diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 00000000..3e4b48e5 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,36 @@ +# This file is part of the tschm/.config-templates repository +# (https://github.com/tschm/.config-templates). +# +root = true + +# Default settings for all files +[*] +end_of_line = lf +trim_trailing_whitespace = true +insert_final_newline = true +charset = utf-8 + +# Python, reStructuredText, and text files +[*.{py,rst,txt}] +indent_style = space +indent_size = 4 + +# YAML, JSON, and other config files +[*.{yml,yaml,json}] +indent_style = space +indent_size = 2 + +# Markdown files +# [*.{md,markdown}] +# trim_trailing_whitespace = false + +# Don't apply editorconfig rules to vendor/ resources +# This is a "defensive" rule for the day we may have +# the vendor folder +[vendor/**] +charset = unset +end_of_line = unset +indent_size = unset +indent_style = unset +insert_final_newline = unset +trim_trailing_whitespace = unset diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 0550400a..00000000 --- a/.flake8 +++ /dev/null @@ -1,5 +0,0 @@ -[flake8] -max-line-length = 120 -extend-ignore = E203 -per-file-ignores = - tests/test_imports.py:F401 \ No newline at end of file diff --git a/.github/actions/setup-project/action.yml b/.github/actions/setup-project/action.yml index f3ee6bc0..8fdea0a4 100644 --- a/.github/actions/setup-project/action.yml +++ b/.github/actions/setup-project/action.yml @@ -79,3 +79,11 @@ runs: else echo "No pyproject.toml found, skipping package installation" fi + + - name: Show dependencies + shell: bash + run: uv pip list + + - name: Show Python version + shell: bash + run: uv run python -c "import sys; print(sys.version)" diff --git a/.github/workflows/codecov.yml b/.github/workflows/codecov.yml deleted file mode 100644 index 7e3afd99..00000000 --- a/.github/workflows/codecov.yml +++ /dev/null @@ -1,55 +0,0 @@ -name: codecov -on: - pull_request: - push: - branches: - - main - -jobs: - codecov: - name: py${{ matrix.python-version }} on ${{ matrix.os }} - runs-on: ${{ matrix.os }} - env: - MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434 - strategy: - matrix: - os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.12"] - - steps: - - uses: actions/checkout@v5 - - - name: Install uv - uses: astral-sh/setup-uv@v7 - with: - enable-cache: true - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v6 - with: - python-version: ${{ matrix.python-version }} - - - name: Display Python version - run: python -c "import sys; print(sys.version)" - - - name: Install dependencies - shell: bash - run: uv pip install ".[dev,all_extras]" --no-cache-dir - env: - UV_SYSTEM_PYTHON: 1 - - - name: Show dependencies - run: uv pip list - - - name: Generate coverage report - run: | - pip install pytest pytest-cov - pytest --cov=./ --cov-report=xml - - - name: Upload coverage to Codecov - # if false in order to skip for now - if: false - uses: codecov/codecov-action@v3 - with: - files: ./coverage.xml - fail_ci_if_error: true diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 4ba4ce6c..7d1d2e5f 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -14,8 +14,86 @@ concurrency: cancel-in-progress: true jobs: + code-quality: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v5 + + - uses: actions/setup-python@v6 + with: + python-version: '3.13' + + - name: install pre-commit + run: python3 -m pip install pre-commit + + - name: Checkout code + uses: actions/checkout@v5 + with: + fetch-depth: 0 + + - name: Get changed files + id: changed-files + run: | + CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }} ${{ github.sha }} | tr '\n' ' ') + echo "CHANGED_FILES=${CHANGED_FILES}" >> $GITHUB_ENV + + - name: Print changed files + run: | + echo "Changed files: $CHANGED_FILES" + + - name: Run pre-commit on changed files + run: | + if [ -n "$CHANGED_FILES" ]; then + pre-commit run --color always --files $CHANGED_FILES --show-diff-on-failure + else + echo "No changed files to check." + fi + + pytest-nosoftdeps: + needs: code-quality + name: nosoftdeps (${{ matrix.python-version }}, ${{ matrix.os }}) + runs-on: ${{ matrix.os }} + env: + MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434 + strategy: + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"] + fail-fast: false # to not fail all combinations if just one fails + + steps: + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Install dependencies + shell: bash + run: uv pip install ".[dev]" --no-cache-dir + env: + UV_SYSTEM_PYTHON: 1 + + - name: Show dependencies + run: uv pip list + + - name: Test with pytest + run: | + pytest ./tests + pytest: - name: py${{ matrix.python-version }} on ${{ matrix.os }} + needs: pytest-nosoftdeps + name: (${{ matrix.python-version }}, ${{ matrix.os }}) runs-on: ${{ matrix.os }} env: MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434 @@ -54,14 +132,99 @@ jobs: run: | pytest ./tests - - name: Check with isort - run: | - isort --check --diff . + codecov: + name: coverage (${{ matrix.python-version }} on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + needs: code-quality + env: + MPLBACKEND: Agg # https://github.com/orgs/community/discussions/26434 + strategy: + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ["3.12"] - - name: Check with black - run: | - black --check --diff . + steps: + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Display Python version + run: python -c "import sys; print(sys.version)" - - name: Check with flake8 + - name: Install dependencies + shell: bash + run: uv pip install ".[dev,all_extras]" --no-cache-dir + env: + UV_SYSTEM_PYTHON: 1 + + - name: Show dependencies + run: uv pip list + + - name: Generate coverage report run: | - flake8 --show-source --statistics . + pip install pytest pytest-cov + pytest --cov=./ --cov-report=xml + + - name: Upload coverage to Codecov + # if false in order to skip for now + if: false + uses: codecov/codecov-action@v5 + with: + files: ./coverage.xml + fail_ci_if_error: true + + notebooks: + needs: code-quality + runs-on: ubuntu-latest + + strategy: + matrix: + python-version: [ '3.10', '3.11', '3.12', '3.13', '3.14' ] + fail-fast: false + + steps: + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + + - name: Display Python version + run: python -c "import sys; print(sys.version)" + + - name: Install dependencies + shell: bash + run: uv pip install ".[dev,all_extras,notebook_test]" --no-cache-dir + env: + UV_SYSTEM_PYTHON: 1 + + - name: Show dependencies + run: uv pip list + + # Discover all notebooks + - name: Collect notebooks + id: notebooks + shell: bash + run: | + NOTEBOOKS=$(find cookbook -name '*.ipynb' -print0 | xargs -0 echo) + echo "notebooks=$NOTEBOOKS" >> $GITHUB_OUTPUT + + # Run all discovered notebooks with nbmake + - name: Test notebooks + shell: bash + run: | + uv run pytest --reruns 3 --nbmake --nbmake-timeout=3600 -vv ${{ steps.notebooks.outputs.notebooks }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..a1c8a676 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,15 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-toml + - id: check-yaml + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.14.5' + hooks: + - id: ruff + args: [ --fix, --exit-non-zero-on-fix, --unsafe-fixes ] + + # Run the formatter + - id: ruff-format diff --git a/cookbook/1-RiskReturnModels.ipynb b/cookbook/1-RiskReturnModels.ipynb index cd262eae..3da6e9e0 100644 --- a/cookbook/1-RiskReturnModels.ipynb +++ b/cookbook/1-RiskReturnModels.ipynb @@ -1,388 +1,391 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "LSMMfr_pzwIP" - }, - "source": [ - "# 1 - Risk and return models\n", - "\n", - "\n", - "In this section, we compare how well the different risk models predict an out-of-sample covariance matrix, and how well the different returns models predict out-of-sample returns.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", - " \n", - "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/1-RiskReturnModels.ipynb)\n", - " \n", - "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", - " \n", - "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", - "\n", - "## Risk models" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ajpb4cfuzzCd", - "outputId": "c075b649-0764-4b2d-cd48-65d6a85e7003" - }, - "outputs": [], - "source": [ - "!pip install pandas numpy matplotlib PyPortfolioOpt\n", - "import os\n", - "if not os.path.isdir('data'):\n", - " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", - " os.chdir('PyPortfolioOpt/cookbook')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "3aAcuLOKzwIT", - "outputId": "456251e3-a9fd-4e83-8847-44cb43dbb422" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.5.6'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pypfopt\n", - "from pypfopt import risk_models, expected_returns, plotting\n", - "pypfopt.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "plt.style.use(\"seaborn-v0_8-deep\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 577 - }, - "id": "7qZAgKeXzwIV", - "outputId": "ab5103ba-600c-4a48-9133-651831294447" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.read_csv(\"data/stock_prices.csv\", parse_dates=True, index_col=\"date\")\n", - "past_df, future_df = df.iloc[:-250], df.iloc[-250:]\n", - "future_cov = risk_models.sample_cov(future_df)\n", - "\n", - "sample_cov = risk_models.sample_cov(past_df)\n", - "plotting.plot_covariance(sample_cov, plot_correlation=True)\n", - "plotting.plot_covariance(future_cov, plot_correlation=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Qq12G1OWzwIW" - }, - "source": [ - "We can see that visually, the sample covariance does not capture some of the new features of the covariance matrix, for example the highly correlated group of FAANG stocks. We may be able to improve this by using an exponentially-weighted covariance matrix, which gives more weight to recent data. We can also look at how each model predicts future variance." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "LSMMfr_pzwIP" + }, + "source": [ + "# 1 - Risk and return models\n", + "\n", + "\n", + "In this section, we compare how well the different risk models predict an out-of-sample covariance matrix, and how well the different returns models predict out-of-sample returns.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", + " \n", + "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/1-RiskReturnModels.ipynb)\n", + " \n", + "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", + " \n", + "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/1-RiskReturnModels.ipynb)\n", + "\n", + "## Risk models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "Ig1n6qISzwIW", - "outputId": "69e03da8-4df2-44a2-ad9e-ede97215ca9b" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "future_variance = np.diag(future_cov)\n", - "mean_abs_errors = []\n", - "\n", - "risk_methods = [\n", - " \"sample_cov\",\n", - " \"semicovariance\",\n", - " \"exp_cov\",\n", - " \"ledoit_wolf\",\n", - " \"ledoit_wolf_constant_variance\",\n", - " \"ledoit_wolf_single_factor\",\n", - " \"ledoit_wolf_constant_correlation\",\n", - " \"oracle_approximating\",\n", - "]\n", - "\n", - "for method in risk_methods:\n", - " S = risk_models.risk_matrix(df, method=method)\n", - " variance = np.diag(S)\n", - " mean_abs_errors.append(np.sum(np.abs(variance - future_variance)) / len(variance))\n", - " \n", - "xrange = range(len(mean_abs_errors))\n", - "plt.barh(xrange, mean_abs_errors)\n", - "plt.yticks(xrange, risk_methods)\n", - "plt.show()" - ] + "id": "ajpb4cfuzzCd", + "outputId": "c075b649-0764-4b2d-cd48-65d6a85e7003" + }, + "outputs": [], + "source": [ + "!pip install pandas numpy matplotlib PyPortfolioOpt\n", + "import os\n", + "\n", + "if not os.path.isdir(\"data\"):\n", + " os.system(\"git clone https://github.com/pyportfolio/pyportfolioopt.git\")\n", + " os.chdir(\"PyPortfolioOpt/cookbook\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 }, + "id": "3aAcuLOKzwIT", + "outputId": "456251e3-a9fd-4e83-8847-44cb43dbb422" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "__2md9QLzwIX" - }, - "source": [ - "We can see that the exponential covariance matrix is a much better estimator of future variance compared to the other models. Its mean absolute error is 2%, which is actually pretty good. Let's visually compare the exponential cov matrix to the realised future cov matrix:" + "data": { + "text/plain": [ + "'1.5.6'" ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import pypfopt\n", + "from pypfopt import expected_returns, plotting, risk_models\n", + "\n", + "pypfopt.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use(\"seaborn-v0_8-deep\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 577 }, + "id": "7qZAgKeXzwIV", + "outputId": "ab5103ba-600c-4a48-9133-651831294447" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 577 - }, - "id": "y4PjMrS0zwIX", - "outputId": "ef412b6e-8bc5-4a62-a26f-6e9932bc8453" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "exp_cov = risk_models.exp_cov(past_df)\n", - "plotting.plot_covariance(exp_cov, plot_correlation=True)\n", - "plotting.plot_covariance(future_cov, plot_correlation=True)\n", - "plt.show()" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "V7OAivD9zwIY" - }, - "source": [ - "## Returns\n", - "\n", - "What about returns? Will the exponentially-weighted returns similarly be the best performer?" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv(\"data/stock_prices.csv\", parse_dates=True, index_col=\"date\")\n", + "past_df, future_df = df.iloc[:-250], df.iloc[-250:]\n", + "future_cov = risk_models.sample_cov(future_df)\n", + "\n", + "sample_cov = risk_models.sample_cov(past_df)\n", + "plotting.plot_covariance(sample_cov, plot_correlation=True)\n", + "plotting.plot_covariance(future_cov, plot_correlation=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qq12G1OWzwIW" + }, + "source": [ + "We can see that visually, the sample covariance does not capture some of the new features of the covariance matrix, for example the highly correlated group of FAANG stocks. We may be able to improve this by using an exponentially-weighted covariance matrix, which gives more weight to recent data. We can also look at how each model predicts future variance." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 }, + "id": "Ig1n6qISzwIW", + "outputId": "69e03da8-4df2-44a2-ad9e-ede97215ca9b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "bgQNHK4bzwIY", - "outputId": "b1230c6a-aeb7-4011-b7f9-636a888eca20" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "future_rets = expected_returns.mean_historical_return(future_df)\n", - "mean_abs_errors = []\n", - "return_methods = [\n", - " \"mean_historical_return\",\n", - " \"ema_historical_return\",\n", - " \"capm_return\",\n", - " ]\n", - "\n", - "for method in return_methods:\n", - " mu = expected_returns.return_model(past_df, method=method)\n", - " mean_abs_errors.append(np.sum(np.abs(mu - future_rets)) / len(mu))\n", - " \n", - "xrange = range(len(mean_abs_errors))\n", - "plt.barh(xrange, mean_abs_errors)\n", - "plt.yticks(xrange, return_methods)\n", - "plt.show()" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "future_variance = np.diag(future_cov)\n", + "mean_abs_errors = []\n", + "\n", + "risk_methods = [\n", + " \"sample_cov\",\n", + " \"semicovariance\",\n", + " \"exp_cov\",\n", + " \"ledoit_wolf\",\n", + " \"ledoit_wolf_constant_variance\",\n", + " \"ledoit_wolf_single_factor\",\n", + " \"ledoit_wolf_constant_correlation\",\n", + " \"oracle_approximating\",\n", + "]\n", + "\n", + "for method in risk_methods:\n", + " S = risk_models.risk_matrix(df, method=method)\n", + " variance = np.diag(S)\n", + " mean_abs_errors.append(np.sum(np.abs(variance - future_variance)) / len(variance))\n", + "\n", + "xrange = range(len(mean_abs_errors))\n", + "plt.barh(xrange, mean_abs_errors)\n", + "plt.yticks(xrange, risk_methods)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "__2md9QLzwIX" + }, + "source": [ + "We can see that the exponential covariance matrix is a much better estimator of future variance compared to the other models. Its mean absolute error is 2%, which is actually pretty good. Let's visually compare the exponential cov matrix to the realised future cov matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 577 }, + "id": "y4PjMrS0zwIX", + "outputId": "ef412b6e-8bc5-4a62-a26f-6e9932bc8453" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yNwdw7oszwIZ", - "outputId": "c91a5204-20fe-4e0c-fd26-31d67ab46818" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[np.float64(0.2882364468648248), np.float64(0.29381479725985077), np.float64(0.2559298526725975)]\n" - ] - } - ], - "source": [ - "print(mean_abs_errors)" + "data": { + "image/png": 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", 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" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "15fVn-_tzwIa" - }, - "source": [ - "The exponential moving average is marginally better than the others, but the improvement is almost unnoticeable. We also note that the mean absolute deviations are above 25%, meaning that if your expected annual returns are 10%, on average the realised annual return could be anywhere from a 15% loss to a 35% gain. This is a massive range, and gives some context to the advice in the docs suggesting that you optimize without providing an estimate of returns." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp_cov = risk_models.exp_cov(past_df)\n", + "plotting.plot_covariance(exp_cov, plot_correlation=True)\n", + "plotting.plot_covariance(future_cov, plot_correlation=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V7OAivD9zwIY" + }, + "source": [ + "## Returns\n", + "\n", + "What about returns? Will the exponentially-weighted returns similarly be the best performer?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 }, + "id": "bgQNHK4bzwIY", + "outputId": "b1230c6a-aeb7-4011-b7f9-636a888eca20" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 607 - }, - "id": "zJsV7D2vzwIa", - "outputId": "8ec9fe02-0ee3-4069-a42b-4e81c7ffb8ed" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots( 1, len(return_methods),sharey=True, figsize=(15,10))\n", - "\n", - "for i, method in enumerate(return_methods):\n", - " mu = expected_returns.return_model(past_df, method=method)\n", - " axs[i].set_title(method)\n", - " mu.plot.barh(ax=axs[i])" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "future_rets = expected_returns.mean_historical_return(future_df)\n", + "mean_abs_errors = []\n", + "return_methods = [\n", + " \"mean_historical_return\",\n", + " \"ema_historical_return\",\n", + " \"capm_return\",\n", + "]\n", + "\n", + "for method in return_methods:\n", + " mu = expected_returns.return_model(past_df, method=method)\n", + " mean_abs_errors.append(np.sum(np.abs(mu - future_rets)) / len(mu))\n", + "\n", + "xrange = range(len(mean_abs_errors))\n", + "plt.barh(xrange, mean_abs_errors)\n", + "plt.yticks(xrange, return_methods)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "yNwdw7oszwIZ", + "outputId": "c91a5204-20fe-4e0c-fd26-31d67ab46818" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "Imoi-m_vzwIa" - }, - "source": [ - "The good news is that we see a good degree of agreement (apart from the `ema` method)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "[np.float64(0.2882364468648248), np.float64(0.29381479725985077), np.float64(0.2559298526725975)]\n" + ] } - ], - "metadata": { + ], + "source": [ + "print(mean_abs_errors)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15fVn-_tzwIa" + }, + "source": [ + "The exponential moving average is marginally better than the others, but the improvement is almost unnoticeable. We also note that the mean absolute deviations are above 25%, meaning that if your expected annual returns are 10%, on average the realised annual return could be anywhere from a 15% loss to a 35% gain. This is a massive range, and gives some context to the advice in the docs suggesting that you optimize without providing an estimate of returns." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { "colab": { - "collapsed_sections": [], - "name": "1-RiskReturnModels.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/", + "height": 607 }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" + "id": "zJsV7D2vzwIa", + "outputId": "8ec9fe02-0ee3-4069-a42b-4e81c7ffb8ed" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "fig, axs = plt.subplots(1, len(return_methods), sharey=True, figsize=(15, 10))\n", + "\n", + "for i, method in enumerate(return_methods):\n", + " mu = expected_returns.return_model(past_df, method=method)\n", + " axs[i].set_title(method)\n", + " mu.plot.barh(ax=axs[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Imoi-m_vzwIa" + }, + "source": [ + "The good news is that we see a good degree of agreement (apart from the `ema` method)." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "1-RiskReturnModels.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/cookbook/2-Mean-Variance-Optimisation.ipynb b/cookbook/2-Mean-Variance-Optimisation.ipynb index 0f9879a5..7aba6f28 100644 --- a/cookbook/2-Mean-Variance-Optimisation.ipynb +++ b/cookbook/2-Mean-Variance-Optimisation.ipynb @@ -1,7035 +1,7523 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "UL9EB0Mn9ny5" - }, - "source": [ - "# Mean-variance optimization\n", - "\n", - "In this cookbook recipe, we work on several examples demonstrating PyPortfolioOpt's mean-variance capabilities. I will discuss what I think should be your \"default\" options, based on my experience in optimising portfolios.\n", - "\n", - "To start, you need a list of tickers. Some people just provide the whole universe of stocks, but I don't think this is a good idea - portfolio optimization is quite different from asset selection. I would suggest anywhere from 10-50 stocks as a starting point.\n", - "\n", - "Some of the things we cover:\n", - "\n", - "- Downloading data and getting it into PyPortfolioOpt\n", - "- Calculating and visualising the covariance matrix\n", - "- Optimising a long/short portfolio to minimise total variance\n", - "- Optimising a portfolio to maximise the Sharpe ratio, subject to sector constraints\n", - "- Optimising a portfolio to maximise return for a given risk, subject to sector constraints, with an L2 regularisation objective\n", - "- Optimising a market-neutral portfolio to minimise risk for a given level of return\n", - "- Optimising along the mean-semivariance frontier\n", - "- Optimising along the mean-CVaR frontier\n", - "- Plotting the efficient frontier:\n", - " - Simple (using CLA)\n", - " - Constrained\n", - " - Complex plots\n", - "\n", - "Please consult the [docs](https://pyportfolioopt.readthedocs.io/) for more info.\n", - "\n", - "## Downloading data\n", - "\n", - "To download data, we will use `yfinance`, an excellent library that provides free price data from Yahoo Finance, no API key needed.\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "69vaYVwZ-Cxz", - "outputId": "dee1bf4a-82ee-4909-e26b-217899258cf2" - }, - "outputs": [], - "source": [ - "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", - "import os\n", - "if not os.path.isdir('data'):\n", - " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", - " os.chdir('PyPortfolioOpt/cookbook')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "shuJGGeo9ny8" - }, - "outputs": [], - "source": [ - "import yfinance as yf\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "UL9EB0Mn9ny5" + }, + "source": [ + "# Mean-variance optimization\n", + "\n", + "In this cookbook recipe, we work on several examples demonstrating PyPortfolioOpt's mean-variance capabilities. I will discuss what I think should be your \"default\" options, based on my experience in optimising portfolios.\n", + "\n", + "To start, you need a list of tickers. Some people just provide the whole universe of stocks, but I don't think this is a good idea - portfolio optimization is quite different from asset selection. I would suggest anywhere from 10-50 stocks as a starting point.\n", + "\n", + "Some of the things we cover:\n", + "\n", + "- Downloading data and getting it into PyPortfolioOpt\n", + "- Calculating and visualising the covariance matrix\n", + "- Optimising a long/short portfolio to minimise total variance\n", + "- Optimising a portfolio to maximise the Sharpe ratio, subject to sector constraints\n", + "- Optimising a portfolio to maximise return for a given risk, subject to sector constraints, with an L2 regularisation objective\n", + "- Optimising a market-neutral portfolio to minimise risk for a given level of return\n", + "- Optimising along the mean-semivariance frontier\n", + "- Optimising along the mean-CVaR frontier\n", + "- Plotting the efficient frontier:\n", + " - Simple (using CLA)\n", + " - Constrained\n", + " - Complex plots\n", + "\n", + "Please consult the [docs](https://pyportfolioopt.readthedocs.io/) for more info.\n", + "\n", + "## Downloading data\n", + "\n", + "To download data, we will use `yfinance`, an excellent library that provides free price data from Yahoo Finance, no API key needed.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/2-Mean-Variance-Optimisation.ipynb)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "69vaYVwZ-Cxz", + "outputId": "dee1bf4a-82ee-4909-e26b-217899258cf2", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:54.267009Z", + "start_time": "2025-11-12T08:10:53.573242Z" + } + }, + "source": [ + "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", + "import os\n", + "if not os.path.isdir('data'):\n", + " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", + " os.chdir('PyPortfolioOpt/cookbook')" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "c4r8LJdC9ny8" - }, - "outputs": [], - "source": [ - "tickers = [\"MSFT\", \"AMZN\", \"KO\", \"MA\", \"COST\", \n", - " \"LUV\", \"XOM\", \"PFE\", \"JPM\", \"UNH\", \n", - " \"ACN\", \"DIS\", \"GILD\", \"F\", \"TSLA\"] " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in 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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + ] + } + ], + "execution_count": 7 + }, + { + "cell_type": "code", + "metadata": { + "id": "shuJGGeo9ny8", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:54.280850Z", + "start_time": "2025-11-12T08:10:54.278023Z" + } + }, + "source": [ + "import yfinance as yf\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np" + ], + "outputs": [], + "execution_count": 8 + }, + { + "cell_type": "code", + "metadata": { + "id": "c4r8LJdC9ny8", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:54.304666Z", + "start_time": "2025-11-12T08:10:54.301956Z" + } + }, + "source": [ + "tickers = [\"MSFT\", \"AMZN\", \"KO\", \"MA\", \"COST\", \n", + " \"LUV\", \"XOM\", \"PFE\", \"JPM\", \"UNH\", \n", + " \"ACN\", \"DIS\", \"GILD\", \"F\", \"TSLA\"] " + ], + "outputs": [], + "execution_count": 9 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "XWdfXpB69ny9", + "outputId": "e60980bf-21e3-470f-a4f7-99da82c820a4", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:56.505627Z", + "start_time": "2025-11-12T08:10:54.345590Z" + } + }, + "source": "ohlc = yf.download(tickers, period=\"max\").loc[\"1990\":]", + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XWdfXpB69ny9", - "outputId": "e60980bf-21e3-470f-a4f7-99da82c820a4" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 15 of 15 completed\n" - ] - } - ], - "source": [ - "ohlc = yf.download(tickers, period=\"max\").loc[\"1990\":]" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_3/k_9k5d5n5zz57w7qfll9rzs40000gn/T/ipykernel_59786/3860718606.py:1: FutureWarning: YF.download() has changed argument auto_adjust default to True\n", + " ohlc = yf.download(tickers, period=\"max\").loc[\"1990\":]\n", + "[*********************100%***********************] 15 of 15 completed\n" + ] + } + ], + "execution_count": 10 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 388 }, + "id": "S7voZG_T9ny-", + "outputId": "de26041a-af6d-4554-eb32-1eb9d57d5f3e", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:56.559652Z", + "start_time": "2025-11-12T08:10:56.550372Z" + } + }, + "source": [ + "prices = ohlc[\"Close\"].dropna(how=\"all\")\n", + "prices.tail()" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 388 - }, - "id": "S7voZG_T9ny-", - "outputId": "de26041a-af6d-4554-eb32-1eb9d57d5f3e" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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TickerACNAMZNCOSTDISFGILDJPMKOLUVMAMSFTPFETSLAUNHXOM
Date
2025-11-05247.820007250.199997935.030029111.36000112.960114122.580002311.67999368.51000231.620001553.309998507.16000424.184153462.070007327.739990113.680000
2025-11-06241.339996243.039993923.580017110.48999812.969999123.400002313.42001369.05999831.510000553.280029497.10000624.420000445.910004321.559998114.500000
2025-11-07245.759995244.410004922.739990110.73999813.210000118.839996314.20999170.55000332.450001551.969971496.82000724.430000429.519989324.209991117.220001
2025-11-10244.550003248.399994915.559998112.23999813.160000118.150002316.89001570.51999732.660000552.960022506.00000024.389999445.230011321.579987118.220001
2025-11-11242.559998249.100006913.859985114.84999813.300000122.559998315.61999571.61000131.990000558.349976508.67999325.510000439.619995327.450012119.779999
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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "prices[prices.index >= \"2008-01-01\"].plot(figsize=(15,10));" - ] + "image/png": 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Teh8Z3HawNHeEZd4/dndFRaP8AkypqYFVMHbDkUceKfvss498+umnIWGZCtZUxdnUqVPltNNOi8vnAQAAAADQ3OjDLV+1mD4sm7FxRkiwlpmcGRJYRVtB84L+F8jRXY6WvVrtVat7+2XTL9rzw38+bDieZq05LPPZVbmrVp+5pyIsU3/AFRWyfPB+jfIL6DN/npjSYv/DjaZv376ycOHCkOOq6uy///2vnHPOOXLFFVfIAQccoIVrF1xwgTbzDAAAAAAA1ExVbQUP7fethhm8uqUvCNuvbWjekGJJiRiWqRbNfdvsW+dfR7nDOMg/eF6angr09AU1vrlnzR0zy/YgwX/kevfff7/k5+fLiy++KAMGDNCeVbi2aNGiBr9PAAAAAAASkT4M880ka5XaKuy5viDMN58s1sqyutKHdtHCshuH3OjfVnPM9D8TYZkHkaG3FVJVeDXWZ8eLGtyvZpFF0rJlSzn99NO1xwMPPCCDBg2Sxx57TN5444243QMAAAAAAM0pLOvfsn+tw7JolWV1HZ302UmfyXGfHhdyPNVqzB3U/DT1KK4ulm1l28SWFbg/wjIPKsu8f4jmtLRGecRrfthPP/2kVYmdeuqpMZ2fnJwsPXr0YDVMAAAAAABipAbmB4dluSm5YYOpBdsWRAzLoq2GaZK65QSdMjtJu/R2NVaWqc/2tZPe8esdhnt4ZM4jca12S1SEZQmoqqpKa6nctGmTzJ8/X6sSO/HEE2X06NHaHLJgX331lZx33nna87///ivLly/XKsq++eYb7X0AAAAAAKBmvqH9FlNgpUvl+v2u15575fTSnlcUrJCfNvxUc1jmqBK7MzAHTR/C1YU+uIsYllltUuHwLHK4eOdi+X7t9/7X1H2/tug12VGxQ5oz2jAT0LRp06Rdu3ZitVolNzdXWwXzmWeekQsvvFDM5tD/qPr37y9paWlyww03yIYNG8Rms0mvXr3k1VdflfPPP79RfgYAAAAAABJJpaNSHC6Hth3cJdYzp6f27HB7Xv9h3Q/+14KDNV9gpagqruCB/HWtLAu38uU9B98TckwFdcd3P16+Xv21tn/37LsNr09aMEl7PHX4U3JUl6OkOSIsSzBTpkzRHjVZu3atf7t79+7y8ssv1/OdAQAAAACwZyq3l8uRHx3prwgLDsAsZs++L0xbUbjC/1qpvTRqG6avystvN6Y1pSUFgjHVknlyr5OlqKooZCGAK/e5UgvLVNVZmb0s7LWemt98wzLaMAEAAAAAAKKYv22+FirtqtwVtlXSavLUIjldnplma4sDBSyFVYVRwzIVxBmDmrpHNZ0zOwfuyey5p2xbtnxzyjf+4y5x+e9fv2BBMLt3rllzRGUZAAAAAABAFGr1SL2QsMwbTPkWAPCFZkpwZVdwWPbzhp8Nr+3OQoAtU1v6t5PMSYbh/3cffLf8ve1vGdJ2iGwv3x5yn8GqndXSXBGWAQAAAAAARBE8pD+4+ssXTKnwS80201eTqVbISNdT56t2x3jNLEuxpIQEeD6n9DpFe+gDOVVlFonD21LaHNGGCQAAAAAAEIWvYswfpgQtruebFabmj435bIy/XfOW/W+RMd3HRF0NM9juVJb9W/Bv2O1gvplr0dowmzPCMgAAAAAAgCjcbrdhP3jAvxqU7xvmn1+W7z++d+u9w4Zf+tUwg+1OZdmB7Q6M6bxYZpY1Z4RlAAAAAAAAUQSHSsGBVqo1NWzrYnCoFq4NM56VZSO7jozpvOCZa+G4xRgQNieEZQAAAAAAALVowwwefu9rw4w1lPKFZZXOyrhWllnMFunXol9cwjLTbtxHoiMsAwAAAAAAqEWVVYm9JGTAf7I5OeZQKiMpQ3suriqO+T2x8lW5RRPLZyRZAqtpNjeEZQAAAAAAAFE4XcbKsnDCVZdFasPMTcnVnndW7oz7937ngXdKy5SWctsBt0U8J9J9RZvT1pwQlgEAAAAAAOzm/C7fkP9oq2bW1LYZj/bHnrk95eczfpZz+p1Tq8qyRw97VPrk9vHvb6/Y3mwDM8KyBDN27Fht2F/wY+XKlY19awAAAAAANIuZZaO7j46p/TFSBVe0QGx32zBjWSQg3GcMzRsqOyp2GI4VV4e2iTYH1sa+AdTeqFGjZPLkyYZjrVu35qsEAAAAAKABVsNMtiTHVC0WKfiKFojtzmqYsQr3+epYcFvoptJNkm3LluaGsCwB2Ww2ycvLa+zbAAAAAACgWYZlaqB/sHRraBtmpO7Nxl5pMlzFW7gAbXPpZunfsr80N4Rl3qF1jmpX4/wCks0NkhoDAAAAAID6C8vCVZZFmnUWtbKsAYK0cDlEuHvaVLpJmiPCMhEtKHv52hmN8gu4/OnDJMlW8yoUel999ZVkZHiWmVWOPfZY+eijj+rh7gAAAAAAaD6qndVhWyyDw7KM5MC/yaPNLOuc1bnWYVk8ZpbF4pRep8inKz6NGgBuIixDojjiiCNk0qRJ/v309DClngAAAAAAICbTN0yXXzb9Ip/8+4k8d9RzMqzDsKhhWVZyVsg1HC6HYX9Ye+M1olV22Sw2qXJWNWiL5t0H36393Lsqd2n7wSHhnQfeKQNaDZDmiMoybyukqvBqlF9Acu0TYxWO9ezZs17uBwAAAACA5kQFRuN/Gu/fv+PXO+TnM34OGd+kF27ovb4yS70/15Yb8TPNYswCumZ1leUFy7XthhzVNLjNYPlh/Q/+/QkHTZB7Zt8jV+xzhZzR5wxprgjLvH+ItW2FBAAAAAAAiW/+1vmG/Yyk0BZLh9tYNZaZlBm1DbNVaquonxncatktu5s/LAuuYqtPtx94u5Q7yv3B2Om9T9cezR1hGQAAAAAAaLZyU4wVYFm20BZLu8te48yycAP+IwmuHuuU2cm/XVBVIA1FhXovHf1Sg31eomiYqXEAAAAAAABNyL8F/8rf2/4Wi8kSUjX266ZftfZMH7vTXmMwNrTd0Drfi/56wfeDhkdlWYKZMmVKY98CAAAAAAAJ79QvTtWeL+x/oeH4r5t/1R7KL2f9os0nC64sS7eGLrQ3vMNweerwp6RHTo9a34uad3bH0Dvkj/w/5Jy+59T6/YgvwjIAAAAAANCs6Af2by7bHPG84urikLBs39b7SueszmHPP6rLUXW6HzXD7My+Z2oPND7aMAEAAAAAQLOiH9jvcBmH94fja8Mc3X20vHnsmyED+nfXX9v+iuv1sHsIywAAAAAAQLPidDkD2+7AdghvAZqvsizZkhwynD8eTBL/a6LuCMsAAAAAAECzoq8mi1ZZ5vamZb6wTM0Wqw8p1pR6uS7qhpllAAAAAACgWdFXk0ULy75e/bXWItkhs0O9hmX1dV3UDWEZAAAAAABoVvQB2ZayLRHPe2HBC96T6jfUcrld9XJd1A1tmAAAAAAAoNmGZRtKNsT8Pqu5fmqOzu13br1cF3VDWAYAAAAAAJqVqEP9o0iyxL+y7Jb9b5E+LfrE/bqoO8IyAAAAAADQrESbUxZNfbRhtkprFfdrYvcwswwAAAAAADQrFY6KRg/L/m/v/5MF2xfIUZ2Pits1ER9UliWg/Px8ufbaa6Vnz56SkpIibdu2lWHDhsmkSZOkvLxcO6dr167y1FNP+d8TvK+3du1aMZlM/kdmZqYMGDBArrrqKlmxYkWD/VwAAAAAADSEkuqSkGO/nf1bje/LtmXH7R6uHnS1vHLMK6yE2QRRWZZgVq9erQVjOTk58sADD8jAgQPFZrPJokWL5OWXX5YOHTrICSecUKdr//DDD1pIpgI3db2nn35a9tlnH/nyyy/lqKNIugEAAAAAicPtdov6P7MptE6ouLo45Fh6UnqN12yZ0jJu94emi7AswVx55ZVitVpl7ty5kp4e+A+5e/fucuKJJ2r/Y1BXLVu2lLy8PP/1xowZo4Vkl1xyiaxatUosFktcfgYAAAAAAOq7zfLQ9w+VzORM+erkryQtKa3GyrJwoVqwFqkt4nqfaJoIy7xps6OqqnF+ATab1voYi507d8r333+vVZTpgzK9WK8VC7PZrLV7nnzyyTJv3jw54IAD4nZtAAAAAADqy2NzHpNKZ6VUVlTKTxt+ktHdR9cYlsWCyrLmgbBMrYJRVSXPXHhao/wCrnnjY0lKSYnp3JUrV2rBXp8+xiVlW7VqJZWVldq2mjP28MMPx+3++vbt659rRlgGAAAAAEgEH/77oX+73O6Z7a333rL3wr7v9qG3y/1/3O/f/+n0n+TmmTfL3K1ztX3CsuaBAf97gD///FP+/vtvbd5YVZwr5HxtnfGsWAMAAAAAoD7pB/HbLLaQ19eXrA/7vuBzW6e1NqycmWSJ32qYaLqoLPO2QqoKr0b5BdhC/6ONRK1+qUKr5cuXG46r+WJKampq3O9v6dKl2nO3bt3ifm0AAAAAAOpDr5xe/mqw4ABsQ/GGiO9zup0hx/RhGZoHwjJv1VSsrZCNSQ3gP/roo+W5556T8ePHR5xbFi8ul0ueeeYZLSgbNGhQvX4WAAAAAADxkpuS699WK2LqHTf1uIjv21a+LeRYlbNxZpyj8dCGmWBeeOEFcTgcMmTIEPnggw+0yi9Vafb222/LsmXLoq5YuWnTJq1dU/8oKCgwLCCQn58vq1evli+++EJGjBihtXi+9tprrIQJAAAAAEgYdpfdv+1yuyKe99axb2nP5/Y71/Ac6VpoHqgsSzA9evSQv/76S1sR87bbbpONGzeKzWaT/v37y4033ihXXnllxPc+9thj2kPvrbfekuHDh2vbKhxT0tLSpEuXLnLEEUfIyy+/rLV/AgAAAACQKOzO2MKyfdvsK3+c84ekJaWFzDrzOajdQfL5qs+lTVqberpbNDWEZQmoXbt28uyzz2qPSNTqldH2Iw3yBwAAAAAg0emrwYLbMIP5gjL9CpjX/HSNnNHnDG3/lgNuke453WVU11H1dLdoagjLAAAAAABAwiusLJSbZt4kJ/Y8MWIbpr5Q5NCOh4a9jloB873R7/n3M5Mz5eK9Lq63+0bTQ1gGAAAAAAAS3osLX5Tft/yuPQa0HBA2IHO4HP7tew6+p8HvEYmBAf8AAAAAACDh6VeyjFRZpj+eak1twLtDIiEsAwAAAAAACU9fQWYIyyQQlm0o2eDfTjInNeDdIZEQlgEAAAAAgISnH+Rf7awOG6J9uPxD/7bVzGQqhEdYBgAAAAAAEp6+3bKwqjDs8TZpbfzbJpOpAe8OiYSwDAAAAAAAJLwKR4V/u8xeFrbizBeindfvvAa+OyQSwjIAAAAAAJDwiqqKwh7XV5bll+Vrzx0zOzbYfSHxEJYBAAAAAICEp2+91NPPLNtStkV7zkvPa7D7QuIhLAMAAAAAAHtsWKavLNtesV17bpvWtsHuC4mHsCzBjB07Vk466ST/thpIqB7JycnSs2dPueeee8ThcGivT58+XXstNzdXKisrDdeZM2eO/70AAAAAACQyVT2mn1kWHJbtqtylbTtcnn8v2yy2Br0/JBbCsgQ3atQo2bJli6xYsUJuuOEGmThxojz66KOGczIzM2Xq1KmGY6+99pp07ty5ge8WAAAAAID4s7vsEV97fN7jctgHh8mMDTPE6XZqxywmC78GRERYluBsNpvk5eVJly5dZNy4cTJixAj54osvDOdceOGF8vrrr/v3Kyoq5P3339eOAwAAAACQ6Iqri2s855m/nhGXy9OSaTETliEywjJvuaar2tkoD/2gwXhITU2V6upqw7Hzzz9fZs2aJevXr9f2P/nkE+natasMHjw4rp8NAAAAAEBjuHXmrTWek5mcKQ63pw3TbCIOQWTWKK81G267SzZP+K1RPrv9PQeLKXn3E20Vuv3444/y3Xffyfjx4w2vtWnTRo499liZMmWKTJgwQasyu/jii3f7MwEAAAAAaAr+yP+jxnOykrP8w/5pw0Q0RKkJ7quvvpKMjAxJSUnRArEzzzxTm1sWTIVjKixbvXq1zJ49W84999xGuV8AAAAAAOLJN7zf56YhN0UMy3wzy6gsQzRUlomIKcmsVXg11mfvjiOOOEImTZqkrYbZvn17sVrD/0pVkHb55ZfLJZdcImPGjJGWLVvu1ucCAAAAANAUFFYV+rc/PeFT6ZnTU9t+dK5x8bssW6CyzGomDkFk/HWowMpkiksrZGNIT0+Xnj09/0MQjQrRLrjgAnnkkUfk22+/bZB7AwAAAACgvpXby7XnvPQ86ZXbS9u+YMAF0j2nu4z7YZz/PJvF5g/LqCxDNLRhNiP33nuvbN++XUaOHNnYtwIAAAAAQFyU2cu053RruuH48A7DZUTnEf59u9Pu32ZmGaKhsizBqGVuI7Va1kS1arZq1Sru9wQAAAAAQKOHZUnGsEzplNnJv13tqvZvU1mGaAjLEsy2bdv8bZdqYH80hx9+uLZKZiQnnXRS1NcBAAAAAEiUsCwtKS3ktYv2ukgmL56sbVc7A2EZlWWIhjbMBFFQUKCtfDl9+nQZMSJQRgoAAAAAQHNW4ajQntOsoWFZbkquXLHPFaFhmTkx55ajYVBZliAuvvhimTNnjtxwww1y4oknNvbtAAAAAADQpMKy1KTUsK/7qshow0SsCMsSxNSpUxv7FgAAAAAAaHJ8FWNqtctwrGZP9EEbJmJFGyYAAAAAAEhYVc4q7TnZnBxTZZlJTAz4R1SEZQAAAAAAIGH5KsaSLTWEZd7zGO6PmhCWAQAAAACAhOWrGIvUhukb5u8Ly8wmohBEx18IAAAAAABI/DbMCJVlVpNnZtmC7Qu0Z1bCRE0IywAAAAAAwB474D84HPOtnglEQlgGAAAAAAD2+JllQKwIywAAAAAAwB7bhul7HYgVYVmCGTt2rJx00kmGYx9//LGkpKTI448/LhUVFXLXXXdJ7969xWazSatWreT000+XxYsXN9o9AwAAAABQXwoqC6K2Yf5b8C9fPmqFsCzBvfrqq3LuuefKpEmT5Oqrr5YRI0bI66+/Lvfdd5/8+++/8s0334jD4ZChQ4fK77//3ti3CwAAAABA3MzJnyN/b/9b2042h68s657dnW8ctUJYlsAeeeQRGT9+vLz//vty0UUXyVNPPSWzZ8+Wr776Ss444wzp0qWLHHDAAfLJJ59Iv3795JJLLhG3293Ytw0AAAAAQFxc/N3F/u2WqS3DnnNGnzP4ttG4YZnT6ZQ777xTunXrJqmpqdKjRw+59957DSGN2p4wYYK0a9dOO0dVQ61YscJwnV27dmkVU1lZWZKTk6MFPaWlpfG+Xf/9VFdXN8qjruHVLbfcon2vKhg7+eSTtWPvvvuuHH300bLPPvsYzjWbzfKf//xHlixZIgsWeJbKBQAAAAAgkbncLsN+t+xuYc+LNMsMiMQqcfbwww9rLYFvvPGGDBgwQObOnatVPWVnZ8s111zjr4h65plntHNUqKbCtZEjR2phjpq9paigbMuWLfK///1P7Ha7do3LL79cC4TiTV3/gQcekMbw3//+V5KTa/cf7rfffiuff/65/Pjjj3LkkUf6j6u2yyOOOCLse1Rlme+cfffddzfvGgAAAACAxrVgu7EYpGVK+Moy5fz+58tbS95qgLvCniDulWW//fabnHjiiXL88cdL165d5bTTTpNjjjlG/vzzT+11VUml2gXvuOMO7by9995b3nzzTdm8ebN89tln2jlLly6VadOmafO41Kyt4cOHy7PPPqu1G6rzmjv1nanvVg3yD662o80SAAAAANAcrClaY9g3mUwRz71+v+sb4I6wp4h7ZdnBBx8sL7/8slbBpFZkVG1/v/zyizzxxBPa62vWrJH8/Hyt9dJHVZ2pUEzN2zrrrLO0Z9V6OWTIEP856nzVTvjHH3/42w71qqqqtIdPcXFxzPeclJSkVXg1BvXZtdWhQwdtBUxVRTZq1Cit0iwzM1P7vlXQGI7vuDoHAAAAAIBE53A5/NsWkyXquVZz3OMP7MHi/tdy6623akFV3759xWKxaDPM7r//fq2tUlFBmdK2bVvD+9S+7zX13KZNG+ONWq3SokUL/znBHnzwQbn77rvrdM8qfa5tK2RjU8P7Z8yY4Q/MVCWeChpvv/12LaDUzy1zuVzy5JNPSv/+/UPmmQEAAAAAkIjsLrt/+6WjX2rUe8GeJe5tmB9++KG888472myx+fPna3PJHnvsMe25Pt12221SVFTkf2zYsEH2dJ06dZLp06fLtm3btJlvV111lbb65ZgxY+Sjjz6S9evXy5w5c+TUU0/VKstee+21qGWpAAAAAAAkip0VO7Xn47sfL0PbDW3s28EeJO5h2U033aRVl6kqp4EDB8r555+vrcSoKr+UvLw87Xnr1q2G96l932vqWQVAeg6HQ1sh03dOMJvNpq2cqX80Bx07dtQCsx07dmiB2ffffy8XXHCB1lbas2dPrepMVfj9/vvvcuCBBzb27QIAAAAAEBevLHpFe56+YXpM5z935HOSa8uVF456gd8AGrYNs7y8XJstpqfCGtUKqKjVL1XgpVZy9K3KqNo21SyycePGafsHHXSQFBYWyrx582S//fbTjv3000/aNdRss+ZsypQpYWeYqRlxPvfdd5/2AAAAAABgT1dmL4vpvMM6HSYzzpxBxxUaPixTLYBqRlnnzp1lwIAB8tdff2nD/S+++GLtddUGeN1112lhTq9evbTw7M4775T27dvLSSedpJ3Tr18/rSLqsssukxdffFHsdrtcffXVWrWaOg8AAAAAAKC2GE2ERgnLnn32WS38uvLKK7VWShVu/d///Z9MmDDBf87NN98sZWVlcvnll2sVZMOHD9cG1KekpPjPUXPPVEB21FFHaZVqau7WM888E+/bBQAAAAAACWbB9gWNfQvYg5ncbrdb9kCqtTM7O1sb9h88v6yyslLWrFmjVbXpAzrEH981AAAAACDezvjyDFm6a6m2/fxRz8uhHQ/lS0adc6J6H/APAAAAAABQn8ymQJwxqM0gvmzEFWEZAAAAAABIKC1SWmjPHTI6SGZyZmPfDvYwhGUAAAAAACChVDortedrB1/b2LeCPRBhGQAAAAAASBifrvhU5uTP0bZTLMwhR/wRlgEAAAAAgEa3omCFLNm5pMbz7vrtLv+2zWqr57tCc2Rt7BsAAAAAAADNm9PllFO+OEXbnn32bMlIzojpfanW1Hq+MzRHVJYBAAAAAIBGVeWs8m8XVRfF/D7aMFEfCMsSzNixY+Wkk04KOX744YfLddddF3J8ypQpkpOTo22PHz9e+vXrF/a669evF4vFIl988UU93DUAAAAAAJE53A7/ttUUexMcbZioD4Rlzcgll1wiy5Ytk99++y1sqNamTRs57rjjGuXeAAAAAADNl8MVCMssZkvM70u10IaJ+CMsa0b23XdfGTx4sLz++uuG4263WwvLLrzwQrFaGWMHAAAAAGi8sKw20pLS4n4vAMmINyxyuSoa5a/BbE4Vk8nUoNVlt956qzz99NOSnp6uHZs+fbqsWbNGLr744ga7DwAAAAAAdjcsy0rO4ktE3BGWiWhB2fQZA6UxHH7YIrFYGi4JP+ecc+SGG26Qjz76SJt/pkyePFmGDx8uvXv3brD7AAAAAADAx+6yGwpaouma1VXWFq+V7tndG7T4BM0HbZjNjBr2f8opp/hbMYuLi+WTTz7RKs4AAAAAAGgMdqcuLJPIYZnT5dSCMmXCQRMa5N7Q/FBZ5m2FVBVejfXZ8ZCVlSVFRaHL6xYWFkp2drbhmArGjjrqKFm5cqX8/PPP2iqYp59+elzuAwAAAACA3VkN0+V2RTzv7+1/+7eTzEl80agXhGUiWtlmQ7ZC1oc+ffrI999/H3J8/vz5Ie2VRxxxhHTr1k1rv1Rh2VlnneWfXwYAAAAAQGNWlsWqNqtmArVBWJaAVAXZ338H0nRl9OjR8txzz8k111wjl156qdhsNvn666/lvffeky+//DIkHFTD/J944gkpKCiQJ598soF/AgAAAAAAaj+zzGIKBGQuV+QKNGB3EJYlILV65aBBg0JaK2fOnCm33367jBgxQqqrq6Vv377aIP9Ro0aFXEMN97/rrrtkwIABMnTo0Aa8ewAAAAAAIq+GGW1mmdUciDFcQliG+kFYlmCmTJmiPSIJ14oZTseOHcXpdMbxzgAAAAAAiENlWZSwTD/PLDM5k68b9YLVMAEAAAAAQJMJy6IN+NdXoHXP7l7v94XmibAMAAAAAAA0mbAsSmGZPyzrkd2jAe4KzRVhGQAAAAAAaFSbSzfH1IbpC8v0s8uAeCMsAwAAAAAACRGW+SrQCMtQnwjLAAAAAABAo9pVuSummWUl9hLtOSM5o0HuC80TYRkAAAAAAGhUOyt3xlRZVlRVpD1nJWc1yH2heSIsAwAAAAAAjWpXxa6YBvwXVxdrz9m27Aa4KzRXhGUAAAAAAKDJtGFGqywrrvKEZVSWoT4RlgEAAAAAgEazs2KnFFQV+Pfd7ihhGZVlaACEZQAAAAAAoNHc/8f9hn2XRB7wT2UZGgJhWYIZO3asmEwmueKKK0Jeu+qqq7TX1Dl6s2fPFovFIscff3wD3ikAAAAAADX737r/GfZjqSyjDRP1ibAsAXXq1Enef/99qaio8B+rrKyUd999Vzp37hxy/muvvSbjx4+XmTNnyubNmxv4bgEAAAAAqF14dtWPV0lBZaA106fC4fl3cKo1la8U9YawLAENHjxYC8w+/fRT/zG1rYKyQYMGGc4tLS2VDz74QMaNG6dVlk2ZMqUR7hgAAAAAgPA6ZxqLPl5a+JLM3DhT3lryVsi5dpdde06yJPF1ot4QlnlLPMuczkZ5RCsvjebiiy+WyZMn+/dff/11ueiii0LO+/DDD6Vv377Sp08fOe+887Tz6vqZAAAAAADEk/r3aX5ZftjXyh3lEcOyZHMyvwjUG2v9XTpxlLtc0mPmokb57FWHDpR0i6XW71PB12233Sbr1q3T9n/99VetNXP69OkhLZjqXGXUqFFSVFQkM2bMkMMPPzxOPwEAAAAAAHWzq3KXVLuqxSQmyU3J1fZ91LFg1c5q7TnZQliG+kNYlqBat27tb6tUSbzabtWqleGc5cuXy59//ilTp07V9q1Wq5x55plagEZYBgAAAABobL5wLMeWYwjKIvGFZUlm2jBRfwjLRCTNbNYqvBrrs+tKtWJeffXV2vbzzz8f8roKxRwOh7Rv395/TAVrNptNnnvuOcnOzq7zZwMAAAAAsLt8Q/xVVVlBlXGgf0l1Scj5zCxDQyAsU6WdJlOdWiEbm2qrrK6u1u5/5MiRhtdUSPbmm2/K448/Lsccc4zhtZNOOknee+89ueKKKxr4jgEAAAAACF3dMj0pPeRr+XzV57J3673l9X9elzP6nCEX73UxM8vQIAjLEpjFYpGlS5f6t/W++uorKSgokEsuuSSkguzUU0/Vqs4IywAAAAAAjclfKRahrfLe3+/Vnp+c96Q4XU5/uMbMMtQnVsNMcFlZWdojmArDRowYEbbVUoVlc+fOlYULFzbQXQIAAAAAEGUGmaXmGWTP/PWMf5uZZahPVJYlGDXQP5rPPvusxmsccMAB2uwyAAAAAACacmVZJFSWoT4RlgEAAAAAgAYPyZbtXCZ3/HqHtv/Lpl9q9f5kc3I93RlAGyYAAAAAAGhgH//7sZzzzTkRX3/+qOejvt9qpvYH9YeZZQAAAAAAoEG9ufhNw/5+bfcz7O/bZt+I7+2Z01NMJlO93RtAWAYAAAAAABrU4LaDDftPH/G0WEwW/35mUmbYVstWqa3knePeaZB7RPNFWAYAAAAAABpUhaPCv314p8Ml25YtmcmZ/mOqciw1KTXkfYd2PFTSktIa7D7RPBGWAQAAAACARgvLfPRhmWKz2ELOSbGk1Ot9AQphGQAAAAAAaHJhWao1tLIsxUpYhvpHWAYAAAAAABotLDukwyH+FkslyZykPa8rXhfyvrz0vAa7RzRfrLUKAAAAAAAajMvtkiU7l2jbZ/c9W07tdaq2fdnAyyTHliPD2g+L+N7OmZ0b7D7RfBGWAQAAAACABvPz+p/92+f1O08sZs8qmMmWZDm337lR30tYhoZAG2aCGTt2rLYqyBVXXBHy2lVXXaW9ps5Rtm/fLuPGjZPOnTuLzWaTvLw8GTlypPz666/+93Tt2lV7j/7RsWNHmThxYsjx4AcAAAAAALW1YMcCz79Hs7pK56zYKsU6ZnSUTpmdpF1GO75w1DsqyxJQp06d5P3335cnn3xSUlM9Aw8rKyvl3Xff1YIxn1NPPVWqq6vljTfekO7du8vWrVvlxx9/lJ07dxqud88998hll13m37dYLNp19YHc/vvvL5dffrnhPAAAAAAAaqvSUak9H93l6JjfM/XEqWI2mcVqJsZA/eOvLAENHjxYVq1aJZ9++qmce66nRFVtq6CsW7du2n5hYaHMmjVLpk+fLocddph2rEuXLnLAAQeEXC8zM1OrOguWkZFhCNAinQcAAAAAQKyqnFURV7vUa5nSUnZW7pSeOT1ZBRMNijZMEXG73VJe7WiUh/rsurj44otl8uTJ/v3XX39dLrroIkPQpR6fffaZVFV5/ocIAAAAAICmshKmzWKLet7bx72tVZ/dN+y+BrozwIPKMvUfqt0p/Sd8J41hyT0jJS259r+G8847T2677TZZt86zlK6aQ6ZaM1UlmWK1WmXKlCla2+SLL76oVaOpCrOzzjpL9t57b8O1brnlFrnjjjv8+w888IBcc801u/2zAQAAAAAQbE3RGu05xZoS9cvpmNlRnjj8Cb5ANDjCsgTVunVrOf7447VATFWnqe1WrVoZzlEzy9Rx1Y75+++/y7fffiuPPPKIvPrqq/5FAJSbbrrJsB98HQAAAAAA4sHldsmyXcu0bYfLwZeKJomwTPVJJ1m0Cq/G+uy6Uq2YV199tbb9/PPPhz0nJSVFjj76aO1x5513yqWXXip33XVXSDjWs2fPOt8HAAAAAAC1Ge6vlFSX8KWhSSIsExGTyVSnVsjGNmrUKG21S3X/I0fGFvb1799fm2MGAAAAAEBjzStTWNkSTVXiJUQwrFC5dOlS/7bezp075fTTT9eqz9SMMrWS5dy5c7U2zBNPPJFvEQAAAADQoJ7/+3l5ccGL/n231G3BO6C+EZYluKysrLDH1UqYQ4cOlSeffFJWrVoldrtdOnXqpA38/+9//9vg9wkAAAAAaN70QZlycs+TG+1egGhMbjUdfg9UXFws2dnZUlRUFBIoVVZWypo1a6Rbt27aTC/UH75rAAAAAIAy8I2B/i+iZ05PmXriVL4YNImcKJi5we4KAAAAAABARDplduJ7QJNFWAYAAAAAABpUu/R2fONosgjLAAAAAABAvQqeAJWZnMk3jiaLsAwAAAAAANSrFYUrDPsOl4NvHE0WYRkAAAAAAKhXp395umHfLXvkWoPYQxCWAQAAAACAeuVyu/iGkTAIywAAAAAAQIPqmtWVbxxNlrWxbwAAAAAAAOy5nC6nf/vxwx7X5ped0OOERr0nIBrCMgAAAAAAUG+qXdX+7WEdhskxXY/h20aTRhsmAAAAAACoN9XOQFiWbEnmm0aTR1iWYMaOHSsmk0l7JCcnS8+ePeWee+4Rh8Mh06dP97+mf9xxxx3aeyO9rh75+fmN/aMBAAAAAPZA64vXa8+ZyZliNdHghqaPv9IENGrUKJk8ebJUVVXJN998I1dddZUkJSXJQQcdpL2+fPlyycrK8p+fkZFheH/w60qbNm0a6O4BAAAAAM3Jrspd2nOnzE5asQbQ1BGWJSCbzSZ5eXna9rhx42Tq1KnyxRdf+MMyFXzl5OREfH9NrwMAAAAAEC9l9jLtOT0pnS8VCYGwTHG7RezljfMbSEoT2c1kPTU1VXbu3Bm3WwIAAAAAYHfM3DhTflj3g/yx5Q/p3aK3doywDImCsExRQdkD7RvnN/DfzSLJdUvX3W63/Pjjj/Ldd9/J+PHj/cc7duxoOG/dunXSsmXLiK936dJFFi9eXKd7AAAAAAAg2FU/XuXf3ly2WXsmLEOiICxLQF999ZU2h8xut4vL5ZJzzjlHJk6cKHPmzNFenzVrlmRmZvrPz83NNbw/+HU17wwAAAAAgHiwu+xhj6dbacNEYiAs87VCqgqvxvrsWjriiCNk0qRJ2mqY7du3F6vV+Gvs1q1b1JlkNb0OAAAAAEBdVTmqwh6nsgyJgrBMUTPD6tgK2RjS09OlZ8+ejX0bAAAAAACEqHRWhv1WWqYGxgMBTRlhWTO0bds2qaw0/o+XmmlGOyYAAAAAYHdVOCrCHu+W3Y0vFwmBsKwZ6tOnT8ix2bNny4EHHtgo9wMAAAAA2HNUOsJXlnXJ6tLg9wLUBWFZgpkyZUrE1w4//HBthcy6vg4AAAAAQH2FZe3T2/PlIiGYG/sGAAAAAADAnj+zLMmS1OD3AtQFYRkAAAAAAKj3mWVAoiAsAwAAAAAA9d6GCSQKwjIAAAAAAFDvbZhAoiAsAwAAAAAAcUNlGRIdYRkAAAAAAKiXmWXDOwznm0XCISwDAAAAAABxY3fZtecTe5woDx3ykJzc82SZMmoK3zAShrWxbwAAAAAAAOw5XG6X9mwxWyTbli33DLunsW8JqBUqywAAAAAAQNzDMrOJyAGJib9cAAAAAAAQ/8oyk4VvFQmJsCxBmEymqI+JEydq502dOlUOPPBAyc7OlszMTBkwYIBcd911/utMmTJFcnJyYvrMvn37is1mk/z8/Hr7uQAAAAAAexan26k9m8TU2LcC1AlhWYLYsmWL//HUU09JVlaW4diNN94oP/74o5x55ply6qmnyp9//inz5s2T+++/X+x2z3DF2vjll1+koqJCTjvtNHnjjTfq5WcCAAAAAOx53G63f2YZkIgY8J8g8vLy/NuqakxVk+mPKV9++aUMGzZMbrrpJv+x3r17y0knnVTrz3vttdfknHPOkcMOO0yuvfZaueWWW3bzJwAAAAAANKfKMmaWIVERlnlT7wpHRaP8AlKtqVrwFQ8qPHv33Xfln3/+kb322qvO1ykpKZGPPvpI/vjjD60Vs6ioSGbNmiWHHHJIXO4TAAAAANAMBvzTzIYERVgmogVlQ98d2ii/gD/O+UPSktLicq3x48drodbAgQOlS5cu2uyyY445Rs4991xt9lis3n//fenVq5c270w566yztEozwjIAAAAAQE1mb55tqDADEg0zy/Yg6enp8vXXX8vKlSvljjvukIyMDLnhhhvkgAMOkPLy8piv8/rrr8t5553n31fbqtJMVZwBAAAAABDN8oLl2vO7y97li0JCorLM2wqpKrwa67PjrUePHtrj0ksvldtvv12bW/bBBx/IRRddVON7lyxZIr///ru2QIB+TpnT6dQqzi677LK43y8AAAAAYM9txwQSDWGZWs7WZIpbK2RT07VrV0lLS5OysrKYzlftloceeqg8//zzhuOTJ0/WXiMsAwAAAAAAezLCsj3IxIkTtXbL4447TptZVlhYKM8884zY7XY5+uijDVVif//9t+G9aqZZz5495a233pJ77rknZIEAVaX2xBNPyOLFi/2zzAAAAAAAAPY0zCzbgxx22GGyevVqueCCC7RVLI899ljJz8+X77//Xvr06eM/r7S0VAYNGmR4jBkzRr744gvZuXOnnHzyySHX7tevn/ZQ1WUAAAAAADQ31WvXyo6XXxFXjJ1bSFwmt9vtlj1QcXGxZGdnS1FRkWRlZRleq6yslDVr1ki3bt0kJSWl0e6xOeC7BgAAAIDmZeAbA/3biy5cJHuKpXsNFHE4pMXYsdL21sCMbyR+ThSMyjIAAAAAABB3nTI77VnfqsOhPVUs2nMCQIRHWAYAAAAAAOLuoUMe2iO/VUtmZmPfAhIxLNu0aZOcd9550rJlS0lNTZWBAwfK3Llz/a+rzs8JEyZIu3bttNdHjBghK1asMFxj165dcu6552qlcTk5OXLJJZdos7YAAAAAAEDTVO2s9m93yeoieyJLdvQWPiS+uIdlBQUFMmzYMElKSpJvv/1WlixZIo8//rjk5ub6z3nkkUe0VRpffPFF+eOPPyQ9PV1GjhypzbfyUUGZWnnxf//7n3z11Vcyc+ZMufzyy+N9uwAAAAAAIE5Kqkv82xlJGXvM96of6m/OJCzb01njfcGHH35YOnXqJJMnT/YfU4P09VVlTz31lNxxxx1y4oknasfefPNNadu2rXz22Wdy1llnydKlS2XatGkyZ84cGTJkiHbOs88+K8cdd5w89thj0r59+3jfNgAAAAAA2E2l9lJ/UGYxW/aY79Oxc6d/22SNe5SCPb2y7IsvvtACrtNPP13atGkjgwYNkldeecX/ulqFMj8/X2u99FGrEQwdOlRmz56t7atn1XrpC8oUdb7ZbNYq0cKpqqrSVjbQPwAAAAAAQMNXlmUm71lzvVzl5f5tt93eqPeCBAzLVq9eLZMmTZJevXrJd999J+PGjZNrrrlG3njjDe11FZQpqpJMT+37XlPPKmjTs1qt0qJFC/85wR588EEtdPM9VHUbAAAAAABoOMXVxXtkWOaurg7Zrlq5UsrnzZOmbkVZpTy8eosUO5zafrXLJd9sL5RCu2d1TzRAWOZyuWTw4MHywAMPaFVlas7YZZddps0nq0+33XabFBUV+R8bNmyo188DAAAAAADNo7LMXVUV2K6ulm2PPSarR4+RdeeeJ/bNm6UpO2PBKnly3Va5ebknJ3lkTb5c/M9aOXvB6sa+teYTlqkVLvv372841q9fP1m/fr22nZeXpz1v3brVcI7a972mnrdt22Z43eFwaCtk+s4JZrPZtJUz9Q8AAAAAANAIYVnSnhWWuaoClWWuqirZ+epr/v2mHpZtqfK0jX62rVCuXLJOnlvvyVv+Kgm0lqKewzK1Euby5csNx/7991/p0qWLf9i/Crx+/PFH/+tqvpiaRXbQQQdp++q5sLBQ5unKGX/66Setak3NNgMAAAAAAE3PnlRZVvH332L3joLSt2FWLlxoOM+UkiqJ4tOtBY19C80zLPvPf/4jv//+u9aGuXLlSnn33Xfl5Zdflquuukp73WQyyXXXXSf33XefthjAokWL5IILLtBWuDzppJP8lWijRo3S2jf//PNP+fXXX+Xqq6/WVspszithHn744dp3F2zKlCnaggjKxIkTte/4iiuuMJzz999/a8fXrl2r7atnta+Ox/o5AAAAAABEs6Fkwx4RllUsWiRrzzpbVh7lWZzQXV0VsZLMlJwUl890Oxyy+fbbpfCTT+JyPTShsGz//feXqVOnynvvvSd77bWX3HvvvfLUU0/Jueee6z/n5ptvlvHjx2vzzNT5paWlMm3aNElJSfGf884770jfvn3lqKOOkuOOO06GDx+uhW6omfoeX3vtNVmxYgVfFwAAAACgwXz878fas8VsSYhv3b51qxR+9pmhckwp8XXDOT1D8YNfN3DH515KfvhBij75VLbcfkfU85xut8zcVSLbvO2V0bjccbq5ZsZaHxcdPXq09ohEVTTdc8892iMStfKlqkpD7fXp00dbTfT222+XDz/8kK8QAAAAANAgOmZ21KrLOmR0SIhvfM2pp4lzxw5x5OdLK12HlmOLp/1SP6csIrcrLvfiLCwKXNLt1rKTYJVOl3Sd6WkDHZiRKv/bv0/Ua1a6CMuaTFiWaNQfobuiolE+25SaGvY/gN310EMPaVV7c+fOlSFDhsT9+gAAAAAABCuzl2nPQ9omxr9DVVCmlM6YaQjLVMWZnls34D+Y21t9trtM1kA1nruyUssLgv1TGsguFum2I6lwBoK8I1pkys+7PDPlEB1hmfojrKiQ5YP3k8bQZ/48MaWlxf26gwcPljPOOENuueUWw2IKwQ4++GAxm43duBUVFbLvvvvG/Z4AAAAAAHt2IUpxVbG2nW3LlkRS8ddf4qqsFLN3PFRV0FijqG2Y8are0rWuOouKxOwNy1Qr5WWL14rFZJJ2tZyPVuHyhGUpZpMckhsalkWqYGvuCMv2YGoRBbVYwvfff6+1ZYbzwQcfaOfo6efLAQAAAAAQi3JHuTjcjoQMy5Sdr7wqrcdfrW07d+40vKYf8B/CFZ/KMv11nIWFkpSXp23/XVwuX28PtGjWxsWL1mjPLZOssr06dMbZ59sK5aS2uXW+5T0VYZm3FVJVeDWGcGWVkWRlZUlRUeh/IIWFhZKdHfo/RD169NBWFL311lu1gf/hdOrUSXr27Gk4llqLewIAAACAxlblrJLv134vB7U/SFqltmrs22m2iqo8/15NNidLiiWwgF9T5fZWXflU/LNIe3YWF9eq1TL4OnW+H13LpH5+2W+FpXW7ntstC72tmjazWS7q0EombdhuOGdjZZSKuWaMsMy74EB9tELWx+B+VSUWbP78+dK7d++w75kwYYIWmr3//vsNcIcAAAAA0PBeXPCivLroVW2o/LRTp/EraOSwTFWVJUJrnyNoLlnZjJlaK+bqE06sXatlnFacdDs9VXm+NkyfecXlYc9PruE7rtDd8zVd2kjnVFvIOQ+vyZf7Vm+RVwd0ldFtcup453se47AqNGnjxo2Tf//9V6655hpZuHChLF++XJ544gl577335IYbbgj7nrZt28r1118vzzzzTIPfLwAAAAA0hOkbpmvPm0o38YU3MJfbJd+u+Vb77lcVrdKOJUp1X+WSJSHHqlas1FbGDOaO1moZpwH/oqssq1i4wL+9sryyTpcrcQTu68y8FmHPsXuDvksXr63TZ+ypCMsSSPfu3WXmzJmybNkyGTFihAwdOlQ+/PBD+eijj2TUqFER33fjjTdKRkZGg94rAAAAADSUJHNg6Plxnx4nk/+ZzJffQKaumCo3z7xZjv3kWLn/9/u1Y8M7DG+S33/l0qWGgGzjVZ75ZHpls2cbD/iqt8JUliV17Kg9u+M14F8XyO167XXteWuVXVaUG+elDcjwtLg6JfrnFnvDsiyr2V/p9+P+feS4VtlyeG5m2LZNeNCGmWD233//sK2YPhMnTtQewbPOtm839iV37do14n8I06d7/r8yAAAAAJAIkiyBsGxDyQZ5Yt4TctFeFzXqPTUXs7d4wiW3uKXU7pmtNaDlAGlqXNXVsubkU7RtNbPcHGEUk33LZsO+yeZtXQxTWWZOTanTgP9p24ukzOmUU4OqvfQzy3z+KCoLOdbBliyLSytrXIRzvrd9M9MSWGVzQEaqvD6wm0xcuUmmFxhXxlxfWS3f7SiSk9vmSutarrq5pyEsAwAAAADsMZVlaFjOMEFRli2ryf0a3FWB6ixnQYGI1SqmlBRxV1aKrW9fqVq2THvNvmGj4X0mqzXyEH+zpdYD/p1ut4z9x7NCZabVIse00i3Wp/su0w85xHMoTJFLjzSbyE4VUHqqwSLNh7t22XrtuTrMNZLCvOeUv1bKpiq7vLRhu8w9qH9CzJ2rL7RhAgAAAAASWiKsvLincrgDQ+l91ID/Jkc3V8xtt0vpjBlaUGbJzpYub7/tf63sl1+M7/MFTWGqvsTsjVRq0YZZpTv3fzuLI1aWmZI8AfAOe+j3qx/UH0tMN7p16OB+a5ggTAVlvudN3u3mirAMAAAAAJDQ0pLCt9ShYQb8B8tKboKVZbqwTLVkls3yhGLZJ50olox0yRw5MsIbveFWmJ/TX3lVizbMal0VWkhcpb+O93N3VIeGZZ1Skv3bDu95wRVo83Xtm1d1bhNyjSSzKebFAZoj2jABAAAAAAktPSm9sW+h2QrbhtkUwzJd+OOuqJCqf//VtpO7dtWeS777Lvz7vCFUuHliYqldG6YKtLaGCb/C3WOsYdkdKzZJqySrvL5ph3y9Xy/paEuWsxaskt91YVlymCqy3KTocVBFuJ+3GaGyDAAAAACQ0AjLmk4b5iOHPtI0K/2cgfvc+uijUvH339q2OT180NrlHW9rpq9iK+zMMl9lmavGOWVzi8rkqXVb5bA/PbPRwnVvusMEjxsrq0OOtbcFZvS9tXmnPLluqxQ5nHLpP2tlcWmFIShTrGGqyPbJSI16z+W1mMO2J6KyDAAAAACQ0JLNgUobNF5l2bAOw+TYbsc2yV+B2xEIyyrmztO94Ems0g89RMpmzvIftrZu7dnwhkbucG2YMQz4V+2Qx81fEfY1Xwuln76ay+3WqtrmFHuCr3a2JNninSMWbji/sqysUv4q8ayAqReusqxXevQ5f5W1mMO2J6KyDAAAAACQ0Jzu5j1fqal899nJTXCwv1flkiVhj6cOGqQ9591xh+G4KTk57ID/3AvOl6wTxkjnKVNiGvAfKSgLG5YFVZapVSzLvJ+bbfUEc4olyiqVqi0zlmH+aiXOs/JayFEtwrfM2pt5ZRlhGQAAAABgjxsyj4avLMtNyW2SX7u7ulp2vj455HiPad9KcufO2nZShw7GF70Bk1o501la5h/wb23RUjo88oikHzi0TgP+9YL/avVz0dziNswNG9fJM6RfhVs1zOaPeZj/U/06yzv7dA8bDFVRWQYAAAAAQOKisqzxbC7b7N8e0HKANDW73nlHlu29j1QuXGg4ntSxo3+4v2KyWKTr++9J5tEjpOePP/jDMmXlYYeJu9rTAumvJqvDgP/aVpa9snG7f/uMvFz5YUhveW2vrrWueopWiaZ9bJhjH+UXyLjFa+WT/F3SHFFZBgAAAADY41ZkRP3bUbFDe/gMauNpaWxKtt57X9jj5tTQmV2p++4rHZ99Vqsy81eNqTCprEyqN2zQtk0Wc60H/Puc066FYb9Ev/plmNUwH1+71b+r7mevzDRJsZi17Rf7d4npM1ceMlDqYm5xmUzdVij/lFZIc0RYlkCcTqccfPDBcsoppxiOFxUVSadOneT222/X9r/66is57LDDJDMzU9LS0mT//feXKaqfWmft2rXaf2AWi0U2bTL2NG/ZskWsVqv2ujoPAAAAAJqyKmdVyDE1HB3xt3jHYpm6Yqr2/ZZUl/iPP3n4k9Ixs2PCfOWm1BpW7NRXkKlFAebP977RHDLgf/NNN8f093ZobqZh/+ddnu/vl4ISOevvVVJcbVz5snuqTXvuF2YYf7o+tAvj+NbZsmjYAMnQzTqL5Km+nUKOFXmDuwxv9VxzQ1iWQFSwpUKvadOmyTvvvOM/Pn78eGnRooXcdddd8uyzz8qJJ54ow4YNkz/++EMWLlwoZ511llxxxRVy4403hlyzQ4cO8uabbxqOvfHGG9pxAAAAAEgE1U5jyKDQmlk/zvr6LJnw2wSZv22+/3tvkdJCRnQZIYnEnBJ9NUh9G6bhcISQqurfyIP8fQ5rkSmpQSGcctrfq2R6QYn8urM4cNAt4ruDB3uHhpDmGlorz2vXUlonJ0kszmrXMuJrmdbmGRs1z586gfXu3VseeughLSBTFWCff/65vP/++1rgtXXrVrnhhhvkuuuukwceeED69+8vPXv21I49+uij8vjjj2sBmt6FF14okycbBx2qfXUcAAAAABJBhcPTKnZ0l6P9xwjL4md7+XYZ+MZAGTN1jP9YYWWhPPjng9r2rsrEm2tlSrHVLSyzBUI2V3l54HhS9GDKahLJTbLKzKF95ZDcjLDnVNkdIathKslh7sVSw5D/vhk1hIExaplkleaIsMxbnmuvcjbKoy6lwSoo22effeT888+Xyy+/XCZMmKDtf/zxx2K328NWkP3f//2fZGRkyHvvvWc4fsIJJ0hBQYH88ssv2r56VvtjxgT+RxAAAAAAmqpXFr4i0zdO17aH5g31H2eOWfwc++mx2vPa4sCYHpvVJvO2zpNEZdK1U4Z/PUIapassc5WWBs6P0O6Y563uenmAZzGBTinJ8lifQNujQ7fqpEW/qqvb7V8AwBpmNUuzv+4svHa2ZKmLtKDKuVGts6U5ap4RYRBHtUtevnZGo3z25U8fJkm22vUAq/9oJ02aJP369ZOBAwfKrbfeqh3/999/JTs7W9q1axfynuTkZOnevbt2jl5SUpKcd9558vrrr8vw4cO1Z7WvjgMAAABAUzdpwST/dmZyYCYUlWXxUemoDDsTLlzra1Nn691bqrz/Jra0MA7bDxGmXVJx66rJnGWBsMztDL/IhG+2WAtdhVY7W+Df22W691n0CwW43VLtDdKSwgR3wfmZqj7zVaK9ObCb1FavNJusKK+SKzu1kcfW5mvHnu3XWdKZWYZEokItNbx/zZo1snHjxt261sUXXywfffSR5Ofna89qHwAAAAASQZesLv65WYd0PMR/nMqymm0r3xb1e1KdUBtKPKtABrO77JIIUvbZO7Cjq/6ytmoV/Y26LjBrXp5/21kSCMhcpWWB8yOsiFnqDcOsusAr2Wz2B2ClzsD7ynRtmOrTd3r3k2OoLLuua1s5Iy9Xbu/eTo5umSW19e1+veWb/XrJJR0D30tzbkWkskx9CclmrcKrUX4BybX/8/vtt9/kySeflO+//17uu+8+ueSSS+SHH37Q5pmplTE3b94s7du3N7ynurpaVq1aJUcccUTI9VR1Wt++feXss8/WqtX22msv+fvvv3fr5wIAAACAhuCrIHvssMckIylDLCaLdqzUXio5KTn8EiL4YtUXcvsvt8sN+90gY/caG/L66sLVcuLnJ0b8/oqrdcPomzBLdqCN0L5xk3/b2jp6WKavFOv2yceyYthwbTupY2AxPFdJSdTKsgK7Q7ZWO8KuXplhMUuBwyllurDMogsuy72rUWrXDnN/wflZ62SrXN81EOrVllo1c3BWumFUVKHuHpqb5hwUGtoaVStkYzwi9kFHUF5eLmPHjpVx48Zpwddrr70mf/75p7z44oty6qmnau2TapB/MPV6WVmZFoiFo6rJpk+fTlUZAAAAgIRSbve0xaVaU7V/X/XO7a3t/72dAoBoVFCmPD7vcVlbtFZmb55teN03vD8SfcVZ1yzPPK4mSZc0uSsrJf3gg0QsFsk67riobzOnpga209Oly7vvSuvrrpPs0aN1F3RHrSxbXlbp3+6ZlhISTiklukDKrLuGW3fjXcIsRmAJyhL6pQfud3dUOVxqxQxtWy1I0Fw13588Qd12221a0qtWxFS6du0qjz32mDbU/9hjj5VHHnlEW/0yJSVFWwBAhWdqxcz//ve/2vGhQwMDL/Uuu+wyOf300yUnh//PCwAAAICGta54nYyeOloOaneQvHT0SzEXFah/GxVUFmjbOTbPv2X2a7ufLN21VBbvWCyju+uCDUR0yfeXaC2Zjx76qIzqNso/qyyajSWBcUDvHW9cSK5J0QVQyd27S6eXXxZXZaVYMsKvSKkPy7q8966YzGYxp6RI2uBB2iOScJVlxd4gbEBGiiQFlYKpVSY3VFbL6oqqsGGZ0zuvrEtKcsh7tXOD9odkp0s83PDhAklZtFWOGdVdTmjdfPMBKssSyIwZM+T555+XyZMna/PK9CtdHnzwwVo75rXXXitTp06VWbNmyZAhQ7SWynfffVdbEECFapFYrVZp1aqV9gwAAAAADUG1+r25+E055fNTtP3ZW2bLD+t/iPn95Y5yqXZV+2eWKdk2T9tdpTN62NOcBc8pU0GZ8tLCl/zHFmxfEPUaOyt2as/7tt5XMpKjB0+Nya37WbNGHiMmq7XGoMwnbdAgSd1nn9g+KExlma9qrFNZiTh27TK81irZ82/vRSUV4cMy73Nw+6aPPk8+pg4zyiL5etEW7bl6TUnYVTibC5KRBHLYYYeJwxEY+Kf33Xff+bdPOOEE7RGNqkjT9yIH23fffaO+DgAAAAC7K9xMrKU7l8rRXY6O6f27Kj0BhM1i09owFavZ88/cT1d8KncddNdu3d+8rfNk+a7lcnbfs2s9Qqcp21QamN2lt7JwpbjcLjGbzIY2wGhtmE05KNN4K7QyRhwlLS65pP4+J1xlmdMltuoqueXasbLSZpM+8+eJybu6pJoxpiwqDayuaXEHwjKXNzhLixCW6dswW3qvFU8mad6oLAMAAAAANBm+AKwm87fOl8u+v8xfVeYLs3z/T38V+hRWFu7WvYydNlab3fX7lt9lT7KmaE3E16qd1RFXyMy15crerT2rS26v2K49t0xpKU2aN8RSs8bMyclxvXTLSwPhmztMZVmx3Smn/DzN83pVlTh2eKrxlFbeeWD/RKos82aVqRHCMv3qmq0jzBajAKbuCMsAAAAAAE2GwxW+mybYhdMu9FdI5abk+o/bXfaw27tjY2lgPteeHpap78wXhAX7+YyfZWCrgYZj+nZXFc647fH5zuPFHxiZ4x9/tL7hBjH7RiQ5neLwVrGpmWibb79dMmf+LJd/9r7/fOfOHYH3eqvBSnSrYeYWF/m3N1dWRa0sa5HkqVCLVFn29cItsvfd38uMf8P/Lmti2oMqKeuCsAwAAAAA0OAWbV8U9rjDHVtYFjz7zEffPmgxBwKF2tJXV1lNe9YEo2iVcqqyLL8sX9tun97ePwPOYrJo32dGkrHtUlXw+Wy6/npZNnBv2XTDjU0nNPNWlqlB/fGmAiVru3ba9h+7iqXXrIXyzLqtUvLDj1L0yady0AN3G853lZWFrIbpc/wvP0qvjev8+6XeeWf6CjK91klJEVfGtDtdctW786Wk0iGXvTm3bj+bNG+EZQAAAACABjd58eSwx+3O2ocs+uomfXij366tUnupf1vN8NpTqBDwz/w/te0TepwQtg323WXvatt56XnyzSnfyGUDL5OPx3ysHTu518mG86/f73r/dsm3npbD4q+/1kKz4KH2jcHfHrkbwWk4Trdb8qvsku/wXP/RVZulwuWWSeu3SfWa8JV7Tl1YZgsKuG5859Ww7xmUGVjcT08/fD8tKAj87K/ATDqHrnKtNkzNPC3bc/6LrwP6d/mOAQAAADSscnu53PXbXdrMsd1pw4zl33m7E5aV2csMq27uKbaWb9VaLZPMSdoCCB0yOshx3Y7zL5Aw7odx8u2ab7XtjpkdJSs5S64ZfI30zO2pHVPnd87s7L+eOieSgrffkUbnD8vil/6ov7GT5q+UIbMXyy5vGOUbzl/gcIqrsiL8rejCsqQaKt18d3tl5zYRz7mlW54clJMuJ7UNtCErpVWB/4ZUZ+iuMs+KseGoMG3qXxtlS1GFlFcH3pdaD4sGJJJmGZYlecsVy8v3nP/Ba6p837HvOwcAAADQvL3w9wvaSpU7KwPDzuvShtmvRT//9ivHvOLf1s/Uildl2QN/POBvTYxm55QpsvG6/zSdFsQw1pes94deyZZkmXbqNHn40Ie1bV+YVlMQlpmcGXKs4IMPQ46ZkpPEVV4ua888S7Y/97w0Bre3nda3CuXuWFVeKY+vyZc1FdUyp7hMHG4Rl7fqUD+c310RPixzVwf+LmwxhHf90lPEHKXE6z9d82TqoF4hc830VWfKEY9Nj3iNKb+tlf98sEDGPPurbCuuCtyftVnGRX7NMiq0WCySk5Mj27Zt0/bT0tKa/fC6eFNJuwrK1Hesvmv1nQMAAADAkl1Lon4JsbZhVjk9/7AfP2i8HNjuQP/x4R2Hx72yTHn2r2fl/uH3RzxfBWTbHnpY2169YoV0fv01SWrbVpqaTSWbwgZhKZYUKZLAgHntnIzwYZkpaKKV+vdf/l13hZxnadlSCj78UCoWLNAera++Shqcd+i+xKGV9vyFa2R1RZV8tzPwPTm9FWImXUVjdXn4sMy5KxAQJ9XQ56iu1ya5bkUnlqCqtaKKyP9Nfb/YE47uKK2SrcWVu92+uadolmGZkpeXpz37AjPUDxWU+b5rAAAAAM2bmpc1J39O1HMWbF9Qq7BsaLuhhuOqvVC1FFY4KuoUls3aOEuu/PHKkFUfawrxKpct929Xr1olm264Qbq+/bY0Nb6KOdVeqacqzfRVZcqAVgPCXsMluioqu11KZ84Me54pKUkc2+q2GmPcB/xHWFWyNlRQpiwsCYRhLm8wZdFVlhWVlIYNW7Y9+phkHHGE2Lp3l2Rd9ddz/QJtrXo5uhUva6M2P2pxZeDvekNB4OeyOwPhX3PUbMMytWpFu3btpE2bNmJvwiWyiUy1XlJRBgAAAMBn8c7FEb+MTpmdZEPJBkmyJNUqLFMVUcF8A/nrEpapoExZtMO4WmdN91W1cqVhv3LBQv92yfTpUvTpVMm7e6JYc43zpRrS2qK1snyXJ9RLsRq/t2O6HiPztwXmyF2xzxXSPbt72OvcOORGufi7i2XsgLGy4eqrpWyGMSxL7tlDqleuEnE4xFkcqMLa9eab0uKCC6Qhud3xGfBfGaHSyheW6dswF23fJYMiXKfgnXcl7847DK2TWUErYyomcddYfRZrZVk0HXPTZFl+iba9YEOh/3g1lWXNmwpzCHQAAAAAoP5FG5TfMqWlFpbVtg3TN2tLz+wdz33zzJvl/dHv1/l+xe2W1GqRCptJq1iLemrwUHfvKJrtzz8vO559Ttsu/eUX6Tt/njSG9cXrZcxnY/z7NovN8Pq+rfc17J/T95yQa9g3bdKCv/1OOUV+PftXsW0tktUzjgk5z5KVrT27HQ5xFXuCGGXrAw9K7vnnN+wYJG/oY9rNAf8bq8IPyXd5r6sPy2zVgdlfwQreeUcLy3qlBcLKSDVcljp+T5kpoXVRG3aVS6cWoStrLssv9m+/9fs6/7a9mYdlzXtiGwAAAACgweyq2BXxtUFtB9VqNcxqZ3XY0EcpsZf4K9mKqwNhQE3UKpF65053yeQnndJrk1sykjKivtdVZQxITGazuCor/UGZ4i4vF7e3LbChTVs7zbAfXJEXXGmWnpQeco3tzz4nW++9T9ZfdLHWxln2009hP8uc4vmduO0OcZUGFkrQNPTP7wuxdnOO9g7dSpF6zjAD/k2VgdlfkWToKsvsvrlqem4Rax3zPVeY663eYZzBt2JricxasV026lov9eyEZQAAAAAA1L9dleHDspuG3OSvZIplNcwdFTu0yjLVbpljy4nb/e0o3+HftlW75cTf3WJ2i1z9pVPaZbST79d+L/fMvscf1Om5yoxhhFoF0uUIrZJzxxCk1Id/dvxj2G+R0sKw3zbNuBhBuEq60hkztOeKv/+W8vnzZeuDD4X9LFOyNyxTbZhlxrCsocNCty/E2s1qtttXbAx73D+zzO2SdG8AZqs2/n1Y27cLeZ++ui7SrdW1sswRJizTt1gWldvl6Cdnyvmv/RnxGvPWFUilvXGC3aaAyjIAAAAAQIPYWRlYDdDnogEXyQUDLvBXOqk5Y2ohgGiKqjxzsFR1U1pSmlbVten662XX2+/IhnFXykFLdAPodasU1qTSGQiyjpsTeF+rYs+A/xtm3CAf/fuR/LLpl5D36ivIfEqrAi2IPqrarCm0wKoZcXoZycbKuXCtkikD9/Jvrzvn3IifZU5L9bemhgz4d8RWORj/Af+7V1m2uDT09/bf7u2kR0aav7JsYs/2kl5ZId03bzCclzdhgn87uUcP//a4Tq1laHa6jMjyfF/Bq2Fa6xiWubx/88N7tpL7T/b8zn5fHfhv74UZxvl64VTaXTJb957mhrAMAAAAANBolWW+2WP6Afo1VZf5WjUtJk8AUjR1qhR/861sve8+Kf35Z/nP54GwzOmOvTqmqqpcrvrSKWfOcMqwpYFrJDlFFm1bELHqSq0IGc6MtT+HHAtpS2wgf2z5w7Bvs4a2r/rsn7d/+BciBF3Zp52qDfVX0ocPl6QOHT2nb9+uPRq1sswXlnrbJWtjVXmlXL9svawur5IWYVamPK1trnRM94S8N3duI+e1aylj/pwVcl5yly7S8cVJ2rY5JdDuelfPDvL54F5i3hGoaHz6zIs8t6v9fUud/LbSE3KZzSbplOsJ83aVBardXpqxOuJ7D+reUo7o01rb3lbcOMFuU9BsV8MEAAAAADSsnRWhlSq+FSut5sA/T1UVV7hZZMFhmu89rvLwc5eUmqrUDNf9Y54c9k/4SrQ2Py0S6RW+6sq+dVvY9zz564PiiUgCVAujCk8aUvAsNiXc96sq9dSMt9HdR4e9jppBFk6rK66Q5I6egEwpeO897bli4aKQgK3BZ7b5K8tqH5b9Z9kG+bOoTN7dsksLxj7eWmB4XbVdFpR62m/bFuzU/i7KtJjLyJSULOa0NH97brDSWZ5Kxc0t28iOnMBqqQW1bINUweD0f7fLB3M3+PfTbZ7/Rsq8M9ccNcwiU+dn2DzBYEllA1cBNiFUlgEAAAAAGq2yzBfk6Ku1woU7PuX2cu2hD8ssWZkRz69NZZmjMvJqnR2WF4QEfP735W8J/dx2reTWjxp/5tMbi9+Q95Z6wqtoA/6VD8d8KI8c+oic3PPksNeKVEGnD8r0rYaV/3jmpFlat1JlTmGvocKz/PsfkB0vvyK14SwtlcJPPhFnYWAWV9h79v2uvJ9fGyoo8/EFZXnJgb/TDKtFKuZ5Vjfd8Zx3xdO0MAtBuJxiTk8PO9uuYuFCyZ84Udtuv3OboQ1zY2X4FTgjeX/OBrlo8hz//pAuLSTDG5Zt2FUhr85aLSu3R69sPHHf9pLmfU9xRWwr0+6JqCwDAAAAADRaZZkvGFPD+lW1k2rLVPO1ciVQYeNT6aiUYe8PC10x02KNT1hWXiaR6tmKRVWvWcKGZfYt+dpz2pAhYm3XToq//FILgboEjevS3ltRoVX8hJsJFm+rC1fLY3MfC/ta8IwypUNGB+0RiaqKC9ZzxvSQY7aePQ37Sa3biKuwSBu2Xz5njmQff3zgmn/9JQVvveW5p8MOlZQ+fSQWWx94UIo+/VS23H6H9Pn7L0N7o4Gz7mFZOF1SkyW/2h5xAH9SmEUdkjp1Elm3Lmxl2a43PT+7j76ucUMtw7LHvltu2L/mqJ6yqTBQdXnf10vDvu+l8/eTV2aulrMO6Cyj924nizZ5ZgI+89NKKat2yp2j+0tzQ2UZAAAAAKDeqYAoWmWZkp7kqb4psxurb3xWFa4yBGWbSjd5rl3lmXu2O22YKoj74K83Ir5erstiQsIyb2WZWvWwxYUXhm03tA7ZV3vOn3i3/HvAUCn/6y+pb9srwqR1EVa/rEnBBx+GPZ7UNvQ61hYtxJIbCDutbdr4K8o233Cj4VzHtkA1Vdlvs2O+HxWU+RR+GP7eNN7VME11CMtSzKFh2CG5mfJI747y1WBvT26QE3I87ZY+WccdpwWjJl0bpn7RCVdJYBGIV084U/dOtxzZMqtW93tob8+sMSXZatY+Nz255hqpkQPy5ONxB8tp+3XU3pOWHJjPlq7bbk4IywAAAAAA9U5Vi+lXm/TRh19pVk+g4GuzVNTKk5d+f6lsLNkYsT3TXR05LKtpsQCfxc8+IBdPi9x2tj3LJPsvd8ml05ziqjZW/Li8c6ssWdlisnrChbLKYsM55s4dDQHJurPPkfqmAsBI1CqitZF/7721Oj+pXTv/tjUoUPOFRSpQ3Pb004HjVbEPlDfpKsnsm0PbYP3X9IZlYrbUekXJSlfo/Lokk0ku6NBKhmR7gl1zprEFeGi1sXLMkpPtefa2Yarwzl0RqPZy62rJvjh0hBqIp213SbHJhB7ta3XPU//yhMfafXlzPt/MsmCtM23SISdVjhuYF/Jaui5gU+c1R4RlAAAAAIAGa8EMnpVV7awOqSwrtQfmKo37YZy2kuPE3yYaztVzRaksc/nCkhqkvfRR2OMZI0Zoz1aXyE2fuuSYv9ximz7XcI6vasqUnOxv98sJKo4z2UJDB32F0cyNM+XCby+UDcWe4ezROHbtkrVnnS0F0SqqVMVVVWHIAP9Te50q35zyjdRWUps2Icc6PPF4xPPt2wMVY9Y2rYNetEvl8uWybMBeYl+33n/YHRRCRqNvu3RHWKXTUFlWywH/kYbrVwdVFba7525/C27lv/9K2ZQpxvvM8lSHaZVl3iBMP7fM7V2cYv1lV0hJeqA1tk2SRTK9wWss/vG2TvqM3ru9v8IsnL55mTLr5iPkhXP3C3ktXRewtc6M0N66hyMsAwAAAADUu2/WeAKa4OoyfbWYr9rp/WXvh7x/R8UOKakOtKzpgzd3VWjI4lswIJaZZVWrVxv2Xz3GLN2//EK6fvKxWHNztGPn/RwISUzemVXlc+dK5ZIlgbAsKUlM1vCVPO7U0LBMP5z+qh+vkvnb5sudv91Z4/1uf/oZbX5Y/oS7Ip6jWkXvnu0JcnyePfJZmXjwROmU2anGz4gU7HWe/Lr/WMZRR0U837l9h387pV9/STvggMC9Vdtl3bnn1Sr0DKYPHyMtPKCO+64Z6fcSyV8lngqx9rbAQH+ltW7Av3bd1FT/38KaE040VI0plkxvWKaCMu93uOUuz0B/xVXp+e+hsktXzz1L3Yx+1rOipnLp8G7ywMkD/fuTzh0ccr7NahZzmDZTJd27GqZCZRkAAAAAAPUkUgulvlrsr22eOV4zNs4IOc9itkhRtbF65t7h9xpmlmWNHu15wWSSFiktYm7DXP1AILxQdmaJ2Hr1ktQBA8Sclh76s2SniWP7dll33vmy5pRTxW2vDoRlYWZj/dPZJBKmssyxbXtMiyCEvG9HIIiK5KUFLxm+c5OYZHDb0NAkVr4QyNq6tXR5523p8vZbYg7zM4WTccTh0uHpp/z7zoJd4ioNXZXR7Q0hY7ofXUCmVfSFUbn8X62KTVV3qYUXamOudyVMNaOsgy4wO62tceEJc0pq6L3pgjlbL+NiB0rpTz/5t10V3rbNoOvo2zNr6/bj+xkqykbtlSenDDYu3JAWZZZZmu61NrRhAgAAAABQP9Rql8qZfc6Uj8d87K8Ku3LfKyO+55vVgXZBi8kixVWBOWD9WvSTUV1HGWaWmb1D1FUFT5Jv5coY2jBXlXlWKvTZkRWouGl9zfiQ8zOn/S7VGzaEBEkqLJMwFUz3nm0Wd2Zo6ObYtjXkWCyrZNo3BWZTRfLCghf82/u13U/uPthYZVZbahVPxZyaKmn77ae1HUbjm+WVNWaM9jNZdQP/Vx19jKTss7d/P2XvvWvVhlk6Y4Y4dwUWiwi3wINqzaz85x9tO3XvvWsc8L+92i6vbtwuhXZPuPr9Dk8wu29Wmuj/gjKCWiPNqaFtiiaHQ7p++IG0ve1WST/kkKifW71ylfbs8l7H7fv9u2Ov+Jv610ZDFVnw35Daf+KMfeWO4/v5j/VrlxV1XltzryyrXR0iAAAAAAB1UGGv8A/x79Oij/x57p9SYi/R5mhFcsusW4xhWbUnLDu046Hy+GGBeVm+Vjuzb4i6asN0mWJuw3Rnq2AnMGNrl25mu/6aPulzlsnOV1717xd9/oW/1S9cKOM2m8R85CEijzwXcSVInzVFa6SgskByU4wVTDW9L5rJIyfHFMJFYt+yxR9I+doOa9L1gw+k6MsvpOVFF4V93eEdyt/xxUlStWKFVC5cGHNYtu2pwKIA4RYG2PHyK7LjhRck4/DDtX1r29B5a8HOXbhaFpZUaBVlz/fvIkvKPNdMM5vFFqFdUTGFqSzzBXTqEY1hdpmawVatC8tidNqLs2XeugL/ftdWoX+vPuXVgf8WjuoX+Ttx68KylCRWwwQAAAAAoF5sr9huGOKvwptoQVm4Nsx/dngqhQa2GigpVt2Ad+/MMn9lmYg8fNc6MbndhtU2I0nK9bRs+pTEkAeVz5kTcsy+NV+ckcKFzMDwdl/1WaTQy9eOGkm4Siq9wkrjYP/dCcqUTf+53r+tKstiYeveTdpce61YvAPug6k2VsWSmSlmbxtlrGGZ73wfV9DMuu1PPCHuykopmTbNc36EQEtPBWXKZ9sK5cttge/P6XbLi/27SseUJHmxf5fQewlTWWbO0P2uo7BvDVQW2tsb2yR9881qog/KlOxU40w1va3FgVCxd1vjKp56R/RtI/t3zZX/O6y7NFdUlgEAAAAA6pWqCJu1aZa2PSQvcvueasl84e8XQqpblNWFq7VKNOX4bscbXnOWFIetAmtR7Bl0XyN9kKXEEC5pA+aD5m5lHHKorHGHziFT1H10++JzqZg3T+ybN2uVaY5dxqAjVi5dqJRfli9t09oaArGv13xdp+uG/azKSm0xgWireu4Oky3FP3Ms1rDM7W2tzRhxlJT+8KOhQiscc1psAZ/PFUsCbbknt82VVItZ5h40IOy5Jt2qnGrFy7Y33yQp/fvH9DnOAs/vP6lz55gr9vSC/xsZe3BXaZ8T+Trq9enLt9cYgtmsFvnoioOlOWM1TAAAAABAvVHVYMPeG6atZKnmlu3bet+I557b71z/tqoIa5/e3r/vC8rUNTplBVZz3Pb4E1pgEi4UcZmjD/h3O51S9OWX0uXNnw3HVctnTZw7jYP4zTk5Mr+bS9aWBWaZGe5FXJLSu7fknn22FhBpn+9dGEDJsXlW3VSu/fla+XLVl5GH++uG2x/98dHy0b8fGc5ZVxwIe/q3DB/cVK9dK9ueekqbvbbrzTdl68OPSNUqz/wsvc033WTYr2n2V22ZU2y1DstcJZ6/hdS999Gey2bNko3XXifV69eHPd/3fUdid0Wu4lJBWTT6SjuTxSK5Z51VY/tl8Gqoltyc0HAmhsqyKocxCA4e4h+sV9tM+fXWI+WCgzwrbyIywjIAAAAAQL15aeFL/u10a7rWThlJsjnQXlftqtYCpmBJZmOb2c5XXokYipjcIk5X5JllG8aNk8033Ww4dvUVFv9iBD623r2lJrsGd5OrfrxKbp55s+Gu3zw5O6QKSM020445AkFehcPTBujz31/+G/Zzir/5NrDtzWnu/d2zKqgvZJy10VPFd1C7g+Tlo18Oe511518gO198SRu2v/WBB2XX5MmyYVzoYgsl//tB6pOqzPKHZbrwMBqnt6Iv/eCDtWoupeS772TDZZeHPV/fnhvOynLjzLPaMOsqy3whXiStrr5ae7a2aWMMy3JyxFcXWJuZZQXlxu9LvwImdg/fJAAAAACg3rRICcwDS0uKHlrog7BqZ7XYnYEKqnCBWjCzLVlaXnapf9/iit6GWTbTEyr5LOpmlu25ZnnqiKeMP8PF4YfU661J8qyeqJTqCtz+HOxpDdXfhynJOxHJWyGmAr0qZ/Q5ZOFW0EyvFLE43Vobps+MjTNkY6lndcRrBl8j2TZPWKengjvfzDA9e1BllltXwaa0e/BBiTezzSampOSQ9tJg1Rs3aQ/tvGJP2601N0dSdS2P1evW1SksW18ZW0gXjrYCaoxSB+6lPVtbtza0YVpzcsQcHJLFUFm2vSTwN3PFYT2kT5Q5ZKgdZpYBAAAAAOqNvr0wuCosmKo6Uy2QagVLu8setoUy2uqWnpUJA6GD1Rm5DVO1YAbr4siWeefNkCSL8T6zjztOG1S/8cqrIn92C/VzesKmat2/tH1VavoqOV/A4rZ77i1SUKaqxKxm4z/bnUWBUM7iFmm3SySvfSdZUbBC3l32rnTM6Ki91rdFX9mrlSecCVa1fHnEn8Pw+UEz1czp0UOnulAz0AJtmKHhqC9EWzVihLbd+88//O2a5uxsMcUwj6ym+94UJixTK2Be2tETasWNt4XV7XZpc9Yqlyz1V5ZlWj0Vl27d329NthV7/m727pgttx7bN7732swRlgEAAAAA6k1mcqDaxVfxFE2yJVlrSYxUWVbuKPdv79C1YPrmX1lycwP7UdowXRWhrXdmpzskKFNUmJN55JFR79uWmWP4XP+2t6HLMIw9qA1T/zPprS5aLb1zjS2gwXO9Oux0yx9b58pdv90li3Ys8h9vnRo56KlcvERi4So1thWmHzxMdkf3r76U1aPH+PdzzjxTCyGDZ5bNLy6Tb7cXyU3d8iTZbDYM8K9es8azYTZrFWMmizHWcIVZKbSmyrJfCowLNSjLhw+UlBrmldWar73X5ZZVY8aIY/MWbVf9zQ7MSK19ZVmp52dtkxnfRRdAGyYAAAAAoB4Fr9hXE18lVaWjMmKI5FPw5lshlWU5p54SuJYzchumuyL02mrGWTT/Xjc68n2nGVfiDK4sU1Vi/s8JCsteWWgM/XwW71gcet8OY/jXcYfnWR+UKdFmw1UsWBDxNX3rpX4GV48f/ieWjPA/Y6xsPXv6t3POPkva3T1R2zYlJxnCsuPmrZBn12+TL7YVhtyTb4i/JTNTW2zA5K3I8lk+ZP9ah2ULSz1/C0OyPOeNaJkV/6BMy8q8VWMulz8o81WWtbV5vwPvKW6JvQ2zNWFZ3DGzDAAAAABQb1Q7ZW34ZpLtqPSmQCLy/vHvhz+3e3fDvpoFZsnOFmu7dv6ZZbsqd4V9r6vCOFBfe38NwV7P08bK+TdY5O5zQv8pbU+xhA3dVKVc8Pfgm9HlC4F2VhpX1gy3qqWPL2BzZXmCq5zS8PccbUVPbUXNCFxVgco1V6UnjEnu2UOSO3raO+NFXxFmDqos65S/SU6c8b2kP/SAOHbuNLRn+hZkUC2Yij0/MMPNc8Aec1hW4XTJR/m7ZGOl5z3P9usiXwzqKS8PqKfVIn0riQYFuKq6ri7W7fSEfK0zqCyLN8IyAAAAAEC90YdElw28rMbzfeHS9nLPAPrMpEwZ0GqA5KXnGc4rnfWLlP/xh38/qXNnSfEOe/fNBLt+qlMe/PNBcVZWyrbHn5Dy+X/5z3fu2hW2DTMadR8HdT9CFncJ/af02up8/7Z+6pTNYguZSxaoLPN8N+V2T+hxbNdjDStxFld7Btnr7SzxhEO7pCyk5VMvtcIV0rIZPPesw1NPSr9lS6XLe+/6X7NvCAz5d1d77tnXJhlPJosuXNSFZVV2u7x5941y3fuTpeu0r2XTjTeGXSXTFzDZtwQqtCKJFJa9tXmHjF8a+HlbJ1vlgJwMSauHqjKN93frdhl/ab4VPT07/tKyqP63ZKt8Mt/T1kxlWfwRlgEAAAAA6o0vJFKD56/aN/KAfJ/0JE/F1IaSDdqzbzXHdKuxBXDDZYHgreOkF6Tn99/5AxjVnqe0KRJJrXTLznfelp2vvCLrzjknanVVTW2YSu8Wnhli8y472HB81s45/u0dmaHhnyEs86+GaZxZNqLLCPnfaf+TG4fcGBKWqXbST1d8Kv9sXWBYREBVzwXLLnPLObf8KGvPOjtkRUvFd8yU7Any0gYN8r+25rTTA+f5Bul7z4sra5iwrKpKdv1tbBEtn/172MH/aft72y1jaPO1tGoV9vjCEmN1YfpuhmRrzzgo6usm3/WDFpcwawtTSMwD/jfsKpfL3pzr30+3MY4+3gjLAAAAAAD1prTaMzz9lF6nRJyjVblkiaw44kgpnjZNWqS00I6tLV5rWE0zeFVIPVexsQLLmheoQlOVV5uXBoKF4LCsIj0w0N8dYTEAvVSrJ9hYNrSdtLz0ksDPoCu+euZEi6zvmyudp0yRFEuKdkwtWBBpZpmqLLvhU6fk/ecpaWm3yd63vi1nznBKcVXg5/r434+1If5mbzgWLSzruMMtVrtL+15LZ84Med1X0RY87ys4yPGFZfGsLPOFXDknnxxS+aUq3grPPz/kPXM3B7VaikjuOWd7NlzhZ9LpqZlg4VQEvdfkq+qqJbv3ayzap4b2TW+I66o0Li5h9q7oOWWvbjIgI6XGEHDJFuPfe9ss73sQN4RlAAAAAIB6U2r3hGUZyRkRz8l/4AFxbNkim677jz8cW1u01lBZFhy0WVu3DrutJOnCMqtL5LvtgcDI5Q2ACt7xtB6u2re1VHizoIK+nlln0aRZPcGOWrHTnBEoISvVLWaY38IkU6/eW9IPHCo2q81/vo+vTVSFUW6XS5buXCJDl7slaeka2frwI5KyYqOc+pvbUFn21hLPYgap1W5DWNY93y1vPuaQC35wyg373aAdS9YVYjkLCkJ/CO8iAb7QLljRV18b5pfFMyzrPGWy9Jr9m2HY/yZb0EqQQSb9uTDkmBrwHxwqpeoq5Hw6vfxSxBCssoa221hdcbVFbh1rkfI+Ncx1896HO2henm8F11Gts+XOnh1q/LxkayDKGdGvrRzco2XdbhwREZYBAAAAAOpNmd0zWysjKXJY5ir1nKP0XVFhqCzzhWU9snsY3+MNvXLPP1/SDjK2v+nDs9SqwAqDigrllKoVK7RnW0GZ3HuWRb4ZYpJ/Lh5e48+TlpTmb53MHjNaq4rKOHaUFKUbA5lfN/2qPeeleYK7TaWbdDfoXfFzyRJZfvophuqwok8/9W/bCwNBl+/76OO9TKY3b+m0QyTFLjJ6jluGLvK0eqboRnxtueNO/3fl46to892H0uKii/zbm2+8USqXLw9Ultni14apWmWt3nDI55ltxVJtDVT4BbOFmVnmvyddWJY1xrhaaY/vpknGoYdGvO6Pu0JnwtVFSZpJVrczRV1UQd8ebFhcwmyW5E6dQk+OUln228pAC/GL5w2uc0UcIiMsAwAAAADUexumbxZZOJbcQJtc2/wqQyWWr9Ls5v1vlhN7nChTDn1RHNu3i8s7pL7l2AtDwoK0oUP926f+6pIMXTZRsTCoSqnaLis7mGTK0RapspljbsNU95fUoYP0njtH0u+/I+L5XbK6GCrlFJMuGHIvXi6dt4V/b1p+ofa8uXRzyGvF4WbW3/2UpFe4tfBMr+DdwAB/fVimryxrc5NnTpqPfdNm/7B/U3LkICsedtidsj3X034bTnKUsMytC5Wcu4xVdEnt20e8pv59ypsDu8nuMtU0b8wblukXXui7+B/jNWIIvl6Ztca/ba2vxQiaOb5VAAAAAEC9UCtaLtzhCacyk3VT74NCC/u6wIqEaWJs+es5f5usGj1aUjfukPuG3yeZ594sKw4JVAtZWoSGLBnDh/m391rn1mZ4+Wy+6WbZ+dpr/v11bQLva5Om26mhDXPe1nnaIgSqWqjM6RnQr5ds9vwcXbM9c6y+X/e93DTjJnG6nIEB/14PTwk/K+2Mr4tl+a7lcsvMW7R9k24Vxfk9w/9z/sqvXWILCssq/1lsPBAmLPNVPfnYt2yWna96vidXcYnUl2qXS6bvKpFtuYFWwjePPVnmvREI+GxhVvX03a9ZX/WmmznX86cf/e2u4VTovsurO7eRY1p5Khh3h34l07CCXlez1CKGYxEqyzYXBpLfE/eNHAbujmlrp8nE3yaK3Rm6sEJzQVgGAAAAAKgXF393sX+7V06vsOdUr1kr9s2ByqkUl7GVba+np0n1ylWy+b+3a/vOXbv8r1latxJzavR5V0kOtTqk8di2Rx/zb3881BNKdMvuJmf0OSPmyjLluE+P0wb3+1pN9d489k3tuVVqK0MIsaJwRdQQR6/nFpHTvjxN/t7+t7af7O2eVMoidEbuv8JtaMNU3E5HjZVlvpZWn4p58wKfNXu21IdtVXYZMedfqXa7ZbsuLDNlZ8l5QwdJcXqGoQ0zdch+knfvPdLts6mhq2Kq+z/3XEkbMkTa3X9fjVVlS0oDodMJbcIvABDvsMxkNgZjppQwg/lrqCzLLw4sDvDIaXtLfbhpxk3yyYpP5KvVX0lzRVgGAAAAAIg7VZXim7N1Qo8TJCclNJAo/PhjWX3ccYZjVZWhwZPiKgs9bk6ueZZWVoVInqebMazC5GqtRfTjMR8bgrBIgs8575vzpKQ6tPJqQKsB2nOK1RiIqHPDzqiKwQP73eXfPl32C3uOWqzAZg+qSnK6ws8ssxjDsrb/vc0fPhV/823ghRhWnKyLCSs3yb/lnvBne06gQtCU5anycnmrx3xtmObkZMk9/XRJ6dvXf27eXRMk95xzpOtHH4m1ZUvp8vZbknPqqVE/94T5K2X0fM/MOmXvzHA9rfVQWRZUvRc1GItQWbaz1PNd7NMpR2zhVjONo7IwIXBzQVgGAAAAAIi77RXb/duRWjDV8PlgPdI8M77Ctd0Fz5mKtkpj6+uuq/EeXSYVyJjk1F6nSrIlthUfg1flXLpraUioYLMEQrxUizFcUytcqva7LS1qP5T90FYHaM+m1FTpN/JMw2vZp57i+bxqkXZBC2CqGVnrL71Mtj35lDbs31XiCfcsGcY5cqolMGy1Uz1ZXeGZT6eU6z7XmZmlPad7K9+S7d52wDCrd1qysyVvwp2SOnAvw/HFm4vk/96aK0s2hw7xn1NcPyHQ1vKt0U8ICtPcjjBtjv4ALVJY5vnOWqXHb4VSPZc7EIxG+u+2OSAsAwAAAADEXX5Zvn+7ZUqgxS4Sc6bnH+Yp7gj/TLVYRHyhiZezNPIsLUvLyAPjfaq83ZC+uWJ1rR7aUuZZYTMzKVMOaneQvHPcO/7XgivL7C67VDoq5bMDYwvLzLrZWhWz//AcS0mRzKOPNpzX9uab/dsHLzUGLaXTp0vZL7/IzpdektKfp4vbbtdmvVnDtCoGt2Zq177tVqkPKbpKK18VmXJKH8/vI9VbOZXsDZX0CyPU5LmfVsp3i7fKcc/Mkg27PDPlSh1OcQYFrrd286xWGo9w6dVFr8qP63+MuQ3TVVIa7qyon/fvVs978rLrJ9ScuXGmoW24uSIsAwAAAADUa5XNWX3PqvH85K6egKRaN+x/n9UuQ2WZqzooLNu+I+L1VMteTaq8uVCKJfbgwWoKDZMWbvcsYjC03VB5+ZiXpU+LPv7XspKzjPfscmoBy/S9TfLWEeH/Sd76+uvFYfN8ziH/eMKd/Va4JP+uu/wrQaqfL++eu7X97BNP1KrNYrHp2mu155S9BoQfLh/UKtjptVelxYUXSn2w6j6/R3Lge93/4KGBgFSbO+dpGy01mbQFAWKxSTcI/7Hvl2szynrNWiTXL9tgWAHz2i5td+tnqHIGquOUlxa8FPnkoO/WXRmYPxYsuIpSeeO3tfL6r56VMA/pFZiFtzveXvK2/LrpV//++J/G+7d/2fSLNFeEZQAAAACAuNta5gnLju12bEztXEkdO4TMJrv9A10wYrGI2zu7KrgaLRwVKNWkOil89VdtK8tUAKao2Wfh2jYXXrBQhuZ5AiCn2xOWuU0m+adrICxydApUOFlbtpC0EUdp21d97ZLBK1xyy8e678IbMuWecYb0XfyPtH/4oZgXDfD/HGmh96pULV/u37b16iUZwwIri8bTzmqH/FYYqKyy6iq0/NVt3kosX2XZT4Xl0nnGQq1CrCb6GLDS7pTPtxVqjY0f5AcWiBiemxl5NcoYqQUe9MLNr4u4GmbrMIFXmPtRwdl9Xy2Ru74IrGp6VL/dC/mUOflz5OE5D8sVP1yx29fa0xCWAQAAAADiTs3mUnJtuTGdb83N9Q+ff/TQR0NeV5Vlzp07DcdUmBNJ5ogRhv3CnKSIbZi1qSwLnlmmFNs9P2taUvhB8SqQsVltgcoy8QRDDt2l3KmBcM+cni4ZeZ7wULlVH5RpF9RtequvwoU+bW65JeLPob7PcJK7dfNv555/ntSHn3YWy4Bf/zEcK8owVuApJm+4lOGtJnN4f9b5xeWyuLRCTv97pXy2NWhAm1dJZWAFULvTLR1sob9/W1BbZDwqy0rt4VorPSyZntU9fTpNejHyhd3GKrlXf/FUlPkkWXY/ztlcGliFFkah9aMAAAAAAOymCkdF1ABJseTmirPAE3aYs7P9YVnv3N5hTraIPd84QD3zyCMiXjt49lZpVpJcd5FLpjzpDG3DtO5eG2ZptScgSTJHru6ymDxBj8PtEJevlVAXiLjTjGFZtPlcpghzrVSV2Y7nX5DCjz6SdvfdK86iosg/SISwTK0uqa6RNvQArb2zPjy2NjDPzmfW8CPkwrJdktKvX+CgNxyzeivLnN59tYLmWyt3yvKySplVUCr7ZadLp5RA263D6ZKNujZMlYkFNzV2SUkWy25WlYUNy7x/C+FYWhkryVL38qyYahDmloIXKbj/ZONiBvGYtwYjKssAAAAAAHHnWyEyzRo5LDNnZPjDobTB+/lXCOyS1UX2br234VzVZuiuNgYTLcaOjfl+rLZUKU8x+QMyQxumZffaMBdsX1Bj+OALy1RQ5qssq9SNVXMlB8IxS06uOPJDAyW/CCGPqjJrfc146TVrpmQcdphImCo43Q8S9nByx47S/sEHJOekk3a7RTESVRkWrNhilXZ3T5TcswKrfPo+v7vV8507vQHfXSs3aUGZz7LSQDC2rqJKRv66VKodgd/Fgd1bij1oBlh5jLPPatuGqcLQSAzfZ5iFFAx097tkizEsO3v/zrW+z7AfoYsQ31z8ZlyuuacgLAMAAAAAxF25o7zGyjLHVk+lWOcpk8Wc6g2s7A6t1fHtY9+W1P32C1zvjz+k4L33a1y5MeL9mDyhRoUuoKpK8oQXvhbJurZh+vyz458a36dVlnlDte05Jnn3MLO8MtIs1k3b/Oem9OktOWcGQqO60q++qFpWbX376l6LEqTVs95poeFkpW7VTz9vOJbunQnnqyxzBp26rToQUF27dL0s2RWYe6ed73KHrIJ5QHb4mW27W1lWk/aPPirJPXpI96mfhn09XED555rAnDXFHIf20eBFBB6dG9r63JwRlgEAAAAA4q7cXh61sqzo88/FXe0JsCyZmf7gy+10+kMD3+s+ZbNm1eoe2j/6iH+7wOVpj6uwhVaWpVpiW0lSXyFW2+DE9z41s8wXUqgqtc8ONsv/BquwbLv/XFNysqQNHqQFK+GYfMFiTXStlua0NLHq2wAjtGE2hJwkz3dxRIvAAg1hV7n0zuVKcXrCMGeEgK9EN/D/96IyMRcbV0198NtlUmE3Lgpwb8/ATLh4VpbVJHvMaOnx9VdR5+1pdEHWqu2RWzvjVVmmbCndUi+fk4gIywAAAAAA9daGGW6FSKVigad1UUnq0kXEO6NLzSzzcVfVrmonWPaYMf5tuzdnMVSWeQvTsmyhw+XrUlkWrkXTx2q2+lfDVA/f+S+NeEnuPPBOsbZrpx1LO/BA/3tS+vYJfw+ZMd6vLhCztmmttbvqblYaS7W3iuyiDoHwripMZZlvwH9SmScs2r+lZ65dsHnets4qFbi53ZK0pDDknF82G4+118042x0bC40tkrvNV1nmDcvsTpdsLQ78d3D83p6/k3gIbhs+5pNj4nbtRMeAfwAAAADAblu+a7kWAvVv2d84syxCG6azuMS/YqOqIjMlef55qmZ1FX39tWQff7y4KgKzqHaXPyyzGVfDHNZ+mGTbwocwNVWWDWw1UBbtWOTfj2VmmfqO/JVlYpaDOxysbVc+v5cUfvyxtBo/3v8ec1b4+zJnBSqyorFv2ODftrbNE1dJcZNow7R7v6dks0n2zkiVhaUVMrp1Tsh5VStWaM/VK1dpz611FXVdU5NlVKtseXHDdllfWaW1X/5VXC7mrYG/GVuqVaoqPOHrr9uK1dKscf9Zrv1gjqRFGSGmZqep/CvW1Sv9FZbe0Hixbrj/N9ccIr3aGlfUjFcbZiROlzNqQLynorIMAAAAAFAnqu3wh3U/SGFloZz25Wly5ldnSlGVZwXGgirPKpe5Kblh3+sq8YRllsyMkPljm2+4UVxVVYawZ3eVeDO7ymSToQ3z8cMfr9V1VKXcuH3GyZX7XimTRkwyvNYxs2ONlWVPz39aZm6cGVKJltK/v+RNmCDW3MD3Zck2VpDlnHGGmFJSpO1NN8V0r75AUvv8li3EbQ9U7bnKQ4fsN3RlWZLJJO/s010e69NJ7u7Zvsb3JesWQVCrXx7jrTRbUFIhH+TvkhXbSiR5gefvTqmwiLiyPO8x2etn5UeTOfJA/1dnrZbed3wrB9z/g7ZCZ0zXS/W0BLsqPQsY7CrzVJWlJ1ukf/usmEO3WPgqHKP5YPkH0hwRlgEAAAAA6uSNxW/If6b/RybOnug/tqpwlVaxsqvSM5S8ha1F2Pc6Cz1tcZacHP9KjnoVf/3l384999zACyaT5N17j3T/8ouY7vGDE1vIsg4iHw03h7RhViap6qbat+OpoEwFZqoiLdUamHf236H/jfieVqmBlsP7/rivxrZN7fUU42yyvLsnSp8//xBbz54x3adJF6yYs7MNbZnV69ZJYyn1BkfpavXO5CQ5r31LSbeGVi+pFVD1knSBqgrLBmcZqxaTFhgH4YvFJO4k789sr7mKqrYWby4SMYUPy1wut9z39VJtu6DcLvnFgdU7ozF7wzK3t6pyZ6lnJtp+XcP/d1RXW8u2yoN/PljjedPWTpPmiLAMAAAAAFCnwebP/vWstv3j+h/9x7eVb5MSe4k4XI6olWXBYZlvZpmPY/sO/7ZJF6SoACX39NNrHpDu9fsBmTLhAquUpZrCDPg3+Su+6qrCURE2EAvWO7d3yLGawjK9lH329rSrJifXqc3OkpUtLS+7NPBa0OIJDanQO2zfN+g/krZ33GHYt+kqyzrYkiUlqMrKVGYMruz9c0S8YVnyPwVi8rZkxsvnf29WJWthX3MEzWCrjLGyTVUO+irL1O+voNzze2qZHp8Zaz4vLHghtvuRxptt15gIywAAAAAAtfbRvx+FPa7CsoLKAn/LYoo1/MqNjuDKMu/MMp9db7/l304ffkjghaAKtNr+Yz94wL8KoBrCwe09s8n0yh0xtEJ6q6tyTjut1p+pXwjA1ruXpPTp0+hhmVr1ssK78mV2mGoyveyTT5Lkbt38+zZdpVmL5NCQ053qnfeVYZXKo9uLOys5UFmm8tjl8R3GP39dQcQ2TGdQWFZRXXPLo76yTNSqsHa77Czz/J5y0+Ibltmd4UM+eBCWAQAAAABitrpwtUz6e5LMzZ8b9vUdFTv8LZi5tvBVZW6nU1zFxcawTNdipx/qnn7YoZK2/5DAe2s59D84DOu8zTjgv6GohQ7O7HOm4Vi0BQF8un38kbR76ME6hWW5Z58lllatJOPIIyWlt7GybXdXGq2rIkcgNMqqISwzJydL5jGBFRqtukA1x/veI1p4FztQg/S9lWPpB7QNrPZp1f3+ve2f+lU4d0dhhT1iG6bDGwj62IP2Y2m9VQtc+NowW2bENyxzSWz3k12LxS/2JKyGCQAAAACI2f/98H+SX5Yf8fVKZ6XsqvDOK0v1zFmq+GexbH/2GWlz/Q2S0qe3OFVQ5m0RtKhZWmHCMleZZzXN1IF7awGCOT3df2x3Ksv2Wheo+Nm/yzCJl4ykmlcpvOPAO+SzlZ9pCyPESlWD6SvCakNVKfX+ZVbY11yNUFlW4XTJJf+s1bYzLWaxxFDVZ7IFQiKzNUk62JJkU5Vdhud6vu9n+3WRF9Zvk04lTrnbvUXaZ6dIVm6q7Cr1hKpuq9lQeXZH93ZyeafWIZ9T5XDKXZ8vlkN7t5bjBraL6ecpr3KIyRpbZZmaYRYLbU6b+m/B4dBaMT/7a5N2vF12+ArNuvK1SdfklgNukeaIyjIAAAAAQMyCg7LDOx1u2Le77LKzcqe23SLFE5atO+88KZsxUzbdcL227yzwtGCaMzL8Q9yDwzIfa9s2/tUi60I/F6xnTk95+djAvi3duNpkXZzW21Px9Z/9/hPT+QNaDpCmoDHaMF/csE3+LPIEngMyAgsjRGO26YbMWS0y44C+smjYAG1hAKVVslUm9GwvrcyeSrNOLdJkQGbg2oZVMJPNckWnNpKsW+jA59P5m+T9ORvkynfmx/zzlFY5REzOmGaWxZiVGarL3p2x3H+dXm28FXRx8t3a7+IWAu+JCMsAAAAAAHV27aBrDfsl1SWyumi1tt3GnSlbJk4Ud6VnJcDqNWvDD/dXglY+9B9u21Z7trYOrQaqLRXs/dUjUM2Ukrb7AcQdQ++QL076Qk7vfXpM5z94SM0rEDaExmjD9AVlypDs9JjeY0oOhGUqUM2welbQDFbl8IRiKUkW+b9OnoBVcafoWj3dbrH62jODzFkTtJJmDdTw/TI1h8zkiqmyLHg/GlOqJyyb8vNy/7HMlIZrDDTrAmbVPtwcEZYBAAAAAGLyz45/Qo71zO0ZUrHyztJ3tO1BP66Xwvc/CLyohpbrw7LcwEwzfWVZ3sS7/NtWb1hmCNbqOLNMhQC3Hn6Pfz/ZtPtDyyxmi3TL7hbzQgHtM9rLNyd/o21nJsW3Wqiph2VbqwJD5S/tGFv4qW/DtLaKPGus0rvCps1qlt7pKTL/IE8lorOjLpSLMqbrU2+7ozJ/vWeBCn0wll/kWZ1SH855ArDwF7V756P5P1r33pqYUzyVcS0sgaq1Vpm6Crt6pP4m7xt2n38/ydyAg/2aEMIyAAAAAEBMpq6YatjvnesZGm+zhP+HfFZR6FwkNa8sXGWZyWyWDk8/Le0feVgyR470H0/u2jUkWKvrzDKLySLH9j3Rv291N8xKmME6ZXWSb0/5Vr4/7fsG/+y0/ffXnrNGj27wzy7zBkhfDu4lebakWg+8T+rYMeJ5+soypX1K8v+zdxZQTtxrFL9xWfddYFnc3anQ0lIopS7U3eVVX91e3d2dulCHlgpQ3N1ddpd113je+Y/PZJLNLrtI+X7ncDKZTCaTZLLvze2998PVrMjfZICzu1BSH6W76/z3FqvuPzZtE0Y9MwszN8vTISqEKZWGMM6y33b/BFPM1paJZcJEzHRBJ7zn5J6ItR0YZ9nQzKFItLVMmP43QQX/BEEQBEEQBEEQRFTsrOYnVIp0iueFrHfGvYOr/rwqZPuY8tBCfm9+vn4Mkwk4E+TJh+1ffQXGmFipsyrpkotR+d23iDt+bIsjZWyZOcFEgplyXO9A0yGuw8F53TffQO0//yBu3EkHTSyLNTXDt6MQmazZ2aqHNuyrxo1frsQlI3PgErrJHIJYxniye3s80DULpxaswG5uX/ovUVandtl5/UHM2VqC43vy58eURXx8+Kule3FSH97puDqXP4djbAbIfjmerRVb8eb6Z+HsCNRufprzKbUkhmnxugErMKIT3/3XWrBewXBc3e9qDEgbgAt6XoA+KS3rCfw3QGIZQRAEQRAEQRAE0exy/+5J3bnpjozhmcNxapdTMX3XdNX2zjU7Qvbh2bcP3qLCJqOV8SefrL54TUpC97lzOQdaS2HOMsZPdw1Hzd4deOS4C3GkwaaPJp555gF/3U/3laHMyzsNnU2IZXvK6rFsTwXOHdIBNsUkUNFxJXLqGwu422dmbEG6EFNUdnuxaGyMyYTusQ5OLAvnZWOim5bbvlmDtY+Ox5aiGmmdWLZfUuvCzV/xgwCssXtDxLIz3vsFlnb8sjXtT3hKJ7YohmlmYhm7H6ZnraV8suGTsC7MQemDuOUHRz2IIxkSywiCIAiCIAiCIIgmYX1NFS65BP2j8R8hyS5HI28bcluIWKZH7Z9/oWY6v50pUYjHRUlLhDJtZxnj/ms+hT/oh9lIl8QHgnyXB/duy5fuswmWkbhr6lqs3FuJveX1uOn4bphz2T0YMiKyy6mklheW4uyhktiQxBiwwKu5oAE+fwBmjVhX2cBHKjsmO5Fb0cAtJzn5/Zz86nxpu1qXDxNemYetxbXSunrwrjMlPrgkYc6WOpcXyyL0pYWLnlrd/GCMcEMJWsobq9/QXZ/uPHhOy0MN6iwjCIIgCIIgCIIgIhIIBnDqT6ei0dfI3X/l+FdUQhkjMyYTMRa5TH2AhY9oMgwWC+JOGscti0LZ/pT2709nGbfOYCChrI3xBoLIc3lw/cY9OHP1dtVjzO0VDhZXZEIZ46dV+zDgsb/wXE06zp9ZFtXrdkhSu8+UYhOLV87YUMQJv0/9tgnvzOFjxXUu3vHWJyse1xzTmVvulBo6rXNNXpVKKAuHwegJfV/NcJZ5i3gH5+WLv+ZujVEOj9hfrCZ5mMKRDollBEEQBEEQBEEQRES2VGxBbm0ut9wzqSfG5fDCl5Y4qzzd8bUGuUi/x5LFcI4cFbK9Kb55zrKWoOwsU/aVEW3Hn2XVyJ67FsMXb8IvJVXId8lBxUUje0d87mRFuX5BtSts19eOEn3RalDHUAE2v5IXeRlr86qwt7wBH8zfjef+2ILSWjcaPPzUSafVhMEdeRFYXNcSDMbQSaOBZnSWubfKgwEYZlPbiWULLuCjrEfy5Es9SCwjCIIgCIIgCIIgIiI6shipjtSw23VL7CYtB6r5rqfY446DMSYG8adMDN1vvCyuHQhnWaRjJ1qPy9dzdfohjEuJRxen/uRUEdFV1hTjXp6nu75zSqgjrFEhfHn9AXy1jBd+GdWNXjR6+cftVhMnmInPaY7ApSJELAtGO4iTP44BA1T3WzuGqSTBJgvWJJbJkFhGEARBEARBEARBRMQX4GNqjNuH3h52u0dGPYIRmSPw+tjXEWjk3Ty23r34i0+nM2R7U8qBFa+S7a07VZBoHu1saudSi8WoMIzvk6Fbhu9VFIaV1Xnw/rxd0n2X1y+JZU6LCQ5BLGvw+PD5kr0RX+/EXnLH1/ic8Ugy9tKPYRr8zYphtnuGTdAUCAYphnkQILGMIAiCIAiCIAiCiEi1h58W2C6mHXol84KAHlmxWfhowkcY23EsAg18UbrRwYtkBmtoH5Ktu+xEayuUBf82U2RXE9G2nJgSLy1vLKjGwMf/wntz+d6wpmDuqqbEtXaJoX1lDJ9ffh6LXSqZvq4QZbW8uOVQOMt2ltbj0V83Rny99HibJMBe2udStEsQ35+mzd/gRb3bhzq3Dzd/uQoz1vPTYMNhycyUlrtWF8C8HxNg9RiSPoS7NRvUgxbIWSZDYhlBEARBEARBEAQRkbWla5vd+RV08c4yo8OuO8ky4dxzYLS1vXhlVFz22s38sRDRwYrw2b/moN0+0SyfM8ckxUrLb8zawU2XfGbGlqj26wsEUSFMrWQ4LPx+zx+WLa2zC+u0jOuTIS2L0y5F3p27Ez+sypeeH2uLfkLq2UM6SAKTxWRBWgwvDCc41e42g6kB9/+4Hqe9sQC/rS/EjV+uirhfg8KFef36X9DKWpkUvbx/5P2q9Q6Lvth4JEJiGUEQBEEQBEEQBBFV71dznFkBNy9sGGx2+AOhZemxRx11QD51cpah2WJXsduLt3JLkDVnLfdva70r6ufX+2VX1V2dMrBidB8clxSHd/rkqKZgxtnNqtecvq4A789Tu8w+uGwYlKnK4hqXtL0YnbSaFWKoRV/iOG1AFu6fyDsii4R96MEEuE4pMbAp9qmNW2onb7JJsWKvHxPMGI32RartjGZ+GMHusno095xN8NS1urPM7efddQ4zL47dP+J+ZMVk4YERD7Tq6xzOkFhGEARBEARBEARBROS3Xb9xt6PbjY76kwq6eFGi2F+JY745Bh+s+wCmVLmjLG5iaOF/W6AUHkRxgAgPm145cNFGPLGzQFr3yb6yqD6y74sqMGLJJv6zNhpxd+csxJpN+HZQV5yVwU+ZFBHjjox9VY2449s1ePp32WX22VUjcFKfDGx6/GR53aK9nFA2+Im/pXV928nRzhirOew5oHSXhYPFMFnnWfsk+TxZdN8J6Ntef2prosMKf9AvTV31BuSpnyp0pmM2Rfp993K3+bHpre4sc/lcKvH7ot4X4a9z/0LH+I6t+0KHMdH7CwmCIAiCIAiCIIgjjmp3NfbU7OGW7aboY4wBN39B/mfBbNSl1eH11a/jiu/+RPHzLyDlqitVIlabokgFxlnbfvrm4c4Nm0JL7RMUUcpwfFVQjju35kn3w5i8JNbm8z14jE8W7oFX0SvGGNMjLSRa+e2KPIzskoyqBlmUOmtIe9z343rVc/TQusXaJzo4kU6JGO2Ms1tUPWgmxbnKHGqxdjNXus/ENaWzzOvXF8sMLRDLjHG8CGgJ+FrdWdbg46OoTkvo0A3iADnLnn32We6P4O23yxNTXC4Xbr75ZqSkpCA2NhbnnHMOiouLVc/Lzc3FpEmT4HQ6kZ6ejrvvvhs+nzyBhSAIgiAIgiAIgmh7yhplV9HR7Y+O+nlBIYZpsssuHUu7dujw6itwDBiAA4XS7eM0kzjQEqq8ka/F2aRHpVDG6OwIH9llccQ1eVXS/Y8W7FY93j1d7jZj3HoCPwji2O6p+GdrqbT+oUm9YTObsPPpU7D2kfHomRleDNX2mSU41JM5GeIkzNQY9TCKJbvKpWUmpF08MgcXjuBdWEpnGfunR5q+MS0iAQt/fMOLt8CkM+Fzf6j18LHQWIv6cyYOkFi2fPlyvPfeexig+UN4xx13YNq0aZg6dSrmzp2LgoICnH322dLjfr+fE8o8Hg8WLVqETz/9FFOmTMEjjzzSlodLEARBEARBEARB6DjLGNlx2RiaMbTZMUzYQqdgHkiUYtkBc7P9S7iqPR+bLWtCLNtYp3ZoMd7snaO7rdcfwNgX50Tcn9mklip6ZfEuq/nbyzBtrRwPPU8o92diUoIzVPyK5Cxj7rD594zVdZY9dGofJDkt+O/4Htz9/Cp5KEB6nFoEVDrLwg3AqIv7CoN6yCIfoyRCdxqjcQ0/VEN8f20hlpHT8iCIZXV1dbj44ovxwQcfIClJziZXV1fjo48+wssvv4wTTjgBQ4cOxSeffMKJYkuWLOG2+euvv7Bp0yZ88cUXGDRoECZOnIgnnngCb731FiegEQRBEARBEARBEG0P64e6/I/LueW8Wt45FPR44Mnf1+TzvIWF3PKcEnXZ+YHGF6CEUrTU++VBDCtH98HgeN6J16Ao7ddDbwBApzDOMqVLKxz5momVouNLi547LBxOTZ9ZvN2M7GQnbh/XPeR1OqfGYNXDJ+GWE/jH0uPk+PFYTdm/KJYxVxkTzMKx0/QSVjw0Dl3SYrj76/fJMVQ9jOny65hbUSxjv816Lz9ogMSygyCWsZglc4eNGzdOtX7lypXwer2q9b169ULHjh2xePFi7j677d+/PzIy5AK+CRMmoKamBhs3bmyrQyYIgiAIgiAIgiAUbKrgy9qVlLz0MnaOG4e6+QvCflYVH3+CQF0dt+wxhwoL4UStnVU7uYv51iRs6ToBX0D9WRe6vVI5fzubhbuNRiyr9vEiW7bdinMzkvBe3xxYwgg8hVVNT9asdasFzhid4v53LxnSrG+QubPibPJ+BnfkTT0sximSrIhfKl2Iz587ACM6J+Pzq0eEuLzESa9MKAsXwxRJjbWht+CS215SF/Fct596unwsgciff3No9DVK0VGKYR5gseybb77BqlWr8Mwzz4Q8VlRUBKvVisTERNV6Joyxx8RtlEKZ+Lj4mB5ut5sT05T/CIIgCIIgCIIgiJZT6aqUlm8edDN3W/Hpp9xt8VNPhX1eyQsvSMtKsUycwqfHPfPuwZm/nImp26a26ldGYpk+j2zfhz4L1yPPJae3NtXx30+vGDsnFjmEOGRjE2JZpRDTPD45Dm/2ycEZ6erJl0ru+WFdk99ZB8U0Su3kTJF4RQl/tMTZ5ZPxnCEduNv8StnF1jmFd31p6ZoWi++uH41ju4cOEBCFJxbBjOQsk45BEOyenbEF13++Mux2AZvsZqt+9x20dgSTHStNhz2AYlleXh5uu+02fPnll7Dbo5+Usr8wYS4hIUH6l53NZ5cJgiAIgiAIgiCIltHg5YWEvil9ccPAG1SPeTVD2pSYs7J0xTJxCp8ef+/9m7v9eMPHrfp1dYrv1Kr7O9wp9/hw3cY9eD+/FDW+AO7YnBvSPdYvjherRGfZurpG5DbyEx0/zi/F27klujHMHHvr9NO9eN7AJsWyGIVLLFqUbrFEoeNM7ENjGJsZd2SuMlEssxqtTTrLGDM3y5/dX5vC/4b8Csdb+TutL5axCCZ1+B1AsYzFLEtKSjBkyBCYzWbuHyvxf/3117ll5hBjvWNVVfLkCwabhpmZmckts1vtdEzxvriNlvvvv5/rQxP/MdGOIAiCIAiCIAiCaDlLCvleaafFGRIZCzY2YvuxY1Av1OkosffpIy2XJagjYAeaJ45+AiflnIQvTvnigL/2ochNm/bi1xL5enxBFR+XZby2l7/ubm/jhaTuMXLv2DdFFThx+RY8sH0fHt9ZgBoheslYU8uLoIOEjrNwBDSxz5vHdsWUK4cj1mbGrSd258rzx/VOx6guKRH7xhgxtqZdXFr2VTWGTMecPKwDnjizH+b89/j9ci1aTBaYjU0LeGV1vOgoEi6K6Y/CpdYS6rz8900RzAMslp144olYv3491qxZI/0bNmwYV/YvLlssFsyaNUt6ztatW5Gbm4vRo0dz99kt2wcT3UT+/vtvxMfHo4/ij64Sm83GPa78RxAEQRAEQRAEQbScqVu/424rVy7DtlGjUfbe+6rHfaWlyL3yKuy5+BIUK6KXQR8vImQ99RSCCjfPwRDL2sW2w8vHv4yBaWq30pGK2C8mkmoxwx0I4L08+fp7VQ0vfqVZLRgguMxe3lOMjUJMk1En7IcNBch38d93n1h1fFLLvO3qiZBmoxHH90zHukfH486TemD+vWPxwWXDohLGWuIs04N1ll06KgedUvUjmFGLZUZLiLPsir5XqO7vLqvHg6f0Vq2rc/vw+eI9eO6PLSrhTKMrthouP/8d2s0HLgl4ONLqYllcXBz69eun+hcTE4OUlBRumUUkr776atx55534559/OCfalVdeyQlko0aN4vYxfvx4ThS79NJLsXbtWvz555946KGHuKEBTBQjCIIgCIIgCIIgmk8wEEDJq6+idubMiNtVuaow9YP/4rtn/Ri7NoDb9vVFoLoapa+8ort948qVqPjoY27/HF5BRLCoBY1wYll1A5XwHygaNWXxZV4fcuauw6M7CqR1dqGrjMGK/vVoEPbz6T55umWSIjqoxxWfLFfdF6c8ivFHJlzpRQPj7BbcdHzXJt1mBxqtWKbsLGPR5TuH3qna/oU/NiM7WS0oltd58PAvG/HOnJ3YUVKnEtFm5Izklg2tqIMEhO8tmn61I5k2m4YZiVdeeQWnnnoqzjnnHIwZM4aLVv7444/S4yaTCdOnT+dumYh2ySWX4LLLLsPjjz9+MA6XIAiCIAiCIAjiX0HV1O9R/u57yL/lPxG3e2XVK+j30m/c8o2/B5Axq+lSdtFpxgh6eBEhqBFPwhX83/SVXHTe2tMwCTW1GmeZHvd3ljvnrEJvmRax9J9FMkWa04HFhLLJw6PvGr/n5F7432ly0ixGp8esKbqk8e6xnhlxaA08fn44AotfsveuFKC+OfWbkM+jHGtgUQiRjDlbZUef2xeQoqr3fL8OU3uM5ZYNptYTtsSOtWj61Y5kDogUO2fOHNV9Vvz/1ltvcf/CkZOTg99///0AHB1BEARBEARBEMSRQc2MGVFtt7J4JS5owf69+wpgychAUHCW+U2GqJxlC3eUI05IpymlsnyXBxtqGzEhNZ7KyPcTFrW8ZVMuCtz8d3NHTgZeETrKlCSaTejslJ1MJ6XEqzrORBr8AZQKoijj0a7tmjwGu8UIlzeAhyb1xiWjcqTesGjxKKZymjWiUzRMuWIEPlywC9ce2wWtwY/bedOPw+SIToAyuHFcD/VEzc+W7JWWH5u2EVsKa/Hi5IFYl1+NJBM/MCHQ2MiJyK1RyB8IkrMsGkhKJAiCIAiCIAiCOELw7NkT1XZ9/Zn7tf+Al3fc+M2hYlnIoIBgECbFFEJlCfywxZtwxYbd+L2sukXHc6RR4vbim8JyyfWlhHWOTSuVRa/RibG6+3BqRKizM5LQX9FFxjrOxDhnmccnrb8gKznisbHv2efnv9tTB7RrtlDG8PhC31dz6JjixONn9EN2cuRBBNGSX5vP3Q7NGMrdmoyh76lfSj9peXepC7+tL0S7RLkvbFdpvbS8fE8lat0+XP8577R0C2IZgkEE3erBAPvtLAvjGCR46NMhCIIgCIIgCIL4l+MJBDBhxVa4Kiqlde5du1E+ZQoCDXyZu5K+i+RonR4p11+P3ls2I/2+e1XrXRvW8wuis8ysvuRcXrQcY74dg2k7p0nrqhu98CsEMp9Os/miSrnLiQjP2Wt24PYteXhud2HIY+uFiZUiRyepxTKH0QgmWT7QRY5gMkwGA05PT5Tud3bYJGfZ1no5Vpuk6afTUlDtkr7beEfLQm77K5a1NqLwNDxzeNgesOfGPCctV9R7cds3a+CMMkLqMgtimeAua81jps6yyJBYRhAEQRAEQRAE8S9nTkUt1lfVweyR3Sm7TjkFJc8+h+Lnn1dtG3C5MPz33RH3Z8niBRWjpnjcV1oG19atcG/fwd/XaAJTt01FlbsKDyx4QFpXXKN2zPg1BfQMcyvEz44EdjTwn+VvpaFOPCZuaUWwt3p3lO5vPbYfVh3VB+dmhjrErmifinEp8Xi1VzYcQrR2n8uLGzbJEcKmuOf7tdKyowWuMsbILik4lJCEJ8FRpidAqaKTQV6C2VFSj9FRvJeAwSiV+wfqG1r1mKmzLDIklhEEQRAEQRAEQfzL8QWD6Fik7xar+uZbVTSy/MOPmtyfpR0vlgUa1YX9jevWYc/5ctuZT9NZpmT6runcbUmteh9Kl5lS2InEpxs/xT1z74E/0HR5/ZGAIcL0SsYT3dpzt2dlJOHT/p2x5qi+XJF/lk12MimJM5vwxYAuuCArBbFC2fzDO/Y165jW5PIR0LMHt29x99bR3VLxxdUjsfj+E3AoIJ5vokimJ0DZDbL4GBQkGIvJgF5Z0Q0ZMMbyDsBAfeu4K2kaZnSQWEYQBEEQBEEQBHEEEN8Q/mK78suvpOWGzZu423q7FT8dNx5uiwV56ZlYdvUN0jamJF4ACNTLfUum1FT4iosRdMnilz/CFef98+/nbj9dpHYn+QRRRxnH1FSfhfDiihcxY88MLCxYiCORYrcXN27cE9ZFxhB7zN7tk4NrOqRyy0aDARNSE5Bps0T9Wsk6ccvvB3VtMj5Z7+GFpUcUEy1bwjHdU5GVIHeoHVLOMp3Osk8X5sPvyuCW4+384wM6JKKqQR6OEA6b2QhjLD/BM1DXOmIZOcuig8QygiAIgiAIgiCIfzk1Pj+crvCdR8VPPsndlnz1ORpmzeaWX7roerx+wZU4+fXPcNljr+D+wcdI25sSE/gFYbIeI2HSKSH71cYwtXj9AczbXqpaJ0pkdX5/1M4yEY+fHyxwpHHNhj34STGxsszrg0sjmNUL9zs5bPs1VbFHjDp6m2wx4Zgk3iX1y5p92FgQGgEtq+PjoWyQQ7w9emHuUEcUnswGXkDMjAkdjFFe70YwwIt7F4/qwN36/AFkJ8mCX69MfZcZG4JgioltVbGMpmFGB4llBEEQBEEQBEEQ/1KYw+jy9bu40vd4TYyr3QsvwDFkiHQ/GAig/PGnpft1MbyjRSRgMnHPyXjwQVizs7l1SRdfDGtODlJv/Y/kNlPiSYuPeHyPT9sUUtouRkJLFJMW/ZoJmkqU0UurOD3wMOPH4kq8sbe4xc/f1Rg6KbHC61N9pgVur+60y+bC+suUiFM1l+wq58rrJ72+IOQ5eRV83xYTiIyKyada7vx2Da79bEXIxNRDFa1L66xuZ+H8nufj1eNflbapdfmAAC8Q+iFMiQ0GcfogPgrLiHfoC4h2C3OW8Z+vv5XFMuosi0zLRlAQBEEQBEEQBEEQhxS7G9ycQDI0QRa5llbV4c+yGm65156d/EqDAYmTJyP+lImIO/EEbB0ylFvt3r5dtb9ap1osYzgmncJ1W4mYU1LQ9c8/uOWip2WhjdHhnbex1Rb5kvPzJaEF8QFBKFlVU68Sgx7bsQ9Xtk9FR2Eao4jbLwtFZuPhdYlb6/Nz7/cmoSh/fGoCesbYm7WPv8uqUa4QxpRl/1P2leGy9qmYV1ErrbdHEKuigX3/E1MTMKOMd5AlmfnPfHMhf57pUS8In+FEIUaDx4cfV/M9aPuqGtEhyYlDHamzTIhfsvPvoVEPqbaZvq4Q9va8i8zlZ99DInz+ILqlx+LJM/thw75qHNcjDct2V+g6y0SxzFda2jrHTNMwo+Lw+ktCEARBEARBEARB6DJ66Wbu9vch3TFEEMxcQv/XmFVLcdbcv7hl38WXIOshfhqlwSkLErvPOFO1vxonf5GupNLrR4ZN35nk2sR3nYnYuveAN5Db7G9L9BStr5Vjo9OF6Y7rahvxw+Buqu0bfPKUQDEOdzjAYpL9F26AS9HNVudr/oCCG8NMpJy8lhdHX88tUa1Pse7/Z6Sc25BoMaG6wYvHpsnff0FVI9olyjHDRk9AEn/C8f3KfBzuMUwtdW5eJAz6+c+ikRPLZEH4klE53G1lvX582G42wTl0COpmz0bN9N+QcsUV+33M5CyLDophEgRBEARBEARBHOYoY4qnrJIdYvvcXjhcjXjsAzkW5q6THVuRKNWJVeo5mETSbrlFdd/odMAbiFxi7rTquJyE96Kc3iiysCo0iubyuULEix+KKvB1YTkOZXY0uFRCmdgt11zqFN1kbLJlU8QI0yz3B2WHXJLZhIGP80KsSK4QuwwEgnj5r634cRUvhDmt4V/7kV82SsubCmqwuyy687QtWbSzDBNfm48Z6wujcpZpqWoQRLAAL6YFgr6Q4RWMpBgrrDrxWBbDjDnqKG7ZW1y0P28l7FACQh8SywiCIAiCIAiCIA5zqjUiiycQQIHLg4e278OFf/6qemzemBNU97vM+D1kf5bTT4fXEtr/Va7oEQt5Tge+x0zEaLfD648sliU4Qi/YfcYq1HpqodETwtLoa1QJAe5AADdvzsUdW/IiHu/BpkbT1cZg39e3hRVRdXYxweUiwT0mkuOwoqsmpqrk5V7q76ilmBViWV2J7OwTueD9JZjwyjx0eeB3vD57B2Zt4d1tMWFcbdr3e93nKzH2xTlcHPNgMmXhHi5eeuOXq+DXOSGbmiwpTrwMBgWxDD5JRNRyzbGhQqfNbALE2HMr1bhpnWXss8+74Ubk3XjTYdMVdyAgsYwgCIIgCIIgCOIQgAlL4oVsc6nUOL5YXHJrPe+4Mmr2OSezI3e7sXwjJnw/AcPmnY6dJ3RXbfPWlTfpvk4kZ5nRJotrBqsVBkfTzrI4h/4l6fHfHo9AlOqASiwL+OFTXPDXKyZqHmroOed2Nrpx25ZcXQedlg/zSzFb0UXG6OywcfvQY0RCDC7KSkFroKw9s4c5ZbcWq4+N0UExAVLJp4v26K5nDrODSXGN7FqsdXnDO8sMJuSWN4SITdWN4nN4UdgfxlnGsOg4y2wWtk74sFtJyBKPWRTL/FVVqJszB3X//ANfURFqZszAvnvuQaDx4AqVBxsSywiCIAiCIAiCIA4yTPCZ+ONEXPvXtS16PhPHtFMwRbdZdaw8kdJnNGFZdT1Wl27BJb9dgoL6Am79/SN2Sdv8dNx4fF1Uqfs68ytDBRClQCYS9HhgMBjgCeh3MYnEhxHL2POi1QYeXPCgtOwL+FSOtEPZJ8O+o3CIQmc4Stxe/G8n/92JLBrZGzbF8AUtr7SSq4xhEgUc1lkmFPxHQ05K6NCI0lo3/qfoO1OSEGEgwIFA7BxjVAouMTaIQBTFRDF45qZyjHnhHzz7xxZdZ1n7BL7/LwB/WGeZ1awXwzRJWllriWWiIM8EPm63DbIz0LVxI/bdcSdqfp2Gmt9DHadHEiSWEQRBEARBEARBHGTWlq5FcUMxlhUta5G7jE3BVOIJBqX+q0v+niatv++We7jbcxZ8Bp/gcuFQxOoKU9PCvs6XhRXIDeNcMsbFhaxrKoYZZ+df15I8P2IPWyT21MiuJPaevIdJlOzvcn5ogR4Pbt+HPFd4obHQE/q5dnGGj192YvFMZ/OmbEbrint5Oj9YgjGiU2jPneo4UvmBEqyPjIlkavdVKI4IAwEOBBWK4v2dJXXc5Mo+j/yJR3/dqBLLvl7KC5fvzZVFZ0al0FnmFCLNgaA3rLNMv7PMxInOreos00RH/fVyN1z+Lf+Rlg3NEEH/jZBYRhAEQRAEQRAEcZBRTtNjfV3NpUqns4yJZR0L9yG+hhdlzBddhJW9B3DLfksO6hIvRHXafxFUuIQYO9vzE/rCMTeMu8xgNMKcphbamophGky8YGLP+C3ksZnlfATPohDyOtgtYcv9xYiZUmQ7lGWzfZpYn1XxPhlv7i0O+9zaZg4CmDG0B1qTUkGsMzT4VGLXQ6f2Dtn2nYuHSMudUmIwfV0B10c2/KmZ+O/UtVyJfjjEqZEHA9ZRVqV4b0w4e+XvbdzyZ4v5CaQePy+GmY36Djjxs3EKrktRqNJ7X3rOMs6BphDLvt/2PS6cfiHKGsN/Zs11lgUUYpkSgy28+HokQGIZQRAEQRAEQRDEQUYZV6zzqvuqoind1naWeQK8syyjUr6odhx7rLQcMCWgMf4UeBwD4bN24tbdd904vHb+FVjVq1/E19per+8sY7R/9RXAZELqf25RiQnhcAeYIKb//mqFmGKMwnGT7/Li6g27w4plzFmm7Cx7QhNVPFRgzqIV1WqR4s9hPTApLUG6X+nzY1NdIyas2Io5FTUqEfHdvNJmxS2TLK3rEioTBycoHFLHdk/FgA6JeO6c/tK6OLsZx/ZIQ/tEB3plxiEz3o5bvlotPf79ynxpCmZOihPLHjiR2+ZQEMuYK0z58jUur9KAqRKDrWHEMnEaZoyVF55qPBWwpsxBuaskJIqp11nGue8UYtljix/DhvINeHP1m63mLPPm5upuF/RE/u3+2yGxjCAIgiAIgiAI4iCzvXK7tKwUmH7Y9gNGfTUKa0rWNKuzzB0IckXvJqHg3tarF2Zl8BMJGV57L8XWJgRhwtLBV+Pn4yeoIpl6RIoHOocORc/ly5B2881ROcu8QQ8Mpshl9ld3SMWQeD6+x/ittFpyVrn9auGOL/iHalsxjnoowSZ2sqgs450+OXikazv0jnXg6vayM6/a68eFa3dibW0jLljLx/sa/QFcsm6X5Lpjky/7xtpxQ7b8vAs1Jf7/DO/Z6scfa+JdSXaFK/HWE/khEecP74gHTuHPr5fOG4hYmxnz7hmLaf85Rool6mE3m5Aeb8eSB05EWhwvLkU7EbUtWLGnQnW/xsUEQoPKeSae3+H04w/mC8JugH8/a8uXw5b+B5w57+Pr5blNOstKal3S71H5UZQ3lreas6zg3vt0twt6I/92/+2QWEYQBEEQBEEQBHEQYc6x6bumS/c/XP+htP5/i/+HBl+DtE4vDnfZul34tkh9YV/m9WFldQMsPt4BZHQ4MC/vL919VGU+Cn/HV0PWf9yPd5wxOjvk8v5inb4sJUanLGzpiWUBozxwYGVeMQwW/WECIhlWC34f2gMDYuVJikVur65zbWNtHY5ZKndoMZROM/aZbqt3HVTHEkPZq8bcZDd1TOeWj0qKxUNdsqRJnsWig0tgsWZKJhNEZw3vhf91a69a/93ArlxPGbtlIlxr83rvjhgeH4OzLfJ3PVzRV3bdmK7Y+NgEjO+byd03GQ2cc0pvQqZ68iMPE9iidVW2BT5/ADd8sUq17vVZ21FQJU+I7PrAdFS4+N9dMBi5W80CdZ+f0VqBdXnqzjq74v2L1LmZ0BvaWTYnfw5ay1kWjiCJZQRBEARBEARBEMT+UuDy4PEdBRGdV8ri+9dXvY4/9/yJvNo8bKmQp+j9uvNX7KrehY3lfDyNkeJQu4WUMcO/ymtQKIhHItdt3IMijxfWAH9h7DcZkFebi4SS53X3U4nEiNG9q9qn4dgkfqLf5rroxSY9scxn6YBeybzzyJn9KUwO/RiYiElw1igNb6JYpnWWzSgt51x16mPg7zOHWdactRizbAte2lOEA4lW9FG638waJ1//OGfItEyH0RDVlEyRMclxWDKqD3fbFnSPsWPa0O74YY4cidUSIwheSrYVhRfLxMmRDPEjOVjOsi+X6p+TmwrlOKw5XuH2DMgxzJ9X7wv5zs8brHRy8tRqfrOjusi/8TibmRPPXjxvQNtNwzSaQqbYKgk2IYr/2yFnGUEQBEEQBEEQRCtw25ZcvJ1XgnNW72hy2592/IQP1n+A/879L77d+m3I42f8fAY2lm0M280lUuJWO49EHK5G2Dxu5AjRruVlq1FYXwiLa0PU76e7044nurXHyanxuLx9Cl7t1ZFb3xgI4OJ1u1DfRLzxt9IqrKrWmfhoMKlcLdbkRRH3IwonBkUETpwG6Q6oxbIid2geTow7vrRbFshe2hO+PF/JzgYXF5ncH/67JQ/DFm9CoVsWUcUhBOxTMGrEsjjhOytV9NClWnkx5o8y9ed5R05Gi46psLoRf2woDOnNai5mQcSLlkU7w8cHcysapGXxMzlYDkBx2iXjtIHtdLdRirzBoCyW3f7tGk4oq1X8Nrsk885BJbVcrFMmNVYu1B/ZJRkb/jcBx/dMl6dhKs7DVEcqWsNZxo4zKLhPRRyDBvHviZxlBEEQBEEQBEEQxP6ysoa/2M+N4CwTRa+COrl4/rNNn+luu7BgobRc7dYRnTRuKxGL14spj/8XUx77L7KFC2O/kBIzNDEf8poOqdh4dD8sHdUbqVYzrs1Ow5T+XWA1GpEuCDaMfypqOYcWK6pXwtxbexrd3EX41Rv2YF5FVchrBA1mqS9JOKiIiLJan1h7kzFMc8PKkOezyaCMXY3hBxPosaSqDkcv3YL/bI7sfGuKLwrLsc/txeglm/FVQbkqhmnREZt6xTi4CaCligimOH2SRUgZNqMBl2Sl4PZOLRPLxr00l4sZ/rAqv0XPl/bTm3/9C0fwQmpTiIJYutBJFg7xYznYcVnGa+cPgtOqjlna0n+HNXmJvCKgdmeNeHoW7vx2LbfMnpsRG+oM5TvQ9GEDEcxi4b/YWab4LDKdfLy1OWwo24DVJasR8PuRXRKExRNE5edfSCKcwW5Hl9+mw9a9G/96R7hY1rojMQiCIAiCIAiCII5QMqxm7G70SBE6p2a63Tm/noNtldvw6vGvym4R5fOdGShukB1P/+T9oyucRSLZYkLvtSuQXsl3KWW+9wZ3a46i4373mAFwCMeconOpyISdeztn4jnBocUEIObwyrZbJbfUwIUb0BgIYsbQHtw6Q1BPEDDDaJBdMkGdKYD8A0FOKBBdRqwE/6vCClVEURvDtLrVfWXiZNA6nx9/C6X43HE18Vk8tmMf3hEmTv5aUoW3egd1ha3m4AoEcefWPPSKtSNFiLiKEVMl7LzpH+fAKkF8FZ+r3P61Xh1xZkZSi4+l3sOfEDM3F+O8YaHTM6OlXGi2P6qrfkw4nJvqvUuHom+7BKzOrcT57/Oik01RcC9+5wdDK6vXuDWNRgOGdUrGvG2lMJhrYIrZDmvKPPWTguaQKZbss2X0yIhDrIWPMIv4Xe1Q6woVo3666Sj8uGof7jiJ//1wCJ+FTxFptpj0p2+Ggw2+uPC3C7nl4dsCeOmHAPDRFCj9lT1XrYTBaITBwu+bpmESBEEQBEEQBEEQ+43SefVmrjrmx6bXMaGMcfuc23Wf//Wkr7H+8vVh919YVxiyzqiRfWJMJvTaszNkuwF79FWHdjb+mD/q10kSyiJxRye1o6VUcHgx18tJy7dyQhm3v3xeaEIwVBAI+o0aZ5kgBpjV+7a4+c9L3DLRYsb1HdJUpf1aZ5kerMNsXqW6K2uoYrqmlkqvTxLKRGZo4o/7w+4GN/IF96Gyl0xJujVUrOy/cAM3uIHRQRAo95cGQTRrCWwa5OZC/nPtmqYWg8JRJwhRcXYLN/1xZJcULLrvBEwakIWvrh0lbSeKyQfSWcbOYTZ9su+jf0rruqXHquKmzk5vwtFuqs6zwwupJ/ZSRCklAiExTMbgjkl44sx+SHQqvl/huaznUAtznP5v0f+wqlg9jEBLva9ePp7VoZ+pwenkhDJu2cK/9pHuLKPOMoIgCIIgCIIgiFaAxRZFFlWqpxZqY5RrS/mIlsicyXOQ5uSFoCHpQ7jbIEyoS7wYbvtA7v598+8LeU3tNTgbLuB0yY4kka+O4y/97hsh7+Pp7u2xeFRvzB3RC6ekJqAlrK/jpwM2BALYpCif/76Yn3BpEGKg6oM2IRBUHLiw2C2xu2ozWwPvOFJ2eomihRhj1DrL9GAxzDqNKOUPhhdMrt+4J2Q9c6a1Fqzc/9ldkQcMnKXjGlPGMltLLGvcD7Fsa1EtJ36xyZU9M5seJMA+W1ksk38r7RIdeOuiIRiaI79n0cTHBLkDxV3frcWIp2ap1n117UjVeWe0yO5EEXfxxIj7Ha3nujMwsSxKMUoccKH4KETh7JWVr+CH7T/g8j8uj7iLeo8slu3OjDzB1iCI/kESywiCIAiCIAiCIIjWJFkxSZJR6ebFI5HlRctV95XTLq/ufzV364o9AY3xJ6Mm/b/wGxOwqmQVd3H81pq3wrofJqUlILaRF7CU/DzawLnWLu59Mc7PTEZ7mwXnZibDZjSiZ4xdNxYaDfdu4zuvGsOpT/DpdpblVyoEPSF+ZzWpe6xMvhLuVnlk4uRIsSDf5VOLZV5rF92Cf5cglolTJUVnmpa1tY2YpxE6GfVhHGBNMVMR/ZSOMRhEdRPi2ympiegVI3e0ackUHIH7y4q9laisb9qdp0dBFX+edU2LgSmKiOotX62WxC+lWKbHwYhh/ihMsVSSHsd/B2ZT+PfXJzNyf1hOSkzIOgMCcHkDWJcf2ukXsq0olinWFdTznYe7q8NPI1VS7ZHF+nMWhX6o5uRk+fXEGKaXnGUEQRAEQRAEQRDEfqKM1I1KVF8g59cWwG9K1H2ecjIk46h2R3G3AZPs1qno8CYCBic+3vAx3l37ruSo0kYnH+3aDtll6ghoBUuSKcSw13p3xPLRfRBvVpeWR8svg/kCcJHFVXUoV0xuVGLQTKvkVxpRXCM7XcRLd6tJ7ZYy+opDer3MwqJXEF1qvdopoQaYPHlAMKCKYbIJnoxY4T2LEzK1lAhF+lqKw6xvikvW7dJ1uk1Mi+zkY/1oc0b0QtFYfjKhkk/6dUJr8vAv0U9IVVLdyH8m8Q5LWDGt98N/oNN9v2FfVSN+Wy/HiB2WyOce6wk7kDHMn1ZHHnRgFiKKetw0pl/E56bG6rgAhc6+09+MootQPP8VH0WFqwK/7fotapFbOVBED2tOjvxyVv54A279CbxHChTDJAiCIAiCIAiCaAUqvbJbyKu5xv+oxIyK9m/Ak3pdyPMsRrXYYDYKrhtNhNFnk11TO6p2YFP5JnRxqN1YCcEA+uTvVa1LrgNeOf4V1TpltLG5jEyMxTM9Okj3z1q9A0/s1L8Yt7nWhKwzBuslsUC1rUntpBKHAyhNS6KzjDnD3sotwQPbeGdN0MB/DhbPTiQXPYDYik9U4tTyal6cizfxIo12iqdSWGstsUyc2KmlwO3Fa3t5IXByZvNL+iem6YuuejAn17nvLMJt36wOu82KPWrXY7RUNvCOtIQwYhlzrTUKv4n358o9esd0S21S5JGnYeKA8PPq0PN31l3HSctiDFOPGIssjPfOiuemX1pNRmTG23HL2G7679XQjPirjrOM8fjix2FoclQF35eYV5sn3a/TMSxmPHC/tGxK5M8vf1XTrrd/MzQNkyAIgiAIgiAIohWo8snuKq/gZBLZ4OYvQKudxyIN70cUyxgPjHwAT+zIhbJ9zG+So1IX/XYRAkwY6/kCC+VJ610//oigO9TNNS5nHFqTK9ql4H4hghkubhguQ2cMVKrEMrHXjDnLRmaNxNLCpcIj/DZr9lRiYkoC5zaSxTJwAp1DGCAQMMbApOgvc9TPQV3K1ZKLbHopH0NzC44zsfNMW7x/rU5fGaNEMyExGsK51N7M5eOl3HvWuAqbYv3RfZu1/aaCGk60Yv9emTyI+wwDGgXKItr1mklhtUvqHNPDpehD+2erPDDhjQsHN7lvUcw9UM4y1rumRTm0YG9FaA+giNPixGdX9cCURXvw1Fn9kOiwcpM9RXecHixFK3srm99Zxjivx3nYVLEp4lP3VO/Bmb+cCb9CeM9PAXopEqfp99wDS1aWdN+UxP+d8Ve0TET9t0DOMoIgCIIgCIIgiFagSuEs08b8PMHwl16Sk0zBhb0uxORel8Dk9+Gt5x7GPZ+9C79FvqBlQhljmWYKnnflSu629rzWFce0RBf/kj+PhrgJ0rLRzxwrslhm8pdxtxajCR+c9AF8uXeqnGXvzdqB6UKET+ks457r5a/6E6yhvVB634tYraYnlj2wPXwUb25lLRqb0VvGyuy/K6pQrcvS6Rk7Nb3pwQrHJclx3DTFxNVoUH5Nostr2jq1i8oSxRRUPWqEGGaiU/+YxNdj5CrEJnsTEUyGqDOxTi+tuNfalNW5VRFRRnuNAHhKf/m3p8VpdmJMjzR8fMVwZCU44LCaIgpl3HOs8uNVgkMvLGF+axaTpUln2a87f1UJZQyj5uO0dpIjmAxziiCWlZfjSIbEMoIgCIIgCIIgiP3EryltFzu1JAThhzEwTd1Dpe3qEnEFguiyLw999uzAxMVzYbScELJN0MALFRdlJXOuo2ADL0p8UT8bz57LX+7l/PQDDgYGwfXFaEg4h+tcY/isHREMhr5ni8HEiXAWo/CYeJHvD+DjBbs50YR1eTFqhc/aUT+Pu61rlGNmWvJdshiRIYhNId+PYp+MTKsFXwzogm8HdpXWvZsnO8Ka4rfSanyYz4uAIm/07hiy3fHJ8U3u652+OTg9PRGf9e+M5qKM29YL0zR3l6k9Tf3bN38SKhMDi2p4Z5k9TPedUixTwlxX0Yqxb/2zE+/PD+19ay3YOTX8qZkh67WF/lcc1QkjOsnOTiUOs76zLhLegHxOrslrKu4oTOLUnLJsImZLBnNo92Prrp5Ca0rmh434KtRi75EGiWUEQRAEQRAEQRD7SY3Pr+zfDi2QVwhHq8u28dvY+6E69Vac1/ta7v7Gukbcty0f5YKoUebxIqAoFh+1Xt07FYSJja7jljvarZzrKCBMwnRZgVXdjfjw3VPg7N2nTb5fcbJkWBQCIesUq8p8DA1xE1GXdAU8VWNCNrcIDjsxlmoQJ2n6gpygwGJuojtrV6MbnR2y4JbkVLtj+BflnWB5CrEsRnBR6U3DHBDHi3mMIo8X41Li0TdWFkLY9xMtrwqdZCIXZiWr9sVIjsJhxW9nxvt9O2F8avNFLZ8iDvzNsjyubP/VmdtV27COrXCEc3Xd9d1azN9eFtEptrs0NGiYEmNt0nXFUG7y7IwtaCue+3OLblpYO92T3f/uhtFhY5jNpcHXgP7ZdlWcNSzioWjFsoC3SWdZo6+xSbHM0r69rrMsUFuLoKdlk1L/DZBYRhAEQRAEQRDEv4Y1NQ0YtHAjXteIFQey3F8skBf7li5cuxM+xSTMMR0ncrfV6ffC4xyOlRiJ9/JKcOLyrZiyrwzXCb1ZuY0eWL2yyDZo20Zp2WPri7Lsj+GKPZ677/LynWEBwVnmEpJxSfbmF8hHy3HJcjwwkrOMF/WMCFgy0WA5D8FaJ3yugSHbsxgmw2oSxLIgf6FuELKTXyzdi54xvMBQ7Pai1heA18KLZJP7hg5OODO+IEQsS4gwDbNMMdHzPx3TQwStOl+Ac1Sxf025DDdohLVXenVEksWMJOH1mWj302C1o6ct8Phksezlv3mRVtvTtX4f3+emhbn5ujzwO976Z0fIYz+ulkuv7JZQWYENFfh2RajbL1y/mRZbCye1Ngf2Pb43V9+1tktH6IsUw2wJXTL530d+Zfg+NEZQUMnETznOyv/ufAGfapKuGM1mjrN/cv9Bg7eBK/fXYlSkiY1xcTBoJn0a4+MBM39u+CqP3N4yEssIgiAIgiAIgvjX8MLuIs4V9PQudQdRW1PoVjswFlTVoef89Xg/rwT/VNSqHsuIk6N9jGml1Xh0h9wjtbCqDn+UVmNdXSMsPlksS66pxrX9r4XbPgjVGfdxApRIRWMxav74A64NG7j7LqETKd7adMyvtQRCBhODfhvSHS/3zMax4kW5gRc+hsQ5YFtYAtuyMniGJMNjOkf13O/n5+LXtQXIipeL1Q3+Wr7NXzDYJAoX8bX+AGr9fhgDvEg4Jq1TyLFUVPH9bStrZDHi1pyMsNMwS4TplRdkJuPuzpkhMcYBcQ7csy0f/RZuRJng/tNjR4N6wIIo0DE2HdMPeccNxM4xAyThry2pV5Tsh2PDvhrUurwhQtLj0/ny+Bf+3Brx+TGacvw6t48bKKBHVkJ079kRpetuf2DH2RrYzS37HmOc/N+F/MrIjkWfIIIx7htxHy7vczm37Al4VM4yl493qN0+53bc+s+t+HLzlyhpDI0Oi4MCUm+6CV1n/B76uMEAcxIvsh/JvWUklhEEQRAEQRAE8a+gyO3FrAqdqYwHgL0K9xJja72LE3QeUYhgIhsbldHKVXj72YfQc89O1TZXbNjN3SqdZUetX4WzlhtRk8YX4Cvx+Wqx7/Y7pPtuwVl2atdT0VY80b09UixmPNujg7Su0ufH0IQYXNQuBdtWFah61eCRL/qN5W5482W3Hb+hEbd+vRojc+RYmMlfziwz0kW8TRGPc/t9MPl5UaZDnHwM0msgNELWTohxenUcYqwjjnFKWgKsCrfNCYKDjglnnxeUo9zrw8IqtQCqPQ9FJqTG4+sBXaT7fCdby6ZPNgd/IIjLP17G/QuH8ij+96t6qqKykL8pspPUzqqtReF/g9E6y1hJvhK3ok+utSiplUXNL68ZiYtGhnbKKdGeL2wa5acnf6pydzULM99VVtmgPzVVRFnQf1a3s6SOw3pvPRYWLJQec/vdnJtsXj7f4/fNlm9Q2iBPIdXGMJ0jRsCcmqr7mqYUobes/MjtLSOxjCAIgiAIgiCIfwUrqqOPTrU2f5bpR9n02Bjsg7SqIO6f8jaeefsF9N67E5MW/qO7bYZiaiSj7pW3MHzzupDtnFC/96ABOL/n+eiSIAs1rQ3r+Np4TD9c0T4VIxL4aZS9FW6pahfvmAmCdx55KuRuJvOeOgRssoOM386A5BgrhndOQcDL93NZVpdIog7TmKwqoUkWEaziUAAFoTNGWdSTf35QMRlTxCP0e9k1sbQUK7+nhZV10jqnZhulO21aCS+CDIh14NP+XTBE+GwOFEzUeX/eLszdFiqUKIl3yFMsC6vV7qaTXuEFFy1Ld5Xj6inLVetyUmSxbPaWYpzzzmLV46mxNmm5a1p0n4W2By2vGeJdtCjjpUd3S8WQjnJk+YcbjwrZnsUelZzQ8QQMyRjS4tc3GHmRrKEJh5tX0f1nNpgkce7vvX+rtmNi2fx986X7TFSrdFWGdZYZIkxBNSfzn0XJ88+hfslSHImQWEYQBEEQBEEQxL8CZeeUxWBosluqNfmzTHDTBJoo6wYwMjERb5WejPFL5QvbcAwsyA1ZN6A+1LmTUKPuaCuLBzrFh0YT24oP+nbCTdnpmCJMbOS6sgyCmGWwwLytGtsXyC47gzsAY6XmcjRo5Hq02ic6gSAvUBkUQgKLnFmV0/8U8bSqhlDBwSyqAtJ9tdjmVpTfV3p92FTPf3dqQQ7Sa7J4r4jegADG2OVb8UVheUiE80CyZFcFnvuj6VL8OLssJ6bF2VQuLmXXmbLs//z3l2DWlpKwMcx7f1gvLU8akIUXzh2AB07pJa0bFmaipJaOyWq32uuzduyXu6y8zo11+fLUSfa34cdVfO/ahL58NPfswe1x/8Re+O760RiaE9r1x8So/ekqu3XwrUh3pmNYxjDuvskUjCoqq3SWmVn/X4QyfzGKyah0V6LepxbRWYQz3Sa4ycIIvsqJmO7tO1A3Zw6OREgsIwiCIAiCIAjiX0GpQsxgMTsxVncgEAUVq0su4ddydTs+zjejrBqmNeoeKJvHjeEb1+CVlx9HdhEvKuUU5uOkH74O2c9eZhvT0HvaAmn5+/8OQ63TAIc5usjb/sKEh/oaNx7skoUcBy+6NHr8MBgEAStggnl3XejzAtquJyMX/6uodyMY5C9VDQr3mNlk4GKM4mdtUIwHPPtttZuJ217zMfWJcXCOMPEiuMDtxWahiP/7ItmBoxXLbIKooBwUoDcggH0OLKIZ7vUPFLvKQj9rJYlOC+fS69NO7rP7ZU0BthXz0dK/NxXr9nttKowccV6xpwKlimgjc2qdNywbE/tloUdGLPq1j0ePjMhDIUT6t1dP/mRddh/M0y/jj4bz3l2M099cyE0Dnba2QCVQOQXnIJvSef1xXTGis76gxzrClDT393XtgGsx89yZaB/Lx4zNgljWEKH/juFTiGXslNKLfQ7fFkD9okXchEwRFtMUS/9FuiR2QYzJ0aRYZhYmYnKbOVs2wOBwh8QygiAIgiAIgiD+FWhL12vboOcoHGKXltUlO2uUjPb9grMz+dJ4RrlRHRJ0ul144LP3MGj7Znz22F3cut67Q6cQMo5drY7Bca9bIEfuvjOv5m7bWixjpfBP/74ZT0zfjBNemosTXpqDTQW8oFLAYn2is8yv74YJEcsEgYxNZzSIDhqDfLHfVxB3ZDFLfqykxovGgvOQZOwZVqx6r28nTmyLEwr32dRR5gT7vqhC5RTrLAh+ImLEVIlXR4hdoIhpMkwHyVmmZ3pLcsqRy2m3HIO5d4/FfRNlxxfjoZ83YGdpHW75ij9/lHy1dC8e/En/3GZUN3hx0QfquN6pA7Kk/rE/bhuD6f85FqYo+9pGdknGcT3SVOtmaxxt0bK5sAa7ymSH1X++Xo0tCuHvstH8RNWm8Pj3TyxjsPPPJEx9NQnlYXvLG7Bsd/huML/iN8C+XGWpPyOhLoi7fwjAdMeT8PjU7jc99xkER6V2CqYSb7H8WRsseoHmfz8klhEEQRAEQRAE8a+LYTJq/G0vls0qr8HvpVVcmT/D2rhGd7vOTgc6OuRerQaNouFwuRDbKF/Qp1ZV4N7P31Nts6Z7b+52+Ka13O1n/Tvj9vQyJBXej7hcPvqX/uAD7IqcWy6oDx0u0JpMXZHPdWN9vHC3dNF/yut8tPS7FXmA6CxzhxFIAlbdy9MGjx8WoyyWiXFBUWhxil1LKteMAb7qocjdeCVuH3K7bgyzs5MXweLM/PM3C7HLWzbnck5ExqlpCUjSiAMnpsRr5Aneuahlg+BSk97NQXKWuXUilG9dxHdrXTyyI7KTndy/9Dg7F5MUqaz34MSX5kr3/3daH9xwHD+5tajaDa+25I0NeTijL3f7+4ZCeITfAGP9/8YjI14WQ5lrqzlYTEZ8etUI9FQ40ZROuOawOleOX4r8tp6flju4YyIGK7rKmiOWOS0tc1yZDGqxjDH5vcXcUAY9vIqutA351ZzgpiReUef2/lr13wyGUlxjYllQjB+LvzEdDAoh7Ugt+SexjCAIgiAIgiCIw54t9Y34rVRdsl/jbVuxjJW5X7xuF67asIe7n26s56c36tAlJhFpVgtGCi6l6o5qN4vT3Qh3vBw9S6wNHRgw5dRzuduAcCE7KM6JoxNsMHvzYW3kL6hNIwZL26fY+d6htoK5kPRgccTdZfUwGIWLfKF/LBT1Rb/ouKl3++DxGWSxTOjEEsWaEslBGNC9tBXFBKUUwCKiIvGmUJGgURB62HekhTnRtDLGDM25xvBrBLTFVQdn4IRe39hR3VKx9pHxePLMfqr1o7rI50hxjbpv78KRHeEQivZdPj/3j8GmRo7snMyJWZeO5nvxXp25TXregnvHIs4e+jm2hNcvlM/nJGfoEIdoEOOlSlYJAtrVx/Ade9GgjDgy7GZtjDg6xBil9jTcU65/vuRWyuvPfntBiLNMea+6IVTYSnOmqQdhiM6yCAX/abfdKi0nX3oJjkRILCMIgiAIgiAI4rDn+GVyB1iCELNra2fZXkWHFaO2indVxZe8GLJtThwv1pyfyXcB1dj4CNeODjmSs8xeK0fDrv/xK9XzH7r+TuzJ6sAtx7gaYQwEuMmOcVbeeWMUBJJGyBf0p3U9DW1JuIv7inoP51ISY5jBoEIVGJwCf5Z+fC3OxoshXJ+UorMsVnCWeQVByyTqaIKzTOw3Y7RLsENsJZu9dzqc1b9wy6WKiK5Y5K/klb3FqjhtUyjL/kXqFc6qg0m4IvwEpyXElcQcZk+dxQtoNS61M/OLJbnwC8JKdaMXu0r573vysGx8e/1oKSbJvpd6N/+aLNrZIan1Oq56ZsZhYj8+vvzxAt7B2FxW51WpivwZZUK3WlZC9FFKZdE+w25qmVhmFiLYRmW8ku0/jLOsrF52LJ63/R8u8qpEaaA06ZyCCbYEPDzqYRzd7mic2+NcBMW/ixFimNacHPRYvQ6dpy+GpWNHHImQWEYQBEEQBEEQxGELczFpp172iuEvYmt0HDatSb1GlLC4ecHO5lqLtNxLYfIWSY+xKXiMJMGp4xEuWCvj+GhZQl0tTB5ZfBu2ZQO/r+7d0GvzJiwcNBxuq+yssXo93MTPeCv/fJPguqoL8kIQE9HEi/K2YF9VIxbu4F1043rz701ka3EtXF42DVPtLNv9zCkwZTnh7S/H3rw1fIyP4bTwbiSvL4CgeKlqCHATMsUSetaTJjvDhO9XMfCgssGrEoRiqr+HIdCIuiiF0wwdZxnDrhHRegrnmJKGQ0Qsm7KIdzoOzE6E2WjAf07oFnH7cKX7T0zfhC+X8tNYf1vHxxYZdotaRthRUscNAGAutKuOjt6pFS2iSNfU1Mhw1Ll4cemikTmq85eREhO9W01blm8xWvYrhmmIUiyrccs9ZJdu+QuzPl0YViwz6pyCzE02uedkvHvSu7B7AX85/7s1p6ZGnBhc8dUWlH24Ho3ry3AkQmIZQRAEQRAEQRCHJRVeH05YvhVZc/gOL8YHfTtJBe5tXfBfUK+eGmhtVBej2+p5pxkjM4Z3x4h9WKL7p9HGiy5JdfqTBm29ekvij8csX5y/8PozMHk9iLXGcqXfJuGt3rP4Ae62Y1zbukHenL1dWhZ7rURYyT97fwaFs2zysA7c+zCz96J0Nyl6y8SphMyp1CczkV9pCMAmfJ+Mu6eu0yn4ly9rG71+6GlW0c5FvaydfnR12pDuyLZb0V3oPXPrCBvRCnJtSVG1C1WC86hHeizW/28C7hovDz3QY2iEzq5y5hDUYFd8HwzORQigfZIDVqEPrjW5/rgu3G2nlJY51jjhlkWbHRYp0iuSHGttsbNM69JrrlimFd9YfJbFl7XUeNRdeCkNLhgCQdz9vR/nzfdjZOYIed86575S1HPv3MX/vUhOBixxKHlzDQqfXw7XDnWvW8Dlg3s7v65uYdt2Hx6qkFhGEARBEARBEMRhydUbdksl7SKnpSdKBfBt7fTZWydfRBr8bIKjH1NOniKtc9b+CXvdXLSrfBc58TkqsazRx7uuXIJYFg5LJh8du7ljOrrGyWJBv13b0DhtOhexYj3h4oVdoZufitk5ofUdPiLMZfT1sjxu+f6JvTCggyBsCeRXNoY4y8QOq/Myk8JekjoF5xybNNk5iRcXHe2/Ro1lgbTNHxuLYBXjY8FQZ5lSHBHJsppxZyd5Emk4fhrcDTEaIUikf5wTy0f3wQ3ZvIvOLZakH2IxzId/4R2JDFa4zyZRrs6txHZNb1eNy4sN+6o5ZxEr35/+n2Oifo12ieroInPzaSdutiapsbxA6RDE1ObCBFT++SbUuuWoaYzVFCKeRSKg8523BLGbTyu+3frNaox9cY7Kxceocav/xgUMBgzYHcTw7UGctyCIHvbj5H3rqMJWkywIunfyE3ZtXbqi6Jll8O6rg7/ChZq/eDeitN0OWTxLPI0XK480SCwjCIIgCIIgCOKwJFyBOude0ilcb20q3bIbLLHkWS4SOShtkLTOEHQjruJDPNBvnLQuWYhhihPp6u2RO5OCwoTPh7u2w/yR/DRMCVcjbCYbzg7KJeg+QetpywjmGwpXWf/2CSFuIibScM45hVgmuoIe7NIOl2SlwDMoGYF4C7KzZOdSjCKG2TO5h7Q+1/gpHB2mwGCuUvWKGRTOMnYMyUKkrtGjFjX+GdGDc4WJPN+D737TMlAhRopoY2ria7t1JkMeCmKZMsrn8wdRVufGWW8vwkmvzFNtd9EHS3DqGwswe0sJvlqai6yE6Pu3tN/3l0v3creJLSzgj/b1NhfW4I1Z27lOvGgJBIKoE7rYnFa1ENotI65Z7jCtuNVSxIJ/X8CH2XfJQhebJssQp8uK1HrUYpnfYIRZcap9u3BPxBim0lnmr+J/Q4YYtWgdEDrnuOOqdKH8i83csnNwOqwd9GO6/3ZILCMIgiAIgiAI4rDEEaaMXbzIaetQnEuI3VlcG7iJlFf1u4pzjXx1irqcf2jGUGk50SyUewsiTHU83zkmEnPssWj3wgswJSRwBdwJZ5we9vWLn34GtbP/weTnV4SIZUxEawtYZ9iWItmlNKIzP7Dgq2tGSuvc3gBqXT5VDFOMUjpMRlyYlYxAhgOe0ekoalwvPS9WcNn9takY8MrTKxnmuC2wt/seJqMBVkngEJ1lRm6qY5rgQKpqUBfVa+NuoxJjQ97XWQpHIuPLzV/i4w0f44SpJ+DTjZ9K6xMtZlgblmPztvuxvZJ36YQTy7Q9ZwcC1h0mwgRLFssUcQkOKyYgbdjHC71Xf7oCD/y0Htd9vhLf3zAaZw9pL20/sEOCJIhGmoi6aCffgRUnDGJobWwKce6lv7fhuRlbon4ui5Ey8ZadMhnxakGQOeuag/I8mtxjMlqK2WCW9tclLRY5mnhpepz6t1vnbQwRy/wKJSfNxYts4cSydaXrpGXPzp3ChurX8BU3oPrvvQh6A2jcJE/0NSW2zd+RwwESywiCIAiCIAiCOOxgrrHGMIXYorMs0MbOMneAFx8MguNkfM547rZ/Wn8svWgpku3J6J7UHZ3iO/Hb79yJYF4uMq0WrnOI4TWa4Bw2TNpnh7feRMJpp6L7ooXosWQx7L01bjIN+TfdpLrvF0ZFiu6V1ub7lfnS8udXj4BZEJiO6paKh0/twy2v3FsBH3t/CmeZWRxhqZk4mZMgO8i6pMqizDPT+WJ5JeaYHTDHrwyJYQZhgM1kQJe0GO7+dyvkY9RzBInnh5LrhXglY1nhMjy77Fm8svIVlDWW4cUV8nTTgXEOxFV8ikDjZty14PGInWWzhkfuCmttSmvdWJMrx+cGd0xSfe4lNXxR/N4KWVwRWbm3EsM6JSNNEGrYBEpR4CytlQU3JlYquf+H9arXbwvELjuRXWV1UT+3QCjyz4izw2IyIl4h6PVrpxaqm0I8j9jv+eHRD6O1YpgmzfmoFfX+2V4YIpYpS/1vXDRDWlY6zkSMBgMnqpe98w6qpn6v6gq0dZMj1LWzclHy/joEFYMU7L15MfxIhMQygiAIgiAIgiAOO/Y0hr8wF/UBXxuKZZ5AAB5BLEOQF4Wy47Olx50WJ34/+3d8O+lbLurlq6zErkmnYueEk/HmvTfimLXLue0CBiM6fv4Zki66EGm33Qqj0NtlMJlg0rjOGJNe/ghL+spRTyVX3ybHzNg0zLZAnEyod1EvOoD2CHEyg1H4fAImTqgQsSuWrxp0l7xeMcAAAX1HiyN1MezC12oQJn8yMe7vzSWYsYGfPqqtltI6yxT6ER7skoWlo3pjULzs7tlUvinMuweSzAYYA7wjqdKtFm20HXldndFHG/eXqgYPhj81k3NRMS4dlcNNpmQuP5HPFvNxvXX56jJ35YTIZbsruGUmnO0p57/rIkFk65wag4X3nqB6zrI9/PaMmBZ2ijVFt3S1E7Ck1o1FO8tCur30EKdetkvkv4uvrxuF4Z2SMKZHGp49Z0CzjkM8j8SC/pYiCtmiWMY645QkKeKsK/ZUIKCxi43vmQobXxPHYfXJd9541y9NxhV5uHYsJ6qXvva6tM5TUMndOoeoJ9l682rhyePP69jjOsDWsXmC4r8JEssIgiAIgiAIgjjsuGjtrpB1n/TrpHJq6NRKRU29z4//bN6LmeWhUyq/KixHx7nrsNzdTuomu7LvlSHbxVhiYDHxAlDtzJnS+pSyUiTU8xekAaORE9MyH3kEqTfe2ORxzTthGIYO6q/7WK2Tf9/DMobhir5XoC0QBRRGoqbQXRmXYxzbnXetBINmtVgmOsNYh5tddq6kxsqCVcDNDzbQ4jMVYtVfe9DFYYVtF9+dFvCkabYyRBTLLAonD+spy3GohTlPILQTyyeIUKf+dKq0zmpNO2Q6yx7+ZaPq/r0Te3GF9hUN8ntZvIuP163P148fXvnJMqwWnGnMJcZEKSXF1Y3YVVqHPwRRUhnxZNw3sRfaAuZme/eSoeiYzJ8f+yobcdEHS3HzV6tQXKM+Bi3i41kJfDdg33YJmHrDUfjsqhHondUyZ5lRcf7uTwzTL4jtLNqsfh35D9dNX65CwKyOFWfH2VVimZaJNZ3U2/8TGlu15pwAc6oDzkHpsHZSfw4uIYZpTj5wYu+hCIllBEEQBEEQBEEcdux1ySLAjmP7I/e4AZiYlqgRy4Itdo11nb8eU4sqcck6tShX7a7GnVv4SZBlAd7xclRKFu4cdmfEfdbNnKW73tTMC+8OdiuyEkMv8r8Zw+/HYXbgk5M/aRNn2V3frcX87WXc8vnDspEep76Y7pGhfs1OaYIIFTTDajboimVJNrlovHNqLDehkCdM35chCGOtF/faE2B27eNWBdzNE8tUxxwTKgh4/KFi2R+bd3L72VfHvyb3tgzqQvt6RQxzXMqBc+QwAWvaWnkyKyNWmPK4ei/vIGKwHjnGVs1kTJFtxbJTTtujxWjwBnDRh0txwxcruZL9TYWy6PbIqX3QKZWPwbYFJ/fLxB+3H8stcxFfgTdn71ANNWB8syyXE9LYxE+3j//umXC4v7S2s8wnOFKLBeeeiF9hjWSCpUGMMwsYfH5Y1atUTMpPw9PHPC3dNxXL7j+GtfcZMMay4v5YGIwGpF2n77BzDtL+ro4sSCwjCIIgCIIgCOKwQjuhMNZsknusOOcG9kssy1UIcdqL5WO+OSZk/bAE9WQ5PfzV+m6eXjoTGJvCGBMqSvw+jH/TjT51GXhr8MPKfBz97Gz8sEruAntgUmiXWnq82qG1tViI+wVNMCu+H+VgBodVjtj5go2YqZgOqA//nd72zRoYLPz+A54U9RZBQ8TOsgybBf1jHTg6MRYZOtFBPWdZuasM+2ploYw7XsV27JwUnWXfD+qKT/p1xoHims/kAQ+MX285mjue75bn4cMF8mRF5mBiwprSHRiOk/tmYuadY8I+XtngkcS3zHg7rjqm7d8v6y4TRUCRz5fsRdcHflf1pd3343ouonnlJ8vhEcQyizJ7u59i2f72AYqdZeFEXDbFVIQbmmBU28hSpszAtX+GF4BTpi/BqV1OxQnZJ2Bi54kwNKrdd768pfz7EJyhTDDTYs5wwqj5rI80SCwjCIIgCIIgCOKwotInix83KYrZlYXW+zMNs1ERp0swm1Do9nDrPlz/IYKG0C6t03xOVP/yS4iIJ8LWN65Zo/uY0dR8l0rcSePgGDIE5nb8xMi0u+6Ey9Y2kxf3ltfjrqlrpe4nkQSHOoLJUDrNjuuRhuV7SqQYptLZY1MIZ0aFSycrNkslqumj+IwNgogQtEZ0lmm/F+Y8/GtYD/wwuBsXgdXS4A0twM+vKcTuGll4EsWy53cX4sP8UuS5PFzsl2kyQ+NjYDlAkzBZPFTZI3fqgCwM6JCI5Xsqcc8P69CgKGuvbPBiwqvzkFfBf5f3ntwLmx8/GU+fFRrrddpM6JYeh5l36ouXJ740F9WN/Oc/QJiaeSAQo5ha7v9xXch3zYYWvPz3Nm5ZGQNuKWJscn+dZdoYphavQixjb0frLDNXNT3gINDgxhPBu/BUz0dhjFV3vlk6H8/dGhyyGJZ0Tnf1ayQeuVMwRY5sqZAgCIIgCIIgiMOOco9PErIe6cb3holUuaowe+9MwDgQb+WW4KSUeIxKVF8sNsXLe4qlZU8giMGLNqFvrB3VeStQm3R5yPb+yZeDheACbjeSJk8OeTxQqx97a0kMk2HNzkanr75Ur/xULu9uLZjwMHsLL3gpWf8/fuqnXrfUticnct1YPdJjMfZzcQCCCV0UET2z0YBLslJQ7vWhq8OG70/7HsUNxeiS0AXVDRHKmNgFvmIMoMHA7z8Y1IoXaqGqzh3qFNMTyRi+gA/fbv02ZP03eY+iV4f/qdbVeN3SuSJGS5lQ5mgFYSZa6hViWGqsFS9P5oc/fDA/tNNPK8Sc2DudEzEvGtkRD/wkT7W0mozcP7FcnznVTn9zYci+dpQIRfAH0IHEjmdTYWiP4CzhPFW+PyWtIZa1lrNM7DwTY5jK3w+LlH68cDdO6Z/JDdCoc/tg1zjLlHzZaywu3vIP5nXsijG5O6X1++7/A0ZnCmr+3gujg+9rE7HkHMUfh0Isixmeicof+A5ARrBtBwkfFpCzjCAIgiAIgiCIw4oawVnGxDItL6x4Afm1e6X7Z63eoSsC/bT9J92ph+5AADPK5Mhko9AftLHOhfykm+CO5XuTRH7KkJ0u1T/+hIbVq1H0+BPw18nuj4BLjkHFHMVfqIqYdN5DJHZW7cTuarXDSdv91Vq8Nms7Hpum/ozG9kzjLuBf+HML5m8v5dbtLqvHKa/NxycLd8NqNnKusrVs4qLgiDl/WGekxKqdKi/2ysYn/TtzolXP5J4Y04GP/DVLOxTEMtaJJnJS7wxWJqbazBvGwaMHE+3CMTv3H81+ZRHuv1v5HrsBcWphoq0RBw8w/rh9DPf5M/7eFP59iHRLk0XkWYr4KxvUoBQTbWHOUbHgn4sKHiC6KyZjntRHHgIxKJvvKxSngbaFWOYNelvVWSaKbz/cOBo3j+2KG4/rKm1z7ruLJVEwURjcoccXvSbirFOfwpSjTsejFyuOK6AQ4gTly5TcFTHjn4HBbA8RyxhpNw6U7/gP3rCKQwUSywiCIAiCIAiCOGxw+QO4YO3OsGLZ/Pz5MEC+0NMzSCwqWIRHFj2C86efH/LYaStld0VTFI0dhJ7LF0v3A42N2HvhRaj86iuUvvyKtL52xgxpuePHHyGvh2JqoL3piXMfrPsATy99GiUNJTjzlzNx7q/nhkS4Hh39KHd7Tf9r0BqwcvRXZ8qfxdlD2mPZAyfi4yuG46GfNuCtf3bi0o+W4ZIPl+KxaRu5C3ulsDZve5nk/BqWE31ReDQuJXvWtzDHr1GIZfJ50DU9BgaTuqPJ64/Qhq7B6w/v4tlezk+BFDFonEGMPY36fXdthVh2z1KfqYIgWaWYgCni1BTcZ7Xbijvm3I5aD+967KoQzlJi1bFWc5i+r40FvJgTZw+N5Lals0zklrHdcO7QDqr3J3aUaWmNzjJx8INdEJtaiuhMm5c/j7sdmpOMuyfw00uV1HGdcEFYLZHEXiNcZhvugB2bOxpQJRg4g4rzOODmz1Nr7zM5t5n0TI1YZsuRh1IY47TR5iMPEssIgiAIgiAIgjhseHxnAWoF14O2wH9b5TZUuisBHRFDyd4a2XmmZV1d8wryi595Vlr2FsgTCV0bN0rLJQrhjOGzyi4rg63pbqDXV7+Or7d8jV93/ioV0DPBT8mJOSdi9nmzcevgW9EafL00V3V/aE4S0uPtnOOorF4WYxbsKMOcrbzDTMnWolrJWWY1Rn/hzfY/7+6xEbexJK6Go/03UpcT60QTeXfuLsQnqov4vYopleV1bs4ZFw5vILxYxiahqtA5z0TX44HCK/wWzArn1OpcYbCCwIvnDeTcYiJjeqShLuETzM6bjXfXviutF81kE/plqp6vnDap7AwTe+xiD6CzrF2iQ7V8/Zgu3PKy3RWcSOgWPn8W9b1wRHarxgpdPl6EtZn2r89L6dpjAng4Qe/eX+YjpvtTqDTyhfyRGOnKwaD6njCB/60FqnMRFJxrfqGjTvqCBYzO0O8t6fyesPdKRuIpB25AxaEKiWUEQRAEQRAEQRwWbK134eN9ZdL9TfVqB9HCfXyvkiHQGLHgPc4aJy0rp0fOqwjfLaZHUIho6nWTBTyKSYlueVKfViwzNSGWKY+9tEEWpW6adVOI6JfmTAvbxdVcnpmxRXVfdC2JQkQ43vqHj73Wujwwx/AOQKupeS6VjinO6JxAOs4yxqkdrlLddwmOoHX5VRj65Ez0e/RP/KiY7KmkqCb8pEg/1I8ZhFiekhEJoZNK2QTK2VuajkW2BHFyonKgQIUgZvZtF48PLxuGswe3V4lpL5w7QFrOr5U/h2m3HIM7xvXAXSf1VL2G8uv+8pqRIcdwIGOYvbLi0Dk1BsM7JXEdbd0z4tArM47rKpu/vUxylrE46qWjOknPK6tT/wb3y1lm2j9nmdIVWlBXELa/0JQ8G0Zz+DL/vwbLX4wJRjyTexvihO/etWoK3Bt/5JaDor/WYECCKIIZDTAnh76PmMHpSL2iL0yaybZHIiSWEQRBEARBEARxyLO8uh7HLVMLOM/14CNYYv/Pzzt+5paNQfU0wwaf+n6NRy4IVwpOk4V4J+Pp7u0jHs+dnTLgLy8P+7h782YEfT74a+TXsvfrx936bbJ4ZGwihin2GjHKGmWhkPHE4idwIBjRORljustRSuWERS0v/LkVxTUu1ATlXjXpYr0ZKGOB0YhlD03qzS0mx1gRa06Ht2qISmRkriNlSf2d363V3eW+CJMGPUFeLOuRxL+W2VcAi2uzapvbc+QeLZETXpqLq6aswOrcSrQ2PkGwVYphotssK8GBcX0yYDQaUFori0XJMXJsMskud931a5+A28Z1l3rPlN/FOUM64IbjuiI72YkrjpJFqANd8M/60/6+Ywy+u360JAyL50puRQM+WbiHW2bvoU87OVYY0wrH6Pa7WyT+RqLKzbsAP1r/EeaUvaOZ9qrvgNx27lBccYcJH04wRhR1vDv+4heEvyEGGOHol4qsB0Yg/ZZBJIg1AYllBEEQBEEQBEEc0uxtdOO0VeousZd6ZuPSdsm4YPoFOPvXs/HX3r+wq5qfAGgIqF0k1S51fO7ZZXJ0cleV/tTAvT88gt67wveXHZcUB88e/sLc0rGj7jYlL70Mb77s3Ml+j4+8+RXOMiTIF/TNFcuWFi1FXi1fLN+aNCrEsOn/OYYTJpR9So3CNNJJA7Lw8Kl9Qp6/qaAGpXWySNgzSe1UioZoCtlNNj7C9uDEfhjWKZlbtpuNXIeWq3AyfHXduXXV7hp8tUwdK+3fPkF3nzVutVuREQP++w0KXXgJNtmZmFjytGrbWE2PnrKAXxuPbCksRsocfCwGKU5/VDrxRLHMatZ359V7ZQdkoo0vxo8EE6VemjwQ903ku/YyE9QCb/wB7CwThUGlgzLRaZGE2imLBLFMOH+ePbs/hnRMxI3Hy+X5LYVNSuVe37h/wpvy2CtcFdztq6texZrqGTDaZaeZAaGitLVTJ2w5vT8a7AYpVjlZiF4yLB2PDn1BwZ0ac2wHzk3GXGPWds2bEHwkQmIZQRAEQRAEQRCHNFOL1I6cN3t3xEVZyVhWtAwbyzdie+V23D33bsUWakdGmVsWbrQ8sOABFNcX47SfTpPWJdVU4YJ35uDtFx5BQq36ue1KijBm1VI4jAb463mnkSk+Hun33huy74pPPsHey6/gli05HWFO4cu1K9xyfM/UQXbH6eFT9GKtKlkV8rhY0N6avPz3Vt2OKO71XF7sKeedemx638AOoaLTlVOWwyDEx9IcGegYry8mNhXFlIkcycxKiEWSIJhUNnhxnlD6HgzwIoLb5w6Z6MgEFiYqiRMdRRq9oeX4vvITVffjLNELDW5F4XykrrTm8M2yXE4YOvrZ2ViTxwtwZkWET3xNpeB49wResDx1QJZKdDUZmz/Z8cxBatflgews00PPNSa64y4Y0RE/3nS0KkbcUgKCWLq/0zDFgn/R9aiKiSvcZKbYbaFPNplCevVugSxeGhxJ4cWywaGuRyI8JJYRBEEQBEEQBNGmsCL+zXWNId1hTbG6pgFv7i1GkUJcmjG0B87NTObcGUzk0iPVrhZwrpt5i8qhpT42P8Z9Pw57anhHCiO96Htp+dQFs1Tbf/noHXjsg1dhOXk8yt55R4pSOkcM192/2GNmdMjij71aFv+cCZGdPeGOW6TOEz422FLyhELwSf2zuFijkvt+WC8tswmErPj/vUuH4oxB7VTbGcz8+x6SPrhFx3CTwgnULaEHOrseCbttZlwskoTjbPT6OfGE9VpBKP5v9LlCerXc3gBu+nIVRj0zSxWPdPlCxbLycvV7i7VGL6Sxz8IAAQAASURBVJa9M0eO9m4pqsGOkvDiJuvbYq68SL8T1r315G9y9PP+H9eHTKyU3Wby5f7NY7txLsFLjzPgg/UfRH1+6cGcZfGKz/NAO8u0KIcXiGijpK2B+L3sby+gQSH+soEkSvHLnvErDCZehDeadfrzDMDEzhO5xU7xnUIftqjF7dij28FoFz4LhUhHNA19WgRBEARBEARBtClfF1Zg7PKteGi7ekphJOp9fkxcuQ1P7irEF4V8N9jj3dphcLwz4lTL83uej8Fp/VXranweTPhhAhq8DVyUSu8ik2H08y6dzvs2Ses6F4SJOVZUwLV2HbdosNvh6NsX6ffcg9Sbb9bft6KbrF2NHMeLbeKiXox+hUM5oKC1qBXEyZP6hDpR/thYJC3bLSZOOJjQN5ObuHiu4OhiGAWxLD1G7jprDkqnlNVkwc1jRoXd1mm1Ic5mlgYPVDZ4uOidKJbVeVxSNFHE5fPj70282Pre3F0hYpkpGIOg3w536YkIBtSupPxyfTHr3T45IXHWN4WBB4zf1xdh3MvzUCBMkdRy+7erccrr8/GlZhKpkt/WFequVwpjNS6vbpSVdZJdM/My/L779/0SyxgjOvMuSUaXtNChBgddLIsixttcxM9K6QxrCTGWGNXvW+xCY5gc+2DL+CXscw0GI4ZmDMW0M6dh6mlT8e55A9UbmDUR2UmdYbDyx2uIMJiDCIXEMoIgCIIgCIIg2pTX9vKixEeKSZZ6sAvHLzZ9geVFyzG3VHZ6iXR1qi8Ec2tDRYUxmUfDtmIl7IruqaDBiqL6Imyp2IKjvz5a5SITCRicCJgSEV9Xg3u/kwWJtPL10kXT0HVf6R630cEfV8pVVyLtP7cg8YLzQ7YxpcriQtzo0dztvrQMDIhTxg1DaUrMaEpMawm1Ln6f8Q4zFx08+dV5uOHzlbj5y1XwB2ShKCVW0ZVkMvJuLgGzhY9qJtv5LrHmohwK4LDYkezgxSs9YqxWTrTzCcc2Y30Rfli1D8EA73gqrauDVxGHZLi8ch9UYY18rrh9vNBkDWahbtsj8JSdJIluIot36Lv5ujltqgmYgx4XCtY1rMtXd+gpxTStG01LuKmO4hRINlxBfr5a1Ctp4DvelLRULGMdYMxgdcvYblH1y7Ul2ogtY0tR7SErlo3pMEZaXlywWJqyKWK08X8vg0EdccvEv9dOCZ1gN9txYg7/d8VgNaEm2QaXXd2BuPvveQiK0zc10zaJyBzccDFBEARBEARBEP967FE6Gn7Y9gOeW/4ct+yKOQZIuV71eO8YtVgiOstYgfzWyq2wu4NIve0lXLt1J7oPceOxa2/nNzTYpDJt7WRMkYCJv8g8d9Y01Xqrpw6f9U3DY9PexovvqB8TcQ4foX6/vfgidCVJ58sC2oC778QvyanoctqpSLZEviRjMdFIeIPq/qLWiJqJYg4ryl+XX8UJD1rxYf49Y0NECqXDxx/0cCKj3RR52mc4OqXIwluM2ckJcQFfHEym0AJ+7XTCx6cLzkBB5KpxyUX4nVKcXOeayyuLREXVjaHOMu5SWXw/GpFB4zSTViuWL/pgqaqvTElTPwe3z8+Jecy5F07I1MLK/hnfLZedkCU1amGNCcbNPb/CweK3G/43AQ6dYzzQ2CyhIpBel97+Igq4yhhlS2ADAkZnjcbiwsWc4K/tIJM7+thtMOLJE6jnn2uMtaDzrYMw7JEiyCFy4NOp83GJKHCTWNYsSFokCIIgCIIgCKJN8flkJ01A08c0c+9MnPT9SViwbwFWlqzktzHGolYjlD3ZvT3a2a0qUUcUy07vejp3e8XMAAxbeVfN8auWytsaLGFdWH5TEucqCxr5CYdplWr3jTEI3DHjZBy74uew7y/pkotV9xPPOQdJF6vXOQYPkZcTEnDBnf/BiO6dw+5TOj7RFXKAnGWv/C2XirNYY3WDvhiXneyM3BNl5I/LZm5ZsbpSKHJYHFxBuzmoPznUYtTvzAoG+fXrC8rxkjC0gAmAWmdZsUJUavDyy9WN4R1XWofbDdlpOCU1Af1iHdwAhKoGD4oUbjUtHk0kVEtZnQe9Hv4DK/dWhohofypisHrkVshicHm92rGkJxRH7EdrLMNNM2/CrFx1b58I64YzHgLRviSnWixlPHWWOop9qDjL/DVu1M7fp5piq3WWsXP0/GHZoUKZEMPUFctiLHDYLag3mvBzl2Olx0vrPECAP24DiWXNgsQygiAIgiAIgiDaFLdXFsvKvWpx54XlL3COlxtn3oj1pXxZuds5UrXNpNQYXNNB3X31xuo3cNN3dfjuGR/GfLACSZYEnLBO/8KfxTAZSgfHgF0BpNY4UNH+dZRnv4f6xMnceptH3SdlDgCT5/lx0Rx9gSPp0ktDLkINFgsyH34IsePkKYpGW+gFfTQ05fyJViyrbvTitZnbMeGVeRFL5r9aJkdbu2fEoaoxVCx74JRe+sciuLcYBgP/vJY6yxjX9L+Ge/4tg27h92VMbJZYJjrLXH635CRj0VJxEICWercPs7bwEdxgILxjKhhQl6j/r1t7fNy/M+fIG/z437jm0xUR35e2P40Jksp4q8g57yySllflVqLnQ3+gUDO9kw1ZUFKhEMjuOKkHd1vlqsLFv1+MTzZ80qzz64nFT2D+vvm4/R/BoXmIwrrYolm3v4jC4v6IZRXfbEX1b7sQUIjQWrGsY3IMnjm7HwwGnb9nmr81vkr+fDDF8r+BAR0S8E3PE6TH+2TESGIZOcuaB8UwCYIgCIIgCIJolQvJx3YWIN1qwY3ZaVyH1DeF5djd6EFBUC6K31pbjrSUTEnoKagvkB7Lr8vnbll3mEhc2TsYkNAHc/MG4aj2R3HCCBPXPlz3Pr7dyl9MNv7xN3688jOU4jL1xY5nN3zWzgiYeNdYjaeGu+2VF8RD37ILyFqM5QdawmvnBaBUwYUm0qWI/Qu9aM169hlUff0N11MWDlOcwgllbtmll1bMYBEupUC2o1IukA8H67Ma+Jjcn8VK5p8/dwAmc+4VNaLR6LLROfhtXYE0bZExsnMy59jplq4/DTLRKX92Fouf88VoI5LN4bYht+HmQTdz75kRzlkW7jXEzjJRuFNObmTTMLX8ui4P1tTZ/HODahHKV9sb5jh+CmXAm4hj0s7BgtIfYDLI2325ZC/Xm7ZC4wjT4vXJ59Nj0zbik4V7MGlAFvf5VYVx8p39tiycKWnwqM8PUdx87PS+OK4HLzC/vfZtrCvlh1E0p7Nsdh7/WRzqsIjup1eNwNq8KpzSP1PXadYaiJ9VS6ZhunNrUD19Fzy5vFAdZOeAoHt5AmqxjHUrFjfIk36v7X8ta7Lj72he272T/w8R1o7837jvrh+N0lo3csv/QfKyeTAHg7J7cD+neB5pkLOMIAiCIAiCIIgWwy7EVtXU45uiCrybV4rHdxbgzzJelLp9S55U7i+yvUqeYHnXnLtUjwWM8QgabGhIOFNaZ2tcjg/Xf4hbZt+Cy367FL9+/ihmzf8UIwShTMRaHCpQBA18+XVt6n+422o3f2HZfZ/iuZoYWmIgugvKxDPPRKdvv4ElKyvsNqZ4/gK2pRfYemKG1kn26aZPm9zHtLWyIClyz/frdN1MSmHs3h9koeyaYzrj2+tHhxXKGKcPbCctZ6fy+46zyp9BSxCFMkan1NiI27x2wSD1A2IxvxAJ5Y7Hbg4bhVxY9DuMFmFSqSCWXTqKn3AZVPRUBRo7YETymSH9VZ0UAw5E7p7QE2sfHS+9LsMrOH3Y1EomlIlTLtPj1JHV/i1wR7EIKKNHRlzErrLmFvy3xSCJ1oQJg7ee2B3d0uOQEtuy6G9TBIRWOmMLZJTKqdskoYzR39wrrLOMcd3f10nLV/S7QvfviL/OA9fWCm7Z3j2Jv7WYuIi0SegT9Hq9CNTwf48phtk8SCwjCIIgCIIgCKLFTCutxikrt+OOLXIHz0f7SrGgUj/qV9RQzt02+hoxK282qlNvQ1Xa3fBZOqC8w1uoyOIL/hlTeifBIBTYZ1YE8cjda9H9qe8w5PYpuOsn9UX+vltvC3kti1/tMGEF/wyzVb6Y/uemi/D1g//BkC0bcOGfvyB7Dd+blnTZpdhf7P32vzdJ2VnWO7l3swQPJmR+snA37pq6Vvfxaz9bIU1RZOSWN0g9Vzd/tVq1bVaiOnqoh9lkxLYnJ2LLEyejwlXKrcuM4V2ErUGvLP1jEGNxE/pm4qQ+GSFimcEgCz1JMeFdR3Ve+Zw1mPh+rxuO78rvyqOc6mlCQJhUGCnGODA7ETeP7YYEhwX//Pd4HNWVF2/FyZzaPri95fxrthc+a+W00Wj4fX0hF7fVuvxqPeFjt1O3TUWDV3/ohZJKV2S33L+Z3JpcXD7jcszJm9Ni4dtfrR62cBnOlpZDC/6hmtjL3Iv2Pn245YSzzpLW+8oa2QkIU4IVlvZqIdkkOFnN9bUhkzSJ6CCxjCAIgiAIgiCIFrOyuj5k3fzKOpy7hi/a1/JmaRK2VxdhTckaLiLpcQ6D1zEAlVnPcI8HzLygMCE1Hidn8q4exuvvNX9qn92jvkD9duu33G19UL0+s6IML732FK77+RtpnWPgwLD7NaWmRvX68RPGI/nyy9BxSmhXVLQoxZi3x72tu01hPd+zpWVNXhUemyZMhgTw3/E9OCFLZPaWEvR8eAb2lNXjoZ/XY8wL/4Q9jjhbdDFSVvJvMPpQ563j7qc6ovusoqEugugjumpePG9gSAzT6JB72KavLVS5vJQ9Yn6/vN5o591YWfF855qnfCysdSeifued3H2/3yBNSBRdVy/8yQ8RUA5IEGEDCjKEfbGopt5kS3F65kUjOvLvN8zky3Dc9OUqbjgAI1khCorfRThGfjUS+bV8BDocyljgkQab0LuqZJVUyt+SaZjmZLXQ6wjYJaFXz1mmhL1ex88+Q85XXyJx8nnS+sb1Zdytv9oTIuCZhSm7jhpZ5LS0b9/s4z6SIbGMIAiCIAiCIIgWs7tRLTw1hd/gwLGrinDDrLtQl3RJ2O3OzeCdPPcMvwfGCHHBSNi8+heh/qYMFmYzrGEuLJ3DhqH9Sy9F9foGqxUZ99+PmFGjsL9iWZojLazw9Nbqt3TX76tSDyu4dHQn2JQTK4UU6vEvzsEXS2RBSQsrkT++l3rAQiSK63lhhfXLxVn2L4apROyci0SMovDeYOS/f6OZCboB6TPRilQM5sCrbJDPZeZGe/CU3ty0x2O7pyLoj8Gtg2/H2f35qaZuj00STarcQnRTg0kjYFhMBlUElE3O1GPxLt59ybqnwvHuJUNx+7juuo+x71gZ6axxqz+3K/pegduHqEv7/9ord9rp8dKK6M75fyPMBaukJQX/RqGAX7oviK3MFer2R/4bajKyc9qCYLA9gl7+b6FrZxXqFobGq7VimdHN79uUktLiKPiRCollBEEQBEEQBEG0iNU1DfirvGkBQ4/iDm/DZ+On9ekxKY3va7q0z6WYfSzvCGPEXndV1K/xzUO34j/fhrq66m1hpicKmNPSWI4pZL0pKQk5X3yOmJEjcKAQxTL+glnGYXZIF+1z8+fqPndfpXyR/+11o7g4YDQXzNlJvAvmhXMHYOuTJ2PFQ+OQHtf0VMtd1btwz9x7MOmnSdx9Jia15gW6LyiLXFajNWwUVMRgVjrRIguuT/++BVuLq1VDB68d00VyhTFYZFUUocpqvUi0JarivVpMCmcZwyIcm1jwryfaMRbs4B1DysmXSUKsUnT4MefY2YM76D7/klE5qs9dG8N0mp2cI06J1t2kjQauKOYnfH6x6QvM3DsThwsFdQX4a89fqjhzc0m2J++XWFa3qADuHWpB1eQ3hhXjtLDXq5q2i+s9K/t4AzcsoOwDuU8wfrzswJX2v2s7dzti9wpJuCeaB4llBEEQBEEQBEG0iKs27JaW/xrWAyckh3ERKUSOaDEqLvZtBbzThtHh1jtgSpSnZTbF2XN4x4woDbico1CXLJdn62Hr3BkGhejCMDgcyP7gAxxoxIt8ceriB+M/QPek7vhw/Ie4b8R93LqeyT1Vz9lSVIML3l+MZ2Zs4e7fN7EXRnbh463RkCeIbDE2M2xmE5zW6CKY1/11HWbsmSHd107521+kqX7s2CyhZfoi43oLvWUGhUBiaLrI3mCQ929QiElW4VxgjjAxSvn1sjxOsGQ0ehu5GKcWs+Ak04plPqHgv9at7ywTYa8n9prVC1MvG738bykz3o6OKU50TQv9HAZlq38ftYouNobT4pSGXYho3U1a8eyknJOwqngVF0m8Y84dqPeGxq/1YPudmzc3ql60tmDCDxNw19y7sLBgYdTPYcfKutzKGst0xbLmCMDMAVb1qxxJj5/Qibu1+MySMzFcjFqE/fYb1/IdgJ69NSh9W91BaOsa+vfQsI3/7YsYSSxrNiSWEQRBEARBEATRbALBIAoVF/t9Yhx4uKs8DVFJQsmz+/UJe/bwZddx48fDYDajx5LFsHbji9f1MCimUIq4Y47hbuuSr4K5CZdJ5uOPIagQPzIfeww9ly2Fo19fHGjE8n5RLBuVNQo/nv4jBqQNQLfEbty60gb+Qlrkm2V5WLJLdjudPSS6rqLvrh+NhybJQwTEovlo0fZaXTcgsijZXJRuqEhTNu89uSfsFiO81UNUzw4XMZUJ6G7vELapqPegot4tCVluL7/+kWmrkVfREFHw1Yth7iptWnD6Yule+PwBaRADuxnYIYETyhgn9QkdoBDviOycZCKfVgzTus9cPtnVJrrnLv/jcum+KCQ1xbPLnuUm2T666FEcTDaWbYx6W/Y+H1/8OD5a/5Guk6w50zCVDjBLh1hYs4Ui/r2NyDDwsep9dfsi7oO9vsGi/5qmRBusmnJ/PZizjJ2/Hy/YzU1hJZqGxDKCIAiCIAiCIJrt8CkXHC6MGUN7wB/0oKtD7kkSScm/BVb3VrzQox138THIvLfJ/V+clawrllk78a4MRrunn+YKq+NOlgvrGVlPPgFze7VoF1tfB7eT7w0z+kph8ocXy5IeeADWDh24yKVI4nnnwmCJLEC0FWJ5vF70S3S8aDuzlPG/tDhbSITy11uOxmsXDMKeZydh7aPj8fnVI7io5YjOyeiWHqua5rg/3DLoFrQmyqmfkcSy7hlxWP+/Cbhk6CC1s8zghTV1Fow2ueupQXBsSdtIy7JYNrgj/zn8vakYr83aIa0vr+W32VBQjm3FocMHlAX/3H3RWeYPotHjxxuz+X2dOagdN3zhgVN6heyDDQ24/nN+QqvI+L6yQHbqgCxoXgbxigEGesX98bZ4DM4YrDv8QhTGftz+Y8hESCVaZ1o4vt/2PXf7x54/cDCxmqKPIW6p4F1ZM3P5uKnX721RDDOgOLfiju+AjFsGq4r+TS7+i4vk0mOvFXD7EPTqOyMz/zsMBk0PoR7lXmDIE3/j8embcMF7S6I6/iMdEssIgiAIgiAI4jAiUF+P3KuvQcVnnx3w155RWoUndxbg1FXb0X8h79RItpjQULsaw74YhueXPx3yHEOAFxEuaZeGHWMG4I9jz8BFGaGiGuPsjCS82ycHz/fMhreoCHsvuRTlH36Iyq++DhHLHAMGoNusmWj3nNq1ZuvZEyY777oRGbBjC4IGXkDwWzvCrBHLxr79lbQsXlJbO7RH9kcfovOvv8DACqwOEqJAZDaGRiGlGKCm84i5/kQm9BUiiQoGdEjEGYN4txnrMTu2exrXy8VE0Cs+Wc6tP6ZbaotEPZGnj3m61QvFlTHMSGKZGHlMsMsCicFUB2vKP7Cl/Y2YLq+HewVpqbtJdlEd14MfbpCrcY+Zhd40g9GLNXmh4tHO0jrdGOZHC3aj9yOyeHTVMZ1xywndud40PWZtKVHdz06Wz+9+7ROw7n8TVIMN2HcaqZg/3hKPCTkTdF+Le7/fHofXV78eMabZ1IRNxvvr3sfBRHm+fLH5i5BzVI9Klzw9MtbCC8cBleMw+hima4vs7ow/kZ9wakqywWDlzwNL0CzFeMPBhmT4q9SfvbVjHGzdE5F0bvewQpn9CnW3494aWfDbVFij+mwIfUgsIwiCIAiCIIjDiPKPP0H9woUofvqZA/q6lV4frtywB2/mlmBljSwa9HTacMPMG0LcKYxr0+phQICPERkMcApiwcnpsvPrl8F8lJCRbjXjzIwkbopg6Wuvo2HFCpS8KF/sK8UyEaPNhpijjpLuMweYyaGOD97/6TswWrPhTzpbf0qm8uJX4V6KPfpo2HuEH0JwIBBL7fXcLHazXRIylK4rZX/W3RN6Re0CWp1XpXKkNQelYPf62NdxWtfT0Noo32O8Nb7J7c8YJBfgO9p/C5Mj1GWlQnCWeasHoqNljLQ60WnV/Tx8PkHANHixtzzUHbSnXC2uWbQWMAGlmy8S4nRNrWMt1mbm+uX0YpjavjJG+7j23O9xco/JYT9fLdrOsalbpzZ5vG+sfgMHE6U4xmKjv+z4pcnniNFLpSimFZaidZaJYpljYBoMFpO0T3M6L3aawK9r8IXvc2ODLESxzGA3cw61pPN6IO3q/ogZFhrBFbENVLgq2TmtEds73/87GjwRxMNgEPA1b9Lxvw0SywiCIAiCIAjiMKL6118PyutWevWji12M+gKE1WDAzytv0L0IH5nAl5L3cNoxMlEWCswK0cpfHSrwWDuHimUMY4xccm50OGCwq2OHsY0NcBvjURF3Fr8fjVh2SZZcfm/rLXd2HQpoO8v0nGXajilx2uLdE3pi6o5Pccw3x+Cn7T+hvM6NQCC8o0TZu/XoaX2iPkYmJny+6XNumZWWH599PNoC1tMmcv3A67nbc3ucG3b77hmyoGZy5MEcu03XfSXDfzZBX6zkAhPpkREqaHkFsYw5yyobmh5mYAnjAnIIQkpTiAZH7ZRNxgm90qXlOEUMc0Cq/JmJpDpSVZ+hSKQSfuUkUjGiGCk+mFebp7qfFZOFA43WDcemtTZFpVt2lgWEQQzayaHRdpZ58nn3nXOI/N0w/BX8b9UcNDU5DdNissAniGW2TvFIOLkzLGlq56weMU61uOvRme67pUgWUhftLOP+Mf7aWIQVr1+I4AtdgZrIwwf+zZBYRhAEQRAEQRCHEd489UXogcIlXDhqCXqLuNtj2x+rWu+JEPNJsJix/dj+mDVcPcUx1SJf0PnK1AXixoSEsFMwleIYm1pptIe6gEx++WLf6lV3EI1NiUOX6dPQ/vXXEDNiBA4ltNMwldhM8vtUXnCLBfJsiuNrq17jlh9Z9AiGPjkTD/wkF44rYW60275Zwy2fPyybc1NFA+uyOurro/DO2nckAa+145ciNw+6GbcOvhW/nPELeiT1wJKLluCRUY+0aF9qt1AQBks5DFJnmREF1WoBo3u6TuwzIAhuBq9qoILIlCuHq+7r9ZpxTw/zecXYTLoDA7TOMsaDk3ojyWlBr8w4boJpOJFLed6kO9Mx+7zZ0vrlRcubFc+LJPJ8uflL1f00Jx9lPZBsLFeX+n+26bMmo5idEzqHiG3azyTa8ztQz/+dMcWr/x7FjumgEstE0VGcjqmNYbIJmNx+EqN3e5ptVl1nWedU+T8sVNbzAi9zmF30wVLuX0mtC9d9vhLDKmfA4K4FVn+BIxUSywiCIAiCIAjiMOorO1g0KqJ9Sqxe3nnQO6U3Hj/q8aj3F2c2SbG0O3IykGm1YFI6L4YFGhrg3qLub+ryy8/hL1IVHWRGZwxgDnVRWHzyRbIyhlmYkoZODhts3bohfvx4HGp4A96wnWUsDmY32SXhgrnGCqsbMVvouLLqOJm+Wa4vtt769WppuU+7piOOIuf8eo6qv0pP1GstnBYnrh1wLbokduHux1hiWizMuYXpkgxL8jzEdnsB1pR5whqDynUTdsKk0DllsssDA5SIXWfvzd2Js95eiB9XRZ56yOKUSurd+m5OPWdZnN2ChfedgJ9vPlq13q0TpVOeS0zEuqT3Jdzygn0LIkYxtUQSnrTTNSMJa23F33v/Dln3846fIz6nxs0LU4z8unzObaf9TKKJYQYDQQQaBbEsRv29xh3bHvbeyZJYJjrexFi1ErPXhIZV/O/ZIExTjQY2NViJx2Th3JFfX8sPOmHUuvjvL79S/m5GPDVLPTn2nyeB+ugmn/7bILGMIAiCIAiCIA5x6pctg2fvXgQa1Recrq1yrKyt2Nvoxif7ylDlC71wH58SD7/gLEu0JaJHsn6/1+is0RFf494uWVh9VB9kC4Xs9UuWIujxwNKhA7rPn4cey5bCkhm+n8dfK1+YG50O3cmVp8/jJ9spnWWlCUm45e7H0M52cCZdhsMfCOKLJXu5WKQoMigjl0rE9UycGP3sLIx+ZjaqhYv0Lmmyi4Rhcu5EtmIan0id24cZG/jvkXH2EL78vymY48bll+OfDONBHIbQUrHMnjFD9VgwaOS+AyUZGncQwxTDT7O0JK7SfQ0m5DHH3jMztmB1rnpi6fE903D9cV24SaQiXYXv67yhcteaEvGYtBFREafVDLsm0lnjkcWfcPRM7imJQ/5g+EmxWrRTIiMJadsrt2NNCe9cPFAU1cvntEhBnb6wKaKdLPvVlq9CYphKR2c4gkyIEk4xo1P998VgMsLeIwkmQSwTsevs11Qnv7Y1J77FYtm61K5wWM3ITLBLkV2P8BtQxq8ZcdAIm38+gCORw+MvGUEQBEEQBEEcoTSsXo3cyy7HnsnnI+BSCxPu7dvb5DWVsaNjl27B/dvy8cTOAqlv7OVe2Vg0sjc+G9BF6vhhYlmn+E6wNizj7tvq5kv7eOG4F5p8TaVDqG7uXO42dswYmNPSYIqPfJGYfMnFMCUno/2rr3CTK/XEspEbhQv1YBCd9+Vyi7+OGYeKhCQkKmJrhwLvz9uFh37egDPfWiiJZR5vqLOMIX7+V/xxBYpr1C6ivu0SVPedOR/AbwjtgtuqcFFN7JfJuZSioaRBPaWRYRamjh4WGF0w2vU694yqIQmMk/uGirVGi/qzZKfwUV3l/julIKHl5rHdcP/E3twkUmlbP/+7O22gPABDiU8Qy5pjpttbs7fJbcSpj3WeuhCRSxuvZiTY+PPKG1SLZaw3j52H7659V3JEKpm5VxasDwTFDcUh65pyhWnFMiauaWOY0YhlgQb+czRYTboTKz376lBn0kxXrdOJYQruRYY1O/IEWPXO1L/hVek9MLRjErdst/DH4/L5MWdrCa7+dIVq2xTt34jskTgSIbGMIAiCIAiCIA4xvCUl8BbxrojGNWulwnttNLH844/gr2vdaGapx4uBizbi0e37sKfRLXWPbannhbochxUXZaWgi9OGxxY/hqWFS7n12XHZXCwuvvx9xJe+jrjKT7n108+aLl1cRwO7MK2bx8fhYo8/LqrnxB53HLovXID4k0/m7hs0F4oMk9D9dcHf0zBk2yZu2W3lnWxt1bHVUhbsKOVuy+s9qPPwF9RLdtbi88V7MGO9fuG2dqIec4dtLAgVxupivwtZx2KCYrzvrYuGRH2c5047V7eQ/HDB3u47xHR+M2R90G8PiTqmxNq4gQmRYD8VrUvHJwhg0UQp690+qavskVPDD1iobgjv6FIfTxC7q3dzy+1i9AU4htXE/w7Wla0LEbkeP1odre4Y15Hr0dJzls3KnYWVxSvx1pq3OCeZyIjMEbqF+21NRWNoj5x47OHQut9+2/UbAqJFTIA5zTwFdSh6eQUqvt2KgCfUjecXviOjU188NifaQh1rgdCeQDGqqdd9FgmDov+R0WC246KR2fx+BPdnUbULHy3gzw8lKVC7EavThuFIhMQygiAIgiAIgjhEaFy7Fq5t27BjzHHYcfxYrrsr6JbdZEVPP63a3r1pM0pffVV3XyzKWPbuu1x8c8f4Cci9+hr4KuVJb+H4KL8MJR4f3ssvxRXrQy+kHEYj5x7p/2l/fL/te25dki0J/VP7c8uGoBu2xuXcbVMX6XoE3W74CnlByDFwYNTPUwpees4yk9Brdv1PX0vrvOboSuwPNMoS7mf/4l0fQb8DD/+yETd+uUoSS8KVsd88titePHcgCqvUTkRGwLERne77DXvL6/HDynxc8cky/LWpWIr5GXVEHD2YA0nrwmGIHWqHMgEP7/6yxPGiqZYYcwLeu2SorhvsdMH1pS3fF8mrbERanBXPn8tPodxcpB+DNGkE2pV7K5ArCG2xNgvOCRPFZDSGmUyrpdpdLfXJKYvrtSinWu6rk3vVru1/LRKsaqF76mlTJcFJdKExR9mLy1/E+jJ5eMSemj3c7VPHPIWRWbwzSc9t1pboDTcIF2cWBwIop2GKIrT2d1bWWIbqP/bAV9KIhtUlqJ6xO2QbsdzfGCP8LfI2AnW8CM6IGZEJS1D9d8qquc+wBHjRK/P+EfvVWWZ2OtFNGFIxbS3vEn57zk5sKgg9P5MN6r65aYXRxz//TZBYRhAEQRAEQRAHAfeuXci7+RY0rucvMN07dmDP+Rdg9+lnSNswdxnr7hLxFYS6iuoXLAhZ56+pQe4VV6D01dewc8LJ8Obmon7hQpS/936zjlF0kymxGgKce0TE5A/i3ZUDsa3/QE6YU/LwqIeb7TQKKiZVssmWLUF7ocgfJ+8OabTJ7gy39dB0QYlTD7llK1+uHfCkSuvK6934cVU+Ot//e8hzbWYj7p7QixO9Cqtd8DXkqB4PNHbkbh/8aQMe/Hk95myVL+C/uz5ytxxjXek6LClcgsJ6+Vz8/azfcdfQuzgR5bGjHsOhjtFaDmtqaPm7yOuTj8HILuo4pcjQHD7KNrxTMgxu9Wcrwj7/inoP3pmzExe8vyTkcTaxckAHtQh1xSfLpeU4uxkJDgtyUpy6+492AIPYJ8e+l0gikTJWKDrRGP8Z/B/u93t619NVQxbEAQGi+DVl4xR8uunTkAmY4mtbjVbJtdWcAQL7i94AAq2bS8ncPD7+rUUr8nXOTYN7pywU1y8u5P7pxTAlZ9lbI4AXuwE1/HZGhyVk9qVdx1nGxTANgCnOul9/A4OK/4AgduOJ7tWQ14T8ua0JdMGe8gM/nOFQgMQygiAIgiAIgjjAlL7xJnadMgl1s2Zhz3mTue6xXaeeFrJd0OVSiWV6sK4u1XOCQWwbod8xUzFlSpPH1qSxKKAW0I7eFIRh2kxuImXe9Tfg8j6Xc+tvHHgjJvecjOYSVEyt1BO9osGgEMS0MczlvXnHD2Nne144OtTwKqJ7TNhRuqEYTIi58zs+nqvEYCnjhBaRV2ZuA/xqwSUo3F+wowwuryxcsMmZIzqrzyUt7Ny6+PeLce1f10rx2y4JXZAdn40r+l2BJRctwZCM6GOcB4JvJn2ju96Wxqb+6ZNi1xfKGA4r7ygrq3Pj7qEPKx6RP8sGtw/PztiC5/5Qx6ZF2KTNWiFyqZ1MyBC/wylX8vHFjsnq77BXZnRimcfvkWKWYzqM4Y9fRzQTH2PMyZsjdc+Jbs1eyb1U22vFMqXApiuWCTHPndU7MXXrVBwo9IYVRBLrxOPUUtrAC8rnVJ6Ex3JvQo/5SWznqm2qft0pv0aDF5VTt6nL/av4nkTs5M87g8UIg0aO0XWWBc0wJdhgiNLxqfe3c0bOSCniy7jjJDaIRX38SqOjQ3hqVTAGF3sebPLvwr8VEssIgiAIgiAI4gDCopVlb72lWrfrNNm5oaTmz78QcIeKZeZ0fpoZQzslsuhxdceQlqYmaC6o5GNbSo5PkoulSxrUE+aGm7vJx5WRgduH3s4JFNcPuB4tIegRXBwmE1fW3xKs2aERNrMQw6yM4x09346bhG05XXEo4g+IF/RBhVgmO8uW79GP0zqyP0WMjb/S9YkF9QatOKDvrAlXRC897vdgxJfy5Mbvtn4ndVg1JTYcTPqm9m1WFDgrJivsVFdGaiz/Hjfsq8H63VYEg7zKYDDJUUZXE58l45nfZSFNG+GLsZqlOO76/43Ht9eNkh577PS+Ub8XSSwzWnFGtzPw4nEv4pczfgnZjolfoiPwjz1/SOtEzu95Pk7rchqeH/O86rFoJmeyQn3lefHB+g9woPALAjkbPBKNWCZuz7h3+L3ScmkjL5Zl+FMwor4fDCGeMJ6G9aUoemUlCh6X3YTmVDvwwQnyRnP5z5ChjTzH+B26Yln6TdHH0eUnysJbYUyKNByC0XfnR1hquxkdDfIAhG+ulc+x9vH8cy3ZQ7HyibMwXme4xZEAiWUEQRAEQRAEcYAI1Nejbg7v3IiG8vfeQ+WXodEm58iRiD3xxJDYIqNx5Spp2dZdFrJE6maHd9QUu71YVh06MOD5nrL4tLBwldRntPzi5TilHX8cDGvHjtyFNBMoTMaWTZgU349e71i0mBITQ9cJF8LibXoK75Y4Mz1024MF6w/6368bUVrL972N6W2FwegFgkYEvXz8jyE6luI0vVkmWyk8CVMxL38e9pSL36NaHDDHbVEJO9Hy/rr3pVgfY3PF5hBR5VAl2R69M+as7mdFLIFn0yutJv4y+rsVBUCA72jrniV/F5U60TbGxSNlYfHrZblYk8dH+bQl60oRhU0mjVW4BY/rIU/PbApPgD8OFqVkotWEThOQFZsVcSKmSLxNdq8xsevpY5/GxM4TVRNPxZhjpAEZbEqu2GfIUC4fqM6y9096PyqxLCdejtVe0ucSSWRjnWzh3qdzkPx9VHy5Bb5i9YAHi6UE2CfH1lElR9UNwnkk0sGTEXpQdmOziv2lfSv+fm5OzsEp/QXBq74Mnde+iAxDFW40/Spt01sR7U208+dyjN0Ku+XQmhR8ICGxjCAIgiAIgiAOAN59+7B16DDsu/Mu7n7CuedE90TJZSTj6N8ficLzPfvyVRFG9zbeOdbpu2+RdPHF0mPmdvxFsidP3l7L3Eq+2LmdTS0WpCgmq9X5A4i3xuOa/tfAbrYDipiov1ZdDN0SGleu2G+xzGALLZnPKSrATVM/Q0o1L1BMbp+G+SN64c3e+r1TBxqX149TXp+PKYv24B+hR6xbO1406xDXDo+fIcdHRWrdoc6eOtt83DzrZmwt4h2CccKF73Ed5MmilmR+2qjYwXXbid3xy81HRzy+TeX6Zfii6+ZQRq/oPRypDtnBp4fFZMREUXjgXGH85xtrl4WUBp3piIyHJqknXJ751kLcPXUtnvyNFx4ZL54X6iJyCk4zRlqcrdnOMmUnWThYF5mSHknh3XWiQKrXCaZkco/JGJw+GD2Te+LY9sdy65hod6AQnWJMLDy3Bz+5NZrOtGEZw1THKp4/Bp1jT76gFxz9w58ztvmXa57QRVo0mtRCVC936BCGRlPLJoiaYmJginfAYLfizScvwwvnCufV1CukbS40/4NE8H+znQpRzATh/DUcuUIZg8QygiAIgiAIgmhjgn4/dpw4TrXOOXiw6n7MmGPR7gU5oqMlbvx4adkxaCCs2dncsjc3T4pxefbwE+g4TCZY2suOMFu3btK0SYavrEw1HbPO58etm/lenXMzkvB09/bysSocEEZ/Jfqk9JEurgMu+WIuUFON/fmMqn/7DQX33sfvq0Ht0GgORru+OHDe7BkYvWG11OnTPcYOczO7gNpKKOv1MB9/EzGYq+AJ1krOqMtGd8LWJ0+Oep8rC3jR1GblxYEku+xMM5rlCXgPTerNdRgNzE5scrKiHgfSKdRSmnOM0Uzz3F6siCoH+d/GmrwKaZXbJ4tlP9w4GhP6ZuDek3tJfWdKpq6Uxet+7eNxrs4UTJPRgEX3nYD594yVYrbNjWE2hdOsFssSbOoBBJHEMqNGVrhp0E34fOLneHj0w5IbS+wv3FW9CwcCJoqJZf4mg0k6xjfXvIm82jzd54iimOgsFN2xeg46e88kpN/G9/PFjg6N+TqHpCPzjj4w+fnJkxIVu4DaYl1nWc+M3iH7qTe2oFw/4Ifh6TR0HbcL3SftRafMRMQY3Px/eNkzX7XpM5YPuVuzySj1lnVPF86Fw8A12paQWEYQBEEQBEEQrQxzWBU98SQaN27k7vuK5W4YEcegQTDG8FPJOk75BB3ffx8Jp4WW/ItkPHA/bD16IH7SJNj794elA39RHairg7+qKqQc396jB+x9ZSeLwcpfMAc9bi4Ouv2YY7FjzHFYV12HLwrKcfuWXPlit2M6Tk/nxZUBsQ7uIvHN7rGw186Cs/YP3D/yfmnboFuO5tUvWozcq66Ce1f4wu9wlLz8Mgru+q+8QugYaxGmpi/yDOZDxzUxe0uJ6r4pZitiuz+LXwqfUZWy23SO2VM5XHefxfWlMNr3oSqwLaS3yWDmY5jnDOmAwR1lES1chxajqF7dVSdy86CbcaijjIoqHXZ6KMvuw/HcOQNCxDJlN1xZHS9SDemYiKE5yXjv0mG48Xi+H+8bRf+YlvtODhVLRNolOpCtKfqPNoYZTZec1lkmRi31YOKTUlxqHycL63+e8yc33GNQ+iDdKGyDt+UieHNQ9o8x0UspdJ3y4ykhoiIbWDE7d7a0vep9imKZ4k9S8oW9YM3i/37bHIr/SMHcfzcORPLknjDbhPfKxLehV8obrOOHThhMaqHekaCOwjIaDKETiZtkHd8naLIGYbIEgVlPAM9kA4+H/tYnmuQprP/cdTyePbs/RqYL53IUnXT/ZkgsIwiCIAiCIIj9hDm0PPn7pPuV33zDdY3tOedc5N96G3acIPd6tXvhBXT+5WfYunZF559+RPb77yFmVPgLaAbbhhX5d/n1F7R/6UXuws9ot8MYz/fM+CurVG4sS05HLsZoVkzKtPfiL8QDbjcaVq2S+sGuWL4J/92ah+mlvHPoug5pSLSYkWo1Y9Mx/fDLkO7c+m7WOsRVTkF2TBpykCIJYmx/SphgVvToo83+DCs++hithb1PbyScfTYyHngg/EaaCFRr8c/WEtzw+UqU10Ufn6rTTEa0psgxyXATDBlfXjMS3mo+MqZlxvpSxHR+Q7rfNVEeZiB2lk0akKk7+W/c9+Pw8sqXsbFsI1YVr+KmHopxy6v7XS1te3rX0xFrDb3AP9QQRQ9G54TQqJvI0ouWRnRUifTvkICju4kTM/l9GzglJQBT7Gb8s53/bZh1BlSM6hJ+0mYL51k06SxjMcTmOstSHClRO8vE17m0z6VoF6s/TIGLbDMXpaL37kBFb5nwpzwHtDy37Dlc89c1+Hvv39L2emKZNZMXx1i/v1HRI4f3FAKrwQtbpiCCVQuuwZg0eRom4+9HdJ1lNmuoq7HB0AJn2V8Pqu/PfzGi8LXkZn7aaafUGFwwoiNMc57iH9imdrseaZBYRhAEQRAEQRD7yd6LLsbOceNQ8tLL3P1S4ZZR+9df0nLylVci4bRTYe/ZUyrEjx2jdrIwMS3+9NMQN2GCtM6hiWyKmOLiVPHHQD0vlhmdwkUdE7nmzuFEOWsn3lkUdHuQd+110uP+KnW87rFu8sVussWMH7Z+hTvn3ClFl1hh994rr8SuU05Bw/LlCCpimPI+9ac1hsO1We5sEuk6cyZaisFkQrunn0LyZZeG36a1lQmBKz9Zjj82FuGZGfK0w0j8tDof93y/jlu+cEQ2JvbLBAKWqMSyo7qmIDUmXB+V2rXSJUHuSkqJ9+D2cd0xtqc8VVXk1VWvoqShBJ9s+AQX/HYBLv/jcuTV5HGRNtbhpCxBb/S14EL+IKAs7A/nsjqq3VEh7qpIfHbVSO42KDnL/DDFboEz+1PE9ngCBksZF5/U491L+PieFncUUzSbQ3NimNpzjHUSNlcsi9SNJsZbxbL8A+0si9SV9t023oklMid/jkosE6d+moTuuLjj+Qi8ShATMATdwEu9gffHAsuFyZ8dhgKDLlK/aGMlhLo7CYvOuVkfaKYTz9sINPATdKMls2Yd8OeDvOjnOTDOv8MBEssIgiAIgiAIYj9oXLcOnt28k6T8gw9Q8/vvYbdNu+P2JvfHxLT2zz+P9q++gu4LF6DX5k2SKKZFcpYJxfqis8zolC/6LRkZsPfuDYONvxDzK3rKGOfMniEt59it3CTFvTV7uQvgzzZ+hm+mP4u6P/7Em6t4l1Kmzwn3Jl7cKnn5FQQUMUwR9/Yd2HX6GaiNUvDafdbZqvtZzzwDawc52rU/mPr2012v7FprLWpc8mTS3IqmLzrzKhpwx7drpfsn9MrATcd3QzCoEcss+mIZ5zCEvkBhMNeGLa6v8pbi9nE9dKf7/bpTnpAnUlhfyB+H2aGKNCbZQmNdhyLKYw43vTMrRn9KZDgkIUxSPAIwWeVhBybnXpg1MTuRk/tl4fEz+oasT4tt/tTDaGKYzS34753cG3FW/b85SueVKEhFI8qJzjK3360b821tRIFLFL0iTezUou0sEzEEhX0odzVf/g8jDCMaAE8tULAK2PADv7LjaKDfOcCF36ieFzCqPwe9KaxX2S+I+rjh8wAv8v8hRj6gKAalVO4FFr8JFK6Vj5nAkd3YRhAEQRAEQRD7SeFDD6vui9MutSSedy6MQm9YNLCLO3NK+CgUQxTRmGDn2rQZpuSkELFMxGjjL5h9lXIROSO9shyfPXUPrJ4KfHRRAm7Zxvdnselx32/7Hm/86EdGFfBB4z54uhpw1reyC6xxNV+Wz7B26QLPLrm8m03lzL/lP+i9JdQ1poRFQbWRU63bbn9IHHciyjduCFkfe+wxaG2emi6/10CgaUHgko+Wqu4f3zONm7Y4IicDqyvXN+ksYxRV2BCXFrreHKt2tmn3UVBXEDYyp6XCVaG7j3RnqDPtUCecGy6a+KWW2Xcdhwt+ewfcHo1eGG1y9xyLZfr84c+BRKf6b8ETZ/ZDv/bNP4bWimEqBxuEExTDOsui6EYTxTImYrHnRXNM+4NyUicTy/RimLurd6Pey0eSlfx+Nv8fPLRuNIPwdRqUjsEC/m+gzbgG7sAgxJp/CT2YjH7shAB6TgT6nwesnwpU5yHA+sQUmBXR8H713XBP2n/Qf0LkKbUc9WXAxp+Ape8C2kEcAfXfV4lLfgC2/A6s+Ahw8TF+jtwlTb/eEQKJZQRBEARBEATRQlhfFxOFIv4f7owM5HzxOSxZzXOuRIMpgXeWlb3xpmq9nlhmsPJimb9cLZYdt3qZtJyzphFrRvMXiEwoYzChjHHtn2JETD9imXLNNdxnUTFlimq9e+dOrp8tHOUffaS67xzJx9taC3+B3CWnRByu0Jp8u0KesrdibyV6PDQDH10+DMd2T9OP75ar3WdMKGP0SE/BasXHLIpU9ZpuM8bNx/XHR1sHwJLARzlFrEny9zo0Yygnvv5+1u845Se+3PyRhY/gwwn8JLymKG8slwQVpUPngl7NcL0cRPbUyAXs2oJ5VjzPRB7Wt9VcuqTFIj3Wib31gDP7M/WDBj8W7wofh7OZZSGGRW8vHSXHW1uLWuZwYsKwpeleOaWLKlK/l0osC/qwqXwTft7xs0oQa0qMY71lbS2WSdFJwVWmFM8YhXWFOP3n00Oe99JxLyEzJlN3yIFBtJQpXWpW/u9IiuUpeAI9YDPKIreEXSGCdhnLi2XuWgQd8mfyZq+XYaiTzwmDw4TB550Q3Zv9+gIgXy7q53Cm6Mcxe58O9Dsb6DYO2MXHTbHgFfnxNV9E95pHABTDJAiCIAiCII4IvIWFKJ8yBf5qzX95bwFVP/+Miq++QsOKFdx9U2Iius35R7VN8tVXIeGcs9H1999gzc6GwRz636nn58/HTTNvQlljWYuOwxjHi2Va7L35wmYlYgwTgfC9SMm1aqeDzRN9XCpu/Hhk3HcvN+VTCRtwEImqH3+SllNvuUVywLUW2gEEbSWWbStWxx4ZHl8Al360DF5/6Gfu06x78byBusICI8HKX2wXVMmuqOn/4Z1x2UlOeKv1J2KKvHECH6HNjpe7lvbVhYqIgaD+ufHSypekOKiy+6wlbqyDgTL2p3ULPTjyQfx1zl+qmGpz6JrQI8wjkScJ2i2yINU7S/93vL9UunjFNcnevLisNn4Y7nEmQJ0//XxpfYo9vBNWGTHcWbUTbY14LovCX6VbLfJP2agW9UWUkVXtueLZWSM+IK8UXFlGQyPsprUwKKaiSij3YxPire5anNH9DG6xe2NHjMoYwe3XKvQVDkvS77XTRSuUMS78Vn/bIZcBfc/il5vq6LswzD6OEEgsIwiCIAiCII4Iyt5+ByXPPoc9k+WLu5bgKy9H4X33o/jxJ1D9Mx+5sQ8cwE2rVJJ6441o99RTYUUZdqF506ybMH/ffLy+6vUWHUu4LrPkq64KXanRvTZ0Cb3It2qMS52KozuOXhvWwxTLv88Ob6ldbv4KtZNNizmVFylijj0WabfcjNbGYNIP0+i57/aH5//YGvaxR34JjYHuKK2Tlj+8bBjOHdoh7EX6KV14N9hWQZDr2y5eiuwd1zMNCIQXN7oldtPtnxrTYUzY2F44iuuL0SelD14+/mV8c6qif+kQ59Yht4a6gwSY66o5fVZa2ERQXbjpmOEJKAS80wdGF4dtLmJ8lrnnmoPWURXu8aWFS6OeoKn8jO+edzfaGtFJJgp7C/YtkB5jzrFwnWxKx5s4AVZ77qh+ni7hP74kdgx/MCkKZ61dEEZdNVzU/d1er+GtHi/DmhPPxTvf3/UIbim8AJclXdj0mwxXxn/pz0C85pw67l7gvCm8o0xEu42SzscBPU/GkQyJZQRBEARBEMS/nqDfj6qpU7llz969+7Wv2tmzpeWaadO4W2s2f6Fk79OHu40/ZSJMsZGjT4sKFknLRfVFYbdjj90480asKl4V8pgxPvSCz9ajh243mkso/xcpyQiNBnZ1dsSaS9cg29EOt/3sxxNf6F/wd18wHzlff8W9Vuajj6hcc6xnrefaNTAL4iEbKBDO3cVoXMW/r+TLLkNbYDDrC0l6Tr+Wwpxjy3aHj9x9vUyOZ87cVIy7vluLLYW88DWwQwLG9cmQHmcX9b/t+k263ymup9QNtnQXL34M7ySLH1kJDjx3dngXyk2DblLfH8jf18bSxPL1SNR4eGfNSTknoW9KaEH9oUrnhM7SslYYixFidC0lOylRdz3rLLv2WPl1tSgjtTkprSvciohuKjbBtjlE21nGhH4l0brzIv29a+0YpijsKV2TXr837DEohxS4azR/My2C8K88h0Sx7IKvgevn811gqZr/EKE8xxTOMiaKHz3yBGSd0JM/L40GZHhTMKlqDOyWJhy2m6cBT7cD/noYUAp/2aOATscAyu+w41HA2Ad4R5ny2DP6h99/ZoTHjhBILCMIgiAIgiD+9RTce1+r7MdfV4+ihx8JWW/v1VOa4tjuxRe5f01R3FCsEimqvT48s6sQOxrU0yVfWvESJ6Bc/sflIfsw6cQwHUP1hZO7Ne6SvQk+vHGqEau6GrDu5G7cuv4JvTknxpfBq3D0Ztn5YkqXi9zNWVmcG8w5eDC6/PoLki4MdUCwKGXXP/+Q7nsLCnSPSZzeyS3XChGn1kZRmt0WsPjldZ+tQI3Lp0pn6bF8TwWu+WwFfliVjxf+5J1o8Q75opZNF2TCaEmjXBS/s6QO/2wt4aKEf2/iz5mju6lFiX7twzt6tJHOWGusqs9KiVcoA9e6r/7NxFnCT32MBjEiq8UUuw0PTuLFcz3G9kxHpxQnzh+Wretsq1KWrrcAdr5sKeeHPOTE57SJWKYlkrOM8d2p30Xdoba/iJM6RWdZvDVedZ7rxZC1Mcxra87D4Do50l5p5v9GBRrccqTdJfzdik0Hsgbwzq1blgO3rub7yc54S/0CdkG4rMkHvOq/9Qbl9NSm/ph8ewlvF170Oj99k3HzMuDqPwHmjlNGac1hBi+0HwKc8DBw7iehjx3fOv+beThDYhlBEARBEATxr6fuH3WfWNAX6qqJRM2ff6Hk1VdRN3uW7uOJ557L3dp79kDCqZNgMDb9f7Nr3LI4tDvYCT0XbMBre4txzFL1FENWhi1SrZl0Jhb8K3H0H6C6X+H1IfOfNfitVu0aKko2YX5/I56dbMK4URdLkynL3nsfRY/+T7Vt8oVykXvsMdFNkWSCmSmJ70oKNvJdW43rN6D8ww8R9HhCJmE6hg5DWxAuhtkaMEHi4g+X4p+tpSE9VIyzBrfnbkd0TsbsLcU4793F0mP7hP6xdfnyeVDnlaOZMkZc+cly1Hv8KKrhz4XRXVPCumH6p6odIdoJhWL8rNZbG9ZZphQMlIzKGoXDHSYE/nfYf1utdy0jRnYFKjFa1RE+LTE2M/757/F47lz175Xx2cbPcOy3x+LrLV+3+LgK6ws50ZU5q/ql9ovqORM7TeRur+qnE+OOQixrSngUi/PZeS4Ks20FGz6g7Cx76fiXpHOfvXZ+XX7EY2QcYxmBp/PkCG+jk/+75Vh2GTDrcUGkEv6jgk3ztzi5C3DZz8BgJmopSOokL3+gKfBXCGQq4UyLP8xnp3SwKX/34bZnIu2Y//KF/1f9qX7Mtn8i8r8BEssIgiAIgiCIfzVMGAvU16vXCWJNUzRu2Ijdk8/HvttuQ/m776HsnXelx1g3l61XL+R89VWLjmtNyRru1mvtim0OoXBZp89IdEgwZu6dKXURLS5YrHJ8iSSerd7XbZtzdV+/zMlP8fz+tO9hstmlz6X0FcVkNAFTiizOOAaGXtyHwxTPX0Dm3XgTSl9/A3vOOw8lL76EPRdeFCJamtP1J0a2VQyzNcitaECpQoRMjZVFprsn9OCmHIoxzTdm7dDdR3WjF3VCJE8poIp4KnmBanWuXFAeYzWFFcS0LiWt8CWJZZ7wYhnbH3MB3TX0Llze53JuHyx2+foJLevWO9Q4tcupbT6kIOhtulQ/XFfaCyte4G6fXvp0i1+/oI53c7aPay9NU22KZ8c8izmT52BIRuRy+XDOw6a635Q9YTsq9X8PbeEsC3j86FrVHj9M+kGKINd59IRpdZQ06OOjmwNieLHxvPGXIb33z7AGNgDzXwIahFgm+3wt4SeBqlA6vko2An75byDrLJM2i9e4wbbPBLb9xS9XhqkSEFyjqm40RpHOhE4tHUcBHUbwyz35jsQjnbb7zywEQRAEQRAEcQgQqAu9KGIdWtEUvBfefx/c2+WLOs/u3fyC2YzYE05A3IkntuiY5uXPw5z8OdxyVabaxcWo8fqRaOX/r/qWCtlp9uWWL3F0+6Nx0vcncfdfHfQYlBXNGQ/cH7Kvv8tlAeb9My/AdT/zxez5qZX4eMIn6JncE9XWHboioiU7G46BA5EwaRKcQ4eifulSJJx5ZtTv0+DgL9J9xcUoe/ttab1r40b4q6oQ9AlCoMm0XyXrEWlist/+8OH83bIjJd4Of0AWOb3+ICwm3puwOrcK5gixKha1HJidKHWCiQQ8yfBV88IFm6rJsFuMIZ+VUiwzavwQysJypfuHiWXbK7fjkYWP4MZBN3KF/6zLicHEsd4pvbl/jP8Ol51Yhzvss2NxweeOfQ5Oi7PJyGFLCQbbNv7bFI0+3rnoNEffh8Y6tJqKUjJ6JIWbABoZ5Wc9O2+2dH61dWdZxVdb4NpSAd/x8ZKzTDtEQ3pehQvmFP7vVtDDi2Xv9nsNVe3cyGaR290KIalGiJfHqYe7NMmom4Alwt/Dsq1ABt//F1RMYbGkKgROnwf48hx++Y5NcuxSi9bd1u8cYMMPwFGyOy4ikz8DVn0KjLqxWW/n3wo5ywiCIAiCIIh/Nf5a/sLCoBDHGpYv526DgQAn2oQj6NWPa2b979EWiztrS9fi5lk3w+UcCVvO8+oHA7xYtae+hHNelDSUqCayMXFDFMoYc+vWyMf01JMhJfkzSuX31jPGjnPuvh0l792P8+81oV1cBwzPHM49ZhAGAjSuXRsy0bP9iy9wEz1tXbsi+aKLmlWMb04O765pXLcO8HlbvWxfi0EQrJQkXczHTlsKK2f/YN4ufL6Ed3ic0j8Ts/97HMrrZZeZzx+EWRGl8imENC1nvLUQu0rrsHC32gVoC2TDKYimIg5N1FMbwwwgoN6HMbyz7MklT2JD+QbufGROm80Vm3Wjm/8msmKypAmjx2cf32avYzA0L+odDuYgVUYWWfR3V/Uu3QENemJZtK6y5nBmtzNDBLNoe8iu7Hsld/vu2ne599JWiJNdmUDHhDKGa15RxGEWV5acCW8x36MYDAThLaqXBOfs+Gxg2fvqJ/x2J3+bHr6bTpdswcHFmPOstOgr4b8zhinZHjpEgFGxE3AL/wEoSTNAQhv/P+Nt4MJvgaNvi+644rP4rjJ727gtDzdILCMIgiAIgiD+1QQEsYxNpzTY+QsQbx7fV1P5xRfYNmo0amfpd5E5hg3VXW9MiO5igl0M7ml0q2KVzy9/Ho0xx6E29RbkB/kLd8YN7RNgDPIXS6+t+Qhn/XoWTpx6YkR3yB97/kD6vfci5qjRiJ/I9w2J1Pn8uHLDHun+38N64JiUBPxh3Iyg0YChGfJ7E6dnBl3qwunYsfsnJjgGh49zuTZt4qaUMgxtWcKvcZBkfvwxMh9+qMW7Y99p30f/xFO/88ISY+62UjR4/HB5ZaGq3uODqamSbgUnvDQXz/8li5+MYR06IiNeHe+Ks6udYlpxi4ko53Q/p8kYprYf7dedv+LhhQ9LMd9/G2+d+BbO73k+Lugp9++1JZmJLbvU1vaUXff3dXhv7XvS/em7puOMn8/APfPuOWhiGfsPBcoBBIPSBmH6WdOjeu4Z3c4ImdbZFoguTWXM1hIML8qPrxqNyeXjuQoy19YKVP64XXpMdJrBp/77iFLB9dutmQ5jNgRAZPOv/G1jJWzt+L8fpkSb+j/GKMWype8BnwoxYrMNmPQSvzzyhtDXYdHQnieHL/gnDqxY9swzz2D48OGIi4tDeno6zjzzTGzdyk96EXG5XLj55puRkpKC2NhYnHPOOSgulqcBMXJzczGJ2b2dTm4/d999N3zNLGIlCIIgCIIgCM8+fuoZK5tPnHwet+yv5MWA4qef4W733X4HAi4XvIWFKlFEjCU6hg6FY4gs/JjioxPLvigsx6glm/HIjn1SQf+60nWoS7kmZNsHuuXAFOQvxhYVqx1el/a5FMe0P0b3gviv0TZ0/PjjkFjpZwXl0vLxSXGwCq4D5mxjnNb1NOlx0VkmYTaj95bNMAsF/S0l6aILw7q6fKVlcgzTEioAtRaOwYNU981xLS+uXrKrHKe/uSBkfb3bj7Ev8rFakZJatxTDjBaDSXaWMC7qcS2Gd1J/B8M7qaeaaqOWTCwT3VPax5RiGTt32sfyAwgYzyzjfwuM8Tnj8W+DxUwfGvVQyOfR2rB4J8NgDHUuRYNeT9l762Sx7JON/OTCv/f+HXE/4jAQ5RTI1kQ5sXVUu1FRxTcZXRO7SnFMt69ln1E0SO9fEU20Bi2wBfS//zg/X45f/vkmlH2yEQ0rZH3CLLq8HElNi1/RoFee/2JP2H8ZirTLOyP9P4PVjykHu2yZrt7P8GuA29YB459s3jEQB14smzt3LieELVmyBH///Te8Xi/Gjx+PekWp6h133IFp06Zh6tSp3PYFBQU4++yzpcf9fj8nlHk8HixatAiffvoppkyZgkceCR3TTRAEQRAEQRBsqqK/Wj0pktG4fj3K3niTW3aOGAFzMn9B596xE5t79VY9P/fqa7Bj7Alw79yJuvnzsX30Uaj5dRr3eMLpp6PjlP+zdx5QTpRtFL6pm2R7751lWXrvHaRLkSaIFLuCDQui2BVUEAELIgIqSlGaIr333ssCC8uyvff09p/vmySTSbKNJr/Oc86cTEsySSbZ/e7c977Lqi3tc8UH15lMmx8zC3Gr/Ba+PvM1zHAerH3VIJKKWRIT4/YxibgDz6aBTdE62HW3yDP5Z1yuz9Sw+WNfJUU6lR/ZD6IdxbKAZ57G3UBocfJZiVm1EtLoaDpvKCpCwbx5Lh1tdxOPHj04y6I7CPx/ZfVZXMhyDuEnVGi4F/Y3nc+BwcgtiayrWNY1PsbJWWZtGmAPyWWyF8vahbazLTuWVNq7FKvqCPheB37cVResTj7yvjUPYsTZMp3z79HdQC6qnVOsVMs4v3xkPvfkOF5p+Ypt3l1s14WxFljPV8eS4WM5x3A8h8nmu2timcCT05jA18C90PF+u/fRP7Y/HtMOdvk4fo8msgvW8kdHfJnftDphX7pJnGNGLW1O6eaZC5G7w98Ie2eZPY/8wD7/PRaB/4vc9XCArVu3cpaJyEWcYadOnULXrl1RVlaGJUuWYMWKFejZk2mVumzZMiQlJVGBrX379ti+fTsuX76MnTt3Ijg4GM2bN8fHH3+MadOm4YMPPoDU8coXDw8PDw8PDw/Pf5rMKS9SgSt+21ZIIxlhiDjF0kaOsu3j0a0b9NmMw6tyL9cFRFCfOkVvUwcOgkAioQKaFWlMDC1V9B4yGJqUFMia1q4jpH355aD1TOmMTm6XV2Ohow+T92PSZQLSeFQEPA9Z+mHb9itaD+gEln0ECtoUQKo6CY+y3xGsCHb53OUW19a78WEIdWP/f7aGuHNyrlRckaa2r68mrGWvhKhlSyFv3tzm3tOlpUF7hSljMmvvncPEMVtOdBv5aBnFKmSVqpFbVrOo92zXOCzan0rnf7Dc2vP9uFZ47lfmXHMkzM8M1g8ogFAowLPd4nE9v5KKZtH+CvRKCqr2NRKxrFlgM/SP6U9Dzn3duG4Y4uoh4eYms6lKofXfnFl2L3i3/buY1HgSojyjoDKobKK0xqCBTFzLLom1pLZllTlK5ntW1e/DnUKcrvNOz7utjqLWcH2TiRHL8pR5VFx8ajvjuD346ME77lJaZikTFZ/n/rb5GjyRKy20LQ+rPwwjGoxA5uYDLh9HEmaXxeaiW22dXWVWBn8D/MjoISizE61L04GI1jWLZW9lcDte8vz/ZZYRcYzg58fYhYloRtxmvXuzJ1WDBg0QFRWFI0eO0GVy26RJEyqUWenbty/Ky8tx6dIll8+j1WrpdvuJh4eHh4eHh4fnv0Hlvn1k5IWiJUugz2bcXNZbK24J9SD2r12pkL1QRu8bxwQph33+OeLWrbNlfFVFilKD2H3noHYR6q6TN6a3AwO9ESuXYk3zeETLmVwpk4gd/FT4PQEzBDTf7L10IX4vZ0RAtVdfGCWhUHs/XO3g2SqWeds5qUgXTmtOkL0gIgnhDqgVLRzKgG4TkkVGyldpV81WTEaayPIZWIWyewEJ4K+S28hH6/LFHjz6w9Fa7dunEev82nOVbc5gpVW0cymXNdssNogdnv3Q81d66+EmxsJxrfDB4EaY1Cm2xsYSRCwj+3zR7Qt82f1Lp/3Jskx0dwWc/zoioQjRXtH0vSXOPZFAxHE33U1qK75lVjACTIRnBO4F9mWXHtLahftbsb4/RMwlbrLea3pj+F9szt7dyMwrrSzhlFcSvPvHIFlxk3ssLrrlSkLZ+wjscwe1LrpQyp3LomtFoJ1jLcWupDb3gvO+GhfaBi+U/X+LZUQpfuWVV9CpUyc0bsz8U5Cbm0udYT4+XDsoEcbINus+9kKZdbt1W1VZad7e3rYp0nJFkYeHh4eHh4eH579D6arVuN6zF/Lnz0fx8uWcbWI/P4h8b29gIwoIqNP+T1y86VIoI5iETFlQN19PHGnfEJ192TIhRdlf7PjIowf0bg2gkzPC1TUtUz5nEnIdF2TAWZ1Y5ikW0o56D69/mHY9tGIvlrklJHDuK6plA4PaEL38F8Rv3mQTGMUBgU77hM5i87JuF5IxR5xfu5Lz0PiDbVh6kDsotnIvmwmMaRuFhqHVuz0CPd04wf8zBibhxswBSPtsIDwUalvHwA6Rt+fuU1eU3JHg0iOSW7bKUzeIYGZ1Rd3NUkxyfu+8tROHs1nHaXVkVTIu2giPeyOW2Tu/6trVUmjJTyTuxj0Ze5y2q/SMO68mtGllyP3yJDTXnM/5kuzz9NbTyPxuSgO0kDeq+ndcHMyWJ9tnhgk9Lb+TJF/t9M/MfJfX2DsG17ETphU3D8D6Pdz5Prte4uLih9VZFtEWCG0GvMRtBMLzfyiWkeyyixcvYtWqVbjXTJ8+nbrYrFNGRsY9f04eHh4eHh4eHp5/HlcDtaKF36N0Jfs/qM/IkbS0UuzPFcvC5syB14D+EDtcqHWkOjePq+dPUXHLCoX6PBjEYSgO+RQ6BVNi4y1xFm3kZq6DoCz4bRjFrLhEnGZmITcfyGhyFsuURiOOljGZwUKTinbUSytnO2PauzusuHfsiHsBEafIe29FHMB198Vt/As+w4be8fPM3JyM9rN24cmfT4J8JB/9fdn1jqLalWGSz7VMzXUY1sSsR5pALq1ZjAuxyyFTSMW28tizBcwguE1IG9wulcaahYaqnDsrB67El90s3fV4bhtrHuDvV3+/a+/ioexDeHXvq9Cb9E75g46Q8s8CNeNqtG/icDeRCNnvdIA84LadZa7KLR07tVYFCeI3FKhRuPQiStanQHmcNdZcqbxFb/0NPvAS/wz/DpnkoNGvpJNtnxaBbPOP4JdaQtE6GL6jE6mbLHRGO4RMbwuhm8g5WN/bToD0i8dtQ7pZOrLnU0BV7FosC2sBPLsf8GOczjz/p2LZlClT8Pfff2PPnj2IiGBPppCQEBrcX1rKtpolkG6YZJt1H8fumNZl6z6OuLm5wcvLizPx8PDw8PDw8PD8+zGrqhcH/CZNQujHH9F5kSUaxIr3oIEInzsXCfv2Im7zZlv4vD0xa9dU+dgXKlRodOgilmYW0Hwyo0U4S3Aw7pgkwSgJ+xxGaZRtXUN3ZwfB0r6WwGY77O+zoM9qaN3b25YNkignZ9m63GLE72dLeVIKuZ01gxRBaB/a3mmAKwkPw/1A6NCN0lqWWRtuFSkxbc15rDvNZvwUVGhxPrMUiw+4dpIRFHYh/4JaBvx/tTMFzT7cjkPX2XyjO4F0tfz1SSZ4n+SfWfFRMKLDK3tfsYWyKyTczqbVYSwvR/6Xc23LeokAJrvmanWhcUDje94t8r+Axshk262+uprmwr257030WdMH6eXp1d6POK2q4niuc/B9hc5FWSCA7EqmBN1D4nHH2V/VMa/HPLzU4iW0CKpb2TYJ2re+Xmu5aG3FMuI6W5G8AtkV2TBr2d8+5bFclKxLgfpyEe30miFlhMQu2qPwEv8BkaEAArEQ0Vq2U+ynXdjOowKRAH4j6sM9UQyc/gUiQTnE3nZiliVnjeITDTy8AGj9JNBgIG4bRRUi4453XYtlfOnl/7dYRq7AEKFs/fr12L17N2Jjuapnq1atIJFIsGvXLtu6q1evIj09HR06dKDL5PbChQvIz2fb0ZLOmkQAa9jwNm2OPDw8PDw8PDw8/0qMDsKAz8gR7IJAAP+nmdBogtCj6mwdkktm0rMdJAmxG9ZD3qhRlfeZdi0TxXoj3k7JQtjecwjfew631FpkWQLr5eWbXd7PXyJGPYWzq6CedwyqY8oNbhZXSein0Dh0XHwhmTsgX3zuK9v87G6zsWvkLizus9jJLRc4dSo8+/VD5OLFuJeQ541auoS6/cK/XkDLY2vDldxydJu9F6tPZmDq7+ew8Vw2vtt7HW0+3YnB3xxy2p+UOlq7UdqH+temDNNoMmPBrhQ6/9iPxziPOfWh+i7vkxDEnlt+7s6Zdn881xGdE5wHx8FebrY8OSt1yRTL/eBDFDl8ZqmPPFLr+/Pcfew/v/FbxmNL2hYauL87fXe198tV5nKcW9/0ZDr5Em6U3nDav1znOqfb2uWUuMpqyri7E3pF9cLTTZ+u83NYnWX7Mvfhrxts6bljGabmRikKf75EBTBrqSVpKjDr+CyM/Xusy8cu+uUyCrZfsy17my0mnD2fQKAvhUnAOoEjPV1EN/39KvDXi9zSSEKGnVgZ3xNoNQEYNBdwkXlWa3o5iGJW8h3yHK2NBWT3TvjkuQ/dMEnpJel0+eeff8LT09OWMUZyxORyOb198sknMXXqVBr6TwSwF198kQpkpBMmoU+fPlQUe/zxx/HFF1/Qx5gxYwZ9bOIg4+Hh4eHh4eHh4XF0lgkUCtQ/dBBCuZwKPyTgX9awIWcgR+ZFPj4wOlQ5WDEWc7NvJFVUNVhROQhVhNFnLkNldoPQWAr3srVQew1w2ud0R+5xWRHXMOgkwpwjx3Ws82zttfVE4uNsJ2WY1o59/WL6VfnYYl9fRMxjhbV7CSn5rGvZ5+HrbJ9IwosrXXdyPPFOb3T6bDd0RhNyyjSI9FMA9m6yWnTDXHPKdaSLyWTGlB71UFipxS9HmDKvRY+3Qr0gD4R6swLJtle6YsvFHLz3p+vmZGffe4gef+d6AWgZ5UudMPbURXxQWbq42qO/lQ5DcXGthUieu0tVTTdqKi+0CmIkZ2zVoFVILUutNserKmeZ1aFoH8L/IGEC87v59Zmvq3yfyrbcRMU+RvTTJDNliUGTm+NAJtO1skjH/T2wp/hwOpAAiMxCuA16B9jCiMfC7COQmappzqJXA5c3MPNnfgOGfMtuO/odO3+3BMiGQ4GOLwGHF3DXZ50Ess8wZZflOcCVzc7lnzz/f2LZwoUL6W337t0565ctW4aJEyfS+a+++oqG+g0fPpx2sSSdLr/7jj35RCIRLeF8/vnnqYjm7u6OCRMm4KOPGPs8Dw8PDw8PDw8PjxVjBTMAFXl5UaHMKvyQyRWRi75H7sefIPitaU7bzBqmfIoikVBhrToCJM7/TqdpmYGUvHwTXmnxPBZVSpGh0aGJhxwvRgfDTSiAmyXg2hGpfee1WlJgYsoa08oz8GK6L2CnC8kqdkBoYlwJsd7/vzk3pPzSmkE2sWMMLmWX4USa6yB74uoK9ZHhVpGKljsSsUxg5/6ojbNs2loXHeloZhwJJxfgoyGNMaBJKIK9ZIgN4GbIWUP8x3eIgd5oxuYLOfh0GNPszIqPQorllpJMQommpMYcuqowW/Z105mhlbLnz80RI5Cwu3onE8FT4okKfQXC3O9PGe5/AbHQ9TBbqa++PNYqjjX0b0jLJ+2dT67uW251HDmwJ50JzXcTPZhGk0J19aXN5LVahTJ7KvdcgNm95mYCOgGT6yY1SSBp1gU41RDIv0wdWr3K2uGg5xm01LlwDJekcQUxskxEs3MrcU8gz9HnY6Ddc0BlHrDYrrnGkj7A6N+AFSPZdf717s1x8Nwfsaw2nTBkMhm+/fZbOlVFdHQ0Nm92bVvn4eHh4eHh4eH5b0LcMhlPPQ3NZefwdpGfa3HMEXmzZohd84fLbR7duqFy3z46H7/RuTzIHrXRhPOVjNvDRyxAZeUVGGSJtu1SzUWMSHgHsUoh5t/KwztxoWjnU3UZKH0Nt+FYkIER+H5PPQiTKMnWUMAnfyZERjYoOs47Dv+vfLP7um2eiF8lKlIuywhMRIh6Z/1FOh/lp6ClkuE+ckYsK2EcW/YNBu5WN8z2cTW7dp7sHEunmnAMaq/v57rU056yvzch+/XXbcseGkBrZ5oxZOfApNPBWFzs5JDcMGQDhv7JNFXoHN4ZY5PGOoupRISrzAe82IwnntphnyMYKA+0he3X5CyziqYkV5BAcgXHJY3Dr8m/unSR/XnjTyo8DYofZAvcN5gM2Jm+s9oyzQeRh+MepgIhea2VOtfvk/HKKRgb652EsQqREo1e7Y3CH88hqyIXX4Qvo9vcoIFAJgWCLGJZeTZCO/lg5tGX4f9oA+fzfe9n7DLJj/ttFFB4Ffcc73BmsseoA1Y+yl2neDCdgv9W7mk3TB4eHh4eHh4eHp67SdGPS1wKZQS3mDt3ToXOmongt6cj4chhSGOc88PS1Fp8cyuP5pItzixAucGEQKkY4tTHIdExGVcEoaEEy3t+Ah+ZD3r5e+Gvlgk1CmWOeInZf9U/iA9DW2+ugylSzAhBJUYJLdFacnWjbds3jRtjTf+FOPjoQQxPGE4zyh7UkqyauFFQiT9OsS4TkQBIDGGbBDzWLhrH3u6FF7rHY90LHW3OLgIjqgFCL7umAjWIZSfTXHeKJPw86fa7VNZWLCOZcrVxBNkLZYRH9zGlbX1K2FKtq02b4Xr3HlAeZXPXCPE+8RgcP5jOT2o8Cc2DmjsHwe/7HJjbAPhlCKBTAXlVdBetK+oSQHd7DQj+X7A3kNgLZFU5wayoDCqnBg+tglvRW+L+sxLizoif29K24b3D72HjjY0uXYr/T2IZee3uEuY3rlJrJ5aJBPB030Rndab6MKpYscwIE16KewnjEt5GliQfMuNOzAn7GVfljEPMzSpaWrO+9nwKxcnHEO45CfLGDvmByRvZEkwr90Mos6fDFO6yQ/MWyPmy6vsJL5bx8PDw8PDw8PD83wxAS1asqHJ7yEcf3vFzkIwnv/HjqyzhnHY1E5+k5qDd0WTMTM2h68p0GghghnsZO9Aa734CrYJb3tYxrG9RD9NjQ7G3TQOO4yxIyhaFxMqlmBLGLOcIIvHkjhdhFAfT5d5+HngkLNxWyvVBxw9o98t/EpL1te1SLu1cWVs0eiNGLTqCXl8yTj8rg5uHY0SrCAxvGYE1zzENwkg55Jv9GiDAgxGZFFJGEFPpmMGmyNOr1s6yg1V0v4z0laNbIuP4uVdiGQlkt7qKqsNVt8tuF81YsNCAmeOXO21LnzgRyQ2SoL50CcZK5r6fdPoEh8ccRpI/40Z0gohlhNS9wMwwYGEHbsh5HauLbJlQC1oCX7ciJwX+rdh3tbTPo6tJvLKWWirErFgmEzNZeGVapiMi+U53i+jGud+pvFMuSxxd5Zw9yO8Z6d5JKDzL5AESQh4VQqo7QefNUMBEi6EZvgpdjluW0uPdl1dBoj5lE8oIngKxyy6SAjXbSNBGlnP2n0tG/oR7RhXluzYktW/8wXPn8GIZDw8PDw8PDw/P/wWlv//ByRSLWrYU8pYt4dG7F+ofOwpRNZ0u7wZakwn7SpxLoXSWZBOBWYtvGoRhboNIfNbxzdvuQtfBxwMvxwQjxI0tHSzWGxAsZZfJ/EOhCbblw6ZWqPSbQOebeTlnaP2TECHlx4OpeHb5KUxY6lpscdWNctbmZBy/ybq83MRCXPukP80kC/KU4ctRzdA6xrXTQm7JklNbGiKIvO0GyzWIZdmljLjhLWff76YR3jgwrSfuFTpSclWHjClDobOgp2jXDo1GPg03P+eOm1bSho/Atdatkf7EEzApVfCU2jnuHJH7wmQQwKgj57FFoEh2Lk1WHjuOK0kNcbVtO5iNNWStVeQA6mLm9n67dv6hMkx7ynSM4FUVVmHN6rBy1SyAiGYTGjHfdVf72ItlVvHp/4GBcQPhLmZet8rSlMS9bQjEN36FAPZdgFmxbJcP65g0HPkGMuFe6IXsvvIAywUH98CaD8D+93rcOu62fp8DAYnAq5eARsNwzxBV03yAHAPPfYUXy3h4eHh4eHh4eP4vyH3/fdt8vX174d6hA2JW/IbIb76ByNuhhOwesDbPdaC8lR0jdmBEaBDGht6dckeh3eCtUG+Av52zLFwmRZiCbT6gdWc7S44Pq1osudfcLFRi+6VcjtPorbUXMHPzFTp/Oad2ZWEzNyfjZ0u3SYJULMS+N3rQ25pENrXOCLmU2U+pZQbOQntnWRXNFejxZZfj95NMyaeY1HtaaGBX9nkvnWW1FssKmAwse6KWLkHQa1NrdX/l4SPIeOaZanYogllVgqtrQnFtXShKb1icTue5WX+q06eRPoERbkzl5bjSqHENB27nLCxgzol/I1U57Woqw7Q5yyTOzjJ7SPC/vUBWrCl2KZZ92vlTPIg8ksB0p7Qyu9ts9InuA2kB876phMxFEbXgJi5eJuccKz5Kza4FJb1AgPMyrjPLz51x26LxCNcHknGC7TSpsryHPWcA0Z24+7V/Dphy/N53o3QllpHP+b1i5hh47iu8WMbDw8PDw8PDw/N/NfiUxsdDEmwZBNUBEpC9K32XU5h6bfk5ixmENhNdh3/GM/DNeQcS9Tn4Zz5Hw/OtOUJ3k3CLu6ynnyfsx99hlvXxImfRJNjOkXa/OlU+9uNR/LD/BnrM2Ytnlp/CX+ey6bb3/ryI1SczOPsn1yCYZRSrsOTgTdvyyFYRWP1Me4R411yCNOaHo0h6bytO3WKEzbQixqEiss8sq4KUvAoMWHDAtlxUybi9CDEuOl7eD7HMrNejYtcu6HOYkt/qxDL78lLS8dUKyd/zHs4VJwjq06erPqCiFBi17FCxMNniUJJ5w1BSAs1VxhV2a+xjTnet0l2WnwysHscu/zER0Pz/ZGrdDWdZnioPlwovVfkbd7PsplMZplzEdZb1jelLb8c3HG9bZy3RJBRpiujtkPghSPRjG448SHzQ4QMcG3sMTQOborF/YyqUwQQY9zG/sefcr8EMM54rfAVjwkNwVME6ij2M3PfDykJfb2x1Z7+n8d7xWNBzAbPgGQy0eJx7B4MOWNIbWDUG2Po2cNNS7i33Zcode77LLDve714icvHbTUL97br58vwfd8Pk4eHh4eHh4eHhuRNMGg2M5eWQBDHZTYU/LIbQnR08hrxrGcTUkTkn52Bdyjq0Dm6NJX2XQCio+boxGcDuy9wHtQk4X0HcSUJkpH8LkVkNoT4dPgVz6H61yZm6Hba3TsQVpRodfTzQzFOB2Wm5dL2vpczQUyyyN138I2w4k41D14voZOXlVWex/MgtnLSIVvb0n38AIV4yHH27l8vHu57Phnt/MrQxHmsXVauS1jK1Hsct4fxHU5nbcjUTBu6WWL1ocDq9BI98d9i2bFd4SJnQwbnZQ5WU5wDph4GkIYCodsMtrcG1WFa+eTOyp70FoYcHYlavgjQujr4X+qwsup2UILvFxsKzVy+nrq5JV5JtywHPv4CytesgCgyAsYB1HplJt0ylEiIvL26WW2EK9Ep2WeppQNktGTy99Mh+/Q0oDx1C0BvcBgNWdLfS4RbnotnGT4MAlUP56O/jgWHfA553X2h+UJm0bRKOP+ZcjvzlyS+Ro8xxcpY5lmF+3Oljevtcs+eoSLbq6iraAdNKkbrI1knzQYWcw+Q1/tr/VyqKCSBAweLzEJrZ3+Qvop7CTSnzffjeT4fG0j8xougheBqrFq5XeLOi+MwuM7m/8f0+A0pvATf3A25ewJd2vwlHv3UO0e/6OtBiXO1KOO+lWOb2/1NK+2+DF8t4eHh4eHh4eHgeCDTJych4YTIMFhcNEQICnn0OBXPncvaTN292W49/Mvckc5t3kjo4SEdAK79kFWJ5dhHCZBJMiQpGG0vnyd+Sf8PnJz6HURQAc/hXgEkHkdE5L+qpJk/hXkBKLztZcqVCXTjGfCRS4PaMcncFrcGITRcYF5kjroQyK7nlGsS8tQlrn++AVtHc3LGMEsYN1qdhMMa1j671sVzKds6DsjYUkEZEIOrnnyHycV2uO38n28kUDkLZ7BFN4e5Wh2HToi6AsgAYNA9oPalOzjKpQxkWEcoIpspKpA4chKDXX4P/U09Bm3Kdrpc3boyA52ouz5JGhNvEs8pDh5DxJHO+Fv/yC/LnfEnng958E7qUZEh1ydBcugI3S0dRgjJHRievQi2UKYfouvzZjFBMiOxWhIJbSdCk5ePmiBGI+GouhJ6eULRsyXbAdBTKCKl7gK8aAy+dBnyi8G+BlD8+t+M5TgdLV4H/9vx8+WfbvKuAfwJp2mEVz8RCMdqHtadimb2Tzeos85M9+J0TiWgmKM+BvtIDurRyKOTsa93rzp5/l2RmXJJtQ4GkBDoB2w2zOpL8kpxFp8HfAPObAkY9UFVJrMLufbvfIq6rMkwpL5b9U/BlmDw8PDw8PDw8PA8E+bNn24QyQuXOXUgbOdJpP4G0mhBkIsQoc6E3cQdU6eXpSK9Ity1nVDClgcSZ8ce1PzDvVi4uVKqxrbAcD59OgdLADD43XGc6XJqFzCBOYGYGup3DO+P046exedhmnB9/Hu1C2+F+DCxbeTGD6EGBjOgTJPtnB1IvrzyLa3msE2xaP7aDpz1/WLpWOvLSyrOc5axSNd7789JtlT4eucE62+wf75HvGHHHvV1byFw4zP48m4VjN53va2Vk68g6HQcVyqxdJOsY8E+EEbPJRLtWkhJMR4p//oXelv35J70Vutfx8z+6EB7qHbZFq1BG57/4AqXrNyJ/03WUp4lRcIHbQZBQnuKcxSX11MMjVAupicmYM6tUyHj2OVqimTp0GFNCmn0GRVfccWNzIHSVDiVl5LtKyjPt88z+zyHlhQfHHLzt+1eVWeaYhSYRMgK60cSKZZU65vtYbfOGB4X0ozB/2QhlP6yii0nquGp33+19HDph7cQyl25Ua7dJh78PHDzqXuJ/T7thuv0ffI7/UnhnGQ8PDw8PDw8Pzz+C8vBhaK5dg1tcHIxl5TR0vDZUF9C+L2MfpuyeghivGKwZvIaWtZHSy/cPs80BCDdKb1AhjGSYEQqilnO2p6q1aOKpgEagQEnwexAaS5nnNmmoo2Nh74V0OdKrjkLKHbK2eT2UG4wIsrjMot0DALvOiE9H3L/SK4PRhK2XmLJQwrn3+9AOkp9v5Qa373qtG+IDXYs6RMz6/WQGzQsL9Zbjo78v27Y91LBug9bdV/Jdrj+dznx2rkgvUtGS0apY9wLbOKFWHJxX9aBbWwH8OhwIqA8M+YazqVLPCBzENVS0ZAkKvpwLaYxz6SfJKiOlk1ZEPmyThxpRFgJbGaea2C8JhuLqOzPWlqBmjHvKSQQjL/nKFeSM7YjQ1mXIP8u8H2V5UQh8YwWw0E5AzTnHlGSO+gUQ167JwYMOKQEUCURV5pdVh303TJmIFct6RPXg7Ecen0Ceg7gT+63tZwv4t3+MB5LSdGBpX6hMPaHRMy4wT9GfWJWVi0fDq3Z0GYXc/D4r3SO6Y2/m3tqVOdqVrTrhz3YZvu+oXIj2fBnmPwYvlvHw8PDw8PDw8Nx3iNsk/YknXW6L2/gXSv74AyW/cAWsmiDi15abW+h8WnkaFc5mHJrhsuxpzbU1yKzMhM6tIcqCpzttX5F5Cx/Wj8dlYywMHuzgKVgqxsahG/FPIRMJ6WTF25LpQ5gRLceUuHvcrc3Cwr03OKIYEcSIUOaKIEs5H3GXrTudhWn9EvH51qtYeZxx+r255rzTfRqHe6FNTN3KyC5lM2VVY9tFYcUx1kXoCuLQmbHhIlQ650FzQpAHhrYIx7h20fBW1LFZwk47UVbm4MxKOwRkHGOmvp/SsHwrpVpG0PN180XRQiacX5eWxjxMkyaI+PYbXO/ajS5facqWIbt3aF/zMZVlAt+2B4Ib2VZ5NApG6QFWLPObMB6qwwegSWEbK9QGj3A1PMI1QNJgGP466nIfUr6ptjRaIGi92gNBDiVyhGtbgU8s2X8vnwd8a1+CC20lIzTU5T73AVJOaS3F/LLbl3ht32tUyK/N/ewdUm+3exsFqgI82/RZzn4iS/C7wWzA3JNzOZ0wPSQPdvmeduuvgKkRtMYWtnUe4vUIi+wEmG5Ueb8bbqxYbGXd4HW0rL7r6q6cZge1cm45UsucwXuCK3elq9JMnvsCX4bJw8PDw8PDw8Nz3yDOmLTHxuF6j54ut4fOnAm3hAQEvfqqbZ17ly6QhIUx8127uLzfkewjePfQuziczYa0f3z0Y45QNqbBGLzVlnHWZFRmQun1sJNQJtIz+VvLcrVos2cDVN7DONu7BNdDhOf9EaRqg6ddKHs86fh2j7mcXU6zxuyFslbRvhznmEwi5IheHpa8LyJ+zXqkCXwUUoxsXf17+POktrd9jF3qObvr9EYTZ/lGgRK/HUvH+jPOeWuxAQpM7lGv7kKZ05M6iLTKfG4TADtKNEy+m4/MB5Jw5ly3ImvY0NbswhGhqBqHjNXN9lUjQFcBZLBilpuCLZ0lzQ+C3noLsWNd57lZ8YxkXo8sSAzPh3pDKHdDSMsyCEjW2OjlMOqqHlpmHWaFT5PBTBQgwK+akjuLA65WkBLE79ozWVTrniFKKB4UJHaB7bHeTNODasUcF2WY1t+ul1q+5NSUxOYsMxmx4xZbXktwlz64zjKz0YyCs11QoPscKhPjllOItkOc1AGyMSvr/HgJvgn0vZnXfR4VCT/q+JHrHV11lez6JuAVAbScADxT+9Lpe0Lbp53XlTCiOc/9hxfLeHh4eHh4eHh47jkkh4m4eco2/g31qVO29eKwUM6g3ecRRpwSyuWI+mkZ5K1bIfCllxCzdg3Cvvgc4XO/cvn45wuc3UlWt46V6W2nI9Q9FCaBDHq3RKh8RnG2exXMg0nECgZ5ovpOjzktlj3eB4EIGTsY97d0yLwb5ZWzNidj71VuWaPRZMaABQec9u/fmFsy9dtT7dE62hdrnuuAPyd3dpkdVFVZJmHJhNbw96hbKZ7JxAokLaJ8aTdLeyo0XFEpt0xT5WOJ7Zx7dULHuKcuSyW4JJUwQpU95XbCXEU2I+qk7ABUxfjzxp82Z5k4lHuOuXnqAKMB7p07Oz2l4Pu2wP45QI7z+U8hLjYXuAezTi/3Tp0gIDlrVzcjoksRAvvGo8GmRYjftpV+5+J3bEfMjwsQ3qEEwS3LENI8B2HjWqDezEcgcTcB0Uw5pV99rjgYMdpFR0yCNYvtkcWMQOEKx/euOgquAmVMBiHOr6Y5WA9SdhnBx82HToQyXRlMZq5464hUWDs3ESkJJ5BumP5yf862B81ZZlLpYSxnnFOaq0y3WnuEqKCNIOw7wj4U/VCVj9cpvBO9ndZmmm1d65DWODTmEIYlcC9yVOnS8gwDer4DTL0EDF4AhLEut38EjyDggzLgPbvmKP+iLL//N/gyTB4eHh4eHh4ennuGSaWC2WBA6qCHYch3zpRK2L0bqjNnUL5pMwJeeJ6zzb19ezpZ8R48uMrnuVh4scZjIaLNbmUgiiIWQapmBTsrUvVZmIXVuzHCZQ9WSUwnX0983zCaZqy1snTwvBOUWgMavb+Nzi/an4q0zwZSYWnftXy0jPJ12t9dKsLAplxxhzjN1jxffdYXKdlcOrE1nviJ6VDaOykYO5PzaAfM7omuXVTVkV7Mij8+Cgkt/cyzdMIkVGj08HNnP7sfD6ZW+VitHbpz1roMcFY4NAIBRocz78cJbQVkVYllRNwios6+z1Gk8AGCmZJND6kHJMGs+OiR6Avvku+A/Z60FFPz7STc+uGMbbuAuJR2fwzsmQm87yxAoCzL5eFKtZch9EqEqbwCirZtGMGJaAfhWni+u55mO5F3SxptKWskDSV2EkFMySxvYZ2fCG3O7DJrGWRfjEXWIeb9czcegsQ9CHold8hp1lnEsojWzEREil+GAjf3EVWd2ZZ2ANg+A+jzSQ1vPKnBe+b2hbZ7zHvt30OERwRGJY6yZYgRoUxj0Di5xyI9I22NR1yG07vA6iyzb15i5UHqhmko1SL3s+N03rNHJCr2WMRNC/LAPHiVrwQ6/8Zxz9mXozoyv8d8XCm+giYBTTjrHd13HCRywM0bsLr74rrjgYTkco5aDmydDgyY/U8fzX8WXizj4eHh4eHh4eG5qxAHWbYyG8GKYGSMnwDNRWchSxwYiLDZX9B5RYsWdLoTrpYwg/1PO3+K/jH9QaxFJ3JO0PKclVdWUofHpUo1fsw1kg4B0CnacO7/ZUIA9unbYqv6DHRy52MhWWVdfB/MrmRDg51FrLpCnFmktJIIZI4B/J0+281Z1yDEE1tf6QqN3git3nTb5Yo9EoMwvX8DJAR7oGeDOysh7T6HLZ+SSUQI8ZFzxLJyNessu1moxN6rlo6VdjyUFIQ2sX54vMNt5F5lMaJfpZAVOVTJGyDLeRUIZdxFqMhxmW2mJSWSYPPNBBaHIMkji4xex6zc9zmEPaZDXroJAFOm6R3DCoSoKkS+3CKWkTKz8kwgMAkoSKZVkHHdU6Au8YBH3hJA0prZL6oDG4JuD8lfe2wtkLoHOMJtToBYpjRakNgLXp/uhPD9XnSZmJ7iB+ajUt8cZWcKIPWXouiUlornToxbS7+XOLkU2DSVWXf465rFMlLqmneBu07LZNc9CAQqAjGt7TTb7yIRcohYptQrncSyQHkgFcteb/16rR9fZbA7ByyMSxpHXVe+sjv/XbgbmA0mm1BGsBfKvMVL4Nm3KdBlKmAYDIi5FyPIe/Rhxw+dGrR4SjypA61ZIJvfV2tIAwnrT8MA5m/QA0nDwczE84/Bi2U8PDw8PDw8PDx3DVIONHLjSKQXpMBTDSy86HoQH/vXnxD73r3BnHXQ2Ni/sS0nqGM4424iWT96kxmR+8453W+wrwHfNW0FsVCAxyJ+QPc1Q3HFQSzb37Y+4hXyf21+CcnzavLBNmj0zqVhw7495LSOlDlaRSky3S7EPfNst3jcC9IKLQ4oC0VKVjhbetB1iP3iCVwBtdac/wPIZ7p4au0cQXoy/9sI4PVrQOo+IGW7y7sb7IpGGxInZgUj5MmV9iKlmeac2Ztm3EMcyrNIWaejI4mIT4TWE4HOrzHzHzGfn0RhgkRRDiT/xUwEV8H7VhJ6M1NhCpDCuA8pIXbOnuDG8Ahlj4scL3GqeSr8ULl/P4qeeRZmaxmmqyyphD7OzQm8I4CrW4DrO4HeH3K7A6YddN318wGEnO/uYnca+E/EskAEcraTkH6rw6y2BMic8/kGxQ9CI3+2mcM/iS67EiW/MxcyXOEh2gCILEKtg1BGiPOOw7B6w+h7MuPgDHoRxjELru4Hxeb1we3BvADC82Dwb/2bz8PDw8PDw8PD8w9AXFym5BT8OseIhd8aOU6y+sePIfyruWhw/txdFcoIKj0jljm6Nax8mZbrcv3jkTFUKLPyfc/PMNS8AnPjZHg7LhSrmsWhvrsCIoGg1qVR/0+QHLKEd7ZwhLKHGgYjNoApGcu3c2dZkYgenPehXKPHpWw2MP2JTpYQdTVXkJm47ASnTNORce1r6SbLvQj80B1Y/TjwxyQg+yyw7ing4Fy6WWWnZunI+VKZBxh0wC9VO0TofhYif58I8yUmv0xgEU9szG3AXZbIgGGL2OUbu5xdVySLjOAdyZR2kam7c/dXG0SYqonBX7PzzR/jbqPB/XYCaPvJgIIpBxRImPfdpVhmxScSiGMC3ynHFrGB/yd+pA47Dmd+ZefdLO480hXzAcX6+6Q0cMVca0i/fQ5ZbajnWw/tQts9sFll+QvOQJ/L/DZLo73gPSCW4yoTCMwuuz2SgP7hCcMxvP5w+rvbJqQNYn3Y+9blPXIiwZKDFswt3+ThcYQXy3h4eHh4eHh4eO4K+ap8fH16Ad5bwXWThS6Yh4QD+yHy8oJX//4QSO9u7pfeqIfexAzA5WK5y33+yHWR5wSgnS8316eBXwN83/MLjI1ugJeig9Hdjy2P+zfi6LI6/k4vLB7fGu893JCzftNLnWkWGWFIc263xn+Syb+dxsAFrLtoQkdG9JrUKcZp34IKLX4/kYEMu3wzKx8PqaUTZ8ubQPYZxol1aR3wQzfOZnUQK2iR/DLKofnVPqTOsluQtTzRLKh2pOZTTwk3bz08X1kINHuU3fDrcMBoEaJMJhrYb6ORXeB5dTlNQdzP3SXugYA1hN0qPNjTbAxzG5AI9JtpWy1wY+5jUjt0CXVVkmkV3EiOmaac7Qh4eAEjUFrRWJp49HwXaP8829Tgt1FABlv69yA5bwkFqoIqt1lzyGrLwNiBtvnuEd0R5RmFBwF9IfdzDpjUCB5dwuHZLQJewQfhKV7PbHBxEYIE9H/Q8QNIhKywTZpfWLFfX2cGzAG6vQVMsLgpeXiqgC/D5OHh4eHh4eHhuW3Sy9ORo8yh7oZj1/fg5ZVKKHTcfaQ9mEyje8W10mv0VgCBS1dFpkaHLC0jImxulYABp1LYYyNOm38Y0n3yyx3XEOjhhvOZpeiZFIzBze6PIJWcy+Y7nZrR29aFsmM8t7NepJ8CP01qg1tFKjQOZzuG/pOQDKgDKdySO+vxkzy0ZYcsAouFMYuP4nq+XQmWHbV2DdaQh6UKawYU7uc6xvY45G6NWQ2sHG1btO4nsTT0tGbcU9cNQerBKR0L7WgCpqewIkPL8cDpX5h5Egh+cS2gthOH2z7L5DRZiWKbZmDwN4w7jIhQfrFAYv+a3wPynZm0Bci7CDQc6ry908uARyBQrzdntTiIadygz8hA5aFD8OjEdDN0xGwyw/DQdxCv6AtBUSpwbiV3h9/HA6+c54b5BzcCSjNYgY1ASkVJZ8EHiCIN43r77ux36B7JFS2Nltw5kbUktZZY70eY33P+A+OAVZ3Ks82HfdgRQjcRLRX27h0CHPuM3dFQdWdae+wz2O5ILCMdJ3tU467k4bHAi2U8PDw8PDw8PDy3zcD1jKth5cCVEK7fjpY3zKzLRgp8MlqEnxzLye4i5wvO47HNTCmYj5uPy4HmIIs4FigVo6XXnXeMvJvoDCaM/uEIzqRbHDIANpzNxsAmoRDZlYfeK0hAP+HDwY1sQhPBTSzCuhc64pHvDtNlLxkzOH1QhDLC6EVHXXbnJEjFziJoVUJZnfAgnSodAuXtUCUNAg7s5zrL7CgVClEWmoRo4m7Z/DqbbUaOmWSOkSB+M3OcAs9g4IMzNKuMU4JJ8prsH7t+f1YsO7G4dqWVEzcDhVcZoYyIX51fQZ2IaMVMriDZU60m0lltejkKvjsHt3hv+D6ayDyXyYSi7xe5FMv0OTm43qMnnXfzDkRc/wKmlNUiImpKJJCZbkFAymFDGjOuM7qzF+DOFXgfZCI8I6p0lokFdRuik4YBteoEeZ/RZTHnsU9iMoSGhoDIE1jYASi6zt2xliWV9t0976gMk4enljw43yYeHh4eHh4eHp4HxrFDyoTKdVW7aEjuUMZrr+H3WQb8OtuAHz8dDdNeRlghCBUKTHhNjJQIAS2TvNuQfJ/ndj5nE8oIrYKdB+9HSiuRq2Oe/4VIxtmSoGBEIR/x7YfT3yn5FRrsuJyHNp/u5AhlVo7cuPe5S4WVWmy7xGS5ySTOw4KWUb5YOrE1Nk7pjAcJ4sR7aeUZHE/jltb6KiQ2Vw3Rne465EGv76h2FzVY4cI+7N9Kn+goDFo/CFkwOjnLqFjWagLMfgnMhqYjmVuvUGDqFfZBxjg4rQw1lDVKXJQmx3QCWj/BiFf3gNK/U5HzxQkqlBG0N8qQ++lx+ExkSlJVZ8443ady3z6bUCaJ6QqDrCWzQcV8zoWXPJG2IxD557yA7zsBx35gnX6kW6dXOB50nm7yNL0lnYKrcojVVQh6kDLK7NFnMq4/yc0fgdlxwB8TnYUyUvbbfGytHu+ulWHy8NQSXpLl4eHh4eHh4eGxcSb/DMZvGW/LzjnWZT3MWi1kDbjh4oVLl6JyE5OJJDUAz27hdlIMmzMbkqzXaZaYNU/sbnIq7xQOZbGdGlsGtcQ77d/h7EM6YI4/n0rnO/i44/koRiz7qUks5tzMxcsxzgPWu0WxUocvt1+lbq1XeiVA6OASGzD/AAorHepV7fhyx1V0TnDudGcVixbtT0WLSB90rOd6n5rIL9eg7Uw2EF5chWjSs8G9e49uh0qtAY3ft+vECFAxj6wP9mKdcUptzW5GmViIesEemNLDIk7VRI5zN1UbXd+gWWAqXY5tlbbTFBh2zWEHXDPyof6N6fx3Sp2DcEexLKQpbnyxH7obWXRZEGgXlE8Es6pKCsO43VudiLm/YqehWIPKg8xrcMRY7AZZiwkQBdRH7pcb4dk5GJpLl+D5UG9kPPscBIpAePT5lH0s3V6ITy2ziWWE4qseCG5RDmx5A7BmFJKuhiQj7QHHzZL15upCxO1mlj0U8xD2Ze5zebGgJkjJK8hEGqm6EMxvB5PGgOwPjliWjJAILOXQVzdxd3zzpq35w30tw+ThqSW8WMbDw8PDw8PDw0M5m3/WJpQRQgoMSO0/gM7H/b0RbvXq0XlDQQEKv5rn8l0r7pCITss20HnJbxJGLLtDZ9lfN/5CRkUGXmj2gs05tOrqKtt2d4k7fu7/s9P9CnR6VBgZEe+HRmzge7xChoV2y/cCIoblljNZPAt2pWD+o80xpDkjj5QodU5C2Zl3H8Lea/nIKdPgi61XcS23giOOfbIpGQevF2JkqwgyrsXsbVfptrTP2HDv2lBUqYXOaMJba7mlhDLJP+eyqw6TUkndT0K5HLeKlOg2e6/TPk0inEtDo/xdd0W1Z0y7KLz/cC2D/QkVrBDmRM8Z9Ob4vjdsq1YXn8dLsUzYep9KJcLPfWvbZvBmXVDWM0FQUgndDTZnzb1zLUUuvzig/2xGPLInvDXQbRoQlFQrN+ndyLrS56uQN/dUtftIopnyS0MBkD1tGgy5ucj/4gu6TtGJWw6acyoIkR0yoC6qQhyxuupIGSYp/5R6AjrLd+cBdJpZG5CQ37R3278LmVh2R90wrcLR510duoTWkvwFp23dKuWN/OE7sj6EsjuTCMo2s01DZMJjEAqcO+qi+bg6CWUEvgyT537Dl2Hy8PDw8PDw8PBQvjr1le2d8C8z46vFbKlY2SbWFaA6ccI2/+2L0Qifx94voU0f27x1IFhdOWdN/HzpZ7xz8B18f+57XCthgvzJ7Y5bO2wDqA2DN2BuWi42F5TaAv3VRpOt/DLUTYJAqaRaoeBO0RtNVNQ6dL2QOp+sQpmVl1cxHfz+OJmBFh/v4LiiiODl6y7FsBYRGNkqkq5X6Y0wEccHacB4MRc/HU6jmVuztlzBZ1vYkjzynLWFvM5Wn+xEh1m7se8atxvfQw0fLAcZQZ+bi2tduuJq6zYo2LPPpVA2c1gTl/etH+yJZRPbVPv4z3a1c27VBmuAvCOPW7r6kXz9tK22+UOl7Oe03cMdyy4yDimCXubNdOVrMAi6od8xK2+m27bHb90CaUQdxJ6kh7nLb6UDT+8C6rPfR0fMBhPMRhPUV4qRM/MY8hedg1F5Z8J28e+MiOtI4PPNEPJGawgchBhDIdctJySdNu1QFzMlhpmHWFcRQVXg0FGXiGWEVy8A7Scz8zolHjS6RbLdU3OVTAm0FYMl27GuAf+3i+ZaiU0oI6gvFaHg+3PUGXY7kN8X3aljUB5nX5e3+BdGtOzBiMmUrm8CQ1nhuLbwzjKe+w3vLOPh4eHh4eHh4aHll6fzT9N3Yl7DdxH2+Pucd6X4xyVQ9HsIu394HwmbLtJ1W1oJYEyMhVfvfsjCq3Sdewu2JKxxQGPsz9yP5cnL8VkXu+5nNVCmLcPKKytxNOcoLbe0z/Qhwtvnx1kXxRvt3kPzE+zgbGiQDzbklyJEKkFnX2agHSN3GFjbsfJ4OmZuSsYP41ujfZwfjCYzDdavrcuG5H71+nIfytS1ExmI6GXlz8mdnFxR7qRjnCUeS603wt1NjBsFVQfTk9yujvE1l2KS1zXyezZTjtAyygev9UlElJ/CZSD+P4XZYED5li2o3LsPlTojhBBhyfJdQFA7un1UyzC8dnMH5K1bw6sd49xyRY8GQfR1kSYKrvBzr/q8cEnJTTZQP+0gENoUePQ3QM4VcmpDma4MxU1H4O3yU0D6drpObNEoBFIppDF1dD6SMs2QJkDuBaDd8wAR46pBl6NE4Y/nYVKywoiuQo/KQ1nw7hNTc+meADDkq1C+4xa8+sRAEsQ4+fSZzueq36OJcItmxCz/sQ1QuJT5/SB4DpoPY/ENqPYTZ5kZ4kDGcWbFZJLCqBfApOeen7d2BSC6dwEUAZbvncgyrCWfRaeXgKPfAppSIOM4ENkWd4ReA4ikdyXfLdormgr8xZpip/L02w34v11K1jIXH+wh4lnZlpvwHWZXmkwy48j7Wt1vYlkmSmb/BJWBFQODpc9AIswGokYA/vG1LxuuhbPsXpT28/A4wotlPDw8PDw8PDwPGKWaUmRWZqKhf8O71t2MBPJDLHYpAhFHwO9Xf4fAbEZMLpB46zKsRYDmKRMg3LQH5pvpyBgyHPbpTmfihXi58ZN0Pnr5LzAUFsK9Qwfb9k5hnahYtil1E2Z1nsV5bvKcO9N3opF/I4R5hHGO56dLP+HHCz86HWeJpgRv7n8Tt8pvwSjyhbt3Fzx1k9sBjwhlBOIqW5NXQucTFDKkFlTi/b8u4UpuBRY+1hIJwZ4Y/M1B3CpinBXvrL+A1ELGiUKyrw682bNWAtJba89XKZRd+rAvMkvU6DuP6Y4Y//ZmKloRlkxojWaRPk73kUtEdExKxLJG72/DmLZRuJDl3ADAytjFx/Dt2JYY2DS0yn2IWDTux2M47dBIYP6jLRDpV3O5Yl0gn6upshIiTyZf6nYo27ABOTPeRanUHS/0noYSEt5ux7BPnkGxrhJYtgwe589BKK1a9CKh/3nlLsrAquiYWS0lFqGzXi9GJHPhAEryS0JycXKtxGkimNjn7kksRk6Rb93FN8pzB4GMEzWWXZrUBhQuPg+TytlBZO80qrL5x6LzMJEyYqEAhgI11BeLEP5xJ+pSs72WCA+bcCZUsK5OaaTzeSHyi4fn0EVQ7psFgYi8p6yjVSAUw6ASwT1Yi4pMbqOCWzsDkfRotvNB2guFSx4C3i+tXuipjqxTwOKeQOIA58YKt4m1zNIqjllLMK0O3PvR6ZF8VtbP36NTGKQRnihezbgCddl2jryzK4ANzwNdXgd6vVvl4xnn94TKsMSyZIKXeDkjlBEGLwDS2PMcEUxuX13xJCW2FsjfRx6eew0vlvHw8PDw8PD85yACjlKvRP/Y/ngQSC1Lxev7Xke4RzhCFCGcPK447zhsGLLhjvKE1BcuIG3UaDof9Ppr8OjRA7q0NJpBZlKrcWLWmxDJrqO/CJi4y4QKrKb7nokTYJbnbxgQZ8JENoaGcraeCI9Nmo3WIczAR9HGueStb0xfzDo+i843/aUp/h72N3VWWMvViPAVpAii4fxkeVzSODTwa+BSKCOQ7pdmSFDp9xQ0Ht3A7YXoGi+RELrrZeh54Lxt3Yjvj+C9QQ1tQhnBKpQRiLhyNbfCZRaWPRq9ETuT811u2/ZKV+oKqxfkQR1MJPDfKpQROlURzE8+Z/uqUOJ8q4kvtl2pUizbdD4Hk1cwjkErK55uhzYxfpCI7pIQazJBeegQhO7uqNi2HcU//4zo336FolXdA8cJ6nPnsDW6Lea3GOW0bf7eefAhQpmFq02b2TL1NMnJ0FxORtCbb9i+L10TAvHHqbs0sC63hNZ7R7oUyghmmihXMwezDqLc2snRQSwLn/vl7R9jZPWlp4TSv244CWVu9XygvV5KS/OIc0xgaUhh1hup0CSwCIvGEi10t5zLqrPetRNDxEIETW6O8m23aIaZWzwrCgvlYnh0DkfloUzAzP1Nc+82HfpcViijiKQoz5ChIs8P4tBEGGiThRreY7scMEp5NmCXEVcrTCZgzUTg8p/M8tXNzLq74C6zhtPbu6PsHbT25Yb3Cl1WJcx6Ey2L9R4YB4G6CMaEFJSlJECfUUE/N3GgHFj/PDSmVhDtWwNJzxnM75PRBH2+GpJAOXNenFsFvZ654CES5CLU7Sn2iZ4/DEjdGYGMlNiSvDoPptHKnZCvcv27y8NzN+HFMh4eHh4eHp7/FCklKZi8i8m06RDaAT4yZ3fP/UJj0GD036OpWGY9NkfItnMF59A8qHmdH1957DhUp06i/K+NjFWJDDJmz6EThbg4jEYQ2eYxF/dfOJAZGG5uK0RwqRn9T5kxb4gQXl274bXO7yCkhgGov5zr+hq0fhCOjT0GhUSBzTc32wY91pynX5N/dXqM3wb8ho+PfoyTpubQuHeFRHcDOnn178U7caEIdpOgq68n1h25hS8OOOcoffT35WofQ2NwGLRbOJBSgO/23ECgpxvNJ7Py9ZgW2HAmC7uuMIM4maWzHCnpfLZrHM0aiw90p43nHm8ffVuB+n881wEjv2e6zAV4uNESUEKraN8qO17aC2XkWIjb7W6H+Rd8/TWKFn7PWVf6+x+3LZZpg8PwdXPn0rnhKXtRv9S18JU6iM3sMhYXI+xzpux3xsCGd08sI+VoBPeAar/TteV8ISvgWssw/Z999rbfN3vsBS9H1MlcmdmjYxg8uoYj97MTxO4EXXo5pOGeMFbqULjsEsw6I0Jeb02FkaJfqv/eEEQeEiqqePdzXc7pMygO8kYiFCxyLQSLg+QQSEXUmSZU+KPwohdkbSdAEtYSEJmgv7YEmgtMbiLJxCe6pT4rC/q8PChatmRcZB2mAEe+sRzQbXROzD7NCmX2627TFVWTWJanyrM5E+0dVPcKzRXmHHCL82bOk1+Gwj03FWWWCyWkSYPYXwYvUycU66fTdfJVV+HZNQLKozlQnsiFJESBoJdaQrD+WSiNrzOvTZAKvHyeEZSNWkBicQOSMP/XnMs+6wq5sELiAh6KfuiOH4uHpyZ4sYyHh4eHh4fnP4W9a0lpUMIH/5xYRkofrUKZI083eRqLLyym81P3TsXuUbvr/Pi5H30E3Y0bVe9gdC0IEeQdO6DUgw3y3zIsHBVTOqO7b32MThx92+WhezL2YGDcQFTauYOqYtWgVbRMU2sC1F5M10d7oWx1s3j8nluM9j7umJeWhywtM/js6OOBVt7uUOkMtLOklXAfObQGI6cTZf1gD1zLY4/FWy6hZZXWnKuMYhVGLTqCce2jMblHPUxfd4GWVtoT46/Aw83CkJJXYRPL7F1bz3aLx/gOMZBLb1+kmtavAVpH++LDwY2g1BkwqWMs3lx7HhvPZdPumq5oO3MXZ3nbK13umlBGulQS/bVs/XonoYxQ9uef8HviCcgS69f5sRerAmGynF8/TWyNUrUBJMqtX+P+0Ge/goL586FLTYXfxInIfuMNl89Nptj16+DVoAHuCjnngTJLwL+CKwLbo7Z2Z7wNiLPM2nH2djHpjCj+LRm6zEoEvdgCIncxitemQCgRwXtQHMxaI8x2Ae4BTzaGLIERW+WN/WlJZdGvyZDV84HqLBsepjpfAEWLIOo4skfgJoLXQ9Eo+5v9HRNYhOJqX2tIEFSHXoXQKxzewydBe41xdsqbB8J3cDxKN99kxDLvCCD7DCOUEYxC+Ix7D7nTGHHUqBPCOGo5bjw8GGaVCjF//AF5k8aAlMkrpJhr3wTDhlFXtVh6h0hEzmIZKcklxHjd2y69VleZ8kSe7TOnX+S8C6SqljrDjOYQus1QpEExGKGMoD5XQCf7kl1DbgUEZn+oTd2Z19ZlFODLuIch5JbN3g1X3pfdv8TGGxsxpN6QO34sHp6a4MUyHh4eHh4env+MSDb/9HzOunmn5uGpJk9hV/ouPBz3MCK9mE6E94vLxYxLo0lAEyzrtwxHs4+iQl+B1sGtEeIeAm83b8w5OQcF6gJcLrpMM8zqEpLuKJT92kOIltdNKPUQYEcLAd5fwQwiy+VMRZS33TjYZ/BgnH54Ebbf2o5Wwa3o8dwO83vMpwIZcdwQB9m80/Po6zuZd7LK+3QJ74LRiY+ivi/zes95cQUR4pfZ17YB6rvL0M2PcWFojGa8e50pkwuCEDM3JyO3jHH5EBfY9P4NkBTqhWOpRfhgI+uOmdgxFm+vv0Dnf3+2Az76+xLKsvQ4m1GK9nH+WHYoDTllGszedhU/H05DfoVz/lW5RXx4pls8Fuy+bnN+2VMXoezz4U2wKzkf2y8zA1pC21g/6taZ0JEdTD/TJY6KZXuuFtDXGuIts+VKbbqQw3nM78e1RL2g2jtWyGO4zLfT6XBz5Chor7ruemjPzSFDkHSFze9Snz8Pt/h4Wq7piq0Xc/Hcr6QcjRlkP4osdG/AiKRWSIfI8NkkDJ7B++FBSH/6GSgPHHB+/mGPcJ7fSsNQL0x9qA4iXsE1YFEXdrkKsYw0prA6hKpj1cBVeHTToy7FMnmzprgTst9jmzjkfnacs42U1ZVtZuupPXtE0vJLK2Yj4z41Veo5Qhmh5Pdr0JMsK7tSYoK8SQA8O4dTcS1vHuNiNFa4Fm/tEcrl8BnaCcbSUviPa2Y7bq8ekTTjTBrqDvJzJPSKgKzVJM59lUdyIA5rCEP2ZeSe9Ebln+zvAxFvqVjmY/dbTuxndUXD7dJJWTESeNPy/lXkAsG1/z125SwrUBVw8hjvRwmmOrkIRT+zv3+08YKdMBgsnYJs7ZpaP57u5EGUaJfalmWJbAj/vSBAHoBJjbnnAw/PvYIXy3h4eHh4eHj+9ZBBiaNQRiDijbUEcMP1DdjyyBaIqsgiuhdY3VXDE4bDTeSGbpFsJzECyfAiYhnhRumNOollulu3OMtPvyRCmbsAf7Vnru4LTWakBgNxecA740XI8xNg20NrUTH0ccgaNYT3kCFULCEusDuhZ1RPOi0+v5i+17nKXPRe09u2fdfIXTS3rMnPTWzrAiJexOjrWuA6ySciMAHu3T0q0T+sAR4O8oGfhPtvbCdL50siUXWZxXXhtYnxxSMtI+h8hZ2rhtC3UTDtBumjkKBxuDfEFvcDEceIeHMhix002wtlpPSRNAIgmWXLn2RKBj3cxLj6ST+aTXYn3SVHt4miE8lNe+zHY+jZIBAtXDQDSAplxa8dyXm0vJPw9vqLnKyzKx/3q5OjLOv1N6A6fQoRX38NeaNGnG269PRaCWVWzEbiZNKg4LvvULyEGVTHbfwLbgn2rSIAg9FkEcpYnpJwBb+qIG4sV2IZfX77ADgL5PPydxAzq+XWQYcndC06fnGCFfGq4oXmL6BRAPc9teLTuRukUVV396wKUi5ZsiYF2psuBB477IUycZAC3n25LiavnlHQOJRo2lN5MIs91ofjoLleCu/+sXRZEuIOtwQfaFNKoWgaWKvjDnrtNdt84PPNYCzXQhLMCKmSUOZW5E3EM2dxUhrZiopllVlc91LJihUIeHEKxE0fJe1mmZV2Qfq15oxzSThl/xzg4hqgMg944RgQVHfnYqCceX/shdUSLSuW0XPWZIZJqYfQU8pEtBnNtXLsVYUmpQTGMi11DtojOvw20P5527JQoEGI9AnSlxQFus9hAiPeiQXZMJjZRixChZjm3pUc9uQIp6Ssk4fn3wIvlvHw8PDw8PD86yCDDVIOtf76elre0jSgZrdGjjIHc0/NxRttnMu6qoK4ARadX4TO4Z3pVBtMZhN1V5HuZxcLL9J1crFDuYoFItyR0P+syiy8ffBttAttR4Wl2lD808/01igWYvKzAiqUvdX2LXx2nMlycpMq0G7rZvi7+WGnUGhzEpkPHmACve+goYArOoV3woIzC5zWW18PKT9KK0+Dl9QLP+S47l64snWnKo+roYcc8yND8eavZzjrI3zltITRlcBEIKJJ5wRWOEkvZu119kKZPUOah9FOklaRR2xXcukmvntia2KIJ07OYIVFR8jzdoz3x+EbRSi3dOS8WajkCGW/PdWuTkIZOQ/K//6bzud9OhMxK36zbTMRV9kjw53uEz5vHjx79UT+wkX4VNwAQZvXYOj1/RDBjLRHx9Cuiepz51EuVUBu0OKld5ej75i+eGRIJ5hMZkz66QTOZXI7dX61bwHc+3Ws1TGLfFghkZR+Ktq0RubzL9BlYwkjQtijqGs5bN4l7nIV5+BfN/6q9mH2jd4HP1nVzpvuXcbhdijfmW7LoKotJDfMERroXks8OoXTyR7fRxKgvlBIyzXrCnU42UHEN4K9UCZr6E9FJPJaRaHE6bfc5WNpr6VA3K4tU4pJLkiY6+gsI+WWV5jvAKXf58DWacx84VVGKCNknbwtscz6m2ctwyT5X3k3mRJfcn6QrpRqi7OPCFDkPaX3e6kFpGF25aXkO6kxQHU6nwqVxlItpOEenO6j9OWcybd1uuQch/RFCE7dBE6xzjCCWMiUkgdJ30Cp4UnIvVLhrlkJTdhTKEwdCkmkJz1/ChZaL2YAspAK+D9m577k4fkXwItlPDw8PDw8PP86VlxZYROF7OkR2QPT2k7DS7tfwrUSNmyYuLq0Ri1+ufwLnmn6DC1/rA2fn/gcm1I34bfk3/BF1y9q7K6ZXZmNvmv7Oq2vSiwjEKHMyqGsQxiWMKza56jYuxeZz7FOgUpvKYq9DOgW0Q1jG4zFY0mPUcHOMXPMKkIJpIyL625DgqvJ+7Pl5hbbuq3DGVcf4Zte3+BYzjH0iBmCJoedS+eOtEtyXRZoNmPt6Sz8dS4b+68V0BJNK4fe6klzyuzxlEngq5CgRKXHmLbOLh7StdIV/RuH4GBKIeaMaoa+jdiSVHuh7J+gfrAnFcvUOiOKKrXoMWevbVuDEE+0jqlbWZexiHWeqE+fhvLwYWiuXoPvqJG0SyUpw7TiO3YsZE2awLNvHxxNLcZSn/bYQUpHGz+MIpkX+qcdRUqJBBKTARXR7fB1i5FILE7HVb8obDtSiqlHNqFLQgAOpLCNErwkAqz+g3Ecubd5uVbH7NGtKwq++orO+4wYDre4OIhDQmDIzXVyWBKktRU0SRfFpX2BUrsg+sSqnZYJvgkum3RYUYgVtvmXWrxkE48/Wm5AYibQcELtxEFH9LlsJ1dXkFyywiWMMG8tv5TVdz4vSP6YPSFvtEbl4WxUHspmX0PLIHj2dO1+E/vKaAD83YAIPgKpAGYd6wz0GRgL1YVCmzAokHrCrKug88EzZiDvk0/ovLHIcj5ZXcJ1LcPU2Am3A78EAhLZ5VJLbh1B7ntnAf9GRiwrWZuCQ0lMPqQ8AzahjGAVyggVezLg/1gS57Eq9meiYjd7TPKmAfB5OB6aqyWQJfoi59NjLo8hMGkTpDcdWhwTPMOACubzFgtzETAmDjBFAetXQla4CkEvvAvx2TkQLvsaACsoin3unyObh+d+wYtlPDw8PDw8PP8aiHCiMWpcCmWEOO846tRaO3gtMisyaVfMMQ3GoHFAY4zZNIbu8+GRD/Fe+/c4XTKVeiXe3P8mWgS1wIRGE7A+ZT0VyOzD+YkrTSqSom1IW5fdzFR6lUuhzFXXSHtmd5uNN/YxbjdSxliuK0esdywa+DVw6TKzOsqsnA0lLi0RxiaNtYlNtxvOfyeQ5yaCIplIB0zyHtmLhNFe0XTaXVRuW/dRvTC8dz0b48P8EatwLpsjIfz1Z7Dim5UvRjTFqNZV588df6c3zqSXolmksyjqJhZCawn3tzJjYBKe6uLsxHkQsGahkdD/vVfZQXa9IA9sfaVrnR9P5zCATn/iSXqb//nn8HvyCdv6bIU/fms6FPGBHljz9UFcymY/N8L6et3o5AgRyuyxF8oI49Ss2OTZr1+tjlnWoAEivvsOAomYCmUEaXS0nVjGddqQrqC1YvcnrFDmEQI8fwioonsuERSFeua8GXHQhDWdnb9jRJS3Qn4rrEQWAOGW7p11xajUQ5fGfe/DZ3ZG3rxTMKlJF8tWELqJ4Ts8gYoyBK/elgB2B+zFaO/+MRD7y6nwYhXL7JsB3A9EvnIY8hinJ+meSY5HaFeKKPQIhrG4AhAI4TtmDO38W7FlK7KmvkY/D3+dCKLbKcO0iFiQuANtngJMdr8HxXY5kHXofOpKLDOYDPTzO6tgXV+mI8QJGeryfmbL+WXbV2fklMYS1OcLadmmvcjmCtGNdYCrPwOjlwM/9mLmY7sCTUYAVy0XNXSVkCbPBk4zXUY9RatQYWSy96T1731jAh6e+w0vlvHw8PDw8PDcNYh4JBKIqOjxT0CcGvbdLq2lfVbsM8EiPCPw59A/6TwRb6zsuLWDBv7/8NAPtOyRQISx/Zn76eQq+4xAsrhe2fMKnW8W2Iw2DugeyXQIO5V3ChO3TnR5v5dbvkwD/quiX0w/5FQyJaKHsw/TycrjDR9H57DOWHpxKXXKrRuyDrnXz8Na0ERC/Jc+xIyIbjeg/17gSuTL0epwXanF4kxG8Bke7IunIwLR098LUTKu201vNNGw/U82OTvQSD7ZCEs+WVWQTpUkMN8VP4xvjQlL2WD0CR2i8WRnJpfpQcTdKpZpDdifwopl7wzgOlBqgyY5GbceH1/ldmvmmFEgxJN9pgNH7RxXdwnpBSYkXiCXQ1CH7nmePXtwlsXBQTan3Mu9umD+rqodX1Wisst3Cm0GuAe4FOizp01D+V8bUf6sCPATwEvpnJPmKEYRJ6sVdy3g0avqctvq0GUyziorYe+1h0AoQNALzWnJIhHKCIpWwTRjTOwvg0BUtVhIssMMRWpOKSURyUgHRftmAPcDn8HxKPrlLGBWw3dsX9vrKN3IXKQQehKx7Do8Bi1A1juHIPJvQy4p0G1FixZBEyZFVNfbcJZZxTKppREFOQ8fXgBsfIkrvGWeAho7lyW7gnQTJZ+HonmQrRumzqhDzsdHcT7AzuVsYn/rxAFyGArZ7qokc4xzmMUamHXOnT5dCWVENFV+9SYqjKMhFmRBJKhCTItoDbxyEcg6BTQcwn0fCEcYoYwgErClv/L2de96y8PzoMOLZTw8PDw8PDx3xNXiq1AZVLheeh0fHfmIs+3HPj/aBKf7wfa07bZ5d4k7NgzZQLPLRmwcgTYhbdA8sLnL+/nLuM4uUqb41HZG7BpZfyS+PkNKTmrPuYJzeHH3i/igwwc03N5eKLOWa5IsNeKusroMqiPKy3XZ0/LLy7HmzC9of8UMQawAXx+eg0cLGSfGH50EWNNFCLNlgB7mzoYzP2jsLCrHuPOsS48QKZNScaGegunwaOVydjkGLHAOc9/xalekFanQIzEQwto6h1zQrX4g0j67s6YG9xOFlPl3/veTmbZ1L/ashx4N6pYbRUQf0j3SitfDD6N840aX+xbOXQzsc84CIzQJ964y682erplnsD+CyX0LVJUgWFUCN6MO3TKZzDmfYdWXG9eEyINxdxorKvBEp1hsPJ+NrBI1Zo9sVnfRhBDm+rfDkJ1NhTJrV1mCfVdZR/K/nAuBXIZ+9cJx0Ksh2qy6QDPQhO5siWZdUJ9jBFJJmDv8Rifa8qqEMu4wjwho/mMa1Co7zDE/jLjJ7qejzPa88T4I/5C54GCFvC739qFQHs2B0CcWUqkHBBaXnrEyEkKfaJhKmdJbZbZFbFWyF0NqhbU7pJ37Dy3HM2KZPUe/BfrNrPJhrEH9xX9cs5VWkgB8WxmmmhG/zAJWXNUKdfDoHA7vfjEwaY0o354Gt1hvFK+6Cn2OEiatwSaAmh0csFUhkedC/E0ovCWAl5hkEJqqit1jIJ1E7buJRhAR0hmFaBeMZj8oRPsgELJlvjw8/xZ4sYyHh4eHh4en1nx39jsq0Pw28Dda0kjcEUSIqgoiOFm7Hd4P0itYp8vzzZ6nAfkeUg9ONpYryH725Y5W9mbspZMrSFj+L5d+wawuszBh6wSX+3xw5APcKGPLdnpH9bblmjkGfWtv3IDQ0xOmSiWksTEcFwppHmDNQ3q6ydNYfGGxbdvEnSb0OG9GsQewtA8zaK+QAW7PjEd/bQkt/yRNC2Riruj0T1Kg0+PD69kwms3wkYixLKvQZWC/K9aeZkUhK8smtUFCsCed/mu4Cqsf267uHRV1N1kHJiHw5ZcQ+uknyHlrOqT14uHRpQsyX5iMsxOm4g0HoWx6/wZ4onMsskvVtLNo7PTNTo8vEQmQ/FE/2thPLBQguc2HMApEkBl1eObCX/DSswqTOCyUdjS8E8h3iUC+T94KCXa/xhVdqoQ4ai7/CXSbBmVaJYr3+yGkdSkk7Z5zubvBkvGmlgJKOfOd9XbhLAsQe0Ofl4+ixex398NmTaE5Z6bHWpuGGqqz+TAbzXBvFUyXSQkfWUfwGVLP1kny347VYSWN7QqznnVeEUI//RpZkwdz77B8GPBeMZNhVpwKLGgBeAQzDiqxQ0ajXgOctATeWxxgFPL5BCYBBQ5uVoPO+THIeac2IG/+aRq6b0/F3kwIPBh3miqbyUYrErPictt2XeHThyklFomF8B2WAGMFmxOY+/kJhL3XgSOWCb2kEHm7QZ/BdRlCLIDUfBV+xs9tJZcCgZ3AFlAfeHIHsH82Iwy3d32OQyID+s4Etr3NWS0UaOEt+RVwETvAc/8xqdUwGwwQWX77bheDTgczzJBI69Ax+F8KL5bx8PDw8PDw1JqF5xbS2yEbLOUZtaDXH70Q6RmJ5f2XV5vN5Qhxd90ovYF6PvVolzlSfhjsHownGj3ByROzsi9jn22ePN+oxFGoC6TcMcozCqP/Hl3jvqPqj6JB+WSyCmfrUtbhicZPYMahGTSLxgoRF61MbT2V8ziVhw5BdfQY/Qe3eNkyp+fxGT0aXgMGwL1dW6wbvI6uq7h+FVsKfkBWAPDbwJUwzyPHoIdfJfD6OmYg5KkBprV7Cw8ilQYjmhxy6C5ooYWnArMTI5Cu0aFfADdPTKUzYO72a1hykMnUahzuhfUvdKIllf9lFBaXiT2h3rXvamjFpGRD4mP27MHqNA3aSbSoP/dL2/qwnbvx0PvbOPfr1SAIT3eJo26+aH93W7fQP89mo2v9QIxpE4m8cg0ebRvFaYaQuGcX3u3aDWY1I3bU272Lfg9IEwGvfv0gkNTsuKwOoQdzLKYKBwHBFX9OAUrSgPF/Aot7MusOzUf6KuLGlKHAMBZhCtdlu8S5RsixM161ePsLIJ0RFkKKzXjhbyPCi4uQe+hDzn01587TW0lkRK3KLYm7iEDC+YXuElrCRxDIRJBG/XcEC/vSU4GEe65XHiqER69eqNy1C0KxnWhJumLKvIFdH1t2zANOLAY6TOY++OEFwJnlHLGsYvceSMLDIJu0GTjwJXB+NaC0lDxf3gA0df5bo71V7iSU0ac9nA2TbykQAvyt34nGnlHY4XOEbntd9hxCejiXMwqk7PfGpDKwnYutYplcjODJzaFNK4P2ZjktzyRdNGUBJcACFy7ZD8oYUZCIh+Q19v0UNZL0MFcsGzAH2Pw6K6bx/KNor19H6qCH6XzwuzPgPXgw7WpMHMIiD24H1erQKCvx8xtTYDIYMOiVafj9w+lo3OMhtBk8An5h3O63/wV4sYyHh4eHh4fnrrF+8HoaXE2cWv3WsuHcGRUZ6P474+xoGtgU0Z7RqNBX4M02b1JhyxGNQUPzvw5lH3LaRpxe5HnIcxBult3E4A1cJ8HmR5ydLbWhoX9DHBh9AGtS1nCyyUgTAHKsJI/tZN5JGq5vj71w1jemL3669JNTttkLTZ+H94ELUEeXQ96oEXJnzkTJL6yQ5orS1atRtn49Eg4fov/w6nNykDloKOZattcfEYdrartSMQt6SynWg4beZMZLya5zroYG+WBhw2g6CGzsqYBGb0TLj3cg2EuGZRPb4JGFhzmdKr8e0/I/L5QR4gLujpvIrGfOI3FUFNp8fw6VWkbwXTCmBX47egvvDEyC2C5D7P2HG2JSJ9dZbp8Oa4KHGgajR2IQ3F2IeQTifkg4sJ+ZtxvMSaPq7oqr6vEJxsrKqneyBrdbxZEsJi+NoKtkHXtmVO2wsHYH1SWR9yIDAW5+CO/aB/iVERYiCs1oYMlgr9y92+VjKJq7LvG0R3uTdR+VrEuBOJAVidxbBtfKmfZvwfeRBBT9fJmzzrN7BHVtETFJHEkueOyGJJQ4mnNYBxj5vDTlMJoDICaZXXaft42r9g1DBKg8cACZL7xAnWVJyZcZYanzq8DseGaX/MvA5b+AgAQgKIk+j9lkRMnvbGi/I8F69qLRZxEWFxvJ0+/UBAKJs1NU4NC9NWv6Qe52S9MDtxhvOtlYN437QGNWAV5htydw+UQx7rwTS4DoDkBQQ1YsM9euHPR2IAJ6xc6dMJaXw2fEiDrlGN4pRqMJGZeKERTjBYXXvekSfbcoXPi9bT7v40/oRCj78y/ErFpZ68c5tv53VFq6yf7+4XR6e3HPDkhkMvSc+Cz+a/BiGQ8PDw8PD0+tIVkrepOzOEPEJFJiWM+3HkewGrBugNO+5wvO08kqfJ19/KxN+CKUacvQdXVX6ixzBRHHrpRcQUO/hjiQdYB2tLSHlH3eCcS1RsL511xbg6xKZpT7djv2ijrJPqsOsVBM75/kl4TndrJlLfGXy5D18WvV3lcSFgbvRx5Bxe5d0F5Otg3GNZcuQ960Ca73sLheLFxr3drl47gtmoN/kuXZhdhaUI4P6oUhwV1G3WRakxnr8kqwuZAd9JNhj8mSTzYnMdI24F93OhNTfz9H528WKtF9DrcU9ren2iH2LolE/+80DGXzpZJCvfD2gJpzqVxhNjDf61KZp00oI7y0kskQG/zNISRaylzJc1YllBE83MQY1LTmjLy6OB44x2o2Q3k8F9IwD0gjXTuqhJbMMlNVYpmyCJjt0OF0CROyb9AKkX2UtYqZ1Nyuh2ajkQ7exb6+MOuY963SUoIZTXK0hOzAOqJRO2At4xyqCrcG1X9mxEFUsZ/teqi5WgI3u86IXg/9Mw1V/inkSQ4OZZGA5nwRsYxgLAU8hy6CLnU9ILrMZJAZtTDrjci+8gLMRikkgmsI1u1wfnBSmphtEdGKUlC6lnH0wmyGPjub/kbTRg+tnwROLgGu7wIOfsXs88YNYMUo6HKEMKnetz2k35hEmjuWM5NpHOJtcH3OdghjyisdIQ0ZhJ4SmCqc//bSl+/hQsghXVyJA86eRCYC4LYhf6fbPeO83uDsoLtbFHz7LYosQpDY3x+evSydOu8DO5dexvVTTJlzv2caI77l/YmTqAu6zCyYdVoojx1zuV199izSPvgAyf4eiG/bAfFJTWjZt1mlYrISFdysRGUJ27SBIJQkQuSWBGWZa2ftvx1eLOPh4eHh4eGpkgJVARXIiIBERDKrUHbw0YPwduOWyTkS4RGBYfWGUQGElChWxWv7XsPERhNRpClCt4hu+OrUVxyhjHRxJI6uyc0n066PpJHAszuepaKaIyPqj7hr+WhEIJt7ci4+6PjBbd2/U3gnBMgDUKgupAOtyI9/cblf4NSpUB45jNAPP7S5agKnTKZX1DOeex7KgweRPsF1Jpo9QdOnI3/WLBjCA9G49UP4p0iuVOONq8ygteyKAT38vDA7LZcuN/Nk3DBPRQTgw3rhVEATCwC92Qx3ESOY3iiotAllrrj8UV9bqD0PadQnwPfjWuFkWjGmD0iC6HabGxgYgSxHUXWp9NU8pvwt2OufybIheV0mjQH5JAuqnHUZivxlCH65JZ1Xnc6n3zeT1h0QiqE6dgza1Jtwi3MQ95L/cvkc6iIJ0nYEctYZS0o4Ql3Wq1NRsX07wufPtznLKq3h/lJvjstLFBEOefPmdNBqpUHyZVxJamhbFvtVPxAl3SlN1twq8vmazNBeZ/Ku/B5LomV4/zVE3lIYyyzvidFMBSOPbhGo3MdmGkrjhkGnXgOpVEcFHfWlIiqUEfTm+lAXXoRTwbLZrnOmUALtNbZTpTYlhRHLrN1RCbnMhR8KKVPMOoUiDesY9h1ZH4pmzN+k8I87Qn25CN5r2K619lTX7CV0WltkzXB2WhNXmWdPB3d2+jESYMkuJw4E+ljKT+8mEW2BzONAg3vXEKViM+v006am3lexzCqUEQ6uSeGIZSW5SmxeeIHmyg2d2gIy97vn5raW2daE6uRJ3Br3OLtCLIY0MhK6m0xUgZX9x/ahwMsdlw/uRcPMAsQUldP1RoEADQ4fgknuCZPRjH3Lv0PyQe6FKaEkGiJJHPS6eyeIPsj8935ZeXh4eHh4eJzQG/XIVeVSgetayTVsvrmZlhpO2z+N5oRtH74deco8uq8AAijENXduI//sfdSJ6Y45oeEE2jGTiF5/p/5NO2RaHWG70nfRiUDyyYgYRuge0R1f9+J2oSTHRba7EsoOjTkELym3i9ud0DWiK53qgqGkBJqLlyBv0QIiD3f80u8XnMg7ga5ZXijAi077+44dg4BnnqaTIwKxGIo2bahY5ki9XTupkEYGb1b8xj0GSWgI3Ool/GMlWSS4v89JdnB5slxFJyvnKph8qrGh/hAJBFCImOO090X8dIgNmn+qcywVz/ZcLUC4jxwHp/X4T5Wb1ZZ+jUPodKdlmCYI8H5EzUIrKY2931Qey0Hpeua3wRFjkQa5c07CZCegASLI2jwDzbHvkDpgAJKuOASzk/wqFzgKZTZ3xqNjaDlT+abNVCgjZL38MmTNmtL5ChmTj+WYp2gWC+n9TFotbZBAvtOO57A0zsHh5iIsnr4iPxntVKk6ww7ixT7/zRBucZCCFcuI4k6Eyr4xUJ/NZ9eTKsnr7RCQmAeZTgl9pv35AahKEyC/tJ5p6ECcYkRAJQ0ArAgEMJYyoiShct9+eHTrxix4hjof1PnV0JoawATGlSjyktqaMdCHk4ggbxqIhKAuANu4mdIyiBF7q0IgFkIa7QXdLUboIARPbQVxgJx2OuVgL5QFNgDGrMA9gZR1XloHNKm6yc+dQBpi6G4xnU11Enec+v0c2nVJg3d9xknp9LrvIkpLEwkrlcVabF9yCUkdQvHXAlb4JuweMR1JnhmIWbkCAstFn9shN7UMa784BQ9fNyrAeQdW/39W6YYNnGVZYiJiVq9C9pvTYKysgN9zz+HWY49RoczK5YhABJcroZZKcCw+DDcHD0J5t8+h1ZpQkm7JfRXIIfUcBaGIvXASGl/9xdF/K7xYxsPDw8PD8x+HBONP2c12ngtWBCNPxQhjhFxlLuadnkddXYQWQS0gse8SVgvifNjBYJJ/Er3dPGwzBqznlmlahTJCnxi7f/gtEKeWI192+9LlvvcbXWYmbvTmCg1eA/qjW8OGyJ/zjm1d/ZMnaPkZKeWq6R9r/ycmQejpgbyPWFeAz6hRkISHI/bPDbjSsBFdJ42Pp4/l1eeffR8WZRRQl1hNNFC44UBKAV5dfQ5uZBAoFqJPo2Bkl2qw8Vw23adHYiBmDGLcN8k55VSg+S8JZcQNVfj9QviOGQNFixYwFBfT0Ps77XRWnVi2NaYdSsSM16Zvo2AqwJHPiPD3i50x6GtGuPV3VfZ1D9EXqKoUyqxwhTIGSWhzaEDOGRfnJAl8d6C6U5cIZiUrVyL305kuQ/orpWabs8wea7MPoZsbopb8aFvvPfwRlK1dB59HR8OtJrFMw7idiIPMs1cURywT/UfFMiJCaVMYIStgUmObeOLeIQzlW+06u8a9hEI9EPZ9b+j1JIOpNcSCLBjM4TAZZMAfE5n9iGjm4nwg5bYCN2+YjRqUrFgBr4cHwazRQLX/EALMTJNMe5RG9m9A4HMW95kd5DfMJ8T579iTTZ6s8TX7Dk9A8e9Xoc+shP/jDSEJqvmiFQqu4J7h7g+0db7Qc7e4+cgj9FYlD8LRdkxZa+pXKfAIzAbp/9Hu8gKI+wzBTfdWaNQlDP7hHtW6tSpLtFSIMpnMMOpMOPD7NVw/XQDvADl8ghV46MmGEFmakJTksA1PrKScyKOT03FGD4DbleUQPvkUopYuqTJXjbjFIRJx/o7tX/ETbpw8hk6jJ2LPb8zFJHKcty4WoWmPGj5fI+PANwmEEIdHwv/JJ6DTmZDWcRLqtwvBT+9NhrlhDPc+AhkuNXgUxe4yCPS3kN5oGFDI/HZK3AfAoDmKgLCeqKhkhTKJoRLx+eT3tyP+a/BiGQ8PDw8Pz38UtUGN53c+j1N5pzjr7YUyK1ahjNA6xHVOVl2J9IrErC6zMP0AEyLryIDYAbZ/colby61ePOK949mBiDyQdoh01RnzfkGOjXTzE8hkqNjunH9TvnkLnaxELVtqy2mqzRVoIo74jR1LBZPS1b9Dn5sDP0tJJvmHPPHMaaiOH4esKeNu+Sf44mYOZEIhHVh+k84M4oOlYrwUHYx3UrIQBRFerxeKl64z5VF/JMYg7m37EG2GRftSuR1HW0dysrj+36GDtb17IW/cGOJAZ/cSoeinn5D/2eegI0FL4H75XxtpuR9xMRGC33kH7h07wJCXB/eOdRu8GMvKqGNR6M44Df44mYFVJzIwslUE3tqpBZozDhGSB7fwsVa0xNNbLkGgh4yTjVahYTPN7jWk7LJsSxqn5JIEvEvDPVC45CLNLCPldUY7J4jf2AYoXsGIBEKfaJhK02jQPycjrdDiyvSLs7mJDDGky+8J9rkCA2AsYMKuCbkfMk5ZV6j8GJHRy417rhrty/rsIGXX/hMnQlqPzXmsCrPl/SZimdif6+ojXTH/i8ibBcJPKKDOKpJdZ0XROAAVu9Nh1nEzL3WGSOhMTCmuu2wfytRjYTB4c22tDpi7TIdo+wEoOr0CY9F1qA58gYKv5tHfXPo4PaVQBLEirdrYFipjXzrv3z4bYj/XDkxXon+ou8WplnkSOLkU6PU+4Mm60ghEHAue0qL6N0bDOs8oXSwh/HcBIuK4uYshkd6+e6om6N/7c+fg1rAhjEVFdF1m05G27SaBCOWFTIbgCUMbFJwg71EmLuzNxPA3W8ErQO4yjP/a8TzsXMZtCmGlKKuSTm4KMXqMa4DLh7KxZznz+xHVyB8xTfyxfxXrmHbFtfqPInT/q8h49jkEvjiF5hAKpcxxmHQ63OjXD4bsHHqhK37rFloyue+ZJ3CqnCnJ3fNrKgx69vPWa51/N8p37EDOW9MR+cMiFC3+kf49MYjcsL8L0/JnfPuO+HEqc0Hj/J5b0KqU9HmsFwt8lBpUBnRHuawlIwLJuG5GkTSOThUO1xGk2nJAzTrE/0vwYhkPDw8PD89/kD+v/4kZh2ZUuf3RxEdpeL414N4eElx/txgUNwgDYwfiVvktyMQy9FnTB2aYManRJAgNRuiLC5D/5VyUb9wI3/GPI358F9t9f+r3U52EMs3ly9T95fnQQ3fFoZTz4YcoXbnKtuw7bly1+0f++CPcO7gOcK4Jcry+j5IOb1yEcjlbFnQPIVfilToDPGXswNxoNiN8r+tssWdMCnz8zXHaQ5DIZ29uS0fOrAG4XlCJh+YyHRCr442+iejT6M7KCh80ChcuROGCr+HRuxciv/nGabtJqWSEMoJFKLNiFcoIeZ9+aptXtG2LiO++oyW/NWEoKkLqwEGcsrI3hjKNIE7dYjO5CJ8Oa0yFMkLPBuwAblTrCKw9nYXRbZw72N5NdNmVNIeqfE8GNJeZAbOV4BdbQChjhjBBk5kukkKFGOU7mS6r0ihPKJoG0uwyzZViiHxjqVimPHAAXv3tAs7VltfcfCx0ni0hLLkCQxApux5FV4d++ikta05/wrXjJ2DKFHg+1Bvpk56gIdmamGAgJ8Upy9Foci2WEdHSLSGhxvfCWKFD8Sqms6JAynWlkDyse1mK9iBD3gfyOTtCxLOw9zuiYn8KyrexF34KjfNs81KfSkBNhJlqyomfPQCjIAiy5kyOpMi/HgSKQCqUiQLqQ+QXD21AFhTYaruL2tTeNi/zY91/tcEqllV+PADFVzwQkpMP6fNrUGcW2zWBef4wEMy4j+9EvMq5XgqJmxhrPjuJwGhPjJh2dy6YWTGp1ShashTegwaifMsWFMxfwG4TiJHj2QgwONs+CwK5wiEpYSSC11Nzu9r+blUWa+DpJ6tSKLPn8sFsGPRGXDvGnjeBUR5o0j2CTis+OIqSXEY06nLwdUgMahT5JuFcsykwCSWo8IiA4MAB+ltjdZYHvvIKSlatpkIZQZ+VhaJlP0F94YJNKBMIfThCGUFtiSywJ+vFl+jtrceY/zVSA72RWv9F2iiHsO4LkjdmEeiMcojl3SCWtYKuchNM+quIiuiIa6i+3NcemboAGnkg4lL/gmTAMPwX4cUyHh4eHh6e/xhrr63FB0e4ofUkPF8ulmPOSWbw3CWiC15r/RqOZB/B8dzjtGslKYEcWm8oekX1uuuDnhhvplRg+4jttJukeOtBXBnHLWGp2LETTV+bDE+JJ+2eSbLU6vIPf/qzz1KXiDg0FAl7dt/RMesyMjhCGaHk11/prf/TTyPwlZeRN3MWSn77ja5TtG4N907/XAnDkRtFOJ1eghe6x9daKCTvmVpvxKXscoz8nunmd+ztXra8qpaHL7m8X3e5HLM3MPlQ9s80+oejOH6T7bQV5i3D12NbQCoS4fv9N3CrSIknO8diWIsI/JsgroLM556D8jDzHlbu3AXl0WNwS6xPSx8lQUxotOoMNwenNpCBO+mIGrnoeyqamk0m6n4yFBYiePpbkEZE0IGa+sJ56K7f4Ahlh0KZ0jV7korS8NOMYQiOdi4TI3z2SFNM758EX/d7V4apOleA4pWuS8fCPuhgE8rs8ewZBaGnFCaVHp5dmPNHJGPcWAIJ4/gi7wNHLNMxZVYGrQg3nmHK0CMWsqH73o8Mg/ZK1SVsh3qH4Gj+Mnx8YC8tSy/bzARt+7hxBXyhwHVJVlUNDEjnQyskDL7ol8tO+WRhH3akOWb/1byymiDvoVscEdudXdKEknNlQBDpk+AU78/Q6BEgtCkMV65CIGebL8iaPgrN+RVQdGbcWiXXd8Lnh28h0JXDPL8FVEamDN5DtAECY9WNMlzhIWXccRn7mPtd+TYV9bpchKKx8/fUCbMZut8mEtUF0iI2x/JOhTLC/pXXcNGuE2vezXLo1AZI71JTCZLzeb17D5i1WhS6uIhwue+HMGpIvSvQ8fwXyFMkICu8KzQy1++vVmXAjdP5iGkagA1zz9AcsLpgL5QRGnUJt8037RmJfSsY4ZoIZYSEYR1wzmKKvtjoSbQ//hGElgZFxFWeZQhDQZkEkWJ3SAxKGIUSXFu6EWeavwKZL2DQnIFYxop+Bs0JiGVtcOvCBXQemcR5n2yvUSzC8bhQVMgVkEnY7LzKUu7vMhHKCFKPgdCUXIU6vj9ww1mEMxmLIRRxm4wEFJ5D04s/2JY9urFi838JXizj4eHh4eH5D0EEkBVX2LDfd9q9g0cbPErnSadL0rmRhPB3Du9MB3k9onrQaVrbaffl+Ejny+x33kHBWufumSTXx9MgxqaH/4JJLICbqHYDxfKtW5H1yqu2ZUNODr2STVxZt0PF7t00qLsq3Lt0piWWQW+8TsO8Pbp0tpW9/RPkV2gwZvFROj9721Ucnd4LId7VB7T/fDgN721NJoFLMLuJYIr2gLBMh7eOXESnuCBklJQhT8c4ZqQ6HSbmpOMveSQKSzU4kp5tE8le71Mfc7Yz5Sv2QtmMgUl4qgub0/Tt2Npf7a4NxKVFwqGlkREo/mU5/bxJhzCfkSOhTb0B7wEDIPK59+W7mqtXcXPIUKf16RMnskJP/34IfPFFXHluCtYk9cOJyGZ46PohDEnlNnYIeHEKFcEcRVoCKf0hHRaJo6F09Wq6rnIX0zSjKlbX54rec/Z/gx5PjYJ/tKXbnwuI2+zeCmX5KF7JDEatiAPlMOtN9NaVUEYg7iqPdpZBY8pO4LfhgP4FUswNab3e0F3bTLtiusos0xWwg8f82czFAo8eTCMJWVISQj74AJLICGQ8+ZRtv3r79mHUVub9axfSDsMShtEGKdbycMLrrV/Hb8m/4cUWzo09XEHKSHO/Og2hmxDiYHd49YriCGX0uLoyA3ehm4hOPFVDGiFURcmxdHg+TOYkMJhCYIIXpEK7Mrumo6nwXPLrWgiErJtZHNIEcjn72y+N6g31DQMUzeJgApslaIbMJsZWBTlPCtTcrphZy9eiyK8hJHolTrV8DWdnncDILwIhD3e+MKSu0CHtQiFimgRg1w/HcCuFKc+fELgXHqJiYMi3d3x6aJR6jlBmZdcvyej/bBPcDXJmvEuFMldcaTge+Rrmd7rNwFg0/3YzCr76ClGL38fx1tOh9AiHX2UqQvp3xuUDTNYlYesPFxHfMrBGoSy+RSCUZTpUlmoQFOWF1LPOXUo97ARpkotGul56oQRFlqaRQVNfRd9zxdi2+CJ1YRV1m4SQCxtoCSlpSnBC1xKk5Wpa5y/QoWIjjst7w2jJhSTYC2W6yr8hsIhWlcVlNnccEScNly7jRuxgZIe0QYV6FanNhkDkupzfFQrfEBTkMhcQ6rUOwvWTjPOxVV8VgqKjseWb7xBqDoSuIhxyTRHa9w9FRUUUhAp32oDI1gX2PwYvlvHw8PDw8PxHMJlNWHRuEe12SVg/eD3q+dbjtK0nbrJ/CkNBATImT4HmPBOYTSAlk5prV6G/lU7zlq737AVpbCztLmel7O9NKFq0CBHffgNpVBQN4RbI5FC0aI4b/eycJHZcbdEScRv/oq3XfYYPh8CSLVIbCr9baJv3f+pJeA8ditRBD8MtMRER33xNW7cThDIZvPox+TV3m+xSNQoqtGgWWbXgo9YZcfB6Ib7dww1GP3azCEOah3Nz1+xcYPN3pWBOSg4MPZy7vZGksS2ZrOhF2PryBHrf852eRWkgW1Z2+t2H4OcutYllVoY2D6MOMoJJpYJALr+rwf3WzoUEEp5euooRjwjlmzbRW9Iwof7Jk7R8kbx+4rgi4tndPA59drZLocweo0CIsi3bMDtTij8HfWJbf6PpUDyceghCS9aMtF48AidPpuerVSwr8/CFu7IMYouLgQi4lXv2VPt8oiHD8KRXd+ghQG6FDmKTAb9u/QjeOpWtecQ/RenGG6g8xA54PTqGwS3eG7Iki4Okth/NSkb8t5XY2Q1MORSngr51noyzj6BLZSwi4mB2nbX8WRIRAX1mJnxGjoA4iB2kHso+RMWyInWRrUEKYUKjCXSqLcRFRrLJjBoinOlQcI11kpBcNmvJKU/t8Z/QEEXLNsBsCIdAylywMOtVgFELk7IAQvdA5OqYpgvuom2QBEsgbtoRhcvIuXOIeKydHlPkzXW+Vuw5A0Wz/jCaWUFLJjwKs6ZhtafsL/1/wdS9U5FcnIwPO35I123ZJ4a26WQIjTqYBSJUeEbjxCe/outC57/LS99w7pJMuKbuhpYe64HGw3Gn3SB/mkbeA2eKMitRWaJBUZYSUY387uh3sypRv2DQq8iuZP8/adojguZ0Br32Gp38v5iN4qUzaa6gb5fRNHhfb2mEQbhx2ln48g93p464no8noThbiRDy++IhoZ+TqkLHEcsadwtHYJQnp8yZvM56rYKgvpAP8m0nLnVSTk3WFWfH4MSmNOTHdkfX71+HqlSN5W9aFDULRxiFtkrMpgoIhIzIK3HzwK1LOfj7a8alHepZgZxo5v8JqWgIdBWrIRQxv1NmkwYCIXsBTKfcAqk7938fE8ZS8ZO8fpLLFtXQH2EJ3raOm5OXLqMxFeSigtfAZ+n/L8GTn8d/HV4s4+Hh4eHh+Zdzo/QGTuefxqXCS1ibspaue7rJ0xyh7J+GZHjkf27Ja7KjYscOOjgtvZUOzcWLNjGElLaR8Fz1pUvIfp0piUkbOQrRK1dUG8JtT+rDg+mtsawcAc89W2NnwtQB3M6dHt27I/DVV6mLrMHlS1V2wLJHozdCJrkzRwi50kzKIrNK1fjh8Va2bC8i+ny29QoNyveSiVFeRQh7pZZZX6DT0wD+v/KZ0rzXf/0B7vWb4bO27YAGtWsT//7iebYB4axDi/Dw4M9gEIpx6cO+cHdj/s1c+3xHGiQ/sVMMGoR42USy1FGjaGkgERyDLJ/hnWCsVCL9iSc4Yqu9UOZI7vvvI/zLOTQouWDuXER89y08e9rl/dQR4mAz67Twe/JJOqgq+eMPzvaY1asgb9aMnrskt+a7E3mYf5qbx2XPwKGzsXl8Y0TmpiI7qSWGfnsIT3WJRe+//sSrh4qx5WoReicF4d0dC6iIZi+URf+6HOWbN6NkxUp4DxmMyr37oC8rx3T/bsgoZvPQ2ucl24Syenv31Cr77F5gNpmhulDIEcp8BrPNPGpN0Q3AxLw+hWgvVKaeEAhFJLkaMOroOUJ+P9xbNIb2ejpubgsDfmfCse0RW0pj7Ylc+B0qDx6C37jHoDKwYdfb0rbhi65fUGcuwU18e6WRgmqcYv7j2fJQntojT/KHRzsf5H70PsThrSEOTIL26t/MRocsOSUJ5idaLavX2jCpCyC0OAYdMRQooTqbD13iV8AFQChRIW9PGgw7ixDROhc3Uo1QV+ipoEI6LlqJ8IzA7w//DpVeBYVEAZNWC62YcaeZyPlqIbtUgYL0Chz/+yZa9o1GaLw3jHpu8wJ7ig2RgHcUYCk/rgvEwZSfXoHgGC8k2wnX1qB7uYcEV4/loqxATUscyW2/ZxojvqXz96W2ePTsicrd3FiEsAULsHsd+314ZkE3p6YCPiOGo3jpUhqtYP7jBzwz7zWYjCac252Jw2vZC0Qjp7empZOl+WoMnNyM5pfR+9t9FgR3bzfqHLt0IBvBsV7oNiaxymMmf78IJKvQSqOu4Ti5OY2WqZLsNOpqE9b0W0DOQZGtFNJszCc/PnRZrQy2CWWEnArWuSgUk4tdUojdmL/9Rl0yzGYVJPJOaDsoAo27foI1XxyAwkuEqEYxOL7xpu2+LftGQSoTI6mj88UwUrZPJh4WXizj4eHh4eH5FznHNqVuos4G0ulyzbU1VBD78QJz5dxKp/BOeKoJW1J0t9BcvUavSgpkbjApVfAZ/ghu9O1HnVdBb74B9ZmzcIuLpXlNpevXw3vYUGguXYKsYSOXQpmV0j+cQ45JJ0Di4LK/Kk2cZ6QczRXEFRI+bx4ZlVNRzZ7in3+mpZphX3wOWf36Lu9PRBh7RH5+CJ8/z9bRsjqh7KdDN/HBRudwYeKw+mp08zpflS9R6ahQRnhm+Sl8MaIpGoV54UB6Mb4/nEbFK1dCWYc4fxxJLcI76y/iyNG9WNOUmwk3Z9wzkGmYLmOOuKuU6H9oL8TXynDZPxZCLxGS3SLweeQIBGUWoSFtKw/8kb4WEb26ouTN1yGd/hYkISFoFe1LJyukvCl/zhwqlBGKflyCwNdeu2NXV9n69RyhzBWigAAYCxlRpnz7dvieOUOFMnoci364bbFMn5ODvJkzmePYtBlx69eh+Odf6HLW4y/gdWMi3jf5IzajFNfyKtA1IRQrU5jXb0+AQgy9WYAyNSO8DPjlIl7pnYCKU1k4m1GKKSvO4OSM3thylckl2pmcD3X8w3j68g0Eq1gnEsnII1PIe+/RZZNGg+Onr+PsX7c4zzd+dDeIUjZCGhNDP6t/CvWFApjKma6CfmMaQFafPV/qxLpnmNugRhDmsvlsiq7ToNrzMc2Oo27Sfp1Qus11LhuB0zXTAgnjJ9PZ/LN4fAuTT2Ylu5IVFmSi6kucHVFfKUbRT67z/wjubUIg8rx3pa//drwGDULOOzOgT91NJyuGgsuQetbunA96uRtEnnLkfcl2jpYEFUOf7wezMdjSgIH5/dJnnoK6UIqc4GbY9jH7u3/sr1RM/LwTFWXskYnkWDfnFHKuuy4ZLPJOxO8z2Q6tA19oivJT5HVw/+YESVKQr09AhTEQ6P8Zboe/FpylYg99vBjmwoaHnxse/7gDhCIhVOU6KpYRiFBGSD6SA69AOXxDFBDfxoUgkRfzPEGvvwbPvoxzatdm8t1lXF5PftnFZfdNkbc3QP7+Go30ggfpDFy2YQP8K5UQiobDZDRTJ1lQtBeGvNqCCozyGr5HXUbVR3STAEQ15OZ3OWIiXSYdxDLyuZL3jLx/juWfvYYEYdefbMMHo+469KrdSGg/HBlXGOdsmHQ70mBEYHQkSp1NcU7IfJmcRUL3x4dj5w9TENNYgDaDmMZNj3/Sm3kug4kKrcSk3H5oHFr1Y/JheWoHL5bx8PDw8PA4QBwCxI1FMrvq+7oWTx5EdqfvxtsH3+as25vJLQPwdfPFd72+q1PodG25OWQIZ9kqgBUvW0YnR5T79t32c5Vt3EjLJ+1LIgn6jAyX+4cvmE9zUchVU3FICAy5zD/8BGNJCZ1uDh6CxDOnnbLMKnbu5OxPiPn9dwjdnK8aG4wmiEXMe/v6H+ew5lRmla9hw9lsTOlZD6UqPVYcT8eUHvUQF+g8SCeodAbIJUwnvFKLkGLlzTXnYfKUQNc+EOjJXC2WnCqEMVRBolJguFQCXy8Z2sb6UbEsxFjsJJRZ0ciYgX6v4wcxec1yRHXsgIAXXkBxcCLmKN2xzsTm1wgMZrSP98eQTzeg4NNPaHmg7OwJFJ5lBnYV27Yx7ruXXoTq1GnaxZEIp7ImjanjyZ6MZ59F1A9smPDtoLtpSVm2QDoVkqYQsiZNELNyBS2XIRT9+CPy53xJO07eGjPWtr/63Dlkvf4G/J+YBFnDhjV2VlWfv0Bdj0Qw1aWl2bZpk5OR3IANZp6qrYdynR6vrnbdOdQKEcWeku9HRsZN9D/LNoOYt9MusBtA6092cpYPad0RFdUW469so8uBU6fS8+6zLcloF+eP4S3DseNyPlYeZ7pFEpY/2Zaed32ahcHc/yANCL8jzq4ANjwPxHUHuk8HwloALhxWJq0R+d+dhSFPBb/RiZA3DyQqvy2nzL1dCBTNap/Dw0FVDGSdZObjukFYeQCwVAyLvJnSaCKUEUq3ui4vqw0Lz3F/cwizjs+yzdc2S9FK+Vb23OE4ycxm+n4pmt++a4eHmHvcIGvalCOkE9Fce341pHFVi+Pqk0sgb/0kRN4ayOIYQSN8VmeYVAYIJEKU/b0HehcNL/XZabhWbyQyI7o7bSNljc993R0iiZCWxB1akwJVub5KocyRoowyYOcHKNxNfkteRID4BkYHMK7cq+ktsVP6LrL1jXFzzVnEzhhI1xOR6MrRHOoMs7qqXKHXGm1CGSE/jZkfNLkZFcoIpGTRkVsXiuiU0CYYfZ6se0MBs44RyUkUArkAlnIyD6lnGLUoJM6LZoS5QuzvT92eGc8wrnDSldZKW/l55DZ7BFEXLyH77b8Q+tGHkMpqFpzJ5xLbtGoR3YqxhBHiHf9XSGwXwnkPY5sFwD/cA4n9YhHTNZEKjWc2L0ZRxmm6vVnvRIhLMiDdvwGx4Ro0DohCWUY6Tsiq/r/TqLsKkZTremvUJRLNev3t+jWJhZi8sCd1nt/NmIH/CrxYxsPDw8PDQ/5RNOqxJW0L0srSsPjCYtt7MqHhBLze5s5LxO4HKaXcQXVVAfp3Sygj/+TqsrLgFhtL3TR3A3FYKBTNm6N8+w4EPPecy+5YhMIFX9OptqQ9wuS3CBQKmC0lFK7In/sVfEeNhDYlBcUrVkASHEJdbFZIaL/3I49A7OtrC62fvu482sT44dStEqTkM6HhhKHX92OgSIxNsYzw0bV+IC5llaFIyQwOCEdTizFjA1Neuu50Fka1jkC3+kEorNTi0baR6PTZbhRWMvtH+MqxeHxr6kxyxOQjJW33bMv6Vsw//PT6d5gCROr7HCqgbzjSwOaVLZo5HfUz0jDoyx+hVDAleL3VQvw6bQpAJgsk2nfuqOYY3TqSdrUkxAa4Y/aIZhCLRQh9/31UbN9BQ43tqdy7l05WyJV/Mjmi3H8AdcVsMCD9qaehOnoUYV/O4Qhwbg2TEPH117SDmMjd3SaUEbyHD2fEMheU//03nRqcP1dljh0p5ct86WWaX5X7wQeot2c3zdpzRe7AUSjXVV0yJYMWGrihpeAaXlRug+jgz4g1k8Fh3TqnFsqZ7Lrgd96B77jH8Pp05vu46XwOnex5tlscuiSwghQdQLkaRBkNgKoQqI37hghl5DO5sReG6zcg7vYYBL2mO+2mvVlGhTJC8eqrwOqrVDSz4tmDEbVui6N2IlanlyHMuGITy+qK10BGaHCFNcDfnv2Z++kt6eRLOvXWBX0uNwg++JWWkIT8cw1B/o1ELV2KvI8/Qtmff9FlkmdJhHTVrpdRf9cmmBXBMCr1yJ113HYfQ+YxVGQeQ9KVZM53RWQRb9yiA6E8zgojVopEHi6FMiup5wqocLXkNde/eYnmbbgq6IswyUWUqQKhlLBZaBUlepTtXYEbyvF0WV6ai+SdTPC6UmEG2jL77bwRg/hlF5BxrRwJrYNxZgcjlD8xpzMV5g6vu06zqpr2jKAuqozkYmxc4Czmh9bzpmKPfYOPqiCZYQERHtBpDAhP9EVEom+N4kzGlWLkanyhEIhwJt0XV57jlmMOe53p5lgVHl27wv/551C08HvOeoW6AHFHF9H5snOgLndFq+ofqy5Y/4ZJwtm/pdZSzLAEH/iFusOgN0FiV1pNRL9mPSNRmBaKogzAJyQU0Y2bwjczB1mrD0Jn+Zl2J/+fdWNcYYSYxqW4emgNpJ5jYdRdhkF7hiOWEUFO7MJ55wgvlN0evFjGw8PDw8NDuv9d/hnzT893uT7BNwFD6nFdUw8Snx//HL8m/2pb7h7RHS+1fAmZFZk4mHUQ3m7e+PnSz9CZdBhRf8RtP4/jlcm8uXNR8tPPEMhkMFdRvlcVxO2juXCBsy7x3FkqUJj1egRNm8bkBlnC140V5Sj/a+NtH7vtNVQjlBHKt25ByfLltmX7JusBLzwP/yeftC1vv5RLyyAJNwqYAW+QshjvHf8J8WVsWdbOyNYY260+3n+4EX0Pk3Mq8OLK0/Q+VqHMyu8nM+lEeP8vbmlWZoka/ecfgMlXCnhKYGzmB6O7GL4QoMQSBF8Xmu08heQXZmLnyfMwp6ogCBdDeq4YC6ez/6g7QpxKaZ8NRJlKDw8ZEQbY8yFs5qc0p8uzR09aEml18VRF1C8/Q+TtY3MkWt1YxPlHHAOkE2FNnSaJUEbIfo0raAdPe4t5LIuoaY/jOnKeGfK5FpErTZshZs0apD/5JBWFSbmN99Ah1HHm+FykuYP1vAp5/z1aEkTC/c9HN8M0iWX0akfTCG+czyyDCEYcd3sBRWZvhAsKoDvZHGJBGNwELkKTamBHdBs8cWkT3PRAS4tQ5oqJHWPwau+a3bKmXV+gdHcFBNDDZ1AsBB2qyfRL3QezWYIyw2OoNJLPUgLsAPwCC6BoyhWWtNfZ0kgrVDSjpc0yiH3qVsLIgQh7BK8IKvAJHPPXyCDU0gzBJWIxdYrU270LIk8mH0h5MhflO27Bf0IjSMMY0cBP7ldtKXxdEcjENNifEPB0E14ouweQLL7QWbNoXqAhv4C6L4lYZlRqYfYIpn9nxN5u8B2egJK1KVCfWkrvR8qTq0ISQVy8zmJZmYTNlrLSQ/019siZrqjbf3RdcutZkY6kK8vRuOM5dPNdBolQC6VJihPn+0BgNuFawkjo3HzwayErDHmnsWWeblrWnUb2Sz7GuLOsQhlh6esHablkWb6aTumXimhAf/ol16pypxFswxYrvSYkYdfPjIBozfiycmQ9U1p+asstqr8/NbcrLd0szKykz7Nv5VUktApGTNMAlOap8Ne8s8yFgW4dAQcTdqt+0dWKc1aCXn4ZbjExyLb85rsi8+VX4NmjO80ejVgwn7rS6gL5u00umomDg1GxbTtUxxlR1dvBTU+O1you2gtlVk5v2Yiz25gmM427P+TyuYQOvyEDpzyCpp2i8ftn79vWteiVi7ZDRqOyVAuFF1+ifS/hxTIeHh4enn8Neco8rLq6CkPihyDGO4YzgCFlle4SdyockVtHHIWyVsGtcCqPEUJmHJqBrhFdUaotRbGmGM0DmZype1HKSChQFdDHD5Cz5QBKvRKn807DaDZSEWzpxaVObeetjEkaQwU+MvWI6kHXjU4cjbMFZ9Enus9tHVPuJ5+iYtcuxK5dA6GHB3I/+hhla5gssboKZQQihpDug9QNJBTSK7/WskYimAmDmavpgS+yjh1JaBjtelknSA6TQwmlFVJeSEKVi5cssa0jYcFV4TNyJGd57Wnuf/fe2gr8vIPJrbLn4mvtIQllyiPJ59owzAseMtelJVUR5i1DpskIk5cEhkY+HCfQ7QhlA3MzsPjjSRCSrLVhLTDxVjEOpBTi2XdaQ16Lq9TeCufj9+jWjU50++CHkfnKq5AlJqLwu++Y1/D5Zyhc9AMVIiJ/WEQzZ4gQ6ggpeb057BG6D3EOEHdY/pdfQhoRCb8nJtHGDtaOlk7H0KMHPbdqgmTjkDJRgZsbwuauRPHq0zAWnoQkOAIV25bCrC5B2ghWWDaq1ShewgygHanQGrGxfk/0CRIi8OFHEDhwMLxkEsxffR64xLoSwwRCfGSWo0XeMaRHnAFyRZCakhArOg2tqRGK9B9CgEoESd/EJski3Bw8H1PWsoPhN/omYvY2RlgiPN89HgdTCnEhixkkT+06BTnXqs76emdAEp7uGlft+2LWm1C24QSUp1rADItw9fcW+Fa8C3R9HZBZGj+YTMDaJ4BL66EydkWxfr3TYxWvuMIRy8wGE1RnmPfDb0yirfTSinvbOmam3TwAbJsOtHseSBoEaCxiQTsmt0zQeCiCkycjT/cts14sA0gnRDvI75k2LQ3uHTpYjtFgE8rI8ZasYdy6lYez4Teivs2FTIjxikFaedodi2XWDhnBU1tBEsQNHee5e5BcyYivvrJ9ztaLPJmTp1Bh31TBOHbFoZEw5DDl/CSXsirEgQFQn3oL8laTbOuURjNSQpnfQIHJCL+Sy4jN3w5BXipaeH+FMy1erfLxmp/7BhKDEhIPI8TDvwHWPwv3QB3quR1CRaYcWjcfpCRw/wb5lVxhj4e0Ua0FJbnc74CjUDbu4w40KJ/g68Lh2KBDKOKaB0KnMULiJoRBZ7LlmNlDKrtXfXwcFcXc47p2LA+TvuiMw7+zjj173NzF6D62AXVM1RavwYPh3rWr7UJI2aZNMGt10Odko/Drb2hOpTX7tGDePIR+7Py3wxHl0aNQHjoMn9GjkTllCrRXmPea/LXN83ZHQFAI3Nu3q/UxlhcWYM9P7P8vbhYnt7xpE6d9W5/8DCdbv0WFSYLIIRM1LCGRusl8+N+Lew4vlvHw8PDw/F+jMWjQbkU7ziBle9p2bHqEuXpHIEH3Hx/92FZCs2X4Fporsy9jHz499ilylGyZUqewTpjfcz7dvuH6Brx76F26vuvqrpznbRrQFMv6LYPUrmsVGUSRMpxrJddwOPswBsUNQqCi5vwdUvqZVZmFIEUQfR0jNo6AWCDGU02fosdLBmUv7HoBWqO2yscgodKvtHoFrYNbI9HPuYtTsHsw+roz4bm3Q8mvjHMtdcBAlwKHFd8JE1Dy8890Xt6iBc0KEYeH41pLtgQi4puvIQ5g/hH2G88Nyq6OoFdfQcmKFbZBTXWIlv+KGftP4kiTlvCuLMcfb3PL5ArGP4Nd/YZjRUEJRo8ch2N7D+Ldz5jPuiqsgpdjV8m2MX44nlaMIXlMGUuRlw/c9Dp4qJlBifLIUcibN6P3t2ac+Lsz5w1xDHzhfgutU0/jRHAivvFrhU4JwejZIAgHUgoQ6i3H011i8fbpm/ilsvrXfblzY/hJxFiRU4SpVzKwvEEEHr/CCHpxmenY1SIe8sbWTJnmnPu2ivaj091CIJEg8lumhJZklllxuhLv7Q1561ZQn2SDs61Ys2jsIQMdR7wGDKCuL1JqKW/dutrjUp7KQ8kf1wD5cPhPbgVZ0w4oWXMDApE3xMG96EDIo+/nqPjzuepdSEIx6u3ZiZtPPIXn40ejQOELetZ/shNxge54uVcCtlqEsmgvGb4uF8PPzAx41Pq2CMxkHGdUmrWLoDPDA3m670AkqXZ7zyHKzxvpxSqE+8jxbNc4BHhIMW3tBVvG2bR+DRDzFvNbl+PBHVw+1TkWybnlOHS9CL8/24Fm1tVExf50VJ4iZb+sw0tp7A/p/nkQ33wbbs9+S0KQgB97ATnnoDE2QbG+6jJ1k0oPoUVYJd0uTUoDhJ4SyBsHIniqB/Lmsp+7Rzvn7mwuMWiBv6cCZy1u2j9fYCYrEovg5BsDifAWBFDDDDkEEgXMDmKZtF49yBq5zlkyFLG+UoGYHazqjJaMpbuQ/0O6gFpdZUI5Pyy7X5iFIuQm9ofX1X2AXRdZglUoIx1lidhfnfhmVqfAUHAV4kBmv2QN22Gza+6nEF3L45QGOkIC1zVXLkD+02dUKIsfmAdxSBTQ7FGgXm9gdjzCO5WgKFkPXfJJjljmrsxG4KPDoTxyBLpUJq+x/bEPUOYVi+SkCU7PRRxPRVlsTIAjgVGeGPpqC0hrcR6Sfaz79Z7UkOaV/f0N8/cv9uZGFAY0Q4VnlJNQZmXZmwfhpikGZNzfpNsNoCffRXvHsLeljJo4CdWnTkF5+IhtG/l7TDImJZGRtgY9juhu3UL6REYELVrMxnIQ8r0UOB3DCPsh6WkIjKrd8f761sucZTdLcwBJWBh8x42jn2Hkou+RRnI0L17E6JY3ENCBydQLCGJ/G2OatUR0s5a1ek6eO0dgJr7CfyHl5eXw9vZGWVkZvCxdNnh4eHh4/v8g4hFxU0mEzk6W1LJUDNngujxybve5eCiasbk3+dn5yt277d+1CWj2nBp3iiOAzTkxh5ZiVsW87vNod8nk4mRM3jkZFXquoPFyy5cxodEECCCgQhrBaDJSJxspnSTNBO4UUlo5ve10znHfTci/CleSXAeeC+RyiIODoE+7Rf/pi9+10+Ug8taEiTTjKfbPP2lJTG0o0OlxokyJWak5SFExQmHT1BTMn810+KuKym8X4mEhk+FkpeeJQ3h36TdICw3HU+98BqPIeUDw24yXEFbEDGhIyY7Y3w95sz6D7uZNRK9YAUXLFihashT5RLDR61Hs5onvmg7DVFk2PJo0wh/JV/DNqImcxxy2Zyue3rAK3w9/DLn+gTjeqDleKs3FsG/n4oAwEMKGjdBpK1v2GfzBB7jYZwBWX7uJKLEIoxrWw8SLN3FVyR10jAvxw0OB3mjppcCZchVaerkjQMq8JtWpU7g57nEIzWacTUjClZh4jNq3HUknjrtsSvAgYKyogObiRZork//lXNqhtLY0uHiBk0lWFZobpShczC39rQqzXo3KbdPovPfIWfDoFoG89ybTck1Zm4mQhHdELkx4Xa5Bmtq586gVH6kZ6/U3IDHf3uBG9pgBF6SN0CXCByWrrkLS0B9bzTq0CfSE14EcKJoEYK+bGZNXMIHRVtY+35F2SJXVpjsdySYzm2Ca1xHZhYzzhuAp+gOVxoEwg3U7+UQeREVeCxh1rr/DHl3C4eF9Crl/sy4xjw4BEHlJULaNuSjhLtoC3/5BMHd4EZVHclH2dyq8B8TCs2tE1cdIhivW35Vzq4H1lq6XrnhkMdB0FKAuBeY1QU7ZAhgRAOWeT2AqY0vSCPZ5VI5o08pQ8D0TCi9vGgD/sUmcvyckz5KUuZtgohdKMisZYfrChNqdY8YKHXWV5XxyjC6Hf9KJI8rx3DuOb0zFiU1pUChz0f6Ea5dRwoH9EAdWf7FLffYs0h4dg5zg9sirPwzFIqYEr35ENhodfhtlqez3hAy493RnXI5yVT76PlEfYd1aIH30UKjOX4NnhBoRnUuAF08D/vFO5/qVDeHY1Z7N8CTCWNKS+fTvEukwXDBvPmQNEpE7cxZuKFpB6+aL5tPG4sDWAnTsJINu9nTsbfAmdXIPebk5diy9jMoS5u9q51EJNE/rTqi8no70QX2pUdIMAfZ0d5036kjTCwvh/8ILCOvdDt6BcghqUXpZV8j7Qzpvp0+YCJOlZJ44xkI//IDZbjDQzprkfxfl8eNIH+8sNlpJCfJFSigj8nV+dDzaDRuF0rxcKLy8IJUrkHU1GbuWLkTjbr3QcgDz/+mNU8ew4QvueTbm49kIq+8cNUBc++SioN+kSQie9iZdV7z8V2TNmolyTw90PHqMzx+7jzoRfwmDh4eHh+eBdYzNOz0PvyX/Rl1Wvw74FY0CuA6A3y7/VuX9p+6dihdbvOgyjJngSig7MPqAk+D0WuvXaEnm5eLLeL7Z8xi/ZTzOFbBBuK/sfaXa10FEMTKJBCJ80+sbzD01FyklNQfxu+KVlq/Q9yDCIwKeUk9aUno/MCmrzvkKnDKZ5niRf0bJgLYqt0XUsqXM9iqu5P6dX4pdxeWYHBWEegoZDpdU4pGz1532Ox+XgBnPTMX7P86DxGSiZX8R336DK42ZAeyOLr0w00EoE2arsLtNJzpVx+MfzcOOyeMghBmlBcWYluWHoMc/wddjW9LXR67g58+ebdvfT1uBKcnrMeuxpwEjcNBBKCOs79GPTvYs8AnB/hEToBdLsOBL5p91QrGnN/r7RENzjsl9AUz46hh3MB8gEWNjywTEKljRq08Aex6Y1Grceoy8BobmKcl0Cnr9tQdWKCOQ0jdrKZxbQj2XgeskB0x95gxnfdicObUSygp/vgRNcvVp7waYcQMmREMImUSOiIUbUbbpJkxqoGhrMfye+x4+g+Lx9cwDmGfNKrKYj5gBojPf6vIhASOUeYjWwl20izrHCLIEBQQyBdQXqi79Fa3aie7PeKBwVR60KaV06mzZRoa5urQyxL/gfDGgVXTV5Zgcdn4IHP8B0FVCb2pgW+0TdQweLyyA+M/NKGFNGSjNsD47F/9JjSBPtLhEiswQ/n0BJjC5QJVHuK/PTXgO2HkQgiub4PnUDnh2DmfEMNLNUu4LXPkbyDwBdHoFkHowLjbyuzzxb8BkBLa8wT5Yy/FA8kZAXcKuS7CUmpOmB69eguDDvwBzAAQSbuc6947M+VYVpOuhbb5Sz3GVEVQGFTYMZYK+V1z+DbNOfGbbZijRQH2xEPJGARD5utl+F81GE5Qn8mAoVKPyINtZlsALZfePm+eZc1Llzoq6kqgo6NMtYipxKjkIZaQ08eCa6zS8vXFXJthd3rw5fMaMxe4c7t+W1vHJqNzDFT7FMiN67p0Mg0hGSybdWw2GLsqXCmWEgBZmYNxaVigjNBsNFCQDB7+CRKKHyKCBUSxDSM4ReAfIIG/R3OZyC5rKlHjmz5uPmHSmM676qVUY+v1CZD7HZG12z56CBpcv0f1Hz2iLLd9fgMJbikadmSYBJatWU+Hcd8yYOr+nxktnrBXFEMCMqPSdSI9i8i87HnkHIuJIDQzGgcTXbPdx05agw98/uMyWvJuQ1ytv0gTyVq2gPMA0VihdvRoh772LzBcmo3LfPohDQ1Fv107aLdkV/s8+Szsla4/sR8oyS/OA/Fxc3r8bW76dS5fFUjcYdIwAuSctFR7+Abi4ZwdunmHzO/tPngqhSORSKKPHKmdcvaSDuFu9eJrhSTpJk79y/hodL5TdZ3ixjIeHh4fngaTNb21s8wazAY9tfgyfd/0cq6+uRqh7KNQGNY5mM+HeD8c9jHfav0OdW8lFyXh8C1Pa9/UZbrfEs4+fxYdHPsT662y+ztvt3qYONH+Zv8t/Qsg6kvtlzf76qvtX6PlH1e3mCUQYI244e8jy8zuZjnGuqO9bHzKxDBW6Cup6a+TfCPmqfBhMBnrfUI9QeEnvr1OaXG2t2LkLWa+7LrOK+vlnuLdra/tntDrIdqPZjEsVKuqEauwhRytvd6SqtJiRkondxYwjb2VOze3rDrVog7defAsfV+SjwRtToTKaEPTlHCR/PBMzxz7F2ddtbw6gNcFgNMPsLobkbBG0nYIBu/BdQamObjNJhJg/ZhJeXbkU6rmzQWQspViGvOjvUfyksxBG2Na+Kw42Z89VQpBUjHxd1W4jwtlERvi9FFcfTbNuQbZuHYbfYgOaHYmqKMX6skyEDBsKkZ1QZu/+0yYnQ5fBzVELfPVVuCXWt2WJ/T9ASjXJQJVk49l3MNNnZUF74wbcu3Sp04BBdb6AI5R59Y1BybabEFmGdoZHE7D+VCaOFVbiaIkSPWUyfKSRUqGMcBYGTCGdRM9XQHQ+leiiTnwNBZpDDG2QDL0szQJ+aXET0Wea2fbxkSyjtxGftCOjKk4pniFfBXGwAsZiDYwlpShZshcGcwSUxn5QL8yHCa6/+2adCTH+XJfXx0NclxUSTFoD9LkqSKM8qWBTttsDbsK+8BBtQIVhlG0/96eZgbeibw9Unt0EvZrtyOcKa/g9xS8OwW4PIUfryh1ogkx4gpnNPA7smQX0mA6smUTzzzgcmg80HgHkMu4ukuGEq1sBazl66yeBQf9j7yrAmzr/7oknTeruDrSU0uLu7hvzwTYGgylzd1fmLswH29hguLs7NeruLmlcvuf33rZpmtQYk2//nOfp0+TGbm6Se+973iNvA/M+4KyZO58G/AdyJFkrpC7gk5K1CZANux3KnY+x+4Zt2AzNRSNTj0nC7E86mJr1Vi2eJo0BFXqLrY4mT1oxxzAF+VUXkNjcD/Ubc1jGGYG+Q3yFCD53J7DygrJXT7YRbw78M6DfW3WRxY7o+egTUAyMYwqtwqXL0HzkCLxbiKf2+OKBQzBouV+/f6Qrzu4owNhr+8Dz0ceB+7g21Fa4mgvQaLA+Joavfg8Ft60EWlTChqoaqM5YSBRpwjDOetkRU54D+s1BgOhhJK5/F+V+wzD1s5VwCnzS7vuTDRxoIf3oZ3a79XkHFdg0bt/BrIbxbm7wf+lFln9lbGxk7b4E5xkzekVg0WNbA/YpN1KdlITI3PUILdwBE18Eia7l2FaWi4lldyGt382o8+iH+ImBfzlR1h5sX9AOmcOGw9TMlfMYysqQHmvZd/o9/zzL3qx47XV2DPW44Qa2XK+zEOY5Z04iee/OtuutRFkrNr39qtX16bffi9hxXZ8/8qUWUr/syaesbgtsydxz4O+Dw4bpgAMOOODAvwakuNpbuBcfnu+ZfJ8Q4xGD72Z+x4imVizeupiF2bfHm+PexIxwTuFDhNTN229GlGsU3hj/Rq/Xk2yUAr4Af2T/wcL/W/HYsMcwMmAkZAIZu11r0LL1oPudKOesNq34YNIHGB80nqnnqtXVeHH0i39ZYUBvQE2UZU8/jYYNf3C2p07SGtyuuw7uTz+FN/MrsLasFsuDvHFvWNcD6mezS/BZkf1Sgu4gOlcDQaUGgiEuaPa0bRvrCH6FGqILtXhiRj80aw14fy+nUjv0yEToBcDHWeX4obIWwjwlBEXN0A90h8nPCXyjEb8/ejtcmzvPdmkUOeH5WStwdoZt0+EL3gosj+OUUUqDEctT8xkRuDTQC/eH+eHd8xfxZbOFbnFvbMD+Uf0xMMWa5Pr6+YfgNmUyDvLFiD6wDzEFrWozC2jGmdRYLjOmo2jF7Va3OQ0ditDvv8P/GojQIIWOSW1A04FiaLPrGDlEELiKobu1P8a/c4BdXwM5AsDHDW4GlNS37z0FU5ddBTHmQoQJxLR0gmchw1Rqf2wHo7ABXo9MQd0rp9oRqA9AzM8EJj8LjH2gmzdhROWTX0Jntm997nBnBDw3GhHPcUqSL24agqmxvtAVNUGbWw9RgALqcyWQxPih7tcMRq6xbeEphbHGYu11EuyCysjZ1kVBCvjenWhDxmoOHEHNdm5/4O35FsQPbUDz8TIIPWWQ9ukw4P1uPgw5ySjvQJgFSK4F/76jwHsWEvGScW8S4B7a7d2qV1+AJpNTAupydkOb/AsCP9mOxh0coRD02libx1C+Wu2P1qpOp0Qf5IxvxK07bkWoUwi+KnmRbRdJmAuMjboulYvMnjo6EOWvcS169mBvPRy4vFDWafDt40etll350CD4R7m1TRBR0yFlH2ZfqIXESYTQ/p7QNOvx1YOcIqk95K5iLHhgEH58lpu4I1zxYCICXKmkZA40NRwpHvjB+3AZOwyml0JRmeSMukyOXFaMHwPlgcOQ+2sQcv8cjvjtDKTepsw9STti2g7oPZA1lGzt7UFNvpo0S2FIK5xnzmC5jyX3rGxb5nHrrfB9pJ2CsxtUf/EFqlZx6iqve+6G14oVKLr9DjQfPQr3G2+0aphuhaRPH0Rs/AN/J6itufzprmMcCM5TpyDoA+vPQq/VIP3IQez87P1uH58wfQ7O79hsteyeb3+FuB0R1hlI2Vb51iqb5QFvvgHXuXO7fbwDl9eG6SDLHHDAAQcc+EdAYfVbc7cycmxB1AIM9R+KGb/NYERWe3w65VPcvtuaCGgFKcKIZOrYbqnSq1h+zPnK89icuxlX97kas8JnMQLrcoMGS0t3LsWp8lM4ev1RZo/sjGDbmLOR3T45ZPK/Tkpf9tzzqF+7tsf3D/ttHWY0C6yytN7rF4J5Pm6QCSykX63egPlns9oyx7pCSHEOCoMsFhTR2RrwazQwK0TgN1C+Dw+6wR4weXV9winIa4KoZXB88snJ8HGW4vtj+WybLxphGVzvTa/Ard+chpdCArOzECXxFkXK7jtvYLojyv1qhVIqw9L7X0GdQQavUDeUCSy3zfRyRaKLE24P9oa4C5WdwWTGTcm5bUo6QqxcirR22/G8pxDe4aHMmkjfr+bDh1F0WxcZTXZA2XDSvlx73/8K9FUqVKyyLQpohdeS/pix8QLyazjyzA88lF9Ckyjhh6sGYsyQIBQ/ZjuA7ghplBReS4cAdXmAWyjQg/2Q9plRqNK9brXMW/wYtKY4aALvBKozYWiWwAR3eNzQF6kuQpwrrMOyMRHg83moWHUU+ip7Grju4b0iHpJwO2orvQbqF2aBz2uCJNIPuHlT509ClsrTq6EsDARq0+FU8zmg8AP//hOcqu7018DmTizsTl7A9WuBr1pVNjzAb4BFYXbNd0Cs/axKe6j6PAnaXE7ZYqi8CPXRdxDw/jY07eWC3P2fHM5egkhWvpQz3XT2uZ66sgzPXHwRCep+eDXfQi70BKTm0xXaJ15JeSYO6n4SwIE/h4M/ZyJ5X7FN8P11T1tPfDQ3aPHNo0fY5Vl3xuPATxlorrd/DLvioUFY/xaXGXjVo0PgG84NvnPHDIS2WmedjZf0K1R7fkPBZ5YoB4Lf0Hq43/caMLjzrKzeovKdd9vao10XLIDPo48gd+asLgt62iNi6xZIIiLaMiX5UikrbmkFxRKQrVgxZjRqVn+Nyje4icfInTsgDgmxeT6KCKDJt+rPP4e0Xz/WSPx3nwfR8bRp2zaUPfV0W3YZIWLzJlR/+hkaN29mLanBH38E+ahRbbfXlZdi9b2dH4PJWlmWnQmPgEAWwO/uH8hyzL598E7IPTww74En4BPWdRNxK2p/+BEVL71ktcxp2DCEftfzHE8HuoaDLHME/DvggAMO/Kuh1CnZ7DyF4ndlZfx9/u+IcI1gpNf+ov3wkHrgzdNvsts2XbEJwc5/LpD2vwiz0cgCfLs6CVUeOYLK116H/8svQejrh6r33kPD77/36nX0e/ZhWqZtXTwhykmC56MCcWMS19DVES9EBjAi6umcUiQ6O0FYnIeEzT/ArakORf5huBg4EQUqPiYU7EezwAl6ngiDtVkYt+hW3FjrDKWPRUVoDxGnarHnvvGQibsnJVJLGxDlo0BpgwYjkyxZcq4NDWhwdcW8g7uYLbPJSY6rnnofOndL2HkrbvT3wFt9g3t14v9geiF+7GA5nebpgu/iIzpVC5Q+/gSa9uyBud1JfntQ5gpZSXqrCvivoPqbVGjSbdU9fLkQzncMxJtHcvHdsYJOHy8W8nHPxCis2sVlCHWEVMSHRm/Cc1P74pbJUW0Wz9qf0uErvgMifhFqdA9DbbK2vPo/PQICuW1BSZc48j7M6dtQkvVE26JAyXzweEbgoWzgrSjU6e9mNk1CwNOJ4MsVaDpQBOWBbBhVnSetOHufQVNVHGk7rJaL/Jzge5/F9moXz7WQaFNfAEZbt7t1ifYB/a3XiSw7841l2fIDQEC7ltaGYiDvIOAZBXj343LMXIMB796RwB2JL2NNDpynj4LqNGepdJsXiYZteRB6yeB776AuybI1ntvwnc8mTK0fiQfK7DT5CnjkuWcXfe8bhPotuSxnrj1kA7zgcX0/VH+ZzEg8aYwHvG7u3DrrwOWByWTG2hdOoK5FaRrY1w0lGfVw9pDiplcsxAihsqARv75qsUh2BcovSzlYwqyZVz5s+f3U//gVyl58C85jByPoix8s69FUj4yh1nl5/a4pBe/ZakDQy/1EFzBptahdvZqRY57LljFruzY7G+qkZLjMnIHGrVttbH4d4fvM03CePBk5U6ayx9MkGd/JiTVFtuZHEjmWM41r2hZHRSJys7Wi6t8IsqDmTJ/RptCO2reXHb8NVVWsoZkvFreRa/u/+xJnt1or4Gbf+whO/fEbKvNz0GfEGMy9n7OgdgSp0YQicbcxFe1R/9tvNp+L61ULEdCBQHPg0uEgyxxkmQMOOODA34oqVRVTdzmJnFDeXI4Vu1awpkoCBfOHOIegUdeI67dcb6Mc64hFMYtw/+D7O212JMsiNY9RhpcDlKvTjPxFi1leVXuIIyNZFkjgu+9A6OXVtrz2p59Q8YL99q/2+PKKGxDeWIfJe7bBadQohK7+CiUPPIDGrdtQMWESrrv2tl5v/n5OUngXNuPUGQvJ5qOtxLWlv/X4OTIi+mPjtHbhwwYTRGn1MLmIYQxTgNeow+FRsYj07tqq0hF0Ujzuu+PICrFVrd31y7f46BrbGf8xbgqsS7QNo+8JqnUGxB2xtskUjx8IYS+bwAy1taj74Uc4DRsK+YgR+F8AEQz12/KgL2qCxw394BTvzbK4Sp9tl0RPJMjcCMhHBuBYbg1u+NLaBt0Rm+4egwFBHBEU9tgWm9v3PTQBXgoxI9QkwnYkbH0R8PEIFpBPMCcsRsnxa9llnlAHv4dHQeDavfWmMxie6Ys6wz3wENEkQQvpMuFxYP+r0Jr6oUr3FlvkFFwDl+tnoPwNi/VTxMuBq/BL8HlKVOreJSYH3uKHIOGnw2j2gNksQqPhRqhMXIaOy9RQuEy2VYRYoegUkLkdmPDY5RvY1+YBEhdAzhUCXG4UP3WYgi97dN+AZ0aA7yRC2RunWH4cg5DX9vj3/X7CNvfDuLFqFhbVzmH3bc0gE7hK4H51H2gu1rDtSLfZI3EDXx3DDcxrNVAnV7HvKL8HxL4Dl4bCtBpUFytx7HeLlf3Wt8aguV6Hn1/ibLFimRBDZoUhcSr3/S9IqcHmD63VX4QpS2JRkd8IhbvE6vkIkYnemLFigNUxRZeby1RW7RVZhOpHrkXVRk4pyROY0O/qcuC5zjMr/yqUPvkkGn6zTJSRHVOWmMgC5e3B7ZprIA4NQeWbb9m//bpr4d+Se2YPtE2S9+7Ars8/hKuPL254+W04ufw9RUUdQblqTbt2M4WbLM5CVqubGnHop29QX16GojTbZtu7v/4FEidu0qwsKwOeQcGsBfNyoWnvPhTfeafVMprYdFu48LK9xv86Gh02zN5tBAcccMABBy4dpcpSzF0/l9kLiQjTmSzhp93hzoQ7MS5oHN498y6Olx1va6R0k1q3GTrAnWQSMSZQcCQQzRZTQ2P+NddCX2xtK7EHxaRJ8FxyC1KX3spUUv711ifmFe6eeP62+/DxG0+z66tuWIbNYyYhLjcT88cMw+LIYNQ1q5B6PhnLzBbb60vRgbjCxx39OxA/HTFECaQcsW5/i21Kw+RqLj+KkCGPQt9m2wbM9tCJxHhvKZc5Ij5YDr6as5qZRTwYgxUYYBZi2wprlUBPYTSZ8VNpDR7O6n57ElJGx8FLfOldSYVqLa44l80EN38kRiNQap8gdsACIhjaE0IE9yujoS1sZEohnkwI88qBeH5TGsZFeyEu0BVXfGydUUS5dWPf2Gd1PdjDCWiuBjbciajkxTC09YkCux8Yz9SHDBWpwCd2vl9kE1y2lxFI5p9uAHz6gze1a9VGj3D4XWD3s53eXKF9B3pztM1yuWAzXIVfg8/jrGNGsyvMEEHIs26m1I76HFV7uSY8v8eHQej6721MvVSUvnwcpqaehepTrh2py8pXnYap2QCfuxIg9JHBbDCj7MXjeDr4Q5xWpOG+0kWYLZwEv4eHwqw3ovlMBaT9PFiIf0fQbXW/cmpF92v7Qp7oc9nfowP2YdAZ8dlKyzGGED3UF9OW9oeyTotvH+eslq248+OJUDXq8M1j1ssJw+aGY+js8Lbr+UnV2PJxkqWUaE44hs2x3N4R+Unn8NvL3PHVNyIK8wZIofxuDWr6+cMwfBEGXnsH+O0s2qqGejTX18E7tPPnbIWyrhbVRQUIjo2DQCi6pPOL9sroi/3sNzV2h/A/NkDat2+nt2/7cBXSDln2vRNvWYFBM/89OVyN1VX44q4ldm8btuBqjLjyWogkXSvbL0dubPqAeKsWTu/77v3XRXf8r/BEjjZMBxxwwAEHeoWjpUfxedLnOFNhnQ1Uo6np1fMkeCdgRfwKFmr/xbQvmDpNJpRBIe6dIujvRIPegO3VjXg+pwSzvd1wf6gvdGYzwmSSy26lJJuAODychfqW3Hc/mnbt6vIxTiNGwGzQg8fjQ3XKmkwgKPfuRePevcgJ8cPAQmv75IHEYdh4/S246OyOt29YimGpF7Bz+Fhmm0qJ7IuUsga8XNZKrlmIsimeLlgW5M0ur4mPgIDHQ1FuHR4qLAPPDAgzuMeYBTwk1+naauX9NOW4usy6+e6U6yAsW7kCF16+gwsy7gRivQ5Lfn4PO3ymolbnCn9NGa6s34O+E2dil9gfK8ZZbIy5Z08h7/xpjL5mMaQtJGNXEPB5uMrfA58UVyFXbZtPM9xVjmejAuApEkLG5/8poowQIpPgzCiH/aonA7n6P3JYmHx71MGEUzBi3O+Z7IR2FTRoNguw93VuMLYrzdJcSCDC691rEzhirB3arq+/HcjehW9FxXjWcDOemBSAxFHT4S4Xcy2Me14AKlPtr+T8j9saLnk3rsFlA7XjdUaWCcTw9XkH1VWLoTFZbF18NMJd8iVnkzzEBUULeO3I8VErAWc/oCoDkkkL4D+aD56A15bX9V+D00AfKA9bE/WdwdigQ+kLlrB2kb+cZZmRY5XvLEKDgFMQuhudIfTjFIM8kQCKERzhaA9ku2w+Wc5sng6i7PKCFGCegXIo3K0JjKR9xTj2ezbGXGNLJI+/gSNzpHLb73t1iRKH1trasPuN8rciygju/tZZqe6+nauLdGpVG1FGqMjNxhe5wKyVr2Lntz8BG7Zj74btuPOrNZApnLH/uy9wZgtn/VvyzqfwCAjq9LlNRiO+f3QlI9dGXX0jRl7VTnndQ3QkYqgchmz/XU3Eha//HXlXXNl23f2mxTZEGb1vZV0dy/IqSD5vRZQRzKZLy1X8q7D7C9tyKSL04qfMgLCDOvCvAqkQI3dsh3L/frhdey3LinPgn4Mj4N8BBxxw4H8YGoOG2R1pMNqT8Hsiyshi2RVG+I/Ax1M+hogvYqH2pDa7aftNyGvIw/L45bgn8R7UaeqYEk3I//8xOKPts7a8Fvenc4HQHXF+VH/4SUQ2s4MsO0zQc3sNEWTlr7yC5gNcDb3nsqXQpGewgPeuQCdU/s8/h+xTx5F+9CASZ8yFf3RfHH3kAZj3H2QR5mYeD05aPRQ6i8JCJxThvvufhjq2Pz7pH4ZKnR43J+ex24Y26FHt6YQ8Q+eKjA2x4cjIqcOzG1MhEvDw1OxYdrkreGurcF3pOqtlfsMn4Yrly+HUQmhlnTiKrFPHMHLhdVh9H/d9G33NIgyaPR9rnn0M1fk5jFy74DoAywotobcP/mzJStGqVPhwyTU2DVVk/ZDI5eg/frLVLH57aIwmvJ5Xhk86NHeWT2yXp+TA3wJtfgOUR0qhTrYookphwjXovK20PcjW2tfPGc/P648hYR5ty9/ZlYn39mRhyegwPDvRG9j/GnD6K9sneLoaKE8Gvpho/wWo0OP+ZEDWoQ3yckGvAV5uaZmd/AxH2LXCJxaY8SqUq79AvYErQRHySuArXgHeMzWAQAjk7geUlUDhcUDhC4x/xDo77H8AZr0JJU/bKoV6gvYNlYZ6DeZunY9ifSk+cnoFo2dMh8DZoQb9p5B9phI7vkiB3E2CW14bDbPJDJ3WiC/v546f9jDhxr7oPzaw7fpHt+/t0WtRphllm7WHVqXHlw9Ysu1ue3ccxHYIZ41Sic/vWgK9xrpxtzPwBUKYjAarZYtff98qIN5kMuLQT98iafc2yJxd0FDJTQx4h0Xgpte7b2vsKajNsm7NWqZu0qSlovwZjrjnKxToc/IEar74EpXvvIMyNwX6fPopghOsMw83vf0qMk8cwbwHn8CB779qW89WDJw6C1OWWVsO/wkQebn2mUdg0HPOiLCEwRg0Yy48g0Ph4sVNCDrw34FDWeaAAw444EC3yKjNwFWbrmq7PshnEFKqUzArYhZrmOz726cVAADuiklEQVQIk9nUKVHmJnHDpgWbbOyTRMDRso0LNqJZ3wwnITfz6i79iwaWfxE2VNZ3SpQRXs0tYwHvopa8KZNOh/yFV0Gbl4fAt95kCjFJeDi0OTmQREVZ5ZcY6upYrbq+vMImZL/my3aDd6EQ4tBQ6HKsc1I8br4ZHg/ej1XXzuHuZjSiesd26AP94VRYgtoQH+hEQvBNJsxoIcIId93/GEac2YvEEB88OigavhIRlLU1mFJThlSDAWG7tiEpeB74Uc4wBcrB0xphlnDkUkJ1KSTlCly3w0Li6Y1mK6JsSowPPl88BMdza9CkNTA7XElhMQq3nEJBqWX9r3/xTQT0sbZ8RA8fxf4I1zzzClIP7EXizHmsdr2VUBvacBZjJJVor/86t2MzEqdz2+HkH7/afE7tq9zrykox7oZb7H6eUgEfz0YFYnmwNxKPprFlH8R0k+X0H4SxUQueVHjZ8pQa9xehcXs+awX0XBwLXXET6tZlMrubYlQAnCeHWAXhqzNqUfO1LfmaNsgDONs1WRbj74K1t41gGWP2ih5WTo7G5BgfxPg4AZ+PAaozuBu8+louE5qrgLLz1g++4xjgGwvQgJYIqb8SIikXcF+TDYSMBKKnAVk7udtG3gUEDYVUsAxoIctchD9yXFjrekVM4P7HWxPH/0vgiboP15ZEukIW58UUjK2g72R7kMWyid/MLgdOjXUQZf8wLuwpZP+ppTJpXxEO/WwpaOkMvh1aXsPivZid0h6oBVOvNUDhJrUhylpzztrnlXUkyojQSt6zA7u//LhtWciABCx84nnWrNiROGp7XAeijHB+5xZMW35P2/Wk3TtwehN3vqCjlskWVOXnMuKHbJ49AUU5JO/dCb+oPnD28sbRX35A31HjENg3lqnNqBGytRWScr1cpk9H8/ETEHh7Yc/Xn8GgV8Hztltw/uQhnH/tOVz91MsIieMshKrGBkaUETauesWK+CtIOoeDP36NC7u2InRgIqI7FB78lbh4aB8OrvkWE29axsL5aTJ0/evPtxFlhMEz5zHCzAEH/n9M6TvggAMOOHDZoDao8dmFz/BVirWK4mwlV32+IXsDlg1YhgBFAM5VnEOluhLTQ6ejTlvXdt/f5v2G4qZiDPMbxhRjRIh1lzNGBQD/H7E8NR8bKy1tZvuG9mXh7FdfsAyqfi6vRX+FFFd6u6Lwj03I+OEnPH7XE9C0ZluUqrHz4ashyuTsHQGr3mKV7JQ7VrjkVpvXrI+IQrVOj4iSQvDNZogjIhCxZTMj7e5IzUcc34zPhsYiUs49f+bxwxAYTehXVoPQmkbuSXItlrUyVzncVBrLLPmzq+BbkQvPhhrgyzchHvQdth1MQ/q3q5Co1yOx5X48jRHilHogpR735H2CU/GjUOIXivH7fsM2j6mYrsxgAf1mHh87vKegSmKZgR0e7slO+ivXf4qc08fRMdHs6qdfgUdgEBTuFrWPPQT3j2d/rWgfpKuttLaIHP35B0aWkfXj5AZbsqw9Tv2xjv2RykzbzA2Ab33vc7j7WQbI/hIxfoyPQI3egKv9ul7PVpRnZyL/wln0nzgFzh6WYoX/b9BXNKPiHW6f4Do7HKIABaSRl54lSMRY48587nJhE8petg7cVx4tZX+tcJ4YjKZ9FoJaEuUGr6VxMJmBQ99ZWupOPDEZz21MRWppI+bE++Pj/dzv8pcVI+As7cQ2U18IQVkS4j0igFc6DNKu+Q5I3wTsbWkeO/wOkN9BleTTQu7+1URZK5bvByrSgJDhgFZpIcsiJgJiOYQSDdyMH0BrGgAZ37rkwAEOfBcxTI06u02pIn8FvJb0Z4q79mQZtWS2Bw2qWwtqSBntwN8LvdbIGi0lLSRVeW6jJXewB0SZUCJgls32mHXHABRdrGUNmYd/sTyHb7gLwuO73n8TmXTd08Nwems+hs+zbTE++suPOLH+l7brY667CQOnzWKK5lvf/RxVhflQN9QjOG4g+AIBNrzxAosOaIXC0wvKGo7II9KN/jqDzMWVBc0Xp6Xgh8fvw63vfsYUbWTRDOjL7a+IXCPF9aBZ89pC9VMP7MGuLz6EUCJBxKBhyDx2COd3bEHcxGkYv+hWRiDRMZqy00jBJnB1hcv0aajMz8WFnR2KUMxmFKZcYGQZZah9dvtNNuu54JGnmUKOyLJWbHzrZXiHhGHR6+91qva+XGiqqcbWDzlr+qZ3XsP0O+5DfXkpe3+tGH3tYoQO5Fpx/0nlk7OzM7RaLY4cOYKEhAR4ev41BSjdwWAwgM/ns7//RThsmA444IAD/0EYTAZ2Ur8tbxu25G7BIN9BSPBJQI26BmvS1yC73hKiPit8FrufmZn1OES7RzMbZVoNp6rxlHpaZZIl32zbEPRfgslsxvkmFQrVOtyeVsCWiXk8pI8dACeB5YThtpR8bKqyEGndYe8d17fldnVErYsrXrnlLpyJsbRpxYr42MRXYr1XIB4qqUN4SSFWv/Ro2+2nP/kSJ3yDsK6iDs99/g7Gn+OavbrC6b6xePi+pzHyzD6MObWny/v+nnAfYrO2IEKVD7G5+4DsMokv/LUVqJf74bbnX8CRL95FaQb3HWoPNz9/LHn7UzZA6C0Or/3OagBCoFl0mk2n4N15Dz1plQ0z7faVCIsfhM/v5FRkMWMnspnlznDzWx/BKzgUl4L6inKsvm85m60PjU/EVU923zr6b4OuRInatekwVNlahtwXRkPW37Ot5Y9gNpgYyUCZV12h8tML0OVbBrc9BeVEuS/sA0mEK0qUWiz55hSyKzlV2S2jwvDcPOvMt9P5tfB3kyHQrZMGSrMZeN4O6Re3EBj3MEeEGXTAS51Yb7xjgLssmVb/CMhS6RrE/RF2PAkca5e182QFp0hzoA1moxklT9ra2QNfHmP13S1+jLPVuUwJgcsU6/0AqaNH/MQ1zp668RSkQsc2/rvQvp3y6seHwMVLhq8etFggO4LILs8AOdKOlCEk1oOF7zt7SiHvosCCWi4LU2tgMpoRPykIMsWlWWy1qmZ8uuImGHQW3fM1z76K4FjLsd0eiIzVazXIOXUcR3/9CXMfeBzOnl74eNkNnT6GSDGpswvLOastLcbX93MK0+4gd3Nnlk2a2GH7xC5A5FdhShIjzej4SsfH92+6yv7zunsgdtwk5J45iZpiTvlHkRAiiQTDr7iuTXVGRN4vLzyOqgKL2n3h48+3qbkaqyvx3SP3ICRuIGbccd9laZs06HTYs/oTpOyzn/8aOmgopt/5ACOp/i7o9Xrk5OQgIiICYjH3fdu5cyeOHj0KDw8P1NZaWnQJgwcPhkwmQ1ZWFoRCIVtXItZoOf3t27cPpaWlWLBgAeRyuQ3pJRAIYDKZ2P+ukJycjJqaGowbNw47duzAiRMnMH78eEyc2Ekcwf8zONowHW2YDjjgwP8gLtZcZESYr9wXn174tEePeWjIQ7gp9iY2Q0oWzNdPvo7zVR0sRx0Q7hrObJX/NSgNRmhNZow+cRH1BtvQ2f3D+iLaqEf977/DWF0N/ZXX4vGMeuyU9jygdkjqBSzZ/CsK/QLx6ZU3wr2pAfkBwV0+Jqy0CAv3bsPMo/sh6HBSmxoejYdXPo73Vj2P6GKO1DvdLw5NcgVic7PgW2dbuvDcsntxYPAIBFdocN36FvVMJ5j9zJvY8sLDuNy46smXEBp/aflf2adP4I83rUmoO7/8qdMBxS2rPmEz7gd/+gZGvR7jF9+Kn558kJFrneG+H9f3qlGMqubJUtL+JFwgFOLeH9b/6xqszEYTIw7MOiNMKgP4MiH4chGMdRoYlXpUfZZEbHGXz8F3EsLvsWEw1mpQ+fF5mHUm8MR8RprJB/vCdW4Ee9/a3Ho07CywIsl8HxjMBmekIjOpDVCM8IfQU8YsmsZ6LVO0NdVqQKfyL0ONgYMDsGxeDL46nIcP92VDR+RcC36/cxQGhfTS0n38U2C7hXBus17e3YFofskXMFjUmAyzVwF9ZlhIqn8Ljn4A7Gxp3pzwODDhsX96jf6VaCXCWuE6JwLOYyz5VQRVUhX7vtJtvBZbfWtTYJmyDNN+m8Ymks4sOvOv+23/l9ExWyx2bADSDrXz87cgcVoI+o7wg2eAAjq1ARePlTGFGJFrfxcoxJ5aH1tx/5o//pRiqjg9lanOWhXQrRi36FYMnWsJ2Cd8uXIZGiqsC3x6C4FIxI6VlwOxYydixl0PdPpb6Ujw9Rk+GtEjRmPnp+8z4rAVRNAlTJ/NMs4uFYfWfNup4tw9diAqxAqm5GoPUnMRQRQXF8fIJiKoLgeMRiPeeOMNq9cLCQmBv78/I6YuF6RSKebNm4czZ84wUo5ApNyiRYvg6+sLicSWPKb93fPPP2+zfOLEiYww+y/AQZY5yDIHHHDg/xkoD6z1T6lXQqVXIci5ZwOyek09dhXuwgvH2gU/dwNqobw78W6b5XqTHldtvAq5Dbl2HzcjbAYj1wZ4dz1DejlBtguNwQinluZBus5vGcRcDtToDLg1JQ8nGqxPRNtjoa87Pgj3QUaitTS/XOaOZ+98AJlRlnyQ4LIKlPGcwStRgacyYlTTRTy76TOrxy198jXkBtlXL4UYeJCfuIDS6GAsPLYTi7dv6PF7WT9rIT6dcxV07TYPqdFu//1HDEvjKu7vevh5pEX0gfhIBfpUZGBmZYulqwXnXQYgodG+crBkzDIEHv6y7fqIhddj5FXXIWXfbuz6/INO14uUX2QHoZPfiTcvR8SgobhUkGrryC8/MDLLxduHnYzz+Py2zLb2sJeHRiCbJtldkvfuQPyUmeg7cizeW3RlW2ZJ/wlT2Gx2t+vSMoim8OamausyAMIdX/zYZnf5J0HkmDq1Gg1b8mBssG35lPRxhzbTYkMhOE8KhrSPO8QhLoxYK32ud/Y+UqHV/ZENGCzEm9BbBr8Hh6CyUYOXtlyERm9kyjB/Vyl+O1sClc6ApOIGrDvTeQObi1SIVdcksEy8XpMV1Lz2gh077dJdQPAw62UNxcA77VRrd58GvGyb9f4VSPoV+H0Zd/nKL/6n88m6QuOeQjTu4iYVAl8eDZ6Aj5T9uxk5QEoLIr07DsZLlCVYtHUR5kTMwciAkSy300PqgQPXHviH3sX/HoxGEz69a7/d24L6uaM4ndt3DZsbbtNY+U+AbIykYCLMvOsBprS6HNA0K1GacRFnt21k7YxELNkDZY8SwSaROeH3155rWy5VOGPxa+9hx6fvMbtkK658/Hns+ORdZkX0CYtEUEx/9hrdgVTa9P6qC/PZb6ijqs3F2xfL3v+CHZ+7Am0r2mY9QW+U35SHdmHXNkYohiUOwcdLLS2hQf0Hwj0gEMm7tkIRFoUyWc8iBoYMGYI5c2zPNXoDUou9/37PSxjIgnn+fNeT2JeKAQMGYPjw4Yw4a1W6rVq1ipF5HfHAAw/AxcUF/wU4yDIHWeaAAw78P4LOqMNtO29rywxrxc6FO+Gv8O/ysdQ2uWTHEpyrtOQ/tGJh9EKk16Zj6YClmBg8EVqjlrVfUsbYEL8hXVo4z1acRahLKFOpJVUloV5bjzivODZI+KtxtrAOAa4yqPVGrFxzDsklDWy5l0KC8X288dvZYrv2q0vB3ppG3JBkSwx6iAT4Oi6cNVwSmRbb3Ii8SfZPeIkKWNZCfi398Uf86NUSqE0qMB4PH+9dhfBGS35YK25/9CVkhLVk4pjNkOh1uH7zBty8q2ty7P5x90AtlePTna9ZLV/dfwZ+jZ5itcykEEI33JsqATE07QLCSovx6+RZEOQpIcpqZA2BM+P8QN5Qb4UEx3JrcOs3p3Br9ucQmGxnlld+u44ptFqD8lsr7Yk0OvH7z+ykePCcK3Dwh9U4t30TO4Fe9v6XUHj89VkbXz9wB2pLuIyroJg4JM6Yw8J7e4qGynJ8ec8yy+fz2feMkJO2FAp0BIU3k92T7CntMW3FSuz95jMYtFos++BLuPr44Z9G3e9ZaD7Zc7UBkWfet8ZZLWs+U8GeQ1fQiZVSyKedR9vVMzBgN/SYDhESIIQozhMe08NQyjfjio+PorbZNj+qO4R4OGHHfePsBvZ3C00jcGENTFsfwcHKcLiKNEj0aPldPlNLFXS2jyF7Y00OcPU3/25bY1MFsKoPd3nR70DU5H96jf6V0BU1ofKj820tl0Q+fHTrdVb3oX2am69/m0X8+WPPY12mdXtvmEsYNl2x6W9c8/9tnN1RgGPrOVVMcKwHitIs1rRb3xqDH546Bp3G2Gkb5d8NygY78MNqdvyZe/8/q/I0GvQoTOaIsYC+sZA4ObVla9I6jrzqhjZrJBUOUIanVK6AXqOBSCpF+pED2PbR25h6293oM3IMs1fu+eoTeAaH4NpnX7MiwugxSXt2wMnFBUKpFD6hEax9ujvQJNonyxfbbQuddc9DOLdtE8qyMzptBu0M79wwn03SEcjGSZNkBKFEiroI6+NbeyWWRqNpu0xqMvprj9tvvx1+fpd+XP/ll1+QlmYbT2EPM2bMwIgRI9oInu+//x51dXVYuXIlI66USiUj+n/44QeUlJQgLCwMo0ePxqFDh1BYyNlgW0H3s0eC9QSzZs3CsGEdJpT+H8NBljnIMgcccOBvBBFWG3M2QiaUYUb4jF49lhRkV2+6GoVN1ge19ifl+Y35LEPsyeFPYrAvl+dACrSc+hxcs+kaGMzWB/Jzi89ByP/nTxgvBR/ty8abO9o10XWBqbG++OiGQaztrqc5ZH9U1kNtNGG2tytEfD4iDloTHYTXwgOQn1SFUfx6jAx2hnLdr6hb9zt4Zo4I+LL/bHir6jA/72jbY5qlMqSHReKoOBr5o2firtJDiPjje6vnbRLLkBUWjkGZ3ElSibcvnl7xAG5dtwVxZSWQqashMGogNHLKH6XcH+cHroReKMMBLwHMKiMGaYVwNlvUNIPOvQ23hhyk+Xvi4WGPwMSzTyLohnjB5MnJ7UVZ9RDkNuPaIcF45coBEHRQ6RHx9fZ1c9uuUw7YFY89y3LGWgPrKdyXTkL9IqO7VIB1N6N8OVGamY7tH7/NlGvhiZ2TwV2hKC0Zvzz/uNWy+3/6A9mnj7Nw5djxk1mGDM3YU55KRyx9/0u4+frhk+WLoGqox01vfsiCi3sK2vakbhOJO8/V6S2ajpSgYZOFEBZ4SCEf4guhhxT6ShWa9lq3vLrNj4R8hH+Xqi1jsx4136Ux4ozUYmStpPs3stfKwdfQYbVVTymHJ2fF4GJZI34/V9Ltevf1dcYniwahoFaFb4/mM7vlnRMiIWyXGdgrfD4BKD2H7CYP/FHMEe33TBdAPHQxMMB+/s7/K6T9wWWZTXvJPvH3P4ySjIuoKS6Ad2g4NGdr4Ne/D2T9vRg58OU9S23uHzZwEBY+wSm1SbH9a6a1dStQEYjtC7ezQT4RBE6ul1584YDtcaMg+TxrZ5Q4yZF6qBQHfspoyyG76tEhzKb9w9PHMGphFAZOCmZ2S1KfXWrG2OVGq91vwKRpbPLk/zuMlHXVzn7Yqqi+3J/7yY2/sWwzasJuKC+Hf59+ba9LTdd7V1viRaYuv5sd5xVuHljHJq0uWKm56fyEyLKOGHXdTdhxwT5RRQQU5YR1VIF9/fXXEIlEVvlhy5cvR0CAdVtuT0HPV1BQADc3N9xxxx3MCkl2TCLBiOAKDg5mJBjdZ+zYsVZWSSK7dDodyy3rCLqtYxYZrTMRaUR2RbU4IJqbm5Gbm4vU1FSkp6d3up5PPfUUIwqbmprg7d1Jhuf/UzjIMgdZ5oADDvwJ0IlAWXMZ/OWWASMtI5WWzqRDjEcMsyn2de/Lbl+5dyX2Fe1ra4mkxxGZ5SR0gkhgyT2q1dRiX+E+lvkV6RaJk+Un8f7Z9xkZRlCIFBgTOAbb87fbXS9vmTfenvA2I8JeOv4SUmtSrW5fO3stYj25uu9/K07l1+L7YwWIDXDBinFcrhFZsZ79IxV51c04mW8dZkpYNCKENd2dK7QN0v9h6XCMie6+cdCobMbPr7yFHQo37Bgxjim+CEPSkuDS3ISJwwfjRhcJCkxS/PbMu1iYc9Du82SFjYVMBzgriyBvLoP7m2+g6YF7225/ZMwd+ChCjebvvrF57MwFb8HkLMQjF3/BsPRCFAZPQYWvrR1RYFDDKOx5vopRuQtZikgMjkpAbD8PNFVrMGB2GK76+gQKa7mZVJNUAMMAd4DaLZPr8PIVcbhxeOdWhvaWxts++houXv+tE6XO0JEoJMy+9xFsee+NLh9HpOBNb3zQZg/54JaroVOr2cn+DS++1Rb6nHpgLzTKJvQfP5nNuDdWq5lqggaEQrEeOlUV9KqDuOl1LkT5UmGo00B5rBSSCDfUfMPtJ5rCXGDs54G4CdYZeQ0782Fs1MF1WhgELr0fbB7MrMLTf6SgslHL1KA9xWeLB2N0lBfWny3G03+kYkyUF+6eFIUoHwW0xTlMwRieMBgJ02ajJCOVCTV9wyOZSrHX+zhSh30wCEazAFvLxyNPNwlmUxNm3zMdUUO4CYh/A+rKm3F+dxEb/JPas+9wP4QN+GcaVf+KAfE/hY4W7Rl33o8zm9ezNsLOQIPuA99/hUcUX9vcllDijcFprlyuE4+HW976CJ5BIfhfA02a0PeEJgh0GjWbTCElE/1WieTozfeHwuDXPvMIU/txEELqbk003fTKKDh7/IsVnh2+b6TYotZnB/48iPza8v6brPm7Kwy/4loMmj0f6197jqnnjGIpeCYjeAY9pEHhqHa2VrnHxMQwxVRoaGi3bY9EMH333Xdt1ydNmoS+ffuyMH4iu6KjoxnZRdbGyMhIq+8/heqvWbOGBfO3YsmSJex1/0moVCqmSMvOzmbEGK1zYmIiAgOtsxz/a3CQZQ6yzAEH/mfQoG3AweKDqFBVYF7kPEYo2TtBI/WXoAez7WSJvHPPnThRxgVsPjfyOSzssxAzf5uJYqVtjs4Dgx/A22fe7jJA/+b+N6NSVYmHDzxsY7VsBZFgU0Ontl1fm74WL594GT2Bn9wPb49/+2/NEesN6GS6olHL2jYnrzoAlc4yoJ4S44vdFytsHuMtF2FIWRruHeSJvksXscd8cSiXPY+bkwif7OcsGa9eOQDbTmbjRLkW78yKgGfaSWRmZKC/nyf6XzEfosBA6PLykLl0GUQ1XNj9mzfehryAYNy39mv0KbI0MXWFBucwJA24HXqxdUuSZ3Uy4j2KUJdyFrlSdwwOcAbvtCWclWrWTWo1XJ5/CVsMkfj+eAECJDyMrREAnajALicGzwxlGS5/JJXioV8vsNz2h6f3wTRnFxSm1IAv5IMv4LHBzfB54ZA4iVge3IY3X0LO6eOYePNtGDTLdnb2vwyyanxwS88znybfegcGTptltd/57PaboKzjiF9qxfQOjcDpTevplBk8gR88ggbBO3Q08pNtCxjMZi2GTDdixJWXnotS/vYZGCo5opRgNJuxucGiQHXxlsFkMGHMNdGITPS55NfZkVqOFd+fsXsbKRaXjQ3HZwdsbc70u/988WC72YMdFQQdIRSJWcNp9PDREIo6lDC0FmDw6DtthkqVB4NGi9Mf/IT0ctsWL4P6OHjmsyz7ztnLGyMXXo/U/btZxh5lAl1O1JY1Q+EusWsTo3UtyazHrtWpUDVY7KkiqQDLVo0Fv0VNp9MYIJIILiuJpVHqUVOihJOrGLu/TkNlQRMGTAhC8v5iZnmbt/LSijj+aWTUZuDefffinoF3I/3JnhXetAeRsk211fh2lkX13bdAgSYnA6ac8gG/Q69xb0tB/k6YzSYYjc0QCi9fyx+pv9a91FIq0Qm6yusi8oPUV3VlJeg/YSr2f/u5FXkpkI6ASDaq7frUW2PRZ9g/b2lvBakSyfbfUcVckHQe617mtsuQuVdi/KJb/6E1/G8i7/wZ/P7qs23XjRIZ9G7eMAsEECobYBJLoPP0Y+QYOw6I7Ku0SdW1YsUKuwqtrrB//3721xNQDlhQUBAL1+8IaqokJZu9gH0H/no4yDIHWeaAA/95UKbW6YrT+OCcdai4gCfA1zO+Zvlav2T8wq4T6UQWyeXxy5FclYxBvoOwKGaRDXmmNqiZYosslZcTA7wGILnafmA6qck+nfopBnoPtLmN1kfIEzKFwYP7H2xTr7WClGsb5m/oNtfsn0Cz1oCfTxUxAdfzm3qWzdCKhYmBuOuHp6HNsrQVCn18EPT+e5DGx0NXUIBlm/NwqESN0SVJeOqUZabvckAvlKPRJRRiXSOzRlZ6JyC93+IuH5Nw/j141GfCxOMjOW4Fajy5PAwXTynGXh2JiycqkXvONgDeSWpCXJgafWYnoqiMxxQlmmY90o+VYdx1fRGR6A2j3oCPb3sCRn0FzKYW5R1PCqnbnVbPRc1t5i5aDIPiPdFvYhB2v2cJ9u0IhYcE8+9LxJaPzkOt1CJ2dDDixgXC2VMKbbMBzQ1aeAYqmCLqxMZcCIR8jFgQCadLUCT9m6HXabHmqYesau07gmwg8ZPtEyp1FVX45oFnIZLPAY/fMyWEUXsRAglXRuDsoUPk4CiUZtWjMr8R/pHO0OvMqC5SYtSVYTDpk+ERGAmJPBCZpyoR1NcdPmHOzIZkrtcg+Y3TUJsAKR8o0ZmgkotQW28/IyxufCBMoU74Or0MS8eEIz7QFUdzathvd9OFUjRpDEz1WaXUWjVRBrrJUFJvyZghZdjh7Gp2+Y2r4nH14CArUierogl3/XQW8YEKvHHVoE5LOja+/QqyTlgszp3BKyQcCx5+DmKZM4RiAUTnv4RuyzMo1cfC9eonkJb5PEobYlCTNgdGrX2SwKjPhV5pPyPwxlfe6dJmXFOqRMr+Embvjkjwxv4f06Gs1WL2nfGQKkQ49HMm6iqaodeYwOOZYDbzYTKUQde0Fk6urrj5zQ8hkbugqrAJv71hn3AkuPnKMO76vkjaW4z8pGpEDvLBjOVxjDg79nsOAvq4IXpI97lA7bHvh3SkHbZtErSHKUtimcLt/ws2525mRBkpwY+XHWfLbtnavXrjtg9Xs4Dzn556sG3ZkbgaZIVwSqdBGW6Iz+m8rCNmzATMvPvBf40az2QygN8Sx5CZ9RKKijiF3JDBv8FoUkOnq4avzyxo1OVQ1xvgEcBtIw1lIIlFzApeW1qCrBNHEDN2AppqalgQe1W+/fKfzjBr5cNwcnZlKisihVuz4E6s/wWH19oeu6n8Zej86/DHuxartqaOm5CkDLA59z7Srb2fXiclJQW//fZb27KRI0cy1Y+XlxemTJnCyIr2SiIKNq+urmZZVK2fIQ2oq6qqEBYait+/XY16rR4DBg1mSqR3rp/H7uMeEMTUw3nnT2PrB5yKmBDcn1RlL1t9H0i906pCIhIlLy8PlZWVWLBgAVMiOTv3jswktRKtH9n1SLFEz9c+X6t///5YuHBhl4op2lZFRUU4cuQIxo0b948riuhzICshrTOtG71Hsh3SZ0cqLMoRo4moX7/9GoXVtTBfAkE9ffp0FmrfnZKsM5SVlTErJa0XgQg3tdo2b60rkP2yNVTfgb8fDrLMQZY54MB/GrsLduP+/ff/qee4bcBtWDnIIvE/WXYSS3faZpd0BFkvKSiflGIqg0W5saT/EpYn9trJ11ibJYXrf5Xylc3jPaWeWDN7DfYX78f8yPlwEnFhq92BLJxfp3zNEWh8IQLkAVgcu/gvOTGntkl62q6em+6TX9PMbFf9A6wHEEezq3HDl51XX7925QA89rs1eXhlYiDLMRoW5oEXsjfCvLnrkPsGF0/o9AZ4qRuhcvJBTsQCGIRS+FaegV/5Cbvh9F/PuQpLNlsHNbeiSSTDOe8oRJnkXRJjHk5qTLx7DGvfSt6ZDZXGcrKVcOEDnB9om2NlD+7+ciy4P7GNZCLryVf3LoemyRKeHpYwGINnL8Cuzz9EYxWnvht51fU4tm4Nxt24BIkzFiDzVAX8I13h7idve1xlQSN+ffU0/k4sXTUWUnnXJ6104ttQqYZYJvx/Qa61b+iKGDwMCx5+utvfG/0udq9ORdbpyh6/jslYDaN6DW5562Osee4VmHBFt4/RKTdDrLCvPPMLlKO8xH6zqybUCVoJD74eMmiOc8RWKyr5JpySGpAuMsLLyEOl0AyFiSuwaG75mgvNQKRegEqBiS2v55sR4umE3Q+MY62X9LuQOAnZ95E+YyJ+Sa0kd5Ng9MIoVFT9ioyspxERfjciIuy3ja555hGUZqSxgfGAydPZdz95XwpqSui3oQB47uAL/cDjW77ztJYDnTbhgoobwHYGuW8q3CIPoLFwOJqKB8Ooy4S+mSur6AjKoSK7nrtfACMPiDjV63xZQHZ1URNObOyZKtUejLoc8IWBNkSqQNqA0Emvo6HAgKoLK8AXthSA2IFAxIdRz5GXy98bzxRnXYGI9CO/ZePCHuuMup4gZpQ/XLxkCIpxh1+4ZX+/7/uLSDvClSRI5Jztjgj+8df3vayNxb3BgG85hbWYL2axCYTrdgVBqu98+8x/6ClEDR3Rlp309vXzmBK6vars5q0hTNVIisPoYaPYgJ1swu0LAibcdBsGz/7rlbhkeSRlk9zNHcUXU+AX2cfKKl9evhGpab07R+IbAyCX9cGxL0pg0PQ88zRxxlwIxWJWdFKRm8XsqGSXTjtkPbkHngJ8oTfbofBF4TCb6mDUpQFmS7Yh/d7Iop6fXI0tH3F5otqmn2E2WIgzIpm9usiBJFJl7dq1zF7WHfrHxmLuvHmoqanBF1980bZ88ODBjLjqTEEkriyGQKUEz2iAQMeFwXcENUzS96M1S+rw4cPYt6/DNumAUaNGsXB2sVjMSCOnljB+e8fSzMxMZuvrDmSpo+ekzK3Tp08zlRM9P1kFiRi8cOECe672iI2NZWReREQEW4c/04BIpER5eTmzKBKRR62Oe/bsgUKhYARhaWkpI+smTJjA1vHAgQNs/ej+RAK2zwkj0DoRgZaf37mFmuAiFjIlfSAF3o8dx8hEen0iTS9HoyOtA5GxRHbOnj2bfV+Ki4sRHh7OSNeGhgYcPHiQ5ZARiKSl+1BgP+Wi0Xt14J+DgyxzkGUOOPCfQ2FjIbNHFjRyte/tQfbFsYFj8czRZ3r9vERcXb/FUifdij/m/4Fgl2BM/XUqajQ1TB32/czvrdRoTbomPHH4CcR7xeOW/rdY5ZMR3jnzDlanrGaXXxz9InxkPohyj4KP06Xbni43jKQU4QHlTVo8sPYc8rLrYeQBQ8UyvPn4aJB5UioSQCGxnDynljZg9vuW3Ii1y0egsEaFR36zhOXLTICeBxhaxkv0+Bh/Z9w7uQ/LGCuuU+GtHRkIcJPh4elc9pvq3DkU3bYcJqUSGok7GmMmIMt7EkwqFUT6Zpbl5awshkEgRUjxXhigQ1r/5dBJrbengW/GudBGROfth0YshV9NFd677hY88OOXGH/+VKfbIrn/bajy7txyFCYtxaxV14PXMjNO1ezrXv4eIvm0Hm1rCieefVc8U8EIRXz2nvPOnbaqdu8KfUeOxZz7HoWqsQEyZ5ceEaVpR0qx73v7Aa6Tb45hKhUadK977TRTuFwqhGI+Zt4+ACGx1nkgFMRMZAkppDoipL8HBs8IhX+UG3svNPDn8Smb/PKWAmScKGe5YKSQ8Q7u+cx9Y3UlC/sPih2A6bff27a9tWoDkvYWMYVd9plKmIxmZmHzDXNh79UujCeQMG0KynJ5CB/oBTefamx44zUIxH1h0hfjgTXfsrud37kVh35pBF94+fcR3ys0KCe2qwW+Bh5uUvY8/4f2BZIO1jOCs7cMTVW9m1WX+yWDLzRBWxsIvaoefYZGYei8EXD1dsKXK5ehoaIck5a9CFWDArnnq9BYbX9A2j1McBGUgh92Er6DtoPH496/ujoCBXsfh9mkxhUPhKOyUI1Da0/AoD6MUTcNQEHuOtTnuEBZwhFyAslQiJzGWp6WNoO5B69uqGJWIB5PCh7ffqtqK/hiJaLnPQQen7Opaxt9kbfjNsDcs0ybhY8Mhl+ErfKJCMvzuwuRd6EataW2JGr/cYFMAUtEL4WlM/tqo46RXd8/dczm/pNviUHRxVpknrC1z7cHqU6nLevPVHcdUZBag6LUWgyZFcZUeB1BCtszOwqQMDkYMmfxJZFl7THjuC/8am2/6/MefIKF+VOJSStoQur05g344uRHONeXa2Ie6zUSb45+nZFTHdFYXYUv7lpid1/9V6leP1l2IysXaI/rXngNJidukiQz68VLfn5dkwgXf46D2cQHj+8Cs7G8S9VYzOjxdm8jdSipRBl4Ckjdltu935CZrvAOpliCAAT394RILMCJTbk4vSUf0UO94R/bgJ3vvgYzjw+dlz/kvv7wiohuy36i0PJrrrmGhZYT4dUTkqw9iAwy6HTQdWg87CmkJbkQNVoTOje//QnOX8xgxAkpyXJyuNiI3oIIFiJeKOSd3heFvhMJQ+RTR9B2IPKJ7kdEAKnM6P+fBR33SHFG/0nVlZCQwP53BcrAOnXqFFtfWo/WBkZS89HndLlAhN6cOXPaFFpEuhHxSTlk3a3j3wVSnxEx9m9RnDrAwUGWOcgyBxz4fwPKCNuSuwVfJn/JGiGDFEGI8Yxh5Fcf9z7o79UfepMeg74fZPPYVeNXMeIpwSehjbyidsjWbDFSX90z6B6MCxoHF7ELkqqScK7yHN46bZHKdwRZG8nGSUH5hDJlGbLrs1nwfm8PdhTyT3ZRyhKTCP59uQTtZ2+7g2RWANZllqO8QsXUJDR8CTDwITIDuRIzBCYz3Iw8DNEKEWYQQN7S2Oge7ozhU0MZqWDQm5h9j8ginxBnFKbVsEGwwlUEt4u7EVh8EA2uEajwHoQ6D86O1isY6wFBu1YyfSEuRIbASS9CSFkBJOCai/r0kyDBvwIF23ei0asfGtMbUBLYbiBMwa03x8AzQI6ClBp4BMjhH+lmo4ZqVcDwBL6QuNzYttw/ohHjbxiMbx5cSd5INuDwCvbA4tfesvoOkZqsvSqhK8RNnMoIm0tFQ5UK53cVsf80QA2Ith70kRqN3uvJTXlWg2KvIAU2fXDBKkepFUSOHViTYfe2PwOvYAUGz6B1dIOyXgN3qRANOiNTttjLe+qMBC5Kq2VWueMbLLahW98ag4YqNTRNengGKXocFk2EDRFuFXk9G3xQ1tOEG/oyEk0osq9oKUlPw68vPYmoISOsBtbbPlmNzJMNMGovQCTWwCs4GKWZF9ltfGEIxM7tmhuNtYgRVYLv4Ye3tBJMbJJCCh40PDOOSwwoFBpRJTCD6Y/a7b54TDfDY4RPoJGPEfS7FYnBV11arfzlgklfBIP2HASiKAgk3D64PTyC+JB5lEImP4/q1FRUFPsCZg1E8nngC9wg8y6Aa9hRyLyyUZXCR3WKH2BWQyA1IHhcGXw8F0Dn8gMjAjJ//xBmky1R45OwFk4+6RDJ6pDyfRQkLvbVokQmLXhwEDa+e47t2zz9c6FTZaGhyhN8cQK0Tb8xRUzM2ImYdfeDqK87z8jrolQ9qvM40lYgaUTY1JcgECvBF+phMtoWWRbsewDqKm5/qGv6HWLnK+2uj8xFjCWvj2b7GCK81E3NKM8pQ/rxEuRdsNhniZC+7qnhbL/WHfIu0Pe+EOW5HGl0KUiYGoKhs8KYopQIuX3fpyH9mIVoE0sFuOX1MW3KuKami/jx8RIYDRxpPuqWczDojFC4KlB6Lgq557QQiGXwDJSgpgRQN+mRMCWY2cHrGo5g4mZrezpheKoHYgosRDmRYze/9REr2WjNNP0t6zecqTiDrXlbbR5/6sZTkArt7ytoW1OYOGUpdZZ9RtbGSUtWsHZEglrZBKFQBNElDOgzjh3C5ndft1nuGVOH4HGdE1vJ3/SBc5ASYVNKoVcJ0FQih0d0I0xGISNoW4nk6tTZqE5d0PY4kZSPMQul8IsMhkdAIHu/pL5rtVN2BvqsT2/NRs5ZIml7RqbLnEUIHeCF9KOcWrHJJQsaJ+5ybyEpy4e4vpqRbFrvQPB1Gmj9uyee+epmmGSW34akvBAmqQyC5iYImxugjE5oKwhqhbi6FJKqUvQfPwXT77iXKadISWb1vHw+Bg4cyP7CwsJQX1/PSBQikC5evIiff/651++RwuUpZL4zOx8pm/bu3dvt89A6kC2QlG/UjkjklkbT+QTFgw8+aGUZJfXU9u3b2ePIYkqkVW9B60CvSe+JkJGRwV4jLi4O8fHxbL/20Ucftd2flGc33mg573LAgd7AQZY5yLIeo1RZiiOlR1gwOg3mibgQ8a0ZcCIXipuKWVU2kRiU/eSAAz0BnVRRa+S6zHVMEVatrmYnnCP8R7ATU7IydgXKG1sSt4QRae0x0n8knh31LPtO2iOoDCYDxAJxl9/76b9Nt1l+9PqjcO4Q4P5Xbx8arFLWVCuoGZIycJzEQquTzrJGDTzlYqby6g2IHCFrVF25iilstM2tTVV8TlX2/wAGKCEk+5UduDTmIaRgC/iGMjx32/3oHzoAI082obaotVGrd/CPcsUVDw5CcVoyjv++luW0UAAx5ag01VSjuqiADRTaI2roSOQlm1kYsVhSjGXvLGafKdlkvrzHYu1d8MjT0KlUzNIncZLj0E/f4OQfnC1UKJbgllU0eOOygerKS5G6fw+zs3gEBv1tTWtECm37NJmpzRa9MAIKd24wp9ca2feV7GcpB0oweFYYPAO4z4Syk4h0tace6zgQCk/wRk2xkpFU9ZUqlsHVU3hLBRgb4ASnBG84DfSGyNf+YJ8I2a2fJLPQ8q5AZMfVDyQi/3wV6hp18Al2xoDJwYz8I4uFi6eMEWtbPk5CXVnPZ8Pp+0NEX0+gaqiHRK5gzXHtkZ9Vg+3nS5FPVmOhAJXZ1XBqNGIghPA28nDOYEaOWQ+12AxfPh8/QMcIMbJN9tELkCw2MmVnR1xb8it8dNXQ8MWgsbHIrGekmcLTE+qaWvAEPuCLfBAzLQDZh2IgkldC6pEPvnYUXL1doHDjSP9BM0IZGXno1yyYDGY4+WTBOegEI6p4Aj1Ulf1QdIDLfgoc9TEkrqWovDAPzeWRkMgk4AkrYBbwoKmNAI+vt0taEdx8nRiJEjlYinrRtUzf1h7pv4ZDwxRD5BsHEpZzyr66HGcU7A7q8Fz+LIessvYXZGW/jKKDK9FcfmmlKPRbGHtNH8RPCoKqSYVfnnsatSXWViYC2fZufe9z0GHl8OHhbctVVdHg8YwwaZToO3gxjnyzB3VFGgybfw3GXLsYTcoUnDrN2XGJ2FOWDUD+Lg8YtRwZIlYMhcnkDoP6AATi+DbV27VPDcWe1WtRVewDHt/2WCZ0qkH4tBcQ3fcuBAZeDbHYogSl/duaZx5GU3UVrnvhTQT2jUFxWgoO/PAVQuMHwWhQIOWwtXLNqMuAvnmrlcyOJwgAXxgAkdM4m9f3DnHuVMHqEypBZYH159sbyD2b4TXsJTxeb6ssmuY2DneHLMHpzesx5ba7WGsjWQdbQa3UXyRbbHgdzzc+n/Z5t6+vrK3Bto/fQWHy+R6tL5VK3PTGh8zW29Pv3JGfv2dZX/YQOKYc3v3r2PG9Jm0uNPWB8B38FURSPTLXh0FVyZ27ixU66JQi8EVRkHlMglHnDBcfQCrfhJqycTBq7Oeyjbk6mn3fezJpSOcsX9x7gJHIHRGR4I6EKeH4/S37ZUftUe1zDGa+baRCV5CW5kHYUGNHAwtGmpE6bdK4Mag4sg/pDWoYFZwlT1aUBZGqiVk8KwsLoHf1gLCxDnwjpzhLnDkXwxdcg/N7dqJco0dZXT1TfbXHiuXLIRKL8eGHH9q8dnfNh/T57ty5k9kS3d3dWS5WRYV99SYp1kjhRVbK7nK3yK5I6i4i1IiQCg7m1KNEbBFJR5ZHUmLZWx8ir3bv3m03oH7y5MnM3kmW1442ToJQKIS/vz9bTyK+SG1GCjUivlpD9Snni6yRrcRbTxp46Xlou1AemyMc34FLhYMs+4+SZWRnoRk4aiu7VNDsYG5aBZQeldit24Rt+VsRUzkSoYa+OBe+AxW6MpbJ9OvcX9kOiyxkZCVrjxM3nOhxzpID/7sgK8P1m69HTkPP5edSgRQSpQuaJDUwtVhR2oNC8En1RaH3pIChsHEasHSX1dKdVeOm2JtYY+XlskeShYUG1w3VamYtq6tQIe98FTz85Yge5suWkTUm5WAJC4S2h1KBCevlWsTqhBigE8DLxEe2M/DKy+Mga0ektf9t0z7i6G/ZbIBJhAEN+u2drHZEgdCILJER5QITfrtnDJqq1fh4RyYk5VqE6npviesz3BdhA7xwfEMOU46JxTzodJbBlEivhF7EES1msxq+lSmo9B1q8zwjjz8NqaaOqWAISnkABEYtlPJAFIROR3j+Fji/fC8GTrQdlJVm1WHbpylsO7dH1GAfpqKyZ+sSioox5+5hCOjTF5/fcXNbm2F3ePDnzcyyRxa6xOlz4Ozp1Xbbvm8+x9lttoURVz31UluTGGWTLXz8efxbQNlGRqOpU0WUPeh1Rhxck4H0Y9bKBrKbTropBtFDLTPfJo0BTQeLoUmrBc/fCTknKlCpN0HM48FFwMN5dffqpliFEONfHg1+h9++yWjCD08fZ7+F9pDyAE0PbHPt4RPqzJoBO/uOU6g65cXRMZmsmTlnKhGY6IVTF8oxOMAVrv4Ktn60PVXnKlH3q2VA4bU0DpIwFzaw5YsFMGkNqD9VjodO5eFIVRO0XRQ19AYTxKWQFKcg1TkWw+pPI1BjUWmQMsQ7NBwVuZYiDULfhbmQeVnvl7SNIvDLlqLfyEnwDouARCFFecV65OW9D53OtriCEN0UClVTOpSBEcja5YbSDoeCgJHl8Inn8lwMKglyd9wDk75vm0oqKsEdA6L3gBcajzMp9vMkvWpCcXSPL0TuJQgcUQGxMzew9cZHGDBxOurKSnHop68ROXg4U2e2oqHhPA7vWYmq5CshEKnQXBnTKUnQiqh5D8Ddsx+OfGQAz6RAnxEDcLFjLlMHLHjkGUQOHob6+tM4c5bIPg4+3jMRGfkgnJzC7T6OBo3Hjk2CWmPJzMreFAJlqYUgFjrp4RyoQlOxHHzB9eCLgjtfEZ4RETOegUhR2SaKMer4SP6a2972kDBjDrKTf4auUQRdE0cskUqWVLQmQw10SgpPt/2tSly1CJ1UitITt8Cg5jKbOsItai+84zYga8P7uFSIXUqga7SeMOOJmrGu3yeoUhTZjVaIcIuwWb49bzsePviwzfLr+l7HioJGB46Gl8yyT+8OqQf24AS1PJbatmd3hH9UXyx49BlGmCXv3QVtsxJBMXG4eHgfLuzaxn5rCx56ihFruWdPYf3rluPEwideQObxw3DzC2A5X3u2jYXcV42CvQ9DXd2n5V4ZGH61BjFDlkDm7AydSg2pQsHyxA6sNcBk7N25k8+wKuSnaOEazIdXoAsKiwrg6qHAvHnzmJqIGgYpjynjcBVObrSOzJhyawxUonJG3hBBYirzRE06HW+42/XiGoh0FvK20TUdI2fGsvyspKQklgc1Ycxo/PzKs+AZdOCZzDAJRVCH9Wt7jCL9DHgtjbj0e+8/bjK2frSKEcCE8MHDMPT6Jcy+R1h9/+0oFSkgbKrDwltuZTmJrcq/nNMncGHXVtSWFGPK0juYQrQjSEVFuVX2yCIC5XARIUSk0qWORWnihggxUqKRZZRIqEsNpr9UELFGr0mfwerVq7mJ3k5AwflECvbr1+9vX08HHOgpHGTZf5AsO7uzgLUeEdxi+bh6+Ri7dhQiDyjfoWOwKqslz6jDH+9az3gl+x3EgHJuoNksasDPCa9CL9DA3CLHtocbY27EY8Mes1l+qPgQy5QiJdDKxJUshNyB/z1sz9+O46XHmXKsPUYFjGLtVBRUT+Dz+EzRuCFrA/hmPr4b+iuS1la3kRgqt1psDvkctfIyuGg8Ma9kBTybAyF3pYYmi8qjzzBfTL21P7tMJJSyXgtXb2v14+n8Wnx+MBe3Dg5B7q+5COjjDsXYZizZezMLAT6zuPMmso44W1iHaB8FnKX2Seuqoib88nLnuViXA5QB4+Itg7ufE/qO8MO2t46gIK/72dfGQAn4jQbwVAY0wowqgQmHpHqoWs5nEoLdsOGu0TbKt+0/ZyDtWBkkLmKMmxzCbC/u/k7wCXFB0v5iJO8rhlAuxK8DJLgYJMYsoQHuyRdQU9+ILWMmYey5E0iJ7IcRyUmoc5Hj+ACLpfbLlx5FZEkhlHJ/5IbPRWDJQZZLxjcZIDTaElpNXt4wf/AhjFIZIgL84OfauRKQrDtEFnYMnifygojK/OQaRCZ6w8WzDj8/Z7HBhcQNRGFK542R0cNHtbX1tQYSdwaT0Ygdn76HtIP2rRBimRNueHkVPAO7GOj+P4bZaILqbCUk0e4wawyoeLd7NQF974hbrTaYIRfzcKDefpZMuJiPoBF+iJkcjKL9xfAZFYDju4uYgrL9fULEfJgEJpxuMrGGyEgJDxcMRiiMPT+R94twYS2hZCGm7xX9p5y+M4W1IKHh4+uTrZoiCYfhAlm8F8w6EzTpnROvnrf0R+0v6XhepcROWP+OfcBDuFCIIJkYxww6lKq7/p1vuG0w8nb+BvcBw1Em9sH8hADy++LE7z8ztSI1yYmkMiTv2QG5uwciEodg83tvoDhvJ1xClFBVyBAxs/NBflOxE5yDus4EEmsEGHq+Cjw1D3oTH81GMX7Is7bROwl0mBaegaLxfBiF1p9DjGEYAk4dQL6fCTnh1upBjzodBqQ1IT9YhoIQ+5N2Tk5RGDliB7rDyZPz0KRMhVQaDD+/+fBwHwWNUor6YheU59Yg7RBnufWM2QyvuD+snFcFewKgbRJBJDPAqBdAXS1FYHQirnj8WaSdewdmgxBy8UiYXPajqIjLrWxFXNyH8PWZ2e36aXXVUKsLkHHxdShVZyDTXQXfqFDkF65Cc5kzRM5qiBXcbyNtzVLwBVxAfSv4OAq/IWVorvGFc9BpZG82wrt/LQJHWRTdlRc8UHqcI7JdwxoRMLISEpfOv2PqWjGyN4bBqBXANbwRkYOGYeCYe6DT10Glu4C8ohfa7ms283BxzRTwBFdbqUWIKPNN+IVZ/wwaZ2Rv5JoOrcAzwKjbDYO6GkJZDAuD9wwdCqHbEXjFbWzLdWuFXuWOkiN3QlPHBb9v6/sFCjxSMEokwVE9R/x68byw7yZrcjOvIQ/zNtiWQiTdlHRZMoZIrbf9k3cx9rqbmOVa18vGvFZQ62726eNMiUqIHTcJM+96gF0uLFzNlJIEs0mAjHWfWj2WciHFTkLWakyqXhdfCb552DaLrhVqWSmc3EqgEAL+Q79BY+EwlJ++2e59zXwtGt2TITKLoRI2gWcWwrkhGmKdR9t9wqc/C7FLKU6eWAqdToOo6JNoVpJyiiNqBQYZyCg+V7gJCrUblCZPRAySwXnGU+C7d66obqqtRnFqMtav+RFmgZDZIHlmEytqGH3NorYSgJriItSUFCJ66EibFs2qwnx89/DdTNm98rt1l/SZUwbXm2++aVd5RVlj/zWQ5fK9996zWX7fffcxstQBB/4/wEGW/QfJss3H96DgG8tOvDGsED/5r2Kh4/cOuhcJroPwwzPHoVFyJzkUZEwZQc4BQvCCVUj64s+FPG7t9zkK3VPZ5f6e/RkZtmL3ii4fs+uqXfCT//+pG3cAPRrErjq9CjsLdmJa6DTcnXg3szzS3/PHnsfuwt02jyGrJCkVW+2NlCtG2WT93PuxsPwtH11gpMWfBdneiARpVYQMnxcBhYcUF7NrsefnTHi2Js23IHiEL+beHNvjkyOyFaz5NgVVJypxwpuHJ6b3hU+QM3stynrauToV+k4UMdQOR7YsClhuj4Ah3sjT6SBJ4vJgmnlmaExqhDTXoFlxeciTXKkJyXw9yoRmNPFtSfArRDV4ZqATqlIyIKqqgGbPLrbc56GH4L7oRmjS0iDt2xd8udy+zVathb5Bh2nJOdCJWral2Qy5Ro1FW9fjut2b0SBX4Kv512Lz6EkwtztZ7ZefjU9ef5pdfvHWu/H0amvrgrhvXwS8/DIkkRHgSaXQ5eVB5O8PfouE/3Igee9O7PzMvrqBgp+n3b4S+7/9ks3gE5a+9wXc/PxZIHB7G09X0KlV+OyOW9j/jm17N77yNly8/pnCB/q9mLVG8GXcxIZRqYM6pQbiQAX4ziJG8KjOV8Ep0QeK4f6dPo9JbYC+UsWeh1RjJpUB0j7uzBZXvz4bzSc7z9FphShIAe/l8UxlpS9vhjqlGtrcBnhc1w8miYBlPR3+hQt07gn6S/mIkPLxNNQ4AI5QEJDlmUhUHnf5gQbL9+gPJy3L4ru5k8D7hU8Nxdk6JSobNdCoDdiWUo4L5V0fVw/AGYKWdDAaIj8FFZRCHoYb+LgeEmab1MEMGXh4BWrsa1lPwvUSGZYPC0Xo1DC2Tdqykc6VoKROjQWJgQh0k7GJMWapMZkh7GExgtlsAo/Hh0qVxxRWEokPzp2/yeZ+eZuGY8T8W3Fy9/NMKdQV8nYGooHlcHW+P3UVqbEs6jS0RgGEfBME1/8ETWMmCtPfQFFQ179pqcaIPjnN8KrRsVeocxXi7ED7A7M+fZ5DcFDnjbatMJn0qK7ZCzfXIVZ2xPZIT38KJaXdN84R3N1GQ63Og0bb+bbi82WYOCEFvcHFi4+jtMy+9a4VzWXhKDr0RNt1T/98jLxFj+ycN9j1IYN/A7T+KEo/jCrDczChdyHo7aGpEzOloWto66QVfe86VzAXHw2C36AGmM3c91Mk44538fGf48KR92AQlEBdHcWsumKXMvDMIujzr0e/YXOZpZEs8KS2IjQ1pSE17UHIpP1gNifCYEiCyeyE5KRUiARGNB23NFwTDIpSrO3zOZQSTsF47NpjUEgtlv616Wvx8gmOaGoFFfb8OPtH/JUwGgzY/eXHSNm3k10383gwKFzBN+jBV6tgFgqhd/OCWSCCSSSGQKuG3tWTKaCW3H0vAvpwaiqVKh/HjnOTNbomX+Rue6lX62EQNEMrq4STMgQ8CNCsyIdKwakZBQIdBiZsh1zegMaiISg91vU5vw14BvS54l7whVymZU7OECiVHhg4kHvPmRljUFFhUVY+B2sXC8Pthyn4FJB0Xo5BLZyV+TkYMGl6jy2tfwVSU1NZVhmNO6nxkBo1/6vKql9++QVpaZzl/ZZbbkFISMh/9r068N+Egyz7D5Jlewr24Ocvv8fAKusT2jpZOVSiRvg3RYJv7l5ObeQZsLH/B5hduAziRo68cA+ToJZfCV5u1xaEZlE9Nsd+jFmm67ATv9uVuXfEfYPuw9IB9u0THVFX3ozqYiWzttDJP5ETgsvciubApYM+k4HfDWQDv55gZthMTAmdgmlhtk2B9NlmnihnNr3mDuHgFO5N6C7LaPRVUTh3uASq8kubqSXkxzrhzZXWs/H28OEvKeDttZ+v5hwkR1OxbZ7RiAURSJzaMitKgcsAXvvhAlLOVjDLY5C7CJFndiKioRRe6nro+UJ4e7kg5CLXZlXvGomziQ8gKvs31vxI0IpdsTfxbogkPuDbUW46NZfDvT4DEXmboRM7Q66qwI6QYXgv8Sp4qRugEwjhq6pDpcwNiQ35eOjYd2yY0xMEffE5ikiFUl6DnMoaxBTlIe7j9xFYUYbNoydi1/CxyAoOQ0hZCV7/6HW4Ntt+fjuHj8WWW+9A//QUhB05iGknOQJKPnUq0p99EZ9s34e7dm9ChK83nIOC4HnrErsk3aWitrQEFw/tRVDMAHiFhDKy6u3r5nZ6/1ZijEAz+jQrTS2Ul4K67GIcWvMNxCIZUs9x6oZxNy7B0HkLcblh1ptgUuvRsC2f2f9Efk4Q+sohi/WA00AfRpLV/pwB9QXOmsKjZk6pEKamzoP6FeOD4DbTMrCh59Bk1qFhUw4MNb1rKpQN9IbHNX3BE/RuFp8y9uhv/6dJyErvOh9tnqsQ+3kGRpZ1hjA9nxVSbHfSQeoiRpBCiroiJYZphQik8grwcF5sQKbICCd/GS5Wd59bFgY+8luIA3/wQXTOxS6IBHtYd/tIDAmzKDMuB4gUS0peAZ3OmrC3B4NaAHHTzZh43ZPsusmkxb791mH7PDWgSlYgN9MPIbwGjPPJR43WCRtLbEP5WzF59jgk5LYQE4/mA7KWkgnKxtl6F0oq1yM/1Hqw68PvA/eMs/Cr1EJoNAMTnwTGPQwk/QxV/lZoRtyC0upNjOwKC72jU9Lrz4DIxdraw3B1HYyKys1IT7eQUj2BWOzVtt37x74DPz9bJVNXyM17H3l5tkoOuTwazc0WArn8zELUZsazEoCZTw5EWdV3bbeNHXOybdtwFioTDuycA6PIvn2sFe6yq6nuD3V1nJK2pzAaRkAgPN7p7SnJk6BQjIBarYJEepCFzHt7zkdSShoEAiNcXX1xxRVXsHwlsrFRthLlHlGeU1NT5629Wr0CQTXWKsZyRR42xL3LeNxX+ryCuSMt+/z3zr5nk4n67MhncVWfdiUafyHIAr3lg7dQrjNC502WUhNCQpOh08nYn1rlArXacm7O5xswesyvLMkzNuYt6HSVbYRo8YGnoazgzjkkrjy4BQlRkdq1ErXa5yjMfAtJT22KpA5qn1Hl65eF6KhTyN3+PPRKX3j03Q6d0peVUjTkda6cUgSehT7gGMLDO89wq64ahOL0YPDMPNzluheSBksZSxt8YoHb9gJlSUBAIiMQ9fo6iMXt9pHN1UDufiByEoWyATuegDl0FPKciiBXREMk5Lahh8dokpwDRccBrz5AZRoQNhZo32ROUuG/mvjZ/zpw9lvgpo2AV9Rf+1oOOOCADRxk2X+QLCv/7BPUvfM+jHwhDoyzPWnqCY6Grkem92lcOWAeHhryEEw6Hgv7poYxQuqhEuz/MaPHz/fpyHvhqvbB0KKZEBtkiFnghqiQUDyw9RH0qxwBnVCNVL/DOHjtQbhLbeu26YRtzzcX2X93PzlObOQOkpRvc2FPIWpKmtnl0DhPNNVoGEmTsr+EKXRix3Ctdv92KHVKPHP0GdRr6+EucWfqKmpmnBQyiQXRHy87Do1Bg8G+g1lbY0eVU3ZdNsuNO1F+goXhD/Edwu7XqGtEP49+TNG1NmMtI6aIlKTXaS1qGOY/7LK9j405G/HkYW7g1B0o5+PRYY92asOlbK2tnyQxK197TL8tjtXeK9y5AOn0Y2XY8y3XADd4ZiiGzQlHdbMOD6w9h0aVHnU6A6qq1WywG63nMr0yREZoeWbE62xf23+IJ05VNmFDfQPubuS+8waYMeK6aIQEuzBbM4XeU3sd5RQ112vZd+/0uXJc+KHnihaCU7QzbrpvMPgmIzSZmUj6fh3WFhmx2zeOkVVx1blYkWKbY9Uebtddi/q1XDuS0NcXhg5BrxqJG84m3A+NzAuBJQfwmZ8PJhWdwZz8zu0VfwZGPh97hoxqI7guJ6L27oEo4M/9psnuuPPzD5B+eD8mL7sTceOnMHKL9i9NNVUs/+XkBhpk2Me0FSsRnjAYv7zwBHwjolh7XUfLRisM9Vo0nypntiyRnxzS/p5tv12zgYgqAwTOYqaM0ubWA0I+Gnfmt4kvTGYj0n2SMGnlHRCKOs+gNBM5wAdMSj34Ttx32lCrQc33F8GTCKAvaoLn4hjI+nsxNVb95lxos7smkZwG+TBb5KWASC6neC9IotxR+sIxgNavB5AP8wNfIYJALoIswYf9/zPIOlaKnd+mt113dxVDbjJDpzZAzuehr5QPJ28Zvmhswmo9R5ZFeMuRW8WRXY/P7Id4z9NQVT4FhetkJAz8CBKhAM0aPQY+t7Odvqt7fAQnRIGbqJK3qKpuc9PgYn3vG0Ifmd6XKcYC3C6PcpK++83NmYyosaccs4cBcR/By2sq+EYdUJ4CyNyoghNQ16Gh4SyaK44h4ABXSmGDkXdDHXM9Lp45h9paJTz8A5i9ifKW3P0D2sorukJx0ffIyHoOMmkIPDzHIiryEQghArJ3A6GjufX5h0HkWX3DGWg1ZaipPYDy8g0QCJxgNKrg7TUVvn7zUVL8A0xmHaKjnoCrayJ7nMGghFDYuUKmM1RUbkVKCtfI6eY2DIMSf4JGUwSx2Bc8ngAlJT8gM+tFdjvfEAiTsMTq8f36voTAwOvtvA8jMrNeRnHxt23Lhg7ZABcX29KDxqYU8HliXEi6DRqNxabbv/83qK2phdlcj8bGSGg0ehw4cIDdFhV1HP4B1sfOCxemobHBfnPfnwGd5y9duhS/5q9Dzk86BDVYZ7H9NuAtNsEbXzMAPz7wU9vyJw49gU25m9qiIkgxf2X0lVbnYso6DXLOVqH/uIBe5TiePXsWGzdyx3nK8aJAcgo4T0lJYU2JNTU1kEqlmD9/Pv744w9oNGr06XMUvn62hJHBIIJa7QxnZ1s7t7YhAPkH74FZbclVq/E+DhNfB7kyHGKtO9ROZZCL3CDhK6CvkkHmwUf4eCEGDo2FQCBgOVgU/t4KvZ6KgMxsOV02mVQ4enAuNM18xCZeg9w8TgVWcfY61GVPhiLgHIw6BSSuxey/2SiEwaMOUvcYzJ8/E+kZK9j+oyMi8poRXtRhQuOOY8AnIzvdrqW+Elzso0Bo6ApERj7CfVZvxwKN3PfeJHUGX8MRqjmhTlYE/NiETRD/vhIobheVMfwOYMqzwNEPgH0tZL5/AjD/IyDvIBB/LbVH4LKgsRQ4vRo42M626TsAiJoMxF8D+HKRIg444MBfCwdZ9h8ky2jAnTdvPrucHLsUVT7WM2eEnxJeRKOsGtPTlyK8Lh4Gng6pfkfAhwBTR4zC+HGDWD4DETOUF2UPFOpMai4KxSaywDNQwYLCv3+qZwNwylCi1r32OBa1Hg/dtBQJPgmsme/i0TIMnByMg2szkX360gZt1z09jK3bPw1qCk2qSmLZW9vytrHK8aTKJIQY+mBO6Fx8VPoW+CY+hhfOw8AyLhw0y+s0Dkb8Ar3ANtidmkgXRi+Em9QNv2b8ihrNpdsTZ4bPZK2PJcoSNGobMTdyLssR83Xyxfczv4e/wmKropMiyhIrV5WzMNG+Hn0ZOXm05CiePfos6rSchYGwKGYRHhn6CLNSvnjsRRaKn+ibyEi8cX7jIRFL2jLzSEFGzVf0XaL/lJtXlmNbQX/TK6MYSXY4uxr9A1zhIefsbfR9aWjS4YMzBUgqbkBGReczyu1BFqvRaj6Gqo0QQoCYjB/hW3kGfIUCJqUSpZOWIN00pNfbtMoJkPrI4JyvZmH4yWIDpqq5dT0nNmC3k56pJCYWn8MNufsRVNe1dYnAk8thbrZWrIhDQ+H/668kHYCEz8Py1HwkvP4yppw6YnW/ImdfaIRyFPUdgKD8NPQpz4JWKILE0Lv2KEKppw++nbOQqb8EJiMGZqXDu64GA7IzMPto1yHWHcF3dob/Sy9BX1wM1ysWsO2ePWkyjB2ao0QhIfB78gkoxo/HnwHZGz9ZvhhGHZnaOAKHiK4Ji5di37f2G87aQ+HpheUffW01QFJn1KJxVwH4UiFkcV4QuIqhPFwCXZESZp213VYc6gKvm2PRfKYCjXuLYFZ3T7c4DfaFYlQAsy9KI1whcOVI4lbCrWF7PpTHSwFDDwgpWu1e5sHTdtoEPd4ApwqbJhRDJeajyWTGqxruO101OxQD/F3Q/GVyy8vw0BBwCGq3TPikLwbfZLGh+tyTiObT5dD5FkMrLIJof1+IA5zhOiMcQg8pTuXXYsX3Z6CQCBEf5Aq1zog5A/1Z62uMnwtCPO3bZ2gfUt6ogZ+LFAezqtj9TQYT8n7NhZuLBDPujG9rkyULqK5EiWyeESdLG/HCZs4mcn1iI66IWAOZLJQN2PT6ephMFjXc5EmW5HldURNe/eg4vgZHdg2GAGdaQswHQIBBELBUMU/wMDPUE+HjQiD0lEJ5tBRNpwtROekr1PNO4L1z9yC5OtrqvVwzJAivXDEAaWWNmPeh9W/50CMTEezRCwsRqYN2PwsceY++7IDCFwgbA1D5zozXYOAbceBggt2H+vtfDZHQBQ2N5+HpPhYisxAlhd/CtaQQfbObwUtcDGRsA1Tdq9DAFwGTnwGGrwCElu+wA5cPZBc9feYqNDWlYOSI3awUgI7TrW11RHJ0ZtUcOvQPuDjHdfP8OpSUcARScPAtnd6PnSfUZeHChfnsXPHcuRlQNdtOhLaC2j7d3MsgkzVBImlGXu5gTJ8+g5Ezp06dYufnRCCdO3eO3Z9sXPS+7D8XD4GBgUxp5uNDymo+4uLi2HJSm9EyavV79OCj2Jq7DbcNWIaVg1diyyfHkX+BOyf9MfF5NElrcfq606xFj17ryl+vRI4mBy+OfBEL+iywfc8mMz5/YB8MGiBuig/GX2W9LSmo/ujRo6zNj5oJc3JyWIZVbW0tI8R6Cqm0EUOH/dGj+zZX9oFJJ4fcLwVmswBZ6z+wur3a5wjM7TLdiKCbOHHin85gI9Jdq62Es3MsI1rpiJCd+TaKC/Zj0IiX2fGBGlxpPztk8K9WKk+lMhMnTnI5fb6+c+HjPBLqHSsRWmKtSjaFjgJ/yTZoCvdButr282hFYaAUWZHcGGCi75vg/7qk0/sWBMlQ6yaCScDD4Au2558MfWcDGVu63gAeEcDkZ4GYudSOgh6juQa4uBEQSoENt3d930fyAKfLqyp2wAEHbOEgy/6DZBlBV1xMbBZyps+ASuoFgUkPnUiB4hufw7Cr+qJCUoisuiy8cvxVuGi80CCtAnXDd6bs6g2ItDj0SxayM4sh0vZ+1psIIt+mcLhoL8/sDJExi14aCbmrGPxLsGo2VKnx4zPHIHeTwCNAgQk39oWzh3VWTVl2PVMYxY4NgKglM0bdpINea8Tuum2sarwjmeXXGIG5aXdBYO6+3GBD//dQ7tJuBpEGu52cy0S4RkBr1DJCilRjDboGeMu8IRaIkVKdAiM7cekdYj1jUa+ph1KvZEq1jqSd3mRNuAzyGYQXR7+IYOdguyddeUnV2L06FTqNkSm0yC7VPkesI2asiMNpvQYvb70INycxCmutSdZYfxdMifVlg+UP91m3tREmBUgQu/MXBNeVIL4mF2onZyTd+jCKqpQYtO17RDRyrW8mHh/81rqlFph4Quwf3zuF5oSV8YhWqNB05ChkU2ZB5uWMN3dkYPfOPITrgCnxOoRmnELKmXSMLOfy/bqC+02L4fv44yjV6uFr0EEpEKKovAKHft+Et/smQCm3TwYvzE7FU6U5EHq4o/qjj9myUi8fvHrzHUiJsrRCEYamnscbH75u8xzF02bgzPAxONioxvwDO/Ht7IXIDbJUml/l644SrQ7H6pvZoPy+Nasx/9Bupi7jm0wQurvDffFiyAbGQxobC7PBgPxrroWhjNvm0UePQOhhfcJnUqlQ/elnEAUGwnnSRAi9vbvdRtQweWHnVrj6+MNb7w+vEdEQuVvvf5ryKnHize8R4hQDJyFnLW82NOBczR6UqKxVDZOWrED08NE4/ttaKOtqWNsVH3zc8sSHcAn3hcBFApPWCNXZCtT/0fMW1x6BB3guioUmsxbN7QLo2yDgQRrtDp5UAPX5qu6fTsRndsuOy1znRqDMXYzPD+fh1wxOjTgn1hcDvZ3heaAUwyBELoxYyldB34vGRXfw0MwzQGcWYHHMz5jkdxb9Ur+E9soLqKzaBn//hTDoG9oUB0KhC+LiPoCnB9cuNuSl3ahW2m9+bQ+5WIDXr4rHnPgAbE8px4O/nEdzB3KS4OsiwcFHJjJFWHsczqrGoq9OWC27ps96TA/rnPAltU542D1Qqwvh738FeEYhyledgbFey0jNY2dKcA5GXAUxfBJ92W/CdW4kyuvXISd3FYQCZ0hlgairI9uZuY3LMpoFGDP6OOQy699CdfU+SKUBEEqiWPHIsHAPjIjowbGxqQKoSKbAReCDQYDSWm3ahrCxSOvvjbKmg1aLnZ0HYNjQDZYFqeuBXzsnR6wgcSUJC3fZI5LLEhI7WrH/LhiNGqjVShw9epZlI9G5bSs8PT1hNueif5y1WnnC+FTo9Vxj34ULF5CVlcUsjQsWLGhrA+wMXBvnMfZH5BaB2vgIQiH3OzYYrMlRauojEopskkQi+fn5sXNxImuISGqvXOoOtM47d+5kLXwJCQmMJGtdj65w07ab2CTmW+PfwvSw6TizPR/HN+RaOSIelT6KRdcuwoFDB7AyeyVMfBOmFU3D1VOuZtsyIiKCvRYRkd99sRb6VM7aqJXUYPCVPuz90DmQUqnEW2+91YN3Y4ZcXgeVypWRW61wcxPBw/MIysv6oH/cXkillkkzUiTW1Z9EdbV1DqxR54SsDdy5C1+ohlf/jai8cK2VrdLFXc4ys/r06dOjbXbJUFYCq/qRBJojkcZyhQOdwWBoQmNjEtzzc8DbyCklO6JuwfMQRs3AyZOz25aFFaoQmW+bsZcXIkOzTIC4DPuRHXR07Gx0QAUhZh4QXniJMR6PFQHHPgTEcmDkPba2TSJ9974AHLaTw9aK2PkcgZbEOQgY3EKAO44Cks6LixxwwIE/DwdZ9h8ly1qhLylB9uQpVstc5s2FbOBANG7aDM2M0cgdF8EIsmj36F5VXncHsv2dS07HyU84RRhfwMP4G/qyFkJSiv0ZnBy8DsPOL4RQKMCExX1Y29/vr3IzjrGj/ak+ERcPl7JBSCvCB3ph1h3xrAX0l1dOQdWgY4HrhKlL+8PNx6mN+Pr9re5b2IjcWf7uOGYJPb+by2SjJj1SzHmFOLPWv46olZWjQVqJCuc8DCqeDrHJfkA0QeYigrqRI6FMfAO8YiUQQYKqTBVMOqBeUY6zfruR6X2qTcX14JAH2yyNep0R6kYd++/kIkbOmUqc2pIP1xAxqrJVMGhMMAuMODLhO6SoOs+J6C3en/g+JobY1mYTIVZTomQWhbM7rGvCW0GiBx0N4j2lCAt3ReQgH2TxDVj6LZfNRSAlU7+6QkTXFeGiRyiWpm5GsmckfukzEVqhhN3u31yDOxZPRGKIB9S//Qqnj3pyktqyDjIZ+32ojlsyVC72XYQyf2upf5MqD85O4XCvS0edu4V4KgvR4W7DSdT9xAU9S/r2ZUSXsakR6eED4bRiEQQlthl+1VJXPDz2TtRIXbBx0+Nty73uvhued96Bueeycaax90HLzs1K6EQiaMXdKzkCqipw1Z6t8Gyow94ho3BswCDoOgTTvxQdiItKNTxFQjwe4c8NBAxGrK+sw2xvNwh27UTlm2/B48Yb4LF0aacz1Lr8fAgDAsDvYfB9R2hVKhRfTEZzfT1S9+9GaeZFeEuDMcn/Bpv7KsYEQpvfAH1x59l2BpMeJ6q2QBSlwKRhS6DcUwShtwyyeG8I3aUo3ZcEWU3nv1d7cJ0VDnGIM1OTmRp1qNuQDc1FizVGGuMBWYwn1KnVMCr1cBrozRRPipEBzJppbNaj/K3T3avPeGCKNrJ5ioMU4DuJoDxWClVqNTJG+CBTxscUZye4nqxEtYyPl2vqcKSqCS5SIRo1nT/3OJkUB9WW2XwfZwkqm7onsdpDyNfjsykPolrtgfyGENRq3eAlq0G8VxqEHVrqPD3GQSbvjxlf9YO5i/D3jnCWCNGk7XobkaCMlGbKLu6X4J2MpXHfw8s1CCpVNkQiD+j1tZBI/KDV2i8eoHbE+AGfwNk5hl3XFjai9qd0uM4OR63HLlRWbmUqHxr8dUa+1defbLuuUMRAq62A2ayHWOwNlSoXQqEzRgzfBYmEI43Tzq5AWf1uOMnCMFh+E2cVkvsACh8ug8eoBUq546ENoqehuXgPSvylcFEa4NxkwPGh3ARZUIkaocUamNyCIKso4D4BygpTE/HRCVkqlAGz3wL6XwnQpAkN6Eg1plMBumbAyfOvz/T5C1FVVcXCqUmZROTI34lWe5tIJGIKJJlM1kZokNKJVFIKhWWihAgmIrhOnDiBI0es1YgdERFxCq5uFUzB1dzsBr3e/sTmyJEjMW3aNLYfr6ioYCotsgaWl5ezc2fKrMrNzWWEVWegxyxcuJC9D1J1EVqb8IjgIvylRI0dkBK+tXjqy2lfYrj/cNaMu/trTmFK+GzEfVisicXw/jpsOuGB7d4nIDKKMLdwLlNGdYSsORCKpsi267Vep2AUqtnnQp9fV6Cxx7x5Y6BWf8cmFGCeDBeX2xAdHc2IxZzc51FautbmcZMmZlts/WYj+76Q3ZaQfDANh9bYkuT+Ua6Yd29Cr2yifwoNJcA7HfIJE24EBlwFRExkOa12UZPDEf3tcHaAC4wCHvgmoMnTHUaj5Zju53cFy0xU5m+EjlqNeTyMzHGDtNx2MuvgCA/oxXwotCIINCoYhDyMOGMbS5AfJENOuBNbxyHn6uHa1O748cBFwCUARTmfoOrCK9BIBBiZxgeP1GGdIWoKcP3PgKBlkpwGKT9exVnHO4COgYa5b0IUPBbwaTe5+cfdwLnvucs0uTJ7FWc59+7H7W8dql0Hegk6P6HfjlDoZjfj+H8djb3giXhmLunzP4f/MllGqPrgQ1R/9FGnt7suvJK1yP2dKEqvxfZPkxnJNefugcg9V9UjAm1bzGeokBdAI2qG0MgNsg0CHXxkPvhy+pcIcwmzGpwf+jkTSe1IK5mLmBFI9tDVbQRXHxmaqjVMvXQ5MXJhJM4fyoUYUjTVajBzxQCEDeBIS7r+3RM9D8uVyIWQyIRM0aZtNvR4XQdMDELEdDkyGtIZcZrfmI+5EXOZioxmX8lG2RHr5q5DUVMRvk79GuXN5YjzjMNDQx9iarL2KEitQcbxcmSdsj1pE4r4MLQoXijgPtNHgOe2WE5W7eGx4r0Yf3prt+9JSPYLV1fosnqWIeZx663wXHKLlYrJrNcjf9EiqC8k48ygB6HWVmFw7jZINdVWCjSlkz/MPD6cm63zXzrCafhwqE5YK1mkQ4ci7OvVqNEYUVynxoBAVwj4PKi1OvyQmoWdGbnIEMtQ6dE1kR1RXIi4nAx4NdRh/Nnj+GXybGwZyzVfdQYhDxjsIseJBvsn8lI+D5qW71C4TIw3+wZjjPvfN4vJNS5WM/LHKZ77XOgwRHlih9daAqkJYYr+GO49p9vn1Jm00Cv0CLlmGExNeqhTa1iT45+B8+QQuEwMZqQVZZDxRAJGegnb2SUJpESj19Jk1UEcoIB8JEc2dgV6Pm1eA7NeEnlX+dF5GCpU4DuLIY12g3yEP3sunpAjJOg336jRw1UmwmcHc/HaNkte15/Bc3NjcctoLrQ/M/cLFOS9jiadAg8ceBkCngH3DfoUx8uG4EjpCLsk1Pkq22yjVgz0Tsbt8d/gYm0ffJm8CCoDpyq5f9DH0BrFGOybBB0C0X/gJqSUqvDUhhTUNutsyD6yZKt0Bgh4PLxy5QBmx3zujyO4WNH5oNDfVYK3p3yD5sZjbMwWEX4fwsNtlQzZOW+ioODTTp+nvT2zVRF2IWmZzf1aCTjSMZBFiTKlKJC9sOgrdAWKSEgUzIUs/QAOR1us7pF5zQjrmONjD/2vhHbWC8gt/NiuBc+lUY+h5zuxHbXHsj2AWyig8ObUa6Rq+JtUYyqVihEu9NdqwWu145WVlTFbm7e3N8LDw5lqic7pSCFF6iVSLhFpQdfT09NZ+xwRNwUFBYzIcHd3Z79Fug+RQpQPVVJSgsxMy3nJDTfcwBQ4PQGRR7QOPQXt14iAonWmdaIMq44EFJF19N5IpUTvo5Vo6gxk9yOyKykpCdXV1ew8NyoqitkaO7MwEih7it4/vU4rkdXda3UEkWFDhw5l237EiBFt5Ni/ARm1GbhqkyWUf8fCHQhQBEDVqMP3Tx2FQcdtmzUJL+GmkCJI+WZcqPXATr0aPiofjC0fC3lTJMx8PTTSSubKEGs94NxobaUmqORFaHbOa7tO2/Xaa69FcnIyhg8bAVc3sjiXIz//biiVXO5qK4iEp5y49rlv7REefi8iwrlGT2WdFhknyliucGSiN7RqA9Y8f8Im75VwxYOJCIj+cw6SHqMqA/ioi0zcgEHArLeAgARbu+KuZzjrOGHk3cDUF1BddwhVlTts9mGUn+jjM4P9jmhiQ6lMh4fHGPB5QmDdEk4Z28GWGRu7Cs6KWJw4ORsKRR8MNc4AvzIDqMuHsS4H+aPGIb95NwQCOQbGf4mLR6/BqFMt+94b1wHRU1FTcxDnL1jbOkV6E3gmM2KylPCq1XetFkuztdNWDp0GVXgicsq/sp8lSEPxjUSY/WD/eRMXAVOeB+SdnDOajL2zhrYQsUnJd0DZdBFDhvzGWpEd+G+gtHQdLqY/2nb9Uopl/utodJBl/32yrBXG+npkjrAfhOl970p43XEH/kl8dDvX4tcZGqXVWJPwMsy8zk/yPpj0ASYET7Baln2mEju+6F39ekdkDdqPB6+7DW4G707Jq4IJB1GZrcTQ4lnsulagQorfIQQ29EF4gicCVFFMWVVfwamDKEeNiMLWkPrOUFnQiPO7CqHTGtnJvNRJCO9QFxz5NavXxJ2LlxRuvnLoNQa7eWDUyiiSCHHij5w2i+TQOWHwHsWHXCSHi8QFR4qPwKs4Ehd2lLD3EjcuEPGTgiBViJB2uBR6jRFimRBFF2tRkllv11qp9BUjNUCIJ+bHoo8PR778fq4ED/16oe0+FHh/Rc5BNInlCPNwwgJfM2RpF6A5f/lUcARZQgIC3l4FcUAADDU1qFuzFk27d0McFoam7dshCgyAvqTrPDHFvHlQtoTztofz1Klo2rXLZrlS5oRFz7+DR1zFeNIkhbOAj2UXz+GqbRsg8vCAwM0V3/Gd8N5s65Yt79oaGAQCLN+wBmPPn4Jx2XL4HD8MzWlLG1UrBJ6eWPf5d3i/0to2S/gpPgKTPLl9HZ1cUtRVtV6PtWW1uM7fg1nufCUiiHk8NBtNUHSwr/0VINKqYVseDNVdDPxHyqGvVkGTVgOZwBnFqgzIha5MUdYeh2vXI0AUgQjngVbLK9QFSJOewqJX37EK5Nfk1KP6Cy5rqztQdpixSQd1MpfRRAoi57Fd25R6A/o8iABKL2vEibxazBsYAC9nCWQiAcvyigt0hVZvhEpnZCqpfRmV+O5oAcvqIluiPRuiPcyM82OZYDH+LrhheAikLSoDev0P92Zj1S4LSXDhmWngGTOQmfkCGhqsv2tagxgqgwzuUsv+xGQm3YUZy3a9b/O6iUEinCvuPicvwjUPTw5/p9tMpSPZ1cipUiLUU46xUV7tMhB1yM//GN8fOYmvU2+0ef4BXqlwVwRglPe3iHa3WK8GDVoLd7ehljs2lXNZW0TK8AWoqt0HJ6MMDemrkcE/ZR1l0+cFNkCj5rUTJ+eyYPVWyGRhiOm3jhEiZFcrLCQbpz+zcQ0bNgwlWfejrJELO+8NBHozxpyogXnCoxBJvYGy84BADGV4PPJKvoZ/LQ8e497B8YKHoFbbV/TyTWammnD2GAo4+wFpGwBnf0Ak49QK+YeBwEFcsyRlnV0m0HeNSK7Tp08zlRSpuEgpRUoaIqyIuBo4cCAjbyjXiQgGIseIcCDijC5bQsV71yJ6qWgl56gJkAgkst/169ePkVikQCPbI6m76Da674QJEzBq1Ci2ngR6LBFdGzZsYPejc06yIdL7uZygbXfXXXexbWkP69evZ3ZLslgWFxez+40ZM4adCxOJSETZt99awvzbg7Y/fU5kdyRy0svLi32XiZyjz5Tee+v7/bfhfOV5LN62uO36/Mj5eGnMS23XNUo9fn/rDMvT3dHnK+R5JnE3mAGZ3hnXJ16FkIv9UH6kdypjCAyA0bJNyNVA0R2EqBmfg+90AXxh74o+SFWWeqiUTeRufNf+eZGrt4zl9n7xwEGYDGYWR7L45VHs3O4vh7YJeKc/oGk5NtB+9JYtwGrb1nPudiEw600gaBiw+zkgu92507P1Vgq0zKyXUFT0NSvLGDFiF6SSLgpBDFrg9+Vsv0bKtDp3MWL6vYqAgGvYzWSpJxWvQNB5bAx9r4+fmAp9fQ5r2iX1rafHWLsTD+3hW6lFXHoTMiLlkGhNCCu2f35Dk62Hh7sxRVxn8PdbiJiY19k4ICf3HfCPfozw7C6ynP3igWHLgZR1nPKYtqe6hewjBdqCT4E+7T4LKrihz4wUyvSZ7XgCCBoKdcxkJKXcCaXSeiJ7+LCtUCisyzEc6Bmo4EWnq4VMdunnjvR4Uq67uMSzv56ClOtisQ/7HrGMy9ML0aS0xMH4+c5H//5v97pYhnIKg4N6Vg70/w0Osux/iCxrVco07d8PWfxAZtEsuOGGNsIgbC1nG/snQO1snz9nrbgh8G/LwsfJH4JvFjDZu7GltvrBwQ8i3DUc36Z9i1Pl1gMWAinMPpr8EUJcuPyIczsLcfR3S5ZVk6QGm2M+QbO4AUa+HosuPg15A2exqJWVYXPsx1CJbUkGwm0DboOH1AOelSHI26pBuuwM9oevgZlnIYV8mkKgFarRIKtCqEsok/n7ybmDOdlAiWSips7uVCUaPRFksMra0RqMaNYasf5kEdwL1Cg7bjlYBvZ1g8lohsJNgohEH/iGu0DiJGSWUzqhokH4jtRyPLKOOwGc2QTEGWXd2k2nLIlB8cU6RjySaq23MMKM3TI9ioQmNPLNMPIAd00jREYD6qXO0PGF8NQ0QisQoU99ERZ5qBFUlAHnC7af7aWA1GKha36Cct9+1Kxe3ZaXZQUiT3ox4Ar68AOoU1LAm78Ae6TOGPPD12j6ipsJDP7sU5j4fLzhF47k7buwaNt6iPUG9CnKY9aAF5auxIHBtgqceQd3YcqJw8wy+dP0eTjXLw5+1ZW4p64UYRoVBocEwGnwYBaCT1lgrdDm5KDq/Q/gPGUKFBMngC+Xs+8WkV5bquoZMbauvBahMgkmt5Bk/yRMZBFOroY2px76ChX0JZ3bI3sDsjx6rxiIguSz+O3VZ9kyAU+IMEUcKtT5CBmZiBl3PWD3d0ekLq2PNqcB0j7ukES4wqQ1oGlfMZwSvMGjLEIemCWzM9Q161DWoEG4lxyyluzCnkBvNKFZa4CzVITIJ7pXTfYGYZ5O2HDXaPx+tgTrz5VgcKg77p4UBS9F1yR9WmkjDmVVYcnocBj1xThxcg6Mxs6tRGRVHJT4A44dt9j+l+60kGXTYn3x5tUDmeLtbGEdrvy4c8WsSMDDttsaUZD7JMsHy8snZTT32ySbQEjwEoSH3233sY2Nyaiu3ou8/PfbiLsXjj+MoqYg3JPwORJ8Op84iY15C/4+c7kwfMqY6QDaw6tA+0sz+DBD6h2O44NdoVJZq8rc3Iajvv4EBAIvhId9i7S0DJw40YktsgUeqEMdzwUisQYmrRiRKEQlPKEUixEbewAuLtYB+g0NPnB1tR0omc0S8HhdW2XJ+mk26dHQyK1TmOttiMzLBCY9DbiHATpllzk4NHCkPyKD2oMUWkQA0e+LVFE6nY6dVxERRBlWRIARiUL3IWKMbr9coNckwqej3Y2IHCKOiMChRkF6zVaVVPuQeCKAWkkgWj9ShZEKKzY2lqnO3nnnHUaM/R0g4o0C6EmZRdZL2nZERF28eJGp4ug2CmEnsoreA70fUqMR+RUQEND2uI6fT29RVFTECEA6P6Y/ApG79Pp/NgD+nwB9Z+O/swwql8cvxz2JtirSLR8nIT+pui23jNCncigm5Szq9jU8YzfByScDqooY1Fy0ZGn1BJFzHkF4nzmorT3C7NftERJyG1xdEpCcchecnftj4MDVKE0Htn7cQuZ1gkHTQzHyikiu7bZeB5mz6NKJMlI0tZEtLdmKxac5coXys9rfj2yFFKSva7GfUxHIvA84wotuJ2U+KaM2ccq4LjHtJWDUPXbbeynvUiq1FFB1hdzc99hxwdt7BuIHdO626QxEUOXn2x4XWlWApLzqEmYz/Cu0iJZOhSh5I5ff1oKjQ92hlnV/zsDjCREZ+TCys19tWybSmWDmkzrYAGelAZFFOvCMvehppgmRGa8Bh9/mGj2pSbjAYuOu8BIjJca5jawUGEzMBks21gnjueOpAEKO7Pyb9gtGoxpZ2a+yYz0djymnUe4Ujri491nG578RnEWa2z5JSbejqnoX5PJouLkNRUXFJqZgpKILuVM0XN0Gw9VlIItl4HVSspeSci8qKjezywEB1zH1Ycf9MrUxNzeTCrmZqeNbIx+oOTokZDnOnV+MujrrUj46xxo39jQ3htA3ICn5dhb54Os7Bx4eo1Fatg4XLz7K1i005Dbk5LwFjZYTFCQMXA2hyA1qVQG7f2fr/v8NDrLsf4Qsax0ISsJd2+w6hOajR1F461JIoqMRsWmjXRtU3fpslpnjMj2MDUZ7c5JkNJlZVgyh9XGkhqKLdJ2ev/lUORp3FCBdY0SGhjtxHT4vnB3kNSYNhv803Oo5KYiVAlnb4+PzH+OTC5/YXYc/5v/BiDVShPGFfFycuAWbimylzzKdM3QCDYyC3rcDtobgk/qqPXm3OHYxa4Ns/QxIESOJdmMHFAoFJ5LQdVoopH2tQ52bNHo8+lsStiaXW7WjkfXoUFY1tAYLqdPPzxljo72wdEwEG4RGeMsZ5/P2rkycLqhFvUoPntmEQRI9ri+rxQjPvlBWZ8J8eBU7yJz1jkbdtQ+Bl9KLg2sL+EIem61sD+8QZxhkfJwqrINEZ0a62IhksRHXDA+GX3kB+m75Hv4FGRAbLsNAifJxDBrwxDKYddyMHU8ig3zKwwDfFyIfE8SBjXAaNhbaLCUkRIKEuDD/oVmnQ+kjj6Jpxw6bp+VJJDC3WGAMfAEOJQzFxLNchhnv0ccQMmkizrm44538Chyu54gesU6HrWf2ojEhEVfK7NfdB5eXgG82o8C/57NJv4R6YFxEiPXvqqnld7OTU4oIfZ1Y4LsszhPiYBfuZJTPa2v++ythNppgIhWiXNTl/bQFjaj54SJkMR5oPmk//6kj6rWV2FP2I/xk4Uj0nNwWyt8RPIUQTgO84TY7om3/VldWAhdvH1w8fABpB/YgceZcRA8bhcuFsgY1Rr7KqWEHBruhukmLknrLrPH9U/qA+kSI6B7XxxvuchEjufOrm7HuTDGmxPrgTEEdzhfVI6XEPjHfGwS4SjE73h/T+vvB3UnEvgJiIZ8prroDzS5mZj4PL69JcHUdhdWrv2WKn1mzJyAi3BOFha+hrt6S4UfIyxsNoWAS5s2bAbHYAMrzJqKhro7CqY9Dp6tDUU08zihdsGxcVFt7Y2VlJRv8h4VHMEUj5YhdLGvEsZwazEsIsEviJafcw2ZQ28PDYyy8PCdCoegHd/fhzCaSlvYIyis22Nge1dp6ljAkoDP8loY1OjlthUQ8ELW1N6O0sAD+dScQbMyDGHocxWCUonPFQjBKQEXL4vgEmIW2M7FZWcNZILf1+ojYsY+sbUTK9BR+/pmIjj4Bg0GEs2fmwNNTiMioX9Fb1NZMQ21dHNSqMri6pqCoKA4mk5ARTWQxpPMhyqBqBRFFRLwQ+UX/fX19kZeXx+x1dN5EnyWRTPR4+mx7C9oW9Jzx8fFMIUYkFZFbRNTQc9N2ovvQaxHxFRoaypRYtC50fyKw6D6khqL/RH5lZ2czgi4sLMzG/ke3t5Jk9Lz2SD97IDvmxo0b29aH1o2II3rvRNARWdWKqVOnsnXbv39/p89H74fyqFpbKlstk62kVK9A2U7ZewDvvpwi0DOKm/yhUHXaEQjFwOnVwPFPuIHx6PuAqMl/28CWgdZDVQOUnAXqC4CmMsA3Duh/BXf737Au7XPKCIeuPcTaxDti6ydJyLvAkWWfjbgfEoMMt5x+pcvnFkp4iJy3zOptNFf0Q/mZRdAr7Z8PEHh8Pcwm7thJjx13fV+EDnBDSeU7kEo84eU9BVJJAIRC6/14YVoNNr1vUeK3IjaiDGm5HHnkF+GCqbf2Z9bMS0JdAXDiM06tWpFiUYgRqFGX/srbkXW0LSkzUVkOmNqdTw68Hriicws7UzRte5Qjz9oXUNF3Y/orLBfscoCOEVXVu+HuNgIiUe9/ZxptOY4cGW21TCRyZ3bY4CBOrUgNoFyzJ5+ReeXlG6BSF6C+/jT0ei7LLDDwBrjKB8Clth71qEB69TdtzxcUtBiBAdeznEoifejYrGzOwKlT83u8nlK1EX2zlfCqs4xlzK7BMPFMMIeORLNcDJdTv4On7zn5T2f5xr5ToK/PYnmWhGp3ETRSAYLK2j0PZahd8x0QPhZ/FUjBdOiw9biwFZdKhF5OEo9swKQk55RbWvbZk8q94zlUT0DnOf36vgCpNIgRT3p9HUxmI7PDnr9gXbgzeNDPcHMb0m5dVEhKugO1dd037VJrcnjYSqSm3c+ux8a8wbZzds4buFT06fPsf0Zp5iDL/gfIMn2VChWrOOuMJNIVXssGtBFX6qQk1kxH8Hl2DXQFOhgq06DL3AbKCDU2CiCOXQABnYC1wGmwC952EeGXtAK8MDQKMwaFMrvQD8cLGDFDZM2jM/ph1GvcQFIq4kOjNzFFgZuTiA0qySqzYcVISD9LgVnDHRzppFFxYz84RblD4mQZeOc35LN8CQqu33LFFrhJ3CCw47evaK7A7sLdeO3ka3a3A6nBiLh67NBjbcsC5AEobba22E0KnoS9Rdy6TwmZgrcnvI1mfTNePP4itubZV338POdnRpa1QqfRQpNaCxLCUYC3sUHLsoa6QpGvFOUVSpwQm3Fap0UOTDaRyiTifwxSzIAY1TDhLjSjpJPg5aDmOkzXaTEBIoSKncG3k1+gLzoBzYWfAIManw64AtPdFZCTFcZdAtnRjVBK3XFy0IPgid1gNOvAh5DtsPlFGzG64CBEBjVO+cYiZ+wSTI4LA/b/hPqGWtSWV8OnuRbfxM5EkNiEuyqOwVBcDFM3IbdW7zUgHIayAm4Gsh0EvnGQ9JsHnsQFfCePttkas07F1BJ8afcnQdK+7pCPCmBnAAJ3PZTbt0N55AjUfCF+ue5mpLh54aOYEByvrMfqpDQcdXKFXNUMnUgMvahrUsju62k0WLrxZ3x0zc3s+vOfvY1hqRewZ9hoRBXlI7K4ENd8+B3q2s3CyPh83BPqg/tDfa0IarPRjPI3T7H2vS63n5cMPncOZHlflwu64iaYVAbwFSL2+qrzlVC3zMC3h+ct/SEOVECb3wh9cROaz1awfLCOoEZGcbAz+y8f6gd+pBPyzp7ClvfftPv6pBIbMftaePYJhcTJF80GDfzDA9nAlAbzuSWVCPT3Q2GDHgfylIj0UWBuvD9cpCK2z6lX6fDV4TyU1Knx+KwYeDtbEzMNai7rqzPQd42Ire+O5WP3xQrUqS6NWO8J3lgYj4QQN3x9JB8KiQCzBvjD3UmMLcmlzIZJBHm4lwJezlL2/ukvPz+fWarIstZq7aLtQuQDBWzTcY5IAroPkVr19WnQaLdAKLSeWTQaBThzeh6GDP0D/BaCiZCaOgG1NdaW156CWcil0jaCiK4TOUPLiAAh8oWOxUSIkAKolZQgO5hKXQhv7y0Qi/N79ZqnTl4Jsdgf8fHl4PHXs5wVsnIa9Bz5s23bd8jKIqXEnx+oe3kVICbW0ihZXR2Mi2nj2KDJ090VwwYNRFxEAORHXwdSf2f3MYGHrZiIZjghEalwGb4Y+rhrWKYUqXdouxDhQ9uCCKyiog0ICxuDvn3HMYKntrYAhw+/BLliL7SaYBhNfIjFRAI1Qq2WIi93ADw9i6DTyVi7Xm1toFXD3l8FWl8iz4gYJAKVCDAi3YhUIrse3UZkFy2jz/vfatnrDei90ftqv6+m32BrBhl93+iPbKV0ntmrQHua/SJSQlMPhIwCarI5q+yB1zlrW4c26h6Bnsc1CEhuZyOLvw4IHgokrwOKT3GW29z9gNgZoONq6EhAQ8S+meU6sWOzyAnov4Aj4Og6kWEiKVBwFGgo5lpYSa3SWRtrKyInAzf8Ygk+v8xo0Dawc7/DJdzA8Vj/+6FQ+AF9ptvkN1F22dePcPc7HLYOY/Kt4xAI/cZI0G+sGXLpIFYMoHVbAr3BfraYrskbdTkT4BWzBTyBAerKKPCEOsi8c2DSS1G09yloGn1sQvgppkMstd0e6fszsWet9Wu5CUpwg9fdHFlH5+sj7wKaq7nPgMgqIq/oHDDIMpi2i+psoDaHqjSB325j54aXDJEcuGEtEE77wR6CSkHKLrSE1VtPIv9b8p2odTQy8hHI5RE9fhwpfI4cHQet1o6roQVDh6zv1E5Hj9+7L9rGhku/RWVzJowGJYqKv0Nl5Za22/lGM0QGE1wCZ4DHF1lPOJnNCCrVIKBewlRDgrIU7jNvQbOXL86H6+Ber0dsVu+V/2bf/uAFDwfiFgIpv3PEasxcjvikMoJOQAQNqcTa2xPpusHQiJTU+xgR1VmcAMHVJRFDhqyzXhfKsdNVQCK2Ppdufzspr4RCRedEf20u4BFhRepT1ENN7SHWcE2fW1b2yygp+QmXgqCgmxi5VVZmve7dwdV1CBoauAK08PD7ENGSt0rq1HPne0ZUEUkWzrIPTTh/YSlqaw/hciAo6Cb07cM5PP6/w0GW/QfJMrPBAHVmLWT9vJmypPixzr/4Qp9K1H3+VJfPJwwaBuXo2yAyAa7NStRmb8GmQCE+X3A94nMy8PA3n2C33yDsCBuOynYHtxjwUQ0z/ExmxNcWIoVnQqaLH8IbyxCkrMHi+jKED7TNkvG9bxBrdTPUali4NzXDFaqLYDKbmEKsJy2cK3atwMlyS8OYPSTdlNS24yxV/l975wEmV1m2//tM79tLdrPphZBe6DVAFCnSRHq1AcKHND+xoCCC/FXEDwsqogioVEW6CKFDSAFCes9ms73OTp855/yv+z0zm90km2wgIdnJ88u112TamXPOe8r73u/9PE895m6ai1NGnYICdwGSehJLWpdgStkUONkZ7QXfo3i2sGkhdFPHrIpZqoooL7j1t7wDM5uwfnewOmRHaTSCIp2z6FE1S+F2WwJRpm4eTH8Jzi2rQlTXQRmqCBruNTwYrnGgaVM3WDMRhm0HNyeS3vg2Eh8+ZFXR4SxfLwzNhpQrBHc6AruvFEasFY7qWXCUT0Sm4QNk6reqHOoKwF48CkZ4M0zOJO8GXGNPhHP0cdAcXmjZSj90U9lcdqQ29a0yp7kZLqfBN7kUscUtMAcQNqp5HcoZNW3m7u+oH7T0I9z2+7vhSad6ZE0edYXnnoPU+g2wFxSg+hc/R0yz4c2OCFrSafhsNswI+THSt5WY8/JGdL+ypdiCs8oPW8CF5KqOfrer8vqZqrLiQMiFV/Gv92BO5dF6cQO6X9/+YGCXsEFVh9QOD6C1sxbtDZvx3pPbVvnKcfzlV0I3dCxdshS28iHYVN/QJ/F1zHRis16AsOnBEr2y3yqKAaeGSHqLsHxEeQZfHs2ZyDLAE8THXW7c+ZJVDOLdm4/D+pYonlm0HprdiaSu4fmPGxBPb/9Y4iE3vsDEcFsb2ju78W5mOIxsIfoiLYYOc/t5g6ZUh3D2rBqVL6y60ItCrwOJjIGRhQ7EoxEUFxcrAez555/vCS/bOtH2riTfpgDDMLRp059HMLhr5+bmzQdg3dpZcDpdmDVrlrpnvvrqq7s1lG4geDxhTJw0Fz5f/268pqaRWLP6EBhZx8ZACCGMMELKLaYHa1DfbV3H2SeYPn26chI5KODZbErw0PQ0/vOXO1ALq1PvRRzDsRmhkgZgzFAcWn0BfE9dgAj8KEFH/3IcQ1c4kGX44zfnWy6gXYSddptt2+8x1JHnCl1LFB3pYKJYyeMgJ2IxNJIJ4Hkc8bN8n59lf+i0005T7UwhlsIXRUy+TrdWziH3/vvvq+UxZJF5vCh+UcgbjGF6+xQcoD37LWDhFtdJ3sOqfgdtWxRjhzAf1ab3gfIDAX8J0LwCeOU2IDQEqJwMtK7Ggy3v4+f6Fjfzr5pacFws28/hBNXxP7RCCSd/uUes6y+HrjPQBH/hOswu+j9UtCZgNwGjZDRasUnd2FtqqmAbdxL8/jFYvaavG80Xy+CwBVbFxYxdU7mvchhwYp7re1hU2zfPZg6XLQaf1oHZh23GP9/akjDfb2vF7ILforw4Cm9pWZ/wue3iL7NyWR1wsrXPKBR+9Hdg1UtAeAcFiljUY9bllviRDFuOMzrNeN3i9av2XUtcVcU/KoBRx+wwlHt/hKIPHVG9K3jmmDnzMRQWzNzh93sXjRk//scYWr1t5W/VV+terMSzjRt/j/aOnRwPWTwJHQXhNFqLXaxXgYzT6r9QFBzRVQTMvQPQ05ZYNHo2ol47bIufgDOVhgYbGosMpB1avznZerC7gWsWWsIcw/15LForjkSyAW+/u0VYpbhFkas/QqGpmDXzCTXW6S0OqfxzNi/iib6FyYYPvwJjRt+ERKIBnZ3z1feZPuL9909GLL6+x5mWSrWgqOhQhAIHIrPwAVTOfwUaj/GSscBV7yJtxLFg4dmqYrYjbaC6IQFTA8JBJzoLHChvTcGVMtBR6ETUv2VMoWmcAPsaopFVqKo+F6Ulx1pjuUy4j9MxmWpFR/s7aG9/Cw2NT+5wdx5z9IdKRGxre63HXVZQMFPl14vFctv0eSXEDa2+EKWlsxGNrcO6dfcooXXIkLNQVrqlEFhX1wdYsLDv5EBpyXE44IA7sGbtXWo5dD4yN6tpZJBJtqIzuhglxccooXD1GqtYINNnjBr1LeQLIpbloVi28dHXUf+BiUJW29rJjDlP1MjTW2zpO+LJ2SfiqMWLUN62JdRi2YgxuOHa7yHh8eCID+djwmuLsXDc0Rg2ZTjOqEtjUpc14NDD9UiueBZ6/QLYikbCf8zNO/4xuwZkOxKBo6pVeNWuwO1KGSm0xFrwx4//iKdWWzP5OR456RElhG0PPZxEfEU7/DMq+oSs9ocKq3ptLSasi2Jq+45DGdMw4cy2yf9DHO+nuvCEaweJSbMzSjnRyeYvQ7r2XSQW/Xmbz2nuIGyhodBb+lZU6k3guONQfNGF0IJj0f5w/59LrnwWNl8ZnJwZoiDx7r3wzLgMNrc188KLZGLRgzBotzdNGCrcI9PXer+LcP1NJhfthb1iMoInXgN7YQi+GeXKLUVRi49bt01iVQeMaBq+6VtmaPVoGuE1HXivQMOTkQjeaOtGJq1jUoeOTpeGVSE7LlubxPkb00jYgVOP6WdmaStC0Shu+vdzaLbFMK52HSatW423ps7CD664Qb3/wG03wZtMYOrV1yG28B04CkIoPOdctD/wAHS6Z674BrxTt+0UMyxZD6eQXN2hxGL11xLvcZGthq4yNy3jY5kXE78wGs3dSRxUU4ihXWmsb4thQW0HSrrCCNS2w6XpGHvSdJQeOWLLb+g6Ip1hJDvjeGfhe0pcbG1pxajRo7B40Ufo6LY688MLqoC4Do/hwsGRkUhBR4cWQZcWRwppeDWn8j76gn4MHTkM/gYTaSbnz4blZqCjzexUudtcuol3Wp+Dzafj0NPOwkdvvo6mtatUXg3T4YKWTkEPhJAOFasy7h6vD+lgAapGjUXt5s39CjIfpKvwkV6NT0qJFkXCdCCKgVet682xzjUYYd++UKmbGuzZPIZLMpVYrZdipqMOAS0Jv5aCLVACR6QZXq9HOayY4JwV6z4t1dXVmDzZcg8ziXya+1Z/BA5nEhs3TEX10OWoqupbeTidngaX8yTE4+/B46U7qu95vHzZVzFt2lFKDMmFuxGu7wsvvKCS1n/lK19RogmPL4ozhOLK66+/roSZXLgdBRq+f//99/csn6IgRUs6c3g/pjBFNxxdZxRm+DqfU8zJVTfs7GR1v0KECuZh2DBrZrWr43oU1X6ERFcTxmEdQohgoTYV75nT+t1fE7ESZ+AlOJAVHE//HTDt/J4wvR2KPhQ0bt3ORETVdKB+xznKUD4ROOWXwLBDsoMQ2y5XKMtHWFmZE151kTo1EUVH+UCpDddiXdc6HDP0mN0n1jEUbf79wIij+ibD3hNEWixXT6wdeP/3ytXVZrOh0DCwzZFRNBIoHWs5iHgs0W32n+8D1TOt75IDTgEOPN1ynlEkYS4ilw/6iudhf+yi7P2aOTFsfcPfCPc7f4PhmqxYyDDOTb3CiPje0TcCz92gUiFslynnWJX3yicAJaOtgbESV3TA7rQcZ3SmMVfSyqzr5fhbgCOvtwbljUuA564H0jEgWGXlxeK55S0C4u1A66ot1RL74ZFQAD8t2TKJe1Ikirta+pkoYPjqab8FFv4Zv3nJSv7emxMO+TbGbxxAdW268E6/D/rY45FM1MOZTKN73RMofvaunX51beJQvNd9ATr1nadqODjwdxwUeAwY9wXgzN8DzFP15OWWG3Dr9ekdQjlQxsyxrlGFn8xNLGwLBQzm9fR4qlFWOgclpbNRUjzwoimcMKfoMpDrG/NNvfHmjJ7nDOVjYQOvdxhsdg/eeedotbz+KC09AZMn3bvdiZitiURWoa39dbQ0PAfPuvko7kyjqmnH0Q/bY/nYABrL3TBsQEE4g4krulWo5+pRfnQH+94LZh+7ArasmSEaXYdFbx6PDFPD7CD9CEWs6vo4RtVuEfUoXG+q8mBjjVdVL2UlU6duoqYuDm+yf/PD1oJ3f6Q8bkQmHInCkx6Gjde7HRGuB+h4ZRj95kVIdW9ApLwCbW2vK4Gv2jUDq5seUMUi7JUzcdBBTylRtHe4JPMb1tb+cfsFiwZAOLxYiY8dHe9hePWlKDZKrZD53sdcrnhHKgZc/mKPYzWd7kR044soiNmg8Z7JSYs8QMSyPBTLbvrOP/A4tszonAMXZsGO4a/dhfKJZ8NRNh5GrF2FsBEj1obof3qJVzYHXBO/BOhJpJZtKbfcHxuGVOPG//ku2gqL8fl3X8fcg45EKpeQ3jTx0O/+hKEfv9Lzee8h34S9dJwSXDSXNYjaGUVnj4N/ppX3IdUQhc1pQ4PNVDl5bnt2GZ5bvMXafMqBpahv7sKi1jSuOW4Mrp8zTv3GSxtewo2v34gvjfsSbjn0FlU1jtXgtqa3E89R5lViRXB2DQKHDsHmVAZzV7fg/rfWqYIDZnsCD8HP4MRtlrPETOFhPYYv2n1Y1fgBzPZ1KIp3qeoyxXYHZna8BmOZVYEkPuHzCDjt0D9+SQ2YuH+c1bNgpKLQW1ZAbxpYpb5dJTV0JAqqT1JuMXvRCNiKRiFT+7YKxdVbV6n2oaPLOfwotQ6pNf+1HGO5pK394fTBUX4gbP5ypFZZnWD3pLNhL58IW7ASLR67cmYUrJsPR+k4FVLJzvOSgI6i5XNR6RqCsquOg2/aGBWitzV00nVnDFS4HNtJaGni5bYwrl1ei47MrhcjIC+/GsFmn4YxcRNPHODDax4DHxY58M1VSRSmTZzQmEYwA8QXPoBMrwGEbrPBZhgY+8brSKxKo+vZdVbI58GV8Iwr3u62ECOpo/2xlUgstTrxyaw/ahF0vIs0nkQa7BLsarBNqRbBaHsbxvr4/SQSySS6TTeeTU1U73uQRrWtC0PtnWgygqi0dcOjZdBm+NBsBNBm+hAxt01oX6DFMcbeCjcyKLbF4ICBQlsC7YYXUdOFsak2VdXJYNKurWGHPhdqQ7FhFwa1TNbeZXqw2DYaGxMeFcbWm9njy3D5oVVY8s4raKxdj6GjxuLZVVF0mV4EHAYOHVOGeCKJR9btmnuH21ukxeHV0jjQ3oRmM4BJhRmUFRX0hJRREGL4I6vh5TjuuOOUuER3GBNl814zEHhM56r8EYarnXzyyUpQ4mt8j8KR5QDsRn09k9qWI1QwDd3di7F5s1WsxeOp7Jld3B6HHfoKfL4tQmoOzjqu33DvoKh4RXGO+8HBDvJjFwMrrIS3vVmEiehECHF44EQG1WjEMNTDj5hK1K9gx27GxcCkL1kd1V2BYXL8zo+2EwJ+7HeBykmWY7dlBTDuxJ2HQu0KPId2kpB/MEC39in/PAWt8b6C8dwvz1XO7Rzru9bj9vduRzgVxvkHnI+jhh6lnOHL25bj9nlWVcOLh30eF5UfioVtSxCIdcId78KhE862wggp1HB/sbNPgal5uZXbi/mSRs+23DYMXaPA1L4OTS3L4KTrwaahas6dQLQFnatewIrO1Tg4kYStYrIVVjT6OEuUYPghlxmgy+ddwBMCjr3ZEsA46UVRqvY9lSh9rlPD/wSBr8VNXJPQoHX2dUIscblwXnUlKhx+/Hv27+CjKNa1yQpPo2A0gH0az8TRFm/D2KKxyj3/rbnWbP9vD7sNR7nKgRG9cjBxHV+8GRgyxRLhtj7GzazI1RsKvaxcuPIFS2w67ntWuBJzVw302s799bdzrFxmn4KkBjwYCmF2LI6xoz+PWj2GkzNWYacpgWE4JTQep5k++Cj08Vx/9jpg8aPWulJ860WDWYqnmqwBJ7m8/GJ4bf33e2IeG3zZnLs9UKxsWAx09W1XJTSe9msrOb7Lr/qEePhM6/jIsiJ+LF7psooLbI9Sx1qcc1YLcMgVAwtXZPuteMY69njcM9yxtwttzAnAtAus47/8gL73aWHQwjxr0egamEYKRUWH9an4mU6H0d7+Bhoa/9njTGLCebqthg/7OoqLdz3HK+/FXFZd3V/R3vo6StpTSLrtSHhsmLokjILuTz6hbtKxnCvCReGc10sKS7yfUnin45GTZkEHnLqGmBtoLnMr8Y0Vn4fXxTGyl0i2K9BJR/cdnaSfiolnWveWtnVWWPT61wcmavPaGm3Z9n1vkSqY9vFIHW0lffu1TPo/4QDL6bVN8Q27y+rzLPjTFtcgc16ySuqQqcCqF4HmZVaYf+9QbO73o26w9v2bzHudZfzJaiIGXZuB2ne2TJac+QfkAyKW5aFY9sHGDpzxu+1XGrv1nfvxq+lno91bgBPgwI9gqdzM9xSPteJvdW9jQrwJUzYvQ8rhwQ9PvBqfW/UaJjWtRaEGODs7cM/FV6Buzudx+73/gOf9R9X3N5dV4MJbf9mnc8QcT3PefxvXPrqtCyqHvfQAeI+4DtGXvo3AidvPU5Tj/sOK8Oq7m/A3BGB47Tg23qFcNjZDh8mcVZoNTj2N38y9GzWcoc2ytHgECr77PZxw8hFoDCdw2zPL8MISy5J//8WzcHShH5G36+EZV4j2v6+E3lmrwhLtZQfAHqqG5i2C0VWrHHG5yh7dMPEG0jgZrh7nHIXF7iFjEWlahJCRhKOxHnaYSGt2OLeetd0OYa8PobhVPp5VIaMuL4rj/Q+u55ePh8vMYGpL30ps6vunnoHkO2/C19EBBwcHHDCOOAAH1q2DZ3ck1u8PzYbASXeg6raTkFzXiei7DSrRvJkxsKolgkeHOfH4sL4X9ENbM+h0alhRYAmsTg14acpoHFAYwMb2GFqjSWxKp1GXSGN9JIH1LgPvdUTVcgN2GyL67gt7JRcWuHFVgRvpVApr1q2Fy18A138aUW0UocnWhQZbJxpsHSg2A2jTutGpReHQNKRgYPKq9Zg+6Sw0RwzEtCQ6tKh6tJs21BilmHT1MbDZbVh7zztIaGlERtqwvGE13BkHug0Pmk0fXtcL0bytj6APHpsBjccULdywI72Tz382sBdhnf9BLYGDHJtUiGSNvRN2GNikF6LBCGG8owVx04lCLa7Wu9Xwq79NRiHG25sRhxMR0420aUdh0K/CvdKZDJKaGw3hpCoa0pvxFUH8+5ojVL6xsoB7u+IpBZVcXiRWnrz92WWYu7IFx4wrg89lx9jyAFZsbECpI6FEv8cWt+NzM8bi/CPGYlNdHdauWaNC2uiomjFjBsaPH68cUp8ECmoMX+Q9hwnCP/jgAyW0MTeVCglV1QoLVSgbQy8pxG0dEhuPb1C5Mmpr70cisYPQme1gs3lUNcnRo2/s9zO6nsTadT9HwD8WVVXbOiz2OAylosNm7OetziEFBybRJHSzMokwq7KtfA4oHAHQ8frEV4BwLkzYBpx4h9UhXPxYX0cMv8vOHsWGf18DTDoT+OKvrTxLhDOl7PR9EhgO9qc5WzqVX33VEk0+CRQu2tdboVEUd5j3hwIP878w9J2/xQInfI/OGw7M2cllh5uTD7xXMWSdjp7ScdbzrYWO3QVzIzEBOF1YvC5xH/L32G5cR/4uxTyuF8UBhm8EKhA10ygIVGFZ+3L8z6v/g6bYtiE3I7zluGnCJXhi0yuY27JVyP8ucFw0hku6ujGjVwh3jodDQdxfEMKodBq/aWpBUtPwUCiIPxT1FT9dhokvd3fj4YIt/UQKadd0dOKyrp1MIG3FWqcDpw/dkrjcaxj4XWMLZtgD0DUbksEKHOrdcv8/vOpw3HfCfTudXOT14aFlD+FnC3bcnyK3H3G7EtRe3vgyFjQtUHlXHz7p4W1ST3wmMNn4Q6db4Xy9oRONgzo6MjiwpOBMyiYAxSOxMbIZvjk/xkOrn8Cf67c4qo4eejTeqNuSQ/Dt895GyBXqX+hu/Bh45lpgs5Xfl3eZG9K3YHTbdLhsnfhq2WVburel4xA/5Sd4Z13f6oezxz0KG0NAew+Ae8NrwkX/ss7J7cFrGgs1jJ0D1M5DJjAcaU8lvIVB1cet+3ADXn5wNY47swRDZk3pk9v3E5GMWNcXhpd9ysqpgrA18fgmLFv+v6qPkkhY92ZvXEdVpAiZ8tEwNr2DkbUxbKjxwhfXUd246060T4o+7BDYa+ch4bKp89LT3bWl0muWTOEQxE+9A0ua70Msvlate2VTQoWp+idchDInK1UyG/7plmDN+yDFaMLJEz0JLHzQClnu7psbe0+wZHwATRUeODIGjnyvE3Ze26ZdaOWLZH+Kedc+S874AzDVyok+2BGxLA/FMvL6hjbc/I+P0NweUdX8enP02FJMrC5AgceJwg1hPLSiCUuzISgOrwNpKs+sWJnaSoRgAv50HBGXD1ceOxr2hI7oE0/jywseUm/H3V5867rvYdXw0TjsowW4475eqjOAV6YeiuM/2rYaiGl3IHDV1Uj8/QXlqNK7G+CdeTk0mwNmOg7Nac2EPIokzjJdSpigKy286gWsbVqMseygs9JR5UTEnB6csMnq7PSmzRPCRZ//vhLUejMeNvwJfUPvoq/fCaNjWzeGvWwCPLO+ohJl5tZJrX8qgsjz12OgsD0oYG0YPgqvn3MpDvzbP3BQ3bIdfufpUUfijeqpuHzpc3hl2Cy8OPzgbbaFup0/ElciW3/YTAPnrXgZ5656BY6tEudvjeP0L6F93RqEFvctTOA//DDoDPM78ywUnXcOtIyJjkdeQ3RBGwpOPggFXxihIvGeaelEJKOjLZ3B/21sRjw7I2RrTcDWGIdZ5IJe5gFcdiCegWN9BLZwClpCh7YD6zMxbRqMMg9Mrx2mzwG91A14rFxl1gdMhDrDKIpFMLarHv6mMJKmAxW2bni17MyWw4ECvx9mWQU2mDbYwl0INDei0Qih0QhinV6CWFYMJcc7V6HGbs3q0EFVb4SUGMrPUvw5yLlJuVSWZSpQZxQgqbxgUE6WNBwIaXEc4dyAYi2m3Fcp2JWbi2vcYXix3ijuyXOVg84vLpOhe+MdzSjVoghptOFn3/d4UFMzDOszBZi7MQmvA7h8ZjEOmTEVzyxrxyvz6zC/tf/krKMCboSjabRudSwMCzlxzOgCOO120HxZt7kJyc42+Fe9hWAmgmXBA7DONxLtrr2bgJfVH3993vR9Mz8S3SlMVMywKIocFGkYNhSssPJ2FPQNH+3qWoS16+5WZbwdjhAC/vFIZ7qQSXfBHxinEt9GIitV2MGOQicIwyaYx6qgYIbK+1FZeZoKqchkOmGz+WDn73+WbHjL6jTyukkXBwUehmbR0cNwRToqOHPKCnkUx7ifPmlIN0W1bSYnNCuZOcWrWIuVhJztwlCzj/5huUrYVr3zL876ijXzy7AvOjAoQqlE2VlnEp0ZDE/b2n1RO8/aJub36S//GMUkhp0xbwu3nTPGvI9x27mPWN2wdSX2CJyJZ6Uy5nPiccBZeYa6McxtytmWSMdjlzQttdaxoAaoe99qLzrj+H2G9THp/LrXrf3Tz/0krml4PBjAfI8b1RkdL/l9aM05z7cDxae7mlvxus+Lp4MDC4vvzYRkCsvd/TtHq9MZtDjsSGkaAiYQ2U2XjvJMBn9qbMN6pw02TyFmTPsqPIFyJN76JT5ItcFTNAIHVR0OzeFCbbILJ7dtPyfWzjhn/Dm4+eCbVZGj7lS3SjHxUctH+N+D/hcV/gq8uP5F3PTGTf1+f3tFjXpz06yb8PvFv1fLDrgC6vl/Nv4H44rG4bqZVqW0Pcrq/1quBrqfDrsaGHvCti40iq4F1UoM++YrWzngtsNfv/BXTC+fvvPf5kTi8zdaTsG3f4W3vB5sSE3CFxLNKHHWWqIdxS67Q4mSH3x4kbpeE4bVHXF4Vpxb/qxVOIHXGV7T6P4adpjl3sqJ8oKwH5FI1GPe+yer/Fzbg6GlnMQbs7oNmJetnPrdBmviii5HdY9cbxUf2TTPKjQy+UuWK5Mh4mtetr5z1Tyreivvo2/8zBLBe2GWjYf2jbfUvZnVUem68/vHQWMfbcOb1nnKPgoneHr1K9et/z+sX/8rVUBgypTfZyue7gLdTcAvelXGZlgjJ5hyXPikleuP/ZT1bwLr5lpuLQrr7/7aur9y4oCvsW/A/hHXmffkV27FJ0cDmG6Hk1l0i23d7zj5bmtikc5hXsu4j3JwAuPEO4HOTdb3mK+waak1IcZ+wrTz8ya1hIhleSiWLe6O4YuLVuM4p4nz422YNP1gPLmuHT97oteJuRv57vt/xVH1W8pHf/eUW3DHs7f1+cxXT/hfbA6UoSgRxriOTfjRvG3dZk3eQjWjWskQUc0OR+UUlUDee+SNKlSvdw6v+Fu/gN6249wRWlkZzJYtDrMfHno53q+0KlbeAA/OgEuJbqnl/0Z603uwBcphLxmH1Mptw3g+CWuG1GB4SwOcmYEP+lq8Bfj3uCORdrkwq245pjeuwrNTjsMbx56DG2ePQ2nAhURax+sb2rGitguvLN9xhanKEi8cLjvqGiIYUxlEdciDEyaU49SpVejK6Jhzz38xZtNGrC2owrF1H2JEuAEflo3F/MoJKrE/k1YeHVuKQ1uW4/EZc7C+qwharwIGpseOzFCfynCul7hxcNCH8WkNzy5vQrQjCaPYDSPkUiKYvTG+rQC72zFpOINGN9FWwlOOIlsCQ+zdiOl0ZNlQY+tEu+lTTqb1xi7eAHcRG4xtBLGtqXSlEHKamO5rR1mkDmYyDm8whJHjD4Dm8SqBjA6kyspKlJSUKAdSjkw6jUh7GxwuF/yFRYiHu5BpyKC5LY4FL61DWyqDCRPLcPR5k+DI5nyj0+rNFQ0oaV2JuS/NRXqtJY72hKdtRfUBE3H6TT+A0+NRN8JkRsez81Zi/rp2zJ4xBqmGDbj13S50xAcWAlsacKOq0INk2kDA48Bho0rUcb6uNYpxFUH43XZkdBMjSv2oCHpQ6HfC47DDad9JPqk9CYVfCiZ0znAmkZ0bupfo9mEFMAphyqFg7cMUnZNjAtAdmnopGMnAPu4UeFKa6qwlYxsR6ogg5bQh6bbaRbcx0BvoCjlUzg4KwKxM1R1wqOS7NpsbgcCBGDr0QlRWnIZIZDkymQgKCw/qu192EE4zt3auqhB3RPUR+MGhP0CRZyehXTmhiMIOO6wtKy0xhUm2KTzVHGyFobHjStcCRStVVnnPzRi32mx4z+vBpGQKcZuGdrsdR8T7yaG0u8mFTQyZBkz5stXZrZho7Q+KaexYMryB7iolOg21HhnqNpAKhr5SS7CjeJb7PDvMFPK4LAqMXPaSJy2BMdZqhXtSBGOoPAXAPUyr3YZ5Hg+qMxlMrpgJO48DOt30FFqdblxob8NmbefXguHpNO5sbsNk5ifkYGXk0fhqYgXmsaT0VlzencA1ceAFLYISbzlmFozBwmARplXOgGfW1zCvaYGqfljpr8SowlG4f/H9+PPS/h3u/cGCP3R0JfQEfvLeT1RBHzIiNAIhdwiLW7b0ewbKmMIxWNNphQYSVuh+bdNrOyxIRKFqVUffPINbMyw4TOViPerRo3peO3nUyUjpKRWKySqQdx51p9qmX3/wayWIfRLKveV45ORHcPeCu/HChhfwuxN+h0klkxB0BbdbpXxPcu8H9+IPi7eE+Vw44ULljrtr/l0ochdhdOFofG3K1zCxxEo7sEv8/XzLuZpjzo+BI1gxbtuk6ytWfl9V2XW7tq04vqupBoRdxzBMLHtzMxrWdanqoTNPHI5AkYiS+xoUmBsbn8Ky5d/uU81x4oE/h9dbs6WPQQcU8zEOfMGWmMYJnK3PNbq9WNmXucB4X2HeyQGEsPcn+LndQz55vzOTnRwLVu7eawIrE/9q+4VB+sDQc08hMPZzQNU0S3BkYZOtHed0tPNaTidsf7Bfw3v9fnJtC++CTqSZPNLzkHwTy25buBS/DVsd65r2JgztaMa7oydD60jCubILtq40TBugbUe3MPwOaEldJTj0+nRM0mqhtSewQavAlO4lCKS68GLZHPjMFNrt1r5yZ5L48bt/wmTGYG/F3KHT8bOZ5ykXlMNII9PL3l8VaVEhkx6edDvAcHgQOvkeS1nPJBF7714YW9lJGSppZi20PEiDJ/+qx/1V//INCEa7sbagEtcd/S083rUa3iVzYQ9WQe/YAKMndGcLcYZYmSa82cTivtmzEZs7t991pMj33cO/jkndG5Tr6bFhx0K32aGZVlYlCk/8/y3z/oJDG7fvIps7bhaMb1yO5lgXHN1dKAh3IJ1MIhK34sVzCbBzVcv4/4qyMjy7ohkvu4ei02XHOHccms+FSX43vqAlUOqxq7Avp9eHYGGRSpzN7zJZNhOBbzY0nL/Wcku53mpS9uLPCi9SiPdybpFh/hQSwwvRWeLHKDONSCSKYZ31GNfegEw6g3bdg4xpR9IZQNrmQWPGi8akAzFzWycBRbOtIvYGRNDjwOTqAvW4riWKcw8ehh8/27/zL+RxIJzYMqDjcOH7c8ahO23gicX1qG23Qmu3ptjvQnvUOr6m1hTi3INqcM6sGqRiUbTV1eL5X/8c4ZYtxTSGT5mOM2/+EUzDVAJZPNINQ9ex+v13kEkmMeGo2XjwpquR6N525q64aih0PYNwUxNGzToYTevWKFFtoBRWDlHi25hZh2Li7DnwBnacHymVMZSIFvQ4sak9pqpIMtSRnYxo0tpXzDfo3F5Os50RbbOqd3Emi4IEXS8zL7VmuXgN4A2cHQAKWeyccDaM5cpzHQLVYWm1Oi20p/MMpehBQZ7fycHrEjtgueTVFH0oEHEWc+tQoe1g8PjTgLST1WRt8DAhq25ukxCWzxZ43Gix23FAKoVR6W3FgYTbDjevy/y8ywdjxkWwe8uAZJc1u0hhigKNUtfqrJk95srha0qospxVa2ach7+Hl0NPdKEu0YZ5Rt/QsfPiGZzhrsar8c2oLBqNM2d9C1rDByo0yGxdjb8601jqcuDs7ggmJ1Pw7OkuwdCDYKZjqPOGVEe4ze1F2leC9R2rsDDZguf17RdXyHHJuHNQZmqofutetW+9homVLhf+GfQjYrNhQiqFc8IRVLBkPMMomK+HYhZdVzyWFvYjsDi8ffN47CIfu1z4VXEhinQdBb5yHBocgcPqlsLP3x91rNWpH3bolgED9zOPw9bVVj6obKGVAcHjg24xngc8XyhkUeDlcbLsX30/y/xbPC/oWqbYRkcM76M8jthxZljrxrexuu49bEi2YXNRFX6HMGKmdQ/3O/34zfG/wb/W/Ev97YxxoVFYFV6HI4IjcVfFcShw+KyBBB04Lh/WdKxRgszsmtmoCdaoStXtsVYUU0TMhe4N0KnD6tWXvnhpz3PmQSv3lavcoywE9PxZz6tK1z9650fqdy468CKMKthSWIiC0+MrH8e44nE4dMihPa+zSje/8881O8/vujVfm/w1/M+M/+kRrhkKGUlH8H7D+6rAAfnmtG9iWvk09dvRTBRvbx5YdTuK318ev/0QahZQOPzvh6ttKnQXKrca98WP3/txz2c8do8SvximORAY4vitmd+CDTYcU3NMnzxz/bGucx3+74P/U9v8rRnfwqTSSdv9XCwdg8vugl1V+dZUdeRn1z2L77/9/R7x8pbDbsFBlbuWyHqn0AH63m+sMMWJp+/eZQufCvaDtGwi949e2YS3Ht8yeV4+PIizb97Nx4Kw22A1Sjrlg8FJ+2ZUwGCEDrr7jrTcr3TbHX6N1afh/Z8TaOwTC58YEcvyUCyb/+67uH5jK1ZXbL+CTSARQ8RDa6sJLZKGGXQqZaGiqw1nfNjLYrkTKEQwX5LDzMAZC2PEmk2YtWJLcuu6QBmumn0DDLuGY51rMcYbh5nOoKszgnnOcagPViIV1fCvf38H7gGE3KS8hXDFrUp9OTYceibWIwxv1Rk4eMN8uOo/gHPUbJVcvt6jodZvw5R5z0Ff8hTSdgdSHh/80Z0n2f7RV/4Hb4yZAS1twtOWgLE5ii+ufBPHb1qoHHIM6xwSbUNJIoz7J52qHGHNO0iyaveYKLInlNHCH+nGlfP/iZquJtQPGYIVkybA8H/GYVG94HD3g5qxWB+sQHyTjlhVCFosoxxk01rWYknUKqxAQxTzZugVXhQUZtDhDMDd0A1baxI61Vezr+haaUbh4CRR2olqP48VG44Z4cf4igCOGleuKuJtaIthTacOl8OO6TVFGFay41xBrIrI3FO93VSkI5rC6qYwljWEcUCxE/FwGAfWFCPJ2f/NXZhUVYCaYp/Ka/Xc4nosrutCSjewuTOO5nBSCVpHjS1V+a++cfRIuBJdCBSXZkWlVgRGHIAT71uIkNeJ8qAbRT4X7r9kVs+Nvr4jhqbuJJavbMVxM6pQWeJXQlZHw2YES0rh8vrU7OerCzajvimCk48ajpLCvuGysXAXPnjxWSx87l9IJz75QHx3cPYP7kDVuANg6BnlVvOFtpO4/NPCGT/avrkPW1ZZAhSdORz80rLORKMMxWGuB7pnKAhxhnAgjpwelLxkiRsUQCiANX7Ub8iYCkVTOZeiloNqAPCwT7gtF6YzY6q/gULH2cteL75T0ndgeVZ3BF/t7MIijwefi8YGJErxCvpYMIA7S4uVs5LJx7/R2YWAYcBhAr8tKsB//b5dDi1rzuZ62xq/YeB7Hd04JWVDTDOx0OcHA5MnwwX71POtEERW58sx7HBLlKRoyUS8dONxX0841RKB2JmjYNn4McIjj8bfC4J4av3zOwwZ212w6MtR1UdhZMFIlHhL8NjKxzC6YDSOrDwEcz+6H4+0LIDP5UeVtwJuuxstiTZs7N6Io3w1uLp2ObT2DZa4RScXc4PkjiUeu3R5lY5FbdsK/Ky0FIs9brT3MyvB8LrzJ5yPtJFWQgLFmxfWv4A5w+eoMLLc9Sb3PsWV9kQ7lrUtU+tfHahW7kC6l3w7q7pFeJy/9ztrpp0hpr1zteVyOWUdAW/Xv632C4WlJW275lL/1exfKcErt/4UmOjS2iu5sfYQX/r3l7CywwphofhU5i1DQ7RBFSI4YdgJsGm2nvBIl82F7x36PZw2+rR+3VgMg1zbuRZTy6b2GVD+6eM/4Z5F96j/nzX2LLxT/476na2Zf8F8eHjd7AcKTmwHZy9Xwcr2lep4OmTIIWp9yeqO1UhkEsqtxXDPgXLGmDOUgNW7mumS1iUIJ8N4re41/H2FVYSkNzxueVzwuzxeOpOd+Nn8n2Fx644dfHcddRdOGnXSgNdNGDxEOpLYtJzFF0xsXtWJspog6lZ2YMPiViWKlQ0PYekb2+btrBpbiOIqPw47Y7RymwmCIHxSRCzLQ7Hszb//FW+9/hqeP+gErB5lhR2SaRtW4rCVi2BPJWDa7fho5IHYVFyBomgY02pXIdjehFHDh2PCMcejq6NduVpMBxPzetDU0orNm60bEp1N7DwbuaokvXCmUqhsaITdlYJ+cBQebxQFBU0wTQ0b1s9AMumHxxOBP9CO4uLN6EyF8GLLGfjf3/4R7mzlt4Hy5ZNuhbfIjeZo3xvhwbBjRKEH/0gnYYtmEKrU8ffffwe2fgacG4IV+OZxNyCYiiHm8KiKhnSCbQ/mxtLoDGEoIsPYkjrMQhfMEhf8zd2o6W5BSTqikpSXa92otoURgxN+battM01odLiYJkzXlg6tLRaBI9IJW8pys2i6DlsmBd3t60mgbnh8MFxuNZjh/yk2GFyGpsEWj8Lw+tVyHdlE/nS9aXoGtmQChtO1ZTlub79JXTOaDfasK475ucKmB0NtnbBpJvz+ACYdOAFDhgxR7jaeP/F4HOF4GoUlZXCOHIYhfh+Ge7cIgKZhoG75EtQuXYxYZycSsSjKaobDGwrBV8BOTY1KfJ9JpaCn0ygeWgOXx4tYVyciHe1o3bQRnY31iHd3w+X1orBiiHJG6ZkMupob0bx+LTRWytE09dme/Wl3wOFywuXzKwEqGY3CEwzB6XYrMYvLL60ZjqLKKhW62LqpFs0bti2YYHc6MeuUMxAsKUO0s0N9htvE3+Qy491h9ZlQaTma1q9Bd+uW8F+7w6HW0+33w+nxomLkGJiGjuLqGoRKy9Q61C79GPUr+7rX6CSrGjcBU044Ef+45SZ0NW8/5NbhciOT2hLmNuagQzHn69dg9by31b5NJ6xjqX7VcrRt3oSCskqU1gxT56/HH8CQseORSiQwZGgF4uFO+JL10LobrVkpDrhzVmvmRuDgmg4X5iigK4tuFTpx+Hm6YVj1j5W+gkMs5w3D9ZjXiJ/noIx/PuY6+sj67ieFLi/mvOJ6bZWTYneTu2rkhqsdIQeayt1wZEy0lriUUKZnQ1qJI22goiUJf0xHPBhU76W1NMaOuwXewChg4V9grv4PMtMvwlyniRs2W9Vid8TYVEo5zzrtdoxOpfCFaAxfCkfwSEEQdQ4HinQDizxurNhBrqbenBvuhmFz4JikgaOSabxtS+Gu4iJscH168aJY13FSJIqTIzH8x+/DnwtDsPF6oyq8akqMOmnkSWpAzMH8uw3vqtApCicUAuio6S+8jc4lDr4ZYjelbIoSJIaFhqkQNAoLdPxwsP/AkgewIbyh33WkA4XOmTc3D3xyaEcwDO24YcfhzLFnwpeIwMvE+nRIsapepAFXvXJVn9C7HVHsKVbbsDXcPxTJKIjxfbqC+sOhOTA0OBSXTrwUbYk2dCQ68EHzB0pUYxjeYVWHqaqT/K0Z5TNUm1BcY8heY7RR/QaFOrqlnlj1xHbXnfuc7XfiiBOVg+nnC36uKk7TAcT2IWyPO468A2U85/OcFe0rcNmLlylh80eH/6hHbNoTLGhcgPHF41XoI9v249aPe3J3HT/seFx84MWYUTFjj/x2Z6KzJ4cZzz0eM3Xddbjspcu2+SyPjf939P9T7rH7P75/t68Lj1MKewxl5bVByC82LmnDs78emEDrcNrwpZtn4a3HVqNuxbaO48pRBUhE0zj63HGomVCMVDyD9oYoYuEURk4tFYfTTsikWHWa/U2rn70/wAnuSHsCwWJPj4tR2D7ppI4V7zagtCYIj9+BcFsCwSKPEqzzARHL8lAsowhAhwpDr1Kjx+O1xx5B98vPoGLkaIycNov1GbHqnXdRUFGJQ8/8MoqHDldhVb0vgBTDcs/D3UuQTnVANzQYRgSlJUeqJI3LV/wA7e1W9SHDcKG+fjJMoxxe3xKUlm4a8PpG4cfXtb9i7pXnqee/Oe18vFw8XRXycnQm8Y9/bHEnMLfWf2tm4enRR26b4H4HfP/9B3FE/ZZB9blf+CGSdpdyh20MVsDoL9cG81/ZrXFP67gK2DUTaYcTwXhUuazo0BvbXIfTmtbD57ArFw6PJwoRdpsNumEoF9TIESMwbsRweF0OJXLFWpoQKimFzW7HqvnvYf2HC5RgdNzFX0XZiFFIxWNK3KHI0r65Du//63Fk0ik4nC443G5UjhmH4ZOnqTBNun58Rbz5x1A6tEbd0Fj9z+l0IJVqQSYTR93yxTCRABxxtG2qRWPTk0h1OxFt9iLSxOpkJpK2YiWguT0epNMpFAUCSHZ3Ielww+n2KHHMnoqjeEg1KkeOxroPFqBt00aUjxyN0TMPgd3lREf9ZuWQirS1ItzajJS5DtE2E+lEGIauIVQThcOTgbcsAT1hR7zNA/+QGBJtbsRaveoxl+LDEwrAtLfD5jCQiTkQb2do7MBuWBTUUtnw1f4x4dQMlats72Ai5Eyi3B2B02YJz4GCEGomTcawyTNgD1VaiYzpiOxowqaP3lfOuqLSYjiMOAoL/TCjrXBG62G6AojqbmiJTvjNsJXDQCUozR68TC7PpMUMzws3AF3Z85PhVXQA0eWVqzK2D0LBKu3QEPXZVaUfGnPCISdsJtBe4FB5vuJuO5IeG1wpA8M3xdWhknbYoNs1FHWl4U4acKUNdJaE0F1aDN1gMYw0NM2JQFcEBeGUErroJm3xOmEW+/EPtwPzUg5sSlvHyDCnjgqniQO9OmaVTUEdKnD4kFkIeKrg94+GrsdVEn6PZ4gKMXA6C/qEErHqHAeNsUwM7fF2ZMwtjtr7P3c/5jXMU+JER3LH4YU7Y2LhWLSlwmjMVhekSGXCxBHOUtwz7kJ4ppxrHQs5mCckvBlmMoLG7jrE6xfh1tpnsDTdhWS2k3hCPIVzfSMx4/CbEKmahkc//hN+s8Iq7kI8pganZkN3tljM7oChd0dUHaHyDjE0jq6ugcJBPJ0wbfE23Pn+nWofULiZVTlLCTqE7/36w1+rfb4rsNIeBT06X7bHogsXoTHWqMLzts5FRYFqculk3DDrBuVkIwx1m/3Y7B0KYP1B0ZD7hcugU6g+Uq/cTHuC4aHhap2vnna1Emu2B9eB2yHixf7Fuq51+Pvyv6sQzDvn3Yna7tp+P+t1eNX5eOroU3HtjGvxy4W/xIctH2J4cDj+W/vfbT5PBx3Pmd9++FslAlOY/uLoL+48x6Kwx6Dw9MHLtWqQPGJyiarMSUGloMyHQNEni5TIpDngbsTGj1ux4eO+aSKcbrv6ra2hu2zElFKMnl6uBubRriQWvbQRi1/dNr1KDgo+mV75c+0OG6YePxRdLXHYbBpmfmEEvEEXfKGBTT7l6GqJ4dW/rkD96s4+6zd0QrGabIt2JnHA4UOU660/wYn7QM+YcLNa0y7A7zmcu68v29EYxar3mxDpTCLakVACJPvmNruG0TPK4Qu6MPSAIgyfXLJXxDM9Y6jxmd1uUyG5FD3ZXsl4Ru3/hjWdan+7vA4cdMpIxLpSat2LhlhmAoMpMVhcLBvSy+d2pw162kBHUwybV3Zg4UsbEQ9bpoNzvn8wSofuesGZfR2eUy21TOlioKDcB3+BSwmDbFMeU5H2pNof3Ecev1PtZ0/AiUhHAus+aEG4NYERU0qw8IUtJoUcMz4/DIedMQb5gIhleSiW6dFm1L9yAdwNq2DXXOj0JGEaaZUrx7BpKvRHp2PM5oBhJpF02VTSaIZL8iLiN0JIGl3IMBn1HroIut2V8HqHo7NzXs9rsboQwguq8ZfPn42PfVuSFdqSGfzxlm/Daeq46vybkXC6YZR4YOtMqkqIJvMe+eywtSRURcXeA+wKTxRh3Q1HNIXHn79FvX7J577bJ2TyiJI4OpI66jMuTDdrUYkw2g0fCm1xnH7i8QgGAhgzdqwK/6OIuDkaR6FmqtfpstvTGAZzdy1HPFEHDTa0ts1FwDUM/mgaYa0druIDEY+sRzxdj3hsIzJ6RCX61vXITqvmbY0y35ksDueGK5BGMuxC17oguuv98JYk4CtNwMjYYHMacHh1GBkNsWavEt74x/d5A3MXJlE+uV1pNZ8K04TdyHrhmLS/04GCZgNawAunPYVM2gN3xgmPs16970v6YM9EYdN1uDQ/7PAgEU/DpSfg1hMq3JifY/44xpXaDR0ZV0BF5cWdQdiNCJxGSvlgMroDjkwSDs3AovYqvNkyEgXOuMqFVuKOorrQRF0HBVsTDfEQNB4TjiSCziQmFjSjwJVQYljKsMNrzyCScWN1uBg+Rxo1BUn4zL75ovYnuPfT/iCSNZPU9UdvmK/CF2MsGMHrjps56TKIB0NIONJI2BIqqf3uhsf7/JgdaVNDWNfwTsSBqMH1G/h174qpV6i8QoQiAUUahsfVhmtVKBVFHgokt7xzi8oVtD3OHX+uCsvKQefQg0sfVGLMv9f+u+d7W1eyo7vj2JpjlduDwtuU0inK3aQGFSbQHYuiOdKCClel6kgHSzyqM8iOIf9SiYzqGHFw02+Hl65AhultJ08WxSgm2KYLiQ4S5hT624q/qUFtDiZZ/8aUb6gQLIaL0eHUnyhEJxJD1ijsnTD8hAHlPRoI3H90+vTn9qH7qz3Zjn+t/pdaR+ZAohhE59RBFQepNuT+oWOKbp7ccugmuvD5C9X27wwWUbjn2Hv6DY1jmz+79ll0p7uVi4yhfLlQSrq9rnj5CjTHmnHamNPUe0xevr3t4T2KDi+6eej4YU4q/jYF2IWNC1Vuqea4lQuR/9+RsFbiKVEuNIapUpjoncNLEPqD18Bvv/FtvFL7Sp/Xnzn9GeVGI/2di2k9rc4FhrTy+sbrh7Dn0HUDLRu70bC2C76gE8Mnl8LlsavwR3+RW4kRdNhQRCIcVP/3L8uxev6Oi0vx8/5Ct7qWc8BdWOHD6Bll8IXcaN3UrQQXCmCtdd1Y/1Grcno1b9y2T0Sx6bTrpveEUvL6RqFkZ8JQMpbGO0+txbK3Pl0Yf1GlT4kDLBjAbZ987NBtxC6uz/J3GvD63wZWwfiY88Zh1PRytY+47M6mGFbOa8TSt+p7xBnCn+A9u3xESAk9R35prBLxckJWd1sCBeVefPxaHeLdabU/uc+5zxLRDNrqulE5uhCjppeq/UVxg4Ie152CIYWjkqqAEpjozKN4xJBXipWpxMAnvSpGhlA1phCJWFpFiMS6kuhuT6h+CAUsb8AFt8+htsFX4FbiatmwvgYNQkFm49I2LHpxozoeuR1cp1CpRzkDKXxxmW8+thqZrHDqDTqRTujIcKLTGsLuFPZ5+Fvqu0ld9Yn4NX+BW+1/Hu9bw3a65I7D1f+5j+rXdKl9xv3v8TnVMUCxldvLNkjGMurYjnallBjL1yk08djnNlHYHTK6AIXlPpQMDfScXzm4/7heXEenh6ln+ppZ2N5sw46GmOrH0f3Gc4zwOV/n7/M3eZy4fA4ECt1qP3FduX5sp7WLtkTB9Mbm0FQfsZ86XzvevxqU8HbgkVWYPmcHRQIGESKW5aFY1vH0WSj6YNvZuZ2RsWuw8aZo9n0t7eTrVrJqpqbK2G3K4UE3Bwe6cY9dfcZuaHCmrQtYmXsyyuxjYHeFVGUNs3vzlqpgnhA0OhnYWdIzSPsLkKx9GRGvhoYKN8JuL95JHYex7Y2Y6F6ANQVV+Lr/txhiM3BkeTHGZNpxdtBAcboDDpsdsbb18KXaMN8+FH/c1IYOM4D3fFPw2xoPhpppDB8+HH98ZSn+8GG0p8rfj04ajYuOOqDvDU/XEYlE4HQ6lTDGxwHPmDCBcqwNhjeIVLKNmdzgjEWUW4MWuZgWQ6x7OaIdH8JtC0JzF0Lr3AxPeyP87hrYi0Yrd1Cy7SPYOxtgb9sILdqCjNOOpIPhS4A9Q+HIVEnCe7eRWncbEA6yM2GtLwUHOnHSTjsMhwN25ciywdQTcDmLkXE6kUw1q+W5UiYifjs6C5yI+eyqnQnFVbb/9gRTFoAob00pFw+PiYSLFSMAT8KAO2XAmTaUk6cnNQ/zOaUpUpmw27zQTTeihhMZF2C4dBi2NBxmWhWBoGjC5fIizaTozq0SouclzM9lGjD1NAw9jrTd2lfcf2xrNp/NoFRqiXPc/5bkNHD4aS6Hx4aePdcpnvMc5iOXGerOIOZ3qOp+nPlPFZZB85fBU3AgMoECVYnP7i6GvXm1OodNdwCO2kXA6v/CVvueOq4SJRVwdNRDD5YhOv4wpIqrEdObEU81IKHFYdMNJPUOpI2B5QTbJv+YuhlTuLYEpJBzOAqCU+AtnomWpgWwmTXQk8Xo7l4Cp9upOhRJYyGi6bVYlbBjVaQAeqoYTtOD17BtUZKtKU6VoDQ9BKv8SxBMF8Fm2NHlbt3mc1M7j8ZHhW8MeGumh2bhG9X/g1KjEnpWvGIHjh1edmbYyWrZGFYdK5fXrgRddpLWmiuwHB9gkn4wqo0RSETS6GqOq86P2+9AKq6rgceu4A254PE5VCeSHbhR08swZHShGhxwubnOH2dsmzeEkU5Zv8FOYK6Dxs8mYhkko2kESt1IOeIIOUOqU8fBWGdLXH2GTWh32ZAu6EZXOApvZwE2l69EgbcAw9PjVJVWbjM7rEaGnUVXTwwsO912h6bWU3VwDRPptAE/1z/gVOvj9NjR2RhT+5CdTHZo2SEtGx5UzgfCmVTu54qRBahb2a4GaZx55kx02+YIph5fozrO/J14hI5em9oHTpcd8Uha3bo4eGMHlZ1e3icoOl3zyjVY1Lyoz769btKNONRxDMpLShDtTKkOP7eBgxSuI2dt+ZvcnlCZV/0WZ7g5AOB+oMOA7c5Or7/EhcISnxrItm6KqHVjZ5/tFSrpmwORy8twH7hsagCzPbhcihIMYX297nXMqpiFCl8FVreswYHOaTiw8gC43E54A0601kXUuaTWLakrZwbDLeiCYJuq1Ay8VvNaZdfUvuJAb6+4DtIGYt0pdW6wndmObHuuN//PGXI14Ain1Hmm9oXBYb3lqOdAhdWceTxVji7AsAOLe1wIu7QeuqH2E/dH7vzmIwfyajCZ0NW6+ApcCBR71D7LF7h/m2JNmN84X4X8smBC7/xlwmfXDus/bEXtsjZLaGmIqmsYrzOb6BbajjDAgbkSDLbcchXV44vQuLbLcvVsPbDehVydO8PBc1YDxswox+FnjlHX9t0BrwFL36xX19IhYwrUuUehaN7T69S+4T4aqDjAe20ymlHXf95vthZYZp00Ao3rutT+HjK2UP2fouE22+q0KQFjX4SCFoUtXsPoICso86o+AIU6hsjujnY2soJQ7jq8L0Bhj2LP0PGFsDvtmP/s+j3+mzzGeb9inyInMPeG/TKejw1rrBQHnzWlNQEloIZb4sp9xr4V+0Tsu/CeGixyq37kF66YrK4f+RaqK2JZHopl8ZYPYfvLSWgu0NXAuLLTqYSqRNlQuJIpuOEHnH6YRgpGshPulm3tk4OdlMcN29DDVdU7beQxsJceiEz5bCQXvQKv+RFsxdWqbG7aH0LMaEcmskm575ylk+DsaoV99atw6ho0u8dKDs4wzWAFtES3lcCZ4UBMSO0vhdGyHFrLahXolA8YNhts2Xx0DDWleJZ0WYKpw3Rk86Gl9976QUOHrQwZ0wW7PQnDbiLudgAx5twJoj0zDAkzAHtRPRyuKAqMDqTsdiTMINLpADqTw6BpOjR3HAh2ocrzMRz2BAxqt6YL6eQQeB0NcJoJtc2BaAZVTdt2ciJ+lwrro2jUO6l7yu1G1GO51ygop11uOEwndKcNgTba802Eg050FjrRVmR1AtX3TajPs5rqDsm6/7SMHa6oE6bDDi1jg0uPIenPwB/PwAyWw1Z+IGw2F5Ld65DOdCFhbD9kbG9ipH0wMkEYqRI4PQyBKICmlwO2TmiOMOzmAUBiOjLxMuixMtjsHrh83UhG6AbMwDAcalaWg89oOIGw2YXVpQuxpnQhSmJVMDQdTt2DhCOKdaUf7nR9SiNDMWfVZXAaLrT7GlASrYY3s62jqtvVjuUV72LR0P/sdJlVXWORssfRGqjDYRtOx9SG2fgsUQIH86Yl9G069Hydr21vwCQMvGNNUYgdR4YtvBJ4AstcCzA6PhnTVn8eZuSzEQgoClHIYluq2eNeA7BcWBRFTl47lOODE2N2mzVQ6UyqASQHjOG2+G4b+HLf8LcZokWRUE/rPevGdaVbg4NFDr571jtD54glxqk/hiVRGOXkid9pddS5rS6bElZ5/KrZ8/akCg1LxTLbDOZ312CG+43iJZevUgVkB9ucuef2cL/Fu1MwsvYGkwLZLuxKCr08J/kb/D0eV9xH3IfcT9w+7ier/SwHAduwaIhPuRsouvF7TevCaK+PIJXUe4ReinTcT6ESjxLCOdjPDWgofFOADZZ6lEiohGa3XQmEXc2xHqGQxweFP77P7aSTgt/j9xc8v6GnzS3B1KbaSg3+svuA36NgQWE6J2Dyu/w/B+JM1s7f5TryszkHBK9d3C9scyXCFlvHc3tjTIkRdPpw+yiGsh1yYVY8ltKJjBJOedxz3W0OiseaWj/1mHXZjJlVoc4Fhpfti/mJKHrxOMsdI3S3MASKEyo5eP6ESr3KGaYmHHQDy95uUN/b3XDfXfyTw61j0bTOXTWZ0J1SA/rWzRGVhL+kyq/OEU4EtG+OqCT9bAs+Z/vz2sDvjzu4QokzRZV+dczsLXh+8bhePLdOObYIt3HU1FLl/FITAv1QUu3HcRdPQPnw7Y8jGbr2n/uXKtdUb3KTVDwHKDiMmVmurgE8nps3hrF5Zadynm0PTlbRtcRzZcLhrFruxvoPW9QEHCd06la0o6PRqshOgZDCv91hVw4uCvY8Rzkx1LQ+jHg0rRxbdHId8aWxO83lxt/c8HGrut6wD8ZuBCeueE3mMtUkH9PCpA10tcbVpB6PiR1B4ZW/OXZmOSpGFaC9PqruAVw/Xge4blz+sANLMHJaqbrmppMZFFb41TWP+5JOPN4bcg6ytvqIunax7XjM8ZH7TE2KdCbVMcd7I699dHn1dtrzt995ci2WvF7Xcx3jdYK5ubgMhmvyusxlMAQ40Z1W12E66Hidyjm3KAxSeOT1TU18hVxo2tCt7rfcrk9KbiJma7iOvOeyHarHFarrNM9NTs7xGkFxi98rqvKr442TbZ3NMdVWdHryuOJ1lcsh+SZ+7bdi2W9+8xv87Gc/Q2NjI6ZOnYp7770XBx988H4plqFrM7D838DIo4GyAyyhZ0fQ5cV8RXxkXiN/uVU+vm2NqoZnak7odh+cvIgxrI+JuVnFrv5DoGO9VZa2dZW1rFC19X75RKCwRrmq1GvFo5WjTLnLmCCcpev5OeZKYq+PM45NA0/UrfO81aAcbnTFUBRkwm2KG6HIrrkq9hR07PDPyY41++4Ol3L0GKau8i5FfZpy5XG9KcjQ1ZVw2xH32BD32mHj9iRd8Le6obfUIJyuQtr0ojE1Dt1GBcJ6OVxaHG6tG5WuFXBqSRimHQaccNmicGtRuG1RuLQYMqbVuTSVr05DyN4E3XTCprFGpY6gvQVlzl2bPdFNOyJ6KaIG8zEYallJI4jOTDViRoFa17TpgW46lJCowyou4NA4mLAh6OqEzUgg5OqA02nC5vbTroGMLajyddn9ftgLKlA6cZzK7bb59dewZnEMnY6xcPoDiMTd6GrpP/zJ6wPiVh8BrIfgcDuRThn93Jisoge9sXu64A42wFO8EdUTHkNZWwotJS5LVNvKcWdj/oNsLq2Uqoz46W4setoDuzOB8KaZsNnT0OxJeEvXwmbfPce2aTCfWQaaGYQNVs4JA60wNavjrSEADTxm3MphpsM6TylsaVoSsCehadtfFxbzYEgqhbBEZzUyiSD0ZAipcCWS4SrY7CkYuhPx1rHWeuwGujwteGry3Ug6sg0+AIodJZhdPgeHlR6OYqMCZrsLeoSuJc722lXISM7hwxk/ukQYksJOl3KrMWyktQNL4otw++ofoNBZhAJHIWaHPo8jtDloXhOFnmCHlSKGHYVVHrjdTnQ2x9G0rkt1VDg75/Zag59cnohknG4YR09IBTuzuTAJvq/+aOTkMZYdsPMzuZwxbj9DRqzwGX4u5ypix5udOnbC2SHl7B/f4+sM6+AjO1ScMeZAjNu8NRwIq3wlIVdPSAQ7ixyMVR9QpJbNXCF8jx11rg9dDBwIcQBhDVI1NUPJPCgMyWHHkr/LTilPw9wgWe0PlRvHOs7ooiLsHLMtOBjk+3zkIIT7lfuag2s+ct/xHGUHsXZJmxrMqRlPm6Y68ZbDJ4NAsRtDDyhGUYVPiQ+ZpKEe2ZHmenIAQ4cbc7fwNQ702ZHksdFWNzB3pBrM6IbaP3Ta9c69Q9Eit01cB6tDb6jONQcByg2laX3ag02vwlhsmhpE7CmUw4Tdvl49v1xel9x6sK1zxyQ/z4/yfe7bvYlykAWcyulIgYQOOx4rXDe1/zXr2MmJb7nzJJebhccFzwUO8HbvillukpyoweObx2/vHEqfBfztkuqAOi/oGhTBfMu5ytAqCpWHnzHGuo4MAF6jw61x5ULhYJMDUP5R/KBoqtyr3SmkYmlrksK0clhp2eOQ55WWXQ4FRN5neB3nZBCvbVx2bwH8k8BwOV6HKaJxcM+BOo+BEZNLrWtU2lDXWV6D+NuxcFq5rXgu8zv8DO9NNROK1P1mf4ND4I9f26zOFV7veC+gm7qsxtqHAz1W6DJTIrTbrkRuiju8Bu0ItgtDSnmdYshj+Yhtwxj7g0JQrv+wt+G6fPhyrUqcz23h/Zl9Cl5znV47yocF+3VC7024nvVrOtV6Vo4ssET5AZKTTra3/5W7vJGhsQwdzqg+E68PPC85OcDjhCHPvEaofgcju7qSqDmwWK0L/7hPmVPurcdWYcbnh6u+AavG7oui/2AlL8SyRx99FBdffDHuu+8+HHLIIbjnnnvw+OOPY+XKlSgvL9/vxLK21/6Jj55+H4WOenhsURjOELrSZdA95XA7U7AZKWQcIZgOP1xeJ0IFOoKJZepETeh+RCI2xDIhdCUKUNs9bqulG/DaIxjuWwqf2QSXI4207kSxcxNqKsLwFAagu0vRGq9Ad109yv0NcJkRpDpa4bN1wGlLWhX2mFza5oQebobOJNj+UjirJ0HLRIGDvgaUjgG6m4DQENW77J53p8qjlgqVIOrV0J5eDactAJ93BALu4Sj0jkNXYg06o0sQ3fAMStqtgYUzw/A/HUOat4gq7G60ZUUPhg3qHDSyKiU7LRlTCVYRrxMpDxCjaKWcVCa8cV2JIRSyVKiipsGT0JXg1RVyIBqtQevqExCpmwKPuwuuYBM0zUDGacLT6YUzxUBIXYlINi0N07QhZfqQZs7/QBRuVyfam2fByHi3K97sChS/SoJdsNF1ZKZR6a1FQ2I0HEwA5vKhtAxoaTRR3xJENB1U36Ek4tQS8Nva4bLF0ZGpVm3mtYUphcGg6KUZSvwK2NrgtseQtJejJVGNmHLfWDcPu92Ay6XBV+iG3eVWN5URE4tgmBykOtQMBjuj7Kx9WjjA5iCSs5W50CiGXOVCw9jxZAewd6JWdnR4g2LHlTOznY1RdDbF1QCZNx7OeKobWEO050bWWr8ZJROeRyZeAM2mQ0/5YWRc8BRtQjI8BEbKB1eoAf7KpUh1VyK84TAkOobB7o5k/3hu1MEZaFbiVypaiiTf94SR6qqCYTiViJSJF8HlrIYv6FODGBVW5s5Vn01Dz8SgOTLwFC+Fzd2OdKwMhiMDBzJIp9rgDK2CnvIi1jQBpmGHzRWHZksj0TEcya5qtf1GivudQgvFCSs81zoAOCsfg54KbOfY27ompIrFVd/XbBn1fz4aGRZhoJMm16nv26GwQt9CqnPI2WMl6jhsaO8Io7WxC5ppg12zIRpOwogw/JAOC4c1IHebaEu1oDZWCwfsiGhdqMcmRNzteLNz7jbHBhORM/cT8ywx9xMrp7E6IPNXMTwol1xd2BYebxzI8dxVAzsl7NlVR20wkhsA8jqQ67ByGykecrDySTuVzP+y7sPWrNhiCXM5kYjXIR63TAqcux5tvX/ZAeY1ZyDwO5xl5zIpWvbk8DFMNG1gNV5bVgDVLHeSz6Feo0tChQJyG7NhVdwfHJCocF+GGzL3Cq97aUukY9hlLoSSIh4HhRT6+Nt91kkNGre/77hvmZ+Fc2HMZaPcQdnjiYN0ulD4+xSuKE6r9eY22C2nD1/jZ5SDLDswofDK0EmVZ4YupWyeGgoLvJ5wm7k/c/tndwwW6GLggIX3GD5SbOB60h2g7i0MoczmM+J6cSY/lwuH8D3uJ0sQtwZMW68X25bCLLctd2/atKzdCjfOGEqMVueg267uU2p+UbUz3VmGqjxGUZDHX66XTgcGB0wczPMeyX2ihJiYJQTviN4J0LktubA8Cr2EYjvFksJyr9q+3P7mbyqXF8Ojk1bIDo8D/p/twuOd681zzu7kfuDxoClRktuayDpbGJKlxJmgS+Xasdx8liuE7geeY7ll8TWuH50cfK7uaHSPURjnc437wmttA91mOid96Dyzcjeyght/j8e9nq34tzW8bjDcjvuZwjmf53L/qNyP8cxeEYerxxeq/gndQgwbo6BmnefWfuEfrz/DJ5WqRxk8C4Ig7KdiGQWygw46CL/+9a/Vc1YirKmpwTXXXIPvfOc7+51YtvzpV/HqC3vntx1aAhmzfwu1kzmLkFFOo7Tphq7cKxaF9jpM8r2EQsdmJSrR6+LQ0nBqMbhLK5GxB9HcEUI44lKinkNLosixWQk8XBZdVQF7q3IugQ4cRxpu5m1xp9FRAsSKMugOaXAkaFn2ABknMhsmoLbhc3AhhahegqSZFRJ6iQc2ZwJ6MgCbI6ncRg5PWAlddM5QNKGAYuoUY3aPis9BqSubZ4Wdcg4UrNw4AWsmIeBUM5bsnLXUhmFnfhgzAqeLHVjOVNnhLCy1qiEOAHb42MmkQ4Gd3lwiSuUcSej40SvfRnmmCMNLxsBW5UFai8GpZWB3+qE5nCqpN7Np6QyZYR4pB11m1uBGDQyy+0VlhGFyVlNXlduYyyScDCOaiSKaisJpd6oqdRQ4fA4fhgSGwGVzqTwnfJ2JtXVDV8vmcvnI5fGfSt6dFWYSegIdiQ71OteN32WFttwff5/LYRJhViXkb3HZ/J00iymkI2pZ/K7dZrd+J60hVgt4Cu3wGgF4fU7VIadYF0/H0R2PIK1nkEyl4fRqCJS6GLOqEh3HEgm13HgygUQqiYyZhm7PwMkqjAjB5XDB43TD4/TA63WpBN+lvlKVHJ77h0ne+f9cUnRue267CV/nOha5i1RVvAz3ETRmzYPNcMBuONR+JD6bH7otg650J7oydPY5wH8pJGGaBopRjqBZoJas9hd0mDBgaKYyfzrcNoSNTrjhhdO0hAf1TzmeLLEyZkSRyehIGUmktTRSJh2POSdKNkmwaah9Q+FK/fWqCrl11bRdqRB4z+x7VN4lJmQXBEHYn6AAREGRl9sdVSSkE6F9cxQdTVF172fybOYDZJ+CeegoRPHexus1Rb4cOfdZvoouFD9rswnOmdtqV6HISLGUghzFcoqEVsiptezi6oAS3RgixtEUBenc73J/s90s4dGu3GQUN63JP68Ss1UeRU1T7UcBMV/bQRAEYV9i0ItlqVQKPp8PTzzxBE4//fSe1y+55BJ0dnbi6aef3uY7yWRS/fXeCRTX8kUsa9nUjXUftqhZ0HQkBqSi8Psy8LpT6O6ixdOuQvbo4opHM2jtKkAk6YPfp6twNT4WltgR8KfgLihCQZkbRQUJpKNxGHChrimAcKeJaLeJRJwJloGu9gzC7Z887npfwuXWkEqacDMtWdCE3WVXs6dOnxfJaArJ7jgyug0uN8NTWBXSB4fXg8rhfmUnzsRjiLd1wu71IR2LI9rcDlfAh6QZUKGIFLzYmWKnxwqJsqFpfZey3XLWelfLVe9JWIVtzhNz9vZqCEIPQWcQ5b5yVZ2PFQFrgjVKcGMlxYmlE2VPCYIgCJ9aeKxb2aFCoCgicsKS4lbFiJBywKmCDablbmc/jnngdlciekEQBGFwimX7ZBmb1tZWVcWwoqKiz+t8vmLFiu1+584778Stt96KfIXWe/7tbnLdgAP6eZ/hAcw9QycUwyJU+Fi2+hNt+7kwA5VQ2jRViIUKr7JpqjoZEz/WLm1Ttnz1PYPVazJIx5JIJJhjx0RphYaCUi8C5YVIJaGSFXL5uQS0DG0xdIZoWOETuVwwFKY4W8fX2LEJlnjVDCqdWwwHLB7iV52jIWMKBxwW0z8BFA3befhvb4aOL8K+CN1WV027CouaFil3Ex07dPvQybS1W4uPdIXxL+ciozcpF1FKB5JyoWma+ozf6cfUsqmoClSpMLlYOtZTOp7uqoZIg3IccbmJTEJVm8stO/e7OVcT/59zW/E3lNPKU2RVdDRSlvNMs9xiuceQK6R+N6knldONv0t3G9eL0H2mXGimrn6fDi/uDzrSuB4k51zjd/gen/Mx9xt8j64u9Zj7y7rlIqkI4npcOazUn5HC6o7V6nusSBdyh5TjjcsudBcqkSjnJiMqRMrQUeotVU46uuS4LXTDqbAePaWe09XGR+4jtqHH4VHbzu9RZOLncvu0I9mhlsX91md/2egetPYz92tXsquPyy0Hl0OnXm5bPXaPcrvl1ikHl6mOFbsTDs0Br9OrPps7jvi73M98zuON28nP8P+5fS8IgiAIewKGKTOJOP8EQRAEYSDsk2LZJ+Hmm2/G9ddfv42zTPh0MASreEjfw0SFEmat4hSp+NefWKQEo7PHbvf9XMW23iEBwp6HgsqVU6+UXb2PE3BtW7FxsEMRVRAEQRAEQRAEYV9nnxTLSktLYbfb0dTU1Od1Pq+srNzud9xut/oTBg8qaankZxAEQRAEQRAEQRAEYR9in7T0uFwuzJw5E6+88krPa0zwz+eHHXbYXl03QRAEQRAEQRAEQRAEIX/ZJ51lhCGVTOg/a9YsHHzwwbjnnnsQjUZx2WWX7e1VEwRBEARBEARBEARBEPKUfVYsO+ecc9DS0oJbbrkFjY2NmDZtGl588cVtkv4LgiAIgiAIgiAIgiAIwu5CM3uXM9tPS4IKgiAIgiAIgiAIgiAI+cuu6ET7ZM4yQRAEQRAEQRAEQRAEQdgbiFgmCIIgCIIgCIIgCIIgCFlELBMEQRAEQRAEQRAEQRCELCKWCYIgCIIgCIIgCIIgCEIWEcsEQRAEQRAEQRAEQRAEIYuIZYIgCIIgCIIgCIIgCIKQRcQyQRAEQRAEQRAEQRAEQcgiYpkgCIIgCIIgCIIgCIIgZBGxTBAEQRAEQRAEQRAEQRCyiFgmCIIgCIIgCIIgCIIgCFlELBMEQRAEQRAEQRAEQRCELCKWCYIgCIIgCIIgCIIgCEIWEcsEQRAEQRAEQRAEQRAEIYuIZYIgCIIgCIIgCIIgCIKQRcQyQRAEQRAEQRAEQRAEQcgiYpkgCIIgCIIgCIIgCIIgZBGxTBAEQRAEQRAEQRAEQRCyiFgmCIIgCIIgCIIgCIIgCFlELBMEQRAEQRAEQRAEQRCELCKWCYIgCIIgCIIgCIIgCEIWEcsEQRAEQRAEQRAEQRAEIYuIZYIgCIIgCIIgCIIgCIKQRcQyQRAEQRAEQRAEQRAEQcjiQJ5imqZ6DIfDe3tVBEEQBEEQBEEQBEEQhL1ITh/K6UX7pVjW3d2tHmtqavb2qgiCIAiCIAiCIAiCIAj7iF5UUFCww89o5kAktUGIYRior69HMBiEpmnIFxWU4t+mTZsQCoX29uoIuwlp1/xE2jV/kbbNT6Rd8xNp1/xF2jY/kXbNT6Rd85fwINMoKH9RKKuqqoLNZts/nWXc8KFDhyIf4UE4GA5EYdeQds1PpF3zF2nb/ETaNT+Rds1fpG3zE2nX/ETaNX8JDSKNYmeOshyS4F8QBEEQBEEQBEEQBEEQsohYJgiCIAiCIAiCIAiCIAhZRCwbRLjdbvzwhz9Uj0L+IO2an0i75i/StvmJtGt+Iu2av0jb5ifSrvmJtGv+4s5jjSJvE/wLgiAIgiAIgiAIgiAIwq4izjJBEARBEARBEARBEARByCJimSAIgiAIgiAIgiAIgiBkEbFMEARBEARBEARBEARBELKIWCYIgiAIgiAIgiAIgiAIWUQsEwRBEARBEARBEARBEIQsIpbtI7S0tKCzsxOGYajnuUdhcNPd3Y3eBWel+Gx+kEgk9vYqCHuItWvXqj+SyWRkP+cJq1evxs9//nOsXLlyb6+KsBtpbGxEfX094vG4ei59p/wh16ZCfiH31fxl48aNqKurU//XdX1vr46wG/tPDzzwADZv3rxf7lMRy/Yy6XQaV1xxBY4++micdNJJuPzyy9UFxmaTphns7fqNb3wDJ554Ik477TQ8+uij6nVN0/b2qgmfglQqheuuuw4XXHABLr74Yrz55puyP/OIV199FWPHjsWXvvQl9dzhcOztVRI+JbyffvOb38TkyZOxfPlyNTEl5M899rDDDsOpp56KL3zhC2oSQ/pO+dG2V155Jc4880x1n33vvfdkojFP+k/f/va38fWvfx3XX3891q1bt7dXSdiNPP300xg5ciSuvvpq9dxut8v+zQNh+8orr1T9p/fff19NTu2PiCKzF1mzZg0OOuggNdP929/+Voll7777Ln72s5/tzdUSPiV0CB533HFYsmQJrrnmGtXx+8EPfqA6B8Lg5V//+hfGjBmDDz/8EMcee6x6vPnmm/Hkk0/u7VUTdhO8FnPigoLKH//4R/WazIIPbu6++2589NFHeP311/GnP/0JRx55pHpdXL6DF85u8zzlbPff/vY3XHvttdi0aRO+853v7O1VEz4lHIwdcsghWLx4sRJB+cgJ5Vy/WJyDg5PHH39cCSkLFizA0KFD1QQy2/Wdd97Z26sm7CYopvDc5bU41y8Wd9nghmPXjz/+WBkD7rvvPsycOXO/7D+JWLYXeeGFFxAIBPDMM89g9uzZasZl+PDhKCgo2JurJXxKODBramrC73//e5x77rlKZPnud7+Le+65By+++KLs30EIw/Iefvhh5fycO3euEkFfeeUVuFwuNWATBje5Gz9DCMaNG4evfOUruO2229RMON1l+1vHIB9gm0WjUfzzn//EpZdeqjrxnIz6wx/+gLfeeku9JwxO2HFniB6FMjrL6D6iCBoMBvf2qgmfkrfffltddx977DFcddVVSuQ+44wz8MMf/hBLly5VzkG5Hg8uOLH45z//WfWb6N7mvXXevHnKMLBhw4a9vXrCpyQnYHd1dSkDyPTp0/GrX/1KGQXoLpPzdfDBNmtubsZLL72E//3f/1XtSqGb12Veh3Mh8vtL24pYthcvLK2trWoWjYIZocDS0dEBv9+PFStW7I1VE3YDbW1tKmZ/0qRJ6rnb7cYll1yiQvduuukmyXc1iMjdCNh5nzJlimrH3GxZWVmZ6gjk8lsJg5dceDQdZSeffDLOPvtsOJ1ONUAjsVhsL6+h8EnalLmsGOrDcPgbbrgBZ511Fh588EH1yAF4OByWHTtI3ducpKisrFTPGxoalAOpuLhYCaHC4O0X8xrMfnB1dbV6zsljhttSDOUjkXQWgwv2nw488EAlahOKKHSXFRUVqdB4YXCTE7Apfl544YXq3spx0O9+97ue9hYGD2xLXmM5juXfscceqyYuTj/9dNx111343Oc+h69+9av71bVYxLLPCM5mcxaUHbxcTo1p06YpdZYdeV5gRo8erYQVOpAYxsdkevuTcjtYbcdbhwaEQiHU1NT02JBzFx4OvHkzyb0u4QSDp10nTJiAW265RYUREIpk7ABSRKGzQRjc52zuGstBOB1HdJcxxJadPYrc/D87f8LgalcOyEpKSvD9739fuQbpBv33v/+tHhcuXIjbb79d7q+DsF15zaWIQrcg8wsOGzZMPX/uuedUOgs6V2SAtu/zxBNP4L///a8SO3P9Yt5bKYL2zgfK5wyxnT9/Pl5++WX1mvSL9/125WQFOfjgg1VxlaqqKvWcE1F0IfFee8QRR+zltRU+6Tmbg5PHHOPw3E0mkzj00EOVYMa0BxzbMhUCXxcGR7vmBDCfz6fGst/+9reVaEZXKPPSUc/gOPbee+/df8ayprBHefHFF82ysjJz2rRp5vDhw82xY8eav/jFL9R7uq6bCxcuNP/617+q15944gn1ekdHh/mTn/zELCkpMdPptLTQPsg///lPs6qqSrXR+vXr1Wu5tlq3bp15/PHHm1dccYUZiUR62prvX3bZZebRRx+9V9dd2LV2zWQyPe8bhtHz/+7ubnXevvfee7JLB2nb8rzMkUgkVHs2NTWp57feeqvp8XhMt9utrtO9214YHOdse3u7+ZWvfMUMBoPmmWeeqdo71+b333+/WVBQYMZisb26/sKu32MJX3vhhRfMAw88UPWhcjz88MOm3+83N23aJLt2H4XtVV5ebh588MGqf3zEEUeYTz75pHpv0aJFqk1/+tOfmslksuc7jY2N5he/+EXzoosu2otrLuxqu/I8Jrx/9r7fbtiwQd1v16xZIzt1kLdt7l5bWVnZc85ed911qv/k9XrNBQsW7MU1F3a1XZ966qmesewXvvAFs6ioyLzmmmv6fO+HP/yhOWTIkP1m54qzbA9z//33K4X9gw8+UDNitJHfeOONePbZZ9X7M2bMUJZz2pEZGsLZssLCQhx11FEqXC83qyrsOzzyyCO44447VIJhOo5++tOfqtdzuY3oPqJtddGiRSpfDuGsKd9nO9M9GIlE9vJWCANt194VfXpbjplbhe1IF1IOhlILg6dtc24Gzozx3OX1mA5g5tz49a9/jXPOOUfNrnEWnG0vyf4H1znL6+3xxx+vcgvmqkznHCkMk+frEgY0uO6xOUaMGKH6Tmxruhdys9sM16Prl2GZwr4Fr5/MZXTnnXeq9qV7jDldGVXBvjIjLXjtZRs+9dRTfZK/V1RUKEeSVDsdXO1KFwpdRbx/9r7+vvbaa+ox5zYj7e3te20bhE/etoTn7jHHHKPOW6Yseeihh3DCCSeoPNy5a7Mk+x8c7criVmxPjmXZhp2dnT3pg3JtOXHiRNU3ZrTU/oCIZXuA3M1g/fr1ytbI8tdk7NixKm/Keeedp2yNfJ/wICwvL1f5U3KDcebdYNUJhmoK+wa5Cz0rInIAxtjtL37xi+qmn7vx50I/WGqXOTd40WGFvRxMmMjOQS5PnTA42nV7N3kKoRRFOSCnGM4iHWz3/cKSnGdty048hU9azBlyycHasmXLVOjInDlzcP75528zWBf27XalYEL4+kUXXaTCL3k/zglpvMfy/ir32MF7LWZfi+cu76s5EYWhmBS9Gfol7Fsw7I45yZj787LLLlNi9eGHH67yWbH/mztnb731VtWX4mCclU9zcADHvHTC4GrX3pNMuTEOB+bMD+r1elUBAOZB+vGPfyzhtYOsbXNjHl6XmfydeelylYp5/eakxvXXX7/NxLMwONqVRc1OP/10ZfDhOCd3n+Vk1OTJk9W9er9gb1vb8olVq1b1CdWJx+PK3viHP/xBPc/ZUzs7O02fz2fedddd6vmDDz5oHnTQQeacOXNUKObll1+u7JD33XffXtoSYUft2jscZMmSJSo04KSTTtrmvTfffFNZWAsLC80bb7zRvOCCC8zi4mLz2WefVe9LWNfgatfen2U4wWmnnWb+7Gc/M6+++mrTZrOZF198sZlKpT7DLRB2V9vm2u2ZZ54x58+f3+d7L730kvnjH/9YLU/O2cHVrrlwTIYT8PxkeB7DMc877zx1Lf7973+v3pd2HVztmgvnevnll81jjjnGnDRpkuovMc0B2/WXv/zlZ7wFwkDb9oMPPug5L3Pt+Mgjj6hUJb3DLh9//HHzqKOOUulLmLqE4ZfsT7NfJQzediVMT3LccceZf//7380rr7zStNvtqn8s/afB3bb/+Mc/zHnz5vVZFq/L7CdL/2nwtuu7775rnnjiiSqdxVVXXWVeeOGFSqPgZ/eX/pOIZbuBRx991BwxYoQ5fvx4Fff7pz/9qeeGwA765z//+Z4DL3czuPnmm81hw4b1ybPBXFZHHnmk6hSuWLFid6yasAfadeuLwwMPPKBybPBx67wqzIP0ve99Tx0HHKRJuw7edu2db6O2ttbUNE39HX744eayZcs+460Q9tQ5u/Xn94eOwP7Sruy433TTTUpUkWtxfrTr22+/bZ566qmqn8UJDGnXfbNtmSOwN73vp+eff7556aWXqv/3HqTV1dWZX//6183TTz9d+sWDvF17n7MffvhhT//p0EMPlf7TIG/b7Ymcuet375y/wuBt13Q6bd52223qenzOOefsd/dZEcs+Jf/5z3/UQfib3/xGJfO//vrrTYfD0eMm+8tf/mJOnz69ZwY7d8Oge4HKbG8Vnu8xiamwb7ar0+lU7ZpLCJ1rS3bomESa7kAmfSdbz6TJDSO/2pVuB94w6GwQ8qNtZVZ730TaNT/5tO3KiajenX069oV9v20ZcUFyThM+nzJlivnQQw/1u7zcd4T8aNc33njDPPbYY6X/lIdtK2Od/L4WG/vp5LGIZZ+Q3AHDimkzZ87sM9CiTZECGcN3wuGwshfTfZKr6JRTelnpieEhwuBp11mzZvVUCukNQyv5HiuEfPTRR+Ypp5yi3EdCfrXrySefLO26jyHnbH4i7ZqfSLvmL5+kbTdv3qwGcwwRInxkJT0h/9r1W9/61me85sLOkHM2P5F23b1Igv9PSC5JJZNAs3oEq/TkEuLdfvvt8Pv9ePjhh1VCw29+85sqKd65556rqvvU1tbi+eefVwn8Kysrd18COmGPt6vH41FJwBsbG/skG2ZydyYUvu2221S78jss2iDkV7syUa20676FnLP5ibRrfiLtmr/satsSFt2oqanBkCFDcO2116oE0xs3blTfyxXLEvKjXTn24fekCNK+g5yz+Ym0625mN4tveW1lvOaaa1Ti2N6hk7QyMuldznqam3Hh62PGjDHfeust9ZzxvZyRYcxwRUWFcp7tbzG/+dKu48aNM1977bWezzI3Hb/PJKW0li9evHgvbInQG2nX/EXaNj+Rds1PpF3zl0/atnPnzu1xP5x99tlmUVGRWVJSYk6cOHGbAivCZ4+0a/4ibZufSLvuWUQs2wn19fUqpI5VeBhOOXnyZLOgoKCnY7By5Uqzurra/MEPfrBNrqrKykrz7rvv7nnOXBsMxXzvvff2TGsKn1m79q62tXTpUvOQQw4x//rXv0oL7GWkXfMXadv8RNo1P5F2zV92V9tGo1G1nKFDh6pKesLeRdo1f5G2zU+kXT8bRCzbAbyRX3LJJSqRd+/cYqwmkasWwZxkt99+u+n1entyGeVihVnS/Ktf/aq5vyfGy/d2FfYNpF3zF2nb/ETaNT+Rds1fdnfbLliw4DPfBmFbpF3zF2nb/ETa9bNDcpbtAJ/PB7fbjUsvvRQjR45U+YrISSedhOXLl6t8CsFgEOeffz5mzJiBL3/5yyrXAmOFGZvf3NyM008/fZsYYiG/2lXYN5B2zV+kbfMTadf8RNo1f9ndbctcoMLeR9o1f5G2zU+kXT87NCpmn+HvDTqYjJLJLAmTUjJR/wUXXKAS+P/hD3/o+dzmzZtx7LHHqo7DrFmzVCL/Aw44AH/7299QUVGxF7dA2B7SrvmJtGv+Im2bn0i75ifSrvmLtG1+Iu2av0jb5ifSrp8NIpZ9Ao488kh87WtfwyWXXNJT1YUi2po1a7Bw4ULMmzcPU6dOVe8Lgwdp1/xE2jV/kbbNT6Rd8xNp1/xF2jY/kXbNX6Rt8xNp192PiGW7yLp163D44Yfjueee67GPp1IpuFyuPdA8wmeFtGt+Iu2av0jb5ifSrvmJtGv+Im2bn0i75i/StvmJtOueQXKWDZBctOpbb72FQCDQI5TdeuutuPbaa1UeBmHwIe2an0i75i/StvmJtGt+Iu2av0jb5ifSrvmLtG1+Iu26Z3Hs4eXnDbnk/O+//z7OOussvPzyy/j617+OWCyGhx56COXl5Xt7FYVPgLRrfiLtmr9I2+Yn0q75ibRr/iJtm59Iu+Yv0rb5ibTrHuYzrLw56InH4+aYMWNMTdNMt9tt/vSnP93bqyTsBqRd8xNp1/xF2jY/kXbNT6Rd8xdp2/xE2jV/kbbNT6Rd9xySs2wXmTNnDsaOHYu7774bHo9nzyiYwmeOtGt+Iu2av0jb5ifSrvmJtGv+Im2bn0i75i/StvmJtOueQcSyXUTXddjt9j3TGsJeQ9o1P5F2zV+kbfMTadf8RNo1f5G2zU+kXfMXadv8RNp1zyBimSAIgiAIgiAIgiAIgiBkkWqYgiAIgiAIgiAIgiAIgpBFxDJBEARBEARBEARBEARByCJimSAIgiAIgiAIgiAIgiBkEbFMEARBEARBEARBEARBELKIWCYIgiAIgiAIgiAIgiAIWUQsEwRBEARBEARBEARBEIQsIpYJgiAIgiAIgiAIgiAIQhYRywRBEARBEAYBl156KTRNU39OpxMVFRWYM2cOHnjgARiGMeDl/OUvf0FhYeEeXVdBEARBEITBjIhlgiAIgiAIg4QTTzwRDQ0N2LBhA1544QXMnj0b1157LU455RRkMpm9vXqCIAiCIAh5gYhlgiAIgiAIgwS3243KykpUV1djxowZ+O53v4unn35aCWd0jJG7774bkydPht/vR01NDa666ipEIhH13muvvYbLLrsMXV1dPS61H/3oR+q9ZDKJG2+8US2b3z3kkEPU5wVBEARBEPY3RCwTBEEQBEEYxBx33HGYOnUqnnrqKfXcZrPh//7v/7B06VI8+OCDePXVV/Htb39bvXf44YfjnnvuQSgUUg41/lEgI1dffTXeffdd/OMf/8DixYtx9tlnKyfb6tWr9+r2CYIgCIIgfNZopmman/mvCoIgCIIgCLucs6yzsxP/+te/tnnv3HPPVQLXsmXLtnnviSeewBVXXIHW1lb1nA60b33rW2pZOWprazFq1Cj1WFVV1fP6CSecgIMPPhh33HGHtJYgCIIgCPsNjr29AoIgCIIgCMKng3OfDKkk//3vf3HnnXdixYoVCIfDKpdZIpFALBaDz+fb7vc//vhj6LqOcePG9XmdoZklJSXSPIIgCIIg7FeIWCYIgiAIgjDIWb58OUaOHKkS/zPZ/5VXXomf/OQnKC4uxltvvYWvfOUrSKVS/YplzGlmt9uxcOFC9dibQCDwGW2FIAiCIAjCvoGIZYIgCIIgCIMY5iSjM+y6665TYpdhGPjFL36hcpeRxx57rM/nXS6XcpH1Zvr06eq15uZmHHXUUZ/p+guCIAiCIOxriFgmCIIgCIIwSGBYZGNjoxK2mpqa8OKLL6qQS7rJLr74YixZsgTpdBr33nsvTj31VLz99tu47777+ixjxIgRykn2yiuvqMIAdJsx/PKCCy5Qy6DQRvGspaVFfWbKlCk4+eST99o2C4IgCIIgfNZINUxBEARBEIRBAsWxIUOGKMGLlSrnzp2rKl8+/fTTKnyS4tfdd9+Nu+66C5MmTcIjjzyixLTesCImE/6fc845KCsrw//7f/9Pvf7nP/9ZiWU33HADxo8fj9NPPx3z58/HsGHD9tLWCoIgCIIg7B2kGqYgCIIgCIIgCIIgCIIgZBFnmSAIgiAIgiAIgiAIgiBkEbFMEARBEARBEARBEARBELKIWCYIgiAIgiAIgiAIgiAIWUQsEwRBEARBEARBEARBEIQsIpYJgiAIgiAIgiAIgiAIQhYRywRBEARBEARBEARBEAQhi4hlgiAIgiAIgiAIgiAIgpBFxDJBEARBEARBEARBEARByCJimSAIgiAIgiAIgiAIgiBkEbFMEARBEARBEARBEARBELKIWCYIgiAIgiAIgiAIgiAIWUQsEwRBEARBEARBEARBEARY/H9igFnq2DaVsgAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 12 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v4vdGWLR9ny-" + }, + "source": [ + "## Calculating the covariance matrix" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 }, + "id": "dTFkI4ll9ny-", + "outputId": "2a0c7a48-d357-4445-bdd1-44dbe901c17a", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:57.706490Z", + "start_time": "2025-11-12T08:10:56.962206Z" + } + }, + "source": [ + "import pypfopt\n", + "pypfopt.__version__" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "v4vdGWLR9ny-" - }, - "source": [ - "## Calculating the covariance matrix" + "data": { + "text/plain": [ + "'1.5.6'" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 13 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 520 }, + "id": "0hDN8ZJG9ny_", + "outputId": "35244fed-56ac-4207-ee84-736e924b1210", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:57.740565Z", + "start_time": "2025-11-12T08:10:57.719628Z" + } + }, + "source": [ + "from pypfopt import risk_models\n", + "from pypfopt import plotting\n", + "\n", + "sample_cov = risk_models.sample_cov(prices, frequency=252)\n", + "sample_cov" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "dTFkI4ll9ny-", - "outputId": "2a0c7a48-d357-4445-bdd1-44dbe901c17a" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.5.6'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "Ticker ACN AMZN COST DIS F GILD JPM \\\n", + "Ticker \n", + "ACN 0.087879 0.045211 0.027071 0.039502 0.042549 0.027293 0.044863 \n", + "AMZN 0.045211 0.307429 0.045653 0.054003 0.051213 0.052769 0.061962 \n", + "COST 0.027071 0.045653 0.092634 0.027938 0.029847 0.025282 0.035684 \n", + "DIS 0.039502 0.054003 0.027938 0.089013 0.042917 0.030226 0.049604 \n", + "F 0.042549 0.051213 0.029847 0.042917 0.150588 0.028601 0.059010 \n", + "GILD 0.027293 0.052769 0.025282 0.030226 0.028601 0.217525 0.039475 \n", + "JPM 0.044863 0.061962 0.035684 0.049604 0.059010 0.039475 0.133386 \n", + "KO 0.019031 0.018045 0.017898 0.021648 0.022414 0.016203 0.024977 \n", + "LUV 0.036189 0.046514 0.028906 0.041307 0.050190 0.029633 0.052660 \n", + "MA 0.043097 0.052424 0.030250 0.046985 0.056848 0.029258 0.062078 \n", + "MSFT 0.039949 0.070593 0.033754 0.037004 0.038372 0.032517 0.045667 \n", + "PFE 0.024414 0.028826 0.022277 0.024385 0.024299 0.031389 0.031981 \n", + "TSLA 0.042784 0.068915 0.030881 0.044845 0.059557 0.028156 0.042675 \n", + "UNH 0.028149 0.031414 0.023572 0.028374 0.028534 0.032010 0.037662 \n", + "XOM 0.027694 0.024803 0.017215 0.027860 0.030204 0.019950 0.034202 \n", + "\n", + "Ticker KO LUV MA MSFT PFE TSLA UNH \\\n", + "Ticker \n", + "ACN 0.019031 0.036189 0.043097 0.039949 0.024414 0.042784 0.028149 \n", + "AMZN 0.018045 0.046514 0.052424 0.070593 0.028826 0.068915 0.031414 \n", + "COST 0.017898 0.028906 0.030250 0.033754 0.022277 0.030881 0.023572 \n", + "DIS 0.021648 0.041307 0.046985 0.037004 0.024385 0.044845 0.028374 \n", + "F 0.022414 0.050190 0.056848 0.038372 0.024299 0.059557 0.028534 \n", + "GILD 0.016203 0.029633 0.029258 0.032517 0.031389 0.028156 0.032010 \n", + "JPM 0.024977 0.052660 0.062078 0.045667 0.031981 0.042675 0.037662 \n", + "KO 0.047342 0.019165 0.023770 0.020717 0.021349 0.014569 0.021770 \n", + "LUV 0.019165 0.137159 0.045174 0.033364 0.024131 0.047666 0.030239 \n", + "MA 0.023770 0.045174 0.105872 0.045315 0.028930 0.046905 0.037390 \n", + "MSFT 0.020717 0.033364 0.045315 0.098125 0.025940 0.052774 0.029705 \n", + "PFE 0.021349 0.024131 0.028930 0.025940 0.070425 0.017049 0.027943 \n", + "TSLA 0.014569 0.047666 0.046905 0.052774 0.017049 0.335304 0.026924 \n", + "UNH 0.021770 0.030239 0.037390 0.029705 0.027943 0.026924 0.130707 \n", + "XOM 0.019547 0.022862 0.037327 0.023631 0.021544 0.026332 0.022519 \n", + "\n", + "Ticker XOM \n", + "Ticker \n", + "ACN 0.027694 \n", + "AMZN 0.024803 \n", + "COST 0.017215 \n", + "DIS 0.027860 \n", + "F 0.030204 \n", + "GILD 0.019950 \n", + "JPM 0.034202 \n", + "KO 0.019547 \n", + "LUV 0.022862 \n", + "MA 0.037327 \n", + "MSFT 0.023631 \n", + "PFE 0.021544 \n", + "TSLA 0.026332 \n", + "UNH 0.022519 \n", + "XOM 0.061641 " ], - "source": [ - "import pypfopt\n", - "pypfopt.__version__" + "text/html": [ + "
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TickerACNAMZNCOSTDISFGILDJPMKOLUVMAMSFTPFETSLAUNHXOM
Ticker
ACN0.0878790.0452110.0270710.0395020.0425490.0272930.0448630.0190310.0361890.0430970.0399490.0244140.0427840.0281490.027694
AMZN0.0452110.3074290.0456530.0540030.0512130.0527690.0619620.0180450.0465140.0524240.0705930.0288260.0689150.0314140.024803
COST0.0270710.0456530.0926340.0279380.0298470.0252820.0356840.0178980.0289060.0302500.0337540.0222770.0308810.0235720.017215
DIS0.0395020.0540030.0279380.0890130.0429170.0302260.0496040.0216480.0413070.0469850.0370040.0243850.0448450.0283740.027860
F0.0425490.0512130.0298470.0429170.1505880.0286010.0590100.0224140.0501900.0568480.0383720.0242990.0595570.0285340.030204
GILD0.0272930.0527690.0252820.0302260.0286010.2175250.0394750.0162030.0296330.0292580.0325170.0313890.0281560.0320100.019950
JPM0.0448630.0619620.0356840.0496040.0590100.0394750.1333860.0249770.0526600.0620780.0456670.0319810.0426750.0376620.034202
KO0.0190310.0180450.0178980.0216480.0224140.0162030.0249770.0473420.0191650.0237700.0207170.0213490.0145690.0217700.019547
LUV0.0361890.0465140.0289060.0413070.0501900.0296330.0526600.0191650.1371590.0451740.0333640.0241310.0476660.0302390.022862
MA0.0430970.0524240.0302500.0469850.0568480.0292580.0620780.0237700.0451740.1058720.0453150.0289300.0469050.0373900.037327
MSFT0.0399490.0705930.0337540.0370040.0383720.0325170.0456670.0207170.0333640.0453150.0981250.0259400.0527740.0297050.023631
PFE0.0244140.0288260.0222770.0243850.0242990.0313890.0319810.0213490.0241310.0289300.0259400.0704250.0170490.0279430.021544
TSLA0.0427840.0689150.0308810.0448450.0595570.0281560.0426750.0145690.0476660.0469050.0527740.0170490.3353040.0269240.026332
UNH0.0281490.0314140.0235720.0283740.0285340.0320100.0376620.0217700.0302390.0373900.0297050.0279430.0269240.1307070.022519
XOM0.0276940.0248030.0172150.0278600.0302040.0199500.0342020.0195470.0228620.0373270.0236310.0215440.0263320.0225190.061641
\n", + "
" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "HZ15mDtz9ny_", + "outputId": "5fe64424-4756-4289-e29e-39edc3eb83df", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:57.933296Z", + "start_time": "2025-11-12T08:10:57.844162Z" + } + }, + "source": [ + "plotting.plot_covariance(sample_cov, plot_correlation=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 520 - }, - "id": "0hDN8ZJG9ny_", - "outputId": "35244fed-56ac-4207-ee84-736e924b1210" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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TickerACNAMZNCOSTDISFGILDJPMKOLUVMAMSFTPFETSLAUNHXOM
Ticker
ACN0.0880540.0451890.0273740.0393400.0429700.0276460.0454890.0195210.0359100.0434810.0405800.0246530.0411800.0285520.028225
AMZN0.0451890.3137430.0464520.0539270.0515600.0542430.0624320.0190210.0459820.0534170.0712070.0294600.0650750.0320880.024862
COST0.0273740.0464520.0938670.0281460.0303430.0256610.0360960.0180670.0291420.0305670.0342140.0226870.0294630.0239930.017536
DIS0.0393400.0539270.0281460.0891830.0430550.0305050.0497340.0220740.0406830.0473570.0371380.0244070.0421010.0290910.027840
F0.0429700.0515600.0303430.0430550.1515770.0289400.0598200.0228210.0498390.0583750.0388140.0241450.0588750.0292000.030198
GILD0.0276460.0542430.0256610.0305050.0289400.2215030.0402500.0162940.0297940.0296090.0333630.0314540.0286450.0324010.020288
JPM0.0454890.0624320.0360960.0497340.0598200.0402500.1353920.0255400.0527070.0635720.0460570.0324690.0400780.0385940.034489
KO0.0195210.0190210.0180670.0220740.0228210.0162940.0255400.0477620.0195440.0242440.0214020.0215320.0160710.0223370.019908
LUV0.0359100.0459820.0291420.0406830.0498390.0297940.0527070.0195440.1360470.0452970.0332170.0238410.0422620.0302000.022661
MA0.0434810.0534170.0305670.0473570.0583750.0296090.0635720.0242440.0452970.1086920.0464920.0293140.0465130.0387440.038239
MSFT0.0405800.0712070.0342140.0371380.0388140.0333630.0460570.0214020.0332170.0464920.0991560.0265460.0507850.0303820.023950
PFE0.0246530.0294600.0226870.0244070.0241450.0314540.0324690.0215320.0238410.0293140.0265460.0704470.0163560.0282720.021650
TSLA0.0411800.0650750.0294630.0421010.0588750.0286450.0400780.0160710.0422620.0465130.0507850.0163560.3286520.0283750.025871
UNH0.0285520.0320880.0239930.0290910.0292000.0324010.0385940.0223370.0302000.0387440.0303820.0282720.0283750.1276690.023130
XOM0.0282250.0248620.0175360.0278400.0301980.0202880.0344890.0199080.0226610.0382390.0239500.0216500.0258710.0231300.061806
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" - ], - "text/plain": [ - "Ticker ACN AMZN COST DIS F GILD JPM \\\n", - "Ticker \n", - "ACN 0.088054 0.045189 0.027374 0.039340 0.042970 0.027646 0.045489 \n", - "AMZN 0.045189 0.313743 0.046452 0.053927 0.051560 0.054243 0.062432 \n", - "COST 0.027374 0.046452 0.093867 0.028146 0.030343 0.025661 0.036096 \n", - "DIS 0.039340 0.053927 0.028146 0.089183 0.043055 0.030505 0.049734 \n", - "F 0.042970 0.051560 0.030343 0.043055 0.151577 0.028940 0.059820 \n", - "GILD 0.027646 0.054243 0.025661 0.030505 0.028940 0.221503 0.040250 \n", - "JPM 0.045489 0.062432 0.036096 0.049734 0.059820 0.040250 0.135392 \n", - "KO 0.019521 0.019021 0.018067 0.022074 0.022821 0.016294 0.025540 \n", - "LUV 0.035910 0.045982 0.029142 0.040683 0.049839 0.029794 0.052707 \n", - "MA 0.043481 0.053417 0.030567 0.047357 0.058375 0.029609 0.063572 \n", - "MSFT 0.040580 0.071207 0.034214 0.037138 0.038814 0.033363 0.046057 \n", - "PFE 0.024653 0.029460 0.022687 0.024407 0.024145 0.031454 0.032469 \n", - "TSLA 0.041180 0.065075 0.029463 0.042101 0.058875 0.028645 0.040078 \n", - "UNH 0.028552 0.032088 0.023993 0.029091 0.029200 0.032401 0.038594 \n", - "XOM 0.028225 0.024862 0.017536 0.027840 0.030198 0.020288 0.034489 \n", - "\n", - "Ticker KO LUV MA MSFT PFE TSLA UNH \\\n", - "Ticker \n", - "ACN 0.019521 0.035910 0.043481 0.040580 0.024653 0.041180 0.028552 \n", - "AMZN 0.019021 0.045982 0.053417 0.071207 0.029460 0.065075 0.032088 \n", - "COST 0.018067 0.029142 0.030567 0.034214 0.022687 0.029463 0.023993 \n", - "DIS 0.022074 0.040683 0.047357 0.037138 0.024407 0.042101 0.029091 \n", - "F 0.022821 0.049839 0.058375 0.038814 0.024145 0.058875 0.029200 \n", - "GILD 0.016294 0.029794 0.029609 0.033363 0.031454 0.028645 0.032401 \n", - "JPM 0.025540 0.052707 0.063572 0.046057 0.032469 0.040078 0.038594 \n", - "KO 0.047762 0.019544 0.024244 0.021402 0.021532 0.016071 0.022337 \n", - "LUV 0.019544 0.136047 0.045297 0.033217 0.023841 0.042262 0.030200 \n", - "MA 0.024244 0.045297 0.108692 0.046492 0.029314 0.046513 0.038744 \n", - "MSFT 0.021402 0.033217 0.046492 0.099156 0.026546 0.050785 0.030382 \n", - "PFE 0.021532 0.023841 0.029314 0.026546 0.070447 0.016356 0.028272 \n", - "TSLA 0.016071 0.042262 0.046513 0.050785 0.016356 0.328652 0.028375 \n", - "UNH 0.022337 0.030200 0.038744 0.030382 0.028272 0.028375 0.127669 \n", - "XOM 0.019908 0.022661 0.038239 0.023950 0.021650 0.025871 0.023130 \n", - "\n", - "Ticker XOM \n", - "Ticker \n", - "ACN 0.028225 \n", - "AMZN 0.024862 \n", - "COST 0.017536 \n", - "DIS 0.027840 \n", - "F 0.030198 \n", - "GILD 0.020288 \n", - "JPM 0.034489 \n", - "KO 0.019908 \n", - "LUV 0.022661 \n", - "MA 0.038239 \n", - "MSFT 0.023950 \n", - "PFE 0.021650 \n", - "TSLA 0.025871 \n", - "UNH 0.023130 \n", - "XOM 0.061806 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "from pypfopt import risk_models\n", - "from pypfopt import plotting\n", - "\n", - "sample_cov = risk_models.sample_cov(prices, frequency=252)\n", - "sample_cov" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0bhjN57j9ny_" + }, + "source": [ + "For reasons discussed in the docs, the sample covariance matrix should not be your default choice. I think a better option is Ledoit-Wolf shrinkage, which reduces the extreme values in the covariance matrix. In the image below, we can see that there are fewer bright spots outside the diagonal:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "htpILjX-9nzA", + "outputId": "254cc6d7-bbee-48bf-c888-82c85a96bf83", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.411124Z", + "start_time": "2025-11-12T08:10:57.991804Z" + } + }, + "source": [ + "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", + "plotting.plot_covariance(S, plot_correlation=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "HZ15mDtz9ny_", - "outputId": "5fe64424-4756-4289-e29e-39edc3eb83df" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "plotting.plot_covariance(sample_cov, plot_correlation=True);" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4mbpNBp39nzA" + }, + "source": [ + "## Return estimation\n", + "\n", + "As discussed in the docs, it is often a bad idea to provide returns using a simple estimate like the mean of past returns. Unless you have a proprietary method for estimating returns, research suggests that you may be better off not providing expected returns – you can then just find the `min_volatility()` portfolio or use `HRP`. \n", + "\n", + "However, in this example we will use the CAPM returns, which aims to be slightly more stable than the default mean historical return. Please see the notebook `1-RiskReturnModels.ipynb` for more information." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "c_syhOUu9nzH", + "outputId": "40e40fd5-06ac-4f42-c375-7a240c15c27e", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.430718Z", + "start_time": "2025-11-12T08:10:58.418328Z" + } + }, + "source": [ + "from pypfopt import expected_returns\n", + "\n", + "mu = expected_returns.capm_return(prices)\n", + "mu" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "0bhjN57j9ny_" - }, - "source": [ - "For reasons discussed in the docs, the sample covariance matrix should not be your default choice. I think a better option is Ledoit-Wolf shrinkage, which reduces the extreme values in the covariance matrix. In the image below, we can see that there are fewer bright spots outside the diagonal:" + "data": { + "text/plain": [ + "Ticker\n", + "ACN 0.202011\n", + "AMZN 0.352581\n", + "COST 0.175672\n", + "DIS 0.206533\n", + "F 0.241105\n", + "GILD 0.240912\n", + "JPM 0.263090\n", + "KO 0.118175\n", + "LUV 0.224131\n", + "MA 0.244448\n", + "MSFT 0.215577\n", + "PFE 0.152560\n", + "TSLA 0.309169\n", + "UNH 0.193748\n", + "XOM 0.139997\n", + "Name: mkt, dtype: float64" ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 }, + "id": "MlqP3XP99nzH", + "outputId": "f5e026c2-d259-4894-d3dc-a667d591e101", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.513425Z", + "start_time": "2025-11-12T08:10:58.464687Z" + } + }, + "source": [ + "mu.plot.barh(figsize=(10,6));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "htpILjX-9nzA", - "outputId": "254cc6d7-bbee-48bf-c888-82c85a96bf83" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", - "plotting.plot_covariance(S, plot_correlation=True);" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 18 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iHNXRsJA9nzI" + }, + "source": [ + "## Long/short min variance\n", + "\n", + "In this section, we construct a long/short portfolio with the objective of minimising variance. There is a good deal of research that demonstrates that these global-minimum variance (GMV) portfolios outperform mean-variance optimized portfolios." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LTjcmcmK9nzI", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.581455Z", + "start_time": "2025-11-12T08:10:58.577196Z" + } + }, + "source": [ + "from pypfopt import EfficientFrontier" + ], + "outputs": [], + "execution_count": 19 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ZD5_4rlY9nzI", + "outputId": "81faef92-6667-4452-8859-3a659d235b14", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.763512Z", + "start_time": "2025-11-12T08:10:58.711335Z" + } + }, + "source": [ + "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", + "\n", + "# You don't have to provide expected returns in this case\n", + "ef = EfficientFrontier(None, S, weight_bounds=(None, None))\n", + "ef.min_volatility()\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "4mbpNBp39nzA" - }, - "source": [ - "## Return estimation\n", - "\n", - "As discussed in the docs, it is often a bad idea to provide returns using a simple estimate like the mean of past returns. Unless you have a proprietary method for estimating returns, research suggests that you may be better off not providing expected returns – you can then just find the `min_volatility()` portfolio or use `HRP`. \n", - "\n", - "However, in this example we will use the CAPM returns, which aims to be slightly more stable than the default mean historical return. Please see the notebook `1-RiskReturnModels.ipynb` for more information." + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.15633),\n", + " ('AMZN', 0.00186),\n", + " ('COST', 0.0855),\n", + " ('DIS', 0.00281),\n", + " ('F', -0.01711),\n", + " ('GILD', 0.03164),\n", + " ('JPM', -0.06418),\n", + " ('KO', 0.2675),\n", + " ('LUV', 0.02184),\n", + " ('MA', 0.17125),\n", + " ('MSFT', -0.00282),\n", + " ('PFE', 0.10258),\n", + " ('TSLA', 0.08633),\n", + " ('UNH', 0.02722),\n", + " ('XOM', 0.12927)])" ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 }, + "id": "RVRwHED99nzJ", + "outputId": "f606418a-1614-4c38-9574-c18b1b9c5ac6", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.841600Z", + "start_time": "2025-11-12T08:10:58.785635Z" + } + }, + "source": [ + "pd.Series(weights).plot.barh();" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "c_syhOUu9nzH", - "outputId": "40e40fd5-06ac-4f42-c375-7a240c15c27e" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Ticker\n", - "ACN 0.205386\n", - "AMZN 0.360796\n", - "COST 0.179589\n", - "DIS 0.208873\n", - "F 0.245274\n", - "GILD 0.247475\n", - "JPM 0.268588\n", - "KO 0.121785\n", - "LUV 0.224938\n", - "MA 0.251706\n", - "MSFT 0.220046\n", - "PFE 0.155214\n", - "TSLA 0.303789\n", - "UNH 0.197618\n", - "XOM 0.142450\n", - "Name: mkt, dtype: float64" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "from pypfopt import expected_returns\n", - "\n", - "mu = expected_returns.capm_return(prices)\n", - "mu" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jpgqSknF9nzJ" + }, + "source": [ + "We can get a quick indication of the portfolio performance as follows. Note that this is an in sample estimate and may have very little resemblance to how the portfolio actually performs!" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ZnDm8UkC9nzJ", + "outputId": "9302f8ed-698e-461b-c4a1-ad8113f1aa11", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:58.878293Z", + "start_time": "2025-11-12T08:10:58.859196Z" + } + }, + "source": [ + "ef.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 374 - }, - "id": "MlqP3XP99nzH", - "outputId": "f5e026c2-d259-4894-d3dc-a667d591e101" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mu.plot.barh(figsize=(10,6));" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Annual volatility: 15.2%\n" + ] + } + ], + "execution_count": 22 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ORCrBIG19nzK" + }, + "source": [ + "Let's say we were happy with this portfolio and wanted to actually go out and buy the shares. To do this, we would need to construct a **discrete allocation** (unless your broker supports fractional shares!)\n", + "\n", + "If we had \\$20,0000 to invest and would like our portfolio to be 130/30 long/short, we can construct the actual allocation as follows:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "w10Rcb-X9nzK", + "outputId": "052edfa3-7811-459c-debb-644dc7aa2368", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.124493Z", + "start_time": "2025-11-12T08:10:58.891311Z" + } + }, + "source": [ + "from pypfopt import DiscreteAllocation\n", + "\n", + "latest_prices = prices.iloc[-1] # prices as of the day you are allocating\n", + "da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=20000, short_ratio=0.3)\n", + "alloc, leftover = da.lp_portfolio()\n", + "print(f\"Discrete allocation performed with ${leftover:.2f} leftover\")\n", + "alloc" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "iHNXRsJA9nzI" - }, - "source": [ - "## Long/short min variance\n", - "\n", - "In this section, we construct a long/short portfolio with the objective of minimising variance. There is a good deal of research that demonstrates that these global-minimum variance (GMV) portfolios outperform mean-variance optimized portfolios." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Discrete allocation performed with $61.86 leftover\n" + ] }, { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "LTjcmcmK9nzI" - }, - "outputs": [], - "source": [ - "from pypfopt import EfficientFrontier" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages/cvxpy/problems/problem.py:1539: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZD5_4rlY9nzI", - "outputId": "81faef92-6667-4452-8859-3a659d235b14" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', 0.15965),\n", - " ('AMZN', 0.00164),\n", - " ('COST', 0.08412),\n", - " ('DIS', 0.00532),\n", - " ('F', -0.01799),\n", - " ('GILD', 0.03099),\n", - " ('JPM', -0.06449),\n", - " ('KO', 0.26104),\n", - " ('LUV', 0.02504),\n", - " ('MA', 0.17178),\n", - " ('MSFT', -0.00689),\n", - " ('PFE', 0.10442),\n", - " ('TSLA', 0.09418),\n", - " ('UNH', 0.02521),\n", - " ('XOM', 0.12599)])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", - "\n", - "# You don't have to provide expected returns in this case\n", - "ef = EfficientFrontier(None, S, weight_bounds=(None, None))\n", - "ef.min_volatility()\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "{'ACN': 12,\n", + " 'COST': 2,\n", + " 'DIS': 1,\n", + " 'GILD': 4,\n", + " 'KO': 69,\n", + " 'LUV': 13,\n", + " 'MA': 6,\n", + " 'PFE': 74,\n", + " 'TSLA': 3,\n", + " 'UNH': 1,\n", + " 'XOM': 20,\n", + " 'F': -92,\n", + " 'JPM': -15}" ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ab3Rk-Aa9nzK" + }, + "source": [ + "## Max Sharpe with sector constraints\n", + "\n", + "If you have your own model for returns (or have read the warnings and want to proceed anyways), you may consider maximising the Sharpe ratio. This theoretically gives the optimal portfolio in terms of risks-returns.\n", + "\n", + "In this section, we construct a long-only max-sharpe portfolio, but also incorporate sector constraints. Sector constraints require three things. A `sector_mapper`, your `sector_lower` bounds, and your `sector_upper` bounds." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mJcyi2-E9nzK", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.146835Z", + "start_time": "2025-11-12T08:10:59.142361Z" + } + }, + "source": [ + "sector_mapper = {\n", + " \"MSFT\": \"Tech\",\n", + " \"AMZN\": \"Consumer Discretionary\",\n", + " \"KO\": \"Consumer Staples\",\n", + " \"MA\": \"Financial Services\",\n", + " \"COST\": \"Consumer Staples\",\n", + " \"LUV\": \"Aerospace\",\n", + " \"XOM\": \"Energy\",\n", + " \"PFE\": \"Healthcare\",\n", + " \"JPM\": \"Financial Services\",\n", + " \"UNH\": \"Healthcare\",\n", + " \"ACN\": \"Misc\",\n", + " \"DIS\": \"Media\",\n", + " \"GILD\": \"Healthcare\",\n", + " \"F\": \"Auto\",\n", + " \"TSLA\": \"Auto\"\n", + "}\n", + "\n", + "sector_lower = {\n", + " \"Consumer Staples\": 0.1, # at least 10% to staples\n", + " \"Tech\": 0.05 # at least 5% to tech\n", + " # For all other sectors, it will be assumed there is no lower bound\n", + "}\n", + "\n", + "sector_upper = {\n", + " \"Tech\": 0.2,\n", + " \"Aerospace\":0.1,\n", + " \"Energy\": 0.1,\n", + " \"Auto\":0.15\n", + "}" + ], + "outputs": [], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1h5ZdZwF9nzL" + }, + "source": [ + "We then set up the optimizer and add our constraints. We can use `ef.add_objective()` to add other constraints. For example, let's say that in addition to the above sector constraints, I specifically want:\n", + "\n", + "- 10% of the portfolio in AMZN\n", + "- Less than 5% of my portfolio in TSLA" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FRjwf0wN9nzL", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.198868Z", + "start_time": "2025-11-12T08:10:59.173173Z" + } + }, + "source": [ + "mu = expected_returns.capm_return(prices)\n", + "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", + "\n", + "ef = EfficientFrontier(mu, S) # weight_bounds automatically set to (0, 1)\n", + "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", + "\n", + "amzn_index = ef.tickers.index(\"AMZN\")\n", + "ef.add_constraint(lambda w: w[amzn_index] == 0.10)\n", + "\n", + "tsla_index = ef.tickers.index(\"TSLA\")\n", + "ef.add_constraint(lambda w: w[tsla_index] <= 0.05)\n", + "\n", + "ef.add_constraint(lambda w: w[10] >= 0.05)\n", + "\n", + "ef.max_sharpe()\n", + "weights = ef.clean_weights()" + ], + "outputs": [], + "execution_count": 25 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "pZAu8ypG9nzL", + "outputId": "2a3703a8-bbd0-468d-f0c6-25be50f7b0c7", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.258345Z", + "start_time": "2025-11-12T08:10:59.223857Z" + } + }, + "source": [ + "weights" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "RVRwHED99nzJ", - "outputId": "f606418a-1614-4c38-9574-c18b1b9c5ac6" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.barh();" + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.15093),\n", + " ('AMZN', 0.1),\n", + " ('COST', 0.04217),\n", + " ('DIS', 0.0),\n", + " ('F', 0.01733),\n", + " ('GILD', 0.06261),\n", + " ('JPM', 0.01013),\n", + " ('KO', 0.05783),\n", + " ('LUV', 0.03754),\n", + " ('MA', 0.34004),\n", + " ('MSFT', 0.05),\n", + " ('PFE', 0.03928),\n", + " ('TSLA', 0.05),\n", + " ('UNH', 0.04214),\n", + " ('XOM', 0.0)])" ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 26 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 }, + "id": "izbZQ6-G9nzL", + "outputId": "1aafb3d3-a32b-469e-e702-6044c7f54116", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.332255Z", + "start_time": "2025-11-12T08:10:59.272898Z" + } + }, + "source": [ + "pd.Series(weights).plot.pie(figsize=(10,10));" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "jpgqSknF9nzJ" - }, - "source": [ - "We can get a quick indication of the portfolio performance as follows. Note that this is an in sample estimate and may have very little resemblance to how the portfolio actually performs!" - ] + "data": { + "text/plain": [ + "
" + ], + "image/png": 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UrAqlYtJWqQ0/A/4+AnjjWiDvA8DVXRGNiEgJ3ApFulFSUuIJIuLi4vCnP/0J48ePR2hoKA4fPoz//Oc/yMjIwGWXXdavY3/++eeegEIKTqTj/fOf/8TEiROxbt06LF682Offi56Jooiqo63I31aDkoNNcLvkf+sqVIYDY2U/jS6JBn7+pHqCEzj6UfclLBYYczkw8eruhnzMkSEiP2JgQbpx1113ISgoCHv37kVkZOSJ64cOHYoVK1Z43tD2V2JiItLS0k4c79JLL/UEFLfeeiuKi4thMpl88j3oWWerHQU7apC/vRbtTTa/ntvW7sJk02wccO/w63n1gD3KNcZmBva/3H2JG9i9VWrKDd3/T0QkM34URbrQ3NyMjRs3erYonRxUnEzayuQrRqPRs+WqvLwc+/bt89lx9cbtFjzVnNb/6xBe+b/t2LW21O9BxTemOuYrcl6tE5m7rV1tFcCWvwH/nAj87wfA0U8AgZvbiEg+DCxIF4qKijwrEiNHjuxxfVJSEqKiojyXBx54wKfnHDVq1Ik8DOqpvakB2958Fc/fcyu+fHU/ynObIQrKfvYd35il6Pm1SmBVKO0The5u329cBfxzQnew0VGv9KiISIe4FYp0bffu3RAEAddddx3sdt8mNX6ztcqXKyFaJgoCSg/uw6HPNqD0wD6I0psZAAMnlKGha4TSw0NXpYio9Bh0ir5PDtcz9rHQGXMl8MUfgM1/6e7yPe1WYOh5So+KiHSCgQXpglQFSnqDf/To0R7XS/kQkvDwcJ+fMz8/3/PvkCFDEMi62s04vOlT5Gz6FO2Np34K2la7FyKyYVD4k2+3S8R8wwX4WHxH0XFoDXMsdJzwfeTD7ktiNjD1JmDStSxbS0Re4VYo0gUpuXrp0qX417/+BYvFu4ZqvSGtgjzxxBOeoGLy5MkIRK11Nfjsv//Cf++6GdvefOW0QYWkvbEOiemtUINsS2A+V97gikUAaD4GbPw/4LHRwLp7gaYipUdERBrFFQvSjaefftpTbnbatGl46KGHMGHCBE+S9Z49e1BQUICpU6ee8bHV1dU4ePBgj+sGDRrUIzlc6pEhlZvNzc319L2Qtll99NFHAVcRqqawAHvXvYeiPTtPbHc6J3cugAVQWnBVHJCt9Ci0ham+AcRlA/a9BOx7GRh5ITDnHmDQbKVHRUQaws7bpCu1tbWeHhbSG/6qqipPH4sxY8Zg5cqVnnK0ERERnoZ4q1ev9lwk0tdSdafvevXVVzFv3rweW52kx0sBx8KFC3Hfffd5tmAFAimfpHjfbuxdtwbVBUf6/HijKQjRaXfA3hUCpX0+798ocndvY6NzeyR0OC4p+IJTFcjdvWf/BBh9mVQOT+nREJHKMbAgojNyOZ04suUL7Fv/PlpqqryaqYETLkVDpfLLBZ2zivCa4Umlh6EZfwobjkvzGVgEvPghwOy7gUnXASERAT8dRHR63ApFRKewWTpxaOMGHPhkHSxtvsmP6E7iHq54EndayzAgUdEhaIrIcrMkaS0FNvwM+PJPwPRbgRk/BqKSOTdE1AMDCyLq0X9i30cf4vAXG+G0WX06M+2NtUgf3YrWugRFZ9xeEYTgxBA44VB0HFrBHAvqwdrS3Qdj+5PAlBuB+T8FotM4SUTkwcCCiDwBxY5338SRLZsguN2yzYhByAOgbAdsp82N2aZF2OL+RNFxaAWrQtEZE713Pwvsf7m7VO28+xhgEBHLzRIFMmmb06YX/o0X7r0duV9ulDWokNQXH0BohBNKG2udofQQNENkJws6V4Cx69/APycCH/+CHb2JAhxXLIgCkLWjHbs/fBcHP/0ILodvO5KfjdvlQmxiORq6lK2mFVWXCgxUdAiawRUL6n2A8Ux3udppNwNzVwPRqZw8ogDDwIIogNi7urB3/fvYv+FDOKxdioyhrXaP1CsdSuqscSFlcDoahFpFx6EFApO3qS9cVmDn08DeF4FptwDzVgNRKZxDogDBotREAcBpt3lWKJ5bdSt2rnlDsaDimyTuhNQWKEkUgfnickXHoBWi0gMgDQcYT3Vvkdr4a8Dqm+pyRKRuXLEg0nkfipzPP8au999Gl7kN6iE12Zun6AgGmccAMYoOQRO4FYq84uzqriC1/1Vgwc+AGbcDQaGcVCKdYmBBpENSEnbu5s+wc81b6GhuhNpISdyRyTPhsAYrNgahMgIYq9jpNYMrFuQTtrbulYvd/wUW/xYYdyVgULanDRH5HrdCEelM6YG9eOlnd+Oz//xLlUGFxO10Ii6pXNEx2NpdmGSapegYtIB9LMin2sqBNbcCzy0Gyrdzcol0hisWRDrRWluNza88h5L9UnK0+pnrlE/inupYgIOmnYqOQe24FYpkUb0PePFCYOTFwNLfAUnZnGgiHWBgQaRxUiL2jjVv4sDHaz3lXLXC3FCL9FGtaK2PV2wMCY1ZAJsGnxW3QpGsjn4EHPu0u8ne+b8EIpM44UQaxq1QRBoliiJyN3+OF1b/GHvXvaepoOIbRkiduJXTVSkiysAM7rMRWW6W5Ca4gD3PAU9MBr7+p7RXknNOpFEMLIg0qPbYUbz+6/vx6TOPe7pna1Vd8QGEhCkXELldIuYbLlDs/FrAHAvyG3s78NlvgX/PA0q3cuKJNIiBBZGGdLa24OOnHsPrv/kZ6ooKoXWeJO5kZZO4sy2TFT2/6rFwD/lbYwHw8iXAmh8BHfWcfyINYY4FkQa4XU7s++hD7HzvLThtVuiJuV5K4h6m2PmDq+OUziFXNa5YkGIOvwMUftqdezHzx4DRxCeDSOW4YkGkclKVp5fuvwtbX39Jd0GFxFxfg/hU5bZzWRqdGGYcpdj51Y45FqT49qhPfwk8uwCoYAU3IrVjYEGkUlLuxLrH/4L3//I7tNXVQs8Mnk7cypklLFH0/GrGFQtShfpc4IXlwAd3AZYmpUdDRGfAwIJIhQ5/sREv/fROFO4IjATG+uL9CA5XrhJMegv3Qp0RuyOTaojAwf8BT04F9jwvlcZTekBE9B0MLIhUpLWuBm///lfY+OwTsFk6ESikJO74pArFzm+vCEIwQhQ7v5pxxYJUx9YGfPRT4OVLgZYSpUdDRCdhYEGkAoLbjd0fvotXfvYTVOblIBCZ6/cqdm6n1Y1ZpoWKnV/N+JkwqVbZVuCZucCOpwGBITCRGjCwIFJYY0UZ/vd/P/UkZ7ucDgQqc3014lPbFDv/OOtMxc6tZny7Rqrm7OpO7n7xQqCpSOnREAU8BhZECpE6ZW9/53W89ovVaCgt5vOgcCfuqPpUPgenwxwL0oLKncC/53Z37hbcSo+GKGAxsCBSQENZCf73q/uw493XIbiV6zytxk7cSiVxd1a7kGJMV+TcasYVC9IMl627c/fzS4GGAqVHQxSQGFgQ+bnR3ddvv+YJKhrLSzn3350fp0OxJG6pwMw88QI+J9+dF84IaU31vu6+F1v+Lr3oKj0aooDCwILIT1pqqvH6r3+GnWve9CRr0+m11+9TbGoGmccqdm614ooFaZLbDnzxMPDcYqCxUOnREAUMBhZEfnD4y4147Rf3MpeiF9rqqxCfokwSt1gZoch5iUgmtQeB/5wH7HmOU0zkBwwsiGRk77J4umdv/PcTcNptnOteMhqV6cRta3dhkmmWIudWK8Gg9AiIfFA56qP7gdevZtduIpkxsCCSSfXRfLzy81UB0z3bl+qK9iMkTJm90VMdCxQ5r1oxx4J0o/Bj4OnZ6CrapvRIiHSLgQWRjwmCGzvefQNvPfQA2hsbOL/9TOKOS1YmiTuhMUuR86oVAwvSE9Flx3Xv1ODh9UfgcDGDiMjXgnx+RKIA1t7UiA1P/h3VBcr1Y9CL9gapE/dQv5+3q1JEVHoMOsV2v59bjURwLxTpx/Pxq3GgLBIHtpVid2kLnrxmMgYnRSo9LCLd4IoFkY8U7voar/58FYMKH2mrq0Jcihn+5naJmGdY5vfzqpXANQvSidLMy/GHspEnvj5cbcYlT27DBweqFR0XkZ4wsCDykpSUvfE/T2LdY4/AZunkfPqQSaEk7hGWyYqcV424YkF64Igbiu9XXHHK9Z12F1a/dRD3v30IXQ72vCDyFgMLIi9ITe5e++V9OLzpU86jDOqL9ymSxB1cHe/3c6qVyBUL0jjRGIxfiPeg2RF8xvus2V/lWb3Iq/H/KimRnjCwIOqn/G2bPQ3vWqorOYcycTkciEvx//xaGp0Yavp2y0Qg44oFad3mjNvwXn3KOe9X0mjBFU9vx9t7+JpO1F8MLIj6UfVp8yv/9SRpuxx2zp/M2uulJG7/m+1aosh51YY5FqRlbWlzcEvR3F7fX6oU9fM1OXjww1y43KwaRdRXDCyI+qCr3Yw1f/wN9n30IefNT9rqKhGnQCfu9NZsv59TjUQDq0KRNgnhCbiu+WaIYt9/hl/eUY4fPr8brRaHLGMj0isGFkS9VF9ajP/96j5U5OZwzvzMZMz3+5w7KoIg/RfoRJGdLEibno5ZjbyO/peS3VHSjMue2ob8WpaeJuotBhYB7qabbsLll19+4v8NBoPnEhISguHDh+P3v/89XK7u5NnNmzd7bouPj4fNZutxnD179px4rF7zKd787c/Z8C6AkrgdVjdmmRYh0PXjw14ixRVmrcTfy4d7fZzKFiuufGY7Nhyu9cm4iPSOgQX1sHz5ctTW1uLYsWO4//778dBDD+Fvf/tbj/tER0fj/fff73Hd888/j4EDB+puNplPoaIk7mT/J1SOs85EoOMuc9IaR3w2VpZd6rPjdTncuPv1/Xh041Gu4BGdAwML6iE0NBRpaWkYNGgQ7rzzTixZsgRr167tcZ8bb7wRL7zwwomvrVYr3nzzTc/1esJ8CnXpaNzn93NG16ch0HEjFGmJaArFatcqmJ2+3cYo7Qh88osi3PbKPk/vCyI6PQYWdFbh4eFwOHomr/3whz/E1q1bUVFR4fl6zZo1GDx4MKZMmaKb2WQ+hfq01lYgLtm/NeY7q11IMaYjkDGwIC3ZmP5jbGhMku34n+fX44qnvkZZk0W2cxBpGQMLOmPC5ueff45PP/0Uixb13GeekpKCCy+8EC+99JLna2n14pZbbtHNTDKfQr1MJv8mcUufUs4TL0AgY2BBWtGcvgB3FMu/ffFYQydWPPU1vipslP1cRFrDwIJ6WL9+PaKiohAWFuYJHq666ipPnsV3SYGEFFiUlJRgx44duO6663QRTG15/SX2p1B5EndwqNuv5xxkHotAxj4WpAVCRBKuabixX6Vl+8NsdeKWl/bg2a+K/XI+Iq1gYEE9LFy4EAcPHvQkb0u5Ey+//DIiI08t1ycFHdLtt956Ky699FIkJiZqeibdLqcnoNjz4btKD4XOQmpIGJ/SvQXPb6oiEMjYeZu04J9Rq1FoCffrOd2CiEc+LsDqNw/A5vTvBx5EasXAgnqQggipzKxU4Sko6MzJb9JtN9xwg6cErda3Qdm7LFjzpwdR8PVXSg+FVJjEbTW7MNE0A4FK5GYoUrkjWdfgnxVDFTv/Bwdr8INnd6C+vWcZdqJAxMCC+u3hhx9GY2MjLrhAu3vQO5qbPP0pKvPY9E5LSdyxyf5tWDXNcR4CFXMsSM1sCaOwsvQipYeBnCozvvf0dpQ0dio9FCJFMbAIcIIgnHVl4mykJnpJSUmabYrXWFGG1399P5oqy5UeCvVRcJB/k7gTmvTXo6W32MeC1EoMCsMqx09gcZmgBtVtVqz89w7kVLUpPRQixTCwCHANDQ2evhUSKRn7gw8+OON9zz//fE+Cc1xc3Glvlzp4S7drQUXuIbz14APobGlWeijUD3VFexEc4r9a8l2VQCSiEIi08RtNgWh96l34rCkBatJsceCa/+zE10VNSg+FSBEMLAJUa2urpwKUlCMhNcELJFI52fceedCTW0EaTuJOrfLb+dxOAfNM2t3y5w3mWJAaNQ5YhFXF06BGFocbN7+4Bx/l1Co9FCK/Y2ARoKSE6zvuuAP3338/VqxYgUCx64N3sOFfj8LtYudUreto8m8S98hO/TSA7AuuWJDauCNTcVXd9VAzh1vAqjf247Wd3GpLgcW3Pe9JM95//30EElEQsOnFZ3Fo40dKD4V8pLWmHKnZ7TA3xfhlToNr4oFhCDjMsSC1lT/+a/h9KGkOg9oJIvDrD3LR1GnH6iUjlB4OkV9wxYJ0z+mw48NH/8SgQoeCgwv8di5LgxNDTNl+Ox8RnSon63o8W6WtYgqPf34MD36Yq5kcRCJvMLAgXZPyKN59+Nco3rtT6aGQDOqL/ZvEPce1DIFG4JshUglr0jhcW6LNXKeXd5TjnjcPwunmGiDpGwML0i1rRzve/v2vUFPo39Kk5D9Ouw3xqdV+O196a+CtWIjarCZNOiMGR+LHXXfB4tbu25Z1h2pwy0t70OVgjh/pl3Z/Q4nOwtLWird/90s0lBZznnSuo2mv387lqAiC9F8g4YoFqcF7KXdjS8vpS51rydZjTbjmv7vQanEoPRQiWTCwIN2Rumm/9dAv2PgugJK4/dWJ22F1Y5ZpEQIKVyxIYXUZy3B/8SToxaHKNnz/39tR02ZVeihEPsfAgnTF3FCHNx98AK21/tseQ8oLDvJfEvc460wEEqmyDZFS3FED8P3qa3T3BBQ3WvD9Z7ajqKFD6aEQ+RQDC9INZ6MF6//6Z7Q31is9FPKz+pK9CApx++Vc0fXdneoDBeMKUuxnz2DEwyGrUWUL1eWTUGO24apnd+JYPYML0g8GFqQLzoYuNP4nF+elX4WICP/0NSD1cNpsSPBTJ+7OaheSjYETXAgMLUgh+zNvxEs1mbqe/2aLA9c+twulTRalh0LkEwwsSPOc9RY0/icHQocDaHTh0vGrEBYWpfSwyM86m/3TiVuqvjof2ix5SaQVluRJuLZ4MQJBY4cd1/53Jyqau5QeCpHXGFiQpjnrpKDiMIRO57dXNrhw2aR7EBoWqeTQyM9aqssQm+SfLQWDzeMQKLhiQf4mhkThR50/hl0InLcotWYbrvnvTlQzoZs0LnB+a0mfKxX/PQzBclJQcZyh3o0Vk+9FSHC4ImMjZQQH+6dniVgZgUDBHAvytzeSVmFHa2zATbwUVFz3352ob7cpPRSifmNgQZrkbOxC43OnDyq+YahzY8X01QgODvPr2EjhJO5g+ZO4rWYXJppmIBCI7LxNflSVeTF+VTI+YOe8rLnLsy1K2h5FpEUMLEhzXM1WNEkrFR1nDiq+YawVPMGFyRTil7GRCpK40/xTaniqYwECAVcsyF9cMVlYWbUy4CdcKkV7/XNsokfaxMCCNMXVZvNsf3K3975rqalWxBWz7oPJFFgdkwOVv5K4E5sGIRAwx4L8QTSY8FvTatTa+CGQ5Gh9B65/fhfMXef+AI1ITRhYkGa4Oxxoei4X7ra+LxGbaoAVs34Ko9Eky9hIPVqqS/2SxN1VCURC/9XHuGJB/rAz8xa8XpvOyT5JXk07bnhxNzpsDC5IOxhYkCa4LU5PToWrydrvYwTXGLBizk9hMPDHXu+Cg+XvxO12Cphn1H/ZWZGhBcmsI2Uabig+n/N8Gocq23Dzi3vQ5XBxfkgT+A6LVE+wudD0Qi5c9d7X+A6pNmLF3PsAg8EnY6PATuIeaZkCveOKBcn68xUagxvNt8Ep8DX5TPaWt+LWl/bC5pT/NY3IWwwsSNVEp4Cml4/AWd3ps2OGVgdhxdzVPjseqY/TZkVCqvxJ3ME18dA7gVWhSEYvJdyL/eZozvE57Chpxm2v7IXdxeCC1I2BBamWKIhofqMAjlKzz48dVh2CS+fd6/Pjknp0tu6X/RyWBieGmLKhZ1yxILmUZV6G35WO5gT30tZjTbjrtf1wugXOGakWAwtSrbb3i2A70izb8SOqw3DJ3FWyHZ+U1VJVghg/JHHPcS+DnjHHguTgjB2MlRVXcnL7aFNBA372ziH2lyHVYmBBqlS7+Rgse+pkP09kTQQumnu37OchZYSGHJX9HOktw6FnDCzI5z9TxiD80nAvGh3BnNx++PBgDR77rJBzR6rEwIJUZ8+ePXjh67dRl+WfzqPRNVG4YM6P/XIu8q+64j0wyZzE7agIRhD02yOFORbka1szbsO7damcWC88+UUR3tlbyTkk1WFgQaqSn5+PDRs2wOl0YkPzdpQPtfjlvHG1cVg6+za/nIv8nMQtcyduh9WNmSb9lspkjgX5kjl1Fm4umstJ9YFfvX8Y24uaOJekKgwsSDUqKyuxZs2aE3tHBUHAZzU7UZDd5pfzJ9QlYPGsW/xyLvKfrhb5k7jHW2dDr7gVinxFCIvHD1tvgVvkWw9fcLpF3PHaPhQ1yJ9LRtRb/O0mVWhqasLrr78Ol+vUJkDbKvdhf3Y9RIP8n50m1Sdj4cwbZD8P+U+zJ4nbd+WKTye6IQ16xRUL8pX/xN6LnHb9d6v3p3abCze/tAdNnf7ZOkx0LgwsSHGdnZ147bXXYLWeuav2/spcbB9WCbdJ/rc5KQ3pWDD9WtnPQ/4TEiJvJ+7OahcSDSnQI0FkaUvyXlHWlfhz+QhOpQwqW6y49WU20CN1YGBBipJyKd544w20tZ17u1N+1TF8mXUMzlD5g4v05izMnXaV7Och/2go2StrErf03nsBlkOPuGJB3nLEDcPKshWcSBkdqmzD6jcPsgwtKY6BBSlq3bp1qK7ufXJtWV0lPk3Ogz1S5k9RRSCzZTBmT/m+vOchv3BYu5CYWiPrOQa3j4MeMceCvPr5MYXgfuEetDr1WzlNLT7Jq8MjH8u7Okt0LgwsSDHbtm1DTk5Onx9X11SP9VEH0Bnvlj24GGgehpmTLpf3POQXljaZk7grI6FHXLEgb2xKvx3rGpI5iX7yny0leG1nOeebFMPAghRRWFiITZs29fvxreY2rDPsQVvqqcnePiUAgztGYfrES+U9D8muubIYMYnyJXFbzS5MME6H3jDHgvqrNW0ubivWb8U0tXpwbR6+PNqg9DAoQDGwIL9raGjoUVa2vyxdFqy17kBDpszVMAQRQy1jMXmcPvfQB5LQUHk7cU9znifr8Ym0QghPwjVNN0MUDUoPJeC4BRGrXj+A/Np2pYdCAYiBBflVV1eXJ1nbbvdNMOBwOPBR6w5UDumCrNwiRtgnYeLYZfKeh2RVXyJvJ+7EpkHQG3bepv54KuZeFHRGcPIU0ml34ZaX9qC+3cbngPyKgQX5jdvtxjvvvIPW1lafH/fT2h0oHG6GrFwiRjmmYNzohfKeh2RN4k5Iq5Xt+F2VQCT0VaefydvUVwVZV+HR8mGcOIXVmm2e4MJil3nLMNFJGFiQ33zyyScoLS2V7fhbqvbi0IgGeRvpuUSMdc/CmJEL5DsHyaqrdZ9sx3Y7Bcwz6mtVS2D6NvWBPX4kVpZezDlTibyadjywpu9FUoj6i4EF+cXevXuxZ88e2c+zp+Iwdg6rgmCUMbhwCpiAuRg5fI585yCZk7gtsh1/hGWKbMcmUjMxKAz3un6CDhdLy6rJ+pxavLKjTOlhUIBgYEGyKysrw4YNG/w203lVhdg8qBiuEPmCC9EhYHLQecgeNlO2c5B8QkPlq/UeWpMAPWFVKOqtj9PuxCeNiZwwFfrD+nxPEz0iuTGwIFlJHbXffvttCILMDe2+o6S2HBtTj8ARId95RbuAqSGLMXTIVNnOQTImcQfJk8Td2eDEIONw6AX7WFBvNKefh7uK9FduWS8cbgF3/W8/2rocSg+FdI6BBcnG5XJ5ggqpEpQSahrrsD7mILpi5asCJNrcmBF+AQYPnCjbOUh7SdxzBf3kWbAqFJ2LOzIFP6i/gROlctVtVvz07UNel3onOhsGFiSbzz77DDU1NYrOcEtbK9YG7UV7snxVMUSrG7OiL8bAzHGynYN8z2o+INu0DmjNhn7wTQid7afDgMciVqO4K5zTpAFfFDTg6c3FSg+DdIyBBckiPz8fu3btUsXsdlo68aFjJxoHyLcELHa5MSduBTIyRst2DvKtpopjsnXidlQEIwj6SGDligWdTW7WtXiqcjAnSUMe+6wQO4qblR4G6RQDC/I5qU/Fhx9+qKqZlRryrTdvR/Vgq2znEC0uzE/4Hgakj5DtHORboWGFskypo8uNGSZ9dOFmHws6E2viWFxdciEnSIOdue958wAaOtg8j3yPgQX5PK9CaoJns6nvBUtqpPdJ/Q4UDW+X7RxipwsLklciNYXNobSgQcYk7gm22dADBhZ02p+L4AjcbbsLFjffRmhRY4cdq14/4AkyiHyJrwiku7yKs5GS1jZX7cHh7Cb5ztHhwsL0q5GcxO0BamfvssiWxB1dnw49YKInnc6HqXfhi+Z4To6G7Sptwd83HlV6GKQzDCz84KabbsLll1/e47p3330XYWFhePTRR2G1WvHggw9ixIgRCA0NRVJSElauXIm8vDxoiZryKs5lV+Uh7Mqulq2RntjuwuKs65GYkCnL8Un9Sdyd1S4kGJOhdey8Td9VP2AJVhexEaQe/PurYmzKr1d6GKQjDCwU8Nxzz+G6667DM888g5/85CdYsmQJXnjhBfzhD39AYWGhp5mctKVo5syZ2LlzJ7RAjXkV53K4sgBbBpfCHSxTcNHmxNLBNyEuTh+fXOs5iTs6wfeduEUBWCBqf/85t0LRydxR6biq9lpOik5IlWelErSVLcqUhSf9YWDhZ3/961+xatUqvPnmm7j55pvx+OOPY8eOHVi/fj1+8IMfYNCgQZgxYwbWrFmD0aNH49Zbb1X9VgQpd0FagVFjXsW5FNWUYmN6ARzh8jTSE1udWD7sVsTGpspyfPKNsHB5kriHtGu/BLHaX3/If0SDEY+ErUaZNYzTriNmqxM/eX0/HC7/NrIlfWJg4UcPPPAAHn74YU8QccUVV3iue/3117F06VJMnNizwZrRaMR9992HI0eO4NChQ1B7XkV1dTW0qrqhBhvicmCNkSm4aHHiwuzbEB2dJMvxyXsNpTIlcVdGQuu4YkHfOJj5QzxXlcUJ0aFDVWY8vP6I0sMgHWBg4Scff/yxZ7VC2i60ePHiE9dLW5+klYnT+eZ66T5qVVBQoJntWmfT1NqMtSF70Z4kT4UgsdmJi0b9GFFRTHZUI7ulEwlpdT4/rtXswgTTdGgZ+1iQpCtpAq4tWcLJ0LFXd5Zjw2F5illQ4GBg4ScTJkzA4MGDPUnanZ2duthqIH0fWsurOJuOzg6sde1Ec7pTnhM0uXDxmLsQERErz/HJK9Z2eZK4pzq13c+CKxYkhkTi9q47YXWbOBk69+sPctHUaVd6GKRhDCz8JCMjA5s3b/ZsGVq+fDk6Ojo810uVoKRqSqfzzfXSfdRo7dq1nopWeiLliazr2I6aQTLlizS6cOn4nyAsPEae41O/NZUXIirB9wmMSU0DoWWilIVOAe3tpFXY1sIPRAJBi8WBX713WOlhkIYxsPAjKTH7q6++Ql1d3Yng4uqrr8bnn39+Sh6FIAj4xz/+gTFjxpySf6EGBw4cUPUWLW9IFbk+btiO0mE9V5Z8psGFyyb+BKFh2t9/rzfh4b6v6d5VYUCEQbvPtTbXU8lXajKW44GSCZzQALLxSD0+OKDdvElSFgMLP8vKyvKsXDQ0NOCCCy7A3Xff7akCdemll3o6VldUVGDPnj248sorPSsWzz//PAwGA9Skra0Nn3zyCfRM2p62qXoX8rJbZDm+od6NFZPvRUhwuCzHp/4ncRuDfPsJvdspYJ5xmWafEoErFgHLFZ2BldVXKT0MUsCDa/PQ0K69So+kPAYWCsjMzPQEF01NTZ7gYuPGjbjhhhvwq1/9CsOHD/esZphMJk9S9KxZs6C2N9xSXoXdHhh7MHdUHsCe7FqIBt9/bmuoc2PF9NUIDmbpRjUlcSfK0Il7ZOdUaBVzLAKTaDDh98H3odoWqvRQSKEStL/glijqB4Oo1cxhUoTUWVuqcBVoRmQMxdzywTC5fL965E434P1dj8Htdvj82NR3yYNGoKP9Ep9OXVRqMP4+9C5NPh0mgwkHS0qVHgb52e6sW/GDY99WMKTA9NfvT8APprHEMPUeVyyo15qbmz35IIGosLoEn2cchTPM94msploRV8y6DyZTkM+PTX3XKCVxx/s2ibuz3olBxuGafDq4YhF4OpOn4IfFC5UeBqmA1Nui1qyvIi0kLwYW1CtSMvkHH3wAp1OmUqwaUFlfjY8TcmGNliG4qAFWzPopjEaWc1SD8AjfFyaYIyyFFjHHIrCIodG4peN22AW+PSCgw+bCA2tYJYp6j68c1Cvbt29HZWVlwM9WQ0sj1oftQ2ei7xvpBdcYsGLOT2Ew8NdSaQ2lu2E0+TaAHNCa7dPjEcnhtYR7sLuN5bDpW1sKG/H6rgpOCfUK38HQOdXX1+PLL7/kTB1n7mjHh8IutKb5fvUmpNqIFXPvA1RWCSwgk7jTfduJ21kRAiO0uSIlgD+PgaAi8xL8pnSs0sMgFfrThnxUtfq+zw/pDwMLOiu3243333/f8y99S2oMuNayA3UDfV+OL7Q6qDu4IEXZfNyJ29HlxizT+dAikatouueMGYQfVH5f6WGQSnXaXfj5uzmeypBEZ8PAgs5q69atnoZ+dCop32RD0w6UDbX4fHrCqoNx6bzVnHYFNZYfRbSPk7gn2GZDiwSuoOmaaAzCr033os4eovRQSMW2Fzfj1Z3lSg+DVI6BBZ1RY2OjJ7Cgsye1f16zE/nZrT6fpojqUFwydxWnX0FhPk7ijq5PhyZxxULXtmfcirdq05QeBmnAnz8uQHmz7z9MI/1gYEGnJS13rl+/nlugeunryv3Yn13v80Z6kTURuGju3T49JvVeY9kenyZxd1a7kGBM1txTwBwL/WpPnYEbiuYrPQzSiC6HG//vnRwIArdE0ekxsKDTOnDgAMrLueTZF/src/H10Aq4Tb59wY2uicLyOXf49JjUO7bODp8mcYsCsAAXam76RSP/VOiRGBqLG1p/BLfI55d6b3dZC17eUcYpo9PiqwmdwmKx4LPPPuPM9ENBdRG+yDoGZ6hvg4vY2lgsm3MbnxMF2DoO+vR4Q8zjoDXMsdCn5+NX42B7lNLDIA16bGMhGjvsSg+DVIiBBZ3i008/9VQ9ov4pr6vEJ0m5sEX6tg9CfG0CFs+6hU+LnzWWFSAqzoe/D1WR0BzmWOhOaebl+EPZSKWHQRrVYXfhL58UKD0MUiEGFtRDSUkJcnJyOCteqm9uwPrIA7DE+7ZMb1J9MhbOvMGnx6RzC4/0XRK3tc2F8aapmpp25ljoiyNuKL5fcYXSwyCNW7O/CvsrfF+4hLSNgQWd4HK58NFHH3FGfKStvQ1rDbvRlury6ZymNKTjvBnX+fSYdHaNZb7txD3Nqa1+FuxjoR+iMRg/F+9BsyNY6aGQxkktLR78ME8zidw7duyAyWTCxRdffMptDocDf/3rXzFx4kREREQgKSkJc+fOxYsvvugpLS+56aabYDAY8Oc//7nHYz/44APP9dSNgQX1+KVrbm7mjPiQpasLa6070JDl272oaU2ZmDftap8ek86RxJ3muyTupKZBmppugX8zdePLjNvxQX2K0sMgnThcbcZbeyuhBc8//zxWrVqFLVu2oKampkdQccEFF3gChttvvx3bt2/H7t27cffdd+PJJ59EXl7eifuGhYXhL3/5C1pbuVJzJgwsyKOtrc3zy0a+J71orW/ejoohPqz9LQIZLYMwZwo75fqLvfOQz47VVWFAhEFDuRbMsdCFtrQ5uLVojtLDIJ3526dHYe7q/lRfrTo7O/HWW2/hzjvv9KxYvPTSSydue/zxxz3vfzZt2uQJJiZNmoShQ4fi2muvxa5du5CdnX3ivkuWLEFaWhoeeeQRhb4T9WNgQR4ff/zxieU+kqeR3sbanSgcbvbdQUUgyzwMMydd7rtj0hk1lOUj0kdJ3G6ngLmGpZqZbeZYaJ8QnoDrmm+GKHL5iXyrxeLAo58dVfW0vv322xg1ahRGjhyJ66+/Hi+88IKnX5fkf//7nydgmDx58imPCw4ORmTktx8CSVup/vSnP3lWMqqqqvz6PWgFAwtCYWEhjh5V94uCXmyp2ouDIxp810hPAAZ3jML0iZf65nh0VpFRx3w2Q6O6tJPAzRwL7Xs6ZjXyOjS0Skaa8r9dFcivbYeat0FJAYVk+fLlMJvN+OqrrzxfHzt2zBN09NYVV1zhWdV48MEHZRuvljGwCHBSwra0WkH+s7fiMHYMq4Lgq0Z6goihlrGYPF57jde0pqF0Nww+SuIOrUmEVrCPhbYVZq3E38uHKz0M0jG3IOLBtd/mIqiJ9MGplDNxzTXXeL4OCgrCVVdd5Qk2JN+sXPSFlGfx8ssvIz8/3+fj1ToGFgFO+mVjEpL/HakqxJcDi+EK8VFw4RYxwjoRk8Yu883x6LRsne1ITK/3yex01jsx0DhMGzPNHAvNcsRnY2UZVzRJfrtLW/DhwWrVTbUUQEgfog4YMMATVEiXZ555BmvWrPGsXIwYMQIFBX3rybFgwQJPwvcvf/lL2catVQwsAlhXVxcTthVUWluOT1OOwB7hu+BipGMKxo9e5Jvj0Wk5On3XiXuuoI1AUGQpRU0STaFY7VoFszNI6aFQgHhkQwEsdt+WWPeGFFC88sorePTRR3Hw4METl0OHDnkCjTfeeMOTpP3555/jwIEDpzxeyj21WE5feEWqIrVu3TpPRU36FgOLACZVQbDZbEoPI6DVNtXho+gDsMT5qEeCS8QY90yMGbnAN8ejUzSUFfgsiXtA67fVRtSMW6G0aWP6HdjQmKT0MCiA1LXb8OQXRVCL9evXe3Zl3HrrrRg3blyPy5VXXulZzVi9erWnZ8XixYvx1FNPeYIOqVmwlPA9a9YsTw7G6YwfPx7XXXcdnnjiCb9/X2rGwCJAtbS0YM+ePUoPg6TnwtyKdabdMCf76FMep4AJmItR2XM5v3IQRUT4KInbWRECI0xQOyZva09z+gLcUTxD6WFQAHphWylKGjuhBlLgIFV8io2NPeU2KbDYu3evJwfjs88+w89//nM8++yznmBi+vTpnoDhnnvu8QQhZ/L73//eU/WRvmUQ+5O1QponReJHjhxRehh0ktDQUCyPmoHk6hCfzIsh1Ij9zi9RWLyT8+xj4dExQOgtEN3efzaTM28ttrs3Qc02tgpIb2NpRa0QIpKw3P4XFFrClR4KBajzRybjpZsZ2AYirlgEoMrKSgYVKmS327G+bTuqBvtmm41oFzAlZBGGDtFOWVOtsHa0IynNN0ncE2yzoXZcsdCWf0atZlBBitp8tBGfHfHNayRpCwOLAPTpp58qPQQ6A7fbjU/rd6BouG/qgYs2N2aEX4DBAydyzn3MbvFNJ+7o+nSoncCeappxJOsa/LNiqNLDIMIfPzoCl5vbhAINA4sAk5uby26RKiftTtxctQc52Y2+OZ7VjVnRF2Ng5pn3iVI/O3HHer+61FntQoIxWdVPAatCaYMtYRRWll6k9DCIPMqau/DW3krORoBhYBFApLJrmzapey83fWt3ZQ52Da+GYPQ+DUrscmNO3ApkZIzmFPuKKCIy2vvqJ6IALMByqJkILlmonRgUjlWOn8DiUn8xAAocT2w6BpvTrfQwyI8YWAQQNsPTnsNVBfhqcAncwT4ILiwuzE/4Hgakj/DJ2AhoLNsNg9H7pf4h7epeTWKOhfqtT7sLnzUlKD0Moh7q2+14eXsZZyWAMLAIEFarlc3wNKq4pgwb0/LhCPf+DazY6cKC5JVITdFIx2eVs3aYkZje4P2BKqOgZuxjoW6NAxZhVRGLNJA6/furYnTYnEoPg/yEgUWAYDM8baturMWGuBxYY3wQXHS4sDD9aiQnDfbJ2AKdw+J9J25rmwvjTFOgVsyxUC93ZCquqrte6WEQnVFrlxP/3VLCGQoQDCwCQEdHB5vh6UBTazPWhuxFe5L3jfTEdhcWZ16PxIRMn4wt0JO4I2K972A/3bkQasUcC/U+L38Nvw8lXWFKD4XorJ7fVormTjtnKQAwsAgAX3/9tSdxm7Svo7MDa5270DTA4fWxRLMTSwffhIT4AT4ZW2AncXvfiTupaRDUijkW6pSTdT2erRqo9DCIzsnicOOpL4s5UwGAgYXOdXZ2elrWk37Y7Dasb9+BmkHef0outjqxbOgtiI1N9cnYAlVT+R6vk7itlQZEGCKhRqxErz7WpHG4tuQCpYdB1Guv7SpHrdk3DWBJvRhY6BxXK/RJWoH6uGE7SoZ3eH0sscWJC7NvQ3R0kk/GFois7W1eJ3G7HALmGpZCjbhioS5icCR+3HUXLG7+CSftcLgEPM1VC93jq5KOcbVC/430vqjajdzsZu+P1ezERaN+jKioeJ+MLRA5u7zvxD2qS52VfZhjoS7vpdyNLS1xSg+DqM+khnlctdA3BhY6X61wOlniTe92Vh7EnuwaiAYve100uXDxmLsQERHrq6EFlPrSI1534g6tSYQaieyPpxp1Gctwf/EkpYdB1O9Vi6e+9L6xKKkXAwud4mpFYDlUmY+tQ8rgCvIyuGh04dLxP0FYeIyvhhY4fNCJu7PeiYFG9fUYYR8LdXBHDcD3q69RehhEXnl7TxVXLXSMgYVObd++nasVAaawpgSbBhyFI8zL4KLBhRUTVyE0TJ2JxGrW6IMk7rmCCvMsDPxToYY8l4dDVqPKFqr0UIi84nBz1ULP+NdChywWC/tWBKjKhmp8nJADW7SXdXzqXVgx+V6EBIf7amgBlMTd6NUxMlpHQG1YFUp5+zNvxEs17DtD+lm1qGljhSg9YmChQ1ytCGyNLU1YG7YPHQlur45jqHNjxbTVCA5m8y1/JnE7KkJghAlqwqpQyrIkT8K1xYsVHgWR73DVQr8YWOhwtWL37t1KD4MU1t7RjrXiLrSke5e8b6wTsGL6fTCZQnw2Nr2rL81DREz/e4w4utyYaToPaiIweVsxYkgUftT5Y9gF/rkmfXlnL3Mt9IivVDrD1Qr6htVqxbrOHagbaPdqUky1Aq6YJQUXQZzc3hBFRMV6l8Q9wTZbZXPNPxVKeSNpFXa0slIb6XPV4uXt5UoPg3yMfy10xGazMbeCepDKDW9o2o6yoZ1ezYypBlgx66cwGtW1RUetmsp2w+BF+d+YhnSoCXMslFGVeTF+VTJeobMTye+N3RXocrg41TrCwEJH9u/fD4fDofQwSGUEQcDnNbuQn93q1XGCawxYMeenMLBC0Dl1SUncA/rfidtSJSDBoJ5O6Myx8D9XTBZWVq1U4MxE/mO2OrFmXxWnXEcYWOjozeOuXbuUHgap2NeV+7Evu86rRnoh1UasmHsfYOCm+3NxWnP6Pc+CIGK+YTnUgjkW/iUaTPitaTVqbcxtIv178esyiKKXZdJJNRhY6ER+fj7MZrPSwyCVO1CZh21DK+A29f9FPLQ6qDu4oLNqKPEuiXtIu4q2wHCVyq92Zt6K12vVtR2OSC4lTRZ8ebT/K7ykLgwsdGLHjh1KD4E04mh1ETZlFsIZ2v/gIqw6GJfOW+3TcemNKAqIiinu9+ONVVFQCwH8NNFfOlKm4YZidVUFI5Lb89tKOck6wcBCB6qqqjwXot6qqK/CJ0m5sEX1Py03ojoUl8y9h5N+Fk0V/U/i7mp1YZxpiirmlzkWfprn0BjcaL4NTu49owDzdVEzCuralR4G+QADCx3gagX1R31zA9ZH7EenF430ImvCcdHcu/kEnEGXuRUJ6f1f4p/uXKiKuWVVKP94KeFe7DdH++lsROryAlctdIGBhca1tbXhyJEjSg+DNKqt3Yy14m60pva/kV50TRSWz7nDp+PSE5et/0ncSU2DoArMsZBdWeZl+F3paPlPRKRSHxysQVOnd32XSHkMLDRO6rLNagrkjS5rF9Z27UB9Vv9f0GNrY7Fszm18Is6UxB3dv7m1VhoQhnDF55UrFvJyxg7GyoorZT4Lkbo5XAJe28mGeVrHwELD7HY79u3bp/QwSCeN9D5q3o6KoZZ+HyO+NgGLZ9/i03HpJom7n524XQ4B80zLoDSR5YXlm1tjMH6Be9HoCJbvJEQa8drOCthd/d+eS8pjYKFhBw8e9AQXRL7qhbKxZieOZrf1+xhJdclYOPNGPiHf0VS5B+hnEvdoyzTF51ME+5bIZUvGj7CmPlW24xNpibQVau3BGqWHQV5gYKHhN4E7d+5UehikQ1sr9+FAdn2/G+mlNKThvBnX+3xcWtbV1oKkAY39emxobSKUxhULeZhTZ+GWorkyHZ1Im174ukzpIZAXGFhoVGFhIVpbW5UeBunUvspc7BhWCaGfjfTSmjIwb9rVPh9XIHbi7qxzYqBxKJTEHAsZ5jQsHj9svQVukX+GiU6WX9uO7cVNnBSN4iuahpO2ieR0pOoYvswqgiukH8GFCGS0DMKcKd+XY2ia1FCSi/Do/nXiniMshZLYx8L3/hN7L3La1dMEkUhNWHpWuxhYaJC0UlFSUqL0MCgAlNZV4NOUPNgj+hdcZJmHYebkK+QYmiaTuKPj+vd7m9E2Ekpi323fKsq6En8uH+HjoxLpx6aCBpQ29b+YCCmHgYUGsRIU+VNtUz3WRx+AJa4fG2IEYHD7SEyfeKkcQ9Oc5ord/UridlWEwAgTlCIyd9tnHHHDsLJshe8OSKRDogi8uoOlZ7WIgYXGuN1uTzUoIn9qNbdirXE32lJcfX+wIGKoZSymTLgIgc7S1oLE9L7vHbZb3Jhhmg+lCKwK5ROiKQT3C/eg1RnkmwMS6diHB6vhdDPDS2sYWGgwabuzs1PpYVAAsnRZsNa2A42Z/Shx7BaR3TUBk8Yq35NBaW57/5K4J9jnQCmsCuUbmwb8GOsakn10NCJ9a7Y4sPlo/6rpkXIYWGgMt0GRkhwOB9a37kDlkK6+P9gtYqRjCsaPXoRAVl9yGOH96MQdWz8ASmGOhfda0+bitqJZPjgSUeBYs69K6SFQHzGw0BCrtQZxcQUIZoNWUng73qe1O3BsuLnvD3aJGOOeibEjz0OgEgUpibu4z4+zVAlIMCRBCVyx8I4QnoRrmm6GyGQVoj75oqABbV0OzpqGMLDQkLq6NQiPeANz532IRYvrMHAQMypJOV9V7cWh7H4sUzsFjBfnYPSIeQhUzf3oxC0IIuYblss2prOeW5Gz6sdTMfeioDNC6WEQaY7DLWDtIXbi1hIGFhohiiJq697z/L/b3Q6n8zMMGvQKli7dhZmzrIiIYJBB/renMgc7h1dBMPbtTbLoFDDRMB8jhgXm1hBLazMS0/selA1pHw8liPxT0W8FWVfh0fJhvnw6iAIKt0NpCwMLjWhr2wOrteKU6232QoSEvIvpM97GkiUVGDGCu6HJv3KrjuKrwSVwB/cxuHAImBKyCMOGTEMgctsP9/kxxiplGqpxB0//2ONHYmXpxT5+NogCy6EqM4oaOpQeBvUSAwuNqK1bc9bbBcEGu+MrpKa9hsVLNmPevA7ExXEVg/yjuKYMn6blwxHet00zos2N6eHLMGTQJASa/iRxd7W6MNY0Gf4mstxs3+csKAz3un6CDhdLyxJ569191ZxEjWBgoQFudxcaGj7u9f0djkoYjB9gwsTXsXRZIcaPd8PQj6ZcRH1R01iLj2IPoSu2j8GF1Y2ZkRdhYOa4AEzi7nsn7unOhfA35lj03cdpd+KTxkQZng2iwPPBgWpPnhmpHwMLDahv2AC3u++t7UXRBZttF+LiX8fCRZ/ivPNbkJbGp5zk09zWgnVBe9Ce7OpzcDEnbgUyMkYjkPQniTu5eTD8jVWh+qY5/TzcVTRdpmeDKPDUtduwrajvzUXJ//guUwNqa7uTtr3hdDZCED5C9ohXsHRZDqZOdSIkxCfDI+qhw9KJtY5daBrQtxKBosWF+Qnfw4D0EQEzo5bWpj534rZWGBCGcPiTyE4WveaOTMEP6m+Q8+kgCkhr9rOnhRYwsFA5KWG7rW23D48owmY7hIjINzFnbnfZ2kEsW0s+ZrPbsL59B6oHWfv209npwoLklUhLDZwqOm5H35K4XQ4Bc01L4U9csejlPMGAxyJWo7jLv4EfUSD4NK8OHTan0sOgc2BgoXL19R/J1vf2m7K1A4+XrZ01y4rISCZ8k2+4XC580rADxcPa+/Q4scOF89OuRnKy/7f8KKG+OAdhUX1b3Rlt8W8lLYHJ272Sm3UtnqoMjJ9bIn+zOQVsOFzLiVc5BhYq19D4iV/OI5WtDQ55F9Omv4UlS8oxcqR0LROlyPv+K19W70FudnPfHtfuwuKM65GYmBUQSdwx8X3rxB1W69+kYL4SnJs1cSyuLrnQD88GUeBaw+pQqsfAQsWs1mp0dOT69ZyCYIfdsQUpqa9i8ZKvWLaWfGJn5UHszq7pUyM90ezE0oE3IiF+gO6fhZaqPX16+95Z50KWcQj8hVuhzjE/wRG423YXLG7+SSWS057yFlQ0d3GSVYyvgirW2Pipouc/UbZ2wv+6y9ZOEGDkTwz1U05lPrYOLoUrqA/BRZsTy4begtjYVF3Pe2dLExIH9C2Je47gvzwLrlic3Yepd+GL5ng/PRtEgUsUmcStdnybqGINCgcW3xDh7i5bG/c/nL/wY5x3fjPS0piLQX13rKYUnw8ogCOsD8FFixMXZt+G6JhkXU95X5O4M1tHwV/Yx+LM6gcsweqiKX57LogC3QcH2SxPzRhYqJTd3gizeT/UxulsgiBsOF629hDL1lKfVTXU4OP4HFhj3L1+jNjsxEUjf4zoqERdJ3GHR/a+E7erMgRGmOAP3Ap1eu6odFxVe61fngMi6lbe3IWjdR2cDpViYKFSjU2fqf5zQpst53jZ2g88ZWsHD+YqBvVOY2sT1oXuR0diHxrpNTlx0Zg7EBERq99O3Amlvb6/3eLGDNN8+AO3Qp1mTgxGPBK2GmXWML88B0T0rc/z6zkdKsXAQqUaG/xTDcoX3O4OT9narIFS2dqdLFtLvdLe0Y617t1oSe9DXfJGFy4d/xOEhcfocpb7msQ9wT4H/urPQD0dyLwBz1Xpv2oZkRp9doSBhVoxsFAhp7MNrW27oEU2+7Fvy9YuZdlaOjurzYq1HdtRO8jW+6lqcGHFxFUIDYvU3fR2tjQiMb33pXlj6/1TMUvda6f+15U0AdeVLFZ6GEQB61BVGxo6+vB3g/yGgYUKNTZ9DlHswxYRFfKUrbV/U7Z2M+bP70B8PH/c6PSN9DY0bEfp0M7eT0+9Cysm34uQkAjdTang7H0St6VKQLzBD3knXLA4QQyJxO1dd8Lq9k9+CxGdvjrUpvwGTo0K8Z2eCjU2boSeOBxVgOEDjB//GpYuO4rxE9wsW0unNNLbVLMLR7Jbej0zhjo3Vky9F8HB+trjXl/S+yRuQRAx37Bc9jFxxeJbbyetwrYWfeb5EGnJ59wOpUoMLFTG5bKgpWUr9Ki7bO1uxMW97ilbe/75zUhP548gfWt75QHsza6FaOhdnoGxTsCK6ffBZArRzTQKbnefkriHto+H3Jhj0a0mYzkeKJkg+3wT0bl9XdwEq6P31QXJPzTzru6mm27C5Zdffsr1559/PlavXn3K9S+99BLi4uI8/79q1SqMHj36tMetqKiAyWTC2rVroQbNzV9CEBzQO6lsrVvYgOHZL2PpsoOYNs2J0FDutyDgYOURbBtaDncvG+mZagVcMUsKLoJ0M30t1b1P4jZWyZ/IzhULwBWdgZXVV8k+10TUOzangK3HGjldKqOZwMIbt956KwoKCrB9+/bTBiApKSm46KKLoAZqaYrnTzbbYYRHvInZc97HosW1GDxY6RGR0o5WF2NTRiGcob0MLmqAy2fdD6NRH/veO5t7n8Td1erEGNMkeQdkCOygXzSY8Lvg1ai2hSo9FCI6CcvOqk9ABBaTJk3ClClT8MILL5yyr1sKLG688UYEBSn/aafbbUdz82YEqu6ytZ8ja+CrWLp0B2bNtiIyMrDf0ASyivoqfJx0GLao3n1eHiQFF7N/CoNBHy9rgiu31/ed7lwo71gQ2HZn3oxXazKUHgYRfccXBQ2eXDNSD338Be7lqsXbb78Ni8Vy4rrNmzejtLQUt9xyC9SgpWUL3O4upYehCjZ7EYKDvylbW4ZRo6Rr+eIRaBqaG7EuYj86E3q3jza4xogVc+/TxSfs9cWHEBbZu22RKc1DZB1LIP/mdSZPwQ3F5ys9DCI6jaZOBw5UtnFuVCRgAotrr70WTqcT77zzzonrXnzxRcybNw8jRoyAGjQ0aqcpnn/L1m5FcopUtvZLzJ/fjoQE7b9ppN4zt5uxVtyNlrTelWAOrQ7SRXAhJXHH9DKJ21phQCjkq44VqCsWYmg0bum4HXYhYP5UEmkOt0OpS8C8WkqJ3N/73vdObIdqb2/HmjVrPCsZaiCKApqaAncbVG84HNWA4UOMG/c/T9naCSxbGzC6rF1YZ9mOuqzelWENqw7GZXPvhda1VO+G2Iv1ApdDwDzTMvkGovEgrb9eS7gHu9v02eWdSC9YdlZdNB9YxMTEwGw2n3J9W1sbYmN71hqXgoitW7eiqKgIb731lqca1MqVK6EGHR25cLm4nNeXsrWxnrK1G1i2NkBIK44bmrejfOi32xnPJrw6FJfMuwfaT+LuXW+P0ZZpso0jEFcsKjIvwW9Kxyo9DCI6h2MNnShv7t3fBZKf5gOLkSNHYv/+/adcL1333S1OCxcuxJAhQzxboKTL1VdfjcjISKhBS8vXSg9Bk5zO5u+UrXWwbK2OCYKAz2p2oiC7d0F4ZHU4Lpp7N7RM7GUSd1itfB24Ay3HwhkzCD+o/L7SwyCiXvqMzfJUQ1OBhbQycfDgwR6XSy65BIWFhbjnnnuQk5ODo0eP4rHHHsMbb7yB+++/v8fjDQaDJ1H7mWeewY4dO1SzDUrS0rJN6SHopGztW5g95z0sXlyLIUMCc/tGINhWuQ8Hsut71UgvuiYKy+fcAa2qLz7YqyTuzjoXsozyJHEH0oqFaAzCr033os6un6aLRHrHPAv10FRgIVVxmjx5co/Lq6++ii1btnj6VCxZsgQzZ870VH+SkrSXL19+2kZ7UoAyduxYz33VwO22wtx+6qoL9Xc+O+Fwfo7MrFewZOkOzJ7dhagoBhl6s68yF9uHVcJtOndwEVsbi2Wzb4d2k7jLenXfOcJS2cejd19n3Iq3atOUHgYR9cGeslaYu5ycMxUwiFIzB1JUc/MWHDx0M58FGRkMIQgJmYmqykEoKPBcw/nWicFpA3F+zXAEOc79nDanNuLznT372WhBdGIKHMJ1MJzj5zZytBuPxv3U5+e/NW48Vh/4CHrXnjoDkyvugVvU1GduRATg39dPxfJx/FBAaXz1VAFug5KfKDqOl619rbts7QKWrdWLsroKfJKSB3vkuTfsJNYnY9HMG6E1Hc0NvUridlWEwgjfdx8PhE+fxNBY3ND6IwYVRBq1u7R3hS5IXgwsVKCllYnbfi9bi2/K1hZgwkQ3TL5/L0Z+VNdUj/VRB9AZf+5GeskNaThvxvXQYxK33eLCdNM8358b+vd8/GocbI9SehhE1E+7y5o5dyrAwEJhdkcTOjuPKj2MAC5buwexsa9jwXkf4fyFTUhP5xYprWo1t2GdYQ/aUs7dSC+tKQPzpl0NrXXiDo089x7iiba5Pj+3qPOtg6WZl+MPZSOVHgYReSG/tgMdNuZZKI2BhcJaW3cEyOeB6uZytcDt/hjDs1/B0mUHMG06y9ZqkaXLgrW2HWjIPEcVJRHIaBmEOVPV0cemNwS3C7G96MQd2zjA9+fW8WuUI24ovl9xhdLDICIvuQUR+8pbOY8KY2ChsLa23UoPgb7DZstFePg3ZWtrMESeCp4kE4fDgY9at6NySNfZ7ygCWa1DMWvy9zTzXLTV7DlnJ25LpYB4g297Wuh1xUI0BuPn4j1odgQrPRQi8gHmWSiPgYXCWlt3KT0EOmvZ2k3IzHoVS5Zux+w5LFurFW63G5/W7kDhcPPZ7ygCg9pHYPrES6EF7U31SEw7e4KiIIiYZ7jAp+c9VzCjVV9m3I4P6lOUHgYR+QgDC+UxsFA4v6Krq1jJIVAv2e3FCApagylT38SSpaUYPVq6Vp9vtvRkS9VeHMxuOPudBBFDLWMxZcJF0AQh75x3GdYxwaen1OOKRVvaHNxaNEfpYRCRD+VUmWFznruIB8mHgYWC2rhaodGytduQlPzqibK1iYn8NVKzvZWHsWN4FQTjWQJBt4jsrgmYNHYZ1K6u6CBCI86eQ2KsivHpOfWWYyGEJ+C65pshivoLmIgCmcMt4EBFm9LDCGh8R6SgVuZX6KJs7Zixr3rK1k5k2VrVyqs6is2DiuEKOXtwMdIxBRPGLIbqk7gTz96Ju6vFidGmiT47p2jQ1xvwp2NWI68jUulhEJEMuB1KWQwsFNTWxvwKfRA8ZWtjTipbO2AAf7XUpqS2HBtTj8ARcZZGei4Ro10zMHbkeVCzttq958x7mOFc6LPziaJ+ViwKs1bi7+XDlR4GEcmE/SyUxXc/CnE4mmGxHFPq9CRz2dphw1/G0mX7j5et5XSrRU1jHdbHHERX7FmCC6eA8eIcjB7h+0ZzvtLeWIeEtLOXVUxpHuqz8+llx5AjPhsry7SRqE9E/bO/vA1O91le40lWDCwU0ta2R6lTk5/YbHk9ytYOHaqTd2ca19LWirVBe9CefOZGeqJTwETDfIwYPgtqZThHEre1woBQhPnkXHr4Ey2aQrHatQpmZ5DSQyEiGVmdbhyuPkdFQJINAwuFmNsPKnVq8jO32+IpW5uR+QqWLP2aZWtVoNPSiQ8dO9E44MxJ0KJDwJSgRRg2ZBrUqK7owFmTuF0OAfNMvklG18NGqI3pd2BDY5LSwyAiP2CehXIYWCiko+PcJSNJf+z2kpPK1pZg9GhRJ2/btMdut2O9eTuqB1vPeB/R7sb0sGUYMngy1JnEXX7W+4zu8k1QpPWf0Ob0BbijeIbSwyAiP2FgoRwGFgphYBHYusvWfo2k5NeweMkXWOApW8utUko00vukfgeKh3ec8T6izY2ZERdiUJZve0P4I4k7rNY3n9BrObAQIpJwTcONLC1LFED2lrV4moWS/zGwUIDVWgmXi/v/qJvDUQPRU7b2NSxdlo+JE10wmTg7/iJVPPqyajcOZzed+T5WN2bHXorMjDGqemLaG2vPmsTdWedEhnFQwPaxkBr7/SPqPhRawpUeChH5UbvNhYK6M39gRPJhYKEArlbQmcvW7kVM7BtYcN56LPSUreUqhr/sqjyE3dnVZ2ykJ1pcmJdwBTIGjIRmkrhFYJ54QcB23s7PuhpPVgxRehhEpIC95S2cdwUwsFBAR0euEqclDXG5WuHylK19xVO2djrL1vpFTmUBtgwuhTv4DMFFpwvzk76PtDT19EGoL5aSuJ1nvD2z1ftA6Fw9M9TIljAKK0svUnoYRKSQvOp2zr0CGFgogCsW1NeytWEnytZWY6jv2hPQaRTVlOKz9AI4ws4QXHS4cH7qVUhOHqyK+XO7zp7E7aoMg8HLRhRaCyvEoHCscvwEFhf3FBIFqoI6BhZKYGChgHZWhKJ+l639AhmZr3rK1s6Z04WoKG1uUVG7qoYabIg/BGuM+7S3i+0uLM64HomJWVCDttoz98Wxd7owPWh+QPWxWJ92Fz5rSlB6GESkoML6TiZwK4CBhZ/ZbLVwOpv9fVrSYdlak6ds7RuesrVjxogwMMbwqabWZqwN2Yf2xDMEF2Ynlg68EQnxA6CKJO7UM+8nnmSfFzArFo0DFmFV0VSlh0FEKmiUV9ZsUXoYAYeBhZ9xGxT5kig6PWVrE5New6LFn2PBAjOSkvhr7SsdnR1Y696J5vTT5zCIbU4sG3IL4uLSoLwjZ7wltmFAQORYuCNTcVXd9UoPg4hUgpWh/I/vQPyMgQXJxeGohYi1GD3mVSxddgSTJrkRFMT59pbNZsO6ju2oHWg77e1iqxPLh/0I0THJiidxh4SfPgCyVAmINcT3+9haCCukylV/Db8PJV1hSg+FiFSioJZ5Fv7GwMLPWBGK/FO2dh+iY17H/AXrcf7CRmRk8FfdGy6XCxsat6N0WOdpbxdbnLho5I8RHZUIpbidTsQlnT6JW3CLWGC4UNc5FjkDr8ezVQOVHgYRqUg+e1n4Hd9t+BlXLMjfZWvd7k8wdNjLWLpsH6bPsCMsjMkY/W2kt6l6F/Kyz5DL0OTERWPuQERELJRirjtzEvewDvV1DvcVa9I4XFvsfb8OItIXVobyPwYWfmR3NMHuqPfnKYlOsNmOICzsbcya9S4WL6nGsGGcnP7YUXkAe7NrIRpOs0Go0YVLx/8EYeExikyuuaEW8amn78RtrOr/mARRvZuhxOBI/LjrLljc/HNGRD1VtVrRaXdxWvyIr8R+xG1QpAZuoQsOxxcYkCGVrd2GOXMsiI7hKkZfHKw8gq1Dy+EOOs0b7gYXVkxYhdCwSCjBiNN34u5qcWK0aWK/jullGwxZvZdyN7a0xCk9DCJSIekzkaPsZ+FXDCz8iNugSG3s9lKYgt7D5MlS2dpilq3tg8LqYmzKOApnmHD64GLyvQgJiYC/1UlJ3GGn/4RuhnOhrlYs6jKW4f7iSUoPg4hULL+2Q+khBBQGFn7EFQtSd9na7cfL1n52vGytij+mVomK+mp8nJALW9SpwYWhzo0VU+9FcHCY/5O4k0+fxJ3SPKR/B1Xhj4I7agC+X32N0sMgIpVjnoV/MbDwo46OfH+ejqhfHI6642VrXztettbFsrVn0dDSiHXh+9B5mkZ6xjoBl0+/D0FBIX79aTTXnz6J21phRCj6HugIKluwEA1GPByyGlW2UKWHQkQqV8AVC79iYOEnbrcdNluVv05H5MOytW94ytYu9JStVeFH1ypg7mjHWmE3WtNO7SNhrBVw+cyfwmTyX1MRc33NaZO4XQ4Bc01L+3w8lcUV2Jd1E16qyVR6GESkAUdZctavGFj4idVapsI/z0S9L1vr8pStfQVLl+3FjBkOlq39ji5rF9ZadqDuNI30TDUiLp91P4xGk99+5Axn6MQ9umtan48lqOi1y5I8CdcVLVJ6GESkER12FypbupQeRsBgYOEnXZ7Agkj7bLZ8hIa9dbxsbRXL1p7E6XRiQ9MOlA89tZFeUA1w+eyfwmDwz8tuffF+BJ+mE3d4bRK0SgyJwo86fwy7wD9dRNR7BVy18Bu+OvuJtYuBBemxbO2Xx8vWbsWcuRbEsGwtBEHAZzW7UJB96lak4BojVsz9KWAw+CWJOz6p4pTrO+tcGGAcqMkVizeSVmFHq3INCIlImwpq25UeQsBgYOEnXQwsSMfs9jKYTO9h0vGytWPHiv5476xq2yr3Y392/SmN9EKrTVgx9z6/BBfm+r2nXikC84S+dalWQ1hRlXkxflUyXulhEJEGHa1nyVl/YWDhJ9wKRYFUtjYh8TUsWiSVrW0L6LK1+ytz8fWwCrhNPd+ah1UH47K598p+fnN9NeJT2065Pss8qk/HERXuY+GKycLKqpWKjoGItN2Bm/yDgYWfcMWCAo3DKZWtXXe8bG0eJk92B2TZ2oKqInyZdQzO0J5vzsOrQ3HJvHtkP7/RcGoSt6siDIY+tNNWMqwQDSb81rQatTb/luwlIv2oaWNg4S8MLPzA5bLA4Wjwx6mIVFq2dj+iol/H/AXrsHBhAzIzA+ulp6yuEp8m58EW2bORXmR1OC6a+xNZz11XdGoSt73ThWlB8zSRY7Ez81a8Xpuu2PmJSPsaO+2wu07tNUS+F1h/3RVSb21HYcyP0BQ+H04DEw8pcLlcbXC5P8WQoS8fL1trR5h/G1Mrpq6pHusjD8AS3/OPW3RNJJbPuVO287qdjtMmcU+2z+/1MZQKKzpSpuGG4vMUOjsR6YW0m7O27dRS4OR7Abgxwf8O2cLxu44LAUgXICXEiMwQNwaYOpFmaEKSWIVEVxHi7LmIcLF6FAVS2dp8zJwVgaCgmSgvy0JRkRrShOXT1t6GtRG7cWHqdMTVf/vyG1sbgwvm3I5Pt/9HlvO21+8DMKzHdbENA4ABvXu8qEBoIYbG4EbzbXAKgZujQ0S+3Q41OCmSUyozBhZ+UGlz9Pi6wSmgwWnAfkRLn1cCGAKg+9PDqCADMkOAjGAr0gytSEEtEl2lSHDmI9qRB6PY81hEWiccL1ubPgAYPGQwuromI/dwNNrb9RlkWLq6sDZkB5ZnzUBKZeiJ6+Nq47Fk1i34fOcLPj9nW30V0ke2obUh7ttxVAmIzYiHWTy1LO53KfFMvJRwL/aXSq+PRETeq2KehV8wsFAgsDibTreIAitQYJX2h6Qfv0zx3BZkANJDjcgMdiLdZEYqGpAkVCLBWYg4ew5ChCYZvwsif5WtLcOkycEIDZmGmpphOHLE4FnG1hOHw4H1zduxZMgMDCz99hO0xPpkLJp5I77Y9bLPz2k0Skncc058LbhFLDAsxzrxjXM+VvDzE1CWeRl+VzTar+ckIn1jArd/MLBQWWBxNi4RqLQLqLSbACQcv0hlI5d6bk+UtlgFuzEgqAtphmYki9VIdBUj3pGHCOcxGFRRjZ6od2VrbfYdSEjcgUWL0uB0TUf+kQQ0NvZMftZ6I72NtTuxYPg0jCj6NvcquSEN5834Ib7a/arPk7gjk2bAYfv2ZX9Yx0Qg8tyBhT9fOZyxg7Gy4ko/npGIAkE1S876BQMLP6i0+mf7UrNTQLPTgEOQPgGVLlJ33dme28JM3VusMoPtSDO2IVXaYuUuQ4LzKGLsOTCJLMVG6i1bC6zDqNEGTJo8CU2No3D4cBBcLujClqq96Moej4lFySdKwKY1DcD86ddg655zv+nvSxJ3XHIFGiqHnrjOWBUDjFRPjoVoDMYvcC8aHcF+OR8RBY4aM9/n+AMDCz+osiufF2ETRBTZgCJPLfiU45eJJ0qDSVusMkKcnoTyVDQiSahAgiehPAdh7lqlh0/keXtrsx1AVPQBzF8gfcI/C8eOpaKqUvsrcXsrD6Nr2AjMKsmEUUpWFoEBzQMxZ+pKbN/3js/O094gdeL+NrDoanFilGkCCtw5qggstmT8CGuOpfrlXEQUWLhi4R8MLGTW5nSh3aXu7RvS6KodAqod0har2OOX4QAWeW6PDTYiK0TwbLFKN7QgWZRWO6QtVkcQ6SiAEawNTf7lcpkBfIohQ4DRo0fDbB6PnEOhsGm4muCRqkLYBtmxoHoYghzdwUVW61DMmvw97Dzwnk/O0VZXhbSRZrQ1fLv1aoZzIQqMOYrnWJhTZ+GWormyn4eIAlON2QZRFGEwsNKcnBhYyKzKR/kVSjK7BJhdQC4iAM8lE8B0z22hRgMGhBo8W6zSjWakoB5JQjkSnAWIteUgSOxQevgUCGVrQ08uW5uJoiJoUkltOWypdixpHo2QLqMnuBjUPgLCxMuw+9Ban5zD5Eni7t4iKUltGQoknf0xcocVQlg8fth6C9wiWysRkTwcLsHTKC8lOkCaJymEgYVGErfVyi6KKLVJF2lPdNLxy1gAF0H6TCA5RFrtkHp2dCDV0IRksQoJUkK5PQfhrlObdhH5pmztIFi7piI3Nxpms7pXDL+rprEO6+PsWB48ERFmk7RcgCGWMXBPcGNfzkdeH7++eB8iEqefSOK2VhgRnBQCJxyKbYX6T+y9yCmPkvUcRETSdigGFvJiYCGzKpsTgUo8qWfHPsQAnou0v3tBz54dQVakG1s9W6yS3KWIZ88O8pLdXg6jqRwTJx0vW1s7DEfypGpT2lgCb2lrxdrIvbgweSpiG4MAt4jhXePhHufCwdxPvTq2y+FAXEolGiqk/jmAyy5gnmkZvnSvP+Nj5AwrirKuxJ+PjZDxDERE3WrabJgs1bUh2TCwkFmjI3ADi1737MDpe3YM8PTscCDd1H68Z0fF8Z4dhxAstCg9fNJS2dqEHVi4KBUuT9naRE2Ure20dGJt6E4sz5iB5OoQT3Ax0j4ZwhgXco5s8urY7fVSEnd3YCEZ0zUNX4aeObAQRHnmyxE3DCvLVshybCKi76pu6+KkyIyBhczaXExs7m/Pjgq7gAp70Hd6dizz3J4k9ezwbLGyeHp2JJ3o2ZGLSKdGN9iTrJzOegDrvy1b2zQKh3PUXbbWbrdjvWs7lg6eicyycM8vxmjDDLhHuZFXsLnfx22rq0TayDa0He/EHV6bDAyGX1csRFMI7hfuQauTf4aIyD9YGUp+fEWXGQMLeTQ5BTQ5DTgIaV+2dBl0oqtwuNSzI1TaYmVDurENKahDkrvMs8Uqxp7Lnh0B73jZ2qhvytbORFFROior1LmK4Xa78Wn9Dpw3fBqGF8UATgHjg2dDGOFCfuG2fh/XZMw/kcTdWefCgKEDUSNU+C3HYtOAH2PdsWSfH5eI6Eyq2zRcOlAjGFj4odws+ZdVEHHMChxDqFTz5vhF6tmxwtOzIy1USih3Il1KKEcjkoVKJLikLVY5CHVLn2pTYJWt3YjBg4FRo0ahXSpbezgc1i519caQSiRurtqDruwJmHAsGaJTwMSQ+XAPd6GwaKf3SdwiME+4AG/jv6c/P3yrNW0ubiua5eOjEhGdXU0bm+TJjYGFzLhioS7SZ9I1DgE1np4dcccv2af07MgI6kKap2dHzfGeHXmIdBSyZ4eO2WwFCAktwIwZ4Z6ytRXlA3HsmLoCjN2VObAOH4XpJQNgdAiYEroIwhA3ikr39C+JO7kSDZXduRZZ5lHdLWxkzrEQwpNwTdPNmkmkJyL9MFuZ9yo3BhYyMzuZY6H9nh0zTvTsyAg1ICPYjgHGNiSjHsmenh1HEWM7hCCxU+nhkw8IghUOx2akpQODBktla6ccL1urjiDjcFUBrIPtmF85BCa7G9PClsI92IXSsgN9PlZH474TSdyuijAYxhkgGuT9Pp+MXo2CCul3i4jIvzoCuFKnvzCwkBlXLPTVs6PEJl2knh3Jxy/jAFzs6dmR4qli5cKAoE6koQlJYiUSXUWIs+eyZ4fmy9YGecrW1tYOQ16eQfFP24tqSmFNt2Nx40iEWIGZERdCyHKjvPLsHbS/q7W2AmkjzGhrjIW904VpQfOwx71Vts7bBVlX4R/HpJLTRET+12l3sfu2zBhYyEj6Y9zOqlABQXrbVe8QUO8wfqdnx3me26ODpCpWAjKCbUg3tCBJrEWyuxRxziOIceTBIDIXR81E0QWbfSfiE3Zi4aIUuFwzkH8kAY2Nyq1iVDfUYEO8HRcET0B4OzA75lIIGW5UVuf16Tgmk5TE3Z3vMMk+D3uCtsqSvG2PH4mVpRd7fRwiov4SxO7gIjpM+oCQ5MDAQkZSUKGOzROktA63gHwrkG+VenYMOH6Z6rkt2GDAAM8WKwcGeHp21CPRXY5EdyFibTkIFlqVHj6dxOlsOKls7UQ0NY1WrGxtU2sz1kbtxYVJUxHTBMxNuBxbRReqa472KYk7PGE6nHYT4hoyun80v0Pw8pVMDArDva6foMPFPzlEpKwOGwMLOfFVXkbcBkW94RRFlNuly8k9O0YDWH6iZ0fW8Z4dqYZmJItVnp4dcZ6eHcWcZEXL1h5EVNRBT9laA2bhWFGa38vWdnR2YG3YTlw4YDoSa4D5Sd/HZuEt1NX1rp+Ly2FHfEqFJ4nbUiUgNiMeZtG3wezHaXfik6JEnx6TiKi/gQXJh4GFjNqYuE0+7NlxoEfPjrknenZkndSzIxnSFqsyxDnyEeOQenawZrf/ytZ+qljZWpvNhnWuHVg2aCYGlIfh/NSrsMn9PzQ2lvUpiVtwi5hvuADrxTd9VhWqOf083FU0vd+PJyLyJSZwy4uBhYzMzK8gP/TsKLQChT16dkwCcDlMx3t2ZIY4McDUgRQ0IFlKKHd29+wIcUtbekjOsrXBQTNRXp6FY8fkn2eXy4WPG7Zj4bDpGFocjcUZ1+Mz4WU0N1f2Kok7dUQ7zI0xGNYxEYjsGVj0NzxyR6bgB/U39PPRRES+187KULJiYCGjVjbHIwVJhY6rHQKqe/TsGAFgsef2uBM9Oywn9ewoQby0xcpxFAZmCHldttZ+ctla6xTkHpa3bK3USO+L6t3oyp6EcccSsXTgjdgovoiWlupzPjY4SEringlTdWz3j4mXVaFEGPBYxGoUN4f3+bFERHLhVih5MbCQEVcsSM3aXALaXMBhRAKeS5bnjaUkzGRARgg8PTvSjWakoA5J7nLEO48i1p7Dnh39KVtrPKlsbd1w5OVCtrK1OysPwpo9BtOK0rBs8M34RHgObW11Z31MXdFehMdPRVczMNI0DkfduSfd2vfAIjfrWjx1bHA/Rk9EJJ925ljIioGFjJhjQVplE0QU24BiW8hJPTvGA7jE07Mj9ZueHaZOpBkakSRUItEt9ew4jDBXldLDV3/Z2nipbG0y3K6ZyM9PREOD7xO+D1UegXWIDXMqBmP5sB/ho2PPoqO98exJ3KlVaKgcjJmuxThqyO33ioU1cSyuLrnQq/ETEcmBORbyYmAhozYl6k8SyUx6i1nnEFDnMB7v1yFdhgE433N7jNSzI1TaYmVFmqEVyahBkqsU8c4jiHYcYc+O45xO6U3+eowcZcDESRPRLJWtPRwEpw8bwxbWlMCaYcfC+pG4aOSPsSH/GXR0Np/x/h1NUhL3YKQ0DwWSTn7Oex9YiMERuNt2Fyxu6eeDiEhd2q18byYnBhYy4lYoCkTtbgFHuoAjkPbWSxepMcK0Hj07Mj09O6QtVg1IEsqR4NlidThAe3Z0l62NjDqIefNiYDDMQlFxGirKfZOLUVlfjY8T7FjWMR4Xjb4D6488DYvl9PPcWlOO1Ox2WCriEJwUAiccx0fY+7F8mHoXviiK98nYiYh8jSsW8mJgISNuhSI6W8+OxOOXb3t2JIdIVayknh2dnp4dKZ6eHUWenh0RzlLdT6fL3Q5gIwYNAkaOGIH2jok4nBOOLi/L1ja2NGFd9D5c6J6CS8bdhQ9znoTNKp3rVMHBBXDZZ2CeaRm+dK8/kRTeG/UDlmB10RSvxkpEJCcmb8uLgYWM2CCPqG8anQIaPT07ogHPRUr+nee5LeKknh1pxtbjCeWliHfkI9qRp7ueHTZ7IUJCCjF9RhiCgmaismIgCgv7f7z2jnasDd+FC03TsWLCKnxw6HHYbZZT7ldfvBdhcVMwpmsavgxd3+vO2+6odFxVe23/B0hE5AdcsZAXAwsZmZljQeQzXYKIo1bgqKdnR9rxi9Sz4wpPz470MCmhXOrZ0Y4UNCJZqECCsxCx9kMIFZo0+0wIgg0Ox1dITQMGDhwIq20q8nJj0NbW94Rvq9XqaaR3QfxMrJh8Lz7Y9zgcjq4e93HabUhPrUZX7bDuuK4XW6FEgxGPhK1GWVNYn8dERORPrAolLwYWMupw+b7SCxGdvmdHlV1AlV0KMeKPX6RmDEs8t8cf79kxwNOzo9nTsyPJXYw4Rx4iHYWa6dlhd1TAaKzAhIlBCA2dhtraYcjLNfSpbK3T6cSGpu1YlDYDK6auxgd7H4PT2XO1p6NpL6y2QUgfmolaoeqcW6EOZN6A545J5YqJiNSNKxbyMoi93TxLfTZ1ex6q7T4s8UJEPhdmNCDzRM+ONiSjHknuMiQ4Czw9O0xiz0/01SY4WCpbOwMFBYmor+/by/ncrCkY2ZmI93c/CperO1H7G6kjfoTS7HK8LT6HqOBI7CiUGuidqitpAqbW/j9Y3VJQR0SkbumxYdjxy+5GseR7XLEgIgR6z44iG1Dk6dmRcvwi9ey4FMaTe3YEdSDVs8WqCgmuY909O9zn7mjtn7K1H2HESAMmTJyA5ubROJwT3KuytV9X7kdX1lismHEfPtj1KNzub8swBgcVILNtChB75j4WYkgkbu+6k0EFEWmGg7tJZMXAgojoDKTNjLUOAbWenh2xxy/DT/TsiO3Rs6PFs8Uq0V3i6dkRZc+H0bNJy59law8hMvIQ5s6LgdE4C0VF5y5be6AyD10Zdlw6+z6s3f4YBKF7zPUlexGdMR2GGGmj2OmP8XbSKmwrkeaEiEgb+trwk/qGgQURUT+Z3QLMXUCep19HxvHLdM9tIcZvenZIW6ykhPL6b3t22HIQLJplm3e3ux1u98llayfgcE7EGcvWHq0ugi3Njgvn3YMNW/8JURTgtNkQFVmBaUFzcVjcc8pjajKW44HiCbJ9D0REcnALDCzkxMCCiEgGDlFEmU26BJ/Us2MMgAtP9OzIOtGzownJYjUSPVuschHhKpOlbG1w0ExUnKFsbXldJWyJdiw9725s/OpfUvMKdDbvw6S0ecgJ7RlYuKIzsLL6Kp+NkYjIX7hgIS8GFkRECvbs2H+iZ8eQEz07IoMMyPIklEtbrLp7diS6S5HgOOLp2WEUeyZa97Zsrf1E2dos2GzTkJsbjba2bz+9q29uwKYYO847/8f4avOzaKkuQ/rgBRBCvq1wJxpM+F3walTbpLK/RETa4mZkISsGFkREKmNxiyiwAgVWqS9E+vHLZADf8/TsGHC8Z0e6ydyjZ0ecPQchvejZYXdUwmCsPF62dirqaochN9foKVvb1m7GF84jmLPwJuz64kWY2koQnfBtHsXuzJvx6jFpyxcRkfYwx0JeLDcrI5abJSJ/Swj6tmdHqqEZKZ4tVsWId+QhwnnsjD07PGVr3TNwtCAJdXUCQkJCMCNxCI5sfw+1S8fgkaLH0Zk8BVOrfwq7ICWzExFpT4jJiMI/dm9JJd/jioWMmB5ERP7W4hLQ4gIOIVLaVAVgIIDZntvCTAZkhgKZQXakGds8W6y+6dkR4+nZ8RGyRxgwfsJ4tDSPwd68IoyZfSHCO2ohhkbjlo7bGVQQkaZxxUJeDCyIiAKpZ4cVKMLJPTukyk6XeXp2pEk9O0KcnoTylIxGJA+oR2uXA7GNiXjNeA92l8Yo/S0QEXmFORby4lYoGU3Znocadt4mIh34a/0mRJflYeCxLkTmlEDs6FB6SEREfWcwYHT+Ec6cTLhiQURE5/R2zBI8mF6CNWNs+Hi+HUu7RmJBbSwGHzXDdKQYcH3btZuISLVMUgkMkgtXLGQ0eXsearliQUQ6YBBFjN7fgtdjnsGx8E78PtSBOmuj57ZEIQKXmIdgekUwUvPqIJZXKT1cIqLTMoSEYFTOIc6OTBhYyIiBBRHpyZwO4OjuCmzPeAom8xE8NmY+3m3NhfidUhVjnMm4qCkDY0vciJK2TbXJ12WciKgvDOHhGHVgPydNJgwsZMTAgoj0JFQEkrY3wuS0YHPy3xHRdBh7Bk/Hg5EGVHbVnfYxJhhwvnUQFtYkYOixDgTlFgFOp9/HTkQkMUZGYuS+vZwMmTCwkBEDCyLSm/ktAvbsqcWwCCs+jvkTQtqKYQ2JwJNjF+J/5jwI4rdduk8nVgzzbJuaWRmGtCP1QEmF38ZORGSMicHI3bs4ETJhYCGjSV/noc7BT+aISD9iRAOCNteiy+HGlNhOvBP8EEydNZ7bDmVNxINxkSju7H2ORbYzERc3Z2J8mYCYnHKIzS0yjp6IAp0pNhYjdu1Uehi6xcBCRgwsiEiP5te5sOdQvef/FyW24jnhNzBauwMChykU/x6/BC+258Ml9q1SlEEE5tsGYnF9EoYXdiIktxii3S7L90BEgcmUkIAR279Wehi6xcBCRgwsiEiP0gQDLF9Uw+nuTtq+MrUef7f+GgaH5cR9CtLH4LfJicjvKO/3eaLEEFzUMRSzq8KRcaQJKCoDxJ6J4kREfWFKTsKIrVs5aTJhYCEjBhZEpFdzKuzYn9904uvbMyvwy9bfwuB2nLjOZQzCi+OX4d+dhXAI317fX4NccbikZSAmlRsQJ22bavj2/EREvRGUmorsrzZzsmTCwEJGs3YeQZnV+z+mRERqM8xlQPWmnrkUvxpciNvqfw/DdxK4S1Ky8dv0TBxqL/bpGGbbMrG0PhnZRVaEHS6GaLX69PhEpD/BWVkY/tlGpYehWwwsZHTB3qM41ME/dESkT9MKu5Bb2trjuseGHcD3qv92yn0FgxH/G7cMT1pLYHXbfD6WMDEIF3UOxdzqKGTmN8NQWAoIZ69QRUSBJ2zsWAxZ867Sw9AtBhYyuupgMb5q7ZDzFEREiplgN6Jwc+Up17+cvRXnVT5z2sdUJg7C77KysctcKOvYMtwxuKRlEKZUGJFwuApibXeyOREFtsg5szHwhReUHoZuMbCQ0W25ZVjX2CbnKYiIFDUupx1Ftad+gLI2+2NMqHz1tI8RYcC7Y5fgMWcVOp3fJnzLaZp9AJY1pGJUsQ3hUjdwi3/OS0TqEr18OTIf/4fSw9CtIKUHoGdxwSalh0BEJKvw4bHAaQKLFUXL8cWwDgyp+uCU2wwQsTLvM8yPy8DDQ8ZhS1u+7M/S3tAa7M2qAbKAkPNMWGYZhQU1MRhY0ApjQQngdss+BiJSnikmRukh6BoDCxnFBjGwICJ92x8hYFB8OGpae+aTiaIBy0pW4uvBHUip2XTax6a1VeOpA9VYN3oR/io0os1h9suYHQY31kcVYf0IACOAlIujcal5MKaVByEptwZiVXfDPyLSH1MsAws5MbCQEQMLItI7NwxIH5mAmp3Vp9zmFAxYXHEjtmVYEFt/5k63l+Z/gTlRyfjTiGnY2JoHf2swdeL5hFw8nwBgMjDeMQAXNg7AmBInInNKIba3+31MRCQPI1csZMXAQkbcCkVEgWBfDBAXEYy2Lucpt3W4gnBB3R34MrkT4U25ZzxGYmcjHt3/MT4fMR9/NHagyd7dyVsJh0MacDijAcgAguYZsaRrJM6rjcXgo2aYjhQDrr51FCci9TDFxCo9BF1j8raMPmxoxY/z+t91lohIK+Y3Cdizr/aMtw+LsOLjmD8ipK3knMcyh8fhr6PnYm3rYahNvBCOy8xDML0yFKm5tRDLe/byICJ1y3j8H4hZvlzpYegWAwsZfdXSgasO+bYhFBGRGsWLAL6sg9V55iToKbEdeCf4IZg6zxyAnGzbsNn4XagDddZGqNUoZxIubsrEuBIXog6XQWxlJUAiNRv4wvOInDNH6WHoFgMLGR1s78LyffLWaiciUot5NU7sPdxw1vssTmzBf92/gdHWs7HemVhCo/HY2AV4pzUXIqToRb0MIrDQOhgL6xIwrLADwXnFEB0OpYdFRCcZ/O67CB83lnMiEwYWMiqz2jFrp/xlFImI1CDDbUDbF9VwC2cPAFam1eGvXb+BwdH7XhJ7Bk3DQ1EmVHT1brVDDWLFMFzcPgSzKsKQdqQBKOHWWCKlDftsI0KyspQehm4xsJBRq9OF0dvOnKxIRKQ3s0ttOFDYfM773ZlVjp+3PAiDu/ef6NuCw/HkuEV4zZwHQRSgNcNcCbikKQvjy0TE5pRBbFYuQZ0oUI3YtROmWCZwy4WBhYzcoojMzYdUvnhPROQ7I1wGVGzqXULzrwcfxa31D8PQxyAhJ3MifhsfieJO7SZOS9um5tkHYkltEoYXWRCSWwzRZlN6WET6ZjBgVF4uDEaj0iPRLQYWMsvekoMOt/Y+WSMi6q8p+RYcqehdEvPjw/fj8qq/9/kcTlMInh2/FM935MMlaL/8a5QYggs7hmBOVSQyjjQCRWVSl0Glh0Wkux4WI3fvUnoYusbAQmbTduShynZqbXciIr2abDcgf3PvVxNezd6C+ZX/7te5jqaNwW9SkpDfUQY9GeSKwyUtWZhUbkRcTjnEhialh0SkecGZmRj++WdKD0PXGFjIbMmeo8jttMp9GiIiVRl9wIzShs5e339d9kcYX/m/fp3LZQzCS+OX4pnOY3AI+qzCNMuWgaUNqRhRZEVYThFEK/+uEPVV2JgxGPLeGk6cjBhYyOzKA0X4uq33f1yJiPRgpsWAQ9t6v2phMIjYPOwtDKpa2+9zlqQMx4PpWTjYru/+QWFiEC7sHIq51VHIKmiG4WgpIHDLLdG5RMyahUEvvciJkhEDC5ndcrgUG5rMcp+GiEhVgkQRGbtaUG/ufUJyqFHAtsEvILnmi36fVzAY8fq4pXjCVgarKzA+1U93R+PS1kGYUh6ExMOVEGvrlR4SkSpFL1uGzCf+qfQwdC1I6QHoXWywSekhEBH5nctgQNaoBNTvqun1Y+yCEYsqbsLXGZ2Iqd/dr/MaRQHXH/4U5ycMxEODRmBXm/6blNaaOvCfpFwgCcBUYKojC8vqUzG62IHww8UQO3vfL4RIz0yxMUoPQfcYWMgsIZhTTESBaX+sATHhQWi39r5qU4crCMvq7sKXSRaEN+f1+9yZLRV4rqUCa8YuwaPOGnQ4A2dL6r6QWuzLqgWygJDzTFhmGYkFNbEYWNAKY0EJ4HYrPUQiRZji4jjzMuNWKJm9VN2EXxRqt9Y6EZE3FjS4sftAXZ8flx1pxYaoPyLYXOL1E1AfOwAPDx2Pr9ryEehS3FG4pG0QplUEIzmvBmJl71eUiLQu9Te/RsJ11yk9DF1jYCGzL5vbcU2O938YiYi0KFE0wPlFDRyuvicXT4vtwFtBD8Jk6XtgcjrrRy3EX8QmtDmY9/aN8Y4UXNg0AGOKnYjMKYXY3u6TuSZSo8xnnkb0woVKD0PXGFjIrKTLjjm7+CkZEQWueVUO7M1r7Ndjlya14FnXr2G09a7h3rk0RyXjkRHT8Glr/7dZ6ZUJBizpGoLzauIwpNAMU14x4NJ+80Gibwz58EOEjRzBCZERAwuZOQURg746BBYCJKJANdBtQNOmKgj9bCR9VXod/tz5GxicvktC3pQ9H38wdaDJ3uKzY+pNvBCOS81DMaMyBKm5tRDLua2XtG3E3r0wRUUqPQxdY2DhB1O356Hazu7bRBS4ZhZbcaio/2/i784qw8+aH4RB8N1rqTk8Dn8bPRcfth722TH1bKQzCRc3ZWBcqYBoadtUq29WkYj8wRgbi5G7dnKyZcbAwg++d6AI29kkj4gC2GinAaVfePeJ92+H5OPmuj/CIPp2DfjrobPwuzAnaq39264ViAwisNA6GAvrEzDsaAeC84ohOvTZ9Zz0IXT0aAx9/z2lh6F7DCz84L6CCrxRy+V2IgpsE/M6cLTKu+Tgfw7bjxXVf4evWUKj8Y+xC/B2ay5E9HPPVgCLFcNwsXkIZlaFIf1IA1BcrvSQiHqIWrwYWU/9i7MiMzZZ8INBYSH+OA0RkaoFDY0BvAws7i2egqTsH2Nu5bPwpUh7B369/yMsHzQVD0UHo9zCMqx9YTbY8HpcPl6X2gSMA4a5UnBxcxYmlIqIO1wGoYkfrpGygjMG8CnwA6M/ThLoBoWHKj0EIiLF7Q8TMDApwuvjXHfsPORlXQs5TCvfh3ePHsJNcRNgMphkOUcgKA5qwROph/CjWTn4/m3teOK+IThyzXQ4po+FISxM6eFRAAoeoGxgcf7552P16tWnXP/SSy8h7njjvoceeggGgwF33HFHj/scPHjQc31ZWZnna+lf6Wvp+t6ex18YWPgBVyyIiADRYEDiiHifTMUlRRejIvNSWaY1zGnF/QfW41VHNIZHZclyjkCzLawSDw0+gOuXHMWN9xrwzt1jUbViBsQRQwCDQenhUQBQOrDorbCwMDz//PM4duwYtIhbofxgIFcsiIg89kUBqdGhaOywezUjomjA0pKrsG1wJ5JrvpRldsdX5eDt2hD8Z/xSPNeRD5fAng6+0GV04p2Yo3hnDIAxUjniRFzSPBCTyg2IP1wBsZ5J9OR7IYMGaWJaR44ciZSUFPzf//0f3n77bWgNAws/SAoJQpTJiE43u1kQUWBzGIBBoxLRuMf7HAa7YMSSypuxLb0D0Q17IYdgtwN3H/wIS9JG47cpmTjS0b0VgXynwtSGp1PagBQA04GZ9kFY1pCKEcesCDtcDLGri9NN3jEYEDJwoGZm8c9//jOmT5+OvXv3Ytq0adASboXyk0HhTOAmIpIcijcgKtQ3n2uZnUFY1vAT2BKlj7/lM7IuH68f/hqro8ci1MS8OTntCq3Gw1n78cNF+bj+Hjde/8loVHxvBsTRwwAj37ZQ3wWlpMAYHq6ZqZsyZQp+8IMf4IEHHjjr/ebMmYOoqKgel61bt0JJXLHwk0FhocjrtPnrdEREqtVhACaNTsKeg3U+OV6tLQSXtd2Pj2L/gGBzKeRiEt24NedjLEoehgczhuOAuUi2c1E3u8GND6KP4YORUnQHpLtjcWnrIEwpNyExtxpijW9+hkjftLIN6mR/+MMfMHr0aGzcuNGzNep03nrrLc99TnbddddBSQws/GQgVyyIiE4oTA5CsMkAp9s3PSMKLeG4LvgBvBH5IEyWellnekhjMV5qKsUb45bin7YyWF1WWc9H36o1deA/SblAEoCpwBR7Fi5oTMOoYjsicoohdlo4XaTKwCImJgZms/mU69va2hAbG3vK9cOGDcNtt92GX/ziF55k7tPJysrC8OHDe1wXrvDKDNcU/YQlZ4mIvlVvFDF+RKJPp2R3WwzuNv4aQlh36UY5GUUB1x3+FO81WzErTvo4nZSwP7QWj2QewI3nHcF1P3Hh5VWjUHrlDAjjRgBB/OyUuoUMHqSKpOz9+/efcr103YgRI077mN/+9rcoLCzEm2++Ca1gYOEnLDlLRNRTbUa4zyuNftKYiF+H/wZisPf9Mnojs6UC/z3wGX4XPgLRwVF+OSednsPgxkdRRXhgxH5cfWkJ7ro/Eh/fPhFNF06DIUsbpUZJvysWd955pydIuOeee5CTk4OjR4/isccewxtvvIH777//tI9JTU3FT3/6UzzxxBPQCgYWfsLkbSKinkpNAsYN9k1fi5O9XpuOx+L/D6Ix2G9T/r0jn+OD+lacH99zvzMpp8lowYuJebhr0kGsvL4Bv/9ZBvbcOA1d8ybCEBPDpyaAqCGwGDp0KLZs2YKCggIsWbIEM2fO9JSTfeedd7B8+fIzPu5nP/uZJylbKwyiKPpmgyudlUMQMPirHLDgLBHRt8bZDSjaXCXLlDw0JB831v4BBvj3z9yGUQvxZ7EJrY5T91OTOphgwGLLYJxfF48hR80w5RUDLvYp0SWDASMPHoAxlNXc/IGBhR9N3Z6HarvTn6ckIlK98YfbcaymQ5ZjPzV8Dy6u+gf8rSUyCY+MnI5PWvP8fm7qu3ghHJe0D8GMilCk5dVBLKvkNOpE0IB0ZH/xhdLDCBgMLPzohzkl+Ky53Z+nJCJSvWldBuRulWfVQvJG9mbMrvwPlPBF9nz8IagDjbYWRc5P/TPSmYSLmzMxrsSF6JwyiK1tnEqNilq0CFlPP6X0MAIGAws/+ntpHf5exprbREQnM4oihuxtRXWLfGVbN2Svw5jKNxSZ+PbwWPxt9Dx80HpYkfOTdwwicL5tMBbVxWNYoQXBuUUQHQ5Oq0YkrfoJku++W+lhBAwGFn60scmMGw7L17yJiEir5rQD+3dUy3Z8g0HElmGvI6vqIyhl+9BZ+F2YCzXWBsXGQN6LFkJxScdQzKoKR3pePVBczmlVsaxn/42o885TehgBg4GFHzXYnZiwnfttiYi+K0wEEr5uRItFvk+Cw01ubBv4HBJrv1LsCegKjcI/xpyHt9pyIfo5qZzkMdQVj0uaszChDIjLKYPQxG1vapK9bSuCkqSOiuQPDCz8bPL2PNQygZuI6BTzmwXs2Vsr68zEBruwLf0JRDfsVfQZ2DdwKh6MCUa5pUbRcZDvzbVmYklDCrKPWRB6uBiizcZpVkhQWhqyN3/J+fcjBhZ+duPhEnzaxARuIqLvihMAw1d1sDrcsk5ORpgdmxL+grCWAkWfBHtQGJ4avxivmI/ALcr7PZMyIoRgXNQ5FHOqIpBR0AxDYSnAKv9+E7V4MbKe+pf/TkgMLPztsbI6/LWUCdxERKczv9aFPTn1sk/O6KgurI14GMHtyu+Pz8sYj18nxKCokyVO9W6gOw6XtAzEpDID4g9XQKxvVHpIupZ87z1IuvNOpYcRULhi4WefN7fj+pwSf5+WiEgT0gUDOjZVwyXIn38wO96M14wPwmRRPpnaaQrBf8cvxX878uES2KgtUMywZ2BZfQpGFtsQllMMsatL6SHpStZ/nkXUggVKDyOgMLDws0aHE+O/ZgI3EdGZzC6z48DRJr9M0IXJTXja8RsY7Orokl2YOgq/TUtFXjsrCAaaUNGE5ZahmFcdjaz8FhiPlgCCoPSwNC37620ISkxUehgBhYGFAtiBm4jozLJdRlRu8t+2oOvSa/CHjt/A4JKvj0ZfuA0mvDx+GZ7uKoLdbVd6OKSQNHcULmsdjCkVQUg8XAWxhtuo+yIoPR3ZX7Ljtr8xsFDAzYdL8XGTOj4dIyJSo6kFFuSV+6/b8b0DS7C66SEYVLQNqSx5GB7MGIT95iKlh0IqMMmRhgsb0zGqyI6IwyUQOzqVHpKqRS1ZjKx/MXHb3xhYKODxsjr8mQncRERnNNFuwNHNVX6doYeH5OH62j/BoKL+EiIMeGPcUvzTXoEuF/ffU7cg0YhlliE4rzYWg462wZhfArjUExSrQfLqe5F0xx1KDyPgMLBQwBfN7biWCdxERGc15qAZJfX+/VT26eF7cFHVP6A2NfED8dDgkdjRdlTpoZAKJQmRuLRtMKZVBCMltxZipXxd7LUi67//QdT8+UoPI+AwsFBAs8OFsV/nKnFqIiLNmGkBDm3z/xukt7K/wMzK56BG749Zgr+5atDh5DYYOrOxjhRc1DQAY0pciMopgWgOvP5Z2Tu2Iyg+XulhBBwGFgqZtiMPVTanUqcnIlK9IFFE5u4W1LX5v3PxJ9kfYlTlW1Cjxpg0/H7YJGxuO6L0UEgDTDBgsWUIzq+Lw5Cj7TDlFel+21TQgHRkf8HEbSUwsFDIrbml+KiRCdxERGczzyxi784av0+SwSBi67DXkVn1EdTq41Hn4xGxGa0O/i2h3osVwnCZeQhmVIYh7Ug9xNIK3U1f9NKlyHzyCaWHEZAYWCjkifJ6/KmkVqnTExFpQrgIxGxtgNnq/xXecJMb2wb+F4m1W6BWrZGJeGTkTHzcyu211D/ZrkRc0pSJ8SUConNKIbb6rxqbXJJXr0bSHT9WehgBiYGFQr5q6cBVh4qVOj0RkWbMb3Rjz35lavjHB7uwNe1xRDXuh5p9mT0PfwiyoMHWrPRQSMMMInCebRAW1SVi2LFOhOQWQ7Rrr5dK1nPPIWreXKWHEZAYWCik1enC6G38hImI6FwSBAPcX9bA7lKmC3FmmB2bEv6M0BZ1V2RqD4/Fo6Pn4b3Ww0oPhXQiWgjFxR1DMLsyHOn5jUBRGbSAidvKYWChoJk7jqDc5lByCEREmjCv2om9uQ2KnX9stAUfhj+MoHb170ffPmQmfh8hoLqrXumhkM4MdcXj4paBmFgmIu5QOYQm9a2QBWdkYPimz5UeRsBiYKGge/Mr8FZdi5JDICLShCy3Ac2bqiAo2LtubrwZrxp+C2NXI9SuKyQSj489H2+25UJUUcM/0pe5tiwsqU9C9jErQnOLIFr9X8Htu2IvvxwD/vyI0sMIWAwsFPRefSvuOlKu5BCIiDRjVokNB48p+wnpJclNeNLxaxjs2ugLsH/gFDwYE4Iyi/8ra1FgiRCCcVHnUMypjkRGfhMMhaWA6P+gNv3PjyDu8sv9fl7qxsBCQU0OF8Z/LX2aRERE5zLKaUTZF5WKT9QNA2rwu/bfwOCyQgvsQWF4evxivGw+ArfoVno4FCAyXbG4tHUQJpcbEJ9TCbHeP1sZh3+1GcGpqX45F52KgYXClu45isOd2vjjRESktElHOlFQqXzfhvsGluCepodgELTTaCwvYzx+kxCLY53qzxMh/ZluG4ALGlMxssiGsMMlEC0Wn58jZPBgDPvkY58fl3qPgYXCHi6uwVMVyiUkEhFpyWSrAflbqqAGfxyai2trHoFBQ+vOTmMwnpuwDP/tKIBT8H9vECJJqGjCBZ1DMb8mGlkFLTAeLQXc3q+mxV1zNdIffJCTrCAGFgrb2tKBlexnQUTUKwZRxIgDZpQ3+v7Tzv749/BdWF71T2jNsdSR+G1aGnLbS5UeChHS3FG4tG0IppSbkHS4CmJN//rWZDz+OGKWX8AZVRADC4XZBQGjth6GVclSJ0REGjKrEzj4dTXU4p3szzG98gVojdtgwivjl+LprmLY3Nprgkb6NcmRhuUN6Rhd4kBETjHEjs5zP8hgQPb2rxEUH++PIdIZMLBQgWsOFePLlg6lh0FEpAnBIpC2swmN7ep5M7wx+32MqHwHWlSeNBS/zRyM/eYipYdCdIog0YhlXUOwoDYWgwraYMovAVyn5jaFjh6Noe+/xxlUGAMLFfh3RQMeKmYpQCKi3prXKmLvbvW8bpoMArYOfQ0Dqj+BFokw4M1xS/G4vQJdri6lh0N0RklCJC5pG4TplaFIya2BWNG9eplw881IfeDnnDmFMbBQgfxOKxbuOar0MIiINCNKAMK31qPDpp6qTJEmAVuz/o2Eum3Qqpr4gfjd4JHY3sa/SaQNYx0puKh5AJYuuwPpU+YqPZyAx8BCJSZ+nYt6h3r+QBIRqd38ejf2HOxfkqdcEkOc+Cr1cUQ1HoCWfTBmCf7mrkW7g9t0Sf1CjCHYds02hAeFKz2UgGcM+BlQifnx0UoPgYhIUwqSTQg2GaAmzY5gXNS0Cvb4kdCyy498jg9qm7EofozSQyE6p8mpkxlUqAQDC5U4P4GBBRFRXzQagQkjk1Q3aRXWMHyv82dwxWRBy5Lb6/DP/Z/gbyFDkBAap/RwiM5o7gBugVILBhYqcV5CNNT1uRsRkfpVp4dKVSZVJ68jEje5fgUhQn2BT18tP/oVPqisxkXx45QeCtFpzRkwhzOjEgwsVCI5JBijI8OUHgYRkaaUBYkYPzQBarStJRb3Bv0GYqj2V6TjLc34y/4NeNI0EClhiUoPh+iElPAUjEzQ9tZDPWFgobJVCyIi6hvLwAjVTtm6hmT8LvI3EIP08cHR+UXb8EFpCa6MH6/0UIg85mRwtUJNGFioCAMLIqK+ywsRMSIjRrVT91JNJv6V8H8QjUHQg2ibGQ/t/wj/RRoyIlKVHg4FuEVZi5QeAp2EgYWKzIqNQphRhZuFiYhULmSYegMLyaMVw/BG2v/zNKLTi1mlu/HesSO4Lm4CjAa+nSD/k8rLcsVCXfhKoCJhJiNmxkYpPQwiIs3ZHyYgM1G9W6IkvyoZj88yV0FPIhwW/OLAerzkjMfgyAylh0MBmLQdagpVehh0EgYWKsPtUEREfScYDEgZEa/6qbu9aBb2Zd0MvZlceQDvFhzArXHjEWTQx5YvUr/FAxcrPQT6DgYWKnNxcqzSQyAi0qR90UBCVAjU7spjS3Es6/vQm1CXDasPfIT/2cIxMnqQ0sMhnZMC2AWZC5QeBn0HAwuVGRQeiikx6l7OJyJSI7sBGDZaG30jlhddjtqMC6BHY2ry8EbuTtwdMw7BxmClh0M6NTV1KmJD+WGs2jCwUKHLU9jhlIioP3LijYgIMal+8tyiEUvKfojWNH12DA4WnLjj0Aa83RmE8TFDlR4O6dCigawGpUYMLFRoRUo8nxgion4wG0SM1ciqhcVtxLKa22FJngS9Gl5/FK/mbMXPoscgjEm25EMMLNSJgYUKpYYGY1Ycq0MREfXHseRgBGmkdHejIxgXN90De/wI6JVJdOPGnE+wps2FqbHZSg+HdGBM4hikRaYpPQw6DQYWKsXtUERE/VNnEjFhRKJmpq/MGobvd/4/uKIzoWcDm0rx4sEv8OvIUYgMYi4h9R+rQakXAwuVuiQlDkHa+MCNiEh16jPCoCWHOyJxi/tXEMK1sY2rvwwQcVXuRrzf9P/buxMwJ+t77eP3k32WTGbfM/sKDAgKVkEQBURxV8QFFRW3Itq6H4XWLp729T1aT1trF0Vt+2rVVqkVl7baulVbBdGKqGyy78sAs88k75VYEBB0ZjKTJ0/y/VxXLobJTObHLyGTO/+tSSPT68wuBxbFaduxi2ARozKdDo3O8JpdBgBY0jJHUIPKYv9ci329ui1d33TNUtAV/1NhC7av1s/f/bO+76lWmovfdei+qvQqVWVU0bIYRbCIYafnWeuXIgDEkvayFFnNHzfm6nve2QomyELn0xa/pD+u26LjMwaYXQosYlLFJLNLwJcgWMSwE7N98lhkASIAxJr33UFV5lvv3fA5a/26P/s2BY3Y3za3L2Tv2qh7F7yg/3GVK9PNG2o4NEOGTio/iRbFMIJFDPM67Do+K83sMgDAslKqrXmA1l0rq/VEwU1KJCd8/Ir+uGq1JmUMMrsUxKihuUNVmFpodhn4EgQLC5xpAQDonfnJARWkJ1myfbcsH6yX/DOVSNKbt+mHC57TfbZi5XrieyE7eo5pULGPYBHjxmelKcXO3QQAvdElQ4V1mZZt3mVLjtIC/8VKNKOX/UNzVyzTWRkNZpeCGOG0OXVC2Qlml4GvwCvWGJdkt2litjWH8gEgFsxPk9KTnbKqM5ecoGX+s5RovK2NumPBPD2gPBUncxhaohtVNEo+N6+HYh3BwgJOy003uwQAsKxWQ6qus/a0monLztSGoglKREeueFtPLflAU9MbZDN42ZKoTq442ewS0A38D7WAsZlpSnckxu4gANAfPsi2yeO07q+8joCh41deqB35RysRJbU365Z35+mRjgyVpxSZXQ6izOv0aox/DH23AOs+yyYQp83QSTkM/wFAb203pEEWH7Vo6rRr/Lor1Zw9RInqsNXv6veLF2i6r0EOw2F2OYiScaXj5E6Qs12sjmBhEWewOxQARGRFnkt2i58NtLndqZO3Xaf29MQ9edjV1abrFs7Toy0e1XpLzS4HUcBuUNZBsLCIkRmpynXx7gwA9NZae1ANVdbdIWqP5c0endN8szq9iT0lqH79h3rsg7d0Tdqg8I5BiE8FKQUanj/c7DLQTQQLi7AZhs7M40wLAIjE9mJrnmlxoIU7U3VZ4DYFkrKUyJyBDl353nN6cpddg9MqzC4H/eDM6jNZtG8hBAsLuagwW9YexAcAc33sDGpASXysWXtla4ZudM1W0JWqRFe56RP95v1XdWPqAHmYix83QutoQsEC1kGwsJCKZLdGZfALBAAiESj3xk0Dn9qYqzvTZivIi2nZggFd/O8X9NT2Dg33VZt916APjC4erdzkXHppIQQLi7m40Nq7mgCA2RZ6girLTVG8eGCNXz/Pvk1Bg23JQ/xbP9WDC1/W7ORapTiSzb57EIHJtZPpn8UQLCwmdAp3votFagAQifTq+Dp49P+srNYfCm40u4yYYSiocxb9RXM379ao9Dqzy0EvFKUW6ejCxDy3xcoIFhbjsBk6v9D6u5oAgJnmp0h5aZ64uhNuXD5Ef/N/3ewyYkr+jjW6/90/605PtXyuNLPLQQ+cVX0Wi7YtiGBhQVMLsmRnFTcA9FqHIfnr4u9NmkuWjNJC/0VmlxFzTl38kuau3aRxGQPNLgXdXLR9RvUZ9MqCCBYWVOhxaXwW77wAQCQWpBvyeuLvfKDTl0zU8mJelB0oe/cm/WjB87rbVaYsN9u3x7KxJWOVncSaUisiWFgUi7gBIDLNhlRXH58vXk5YfrY2FI43u4yYNOHjV/XHVat0SkaD2aXgEM6uOZveWBTBwqKOzfSqLMlldhkAYGmLsx1yOeLvV2FHwNDxqy5SY97XzC4lJvmat+u/F8zTfbZi5fHOeEzxe/06quAos8tAL8Xfs2mCMAxDlxTF5zttABAtW2xBDa6Nz+fSpk67xm+4Ws3ZvDN/KKOX/UNzly/V2RkNMjiCNmZGK0KvcWBNBAsLO78gS6l27kIAiMTqApfi9XXMpjanTtn2DbWnV5pdSsxKbd2pby+YpweCuSpOzje7nISW5EgK7wYF6+JVqYV5HXZNyY+/XU0AIJpW2oMaXBG/z6XLmpN0bsst6kotNLuUmDbi07f11JIPNDW9gW1OTXJq5anyuX1m/Xj0AYKFxU0vzmHwFgAitKskfk7iPpgFjam6PHi7AknxG6D6QlJ7s255d54e6UhXRWqx2eUklNBUtKn1U80uAxEiWFhcebJb49h6FgAi8qEroNqi+N7G++WtGbrJNVtBV3yHqL5w2OqFevLD+brc1xA+UwH9b1TRKJX5ymi1xREs4sDlxTlmlwAAlueojO9gEfKHjXn6QdpsBe3sKvhVXF1tunbhPD3a4lGdtzQq908iu3DAhWaXgD5AsIgDozO9qk3xmF0GAFjaAk9A/qxkxbtfrinRr3JuU9DgJUB31K//UI998KZmpg2Sy0Yg6w81GTU6qpAtZuMBzypxYnpxfG6XCADREjQM5dQmxonM//1pjZ4uvMHsMizDEejUFe89pyd3GRqcxg5bfW3awGl9fpswB8EiTpydl6lMp93sMgDA0uanStletxLB9cuG6hX/1WaXYSkVm5boN++/optSByjJzkyBvpCfkq8Ty0/sk9uC+QgWcSLJbtM0DswDgIi0GVJ5XeLsnHTxkmP0vp+57T1hCwZ00b9f0B+2t2uEr6bf7ptEEdoJymFjgXy8MILBYNDsItA3Gjs6NfytD7WzM0BLAaCXvEHJ9coGNbV1JUQPDSOolyufVPmauWaXYjlBGfr9wHG6p2ONdnc0mV2O5XidXv1l8l+U4mSnsnjBiEUc8Tkd7BAFABHaZUgD6hJn3VowaGjC8snaVHi82aVYjqGgJi/6i57etFPHpNebXY7lTK6dTKiIMwSLOHOlP1c+B2stACASS3KdctqNhGliR8DQ8asuVmPe18wuxZLyd6zVz959Uf/tqZLPFf/bFveFJEeSLh54sdlloI8RLOJMmsOuKzjXAgAissEW1OCaxBm1CNnV6dAJG65SS/Ygs0uxrFMWv6y5azdpfMYAs0uJeVNqpyjTY956JsMwvvRyxx13hL/u6aef1te+9jX5fD55vV4NHDhQ3/jGN/bezsMPP6z09PRu/cy6ujq53W5t2LBB8YpgEYeu8OconVELAIjI+sLE2B1qXxvaXDp52zfVnl5hdimWlb17k+5Z8ILucZYpy50Y2xf3ZrTC7C1m169fv/dy7733Ki0tbb/P3XjjjXrppZc0ZcoUnXXWWfrXv/6l+fPn684771RHR0ePf97rr7+ulpYWnX322XrkkUcUrwgWccjrsOtKP6dxA0AkljuCGlSeeC8MlzUn6dyWW9SVWmB2KZY2/pNX9cdVq3RqRoPZpcScyTWTlZWUZWoN+fn5ey+h0YjQKMW+n0tNTdWf/vQnjRw5UjfddJNqa2tVU1Oj008/Xffdd1+Pf96DDz6o888/XxdeeKHmzJmjeEWwiFPTi3OUwagFAESkrSw1ITu4oNGrK4K3K+BJvGDVl3zN23Xngnn6ma1I+Um84RfisXt0yaBLZAWhgLFo0SJ98MEHEd3Orl279OSTT2rq1KkaP368Ghsb9dprrykeESziFKMWABC5f7sCqirwJmQrX9qaqVs8sxVkK9CIHbPsTc1d9onOyWiQocTZFOBQO0FlJ1lj/dLMmTM1fPhwNTQ0qKysTOeee254tKGtra1Ht/O73/1O1dXV4fUZdrs9fDuhEYx4RLCI81ELTuMGgMgkVfkStoVPbsjXXem3K2h3mV2K5aW07dLsBfP0YDBX/uR8JepoxaWDLpVVpKSkaN68eVq6dKlmzZoVnh51ww03aMSIEWpubu727cyZMyc8WrFH6OPQCEZoJCPeECziWKrDrqv8uWaXAQCWNj8poMKMJCWq+1eXaU7urQoavGToC8M/fVt/WPKBLkofLFuC9fTsmrMtM1qxr8rKSk2fPl0PPPCAFixYoA8//FCPP/54t773ww8/1FtvvaWbb75ZDocjfAntMhUKJqGRjHiTWI/oBHRZUTajFgAQgUBoUWdtYq81+N6KOv2x6Hqzy4gbSe3NuundZ/XrDp8qU4uVCNx2t6VGKw4lNCUqOTlZTU3dO2n9wQcf1OjRo/Xee+9p4cKFey/XX399XE6HcphdAPpXisOuq/25unP5eloNAL20IM1QRopL25vaE7aH31g6TFnVV+mY1T83u5S4MWT1e3pynUs/bxivOTsXqzPYqXgerchJttYC9tBZFqGRhZNOOkmlpaXasWOHfvzjH4e3mw0twt6jq6srHBb2FTqvoqqqSr/5zW/03e9+V4MG7X8+TGgE5J577gkvDg+tvYgXjFgkgEuLs5XlJEMCQG+1GlJVnbnbY8aCC5eM1r/9F5hdRlxxdrVr5sJ5eqzFo3pvqeKR1dZW7DFmzBgtX75cF110UfhwuxNPPDF8uN2f//zn8Paze+zevVtDhw7d73LKKafomWee0datW3XGGWd84bbr6+vDl3gbtTCCwWDQ7CLQ/+5btUnfW7aOVgNAL2WEflv+bYNaOroSuoeGEdTfKx9X6ZpnzC4l7nTaHHq4YYLu3/2J2gPxMzo2vWG6rht2ndllIAoYsUgQlxRlK5tRCwDote2GNJBRCwWDhiYsP0ebC4/j0dTHHIFOTX/vOT25SzosrTIu+pvhztBlgy4zuwxECcEiQSTbbZpRwg5RABCJ5fku2W2JfQ5BSFvApuNWTdPOvBFmlxKXKjYt1SPvv6JbUgcoye6RlV055EqluhLzoMlERLBIIBcXZSvHxVoLAOitdbagBlez1iJkV6dDJ2z8ulqy4mfhaSyxBQOa+u8X9IftbTrSVyMr8nv9Oqf2HLPLQBQRLBJs1GImoxYAEJGtRdZ+B7kvrW916dQd16vDV2F2KXHLv3WlHlj4V307uVapFjsF/dph18ppc5pdBqKIYJFgLinKUXWy2+wyAMCyPnEGNaA03ewyYsaSpiSd13qLulIS8zTpaDl70V80d2OjRqfXywoashs0sWyi2WUgyggWCcZpM/S96iKzywAASwuUec0uIaa80+jVVcYsBTwErv6U17hO9737on7grlK6y6dYdv3hHKiYiAgWCejYzDSdmB3bT0gAEMsWegIqz2NB6r7+siVT/5X0LQUtNl3Hik7+6GXNXbtBEzJic33LscXH6oj8I8wuAyYgWCSoO6oK5WFnEwDotbRq3p0/0OPr8/U/6bcryLz6fpe1e7PuXvC87nWUKtudqVhhN+z65uHfNLsMmIRgkaBKk9z6Ogu5AaDX5icHlOdjIfeB7ltdpofzblXQ4CVGNBy/5DXNXfmpTs1oUCw4vep0VaSzmD9R8b8+gc0syVOxh90aAKA3Og1D/rrYeac4lnxnRb3+VMS71tHia9mhOxfM0/22IhUk5cgsXqdX1wy9xrSfD/MRLBJYkt2mOypZyA0AvTXfZ8iXxBs0B3Pt0sP1hv9KHlxRNGrZm3p62SeaktEgQ9E/yPHrh31d2UnZUf+5iB0EiwR3cm66jslgASIA9EaLIdXWc2DeoVywZIwW+c/nwRVFKW27NGvBPD0YyFFJckHUfm5NRo3Oqzsvaj8PsYlgAX2/uliO6L+xAQBxYVG2Q24Hv04P5eSlk7Sq+JSo3ieQhq98R3/45H1dnN4gWxTWu9x+5O2y2+y0PsHxTAjVpnh0WZF5czIBwMq2GkE11DL941CCQUPjl0/R5sKxUb1fIHk6WnTju/P02/Y0VaX6+60lp1ScomF5w2g5CBb4zI3l+cp1OWgHAPTCqgK32MH70NoCNo1bfYl25XK2gRka1ryvJz58W1f6GuSw9e3v+lRnqq4/gsPw8BlGLBDmddh1e0Uh3QCAXlhlD6ihkh2ivkxjh0MTNl2j1qwBPMZM4Oxq1zUL5+l3TW7Ve8v67HZZsI19ESyw1zn5GToiLZmOAEAvNJZw4vRXWd/q0umN16vD13cvbNEztRsW69EP/qHr0gbJbXdH1L7qjGqdX8fifHyOYIG9DMPQnTXFPCgAoBc+cgZUV+yjd1/Vp93JuqDtVnWl5NErkzgCnZr+3nN6sjGgob6qXt8OC7ZxIIIF9jPEm6zzC9g6EQB6w1bppXHd8K8daZphm6WAJ51+mah88zI9/N7fdWtKvZIcST363kkVk3R43uH9VhusiWCBL/ivigKlO9gyDgB66l13QCXZTInqjhc2Z2lW0mwFnUzBNZMtGNAFH7yop7a26Mj0mm59j9fl1Y1H3NjvtcF6CBb4giyXQzeV59MZAOihoGEos4Z34bvr0fUFuifjdgVtnF5utuJtq/TAu3/VHUk18jq//ODcGw6/gRO2cVAECxzUtKJsDfb2bFgUACAtSJVyvJEtik0kP1lVrkfyblVQnNQaC8768K96euMOHZtRf9DrR+SP0Fk1Z0W9LlgDwQIHZTcM/aS+VB42ZgeAHmk3pNI61qr1xB0r6vVc8Td4pMWIvMZ1+smCF/VDd6UyXJ9vSOCxe/Tto75tam2IbQQLfOmJ3LeWF9AhAOihhemGUt0cOtoTM5YO15v+y3msxZBJH/1Nc9es18SMgeG/X33Y1SpJKzG7LMQwIxgMBs0uArErEAzqzHeX6q3GJrNLAQBLOWZjl95euMHsMiznueo/acDqx8wuAwf4xxEX6MiTfiK7jc1dcGiMWOBL2QxD/1tfohQ7DxUA6IlPchxy2lk30FOTlp6s1cWTeLDFErtLRw+/hlCBr8SrRXyl0iS3vlNVRKcAoAc22oIaXJNNz3ooGDQ0YcW52lowht7FijE3S3kDzK4CFkCwQLdMLczS8ZlpdAsAemBdoUcGgxY91tJl13FrLtOu3CN4vJmtcKg06nqzq4BFECzQbffU+ZXBwXkA0G0rHAENKs+gY73Q2OHQxE0z1JpZR//MYndLp98vsa4C3USwQLfluZ36QU0xHQOAHmgp5STu3lrb6tYZO29UR1opjzkzHHuLlHvw8yyAgyFYoEdOz8vQqbmcKgsA3fWBK6jqQi8N66XFu5N1Ucet6krJpYfRVDhMGsnZIugZggV67Ic1xcp1sT87AHSXp+rzQ8bQc29u9+ka2ywF3fQxKpgChV4iWKDHMp0O3V3rp3MA0E3zPQEVZybTrwg8vzlbs5JnK+hIoo/97bjbpVzWtqDnCBbolfHZPp1fkEn3AKAbAoah3FoWcUfq/60v1L2ZtytoY9S831SNl46+tv9uH3GNYIFe+25VkfweFx0EgG5Y4JUyU3jOjNT/rqrQb/NuUVDs49vnvIXSGb8QeySjtwgW6LVUh1331vl5ageAbmg1pMr6LHrVB2avGKjni1lY3KcMu3T2g1IKj1H0HsECERmZ4dXlxTl0EQC64f1Mu5JcdnrVB76+dLj+6Z9OL/vK2P+SSo+mn4gIwQIRu62iQNXJbjoJAF+h0QhqUF02feojU5Ycp4/9U+hnpCrGSqNuoI+IGMECEfPYbbpvQKk8Nua7AsBXWZrnlIPnyz5z0tJTtKb4JB54vZWaJ535K8nGS0JEjkcR+sRgbzKncgNAN6y3BdVQzTz2vtIVtGn8ivO0tWA0j7+eMmyfhYpUpjSjbxAs0GfOK8jSRYX8sgSAr7K52EOT+lBLl13j1kzX7pxh9LUnRt8sVYyhZ+gzBAv0qe9XF2lYGodAAcCXWeoIamBZOk3qQ9s7HJq4eabaMmvpa3eUHSONuYVeoU8RLNCnXDabHhxUpmwnhxcBwJfpKEulQX1sTatbZ+66UZ1pJfT2y6TkSGc9wLoK9DmCBfpcgdulXwwslZ213ABwSO+7g6rII1z0tUW7UnRxx38pkMy6gUOvq/il5M3nfyf6HMEC/Xa+xayKQroLAF8itZrpUP3hje0+XWufraA7jcffgUZ9U6o8jr6gXxAs0G+uLsnVabn80gSAQ5mfElB+Ogu5+8Ozm7P17ZRvKehI4gG4R8nR0tjb6Qf6DcEC/eqeOr9qU/ilCQAH0yVDxXWZNKef/HpdoX6cebuCNtb9KTlLOvtBycbJ7+g/BAv0qxS7XXMGlSnNwUMNAA7mnTRD6clOmtNPfrSqQo/m36ygjMReV3HGL6Q0piijf/FqD/2uMtmjn9SXJvJTOgAcUqshVddl06F+dPvyQXqx+NrE7fFxs6Xq8WZXgQRAsEBUnJDt03WleXQbAA5iUZZdHie/kvvTVUuP1Nv+SxPv8Td4inTM9WZXgQTBsxii5ubyfI3N9NJxADjANltQg2oZtehvk5eM0yf+yYnz+CseLp36E7OrQAIhWCB6DzbD0M8GlKrE46LrAHCATwtcsjFntN+duPQ0rSuaGP+Pv7Ri6dxHJYfb7EqQQAgWiKoMpyN8MncSvz0BYD9rbEENrsqiK/2sK2jT+E+nalv+qPjttTNZOu9RKTXX7EqQYAgWiLoGb7L+T62fzgPAAbb7k+lJFDR12TR+3eXanTM0DvttSKffLxUMMbsQJCCCBUxxTn6mrijOofsAsI+PnQHV+330JAq2tjt10paZasuoja9+H3urNPB0s6tAgiJYwDR3VBXqVE7mBoD9VbDJRbSsavHozN03qjMtTkbRG875LFgAJiFYwNTF3D+tL9HI9FTuBQD4j3c9QZXlpNCPKFm0K0XTOm9TINniu3KVjpROu8/sKpDgCBYwlctm08MN5RqUmsQ9AQD/kV6TTi+i6PVtPl3nmK2g26KjRVlV0pTfSg52XYS5CBYwnddh16ODK+RnG1oACJufIuWmsU1oNP1pU46+kzJbQYfHWo/C5Czpgiel5EyzKwEIFogNuW6nfjekQplOu9mlAIDpOgyppI6tZ6Pt4XXF+mnm7QoaFvldFApB5/1OyqwwuxIgjBELxIzKZI9+O7hCyXYelgDwbrohr8dBI6Ls7lWVerzgZgVD27bGNEM64xeSf4TZhQB78QoOMWVYWop+NbBMjlh/PgeAftZkSHX1Fl9QbFG3Lm/QX4tnKqaNu4NtZRFzCBaIOcdnpenu2hKzywAA032UbZfTzjstZrh86dc033+JYtKRV0ujvmF2FcAXECwQk6YUZOr2igKzywAAU222SYNrGbUwy1lLxmuJ/2zFlGEXSyf+0OwqgIMiWCBmzSzN0/RifqECSGxrC90yGLQwzcSlp2t90QmKmQPwTr7X7CqAQyJYIKZ9r6pIp3E6N4AE9qk9qMEVbCVqlq6gTeM+vVDb80fKVPWnSGf8XLLx0g2xi0cnYpphGPpJfYlGcTo3gAS2u4STuM3U1GXThHVXqCnnMHMKqJ4gnTVHsllkG1wkLIIFLHE690Oczg0ggS1yBVRblGZ2GQltc7tTk7Zcq/aM6uj+4PLR0jm/4VRtWALBApbA6dwAEp2jkmBhtk9bPDq76SZ1eoui8wP9R352AJ7TYqeBI2ERLGCp07kfH1KpHBcHRgFIPAs8Afmzks0uI+G9vzNVl3bdrkBSP28uUnCYdMGTkiv2psFNmzYtPFU5dHG5XKqqqtJ3v/tddXZ26u9///ve6/a9zJo1K/y9h7o+dNmwYYPZ/zREiFdosJSKZLeeOqxKkxcu04b2DrPLAYCoCRqGcmoztPofzXTdZK9uS9c382bp3q5ZMtp39/0PyB0gXfi05PEpVk2cOFEPPfSQ2tra9Nxzz2nGjBlyOp066qijwtd//PHHSkv7fJQtNTV1v+8/8PqQ3NzcKFWP/sKIBSynOsWjp4dWqcjtNLsUAIiq+alSVqqLrseAP27M1fe8sxW0u/v2hrOqpIv+KCXH9k5gbrdb+fn5Ki0t1dVXX61x48bpmWee2S8khK7fczkwWBx4fehiY8cryyNYwJLKk93hcFHi4RcsgMTRZkgV9Vlml4H/mLPWr/uzb1PQ6KPdmtJLpIuekVKt9859UlKS2tvbzS4DJiNYwLJKktyaO7RKFUl9/G4RAMSw9zLsSnax7WisuGtltZ4ouCnyG/IWfhYqfFFaGN5HgsGg/vrXv+rFF1/Ucccdt/fzxcXF4VGKPZetW7fu930HXj9w4EATqkdfY40FLK3Q4wqPXJy9cKmWNLeZXQ4A9LudRlDH1Gfr7fc20u0YccvywcqunqnjV/+kdzeQkiNd/IyUWS6rePbZZ8OBoKOjQ4FAQOeff77uuOMOvf322+HrX3vtNXm93r1fn5GRsd/3H3h9aH0GrI9gAcvLczv11NAqnbNwmRY3tZpdDgD0uyW5TjlshjoDQbodIy5bcpSeqt6lYasf7tk3JmVIF86VsqN8PkaExo4dq/vvvz+8K1RhYaEcjv1fUpaXlys9Pf2Q3/9V18OamAqFuJDjcuoPQ6vUkJpkdikA0O822IIaXMNai1hz5pIJWuY/q/vfkJIrXfyslD9IVpOSkhLeZrakpOQLoQKJi2CBuJHpdOj3h1VqqJd93gHEv41FHJoWiyYuO1MbiiZ89Rf6/NKlL1gyVPSFTZs2hc+t2PcSmlYFayNYIK74nA49eVilRvhi70AhAOhLyxxBDSrff946zNcRMHT8ygu1I//oL99SNhQqsiqVqGpra1VQULDfZf78+WaXhQgZwdByfiDONHV16cL3V+gfO/rh4CIAiBENbYaW/H2N2WXgIHJcHXol9x4lb3lv/yvyGj47/C41h74h7jBigbiUYrfr/w2u0LEZn+84AQDx5t/uoKoKeJ6LRZvbnTp523VqT6/6/JPFI6RpzxIqELcIFohbSXabHhlcrnFZaWaXAgD9JrnKR3dj1PJmj85pvlmd3iKp4ljporlSEjshIX4xFQpxrz0Q0FWLVuq5LY1mlwIAfc6uoEr/tV3rtrfQ3Rh1w3CPZp42SnJwoCviGyMWiHsum02/HFim03J5lwhA/OmSoYLaTLPLwCGcN6JEM844jlCBhECwQEIIHSR1/4BSXVnMYjkA8Wd+mpSezMnFseba46r0gzMbZLMZZpcCRAXBAgnDZhj6TnWR7qoploPneABxpNWQqus5MC9WhHLEd04dqOsn1JpdChBVrLFAQnpl2y5dvmiFdnYGzC4FAPpERmjz+L9tUEtHFx01kcth092Th+iUIYXcD0g4jFggIY3J9OpPw2pU4nGZXQoA9InthjSwjlELM2WluPTY5UcSKpCwCBZIWLUpHj13eI2OSEs2uxQA6BMr8lyyM5/fFFW5qZo7Y6QOL2UhPRIXwQIJLdvl0O8Pq9Lp7BgFIA6stQfVUMUL22gbVZWtp75+tPyZvFGFxEawQMLz2G3hHaOuL8tL+F4AsL5t/iSzS0go543w6+FLhivNw65cAIu3gX38fsM23fDxarUFQqsgAcCahn3UpA9X7jC7jLgWmnF264l1umJ0pdmlADGDEQtgH2fnZ+rJIZXKdNrpCwDLCpSlml1CXEty2nX/1MMJFcABGLEADmJlS5umvr9cS5rb6A8AS6p/t1ErNu02u4y4k5fm1gMXDVdDsc/sUoCYw4gFcBClSW49O6xax2Twrh8Aa/LVpJtdQtwZUuwL7/xEqAAOjmABHILP6dBjgyt1QQE7rACwnneSA8rzecwuI26cO9yvJ646SgU+FscDh8JUKKAbfrZqk76/bJ04pxuAlYzaEdQ7/1xndhmWP0n7u6cO1LkjSswuBYh5BAugm/68pVHXLl6lHZ1d9AyAJSQHpZRXN2pXa6fZpVhSoc8TXqQ9xM+0MqA7mAoFdNOEbJ/+fESNDvNyABIAa2g2pLr6bLPLsKSjK7P0p5mjCBVADzBiAfRQeyCgO5au05y1W+gdgJiXFTTU8fI6tXcymbO7rhxdoZsn1skeOqwCQLcRLIBeembTDt3w0Srt6uKXNYDYNmpNh95ZtMnsMmJeisuu/zt5iE5qKDC7FMCSCBZABJY3t+nyRSu0aHcrfQQQs0o6DW15eY0CQbMriV0VOSn65YWHqyrXa3YpgGWxxgKIQEWyW/OG1bAlLYCYtsoRVEMlW2cfyjlHFOvZmaMIFUCEGLEA+siTG7bp1k/WqImpUQBi0IAOQ8tfXmN2GTHF63Hov89o0ClDCs0uBYgLBAugj6dGXf3hp3pvVwt9BRBzhizapY/X7DS7jJgwrCRd/3vuUPkz2ekP6CtMhQL6eGrUs8Nq9HV/rthLBECscVSkKdGFNnq6ZmyVnrjyKEIF0McYsQD6ySvbdmnm4pXa1M7BVABigxEMqnrBDq3a0qxElJ/m0Y+mHKajKrPMLgWIS4xYAP1kTKZXLw+v0/GZvEMIIDYEDUNZtRlKROMH5On5644hVAD9iBELoJ8Fg0E9sGaLvr98ndrY6xGAyVxBKffNLdqyq02JwO2w6fZJ9broqDKzSwHiHsECiJIPdjXrmsWr9FETZ14AMNeobUG98/a6uL8bhvjTdffkIarKTTW7FCAhECyAKOoIBPWTVRv1vys3MnoBwDTeoOR6ZYOa2rri8l5w2W26bly1rhpTKXtotTaAqCBYACb4pKlVN368Wv9qbKL/AExxzIZOvf3exrjr/sDCNN19zhDV5bO+DYg2ggVg4tqLh9dt1Z3L1mk3h+oBiLK8gKHml9eqoysYF7132g3NGFsVvjjt7E0DmIFgAZhsXWt7+MTuP2/l0CoA0TVyVbvmL95s+bYPLvbprrMHM0oBmIxgAcSIuRu3a9aStdrSwbkXAKKjotPQ+pfXKBi07o5PobUUV45mLQUQCwgWQAzZ3tGpO5au0+MbtpldCoAEMfyTZv17xXZZzfCyDP3gzMHs+ATEEIIFEINe3bZLN328Witb280uBUCcG9RuaOnf1sgqMlNcuvXEOk0+vFiGwY5PQCwhWAAxqrkroLtWrNev1mxWnKytBBCjGv69U0vW7VIsC2WIc4f7dcvEOqUnu8wuB8BBECyAGPfermbd8NFqfbC7xexSAMSpI5oNffBa7I5aDChI0/fPGKRhJRlmlwLgSxAsAAvoDAR1/+pNuvvTDWoNMHwBoG/ZgkGVv7Nda7fF1hsYqW6Hvjm+RtOOLuOgO8ACCBaAhSxvbtMtn6zWa9t3m10KgDhz9E5pwZtrFSsmDS7Qt04eoLw0j9mlAOgmggVgQX/e0qjvL1uvT5pbzS4FQJzwBKXMNzZrW5O5m0aUZ6foO6cO1OiaHFPrANBzHE0Jy5k2bVp4J5CrrrrqC9fNmDEjfF3oa/b15ptvym63a9KkSYoHE7J9+tuIWv3f2mLluhxmlwMgDrQaUmV9lmk/35fk1KxJ9XrxG6MJFYBFESxgSX6/X7/73e/U0vL5fODW1lY9+uijKikp+cLXP/jgg5o5c6ZeffVVrVu3TvHAbhi6sDBbbx5ZrxvK8pRs578zgMj8O8OmJJc9qm102g1dOrJcr9x0rKYfUyGXg+cywKr43wtLGjZsWDhcPPXUU3s/F/o4FCqGDh2639fu3r1bjz/+uK6++urwiMXDDz+seJLisOum8oJwwJhakCU727oD6KUdNmlQXXbU+jdxYL7+8s0x+tYpA9hCFogDBAtY1qWXXqqHHnpo79/nzJmjSy655Atf98QTT6iurk61tbWaOnVq+OuCwfjbWSnP7dT/1Pn18vA6jctKM7scABa1NM8ph61/36EYXOzTE1cepZ9feLjKslP69WcBiB6CBSwrFBJef/11rVy5Mnx54403wp872DSoPZ+fOHGiGhsb9corryhe1aZ49NvBFfr9YZUanJpkdjkALGa9LajB1f2z1qIoPUn3TjlMf5wxUiPKM/vlZwAwD6s+YVk5OTl7pzaFRiBCH2dn7z+E//HHH+tf//qXnn766fDfHQ6HpkyZEg4bxx57rOLZqAyvXjyiRk9t3K4frFivNa0dZpcEwCI2FydJH/fd7XndDl11bKUuG1UujzO6azgARA/BApafDnXNNdeEP77vvvu+cH0oQHR2dqqwsHDv50IhxO1266c//al8Pp/iWWiHrLPyM3VybroeWLNFP165UY2dXWaXBSDGLXEEdHhpuhat3BHR7aS47Jo2skxXHFMpX7Kzz+oDEJsIFrC00NSm9vb28AvoE044Yb/rQoHi17/+te6++25NmDBhv+tOP/10PfbYYwfdsjYeuW02zSjJ1XkFmbr30416eO0WtcfhOhMAfaer3Cv1Mlgku+y68KhSXTW6UhkpLu4WIEEQLGBpobMpFi9evPfjfT377LPavn27Lrvssi+MTJx11lnh0YxECRZ7ZDod+m51kS4rztYPl6/XM5t3qIt8AeAgFroDGpCXquUbd3e7Px6nTVOPLA1Pe8pOddNXIMGweBuWl5aWFr4cKBQcxo0bd9DpTqFg8c477+j9999XIipNcuv+gWV648h6XVSYJU8/7wADwJq81d2bLup22DTt6DK9evNYzTp5AKECSFBGMB733QTQI5vbO/Sr1Zv18Lot2tkZoHsAwhzBoIr/uU0bGlsP2hGX3aYpw/2aMbZK+T5PXHVt2rRpeuSRR3TllVfq5z//+X7XzZgxQz/72c908cUXhzcQ2bx5s771rW9p3rx52rhxozIyMjRkyJDw50aOHBn+nrKysvAOhvsqKirS9OnT9Z3vfOdLa+GlGqyCYAFgr92dXXpk3Vb9cvUmbWzvpDMANGpHUO/8c90XpjxNOcKvK8ZUhreQjUehYPHyyy9r586dWr9+vZKSPvt3tra2qqCgIDxSPnbs2HCwGD16dHi93w9+8ANVVFSEw8VLL72kgQMH6tRTT90bLEJTcy+//PK9PyM0hTd0u6GDXPcYPny4rrjiiv2+Lj8/P6r/dqC3WGMBYK9Uhz28yHt6cbZ+v2G7frZqk5a1tNEhIIHN9xnyJTnV2NIR/vPio0p18dFlykqANRTDhg3TsmXL9NRTT+mCCy4Ify70cUlJicrLy8N/37Fjh1577TX9/e9/15gxY8KfKy0t1YgRI75we16v96AhITU1db+wcaivA2IdaywAHHQXqQsKs/TakXV6YGCZDvMm0yUgQbUY0uHD8jVrUr3+cetxun5CbUKEin23NX/ooYf2/n3OnDm65JJL9gsFocvcuXPV1sYbMUhsBAsAh36CMIzwGRgvHFETPsl7TIaXbgEJZHBqku6rL9EvJjVo+jEVSnEn3kSHqVOn6vXXXw+vjwhd3njjjfDn9ggdvBqaDhVaj5Genh5eU3HbbbcddHOQW265ZW8QCV1+/OMfR/lfA/SvxHuGANDrk7xDl/d3NeunqzZpHlvVAnEptEfc+Kw0XenP0UjeTFBOTo4mTZoUDg+hRdShj7Ozs7+w02Do86EpUW+99Zaef/553XXXXXrggQfCazX2uOmmm/b7+4G3A1gdwQJAjwz2JuuXA8v0aUtbeA3G4xu2qS3A5nKA1SXZbJqcnxEOFJXJ8bXDU19Mh7rmmmvCH993330H/RqPx6Px48eHL7Nnzw7v9vTtb3/7C0GiqqoqanUD0cZUKAC9Upbk1l21fr1z1ADdXJ4vv4fTdQErynM5dGt5vuYfPSD8f5pQ8UUTJ04M7/rU0dGhE044oVt9HTBggJqamvr8/gJiGSMWACKS43Lq+rJ8fbM0T69t361H12/V81saGcUAYpjdkI7NSNO5BZk6ITtNLhvvM35pv+x2LV68eO/H+9q6dasmT54cHtUYPHhweEen0AGsoalQp512Wr/ej0CsIVgA6BOGYWh0pjd82d7RqT9s3K5H123Vh00HP1gLQPRVJrnDYWJyfqby3U7ugh4InVtxMKFF2EceeaR+9KMfhbemDY1q+P3+8DkUoUXcQCLhgDwA/erdnc16bP1WPb1xu3Z1cao3EG2pdptOzU3XufmZGpH++XkJANDXCBYAoqK5K6A/bdoRDhlvNTLvGOjvnZ2+lp6ic/OzdHKuTykHTN8BgP5AsAAQdUubW/XY+m16YsM2bW7v5B4A+kiR26lz8jM1pSAzvMECAEQTwQKAaToDQf1la6MeXb9NL2/bqS52rQV6zG0zNDHbF57qNCbTGz7YEgDMQLAAEBM2tHXo8fXb9PuN27Skuc3scoCYN9ibFA4TZ+ZlKN3JXiwAzEewABCTU6We39wY3rY2tPibgQzgMzXJnvD2sGfkZWhAahJtARBTCBYAYtrGtg69sKUxfHlj+261B4kZSKzzJkb4UnRClk8nZPtUnsy6CQCxi2ABwDJ2dnbppa07wyMZL2/dqd1sX4s4lGK3aWymNxwkxmWlKYNpTgAsgmABwJLaAoHwSd8vbG7Ui1sb2V0Kllbgdmp8Vlp4EfbIjFS5OQkbgAURLABYXiAY1PydzXpu847wlKkVLe1mlwR8pYGpHk3I8mlijk9DvMl0DIDlESwAxJ2PmlrCIxkvbNmp93c1i/O+EQuchqGj0lM0Ifuz9RJ+j8vskgCgTxEsAMT9uox/7tgdPu37rR279f6uFnWwABxREDpNoibFoyN9KTo6PVXHZaUpzcEJ2ADiF8ECQEJp7gpowc4mvRkKGzuawh+3BNhpCn1zUF1oSlNoF6c9F86XAJBICBYAElpHIKiFu5rDoxmhoPH2zt3a2cnkKXy1dIddR/hSwiMSoRBxWFoyi64BJDSCBQAcsBB80e6WcMh4q/GzsLG1o5MeQcUep470pe4djahL8cgwQhOeAAAECwDohiVNreGQ8c8dTfpgd4uWNbexTiPO2STVp3o0wpe6d0SikMXWAPClGLEAgF5Mn1rW0qqPdrfq46ZWfdTUqsVNLVrV0s4OVBZU6HaqNsWjmmRPeLF16BIajfCy0BoAeoRgAQB9uDD8k3DQaAmHjT2hY31bBz02mbFvgNgTHpI9qiZAAECfIVgAQD9r7OgMB4zPLy3h0LGto4ve90OACJ0PUbN3BMKt2pQk1SS7lcIIBAD0K4IFAJhkU1uHVrS0aW1bh9a2tmvdAX9u7yR4HMhlGMpxOZTndirP5VTufz7eEyaqkz1KtodWSAAAoo1gAQAxPLVqXVu71rZ2hP/c3N6pze0d2hT+8/OPG+MggITCQF4oJITCQjg0OJTrcn4hQGQ47OzEBAAximABABbXFgiEg8am9g5tae8MB5LWQOgSVMtBPm4J/b0r+J8/9//cno9b/vPxvkcHOozQIXC28EFwHptNLpux39/d+/z9wD/DX2+E/m7IY7cp27knOHwWJlKZpgQAlkewAAB8aWjpCn52qrSdMxsAAF+CYAEAAAAgYqxwAwAAABAxggUAAACAiBEsAAAAAESMYAEAAAAgYgQLAAAAABEjWAAAAACIGMECAAAAQMQIFgAAAAAiRrAAAAAAEDGCBQAAAICIESwAAAAARIxgAQAAACBiBAsAAAAAESNYAAAAAIgYwQIAAABAxAgWAAAAACJGsAAAAAAQMYIFAAAAgIgRLAAAAABEjGABAAAAIGIECwAAAAARI1gAAAAAiBjBAgAAAEDECBYAAAAAIkawAAAAABAxggUAAACAiBEsAAAAAESMYAEAAAAgYgQLAAAAABEjWAAAAACIGMECAAAAAMECAAAAgPkYsQAAAAAQMYIFAAAAgIgRLAAAAABEjGABAAAAIGIECwAAAAARI1gAAAAAiBjBAgAAAEDECBYAAAAAIkawAAAAABAxggUAAACAiBEsAAAAAESMYAEAAAAgYgQLAAAAABEjWAAAAACIGMECAAAAQMQIFgAAAAAiRrAAAAAAEDGCBQAAAICIESwAAAAARIxgAQAAACBiBAsAAAAAESNYAAAAAIgYwQIAAABAxAgWAAAAACJGsAAAAAAQMYIFAAAAgIgRLAAAAABEjGABAAAAIGIECwAAAAARI1gAAAAAiBjBAgAAAEDECBYAAAAAIkawAAAAABAxggUAAACAiBEsAAAAAESMYAEAAAAgYgQLAAAAABEjWAAAAACIGMECAAAAQMQIFgAAAAAiRrAAAAAAEDGCBQAAAICIESwAAAAARIxgAQAAACBiBAsAAAAAESNYAAAAAIgYwQIAAABAxAgWAAAAACJGsAAAAAAQMYIFAAAAgIgRLAAAAABEjGABAAAAIGIECwAAAACK1P8HSo33ywBvoPIAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 27 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kGa9xMQ19nzM" + }, + "source": [ + "We can immediately see that our explicit constraints were satisfied, and can check all the sector constraints as follows:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "JtA8KzWp9nzM", + "outputId": "cd007325-fa24-4f41-f8fa-3d149ae7fc23", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.357800Z", + "start_time": "2025-11-12T08:10:59.354334Z" + } + }, + "source": [ + "# O(N^2) loop not a good idea in a coding interview :)\n", + "for sector in set(sector_mapper.values()):\n", + " total_weight = 0\n", + " for t,w in weights.items():\n", + " if sector_mapper[t] == sector:\n", + " total_weight += w\n", + " print(f\"{sector}: {total_weight:.3f}\")" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZnDm8UkC9nzJ", - "outputId": "9302f8ed-698e-461b-c4a1-ad8113f1aa11" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Annual volatility: 15.2%\n" - ] - } - ], - "source": [ - "ef.portfolio_performance(verbose=True);" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Consumer Discretionary: 0.100\n", + "Consumer Staples: 0.100\n", + "Healthcare: 0.144\n", + "Tech: 0.050\n", + "Energy: 0.000\n", + "Aerospace: 0.038\n", + "Misc: 0.151\n", + "Auto: 0.067\n", + "Media: 0.000\n", + "Financial Services: 0.350\n" + ] + } + ], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qOZ-z4AK9nzM" + }, + "source": [ + "## Maximise return for a given risk, with L2 regularisation\n", + "\n", + "Let's imagine that we've put a lot of thought into our risk tolerance, and have decided that we can't accept anything more than 15% volatility. We can use PyPortfolioOpt to construct a portfolio that maximises return for a given risk (with the same caveats about expected returns)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "sKantkrg9nzM", + "outputId": "3b60679f-80ef-4ec7-e029-989ef9d27f14", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.468831Z", + "start_time": "2025-11-12T08:10:59.450933Z" + } + }, + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", + "ef.efficient_risk(target_volatility=0.20)\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "ORCrBIG19nzK" - }, - "source": [ - "Let's say we were happy with this portfolio and wanted to actually go out and buy the shares. To do this, we would need to construct a **discrete allocation** (unless your broker supports fractional shares!)\n", - "\n", - "If we had \\$20,0000 to invest and would like our portfolio to be 130/30 long/short, we can construct the actual allocation as follows:" + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.02674),\n", + " ('AMZN', 0.15123),\n", + " ('COST', 0.1),\n", + " ('DIS', 0.0),\n", + " ('F', 0.0),\n", + " ('GILD', 0.06766),\n", + " ('JPM', 0.06285),\n", + " ('KO', 0.0),\n", + " ('LUV', 0.01736),\n", + " ('MA', 0.37416),\n", + " ('MSFT', 0.05),\n", + " ('PFE', 0.0),\n", + " ('TSLA', 0.15),\n", + " ('UNH', 0.0),\n", + " ('XOM', 0.0)])" ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 29 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "46ylv28y9nzM", + "outputId": "de8f0548-437e-4997-cc46-d927d5f61df2", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.515789Z", + "start_time": "2025-11-12T08:10:59.512558Z" + } + }, + "source": [ + "num_small = len([k for k in weights if weights[k] <= 1e-4])\n", + "print(f\"{num_small}/{len(ef.tickers)} tickers have zero weight\")" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "w10Rcb-X9nzK", - "outputId": "052edfa3-7811-459c-debb-644dc7aa2368" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Discrete allocation performed with $65.59 leftover\n" - ] - }, - { - "data": { - "text/plain": [ - "{'ACN': np.int64(8),\n", - " 'COST': np.int64(2),\n", - " 'DIS': np.int64(1),\n", - " 'GILD': np.int64(6),\n", - " 'KO': np.int64(75),\n", - " 'LUV': np.int64(14),\n", - " 'MA': np.int64(6),\n", - " 'PFE': np.int64(74),\n", - " 'TSLA': np.int64(5),\n", - " 'XOM': np.int64(20),\n", - " 'F': np.int64(-114),\n", - " 'JPM': np.int64(-17),\n", - " 'MSFT': np.int64(-1)}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pypfopt import DiscreteAllocation\n", - "\n", - "latest_prices = prices.iloc[-1] # prices as of the day you are allocating\n", - "da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=20000, short_ratio=0.3)\n", - "alloc, leftover = da.lp_portfolio()\n", - "print(f\"Discrete allocation performed with ${leftover:.2f} leftover\")\n", - "alloc" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "6/15 tickers have zero weight\n" + ] + } + ], + "execution_count": 30 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "3HWpBJxZ9nzM", + "outputId": "cae10c97-978e-40ee-8dd5-796d9635f53f", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.551022Z", + "start_time": "2025-11-12T08:10:59.538041Z" + } + }, + "source": [ + "ef.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "ab3Rk-Aa9nzK" - }, - "source": [ - "## Max Sharpe with sector constraints\n", - "\n", - "If you have your own model for returns (or have read the warnings and want to proceed anyways), you may consider maximising the Sharpe ratio. This theoretically gives the optimal portfolio in terms of risks-returns.\n", - "\n", - "In this section, we construct a long-only max-sharpe portfolio, but also incorporate sector constraints. Sector constraints require three things. A `sector_mapper`, your `sector_lower` bounds, and your `sector_upper` bounds." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 26.2%\n", + "Annual volatility: 20.0%\n", + "Sharpe Ratio: 1.31\n" + ] + } + ], + "execution_count": 31 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sFdT8Xar9nzN" + }, + "source": [ + "While this portfolio seems like it meets our objectives, we might be worried by the fact that a lot of the tickers have been assigned zero weight. In effect, the optimizer is \"overfitting\" to the data you have provided -- you are much more likely to get better results by enforcing some level of diversification. One way of doing this is to use **L2 regularisation** – essentially, adding a penalty on the number of near-zero weights." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "wiaa_L2u9nzN", + "outputId": "0adbf717-faaa-4142-9fe3-6ca9368cc23f", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.681090Z", + "start_time": "2025-11-12T08:10:59.626034Z" + } + }, + "source": [ + "from pypfopt import objective_functions\n", + "\n", + "# You must always create a new efficient frontier object\n", + "ef = EfficientFrontier(mu, S)\n", + "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", + "ef.add_objective(objective_functions.L2_reg, gamma=0.1) # gamma is the tuning parameter\n", + "ef.efficient_risk(0.2)\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "mJcyi2-E9nzK" - }, - "outputs": [], - "source": [ - "sector_mapper = {\n", - " \"MSFT\": \"Tech\",\n", - " \"AMZN\": \"Consumer Discretionary\",\n", - " \"KO\": \"Consumer Staples\",\n", - " \"MA\": \"Financial Services\",\n", - " \"COST\": \"Consumer Staples\",\n", - " \"LUV\": \"Aerospace\",\n", - " \"XOM\": \"Energy\",\n", - " \"PFE\": \"Healthcare\",\n", - " \"JPM\": \"Financial Services\",\n", - " \"UNH\": \"Healthcare\",\n", - " \"ACN\": \"Misc\",\n", - " \"DIS\": \"Media\",\n", - " \"GILD\": \"Healthcare\",\n", - " \"F\": \"Auto\",\n", - " \"TSLA\": \"Auto\"\n", - "}\n", - "\n", - "sector_lower = {\n", - " \"Consumer Staples\": 0.1, # at least 10% to staples\n", - " \"Tech\": 0.05 # at least 5% to tech\n", - " # For all other sectors, it will be assumed there is no lower bound\n", - "}\n", - "\n", - "sector_upper = {\n", - " \"Tech\": 0.2,\n", - " \"Aerospace\":0.1,\n", - " \"Energy\": 0.1,\n", - " \"Auto\":0.15\n", - "}" + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.06969),\n", + " ('AMZN', 0.16698),\n", + " ('COST', 0.08443),\n", + " ('DIS', 0.00642),\n", + " ('F', 0.0),\n", + " ('GILD', 0.07658),\n", + " ('JPM', 0.09307),\n", + " ('KO', 0.01557),\n", + " ('LUV', 0.04691),\n", + " ('MA', 0.21322),\n", + " ('MSFT', 0.05),\n", + " ('PFE', 0.0),\n", + " ('TSLA', 0.15),\n", + " ('UNH', 0.02714),\n", + " ('XOM', 0.0)])" ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 32 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "G0ZO5l4D9nzN", + "outputId": "aefa431d-90b2-493b-8232-8dc8c8213212", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.724312Z", + "start_time": "2025-11-12T08:10:59.719794Z" + } + }, + "source": [ + "num_small = len([k for k in weights if weights[k] <= 1e-4])\n", + "print(f\"{num_small}/{len(ef.tickers)} tickers have zero weight\")" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "1h5ZdZwF9nzL" - }, - "source": [ - "We then set up the optimizer and add our constraints. We can use `ef.add_objective()` to add other constraints. For example, let's say that in addition to the above sector constraints, I specifically want:\n", - "\n", - "- 10% of the portfolio in AMZN\n", - "- Less than 5% of my portfolio in TSLA" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "3/15 tickers have zero weight\n" + ] + } + ], + "execution_count": 33 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n8yo6ey19nzN" + }, + "source": [ + "We can tune the value of gamma to choose the number of nonzero tickers. Larger gamma pulls portfolio weights towards an equal allocation." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "hIdHgQnE9nzO", + "outputId": "76b74cec-dbad-4278-ad5c-6a3bb99b404d", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.763277Z", + "start_time": "2025-11-12T08:10:59.743844Z" + } + }, + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", + "ef.add_objective(objective_functions.L2_reg, gamma=1) # gamma is the tuning parameter\n", + "ef.efficient_risk(0.2)\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "FRjwf0wN9nzL" - }, - "outputs": [], - "source": [ - "mu = expected_returns.capm_return(prices)\n", - "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", - "\n", - "ef = EfficientFrontier(mu, S) # weight_bounds automatically set to (0, 1)\n", - "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", - "\n", - "amzn_index = ef.tickers.index(\"AMZN\")\n", - "ef.add_constraint(lambda w: w[amzn_index] == 0.10)\n", - "\n", - "tsla_index = ef.tickers.index(\"TSLA\")\n", - "ef.add_constraint(lambda w: w[tsla_index] <= 0.05)\n", - "\n", - "ef.add_constraint(lambda w: w[10] >= 0.05)\n", - "\n", - "ef.max_sharpe()\n", - "weights = ef.clean_weights()" + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.05848),\n", + " ('AMZN', 0.13376),\n", + " ('COST', 0.06437),\n", + " ('DIS', 0.06074),\n", + " ('F', 0.05798),\n", + " ('GILD', 0.07793),\n", + " ('JPM', 0.08902),\n", + " ('KO', 0.03563),\n", + " ('LUV', 0.06954),\n", + " ('MA', 0.0797),\n", + " ('MSFT', 0.06526),\n", + " ('PFE', 0.03375),\n", + " ('TSLA', 0.09202),\n", + " ('UNH', 0.05435),\n", + " ('XOM', 0.02747)])" ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 34 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 }, + "id": "p0DzMHim9nzO", + "outputId": "baaaab0c-4bee-4883-c0fb-70b4d650ef90", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.843799Z", + "start_time": "2025-11-12T08:10:59.793776Z" + } + }, + "source": [ + "pd.Series(weights).plot.pie(figsize=(10, 10));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pZAu8ypG9nzL", - "outputId": "2a3703a8-bbd0-468d-f0c6-25be50f7b0c7" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', 0.15332),\n", - " ('AMZN', 0.1),\n", - " ('COST', 0.04244),\n", - " ('DIS', 0.0),\n", - " ('F', 0.01498),\n", - " ('GILD', 0.06199),\n", - " ('JPM', 0.00824),\n", - " ('KO', 0.05756),\n", - " ('LUV', 0.03723),\n", - " ('MA', 0.34662),\n", - " ('MSFT', 0.05),\n", - " ('PFE', 0.03744),\n", - " ('TSLA', 0.05),\n", - " ('UNH', 0.04018),\n", - " ('XOM', 0.0)])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "weights" - ] + "image/png": 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" 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.pie(figsize=(10,10));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kGa9xMQ19nzM" - }, - "source": [ - "We can immediately see that our explicit constraints were satisfied, and can check all the sector constraints as follows:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JtA8KzWp9nzM", - "outputId": "cd007325-fa24-4f41-f8fa-3d149ae7fc23" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tech: 0.050\n", - "Aerospace: 0.037\n", - "Media: 0.000\n", - "Consumer Staples: 0.100\n", - "Misc: 0.153\n", - "Financial Services: 0.355\n", - "Energy: 0.000\n", - "Auto: 0.065\n", - "Healthcare: 0.140\n", - "Consumer Discretionary: 0.100\n" - ] - } - ], - "source": [ - "# O(N^2) loop not a good idea in a coding interview :)\n", - "for sector in set(sector_mapper.values()):\n", - " total_weight = 0\n", - " for t,w in weights.items():\n", - " if sector_mapper[t] == sector:\n", - " total_weight += w\n", - " print(f\"{sector}: {total_weight:.3f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qOZ-z4AK9nzM" - }, - "source": [ - "## Maximise return for a given risk, with L2 regularisation\n", - "\n", - "Let's imagine that we've put a lot of thought into our risk tolerance, and have decided that we can't accept anything more than 15% volatility. We can use PyPortfolioOpt to construct a portfolio that maximises return for a given risk (with the same caveats about expected returns)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 24.0%\n", + "Annual volatility: 19.5%\n", + "Sharpe Ratio: 1.23\n" + ] + } + ], + "execution_count": 36 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wggcvvSR9nzO" + }, + "source": [ + "The resulting portfolio still has a volatility of less than our 15% limit. It's in-sample Sharpe ratio has gone down, but this portfolio is a lot more robust for actual investment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q1r-Z6ws9nzO" + }, + "source": [ + "## Minimise risk for a given return, market-neutral\n", + "\n", + "We may instead be in the situation where we have a certain required rate of return (maybe we are a pension fund that needs 7% return a year), but would like to minimise risk. Additionally, suppose we would like our portfolio to be market neutral, in the sense that it is equally exposed to the long and short sides. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "XB4Cw8at9nzO", + "outputId": "26404432-1324-435e-cde0-c28a242a571e", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.900594Z", + "start_time": "2025-11-12T08:10:59.884843Z" + } + }, + "source": [ + "# Must have no weight bounds to allow shorts\n", + "ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n", + "ef.add_objective(objective_functions.L2_reg)\n", + "ef.efficient_return(target_return=0.07, market_neutral=True)\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sKantkrg9nzM", - "outputId": "3b60679f-80ef-4ec7-e029-989ef9d27f14" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', 0.01923),\n", - " ('AMZN', 0.15319),\n", - " ('COST', 0.1),\n", - " ('DIS', 0.0),\n", - " ('F', 0.0),\n", - " ('GILD', 0.06686),\n", - " ('JPM', 0.06198),\n", - " ('KO', 0.0),\n", - " ('LUV', 0.01155),\n", - " ('MA', 0.38719),\n", - " ('MSFT', 0.05),\n", - " ('PFE', 0.0),\n", - " ('TSLA', 0.15),\n", - " ('UNH', 0.0),\n", - " ('XOM', 0.0)])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", - "ef.efficient_risk(target_volatility=0.20)\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('ACN', -0.02187),\n", + " ('AMZN', 0.16537),\n", + " ('COST', -0.05722),\n", + " ('DIS', -0.01548),\n", + " ('F', 0.03129),\n", + " ('GILD', 0.0305),\n", + " ('JPM', 0.06344),\n", + " ('KO', -0.13748),\n", + " ('LUV', 0.00881),\n", + " ('MA', 0.03921),\n", + " ('MSFT', -0.00471),\n", + " ('PFE', -0.08735),\n", + " ('TSLA', 0.12025),\n", + " ('UNH', -0.02798),\n", + " ('XOM', -0.10678)])" ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 37 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Uf3oUKFP9nzO", + "outputId": "aa726cf0-1200-4fa9-f61d-ca5807679a34", + "ExecuteTime": { + "end_time": "2025-11-12T08:10:59.922466Z", + "start_time": "2025-11-12T08:10:59.912001Z" + } + }, + "source": [ + "ef.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "46ylv28y9nzM", - "outputId": "de8f0548-437e-4997-cc46-d927d5f61df2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6/15 tickers have zero weight\n" - ] - } - ], - "source": [ - "num_small = len([k for k in weights if weights[k] <= 1e-4])\n", - "print(f\"{num_small}/{len(ef.tickers)} tickers have zero weight\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 7.0%\n", + "Annual volatility: 11.1%\n", + "Sharpe Ratio: 0.63\n" + ] + } + ], + "execution_count": 38 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 }, + "id": "4rrQKk1Q9nzP", + "outputId": "95a52da6-37ed-4dad-cd5b-2701affa6695", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.010248Z", + "start_time": "2025-11-12T08:10:59.940068Z" + } + }, + "source": [ + "pd.Series(weights).plot.barh(figsize=(10,6));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3HWpBJxZ9nzM", - "outputId": "cae10c97-978e-40ee-8dd5-796d9635f53f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 26.7%\n", - "Annual volatility: 20.0%\n", - "Sharpe Ratio: 1.33\n" - ] - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "ef.portfolio_performance(verbose=True);" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 39 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "YwR1iKqv9nzP", + "outputId": "478e0b8b-c1ed-4826-a1bc-1ad928927123", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.025904Z", + "start_time": "2025-11-12T08:11:00.017340Z" + } + }, + "source": [ + "print(f\"Net weight: {sum(weights.values()):.2f}\")" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "sFdT8Xar9nzN" - }, - "source": [ - "While this portfolio seems like it meets our objectives, we might be worried by the fact that a lot of the tickers have been assigned zero weight. In effect, the optimizer is \"overfitting\" to the data you have provided -- you are much more likely to get better results by enforcing some level of diversification. One way of doing this is to use **L2 regularisation** – essentially, adding a penalty on the number of near-zero weights." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Net weight: -0.00\n" + ] + } + ], + "execution_count": 40 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PBvVeqtm9nzP" + }, + "source": [ + "## Efficient semi-variance optimization\n", + "\n", + "In this example, we will minimise the portfolio semivariance (i.e downside volatility) subject to a return constraint (target 20%).\n", + "\n", + "There are actually two ways of doing this in PyPortfolioOpt. The first is the \"intuitive\" way. We compute a semicovariance matrix, and pass this into `EfficientFrontier` (just like we would do for the exponential cov matrix or the Ledoit-Wolf shrunk matrix)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 }, + "id": "8FsS5Yqu9nzP", + "outputId": "94948fd4-598a-4fa7-a41a-371fbda43f43", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.156565Z", + "start_time": "2025-11-12T08:11:00.050782Z" + } + }, + "source": [ + "semicov = risk_models.semicovariance(prices, benchmark=0)\n", + "plotting.plot_covariance(semicov);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wiaa_L2u9nzN", - "outputId": "0adbf717-faaa-4142-9fe3-6ca9368cc23f" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', 0.06766),\n", - " ('AMZN', 0.17041),\n", - " ('COST', 0.08584),\n", - " ('DIS', 0.0024),\n", - " ('F', 0.0),\n", - " ('GILD', 0.07689),\n", - " ('JPM', 0.09414),\n", - " ('KO', 0.01416),\n", - " ('LUV', 0.04312),\n", - " ('MA', 0.22189),\n", - " ('MSFT', 0.05),\n", - " ('PFE', 0.0),\n", - " ('TSLA', 0.15),\n", - " ('UNH', 0.02351),\n", - " ('XOM', 0.0)])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "from pypfopt import objective_functions\n", - "\n", - "# You must always create a new efficient frontier object\n", - "ef = EfficientFrontier(mu, S)\n", - "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", - "ef.add_objective(objective_functions.L2_reg, gamma=0.1) # gamma is the tuning parameter\n", - "ef.efficient_risk(0.2)\n", - "weights = ef.clean_weights()\n", - "weights" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 41 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "cK0NfwQ69nzP", + "outputId": "16b22337-b5ac-42cc-fe4c-8543d96275fd", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.187295Z", + "start_time": "2025-11-12T08:11:00.171234Z" + } + }, + "source": [ + "ef = EfficientFrontier(mu, semicov)\n", + "ef.efficient_return(0.2)\n", + "weights = ef.clean_weights()\n", + "weights\n" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G0ZO5l4D9nzN", - "outputId": "aefa431d-90b2-493b-8232-8dc8c8213212" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3/15 tickers have zero weight\n" - ] - } - ], - "source": [ - "num_small = len([k for k in weights if weights[k] <= 1e-4])\n", - "print(f\"{num_small}/{len(ef.tickers)} tickers have zero weight\")" + "data": { + "text/plain": [ + "OrderedDict([('ACN', 0.16819),\n", + " ('AMZN', 0.0),\n", + " ('COST', 0.0607),\n", + " ('DIS', 0.0),\n", + " ('F', 0.0),\n", + " ('GILD', 0.00822),\n", + " ('JPM', 0.0),\n", + " ('KO', 0.25592),\n", + " ('LUV', 0.0),\n", + " ('MA', 0.31923),\n", + " ('MSFT', 0.0),\n", + " ('PFE', 0.07272),\n", + " ('TSLA', 0.10584),\n", + " ('UNH', 0.0),\n", + " ('XOM', 0.00918)])" ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 42 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "TUmSYq629nzP", + "outputId": "62eba498-9e70-41b9-c361-5ad7f7814348", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.211822Z", + "start_time": "2025-11-12T08:11:00.206614Z" + } + }, + "source": [ + "ef.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "n8yo6ey19nzN" - }, - "source": [ - "We can tune the value of gamma to choose the number of nonzero tickers. Larger gamma pulls portfolio weights towards an equal allocation." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 20.0%\n", + "Annual volatility: 11.9%\n", + "Sharpe Ratio: 1.68\n" + ] + } + ], + "execution_count": 43 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ZZeruY69nzP" + }, + "source": [ + "However, this solution is not truly optimal in mean-semivariance space. To do the optimization properly, we must use the `EfficientSemivariance` class. This requires us to first compute the returns and drop NaNs." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "1qGD5WW69nzQ", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:00.242145Z", + "start_time": "2025-11-12T08:11:00.234199Z" + } + }, + "source": [ + "returns = expected_returns.returns_from_prices(prices)\n", + "returns = returns.dropna()" + ], + "outputs": [], + "execution_count": 44 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Tyeys0NY9nzQ", + "outputId": "bf3aa95f-99ab-4410-e9f9-c04e10bf82eb", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:01.171117Z", + "start_time": "2025-11-12T08:11:00.267704Z" + } + }, + "source": [ + "from pypfopt import EfficientSemivariance\n", + "\n", + "es = EfficientSemivariance(mu, returns)\n", + "es.efficient_return(0.2)\n", + "es.portfolio_performance(verbose=True);\n" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hIdHgQnE9nzO", - "outputId": "76b74cec-dbad-4278-ad5c-6a3bb99b404d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', 0.05801),\n", - " ('AMZN', 0.13571),\n", - " ('COST', 0.06445),\n", - " ('DIS', 0.05975),\n", - " ('F', 0.06037),\n", - " ('GILD', 0.07905),\n", - " ('JPM', 0.08961),\n", - " ('KO', 0.03555),\n", - " ('LUV', 0.06778),\n", - " ('MA', 0.08117),\n", - " ('MSFT', 0.06534),\n", - " ('PFE', 0.03292),\n", - " ('TSLA', 0.08963),\n", - " ('UNH', 0.05412),\n", - " ('XOM', 0.02654)])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.add_sector_constraints(sector_mapper, sector_lower, sector_upper)\n", - "ef.add_objective(objective_functions.L2_reg, gamma=1) # gamma is the tuning parameter\n", - "ef.efficient_risk(0.2)\n", - "weights = ef.clean_weights()\n", - "weights" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 20.0%\n", + "Annual semi-deviation: 10.6%\n", + "Sortino Ratio: 1.89\n" + ] + } + ], + "execution_count": 45 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6g-7u4YA9nzQ" + }, + "source": [ + "To compare this with the heuristic solution, I will use a quick hack: replacing the `es.weights` with `es.weights` and running `es.portfolio_performance` again. Please don't be encouraged to do this in real life!" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "6jLzfOCj9nzQ", + "outputId": "ba8f4fcc-882a-4764-b0b8-e3906677dbd7", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:01.223479Z", + "start_time": "2025-11-12T08:11:01.215394Z" + } + }, + "source": [ + "es.weights = ef.weights\n", + "es.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 575 - }, - "id": "p0DzMHim9nzO", - "outputId": "baaaab0c-4bee-4883-c0fb-70b4d650ef90" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.pie(figsize=(10, 10));" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 20.0%\n", + "Annual semi-deviation: 12.9%\n", + "Sortino Ratio: 1.55\n" + ] + } + ], + "execution_count": 46 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FK4C6ah89nzQ" + }, + "source": [ + "We see that the heuristic method has a significantly lower Sortino ratio, and much higher semivariance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yJAcvz899nzQ" + }, + "source": [ + "## Efficient CVaR optimization\n", + "\n", + "In this example, we will find the portfolio that maximises return subject to a CVaR constraint.\n", + "\n", + "Before doing this, let's first compute the 95%-CVaR for the max-sharpe portfolio." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 }, + "id": "x1d3qupJ9nzR", + "outputId": "daadeac7-706a-445e-fdd6-ab02efc6d005", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:01.281295Z", + "start_time": "2025-11-12T08:11:01.260523Z" + } + }, + "source": [ + "returns = expected_returns.returns_from_prices(prices).dropna()\n", + "returns.head()" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "zFLNfMLr9nzO", - "outputId": "69c1cead-6233-465a-a3c3-6c5def23d624" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 24.5%\n", - "Annual volatility: 19.5%\n", - "Sharpe Ratio: 1.25\n" - ] - } + "data": { + "text/plain": [ + "Ticker ACN AMZN COST DIS F GILD \\\n", + "Date \n", + "2010-06-30 0.000000 0.005985 -0.014380 -0.024767 0.020243 -0.019731 \n", + "2010-07-01 -0.009573 0.015559 0.001276 -0.000318 0.048611 -0.004084 \n", + "2010-07-02 -0.008882 -0.016402 -0.012204 -0.003493 -0.027436 0.021383 \n", + "2010-07-06 0.012388 0.008430 -0.004241 0.010835 -0.011673 -0.002868 \n", + "2010-07-07 0.022130 0.030620 0.005370 0.044767 0.042322 0.004889 \n", + "\n", + "Ticker JPM KO LUV MA MSFT PFE \\\n", + "Date \n", + "2010-06-30 -0.012143 -0.004173 0.000000 -0.017045 -0.012870 -0.001400 \n", + "2010-07-01 -0.013129 -0.001796 -0.010801 0.016651 0.006519 -0.002104 \n", + "2010-07-02 -0.006929 0.000400 -0.021838 0.000346 0.004749 -0.006325 \n", + "2010-07-06 0.013955 0.007593 -0.011163 -0.013513 0.023636 0.010608 \n", + "2010-07-07 0.050097 0.020622 0.062089 0.037694 0.020151 0.023093 \n", + "\n", + "Ticker TSLA UNH XOM \n", + "Date \n", + "2010-06-30 -0.002511 -0.008034 -0.003840 \n", + "2010-07-01 -0.078473 -0.019366 -0.008060 \n", + "2010-07-02 -0.125683 0.016158 -0.000707 \n", + "2010-07-06 -0.160937 0.020848 0.015733 \n", + "2010-07-07 -0.019243 0.010730 0.016882 " ], - "source": [ - "ef.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wggcvvSR9nzO" - }, - "source": [ - "The resulting portfolio still has a volatility of less than our 15% limit. It's in-sample Sharpe ratio has gone down, but this portfolio is a lot more robust for actual investment." + "text/html": [ + "
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TickerACNAMZNCOSTDISFGILDJPMKOLUVMAMSFTPFETSLAUNHXOM
Date
2010-06-300.0000000.005985-0.014380-0.0247670.020243-0.019731-0.012143-0.0041730.000000-0.017045-0.012870-0.001400-0.002511-0.008034-0.003840
2010-07-01-0.0095730.0155590.001276-0.0003180.048611-0.004084-0.013129-0.001796-0.0108010.0166510.006519-0.002104-0.078473-0.019366-0.008060
2010-07-02-0.008882-0.016402-0.012204-0.003493-0.0274360.021383-0.0069290.000400-0.0218380.0003460.004749-0.006325-0.1256830.016158-0.000707
2010-07-060.0123880.008430-0.0042410.010835-0.011673-0.0028680.0139550.007593-0.011163-0.0135130.0236360.010608-0.1609370.0208480.015733
2010-07-070.0221300.0306200.0053700.0447670.0423220.0048890.0500970.0206220.0620890.0376940.0201510.023093-0.0192430.0107300.016882
\n", + "
" ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 47 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "4RdDFhAt9nzR", + "outputId": "76d8a6e4-c2b9-4c39-e525-e7b8cf2444e7", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:01.949272Z", + "start_time": "2025-11-12T08:11:01.926301Z" + } + }, + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.max_sharpe()\n", + "weight_arr = ef.weights\n", + "ef.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "Q1r-Z6ws9nzO" - }, - "source": [ - "## Minimise risk for a given return, market-neutral\n", - "\n", - "We may instead be in the situation where we have a certain required rate of return (maybe we are a pension fund that needs 7% return a year), but would like to minimise risk. Additionally, suppose we would like our portfolio to be market neutral, in the sense that it is equally exposed to the long and short sides. " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 24.2%\n", + "Annual volatility: 17.7%\n", + "Sharpe Ratio: 1.36\n" + ] + } + ], + "execution_count": 48 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 }, + "id": "29a0FtJY9nzR", + "outputId": "a053e96e-e6fc-4c93-92ce-2cd3373ae112", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.091064Z", + "start_time": "2025-11-12T08:11:02.040248Z" + } + }, + "source": [ + "# Compute CVaR\n", + "portfolio_rets = (returns * weight_arr).sum(axis=1)\n", + "portfolio_rets.hist(bins=50);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XB4Cw8at9nzO", - "outputId": "26404432-1324-435e-cde0-c28a242a571e" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('ACN', -0.02186),\n", - " ('AMZN', 0.1691),\n", - " ('COST', -0.05621),\n", - " ('DIS', -0.01695),\n", - " ('F', 0.03174),\n", - " ('GILD', 0.03354),\n", - " ('JPM', 0.06534),\n", - " ('KO', -0.13622),\n", - " ('LUV', 0.00524),\n", - " ('MA', 0.04403),\n", - " ('MSFT', -0.00338),\n", - " ('PFE', -0.08792),\n", - " ('TSLA', 0.10838),\n", - " ('UNH', -0.02754),\n", - " ('XOM', -0.10729)])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "# Must have no weight bounds to allow shorts\n", - "ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n", - "ef.add_objective(objective_functions.L2_reg)\n", - "ef.efficient_return(target_return=0.07, market_neutral=True)\n", - "weights = ef.clean_weights()\n", - "weights" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 49 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "8LLBZ15l9nzR", + "outputId": "5f81ae25-bf09-495e-c389-9df5890717d0", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.123937Z", + "start_time": "2025-11-12T08:11:02.110772Z" + } + }, + "source": [ + "# VaR\n", + "var = portfolio_rets.quantile(0.05)\n", + "cvar = portfolio_rets[portfolio_rets <= var].mean()\n", + "print(\"VaR: {:.2f}%\".format(100*var))\n", + "print(\"CVaR: {:.2f}%\".format(100*cvar))" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Uf3oUKFP9nzO", - "outputId": "aa726cf0-1200-4fa9-f61d-ca5807679a34" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 7.0%\n", - "Annual volatility: 11.0%\n", - "Sharpe Ratio: 0.63\n" - ] - } - ], - "source": [ - "ef.portfolio_performance(verbose=True);" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "VaR: -2.10%\n", + "CVaR: -3.12%\n" + ] + } + ], + "execution_count": 50 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PxHWPjSz9nzR" + }, + "source": [ + "This value of the CVaR means that our average loss on the worst 5% of days will be -3.35%. Let's say that this were beyond our comfort zone (for a \\\\$100,000 portfolio, this would mean losing \\\\$3350 in a day).\n", + "\n", + "Let's firstly construct the portfolio with the minimum CVaR:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "bQ79Y0Gr9nzR", + "outputId": "b92a041c-9e59-45ba-8cab-ac427d889126", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.320595Z", + "start_time": "2025-11-12T08:11:02.225714Z" + } + }, + "source": [ + "from pypfopt import EfficientCVaR\n", + "\n", + "ec = EfficientCVaR(mu, returns)\n", + "ec.min_cvar()\n", + "ec.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 374 - }, - "id": "4rrQKk1Q9nzP", - "outputId": "95a52da6-37ed-4dad-cd5b-2701affa6695" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.barh(figsize=(10,6));" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 15.1%\n", + "Conditional Value at Risk: 2.07%\n" + ] + } + ], + "execution_count": 51 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hVEjdNOv9nzS" + }, + "source": [ + "We have significantly reduced the CVaR, but at the cost of a large reduction in returns. We can use `efficient_risk` to maximise the return for a target risk. Let's say that a 2.5% CVaR is acceptable." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "onq8FVDr9nzS", + "outputId": "32b82ef5-e86c-414d-b965-d44e27813ff3", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.500080Z", + "start_time": "2025-11-12T08:11:02.346234Z" + } + }, + "source": [ + "from pypfopt import EfficientCVaR\n", + "\n", + "ec = EfficientCVaR(mu, returns)\n", + "ec.efficient_risk(target_cvar=0.025)\n", + "ec.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YwR1iKqv9nzP", - "outputId": "478e0b8b-c1ed-4826-a1bc-1ad928927123" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Net weight: -0.00\n" - ] - } - ], - "source": [ - "print(f\"Net weight: {sum(weights.values()):.2f}\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 23.4%\n", + "Conditional Value at Risk: 2.50%\n" + ] + } + ], + "execution_count": 52 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6wjVOkqd9nzS" + }, + "source": [ + "We now have similar returns to before (24.7% vs 25.8%), but with a lower tail risk (2.50% CVaR vs 3.35%). " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LJ_DCIo19nzS" + }, + "source": [ + "## Plotting - Unconstrained\n", + "\n", + "To plot the unconstrained efficient frontier, it is easiest to use the critical line algorithm. " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "n0UWSKGj9nzS", + "outputId": "08a6de2a-17e4-4f70-92ea-fe86a9c8f442", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.560828Z", + "start_time": "2025-11-12T08:11:02.535448Z" + } + }, + "source": [ + "from pypfopt import CLA, plotting\n", + "\n", + "cla = CLA(mu, S)\n", + "cla.max_sharpe()\n", + "cla.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "PBvVeqtm9nzP" - }, - "source": [ - "## Efficient semi-variance optimization\n", - "\n", - "In this example, we will minimise the portfolio semivariance (i.e downside volatility) subject to a return constraint (target 20%).\n", - "\n", - "There are actually two ways of doing this in PyPortfolioOpt. The first is the \"intuitive\" way. We compute a semicovariance matrix, and pass this into `EfficientFrontier` (just like we would do for the exponential cov matrix or the Ledoit-Wolf shrunk matrix)." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 24.2%\n", + "Annual volatility: 17.7%\n", + "Sharpe Ratio: 1.36\n" + ] + } + ], + "execution_count": 53 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "Sk2eFFZ79nzS", + "outputId": "6f967d10-32c4-4f83-ecac-aed15aa8180a", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:02.649351Z", + "start_time": "2025-11-12T08:11:02.580697Z" + } + }, + "source": [ + "ax = plotting.plot_efficient_frontier(cla, showfig=False)" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "8FsS5Yqu9nzP", - "outputId": "94948fd4-598a-4fa7-a41a-371fbda43f43" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "semicov = risk_models.semicovariance(prices, benchmark=0)\n", - "plotting.plot_covariance(semicov);" - ] - }, + "image/png": 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" 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To do the optimization properly, we must use the `EfficientSemivariance` class. This requires us to first compute the returns and drop NaNs." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "1qGD5WW69nzQ" - }, - "outputs": [], - "source": [ - "returns = expected_returns.returns_from_prices(prices)\n", - "returns = returns.dropna()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + ], + "hovertemplate": "Risk: %{x}
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F: %{customdata[4]:.4%}
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PFE: %{customdata[11]:.4%}
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UNH: %{customdata[13]:.4%}
XOM: %{customdata[14]:.4%}
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AMZN: %{customdata[1]:.4%}
COST: %{customdata[2]:.4%}
DIS: %{customdata[3]:.4%}
F: %{customdata[4]:.4%}
GILD: %{customdata[5]:.4%}
JPM: %{customdata[6]:.4%}
KO: %{customdata[7]:.4%}
LUV: %{customdata[8]:.4%}
MA: %{customdata[9]:.4%}
MSFT: %{customdata[10]:.4%}
PFE: %{customdata[11]:.4%}
TSLA: %{customdata[12]:.4%}
UNH: %{customdata[13]:.4%}
XOM: %{customdata[14]:.4%}
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Return: %{y}", + "marker": { + "color": "silver", + "size": 10, + "symbol": "star-diamond" + }, + "mode": "markers", + "name": "Assets", + "x": [ + 0.24455768468095113, + 0.493331722962542, + 0.30453601208018777, + 0.2985645845530357, + 0.3877886662640967, + 0.4522288096303457, + 0.3650640671954181, + 0.2183951319084959, + 0.3701679309281042, + 0.24038998679008425, + 0.3133726691621561, + 0.2658080261671054, + 0.3790015926095876, + 0.3613958916746025, + 0.24883349140454966 + ], + "y": [ + 0.20201127420114892, + 0.35258061107335176, + 0.1756723529878449, + 0.2065326755220123, + 0.24110517437363252, + 0.24091210894821097, + 0.26308967252098153, + 0.11817473805046842, + 0.2241305400606434, + 0.24444845321680575, + 0.21557714269675948, + 0.1525603997308656, + 0.3091685882934678, + 0.19374826076199883, + 0.139997175275977 + ], + "type": "scatter" } - ], - "source": [ - "es.weights = ef.weights\n", - "es.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FK4C6ah89nzQ" - }, - "source": [ - "We see that the heuristic method has a significantly lower Sortino ratio, and much higher semivariance." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yJAcvz899nzQ" - }, - "source": [ - "## Efficient CVaR optimization\n", - "\n", - "In this example, we will find the portfolio that maximises return subject to a CVaR constraint.\n", - "\n", - "Before doing this, let's first compute the 95%-CVaR for the max-sharpe portfolio." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 281 - }, - "id": "x1d3qupJ9nzR", - "outputId": "daadeac7-706a-445e-fdd6-ab02efc6d005" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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TickerACNAMZNCOSTDISFGILDJPMKOLUVMAMSFTPFETSLAUNHXOM
Date
2010-06-300.0000000.005985-0.014381-0.0247680.020243-0.019731-0.012143-0.0041720.000000-0.017045-0.012870-0.001400-0.002511-0.008034-0.003840
2010-07-01-0.0095730.0155590.001277-0.0003180.048611-0.004084-0.013130-0.001796-0.0108010.0166510.006519-0.002104-0.078473-0.019366-0.008060
2010-07-02-0.008882-0.016402-0.012204-0.003493-0.0274360.021382-0.0069290.000400-0.0218380.0003460.004749-0.006325-0.1256830.016158-0.000707
2010-07-060.0123880.008430-0.0042410.010835-0.011673-0.0028680.0139550.007593-0.011163-0.0135130.0236350.010608-0.1609370.0208480.015733
2010-07-070.0221290.0306200.0053700.0447670.0423230.0048890.0500960.0206220.0620880.0376940.0201510.023093-0.0192430.0107310.016881
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" - ], - "text/plain": [ - "Ticker ACN AMZN COST DIS F GILD \\\n", - "Date \n", - "2010-06-30 0.000000 0.005985 -0.014381 -0.024768 0.020243 -0.019731 \n", - "2010-07-01 -0.009573 0.015559 0.001277 -0.000318 0.048611 -0.004084 \n", - "2010-07-02 -0.008882 -0.016402 -0.012204 -0.003493 -0.027436 0.021382 \n", - "2010-07-06 0.012388 0.008430 -0.004241 0.010835 -0.011673 -0.002868 \n", - "2010-07-07 0.022129 0.030620 0.005370 0.044767 0.042323 0.004889 \n", - "\n", - "Ticker JPM KO LUV MA MSFT PFE \\\n", - "Date \n", - "2010-06-30 -0.012143 -0.004172 0.000000 -0.017045 -0.012870 -0.001400 \n", - "2010-07-01 -0.013130 -0.001796 -0.010801 0.016651 0.006519 -0.002104 \n", - "2010-07-02 -0.006929 0.000400 -0.021838 0.000346 0.004749 -0.006325 \n", - "2010-07-06 0.013955 0.007593 -0.011163 -0.013513 0.023635 0.010608 \n", - "2010-07-07 0.050096 0.020622 0.062088 0.037694 0.020151 0.023093 \n", - "\n", - "Ticker TSLA UNH XOM \n", - "Date \n", - "2010-06-30 -0.002511 -0.008034 -0.003840 \n", - "2010-07-01 -0.078473 -0.019366 -0.008060 \n", - "2010-07-02 -0.125683 0.016158 -0.000707 \n", - "2010-07-06 -0.160937 0.020848 0.015733 \n", - "2010-07-07 -0.019243 0.010731 0.016881 " + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "returns = expected_returns.returns_from_prices(prices).dropna()\n", - "returns.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4RdDFhAt9nzR", - "outputId": "76d8a6e4-c2b9-4c39-e525-e7b8cf2444e7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 24.6%\n", - "Annual volatility: 17.6%\n", - "Sharpe Ratio: 1.39\n" - ] - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.max_sharpe()\n", - "weight_arr = ef.weights\n", - "ef.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 265 - }, - "id": "29a0FtJY9nzR", - "outputId": "a053e96e-e6fc-4c93-92ce-2cd3373ae112" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + } + ], + "choropleth": [ + { + "type": "choropleth", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "histogram2d": [ + { + "type": "histogram2d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compute CVaR\n", - "portfolio_rets = (returns * weight_arr).sum(axis=1)\n", - "portfolio_rets.hist(bins=50);" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8LLBZ15l9nzR", - "outputId": "5f81ae25-bf09-495e-c389-9df5890717d0" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VaR: -2.12%\n", - "CVaR: -3.14%\n" - ] - } - ], - "source": [ - "# VaR\n", - "var = portfolio_rets.quantile(0.05)\n", - "cvar = portfolio_rets[portfolio_rets <= var].mean()\n", - "print(\"VaR: {:.2f}%\".format(100*var))\n", - "print(\"CVaR: {:.2f}%\".format(100*cvar))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PxHWPjSz9nzR" - }, - "source": [ - "This value of the CVaR means that our average loss on the worst 5% of days will be -3.35%. Let's say that this were beyond our comfort zone (for a \\\\$100,000 portfolio, this would mean losing \\\\$3350 in a day).\n", - "\n", - "Let's firstly construct the portfolio with the minimum CVaR:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bQ79Y0Gr9nzR", - "outputId": "b92a041c-9e59-45ba-8cab-ac427d889126" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 15.6%\n", - "Conditional Value at Risk: 2.09%\n" - ] - } - ], - "source": [ - "from pypfopt import EfficientCVaR\n", - "\n", - "ec = EfficientCVaR(mu, returns)\n", - "ec.min_cvar()\n", - "ec.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hVEjdNOv9nzS" - }, - "source": [ - "We have significantly reduced the CVaR, but at the cost of a large reduction in returns. We can use `efficient_risk` to maximise the return for a target risk. Let's say that a 2.5% CVaR is acceptable." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "onq8FVDr9nzS", - "outputId": "32b82ef5-e86c-414d-b965-d44e27813ff3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 23.7%\n", - "Conditional Value at Risk: 2.50%\n" - ] - } - ], - "source": [ - "from pypfopt import EfficientCVaR\n", - "\n", - "ec = EfficientCVaR(mu, returns)\n", - "ec.efficient_risk(target_cvar=0.025)\n", - "ec.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6wjVOkqd9nzS" - }, - "source": [ - "We now have similar returns to before (24.7% vs 25.8%), but with a lower tail risk (2.50% CVaR vs 3.35%). " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LJ_DCIo19nzS" - }, - "source": [ - "## Plotting - Unconstrained\n", - "\n", - "To plot the unconstrained efficient frontier, it is easiest to use the critical line algorithm. " - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n0UWSKGj9nzS", - "outputId": "08a6de2a-17e4-4f70-92ea-fe86a9c8f442" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 24.6%\n", - "Annual volatility: 17.6%\n", - "Sharpe Ratio: 1.39\n" - ] - } - ], - "source": [ - "from pypfopt import CLA, plotting\n", - "\n", - "cla = CLA(mu, S)\n", - "cla.max_sharpe()\n", - "cla.portfolio_performance(verbose=True);" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "Sk2eFFZ79nzS", - "outputId": "6f967d10-32c4-4f83-ecac-aed15aa8180a" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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This allows you to build complex plots. \n", - "\n", - "In this example, we will plot the efficient frontier as well as 10,000 simulated portfolios. \n", - "\n", - "To generate the simulated portfolios, we will sample random weights from the Dirichlet distribution (these are already normalised):" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + "xaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "yaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } }, - "id": "esHxc73V9nzT", - "outputId": "decb7120-4d2e-49f3-f7d9-fcb5b78cdcce" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample portfolio returns: [0.2237915 0.22173136 0.21968847 ... 0.19190574 0.21576692 0.21962956]\n", - "Sample portfolio volatilities: 0 0.179384\n", - "1 0.174740\n", - "2 0.190086\n", - "3 0.214345\n", - "4 0.188124\n", - " ... \n", - "9995 0.180551\n", - "9996 0.169346\n", - "9997 0.183431\n", - "9998 0.173361\n", - "9999 0.172951\n", - "Length: 10000, dtype: float64\n" - ] + "xaxis": { + "title": { + "text": "Volatility" + } + }, + "yaxis": { + "title": { + "text": "Return" + } } - ], - "source": [ - "n_samples = 10000\n", - "w = np.random.dirichlet(np.ones(len(mu)), n_samples)\n", - "rets = w.dot(mu)\n", - "stds = np.sqrt((w.T * (S @ w.T)).sum(axis=0))\n", - "sharpes = rets / stds\n", - "\n", - "print(\"Sample portfolio returns:\", rets)\n", - "print(\"Sample portfolio volatilities:\", stds)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ad_x63489nzT" + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } }, - "source": [ - "Note that the above code is equivalent to generating samples via a for loop (just more efficient)" + "text/html": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 55 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aURS39QR9nzS" + }, + "source": [ + "## Plotting - Constrained" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2xPJH-s69nzT" + }, + "source": [ + "In this example, we will plot the efficient frontier corresponding to portfolios with a constraint on exposure to MSFT, AMZN, and TSLA (e.g maybe we want to avoid big tech)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "OavRfyqJ9nzT", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:03.546654Z", + "start_time": "2025-11-12T08:11:03.525307Z" + } + }, + "source": [ + "import cvxpy as cp\n", + "\n", + "mu = expected_returns.capm_return(prices)\n", + "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", + "\n", + "ef = EfficientFrontier(mu, S,)\n", + "big_tech_indices = [t in {\"MSFT\", \"AMZN\", \"TSLA\"} for t in tickers]\n", + "ef.add_constraint(lambda w: cp.sum(w[big_tech_indices]) <= 0.3)" + ], + "outputs": [], + "execution_count": 56 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:11:03.581303Z", + "start_time": "2025-11-12T08:11:03.565993Z" + } + }, + "source": [ + "mu" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "id": "H3nL43aM9nzT" - }, - "outputs": [], - "source": [ - "# mus = []\n", - "# stds = []\n", - "# sharpes = []\n", - "# for _ in range(10000):\n", - "# w = np.random.dirichlet(np.ones(len(mu)))\n", - "# # w = np.random.rand(len(mu))\n", - "# # w /= w.sum()\n", - "# ret = mu.dot(w)\n", - "# std = np.sqrt(w.dot(S @ w))\n", - "# mus.append(ret)\n", - "# stds.append(std)\n", - "# sharpes.append(ret / std)" + "data": { + "text/plain": [ + "Ticker\n", + "ACN 0.202011\n", + "AMZN 0.352581\n", + "COST 0.175672\n", + "DIS 0.206533\n", + "F 0.241105\n", + "GILD 0.240912\n", + "JPM 0.263090\n", + "KO 0.118175\n", + "LUV 0.224131\n", + "MA 0.244448\n", + "MSFT 0.215577\n", + "PFE 0.152560\n", + "TSLA 0.309169\n", + "UNH 0.193748\n", + "XOM 0.139997\n", + "Name: mkt, dtype: float64" ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 57 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iPJ5mK8p9nzT" + }, + "source": [ + "As per the docs, *before* we call any optimization function, we should pass this to the plotting module:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "BOehDOq19nzT", + "outputId": "171cb51c-a5c4-402a-e17e-86d1dda072b1", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:03.791655Z", + "start_time": "2025-11-12T08:11:03.644554Z" + } + }, + "source": [ + "ax = plotting.plot_efficient_frontier(ef, ef_param=\"risk\", \n", + " ef_param_range=np.linspace(0.2, 0.5, 50), \n", + " showfig=False);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "WjCjkC9p9nzU", - "outputId": "386b35b3-75fd-4186-8f65-f16317246965" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "# Plot efficient frontier with Monte Carlo sim\n", - "ef = EfficientFrontier(mu, S)\n", - "\n", - "fig, ax = plt.subplots()\n", - "plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)\n", - "\n", - "# Find and plot the tangency portfolio\n", - "ef2 = EfficientFrontier(mu, S)\n", - "ef2.max_sharpe()\n", - "ret_tangent, std_tangent, _ = ef2.portfolio_performance()\n", - "\n", - "# Plot random portfolios\n", - "ax.scatter(stds, rets, marker=\".\", c=sharpes, cmap=\"viridis_r\")\n", - "\n", - "# Format\n", - "ax.set_title(\"Efficient Frontier with random portfolios\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 58 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ht1M5kIo9nzT" + }, + "source": [ + "## Complex plots\n", + "\n", + "The `plotting` module allows you to pass in an `ax`, on top of which the plots are added. This allows you to build complex plots. \n", + "\n", + "In this example, we will plot the efficient frontier as well as 10,000 simulated portfolios. \n", + "\n", + "To generate the simulated portfolios, we will sample random weights from the Dirichlet distribution (these are already normalised):" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "esHxc73V9nzT", + "outputId": "decb7120-4d2e-49f3-f7d9-fcb5b78cdcce", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:03.861119Z", + "start_time": "2025-11-12T08:11:03.853260Z" + } + }, + "source": [ + "n_samples = 10000\n", + "w = np.random.dirichlet(np.ones(len(mu)), n_samples)\n", + "rets = w.dot(mu)\n", + "stds = np.sqrt((w.T * (S @ w.T)).sum(axis=0))\n", + "sharpes = rets / stds\n", + "\n", + "print(\"Sample portfolio returns:\", rets)\n", + "print(\"Sample portfolio volatilities:\", stds)" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "id": "dZ0tR7Lt9nzU" - }, - "outputs": [], - "source": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample portfolio returns: [0.20296863 0.22650091 0.21093727 ... 0.24826296 0.22017591 0.19811965]\n", + "Sample portfolio volatilities: 0 0.176258\n", + "1 0.212849\n", + "2 0.186047\n", + "3 0.182062\n", + "4 0.202531\n", + " ... \n", + "9995 0.199046\n", + "9996 0.204033\n", + "9997 0.220805\n", + "9998 0.190212\n", + "9999 0.187209\n", + "Length: 10000, dtype: float64\n" + ] + } + ], + "execution_count": 59 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ad_x63489nzT" + }, + "source": [ + "Note that the above code is equivalent to generating samples via a for loop (just more efficient)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H3nL43aM9nzT", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:03.903943Z", + "start_time": "2025-11-12T08:11:03.892669Z" } - ], - "metadata": { + }, + "source": [ + "# mus = []\n", + "# stds = []\n", + "# sharpes = []\n", + "# for _ in range(10000):\n", + "# w = np.random.dirichlet(np.ones(len(mu)))\n", + "# # w = np.random.rand(len(mu))\n", + "# # w /= w.sum()\n", + "# ret = mu.dot(w)\n", + "# std = np.sqrt(w.dot(S @ w))\n", + "# mus.append(ret)\n", + "# stds.append(std)\n", + "# sharpes.append(ret / std)" + ], + "outputs": [], + "execution_count": 60 + }, + { + "cell_type": "code", + "metadata": { "colab": { - "collapsed_sections": [ - "PBvVeqtm9nzP" - ], - "name": "2-Mean-Variance-Optimisation.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/", + "height": 297 }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" + "id": "WjCjkC9p9nzU", + "outputId": "386b35b3-75fd-4186-8f65-f16317246965", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:04.201554Z", + "start_time": "2025-11-12T08:11:03.927248Z" + } + }, + "source": [ + "# Plot efficient frontier with Monte Carlo sim\n", + "ef = EfficientFrontier(mu, S)\n", + "\n", + "fig, ax = plt.subplots()\n", + "plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)\n", + "\n", + "# Find and plot the tangency portfolio\n", + "ef2 = EfficientFrontier(mu, S)\n", + "ef2.max_sharpe()\n", + "ret_tangent, std_tangent, _ = ef2.portfolio_performance()\n", + "\n", + "# Plot random portfolios\n", + "ax.scatter(stds, rets, marker=\".\", c=sharpes, cmap=\"viridis_r\")\n", + "\n", + "# Format\n", + "ax.set_title(\"Efficient Frontier with random portfolios\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } + ], + "execution_count": 61 + }, + { + "cell_type": "code", + "metadata": { + "id": "dZ0tR7Lt9nzU", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:04.252440Z", + "start_time": "2025-11-12T08:11:04.243455Z" + } + }, + "source": [], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "PBvVeqtm9nzP" + ], + "name": "2-Mean-Variance-Optimisation.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "name": "python3", + "language": "python" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb b/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb index 3e2adedb..d15ccf97 100644 --- a/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb +++ b/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb @@ -1,1406 +1,1431 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "pQhw2JJu_YO0" - }, - "source": [ - "# Advanced MVO - custom objectives\n", - "\n", - "PyPortfolioOpt has implemented some of the most common objective functions (e.g `min_volatility`, `max_sharpe`, `max_quadratic_utility`, `efficient_risk`, `efficient_return`). However, sometimes yoy may have an idea for a different objective function.\n", - "\n", - "In this cookbook recipe, we cover:\n", - "\n", - "- Mininimising transaction costs\n", - "- Custom convex objectives\n", - "- Custom nonconvex objectives\n", - "\n", - "## Acquiring data\n", - "\n", - "As discussed in the previous notebook, assets are an exogenous input (i.e you must come up with a list of tickers). We will use `yfinance` to download data for thesee tickers\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", - " \n", - "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kWJJAvG4_iEz", - "outputId": "26b7d743-eb20-4fb5-a99d-f4a5098ee8ad" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (2.2.3)\n", - "Requirement already satisfied: numpy in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (2.0.2)\n", - "Requirement already satisfied: matplotlib in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (3.9.3)\n", - "Requirement already satisfied: yfinance in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (0.2.50)\n", - "Requirement already satisfied: PyPortfolioOpt in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (1.5.5)\n", - 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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2.2.3)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (from requests>=2.31->yfinance) (2024.8.30)\n", - "Requirement already satisfied: qdldl in /Users/robert/Library/Caches/pypoetry/virtualenvs/pyportfolioopt-AdMegrYY-py3.12/lib/python3.12/site-packages (from osqp>=0.6.2->cvxpy>=1.1.19->PyPortfolioOpt) (0.1.7.post4)\n" - ] - } - ], - "source": [ - "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", - "import os\n", - "if not os.path.isdir('data'):\n", - " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", - " os.chdir('PyPortfolioOpt/cookbook')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "yd0GLGye_YO1" - }, - "outputs": [], - "source": [ - "import yfinance as yf\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "HRko0Vbr_YO2" - }, - "outputs": [], - "source": [ - "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\",\n", - " \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\",\n", - " \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b8QidoAw_YO2", - "outputId": "cc6b4958-fa84-48d8-9a8a-3dd63d59d085" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 15 of 15 completed\n" - ] - } - ], - "source": [ - "ohlc = yf.download(tickers, period=\"max\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 368 - }, - "id": "41-UTlPR_YO3", - "outputId": "45e79da8-c066-4962-d5e2-57baafa8d68c" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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TickerAAPLAMDBACBLKCVSDISINTUJDMANVDAPBITGTTMULWMT
Date
2024-11-22229.869995138.35000647.0000001036.45996158.009998115.650002640.11999534.680000520.859985141.9499978.05125.010002174.39999458.61000190.440002
2024-11-25232.869995141.13000547.5000001031.48999060.080002116.000000634.61999534.509998526.599976136.0200048.16130.529999175.83000258.77999989.500000
2024-11-26235.059998137.72000147.7500001026.47998059.009998115.449997638.83001735.330002528.479980136.9199988.20126.550003172.52000459.09999891.309998
2024-11-27234.929993136.24000547.7700001019.45001259.959999117.599998636.16998337.189999532.380005135.3399968.14130.089996169.72000159.74000291.879997
2024-11-29237.330002137.17999347.5099981022.79998859.849998117.470001641.72998037.380001532.940002138.2500008.06132.309998170.63000559.84000092.500000
\n", - "
" - ], - "text/plain": [ - "Ticker AAPL AMD BAC BLK CVS \\\n", - "Date \n", - "2024-11-22 229.869995 138.350006 47.000000 1036.459961 58.009998 \n", - "2024-11-25 232.869995 141.130005 47.500000 1031.489990 60.080002 \n", - "2024-11-26 235.059998 137.720001 47.750000 1026.479980 59.009998 \n", - "2024-11-27 234.929993 136.240005 47.770000 1019.450012 59.959999 \n", - "2024-11-29 237.330002 137.179993 47.509998 1022.799988 59.849998 \n", - "\n", - "Ticker DIS INTU JD MA NVDA PBI \\\n", - "Date \n", - "2024-11-22 115.650002 640.119995 34.680000 520.859985 141.949997 8.05 \n", - "2024-11-25 116.000000 634.619995 34.509998 526.599976 136.020004 8.16 \n", - "2024-11-26 115.449997 638.830017 35.330002 528.479980 136.919998 8.20 \n", - "2024-11-27 117.599998 636.169983 37.189999 532.380005 135.339996 8.14 \n", - "2024-11-29 117.470001 641.729980 37.380001 532.940002 138.250000 8.06 \n", - "\n", - "Ticker TGT TM UL WMT \n", - "Date \n", - "2024-11-22 125.010002 174.399994 58.610001 90.440002 \n", - "2024-11-25 130.529999 175.830002 58.779999 89.500000 \n", - "2024-11-26 126.550003 172.520004 59.099998 91.309998 \n", - "2024-11-27 130.089996 169.720001 59.740002 91.879997 \n", - "2024-11-29 132.309998 170.630005 59.840000 92.500000 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prices = ohlc[\"Adj Close\"]\n", - "prices.tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8fZeMKRg_YO4" - }, - "source": [ - "## Expected returns and risk models\n", - "\n", - "In this notebook, we will use James-Stein shrinkage and semicovariance (which only penalises downside risk)." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "5_0F7H7k_YO4", - "outputId": "973ab4df-85ba-45bc-a907-2be2ec28793c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.5.6'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pypfopt\n", - "pypfopt.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "U6m_oOBG_YO4" - }, - "outputs": [], - "source": [ - "from pypfopt import risk_models, expected_returns\n", - "from pypfopt import plotting\n", - "\n", - "mu = expected_returns.capm_return(prices)\n", - "S = risk_models.semicovariance(prices)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 320 - }, - "id": "V-3Q-NM__YO5", - "outputId": "57e931b0-70d9-4cb7-c0e8-741fb86db943" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plotting.plot_covariance(S, plot_correlation=True);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8V6jyzsw_YO5" - }, - "source": [ - "## Min volatility with a transaction cost objective\n", - "\n", - "Let's say that you already have a portfolio, and want to now optimize it. It could be quite expensive to completely reallocate, so you may want to take into account transaction costs. PyPortfolioOpt provides a simple objective to account for this.\n", - "\n", - "Note: this objective will not play nicely with `max_sharpe`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "70ib0FwA_YO5" - }, - "outputs": [], - "source": [ - "# Pretend that you started with a default-weight allocation\n", - "initial_weights = np.array([1/len(tickers)] * len(tickers))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h4DCRaLe_YO6", - "outputId": "bab1cd04-1fb4-4d87-d784-ec3006753ba3" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.04712),\n", - " ('AMD', 0.0),\n", - " ('BAC', 0.03863),\n", - " ('BLK', 0.06667),\n", - " ('CVS', 0.06667),\n", - " ('DIS', 0.06667),\n", - " ('INTU', 0.06667),\n", - " ('JD', 0.19043),\n", - " ('MA', 0.06667),\n", - " ('NVDA', 0.05715),\n", - " ('PBI', 0.06667),\n", - " ('TGT', 0.06667),\n", - " ('TM', 0.06667),\n", - " ('UL', 0.06667),\n", - " ('WMT', 0.06667)])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pypfopt import EfficientFrontier, objective_functions\n", - "\n", - "ef = EfficientFrontier(mu, S)\n", - "\n", - "# 1% broker commission\n", - "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.01)\n", - "ef.min_volatility()\n", - "weights = ef.clean_weights()\n", - "weights" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iTTvy6xH_YO6" - }, - "source": [ - "Notice that many of the weights are 0.06667, i.e your original equal weight. In fact, the only change has been an allocation of AMD's weight to JD. If we lower the cost `k`, the allocation will change more:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pTepg1-Q_YO6", - "outputId": "335af198-0d41-482c-a923-617240ee5ffd" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.0),\n", - " ('AMD', 0.0),\n", - " ('BAC', 0.0),\n", - " ('BLK', 0.06667),\n", - " ('CVS', 0.0538),\n", - " ('DIS', 0.0),\n", - " ('INTU', 0.00851),\n", - " ('JD', 0.298),\n", - " ('MA', 0.27222),\n", - " ('NVDA', 0.0),\n", - " ('PBI', 0.0),\n", - " ('TGT', 0.0),\n", - " ('TM', 0.06667),\n", - " ('UL', 0.14875),\n", - " ('WMT', 0.08539)])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", - "ef.min_volatility()\n", - "weights = ef.clean_weights()\n", - "weights" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YM-gPp1S_YO7" - }, - "source": [ - "The optimizer seems to really like JD. The reason for this is that it is highly anticorrelated to other assets (notice the dark column in the covariance plot). Hence, historically, it adds a lot of diversification. But it is dangerous to place too much emphasis on what happened in the past, so we may want to limit the asset weights. \n", - "\n", - "In addition, we notice that 4 stocks have now been allocated zero weight, which may be undesirable. Both of these problems can be fixed by adding an [L2 regularisation objective](https://pyportfolioopt.readthedocs.io/en/latest/EfficientFrontier.html#more-on-l2-regularisation). " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Mh22zJrI_YO7", - "outputId": "f6b4707d-c5cb-4c81-d676-7f1cc9c412e5" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.0623),\n", - " ('AMD', 0.05356),\n", - " ('BAC', 0.06263),\n", - " ('BLK', 0.07041),\n", - " ('CVS', 0.06715),\n", - " ('DIS', 0.06528),\n", - " ('INTU', 0.06667),\n", - " ('JD', 0.07589),\n", - " ('MA', 0.07336),\n", - " ('NVDA', 0.06317),\n", - " ('PBI', 0.0644),\n", - " ('TGT', 0.06588),\n", - " ('TM', 0.06934),\n", - " ('UL', 0.07131),\n", - " ('WMT', 0.06866)])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", - "ef.add_objective(objective_functions.L2_reg)\n", - "ef.min_volatility()\n", - "weights = ef.clean_weights()\n", - "weights" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zQrFckqY_YO7" - }, - "source": [ - "This has had too much of an evening-out effect. After all, if the resulting allocation is going to be so close to equal weights, we may as well stick with our initial allocation. We can reduce the strength of the L2 regularisation by reducing `gamma`:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VdeeHz8J_YO7", - "outputId": "735a86d3-078e-4a28-cb2e-19da01de4542" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.02309),\n", - " ('AMD', 0.0),\n", - " ('BAC', 0.01226),\n", - " ('BLK', 0.099),\n", - " ('CVS', 0.06667),\n", - " ('DIS', 0.04427),\n", - " ('INTU', 0.05892),\n", - " ('JD', 0.16678),\n", - " ('MA', 0.14089),\n", - " ('NVDA', 0.02294),\n", - " ('PBI', 0.03273),\n", - " ('TGT', 0.04921),\n", - " ('TM', 0.08801),\n", - " ('UL', 0.11292),\n", - " ('WMT', 0.08232)])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S)\n", - "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", - "ef.add_objective(objective_functions.L2_reg, gamma=0.05) # default is 1\n", - "ef.min_volatility()\n", - "weights = ef.clean_weights()\n", - "weights" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "pQhw2JJu_YO0" + }, + "source": [ + "# Advanced MVO - custom objectives\n", + "\n", + "PyPortfolioOpt has implemented some of the most common objective functions (e.g `min_volatility`, `max_sharpe`, `max_quadratic_utility`, `efficient_risk`, `efficient_return`). However, sometimes yoy may have an idea for a different objective function.\n", + "\n", + "In this cookbook recipe, we cover:\n", + "\n", + "- Mininimising transaction costs\n", + "- Custom convex objectives\n", + "- Custom nonconvex objectives\n", + "\n", + "## Acquiring data\n", + "\n", + "As discussed in the previous notebook, assets are an exogenous input (i.e you must come up with a list of tickers). We will use `yfinance` to download data for thesee tickers\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)\n", + " \n", + "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/3-Advanced-Mean-Variance-Optimisation.ipynb)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kWJJAvG4_iEz", + "outputId": "26b7d743-eb20-4fb5-a99d-f4a5098ee8ad", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:09.802872Z", + "start_time": "2025-11-12T08:11:09.179468Z" + } + }, + "source": [ + "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", + "import os\n", + "if not os.path.isdir('data'):\n", + " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", + " os.chdir('PyPortfolioOpt/cookbook')" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EFN7ul8-_YO8", - "outputId": "575da3e2-c1ed-4821-dbec-8475d1f0c3b7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 17.0%\n", - "Annual volatility: 10.1%\n", - "Sharpe Ratio: 1.69\n" - ] - } - ], - "source": [ - 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\u001B[0m\u001B[32;49m25.3\u001B[0m\r\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "code", + "metadata": { + "id": "yd0GLGye_YO1", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:10.264980Z", + "start_time": "2025-11-12T08:11:09.806374Z" + } + }, + "source": [ + "import yfinance as yf\n", + "import pandas as pd\n", + "import numpy as np" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "code", + "metadata": { + "id": "HRko0Vbr_YO2", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:10.269816Z", + "start_time": "2025-11-12T08:11:10.268385Z" + } + }, + "source": [ + "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\",\n", + " \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\",\n", + " \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]" + ], + "outputs": [], + "execution_count": 3 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "b8QidoAw_YO2", + "outputId": "cc6b4958-fa84-48d8-9a8a-3dd63d59d085", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:12.635987Z", + "start_time": "2025-11-12T08:11:10.275576Z" + } + }, + "source": [ + "ohlc = yf.download(tickers, period=\"max\")" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "ra5fBWi8_YO8" - }, - "source": [ - "This portfolio is now reasonably balanced, but also puts significantly more weight on JD. " - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_3/k_9k5d5n5zz57w7qfll9rzs40000gn/T/ipykernel_60284/3917095853.py:1: FutureWarning: YF.download() has changed argument auto_adjust default to True\n", + " ohlc = yf.download(tickers, period=\"max\")\n", + "[*********************100%***********************] 15 of 15 completed\n" + ] + } + ], + "execution_count": 4 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368 }, + "id": "41-UTlPR_YO3", + "outputId": "45e79da8-c066-4962-d5e2-57baafa8d68c", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:12.676295Z", + "start_time": "2025-11-12T08:11:12.665179Z" + } + }, + "source": [ + "prices = ohlc[\"Close\"]\n", + "prices.tail()" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 575 - }, - "id": "3Klc-L_8_YO8", - "outputId": "9bc2e6e6-9411-4be4-a851-918a648b5306" - }, - "outputs": [ - { - "data": { - "image/png": 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TickerAAPLAMDBACBLKCVSDISINTUJDMANVDAPBITGTTMULWMT
Date
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2025-11-07268.209991233.53999353.2000011082.19995178.989998110.739998648.84997631.790001551.969971188.1499949.34000091.239998201.97999661.470001102.589996
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2025-11-11275.250000237.52000453.6300011085.76001079.870003114.849998654.32000731.610001558.349976193.1600049.44000091.580002205.99000561.070000103.440002
\n", + "
" ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8fZeMKRg_YO4" + }, + "source": [ + "## Expected returns and risk models\n", + "\n", + "In this notebook, we will use James-Stein shrinkage and semicovariance (which only penalises downside risk)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 }, - { - "cell_type": "markdown", - "metadata": { - "id": "A4s-3rWa_YO9" - }, - "source": [ - "## Custom convex objectives\n", - "\n", - "PyPortfolioOpt comes with the following built-in objective functions, as of v1.2.1:\n", - "\n", - "- Portfolio variance (i.e square of volatility)\n", - "- Portfolio return\n", - "- Sharpe ratio\n", - "- L2 regularisation (minimising this reduces nonzero weights)\n", - "- Quadratic utility\n", - "- Transaction cost model (a simple one)\n", - "\n", - "However, you may want have a different objective. If this new objective is **convex**, you can optimize a portfolio with the full benefit of PyPortfolioOpt's modular syntax, for example adding other constraints and objectives.\n", - "\n", - "To demonstrate this, we will minimise the **logarithmic-barrier** function suggested in the paper 60 Years of Portfolio Optimization, by Kolm et al (2014):\n", - "\n", - "$$f(w, S, k) = w^T S w - k \\sum_{i=1}^N \\ln w$$\n", - "\n", - "We must first convert this mathematical objective into the language of cvxpy. Cvxpy is a powerful modelling language for convex optimization problems. It is clean and easy to use, the only caveat is that objectives must be expressed with `cvxpy` functions, a list of which can be found [here](https://www.cvxpy.org/tutorial/functions/index.html)." - ] + "id": "5_0F7H7k_YO4", + "outputId": "973ab4df-85ba-45bc-a907-2be2ec28793c", + "jupyter": { + "is_executing": true + } + }, + "source": [ + "import pypfopt\n", + "pypfopt.__version__" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "U6m_oOBG_YO4" + }, + "outputs": [], + "source": [ + "from pypfopt import risk_models, expected_returns\n", + "from pypfopt import plotting\n", + "\n", + "mu = expected_returns.capm_return(prices)\n", + "S = risk_models.semicovariance(prices)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 }, + "id": "V-3Q-NM__YO5", + "outputId": "57e931b0-70d9-4cb7-c0e8-741fb86db943" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "5WjglE6d_YO9" - }, - "outputs": [], - "source": [ - "import cvxpy as cp\n", - "\n", - "# Note: functions are minimised. If you want to maximise an objective, stick a minus sign in it.\n", - "def logarithmic_barrier_objective(w, cov_matrix, k=0.1):\n", - " log_sum = cp.sum(cp.log(w))\n", - " var = cp.quad_form(w, cov_matrix)\n", - " return var - k * log_sum" + "data": { + "image/png": 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AAIBBRkNZcnKyLMvS5MmTvfqfeOIJWZaloUOHqqSkRK1bt67w/MzMTM2aNUuSlJaWJsuyZFmWLl68qBMnTmjFihUaMmTIZZ//4MGDunjxosLDw2vvogAAAACgCozvlF24cEGTJ0/WddddV+7YihUrdOrUKY0dO7bcsT59+uiOO+7Q/PnzPX3//d//rYiICN1222168skndeDAAS1atEjvvfdeufN79eqlZs2aaenSpRXODwAAAAD1wXgo27Bhg/Ly8pSQkFDuWElJif7+978rNja23LH4+Hilp6frwIEDnr7z588rPz9fOTk52rlzp1599VWNHz9eTz/9tPr37+91vsPh0EcffaS///3vio+Pv2qdwcHBstlsXg0AAAAAasp4KCstLdWUKVP0wgsvKDIystzx+fPnq3379urTp4+nLzQ0VMOGDfPaJbucBQsWqKCgQEOHDvX0hYWFafjw4UpJSdH69evVvHlz9e7d+4rzJCQkyOl0elpOTk4VrhIAAAAAKmY8lEnSxx9/rN27d2vatGnljh08eFA7duzw2s0aMWKEAgICtGjRoqvObVmWMjMz1bZtW0/fqFGjdPjwYR04cEBlZWVatGiRHA7HFedJTEyU3W73tIoCJAAAAABUVYMIZZI0efJkjR07Vh07dix3LCkpScOGDVNYWJikHz66uGTJEp09e7ZScwcEBMiyLM/j+Ph4paSkeB6npKRo+PDhnvkr4na75XK5vBoAAAAA1FSDCWVbt27VunXrlJiYWO7YpR2xESNG6Pbbb1fv3r0r9dFFSQoMDNQdd9yh7OxsSVKnTp0UHR2tmTNnqri4WMXFxUpPT1doaKhGjRpVexcEAAAAAJXQxHQBP/bqq69q9+7dOnTokFf/2bNntWTJEsXHx+u2227ToUOHtG3btkrNOXbsWF1//fVatmyZpB9u8LFlyxY999xzXuPi4uLkcDj0t7/9rXYuBgAAAAAqoUGFsn379unDDz/UhAkTyh2bP3++tm3bpk6dOunNN9+s8Pxrr71W4eHhatKkiW6++WYNGTJEv/3tb/XOO+9o8+bNatKkiX75y1/qtdde0/79+73O/dvf/qZJkybpzjvv9LqjIwAAAADUpQbz8cVLXnvtNQUGli9r+/btysjIkN1u1wcffFDhuU8//bTy8vKUlZWlf/7zn7rzzjs1cuRIz67Y4MGD1bJlSy1fvrzcuRkZGTpw4MBVb/gBAAAAALUpQJJ11VEox2azyel0ym63c9MPAAAAwI/VNBs0uJ0yAAAAAPAnhDIAAAAAMIhQBgAAAAAGEcoAAAAAwCBCGQAAAAAYRCgDAAAAAIMIZQAAAABgEKEMAAAAAAwilAEAAACAQYQyAAAAADCIUAYAAAAABhHKAAAAAMCgJqYL8HVvpG+Uu6zUdBkAAFTbpKho0yUAgF9jpwwAAAAADGoUoSw5OVmWZWnevHnljs2dO1eWZSk5Odmrv2fPniopKdGqVavqq0wAAAAAKKdRhDJJOnbsmEaNGqVrrrnG0xcSEqJf/OIXOnr0aLnxDodDc+bM0YMPPqhWrVrVZ6kAAAAA4NFoQtm//vUvHT9+XEOHDvX0DR06VMeOHdOuXbu8xoaGhmrkyJGaN2+eVq9erdjY2HquFgAAAAB+0GhCmSQlJSUpLi7O8zg+Pr7cxxYlacSIEcrIyFBmZqZSUlIUHx9/1bmDg4Nls9m8GgAAAADUVKMKZSkpKerdu7duueUW3XLLLerVq5dSUlLKjXM4HJ7+tWvXqnnz5oqJibni3AkJCXI6nZ6Wk5NTJ9cAAAAAwL80qlB28uRJz8cR4+LitHr1ap06dcprTPv27dW9e3ctXLhQklRaWqrFixfL4XBcce7ExETZ7XZPi4yMrLPrAAAAAOA/Gt3fKUtKStLcuXMlSc8991y54w6HQ02bNlVubq6nLyAgQEVFRXr++efldDornNftdsvtdtdN0QAAAAD8VqPaKZN++DhicHCwmjZtqnXr1nkdCwoK0pgxY/Tiiy+qa9euntalSxfl5uZq9OjRhqoGAAAA4K8a3U5ZWVmZOnXq5Pn5xwYNGqQWLVpo/vz55XbEli1bJofDoffee6/eagUAAACARrdTJkkul0sul6tcv8Ph0IYNGyr8iOKyZcvUrVs3RUVF1UeJAAAAACBJCpBkVXZwUFCQpkyZoqSkJL+/+6DNZpPT6dTcA1/IXVZquhwAAKptUlS06RIAwKddygZ2u73CzaGrqVIokySn06moqCgdPXq0yk/WmNT0Fw8AAACgcahpNqjyxxc3bdp01b/pBQAAAAConCrf6CM1NVUzZsxQVFSUvvrqK507d87r+MqVK2utOAAAAABo7Kr88cXS0st/f8qyLDVp0uhu6FghPr4IAAAAQKp5NqhyggoKCqrykwAAAAAAKlajW+KHhITUVh0AAAAA4JeqHMoCAwP1u9/9TidOnNDZs2fVrl07SdL06dMVHx9f6wUCAAAAQGNW5VD2n//5n4qNjdUrr7wit9vt6d+3b5/GjRtXq8UBAAAAQGNX5VA2ZswYPf300/roo4+8bvqxZ88edezYsVaLAwAAAIDGrsqhLDIyUv/+97/LTxQYqKZNm9ZKUQAAAADgL6ocyg4cOKA+ffqU6x82bJh27dpVK0UBAAAAgL+o8i3xp0+frgULFigyMlKBgYEaOnSoOnTooDFjxmjQoEF1USMAAAAANFpV/uPRktS7d2+99tpr6tKli8LCwvSvf/1L06dP1/r16+ugxIbp0h+Im3vgC7nLLv8HtQEAaOwmRUWbLgEAjKr3Px4dGRmpbdu26ZFHHil3rEePHtq5c2eViwAAAAAAf1Xl75R9+umnatGiRbn+Bx54QGvXrq2VogAAAADAX1Q5lKWnp+vTTz9VWFiYp69Pnz5as2aNpk2bVqvF1Ybk5GQtX77c87NlWbIsS263W3l5efr0008VFxengIAAw5UCAAAA8EdVDmXjxo3TsWPHtHLlSgUHB6tv375avXq1XnvtNf3lL3+pgxJrV2pqqiIiItS2bVsNGDBAaWlpmj17tlatWqWgoCDT5QEAAADwM1X+TpllWRo1apRWr16tTZs2qXPnzkpISNDbb79dF/XVuqKiIuXn50uScnNztWvXLqWnp2vTpk2KjY3V/PnzDVcIAAAAwJ9UKpRFRUWV63v99de1cOFCpaSk6LPPPvOM2bt3b+1WWA/S0tK0e/duDR069LKhLDg4WCEhIZ7HNputvsoDAAAA0IhVKpTt3r1blmV5fe/q0uPx48fr6aefVkBAgCzLUpMmVd58axAyMjLUuXPnyx5PSEjQ66+/Xn8FAQAAAPALlUpQ7dq1q+s6jLsUKi8nMTFRb731luexzWZTTk5OfZQGAAAAoBGrVCg7duxYXddhXKdOnZSdnX3Z4263W263ux4rAgAAAOAPqnz3xVdffVVxcXHl+uPi4vTKK6/USlH1rV+/furcubOWLVtmuhQAAAAAfqbKoWz8+PHKyMgo179//34988wztVJUXQoJCVF4eLhat26te+65RwkJCfrkk0+0cuVKffDBB6bLAwAAAOBnqnxXjoiICH377bfl+r///nu1atWqVoqqTYGBgSopKfE8HjBggPLy8lRcXKzTp09rz549mjBhghYsWHDF75QBAAAAQF2ocig7fvy4evXqpW+++carv1evXsrNza2tumrNTTfdpH//+9+SfviIZUUfvayJKT37y+Vy1eqcAAAAAPxHlUPZ+++/r7/85S9q2rSpNm3aJEnq37+/Zs6cqVmzZtV6gdV13XXXqVevXurbt6/effdd0+UAAAAAQIWqHMr++Mc/qmXLlnrnnXcUHBwsSbp48aLefPNNzZgxo9YLrK6kpCR169ZNs2bN0ieffGK6HAAAAACoUICkan2RKjQ0VJ06ddKFCxd0+PBhv7tdvM1mk9PplN1u5+OLAAAAgB+raTao8k7ZJefOndOXX35Z3dMBAAAAAKpkKFu2bJliY2Plcrmu+re8nnzyyVopDAAAAAD8QaVCWWFhoed28YWFhXVaEAAAAAD4k0p/p+z3v/+9/vSnP+nChQt1XJJv4DtlAAAAAKSaZ4PAyg6cOnWqwsLCqvwEAAAAAIDLq3QoCwgIqMs6AAAAAMAvVTqUSfJ8rwwAAAAAUDuqdEv8zMzMqwazli1b1qggAAAAAPAnVQplU6dO5e6LAAAAAFCLqhTKFi1apO+//76uavFJb6RvlLus1HQZAADgRyZFRZsuAQAqrdLfKeP7ZAAAAABQ+7j7IgAAAAAYVOlQFhQUVK8fXUxOTtby5cs9P1uWpcmTJ3uNeeKJJzw7eJfGXK5lZ2dLkrKzszVx4sRyzzd16lTt2rWrjq8KAAAAALxV6Zb4Jl24cEGTJ0/WddddV+HxiRMnKiIiwtMkKTY21vO4W7du9VgtAAAAAFSOz4SyDRs2KC8vTwkJCRUedzqdys/P9zRJOnPmjOfxyZMna/T8wcHBstlsXg0AAAAAaspnQllpaammTJmiF154QZGRkfX+/AkJCXI6nZ6Wk5NT7zUAAAAAaHx8JpRJ0scff6zdu3dr2rRp9f7ciYmJstvtnmYiGAIAAABofHwqlEnS5MmTNXbsWHXs2LFen9ftdsvlcnk1AAAAAKgpnwtlW7du1bp165SYmFit851Op5o3b16u/7rrrlNhYWFNywMAAACAKmliuoDqePXVV7V7924dOnSoyuceOnRI9913X7n+e++9t1rzAQAAAEBN+GQo27dvnz788ENNmDChyuf++c9/1tatWzVlyhT985//VFBQkEaPHq3o6Gj9+te/roNqAQAAAODyfO7ji5e89tprCgysevk7duzQgAEDNGDAAG3fvl2bN2/WAw88oP79+2v//v11UCkAAAAAXF6AJMt0Eb7IZrPJ6XTKbrdz0w8AAADAj9U0G/jsThkAAAAANAaEMgAAAAAwiFAGAAAAAAYRygAAAADAIEIZAAAAABhEKAMAAAAAgwhlAAAAAGAQoQwAAAAADCKUAQAAAIBBhDIAAAAAMIhQBgAAAAAGEcoAAAAAwKAmpgvwdW+kb5S7rNR0GQAAVNukqGjTJQCAX2OnDAAAAAAM8ulQlpycLMuyZFmW3G638vLy9OmnnyouLk4BAQGecdnZ2Zo4caLncefOnfXJJ58oPz9fFy5cUHZ2thYtWqQbb7zRxGUAAAAA8GM+HcokKTU1VREREWrbtq0GDBigtLQ0zZ49W6tWrVJQUFC58TfccIM2btyogoICPfroo+rUqZPi4uKUm5ur0NBQA1cAAAAAwJ/5/HfKioqKlJ+fL0nKzc3Vrl27lJ6erk2bNik2Nlbz58/3Gt+rVy81b95c48aNU2npD98F++abb7R58+b6Lh0AAAAAfH+nrCJpaWnavXu3hg4dWu5YXl6emjZtqiFDhlRpzuDgYNlsNq8GAAAAADXVKEOZJGVkZKht27bl+nfu3Kk//OEP+uijj3Ty5EmtWbNGL730km666aYrzpeQkCCn0+lpOTk5dVQ5AAAAAH/SaENZQECALMuq8Njvfvc7RURE6JlnntH+/fv1zDPPKCMjQ3ffffdl50tMTJTdbve0yMjIuiodAAAAgB9ptKGsU6dOys7OvuzxgoICLV26VC+//LI6deqk3NxcvfTSS5cd73a75XK5vBoAAAAA1FSjDGX9+vVT586dtWzZskqNLy4uVlZWFndfBAAAAFDvfP7uiyEhIQoPD1dQUJDCw8P12GOPKSEhQStXrtQHH3xQbvzAgQM1atQoLVq0SJmZmQoICNDjjz+un/3sZ4qLizNwBQAAAAD8mc+HsgEDBigvL0/FxcU6ffq09uzZowkTJmjBggUVfqfswIEDOn/+vGbNmqWf/OQnKioq0uHDhzVu3DilpKQYuAIAAAAA/ixAUsV3w8AV2Ww2OZ1OzT3whdxlpabLAQCg2iZFRZsuAQB82qVsYLfbq3XvCZ/fKTNtSs/+3PQDAAAAQLU1yht9AAAAAICvIJQBAAAAgEGEMgAAAAAwiFAGAAAAAAYRygAAAADAIEIZAAAAABhEKAMAAAAAgwhlAAAAAGAQoQwAAAAADCKUAQAAAIBBhDIAAAAAMKiJ6QJ83RvpG+UuKzVdBgAfNikq2nQJAADAIHbKAAAAAMAgQhkAAAAAGORzoSw8PFx//etflZWVpYsXL+rYsWNasWKFHnnkEX3//feaPHlyhef97ne/U15enpo0aaLAwEBNnjxZBw8e1Pnz53Xq1Cmlp6fL4XDU89UAAAAA8Hc+9Z2yNm3aaPv27Tpz5oxefvll7d27V02bNtWjjz6q2bNnKyUlRXFxcXrzzTfLnRsbG6sPPvhAJSUlmjZtmsaPH6/nn39eX375pex2u+6//361aNHCwFUBAAAA8GcBkizTRVTW6tWr1blzZ3Xo0EHnz5/3Ota8eXP95Cc/0d69e9W7d29t377dcywmJkabN29Wx44ddejQIe3atUvLly/X9OnTq12LzWaT0+nU3ANfcKMPADXCjT4AAPBtl7KB3W6Xy+Wq8vk+8/HFFi1a6LHHHtPbb79dLpBJUmFhofbt26f//d//VXx8vNexuLg4bd++XYcOHZIk5eXl6aGHHtINN9xQ6ecPDg6WzWbzagAAAABQUz4Tym6//XYFBgYqIyPjiuPmz5+v4cOHKzQ0VJIUFhamYcOGKSkpyTPmxRdf1I033qi8vDzt2bNH8+bN02OPPXbFeRMSEuR0Oj0tJyen5hcFAAAAwO/5TCgLCAio1LiFCxcqKChII0aMkCSNHDlSZWVlWrx4sWfMwYMHdffdd6tnz55KSkrSTTfdpJUrV+r999+/7LyJiYmy2+2eFhkZWbMLAgAAAAD5UCg7fPiwysrK1LFjxyuOc7lcWrp0qeLi4iT98NHFf/zjHzp37pzXOMuy9OWXX2r27Nl68sknFRsbq3Hjxqlt27YVzut2u+VyubwaAAAAANSUz4Sy06dPa926dXruued07bXXljvevHlzz8/z589Xnz59NHDgQPXq1Uvz58+/6vwHDhyQJM/HHgEAAACgPvjU3RfbtWun7du3q6CgQK+99pq+/vprNWnSRA8//LCeffZZ3XnnnZ6xmZmZatmypfLz8736JWnJkiXavn27Pv/8c+Xl5aldu3ZKTEzU9ddfr7vuukulpVe/myJ3XwRQW7j7IgAAvs1v7r4oSdnZ2br33nuVlpamWbNmad++fVq/fr369++vZ5991mtsUlKSrr/+eq8bfFyybt06Pf7441q5cqUyMzO1YMECZWRk6JFHHqlUIAMAAACA2uJTO2UNSU3TMAAAAIDGwa92ygAAAACgsSGUAQAAAIBBhDIAAAAAMIhQBgAAAAAGEcoAAAAAwCBCGQAAAAAYRCgDAAAAAIMIZQAAAABgEKEMAAAAAAwilAEAAACAQYQyAAAAADCIUAYAAAAABjUxXYCveyN9o9xlpabLAADAY1JUtOkSAABVwE4ZAAAAABhEKAMAAAAAg3wulCUnJ8uyLE87efKkUlNTFRUV5RljWZaeeOKJCs+PiYmRZVlq3ry5p69Vq1b6+uuvtWXLFtnt9jq/BgAAAAC4xOdCmSSlpqYqIiJCERER6t+/v0pKSrRq1apqzXXrrbdq27ZtOnr0qB599FE5nc5arhYAAAAALs8nQ1lRUZHy8/OVn5+vPXv2aMaMGbrlllt0ww03VGmeqKgobdu2TTt27NDPf/5zXbx48bJjg4ODZbPZvBoAAAAA1JRPhrIfCw0N1VNPPaXDhw/r1KlTlT7vgQce0JYtW7Rs2TI99dRTKi298h0UExIS5HQ6PS0nJ6empQMAAACAb4ayQYMGyeVyyeVy6ezZsxo8eLBGjhwpy7IqPcfy5cu1cuVKvfDCC5Uan5iYKLvd7mmRkZHVLR8AAAAAPHwylKWlpalr167q2rWrunXrpnXr1ik1NVW33HJLpef45JNPNGTIEPXu3btS491utycIXmoAAAAAUFM+GcrOnTunrKwsZWVl6csvv9S4ceMUGhqqX/3qV5WeY/z48Vq0aJFSU1PVp0+fOqwWAAAAAC6viekCaoNlWSorK1OzZs2qdM7TTz+tsrIyrVmzRgMHDtRnn31Wh1UCAAAAQHk+GcpCQkIUHh4uSWrRooWef/55hYWFaeXKlZ4x7dq1U5cuXbzOO3z4cLm5nnnmGZWWlnqC2ZYtW+q2eAAAAAD4EZ8MZQMGDFBeXp4kyel0KiMjQ8OHD/cKVH/+85/LnXe5748999xzKisr0+rVqzVo0CBt3ry5TuoGAAAAgP8rQFLlb1kID5vNJqfTKbvdzk0/AAAAAD9W02zgkzf6AAAAAIDGglAGAAAAAAYRygAAAADAIEIZAAAAABhEKAMAAAAAgwhlAAAAAGAQoQwAAAAADCKUAQAAAIBBhDIAAAAAMIhQBgAAAAAGEcoAAAAAwCBCGQAAAAAY1MR0Ab7ujfSNcpeVmi4DAODjJkVFmy4BAGAIO2UAAAAAYJDPhLLk5GRZluVpJ0+eVGpqqqKiosqNfffdd1VSUqJhw4ZVONdtt92mpKQkHT9+XBcvXtSRI0f00Ucf6b777qvrywAAAAAALz4TyiQpNTVVERERioiIUP/+/VVSUqJVq1Z5jWnWrJlGjRqlmTNnKj4+vtwc9913n7766iu1b99e48eP15133qkhQ4YoIyNDs2bNqq9LAQAAAABJPvadsqKiIuXn50uS8vPzNWPGDG3btk033HCDTp48KUkaPny4Dhw4oBkzZig3N1c333yzTpw44Znjf/7nf3T48GH16dNHlmV5+vfs2aPZs2fX7wUBAAAA8Hs+tVP2Y6GhoXrqqad0+PBhnTp1ytPvcDiUkpIip9Op1NRUxcbGeo517dpVd999t2bNmuUVyC4pLCy87PMFBwfLZrN5NQAAAACoKZ8KZYMGDZLL5ZLL5dLZs2c1ePBgjRw50hOwbr/9dvXs2VOLFy+WJKWkpCguLs5z/h133CFJysjIqPJzJyQkyOl0elpOTk4tXBEAAAAAf+dToSwtLU1du3ZV165d1a1bN61bt06pqam65ZZbJEnx8fFat26dZ+dszZo1at68uR566CFJUkBAQLWfOzExUXa73dMiIyNrfkEAAAAA/J5PhbJz584pKytLWVlZ+vLLLzVu3DiFhobqV7/6lQIDAzV27FgNHDhQxcXFKi4u1vnz59WyZUvPDT8yMzMlSR07dqzyc7vdbs8u3aUGAAAAADXlUzf6+L8sy1JZWZmaNWumn/3sZ7LZbLrnnntUWvr//pjz3XffreTkZDVv3ly7d+/W/v37NWnSJC1evLjc98qaN29+xe+VAQAAAEBt86mdspCQEIWHhys8PFwdO3bUnDlzFBYWppUrV8rhcGj16tX6+uuvtX//fk/7xz/+oTNnzug//uM/JElxcXFq3769tm7dqgEDBqhdu3aKiorSlClT9Mknnxi+QgAAAAD+xqdC2YABA5SXl6e8vDzt3LlT3bp10/Dhw3Xw4EENHDhQy5YtK3eOZVlavny5HA6HJOmLL77Q/fffr3//+996//33dfDgQa1YsUJ33XWXfvOb39TzFQEAAADwdwGSyt8bHldls9nkdDo198AXcpeVXv0EAACuYFJUtOkSAADVdCkb2O32at17wqe/U9YQTOnZn5t+AAAAAKg2n/r4IgAAAAA0NoQyAAAAADCIUAYAAAAABhHKAAAAAMAgQhkAAAAAGEQoAwAAAACDCGUAAAAAYBChDAAAAAAMIpQBAAAAgEGEMgAAAAAwiFAGAAAAAAY1MV2Ar3sjfaPcZaWmywAAAIAPmxQVbboEGMROGQAAAAAYRCgDAAAAAIMafCjr2bOnSkpKtGrVKq/+Nm3ayLIslZSUqHXr1l7HIiIiVFxcLMuy1KZNG6/xl5rT6dS+ffs0d+5c3X777fV2PQAAAADwYw0+lDkcDs2ZM0cPPvigWrVqVe54Tk6OxowZ49U3duxY5eTkVDhf//79FRERoS5dumjKlCnq1KmT9uzZo4ceeqhO6gcAAACAK2nQoSw0NFQjR47UvHnztHr1asXGxpYbs2DBAsXFxXn1xcXFacGCBRXOeerUKeXn5ys7O1srVqzQT3/6U+3cuVPz589XYGCD/nUAAAAAaIQadAoZMWKEMjIylJmZqZSUFMXHx5cbs2LFCrVo0UK9evWSJPXq1UstWrTQypUrK/UclmVp9uzZatu2re67777LjgsODpbNZvNqAAAAAFBTDTqUORwOpaSkSJLWrl2r5s2bKyYmxmtMcXGxV2CLj49XSkqKiouLK/08GRkZkqS2bdtedkxCQoKcTqenXe7jkQAAAABQFQ02lLVv317du3fXwoULJUmlpaVavHixHA5HubFJSUkaPny4wsPDNXz4cCUlJVXpuQICAiT9sGt2OYmJibLb7Z4WGRlZpecAAAAAgIo02D8e7XA41LRpU+Xm5nr6AgICVFRUpOeff95r7L59+5SRkaGFCxfq4MGD2r9/v7p06VLp5+rUqZMkKTs7+7Jj3G633G53Fa8CAAAAAK6sQe6UBQUFacyYMXrxxRfVtWtXT+vSpYtyc3M1evTocuckJSWpX79+1dolmzBhgo4cOaJdu3bV1iUAAAAAQKU0yJ2yQYMGqUWLFpo/f76cTqfXsWXLlsnhcGjt2rVe/e+//76WLFmiM2fOXHHuli1bKjw8XNdee63uvvtu/eY3v1H37t01cOBAlZWV1falAAAAAMAVNchQ5nA4tGHDhnKBTPohlE2ePFl2u92rv7S0VKdOnbrq3Bs3bpQknTt3TkePHlVaWpqefvppZWVl1U7xAAAAAFAFAZIuf3cLXJbNZpPT6ZTdbpfL5TJdDgAAAABDapoNGuR3ygAAAADAXxDKAAAAAMAgQhkAAAAAGEQoAwAAAACDCGUAAAAAYBChDAAAAAAMIpQBAAAAgEGEMgAAAAAwiFAGAAAAAAYRygAAAADAIEIZAAAAABhEKAMAAAAAg5qYLsDXvZG+Ue6yUtNlAADg8yZFRZsuAQCMYKcMAAAAAAwilAEAAACAQQ0ylPXs2VMlJSVatWrVZceMGjVKJSUlmjt3brljMTExsizL0/Ly8rR06VK1a9fOMyY7O1sTJ06sk/oBAAAAoLIaZChzOByaM2eOHnzwQbVq1eqyY2bOnKnRo0crJCSkwjHt27dXq1atNHz4cN11111auXKlAgMb5CUDAAAA8FMNLqGEhoZq5MiRmjdvnlavXq3Y2NhyY9q2basHHnhAM2bMUGZmpoYOHVrhXN99953y8vK0detWTZ8+XXfddZduv/32atUVHBwsm83m1QAAAACgphpcKBsxYoQyMjKUmZmplJQUxcfHlxsTFxen1atXy+l0KiUlRQ6H46rzXrhwQdIP4ao6EhIS5HQ6PS0nJ6da8wAAAADAjzW4UOZwOJSSkiJJWrt2rZo3b66YmBjP8YCAAMXGxnrGLFq0SL1791bbtm0vO2dERIReeuklnThxQocOHapWXYmJibLb7Z4WGRlZrXkAAAAA4McaVChr3769unfvroULF0qSSktLtXjxYq+dsIcfflihoaFas2aNJOnUqVNav359hTtqJ06c0NmzZ/Xtt98qNDRUTz75pIqLi6tVm9vtlsvl8moAAAAAUFMN6o9HOxwONW3aVLm5uZ6+gIAAFRUV6fnnn5fT6ZTD4VDLli09H0eUpMDAQHXu3FlTp06VZVme/j59+sjpdOq7777T2bNn6/VaAAAAAKAyGkwoCwoK0pgxY/Tiiy/q008/9Tr28ccfa/To0VqyZImeeOIJjRw5Uvv37/c6d9u2bXrkkUe0bt06T392drYKCwvr7RoAAAAAoKoaTCgbNGiQWrRoofnz58vpdHodW7ZsmRwOh6655hqdOnVK//jHP8qdv2bNGjkcDq9QdjWRkZHq0qWLV9/Ro0d15syZal0DAAAAAFRVg/lOmcPh0IYNG8oFMumHUNatWze99dZbWr58eYXnL1u2TIMHD1bLli0r/Zwvv/yydu/e7dUGDhxY7WsAAAAAgKoKkGRddRTKsdlscjqdstvt3PQDAAAA8GM1zQYNZqcMAAAAAPwRoQwAAAAADCKUAQAAAIBBhDIAAAAAMKjB3BLfV9lsNtMlAAAAADCoppmAUFZN119/vSQpJyfHcCUAAAAAGgKbzVatuy8SyqqpoKBA0g9/gJpb4vsOm82mnJwc1s2HsGa+hzXzTayb72HNfA9r5psqu242m025ubnVeg5CWQ25XC5eVD6IdfM9rJnvYc18E+vme1gz38Oa+aarrVtN1pQbfQAAAACAQYQyAAAAADCIUFZNRUVFev3111VUVGS6FFQB6+Z7WDPfw5r5JtbN97Bmvoc18031sW4Bkqw6mx0AAAAAcEXslAEAAACAQYQyAAAAADCIUAYAAAAABhHKAAAAAMAgQhkAAAAAGEQo+5Ff//rXys7O1oULF5Senq5u3bpdcfywYcN08OBBXbhwQV9//bUGDBhQbsy0adOUm5ur8+fPa/369br99tvrqny/VNtrlpycLMuyvFpqampdXoLfqcqa3XnnnVq6dKmys7NlWZYmTpxY4zlRPbW9blOnTi33Wjt48GBdXoLfqcqajRs3Tp999pkKCgpUUFCg9evXVzie97S6Vdtrxnta/ajKug0ZMkRffPGFTp8+rbNnz2rXrl166qmnyo3jtVa3anvNauu1ZtFkjRgxwrp48aIVGxtrderUyXrvvfesgoIC68Ybb6xwfHR0tFVcXGy99NJLVseOHa3p06dbRUVF1l133eUZ88orr1inT5+2Bg8ebEVFRVkff/yxlZWVZYWEhBi/3sbQ6mLNkpOTrTVr1ljh4eGedt111xm/1sbSqrpm999/vzVz5kxr5MiRVm5urjVx4sQaz0lrGOs2depUa+/evV6vtZYtWxq/1sbSqrpmKSkp1rPPPmt16dLF6tChg5WUlGSdPn3aat26tWcM72m+t2a8pzW8dYuJibF+/vOfWx07drRuvfVWa8KECVZxcbH1yCOPeMbwWvO9Naul15r5X05DaOnp6dacOXM8jwMCAqwTJ05YkydPrnD8okWLrJUrV3r17dixw5o3b57ncW5urjVp0iTPY7vdbl24cMEaOXKk8ettDK0u1iw5Odlavny58WtrrK2qa/bjlp2dXeE/7msyJ83cuk2dOtXatWuX8WtrrK2mr4vAwECrsLDQ+uUvf+np4z3N99aM97SGv26SrK+++sqaPn265zGvNd9bs9p4rfHxRUlNmzbVfffdpw0bNnj6LMvShg0bFB0dXeE50dHRXuMlad26dZ7x7dq1U6tWrbzGOJ1O7dy587JzovLqYs0u6du3r/Lz85WRkaF33nlH119/fe1fgB+qzpqZmBPe6vJ3fMcddygnJ0dZWVlKSUnRT37yk5qWC9XOml177bVq2rSpCgoKJPGeVtfqYs0u4T2t7tTGuj300EPq0KGDPvvsM0m81upaXazZJTV9rRHKJN1www1q0qSJ8vPzvfrz8/MVERFR4TkRERFXHH/pv1WZE5VXF2smSWvXrtWYMWPUv39/TZ48WTExMUpNTVVgIC+VmqrOmpmYE97q6ne8c+dOxcbG6rHHHtOzzz6rdu3aaevWrQoLC6tpyX6vNtbszTffVG5urucfLryn1a26WDOJ97S6Vt11s9vtcrlccrvdWr16tV544QVea/WkLtZMqp3XWpOqXw7QeC1evNjz8759+/T111/ryJEj6tu3rzZt2mSwMqBxWbt2refnvXv3aufOnTp69KhGjBihpKQkg5Vh8uTJGjVqlPr27auioiLT5aASLrdmvKc1TC6XS127dlVYWJj69++vt956S0eOHNGWLVtMl4bLuNqa1cZrjf+rRNLJkydVUlKi8PBwr/7w8HDl5eVVeE5eXt4Vx1/6b1XmROXVxZpVJDs7W99//z13PaoF1VkzE3PCW339jgsLC5WZmclrrRbUZM0mTZqkV199VY888oj27t3r6ec9rW7VxZpVhPe02lXddbMsS1lZWdqzZ4/eeustLV26VAkJCZJ4rdW1ulizilTntUYok1RcXKyvvvpK/fv39/QFBASof//+2rFjR4Xn7Nixw2u8JD388MOe8dnZ2fr222+9xthsNvXo0eOyc6Ly6mLNKhIZGamWLVvq22+/rZ3C/Vh11szEnPBWX7/j0NBQ3XbbbbzWakF11+zll1/W73//ez322GP66quvvI7xnla36mLNKsJ7Wu2qrf99DAwMVEhIiCRea3WtLtasItV9rRm/C0pDaCNGjLAuXLhgjRkzxurYsaP17rvvWgUFBdZNN91kSbIWLFhgvfHGG57x0dHRltvttl588UWrQ4cO1tSpUyu8JX5BQYH1+OOPW3fffbe1fPlybmnagNcsNDTUmjlzptWjRw+rTZs21kMPPWR9+eWX1qFDh6zg4GDj19sYWlXXrGnTplaXLl2sLl26WDk5OdbMmTOtLl26WLfddlul56Q1zHX74x//aD344INWmzZtrOjoaOvTTz+1vvvuO+uGG24wfr2NoVV1zV555RXr4sWL1tChQ71u6RwaGuo1hvc031kz3tMa5rq9+uqr1k9/+lOrXbt2VseOHa0XX3zRcrvdlsPh8FpbXmu+s2a1+Foz/8tpKO25556zvvnmG+vixYtWenq61b17d8+xtLQ0Kzk52Wv8sGHDrIyMDOvixYvW3r17rQEDBpSbc9q0ada3335rXbhwwVq/fr11xx13GL/OxtRqc82uueYaa+3atVZ+fr5VVFRkZWdnW++99x7/uDe4Zm3atLEqkpaWVuk5aQ1z3RYuXGjl5ORYFy9etI4fP24tXLjQuvXWW41fZ2NqVVmz7OzsCtds6tSpXnPynuY7a8Z7WsNct//6r/+yMjMzrfPnz1unTp2ytm/fbo0YMaLcnLzWfGfNauu1FvD//wAAAAAAMIDvlAEAAACAQYQyAAAAADCIUAYAAAAABhHKAAAAAMAgQhkAAAAAGEQoAwAAAACDCGUAAAAAYBChDAAAAAAMIpQBAAAAgEGEMgAAAAAwiFAGAAAAAAb9fwgpkdCLMSSrAAAAAElFTkSuQmCC", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mu.plot.barh(figsize=(10,5));" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "pgEDbNJ7_YO5", + "outputId": "ac4edb81-60a7-48dc-ea68-02926985e564" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "UxmNQahn_YO9" - }, - "source": [ - "Once we have written the objective function, we can just use the `ef.convex_objective()` to minimise the objective." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotting.plot_covariance(S, plot_correlation=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8V6jyzsw_YO5" + }, + "source": [ + "## Min volatility with a transaction cost objective\n", + "\n", + "Let's say that you already have a portfolio, and want to now optimize it. It could be quite expensive to completely reallocate, so you may want to take into account transaction costs. PyPortfolioOpt provides a simple objective to account for this.\n", + "\n", + "Note: this objective will not play nicely with `max_sharpe`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "70ib0FwA_YO5" + }, + "outputs": [], + "source": [ + "# Pretend that you started with a default-weight allocation\n", + "initial_weights = np.array([1/len(tickers)] * len(tickers))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "h4DCRaLe_YO6", + "outputId": "bab1cd04-1fb4-4d87-d784-ec3006753ba3" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SMsaFWHB_YO9", - "outputId": "5a25c217-5d8b-4185-89de-a7379a5ef5fe" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.03946),\n", - " ('AMD', 0.02568),\n", - " ('BAC', 0.0386),\n", - " ('BLK', 0.07889),\n", - " ('CVS', 0.05889),\n", - " ('DIS', 0.04687),\n", - " ('INTU', 0.05174),\n", - " ('JD', 0.17291),\n", - " ('MA', 0.12155),\n", - " ('NVDA', 0.03982),\n", - " ('PBI', 0.04325),\n", - " ('TGT', 0.04885),\n", - " ('TM', 0.07227),\n", - " ('UL', 0.09158),\n", - " ('WMT', 0.06963)])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.2))\n", - "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.04712),\n", + " ('AMD', 0.0),\n", + " ('BAC', 0.03863),\n", + " ('BLK', 0.06667),\n", + " ('CVS', 0.06667),\n", + " ('DIS', 0.06667),\n", + " ('INTU', 0.06667),\n", + " ('JD', 0.19043),\n", + " ('MA', 0.06667),\n", + " ('NVDA', 0.05715),\n", + " ('PBI', 0.06667),\n", + " ('TGT', 0.06667),\n", + " ('TM', 0.06667),\n", + " ('UL', 0.06667),\n", + " ('WMT', 0.06667)])" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pypfopt import EfficientFrontier, objective_functions\n", + "\n", + "ef = EfficientFrontier(mu, S)\n", + "\n", + "# 1% broker commission\n", + "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.01)\n", + "ef.min_volatility()\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iTTvy6xH_YO6" + }, + "source": [ + "Notice that many of the weights are 0.06667, i.e your original equal weight. In fact, the only change has been an allocation of AMD's weight to JD. If we lower the cost `k`, the allocation will change more:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "pTepg1-Q_YO6", + "outputId": "335af198-0d41-482c-a923-617240ee5ffd" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TGkjjU3z_YO-", - "outputId": "d8e09c26-503d-4ac6-e57b-f81af6456544" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 18.0%\n", - "Annual volatility: 10.7%\n", - "Sharpe Ratio: 1.68\n" - ] - } - ], - "source": [ - "ef.portfolio_performance(verbose=True);" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.0),\n", + " ('AMD', 0.0),\n", + " ('BAC', 0.0),\n", + " ('BLK', 0.06667),\n", + " ('CVS', 0.0538),\n", + " ('DIS', 0.0),\n", + " ('INTU', 0.00851),\n", + " ('JD', 0.298),\n", + " ('MA', 0.27222),\n", + " ('NVDA', 0.0),\n", + " ('PBI', 0.0),\n", + " ('TGT', 0.0),\n", + " ('TM', 0.06667),\n", + " ('UL', 0.14875),\n", + " ('WMT', 0.08539)])" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", + "ef.min_volatility()\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YM-gPp1S_YO7" + }, + "source": [ + "The optimizer seems to really like JD. The reason for this is that it is highly anticorrelated to other assets (notice the dark column in the covariance plot). Hence, historically, it adds a lot of diversification. But it is dangerous to place too much emphasis on what happened in the past, so we may want to limit the asset weights. \n", + "\n", + "In addition, we notice that 4 stocks have now been allocated zero weight, which may be undesirable. Both of these problems can be fixed by adding an [L2 regularisation objective](https://pyportfolioopt.readthedocs.io/en/latest/EfficientFrontier.html#more-on-l2-regularisation). " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Mh22zJrI_YO7", + "outputId": "f6b4707d-c5cb-4c81-d676-7f1cc9c412e5" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "mOAd1ncH_YO-" - }, - "source": [ - "This is compatible with all the constraints discussed in the previous recipe. Let's say that we want to limit JD's weight to 15%." + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.0623),\n", + " ('AMD', 0.05356),\n", + " ('BAC', 0.06263),\n", + " ('BLK', 0.07041),\n", + " ('CVS', 0.06715),\n", + " ('DIS', 0.06528),\n", + " ('INTU', 0.06667),\n", + " ('JD', 0.07589),\n", + " ('MA', 0.07336),\n", + " ('NVDA', 0.06317),\n", + " ('PBI', 0.0644),\n", + " ('TGT', 0.06588),\n", + " ('TM', 0.06934),\n", + " ('UL', 0.07131),\n", + " ('WMT', 0.06866)])" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", + "ef.add_objective(objective_functions.L2_reg)\n", + "ef.min_volatility()\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zQrFckqY_YO7" + }, + "source": [ + "This has had too much of an evening-out effect. After all, if the resulting allocation is going to be so close to equal weights, we may as well stick with our initial allocation. We can reduce the strength of the L2 regularisation by reducing `gamma`:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "VdeeHz8J_YO7", + "outputId": "735a86d3-078e-4a28-cb2e-19da01de4542" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "J3rM2vkz_YO-", - "outputId": "b00c2c60-47c5-419d-97d1-52957b32a56c" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.03989),\n", - " ('AMD', 0.02581),\n", - " ('BAC', 0.03888),\n", - " ('BLK', 0.08151),\n", - " ('CVS', 0.06007),\n", - " ('DIS', 0.04754),\n", - " ('INTU', 0.05276),\n", - " ('JD', 0.15),\n", - " ('MA', 0.12901),\n", - " ('NVDA', 0.04043),\n", - " ('PBI', 0.04397),\n", - " ('TGT', 0.04958),\n", - " ('TM', 0.07429),\n", - " ('UL', 0.09494),\n", - " ('WMT', 0.0713)])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.2))\n", - "jd_index = ef.tickers.index(\"JD\") # get the index of JD\n", - "ef.add_constraint(lambda w: w[jd_index] <= 0.15)\n", - "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.02309),\n", + " ('AMD', 0.0),\n", + " ('BAC', 0.01226),\n", + " ('BLK', 0.099),\n", + " ('CVS', 0.06667),\n", + " ('DIS', 0.04427),\n", + " ('INTU', 0.05892),\n", + " ('JD', 0.16678),\n", + " ('MA', 0.14089),\n", + " ('NVDA', 0.02294),\n", + " ('PBI', 0.03273),\n", + " ('TGT', 0.04921),\n", + " ('TM', 0.08801),\n", + " ('UL', 0.11292),\n", + " ('WMT', 0.08232)])" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S)\n", + "ef.add_objective(objective_functions.transaction_cost, w_prev=initial_weights, k=0.001)\n", + "ef.add_objective(objective_functions.L2_reg, gamma=0.05) # default is 1\n", + "ef.min_volatility()\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "EFN7ul8-_YO8", + "outputId": "575da3e2-c1ed-4821-dbec-8475d1f0c3b7" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "v-ObCWVv_YO-" - }, - "source": [ - "## Custom nonconvex objectives\n", - "\n", - "In some cases, you may be trying to optimize for nonconvex objectives. Optimization in general is a very hard problem, so please be aware that you may have mixed results in that case. Convex problems, on the other hand, are well understood and can be solved with nice theoretical guarantees.\n", - "\n", - "PyPortfolioOpt does offer some functionality for nonconvex optimization, but it is not really encouraged. In particular, nonconvex optimization is not compatible with PyPortfolioOpt's modular constraints API.\n", - "\n", - "As an example, we will use the Deviation Risk Parity objective from Kolm et al (2014). Because we are not using a convex solver, we don't have to define it using `cvxpy` functions." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 17.0%\n", + "Annual volatility: 10.1%\n", + "Sharpe Ratio: 1.69\n" + ] + } + ], + "source": [ + "ef.portfolio_performance(verbose=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ra5fBWi8_YO8" + }, + "source": [ + "This portfolio is now reasonably balanced, but also puts significantly more weight on JD. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 }, + "id": "3Klc-L_8_YO8", + "outputId": "9bc2e6e6-9411-4be4-a851-918a648b5306" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "WYIshUU__YO-" - }, - "outputs": [], - "source": [ - "def deviation_risk_parity(w, cov_matrix):\n", - " diff = w * np.dot(cov_matrix, w) - (w * np.dot(cov_matrix, w)).reshape(-1, 1)\n", - " return (diff ** 2).sum().sum()" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(weights).plot.pie(figsize=(10,10));" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A4s-3rWa_YO9" + }, + "source": [ + "## Custom convex objectives\n", + "\n", + "PyPortfolioOpt comes with the following built-in objective functions, as of v1.2.1:\n", + "\n", + "- Portfolio variance (i.e square of volatility)\n", + "- Portfolio return\n", + "- Sharpe ratio\n", + "- L2 regularisation (minimising this reduces nonzero weights)\n", + "- Quadratic utility\n", + "- Transaction cost model (a simple one)\n", + "\n", + "However, you may want have a different objective. If this new objective is **convex**, you can optimize a portfolio with the full benefit of PyPortfolioOpt's modular syntax, for example adding other constraints and objectives.\n", + "\n", + "To demonstrate this, we will minimise the **logarithmic-barrier** function suggested in the paper 60 Years of Portfolio Optimization, by Kolm et al (2014):\n", + "\n", + "$$f(w, S, k) = w^T S w - k \\sum_{i=1}^N \\ln w$$\n", + "\n", + "We must first convert this mathematical objective into the language of cvxpy. Cvxpy is a powerful modelling language for convex optimization problems. It is clean and easy to use, the only caveat is that objectives must be expressed with `cvxpy` functions, a list of which can be found [here](https://www.cvxpy.org/tutorial/functions/index.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "5WjglE6d_YO9" + }, + "outputs": [], + "source": [ + "import cvxpy as cp\n", + "\n", + "# Note: functions are minimised. If you want to maximise an objective, stick a minus sign in it.\n", + "def logarithmic_barrier_objective(w, cov_matrix, k=0.1):\n", + " log_sum = cp.sum(cp.log(w))\n", + " var = cp.quad_form(w, cov_matrix)\n", + " return var - k * log_sum" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UxmNQahn_YO9" + }, + "source": [ + "Once we have written the objective function, we can just use the `ef.convex_objective()` to minimise the objective." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "SMsaFWHB_YO9", + "outputId": "5a25c217-5d8b-4185-89de-a7379a5ef5fe" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_fgoI8gj_YO-", - "outputId": "a3f3c341-503e-4b7a-ef96-8bdf462c6d8a" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.04227),\n", - " ('AMD', 0.03306),\n", - " ('BAC', 0.04204),\n", - " ('BLK', 0.08771),\n", - " ('CVS', 0.0706),\n", - " ('DIS', 0.0552),\n", - " ('INTU', 0.06376),\n", - " ('JD', 0.10104),\n", - " ('MA', 0.09691),\n", - " ('NVDA', 0.04684),\n", - " ('PBI', 0.05062),\n", - " ('TGT', 0.0585),\n", - " ('TM', 0.08268),\n", - " ('UL', 0.09063),\n", - " ('WMT', 0.07813)])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.12))\n", - "ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.03946),\n", + " ('AMD', 0.02568),\n", + " ('BAC', 0.0386),\n", + " ('BLK', 0.07889),\n", + " ('CVS', 0.05889),\n", + " ('DIS', 0.04687),\n", + " ('INTU', 0.05174),\n", + " ('JD', 0.17291),\n", + " ('MA', 0.12155),\n", + " ('NVDA', 0.03982),\n", + " ('PBI', 0.04325),\n", + " ('TGT', 0.04885),\n", + " ('TM', 0.07227),\n", + " ('UL', 0.09158),\n", + " ('WMT', 0.06963)])" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.2))\n", + "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "TGkjjU3z_YO-", + "outputId": "d8e09c26-503d-4ac6-e57b-f81af6456544" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "1JJVTSOb_YO_" - }, - "source": [ - "However, let's say we now want to enforce that JD has a weight of 10%. In the convex case, this would be as simple as:\n", - "\n", - "```python\n", - "ef.add_objective(lambda w: w[jd_index] == 0.10)\n", - "```\n", - "\n", - "But unfortunately, scipy does not allow for such intuitive syntax. You will need to rearrange your constraints to make them either `=0` or `<= 0`. \n", - "\n", - "```python\n", - "constraints = [\n", - " # First constraint\n", - " {\"type\": \"eq\", # equality constraint,\n", - " \"fun\": lambda w: w[1] - 0.2}, # the equality functions are assumed to = 0 \n", - " \n", - " # Second constraint\n", - " {\"type\": \"ineq\", # inequality constraint\n", - " \"fun\": lambda w: w[0] - 0.5} # inequality functions <= 0\n", - "]\n", - "```\n", - "\n", - "For more information, you can consult the [scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html), but they aren't very helpful." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 18.0%\n", + "Annual volatility: 10.7%\n", + "Sharpe Ratio: 1.68\n" + ] + } + ], + "source": [ + "ef.portfolio_performance(verbose=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mOAd1ncH_YO-" + }, + "source": [ + "This is compatible with all the constraints discussed in the previous recipe. Let's say that we want to limit JD's weight to 15%." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "J3rM2vkz_YO-", + "outputId": "b00c2c60-47c5-419d-97d1-52957b32a56c" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "84sElIfw_YO_", - "outputId": "f4e9713d-2215-4093-9900-17a2035c5906" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.05695),\n", - " ('AMD', 0.0304),\n", - " ('BAC', 0.05892),\n", - " ('BLK', 0.07322),\n", - " ('CVS', 0.06945),\n", - " ('DIS', 0.0644),\n", - " ('INTU', 0.06689),\n", - " ('JD', 0.1),\n", - " ('MA', 0.07491),\n", - " ('NVDA', 0.05933),\n", - " ('PBI', 0.06263),\n", - " ('TGT', 0.06564),\n", - " ('TM', 0.07206),\n", - " ('UL', 0.07388),\n", - " ('WMT', 0.07132)])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.12))\n", - "\n", - "ef.nonconvex_objective(\n", - " deviation_risk_parity,\n", - " objective_args=S,\n", - " weights_sum_to_one=True,\n", - " constraints=[\n", - " {\"type\": \"eq\", \"fun\": lambda w: w[jd_index] - 0.10}, \n", - " ],\n", - ")\n", - "\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.03989),\n", + " ('AMD', 0.02581),\n", + " ('BAC', 0.03888),\n", + " ('BLK', 0.08151),\n", + " ('CVS', 0.06007),\n", + " ('DIS', 0.04754),\n", + " ('INTU', 0.05276),\n", + " ('JD', 0.15),\n", + " ('MA', 0.12901),\n", + " ('NVDA', 0.04043),\n", + " ('PBI', 0.04397),\n", + " ('TGT', 0.04958),\n", + " ('TM', 0.07429),\n", + " ('UL', 0.09494),\n", + " ('WMT', 0.0713)])" ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.2))\n", + "jd_index = ef.tickers.index(\"JD\") # get the index of JD\n", + "ef.add_constraint(lambda w: w[jd_index] <= 0.15)\n", + "ef.convex_objective(logarithmic_barrier_objective, cov_matrix=S, k=0.001)\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v-ObCWVv_YO-" + }, + "source": [ + "## Custom nonconvex objectives\n", + "\n", + "In some cases, you may be trying to optimize for nonconvex objectives. Optimization in general is a very hard problem, so please be aware that you may have mixed results in that case. Convex problems, on the other hand, are well understood and can be solved with nice theoretical guarantees.\n", + "\n", + "PyPortfolioOpt does offer some functionality for nonconvex optimization, but it is not really encouraged. In particular, nonconvex optimization is not compatible with PyPortfolioOpt's modular constraints API.\n", + "\n", + "As an example, we will use the Deviation Risk Parity objective from Kolm et al (2014). Because we are not using a convex solver, we don't have to define it using `cvxpy` functions." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "WYIshUU__YO-" + }, + "outputs": [], + "source": [ + "def deviation_risk_parity(w, cov_matrix):\n", + " diff = w * np.dot(cov_matrix, w) - (w * np.dot(cov_matrix, w)).reshape(-1, 1)\n", + " return (diff ** 2).sum().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_fgoI8gj_YO-", + "outputId": "a3f3c341-503e-4b7a-ef96-8bdf462c6d8a" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "0SoIPcj2_YO_" - }, - "source": [ - "## More examples of nonconvex objectives\n", - "\n", - "The scipy format is not intuitive and is hard to explain, so here are a bunch of examples (adapted from the tests). Some of these are actually convex, so you should use `convex_objective` instead. " + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.04227),\n", + " ('AMD', 0.03306),\n", + " ('BAC', 0.04204),\n", + " ('BLK', 0.08771),\n", + " ('CVS', 0.0706),\n", + " ('DIS', 0.0552),\n", + " ('INTU', 0.06376),\n", + " ('JD', 0.10104),\n", + " ('MA', 0.09691),\n", + " ('NVDA', 0.04684),\n", + " ('PBI', 0.05062),\n", + " ('TGT', 0.0585),\n", + " ('TM', 0.08268),\n", + " ('UL', 0.09063),\n", + " ('WMT', 0.07813)])" ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.12))\n", + "ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1JJVTSOb_YO_" + }, + "source": [ + "However, let's say we now want to enforce that JD has a weight of 10%. In the convex case, this would be as simple as:\n", + "\n", + "```python\n", + "ef.add_objective(lambda w: w[jd_index] == 0.10)\n", + "```\n", + "\n", + "But unfortunately, scipy does not allow for such intuitive syntax. You will need to rearrange your constraints to make them either `=0` or `<= 0`. \n", + "\n", + "```python\n", + "constraints = [\n", + " # First constraint\n", + " {\"type\": \"eq\", # equality constraint,\n", + " \"fun\": lambda w: w[1] - 0.2}, # the equality functions are assumed to = 0 \n", + " \n", + " # Second constraint\n", + " {\"type\": \"ineq\", # inequality constraint\n", + " \"fun\": lambda w: w[0] - 0.5} # inequality functions <= 0\n", + "]\n", + "```\n", + "\n", + "For more information, you can consult the [scipy docs](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html), but they aren't very helpful." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "84sElIfw_YO_", + "outputId": "f4e9713d-2215-4093-9900-17a2035c5906" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vfzCiKQT_YO_", - "outputId": "a6413d3f-22d7-4406-9904-c480be20bd53" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.04699),\n", - " ('AMD', 0.03341),\n", - " ('BAC', 0.04672),\n", - " ('BLK', 0.07872),\n", - " ('CVS', 0.06334),\n", - " ('DIS', 0.05386),\n", - " ('INTU', 0.05806),\n", - " ('JD', 0.13128),\n", - " ('MA', 0.10312),\n", - " ('NVDA', 0.04802),\n", - " ('PBI', 0.05095),\n", - " ('TGT', 0.05554),\n", - " ('TM', 0.07354),\n", - " ('UL', 0.08566),\n", - " ('WMT', 0.07079)])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Another example of deviation risk parity\n", - "def deviation_risk_parity(w, cov_matrix):\n", - " n = cov_matrix.shape[0]\n", - " rp = (w * (cov_matrix @ w)) / cp.quad_form(w, cov_matrix)\n", - " return cp.sum_squares(rp - 1 / n).value\n", - "\n", - "ef = EfficientFrontier(mu, S)\n", - "ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.05695),\n", + " ('AMD', 0.0304),\n", + " ('BAC', 0.05892),\n", + " ('BLK', 0.07322),\n", + " ('CVS', 0.06945),\n", + " ('DIS', 0.0644),\n", + " ('INTU', 0.06689),\n", + " ('JD', 0.1),\n", + " ('MA', 0.07491),\n", + " ('NVDA', 0.05933),\n", + " ('PBI', 0.06263),\n", + " ('TGT', 0.06564),\n", + " ('TM', 0.07206),\n", + " ('UL', 0.07388),\n", + " ('WMT', 0.07132)])" ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ef = EfficientFrontier(mu, S, weight_bounds=(0.01, 0.12))\n", + "\n", + "ef.nonconvex_objective(\n", + " deviation_risk_parity,\n", + " objective_args=S,\n", + " weights_sum_to_one=True,\n", + " constraints=[\n", + " {\"type\": \"eq\", \"fun\": lambda w: w[jd_index] - 0.10}, \n", + " ],\n", + ")\n", + "\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0SoIPcj2_YO_" + }, + "source": [ + "## More examples of nonconvex objectives\n", + "\n", + "The scipy format is not intuitive and is hard to explain, so here are a bunch of examples (adapted from the tests). Some of these are actually convex, so you should use `convex_objective` instead. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vfzCiKQT_YO_", + "outputId": "a6413d3f-22d7-4406-9904-c480be20bd53" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "P2jqcyK7_YO_", - "outputId": "59979140-7fd0-41cc-fb52-8b12586b2066" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.1),\n", - " ('AMD', 0.0339),\n", - " ('BAC', 0.04866),\n", - " ('BLK', 0.07516),\n", - " ('CVS', 0.06205),\n", - " ('DIS', 0.05424),\n", - " ('INTU', 0.05646),\n", - " ('JD', 0.11054),\n", - " ('MA', 0.09092),\n", - " ('NVDA', 0.049),\n", - " ('PBI', 0.05258),\n", - " ('TGT', 0.05526),\n", - " ('TM', 0.06836),\n", - " ('UL', 0.0771),\n", - " ('WMT', 0.06577)])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Deviation risk parity with weight bound on the first asset\n", - "ef = EfficientFrontier(mu, S)\n", - "ef.nonconvex_objective(deviation_risk_parity, \n", - " ef.cov_matrix, \n", - " constraints=[{\"type\":\"eq\", \"fun\":lambda w: w[0] - 0.1}])\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.04699),\n", + " ('AMD', 0.03341),\n", + " ('BAC', 0.04672),\n", + " ('BLK', 0.07872),\n", + " ('CVS', 0.06334),\n", + " ('DIS', 0.05386),\n", + " ('INTU', 0.05806),\n", + " ('JD', 0.13128),\n", + " ('MA', 0.10312),\n", + " ('NVDA', 0.04802),\n", + " ('PBI', 0.05095),\n", + " ('TGT', 0.05554),\n", + " ('TM', 0.07354),\n", + " ('UL', 0.08566),\n", + " ('WMT', 0.07079)])" ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Another example of deviation risk parity\n", + "def deviation_risk_parity(w, cov_matrix):\n", + " n = cov_matrix.shape[0]\n", + " rp = (w * (cov_matrix @ w)) / cp.quad_form(w, cov_matrix)\n", + " return cp.sum_squares(rp - 1 / n).value\n", + "\n", + "ef = EfficientFrontier(mu, S)\n", + "ef.nonconvex_objective(deviation_risk_parity, ef.cov_matrix)\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "P2jqcyK7_YO_", + "outputId": "59979140-7fd0-41cc-fb52-8b12586b2066" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "voar0fB2_YPA", - "outputId": "2721f2ad-7b9f-4a8a-967a-48921b88e853" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.09422),\n", - " ('AMD', 0.1627),\n", - " ('BAC', 0.04346),\n", - " ('BLK', -0.04221),\n", - " ('CVS', -0.1778),\n", - " ('DIS', 0.20785),\n", - " ('INTU', 0.1059),\n", - " ('JD', 0.31068),\n", - " ('MA', 0.37613),\n", - " ('NVDA', 0.41065),\n", - " ('PBI', -0.02656),\n", - " ('TGT', 0.01202),\n", - " ('TM', -0.47356),\n", - " ('UL', -0.83251),\n", - " ('WMT', -0.17096)])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Market-neutral efficient risk.\n", - "# Please use ef.efficient_risk() for anything serious.\n", - "target_risk = 0.19\n", - "ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n", - "\n", - "# Weights sum to zero\n", - "weight_constr = {\"type\": \"eq\", \"fun\": lambda w: np.sum(w)}\n", - "\n", - "# Portfolio vol less than target vol\n", - "risk_constr = {\n", - " \"type\": \"eq\",\n", - " \"fun\": lambda w: target_risk ** 2 - np.dot(w.T, np.dot(ef.cov_matrix, w)),\n", - "}\n", - "constraints = [weight_constr, risk_constr]\n", - "\n", - "ef.nonconvex_objective(\n", - " lambda w, mu: -w.T.dot(mu), # min negative return i.e max return\n", - " objective_args=(ef.expected_returns),\n", - " weights_sum_to_one=False,\n", - " constraints=constraints,\n", - ")\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.1),\n", + " ('AMD', 0.0339),\n", + " ('BAC', 0.04866),\n", + " ('BLK', 0.07516),\n", + " ('CVS', 0.06205),\n", + " ('DIS', 0.05424),\n", + " ('INTU', 0.05646),\n", + " ('JD', 0.11054),\n", + " ('MA', 0.09092),\n", + " ('NVDA', 0.049),\n", + " ('PBI', 0.05258),\n", + " ('TGT', 0.05526),\n", + " ('TM', 0.06836),\n", + " ('UL', 0.0771),\n", + " ('WMT', 0.06577)])" ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Deviation risk parity with weight bound on the first asset\n", + "ef = EfficientFrontier(mu, S)\n", + "ef.nonconvex_objective(deviation_risk_parity, \n", + " ef.cov_matrix, \n", + " constraints=[{\"type\":\"eq\", \"fun\":lambda w: w[0] - 0.1}])\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "voar0fB2_YPA", + "outputId": "2721f2ad-7b9f-4a8a-967a-48921b88e853" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2kgcF-V9_YPH", - "outputId": "862dc21d-2005-438b-eb0d-3ebe2d7e0222" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.0),\n", - " ('AMD', 0.05561),\n", - " ('BAC', 0.0),\n", - " ('BLK', 0.0),\n", - " ('CVS', 0.0),\n", - " ('DIS', 0.0),\n", - " ('INTU', 0.0),\n", - " ('JD', 0.0),\n", - " ('MA', 0.0),\n", - " ('NVDA', 0.94439),\n", - " ('PBI', 0.0),\n", - " ('TGT', 0.0),\n", - " ('TM', 0.0),\n", - " ('UL', 0.0),\n", - " ('WMT', 0.0)])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Utility objective - you could actually use ef.max_quadratic_utility\n", - "ef = EfficientFrontier(mu, S)\n", - "\n", - "def utility_obj(weights, mu, cov_matrix, k=1):\n", - " return -weights.dot(mu) + k * np.dot(weights.T, np.dot(cov_matrix, weights))\n", - "\n", - "ef.nonconvex_objective(\n", - " utility_obj,\n", - " objective_args=(ef.expected_returns, ef.cov_matrix, 1)\n", - " # default is for weights to sum to 1\n", - ")\n", - "\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.09422),\n", + " ('AMD', 0.1627),\n", + " ('BAC', 0.04346),\n", + " ('BLK', -0.04221),\n", + " ('CVS', -0.1778),\n", + " ('DIS', 0.20785),\n", + " ('INTU', 0.1059),\n", + " ('JD', 0.31068),\n", + " ('MA', 0.37613),\n", + " ('NVDA', 0.41065),\n", + " ('PBI', -0.02656),\n", + " ('TGT', 0.01202),\n", + " ('TM', -0.47356),\n", + " ('UL', -0.83251),\n", + " ('WMT', -0.17096)])" ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Market-neutral efficient risk.\n", + "# Please use ef.efficient_risk() for anything serious.\n", + "target_risk = 0.19\n", + "ef = EfficientFrontier(mu, S, weight_bounds=(None, None))\n", + "\n", + "# Weights sum to zero\n", + "weight_constr = {\"type\": \"eq\", \"fun\": lambda w: np.sum(w)}\n", + "\n", + "# Portfolio vol less than target vol\n", + "risk_constr = {\n", + " \"type\": \"eq\",\n", + " \"fun\": lambda w: target_risk ** 2 - np.dot(w.T, np.dot(ef.cov_matrix, w)),\n", + "}\n", + "constraints = [weight_constr, risk_constr]\n", + "\n", + "ef.nonconvex_objective(\n", + " lambda w, mu: -w.T.dot(mu), # min negative return i.e max return\n", + " objective_args=(ef.expected_returns),\n", + " weights_sum_to_one=False,\n", + " constraints=constraints,\n", + ")\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2kgcF-V9_YPH", + "outputId": "862dc21d-2005-438b-eb0d-3ebe2d7e0222" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LnNkM5DJ_YPH", - "outputId": "874299e2-37a8-4783-9f8e-7608ff293837" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(1.0)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ef.weights.sum()" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.0),\n", + " ('AMD', 0.05561),\n", + " ('BAC', 0.0),\n", + " ('BLK', 0.0),\n", + " ('CVS', 0.0),\n", + " ('DIS', 0.0),\n", + " ('INTU', 0.0),\n", + " ('JD', 0.0),\n", + " ('MA', 0.0),\n", + " ('NVDA', 0.94439),\n", + " ('PBI', 0.0),\n", + " ('TGT', 0.0),\n", + " ('TM', 0.0),\n", + " ('UL', 0.0),\n", + " ('WMT', 0.0)])" ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Utility objective - you could actually use ef.max_quadratic_utility\n", + "ef = EfficientFrontier(mu, S)\n", + "\n", + "def utility_obj(weights, mu, cov_matrix, k=1):\n", + " return -weights.dot(mu) + k * np.dot(weights.T, np.dot(cov_matrix, weights))\n", + "\n", + "ef.nonconvex_objective(\n", + " utility_obj,\n", + " objective_args=(ef.expected_returns, ef.cov_matrix, 1)\n", + " # default is for weights to sum to 1\n", + ")\n", + "\n", + "weights = ef.clean_weights()\n", + "weights" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "LnNkM5DJ_YPH", + "outputId": "874299e2-37a8-4783-9f8e-7608ff293837" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FSFhcUz7_YPH", - "outputId": "0a086117-7335-4558-aaee-c7c70c295f90" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.06667),\n", - " ('AMD', 0.06667),\n", - " ('BAC', 0.06667),\n", - " ('BLK', 0.06667),\n", - " ('CVS', 0.06667),\n", - " ('DIS', 0.06667),\n", - " ('INTU', 0.06667),\n", - " ('JD', 0.06667),\n", - " ('MA', 0.06667),\n", - " ('NVDA', 0.06667),\n", - " ('PBI', 0.06667),\n", - " ('TGT', 0.06667),\n", - " ('TM', 0.06667),\n", - " ('UL', 0.06667),\n", - " ('WMT', 0.06667)])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Kelly objective with weight bounds on zeroth asset\n", - "def kelly_objective(w, e_returns, cov_matrix, k=3):\n", - " variance = np.dot(w.T, np.dot(cov_matrix, w))\n", - " objective = variance * 0.5 * k - np.dot(w, e_returns)\n", - " return objective\n", - "\n", - "lower_bounds, upper_bounds = 0.01, 0.3\n", - "ef = EfficientFrontier(mu, S)\n", - "ef.nonconvex_objective(\n", - " kelly_objective,\n", - " objective_args=(ef.expected_returns, ef.cov_matrix, 1000),\n", - " constraints=[\n", - " {\"type\": \"eq\", \"fun\": lambda w: np.sum(w) - 1},\n", - " {\"type\": \"ineq\", \"fun\": lambda w: w[0] - lower_bounds},\n", - " {\"type\": \"ineq\", \"fun\": lambda w: upper_bounds - w[0]},\n", - " ],\n", - ")\n", - "\n", - "weights = ef.clean_weights()\n", - "weights" + "data": { + "text/plain": [ + "np.float64(1.0)" ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { + ], + "source": [ + "ef.weights.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { "colab": { - "collapsed_sections": [], - "name": "3-Advanced-Mean-Variance-Optimisation.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" + "base_uri": "https://localhost:8080/" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" + "id": "FSFhcUz7_YPH", + "outputId": "0a086117-7335-4558-aaee-c7c70c295f90" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.06667),\n", + " ('AMD', 0.06667),\n", + " ('BAC', 0.06667),\n", + " ('BLK', 0.06667),\n", + " ('CVS', 0.06667),\n", + " ('DIS', 0.06667),\n", + " ('INTU', 0.06667),\n", + " ('JD', 0.06667),\n", + " ('MA', 0.06667),\n", + " ('NVDA', 0.06667),\n", + " ('PBI', 0.06667),\n", + " ('TGT', 0.06667),\n", + " ('TM', 0.06667),\n", + " ('UL', 0.06667),\n", + " ('WMT', 0.06667)])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" } + ], + "source": [ + "# Kelly objective with weight bounds on zeroth asset\n", + "def kelly_objective(w, e_returns, cov_matrix, k=3):\n", + " variance = np.dot(w.T, np.dot(cov_matrix, w))\n", + " objective = variance * 0.5 * k - np.dot(w, e_returns)\n", + " return objective\n", + "\n", + "lower_bounds, upper_bounds = 0.01, 0.3\n", + "ef = EfficientFrontier(mu, S)\n", + "ef.nonconvex_objective(\n", + " kelly_objective,\n", + " objective_args=(ef.expected_returns, ef.cov_matrix, 1000),\n", + " constraints=[\n", + " {\"type\": \"eq\", \"fun\": lambda w: np.sum(w) - 1},\n", + " {\"type\": \"ineq\", \"fun\": lambda w: w[0] - lower_bounds},\n", + " {\"type\": \"ineq\", \"fun\": lambda w: upper_bounds - w[0]},\n", + " ],\n", + ")\n", + "\n", + "weights = ef.clean_weights()\n", + "weights" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "3-Advanced-Mean-Variance-Optimisation.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "name": "python3", + "language": "python" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/cookbook/4-Black-Litterman-Allocation.ipynb b/cookbook/4-Black-Litterman-Allocation.ipynb index 125935d3..46c1a0b8 100644 --- a/cookbook/4-Black-Litterman-Allocation.ipynb +++ b/cookbook/4-Black-Litterman-Allocation.ipynb @@ -1,1184 +1,1353 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "WzHr8McwAbNd" - }, - "source": [ - "# Black-Litterman allocation\n", - "\n", - "The Black-Litterman method is a very powerful way of converting your views on asset returns, along with your uncertainty in these views, into a portfolio.\n", - "\n", - "For a description of the theory, please read the [documentation page](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) and the links therein.\n", - "\n", - "In this recipe, we will cover:\n", - "\n", - "- Downloading data for the Black-Litterman method\n", - "- Constructing the prior return vector based on market equilibrium\n", - "- Two ways of constructing the uncertainty matrix\n", - "- Combining Black-Litterman with mean-variance optimization\n", - "\n", - "## Downloading data\n", - "\n", - "In addition to price data, constructing a market prior requires market-caps.\n", - "\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n", - " \n", - "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/4-Black-Litterman-Allocation.ipynb)\n", - " \n", - "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n", - " \n", - "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "sXdbiHc0AnfS", - "outputId": "5ced30c2-5248-4827-a15d-955380574b51" - }, - "outputs": [], - "source": [ - "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", - "import os\n", - "if not os.path.isdir('data'):\n", - " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", - " os.chdir('PyPortfolioOpt/cookbook')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "bs5cBlLfAbNf" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import yfinance as yf" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "_2kfAeqOAbNg" - }, - "outputs": [], - "source": [ - "tickers = [\"MSFT\", \"AMZN\", \"NAT\", \"BAC\", \"DPZ\", \"DIS\", \"KO\", \"MCD\", \"COST\", \"SBUX\"]" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "WzHr8McwAbNd" + }, + "source": [ + "# Black-Litterman allocation\n", + "\n", + "The Black-Litterman method is a very powerful way of converting your views on asset returns, along with your uncertainty in these views, into a portfolio.\n", + "\n", + "For a description of the theory, please read the [documentation page](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) and the links therein.\n", + "\n", + "In this recipe, we will cover:\n", + "\n", + "- Downloading data for the Black-Litterman method\n", + "- Constructing the prior return vector based on market equilibrium\n", + "- Two ways of constructing the uncertainty matrix\n", + "- Combining Black-Litterman with mean-variance optimization\n", + "\n", + "## Downloading data\n", + "\n", + "In addition to price data, constructing a market prior requires market-caps.\n", + "\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n", + " \n", + "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/4-Black-Litterman-Allocation.ipynb)\n", + " \n", + "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)\n", + " \n", + "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/4-Black-Litterman-Allocation.ipynb)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "sXdbiHc0AnfS", + "outputId": "5ced30c2-5248-4827-a15d-955380574b51", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:48.286018Z", + "start_time": "2025-11-12T08:11:47.649567Z" + } + }, + "source": [ + "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", + "import os\n", + "if not os.path.isdir('data'):\n", + " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", + " os.chdir('PyPortfolioOpt/cookbook')" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 255 - }, - "id": "ghoX_hFkAbNg", - "outputId": "6104b7ab-5a41-47c3-9d2b-5787517a4b99" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 10 of 10 completed\n" - ] - }, - { - "data": { - "text/html": [ - "
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TickerAMZNBACCOSTDISDPZKOMCDMSFTNATSBUX
Date
2024-11-22197.11999547.000000964.010010115.650002453.35000663.438839290.279999417.0000002.97102.500000
2024-11-25201.44999747.500000960.890015116.000000469.17999363.895374296.190002418.7900092.95101.839996
2024-11-26207.86000147.750000971.500000115.449997472.91000464.064102296.329987427.9899902.88100.680000
2024-11-27205.74000547.770000961.549988117.599998472.33999663.945000295.079987422.9899902.88101.510002
2024-11-29207.88999947.509998971.880005117.470001476.19000264.080002296.010010423.4599912.68102.459999
\n", - "
" - ], - "text/plain": [ - "Ticker AMZN BAC COST DIS DPZ \\\n", - "Date \n", - "2024-11-22 197.119995 47.000000 964.010010 115.650002 453.350006 \n", - "2024-11-25 201.449997 47.500000 960.890015 116.000000 469.179993 \n", - "2024-11-26 207.860001 47.750000 971.500000 115.449997 472.910004 \n", - "2024-11-27 205.740005 47.770000 961.549988 117.599998 472.339996 \n", - "2024-11-29 207.889999 47.509998 971.880005 117.470001 476.190002 \n", - "\n", - "Ticker KO MCD MSFT NAT SBUX \n", - "Date \n", - "2024-11-22 63.438839 290.279999 417.000000 2.97 102.500000 \n", - "2024-11-25 63.895374 296.190002 418.790009 2.95 101.839996 \n", - "2024-11-26 64.064102 296.329987 427.989990 2.88 100.680000 \n", - "2024-11-27 63.945000 295.079987 422.989990 2.88 101.510002 \n", - "2024-11-29 64.080002 296.010010 423.459991 2.68 102.459999 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ohlc = yf.download(tickers, period=\"max\")\n", - "prices = ohlc[\"Adj Close\"]\n", - "prices.tail()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (2.3.3)\r\n", + "Requirement already satisfied: numpy in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (2.3.4)\r\n", + "Requirement already satisfied: matplotlib in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (3.10.7)\r\n", + "Requirement already satisfied: yfinance in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (0.2.66)\r\n", + "Requirement already satisfied: PyPortfolioOpt in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (1.5.6)\r\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (from pandas) 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\u001B[0m\u001B[32;49m25.3\u001B[0m\r\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "code", + "metadata": { + "id": "bs5cBlLfAbNf", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:48.297230Z", + "start_time": "2025-11-12T08:11:48.294399Z" + } + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import yfinance as yf" + ], + "outputs": [], + "execution_count": 7 + }, + { + "cell_type": "code", + "metadata": { + "id": "_2kfAeqOAbNg", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:48.315503Z", + "start_time": "2025-11-12T08:11:48.312797Z" + } + }, + "source": [ + "tickers = [\"MSFT\", \"AMZN\", \"NAT\", \"BAC\", \"DPZ\", \"DIS\", \"KO\", \"MCD\", \"COST\", \"SBUX\"]" + ], + "outputs": [], + "execution_count": 8 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:11:49.603251Z", + "start_time": "2025-11-12T08:11:48.335629Z" + } + }, + "cell_type": "code", + "source": [ + "ohlc = yf.download(tickers, period=\"max\")\n", + "prices = ohlc[\"Close\"]\n", + "prices.tail()" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "WmtKlXfLAbNi", - "outputId": "ea14863a-b8a0-4c19-9c85-bea9fbdf29bc" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 1 of 1 completed\n" - ] - }, - { - "data": { - "text/html": [ - "
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TickerSPY
Date
1993-01-2924.608627
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" - ], - "text/plain": [ - "Ticker SPY\n", - "Date \n", - "1993-01-29 24.608627\n", - "1993-02-01 24.783642\n", - "1993-02-02 24.836149\n", - "1993-02-03 25.098694\n", - "1993-02-04 25.203718" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "market_prices = yf.download(\"SPY\", period=\"max\")[\"Adj Close\"]\n", - "market_prices.head()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_3/k_9k5d5n5zz57w7qfll9rzs40000gn/T/ipykernel_60334/2708075403.py:1: FutureWarning: YF.download() has changed argument auto_adjust default to True\n", + " ohlc = yf.download(tickers, period=\"max\")\n", + "[*********************100%***********************] 10 of 10 completed\n" + ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rZwXHHxxAbNi", - "outputId": "dee319be-67e2-44e7-e576-96c2b934d139" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'MSFT': 3148374343680,\n", - " 'AMZN': 2185963372544,\n", - " 'NAT': 601333056,\n", - " 'BAC': 366533443584,\n", - " 'DPZ': 16443793408,\n", - " 'DIS': 212966064128,\n", - " 'KO': 277551644672,\n", - " 'MCD': 212126695424,\n", - " 'COST': 430614740992,\n", - " 'SBUX': 116169138176}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "Ticker AMZN BAC COST DIS DPZ \\\n", + "Date \n", + "2025-11-05 250.199997 52.450001 935.030029 111.360001 400.410004 \n", + "2025-11-06 243.039993 53.290001 923.580017 110.489998 401.459991 \n", + "2025-11-07 244.410004 53.200001 922.739990 110.739998 410.179993 \n", + "2025-11-10 248.399994 53.419998 915.559998 112.239998 407.589996 \n", + "2025-11-11 249.100006 53.630001 913.859985 114.849998 409.230011 \n", + "\n", + "Ticker KO MCD MSFT NAT SBUX \n", + "Date \n", + "2025-11-05 68.510002 305.670013 507.160004 3.57 82.879997 \n", + "2025-11-06 69.059998 298.410004 497.100006 3.59 82.220001 \n", + "2025-11-07 70.550003 299.660004 496.820007 3.56 85.570000 \n", + "2025-11-10 70.519997 299.100006 506.000000 3.65 84.599998 \n", + "2025-11-11 71.610001 306.829987 508.679993 3.63 86.419998 " ], - "source": [ - "mcaps = {}\n", - "for t in tickers:\n", - " stock = yf.Ticker(t)\n", - " mcaps[t] = stock.info[\"marketCap\"]\n", - "mcaps" + "text/html": [ + "
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TickerAMZNBACCOSTDISDPZKOMCDMSFTNATSBUX
Date
2025-11-05250.19999752.450001935.030029111.360001400.41000468.510002305.670013507.1600043.5782.879997
2025-11-06243.03999353.290001923.580017110.489998401.45999169.059998298.410004497.1000063.5982.220001
2025-11-07244.41000453.200001922.739990110.739998410.17999370.550003299.660004496.8200073.5685.570000
2025-11-10248.39999453.419998915.559998112.239998407.58999670.519997299.100006506.0000003.6584.599998
2025-11-11249.10000653.630001913.859985114.849998409.23001171.610001306.829987508.6799933.6386.419998
\n", + "
" ] - }, + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:11:50.666914Z", + "start_time": "2025-11-12T08:11:49.678109Z" + } + }, + "cell_type": "code", + "source": [ + "market_prices = yf.download(\"SPY\", period=\"max\")[\"Close\"]\n", + "market_prices.head()" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "w6cnqTB8AbNi" - }, - "source": [ - "## Constructing the prior" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_3/k_9k5d5n5zz57w7qfll9rzs40000gn/T/ipykernel_60334/3562423400.py:1: FutureWarning: YF.download() has changed argument auto_adjust default to True\n", + " market_prices = yf.download(\"SPY\", period=\"max\")[\"Close\"]\n", + "[*********************100%***********************] 1 of 1 completed\n" + ] }, { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "KMyL_d0mAbNj", - "outputId": "83c8ff0b-6ac3-4c80-9bea-8f6216384238" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.5.6'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "Ticker SPY\n", + "Date \n", + "1993-01-29 24.313040\n", + "1993-02-01 24.485947\n", + "1993-02-02 24.537836\n", + "1993-02-03 24.797226\n", + "1993-02-04 24.900978" ], - "source": [ - "import pypfopt\n", - "pypfopt.__version__" + "text/html": [ + "
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TickerSPY
Date
1993-01-2924.313040
1993-02-0124.485947
1993-02-0224.537836
1993-02-0324.797226
1993-02-0424.900978
\n", + "
" ] - }, + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:11:59.042866Z", + "start_time": "2025-11-12T08:11:50.786533Z" + } + }, + "cell_type": "code", + "source": [ + "mcaps = {}\n", + "for t in tickers:\n", + " stock = yf.Ticker(t)\n", + " mcaps[t] = stock.info[\"marketCap\"]\n", + "mcaps" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "V35ccSUSAbNj", - "outputId": "bdc6d9bc-33c2-47b6-ac60-c43a4cfed6b5" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "np.float64(3.401732529775816)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pypfopt import black_litterman, risk_models\n", - "from pypfopt import BlackLittermanModel, plotting\n", - "\n", - "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", - "delta = black_litterman.market_implied_risk_aversion(market_prices)\n", - "delta" + "data": { + "text/plain": [ + "{'MSFT': 3780701847552,\n", + " 'AMZN': 2662932938752,\n", + " 'NAT': 768654912,\n", + " 'BAC': 391632846848,\n", + " 'DPZ': 13826333696,\n", + " 'DIS': 206492696576,\n", + " 'KO': 308038205440,\n", + " 'MCD': 218510311424,\n", + " 'COST': 405003698176,\n", + " 'SBUX': 98250891264}" ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w6cnqTB8AbNi" + }, + "source": [ + "## Constructing the prior" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 }, + "id": "KMyL_d0mAbNj", + "outputId": "83c8ff0b-6ac3-4c80-9bea-8f6216384238", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:59.682503Z", + "start_time": "2025-11-12T08:11:59.065472Z" + } + }, + "source": [ + "import pypfopt\n", + "pypfopt.__version__" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "tgz3bOltAbNj", - "outputId": "41740df8-87ca-45c7-b0ef-4af343ae3b0a" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plotting.plot_covariance(S, plot_correlation=True);" + "data": { + "text/plain": [ + "'1.5.6'" ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 12 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "V35ccSUSAbNj", + "outputId": "bdc6d9bc-33c2-47b6-ac60-c43a4cfed6b5", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:59.856517Z", + "start_time": "2025-11-12T08:11:59.688275Z" + } + }, + "source": [ + "from pypfopt import black_litterman, risk_models\n", + "from pypfopt import BlackLittermanModel, plotting\n", + "\n", + "S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n", + "delta = black_litterman.market_implied_risk_aversion(market_prices)\n", + "delta" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qknxazUhAbNk", - "outputId": "eb6b5bdf-7a31-4c76-90e0-ca2a751126ee" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Ticker\n", - "AMZN 0.209014\n", - "BAC 0.104631\n", - "COST 0.078294\n", - "DIS 0.092434\n", - "DPZ 0.038470\n", - "KO 0.053045\n", - "MCD 0.058147\n", - "MSFT 0.156181\n", - "NAT 0.041635\n", - "SBUX 0.078083\n", - "dtype: float64" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "market_prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)\n", - "market_prior" + "data": { + "text/plain": [ + "np.float64(3.426860188963759)" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 13 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "tgz3bOltAbNj", + "outputId": "41740df8-87ca-45c7-b0ef-4af343ae3b0a", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:59.931229Z", + "start_time": "2025-11-12T08:11:59.862786Z" + } + }, + "source": [ + "plotting.plot_covariance(S, plot_correlation=True);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 320 - }, - "id": "9exlhi1BAbNk", - "outputId": "a1292c4b-38b9-4c02-919d-b9cf33a4309e" - }, - "outputs": [ - { - "data": { - "image/png": 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vVe1DmCQdPHhQw4cPV/369Z199erV04MPPqgDBw44+5o3b64tW7aoT58+euqppxQREaF+/fopPT1ds2fPdtnn5MmTFRISojZt2mjEiBE6efKk0tLSNGnSJMvOCwAAAID3qRHPCfvmm2/UqlUrDRo0SIsWLZIkDRo0SAcPHlRmZqZz3Jw5c2SM0W233aazZ886+3fu3KnExESXfdrtduXm5kqSDh06pM8//1yHDx/W888/r2XLlmnPnj0WnBkAAAAAb1MjVsIkKTExUTExMc7XsbGxLh9DbNCggfr166fZs2e7BLDzTp06ddFjvPrqq/Lx8dG9995b7hh/f3/ZbDaXBgAAAAAVVWNCWHJysrp166ZmzZqpWbNm6tq1q5KTk53br7/+etWpU0cZGRmXfIwTJ07o559/VosWLcodEx8fr7y8PGfLysq65OMBAAAA8D414uOIknT06FGtXr1a0dHR8vHx0erVq3Xs2DHndh8fnyo5jo+Pj4wx5W5PSEjQv//9b+drm81GEAMAAABQYTUmhEm/fCRx1qxZkqTRo0e7bNu7d69KSkoUFhZ2yftv2LChfve737l8z+y3HA6HHA7HJR8DAAAAgHerMR9HlKSUlBT5+/vLz89PqampLttOnDih1NRUjR49WldeeWWp9wYFBV10/2PGjFFJSYlWrlxZVSUDAAAAgIsatRJWUlKi8PBw58+/NXr0aP3vf//TV199pWeffVbbtm2Tr6+v7rjjDo0aNUpt27Z1jrXZbAoODpafn59atmyphx9+WCNHjlR8fLz27dtn2TkBAAAA8C41KoRJv9xavjyZmZnq2LGjnn76ac2cOVNNmjTRkSNHtGXLFo0aNcpl7D/+8Q/94x//UGFhoXJycrRx40b17dtX69evd/MZAAAAAPBmPpLKvwsFLspmsykvL0+zdm6Wo6TY0+UANcLYiEhPlwAAAFDlzmeDwMDACy4e1ajvhAEAAABATUcIAwAAAAALEcIAAAAAwEI17sYc1dWkLn0v+LlPAAAAAJBYCQMAAAAASxHCAAAAAMBChDAAAAAAsBAhDAAAAAAsRAgDAAAAAAsRwgAAAADAQoQwAAAAALAQIQwAAAAALEQIAwAAAAALEcIAAAAAwEKEMAAAAACwECEMAAAAACxECAMAAAAACxHCAAAAAMBChDAAAAAAsBAhDAAAAAAsRAgDAAAAAAsRwgAAAADAQr6eLqC2mLbxUzlKij1dBuBWYyMiPV0CAABAjcdKGAAAAABYiBAGAAAAABYihAEAAACAhQhhAAAAAGAhQhgAAAAAWKhWhrCkpCStWLHCpW/w4MHKz8/Xk08+qfr16+u5557T7t27VVBQoCNHjujdd99V27ZtPVQxAAAAAG9RK0PYb8XFxemdd97RqFGjNGvWLKWlpSk2NlbPPPOMWrdurT/+8Y/y9fXVpk2b1LlzZ0+XCwAAAKAWq/XPCXvqqac0depUDR8+XCtXrtT48eMVGRmpm2++Wdu2bZMkHTx4UIMHD9amTZs0f/58tWvXzsNVAwAAAKitKrUSVrduXU2ePFmhoaHuqqdKTZ8+XZMnT9aAAQO0cuVKSdKDDz6oTz75xBnAzjPG6OWXX9aNN96om266qdx9+vv7y2azuTQAAAAAqKhKhbDi4mI99dRT8vWt/gto/fv314QJE3Tvvfdq3bp1zv7WrVtr165dZb7nfH/r1q3L3W98fLzy8vKcLSsrq2oLBwAAAFCrVfo7YevWrVPPnj3dUUuV2rZtmzIzMzV16lQFBAS4bPPx8bnk/SYkJCgwMNDZasqqIAAAAIDqodJLWmvWrNH06dMVERGhLVu26MyZMy7bV61aVWXFXY6srCwNGTJE6enpSklJUf/+/XX69Gnt2bNH4eHhZb7nfP+ePXvK3a/D4ZDD4XBLzQAAAABqv0qHsDlz5kiSnnzyyVLbjDHV6qOKBw8eVM+ePZ1BrF+/flqyZIleeOEFtW/f3uV7YT4+PnriiSe0Y8cOfffddx6sGgAAAEBtVumPI9atW7fcVp0C2Hk//fSTevXqpWuuuUapqamaPXu2vvrqK61atUpDhgzRtddeq1tvvVXLly9XeHi44uLiPF0yAAAAgFrssp4TVq9evaqqw62ysrLUq1cvNW7cWKmpqbrzzjv19ttva9q0afrhhx+UkpKi4uJidenSRZs2bfJ0uQAAAABqMR9JpjJvqFOnjiZNmqRHH31UwcHBat26tTIzM/X8889r//79SkxMdFOp1ZPNZlNeXp5m7dwsR0mxp8sB3GpsRKSnSwAAAKi2zmeDwMBA2e32csdVeiXs6aefVnR0tMaPH+9yg4rvv/9eI0eOvLRqAQAAAMBLVDqEjRgxQn/961+1aNEiFRf/38rPd999p7CwsCotDgAAAABqm0rfSSM0NFQ//PBDqf46derIz8+vSoqqiSZ16XvBJUcAAAAAkC5hJWznzp3q3r17qf4hQ4bo22+/rZKiAAAAAKC2qvRK2PPPP6+FCxcqNDRUderU0aBBg9SmTRuNGDFCAwYMcEeNAAAAAFBrVHol7MMPP9TAgQP1hz/8QWfOnNHzzz+v8PBwDRw4UGlpae6oEQAAAABqjUv6TtgXX3yhO++8s9S2zp0785wtAAAAALiASq+ErV27Vg0aNCjVf/vttyslJaVKigIAAACA2qrSIWzjxo1au3atrrrqKmdf9+7d9fHHH2vq1KlVWhwAAAAA1DaVDmEjR47UwYMHtWrVKvn7+6tXr15avXq1nn32Wb3yyituKBEAAAAAao9KhzBjjIYPH66ioiKtW7dOH374oeLj4/Xaa6+5oz4AAAAAqFV8JJmLDYqIiCjVZ7PZtHjxYq1evVpz58519m/fvr1KC6zubDab8vLyFBgYyMOaAQAAAC9W0WxQoRBWXFwsY4x8fHycfb9+ff5nY4x8fSt9w8UajRAGAAAAQKp4NqhQYmrZsmWVFQYAAAAA3qxCIezgwYPurgMAAAAAvEKlb8wxceJExcTElOqPiYnR+PHjq6QoAAAAAKitKh3CHnnkEWVkZJTq37Fjhx599NEqKQoAAAAAaqtKh7CQkBAdPny4VP+RI0fUpEmTKikKAAAAAGqrSoewQ4cOqWvXrqX6u3btquzs7CopCgAAAABqq0rfT37evHl65ZVX5Ofnp3Xr1kmS+vbtqxdffFEzZ86s8gIBAAAAoDapdAh76aWX1KhRI82ZM0f+/v6SpIKCAs2YMUPTp0+v8gIBAAAAoDap0MOayxIQEKDw8HDl5+dr7969cjgcVVxazXD+gWyzdm6Wo6TY0+WgGhobEenpEgAAAGCBKn1Yc1nOnDmjr7/++lLfDgAAAABeqUIhbPny5YqOjpbdbtfy5csvOHbw4MFVUhgAAAAA1EYVCmGnTp2SMcb5MwAAAADg0lQohMXGxmry5Mn617/+pdjYWHfXBAAAAAC1VoWfEzZlyhRdddVV7qwFAAAAAGq9CocwHx8fd9ZRYUlJSTLGyBgjh8OhnJwcrV27VjExMS41ZmZmOsedPn1aW7Zs0ZAhQyRJzZs3d24rq/3444+eOj0AAAAAtVyFQ5gk5/fCPG3NmjUKCQlRixYt1L9/f6Wnp+vVV1/VRx99pLp16zrHTZ48WSEhIbr55pu1efNmLV26VJGRkTp06JBCQkJKtQEDBujcuXOaPXu2B88OAAAAQG1WqVvU79mz56JBrFGjRpdVUEUUFhYqNzdXkpSdna1vv/1WGzdu1Lp16xQdHa358+dLkux2u3Jzc5Wbm6vRo0fr4Ycf1sCBA7Vhwwbn+8+75pprNHfuXC1evFgzZ850+zkAAAAA8E6VCmFTpkyptndHTE9P19atWzVo0CBnCPu14uJiFRUVyd/fv9Q2X19fLV++XDk5OfrLX/5yweP4+/urXr16ztc2m+3yiwcAAADgNSoVwpYsWaIjR464q5bLlpGRofbt25fq9/Pz09ixY3X11Vdr3bp1pbbPmjVLrVq1UqdOnVRYWHjBY8THx+u5556rqpIBAAAAeJkKfyesunwf7EJ8fHxc6pwxY4bsdrvOnj2rCRMmaMKECfr4449d3vPII48oOjpagwcPVlZW1kWPkZCQoMDAQGcLDQ2t8vMAAAAAUHtVeCWsutwd8ULCw8OVmZnpfP3SSy9pwYIFOn36dKnvgElS165d9dprr+lvf/ubNmzYUKFjOBwOORyOKqsZAAAAgHepcAj79V0Hq6PevXurffv2evnll519R48e1b59+8oc//vf/17Lly/Xf/7znzK/QwYAAAAA7lCp74RVF/Xq1VNwcLDq1q2r4OBg9evXT/Hx8Vq1apXefvvtCr1/xYoVysrK0vTp0xUcHFxqTFkrZwAAAABwuWpkCOvfv79ycnJUVFSkEydO6LvvvtPjjz+uhQsXVui7a507d9att94qSfrpp5/KHFMTPn4JAAAAoObxkVT977hRjdlsNuXl5WnWzs1ylBR7uhxUQ2MjIj1dAgAAACxwPhsEBgbKbreXO67Cd0cEAAAAAFw+QhgAAAAAWIgQBgAAAAAWqpE35qiOJnXpe8HPfQIAAACAxEoYAAAAAFiKEAYAAAAAFiKEAQAAAICFCGEAAAAAYCFCGAAAAABYiBAGAAAAABYihAEAAACAhQhhAAAAAGAhQhgAAAAAWIgQBgAAAAAWIoQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFCGEAAAAAYCFCGAAAAABYiBAGAAAAABby9XQBtcW0jZ/KUVLs6TIAoFoYGxHp6RIAAKi2WAkDAAAAAAsRwgAAAADAQoQwAAAAALAQIQwAAAAALEQIAwAAAAAL1YoQlpSUJGOMjDFyOBzKycnR2rVrFRMTIx8fH+e4zMxMjRkzxvm6ffv2+uCDD5Sbm6v8/HxlZmZqyZIl+t3vfueJ0wAAAADgBWpFCJOkNWvWKCQkRC1atFD//v2Vnp6uV199VR999JHq1q1banzjxo316aef6vjx47rrrrsUHh6umJgYZWdnKyAgwANnAAAAAMAb1JrnhBUWFio3N1eSlJ2drW+//VYbN27UunXrFB0drfnz57uM79q1q4KCgjRy5EgVF//yfK/9+/dr/fr1VpcOAAAAwIvUmpWwsqSnp2vr1q0aNGhQqW05OTny8/PT/fffX6l9+vv7y2azuTQAAAAAqKhaHcIkKSMjQy1atCjVv2nTJr3wwgtatGiRjh49qo8//ljjxo3TNddcc8H9xcfHKy8vz9mysrLcVDkAAACA2qjWhzAfHx8ZY8rc9swzzygkJESPPvqoduzYoUcffVQZGRlq165duftLSEhQYGCgs4WGhrqrdAAAAAC1UK0PYeHh4crMzCx3+/Hjx7Vs2TI99dRTCg8PV3Z2tsaNG1fueIfDIbvd7tIAAAAAoKJqdQjr3bu32rdvr+XLl1dofFFRkfbt28fdEQEAAAC4Ta25O2K9evUUHBysunXrKjg4WP369VN8fLxWrVqlt99+u9T4u+++W8OHD9eSJUu0Z88e+fj4aODAgfrjH/+omJgYD5wBAAAAAG9Qa0JY//79lZOTo6KiIp04cULfffedHn/8cS1cuLDM74Tt3LlTZ8+e1cyZM3XttdeqsLBQe/fu1ciRI5WcnOyBMwAAAADgDXwklX3XClSIzWZTXl6eZu3cLEdJsafLAYBqYWxEpKdLAADAcuezQWBg4AXvHVGrvxMGAAAAANUNIQwAAAAALFRrvhPmaZO69OV29QAAAAAuipUwAAAAALAQIQwAAAAALEQIAwAAAAALEcIAAAAAwEKEMAAAAACwECEMAAAAACxECAMAAAAACxHCAAAAAMBChDAAAAAAsBAhDAAAAAAsRAgDAAAAAAsRwgAAAADAQoQwAAAAALAQIQwAAAAALEQIAwAAAAALEcIAAAAAwEKEMAAAAACwECEMAAAAACzk6+kCaotpGz+Vo6TY02UAqOHGRkR6ugQAAOBmrIQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFql0ICw4O1muvvaZ9+/apoKBABw8e1Icffqg+ffo4x0RGRmr16tU6fvy48vPztW3bNj3xxBOqU8f1dHr06KFPP/1Ux44d05kzZ7Rnzx4tWLBAfn5+SkpKkjGm3JaZmWn1qQMAAADwAtUqhDVv3lxbtmxRnz599NRTTykiIkL9+vVTenq6Zs+eLUm677779N///lc//fSTevfurbCwML366qt65plntGTJEue+wsPDlZKSoq+//lo9evRQRESEHnvsMTkcDtWtW1djxoxRSEiIs0lSdHS083WnTp08MgcAAAAAajcfScbTRZy3evVqtW/fXm3atNHZs2ddtgUFBamoqEgHDhzQf//7Xw0ZMsRl+4ABA7Rq1SoNGzZM7777rsaMGaMxY8bouuuuq9CxjTG677779MEHH1SqZpvNpry8PM3auZnnhAG4bDwnDACAmut8NggMDJTdbi93XLVZCWvQoIH69eun2bNnlwpgknTq1Cndeeedaty4sf71r3+V2v7RRx9p9+7d+tOf/iRJysnJUZMmTdS9e/cqrdPf3182m82lAQAAAEBFVZsQdv3116tOnTrKyMgod0zr1q0lSbt27Spze0ZGhnPMe++9p8WLF+uzzz5Tdna23n//fY0ePfqyQ1N8fLzy8vKcLSsr67L2BwAAAMC7VJsQ5uPjU6VjS0pKFBsbq9DQUI0fP15ZWVmaNGmSduzY4fwO2KVISEhQYGCgs4WGhl7yvgAAAAB4n2oTwvbu3auSkhKFhYWVO2bPnj2SfrnpRlnCw8OdY87Lzs5WcnKyHnvsMd14442qX7++Hn300Uuu0+FwyG63uzQAAAAAqKhqE8JOnDih1NRUjR49WldeeWWp7UFBQVq7dq2OHTumsWPHlto+cOBAtW7dWosXLy73GCdPntThw4cVEBBQpbUDAAAAQEVVmxAmSaNHj1bdunX11VdfadCgQbr++usVFhamxx57TBs2bNDZs2f1yCOP6N5779Wbb76piIgINW/eXLGxsVqwYIHee+89vfvuu5Kkv/71r5ozZ47uuOMOXXfddWrbtq2mT5+uG2+8UatWrfLwmQIAAADwVr6eLuDXMjMz1bFjRz399NOaOXOmmjRpoiNHjmjLli0aNWqUJGn58uXq3bu3nn76aX3++eeqX7++9u7dqxdeeEGvvPKKc19fffWVunXrpjfeeENNmzbV6dOntWPHDt1333367LPPPHSGAAAAALxdtXpOWE3Ec8IAVCWeEwYAQM1V454TBgAAAADegBAGAAAAABYihAEAAACAharVjTlqskld+vLMMAAAAAAXxUoYAAAAAFiIEAYAAAAAFiKEAQAAAICFCGEAAAAAYCFCGAAAAABYiBAGAAAAABYihAEAAACAhQhhAAAAAGAhQhgAAAAAWIgQBgAAAAAWIoQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFCGEAAAAAYCFCGAAAAABYiBAGAAAAABby9XQBtcW0jZ/KUVLs6TIAAJU0NiLS0yUAALwMK2EAAAAAYCFCGAAAAABYiBAGAAAAABYihAEAAACAhQhhAAAAAGChGhfCkpKSZIxxtqNHj2rNmjWKiIgoNfaNN97QuXPnNGTIkDL31apVKyUmJurQoUMqKCjQjz/+qEWLFumWW25x92kAAAAA8FI1LoRJ0po1axQSEqKQkBD17dtX586d00cffeQy5oorrtDw4cP14osvKjY2ttQ+brnlFm3ZskWtW7fWI488orZt2+r+++9XRkaGZs6cadWpAAAAAPAyNfI5YYWFhcrNzZUk5ebmavr06friiy/UuHFjHT16VJI0dOhQ7dy5U9OnT1d2drZ+//vf66effnLuY8GCBdq7d6+6d+8uY4yz/7vvvtOrr75q7QkBAAAA8Bo1ciXs1wICAvTwww9r7969OnbsmLM/Li5OycnJysvL05o1axQdHe3c1qFDB7Vr104zZ850CWDnnTp1qtzj+fv7y2azuTQAAAAAqKgaGcIGDBggu90uu92u06dP65577tGwYcOcger6669Xly5dtHTpUklScnKyYmJinO+/4YYbJEkZGRmVPnZ8fLzy8vKcLSsrqwrOCAAAAIC3qJEhLD09XR06dFCHDh3UqVMnpaamas2aNWrWrJkkKTY2Vqmpqc6VsY8//lhBQUHq06ePJMnHx+eSj52QkKDAwEBnCw0NvfwTAgAAAOA1amQIO3PmjPbt26d9+/bp66+/1siRIxUQEKC//OUvqlOnjqKionT33XerqKhIRUVFOnv2rBo1auS8QceePXskSWFhYZU+tsPhcK7CnW8AAAAAUFE18sYcv2WMUUlJia644gr98Y9/lM1m080336zi4mLnmHbt2ikpKUlBQUHaunWrduzYobFjx2rp0qWlvhcWFBR0we+FAQAAAMClqpErYfXq1VNwcLCCg4MVFham119/XVdddZVWrVqluLg4rV69Wtu2bdOOHTuc7d1339XJkyf10EMPSZJiYmLUunVrff755+rfv79atmypiIgITZo0SR988IGHzxAAAABAbVUjQ1j//v2Vk5OjnJwcbdq0SZ06ddLQoUO1a9cu3X333Vq+fHmp9xhjtGLFCsXFxUmSNm/erFtvvVU//PCD5s2bp127dunDDz/UjTfeqL///e8WnxEAAAAAb+EjqfQ92lFhNptNeXl5mrVzsxwlxRd/AwCgWhkbEenpEgAAtcT5bBAYGHjBe0fUyJUwAAAAAKipCGEAAAAAYKFacXfE6mBSl77crh4AAADARbESBgAAAAAWIoQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFCGEAAAAAYCFCGAAAAABYiBAGAAAAABYihAEAAACAhQhhAAAAAGAhQhgAAAAAWIgQBgAAAAAWIoQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFfD1dQG0xbeOncpQUe7oMAAAAwGuMjYj0dAmXhJUwAAAAALAQIQwAAAAALEQIAwAAAAALEcIAAAAAwEKEMAAAAACwULUIYV26dNG5c+f00UcfufQ3b95cxhidO3dOTZs2ddkWEhKioqIiGWPUvHlzSVJ6erqMMeW2Hj16SJKSkpJkjNGECRNc9nnvvffKGOPGMwUAAADg7apFCIuLi9Prr7+uHj16qEmTJqW2Z2VlacSIES59UVFRysrKcukbNGiQQkJCXFqzZs20fft2bd68WZs2bXKOzc/P14QJE3T11Ve75ZwAAAAAoCweD2EBAQEaNmyY5s6dq9WrVys6OrrUmIULFyomJsalLyYmRgsXLnTpO3HihHJzc13a5MmT1bhxY91///0qLCx0jk1LS1NOTo7i4+Pdcl4AAAAAUBaPh7AHHnhAGRkZ2rNnj5KTkxUbG1tqzIcffqgGDRqoa9eukqSuXbuqQYMGWrVq1QX3PWrUKI0YMUKDBw8utWpWXFysSZMm6bHHHlNoaGiF6/X395fNZnNpAAAAAFBRHg9hcXFxSk5OliSlpKQoKChIPXv2dBlTVFTkEtBiY2OVnJysoqKicvfbvXt3vfLKKxo9erQ2bNhQ5piVK1dq69atmjp1aoXrjY+PV15enrP9NtwBAAAAwIV4NIS1bt1at912mxYvXizpl9WppUuXKi4urtTYxMREDR06VMHBwRo6dKgSExPL3e+1116rZcuW6T//+Y/mz59/wRomTJigqKgohYWFVajmhIQEBQYGOltlVtEAAAAAwKMhLC4uTn5+fsrOzlZRUZGKioo0atQoDR48WIGBgS5jv//+e2VkZGjx4sXatWuXduzYUeY+69evrxUrVmjHjh36+9//ftEaPv/8c6WmpiohIaFCNTscDtntdpcGAAAAABXl66kD161bVyNGjNCTTz6ptWvXumxbuXKl/vSnPyklJcWlPzExUXPnztWjjz5a7n7feustNWzYUHfddZeKi4srVMvEiRO1detW7d69u/InAgAAAACV4LEQNmDAADVo0EDz589XXl6ey7bly5crLi6uVAibN2+e3nvvPZ08ebLMfY4bN05Dhw7VwIED5evrq+DgYJftp06dUkFBQan3ff/993rnnXf0+OOPX95JAQAAAMBFeOzjiHFxcUpLSysVwKRfQlinTp1KfSSxuLhYx44dK3eF629/+5v8/f2VmpqqnJycUm3YsGHl1vPss8+qTh2P36cEAAAAQC3nI8l4uoiazGazKS8vT7N2bpajpGIffwQAAABw+cZGRHq6BBfns0FgYOAF7x3B0g8AAAAAWIgQBgAAAAAWIoQBAAAAgIU8dnfE2mZSl748MwwAAADARbESBgAAAAAWIoQBAAAAgIUIYQAAAABgIUIYAAAAAFiIEAYAAAAAFiKEAQAAAICFCGEAAAAAYCGeE1ZFbDabp0sAAAAA4EEVzQSEsMvUsGFDSVJWVpaHKwEAAABQHdhsNtnt9nK3E8Iu0/HjxyVJoaGhF5xoXBqbzaasrCzm102YX/djjt2L+XUv5te9mF/3Y47di/ktm81mU3Z29gXHEMKqiN1u5+JzI+bXvZhf92OO3Yv5dS/m172YX/djjt2L+XVVkbngxhwAAAAAYCFCGAAAAABYiBB2mQoLC/Xcc8+psLDQ06XUSsyvezG/7sccuxfz617Mr3sxv+7HHLsX83vpfCQZTxcBAAAAAN6ClTAAAAAAsBAhDAAAAAAsRAgDAAAAAAsRwgAAAADAQoQwAAAAALCQ14ewv/3tb8rMzFR+fr42btyoTp06XXD8kCFDtGvXLuXn52vbtm3q379/qTFTp05Vdna2zp49q08++UTXX3+9y/YGDRooOTlZp06d0okTJ/TWW28pICCgSs+ruqjK+fX19dX06dO1bds2nT59WllZWVq4cKGaNGniso/MzEwZY1zahAkT3HJ+nlbV129SUlKpuVuzZo3LGG+6fqWqn+Pfzu/5Nm7cOOcYruGytW3bVsuWLXPOz5gxYy5pn/Xq1dOsWbN09OhR2e12LVu2TNdcc02Vnld1UdXzO3HiRH311VfKy8tTbm6uVqxYodatW7uMSU9PL3X9zp07t8rPrTqo6vmdMmVKqbnbtWuXyxhvun6lqp/jsn6/GmM0a9Ys5xiu4bKNHDlSn332mY4fP67jx4/rk08+KXM8fwdXnPHW9sADD5iCggITHR1twsPDzZtvvmmOHz9ufve735U5PjIy0hQVFZlx48aZsLAw8/zzz5vCwkJz4403OseMHz/enDhxwtxzzz0mIiLCrFy50uzbt8/Uq1fPOebjjz823377rbnttttM165dzZ49e8w777zj8fmo7vMbGBho1q5da4YOHWpat25tOnfubDZu3Gg2b97ssp/MzEzzzDPPmODgYGe78sorPT4f1X1+JZmkpCTz8ccfu8zd1Vdf7bIfb7l+3TXHv57b4OBgEx0dbYqLi03Lli25hi8yv7feeqt58cUXzbBhw0x2drYZM2bMJe1zzpw55sCBA6Z3796mY8eO5ssvvzRffPGFx+ejJszvmjVrTFRUlGnbtq1p3769+eijj8z+/ftdrs/09HTz5ptvuly/NpvN4/NRE+Z3ypQpZvv27S5z16hRI5cx3nL9umuOGzdu7DK/ffv2NcYY07NnT67hi8xvcnKyGTVqlLnppptMmzZtTGJiojlx4oRp2rSpcwx/B1eqebwAj7WNGzea119/3fnax8fH/PTTT2bChAlljl+yZIlZtWqVS9+GDRvM3Llzna+zs7PN2LFjna8DAwNNfn6+GTZsmJFkwsLCjDHG3HLLLc4xd911lykuLjZNmjTx+JxU9/n9bbv11luNMcZce+21zr7MzMwyf/HWtuaO+U1KSjIrVqwo95jedP26a45/21asWGHS0tJc+riGy57fiszRxfYZGBhoCgsLzeDBg51j2rRpY4wxpnPnzh6fk+o+v79tjRs3NsYY0717d2dfenq6efnllz1+/jVxfqdMmWK+/fbbct/nTdevu+b4t+3ll182e/fudenjGr74/EoyderUMadOnTJ//vOfnX38HVzx5rUfR/Tz89Mtt9yitLQ0Z58xRmlpaYqMjCzzPZGRkS7jJSk1NdU5vmXLlmrSpInLmLy8PG3atMk5JjIyUidOnNCWLVucY9LS0lRSUqLOnTtX2fl5mjvmtyxBQUEqKSnRyZMnXfonTpyoo0eP6ptvvtG4ceNUt27dSz+Zasid89urVy/l5uYqIyNDc+bMUcOGDV324Q3Xr2TNNXzNNdfo7rvv1vz580tt4xp2zz5vueUW+fv7u4zZvXu3Dhw4cMnHrY7cMb9lCQoKkiQdP37cpf+hhx7SkSNHtH37dk2bNk1XXHFFlR2zOnDn/N5www3KysrSvn37lJycrGuvvda5zVuuX8maa9jPz08PP/ywEhMTS23jGr64K6+8Un5+fs5///wdXDm+ni7AUxo3bixfX1/l5ua69Ofm5iosLKzM94SEhJQ5PiQkxLn9fN+Fxvz8888u24uLi3X8+HHnmNrAHfP7W/Xq1dOMGTO0ePFi2e12Z/9rr72mb775RsePH9ftt9+uhIQENWnSRGPHjr3Ms6o+3DW/KSkpev/995WZmalWrVpp2rRpWrNmjSIjI1VSUuI1169kzTUcFRUlu92u999/36Wfa7js+a2KfYaEhKiwsFCnTp0qNaY2XcPumN/f8vHx0SuvvKIvvvhCO3bscPYvWrRIBw4cUHZ2ttq3b68ZM2aoTZs2Gjx4cJUctzpw1/xu2rRJ0dHR2r17t5o0aaIpU6bo888/V7t27XT69GmvuX4la67h++67T1dffbUWLFjg0s81XLH5nTFjhrKzs52hi7+DK8drQxhqNl9fX7377rvy8fHRqFGjXLa9/PLLzp+3b98uh8OhN998U/Hx8XI4HFaXWqMsXbrU+fP333+vbdu26ccff1SvXr20bt06D1ZWO8XGxuqdd95RYWGhSz/XMGqC2bNnq127durWrZtL/7x585w/f//99zp8+LDWrVun6667Tj/++KPVZdYoKSkpzp+3b9+uTZs26cCBA3rggQfKXK3B5YmLi9OaNWt0+PBhl36u4YubMGGChg8frl69epX6bxgqxms/jnj06FGdO3dOwcHBLv3BwcHKyckp8z05OTkXHH/+fy825rd3Mapbt64aNmxY7nFrInfM73nnA1jz5s11xx13uKyClWXTpk3y8/NTixYtKn8i1ZQ75/fXMjMzdeTIEeedjbzl+pXcP8fdunVTWFiY3nrrrYvWwjVcdfvMyclRvXr1nB+jq4rjVkfumN9fe/311zVgwAD17t1bWVlZFxy7adMmSSp1h7SazN3ze96pU6e0Z88el9/B3nD9Su6f42bNmukPf/hDhX8HS1zD540dO1YTJ07UnXfeqe3btzv7+Tu4crw2hBUVFWnLli3q27evs8/Hx0d9+/bVhg0bynzPhg0bXMZL0h133OEcn5mZqcOHD7uMsdls6ty5s3PMhg0b1KBBA3Xs2NE5pk+fPqpTp47zH3lt4I75lf4vgN1www36wx/+UOp7CGXp0KGDiouLSy1/12Tumt/fCg0NVaNGjZz/L6G3XL+S++c4Li5OX3/9tbZt23bRWriGq26fW7ZskcPhcBnTunVrNW/e/JKPWx25Y37Pe/3113X//ferT58+2r9//0XHd+jQQZJKrTbUZO6c318LCAhQq1atnHPnLdev5P45jomJ0c8//6zVq1dfdCzX8P956qmnNHnyZPXr18/le10SfwdfCo/fHcRT7YEHHjD5+flmxIgRJiwszLzxxhvm+PHj5pprrjGSzMKFC820adOc4yMjI43D4TBPPvmkadOmjZkyZUqZt6g/fvy4GThwoGnXrp1ZsWJFmbfm3LJli+nUqZO5/fbbze7du2vlrTmren59fX3NypUrzcGDB0379u1dbh3r5+dnJJkuXbqYMWPGmPbt25uWLVuaBx980OTm5poFCxZ4fD6q+/wGBASYF1980XTu3Nk0b97c9OnTx3z99ddm9+7dxt/f3+uuX3fM8flms9nM6dOnzSOPPFLqmFzD5c+vn5+fuemmm8xNN91ksrKyzIsvvmhuuukm06pVqwrvU/rlFt/79+83vXr1Mh07djT/+9//zP/+9z+Pz0dNmN/Zs2ebEydOmB49erj8Dq5fv76RZK677jrzzDPPmI4dO5rmzZubgQMHmh9++MGsX7/e4/NRE+b3pZdeMj169DDNmzc3kZGRZu3atebnn382jRs39rrr111zLP1yF8D9+/ebhISEUsfkGi5/fsePH28KCgrMoEGDXP79BwQEuIzh7+AKN48X4NE2evRos3//flNQUGA2btxobrvtNue29PR0k5SU5DJ+yJAhJiMjwxQUFJjt27eb/v37l9rn1KlTzeHDh01+fr755JNPzA033OCyvUGDBuadd94xeXl55uTJk2b+/PkuF3BtalU5v82bNzflOf98j5tvvtls2LDBnDhxwpw9e9bs2LHDTJw40SVE1KZWlfNbv359k5KSYnJzc01hYaHJzMw0b775pssfr952/Vb1HJ9vf/nLX8yZM2dMYGBgqW1cw+XPb3m/A9LT0yu8T0mmXr16ZtasWebYsWPm9OnTZvny5SY4ONjjc1ET5rc8UVFRRpL5/e9/b9avX2+OHj1q8vPzzZ49e8yMGTNq5TOW3DG/ixcvNllZWaagoMAcOnTILF682Fx33XVee/26Y44lmTvuuMMYY0r9fcY1fOH5zczMLHN+p0yZ4rJP/g6uWPP5/38AAAAAAFjAa78TBgAAAACeQAgDAAAAAAsRwgAAAADAQoQwAAAAALAQIQwAAAAALEQIAwAAAAALEcIAAAAAwEKEMAAAAACwECEMAAAAACxECAMAAAAACxHCAAAAAMBC/x+M3aPNDj9yagAAAABJRU5ErkJggg==", - "text/plain": [ - "
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" ], - "source": [ - "market_prior.plot.barh(figsize=(10,5));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v1YzJTJFAbNl" - }, - "source": [ - "## Views\n", - "\n", - "In the BL method, views are specified via the matrix P (picking matrix) and the vector Q. Q contains the magnitude of each view, while P maps the views to the assets they belong to. \n", - "\n", - "If you are providing **absolute views** (i.e a return estimate for each asset), you don't have to worry about P and Q, you can just pass your views as a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "uTP2BVZIAbNl" - }, - "outputs": [], - "source": [ - "# You don't have to provide views on all the assets\n", - "viewdict = {\n", - " \"AMZN\": 0.10,\n", - " \"BAC\": 0.30,\n", - " \"COST\": 0.05,\n", - " \"DIS\": 0.05,\n", - " \"DPZ\": 0.20,\n", - " \"KO\": -0.05, # I think Coca-Cola will go down 5%\n", - " \"MCD\": 0.15,\n", - " \"MSFT\": 0.10,\n", - " \"NAT\": 0.50, # but low confidence, which will be reflected later\n", - " \"SBUX\": 0.10\n", - "}\n", - "\n", - "bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fA5PRsozAbNm" - }, - "source": [ - "Black-Litterman also allows for relative views, e.g you think asset A will outperform asset B by 10%. If you'd like to incorporate these, you will have to build P and Q yourself. An explanation for this is given in the [docs](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html#views)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QIMKGZEoAbNm" - }, - "source": [ - "## View confidences\n", - "\n", - "In this section, we provide two ways that you may wish to construct the uncertainty matrix. The first is known as Idzorek's method. It allows you to specify a vector/list of percentage confidences." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "Z0Pzoio1AbNm" - }, - "outputs": [], - "source": [ - "confidences = [\n", - " 0.6,\n", - " 0.4,\n", - " 0.2,\n", - " 0.5,\n", - " 0.7, # confident in dominos\n", - " 0.7, # confident KO will do poorly\n", - " 0.7, \n", - " 0.5,\n", - " 0.1,\n", - " 0.4\n", - "]" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 14 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "qknxazUhAbNk", + "outputId": "eb6b5bdf-7a31-4c76-90e0-ca2a751126ee", + "ExecuteTime": { + "end_time": "2025-11-12T08:11:59.947356Z", + "start_time": "2025-11-12T08:11:59.944497Z" + } + }, + "source": [ + "market_prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)\n", + "market_prior" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "EaRmLt4IAbNm" - }, - "outputs": [], - "source": [ - "bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict, omega=\"idzorek\", view_confidences=confidences)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 428 - }, - "id": "fo6SKTGAAbNn", - "outputId": "49517772-73fa-405b-a164-8a39c1456faf" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7,7))\n", - "im = ax.imshow(bl.omega)\n", - "\n", - "# We want to show all ticks...\n", - "ax.set_xticks(np.arange(len(bl.tickers)))\n", - "ax.set_yticks(np.arange(len(bl.tickers)))\n", - "\n", - "ax.set_xticklabels(bl.tickers)\n", - "ax.set_yticklabels(bl.tickers)\n", - "plt.show()" + "data": { + "text/plain": [ + "Ticker\n", + "AMZN 0.217688\n", + "BAC 0.104540\n", + "COST 0.077448\n", + "DIS 0.092606\n", + "DPZ 0.039139\n", + "KO 0.051818\n", + "MCD 0.056672\n", + "MSFT 0.160434\n", + "NAT 0.041953\n", + "SBUX 0.078963\n", + "dtype: float64" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 15 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 320 }, + "id": "9exlhi1BAbNk", + "outputId": "a1292c4b-38b9-4c02-919d-b9cf33a4309e", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.051238Z", + "start_time": "2025-11-12T08:11:59.998069Z" + } + }, + "source": [ + "market_prior.plot.barh(figsize=(10,5));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "58aBtbhuAbNn", - "outputId": "31bcfa00-46ba-4600-b626-c7cf15a352b8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00456881, 0.00877776, 0.01202519, 0.00494424, 0.0008738 ,\n", - " 0.00112756, 0.00168424, 0.00344649, 0.04131355, 0.00547976])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "np.diag(bl.omega)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lUGduOmsAbNn" - }, - "source": [ - "Note how NAT, which we gave the lowest confidence, also has the highest uncertainty.\n", - "\n", - "Instead of inputting confidences, we can calculate the uncertainty matrix directly by specifying 1 standard deviation confidence intervals, i.e bounds which we think will contain the true return 68% of the time. This may be easier than coming up with somewhat arbitrary percentage confidences" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "QQtnWs9xAbNn" - }, - "outputs": [], - "source": [ - "intervals = [\n", - " (0, 0.25),\n", - " (0.1, 0.4),\n", - " (-0.1, 0.15),\n", - " (-0.05, 0.1),\n", - " (0.15, 0.25),\n", - " (-0.1, 0),\n", - " (0.1, 0.2),\n", - " (0.08, 0.12),\n", - " (0.1, 0.9),\n", - " (0, 0.3)\n", - "]" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 16 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v1YzJTJFAbNl" + }, + "source": [ + "## Views\n", + "\n", + "In the BL method, views are specified via the matrix P (picking matrix) and the vector Q. Q contains the magnitude of each view, while P maps the views to the assets they belong to. \n", + "\n", + "If you are providing **absolute views** (i.e a return estimate for each asset), you don't have to worry about P and Q, you can just pass your views as a dictionary." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uTP2BVZIAbNl", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.073557Z", + "start_time": "2025-11-12T08:12:00.071602Z" + } + }, + "source": [ + "# You don't have to provide views on all the assets\n", + "viewdict = {\n", + " \"AMZN\": 0.10,\n", + " \"BAC\": 0.30,\n", + " \"COST\": 0.05,\n", + " \"DIS\": 0.05,\n", + " \"DPZ\": 0.20,\n", + " \"KO\": -0.05, # I think Coca-Cola will go down 5%\n", + " \"MCD\": 0.15,\n", + " \"MSFT\": 0.10,\n", + " \"NAT\": 0.50, # but low confidence, which will be reflected later\n", + " \"SBUX\": 0.10\n", + "}\n", + "\n", + "bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict)" + ], + "outputs": [], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fA5PRsozAbNm" + }, + "source": [ + "Black-Litterman also allows for relative views, e.g you think asset A will outperform asset B by 10%. If you'd like to incorporate these, you will have to build P and Q yourself. An explanation for this is given in the [docs](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html#views)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QIMKGZEoAbNm" + }, + "source": [ + "## View confidences\n", + "\n", + "In this section, we provide two ways that you may wish to construct the uncertainty matrix. The first is known as Idzorek's method. It allows you to specify a vector/list of percentage confidences." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z0Pzoio1AbNm", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.083852Z", + "start_time": "2025-11-12T08:12:00.081123Z" + } + }, + "source": [ + "confidences = [\n", + " 0.6,\n", + " 0.4,\n", + " 0.2,\n", + " 0.5,\n", + " 0.7, # confident in dominos\n", + " 0.7, # confident KO will do poorly\n", + " 0.7, \n", + " 0.5,\n", + " 0.1,\n", + " 0.4\n", + "]" + ], + "outputs": [], + "execution_count": 18 + }, + { + "cell_type": "code", + "metadata": { + "id": "EaRmLt4IAbNm", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.091810Z", + "start_time": "2025-11-12T08:12:00.090062Z" + } + }, + "source": [ + "bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict, omega=\"idzorek\", view_confidences=confidences)" + ], + "outputs": [], + "execution_count": 19 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 }, + "id": "fo6SKTGAAbNn", + "outputId": "49517772-73fa-405b-a164-8a39c1456faf", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.127798Z", + "start_time": "2025-11-12T08:12:00.096978Z" + } + }, + "source": [ + "fig, ax = plt.subplots(figsize=(7,7))\n", + "im = ax.imshow(bl.omega)\n", + "\n", + "# We want to show all ticks...\n", + "ax.set_xticks(np.arange(len(bl.tickers)))\n", + "ax.set_yticks(np.arange(len(bl.tickers)))\n", + "\n", + "ax.set_xticklabels(bl.tickers)\n", + "ax.set_yticklabels(bl.tickers)\n", + "plt.show()" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "avW4ld8bAbNo", - "outputId": "540d7109-e20f-4eb4-e32d-fb16ae670a5b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.015625, 0.022500000000000006, 0.015625, 0.0056250000000000015, 0.0025000000000000005, 0.0025000000000000005, 0.0025000000000000005, 0.00039999999999999986, 0.16000000000000003, 0.0225]\n" - ] - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "variances = []\n", - "for lb, ub in intervals:\n", - " sigma = (ub - lb)/2\n", - " variances.append(sigma ** 2)\n", - "\n", - "print(variances)\n", - "omega = np.diag(variances)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VlVq9sICAbNo" - }, - "source": [ - "## Posterior estimates\n", - "\n", - "Given the inputs, we can compute a posterior estimate of returns\n" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 20 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "58aBtbhuAbNn", + "outputId": "31bcfa00-46ba-4600-b626-c7cf15a352b8", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.136042Z", + "start_time": "2025-11-12T08:12:00.132744Z" + } + }, + "source": [ + "np.diag(bl.omega)" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "478dHIDHAbNo" - }, - "outputs": [], - "source": [ - "# We are using the shortcut to automatically compute market-implied prior\n", - "bl = BlackLittermanModel(S, pi=\"market\", market_caps=mcaps, risk_aversion=delta,\n", - " absolute_views=viewdict, omega=omega)" + "data": { + "text/plain": [ + "array([0.0045619 , 0.00873095, 0.01198575, 0.00493244, 0.00088379,\n", + " 0.00112096, 0.0016701 , 0.0034398 , 0.04152296, 0.00552791])" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 21 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lUGduOmsAbNn" + }, + "source": [ + "Note how NAT, which we gave the lowest confidence, also has the highest uncertainty.\n", + "\n", + "Instead of inputting confidences, we can calculate the uncertainty matrix directly by specifying 1 standard deviation confidence intervals, i.e bounds which we think will contain the true return 68% of the time. This may be easier than coming up with somewhat arbitrary percentage confidences" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QQtnWs9xAbNn", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.143365Z", + "start_time": "2025-11-12T08:12:00.140675Z" + } + }, + "source": [ + "intervals = [\n", + " (0, 0.25),\n", + " (0.1, 0.4),\n", + " (-0.1, 0.15),\n", + " (-0.05, 0.1),\n", + " (0.15, 0.25),\n", + " (-0.1, 0),\n", + " (0.1, 0.2),\n", + " (0.08, 0.12),\n", + " (0.1, 0.9),\n", + " (0, 0.3)\n", + "]" + ], + "outputs": [], + "execution_count": 22 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "avW4ld8bAbNo", + "outputId": "540d7109-e20f-4eb4-e32d-fb16ae670a5b", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.151331Z", + "start_time": "2025-11-12T08:12:00.149767Z" + } + }, + "source": [ + "variances = []\n", + "for lb, ub in intervals:\n", + " sigma = (ub - lb)/2\n", + " variances.append(sigma ** 2)\n", + "\n", + "print(variances)\n", + "omega = np.diag(variances)" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "A9VSO7xAAbNo", - "outputId": "c501cc36-8719-4826-d2b9-c138ab2f46c5" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Ticker\n", - "AMZN 0.178794\n", - "BAC 0.137065\n", - "COST 0.068768\n", - "DIS 0.074427\n", - "DPZ 0.107061\n", - "KO 0.008443\n", - "MCD 0.103680\n", - "MSFT 0.107061\n", - "NAT 0.063914\n", - "SBUX 0.086824\n", - "dtype: float64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Posterior estimate of returns\n", - "ret_bl = bl.bl_returns()\n", - "ret_bl" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.015625, 0.022500000000000006, 0.015625, 0.0056250000000000015, 0.0025000000000000005, 0.0025000000000000005, 0.0025000000000000005, 0.00039999999999999986, 0.16000000000000003, 0.0225]\n" + ] + } + ], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VlVq9sICAbNo" + }, + "source": [ + "## Posterior estimates\n", + "\n", + "Given the inputs, we can compute a posterior estimate of returns\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "478dHIDHAbNo", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.159726Z", + "start_time": "2025-11-12T08:12:00.155670Z" + } + }, + "source": [ + "# We are using the shortcut to automatically compute market-implied prior\n", + "bl = BlackLittermanModel(S, pi=\"market\", market_caps=mcaps, risk_aversion=delta,\n", + " absolute_views=viewdict, omega=omega)" + ], + "outputs": [], + "execution_count": 24 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "A9VSO7xAAbNo", + "outputId": "c501cc36-8719-4826-d2b9-c138ab2f46c5", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.168392Z", + "start_time": "2025-11-12T08:12:00.165958Z" + } + }, + "source": [ + "# Posterior estimate of returns\n", + "ret_bl = bl.bl_returns()\n", + "ret_bl" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "eRKmlKABAbNo" - }, - "source": [ - "We can visualise how this compares to the prior and our views:" + "data": { + "text/plain": [ + "Ticker\n", + "AMZN 0.183882\n", + "BAC 0.135815\n", + "COST 0.067281\n", + "DIS 0.074143\n", + "DPZ 0.107602\n", + "KO 0.007914\n", + "MCD 0.102838\n", + "MSFT 0.107538\n", + "NAT 0.063471\n", + "SBUX 0.086751\n", + "dtype: float64" ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eRKmlKABAbNo" + }, + "source": [ + "We can visualise how this compares to the prior and our views:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 }, + "id": "KHfvi6KvAbNp", + "outputId": "ee876b7b-a24c-4dea-c344-a1c8a5c6c7d4", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.193309Z", + "start_time": "2025-11-12T08:12:00.188595Z" + } + }, + "source": [ + "rets_df = pd.DataFrame([market_prior, ret_bl, pd.Series(viewdict)], \n", + " index=[\"Prior\", \"Posterior\", \"Views\"]).T\n", + "rets_df" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "KHfvi6KvAbNp", - "outputId": "ee876b7b-a24c-4dea-c344-a1c8a5c6c7d4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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PriorPosteriorViews
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BAC0.1046310.1370650.30
COST0.0782940.0687680.05
DIS0.0924340.0744270.05
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MSFT0.1604340.1075380.10
NAT0.0419530.0634710.50
SBUX0.0789630.0867510.10
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" ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 26 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 503 }, + "id": "n55wKWFuAbNp", + "outputId": "9445d68a-279e-4cfe-85a6-0609e411b03c", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.515591Z", + "start_time": "2025-11-12T08:12:00.468401Z" + } + }, + "source": [ + "rets_df.plot.bar(figsize=(12,8));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 503 - }, - "id": "n55wKWFuAbNp", - "outputId": "9445d68a-279e-4cfe-85a6-0609e411b03c" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "rets_df.plot.bar(figsize=(12,8));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fuizxt0NAbNp" - }, - "source": [ - "Notice that the posterior is often between the prior and the views. This supports the fact that the BL method is essentially a Bayesian weighted-average of the prior and views, where the weight is determined by the confidence.\n", - "\n", - "A similar but less intuitive procedure can be used to produce the posterior covariance estimate:" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 27 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fuizxt0NAbNp" + }, + "source": [ + "Notice that the posterior is often between the prior and the views. This supports the fact that the BL method is essentially a Bayesian weighted-average of the prior and views, where the weight is determined by the confidence.\n", + "\n", + "A similar but less intuitive procedure can be used to produce the posterior covariance estimate:" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "i6S1l2-aAbNp", + "outputId": "411f2876-67fb-4d2d-8f88-4b63463bf10f", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.645620Z", + "start_time": "2025-11-12T08:12:00.522725Z" + } + }, + "source": [ + "S_bl = bl.bl_cov()\n", + "plotting.plot_covariance(S_bl);" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "i6S1l2-aAbNp", - "outputId": "411f2876-67fb-4d2d-8f88-4b63463bf10f" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "S_bl = bl.bl_cov()\n", - "plotting.plot_covariance(S_bl);" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 28 + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YC9fNRAbAbNp" + }, + "source": [ + "## Portfolio allocation\n", + "\n", + "Now that we have constructed our Black-Litterman posterior estimate, we can proceed to use any of the optimizers discussed in previous recipes." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hDS0vIUCAbNp", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.665877Z", + "start_time": "2025-11-12T08:12:00.663664Z" + } + }, + "source": [ + "from pypfopt import EfficientFrontier, objective_functions" + ], + "outputs": [], + "execution_count": 29 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "qylII3-oAbNp", + "outputId": "f81f870a-3181-42ba-afcf-321674ddf5d7", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.685494Z", + "start_time": "2025-11-12T08:12:00.673018Z" + } + }, + "source": [ + "ef = EfficientFrontier(ret_bl, S_bl)\n", + "ef.add_objective(objective_functions.L2_reg)\n", + "ef.max_sharpe()\n", + "weights = ef.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "YC9fNRAbAbNp" - }, - "source": [ - "## Portfolio allocation\n", - "\n", - "Now that we have constructed our Black-Litterman posterior estimate, we can proceed to use any of the optimizers discussed in previous recipes." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/thomasschmelzer/projects/PyPortfolioOpt/pypfopt/efficient_frontier/efficient_frontier.py:259: UserWarning: max_sharpe transforms the optimization problem so additional objectives may not work as expected.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "hDS0vIUCAbNp" - }, - "outputs": [], - "source": [ - "from pypfopt import EfficientFrontier, objective_functions" + "data": { + "text/plain": [ + "OrderedDict([('AMZN', 0.20061),\n", + " ('BAC', 0.14324),\n", + " ('COST', 0.0697),\n", + " ('DIS', 0.07017),\n", + " ('DPZ', 0.12808),\n", + " ('KO', 0.0),\n", + " ('MCD', 0.11522),\n", + " ('MSFT', 0.1165),\n", + " ('NAT', 0.06627),\n", + " ('SBUX', 0.09021)])" ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 30 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 }, + "id": "9_pWobQoAbNq", + "outputId": "e50c07ee-7274-4936-8f0f-e347309013b9", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.718526Z", + "start_time": "2025-11-12T08:12:00.691823Z" + } + }, + "source": [ + "pd.Series(weights).plot.pie(figsize=(10,10));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qylII3-oAbNp", - "outputId": "f81f870a-3181-42ba-afcf-321674ddf5d7" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/robert/Dev/PyPortfolioOpt/pypfopt/efficient_frontier/efficient_frontier.py:259: UserWarning: max_sharpe transforms the optimization problem so additional objectives may not work as expected.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "OrderedDict([('AMZN', 0.19488),\n", - " ('BAC', 0.14496),\n", - " ('COST', 0.07173),\n", - " ('DIS', 0.07076),\n", - " ('DPZ', 0.12771),\n", - " ('KO', 0.0),\n", - " ('MCD', 0.11622),\n", - " ('MSFT', 0.11613),\n", - " ('NAT', 0.0669),\n", - " ('SBUX', 0.0907)])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "ef = EfficientFrontier(ret_bl, S_bl)\n", - "ef.add_objective(objective_functions.L2_reg)\n", - "ef.max_sharpe()\n", - "weights = ef.clean_weights()\n", - "weights" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 31 + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "8YHugSK8AbNq", + "outputId": "095c7b24-0f63-4c6a-a80b-91247548486b", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:00.746224Z", + "start_time": "2025-11-12T08:12:00.725538Z" + } + }, + "source": [ + "from pypfopt import DiscreteAllocation\n", + "\n", + "da = DiscreteAllocation(weights, prices.iloc[-1], total_portfolio_value=20000)\n", + "alloc, leftover = da.lp_portfolio()\n", + "print(f\"Leftover: ${leftover:.2f}\")\n", + "alloc" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 575 - }, - "id": "9_pWobQoAbNq", - "outputId": "e50c07ee-7274-4936-8f0f-e347309013b9" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.pie(figsize=(10,10));" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Leftover: $16.30\n" + ] }, { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8YHugSK8AbNq", - "outputId": "095c7b24-0f63-4c6a-a80b-91247548486b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Leftover: $3.37\n" - ] - }, - { - "data": { - "text/plain": [ - "{'AMZN': np.int64(18),\n", - " 'BAC': np.int64(60),\n", - " 'COST': np.int64(2),\n", - " 'DIS': np.int64(12),\n", - " 'DPZ': np.int64(5),\n", - " 'MCD': np.int64(8),\n", - " 'MSFT': np.int64(5),\n", - " 'NAT': np.int64(500),\n", - " 'SBUX': np.int64(18)}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pypfopt import DiscreteAllocation\n", - "\n", - "da = DiscreteAllocation(weights, prices.iloc[-1], total_portfolio_value=20000)\n", - "alloc, leftover = da.lp_portfolio()\n", - "print(f\"Leftover: ${leftover:.2f}\")\n", - "alloc" + "data": { + "text/plain": [ + "{'AMZN': 16,\n", + " 'BAC': 54,\n", + " 'COST': 2,\n", + " 'DIS': 13,\n", + " 'DPZ': 6,\n", + " 'MCD': 7,\n", + " 'MSFT': 4,\n", + " 'NAT': 366,\n", + " 'SBUX': 21}" ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "4-Black-Litterman-Allocation.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" - } + ], + "execution_count": 32 + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "4-Black-Litterman-Allocation.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "name": "python3", + "language": "python" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/cookbook/5-Hierarchical-Risk-Parity.ipynb b/cookbook/5-Hierarchical-Risk-Parity.ipynb index a7b048e1..694701bd 100644 --- a/cookbook/5-Hierarchical-Risk-Parity.ipynb +++ b/cookbook/5-Hierarchical-Risk-Parity.ipynb @@ -1,721 +1,804 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "consolidated-vault", - "metadata": { - "id": "consolidated-vault" - }, - "source": [ - "# Hierarchical Risk Parity\n", - "\n", - "HRP is a modern portfolio optimization method inspired by machine learning.\n", - "\n", - "The idea is that by examining the hierarchical structure of the market, we can better diversify. \n", - "\n", - "In this cookbook recipe, we will cover:\n", - "\n", - "- Downloading data for HRP\n", - "- Using HRP to find the minimum variance portfolio\n", - "- Plotting dendrograms\n", - "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", - " \n", - "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", - " \n", - "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", - " \n", - "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n" - ] - }, - { - "cell_type": "markdown", - "id": "saving-safety", - "metadata": { - "id": "saving-safety" - }, - "source": [ - "## Downloading data\n", - "\n", - "HRP only requires historical returns" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "consolidated-vault", + "metadata": { + "id": "consolidated-vault" + }, + "source": [ + "# Hierarchical Risk Parity\n", + "\n", + "HRP is a modern portfolio optimization method inspired by machine learning.\n", + "\n", + "The idea is that by examining the hierarchical structure of the market, we can better diversify. \n", + "\n", + "In this cookbook recipe, we will cover:\n", + "\n", + "- Downloading data for HRP\n", + "- Using HRP to find the minimum variance portfolio\n", + "- Plotting dendrograms\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", + " \n", + "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", + " \n", + "[![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n", + " \n", + "[![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/pyportfolio/pyportfolioopt/blob/master/cookbook/5-Hierarchical-Risk-Parity.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "id": "saving-safety", + "metadata": { + "id": "saving-safety" + }, + "source": [ + "## Downloading data\n", + "\n", + "HRP only requires historical returns" + ] + }, + { + "cell_type": "code", + "id": "kvgN-hAEBYHZ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "kvgN-hAEBYHZ", + "outputId": "2cd60bf1-d484-405c-8d92-a8101f6e6750", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:02.268679Z", + "start_time": "2025-11-12T08:12:01.796130Z" + } + }, + "source": [ + "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", + "import os\n", + "if not os.path.isdir('data'):\n", + " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", + " os.chdir('PyPortfolioOpt/cookbook')" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "id": "kvgN-hAEBYHZ", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kvgN-hAEBYHZ", - "outputId": "2cd60bf1-d484-405c-8d92-a8101f6e6750" - }, - "outputs": [], - "source": [ - "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n", - "import os\n", - "if not os.path.isdir('data'):\n", - " os.system('git clone https://github.com/pyportfolio/pyportfolioopt.git')\n", - " os.chdir('PyPortfolioOpt/cookbook')" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (2.3.3)\r\n", + "Requirement already satisfied: numpy in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (2.3.4)\r\n", + "Requirement already satisfied: matplotlib in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (3.10.7)\r\n", + "Requirement already satisfied: yfinance in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (0.2.66)\r\n", + "Requirement already satisfied: PyPortfolioOpt in /Users/thomasschmelzer/projects/PyPortfolioOpt/.venv/lib/python3.12/site-packages (1.5.6)\r\n", + "Requirement already satisfied: 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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m24.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.3\u001B[0m\r\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:12:02.992222Z", + "start_time": "2025-11-12T08:12:02.272939Z" + } + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import yfinance as yf\n", + "import pypfopt\n", + "\n", + "pypfopt.__version__" + ], + "id": "c442e17aa1dc9965", + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "id": "committed-riverside", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "committed-riverside", - "outputId": "a3c8fb63-b1ef-4696-c0df-5b16f37700d8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.5.6'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import yfinance as yf\n", - "import pypfopt\n", - "\n", - "pypfopt.__version__" + "data": { + "text/plain": [ + "'1.5.6'" ] - }, + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:12:05.557248Z", + "start_time": "2025-11-12T08:12:02.995259Z" + } + }, + "cell_type": "code", + "source": [ + "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\",\n", + " \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\",\n", + " \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]\n", + "\n", + "ohlc = yf.download(tickers, period=\"max\")\n", + "prices = ohlc[\"Close\"]\n", + "prices.tail()" + ], + "id": "575bf519e8a54e4a", + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "id": "accredited-enterprise", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 385 - }, - "id": "accredited-enterprise", - "outputId": "8ffbfbd3-9b0f-4c86-ec5a-0508c068314d" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[*********************100%***********************] 15 of 15 completed\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Ticker AAPL AMD BAC BLK CVS \\\n", - "Date \n", - "2024-11-22 229.869995 138.350006 47.000000 1036.459961 58.009998 \n", - "2024-11-25 232.869995 141.130005 47.500000 1031.489990 60.080002 \n", - "2024-11-26 235.059998 137.720001 47.750000 1026.479980 59.009998 \n", - "2024-11-27 234.929993 136.240005 47.770000 1019.450012 59.959999 \n", - "2024-11-29 237.330002 137.179993 47.509998 1022.799988 59.849998 \n", - "\n", - "Ticker DIS INTU JD MA NVDA PBI \\\n", - "Date \n", - "2024-11-22 115.650002 640.119995 34.680000 520.859985 141.949997 8.05 \n", - "2024-11-25 116.000000 634.619995 34.509998 526.599976 136.020004 8.16 \n", - "2024-11-26 115.449997 638.830017 35.330002 528.479980 136.919998 8.20 \n", - "2024-11-27 117.599998 636.169983 37.189999 532.380005 135.339996 8.14 \n", - "2024-11-29 117.470001 641.729980 37.380001 532.940002 138.250000 8.06 \n", - "\n", - "Ticker TGT TM UL WMT \n", - "Date \n", - "2024-11-22 125.010002 174.399994 58.610001 90.440002 \n", - "2024-11-25 130.529999 175.830002 58.779999 89.500000 \n", - "2024-11-26 126.550003 172.520004 59.099998 91.309998 \n", - "2024-11-27 130.089996 169.720001 59.740002 91.879997 \n", - "2024-11-29 132.309998 170.630005 59.840000 92.500000 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tickers = [\"BLK\", \"BAC\", \"AAPL\", \"TM\", \"WMT\",\n", - " \"JD\", \"INTU\", \"MA\", \"UL\", \"CVS\",\n", - " \"DIS\", \"AMD\", \"NVDA\", \"PBI\", \"TGT\"]\n", - "\n", - "ohlc = yf.download(tickers, period=\"max\")\n", - "prices = ohlc[\"Adj Close\"]\n", - "prices.tail()" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_3/k_9k5d5n5zz57w7qfll9rzs40000gn/T/ipykernel_60451/1837682728.py:5: FutureWarning: YF.download() has changed argument auto_adjust default to True\n", + " ohlc = yf.download(tickers, period=\"max\")\n", + "[*********************100%***********************] 15 of 15 completed\n" + ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "confident-plant", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 281 - }, - "id": "confident-plant", - "outputId": "3ca52fc5-22ff-4a25-d58d-9f3700b04bb3" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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TickerAAPLAMDBACBLKCVSDISINTUJDMANVDAPBITGTTMULWMT
Date
2024-11-220.0059080.0062550.0116230.0082300.0159370.008107-0.056844-0.0197850.011182-0.0321810.0468140.0281270.0058830.0170050.023193
2024-11-250.0130510.0200940.010638-0.0047950.0356840.003026-0.008592-0.0049020.011020-0.0417750.0136650.0441560.0082000.002900-0.010394
2024-11-260.009404-0.0241620.005263-0.004857-0.017810-0.0047410.0066340.0237610.0035700.0066170.004902-0.030491-0.0188250.0054440.020223
2024-11-27-0.000553-0.0107460.000419-0.0068490.0160990.018623-0.0041640.0526460.007380-0.011540-0.0073170.027973-0.0162300.0108290.006242
2024-11-290.0102160.006899-0.0054430.003286-0.001835-0.0011050.0087400.0051090.0010520.021501-0.0098280.0170650.0053620.0016740.006748
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" - ], - "text/plain": [ - "Ticker AAPL AMD BAC BLK CVS DIS \\\n", - "Date \n", - "2024-11-22 0.005908 0.006255 0.011623 0.008230 0.015937 0.008107 \n", - "2024-11-25 0.013051 0.020094 0.010638 -0.004795 0.035684 0.003026 \n", - "2024-11-26 0.009404 -0.024162 0.005263 -0.004857 -0.017810 -0.004741 \n", - "2024-11-27 -0.000553 -0.010746 0.000419 -0.006849 0.016099 0.018623 \n", - "2024-11-29 0.010216 0.006899 -0.005443 0.003286 -0.001835 -0.001105 \n", - "\n", - "Ticker INTU JD MA NVDA PBI TGT \\\n", - "Date \n", - "2024-11-22 -0.056844 -0.019785 0.011182 -0.032181 0.046814 0.028127 \n", - "2024-11-25 -0.008592 -0.004902 0.011020 -0.041775 0.013665 0.044156 \n", - "2024-11-26 0.006634 0.023761 0.003570 0.006617 0.004902 -0.030491 \n", - "2024-11-27 -0.004164 0.052646 0.007380 -0.011540 -0.007317 0.027973 \n", - "2024-11-29 0.008740 0.005109 0.001052 0.021501 -0.009828 0.017065 \n", - "\n", - "Ticker TM UL WMT \n", - "Date \n", - "2024-11-22 0.005883 0.017005 0.023193 \n", - "2024-11-25 0.008200 0.002900 -0.010394 \n", - "2024-11-26 -0.018825 0.005444 0.020223 \n", - "2024-11-27 -0.016230 0.010829 0.006242 \n", - "2024-11-29 0.005362 0.001674 0.006748 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "Ticker AAPL AMD BAC BLK CVS \\\n", + "Date \n", + "2025-11-05 269.878387 256.329987 52.450001 1073.569946 78.550003 \n", + "2025-11-06 269.508728 237.699997 53.290001 1069.439941 78.660004 \n", + "2025-11-07 268.209991 233.539993 53.200001 1082.199951 78.989998 \n", + "2025-11-10 269.429993 243.979996 53.419998 1082.630005 77.540001 \n", + "2025-11-11 275.250000 237.520004 53.630001 1085.760010 79.870003 \n", + "\n", + "Ticker DIS INTU JD MA NVDA \\\n", + "Date \n", + "2025-11-05 111.360001 655.330017 32.040001 553.309998 195.210007 \n", + "2025-11-06 110.489998 653.640015 31.950001 553.280029 188.080002 \n", + "2025-11-07 110.739998 648.849976 31.790001 551.969971 188.149994 \n", + "2025-11-10 112.239998 653.270020 31.410000 552.960022 199.050003 \n", + "2025-11-11 114.849998 654.320007 31.610001 558.349976 193.160004 \n", + "\n", + "Ticker PBI TGT TM UL WMT \n", + "Date \n", + "2025-11-05 9.389523 91.940002 199.149994 60.760719 101.470001 \n", + "2025-11-06 9.280573 89.150002 202.940002 60.383999 101.680000 \n", + "2025-11-07 9.340000 91.239998 201.979996 61.470001 102.589996 \n", + "2025-11-10 9.270000 90.730003 203.940002 60.810001 102.419998 \n", + "2025-11-11 9.440000 91.580002 205.990005 61.070000 103.440002 " ], - "source": [ - "from pypfopt import expected_returns\n", - "\n", - "rets = expected_returns.returns_from_prices(prices)\n", - "rets.tail()" - ] - }, - { - "cell_type": "markdown", - "id": "answering-tamil", - "metadata": { - "id": "answering-tamil" - }, - "source": [ - "## HRP optimization\n", - "\n", - "HRP uses a completely different backend, so it is currently not possible to pass constraints or specify an objective function." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "crazy-minority", - "metadata": { - "id": "crazy-minority" - }, - "outputs": [], - "source": [ - "from pypfopt import HRPOpt" + "text/html": [ + "
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TickerAAPLAMDBACBLKCVSDISINTUJDMANVDAPBITGTTMULWMT
Date
2025-11-05269.878387256.32998752.4500011073.56994678.550003111.360001655.33001732.040001553.309998195.2100079.38952391.940002199.14999460.760719101.470001
2025-11-06269.508728237.69999753.2900011069.43994178.660004110.489998653.64001531.950001553.280029188.0800029.28057389.150002202.94000260.383999101.680000
2025-11-07268.209991233.53999353.2000011082.19995178.989998110.739998648.84997631.790001551.969971188.1499949.34000091.239998201.97999661.470001102.589996
2025-11-10269.429993243.97999653.4199981082.63000577.540001112.239998653.27002031.410000552.960022199.0500039.27000090.730003203.94000260.810001102.419998
2025-11-11275.250000237.52000453.6300011085.76001079.870003114.849998654.32000731.610001558.349976193.1600049.44000091.580002205.99000561.070000103.440002
\n", + "
" ] - }, + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-11-12T08:12:05.687775Z", + "start_time": "2025-11-12T08:12:05.676600Z" + } + }, + "cell_type": "code", + "source": [ + "from pypfopt import expected_returns\n", + "\n", + "rets = expected_returns.returns_from_prices(prices)\n", + "rets.tail()" + ], + "id": "5a2e5f13dd84fb3d", + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "id": "determined-license", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "determined-license", - "outputId": "ba776958-535f-475b-b6bf-7c6994d311f8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('AAPL', 0.03543),\n", - " ('AMD', 0.01748),\n", - " ('BAC', 0.0462),\n", - " ('BLK', 0.05103),\n", - " ('CVS', 0.11493),\n", - " ('DIS', 0.06638),\n", - " ('INTU', 0.03285),\n", - " ('JD', 0.04617),\n", - " ('MA', 0.05374),\n", - " ('NVDA', 0.01695),\n", - " ('PBI', 0.07773),\n", - " ('TGT', 0.07838),\n", - " ('TM', 0.11072),\n", - " ('UL', 0.156),\n", - " ('WMT', 0.09599)])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/plain": [ + "Ticker AAPL AMD BAC BLK CVS DIS \\\n", + "Date \n", + "2025-11-05 0.000370 0.025115 -0.020359 0.012888 0.006019 -0.000987 \n", + "2025-11-06 -0.001370 -0.072680 0.016015 -0.003847 0.001400 -0.007813 \n", + "2025-11-07 -0.004819 -0.017501 -0.001689 0.011931 0.004195 0.002263 \n", + "2025-11-10 0.004549 0.044703 0.004135 0.000397 -0.018357 0.013545 \n", + "2025-11-11 0.021601 -0.026478 0.003931 0.002891 0.030049 0.023254 \n", + "\n", + "Ticker INTU JD MA NVDA PBI TGT \\\n", + "Date \n", + "2025-11-05 -0.007707 0.006914 0.001013 -0.017515 -0.010438 0.021783 \n", + "2025-11-06 -0.002579 -0.002809 -0.000054 -0.036525 -0.011603 -0.030346 \n", + "2025-11-07 -0.007328 -0.005008 -0.002368 0.000372 0.006403 0.023444 \n", + "2025-11-10 0.006812 -0.011953 0.001794 0.057933 -0.007495 -0.005590 \n", + "2025-11-11 0.001607 0.006367 0.009747 -0.029591 0.018339 0.009368 \n", + "\n", + "Ticker TM UL WMT \n", + "Date \n", + "2025-11-05 -0.023248 0.007893 -0.007822 \n", + "2025-11-06 0.019031 -0.006200 0.002070 \n", + "2025-11-07 -0.004730 0.017985 0.008950 \n", + "2025-11-10 0.009704 -0.010737 -0.001657 \n", + "2025-11-11 0.010052 0.004276 0.009959 " ], - "source": [ - "hrp = HRPOpt(rets)\n", - "hrp.optimize()\n", - "weights = hrp.clean_weights()\n", - "weights" + "text/html": [ + "
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TickerAAPLAMDBACBLKCVSDISINTUJDMANVDAPBITGTTMULWMT
Date
2025-11-050.0003700.025115-0.0203590.0128880.006019-0.000987-0.0077070.0069140.001013-0.017515-0.0104380.021783-0.0232480.007893-0.007822
2025-11-06-0.001370-0.0726800.016015-0.0038470.001400-0.007813-0.002579-0.002809-0.000054-0.036525-0.011603-0.0303460.019031-0.0062000.002070
2025-11-07-0.004819-0.017501-0.0016890.0119310.0041950.002263-0.007328-0.005008-0.0023680.0003720.0064030.023444-0.0047300.0179850.008950
2025-11-100.0045490.0447030.0041350.000397-0.0183570.0135450.006812-0.0119530.0017940.057933-0.007495-0.0055900.009704-0.010737-0.001657
2025-11-110.021601-0.0264780.0039310.0028910.0300490.0232540.0016070.0063670.009747-0.0295910.0183390.0093680.0100520.0042760.009959
\n", + "
" ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "answering-tamil", + "metadata": { + "id": "answering-tamil" + }, + "source": [ + "## HRP optimization\n", + "\n", + "HRP uses a completely different backend, so it is currently not possible to pass constraints or specify an objective function." + ] + }, + { + "cell_type": "code", + "id": "crazy-minority", + "metadata": { + "id": "crazy-minority", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:05.760765Z", + "start_time": "2025-11-12T08:12:05.758585Z" + } + }, + "source": [ + "from pypfopt import HRPOpt" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "determined-license", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "determined-license", + "outputId": "ba776958-535f-475b-b6bf-7c6994d311f8", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:05.831839Z", + "start_time": "2025-11-12T08:12:05.809500Z" + } + }, + "source": [ + "hrp = HRPOpt(rets)\n", + "hrp.optimize()\n", + "weights = hrp.clean_weights()\n", + "weights" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "id": "existing-memphis", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 575 - }, - "id": "existing-memphis", - "outputId": "721f641a-3871-4bae-84f8-94cc0b75be87" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pd.Series(weights).plot.pie(figsize=(10, 10));" + "data": { + "text/plain": [ + "OrderedDict([('AAPL', 0.03963),\n", + " ('AMD', 0.02907),\n", + " ('BAC', 0.0417),\n", + " ('BLK', 0.04516),\n", + " ('CVS', 0.11381),\n", + " ('DIS', 0.05955),\n", + " ('INTU', 0.03699),\n", + " ('JD', 0.0467),\n", + " ('MA', 0.04823),\n", + " ('NVDA', 0.02293),\n", + " ('PBI', 0.07645),\n", + " ('TGT', 0.07642),\n", + " ('TM', 0.09703),\n", + " ('UL', 0.17211),\n", + " ('WMT', 0.09423)])" ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "existing-memphis", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 575 }, + "id": "existing-memphis", + "outputId": "721f641a-3871-4bae-84f8-94cc0b75be87", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:06.356024Z", + "start_time": "2025-11-12T08:12:05.897973Z" + } + }, + "source": [ + "pd.Series(weights).plot.pie(figsize=(10, 10));" + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "id": "dramatic-spyware", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dramatic-spyware", - "outputId": "03958175-ebf7-4eb0-cc3a-57d969056f36" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expected annual return: 18.7%\n", - "Annual volatility: 18.9%\n", - "Sharpe Ratio: 0.99\n" - ] - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "hrp.portfolio_performance(verbose=True);" - ] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 7 + }, + { + "cell_type": "code", + "id": "dramatic-spyware", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "dramatic-spyware", + "outputId": "03958175-ebf7-4eb0-cc3a-57d969056f36", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:06.396576Z", + "start_time": "2025-11-12T08:12:06.371921Z" + } + }, + "source": [ + "hrp.portfolio_performance(verbose=True);" + ], + "outputs": [ { - "cell_type": "markdown", - "id": "occupational-costume", - "metadata": { - "id": "occupational-costume" - }, - "source": [ - "## Plotting\n", - "\n", - "It is very simple to plot a dendrogram (tree diagram) based on the hierarchical structure of asset returns" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected annual return: 18.9%\n", + "Annual volatility: 18.9%\n", + "Sharpe Ratio: 1.00\n" + ] + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "occupational-costume", + "metadata": { + "id": "occupational-costume" + }, + "source": [ + "## Plotting\n", + "\n", + "It is very simple to plot a dendrogram (tree diagram) based on the hierarchical structure of asset returns" + ] + }, + { + "cell_type": "code", + "id": "upset-meaning", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "id": "upset-meaning", + "outputId": "65f2985a-e74f-405c-a01a-d7877a0e5326", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:06.500586Z", + "start_time": "2025-11-12T08:12:06.418947Z" + } + }, + "source": [ + "from pypfopt import plotting\n", + "\n", + "plotting.plot_dendrogram(hrp); " + ], + "outputs": [ { - "cell_type": "code", - "execution_count": 9, - "id": "upset-meaning", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "id": "upset-meaning", - "outputId": "65f2985a-e74f-405c-a01a-d7877a0e5326" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } + "data": { + "text/plain": [ + "
" ], - "source": [ - "from pypfopt import plotting\n", - "\n", - "plotting.plot_dendrogram(hrp); " - ] - }, - { - "cell_type": "markdown", - "id": "brave-shock", - "metadata": { - "id": "brave-shock" - }, - "source": [ - "If you look at this dendogram closely, you can see that most of the clusters make a lot of sense. For example, AMD and NVDA (both semiconductor manufacturers) are grouped." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "macro-found", - "metadata": { - "id": "macro-found" - }, - "outputs": [], - "source": [] + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "5-Hierarchical-Risk-Parity.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.4" + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "brave-shock", + "metadata": { + "id": "brave-shock" + }, + "source": [ + "If you look at this dendogram closely, you can see that most of the clusters make a lot of sense. For example, AMD and NVDA (both semiconductor manufacturers) are grouped." + ] + }, + { + "cell_type": "code", + "id": "macro-found", + "metadata": { + "id": "macro-found", + "ExecuteTime": { + "end_time": "2025-11-12T08:12:06.518660Z", + "start_time": "2025-11-12T08:12:06.514826Z" } + }, + "source": [], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "5-Hierarchical-Risk-Parity.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "name": "python3", + "language": "python" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/pypfopt/black_litterman.py b/pypfopt/black_litterman.py index f24df06d..61281d4a 100644 --- a/pypfopt/black_litterman.py +++ b/pypfopt/black_litterman.py @@ -135,7 +135,7 @@ def __init__( view_confidences=None, tau=0.05, risk_aversion=1, - **kwargs + **kwargs, ): """ :param cov_matrix: NxN covariance matrix of returns @@ -296,8 +296,7 @@ def _set_omega(self, omega, view_confidences): elif omega == "idzorek": if view_confidences is None: raise ValueError( - "To use Idzorek's method, please supply a vector of percentage " - "confidence levels for each view." + "To use Idzorek's method, please supply a vector of percentage confidence levels for each view." ) if not isinstance(view_confidences, np.ndarray): try: diff --git a/pypfopt/cla.py b/pypfopt/cla.py index 910d5984..884fbb58 100644 --- a/pypfopt/cla.py +++ b/pypfopt/cla.py @@ -314,11 +314,11 @@ def _solve(self): covarF_inv = np.linalg.inv(covarF) j = 0 for i in f: - l, bi = self._compute_lambda( + lam, bi = self._compute_lambda( covarF_inv, covarFB, meanF, wB, j, [self.lB[i], self.uB[i]] ) - if CLA._infnone(l) > CLA._infnone(l_in): - l_in, i_in, bi_in = l, i, bi + if CLA._infnone(lam) > CLA._infnone(l_in): + l_in, i_in, bi_in = lam, i, bi j += 1 # 2) case b): Free one bounded weight l_out = None @@ -327,7 +327,7 @@ def _solve(self): for i in b: covarF, covarFB, meanF, wB = self._get_matrices(f + [i]) covarF_inv = np.linalg.inv(covarF) - l, bi = self._compute_lambda( + lam, bi = self._compute_lambda( covarF_inv, covarFB, meanF, @@ -335,10 +335,10 @@ def _solve(self): meanF.shape[0] - 1, self.w[-1][i], ) - if (self.ls[-1] is None or l < self.ls[-1]) and l > CLA._infnone( - l_out - ): - l_out, i_out = l, i + if ( + self.ls[-1] is None or lam < self.ls[-1] + ) and lam > CLA._infnone(l_out): + l_out, i_out = lam, i if (l_in is None or l_in < 0) and (l_out is None or l_out < 0): # 3) compute minimum variance solution self.ls.append(0) diff --git a/pypfopt/discrete_allocation.py b/pypfopt/discrete_allocation.py index d67f9116..4e4d8d4c 100644 --- a/pypfopt/discrete_allocation.py +++ b/pypfopt/discrete_allocation.py @@ -4,10 +4,12 @@ """ import collections +from warnings import warn import cvxpy as cp import numpy as np import pandas as pd +from skbase.utils.dependencies import _check_soft_dependencies from . import exceptions @@ -252,7 +254,8 @@ def greedy_portfolio(self, reinvest=False, verbose=False): self._allocation_rmse_error(verbose) return self.allocation, available_funds - def lp_portfolio(self, reinvest=False, verbose=False, solver="ECOS_BB"): + # todo 1.7.0: remove ECOS_BB defaulting behavior from docstring + def lp_portfolio(self, reinvest=False, verbose=False, solver=None): """ Convert continuous weights into a discrete portfolio allocation using integer programming. @@ -262,11 +265,23 @@ def lp_portfolio(self, reinvest=False, verbose=False, solver="ECOS_BB"): :param verbose: print error analysis? :type verbose: bool :param solver: the CVXPY solver to use (must support mixed-integer programs) - :type solver: str, defaults to "ECOS_BB" + :type solver: str, defaults to "ECOS_BB" if ecos is installed, else None :return: the number of shares of each ticker that should be purchased, along with the amount of funds leftover. :rtype: (dict, float) """ + # todo 1.7.0: remove this defaulting behavior + if solver is None and _check_soft_dependencies("ecos", severity="none"): + solver = "ECOS_BB" + warn( + "The default solver for lp_portfolio will change from ECOS_BB to" + "None, the cvxpy default solver, in release 1.7.0." + "To continue using ECOS_BB as the solver, " + "please set solver='ECOS_BB' explicitly.", + FutureWarning, + ) + # end todo + if any([w < 0 for _, w in self.weights]): longs = {t: w for t, w in self.weights if w >= 0} shorts = {t: -w for t, w in self.weights if w < 0} diff --git a/pypfopt/plotting.py b/pypfopt/plotting.py index 54fcf48a..4bc3c8fa 100644 --- a/pypfopt/plotting.py +++ b/pypfopt/plotting.py @@ -16,10 +16,15 @@ from . import CLA, EfficientFrontier, exceptions, risk_models -try: - import matplotlib.pyplot as plt -except (ModuleNotFoundError, ImportError): # pragma: no cover - raise ImportError("Please install matplotlib via pip or poetry") + +def _import_matplotlib(): + """Helper function to import matplotlib only when needed""" + try: + import matplotlib.pyplot as plt + + return plt + except (ModuleNotFoundError, ImportError): # pragma: no cover + raise ImportError("Please install matplotlib via pip or poetry") def _get_plotly(): @@ -46,6 +51,8 @@ def _plot_io(**kwargs): :param showfig: whether to plt.show() the figure, defaults to False :type showfig: bool, optional """ + plt = _import_matplotlib() + filename = kwargs.get("filename", None) showfig = kwargs.get("showfig", False) dpi = kwargs.get("dpi", 300) @@ -73,6 +80,8 @@ def plot_covariance(cov_matrix, plot_correlation=False, show_tickers=True, **kwa :return: matplotlib axis :rtype: matplotlib.axes object """ + plt = _import_matplotlib() + if plot_correlation: matrix = risk_models.cov_to_corr(cov_matrix) else: @@ -110,6 +119,8 @@ def plot_dendrogram(hrp, ax=None, show_tickers=True, **kwargs): :return: matplotlib axis :rtype: matplotlib.axes object """ + plt = _import_matplotlib() + ax = ax or plt.gca() if hrp.clusters is None: @@ -337,6 +348,8 @@ def plot_efficient_frontier( :return: matplotlib axis :rtype: matplotlib.axes object """ + plt = _import_matplotlib() + if interactive: go, _ = _get_plotly() ax = go.Figure() @@ -393,6 +406,8 @@ def plot_weights(weights, ax=None, **kwargs): :return: matplotlib axis :rtype: matplotlib.axes """ + plt = _import_matplotlib() + ax = ax or plt.gca() desc = sorted(weights.items(), key=lambda x: x[1], reverse=True) diff --git a/pypfopt/risk_models.py b/pypfopt/risk_models.py index 9d1ff85a..cf62509f 100644 --- a/pypfopt/risk_models.py +++ b/pypfopt/risk_models.py @@ -181,7 +181,7 @@ def semicovariance( benchmark=0.000079, frequency=252, log_returns=False, - **kwargs + **kwargs, ): """ Estimate the semicovariance matrix, i.e the covariance given that @@ -290,7 +290,7 @@ def min_cov_determinant( frequency=252, random_state=None, log_returns=False, - **kwargs + **kwargs, ): # pragma: no cover warnings.warn("min_cov_determinant is deprecated and will be removed in v1.5") diff --git a/pyproject.toml b/pyproject.toml index 9802077a..f18b8fbc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "pandas>=0.19", "scikit-learn>=0.24.1", "scipy>=1.3.0", + "scikit-base<0.14.0", ] [project.optional-dependencies] @@ -55,18 +56,21 @@ all_extras = [ "plotly>=5.0.0,<6", "scikit-learn>=0.24.1", "ecos>=2.0.14,<2.1", - "plotly>=5.0.0,<6", + "plotly>=5.0.0,<7", "cvxopt; python_version < '3.14'", ] # dev - the developer dependency set, for contributors and CI dev = [ - "pytest>=7.1.2", - "flake8>=4.0.1", - "black>=22.3.0", - "pytest-cov>=3.0.0", - "yfinance>=0.1.70", - "isort", + "pytest>=9.0.0", + "pytest-cov>=7.0.0", + "yfinance>=0.2.66", +] + +# notebook tests +notebook_test = [ + "nbmake", + "pytest-rerunfailures", ] [project.urls] @@ -85,8 +89,28 @@ requires = [ [tool.setuptools.packages.find] exclude = ["example", "example.*", "tests", "tests.*"] -[tool.black] +[tool.ruff] line-length = 88 +# Keep Ruff aligned with project target version +target-version = "py311" +# Exclude individual files/patterns from Ruff linting/formatting +exclude = [ + "tests/test_imports.py", + "cookbook/*.ipynb", +] + +[tool.ruff.lint] +# Keep the same selected rule sets as in ruff.toml +select = ["F", "I"] + +[tool.ruff.format] +# Formatting configuration +quote-style = "double" +indent-style = "space" +line-ending = "auto" +skip-magic-trailing-comma = false -[tool.isort] -profile = "black" +[tool.ruff.lint.isort] +known-first-party = ["pypfopt"] +combine-as-imports = true +force-sort-within-sections = true diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..7c90d815 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +cvxpy>=1.1.19 +numpy>=1.0.0 +pandas>=0.19 +scikit-base<0.14.0 +scikit-learn>=0.24.1 +scipy>=1.3.0 diff --git a/tests/test_efficient_cdar.py b/tests/test_efficient_cdar.py index 4ed6d57b..746efdec 100644 --- a/tests/test_efficient_cdar.py +++ b/tests/test_efficient_cdar.py @@ -1,5 +1,6 @@ import numpy as np import pytest +from skbase.utils.dependencies import _check_soft_dependencies from pypfopt import EfficientCDaR, expected_returns, objective_functions from pypfopt.exceptions import OptimizationError @@ -151,6 +152,10 @@ def test_min_cdar_extra_constraints(): assert w["GOOG"] >= 0.025 and w["MA"] <= 0.035 +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_cdar_different_solver(): cd = setup_efficient_cdar(solver="ECOS") w = cd.min_cdar() @@ -182,6 +187,10 @@ def test_min_cdar_tx_costs(): assert np.abs(prev_w - w2).sum() < np.abs(prev_w - w1).sum() +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_cdar_L2_reg(): cd = setup_efficient_cdar(solver="ECOS") cd.add_objective(objective_functions.L2_reg, gamma=0.1) diff --git a/tests/test_efficient_cvar.py b/tests/test_efficient_cvar.py index 14a2d863..2d20c79e 100644 --- a/tests/test_efficient_cvar.py +++ b/tests/test_efficient_cvar.py @@ -1,5 +1,6 @@ import numpy as np import pytest +from skbase.utils.dependencies import _check_soft_dependencies from pypfopt import EfficientCVaR, expected_returns, objective_functions from pypfopt.exceptions import OptimizationError @@ -156,6 +157,10 @@ def test_min_cvar_extra_constraints(): assert w["GOOG"] >= 0.025 and w["AAPL"] <= 0.035 +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_cvar_different_solver(): cv = setup_efficient_cvar(solver="ECOS") w = cv.min_cvar() @@ -186,6 +191,10 @@ def test_min_cvar_tx_costs(): assert np.abs(prev_w - w2).sum() < np.abs(prev_w - w1).sum() +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_cvar_L2_reg(): cv = setup_efficient_cvar(solver="ECOS") cv.add_objective(objective_functions.L2_reg, gamma=0.1) diff --git a/tests/test_efficient_frontier.py b/tests/test_efficient_frontier.py index 580f2dd5..4028e1a7 100644 --- a/tests/test_efficient_frontier.py +++ b/tests/test_efficient_frontier.py @@ -5,6 +5,7 @@ import pandas as pd import pytest import scipy.optimize as sco +from skbase.utils.dependencies import _check_soft_dependencies from pypfopt import ( EfficientFrontier, @@ -106,6 +107,10 @@ def test_min_volatility(): ) +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_volatility_different_solver(): ef = setup_efficient_frontier(solver="ECOS") w = ef.min_volatility() @@ -1042,6 +1047,10 @@ def test_efficient_risk_market_neutral_L2_reg(): ) +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_efficient_risk_market_neutral_warning(): ef = setup_efficient_frontier(solver=cp.ECOS) with pytest.warns(RuntimeWarning) as w: @@ -1088,6 +1097,10 @@ def test_efficient_frontier_error(): EfficientFrontier(ef.expected_returns, 0.01) +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_efficient_return_many_values(): ef = setup_efficient_frontier(solver=cp.ECOS) for target_return in np.arange(0.25, 0.28, 0.01): @@ -1217,6 +1230,10 @@ def test_efficient_return_market_neutral_unbounded(): assert long_only_sharpe < sharpe +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_efficient_return_market_neutral_warning(): # This fails ef = setup_efficient_frontier(solver=cp.ECOS) diff --git a/tests/test_efficient_semivariance.py b/tests/test_efficient_semivariance.py index 4aa9cc51..7da9655b 100644 --- a/tests/test_efficient_semivariance.py +++ b/tests/test_efficient_semivariance.py @@ -1,6 +1,7 @@ +from cvxpy.error import SolverError import numpy as np import pytest -from cvxpy.error import SolverError +from skbase.utils.dependencies import _check_soft_dependencies from pypfopt import ( EfficientFrontier, @@ -176,6 +177,10 @@ def test_min_semivariance_extra_constraints(): assert w["GOOG"] >= 0.025 and w["AAPL"] <= 0.035 +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_min_semivariance_different_solver(): es = setup_efficient_semivariance(solver="ECOS") w = es.min_semivariance() @@ -347,6 +352,10 @@ def test_max_quadratic_utility_with_shorts(): ) +@pytest.mark.skipif( + not _check_soft_dependencies(["ecos"], severity="none"), + reason="skip test if ecos is not installed in environment", +) def test_max_quadratic_utility_market_neutral(): es = setup_efficient_semivariance(solver="ECOS", weight_bounds=(-1, 1)) es.max_quadratic_utility(market_neutral=True) diff --git a/tests/test_plotting.py b/tests/test_plotting.py index 21db2d3b..a4e5a3fb 100644 --- a/tests/test_plotting.py +++ b/tests/test_plotting.py @@ -1,11 +1,10 @@ import os import tempfile -import matplotlib -import matplotlib.pyplot as plt import numpy as np import pandas as pd import pytest +from skbase.utils.dependencies import _check_soft_dependencies from pypfopt import ( CLA, @@ -18,7 +17,13 @@ from tests.utilities_for_tests import get_data, setup_efficient_frontier +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_correlation_plot(): + import matplotlib.pyplot as plt + plt.figure() df = get_data() S = risk_models.CovarianceShrinkage(df).ledoit_wolf() @@ -49,7 +54,14 @@ def test_correlation_plot(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_dendrogram_plot(): + import matplotlib + import matplotlib.pyplot as plt + plt.figure() df = get_data() returns = df.pct_change().dropna(how="all") @@ -58,12 +70,12 @@ def test_dendrogram_plot(): ax = plotting.plot_dendrogram(hrp, showfig=False) assert len(ax.findobj()) > 180 - assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection + assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection) plt.clf() ax = plotting.plot_dendrogram(hrp, show_tickers=False, showfig=False) assert len(ax.findobj()) > 60 - assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection + assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection) plt.clf() plt.close() @@ -78,12 +90,18 @@ def test_dendrogram_plot(): == "hrp param has not been optimized. Attempting optimization." ) assert len(ax.findobj()) > 60 - assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection + assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection) plt.clf() plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_cla_plot(): + import matplotlib.pyplot as plt + plt.figure() df = get_data() rets = expected_returns.mean_historical_return(df) @@ -100,7 +118,13 @@ def test_cla_plot(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_cla_plot_ax(): + import matplotlib.pyplot as plt + plt.figure() df = get_data() rets = expected_returns.mean_historical_return(df) @@ -114,7 +138,13 @@ def test_cla_plot_ax(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_default_ef_plot(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() ax = plotting.plot_efficient_frontier(ef, show_assets=True) @@ -131,7 +161,13 @@ def test_default_ef_plot(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_default_ef_plot_labels(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() ax = plotting.plot_efficient_frontier(ef, show_assets=True, show_tickers=True) @@ -139,7 +175,13 @@ def test_default_ef_plot_labels(): plt.clf() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_ef_plot_utility(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() delta_range = np.arange(0.001, 50, 1) @@ -151,7 +193,13 @@ def test_ef_plot_utility(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_ef_plot_errors(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() delta_range = np.arange(0.001, 50, 1) @@ -169,7 +217,13 @@ def test_ef_plot_errors(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_ef_plot_risk(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() ef.min_volatility() @@ -185,7 +239,13 @@ def test_ef_plot_risk(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_ef_plot_return(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() # Internally _max_return() is used, so subtract epsilon @@ -199,7 +259,13 @@ def test_ef_plot_return(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_ef_plot_utility_short(): + import matplotlib.pyplot as plt + plt.figure() ef = EfficientFrontier( *setup_efficient_frontier(data_only=True), weight_bounds=(None, None) @@ -213,7 +279,13 @@ def test_ef_plot_utility_short(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_constrained_ef_plot_utility(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() ef.add_constraint(lambda w: w[0] >= 0.2) @@ -229,7 +301,13 @@ def test_constrained_ef_plot_utility(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_constrained_ef_plot_risk(): + import matplotlib.pyplot as plt + plt.figure() ef = EfficientFrontier( *setup_efficient_frontier(data_only=True), weight_bounds=(None, None) @@ -249,7 +327,13 @@ def test_constrained_ef_plot_risk(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_weight_plot(): + import matplotlib.pyplot as plt + plt.figure() df = get_data() returns = df.pct_change().dropna(how="all") @@ -262,7 +346,13 @@ def test_weight_plot(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_weight_plot_multi(): + import matplotlib.pyplot as plt + ef = setup_efficient_frontier() w1 = ef.min_volatility() ef = setup_efficient_frontier() @@ -278,7 +368,13 @@ def test_weight_plot_multi(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_weight_plot_add_attribute(): + import matplotlib.pyplot as plt + plt.figure() ef = setup_efficient_frontier() @@ -289,7 +385,13 @@ def test_weight_plot_add_attribute(): plt.close() +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_plotting_edge_case(): + import matplotlib.pyplot as plt + # raised in issue #333 mu = pd.Series([0.043389, 0.036194]) S = pd.DataFrame([[0.000562, 0.002273], [0.002273, 0.027710]]) @@ -306,6 +408,10 @@ def test_plotting_edge_case(): ) +@pytest.mark.skipif( + not _check_soft_dependencies(["matplotlib"], severity="none"), + reason="skip test if matplotlib is not installed in environment", +) def test_plot_efficient_frontier(): ef = setup_efficient_frontier() ef.min_volatility()