30_programming_project.ipynb

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        "<a href=\"https://colab.research.google.com/github/heispv/programming-feb2023/blob/master/30_programming_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Importing the libarries"
      ],
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        "id": "c3SnLNUN2r1A"
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      "cell_type": "code",
      "execution_count": 230,
      "metadata": {
        "id": "_pA3aDyVDY10"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mplfinance"
      ],
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      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mplfinance in /usr/local/lib/python3.10/dist-packages (0.12.10b0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mplfinance) (3.7.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from mplfinance) (1.5.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.1.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (0.11.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (4.42.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mplfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->mplfinance) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mplfinance as mpf"
      ],
      "metadata": {
        "id": "11Kf7cmg2mex"
      },
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocess the data"
      ],
      "metadata": {
        "id": "PsaPavYK2wJv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Miam va be koamke `pd.read_csv('file_location')` file csv ro be onave yek pandas dataframe mikhoonim."
      ],
      "metadata": {
        "id": "YjJfenX4G4BE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/ML_codes/GBP_CAD-15.csv')"
      ],
      "metadata": {
        "id": "kDQGcsXnGkJi"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Baraye inke 5 khat aval yek dataframe ro bebinim mitoonim az method e `.head()` estefade konim. agar khastim 5 khat akhar oon ro bebinim, mitoonim az method e `.tail()` estefade konim."
      ],
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        "data.head()"
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              "   2022.10.17  5:00  1.55482    1.555  1.55409  1.55464\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "> Ba tavajoh be inke data ma sutun hash esmi nadasht, bayad baraye harkudum azoona yek esm entekhab konim."
      ],
      "metadata": {
        "id": "S-43iKaUKx_A"
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        "data.columns = column_names"
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      "metadata": {
        "id": "rLLUP-PyHFGp"
      },
      "execution_count": 235,
      "outputs": []
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      },
      "execution_count": 236,
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              "         date  time     open     high      low    close\n",
              "0  2022.10.17  5:15  1.55464  1.55488  1.55401  1.55404\n",
              "1  2022.10.17  5:30  1.55404  1.55518  1.55362  1.55383\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-77eb47c3-4f4d-4ea7-868a-bce90c1bebb4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-77eb47c3-4f4d-4ea7-868a-bce90c1bebb4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-70ed38a6-8c4e-4005-91cf-81c41b8d2f2f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70ed38a6-8c4e-4005-91cf-81c41b8d2f2f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-70ed38a6-8c4e-4005-91cf-81c41b8d2f2f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> be komake raveshe bala avvalin radif data e ma hazf shode, baraye inke in etefagh nayofte, mitoonim moghei ke darim az `.read_csv()` estefade mikonim argument `names` ro be onave yek optinal argument be method emoon pass bedim va tooye in list e esme sutun haro benvisim."
      ],
      "metadata": {
        "id": "KkOXHJ_KK6ep"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/ML_codes/GBP_CAD-15.csv', names = column_names)"
      ],
      "metadata": {
        "id": "DoHsDwq0IJRm"
      },
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXNSdVtYzoEJ",
        "outputId": "d8121f8f-f5c2-4cb3-ee8e-eebc5683eb28"
      },
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uoiUOz3JIc0W",
        "outputId": "62ba4494-af33-4c51-defe-47474d37762e"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date  time     open     high      low    close\n",
              "0  2022.10.17  5:00  1.55482  1.55500  1.55409  1.55464\n",
              "1  2022.10.17  5:15  1.55464  1.55488  1.55401  1.55404\n",
              "2  2022.10.17  5:30  1.55404  1.55518  1.55362  1.55383\n",
              "3  2022.10.17  5:45  1.55383  1.55432  1.55304  1.55322\n",
              "4  2022.10.17  6:00  1.55322  1.55356  1.55244  1.55278"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00c18d53-9d42-47dd-9102-2cf2198b5caf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>time</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:00</td>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:15</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:30</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:45</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>6:00</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00c18d53-9d42-47dd-9102-2cf2198b5caf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-00c18d53-9d42-47dd-9102-2cf2198b5caf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-00c18d53-9d42-47dd-9102-2cf2198b5caf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ce9bb3ac-3984-4dba-82d2-6d5bf2d7c35b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce9bb3ac-3984-4dba-82d2-6d5bf2d7c35b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce9bb3ac-3984-4dba-82d2-6d5bf2d7c35b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "close_bars = data['close'][:100]\n",
        "close_bars"
      ],
      "metadata": {
        "id": "ZCZtQa1xJXL1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a97a8a2-3b95-4aa1-96a8-559347ee5a5e"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1.55464\n",
              "1     1.55404\n",
              "2     1.55383\n",
              "3     1.55322\n",
              "4     1.55278\n",
              "       ...   \n",
              "95    1.55652\n",
              "96    1.55744\n",
              "97    1.55705\n",
              "98    1.55668\n",
              "99    1.55705\n",
              "Name: close, Length: 100, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(close_bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_YAZmURrzamQ",
        "outputId": "d141c771-224b-4525-e57c-afe877ce28ad"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "close_bars = np.array(close_bars)\n",
        "close_bars"
      ],
      "metadata": {
        "id": "94Gfz0oQKp8s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c9e4d8c-8b59-4c59-de24-1f3b9b695bb3"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.55464, 1.55404, 1.55383, 1.55322, 1.55278, 1.55273, 1.55338,\n",
              "       1.55386, 1.55435, 1.55408, 1.55275, 1.55202, 1.55288, 1.55297,\n",
              "       1.55454, 1.55636, 1.55802, 1.55964, 1.56111, 1.55984, 1.55797,\n",
              "       1.55698, 1.555  , 1.55587, 1.55844, 1.55522, 1.55522, 1.55554,\n",
              "       1.55624, 1.55798, 1.55958, 1.55887, 1.55726, 1.55922, 1.56122,\n",
              "       1.55777, 1.5559 , 1.55715, 1.5598 , 1.56116, 1.55924, 1.55882,\n",
              "       1.5597 , 1.55969, 1.56116, 1.55994, 1.56161, 1.55914, 1.55728,\n",
              "       1.55912, 1.56091, 1.56431, 1.56825, 1.56968, 1.56572, 1.56714,\n",
              "       1.56722, 1.56784, 1.56576, 1.56318, 1.56312, 1.56186, 1.56072,\n",
              "       1.56197, 1.56101, 1.55936, 1.55898, 1.55866, 1.5591 , 1.55892,\n",
              "       1.55799, 1.55844, 1.55855, 1.55842, 1.55782, 1.55796, 1.55836,\n",
              "       1.5584 , 1.55792, 1.55768, 1.5586 , 1.55888, 1.5582 , 1.55652,\n",
              "       1.55761, 1.55756, 1.55729, 1.55692, 1.55811, 1.55694, 1.55733,\n",
              "       1.55734, 1.5579 , 1.55734, 1.55716, 1.55652, 1.55744, 1.55705,\n",
              "       1.55668, 1.55705])"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Number of dimensions for close_bars: {close_bars.ndim}\\nShape of the close_bars: {close_bars.shape}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6sX0RD51DHo",
        "outputId": "c462159b-40c7-45a5-e375-fb42b2f2b764"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of dimensions for close_bars: 1\n",
            "Shape of the close_bars: (100,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing the data with close of the candles"
      ],
      "metadata": {
        "id": "HxDurftr23gG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the figure\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(close_bars)\n",
        "plt.title('GPBUSD 15 Min, Based on Close of the candles')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('# of candle')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4rSbzAZgLnB6",
        "outputId": "cd7093c9-d41f-4274-bd81-81552e951de2"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Changing the index"
      ],
      "metadata": {
        "id": "xLsQH8Co3MZC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['datetime'] = pd.to_datetime(data['date'] + ' ' + data['time'])\n",
        "data.head()"
      ],
      "metadata": {
        "id": "kENThPzMMkdL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "321a4c48-4e03-4f83-b726-6aa867d79559"
      },
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date  time     open     high      low    close            datetime\n",
              "0  2022.10.17  5:00  1.55482  1.55500  1.55409  1.55464 2022-10-17 05:00:00\n",
              "1  2022.10.17  5:15  1.55464  1.55488  1.55401  1.55404 2022-10-17 05:15:00\n",
              "2  2022.10.17  5:30  1.55404  1.55518  1.55362  1.55383 2022-10-17 05:30:00\n",
              "3  2022.10.17  5:45  1.55383  1.55432  1.55304  1.55322 2022-10-17 05:45:00\n",
              "4  2022.10.17  6:00  1.55322  1.55356  1.55244  1.55278 2022-10-17 06:00:00"
            ],
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>time</th>\n",
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              "      <th>0</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:00</td>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>2022-10-17 05:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:15</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>2022-10-17 05:15:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:30</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>2022-10-17 05:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>5:45</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>2022-10-17 05:45:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022.10.17</td>\n",
              "      <td>6:00</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "      <td>2022-10-17 06:00:00</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-aca35c16-f95f-4f2b-8e5f-8d33fadc5721 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-aca35c16-f95f-4f2b-8e5f-8d33fadc5721');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ccfcf520-54d9-4b43-abc4-40c727d075a2 button');\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns=['date', 'time'], inplace=True)\n",
        "data.head()"
      ],
      "metadata": {
        "id": "ILW46P98N8xR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a8b87ca5-f6c6-453f-c400-353fa03c8fda"
      },
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      open     high      low    close            datetime\n",
              "0  1.55482  1.55500  1.55409  1.55464 2022-10-17 05:00:00\n",
              "1  1.55464  1.55488  1.55401  1.55404 2022-10-17 05:15:00\n",
              "2  1.55404  1.55518  1.55362  1.55383 2022-10-17 05:30:00\n",
              "3  1.55383  1.55432  1.55304  1.55322 2022-10-17 05:45:00\n",
              "4  1.55322  1.55356  1.55244  1.55278 2022-10-17 06:00:00"
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              "      <th>2</th>\n",
              "      <td>1.55404</td>\n",
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              "      <td>2022-10-17 05:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>2022-10-17 05:45:00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
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              "      <td>1.55278</td>\n",
              "      <td>2022-10-17 06:00:00</td>\n",
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              "  <style>\n",
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              "      background-color: #E2EBFA;\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6ced4348-9c6c-441c-ad3f-782803563194');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c5d0947-f4ba-453a-8638-f837b9e114bb button');\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.set_index('datetime', inplace=True)\n",
        "data.head()"
      ],
      "metadata": {
        "id": "Jjs-VTFsOKsi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "1d606a99-2eff-4760-d36d-29cd95c5bdcd"
      },
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close\n",
              "datetime                                               \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464\n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404\n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383\n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322\n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278"
            ],
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              "\n",
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              "      <td>1.55383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
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              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-12ec5adb-7fb1-4cdc-b3b9-1b7184bef88a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6bc72edb-a46b-4997-b012-46253051fd24 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_example = data[:100]\n",
        "data_example.head()"
      ],
      "metadata": {
        "id": "wigo0QMoQYct",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "843e6684-42cb-4b1d-a72b-9aad59d1afa8"
      },
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close\n",
              "datetime                                               \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464\n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404\n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383\n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322\n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16c0e7c7-9785-4167-b2c7-28d7541777e3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:15:00</th>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:30:00</th>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16c0e7c7-9785-4167-b2c7-28d7541777e3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-16c0e7c7-9785-4167-b2c7-28d7541777e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-16c0e7c7-9785-4167-b2c7-28d7541777e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-021a79dc-164b-41aa-987c-3192beac4619\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-021a79dc-164b-41aa-987c-3192beac4619')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-021a79dc-164b-41aa-987c-3192beac4619 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the Candlestick Chart"
      ],
      "metadata": {
        "id": "aYEMjX4D6vHX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mpf.plot(data_example, type='candle', style='yahoo', title=\"Candlestick Chart\")\n",
        "mpf.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "ImQKdn9W5myU",
        "outputId": "396ffaf7-2d76-40e7-cea0-1364926c8f77"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x575 with 2 Axes>"
            ],
            "image/png": 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woBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIiIiIpMKASkRERERSYUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIiIiIpMKASkRERERSYUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIiIiIpMKASkRERERSYUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIiIiIpOJY1xWUlJQo/1apVHVdHREREZH0hBDKvx0ceL7P1OocUAEgNzfXFKshIiIisimNGjWydhPqJZMEVABwdXW1+BlUnU6HU6dOITAwEGq12qLbpqqxPnJjfeTF2siN9ZGbpeojhEBBQYHZ1m/v6hxQ9aFUpVJZPKCqVCoIIayybaoa6yM31kderI3cWB+5Wbo+/BkwD3aaICIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEBCA1NdXaTSAion8woBIRgQGViEgmDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIiIiIpMKASkRERERSYUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqERERkZkdOHAAY8eORUhICLy8vLBt27ZKl9+/fz+8vLzK/cnKylKW6dy5s9Fl5s6dqyxTUFCAuXPnol27dmjVqhUmTpyI7Oxsg21duHABo0ePRosWLRAYGIhFixahuLjYtDughhytunUiIiIiO3Djxg2EhoZi/PjxmDBhQrXfd+TIETRq1Eh57e3trfz7m2++gU6nU17//vvvGDFiBIYPH65Mmz9/Pnbu3Il3330XHh4eePrppzFhwgTs2LEDAKDT6TBmzBj4+vpix44duHz5Mh577DE4OTlh8eLFdfjEdcOASkRUgdTUVAwdOtTazSCieiAmJgYxMTE1fp+3tzc8PT2NzmvWrJnB68TERLRt2xZ33XUXACAnJwcpKSl444030KdPHwDAf/7zH0RERODIkSMIDw/HN998g/T0dGzZsgU+Pj7o1KkT5s+fjyVLliAhIQHOzs41brMp8BI/EVEFUlNTrd0EIrJzUVFRCA4OxogRI3Dw4MEKlysqKsInn3yCBx98ECqVCgBw/Phx3Lx5E3fffbeyXPv27dGqVSscOXIEQOkZ2g4dOsDHx0dZJjo6Grm5uTh58qR5PlQ1mOwMqk6nU3aIpehPa5c9vU3yYH3kxvqUZ2xfWGP/sDZyY33kZqn6CCEAABqNxiD/uLi4wMXFpc7r9/X1xZo1a9C1a1cUFhYiOTkZcXFx2L17N8LCwsotv23bNuTk5OCBBx5QpmVlZcHZ2bncGVgfHx+lL2t2drZBOAVudSMo29/V0kwWUE+dOqUUy9JOnz5tle1S9bA+cmN9SgkhkJ6eXuU0S2Jt5Mb6yM3c9VGpVPD390doaCi0Wq0yfd68eUhISKjz+gMDAxEYGKi8joiIQEZGBpKSkrBhw4Zyy6ekpKB///7w8/Or87ZlYLKAGhgYaJUzqKdPn0ZAQADUarVFt01VY33kxvoYUqlUCAoKqnKaJbA2cmN95Gap+gghUFRUhLS0tHJnUM2lW7duOHToULnpf/75J7799lskJycbTPf19UVRURFycnIMzqJmZ2fD19cXQOnZ1KNHjxq878qVK8r7rcVkAVWtVls8oJbdNg8S8mJ95Mb63GJsP1hz37A2cmN95Gbu+uivGnt4eFgs/6SlpRkNjR9++CG8vb0xYMAAg+lhYWFwcnLCf//7XwwbNgxA6RXvixcvIjw8HAAQHh6Ol19+GVeuXFEu7e/duxeNGjWyyi/oeryLn4iIiMjMtFotMjIylNfnz5/HiRMn0LhxY7Ru3RpLly5FZmYm1q9fDwBYt24d/P39ERwcjIKCAiQnJ2Pfvn3YvHmzwXpLSkrwwQcfYOzYsXB0NIx1np6eGD9+PBYsWAAvLy80atQITz/9NMLDw5WA2q9fPwQFBWH69OlYsmQJsrOzsWLFCkydOtWsZ4OrwoBKREREZGbHjh1DXFyc8nrBggUAgHHjxiEpKQlZWVm4ePGiMr+oqAgLFy5EZmYm3Nzc0LFjR2zZsgVRUVEG6/32229x8eJFjB8/3uh2V65cCQcHB0ycOBFFRUXo168fXnrpJWW+Wq3Gxo0b8eSTT2LgwIFo0KABxo0bh/nz55vy49cYAyoRERGRmfXu3RvXrl2rcH5SUpLB6/j4eMTHx1e53n79+lW6XldXV7z00ksGofR2d9xxBzZt2lTltiyJ46ASkd3z2TkLP7Uvgc/OWco0dXQM4v7KhDqiJ9TRNR9cm4iIao8BlYjsnqY4H1lhntAU59+aqNViWLEOaNoUKDOEDBERmR8DKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIzCQ1NdXaTSAiskkMqERkdywVHBlQiYhqhwGViOwOgyMRkdwYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImITICD/xMRmQ4DKhGRCTCgEhGZDgMqEdkVn52z8FP7EvjsnKVM83B0Q1ahBh6ObtZrGBERKRyt3QAiIkvSFOcjK8wTmkKNMi17QKL1GkREROXwDCoRERERSYUBlYiIiIikwoBKRERERFJhQCUiIiIiqTCgEhEREZFUGFCJiMzA2HBW6ugYqCN6Qh0dY72GERHZAAZUIiIzUIazKs6/NVGrBZo2Lf2biIgqxIBKRERERFJhQCUiIiIyswMHDmDs2LEICQmBl5cXtm3bVuny+/fvh5eXV7k/WVlZBsv99ddfmDZtGtq1awc/Pz/06tULP//8szLf2Dq8vLzw73//W1mmc+fO5ea/8sorpt0BNcQnSRER1VBqaiqGDh1q7WYQkQ25ceMGQkNDMX78eEyYMKHa7zty5AgaNWqkvPb29lb+ff36dcTGxiIqKgqbNm1Cs2bNcObMGTRu3FhZ5uTJkwbr2717Nx5//HEMGzbMYPr8+fMxceJE5bW7u3u122gODKhERDXEgEpENRUTE4OYmJrfIOnt7Q1PT0+j8xITE9GyZUusXbtWmebv72+wjK+vr8Hr7du3IyoqCm3atDGY7u7uXm5ZazJZQNXpdFCpVKZaXbW3WfZvkgvrIzd7ro/45+/KPrvaYMnyyxp77+3Tbt9O2XVWtm17ro0tYH3kZqn6CFH6XdZoNAb5x8XFBS4uLibbTlRUFIqKihASEoJ58+YhMjJSmff111+jX79+mDx5Mg4cOAA/Pz9MmTIFkyZNMrqu7Oxs7Ny5E0lJSeXmJSYmYvXq1WjVqhVGjRqFGTNmwNHReucxTbblU6dOKcWytNOnT1tlu1Q9rI/c7K0+AgK64mIICKSnp1e4XCcAxcXFAEoPlGWXFaL8e2+fZmw7+nXevr6K2FttbA3rIzdz10elUsHf3x+hoaHQlhmZY968eUhISKjz+n19fbFmzRp07doVhYWFSE5ORlxcHHbv3o2wsDAAwLlz5/D2229jxowZmDNnDn766SckJCTA2dkZ48aNK7fOjz76CO7u7oiLizOY/uijjyIsLAyNGzfG4cOHsWzZMmRlZWHFihV1/hy1ZbKAGhgYaJUzqKdPn0ZAQADUarVFt01VY33kZq/1UZ1TQe3oCJVOhaCgoEqXLXv2oOyyKlX5994+raLt6NdZ2bbttTa2gvWRm6XqI4RAUVER0tLSyp1BNYXAwEAEBgYqryMiIpCRkYGkpCRs2LABAFBSUoIuXbpg8eLFAEpvdvr999/xzjvvGA2oH3zwAe6//364uroaTJ85c6by79DQUDg7O2P27NlYvHixSc8G14TJAqparbZ4QC27bR4k5MX6yM0e66M/UlX9uW8d025f1th7b59mfDuqam7bPmtjS1gfuZm7Pvqrxh4eHhbLP926dcOhQ4eU176+vggODjZYpn379ti6dWu5937//fc4deoU3nrrrSq30717dxQXF+PPP/80CMmWxGGmiIiIiGxAWlqawY1MEREROHXqlMEyZ86cQatWrcq9NyUlBV26dEGnTp2q3M6JEyfg4OBgMGKApfEufiIiIiIz02q1yMjIUF6fP38eJ06cQOPGjdG6dWssXboUmZmZWL9+PQBg3bp18Pf3R3BwMAoKCpCcnIx9+/Zh8+bNyjpmzJiBgQMH4uWXX8aIESNw9OhRvPfee+XGMNVoNPjiiy+wfPnycu06fPgwjh49it69e6NRo0Y4fPgwFixYgNGjRxsMV2VpDKhERHXgs3MWNMX56NxewGfnLGQPSAQAeDi6wfd4DvJDG1i3gUQkhWPHjhncnLRgwQIAwLhx45CUlISsrCxcvHhRmV9UVISFCxciMzMTbm5u6NixI7Zs2YKoqChlmW7duiE5ORnLli3D6tWr4e/vj5UrV2L06NEG2968eTOEELjvvvvKtcvFxQWbN2/GqlWrUFRUBH9/fzz22GMG/VKtgQGViKgONMX58HXxQFYYoCnUKNOzByRi+ubpODwn0XqNIyJp9O7dG9euXatw/u1DP8XHxyM+Pr7K9cbGxiI2NrbSZSZPnozJkycbnRcWFoZdu3ZVuR1LYx9UIqJq8tk5C67bH8VP7Uvgs3MWgNIzpVmFGvgez4GHo5t1G0hEVE8woBIRVdOts6We0BTnAyg9U1oweAO6/eGgXN4nIqK6YUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWIyEyGDh1q7SYQEdkkBlQiIjNhQCUiqh0GVCIiE2AYJSIyHQZUIiITYEAlIjIdBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASEVlZamqqtZtARCQVBlQiIitjQCUiMsSASkRkjLs7vnRUA1evAu7uAAAPRzdkFWrgezwHHo5utV/nP+sDAHV0DOL+yoQ6oifU0TGmaj0RkU1jQCUiMkK3Zxe2tvCD7tAP0O3ZBQDIHpCIgsEb0O0PB2QPSKz9Ov9ZHwBAq8WwYh3QtCmg1Zqo9UREto0BlYiIiIikwoBKRERERFJhQCUiIiIiqTCgEhFZ0NChQ63dBCIi6TGgEhFZEAMqEVHVGFCJiCrAMElEZB0MqEREFWBAJSKyDgZUIiIiIpIKAyoRERGRmR04cABjx45FSEgIvLy8sG3btkqX379/P7y8vMr9ycrKMljur7/+wrRp09CuXTv4+fmhV69e+Pnnn5X5M2bMKLeOUaNGGazj2rVreOSRR3DHHXfA398fjz/+OLRWfnCIo1W3TkRERGQHbty4gdDQUIwfPx4TJkyo9vuOHDmCRo0aKa+9vb2Vf1+/fh2xsbGIiorCpk2b0KxZM5w5cwaNGzc2WEd0dDTWrl2rvHZxcTGY/8gjjyArKwubN2/GzZs38X//93+YNWsW3nzzzRp+StNhQCUiIiIys5iYGMTExNT4fd7e3vD09DQ6LzExES1btjQIn/7+/uWWc3Fxga+vr9F1pKenY8+ePfjmm2/QtWtXAMALL7yA0aNHY/ny5fDz86txm03BZAFVp9NBpVKZanXV3mbZv0kurI/c7Lk+4p+/a/vZBw8ebLL9pjZoUWmb7Lk2toD1kZul6iNE6fdWo9EY5B8XF5dyZyjrIioqCkVFRQgJCcG8efMQGRmpzPv666/Rr18/TJ48GQcOHICfnx+mTJmCSZMmGaxj//79CAwMROPGjREVFYWFCxeiSZMmAErP0Hp6eirhFADuvvtuODg44OjRo1a7WdRkAfXUqVNKsSzt9OnTVtkuVQ/rIzd7q4+AgK64GAIC6enptVpHQEBArd97u04AiouLAZQekMuu195qY2tYH7mZuz4qlQr+/v4IDQ016K85b948JCQk1Hn9vr6+WLNmDbp27YrCwkIkJycjLi4Ou3fvRlhYGADg3LlzePvttzFjxgzMmTMHP/30ExISEuDs7Ixx48YBKL28P3ToUPj7++PcuXNYvnw57r//fuzcuRNqtRpZWVkG3QYAwNHR0Wh/V0syWUANDAy0yhnU06dPIyAgAGq12qLbpqqxPnKz1/qozqmgdnSESqdCUFCQtZsDoPQ/A72goCC7rY2tYH3kZqn6CCFQVFSEtLS0cmdQTSEwMBCBgYHK64iICGRkZCApKQkbNmwAAJSUlKBLly5YvHgxAKBz5874/fff8c477ygB9b777lPW0bFjR3Ts2BFdu3bF/v370bdvX5O01RxMFlDVarXFA2rZbfMgIS/WR271vT6pqanlLlHpj1TyfO5bx86ybarvtbF1rI/czF0f/VVjDw8Pi+Wfbt264dChQ8prX19fBAcHGyzTvn17bN26tcJ1tGnTBk2bNsXZs2fRt29f+Pr64sqVKwbLFBcX49q1axX2W7UEDjNFRPVaamqqtZtARGQSaWlpBqExIiICp06dMljmzJkzaNWqVYXruHTpEv7++29lPeHh4cjJycGxY8eUZfbt24eSkhJ0797dtB+gBngXPxEREZGZabVaZGRkKK/Pnz+PEydOoHHjxmjdujWWLl2KzMxMrF+/HgCwbt06+Pv7Izg4GAUFBUhOTsa+ffuwefNmZR0zZszAwIED8fLLL2PEiBE4evQo3nvvPbzyyivKNl944QUMGzYMvr6+yMjIwLPPPot27dohOjoaQGm3oujoaMTHx2PNmjW4efMmnn76aYwcOdJqd/ADDKhEREREZnfs2DHExcUprxcsWAAAGDduHJKSkpCVlYWLFy8q84uKirBw4UJkZmbCzc0NHTt2xJYtWxAVFaUs061bNyQnJ2PZsmVYvXo1/P39sXLlSowePRpAaTeH3377DRs3bkROTg6aN2+Ofv36Yf78+QZ9Zd944w089dRTGD58OFQqFYYNG4ZVq1aZe5dUigGViIiIyMx69+6Na9euVTg/KSnJ4HV8fDzi4+OrXG9sbCxiY2ONznNzc8Nnn31W5Tq8vLysOii/MeyDSkRERERSYUAlIiIiIqkwoBIRERGRVBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZWI6i2fnbPwU/sS+OycpUzzcHSD7/EceDi6Wa9hRERUKQZUIqq3NMX5yArzhKY4X5mWPSAR3f5wQPaAROs1jIiIKsWASkR2Z+jQodZuAhERVYIBlYjsDgMqEZHcGFCJiIiISCoMqEREREQkFQZUIiIiIpIKAyoRkYQOHjxo7SYQEVkNAyoRkYQOHTpk7SYQEVkNAyoRERERSYUBlYiIiIikwoBKRERERFJhQCUiIiIiqTCgEhEREZFUGFCJiIiISCoMqERENiQ1NdXaTSAiMjsGVCIiG8KASkT2gAGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIrMndHV86qoGrVwF3dwCA84BYDMu8DOdevaGOjrFyA4mILM/R2g0gIrJnuj27IFJToRs6tNLl1NExgFaLOF8fqKNjoNuzy0ItJCKyPJ5BJSKysqG3hdOinV/jS7/mKPp+/60gqtUCTZtiWLGu9N9ERPUYAyoRERERSYUBlYiIiIikwoBKRERERFJhQCUiklBERIS1m0BEZDUMqEREEoqMjLR2E4iIrIYBlYiIiIikwoBKREREZGYHDhzA2LFjERISAi8vL2zbtq3S5ffv3w8vL69yf7KysgyW++uvvzBt2jS0a9cOfn5+6NWrF37++WcAwM2bN/Hss8+iV69eaNmyJUJCQjB9+nRkZmYarKNz587ltvPKK6+YdgfUEAfqJyIiIjKzGzduIDQ0FOPHj8eECROq/b4jR46gUaNGymtvb2/l39evX0dsbCyioqKwadMmNGvWDGfOnEHjxo2Vbf7yyy946qmnEBoaiuvXr+OZZ57BAw88gL179xpsZ/78+Zg4caLy2v2fJ9tZCwMqERERkZnFxMQgJqbmjy729vaGp6en0XmJiYlo2bIl1q5dq0zz9/dX/u3p6YnPP//c4D0vvvgioqOjceHCBbRu3VqZ7u7uDl9f3xq3z1xMFlB1Oh1UKpWpVlftbZb9m+TC+sjNXuoj/vnblj6nsdqoAdz6NLb1eeobe/nu2CpL1UeI0u+jRqMxyD8uLi5wcXEx2XaioqJQVFSEkJAQzJs3z+AGyq+//hr9+vXD5MmTceDAAfj5+WHKlCmYNGlShevTt/f20JuYmIjVq1ejVatWGDVqFGbMmAFHR+udxzTZlk+dOqUUy9JOnz5tle1S9bA+lTt48KBV79i2Vn0s8bkFBHTFxRAQSE9PN+u2zKFsbToBKC4uBlB64LbFz1Pf8NgmN3PXR6VSwd/fH6GhodCWefzwvHnzkJCQUOf1+/r6Ys2aNejatSsKCwuRnJyMuLg47N69G2FhYQCAc+fO4e2338aMGTMwZ84c/PTTT0hISICzszPGjRtXbp0FBQVYsmQJ7rvvPnh4eCjTH330UYSFhaFx48Y4fPgwli1bhqysLKxYsaLOn6O2VKKOqVIIAY1GA2dnZ6ucQT19+jQCAgKgVqstum2qGutTPTNnzjS4PGMp1q6PJT53wx0z4OPigexCDfIGJpl1W6ZkrDbOvXoDTZuULnD1bxR9v9+KLbRv1v7uUOUsVR8hBIqKigCgxmdQvby8kJKSgiFDhtRom0OGDEGrVq2wYcMGAICPjw+6dOmCnTt3KsvMmzcPP//8s8E0oPSGqYkTJ+Kvv/7C1q1bDQLq7VJSUjB79mxcvHjRpGeDa8JkZ1DVarXFA2rZbfMgIS/Wp2rW3D/WrI8ltqs/Ktniz2D52qgM5lUmNTUVQ4cONVPLCOCxTXbmro/+/J6Hh4fF8k+3bt1w6NAh5bWvry+Cg4MNlmnfvj22bt1qMO3mzZt46KGHcOHCBXz55ZeVhlMA6N69O4qLi/Hnn38iMDDQdB+gBjjMFBGRjUtNTa3WNCKybWlpaQY3MkVERODUqVMGy5w5cwatWrVSXuvD6ZkzZ7BlyxY0adKkyu2cOHECDg4OBiMGWBrv4icisnE8W0okP61Wi4yMDOX1+fPnceLECTRu3BitW7fG0qVLkZmZifXr1wMA1q1bB39/fwQHB6OgoADJycnYt28fNm/erKxjxowZGDhwIF5++WWMGDECR48exXvvvaeMYXrz5k1MmjQJx48fx8aNG6HT6ZRxVL28vODs7IzDhw/j6NGj6N27Nxo1aoTDhw9jwYIFGD16tDJclTUwoBIRERGZ2bFjxxAXF6e8XrBgAQBg3LhxSEpKQlZWFi5evKjMLyoqwsKFC5GZmQk3Nzd07NgRW7ZsQVRUlLJMt27dkJycjGXLlmH16tXw9/fHypUrMXr0aABAZmYmvvrqKwBAnz59DNqzdetW9O7dGy4uLti8eTNWrVqFoqIi+Pv747HHHsPMmTPNti+qgwGViMhO8EwrkfX07t0b165dq3B+UpLhjZzx8fGIj4+vcr2xsbGIjY01Ou+OO+6odJsAEBYWhl27dlW5HUtjH1QiIjvBfqlEZCsYUImIiIjIJIqKinDq1Cll3ObaYkAlIiIiojq5ceMGHn/8cbRo0QI9e/ZU+tM+/fTTyk1bNcGASkRERER1smzZMqSlpWHr1q1wdXVVpt999934/PPPa7w+BlQionrGZ+cs/NS+BD47ZynT1NExiPsrE+roGOs1jIjqrW3btuHFF19Ez549DR5cEBwcjHPnztV4fQyoRET1jKY4H1lhntAU59+aqNViWLEOKPPMcCIiU7l69arRgf3z8vJqtT4GVCIiW+DuDly9ii8d1aX//gfPjBKRDLp06YKdO3cqr/VnUZOTk9GjR48ar4/joJLJcIxFIvPR7Skdp3Dr9OkY8s+TZgCUnhl1ceGZUSKyqkWLFmH06NE4efIkiouLsX79eqSnp+Pw4cO1GuKOZ1DJZDjGIlWkuj8b/BkiIrJNPXv2xL59+6DT6dChQwfs3bsXzZo1w44dO9ClS5car49nUInI7Kp7dp1n4avG/UNEsmrbti1effVVk6yLZ1CJiGwIAyoRyWjnzp3Ys2dPuel79uyp1aNUGVCJyGSqe4mewyAREdUvS5cuhU6nKzddCIGlS5fWeH0MqERkMtUNqBwGiYiofjl79iyCg4PLTW/fvj0yMjJqvD4GVCI7wRuQiIjIXDw8PIwOyH/27Fk0aNCgxutjQKVKVRRqGHZsD2tGRETmMmjQIDzzzDMGZ0vPnj2LhQsXYtCgQTVeHwMqVYoBlYiIiKqydOlSNGzYED169EBYWBjCwsIQERGBJk2aYPny5TVeH4eZIiKyYxzai4hMwdPTEzt27MDevXuRlpYGV1dXdOzYEXfddVet1seASiQhS4WGgwcPIigoyOzbIXkxoBKRqahUKvTr1w/9+vWr87oYUImsxGfnLGiK89G5vYDPzlnIHpAIoHS4JVXDBlC/8qryeEtzOXToECZNmmTWbRARUf20YcMGTJo0Ca6urtiwYUOlyz766KM1WjcDKpGVaIrz4evigawwQFOouTXDxM9WNxaE1dExUGu1GObrA+cBsSYJwj47Z6FN+xK4bn8UHo5uynag1SLO1wfq6BizB24iIrKcpKQk3H///XB1dUVSUlKFy6lUKgZUMq/KzvrFNWzAECIho0FYqwWaNsHgggKTBWH92Ka+ALIMttOU45sSEdVDx48fN/pvU+Bd/FQjt8IOB1knIiIi4ObNm+jatSvS09NNtk4GVCIie+Duji8d1YC7uzJJebxsRE8+YpaIas3JyQmFhYUmXScDKhGRHdDt2YWtLfwMu+Dor3w0bcqrH0RUJ1OmTMGrr76K4uJik6yPfVCJyObp+0YDUG7Q0v/b93gO8kNr/pg9IiKqvp9//hn79u3D3r170aFDh3KPN01OTq7R+hhQicjm6ftGA2Vu0AKQPSAR0zdPx+E5iVZqGRGRffD09ERcXJzJ1seASkRkJ2oyID8H8Cei6igpKcG///1vnD59Gjdv3kRUVBQSEhLg5uZWp/WyDyoRkZ2oaUAlIqrKyy+/jOXLl8Pd3R1+fn54/fXX8dRTT9V5vQyoRES2zMjd+fq+tx6OdTuDQURUlY0bN+Kll17CZ599hg8++AAfffQRNm3ahJKSkjqtlwGViMiG6fbsgpj2iMHd+dkDEtHtDwflZjEiInO5ePEiYmJuDVN39913Q6VSITMzs07rZUAlqiNeCiVrM3bpnv1HicgSiouL4erqajDNycmpzsNN8SYpojrizSQkI/5MEpElCCEwY8YMuLi4KNMKCgowZ84cg6GmOMwUEREREVnEuHHjyk0bPXp0ndfLgEpEtcIzx0REtHbtWrOsl31QiahW2PeWiIjMhQGVqJ5hcCQiIlvHgEpUzzCgEhGRrWNAJSIiIjKzAwcOYOzYsQgJCYGXlxe2bdtW6fL79++Hl5dXuT9ZWVkGy/3111+YNm0a2rVrBz8/P/Tq1Qs///yzMl8IgZUrVyI4OBh+fn4YPnw4zpw5Y7COa9eu4ZFHHsEdd9wBf39/PP7449Bqtab78LXAgEpEVeJZWSKiurlx4wZCQ0OxevXqGr3vyJEjOHnypPLH29tbmXf9+nXExsbCyckJmzZtwsGDB/Hcc8+hcePGyjKvvvoqNmzYgDVr1mDXrl1o0KAB7rvvPhQUFCjLPPLIIzh58iQ2b96MjRs34vvvv8esWbPq+pHrhHfxE1GVeMc+EVHdxMTEGDxxqbq8vb3h6elpdF5iYiJatmxpcCe9v7+/8m8hBNavX4+5c+di8ODBAIB169YhKCgI27Ztw3333Yf09HTs2bMH33zzDbp27QoAeOGFFzB69GgsX74cfn5+NW6zKfAMKhEREZGkoqKiEBwcjBEjRuDgwYMG877++mt07doVkydPRmBgIPr06YP33ntPmX/+/HlkZWXh7rvvVqZ5enqie/fuOHLkCIDSM7Senp5KOAVKH1fq4OCAo0ePmvfDVcJkZ1B1Oh1UKpWpVlftbZb9m8zj9v0rjMxTl5lze13soT61/YzV3Zd1bc/t21EDEKLi5auzzupup+zUssuL26bV9XMb25cVvZadpb47ldXHEtu3VfZ0bLNFlqqP+OcgqtFoDPKPi4uLwVOVasvX1xdr1qxB165dUVhYiOTkZMTFxWH37t0ICwsDAJw7dw5vv/02ZsyYgTlz5uCnn35CQkICnJ2dMW7cOKW/atluAQDg4+OD7OxsAEBWVla5+Y6Ojkb7u1qSyQLqqVOnlGJZ2unTp62yXXsghEB6evqt1xDQ/fN8XYFb8zqh9Hm8joDB8kD9r8/t+6ja76vFvqxNe4xtpxMAne7Wc5Kr2o6xz1jd7eifx1z28+iXNdXnrmhfVtR2W2Hu705F9QFse79ZSn0/ttk6c9dHpVLB398foaGhBjcUzZs3DwkJCXVef2BgIAIDA5XXERERyMjIQFJSEjZs2AAAKCkpQZcuXbB48WIAQOfOnfH777/jnXfeMfqEJ1tisoAaGBholTOop0+fRkBAANRqtUW3bS9UKhWCgoJuvT6ngtqx9MdGpTOc5/jPdP00e6nP7fuo2u+rwb6sS3sq2o5a7aiE1LLLb9u2DUOGDKl0ndXejrs7HHM0+NLXB8N0Jcrynhfd0DItDzdD3UzyuSvbl7WtjzVZ8ruj3+cAbH6/WYq9HNtslaXqI4RAUVER0tLSyp1BNZdu3brh0KFDymtfX18EBwcbLNO+fXts3bpVmQ8AV65cQfPmzZVlsrOz0alTJ2WZK1euGKyjuLgY165dU95vDSYLqGq12uIBtey2eZAwn9v3rarCeSqjy9tDfWr7+Wq6L2vbHmPbKft1Lbv89u3bMWzYsCrXWZ3t6PbsAgBsnT4dQ9avh37p7AGvYvrm6Tg859Xb1lj7z13xvqz9frQ2y3x3bu25+rLfLMUejm22zNz10V819vDwsFj+SUtLMwiNEREROHXqlMEyZ86cQatWrQCU3jDl6+uL//73v0og1Wg0OHr0KB5++GEAQHh4OHJycnDs2DF06dIFALBv3z6UlJSge/fuFvhUxvEufjINd3d86ajGMHd3ZZLzgFh00moBd3clqJB5+eychTbtS+C6/VF4OLohe0AiPBzdkFWoQeeTAvmhDazdRCIiu6TVapGRkaG8Pn/+PE6cOIHGjRujdevWWLp0KTIzM7F+/XoApXfb+/v7Izg4GAUFBUhOTsa+ffuwefNmZR0zZszAwIED8fLLL2PEiBE4evQo3nvvPbzyyisASq+ETJ8+HS+99BLatWsHf39/rFy5Es2bN1eulAUFBSE6Ohrx8fFYs2YNbt68iaeffhojR4602h38AO/iJxPR7dmFrS38DIOoVotiTw/AyoP9mlJdxgO1xFiimuJ8ZIV5wtfFA5rifABA9oBEFAzegG5/OCB7QKLZ20BEROUdO3YMffr0QZ8+fQAACxYsQJ8+ffD8888DKL1Z6eLFi8ryRUVFWLhwIe666y4MHToUaWlp2LJlC/r27ass061bNyQnJ+Ozzz5Dr1698NJLL2HlypUYPXq0skx8fDymTZuG2bNnIzo6Gnl5efj000/h6uqqLPPGG28gMDAQw4cPx+jRoxEZGYnExEQz75HK8QwqUQ3UZTxQa48lWttt68/K+uycpQRcdXQM4ho2gDqip/RnyDl+q+lY+2eYyJb17t0b165dq3B+UlKSwev4+HjEx8dXud7Y2FjExsZWOF+lUmH+/PmYP39+hct4eXnhzTffrHJblsQzqER2orbBQn9WVn9GtiIejm7wPZ6DrEINPBzdarUtc2CgMp3qXgXgk8eIqK4YUKlCPjtn4ad/+jP67JwFAEp/Rt/jOVKFELIspUvHoR+Us6fZAxLR7Q8HFAzewK4Edo4BlYjqigGVKsT+jIb0gV0f1oF/LnX/lQl1dM0fX0fVw7BDRGR/GFCJqsnopW6tFsOKdfXqRjBzqMtldgZUIiL7w4BKRGbHfqBERFQTDKhEREREJBUGVCKyCp5VlcA/D9jA1avAPw/ZUEfHQB3Rk32riciqGFCJyCoYUK3P2GgM0GqBpk2r3beafYSJyBwYUIlsWK3Dgbs7cPVvbHd1Vc6cARyVgGqOAfUW7gsi02FAJbIAc40pW9v/EHV7dqHo+/340q85inZ+fWuGjYxKwCBAMuLPJZHpMKASWQDHlK0GfX/IapzRZRAgIqrfGFCJSApKf0h9X0jA6BldPuFMHjV5eIU9/1Jhz5+dqLYYUMnm8GBff1XnximejZZHTR5eUd++twcPHqz2svXtsxNZAgMq2RxbONjbQhtlxDv7JeDuXjrsFIeeqtShQ4es3QSieo0BlcgMGFDJVun27Coddqq2Q09V1pc4ome1Ai6/P0TEgEpUFyb4z5ioPqm0L3HTpvV2bFVjfaN55pmo9hhQqVZ4KbaUKf4zloqRwO3h6Mabj+oxW/kuyx5ajfWNrulDD4joFgZUqhVb+U+NasZY4M4ekMibj+oxa36XaxI6ZQ+oRGRaDKhUL/A/LyLbw+8tEVWEAZXqBWv+R8ezyVTv/XNnP7t/EJGlMKAS1REDKtV3+jv7Zev+UddfTHkGl0heDKhERGSTrBVQ/fbMwU9BAn575ijT9GeTswo1PKNMZAIMqEQ2gt0YqL7z2TkLrtsfrfbjU61FU1yAvzo2hKa4QJmmP5tcMHgDbygkMgEGVCIbIVtAZWglU9MU58PXxaPaj0+tLl7KJ7I9DKhkU/SDYct8dsWWRERElJtW3eDJgEp1YWxgew9HN2QVasrfeFXZAzFq8WSqys7UGnvABgMukeUxoJJN0Q+GbcqzK/YsMjKy3DQGT7IEYwPbZw9IRMHgDeVuvKr0gRhVfO+NBeEK/TOw/u3rNEdAtWboZeAmW8CASkREJiXTLzk1CcKWVN2QWN/CMVF1MaCSFHjAJKo/ZAqots5Wjo220k6yHQyoJAUe3IjIFKQ6lhh5wIG+n6ul+s1ban9Itd+pXmBAJUVdDzD17awJD7i2p779DNoC2fa5TN9bow84+Kefq6X6zcu0P4hqggGVFAyohnhgtz317WfQFtjyPjd122XaF+YaU5bHRbIUBlQiIqoWmQKYKdTngFrTMWWtedMWkTEMqASg4jEJ+eg+ItKrdQD7py8mrl5V+mJWdnyRKejZCwZPkg0DKgGoeCiWOj26z90d211dyw2ubWwgbJlUdGlM/x8qwzpRzej7YuoO/aD0xazs+FLbgGrsQR78RZvINjGgktkU7fwaX/o1R9HOr29NrGAgbJlUdGlM/x8qn7NNJCdjD/Ko0S/adXxiVX1n7EqbpUclIPvBgEpERIS6PbHKHhi70mbpUQnIfjCgEhGR7TFyttPWu+GwHyjRLQyoRDVgkZs3eJmR7ERdvk/GznZW1A3HVm66YkCt3w4cOICxY8ciJCQEXl5e2LZtW6XL79+/H15eXuX+ZGVlKcusWrWq3PwePXoo8//880+j6/Dy8sKWLVuU5YzN/+yzz0y+D2rC0apbJ+nZyoHdUiyxP3R7dmHr9OkYsn79rYlaLYa5uEh/CY0/L1QTlvp5qdN2/vmFcZjBmVpXtEjLw81Q2zxTS9Zx48YNhIaGYvz48ZgwYUK133fkyBE0atRIee3t7W0wPzg42CBsOjreinYtW7bEyZMnDZZ/77338Nprr6F///4G09euXYvo6GjltaenZ7XbaA4MqFQpBg6qCf68kLlY62dLt2cXRGoqdGW2nxm9Bu9dfA+ToidZpU2pqan8rtmgmJgYxMTU/CqYt7d3pWHR0dERvr6+Ruep1epy81JTUzF8+HC4l/mlCygNpBWtxxp4iZ+IiKRnzbOtxqZFRkaadLvGhsiqqGuPpboCsMuBHKKiohAcHIwRI0bg4MGD5eafPXsWISEh6NKlCx555BFcuHChwnUdO3YMJ06cwPjx48vNe+qpp/Cvf/0L0dHRSElJgRDCpJ+jpkx2BlWn00GlUplqddXeZtm/qW7K/iiaYp8aq4+6zJZu30Z1t6lvp375ytZZW3XZF2XbU/b9la3z9teDBw+u9X6rbttt4ftT0ec29c+qbGyhNrIwto/qst8GDRpU5ftrUx9jx6uy0zXF+bgc5glNoea2ZQ2Xcx4Qi7iGDaCO6Am4uxsO43cbY9+TmhxLUlNTMWjQoErXWdHnsSZLfX/0AU6j0RjkHxcXF7i4uNR5/b6+vlizZg26du2KwsJCJCcnIy4uDrt370ZYWBgAoHv37li7di0CAgKQlZWFF154AYMHD8b3339v0C1ALzk5GUFBQYiIiDCYPn/+fERFRaFBgwb45ptvMHfuXOTl5eHRRx+t8+eoLZMF1FOnTlktbZ8+fdoq261PBAR0xcXKv9PT00227rL16QSguLgYjoDBNoSo3jb17SzbxorWWVt13Rf69gBQ2lTZOo199oCAAINp1d1vtWm7zN8fY5/bnD+rspG5NjIw9t2p7rHEFKpbn7JtMnZ8AIwf2/Daq/jy1VfRNj4e0L9fq8VgtQOKPT3gmKOp8LNW9D2pyTG4OseXij6PDMz9/VGpVPD390doaCi0Ze4PmDdvHhISEuq8/sDAQAQGBiqvIyIikJGRgaSkJGzYsAEADLoMhIaG4s4770SnTp2wZcuWcv1c8/Pz8emnn+Kpp54qt62y0zp37owbN27g3//+d/0IqIGBgVY5g3r69GkEBARArVZX/QaqkOqcCup/OlardCoEBQXVeZ36315vr4++A3fZbahU1dumvp23t9HYOmvLFPuibCf1oKCgStc5ZMiQam2jOvutJm23le/P7Z/bHD+rsrGV2libseNGdY8ldVHT+tzeptuPD0DFxzZjn8fY+8tts5LvSXWPwdU9vlTUnm3btmHIkCFG26fnt2cONMUF8HB0RWb0GgClZ4mVG0KrOEtsjKW+P0IIFBUVIS0trdwZVHPp1q0bDh06VOF8T09PBAQE4OzZs+XmffHFF8jPz8fYsWOr3E737t2xevVqFBYWmvXzVMZkAVWtVls8oJbdNg/idVe2eqbcn+XrozK6jepuU9/O6qyztuq+L26tQf/+itY5bNiwGq2zqv1W07bL//0p/7nN9bMqG/lrY33G9o+l9llN6mPseHX7dOPHNmOfx/j7b1fx96T6x+DqHV+Mt2f79u1VHt80xQWlT+4r1Nx6r/6JgwBw9Wqt62nu74/+qrGHh4fF8k9aWlqlNzJptVpkZGRgzJgx5ealpKRg0KBBaNasWZXbOXHiBBo3bmy1cArwJikiIqpH7PXu9rrc0GTsBq3nTqXiUpMSPHfq1nr1D0LIKtTcehiCuztw9SrHbq4GrVaLEydO4MSJEwCA8+fP48SJE8pNTUuXLsX06dOV5detW4ft27fj7Nmz+O233/DMM89g3759mDp1qrLMokWLcODAAfz55584dOgQJkyYALVajfvuu89g22fPnsX3339vdHirr776Cu+//z5+++03nD17Fm+99RZeeeUVTJs2zRy7odoYUIlMTT/Q/tWrygHb6IGdqlbJ04K4L8mYeh9QjRxf1NExUL3+BtQRPWsVCPWPMFUeXwpgYeBQtPzbAQsDb+1P/YMQCgZvUB6GoNuzC7pDP1T7EbHmGBnAVkYbOHbsGPr06YM+ffoAABYsWIA+ffrg+eefBwBkZWXh4sWLyvJFRUVYuHAh7rrrLgwdOhRpaWnYsmUL+vbtqyxz6dIlTJ06FeHh4Xj44Yfh5eWFXbt2lTtLmpKSghYtWqBfv37l2uXk5IQ333wTAwcORJ8+ffDuu+/iueeew7x588yxG6qN46ASmZgy0P6XXyjTsgckYvrm6Tg8Z30l76TbGXtoAfcl2TNjxxflQR5Nm5YGV4mZYwxXWxkXtnfv3rh27VqF85OSkgxex8fHIz4+vtJ1vv3229Xa9uLFi7F48WKj8/r3719u0H4Z8AwqkYXYwgGUyJbYynfKVtppUnxkM9URAyqRhZj6Pylj/cZ4+Zvsia0EP1tppynp9uyq9mX/6rKVS/lkGgyoRDbKWL8xY33EiIjqAwZU+8KASkRSs8ezT0QysNp3r4IbweL+yjS4Ecxn5yy4bn+03JUkqh8YUMmyKuuXVMs7UMlQfQt09e3zENWEsZ9/S30n6rqd2r5f6R5w6IdbXQT03QOaNlW6CGiK80vHUL3tShLVDwyoZFGV9ksqc+CxddYMVQx0RPVHtQJqBUPbZRVq4Hs8x7A/eiVDt5m63zqPRVQXDKhEZsADMxFZirEzjtkDElEweAO6/eFg0B/d2EkCfd919lsnmTCgkl1hJ3siIiL5MaDaKUsFtYiICItsp7oYUImIyuNVH5INA6qdslRQi4yMtMh2iIio9hhQSTYMqERERPWUrdzxX6Wa3AhG9QIDKkmNl+TJGJ7tIaqe+hJQa3IjGNUPDKgkNQZUMoYBlchQfQmilt4OyYsBlSyOBx4islvu7sDVq0bHIs0q1NT6UrVNBMcajMHK/yeIAZUsjgceIrJXuj27oDv0A8S0R8qNRVoweINFLlVb6xjMMVipJhhQiYiILMxenzbHExRUXQyodshn5yz81L4EPjtnKdP0l1nqcomJiIioMnUJqAy39oUB1Q5pivORFeYJTXG+Mk1/mcVSl5hkw5uxiIiso7rBkwHVvjhauwFE1uSzcxY0xfno/IfAwzt3I3tAonKzQueTAvmhDazdRCKieq2uwZPBtX5iQCWFPX7JNcX58HXxQFYYoCnUAIByBjm1JBVDB9jfPiEisiX2+H+XPeAlflLwS26I+4OIiMg6GFCJiIiISCoMqEREREQkFQZUIiIiIpIKAyoRERERSYUBlaTks3MWXLc/WuEDBezuYQI1eIY1ERGRrWNAJSndGv7J+AMF7O1hAnyGNRER2RMGVKq36tvToTjsFRER2QsGVKq3GFCJiIhsEwMqEREREUmFAdUO1LcziURERFS/MaDaAQZUIiIisiUMqGRzatsX02fnLPzUvgSu2x9Vhq7ycHRDVqGGwzURERFJhAGVbE5tA6qmOB9ZYZ7wdfFQhq7KHpCIgsEbOFwTERGRRBhQiYiIiEgqDKhEREREJBUGVCIiIiKSCgMqEREREUmFAZVsn7s7vnRUA+7uyiTVm2+hvUYD1ZtvKdP0d+xnFWp4xz4REVnUgQMHMHbsWISEhMDLywvbtm2rdPn9+/fDy8ur3J+srCxlmVWrVpWb36NHD4P1DB06tNwys2fPNljmwoULGD16NFq0aIHAwEAsWrQIxcXFpvvwteBo1a1bWGpqKh8XWQ/p9uzC1unTMWT9emWamDoFf/x4BGLqFGVafbxLnz/PRES24caNGwgNDcX48eMxYcKEar/vyJEjaNSokfLa29vbYH5wcDC2bNmivHZ0LB/tJk2ahGeeeUZ57eZ26ySNTqfDmDFj4Ovrix07duDy5ct47LHH4OTkhMWLF1e7nabGgEpkw/jzTERkG2JiYhATE1Pj93l7e8PT07PC+Y6OjvD19a10HW5ubhUu88033yA9PR1btmyBj48POnXqhPnz52PJkiVISEiAs7NzjdtsCiYLqDqdDiqVylSrq/Y2y/5dk/fYm9s/t6hgujm2WdU21AYturW8KLNMddppbBn+bFSsNt8fsgzWRm6sj9wsVR8hSv+X0mg0BvnHxcUFLi4uJttOVFQUioqKEBISgnnz5iEyMtJg/tmzZxESEgIXFxeEh4dj8eLFaN26tcEymzZtwieffAIfHx/ExsbiqaeeQoMGDQCUnqHt0KEDfHx8lOWjo6Px5JNP4uTJk+jcubPJPktNmCygnjp1SimWJR08eLDaywohkJ6ebsbWyOn2zy0goCsuhoBl9sfp06crnd/BzQ3bHB0xLCsbOjc3pKeno6GDM7IKc9D5JJDXwaXKdhqrbU3qba8/G0DV9SHrYW3kxvrIzdz1UalU8Pf3R2hoKLRarTJ93rx5SEhIqPP6fX19sWbNGnTt2hWFhYVITk5GXFwcdu/ejbCwMABA9+7dsXbtWgQEBCArKwsvvPACBg8ejO+//17pFjBq1Ci0bt0azZs3x6+//oqlS5fi9OnTSE5OBgBkZ2cbhFPgVjeCsv1dLc1kATUwMNAqZ1BfffVVjB8/Hmq1usrlVSoVgoKCLNAyudz+uVXnVFA7OkKlM+/+0Ol0OH36NAICAiqtj27PLmydOROxn28GAAQByA5KBADM/HImDsYnVrktY7WtSb3t8WejuvUhy2Nt5Mb6yM1S9RFCoKioCGlpaeXOoJpCYGAgAgMDldcRERHIyMhAUlISNmzYAAAGXQZCQ0Nx5513olOnTtiyZYvSz3Xy5MnKMh07dkTz5s1x7733IiMjA23btjVJW83BZAFVrVZbPKCW3fbtP4QV9Te114PJ7Z9bVcF0c227OtupaJnqttHYcjX5fPb8s2Gvn112rI3cWB+5mbs++qvGHh4eFss/3bp1w6FDhyqc7+npiYCAAJw9e7bCZbp37w6gtGtA27Zt4ePjg6NHjxosc+XKFQCosm+rOdn0MFPOA2IxLPMynHv1hjrasONxamqqlVpF1sCbhYiIqL5LS0urNDRqtVpkZGSgefPmFS5z4sQJALfCZ3h4OH777TcllALA3r170ahRI6teWbTtu/i1WgxWOwBNmwBX/7Z2a8gMqhs8GVCJiEhm+vCod/78eZw4cQKNGzdG69atsXTpUmRmZmL9P0Mmrlu3Dv7+/ggODkZBQQGSk5Oxb98+bN68WVnHokWLEBsbi9atWyMzMxOrVq2CWq3GfffdBwDIyMjAp59+ipiYGDRp0gRpaWlYsGABevXqhdDQUABAv379EBQUhOnTp2PJkiXIzs7GihUrMHXqVJPe7FVTth1Qqd5j8CQiovrg2LFjiIuLU14vWLAAADBu3DgkJSUhKysLFy9eVOYXFRVh4cKFyMzMhJubGzp27IgtW7YgKipKWebSpUuYOnUq/v77bzRr1gwRERHYtWsXmjVrBgBwcnLCt99+i3Xr1uHGjRto2bIl4uLiMHfuXGUdarUaGzduxJNPPomBAweiQYMGGDduHObPn2/uXVIpBlQL4RisRERE9qt37964du1ahfOTkpIMXsfHxyM+Pr7Sdb799tuVzm/VqlWVT6wCgDvuuAObNm2qcjlLsuk+qHB3x3ZX19LL+/885lIdHQN1RE/E/ZVZrl+qNbFPLBEREVH12HRALdr5Nb70a46i7/dDt2dX6UStFmjaFMOKdaX/JiIiIiKbYtMBlarms3MWfmpfAp+ds5RpHo5u8D2eAw9Ht4rfSERERGQlDKhWZInL/prifGSFeUJTnK9Myx6QiG5/OCB7QKLZt19d7J9LREREegyoVsR+qbcwoBIREZEeAyoRERERScXuA6olzmIa6weqjo6RbqQBIiIiIhkwoFqpHyi0Wo40QERERGSE3QdUIiIiIpILAyoRERERSYUBVTK8s990ODIAERGRbbL5gBoREWHtJtSOuzu+dFQrj2gFSm+cUr3+Bm+cMhEGVCIiIttk8wE1MjLScIK7O3D1aumff8KfOjoG6oieUt01r9uzC1tb+N16RCvAG6eIiIiIADhauwGmZhD49LRaoGlTi4W/1NRU6c/eyd4+IiIisl82fwZVRrbQj5QBlYiIiGTFgGpCPjtnwXX7o+UG5fdwdIPv8Rx4OLpZr3FERERENsI+Auo//VKN3ZRkyn6pmuJ8+Lp4lBuUP3tAIrr94YDsAYkm2Q4RERFRfWYXAVW3Zxd0h36Q7qYkXmYnIiIiKs8uAqqsGFCJiIiIymNAJSIiIiKp2FVA5RlLIiIiIvkxoBphC8NEkWnxlxciIiJ52FVArS4GVPvDgEpERCQPBtQ6YJAlIiIiMr1696hTS/HZOQtt/riBUdu3wsPRDdkDEuHh6IasQg06nxTID21g7SYSERER2SQG1FrSFOcjK8wTvgCyCjUAoAzEn1qSiqEDeMmYiIiIqDZ4ib+aanI531L9GdnFgIiIiOoj+w6o7u7VfvypjGFQxjYRERER1ZVdB1Tdnl3SPf60rhhaiYiIyNbZdUCtjxhQiYiIyNYxoFoIx9kkIiIiqh67D6iWCo4MqERERETVw4BaT4Kjz85ZcN3+KH5qXwKfnbOU6R6ObvA9ngMPRzfrNY6IiIioBuw+oNYXmuJ8+Lp4ICvME5rifGV69oBEdPvDQRmjlYiIiEh2DKg24PYbn4ydLdU/xYpnS4mIiMjWMaDagNsDqrGzpdkDElEweIPRs6X1pRsDERER2QcG1NvVYPB+i247omett8+ASkRERLaEAfU2RgfvN0J/81FWocZkl9QrfXBA06Y2+/AAIiIie3fgwAGMHTsWISEh8PLywrZt2ypdfv/+/fDy8ir3JysrS1lm1apV5eb36NFDmX/t2jU8/fTTCA8Ph5+fH0JDQzFv3jzk5OQYbMvYdj777DPT7oAacrTq1m2Ebs8ubJ0+HUPWr1emZQ9IRGpRKoYONu3ZSZ7tJCIiqn9u3LiB0NBQjB8/HhMmTKj2+44cOYJGjRopr729vQ3mBwcHY8uWLcprR8db0S4zMxOXL1/GsmXLEBwcjAsXLmDOnDm4fPky3nvvPYP1rF27FtHR0cprT0/ParfRHBhQq8lYcDRHmGRAJSIiqn9iYmIQE1Pzrnre3t6VhkVHR0f4+voandehQwe8//77yuu2bdti4cKFePTRR1FcXGwQZj09PStcjzWYLKDqdDqoVCpTra7a2yz7tznWrTdo0CCzbKcqagCAUF7r2yDKLFO2XYMHD7ZKO40xZ32o7lgfebE2cmN95Gap+ghR+j+xRqMxyD8uLi5wcXEx2XaioqJQVFSEkJAQzJs3D5GRkQbzz549i5CQELi4uCA8PByLFy9G69atK1yfRqNBo0aNDMIpADz11FN44okn0KZNGzz00EN48MEHLZ7ryjJZQD116pRSLEs7ffq0SdcnhEB6erpJ11lbnQAUFxcDKC1Weno6BAR0/0wTMGxrQECANG3XM3V9yLRYH3mxNnJjfeRm7vqoVCr4+/sjNDQU2jL3iMybNw8JCQl1Xr+vry/WrFmDrl27orCwEMnJyYiLi8Pu3bsRFhYGAOjevTvWrl2LgIAAZGVl4YUXXsDgwYPx/fffG3QL0Lt69SpWr16NSZMmGUyfP38+oqKi0KBBA3zzzTeYO3cu8vLy8Oijj9b5c9SWStQxVQohoNFo4OzsbJUzqKdPn0ZAQADUarXJ1rtt2zYMGTLEZOurC+devYGmTUpfXP0bRd/vR8MdM+Dj4gEAyC7UIG9gkhVbWDFz1YdMg/WRF2sjN9ZHbpaqjxACRUVFAFDjM6heXl5ISUmpcdYYMmQIWrVqhQ0bNhidn5OTg06dOmHFihXl+rlqNBqMHDkSjRs3xkcffQQnJ6cKt7Ny5Up88MEH+PXXX2vUPlMy2RlUtVpttVPBarXapD+Ew4YNM9m6TOPWftV/zrJ7WvYDpKnrQ6bF+siLtZEb6yM3c9dHf37Pw8PDYvmnW7duOHToUIXzPT09ERAQgLNnzxpMz83NxahRo+Du7o6UlJRKwylQemZ29erVKCwsNGl3hZrgMFNERERENiAtLa3SG5m0Wi0yMjLQvHlzZZpGo8F9990HZ2dnfPjhh3B1da1yOydOnEDjxo2tFk4B3sVPREREZHb68Kh3/vx5JQi2bt0aS5cuRWZmJtb/M6TlunXr4O/vj+DgYBQUFCA5ORn79u3D5s2blXUsWrQIsbGxaN26NTIzM7Fq1Sqo1Wrcd999AG6F0xs3bmDDhg3Izc1Fbm4uAKBZs2ZQq9X46quvcOXKFdx5551wdXXF3r178corr+D//u//LLh3ymNAJSIiIjKzY8eOIS4uTnm9YMECAMC4ceOQlJSErKwsXLx4UZlfVFSEhQsXIjMzE25ubujYsSO2bNmCqKgoZZlLly5h6tSp+Pvvv9GsWTNERERg165daNasGQDgl19+wY8//gigtHtAWcePH8cdd9wBJycnvPnmm1iwYAGEEGjbti2ee+65cjdSWRoDKhEREZGZ9e7dG9euXatwflKS4Q3P8fHxiI+Pr3Sdb7/9dp22CQD9+/dH//79K13GGtgHlYiIiIikwoBKRERERFJhQJWduzu+dFQDV68C7u4AAA9HN2QVauB7PAcejm5WbiARERGRaTGgSk63Zxe2tvCD7tAP0O3ZBQDIHpCIgsEb0O0PB2QPSLRuA4mIiIhMjAGViIiIiKTCgGoDhg4dWqPpRERERLaMAdUGMKASERGRPWFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTiWNcVCCEM/rYkIQRUKhWEEFbZPlWO9ZEb6yMv1kZurI/cLFWfsvlHpVKZbTv2SiXqWL2SkhLk5uaaqj1ERERENqNRo0ZwcOAFaVMzSUBVVmbh3yA0Gg1CQ0ORlpYGDw8Pi26bqsb6yI31kRdrIzfWR26Wqk/Z+MSAanp1vsRvzaKoVCpotVqoVCqeXpcQ6yM31kderI3cWB+5Wao+rL15MfITERERkVQYUImIiIhIKjYdUF1cXDBv3jy4uLhYuylkBOsjN9ZHXqyN3FgfubE+9UOdb5IiIiIiIjIlmz6DSkRERET1DwMqEREREUmFAZWIiIiIpMKASkRERERSYUCtId5TJjc+G1turI+8WBf5sUZkTxhQq0l/YLj9b5KD/pG7JSUlfLqHhFgfefHYJjedTgedToeCggJ+d8iu1PlRp/YgNzcXzz77LK5fvw4AmDFjBrp168aDhSRyc3Mxe/Zs5OTkICcnBzNnzkSvXr3g7e1t7aYRWB+Z8dgmt9zcXDzyyCPQaDS4dOkSHn30Udxzzz0ICQmxdtOIzI5nUKuQl5eHPn364MKFC2jYsCFyc3MxYMAAvPzyy7h48aK1m2f3bty4gXvuuQdarRbdu3dHYGAgHn/8caxatQrHjx+3dvPsHusjLx7b5Jafn4/+/ftDpVJhzJgxGDlyJDZs2IAVK1Zgz5491m4ekdnxDGoVkpOT4eXlhU2bNinT1q9fjxdffBFarRaPPfYYmjdvbsUW2rcvvvgCDRo0QEpKChwdS3+c+/Tpg3//+9/Iy8vD448/jo4dO1q5lfaL9ZEXj21y27t3L5ydnfH666+jUaNGAICePXvi9ddfx3/+8x84OTmhT58+Vm4lkfnwDGoVCgsLoVarUVRUBJ1OBwCYPn06Fi5ciLfffhtffvklgFt97Mjy8vLycO3aNaXv3JgxY/D000/jp59+wqZNm1BUVMR+dVbE+siJxza5OTo64vLly7h8+bIybcCAAYiPjwcAfPjhhwbziOobBtQqNGnSBL///juuXLmiHMwB4OGHH0Z8fDwWL16Mc+fOwcGBu9IaGjdujKysLPz5559QqVRKfe69917MmDED69atw2+//cY+dVbC+siLxza5eXl5Qa1W48SJEwCg/BIRFRWFhx9+GKmpqUhLS7NmE4nMikeeKowfPx5du3bFxIkTce3aNTg7O6OgoAAA8Nhjj6Fly5b473//a+VW2q9BgwbhnnvuwcSJE3HlyhU4OzujsLAQADB58mQEBgZi+/btVm6l/WJ95MVjm3zKnq0ODw/H4MGDMWfOHJw4cQJqtVoJqXFxcejcubNylpuoPmJALSMvLw+vvfYali9fjpSUFFy8eBEqlQrz5s0DAEyZMgXXr1+Hq6srAMDBwQENGzaEi4uLNZttN7RaLZ577jnEx8cjMTERhw8fBgCsWLECfn5+GDhwIDIzM5V66HQ6eHp6omnTptZstt1gfeTFY5vccnNzMW/ePEycOBELFizAli1bAABr1qxBeHg4Ro4ciaNHj0KtVivv8fT0RKtWrazUYiLzUwl2/gJQ+p9rnz594OnpCQ8PDxw+fBg9e/bEuHHjcP/992P79u14+eWXodFokJSUBBcXF/z6669ISEjA5s2b0b17d2t/hHpNXx9vb2+0bNkSBw8eRMuWLREbG4snn3wSv//+O2bNmoXTp09j9erV8PT0xF9//YVnnnkGn3zyCXr16mXtj1CvsT7y4rFNbnl5eejbty9atmyJwMBA/Pzzz8jPz0ePHj2QmJiI3NxcTJ8+Hfv27cPcuXPh6+sLrVaLRYsW4eOPP+aNUlR/CRIlJSVi/vz5YtSoUaKoqEgIIcSpU6fExIkTRXR0tHjnnXeEEEL88ssvYvz48eKOO+4QXbp0EaGhoeLTTz+1Ysvtx0svvSSGDRsmCgsLhRBCZGZmikWLFonIyEixdOlSIYQQ169fF/Hx8aJTp04iNDRU9OjRQ3z22WfWbLbdYH3kxGOb/N566y0xcOBAodVqhRCl35M33nhDdOrUSUydOlVZbsWKFaJ///4iNDRU9OvXT3z++edCiNIaE9VHHGYKgEqlwuXLl9GoUSM4OTlBCIGAgAAsX74cL7zwAj766CM0b94csbGxSE5Oxk8//YSGDRvCyckJ7dq1U+5A5o0e5nPp0iUUFxfD2dkZANC8eXM88cQTcHd3x9atW9G8eXNMmzYNiYmJOHPmDFxdXSGEQKtWrVgfC2B95MRjm/wuXbqEnJwcNGzYEEDppftx48bB3d0dL774IhYtWoTly5dj/vz5eOSRR+Do6Iji4mJ4e3tz9Auq1+y+D6r459ng+ssmeXl5AEo7q99xxx146qmn4OzsjA8++EA5GHTr1g1BQUFo164dgNKDNw/g5qHf5yEhIdDpdMjIyFDmNWvWDBMnTkTnzp2RmpqKv//+GwDQrl07tGzZUumfxfqYD+sjLx7bbEOPHj3g4OCAAwcOKNMaNmyIQYMGYdy4cfj++++Rnp4OoPQ75eXlpTyFjfWh+szuA6r+Cz5s2DDs3bsXycnJyjSdToc2bdpg6dKl2LZtG37++WdrN9fu6A++kZGRSE9Px9tvv63cBS6EQPPmzTFnzhzs379fqQ8P2JbD+siLxzbbEBQUBAcHB6SkpODChQvKdE9PTzz44IP4/ffflaeu8btD9sSuA6p+SA8hBCIjI7FkyRLMnz8fKSkpUKlUyh2TTZs2RUBAAJycnKzZXLujH1JFCIFOnTphzZo1SEpKQmJiIrRarXKwbtasGTp27Mj6WBjrIy8e2+Sm/+4AQJs2bbB8+XJ8/vnnWL16Nc6ePavM8/HxQVhYmNJ1hsie2HUfVP0A1FeuXIGPjw8eeeQR5Obm4oknnsBff/2FUaNGoUWLFti3bx80Gg3c3Nys3GL7ov9PND09Hf/6178wYsQIaLVaxMfHIzMzE/fffz86dOiArVu34tKlSxyuyEKEEAYhh/WRD49tctN/dw4cOICwsDDcfffdSElJwZQpU6DRaDBkyBBERERg9+7d+PXXX+Hv72/lFhNZgeXux7I+nU5X7o7HhQsXiq5du4oLFy4oy7z//vuiTZs2okuXLqJXr16iTZs2vNvYwoqLi4UQQixfvly0atVKHDlyRJm3Y8cOERkZKTp27Ci6d+8uAgMDWR8LuHnzplIXveeee471kYxOpxNC8Ngmu6SkJOHv7y+2bNmifK9++OEHMXr0aBESEiI6d+4sOnToIDZv3mzllhJZh92cQc3JycGCBQswe/Zs/Otf/1Kmjxs3Dr/++qvymD8HBwdMmDABkZGROHXqFPLz8xEYGIjOnTsrZ47I9AoLC/HHH3+gpKQEAQEByh2tc+fOxW+//QZPT08ApWfvBgwYgI4dO+Ly5cu4fv06WrVqhaCgINbHjHJycjBz5kzEx8cjPDxcmf7kk0+yPlZWVFSES5cuQa1Wo3nz5srl4HHjxiEtLY3HNknodDqDgfanTJmC06dPo2PHjlCr1Up3jNdffx05OTm4evUqmjRpAn9/f46mQHbJLgbq12g06NmzJzp27IhPPvmk3PyioiKDPj4VHax5EDcPjUaDe++9FxqNBjdu3ICrqyuef/553HnnnWjWrFm5/V5SUsLng1uQ/vsTGhqKjz/+WJleXFwMR8fyv+OyPpaj0WgwZswYXLlyBTdv3oSPjw9eeeUVdOjQAQ4ODjy2WVlubi4eeeQRPPvss8pIF2q1usLvTkXTiexRvf9fJDc3F3369EF4eLgSTgsKClBQUKB0VC/7n+nnn3+OM2fOGF0XD+Cmd/PmTYwbNw5t27ZFSkoKUlJS0KtXL0ybNg0pKSnIysoy2O8fffQR9u3bZ3CTAZlPbm4uoqKiEBERoYTTnJwcXL9+XTmrU/b54ayP5RQWFmLEiBHw9fXF2rVrsXTpUnh5eWHw4MHYvHkzCgsLDcIpj22WlZ+fj9GjR2Pnzp0YM2YMfv/9d6jVauh0OqMhdOPGjfjggw9QUFBghdYSScjyvQosp7CwUISEhIhevXop01544QXxwAMPiP79+4tHHnlE/P3330KI0v51169fFx06dBD9+/cXeXl51mq2Xbl8+bKIiIgQu3fvNpj+/PPPi/bt24u1a9eKwsJCUVJSInQ6nQgPDxdhYWFK3ch8ioqKRJ8+fURYWJgoKCgQQgixbNkyMXz4cBEeHi5iY2PFqVOnhBCl/RtZH8s6e/as6NGjh/jpp58Mps+ePVv4+fmJrVu3CiFK+3Pz2GZZJSUl4pVXXhEjRowQmzZtEg888IDo0KGD+O2334QQolxfbp1OJ+677z7RrVs3cfHiRWs0mUg69fpagrOzM+666y7s3bsXu3fvxsaNG/Hrr79i2LBhyMvLw/fff48+ffpg//798PT0hKenJz7//HNkZWWhQYMG1m5+vSeEQF5eHnJycpRp+fn5cHNzQ0JCAoqLi/Hcc8+hd+/e6Ny5M1QqFb777jscPXoUXl5eVmy5fXByckKfPn2wfft2pKSk4Ntvv0VGRgYeeOABODg44Ouvv0ZsbCx27Nih9OtmfSxDCAGNRoOLFy8q/bX1l/PXrFkDnU6H+Ph4dO/eHX5+fjy2WZhKpUJAQADc3NwwfPhwdOzYEcuWLcOoUaPw6aefGlzuF0LAwcEBH374IdLS0tCyZUtrN59ICvW2D+rNmzeVsf0ee+wxfPzxx+jevTs2bNigPCXljz/+wOTJk9G7d2+sWrXK4Kkcgn2yLGbs2LG4dOkS9u3bB5VKZdBv7v7774cQAp988gl0Op1SU9bHvMr2I126dCmSkpLQqVMnrF+/HgEBAQAArVaLUaNGoUmTJkhJSWF9LKTsvo2JiYGXlxc++ugjqNVq5btTUFCAYcOGoUOHDlizZg2AW12ZWBvzKvvdKSwshIuLCwDgxIkTeP7553H8+HGDkHrhwgV4e3srv2gQUal61wdVp9NBCKE8zQYA1q1bh9mzZ2Po0KFo06aN0neuffv2aNmyJbKysuDg4GBw0OYB3Pz0fRcTEhKg0+kwefJk5Xnu+juPu3Xrhry8PDg4OBgMJs76mJf+aUMA8Oyzz+LZZ5/FqFGj0LZtW2UZd3d3BAUFQavVsj4WoP++3LhxQ3n92GOP4cqVK1iyZAl0Oh2cnZ2h0+ng6uqK9u3b4/Lly3BwcDDoZ8/amEdxcTFu3LiBa9euKdNcXFxw8+ZNAECnTp3wzDPPICwsDKNGjcLJkyfx3nvvKTeIEpGhehVQc3NzMXHiRAwZMgT33HMP1q1bp9wUsGjRIkyePFkJovqDvY+PD9q3bw/g1nPFyTy0Wi2++OIL5bX+P8oOHTpg5syZyMjIwIQJE3Dz5k3lDKpKpYKbmxvy8vJYHzMrWx/9QPz6kDpjxgyMHz9eGSZH//1xdnZGUFCQ8oshmYdWq8VTTz2FMWPGYMyYMdi7dy8cHBwQHR2N/v3748CBA5g/fz6EEEqNmjRpggYNGqCoqIi1MTONRoNRo0YhLi4O3bt3x6JFi3Do0CEApV1liouLAZSG1Pnz56Nbt27o3bs3nn76aSQkJMDPz8+azSeSUr3pg5qfn4/+/fsjMDAQw4cPx9WrV/H888/jyJEjmDhxIu6++25lrEag9HLXxo0b8fXXX+P9998HwDML5pSXl4fo6GicOnUKS5cuxeOPP678ouDs7IyRI0fC2dkZiYmJ6N69O2JjY6HT6ZCcnIx3332Xl7/MzFh9ACh95FQqFdzd3ZXlVSoVNm7ciE8++QTJyckG4zuSaeXm5uKee+5Bhw4d0L59e2RlZWHs2LH46quv0K1bN/zf//0fXFxc8MUXX+Duu+/GqFGjkJ2djQ0bNiA5OZmPyTSzgoICDBo0CG3btsVDDz2Eq1evYv369fj111+VXygcHR2VPqehoaFo1aoVSkpK8NFHH2HgwIHsdkFkjGXvyTKf1NRUERkZKTQajTLtu+++E/fcc48YN26cOHDggDL9+PHjYuHChaJ169Z8SocF3Lx5UyQkJIi4uDixYsUK0bRpU7FmzRplvv6O1uLiYpGZmSkSEhLEAw88IKZOnSp27NghhBDlngBGplNVfW73yy+/iNmzZwt/f39+f8yssLBQjB8/XkyePFn5nly/fl0MGzZMvPjii8pyN27cEAcPHhQPPfSQiImJEffff7/Ytm2bEILfHXP77rvvRGRkpMjKylKm/fzzz2LSpEkiNjbW4EldJSUl4vPPPxdNmjRRppeUlLBGREbUmzOoKpUKubm5uHbtGho1agSdTofevXtj5cqVmDt3Lj788EN06tQJrq6uyM7OhlarxZtvvokBAwbwt1czu379OlxdXTFmzBiMGDECPj4+mDdvHgBg9uzZUKvVKCkpUZ6E8/zzzwO4dbOB4OVJs6qqPmXpa+Hu7o633noL0dHRfMqNGWVkZOB///sfZs2apZyl9vT0ROvWrXHy5EkApX0f3dzcEBERgYiICBQXFytXJvjdMT9nZ2dcuXIFFy5cgI+PD4QQ6NKlC5566imsXLkSmzZtQpcuXdCuXTuoVCo4OTnhs88+w913383vDlFlrBiOTerHH38Ufn5+YsuWLUKI0rNC+t9K9+zZI5o0aSJSU1OFEKVn6vRnWvnbq2WcP39eGUszPz9fvP7666JJkybi5ZdfVpYpKSkRV69eNXhNllHd+uTm5gohSs/s6aexTuZTUFAgPv/8c6HVaoUQpcc1IYRYvHixmDRpkhDC+PeENbGcU6dOiQ4dOojXXntNCFE6pql+/x85ckS0bt1avPvuu0bfyzoRVaze3CTVvXt3TJkyBTNnzsTPP/8MR0dHFBcXQwiBfv36oXfv3ti7dy+A0n51jRo1AgCDoaXIfO644w64uLhACAFXV1dMmjQJq1atwooVK5RhcNavX4+XXnoJWq0WAM8qWFJ167NixQpotVqDm9hYJ/NxcXHB8OHD0bBhQ5SUlChPIGrUqJFyN79KpcInn3yCzZs3K+9jTSwnICAAM2fOxNKlS/HVV18ZXPW588470b9/f+zevRtA+RtxWSeiitWbS/wAMH36dFy4cAEjRozApk2bEB4ersxzcXFB06ZNrdg6Am4dkJ2dnTFp0iQApSMsHDhwAHv37sWbb75pcDMOWRbrI6+yQ0UJIZSRFDZu3IgZM2bg9ddft1bT7Jb4p3vY9OnTce7cOUyePBmvv/467r33XmUZBwcHZfB9BlKi6qtXAbVly5aYP38+1Go1Bg8ejGeffRY+Pj7QaDT47rvvlDuTSQ76EPTNN99gx44dSE5OxpAhQ9gnWBKsj3z0/bLz8vLg7e2NXbt2YcaMGXjjjTdw3333sTYWpt/XDg4OWLBgAZydnTFlyhQcOnQIfn5+cHBwwNatW/Hhhx9auaVEtqdePkkqNzcX7777LlJSUlBcXAx3d3fMnj0bw4cP5wFcMu+88w6efPJJvP/++xg6dChvGpAM6yOnxMRELFu2DGq1Gv/5z38wZswY1kYSGzduxMcff4y//voL3t7eePTRRxEXF8f/e4hqqF4GVL3Lly/Dzc0NhYWFyt2VAA/gstBoNHjppZcQHh6uHMAB1kcWrI+83n77bcydOxcpKSkYPHgwayMZjUYDFxcX5Ofno3HjxqwPUS3YREDV/+ZZ9hnHJI+61OfGjRto0KABD+BmxPrIq7a1uXLlCq5evYrg4GDWxsxqc+ZT/x6eNSWqPenTnlarxcMPP4yzZ88ynEqorvVp0KABAN4Nbi6sj7zqUhtvb28EBwcrr1kb09PfhJafn2/wujr09WBdiGpP6sSn0WjQv39/fPHFFzh8+DCAmh0kyLxYH7mxPvIyZW0YgkxPq9XiiSeewOjRo/Hggw/i2LFjPEFCZGHSXuLXaDTo2bMnoqKiUFhYiIyMDHz77bdGly17GaW4uFgZK5DMh/WRG+sjL9ZGbrm5ubjnnnsQEhKCdu3a4dSpU7h27Ro2bdqEhg0bGvxCwG5nROYj5TdLfwAPDw/H+vXr8cQTT+DKlSvYuHGj0eVVKhXS0tIAAI6Ojny8n5mxPnJjfeTF2sitpKQECxYsQEBAAN5//30sXboUo0aNQuPGjeHu7q48HEHPwcEBv/zyCy5cuKC8n4hMQ7qAevPmTUycOBF33nkn3n33XQClT7lp2bKl8jSO22k0GkyYMAEjRowAwEte5sT6yI31kRdrI7+ioiKcO3cOkZGRyr4uKCjA77//jsGDB2PgwIHKE7uEECgoKMCcOXMwevRoAODZVCJTMsPjU+vsm2++Uf5dXFwshBBi27ZtolmzZmLfvn3llr9586b4+OOPxbhx44RGo7FYO+0V6yM31kderI38pk2bJu666y6xf/9+8e6774pmzZqJ559/XqSkpIhly5YJLy8vsX//fmX5gwcPipEjR4r09HQrtpqo/pHq173i4mIAwD333KNMU6vVAICuXbuiW7du2LlzJwDDSymOjo64++67UVxcjJs3b1qwxfaF9ZEb6yMv1kZuokzXifHjx6Nly5ZYsmQJXn31VcTHxyMhIQEPPvggFi1ahK5du2LLli3K8sHBwfD39+ejtIlMTIqbpHQ6nXKwrqzT+apVq/D666/j4MGDysD7ZccQvHnzJpycnCzZdLvA+siN9ZEXayO3svUp+++bN28iLy8Po0ePxvTp0zFy5EiUlJRAp9Nh9OjR6Nu3L2bNmqXUhzewEZme1c+g6scCfO+99wCU9uG5vaO5/vXUqVPRqlUrJCUlGdzdqj/o8wBheqyP3FgfebE2cru9Pmq1WjnT7eTkhMaNG6N169Y4fPgwcnNz4eDggM8++wy//fYbIiMjAbA+ROZk1W9Vfn4+xowZg59//hlXrlyBk5MTHnjgAeVArv/y6/9u0qQJ2rVrh71792Lu3Llwd3c3WB9vIDAt1kdurI+8WBu5VVQfR0dHg/qEhoZi69atuOeee9ChQwd89913WLNmjRJQich8rBpQ33jjDQDAq6++ij179ii/yRo7kAsh4ODggCVLliAjI6PcAZxMj/WRG+sjL9ZGblXVR3/Jfvbs2WjXrh1+/PFHNGrUCI899hh69uzJR5gSWYBVA+rAgQMhhMD999+Prl27YvXq1eUOFPoDgUqlws2bN9GmTRu0adOGBwgLYH3kxvrIi7WRW1X1cXR0VPr93nvvvbj33nut3GIi+2O1m6T0B+GyHdNPnjyJV155BefOncOkSZPwwAMPAAB++eUXdO7c2RrNtFusj9xYH3mxNnKrSX2OHz+OsLAwazaXyG5JcRd/2TMGv//+OxITE3Hu3Dk89NBD0Gg0SEhIwI8//og2bdpwIGQrYH3kxvrIi7WRW3Xr07ZtW57VJrIwKQKqnv5gcfLkSfz73//G7t27ce3aNaxdu1Z5UgdZD+sjN9ZHXqyN3FgfIvlYrA9q2cspFdH/hhocHIymTZvif//7Hz744AMMGjRIGUiZv8WaB+sjN9ZHXqyN3FgfIhtlwqdSGaV/nJ8QQpw9e1b88MMPVb5n48aNwsvLS3z++edCCCFKSkpESUmJuZpo11gfubE+8mJt5Mb6ENk2s3Z6KioqQnR0NL7++mucP38effv2xdmzZ6t8X48ePbBp0yYMHz6cv72aEesjN9ZHXqyN3FgfIttn1kv8hYWFuOeeezBp0iQ4OTnhiSeeUO6OrEhJSQnatm2Ltm3bKtN4gDAP1kdurI+8WBu5sT5Ets+sZ1AbNWqEsWPH4ubNm8jPz0fHjh0BoNzj/gwadNudrDxAmA/rIzfWR16sjdxYHyLbZ5aAqtPpAAAFBQVo1qwZ3nzzTTz55JOYOHEiPvvsM2WQaiHPAAJ2hfWRG+sjL9ZGbqwPUf1h8kv8+jsm9cN1xMTEYPjw4cjLy0NRURGmTZsGBwcHjBgxAgBw4MABqFQq9OrVy9RNISNYH7mxPvJibeTG+hDVLyYNqEIIqNVq/Pbbbxg0aBBGjx6tDEDdqFEjzJ07FwAwbdo0XLhwAS4uLliyZAk++eQTUzaDKsD6yI31kRdrIzfWh6geMvWwABqNRgwbNkwkJCQYna/VasWaNWtEUFCQuOuuu8Snn35q6iZQJVgfubE+8mJt5Mb6ENUvJn+S1PXr1zFw4EA8/fTTuO+++/QhWOlwrr8Mc+HCBajVarRo0YLDeVgQ6yM31kderI3cWB+i+sXkN0llZ2cjJycHDRo0AFA63If+y3/27Fls3boVRUVFaN26NVq0aAGg9ODAA4RlsD5yY33kxdrIjfUhql9q3Qe1oKAABw4cwB9//AEPDw+0bt0affr0Qfv27dG9e3ckJCSgb9++ysECAHbu3InDhw+jT58+aNKkiUk+ABnH+siN9ZEXayM31ofIPtTqEr9Go8HIkSNRXFyMv//+G1lZWXBxcUFMTAzeeOMNnDlzBg899BBu3LiBtWvXorCwEH/88QcWL16Mt956C0OGDDHHZ6F/sD5yY33kxdrIjfUhsiM17bSq1WpFjx49xOTJk8X58+eFEEKcOXNGrFy5Uvj5+Ylx48YJIYQ4f/68uP/++0VQUJDo0KGD6NWrl/jiiy+EEILPNjYj1kdurI+8WBu5sT5E9qXGAfXFF18U9957rygsLDSYfv36dfHGG28Ib29vsWDBAmV6WlqaOHfunLhw4YIQovQAwYOE+bA+cmN95MXayI31IbIvNb5J6uTJk2jZsiWcnZ2Vp3YAgKenJ0aOHIkRI0bgq6++wpUrVwAAHTt2hL+/P1q1agWAndLNjfWRG+sjL9ZGbqwPkX2pcUC9fPkycnJyAABqtVqZLoRAkyZNMH78eGRkZODq1aumayVVG+sjN9ZHXqyN3FgfIvtS7YAq/rmXqn379vjll19w4sQJg3n630wLCgrQokUL3ilpYayP3FgfebE2cmN9iOxTtQOq/iDw0EMP4fLly/jPf/6DCxcuKPP0l1zS09PRpk0bODo6KgcWMj/WR26sj7xYG7mxPkR2qjYdV999913h6+srxo8fL3bs2CGEEOLSpUsiJSVF+Pr6im3bttW6UyzVHesjN9ZHXqyN3FgfIvtRq3FQdTodtmzZgieffBKFhYVwc3NDixYtkJeXh2effRbDhw83uPRClsX6yI31kRdrIzfWh8h+1Cqg6p0/fx7Hjx/HH3/8gbCwMLRq1QohISF8vrEkWB+5sT7yYm3kxvoQ1X91CqhERERERKZW42GmiIiIiIjMiQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVQYUImIiIhIKgyoRERERCQVBlQiIiIikgoDKhERERFJhQGViIiIiKTCgEpEREREUmFAJSIiIiKpMKASERERkVT+H2DVWVE5WCboAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['is_green'] = data.apply(lambda row: 1 if row['close'] - row['open'] > 0 else 0, axis=1)\n",
        "data[:20]"
      ],
      "metadata": {
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        "id": "I3nb6mZ75vE_",
        "outputId": "d91de914-ebe4-430c-840a-502125782caf"
      },
      "execution_count": 250,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green\n",
              "datetime                                                         \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0\n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0\n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0\n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0\n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0\n",
              "2022-10-17 06:15:00  1.55278  1.55330  1.55262  1.55273         0\n",
              "2022-10-17 06:30:00  1.55273  1.55344  1.55266  1.55338         1\n",
              "2022-10-17 06:45:00  1.55338  1.55396  1.55336  1.55386         1\n",
              "2022-10-17 07:00:00  1.55386  1.55470  1.55310  1.55435         1\n",
              "2022-10-17 07:15:00  1.55435  1.55447  1.55374  1.55408         0\n",
              "2022-10-17 07:30:00  1.55408  1.55412  1.55267  1.55275         0\n",
              "2022-10-17 07:45:00  1.55275  1.55286  1.55188  1.55202         0\n",
              "2022-10-17 08:00:00  1.55202  1.55328  1.55190  1.55288         1\n",
              "2022-10-17 08:15:00  1.55288  1.55317  1.55254  1.55297         1\n",
              "2022-10-17 08:30:00  1.55297  1.55562  1.55282  1.55454         1\n",
              "2022-10-17 08:45:00  1.55454  1.55676  1.55396  1.55636         1\n",
              "2022-10-17 09:00:00  1.55636  1.55814  1.55620  1.55802         1\n",
              "2022-10-17 09:15:00  1.55802  1.56143  1.55771  1.55964         1\n",
              "2022-10-17 09:30:00  1.55964  1.56144  1.55882  1.56111         1\n",
              "2022-10-17 09:45:00  1.56111  1.56182  1.55814  1.55984         0"
            ],
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              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>is_green</th>\n",
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              "      <th>datetime</th>\n",
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              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
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              "      <td>0</td>\n",
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              "      <th>2022-10-17 06:00:00</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 06:15:00</th>\n",
              "      <td>1.55278</td>\n",
              "      <td>1.55330</td>\n",
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              "      <th>2022-10-17 06:30:00</th>\n",
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              "      <td>1</td>\n",
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              "      <th>2022-10-17 07:00:00</th>\n",
              "      <td>1.55386</td>\n",
              "      <td>1.55470</td>\n",
              "      <td>1.55310</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 07:15:00</th>\n",
              "      <td>1.55435</td>\n",
              "      <td>1.55447</td>\n",
              "      <td>1.55374</td>\n",
              "      <td>1.55408</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 07:30:00</th>\n",
              "      <td>1.55408</td>\n",
              "      <td>1.55412</td>\n",
              "      <td>1.55267</td>\n",
              "      <td>1.55275</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 07:45:00</th>\n",
              "      <td>1.55275</td>\n",
              "      <td>1.55286</td>\n",
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              "      <td>0</td>\n",
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              "      <th>2022-10-17 08:00:00</th>\n",
              "      <td>1.55202</td>\n",
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              "      <th>2022-10-17 08:15:00</th>\n",
              "      <td>1.55288</td>\n",
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              "      <td>1.55254</td>\n",
              "      <td>1.55297</td>\n",
              "      <td>1</td>\n",
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              "      <th>2022-10-17 08:30:00</th>\n",
              "      <td>1.55297</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding signals column"
      ],
      "metadata": {
        "id": "h2qQyUecsszq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "signals = [''] * len(data)\n",
        "\n",
        "for i in range(0, len(data)-2):\n",
        "    if (data['is_green'][i] + data['is_green'][i+1] + data['is_green'][i+2]) == 3:\n",
        "        signals[i+2] = 'buy'\n",
        "    elif (data['is_green'][i] + data['is_green'][i+1] + data['is_green'][i+2]) == 0:\n",
        "        signals[i+2] = 'sell'\n",
        "    else:\n",
        "        signals[i+2] = 'neutral'\n",
        "\n",
        "data['buy_sell'] = signals"
      ],
      "metadata": {
        "id": "8IyY8qQlgci7"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
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      "source": [
        "data[:20]"
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        "id": "KUvQEa9qh4VF",
        "outputId": "cb601569-a9ac-47bc-becc-5aca13c7e088"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green buy_sell\n",
              "datetime                                                                  \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0         \n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0         \n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0     sell\n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0     sell\n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0     sell\n",
              "2022-10-17 06:15:00  1.55278  1.55330  1.55262  1.55273         0     sell\n",
              "2022-10-17 06:30:00  1.55273  1.55344  1.55266  1.55338         1  neutral\n",
              "2022-10-17 06:45:00  1.55338  1.55396  1.55336  1.55386         1  neutral\n",
              "2022-10-17 07:00:00  1.55386  1.55470  1.55310  1.55435         1      buy\n",
              "2022-10-17 07:15:00  1.55435  1.55447  1.55374  1.55408         0  neutral\n",
              "2022-10-17 07:30:00  1.55408  1.55412  1.55267  1.55275         0  neutral\n",
              "2022-10-17 07:45:00  1.55275  1.55286  1.55188  1.55202         0     sell\n",
              "2022-10-17 08:00:00  1.55202  1.55328  1.55190  1.55288         1  neutral\n",
              "2022-10-17 08:15:00  1.55288  1.55317  1.55254  1.55297         1  neutral\n",
              "2022-10-17 08:30:00  1.55297  1.55562  1.55282  1.55454         1      buy\n",
              "2022-10-17 08:45:00  1.55454  1.55676  1.55396  1.55636         1      buy\n",
              "2022-10-17 09:00:00  1.55636  1.55814  1.55620  1.55802         1      buy\n",
              "2022-10-17 09:15:00  1.55802  1.56143  1.55771  1.55964         1      buy\n",
              "2022-10-17 09:30:00  1.55964  1.56144  1.55882  1.56111         1      buy\n",
              "2022-10-17 09:45:00  1.56111  1.56182  1.55814  1.55984         0  neutral"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>is_green</th>\n",
              "      <th>buy_sell</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:15:00</th>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:30:00</th>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:15:00</th>\n",
              "      <td>1.55278</td>\n",
              "      <td>1.55330</td>\n",
              "      <td>1.55262</td>\n",
              "      <td>1.55273</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:30:00</th>\n",
              "      <td>1.55273</td>\n",
              "      <td>1.55344</td>\n",
              "      <td>1.55266</td>\n",
              "      <td>1.55338</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:45:00</th>\n",
              "      <td>1.55338</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55336</td>\n",
              "      <td>1.55386</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:00:00</th>\n",
              "      <td>1.55386</td>\n",
              "      <td>1.55470</td>\n",
              "      <td>1.55310</td>\n",
              "      <td>1.55435</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:15:00</th>\n",
              "      <td>1.55435</td>\n",
              "      <td>1.55447</td>\n",
              "      <td>1.55374</td>\n",
              "      <td>1.55408</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:30:00</th>\n",
              "      <td>1.55408</td>\n",
              "      <td>1.55412</td>\n",
              "      <td>1.55267</td>\n",
              "      <td>1.55275</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:45:00</th>\n",
              "      <td>1.55275</td>\n",
              "      <td>1.55286</td>\n",
              "      <td>1.55188</td>\n",
              "      <td>1.55202</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:00:00</th>\n",
              "      <td>1.55202</td>\n",
              "      <td>1.55328</td>\n",
              "      <td>1.55190</td>\n",
              "      <td>1.55288</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:15:00</th>\n",
              "      <td>1.55288</td>\n",
              "      <td>1.55317</td>\n",
              "      <td>1.55254</td>\n",
              "      <td>1.55297</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:30:00</th>\n",
              "      <td>1.55297</td>\n",
              "      <td>1.55562</td>\n",
              "      <td>1.55282</td>\n",
              "      <td>1.55454</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:45:00</th>\n",
              "      <td>1.55454</td>\n",
              "      <td>1.55676</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55636</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:00:00</th>\n",
              "      <td>1.55636</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55620</td>\n",
              "      <td>1.55802</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:15:00</th>\n",
              "      <td>1.55802</td>\n",
              "      <td>1.56143</td>\n",
              "      <td>1.55771</td>\n",
              "      <td>1.55964</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:30:00</th>\n",
              "      <td>1.55964</td>\n",
              "      <td>1.56144</td>\n",
              "      <td>1.55882</td>\n",
              "      <td>1.56111</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:45:00</th>\n",
              "      <td>1.56111</td>\n",
              "      <td>1.56182</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data = data.copy()"
      ],
      "metadata": {
        "id": "glhU6BUUUOKE"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding TP and SL columns"
      ],
      "metadata": {
        "id": "RdzinWS1sx2C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sl = [''] * len(data)\n",
        "tp = [''] * len(data)\n",
        "\n",
        "for i in range(2, len(data)):\n",
        "    if data['buy_sell'][i] == 'sell':\n",
        "        sl[i] = data['high'][i-2]\n",
        "        tp_amount = (data['high'][i-2] - data['close'][i]) * 2\n",
        "        tp[i] = data['close'][i] - tp_amount\n",
        "    elif data['buy_sell'][i] == 'buy':\n",
        "        sl[i] = data['low'][i-2]\n",
        "        tp_amount = (data['close'][i] - data['low'][i-2]) * 2\n",
        "        tp[i] = data['close'][i] + tp_amount\n",
        "\n",
        "\n",
        "data['tp'] = tp\n",
        "data['sl'] = sl"
      ],
      "metadata": {
        "id": "pYBOTUb1h6RY"
      },
      "execution_count": 254,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[:30]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oX2LW8LXrx9s",
        "outputId": "6bd1b711-b507-4ea3-8d58-f173560b7bb3"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green buy_sell  \\\n",
              "datetime                                                                     \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0            \n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0            \n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0     sell   \n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0     sell   \n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0     sell   \n",
              "2022-10-17 06:15:00  1.55278  1.55330  1.55262  1.55273         0     sell   \n",
              "2022-10-17 06:30:00  1.55273  1.55344  1.55266  1.55338         1  neutral   \n",
              "2022-10-17 06:45:00  1.55338  1.55396  1.55336  1.55386         1  neutral   \n",
              "2022-10-17 07:00:00  1.55386  1.55470  1.55310  1.55435         1      buy   \n",
              "2022-10-17 07:15:00  1.55435  1.55447  1.55374  1.55408         0  neutral   \n",
              "2022-10-17 07:30:00  1.55408  1.55412  1.55267  1.55275         0  neutral   \n",
              "2022-10-17 07:45:00  1.55275  1.55286  1.55188  1.55202         0     sell   \n",
              "2022-10-17 08:00:00  1.55202  1.55328  1.55190  1.55288         1  neutral   \n",
              "2022-10-17 08:15:00  1.55288  1.55317  1.55254  1.55297         1  neutral   \n",
              "2022-10-17 08:30:00  1.55297  1.55562  1.55282  1.55454         1      buy   \n",
              "2022-10-17 08:45:00  1.55454  1.55676  1.55396  1.55636         1      buy   \n",
              "2022-10-17 09:00:00  1.55636  1.55814  1.55620  1.55802         1      buy   \n",
              "2022-10-17 09:15:00  1.55802  1.56143  1.55771  1.55964         1      buy   \n",
              "2022-10-17 09:30:00  1.55964  1.56144  1.55882  1.56111         1      buy   \n",
              "2022-10-17 09:45:00  1.56111  1.56182  1.55814  1.55984         0  neutral   \n",
              "2022-10-17 10:00:00  1.55984  1.55984  1.55674  1.55797         0  neutral   \n",
              "2022-10-17 10:15:00  1.55797  1.55797  1.55649  1.55698         0     sell   \n",
              "2022-10-17 10:30:00  1.55698  1.55826  1.55450  1.55500         0     sell   \n",
              "2022-10-17 10:45:00  1.55500  1.55660  1.55480  1.55587         1  neutral   \n",
              "2022-10-17 11:00:00  1.55587  1.55904  1.55554  1.55844         1  neutral   \n",
              "2022-10-17 11:15:00  1.55844  1.55871  1.55482  1.55522         0  neutral   \n",
              "2022-10-17 11:30:00  1.55522  1.55576  1.55410  1.55522         0  neutral   \n",
              "2022-10-17 11:45:00  1.55522  1.55680  1.55522  1.55554         1  neutral   \n",
              "2022-10-17 12:00:00  1.55554  1.55632  1.55356  1.55624         1  neutral   \n",
              "2022-10-17 12:15:00  1.55624  1.55818  1.55620  1.55798         1      buy   \n",
              "\n",
              "                          tp       sl  \n",
              "datetime                               \n",
              "2022-10-17 05:00:00                    \n",
              "2022-10-17 05:15:00                    \n",
              "2022-10-17 05:30:00  1.55149    1.555  \n",
              "2022-10-17 05:45:00   1.5499  1.55488  \n",
              "2022-10-17 06:00:00  1.54798  1.55518  \n",
              "2022-10-17 06:15:00  1.54955  1.55432  \n",
              "2022-10-17 06:30:00                    \n",
              "2022-10-17 06:45:00                    \n",
              "2022-10-17 07:00:00  1.55773  1.55266  \n",
              "2022-10-17 07:15:00                    \n",
              "2022-10-17 07:30:00                    \n",
              "2022-10-17 07:45:00  1.54712  1.55447  \n",
              "2022-10-17 08:00:00                    \n",
              "2022-10-17 08:15:00                    \n",
              "2022-10-17 08:30:00  1.55982   1.5519  \n",
              "2022-10-17 08:45:00    1.564  1.55254  \n",
              "2022-10-17 09:00:00  1.56842  1.55282  \n",
              "2022-10-17 09:15:00    1.571  1.55396  \n",
              "2022-10-17 09:30:00  1.57093   1.5562  \n",
              "2022-10-17 09:45:00                    \n",
              "2022-10-17 10:00:00                    \n",
              "2022-10-17 10:15:00   1.5473  1.56182  \n",
              "2022-10-17 10:30:00  1.54532  1.55984  \n",
              "2022-10-17 10:45:00                    \n",
              "2022-10-17 11:00:00                    \n",
              "2022-10-17 11:15:00                    \n",
              "2022-10-17 11:30:00                    \n",
              "2022-10-17 11:45:00                    \n",
              "2022-10-17 12:00:00                    \n",
              "2022-10-17 12:15:00   1.5635  1.55522  "
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Backtesting the Strategy"
      ],
      "metadata": {
        "id": "-yI_lT-BTtJt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adding PNL column"
      ],
      "metadata": {
        "id": "Zf9QyiYYTjj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pnl = [0] * len(data)\n",
        "\n",
        "for i in range(len(data)-1000):\n",
        "    row = data.iloc[i]\n",
        "    if row['buy_sell'] in ['sell', 'buy']:\n",
        "        tp = row['tp']\n",
        "        sl = row['sl']\n",
        "    else:\n",
        "        continue\n",
        "\n",
        "    j = i + 1\n",
        "    while True:\n",
        "        row_to_check = data.iloc[j]\n",
        "        high = row_to_check['high']\n",
        "        low = row_to_check['low']\n",
        "\n",
        "        if low < tp < high:\n",
        "            pnl[i] = 2\n",
        "            break\n",
        "        elif low < sl < high:\n",
        "            pnl[i] = -1\n",
        "            break\n",
        "        else:\n",
        "            j += 1\n",
        "\n",
        "data['pnl'] = pnl"
      ],
      "metadata": {
        "id": "5oVecKErrzmm"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[:40]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eIa0q0_NPsKl",
        "outputId": "dff792f5-12a4-451a-c770-6ad1ffd3f29f"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green buy_sell  \\\n",
              "datetime                                                                     \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0            \n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0            \n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0     sell   \n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0     sell   \n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0     sell   \n",
              "2022-10-17 06:15:00  1.55278  1.55330  1.55262  1.55273         0     sell   \n",
              "2022-10-17 06:30:00  1.55273  1.55344  1.55266  1.55338         1  neutral   \n",
              "2022-10-17 06:45:00  1.55338  1.55396  1.55336  1.55386         1  neutral   \n",
              "2022-10-17 07:00:00  1.55386  1.55470  1.55310  1.55435         1      buy   \n",
              "2022-10-17 07:15:00  1.55435  1.55447  1.55374  1.55408         0  neutral   \n",
              "2022-10-17 07:30:00  1.55408  1.55412  1.55267  1.55275         0  neutral   \n",
              "2022-10-17 07:45:00  1.55275  1.55286  1.55188  1.55202         0     sell   \n",
              "2022-10-17 08:00:00  1.55202  1.55328  1.55190  1.55288         1  neutral   \n",
              "2022-10-17 08:15:00  1.55288  1.55317  1.55254  1.55297         1  neutral   \n",
              "2022-10-17 08:30:00  1.55297  1.55562  1.55282  1.55454         1      buy   \n",
              "2022-10-17 08:45:00  1.55454  1.55676  1.55396  1.55636         1      buy   \n",
              "2022-10-17 09:00:00  1.55636  1.55814  1.55620  1.55802         1      buy   \n",
              "2022-10-17 09:15:00  1.55802  1.56143  1.55771  1.55964         1      buy   \n",
              "2022-10-17 09:30:00  1.55964  1.56144  1.55882  1.56111         1      buy   \n",
              "2022-10-17 09:45:00  1.56111  1.56182  1.55814  1.55984         0  neutral   \n",
              "2022-10-17 10:00:00  1.55984  1.55984  1.55674  1.55797         0  neutral   \n",
              "2022-10-17 10:15:00  1.55797  1.55797  1.55649  1.55698         0     sell   \n",
              "2022-10-17 10:30:00  1.55698  1.55826  1.55450  1.55500         0     sell   \n",
              "2022-10-17 10:45:00  1.55500  1.55660  1.55480  1.55587         1  neutral   \n",
              "2022-10-17 11:00:00  1.55587  1.55904  1.55554  1.55844         1  neutral   \n",
              "2022-10-17 11:15:00  1.55844  1.55871  1.55482  1.55522         0  neutral   \n",
              "2022-10-17 11:30:00  1.55522  1.55576  1.55410  1.55522         0  neutral   \n",
              "2022-10-17 11:45:00  1.55522  1.55680  1.55522  1.55554         1  neutral   \n",
              "2022-10-17 12:00:00  1.55554  1.55632  1.55356  1.55624         1  neutral   \n",
              "2022-10-17 12:15:00  1.55624  1.55818  1.55620  1.55798         1      buy   \n",
              "2022-10-17 12:30:00  1.55798  1.55976  1.55777  1.55958         1      buy   \n",
              "2022-10-17 12:45:00  1.55958  1.56060  1.55868  1.55887         0  neutral   \n",
              "2022-10-17 13:00:00  1.55887  1.55901  1.55692  1.55726         0  neutral   \n",
              "2022-10-17 13:15:00  1.55726  1.55975  1.55720  1.55922         1  neutral   \n",
              "2022-10-17 13:30:00  1.55922  1.56254  1.55742  1.56122         1  neutral   \n",
              "2022-10-17 13:45:00  1.56122  1.56130  1.55753  1.55777         0  neutral   \n",
              "2022-10-17 14:00:00  1.55777  1.55852  1.55576  1.55590         0  neutral   \n",
              "2022-10-17 14:15:00  1.55590  1.55927  1.55514  1.55715         1  neutral   \n",
              "2022-10-17 14:30:00  1.55715  1.56032  1.55698  1.55980         1  neutral   \n",
              "2022-10-17 14:45:00  1.55980  1.56146  1.55973  1.56116         1      buy   \n",
              "\n",
              "                          tp       sl  pnl  \n",
              "datetime                                    \n",
              "2022-10-17 05:00:00                      0  \n",
              "2022-10-17 05:15:00                      0  \n",
              "2022-10-17 05:30:00  1.55149    1.555   -1  \n",
              "2022-10-17 05:45:00   1.5499  1.55488   -1  \n",
              "2022-10-17 06:00:00  1.54798  1.55518   -1  \n",
              "2022-10-17 06:15:00  1.54955  1.55432   -1  \n",
              "2022-10-17 06:30:00                      0  \n",
              "2022-10-17 06:45:00                      0  \n",
              "2022-10-17 07:00:00  1.55773  1.55266   -1  \n",
              "2022-10-17 07:15:00                      0  \n",
              "2022-10-17 07:30:00                      0  \n",
              "2022-10-17 07:45:00  1.54712  1.55447   -1  \n",
              "2022-10-17 08:00:00                      0  \n",
              "2022-10-17 08:15:00                      0  \n",
              "2022-10-17 08:30:00  1.55982   1.5519    2  \n",
              "2022-10-17 08:45:00    1.564  1.55254    2  \n",
              "2022-10-17 09:00:00  1.56842  1.55282    2  \n",
              "2022-10-17 09:15:00    1.571  1.55396   -1  \n",
              "2022-10-17 09:30:00  1.57093   1.5562   -1  \n",
              "2022-10-17 09:45:00                      0  \n",
              "2022-10-17 10:00:00                      0  \n",
              "2022-10-17 10:15:00   1.5473  1.56182   -1  \n",
              "2022-10-17 10:30:00  1.54532  1.55984   -1  \n",
              "2022-10-17 10:45:00                      0  \n",
              "2022-10-17 11:00:00                      0  \n",
              "2022-10-17 11:15:00                      0  \n",
              "2022-10-17 11:30:00                      0  \n",
              "2022-10-17 11:45:00                      0  \n",
              "2022-10-17 12:00:00                      0  \n",
              "2022-10-17 12:15:00   1.5635  1.55522   -1  \n",
              "2022-10-17 12:30:00  1.57162  1.55356   -1  \n",
              "2022-10-17 12:45:00                      0  \n",
              "2022-10-17 13:00:00                      0  \n",
              "2022-10-17 13:15:00                      0  \n",
              "2022-10-17 13:30:00                      0  \n",
              "2022-10-17 13:45:00                      0  \n",
              "2022-10-17 14:00:00                      0  \n",
              "2022-10-17 14:15:00                      0  \n",
              "2022-10-17 14:30:00                      0  \n",
              "2022-10-17 14:45:00   1.5732  1.55514   -1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bdd76cbb-7af8-432c-aeeb-89cb29ef637b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>is_green</th>\n",
              "      <th>buy_sell</th>\n",
              "      <th>tp</th>\n",
              "      <th>sl</th>\n",
              "      <th>pnl</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:15:00</th>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:30:00</th>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55149</td>\n",
              "      <td>1.555</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.5499</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.54798</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:15:00</th>\n",
              "      <td>1.55278</td>\n",
              "      <td>1.55330</td>\n",
              "      <td>1.55262</td>\n",
              "      <td>1.55273</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.54955</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:30:00</th>\n",
              "      <td>1.55273</td>\n",
              "      <td>1.55344</td>\n",
              "      <td>1.55266</td>\n",
              "      <td>1.55338</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:45:00</th>\n",
              "      <td>1.55338</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55336</td>\n",
              "      <td>1.55386</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:00:00</th>\n",
              "      <td>1.55386</td>\n",
              "      <td>1.55470</td>\n",
              "      <td>1.55310</td>\n",
              "      <td>1.55435</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.55773</td>\n",
              "      <td>1.55266</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:15:00</th>\n",
              "      <td>1.55435</td>\n",
              "      <td>1.55447</td>\n",
              "      <td>1.55374</td>\n",
              "      <td>1.55408</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:30:00</th>\n",
              "      <td>1.55408</td>\n",
              "      <td>1.55412</td>\n",
              "      <td>1.55267</td>\n",
              "      <td>1.55275</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:45:00</th>\n",
              "      <td>1.55275</td>\n",
              "      <td>1.55286</td>\n",
              "      <td>1.55188</td>\n",
              "      <td>1.55202</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.54712</td>\n",
              "      <td>1.55447</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:00:00</th>\n",
              "      <td>1.55202</td>\n",
              "      <td>1.55328</td>\n",
              "      <td>1.55190</td>\n",
              "      <td>1.55288</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:15:00</th>\n",
              "      <td>1.55288</td>\n",
              "      <td>1.55317</td>\n",
              "      <td>1.55254</td>\n",
              "      <td>1.55297</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:30:00</th>\n",
              "      <td>1.55297</td>\n",
              "      <td>1.55562</td>\n",
              "      <td>1.55282</td>\n",
              "      <td>1.55454</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.55982</td>\n",
              "      <td>1.5519</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:45:00</th>\n",
              "      <td>1.55454</td>\n",
              "      <td>1.55676</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55636</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.564</td>\n",
              "      <td>1.55254</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:00:00</th>\n",
              "      <td>1.55636</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55620</td>\n",
              "      <td>1.55802</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56842</td>\n",
              "      <td>1.55282</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:15:00</th>\n",
              "      <td>1.55802</td>\n",
              "      <td>1.56143</td>\n",
              "      <td>1.55771</td>\n",
              "      <td>1.55964</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.571</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:30:00</th>\n",
              "      <td>1.55964</td>\n",
              "      <td>1.56144</td>\n",
              "      <td>1.55882</td>\n",
              "      <td>1.56111</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.57093</td>\n",
              "      <td>1.5562</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:45:00</th>\n",
              "      <td>1.56111</td>\n",
              "      <td>1.56182</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:00:00</th>\n",
              "      <td>1.55984</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>1.55674</td>\n",
              "      <td>1.55797</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:15:00</th>\n",
              "      <td>1.55797</td>\n",
              "      <td>1.55797</td>\n",
              "      <td>1.55649</td>\n",
              "      <td>1.55698</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.5473</td>\n",
              "      <td>1.56182</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:30:00</th>\n",
              "      <td>1.55698</td>\n",
              "      <td>1.55826</td>\n",
              "      <td>1.55450</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.54532</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:45:00</th>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55660</td>\n",
              "      <td>1.55480</td>\n",
              "      <td>1.55587</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:00:00</th>\n",
              "      <td>1.55587</td>\n",
              "      <td>1.55904</td>\n",
              "      <td>1.55554</td>\n",
              "      <td>1.55844</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:15:00</th>\n",
              "      <td>1.55844</td>\n",
              "      <td>1.55871</td>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:30:00</th>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55576</td>\n",
              "      <td>1.55410</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:45:00</th>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55680</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55554</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:00:00</th>\n",
              "      <td>1.55554</td>\n",
              "      <td>1.55632</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55624</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:15:00</th>\n",
              "      <td>1.55624</td>\n",
              "      <td>1.55818</td>\n",
              "      <td>1.55620</td>\n",
              "      <td>1.55798</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.5635</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:30:00</th>\n",
              "      <td>1.55798</td>\n",
              "      <td>1.55976</td>\n",
              "      <td>1.55777</td>\n",
              "      <td>1.55958</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.57162</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:45:00</th>\n",
              "      <td>1.55958</td>\n",
              "      <td>1.56060</td>\n",
              "      <td>1.55868</td>\n",
              "      <td>1.55887</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 13:00:00</th>\n",
              "      <td>1.55887</td>\n",
              "      <td>1.55901</td>\n",
              "      <td>1.55692</td>\n",
              "      <td>1.55726</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 13:15:00</th>\n",
              "      <td>1.55726</td>\n",
              "      <td>1.55975</td>\n",
              "      <td>1.55720</td>\n",
              "      <td>1.55922</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 13:30:00</th>\n",
              "      <td>1.55922</td>\n",
              "      <td>1.56254</td>\n",
              "      <td>1.55742</td>\n",
              "      <td>1.56122</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 13:45:00</th>\n",
              "      <td>1.56122</td>\n",
              "      <td>1.56130</td>\n",
              "      <td>1.55753</td>\n",
              "      <td>1.55777</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 14:00:00</th>\n",
              "      <td>1.55777</td>\n",
              "      <td>1.55852</td>\n",
              "      <td>1.55576</td>\n",
              "      <td>1.55590</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 14:15:00</th>\n",
              "      <td>1.55590</td>\n",
              "      <td>1.55927</td>\n",
              "      <td>1.55514</td>\n",
              "      <td>1.55715</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 14:30:00</th>\n",
              "      <td>1.55715</td>\n",
              "      <td>1.56032</td>\n",
              "      <td>1.55698</td>\n",
              "      <td>1.55980</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 14:45:00</th>\n",
              "      <td>1.55980</td>\n",
              "      <td>1.56146</td>\n",
              "      <td>1.55973</td>\n",
              "      <td>1.56116</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.5732</td>\n",
              "      <td>1.55514</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdd76cbb-7af8-432c-aeeb-89cb29ef637b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-bdd76cbb-7af8-432c-aeeb-89cb29ef637b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bdd76cbb-7af8-432c-aeeb-89cb29ef637b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d187f809-e4d0-4c4d-8d8c-d0cc1e0e2915')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d187f809-e4d0-4c4d-8d8c-d0cc1e0e2915 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['pnl']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NozawpUEIHg1",
        "outputId": "3268eac5-a730-4b94-8185-4a120f9a05f4"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime\n",
              "2022-10-17 05:00:00    0\n",
              "2022-10-17 05:15:00    0\n",
              "2022-10-17 05:30:00   -1\n",
              "2022-10-17 05:45:00   -1\n",
              "2022-10-17 06:00:00   -1\n",
              "                      ..\n",
              "2023-08-04 19:45:00    0\n",
              "2023-08-04 20:00:00    0\n",
              "2023-08-04 20:15:00    0\n",
              "2023-08-04 20:30:00    0\n",
              "2023-08-04 20:45:00    0\n",
              "Name: pnl, Length: 20000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum = [0]\n",
        "for i in range(len(data)-500):\n",
        "    sum.append(sum[i] + data['pnl'][i])"
      ],
      "metadata": {
        "id": "mn5QtLwTOnF0"
      },
      "execution_count": 259,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot the PNL plot"
      ],
      "metadata": {
        "id": "hqUw8SIDT10e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(sum)\n",
        "plt.title('3 Consecutve Candles Strategy')\n",
        "plt.xlabel('Total PNL')\n",
        "plt.ylabel('# of Candle')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "XqBZAKUuPwnF",
        "outputId": "e027b257-cd4c-4c6b-eb06-2653ba1435a6"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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fNlu/1NRUAPphjxEjRuC3334zm/Hb0NNi+JLfsWOHdCwvL09qozFXV9dKA5F27dqhU6dOWLt2LdauXYugoCAMGDBAOq5SqTBy5Ej89NNPZoM7Q70rExoaiilTpmDTpk1mvyh1Oh3eeecdKSGnSqUy6U364IMP6pyVfujQoXBwcMAHH3wgu+57771X7bk1vWc1uf+V2bp1q9nesz/++AMAZL2wVd1Hcww9SBWvb67thpxTFa9/3333QaVSYeHChSbXEUIgLS0NgD7w8/HxwaefforS0lKpzJo1ayoNWCdMmIBNmzbhvffeg4+PT50CTrJv7JEiMvL4448jOzsbAwYMQPPmzXHt2jWsWbMGJ0+exDvvvFPtEELfvn2xfPlyTJ8+HW3btsWECRMQGRmJnJwcbNu2Db/++itee+01AMBdd92Fm2++GS+++CLOnz+PLl26YNOmTfjll1/w1FNPmTxWXp2cnByEhIRg1KhR6NKlC9zc3LB582bExsZKPStKpRKrVq3CsGHD0KFDB0yaNAnNmzfH5cuXsXXrVnh4eEjL4bz++uvYtGkTBg4ciMceewzt2rXD1atX8cMPP2Dnzp3w8vLCrbfeirCwMEyePBlz586FSqXC559/Dj8/P1y4cEFWv+joaKxcuRKvvfYaIiIi4O/vL82BAvS9Ui+//DKcnJwwefJkKJXy/+e98cYb2Lp1K3r16oUpU6agffv2SE9Px8GDB7F58+ZqhyPfeecdnD17Fk888QTWrVuH4cOHw9vbGxcuXMAPP/yAkydPSr1gw4cPx9dffw1PT0+0b98eu3fvxubNm6U0E7Xl5+eHOXPmYPHixRg+fDjuuOMOHDp0CBs3bpQe869MTe9ZTe5/ZWbNmoX8/Hzce++9aNu2LYqLi7Fr1y6sXbtWWkLHIDo6Gps3b8a7776L4OBgtGzZEr169ar02h4eHhgwYADefPNNlJSUoHnz5ti0aRMSExNNykZHRwMAXnzxRYwZMwYODg6466670Lp1a7z22mt4/vnnpXQG7u7uSExMxM8//4zHHnsMc+bMgaOjIxYsWIBZs2Zh8ODBGD16NM6fP48vvvgCrVu3NtvjNW7cODz77LP4+eefMW3aNGm+FVGNNf6DgkSW67vvvhNDhw4VAQEBQq1WC29vbzF06FDxyy+/1Oo6Bw4cEOPGjRPBwcHCwcFBeHt7iyFDhogvv/xSaLVaqVxOTo6YPXu2VC4yMrLKhJwVGSdzLCoqEnPnzhVdunQR7u7uwtXVVXTp0kWsWLHC5LxDhw6J++67T/j4+AiNRiPCw8PF6NGjxZYtW2TlkpKSxMSJE4Wfn5/QaDSiVatWYsaMGVJCTkNbe/XqJRwdHUVYWJh49913zaY/uHbtmrjzzjuFu7u7yaPoQgiRkJAgpZwwlxxUCCGSk5PFjBkzRGhoqHBwcBCBgYFiyJAh4pNPPjFbvqLS0lKxatUq0b9/f+Hp6SkcHBxEeHi4mDRpkiw1QkZGhpg0aZLw9fUVbm5u4rbbbhMnT540SZ5paGfFFBHmkmpqtVqxcOFCERQUVKeEnNXds9rc/4o2btwoHnnkEdG2bVvh5uYmHB0dRUREhJg1a5ZITk6WlT158qQYMGCAcHZ2NpuQMzU11eT6ly5dEvfee6/w8vISnp6e4v777xdXrlwRAMT8+fNlZQ1JS5VKpcnP0E8//ST69esnXF1dhaurq2jbtq2YMWOGOHXqlOwa77//vggPDxcajUb07NlT/PfffyI6OlrcfvvtZtt/xx13CABi165d1X5WRBUphKiH2ZBEREQWSqfTwc/PD/fddx8+/fRTk+P33nsv4uLicObMmSaoHVk7zpEiIiKbUVhYaDKP6quvvkJ6ejoGDRpkUv7q1avYsGFDjbPeE1XEHikiIrIZ27Ztw+zZs3H//ffDx8cHBw8exGeffYZ27drhwIED0tJGiYmJ+O+//7Bq1SrExsbi7NmzCAwMbOLakzXiZHMiIrIZLVq0QGhoKN5//32kp6ejWbNmmDhxIt544w3Z+pDbt2/HpEmTEBYWhi+//JJBFNUZe6SIiIiI6ohzpIiIiIjqiIEUERERUR1xjlQtGa8LVdulEoiIiKhpGM9kqpjw90YwkKqDmq4gT0RERJbFeG3T+sBAqo6cnJzqvUdKq9UiISEBkZGRla4mbwvsoZ320EaA7bQ1bKftsIc2ArVrpxAChYWF9V4HBlK1ZAieFApFvQdSCoUCQogGubYlsYd22kMbAbbT1rCdtsMe2gjUrZ31/XlwsjkRERFRHTGQIiIiIqojBlJEREREdcRAioiIiKiOGEgRERER1REDKSIiIqI6YiBFREREVEcMpIiIiIjqiIEUERERUR0xkCIiIiKqIwZSRERERHXEQIqIiIiojhhIkd0pKNY2dRWIiMhGMJAiu/LVngvo/OoW/HL4SlNXhYiIbAADKbIrr244CQCY81N8E9eEiIhsAQMpIiIiojqyqkDqv//+w5gxY9CuXTt4e3tjw4YNsuNCCLz++uto27YtgoKCMGLECJw9e1ZWJiMjA1OmTEFYWBjCw8Mxa9Ys5ObmNmYzyEIUlnCuFBER3RirCqTy8/PRsWNHvPXWW2aPL1u2DB9//DHeffdd/P3333BxccHIkSNRWFgolZkyZQpOnjyJdevW4fvvv8euXbvw1FNPNVILqCmVaHVQKMq3L2cWVl6YiIioBtRNXYHauOWWW3DLLbeYPSaEwEcffYQ5c+bgjjvuAACsXLkSUVFR2LBhA0aOHIlTp05hy5Yt+Oeff9CtWzcAwJIlSzB69Gi8+uqrCAoKarS2UOO7nlsMIcq3L2cWoLWfa9NViIiIrJ5VBVJVSUpKQnJyMgYNGiTt8/T0RHR0NGJjYzFy5EjExsbC09NTCqIAYNCgQVAqlThw4ACGDx9uct2ioiIUFRVJ26Lsm1ir1UJh3L1RD7RarexvW9VU7byckS/bvpCWB20rb2k7Pa8Yy7cnYmS3YLQPcr+h9+K9tC1sp22xh3baQxuB2rVTGP9Puh7ZTCCVnJwMAPDz85Pt9/f3R0pKilSm4nG1Wg1vb2/p/IqWLl2KJUuWSNtubm6Ij49HQkJCg92UM2fONMh1LU19t1MnBJRVBLf7z8sDqaNnL6O7R560/cHedGw+l481+y7hoa6euK/djQVTAO+lrWE7bYs9tNMe2gjUrJ0KhQLh4eH1/t42E0g1lNmzZ2P69OnStiF4ioyMbJAeqTNnziAiIgIqlaper21JGqKdj359CJcyC/HdI9HwdnU0Of7iL8fx06F02b4CpQuioqIAAEUlWmz+bpt07MvDWXh+RI8614f30rawnbbFHtppD20EatdOIQSKi4vrvQ42E0gFBAQAAFJTUxEYGCjtT0lJQadOnaQyqampsvNKS0uRkZEhnV+RRqOBRqORtoUQyM7OhkqlqvdAykClUtn0D75BfbUzt6gUO8/qg6TlO85j/vB2JmV+OnRVet3cywmXMwtxOatQev+cvBKTc4q1gLPjjdWP99K2sJ22xR7aaQ9tBGrWzoYaRbKqp/aqEh4ejoCAAGzfvl3al52djQMHDiAmJgYAEBMTg6ysLBw+fFgqs2PHDuh0OkRHRzd2lameXM0qf/ruUkaByfGK/3hmD4kAAJy7nofiUh0AQK00DYqvZPGpPiIiqppVBVK5ubmIi4tDXFwcAP0E87i4OFy8eBEKhQJTp07F22+/jT/++APHjh3DtGnTEBgYiDvvvBMAEBUVhSFDhuDJJ5/EgQMHsGfPHjz77LO47777+MSeFTp1LQcTPo/Fb0fLe5uuZReZlMsqKJVtD+8cBD83R+QVadFh4Wb8dSwZ3++/bHLe5UzToIyIiMiYVQ3tHT58GHfddZe0/eKLLwIAxo4dixUrVuDJJ59Efn4+Zs+ejaysLPTu3Rs//vgjnJycpHM+/fRTzJ07FyNGjIBCocDdd9+NN954o9HbQjduwur9yMgvwZ7EDGnfhfR8XM0qhE4IBLhroFYpcSWrPCDqH+EDlVKB/pG+WHdIv97ezO+P4M6OgSbXZyBFRETVsapAql+/fsjIyKj0uEKhwAsvvIAXXnih0jLe3t5YtWpVQ1SPGsmxK9koKtUhI990XlN+sRYD3t4BALi7cxDeub8TrpYl3vR0VmPleH3qi/4RPlIgBQD7zuvnWD1/extcyijA13sv4r8zaRgbE9rQzSEiIitmVUN7REIIjFi5Bw98uq/asr+WDfkZ5jr1bNEMGrX+R75vax9Z2dRc/ZMc/SN9kVusz0fy1/GUeqs3ERHZJgZSZFVKdbV/6sIQSAV7lQ/xNnN1RCtf06zmYc1cZNv5xaUmZYiIiAwYSJFVMTxlV1OlWh3iLmcBAII9nWTHPpnQzaS8Rq3EI33LE7adS803KUNERGTAQIqsSrG2doHU3ydSsLdsMnpQhUAqvELvk0HbQHcEuOtzh51NzcWRS1lYuf0csgtM52QREZF9YyBFVqVEW/nQ3ucTu0tzoAyeWHtUeh3s5WxyTliz8n3dQj2l10Pa6ZcSmvNTPEZ9vBfvbj6Dad8ermu1iYjIRjGQIqtScWivQ7A7HugRgkX3tEf/SF8ceOHmSs9tG2i6dt7wTuX5w94a1Ul63SbAtOy+85U/MUpERPbJqtIfEFUMpGYPicDANuULUWscVPh6Ug+sO3QZPx8uT9QZ08LbpLcKAGYMaoXmXk7oF+Ej67G6u3MgFvx2ogFaQEREtoSBFFkVwxwpV40K/5vS02zPUe9WzeCqUckCqQERPiblAMBRrcToHiEm+92dHEz2ebuY7iMiIvvGoT2yKiVlgZSHk4PZIMqg4hN6Pm6ON/zemQUl0vsTEREBDKTIyhiG9hxVVf/oNnN1lA3l+bhpav1eFZ/qEwJIzyuu9XWIiMh2cWiPrIphaM9RraiynEKhQLCnExLT9HmgfF1r3yP19SM9sPlEClwcVXju52MAgOu5xXB3UsNRpYS6mmCOiIhsH78JyKpIPVJmJo5XZDx5vC5De0GeTpjQOwwjuzdH+yD9MGLi9TwMeudfTFi9v9bXIyIi28NAiqxKcak+j5RDDXqDjJeE8alDj5Qxv7Khwc0nU5GRX4L9SZnIKeTyMURE9o6BFFkVw2Tv6uZIAeUTzl01Kjg5qG7ofX3d9YHYxYzyJWPOpOTe0DWJiMj6MZAiq5JTpO8FqtnQnj6Q8nWt/UTzigzXOHopW9q3/wITdBIR2TsGUmQ1fjlyFS/9chxAzXqkuoV6Qa1UyJZ+qasOwaapFg4mZd7wdYmIyLrxqT2yGnN+jJNe16RHqqWvK/Y8Nwjumhv/Mb+9QwCiAtxwKrl8OO/ktZwbvi4REVk39kiRVXJQVZ3+wMDT2QFKZc3KVkWhUCDC302271JmIXIKS2742kREZL0YSJFVqkmPVH3zdzedaxV/JdtMSSIishcMpMgq1WSOVH1TG/VsdS2bdzVx9QGkMds5EZHdYiBFVulCekGjv6ezY3kKhT4tm0mvN8Rda/S6EBGRZeBkc7IKRSVa2XZLX5dKSjacB3uFIv5yNkZ0DWr09yYiIsvEQIosnhACqbnlw2ezbm6FsTGhjV4PbxdHfPxgNwDliUEBIK+IGc6JiOwVh/bIYiWl5aNEq8OUrw/h5nf/BQAEuGvwxOAI+JmZ+N2YHFRKTBvQEgCQmlPUpHUhIqKmwx4pskhbTqRg6reH0SPcC/uNEl82u8E18+qTIZhLYSBFRGS3GEiRRfpsVxIAyIIowLICKf9KAqm/jiUj8Xou+vuKpqgWERE1IgZSZFWaIOtBpQyBlPHQnhACM78/AgBwHuCDtm2bpGpERNRILOhriaicEOZ7czLyLSeTeKCnflHka9lFKNXqsHpXEnou3iYd35KY30Q1IyKixsJAiiySt4t8CG9MTAhcNSrMvTWyiWpkKsBdAycHJUp1ApcyC/D6xlPILCgP9HZfLGDmcyIiG8dAiiySpsISMM/eGonDLw1Bn1Y+TVQjU0qlAi18XAEA566b73367SiTdRIR2TIGUmSRcirkZnJ3cmiimlStVVli0MTreU1cEyIiagoMpKjRZeSXoESrnwNVVKJFRr7pWnXGSS7HxoQ0Wt1qq6Wvvkcq7pL5IbzLmYWNWR0iImpkfGqPGlV6XjH6vLkD7o5KxLZvi0e/Pog9iRnY9kx/NPdylsrlFOoDqfnD22JU9+ZNVd1qGQKpPYnpZo9fzmz8NQGJiKjxsEeKGtW206kAgJxiHXILS7EnMQMA8PWeC7JyhkCqU3NPODmoYKkMQ3tpefJeNcMcryvskSIismkMpKhRfbzjvPT63zNp0uujl+VDY4Y5Uu4ay+40NUw2N2jt54qxMSH4bnIPAEBWYSlyCi0nZQMREdUvBlLUqM4ZTcr+41iy9PropSwpd5QQArllgZSbk2UHUu5OargZBXvBnk545e72aB/kDg+N/p/XpQz2ShER2SoGUtRosgrkPTN/n0iVXheV6nDiWg4AIK9YC0M+TkvvkQKAYR0DpNdnU8sDxXBP/ZOGOxKuN3qdiIiocTCQokZRotXhxfXHZPtcHOVznxJS9EGIYX6UWqmAk4Pl/4gOaesnvTZu06AW+vlT3+67iFKtrtHrRUREDc/yv6XIJnwfewl/HU8BAHi76Htq8ou1sjKXM/RPuEnDeho1FApFI9aybm5qXZ4kNDrcW3o9oIULmrk44EpWITaVtZ2IiGwLAylqFLHnM6TXN7fxNVvGkCogt9A65kcZODmoEFy27t7NUeVtc1QpMLJbMIDypxWJiMi2WMc3FVm90ym50uu2ge7w0CQju0g+3GVIXmktT+wZ+31mXySk5KJbqKdsf1SgGwDgEtMgEBHZJPZIUYM7l5onm4R9W3t/BLmZBkmGHinDHCl3K+mRAvR17R7mZTIUGeSh76m6lsVAiojIFjGQoga1Mf4a7v1oj7S9ZnIPBHhoEOReHiS18NFPyr6cWQCdTkjLw7hqLDcRZ00FemoAANeyC6X0DkREZDsYSFGDemLtUdmk8tZlS6oY90i18HGBSqlAiVYgNbcIBSX68s4WnNG8pvzcNFAogBKtQHqe6ZqCRERk3RhIUaNp4eMCHzd9D02gUY+Uh5MDAj30+w8kZaKoVD93SmMDgZSjWglfV0cAwLXsoiauDRER1TcGUtRgikvlk8n7tm4mvY5o5iC9VqkUaFYWbDz5v6PIK9L3SBnWq7N2QZ6cJ0VEZKts45uKLFJmhUzm54wmnDc36pHKLypFYUn58F9anr7nxslGAqmAsgnnV7MZSBER2Rrb+KYii5SZL58TdHuH8qVUjJ9uKyjR4p4uwdJ2ep4+AHOygaE9AAiUeqTKh/aqy3TOTOhERNaBgRQ1mIx8fUDk6+aIjx/shtE9QsyWKyjR4eE+YdL2lSx9GgRHG+mRCjJ6cg8A3v/nDKJf34oEo9xaAJCQkovCEi3e2nQaPV7fKuvBIyIiy2Qb31RkkQyBVHgzFwyO8oODyvyPm6+rIzQOKoQ1cwYAHLuiX7zYGtbZq4lAw9Be2RypD7aeQ36xFov+OCWV+S72Iu74YBdmfn8En/x7HnnFWryzOaFJ6ktERDVnG99UZJEyywIpLxcHs8c/Hd8VA9v44vlhUQCALiHyrOBOahsZ2jMk5awwR+pCer70+uVfTwAAtp++Lu0zJCYlIiLLxUCK6sWWkykYu2ofLpUtPAwAGWVzpCoLpPpH+mDVhO7SU22v3N1e9qSezT21l10kS8p50eizMievmIEUEZGls41vKmpyU9ccxv6kTDy59oi0z9Aj5e3iWKNruGnUGGi0oLEt5JECgAAPDRzVShSX6nDuej68jQLL7IISKZN7Rem5TOBJRGTpGEhRvTp6OVt6/eexZACAl7P5Hilz7ugYKL22lR4pB5USHYLcAQAnr+XInkY8mZyD82n5Zs+7lFmInMISs8eIiMgy2MY3FVmc5OxCXCmbXF3Z0J45AyJ9pNdFRrmlrF0rP/3SOOeu56GotLxdp67lIvF65U/nffZfUoPXjYiI6o6BFNULwzwgANDqhPSEGgBEh3nV+DruTuVBV5YNTbZu6aMPpBKv50lL4AD6HqoPtp6r9Lzl285xsWMiIgvGQIrqhWGtPED/NJpxVvMIf7daXcsQlPVr7VNNSevR0tcFAJB4PV+2dM6m4yk4V0WPFABcqGZSOhERNR119UWIqqc16jXZn5QJZVnicuOhupr684m+SM8rQYi3c31Vr8m19NX3SJ1NzUWJtvyzMg44+0X4YOeZNABA2wA3XMwsQF6RFvGXsxHezKVxK0xERDXCHimqlb+OJeP72Esm+0uNgoPzaXlIy9M/cebjWrMn9oy5OKptKogCgLBmLlAq9FncK9M/ojzoPJmci3u6BAEA5v92nMN7REQWij1SVCM6ncCLvxzHjwcvAwA6BnugY3MP6bhxIHU1q1DablaHQMoWadRKhHg740J65cN0PcK9Zdsdg/Wfb1ZBKf6IT8adnQLNnUZERE2IPVJUIyeu5UhBFADEJmXIjpfqyntajl7KxtmydeJ83BhIGRiG9wwM6wve2y0YK8d1RecQT7x6d3sAwOjo5mgb6C6V3XoqtfEqSkRENcYeKaqTT/9NxLiYEClppvG8n6T0fCSVLX9Sl6E9WxXh5ypbAubZ29rgni7BaB/kDmXZpLIHejRHp+YeiPR3g0JRfm4280kREVkk9khRjRg/sg8AqbnFWLPvorRdqjM/h4eBVLkWPvIeKQeVEh2be0hBFAAoFAp0CPaAo1oJB5USn07oBgC4nCFfp4+IiCwDAymqkUIzyTFjz5cP75VqzU+i9nHVmN1vj9ydat8B3MJH/7TexYx8TjgnIrJADKSoRgpLK3/aDKiiR4pzpCTdjRKTrpvaq0bnBHk6Q1H2tJ/hSUgiIrIcDKSoRgzLtSgVwOsj9BOijb/YDXOkvn80RnZes1osD2PrgjydsHFWX/z37EB0au5Zo3M0aiUCPfQJSi+mF+BKZgFOXstpyGoSEVEtMJCiGjHMkerb2getyp4+Mw6kDE/tBXg4YWT3YGm/xmiBXtJnefd3r91wpyHT+79nrmPgO//iruW7kZHP3ikiIkvAQIpqxLCMiZNaKQ3XyQKpsh4ptUqBB6JDGr+CNizuchYAyNbkq2QklYiIGhnTH1CNGNJEXc8rlp7EyyvSorBEC41aKc2RclAq0C3MC+8/0Fm2ADHV3aP9WmDl9kTZPi0jKSIii8AeKaoRw9Bdu0B3uGnU8C6b+3TsSrZsorlapf+RGtYxEP0ibGfR4aY06+bWJvtW7Tzf+BUhIiITdhtIffrpp+jcuTMCAwMxdOhQHDhwoKmrZFFKtTp8s/cCtp3WZ9Q2zJFq5uoIhUKBdkH6rNvvbTkjyzGlNsqJRPXDQaXE0LZ+sn2rdyXh+NXsJqoREREZ2GUgtW7dOrz00kv4v//7P2zbtg0dO3bEyJEjkZrKZTgMdp5Nw8LfT2LK14dwNasQBWVP7TmXTR73dtYP7+1JzEC31/6RzjP0SFH90prJIXXPij146ZdjsnxeRETUuOzyW2/FihWYOHEixo8fj7Zt2+Ldd9+Fi4sLvvnmmyark04nkJ5XjOTc0iarg7HUnCLp9dZTqSgs0fc6aRz0PzJelaQ1cGCPVINIvJ5vdv/a/Zcx7rNYk/1FJVo89MV+vPN3QkNXjYjIrtndZPPi4mIcPnwYs2fPlvYplUoMHDgQsbFmvpCKilBUVB5UGLJLa7VaKBT1FzTsPpeOSV8dAgBsa90SgV4u9Xbtujh8MVN6HX85CwXF+gBPo1JAq9XC08k0rcGACB8IoYPWNAm6jLasgLa6glasvts4e0hrPPm/OLxweySSs4vw2a4LZt/PYP/5dOw6q/8zJMoXnZp71Es9KrKHewmwnbbGHtppD20EatfOhlodwu4CqbS0NGi1Wvj5yeec+Pn5ISHB9H/vS5cuxZIlS6RtNzc3xMfHIyEhoV5vSnZm+aK0P+85icEtXaso3bCSc0vxvwPXpO0fDl5Bez/9UF7G9RScOpWL62lZJueFuZTg1KlTNX6fM2fO3HhlLVx9tTFcKbBmZDBcHfIBL+CzXfLjx0+chMqoN/BIYp70+p/DCXDMdauXelTGHu4lwHbaGntopz20EahZOxUKBcLDw+v9ve0ukKqt2bNnY/r06dK2IXiKjIys1x6pNkIAG5MBAMv2ZGDsgE6VDp81pIJiLR59f5fJ/uOp+pxRLUObIyrKH67nEwDIM2z3bd8CUVF+JudWpNVqcebMGUREREClss2EnQ3fxsuyLY/AcIR4O0vbO1LPA9DPncpVuiEqKgoAsOl4CracSsXswa0RWJbo80bYw70E2E5bYw/ttIc2ArVrpxACxcX1n8zY7gIpHx8fqFQqk4nlqamp8Pf3Nymv0Wig0ZRnohZCIDs7GyqVql4DKQB4qHcovtxzEQDw4q8n8NH4bvV6/ZpYd+QyUnMr/0FzdXKASqVCv0hfrN4tH14a2j6gVp+JSqWy6X/gQMO18b5uwVh36Iq0fTGzCOG+5b1OKTnl9/BUcp5Uhzc3JeBSZiGOX83BH7Nuqrf62MO9BNhOW2MP7bSHNgI1a2dDDe3Z3WRzR0dHdO3aFdu3b5f26XQ67NixAzExMVWc2fAMa6oBwJaTjf8EoVYnqn0CTKPW/8j0j/DB5xO7y47Vd2BJlZs/vK1se9KXB3A1q1DaTkjJlV6fvJYj/QK5lFlYdjyv1svMJF7Pk6W6ICIiOwykAGD69On46quv8N133+HUqVN4+umnkZeXh/HjxzdpvQI9arcGW337PvYi/jyWLNu3bmov2bYh/YFCoUD/SF+4Ouq3a7t+HN0YF0c1KsatK7eXLyGzJ7E8IM4uLMW1bP0DE95Gw8Xxl2ueh+rfhOu4ddl/mPwV860RERmzy0DqvvvuwyuvvILXX38dAwYMQHx8PH788UezQ3uNKaBCIFWqLf/f/8b4a/h4R2LFU+rV+iNXZduL7+2ATs09ZfucKyxC/N2jPdEvwgcfje/aoHUjUz9P7S3b/i72EgB997Vh4rmDSv/3iWv6+WzGS8vE1SKQ+naffsh5byJzVhERGbPLQAoAHnvsMcTFxSE5ORmbN29Gjx49mrpKJoFUYlp57qAn1h7F238n4NCFzAYb5zWsoWfQNUQfRK2fZvSFXaEXpF2QO1Y/FG0ScFHD6xDsgc2z+8n2PfH9ESz567QUMPWP8AUAnLxqLpAyffKyMhy2JSIyz24DKUvk5yYPpH6r0EMEAKM/3Ycer2+t1bBMTWXml8i2I/z1k5fbB7nD8GR9cD086UX1J9TLWba98VgyPvsvSdqODvcCUB40GWdIj7tS9c9QUakOV7MKkZxdiKJS285FQ0RUV3b31J4lc1QrEdPcCbGX9ROCdyemQ6cT+CP+mqxcdmEp7v1oD/73WE90C/Wqt/c3nnxsPFSnUCiw/4WbUVSqg6uGPzKWRFlFJnknByU6BusTcZ67no+cwhIpQz0AJGcXISWnqNL5bXd+sAtJ6eYzqhMRkR57pCzMi/19sGGGfijt5LUcLN92DrN/iDNbdvQn+5BdUGL2WF1klPVI/T6jD4a0lc8Xc3dygK8bJ5RbExdHFZqX9VhdySrA8m3lk9HdnfQB8enkXLPnAqg0iCrmk3tERBIGUhZGoVCghY8LXBxVKCzR4f2tZ6ssf9BoKZcbodUJZJYFZT5ujtWUJktyW3vzD0m4OKqlJ0ELS3SyIb8WPvoliCZ9eQCXMgpMzq1qHl5OkWWsB0lEZAkYSFkglVIBD6eaDaGdTc2rvlANZBWUwPDd6enc+BnVqe4e69/S7H5XRxU0DuYT1DU3mls13syix/nFlc+Jyimsv15QIiJrx0DKQhny/lSnvgIpw7Ceh5MaDir+WFiTziGeGNk92GS/qor5U20CyrOgX8kqxIEkeVqD1NzKf/5+OHC50mNERPaG35gWalzPEJN9Twxujck3yRdcPFdvgZR+orm3C4f1rNGo7s1N9hlnOjf279wBaO4lf/pybYXg6GK66XCfwSf/nq99BYmIbBQDKQv13G1Rsm21UoFZN7fGc7fL959NzauXvFIZefoeKW9XDutZo+gwL5N9hl7GIW3lC0n7u2lMhm9LKkwgv1DF03qGJJ9ERMRAymI5O8rntvzf7W2k11tm98PCu9oBADILSjBmVSwGv/svUnJqNhxojqFHqhl7pKxSVQkzl9zXEdMG6udRuWnUUCoVJglUE9PKezZX7TyPBb+frPR6g9r4mewTQiAtr+q1+45eykKP1//BjwfLe7+EEJi65hDGrtonSxZKRGQtGEhZMOO19x7qHSa9DmvmgnE9Q6XtgxcycTGjAB8ZrbVWW4beC/ZIWa8Fd7WDg0qBCb3D4KBS4NW72wPQPzzw9NBIbHryJvzztD4Tur+7BpuevAnfTtYv1H3+er7Us7nkr9NVvo+5xY4/2nEevd/Yhl/NJJEF9E+Fjvx4L7IKSvH8z8cA6JOE/nMqFVtOpmJ/UiYuZjBnFRFZH2ZXtGAfjOmCeb+ewLO3RdZoiY4rlcyJqQnOkbJ+43uGYlT35tColfi/29pAo5b/P6mlr6vJdnMvZ6iUCuQVa5GSU4QAD9PM9UGeTrL5Vmm5poHUsq36IP6F9cdwd5cgk+PZFZ7023MuHRNW75ftu55bjBY+8joSEVk69khZsK6hXvhtRh9pvbSKKk4wPnyx5munVZRu6JFyYY+UNTMETxWDqMo4qpUIKUuFkGjUK2Vs+zP9ZduJafl4b8sZs9crMpOss6hUhye+PyLbVzGIAvSZ1omIrA0DKSs2tJ18rkpaXnGdJp6vO3QZPx+6AoA9Uvaopa8+OefZ1FzkmckfpVAo4OQg/1WxfNs5XM4sQE6h+eScOYUlEEIgp7AUW0+lYk9ihtlyxurrCVQiosbEQMqKBbibDsNU9sh7Vf5v3THpdefmHjdUJ7I+pWWTvBf8frLSCePbnhmAX6b3lu17bl08Yt7YjgNX5KkSPt6RiO6LtqLPku3o8fo/SEqr2dyn97eeRXJ23YeniYiaAgMpK+bnbtp7dCo5F9/uu4j4y9m1vt5dnQMRFeheH1UjK2KcRX/C56ZDbgDg4+qI9kHyINvQy/TK9jTZ/rf/TgCg7yHVifLtmvj7REqNyxIRWQIGUlbMx9U0kHrsm0OY/9sJ3PvRnhpfp1XZ0M6YGNMkoGT7pg1sJb029Gg2c3XAPV2CTHqhGtrC30/WS140IqLGwkDKiqmrWcpFV8O8PIbFir24xp5damumF7JTc0+8PaqTSS/UjQryNB2Orjj/ak9ier2+JxFRQ2IgZeUGRvrK/jZWk3QIQghkFegnDHOxYvs1dYB84WNzvZ0A8OTg1nV+D3cnNf73WE/Zvg/GdME/s/vj4IuDpX2Z+VwUmYisBwMpK/fJg91w8MWbsXR0J5Njm09WP98kt0grZZRmIGW/nrklUraOY7NKAqkZg1ph/ws3S8PBtdGvtQ8CPZzQJaQ8q/pNrX3g566Bu5Mag6P0T6HWdMFuIiJLwEDKyimVCrg7OcDdyQF7nxskO2acV+p8Wh5eWH8M59Pkj5hnlQ3rOTko4eQgX5aG7EvH4PJhvMp6pBQKBTydHXBTJbnNDBzVSgzrEIDbOwRI+wy5rX58vBdmDGqFqQNawt1oontrP30yzosZlS+YTERkaRhI2ZCKyTSNn9x75oc4/HDgMsauipWVMQRS7I2i5mWJOYHKAymDB6L1yWCDPZ3Qq7l+3pPxHLuBkb54f0wX9GrpLe1zNEoS+tSQCDxzS6TsmqHN9O9/sYoFk4mILA2XiLEhCoUCfz91E5LS8vHo14eQlJ6PQxcyEeChwdmyZIfXKyzvwYnmZNDcuzyQ8nCu+ldDVKA7vn80Bt7OaiRfOo9p7oFwcFBh/Gf69AnBXvrgynhyuWM12dbDmumHC9kjRUTWhIGUjWnh4ypbr2z0p/ugUACh3s7ISzf9gsrKZ48U6fka9UK5Olb/qyE63BtarRaF11WIatUMqXnlk8T93fULbhv3clW3bE1oWSB3NjUPa/dfwp2dAuGm4a8oIrJs/C1lB4QA1MryRY9LtTqs2XcROxKu41DZPCoGUqRUKvB/t7XB6eQcxLTwrv6ECvzdNNJrw4TyYKMeKW016TiCPJ2gUiqg1Qm89MtxnLiagwV3tat1PYiIGhMDKRs1sXcYvtpzQdo2znEYfyUbr/1xSlbei4sVE4BH+7Wo87lKpQKrJnTD9dxi9GrZDADgYRSgVxxWrshBpUSQpxMulQ3t/XLkKgMpIrJ4nGxuo14YFiXbTjRa72z76esm5dkjRfVhYBs/jOze3OyxytbxM9bVKDWCg0qB08k5zHRORBaNgZSNUhkN5VW0Ie6ayT5ONqeGVpP5Ti8Pbyu9zsgvwZ0f7sZ/Z5npnIgsFwMpO2TcO2XAHilqKJ8/1B39InxMeknN8XZxxP+myLOffx97saGqRkR0wxhI2bD103qjp9Gk4RAv03XODBxUlfdgEd2I/hG+WP1QNEKM0itUxc9dI9s2pOggIrJEDKRsWIdgD6yZHCNt5xVrpcfSKzJetoOoKfm6yZOBZuQxkCIiy8VAyo5k5JcgKsDNZP8HY7ogwt90P1FTqLhU0cWMfOiqSZ1ARNRUGEjZgR7hXgCAFj4usqU/pg1sidva++PWdv5NVDOi6hWU6JCcw4WMicgyMY+UHfhgTBes2J6IsTEh2HIiRdr/9NDIKs4iajreLg7IyC8f0jt3PU+23AwRkaVgj5Qd8HXT4OU72yLS3w13dg4EIM84TWRpFgyXJ+JMvJ7XRDUhIqpanXqkEhMTsWbNGiQmJuKNN96An58f/v77b4SEhKBdO2YitmSh3i745+l+cNcw3QFZrmEdA9Darw9+OHgZX+6+gHPXTVN2EBFZglr3SP3333+46aabcODAAfz+++/Iy9P/TzE+Ph5vvPFGvVeQ6l+otwuXhCGLplAoEBXoLj0ccTYlt9HrUKLV4aPt53Diak6jvzcRWY9aB1ILFy7Eiy++iJ9//hmOjuUTlwcMGID9+/fXa+WIyL51CPYAAMRdyW70pWK2nkrFO5vP4O4Vu5Feg+VtiMg+1TqQOn78OIYPH26y38/PD2lpafVSKSIiAGjt5waVUoGcwtJGf3Lvcmah9HpHAn+3EZF5tQ6kPD09ce2a6VptR48eRVBQUL1UiogIADRqJcKb6TOin0lp3AnnuUWl0mtzyyoREQF1CKTuu+8+LFiwAMnJyVAoFNDpdNizZw/mzZuHMWPGNEQdiciOGZLFnklt3HlSWUZL0/CpQSKqTK0DqXnz5qFNmzbo2LEjcnNz0bt3b9x5553o2bMn5syZ0xB1JCI7FuGnD6QOX8zCnB/j8M+p1EZ53yyjPFbskSKiytQ6/YGjoyOWLVuGOXPm4MSJE8jLy0Pnzp3RunXrhqgfEdm5SH9XAMCGOP2Ugl+OXEXCq7fW+Xrfx17C6l3nsWpid4R6u1Raznix5KS0Amh1XnV+TyKyXXXObB4aGorQ0ND6rAsRkYn6Xgdy3q/HAQDP/hSP7x7tWWk546G9Yq0O1/O19VoPIrINNQqkXnzxxRpfcNGiRXWuDBFRRS19XKBSKqA1WrhYqxNQKRU3dN1DF7OqPG4cSAHA5ezSSkoSkT2rUSB19OjRGl1MobixX2xERBVpHFRoH+SOuMvZ0r60vGL4u2tu6LrGgZk5hqG98GYuSErPx+WckirLE5F9qlEg9dtvvzV0PYiIKtUvwkcWSG2Mv4afDl7Bi3dEoVfLZpWeV1iihUathEKhQGGJFk4OKoR4OeFSWY6o/OJSuDia/hoUQiCrQN8D1aulN5LS87H/SqFJOSIiLlpMRBZvYKSvbPu1P07hxLUcPPh55aspJGcXoveSbZj9vzjM/t9RdHplC3YkXEcz1/IVGU5dM59SIbdIK/VYjYkJAQAcTy1q9OzqRGT5atQjNWHChBpf8Ouvv65zZYiIzOkS4lnpMUNPU0WL/jiFvCItNsSXJxCeuuYQIo0mrx++lIVuYV4m5xrmR2nUSrQpK1+sBTILSuHrXudndIjIBtWoR8rDw0P64+7uju3bt+PQoUPS8cOHD2P79u3w8PBosIoSkf1Sq5RYen8n+Lo5mhw7X0mOp43Hkk32lWiFbG7UpuOmZYDyQMrL2QEaBxWalS3ynZzN4T0ikqvRf62WL18uvZ4/fz7uvfdevPvuu1Cp9P8L1Gq1eOaZZ+Du7t4wtSQiuze8cxBiWnij31s7ZPsTr+chws8VSoUCyho8yVdUqpNe70/KxLXsQgR6OMnKGCaaezrrA6hATyek55fgalYROjS/0ZYQkS2p9RypNWvWYObMmVIQBQAqlQozZszAmjVr6rVyRETGAjycTJ7WO5OSh3tW7MGIlXug1Qmk5RZhY7zpeqAGFXuwNsYn42pWIVKNFkU2ZDX3LOuJCvTQv+fBi5m4ntu4iycTkWWrdSBVWlqKhIQEk/0JCQnQ6XRmziAiqj+dmsunEOw7n47TKbk4cS0H59PyMeyDXXhibfUpW+7qHAgA+GjHOQx7/z/cu3IPist6q/4sGxb0MvRIlfVYfbozCTe9uZ2TzolIUutZk+PHj8esWbOQmJiI7t27AwAOHDiA9957D+PHj6/3ChIRGesY7IEtJ8vX2ztwIVN6feRSJjLy5fmeWvm6wsfNEQoA+85nSPsHtvHDb0evIT1PXz6vWItDFzPRubmnyfwqQ48UAOgEcDmzECHezvXYKiKyVrUOpF599VX4+/tj+fLluHZN330eGBiIWbNmYebMmfVeQSIiYy19XWXbJdry3qH/zqSblP91Rh9o1PrO9xErd+PYlRwAQAsf03X2/jqWjBfWH5O2DXOljAMpADh1LYeBFBEBqEMgpVQq8eSTT+LJJ59EdrY+QR6f1iOixmLuyT2DbadTTfY5qsonoEeHeUuBlKPKdGbDlpOpuJJV/mTeA9H6meWBnvLJ6FtPX0e/SF8pQCMi+3VDCVEYQBFRY+sW6oW2ge4I8NDg5LUcJGeXT/7OLpSvhzeoja9s6aqOweW/s4K95MERAFkQBQB3dwkCYNojtXb/JaTmFOHjB7vVvSFEZBNqHUilpKRg3rx52LFjB1JTU00mXaalpdVb5YiIKnJUK/Hr9N4AgEe+OigLpIz1bd0Mj/VvKds3rGMAtp5KRY8W3vB0dsDANr7Yfvo6Qr2dEe7jgp1nyn9/LbqnvRSEBZhZ1++fU6a9X0Rkf2odSE2fPh2XL1/GnDlzEBgYyIWKiajRGX7vRId5yYIfg0APDb58uIfJficHFd4f00XaXnJvB3y68zzujw5BdkGJdK1JfcIwukeIVE5jJnM6ERFQh0Bq7969+OOPP9CpU6eGqA8RUY31CPeWXgd7OklDc11DvWp0vo+bBs/dHiVtu2lUyC3SIrOwxKRst0ANDl2T934Vl+rgyHlSRHat1oFU8+bNmUOFiCyC8Rp8E3qHYclfpwEATnUMbl6+sy1KdQIjuwWbHHu+vw/c/MPw5d6LWLv/MgAgLa8YQZ6mc62IyH7U+rfN66+/joULF+LChQsNUR8iohpzdlRh1s2tMDjKD6Ojy9duyS/R1ul693ZrjvujQ6BUmv5q1KiVaOXnitfu6SBNPjfOhk5E9qnWPVKPPPIICgoK0K1bN7i4uECtll8iMTGx3ipHRFSdJwZHmOy7mF7QoO/p567BtewiBlJEVPtAavHixQ1RDyKiepORX9yg1/dz0/dIpXDdPSK7V+tAauzYsQ1RDyKiG7b43g54+dfjWHBXuwZ9n5a+LsAp4PDFLIyNCW3Q9yIiy3ZDCTkLCwtRXCz/nx+TdBJRUxnVvTnu6RIEBzNZy+tTvwhffPZfEv47kwYhBNPAENmxWgdSeXl5WLBgAdavX4/0dNN1rZiQk4iaUkMHUQAQE+4FJwclknOKkJCSizYB7g3+nkRkmWr9G2f+/Pn4999/8c4770Cj0eD999/H888/j8DAQKxcubIh6khEZFE0Dir0bKHPYWUuISgR2Y9aB1J//vkn3n77bdx9991Qq9Xo06cP5syZg3nz5uGHH35oiDoSEVmcfhG+ABhIEdm7WgdSmZmZCA8PBwC4u7sjIyMDANCnTx/s3r27fmtHRGSherdsBgA4eDGTSYqJ7FitA6nw8HApGWdkZCTWr18PQN9T5enpWcWZN+btt9/GrbfeiuDgYCmQq+jixYsYPXo0goODERkZiXnz5qG0VL4a/M6dOzFw4EAEBASge/fu+PbbbxuszkRku1r7uUKpAPKKtEjNbdh0C0RkuWodSI0fPx7x8fEAgKeeegqrVq1CYGAgXnjhBcyaNaveK2hQUlKCESNG4JFHHjF7XKvV4oEHHkBJSQn++usvrFixAt999x1ef/11qUxSUhIeeOAB9O/fHzt27MDUqVPxxBNPYMuWLQ1WbyKyTY5qJYK9nAEAF9Lzm7g2RNRUav3U3vTp06XXgwYNwt69e3HkyBG0bNkSHTt2rNfKGXv++ecBoNIepH/++QenTp3C+vXr4e/vj06dOuGFF17AggUL8Nxzz8HR0RGff/45wsLC8NprrwEAoqKisGfPHqxcuRJDhgxpsLoTkW0Kb+aCSxkF2H76umwBZSKyHzf8nHBYWBjuuuuuBg2iaiI2Nhbt27eHv7+/tG/IkCHIycnByZMnpTKDBg2SnTdkyBDs27evMatKRDYirJm+R+qjHYnILihp4toQUVOocY/Ujh078Oyzz2LTpk0mSTezsrJw++2345133kHfvn3rvZI1kZKSIguiAMDPzw8AkJycLJUx7DMuk5OTg4KCAjg7O5tct6ioCEVF5ctAGCaVarXaek/Cp9VqZX/bKntopz20EWA7DYsXA8Dp5Bx0C224eaKNwd7vpy2xhzYCtWtnQz0UUuNAauXKlZg4caLZzOWenp54+OGHsWLFiloFUgsWLMCyZcuqLLN37160adOmxtesb0uXLsWSJUukbTc3N8THxyMhIaHBbsqZM2ca5LqWxh7aaQ9tBOy4nXl50su404lwyTf9z5g1stv7aYPsoY1AzdqpUCgqfVjtRtQ4kIqPj8eCBQsqPX7zzTfjgw8+qNWbz5w5E+PGjauyTIsWLWp0LX9/fxw4cEC2LzU1FQAQEBAglTHsMy7j7u5utjcKAGbPni2bF2YIniIjIxukR+rMmTOIiIiASqWq12tbEntopz20EWA7c5wygD36FDAaTz+UurvDVaOGg0qB5l7WF1TZ+/20JfbQRqB27RRCmCxrVx9qHEilpqbCwcGh8gup1bVeHsbX1xe+vr61OqcyMTExeOedd5CamioN323duhXu7u6IioqSyvz999+y87Zu3YqePXtWel2NRgONprz7XgiB7OxsqFSqBltfS6VS2fQPvoE9tNMe2gjYbzujw5tJr1f9l4SLGQXS9uGXBsNVc0PLmTYZe72ftsge2gjUrJ0NNYpU48nmQUFBOHHiRKXHjx07JvX8NISLFy8iLi4Oly5dgk6nQ1xcHOLi4pCbmwsAGDx4MKKiojB16lTExcVhy5YtWLRoER599FEpEHrkkUeQlJSEl19+GadPn8aqVauwfv16TJs2rcHqTUS2S61S4umhEQAgC6IA4HwaUyIQ2YMaB1K33HILFi1ahMLCQpNjBQUFWLx4MW677bZ6rZyxxYsXY8CAAVi8eDFyc3MxYMAADBgwAIcPHwagj0a///57KJVK3HbbbXj88ccxZswYvPDCC9I1wsPDsXbtWmzbtg39+/fH8uXL8f777zP1ARHVWbCnk9n917LkvytLtbrGqA4RNbIa9zvPmTMHv//+O3r06IEpU6YgMjISAKSeHZ1Oh2eeeabBKrpixQqsWLGiyjJhYWHVrvfXr18/7Nixoz6rRkR2LMjLfCCVnFP+tO/Z1DyM/GgP7u4ShEf7tUBYM5fGqh4RNbAaB1L+/v74888/8cwzz+CVV16RxhoVCgUGDx6Mt99+2yT9ABGRrQv2ND+pPC2vfFLr+sNXkFesxXexl/Bd7CX8+cRNaO3n2lhVJKIGVKuZkIYen8zMTJw7dw5CCLRu3RpeXl4NVD0iIssWYJRLytj+8xl4ZcNJTL4pHL5ujrJjm0+koLVfy8aoHhE1sDo9UuLl5YXu3bvXd12IiKyOg8r8VNNd59Kx61w6/oi7hrxi+eLppbqGeXqIiBqfdT6bS0RkJYyH+AycHG54dS4ishD810xEdIOeu712qy9cz63/pIBE1DQYSBER3aDJN7WQXrfwqfyJvM4h+iW20nKLKi1DRNalRoHUwIEDkZmZCQB48803kZ/PRHNEROZkF5bA3cn8rImhbfVPNpsb7iMi61SjQOr06dPIK1ucc8mSJdJrIiKSK9UKBHmYzy3l565/wo9De0S2o0aTzTt16oSZM2eid+/eEELggw8+gJubm9myzz77bL1WkIjIGrwzqhNe+uUY3rm/E77ecwGnU3JlxyP9XRFRljuKPVJEtqNGgdTy5cuxePFi/PXXX1AoFNi8eTPUatNTFQoFAykiskt3dwnCnZ0CoVIqsPlEisnx32b0RUpZtvP0vGIIIRps4XMiajw1CqQiIyPx+eefAwCaNWuGX375BX5+fg1aMSIia6NS6gOjAKOhPXcnNd64twNUSgWauTpCpVSgRCtwKbMAod5cKobI2tX6qb309HQGUUREVQg0CqTm3hqJW9sHAAA0aiU6Ndc/uXfoQlaT1I2I6ledEnImJiZi5cqVOH36NAAgKioKU6dORcuWXPKAiCimhRecHZTQCaBjsIfsWFSAGw5fzML3+y/h7i5BsmPxl7Ph4qhCK67DR2Q1at0jtWXLFvTu3RsHDx5Ehw4d0KFDBxw4cAB9+vTB1q1bG6KORERWpYWPK/Y+dzP2PjcInZp7yo619tM/qBN7PgNf7E6S9l/PLcK9H+3Bbe//Jy0KT0SWr9Y9UgsXLsS0adOwYMEC2f4FCxZgwYIFuPnmm+urbkREVsvZUWV2f4RRb9OiP07h4T7hAICktPL8fG1e/hsbZvZBmwD3hq0kEd2wWvdInT59GhMmTDDZ/+CDD+LUqVP1UikiIlvVupJhu6JSnWz7zg93N0Z1iOgG1TqQ8vHxQVxcnMn+uLg4+Pr61kuliIhsVZCnEzyMMp9rdfphvMz8kqaqEhHdgFoP7T300EN46qmncP78efTs2RMAsHfvXixbtgwzZsyo9woSEdkShUKBXf83CB0XbgYApOQUIcjTCRn5TNJJZI1qHUjNnTsXbm5uWL58OV555RUAQFBQEJ577jk8/vjj9V5BIiJbo1ErEeLtjEsZBbiSWYAgTyeks0eKyCrVOpBSKBSYPn06pk+fjpycHACAuzsnRBIR1UZzLydcyijApcxCRIcDGWYCqcuZBWju5Szbl19cCmcHFbOiE1mIWs+RMubu7s4gioioDgwB0qbjyQCADDPr7+06mybbPn41G11e/QeLNvLBHiJLcUOBFBER1U1zL332803HU7A3MV3qkXp9RAfcH90cAHDiao7snPe2nAEAfLn7QiPWlIiqwkCKiKgJGA/Z/XrkqjTZ3N/dET1beAMAvt57Eb8cviKV0zFPJ5HFqdMSMUREdGNCvMsDqf8duCy9bubqKFv0eM5P8fB112DJn6dx4pq8h4qImh4DKSKiJhDs5WR2v7eLI/zdNbJ9D39xoDGqRER1UKehvblz5yIjI6O+60JEZDeaezqb3e/t4gBHdfW/mku0umrLEFHDq3Egdflyedfzjz/+iNzcXABA3759cenSpfqvGRGRDVMqFXj/gc4m+101+oECd6eqBwxyi0obpF5EVDs1DqR69eqFTp064dFHH0VRUZEUWF28eBGlpfwHTURUWx2be8i2n7+9jfR6xqBWVZ6bXcjfu0SWoMaB1Pnz5/HFF1+ga9eu0Ol0GD16NHr06IGioiJs2bIFKSkpDVlPIiKbE+rtgu8fjcH/HuuJTyd0w8TeYdKxSX3CsWpCN4RUMpdq9X9JjVVNIqpCjQOpkpISREdHY+bMmXBycsKOHTvw4YcfQqVSYc2aNejatStiYmIasq5ERDYnOtwb3UK9MKiNH9Sq8l/JSqUCA9v4IbSZi9nz1uy72FhVJKIq1PipvRYtWqBTp07o1asXSkpKUFBQgN69e0OtVuPzzz9HUFAQDh482JB1JSKyO81cHaXXk28Kx2fsiSKyKDXukTp+/DieeeYZODo6orS0FDfffDOGDRuG4uJiHDlyBAqFAn369GnIuhIR2R0fo0DqudujmrAmRGROjQMpHx8fDBs2DPPnz4ezszO2bNmCxx57DAqFAvPmzUN4eDjuvPPOhqwrEZHd8XNzlG3f1t5fel1cyhQIRE2tzkvEeHh44N5774WDgwN+/fVXHD58GJMnT67PuhER2b2xPUMR6KGR1t+7o2OgdCwpLb+pqkVEZeqU2Xznzp0IDg4GAISEhECtViMgIAD33XdfvVaOiMjeeTo7YPszA6BUKgAAXUM9pWNTvz2EzU/1g0KhaKrqEdm9OvVIhYSEQKnUn7p7926EhITUa6WIiKicIYgCgECjdfgupBegz5LtKGWWc6ImU+ehPSIianzGQRUApOUV46/jzONH1FQYSBERWTnOlSJqOgykiIiszGv3tJdtn0rOaaKaEBEDKSIiK/NAjxC8PqKDtL0/KRM6nWjCGhHZLwZSRERW6P7o5oifPxSuGhVScopw+FJWU1epztLyiiEEA0GyTgykiIislEatxNC2+gSdf8Rfkx07eCETV7MKm6JatfLLkavo/cY2LN92rqmrQlQnDKSIiKzYsI4BAIA/45OhLRveO3UtBw98ug8D3t7RlFWrkZd+OQYAWPbP2SauCVHdMJAiIrJi/SJ84eGkRnJOET7ekQgAOH61fPK5pQ+ZqZX8GiLrxp9gIiIrplErMXtoBADgu9iLAABfo/X5MgtKGrwOOYV1fw8HFbOyk3VjIEVEZOWGdwoCAFzLLkJ+cSnURsFJcnZRg773F7uT0H3RVvxdx6Sgjip+DZF1408wEZGV83JxgLeLAwAg8Xo+So1SIVzLbtgJ54v+OAUAmP3D0Tqdr2aPFFk5BlJERDagla8rAOCNP08h7lK2tP9aVsP2SBmolHULiByMeqS4ZiBZIwZSREQ2oKQsCNmTmIGlW85I+5PS85FfXGr2nO9jL2H4h7vqJU1CXSe1O6nLv4ZSc4tvuB5EjY2BFBGRDfAxmmBubNXO8+jy6j+IPZ9hcmzer8dxKjkXr288Vaf3LDHqQSoo0eHfhOu1vkZBSfk1kht4GJKoITCQIiKyAf93W1SVxz//73ylx07Xca2+1Bz5sOEjXx2s9TWMe8saemI8UUNgIEVEZANa+7nihWGVB1N+7ppKj527nl+n90zOMQ18ikprN88pr1grvW7oifFEDYGBFBGRjWgX6C699nZxwNeTekjb1QU4uUXlPUMHL2RixMrd2J9kOhxorLBEHwRF+LnCvyxQ25eYXuP66nQC+UaBFHukyBoxkCIishFtZYGUI3q3aob7ugUDANLz5BO5dToBhdGDdudS86TXj605jGNXcjB2VazJe1zNKpTmMhWXBWdODir0j/QBALNzsSqTX6KVbTOQImvEQIqIyEZ4leWSAsrnHt3VWZ+s83yafPgur7gUxg/anU3Nw97EDBxNLkRukTzAAYC03CJ8vecCBry9A/3e2oFSrU7q5dKolega4gUAOHQxs8b1Ne6NAoDkHA7tkfVRN3UFiIio/nQO8cDRS9m4pZ0/AKBdoBuA8jQILo76X/s5hfKUCM+ui6/yui/9chybT6ZK26m5xbJAKjrcCwBw+FIWSrQ6XM0qxOpdSXjkpnCEeruYvWZekbwO7JEia8QeKSIiG/LGvR3xeP+WmD6oFQDAx00DPzdHCAGcSs6VylUMpKpjHEQB+iE+QyDloFaita8rvJwdUFiiw/GrOZjx7WF8s/ciHvmy8if5rlTIX5WcXWjxiywTVcRAiojIhkT6u2HOrZHwdSt/Si+qbO7UqWtGgVRR7QIpV41Ktn0lswAnrurTJmjUSiiVCnQP8wIA/Hb0Kk6WBW0VhxQNXvrlOB7+4oBsX0GJrtYBHlFTYyBFRGTjDJPQT14rzxdlCFg6Bnvgj5l9ZeWNV3spKpsQrlHLvy6+3HMBX+25IDtmGN77cveFauu0dv8l6XWvlt7wdNYPOXJ4j6wNAykiIhtXVSDl7qRGZICbrLzRmsdIK3vaz0Ep/7o4fDFLeq1R63urBkb61ql+Xs4OCHB3AsBcUmR9GEgREdk4w4Tzk9dyoCuLknIKSwAAbpqqnzkyBEyFpfqeKWcH068NXdm8piij9AtVqTgPys1JjQAP/VDkNfZIkZVhIEVEZONa+brCQaVAXrEWV8qeplvw+0kA+h4pQJ/A05wdZ65DpxNSD9b/3dbGpMz13PLgZ/rAVibHKwZOFTOiuzqq0dLXFQCw+1xaTZtFZBEYSBER2Ti1SokQL2cAwKWMAtkixYZA6uPx3eBgPDmqzLErOcgrLpWG++7uEmxSptBo4eHWfq4mxyvmpTJMUjdw1agwrGMAAOC/s2l8co+sCgMpIiI7ENpMH0hdzJA/RedeNrTXLcwLR166GV0D9UNshiG/Mym5SM3Rz5NyVCvh7qTGw33CZNcwTqxpLpAy7rECTAMppUKBTs094ahWIj2vpNIn/YgsEQMpIiI7EOJdFkilF8j2G3qkAECpVGBmT2881DsUfz15EwCgVCfw7uYEAIBnWdnOIZ6yaxgn1mzpa5p889iVbNn28avy7fNp+dColejc3AMAsD8ps8btImpqDKSIiOyAIbv4xYzKAykA8HNV4/nb20iLEAPAX8dTysrq51F1bi4PpB7t10J67eKoRlSAGxQKSBPIE8t6mHIKSxF7PkO6nrpsKHFU9+YAgB7h3gCAAxdqvl4fUVPjEjFERHYgtKxH6kKFob32QR41voYh6Apr5ozOzT2QVVCKD8Z0QdtAefqEbx6JQUZ+MbaeSsXiP0/j1LUclGh16L7oH1m5f57uj9yiUkT668835KHam5gBIQQUCtM5W0SWhoEUEZEdMAztHb1UPqw2e0gEOgRXHkj9+cRNuP39/6RtQ+oDhUKBHx/vBa1OQK0yHdjwcnGAl4uDtATMX8dTzOaHCvJ0km33CPeGRq3EpYwCnEnNkwIsIkvGoT0iIjsQVjbZ3EChAKYNbFnlOa39XDH5pnBpO89oUrlCoTAbRBlra5To88X1x2XHRnQJMinvplGjU9k8qeMV5lURWSoGUkREdsAwv8lACNRo6GzOLZHS6/haBjc+Ruv97T6XLjumUpl/76gAfVLPPWXDe0SWzioCqQsXLmDWrFno0qULgoKC0K1bNyxevBjFxcWycvHx8Rg2bBgCAwPRoUMHLFu2zORa69evR8+ePREYGIi+ffti06ZNjdUMIiKro1Yp8eXD0XBxVOGpwRG1Pr9fhI/Z/apKgjjDcjY/HryMZf+crfX7ETU2qwikTp8+DZ1Oh6VLl2L37t1YtGgRVq9ejVdffVUqk52djZEjRyI0NBRbt27FK6+8giVLluCLL76QyuzduxePPvooHnzwQWzfvh133nknHnzwQRw/ftzMuxIR2ZbX7mlfp/P6tvZB7PM3Y/og06zl1fl8Ynez+5Vmkn8CkE1cX77tXK3fj6ixWUUgNXToUCxfvhyDBw9GixYtcMcdd2DmzJn47bffpDI//PADiouL8eGHH6Jdu3YYOXIkHnvsMaxYsUIq8/HHH2PIkCF44oknEBUVhRdffBFdunTBp59+2hTNIiJqVA/0CKnzuY7qun1dKBQK9DfTK5VcyeLEbQI4wZysi9U+tZednQ1vb29pOzY2Fn379oWjo6O0b8iQIVi2bBkyMzPh5eWFffv2YcaMGbLrDB48GBs2bKj0fYqKilBUVJ6V1zBmr9Vq6/3RXK1WK/vbVtlDO+2hjQDbaY3USgVKdQIatdKkPQ3Vzvfu74joxdtl+05czTH7PpoKc6cupOWiuZezSbkbYUv3szL20Eagdu1sqDl3VhlInTt3Dp988olsaC8lJQVhYfJlC/z8/AAAycnJ8PLyQkpKirTPuExKSkql77V06VIsWbJE2nZzc0N8fDwSEhIa7KacOXOmQa5raeyhnfbQRoDttCbjOrnjqyPZmNbDE6dOnTJbpjHa+UA7l0rfv62vI05e18+B/fyfeIzuUPNcV7VhC/ezOvbQRqBm7VQoFAgPD6+2XG01aSC1YMECsxPCje3duxdt2pSvNn7lyhWMGjUKI0aMwEMPPdTQVcTs2bMxffp0adsQPEVGRjZIj9SZM2cQEREBlUpVr9e2JPbQTntoI8B2WqMXooCZt5fAw9nB5FhDtvPFYS74PvYSVk3oBncntbSWnzlrW0Wgy6JtAACdozuioqLqtS62dD8rYw9tBGrXTiGEyUNq9aFJA6mZM2di3LhxVZZp0aKF9Prq1au4++670bNnT7z33nuycv7+/khNTZXtM2wHBARUWcbf37/S99doNNBoyh/hFUIgOzsbKpWqwbLuqlQqm/7BN7CHdtpDGwG209p4u1XdhoZo58N9W+Dhvi1qVNZFpcIb93bAcz8fQ1J6QYN95rZyP6tiD20EatZOmxza8/X1ha+vb43KXrlyBXfffTe6dOmC5cuXQ6mUT3yMiYnBa6+9hpKSEjg46P+ntXXrVkRGRsLLywsA0LNnT2zfvh3Tpk2Tztu6dStiYmLqp0FERFQvWvm6AgDOXc9DqVZXbfJPoqZiFT+ZV65cwV133YWQkBC8+uqruH79OpKTk5GcnCyVGTVqFBwdHTFr1iycOHEC69atw8cffywblnv88cexZcsWfPjhhzh9+jTeeOMNHD58GFOmTGmKZhERUSValgVS17KL0PnVLUhKz6/mDKKmYRWTzbdt24Zz587h3Llz6NChg+xYRoZ+lXBPT0/89NNPmDt3Lm6++Wb4+Phg7ty5ePjhh6WyvXr1wqeffopFixbh1VdfRatWrfDNN9+gffu65VYhIqKG4eXiAG8XB2Tkl6BEK/Dq7yexqpKcVERNySoCqXHjxlU7lwoAOnbsiI0bN1ZZZsSIERgxYkQ91YyIiBpKK19XHLiQCQDYdz4dxaW6OuezImoo/IkkIiKLFOjpJL0uKNHh4S8PQKvj+ntkWRhIERGRRRrUpvxhJI1aidjzGTidnNuENSIyZRVDe0REZH9GdA1Gt1Av+Lk74tGvDyH2fAZOXstBuyD3pq4akYQ9UkREZLHCfVzg4qhG20B98HTyWk4T14hIjoEUERFZvHaB+sWMTyYzkCLLwkCKiIgsXlRAeY9UQ2WoJqoLBlJERGTx2gS4QakA0vNKkJpb/+ulEdUVAykiIrJ4Tg4qtPDRZzuPv5LdxLUhKsdAioiIrEL3MC8AwJGLWU1bESIjDKSIiMgqRPjre6S47h5ZEgZSRERkFcKbuQAALjCQIgvCQIqIiKyCIZA6n5bPJ/fIYjCQIiIiqxDazBkAkFNYitPJuYictwndF/2DwhJtE9eM7BkDKSIisgpODioEemgAAMOX7wagD6p+Oni5KatFdo6BFBERWY1r2UUm+5LN7CNqLAykiIjIqiWm1Xzy+R9x1zB46X+4mlPagDUie8JAioiIrEbn5h7S6x7hXgCAvYnpKCiu2TypJ/93FFeyCjH192sNUT2yQwykiIjIaqwY11V63T/CF829nJCRX4ItJ1OqPZdP+lFDYCBFRERWw99dI70u1elwV+cgAMAf8cmVnpNTWIo959KRX8NeK6LaYCBFRERWQ6FQSK993DS4o2MgAGB7wnXkFJqf9zTt20OYsHo/Pv43Ubb/ox2JyC/mXCm6MQykiIjIqqwY2xUP9GiOUd2bo22gG8KbuaC4VId9ielmy+9NzAAArNwuD6Te++ccPv8vqcHrS7ZN3dQVICIiqo1b2vvjlvb+0nbH5h5ISs/H2et5GFLLax26mFmvdSP7wx4pIiKyahF++sWM9ydl4N8z11Fcqqvxub5umuoLEVWBgRQREVm1CH83AMDWU9fxyJcH8YnRXKijl7KqPDctr7hB60a2j4EUERFZNUOPlMG6Q1ek16/+cdKkfLCnEzw1+q+/o5eyLCYtghACb206jR8OcMkba8JAioiIrFq4jwscVOVP8yXn6JeMKdHqcOxKtkn57mGe+OyeIGjUSmTklyDxes0zozek41dz8Mm/5/HC+mPQ6iwjuKPqMZAiIiKr5qBSooWPi7RtmCP157FklGgF3DTy56rcNGo4qBToVJYl/cCFjMarbA3tPHO9qatANcRAioiIrF5k2Twpg51n0vD0D3EAgHZB7ph7a6R0zLDwcXSYFwAgNimzUepYHZ3REOO+86bBXez5DOxIYIBlaRhIERGR1YvwkwdSS/46Lb1uF+iOx/q3RKS/fi5V1xB9T1SPskBq0/FkXMsubJyKVqFEWx5IxV2WD0meS83DuM9iMfmrg/j7ePXL4VDjYSBFRERWr1WFCecnr+VIr9sFuQMAPhjTFbNuboUxPUIAAH1aeaNdoDvyirT45fDVxqtsJYzTNsRfyYaubJ5UqVaH297/Tzq2+M9TGPnxHvzL3imLwECKiIis3tB2/gjxdjZ7rH1ZINXazxVPDI6Al4sDAECtUuKuLvolZo5erjpNQkMrLtVhwur90nZOYSm+2nsBvxy+gq6v/SMrezGjAEcvZePxNYcau5pkBjObExGR1dOolfhjZl8882Mc/j4hH/pqG+Be6Xmdm3sCAOIvmz7d15h2n0sz2bfoj1NVnuPl7NBQ1aFaYI8UERHZBGdHFfzcHU32K5UKM6X12gfp50tdySrE97GXGqxu1fn7RGqtz0nNZTJRS8BAioiIbEZt8y+5O5UPzMz79Xh9V6fG1u6vPohzdVSZ7EtKs4wcWPaMgRQREdmM8T3DZNtv3Nuh2nNCvJyk15n5JfVep+psOVmzp/Ba+LqY7LOEpw3tHQMpIiKyGYYn9AxGdm9e7TkvD28nvU5Kr1kPT35xKX4/ehU5hTceeL26wXQZG3NaNHM1SS56LYuBVFNjIEVERDalV0tvAEArMz045twc5YfOZVnO1x++Uk1pIK+oFF1e/Qezf4jDK7/XLAiqiq+bRrY9f3hbs+XG9gzB2yM7yvZdtYJA6pcjV/HtvotNXY0Gw6f2iIjIprw9qhNW/5eE8b1Ca3zO0bKn9r7ZexHzjXqozNl8snxi+PojV/HWqE51q2iZlj4uOHJJn37hy4ejER3mhYUVArTPJ3ZHr5bNAAAJr96K97acwfJt53DFwgOplJwizPlRn2H+s//Ow0ltOs/LnLaB7njn/hv7XBsLAykiIrIpgR5OeH5YVINdPzO//Gm5ikNtdVFYlohzcJQf+rb2MVvGzUn+PkGe+nldlt4jtSHumvT6QnpBjc9z1dQs4LIEDKSIiMjufT6xOx756iAAfSZxtarymS95RVrptVs9fOEXlOivd0t7/0rLuGusM5BavStJev3lw9FQVJ6JQqY+AtTGYj01JSIiaiA3tfaBQgEIoc/PZAhUKrqQno+lW85I26m5xdUGXtUpLAuknB3Kg7IFw9viz2PJCGvmgrxiLVr5ypfAMdTvVHIuikp10Kgtb8qzEEIW6FXW22btGEgREZHdUyoVCPRwwtWsQiRnF1YaSH3233nZtlYncD2vGIEe5svXxN7EDACAk1EwNL5XGMb3CqvsFFn93v/nDObe2qbO798QhBCY9f2Rpq5Go7C8EJaIiKgJBHjon55Lzi6qtIy5Y7P/dxS3LduJxOt5EKJ2CUFLtOULFV/MrPkcIuOhr5ok82xsV7IK8dfx8vxYg6P8mrA2DYuBFBEREYAA97JAKqfyQMp4+M1gf1Imzl3Px63L/sNbmxJq9Z7GgZmHU90GibIKSvH1ngt1Oreh5BaWSq9/nd4HH4zp0oS1aVgMpIiIiAAElA3PJZdlC//vbJpJ/qPqlqD5dOf5Wr3nlazyXqg7OwbW6tw5t0RKr1+pYVLPhlZYosVH288h/oo+nUSIlxPaBbnD0QLncNUXzpEiIiJC+dDe6l1J2HIyFWdT8wDocxp1D/MCAJQaBVKfTeyOyWVP+tXV1Ux90NanVTNozPR2VcXcAs1N7cOt5/Dxv4nStrOZ9QFtje2GiERERLVg6JEq0QopiAKAU8k50mtDj9Sie9pjQKQvIvzkT9MBqNE8qaJSHfKLS6WEmpVNbq+KX4WM6LVdsLkh7E5Mk23bQyDFHikiIiIAgR4as/svphcgt6gUcZezUKrTTw5XKvUJkdzNzGvKLiyFp7NDle81+pO9OJOaBx9Xfa9ScB0CqfYV1hV8+oejWPZA085FMrTHwNXR9sMM9kgRERGhfGivooSUXLzw8zFMXH0AOxL0PS7qskDKXI/L5hMpOJeah9W7klBUqjM5XlCsxfGrOSgu1Ul5loK9ah9I+bhpMHVAS2n7j/jkWl+jvuUXa2Xb5ibn2xrbDxWJiIhqIMBdHsz4uDoiLa8Y+5MykVtUKjumKguklGZSdT/38zHpdXGpDo8bBTsAcD3P9KnAIE/nOtX5mVsi8dGO8jlJWQUl1faGNaQrmfJM6/YwtMceKSIiIui/9P3dy3ul1k/vjdZ+riZBFACoygIo4zDq5ihfk3K7zqWZ7LueW2yyry5De+Z8sSsJGfnFeHD1AXywN73Wea1uhE4ncC1bHki5MJAiIiKyH/0iypcxcXVU475uwWbLmeuRevf+ziblzPVYXTOzPl5dJpsbGKdB+HDbOfRcvA37kzKx+Vw+XvzlRJ2vW1vX84pRopUHbgykiIiI7IjxZGlnByWGdQwwW86wPp5xl5SbRo0Qb/kQXanWdI6UYZ6VwW3t/W9oCOzxAS2x/Zn+ZhcEXnf4Kr7ZewE5hSV1vn5NHUgqW+rGoTy0yCk07c2zNQykiIiIyhhnF1erlAj1dkGn5h4m5QyTxJUVgpewZvJA6mKG6bIvFYOmD8d2rWNtywV7OaOyUbyFv5/E0i1nb/g9qvPE2qMAgMKS8uDxeFliTlvGQIqIiKiMu5mJ2m0C3Ez2dStL0KmAPJLyr5Db6XJmocm8IV0D5Xt6pG94pcfquoTMoj9O4pEvD+CtTacxcfV+FJVoqz1HqQB6tvAGAIyoZGjUljCQIiIiKnNrO38AQJcQT2lfkEf5/KVvJ8fgm0d6SIFCxd6lx/rrn9C7s2OglOfpYFKmrIwhO/qASB/smDOg3ur+3O1tpNej6iGAmfzVAXyx+wL+PZOGT/49j93n0rHu8JVKyxs+k5fuaIuPxnfD5w91x0O9w264HpaOgRQREVEZP3cNDrxwM76dHCPtM54IHuChQa+WzaAom5A055ZIhHg54YVhUQCAyAA37Ht+EN65vxOiw/WBxf4LmbL30JWNwfUI976hSeYVKYwmSfVu5Q1vZ/lXfFU9YRvjr+H41WxZ2YpzuQDg+9hLWH/4itmnAQ0T8L1cHODupEb/CF+oVbYfZth+C4mIiGrBw9lBtshuoFGwo64wKSrE2xlbnxmASUbDat4ujlApFehRNvx3ICkDQgi883cCNsZfk3qkVBUnWNWDRfe0x92dg3B7e3/kFcuDnRPXcsyec+RSFp5YexT3rNgj7fv16FWzZY9fzcHcn+KxI+G6ybGSson1FT8jW8dAioiIqArGS8fUJviJDvcCAJy8loO/T6Tgox2JeGLtUfx8SD881hABx+geIXjn/k5Qq5S4p618btfOM6Y9TABwPi1fer1m30UAwK5z6VW+z79mrlVclsXdwQ56oYzZV2uJiIhqyXj4rTYLAwd4OKGljwt0Ath22rQHpyF6pIzd316+Ft/vR68iv9g0HYHGqPdtwW/6vFNabdXtPFmhd2vxxlM4elk/NKhWsUeKiIiIyrhp1BjYxhddQjwR6FG7OU2GeVJbT6WaHFOYS/xUjzRqJfq09EZ4Mxe4OqpwMjkX7/9jmgYhz0zm9uoCxvgr2bI5V5/vSpJeq5X2FVpwrT0iIqIqKBQKrJrQHUKIWgc/0eFe+PHgZbPLwmTmm+6rb59P7Aa1Wo0fD17G8z8fw5FLWSZlcioEUoUlWhSWVp3mIK9Ii8S0fLT2czU55sAeKSIiIqqoLj1IPcrmSZmTktPwgZShzlFlubCM50MZ5FbIPp6SU4TUXP3Cyu8/0BnTBrQ02464y6ZBGcDJ5kRERFRPwpu5yJadMVYxgGlILXxcAOgXTK64bEvFRZkvpBcgtSzIC/ZyxtO3ROK7R3viycGtZeUMc6IqsoeUB8bsq7VERESNSKFQoHfLZtL2HUZr9824uVWj1cPdyUEK6JIq9EpVDKwmfXlAWgLHz608CJwxqBU2P9UPb4/qBKB8+ZfCCtnO7a1HinOkiIiIGtDI6GBsiL8GQP8EYMKrt0KnE1A2csDR0tcFaXnFOJ+Wh45G6wdW7JEy5mu05I1CoUC4jwvyyp78O3c9DwDw7uYzsnMqBla2jj1SREREDahfax84O+i/btsF6QOYxg6igPI0Dm/8dVq239Aj5els2rdinJjUIKyZfpgwI78E2QUlWG30xB4A+LprTM6xZQykiIiIGpBCocDOuQOx9P5OGNYhoPoTGogh91NydpFsv+GpvVfvbi/bv2ZyD7PXcdOo4Vs25HchvQCh3s7SsVUTuiG8LNCyFwykiIiIGpiHswOGdw4y28PTWLqGekmvjYffDD1S3i6O6GQ05NezRfncrooMwVJSej6Ky5aG+Xxidwxs41efVbYKVhNIjR07Fh07dkRgYCDatm2Lxx9/HFevytcCio+Px7BhwxAYGIgOHTpg2bJlJtdZv349evbsicDAQPTt2xebNm1qrCYQERE1medvj5JeX8ookF4b5ki5O6nRr7UPACC4msWUw8ueAjyVnCP1cHUI9qjqFJtlNYFU//79sXr1auzbtw9ffvklEhMT8dBDD0nHs7OzMXLkSISGhmLr1q145ZVXsGTJEnzxxRdSmb179+LRRx/Fgw8+iO3bt+POO+/Egw8+iOPHjzdBi4iIiBqPu5MaLcsCIONM5IY0DG5OajzarwXGxoTgzZEdq7yWoUfqv7P6NffcNGp4uzg0RLUtntUEUtOnT0dMTAzCwsLQq1cvPPXUU9i/fz9KSkoAAD/88AOKi4vx4Ycfol27dhg5ciQee+wxrFixQrrGxx9/jCFDhuCJJ55AVFQUXnzxRXTp0gWffvppUzWLiIio0SSWpT744cBl6HQC6XnFyCvWD/O5adTwcHbAK3e3R6+WlQ/rAeU9Ukcv6VMghHo7N/iSN5bKagIpYxkZGfjxxx/Rs2dPODjoI+DY2Fj07dsXjo7lOS+GDBmChIQEZGZmAgD27duHQYMGya41ePBgxMbGNlbViYiImkyAR/kTdQs3nMDM749I226ammdEqjihvFSnu/HKWSmryiM1f/58rFq1Cvn5+YiJicH3338vHUtJSUFYWJisvJ+fftJbcnIyvLy8kJKSIu0zLpOSklLpexYVFaGoqPwJByH0izRqtdp6j761Wq3sb1tlD+20hzYCbKetYTttR2VtXDGmM0Z+ou88+HbfJdkxtULU+DMJ8ZKnOOga4tkkn2dt7qXh+7u+NWkgtWDBArMTwo3t3bsXbdq0AQA88cQTmDBhAi5evIglS5Zg6tSpWLt2bYN2Jy5duhRLliyRtt3c3BAfH4+EhIQGuylnzpypvpANsId22kMbAbbT1rCdtqNiG9UAXhrgg9d2pMn29w5xwqlTp+r8Prc0193Q+TeqJvdSoVAgPDy83t+7SQOpmTNnYty4cVWWadGihfTax8cHPj4+iIiIQJs2bdCxY0fExsaiZ8+e8Pf3R2pqquxcw3ZAgD5vR2Vl/P39K33/2bNnY/r06dK2IXiKjIxskB6pM2fOICIiAiqVql6vbUnsoZ320EaA7bQ1bKftqKqNLVvr8NqOrbJ9zf28ERUVhdpwdriCghL9kN6A6A43VuE6qs29FEKguLj+F4pu0kDK19cXvr6+dTpXVzYea/hQYmJi8Nprr6GkpESaN7V161ZERkbCy8sLANCzZ09s374d06ZNk66zdetWxMTEVPo+Go0GGk15F6YQAtnZ2VCpVA3WE6ZSqWz2H7cxe2inPbQRYDttDdtpO8y10dlMmz2dHWv9WXi5OKKgbE2+pv4ca3IvG2oUySomm+/fvx+ffPIJ4uLicOHCBezYsQOPPvooWrZsKQVBo0aNgqOjI2bNmoUTJ05g3bp1+Pjjj2W9SY8//ji2bNmCDz/8EKdPn8Ybb7yBw4cPY8qUKU3VNCIiokZ3V+dA2baHc+1TF7z/QGe09nPFJw92q69qWSWrCKScnZ3x+++/45577kHPnj0xa9YsdOjQAb///rvUW+Tp6YmffvoJSUlJuPnmmzFv3jzMnTsXDz/8sHSdXr164dNPP8WXX36J/v3745dffsE333yD9u3bV/LOREREtmfyTS1k29o6PHXXNdQLfz5xE26Osr9s5sas4qm9Dh064Ndff622XMeOHbFx48Yqy4wYMQIjRoyop5oRERFZnw7BHvhgTBfMKkt/YJjrRLVnFT1SREREVL9uN1pA2cXRtueKNSQGUkRERHbqiZtbI8LPFRN7h1VfmMyyiqE9IiIiqn+zBrfGrMGtm7oaVo09UkRERER1xECKiIiIqI4YSBERERHVEQMpIiIiojpiIEVERERURwykiIiIiOqIgRQRERFRHTGQIiIiIqojBlJEREREdcRAioiIiKiOGEgRERER1REDKSIiIqI6YiBFREREVEcMpIiIiIjqSN3UFbA2QgjZ3/V9bYVCASFEg1zfUthDO+2hjQDbaWvYTtthD20EatdO4+9vhUJRb3VQCFv+hBuATqdDTk5OU1eDiIiI6sDd3R1KZf0NyDGQqiWdTie9rs+IFgCys7PRsWNHxMfHw8PDo16vbUnsoZ320EaA7bQ1bKftsIc2ArVrp3G4U5+BFIf2aqk+P/yKFAoFcnNzoVAo6j1IsyT20E57aCPAdtoattN22EMbgdq1s6E+B042JyIiIqojBlJEREREdcRAyoJoNBr83//9HzQaTVNXpUHZQzvtoY0A22lr2E7bYQ9tBCyjnZxsTkRERFRH7JEiIiIiqiMGUkRERER1xECKiIiIqI4YSBERERHVEQMpC/Hpp5+ic+fOCAwMxNChQ3HgwIGmrlKl3n33XQwePBihoaGIjIzE+PHjkZCQICszfPhweHt7y/7Mnj1bVubixYsYPXo0goODERkZiXnz5qG0tFRWZufOnRg4cCACAgLQvXt3fPvttw3ePoM33njDpA09e/aUjhcWFmLOnDlo1aoVQkJCMHHiRKSkpMiuYeltBIDOnTubtNPb2xtz5swBYL338r///sOYMWPQrl07eHt7Y8OGDbLjQgi8/vrraNu2LYKCgjBixAicPXtWViYjIwNTpkxBWFgYwsPDMWvWLOTm5srKxMfHY9iwYQgMDESHDh2wbNkyk7qsX78ePXv2RGBgIPr27YtNmzY1eBtLSkowf/589O3bF82bN0e7du0wdepUXL16VXYNc/d/6dKlFtPG6toJANOnTzdpw6hRo2RlLP1e1qSd5v6dent74/3335fKWPr9rMn3R2P+bq2X715BTe6nn34S/v7+4ptvvhEnTpwQTz75pAgPDxcpKSlNXTWzRo4cKdasWSOOHz8ujh49Ku6//37RsWNHkZubK5W58847xZNPPimuXbsm/cnKypKOl5aWij59+ogRI0aIo0ePik2bNonWrVuLhQsXSmXOnz8vgoODxYsvvihOnjwpPv74Y+Hj4yM2b97cKO1cvHix6N27t6wN169fl47Pnj1bdOjQQWzfvl0cOnRI3HLLLeLWW2+1qjYKIURqaqqsjVu3bhVeXl7i33//FUJY773ctGmTePXVV8Vvv/0mvLy8xO+//y47vnTpUhEWFiY2bNgg4uLixNixY0WXLl1EQUGBVGbkyJGiX79+IjY2VuzatUt0795dTJ48WTqelZUl2rRpI6ZMmSKOHz8ufvzxRxEUFCRWr14tldmzZ4/w8fERy5YtEydPnhSvvfaa8PPzE8eOHWvQNmZmZooRI0aIdevWidOnT4t9+/aJIUOGiEGDBsmu0alTJ/Hmm2/K7q/xv+WmbmN17RRCiGnTpomRI0fK2pCRkSErY+n3sibtNG7ftWvXxDfffCO8vb1FYmKiVMbS72dNvj8a63drfX33MpCyAEOGDBFz5syRtrVarWjXrp149913m7BWNZeamiq8vLzEzp07pX133nmneO655yo9Z9OmTaJZs2YiOTlZ2vfZZ5+J0NBQUVRUJIQQ4uWXXxa9e/eWnTdp0iQxcuTIem6BeYsXLxb9+vUzeywzM1P4+fmJ9evXS/tOnTolvLy8xL59+4QQ1tFGc5577jnRrVs3odPphBC2cS8rfinpdDoRFRUl3n//fWlfZmamCAgIED/++KMQQoiTJ08KLy8vcfDgQanM33//Lby9vcWVK1eEEEKsWrVKtGjRQmqnEELMnz9fxMTESNuTJk0So0ePltVn6NCh4qmnnmrQNppz4MAB4eXlJS5cuCDt69Spk1ixYkWl51hSG4Uw385p06aJcePGVXqOtd1LIWp2P8eNGyfuvvtu2T5ru58Vvz8a83drfX33cmiviRUXF+Pw4cMYNGiQtE+pVGLgwIGIjY1tuorVQnZ2NgB9t7OxH374Aa1bt0afPn2wcOFC5OfnS8diY2PRvn17+Pv7S/uGDBmCnJwcnDx5Uipj/LkYyuzbt6+BWmLq3LlzaNeuHbp27YopU6bg4sWLAIAjR46gpKREVr82bdogJCREum/W0kZjxcXF+N///ofx48fL1qWyhXtpLCkpCcnJybI6eXp6Ijo6Wnb/PD090a1bN6nMoEGDoFQqpe7/2NhY9O3bF46OjlKZIUOGICEhAZmZmQCAffv2mbR98ODBTfLvOzs7GwqFAp6enrL97733Hlq1aoUBAwbg/ffflw2RWEsbd+7cicjISMTExODpp59Genq6dMwW72VKSgo2bdqEBx980OSYNd3Pit8fjfW7tT6/e7locRNLS0uDVquFn5+fbL+fn5/JuLEl0ul0eP7559GrVy+0b99e2j9q1CiEhoYiMDAQx44dw8KFC3HmzBl8/fXXAPS/BIz/EQCQPoPk5GSpjLnPJScnBwUFBXB2dm7IpiE6OhrLly9HREQEkpOTsWTJEtxxxx3YtWsXkpOT4ejoaPKF5O/vL6u/pbexog0bNiArKwvjxo2T9tnCvazIUK+KdfL395fmYiQnJ5scV6vV8Pb2lrUrLCxMVsa47V5eXpW2veKcj4ZWWFiIBQsWYOTIkfDw8JD2P/744+jSpQu8vLywb98+vPLKK0hOTsaiRYsAWEcbhwwZguHDhyM8PBznz5/Hq6++ivvvvx+bNm2CSqWyuXsJAN999x3c3Nxw1113yfZb0/009/3RWL9bMzMz6+27l4EU3ZA5c+bgxIkT2Lhxo2z/ww8/LL3u0KEDAgMDcc899yAxMREtW7Zs5FrWzS233CK97tixI3r06IFOnTph/fr1cHJyasKaNZxvvvkGQ4cORVBQkLTPFu6lvSspKcGkSZMghMA777wjOzZjxgzpdceOHeHo6IjZs2fj5ZdftprlRUaOHCm97tChAzp06IBu3bpJk41t0Zo1a3D//feb/C6ypvtZ2feHteHQXhPz8fGBSqVCamqqbH9qaqpJxG1p5s6di7/++gu//fYbmjdvXmXZ6OhoAPqhMkD+P38Dw2cQEBAglTH3ubi7uzd6DwagH/qJiIjAuXPnEBAQgOLiYmRlZcnKpKSkyOpvTW28cOECtm3bhokTJ1ZZzhbupaFeFetk/D/dgIAAk+OlpaXIyMiotl3G71FZmcb6920Ioi5evIiff/5Z1htlTnR0NEpLS3HhwgUA1tHGilq0aAEfHx/pZ9RW7qXBrl27kJCQgAkTJlRb1lLvZ2XfH431u7U+v3sZSDUxR0dHdO3aFdu3b5f26XQ67NixAzExMU1Ys8oJITB37lxs2LABv/76K8LDw6s9Jy4uDkD5D3lMTAyOHz8u+yHeunUr3N3dERUVJZUx/lwMZYxTEDSm3NxcJCYmIjAwEF26dIGDg4OsfgkJCbh06ZJ036ytjd9++y38/Pxw6623VlnOFu5leHg4AgICZHXKzs7GgQMHZPcvKysLhw8flsrs2LEDOp1OCiZjYmKwa9culJSUSGW2bt2KyMhIeHl5AQB69uxptu2N8e/bEESdPXsW69evR7Nmzao9Jy4uDkqlUhrysPQ2mnP58mWkp6fLfkat/V4a++abb9C1a1d06tSp2rKWdj+r+/5orN+t9frdW6up6dQgfvrpJxEQECC+/fZbcfLkSekRTOMnEizJ008/LcLCwsTOnTtlj9jm5+cLIYQ4d+6cePPNN8WhQ4dEUlKS2LBhg+jatau44447pGsYHl+97777xNGjR8XmzZtFRESE2cdX582bJ06dOiU+/fTTRk0N8NJLL4mdO3eKpKQksWfPHjFixAjRunVrkZqaKoTQP6LbsWNHsWPHDukR3VtuucWq2mig1WpFx44dxfz582X7rfle5uTkiKNHj4qjR48KLy8vsXz5cnH06FHpibWlS5eK8PBwsWHDBhEfHy/GjRtnNv3BgAEDxP79+8Xu3btFdHS07JH5zMxM0aZNG/H444+L48ePi59++kkEBwebPEru6+srPvjgA3Hq1CmxePHienuUvKo2FhcXi7Fjx4r27duLo0ePyv6tGp5s2rt3r1ixYoU4evSoSExMFGvXrhURERFi6tSpFtPG6tqZk5MjXnrpJbFv3z6RlJQktm3bJgYOHCiio6NFYWGhdA1Lv5fVtdMgKytLBAcHi88++8zkfGu4n9V9fwjReL9b6+u7l4GUhfj4449Fx44dhb+/vxgyZIiIjY1t6ipVysvLy+yfNWvWCCGEuHjxorjjjjtEy5YtRUBAgOjevbuYN2+eLPeQEEIkJSWJUaNGiaCgING6dWvx0ksviZKSElmZf//9V/Tv31/4+/uLrl27Su/RGCZNmiTatm0r/P39Rfv27cWkSZPEuXPnpOMFBQXimWeeES1atBDBwcHiwQcfFNeuXZNdw9LbaLBlyxbh5eUlEhISZPut+V7++++/Zn9Op02bJoTQp0BYtGiRaNOmjQgICBD33HOPSfvT09PF5MmTRUhIiAgNDRUzZswQOTk5sjJxcXHi9ttvFwEBAaJ9+/Zi6dKlJnX5+eefRY8ePYS/v7/o3bu3+Ouvvxq8jUlJSZX+WzXkCDt8+LAYOnSoCAsLE4GBgaJXr17inXfekQUgTd3G6tqZn58v7rvvPhERESH8/PxEp06dxJNPPmnyZWjp97K6dhqsXr1aBAUFiczMTJPzreF+Vvf9IUTj/m6tj+9ehRBC1K4Pi4iIiIgAzpEiIiIiqjMGUkRERER1xECKiIiIqI4YSBERERHVEQMpIiIiojpiIEVERERURwykiIiIiOqIgRQRURlvb29s2LChqatBRFaEgRQRWRxvb+8q/7zxxhuVnnvhwgV4e3tLawLWp+nTp0t18Pf3R/fu3fHmm2+itLQUALBz5054e3ujT58+0Gq1snPDw8Px7bffStudO3fGypUr672ORNS41E1dASKiik6ePCm9/vnnn/H6668jNjZW2ufq6toU1QIADBkyBMuXL0dRURH+/vtvzJ07F2q1Gk8//bRU5vz58/j+++8xfvz4JqsnETUO9kgRkcUJCAiQ/nh4eEChUEjbfn5+WLFiBTp06ICAgAD0798fmzdvls7t0qULAGDAgAHw9vbG8OHDAQAHDx7Evffei9atWyMsLAx33nknjhw5Uuu6aTQaBAQEICwsDJMnT8agQYPw559/yspMmTIFixcvRlFR0Q18CkRkDRhIEZFV+eijj/Dhhx/ilVdewc6dOzF48GCMGzcOZ8+eBQBs2bIFALB+/XqcPHkSX3/9NQAgNzcXY8aMwcaNG/H333+jdevWGD16NHJycm6oPk5OTiguLpbtmzZtGrRaLT755JMbujYRWT4GUkRkVT788EM8+eSTGDlyJCIjI7Fw4UJ06tRJmm/k6+sLAGjWrBkCAgLg7e0NQN9D9cADD6BNmzaIiorCe++9h4KCAvz33391qocQAtu2bcM///yD/v37y465uLjg2WefxdKlS5GVlXUDrSUiS8dAioisRnZ2Nq5evYrevXvL9vfq1QunT5+u8tyUlBQ8+eSTiI6ORlhYGMLCwpCbm4tLly7Vqg5//fUXQkJCEBgYiPvvvx/33nsvnnvuOZNyEyZMQLNmzbBs2bJaXZ+IrAsnmxORXZg+fTrS09OxePFihIaGQqPR4NZbb0VJSUmtrtO/f3+88847cHBwQFBQENRq879G1Wo1XnrpJcyYMQNTpkypjyYQkQVijxQRWQ0PDw8EBQVhz549sv179+5FVFQUAMDBwQEATNIP7N27F4899hhuvfVWtGvXDo6OjkhLS6t1HVxcXNCqVSuEhoZWGkQZjBgxAm3btsWSJUtq/T5EZB3YI0VEVmXWrFlYvHgxWrZsiU6dOmHNmjWIi4uTJnb7+fnB2dkZmzdvRnBwMDQaDTw9PdGqVSv873//Q7du3ZCTk4OXX34Zzs7ODV7f+fPnY+TIkWaPXblyxSTfVWhoKLy8vBq8XkRUP9gjRURW5fHHH8eMGTMwb9483HTTTdiyZQu+/fZbtG7dGoB+SO2NN97AF198gXbt2km5nD744ANkZmZi0KBBmDp1Kh5//HFpYnpDGjBgAAYMGCAl7TT24YcfSscNf/76668GrxMR1R+FEEI0dSWIiIiIrBF7pIiIiIjqiIEUERERUR0xkCIiIiKqIwZSRERERHXEQIqIiIiojhhIEREREdURAykiIiKiOmIgRURERFRHDKSIiIiI6oiBFBEREVEdMZAiIiIiqiMGUkRERER19P83mFaJA1ND+QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check another strategy"
      ],
      "metadata": {
        "id": "EUNrD7L0UdQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "prkiejZkP9mj",
        "outputId": "068f850c-96f2-4482-92dd-9e4f6ff9891b"
      },
      "execution_count": 261,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green buy_sell\n",
              "datetime                                                                  \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0         \n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0         \n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0     sell\n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0     sell\n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0     sell\n",
              "...                      ...      ...      ...      ...       ...      ...\n",
              "2023-08-04 19:45:00  1.70296  1.70339  1.70278  1.70333         1  neutral\n",
              "2023-08-04 20:00:00  1.70333  1.70333  1.70222  1.70252         0  neutral\n",
              "2023-08-04 20:15:00  1.70252  1.70290  1.70214  1.70274         1  neutral\n",
              "2023-08-04 20:30:00  1.70274  1.70390  1.70269  1.70390         1  neutral\n",
              "2023-08-04 20:45:00  1.70390  1.70442  1.70380  1.70411         1      buy\n",
              "\n",
              "[20000 rows x 6 columns]"
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 05:15:00</th>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 05:30:00</th>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
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              "    <tr>\n",
              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2023-08-04 19:45:00</th>\n",
              "      <td>1.70296</td>\n",
              "      <td>1.70339</td>\n",
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              "      <th>2023-08-04 20:00:00</th>\n",
              "      <td>1.70333</td>\n",
              "      <td>1.70333</td>\n",
              "      <td>1.70222</td>\n",
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              "      <th>2023-08-04 20:15:00</th>\n",
              "      <td>1.70252</td>\n",
              "      <td>1.70290</td>\n",
              "      <td>1.70214</td>\n",
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              "      <th>2023-08-04 20:30:00</th>\n",
              "      <td>1.70274</td>\n",
              "      <td>1.70390</td>\n",
              "      <td>1.70269</td>\n",
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              "      <th>2023-08-04 20:45:00</th>\n",
              "      <td>1.70390</td>\n",
              "      <td>1.70442</td>\n",
              "      <td>1.70380</td>\n",
              "      <td>1.70411</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sl = [''] * len(raw_data)\n",
        "tp = [''] * len(raw_data)\n",
        "\n",
        "for i in range(2, len(raw_data)):\n",
        "    if raw_data['buy_sell'][i] == 'sell':\n",
        "        sl[i] = raw_data['high'][i-2]\n",
        "        tp_amount = (raw_data['high'][i-2] - raw_data['close'][i])\n",
        "        tp[i] = raw_data['close'][i] - tp_amount\n",
        "    elif raw_data['buy_sell'][i] == 'buy':\n",
        "        sl[i] = raw_data['low'][i-2]\n",
        "        tp_amount = (raw_data['close'][i] - raw_data['low'][i-2])\n",
        "        tp[i] = raw_data['close'][i] + tp_amount\n",
        "\n",
        "\n",
        "raw_data['tp'] = tp\n",
        "raw_data['sl'] = sl"
      ],
      "metadata": {
        "id": "pirtHNODUalm"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data[:30]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8ELdp9gWU7F9",
        "outputId": "3799ad41-0429-4390-ef2d-87cbb647466d"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        open     high      low    close  is_green buy_sell  \\\n",
              "datetime                                                                     \n",
              "2022-10-17 05:00:00  1.55482  1.55500  1.55409  1.55464         0            \n",
              "2022-10-17 05:15:00  1.55464  1.55488  1.55401  1.55404         0            \n",
              "2022-10-17 05:30:00  1.55404  1.55518  1.55362  1.55383         0     sell   \n",
              "2022-10-17 05:45:00  1.55383  1.55432  1.55304  1.55322         0     sell   \n",
              "2022-10-17 06:00:00  1.55322  1.55356  1.55244  1.55278         0     sell   \n",
              "2022-10-17 06:15:00  1.55278  1.55330  1.55262  1.55273         0     sell   \n",
              "2022-10-17 06:30:00  1.55273  1.55344  1.55266  1.55338         1  neutral   \n",
              "2022-10-17 06:45:00  1.55338  1.55396  1.55336  1.55386         1  neutral   \n",
              "2022-10-17 07:00:00  1.55386  1.55470  1.55310  1.55435         1      buy   \n",
              "2022-10-17 07:15:00  1.55435  1.55447  1.55374  1.55408         0  neutral   \n",
              "2022-10-17 07:30:00  1.55408  1.55412  1.55267  1.55275         0  neutral   \n",
              "2022-10-17 07:45:00  1.55275  1.55286  1.55188  1.55202         0     sell   \n",
              "2022-10-17 08:00:00  1.55202  1.55328  1.55190  1.55288         1  neutral   \n",
              "2022-10-17 08:15:00  1.55288  1.55317  1.55254  1.55297         1  neutral   \n",
              "2022-10-17 08:30:00  1.55297  1.55562  1.55282  1.55454         1      buy   \n",
              "2022-10-17 08:45:00  1.55454  1.55676  1.55396  1.55636         1      buy   \n",
              "2022-10-17 09:00:00  1.55636  1.55814  1.55620  1.55802         1      buy   \n",
              "2022-10-17 09:15:00  1.55802  1.56143  1.55771  1.55964         1      buy   \n",
              "2022-10-17 09:30:00  1.55964  1.56144  1.55882  1.56111         1      buy   \n",
              "2022-10-17 09:45:00  1.56111  1.56182  1.55814  1.55984         0  neutral   \n",
              "2022-10-17 10:00:00  1.55984  1.55984  1.55674  1.55797         0  neutral   \n",
              "2022-10-17 10:15:00  1.55797  1.55797  1.55649  1.55698         0     sell   \n",
              "2022-10-17 10:30:00  1.55698  1.55826  1.55450  1.55500         0     sell   \n",
              "2022-10-17 10:45:00  1.55500  1.55660  1.55480  1.55587         1  neutral   \n",
              "2022-10-17 11:00:00  1.55587  1.55904  1.55554  1.55844         1  neutral   \n",
              "2022-10-17 11:15:00  1.55844  1.55871  1.55482  1.55522         0  neutral   \n",
              "2022-10-17 11:30:00  1.55522  1.55576  1.55410  1.55522         0  neutral   \n",
              "2022-10-17 11:45:00  1.55522  1.55680  1.55522  1.55554         1  neutral   \n",
              "2022-10-17 12:00:00  1.55554  1.55632  1.55356  1.55624         1  neutral   \n",
              "2022-10-17 12:15:00  1.55624  1.55818  1.55620  1.55798         1      buy   \n",
              "\n",
              "                          tp       sl  \n",
              "datetime                               \n",
              "2022-10-17 05:00:00                    \n",
              "2022-10-17 05:15:00                    \n",
              "2022-10-17 05:30:00  1.55266    1.555  \n",
              "2022-10-17 05:45:00  1.55156  1.55488  \n",
              "2022-10-17 06:00:00  1.55038  1.55518  \n",
              "2022-10-17 06:15:00  1.55114  1.55432  \n",
              "2022-10-17 06:30:00                    \n",
              "2022-10-17 06:45:00                    \n",
              "2022-10-17 07:00:00  1.55604  1.55266  \n",
              "2022-10-17 07:15:00                    \n",
              "2022-10-17 07:30:00                    \n",
              "2022-10-17 07:45:00  1.54957  1.55447  \n",
              "2022-10-17 08:00:00                    \n",
              "2022-10-17 08:15:00                    \n",
              "2022-10-17 08:30:00  1.55718   1.5519  \n",
              "2022-10-17 08:45:00  1.56018  1.55254  \n",
              "2022-10-17 09:00:00  1.56322  1.55282  \n",
              "2022-10-17 09:15:00  1.56532  1.55396  \n",
              "2022-10-17 09:30:00  1.56602   1.5562  \n",
              "2022-10-17 09:45:00                    \n",
              "2022-10-17 10:00:00                    \n",
              "2022-10-17 10:15:00  1.55214  1.56182  \n",
              "2022-10-17 10:30:00  1.55016  1.55984  \n",
              "2022-10-17 10:45:00                    \n",
              "2022-10-17 11:00:00                    \n",
              "2022-10-17 11:15:00                    \n",
              "2022-10-17 11:30:00                    \n",
              "2022-10-17 11:45:00                    \n",
              "2022-10-17 12:00:00                    \n",
              "2022-10-17 12:15:00  1.56074  1.55522  "
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:00:00</th>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55409</td>\n",
              "      <td>1.55464</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:15:00</th>\n",
              "      <td>1.55464</td>\n",
              "      <td>1.55488</td>\n",
              "      <td>1.55401</td>\n",
              "      <td>1.55404</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:30:00</th>\n",
              "      <td>1.55404</td>\n",
              "      <td>1.55518</td>\n",
              "      <td>1.55362</td>\n",
              "      <td>1.55383</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55266</td>\n",
              "      <td>1.555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 05:45:00</th>\n",
              "      <td>1.55383</td>\n",
              "      <td>1.55432</td>\n",
              "      <td>1.55304</td>\n",
              "      <td>1.55322</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55156</td>\n",
              "      <td>1.55488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:00:00</th>\n",
              "      <td>1.55322</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55244</td>\n",
              "      <td>1.55278</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55038</td>\n",
              "      <td>1.55518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:15:00</th>\n",
              "      <td>1.55278</td>\n",
              "      <td>1.55330</td>\n",
              "      <td>1.55262</td>\n",
              "      <td>1.55273</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55114</td>\n",
              "      <td>1.55432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:30:00</th>\n",
              "      <td>1.55273</td>\n",
              "      <td>1.55344</td>\n",
              "      <td>1.55266</td>\n",
              "      <td>1.55338</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 06:45:00</th>\n",
              "      <td>1.55338</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55336</td>\n",
              "      <td>1.55386</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:00:00</th>\n",
              "      <td>1.55386</td>\n",
              "      <td>1.55470</td>\n",
              "      <td>1.55310</td>\n",
              "      <td>1.55435</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.55604</td>\n",
              "      <td>1.55266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:15:00</th>\n",
              "      <td>1.55435</td>\n",
              "      <td>1.55447</td>\n",
              "      <td>1.55374</td>\n",
              "      <td>1.55408</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:30:00</th>\n",
              "      <td>1.55408</td>\n",
              "      <td>1.55412</td>\n",
              "      <td>1.55267</td>\n",
              "      <td>1.55275</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 07:45:00</th>\n",
              "      <td>1.55275</td>\n",
              "      <td>1.55286</td>\n",
              "      <td>1.55188</td>\n",
              "      <td>1.55202</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.54957</td>\n",
              "      <td>1.55447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:00:00</th>\n",
              "      <td>1.55202</td>\n",
              "      <td>1.55328</td>\n",
              "      <td>1.55190</td>\n",
              "      <td>1.55288</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:15:00</th>\n",
              "      <td>1.55288</td>\n",
              "      <td>1.55317</td>\n",
              "      <td>1.55254</td>\n",
              "      <td>1.55297</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:30:00</th>\n",
              "      <td>1.55297</td>\n",
              "      <td>1.55562</td>\n",
              "      <td>1.55282</td>\n",
              "      <td>1.55454</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.55718</td>\n",
              "      <td>1.5519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 08:45:00</th>\n",
              "      <td>1.55454</td>\n",
              "      <td>1.55676</td>\n",
              "      <td>1.55396</td>\n",
              "      <td>1.55636</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56018</td>\n",
              "      <td>1.55254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:00:00</th>\n",
              "      <td>1.55636</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55620</td>\n",
              "      <td>1.55802</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56322</td>\n",
              "      <td>1.55282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:15:00</th>\n",
              "      <td>1.55802</td>\n",
              "      <td>1.56143</td>\n",
              "      <td>1.55771</td>\n",
              "      <td>1.55964</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56532</td>\n",
              "      <td>1.55396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:30:00</th>\n",
              "      <td>1.55964</td>\n",
              "      <td>1.56144</td>\n",
              "      <td>1.55882</td>\n",
              "      <td>1.56111</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56602</td>\n",
              "      <td>1.5562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 09:45:00</th>\n",
              "      <td>1.56111</td>\n",
              "      <td>1.56182</td>\n",
              "      <td>1.55814</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:00:00</th>\n",
              "      <td>1.55984</td>\n",
              "      <td>1.55984</td>\n",
              "      <td>1.55674</td>\n",
              "      <td>1.55797</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:15:00</th>\n",
              "      <td>1.55797</td>\n",
              "      <td>1.55797</td>\n",
              "      <td>1.55649</td>\n",
              "      <td>1.55698</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55214</td>\n",
              "      <td>1.56182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:30:00</th>\n",
              "      <td>1.55698</td>\n",
              "      <td>1.55826</td>\n",
              "      <td>1.55450</td>\n",
              "      <td>1.55500</td>\n",
              "      <td>0</td>\n",
              "      <td>sell</td>\n",
              "      <td>1.55016</td>\n",
              "      <td>1.55984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 10:45:00</th>\n",
              "      <td>1.55500</td>\n",
              "      <td>1.55660</td>\n",
              "      <td>1.55480</td>\n",
              "      <td>1.55587</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:00:00</th>\n",
              "      <td>1.55587</td>\n",
              "      <td>1.55904</td>\n",
              "      <td>1.55554</td>\n",
              "      <td>1.55844</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:15:00</th>\n",
              "      <td>1.55844</td>\n",
              "      <td>1.55871</td>\n",
              "      <td>1.55482</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:30:00</th>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55576</td>\n",
              "      <td>1.55410</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>0</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 11:45:00</th>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55680</td>\n",
              "      <td>1.55522</td>\n",
              "      <td>1.55554</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:00:00</th>\n",
              "      <td>1.55554</td>\n",
              "      <td>1.55632</td>\n",
              "      <td>1.55356</td>\n",
              "      <td>1.55624</td>\n",
              "      <td>1</td>\n",
              "      <td>neutral</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-10-17 12:15:00</th>\n",
              "      <td>1.55624</td>\n",
              "      <td>1.55818</td>\n",
              "      <td>1.55620</td>\n",
              "      <td>1.55798</td>\n",
              "      <td>1</td>\n",
              "      <td>buy</td>\n",
              "      <td>1.56074</td>\n",
              "      <td>1.55522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f180cd65-5ea1-4b4c-89e2-05268afcedaf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f180cd65-5ea1-4b4c-89e2-05268afcedaf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f180cd65-5ea1-4b4c-89e2-05268afcedaf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a57dfde1-43ab-4622-b499-7c93a79bfe69\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a57dfde1-43ab-4622-b499-7c93a79bfe69')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a57dfde1-43ab-4622-b499-7c93a79bfe69 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pnl = [0] * len(raw_data)\n",
        "\n",
        "for i in range(len(raw_data)-1000):\n",
        "    row = raw_data.iloc[i]\n",
        "    if row['buy_sell'] in ['sell', 'buy']:\n",
        "        tp = row['tp']\n",
        "        sl = row['sl']\n",
        "    else:\n",
        "        continue\n",
        "\n",
        "    j = i + 1\n",
        "    while True:\n",
        "        row_to_check = raw_data.iloc[j]\n",
        "        high = row_to_check['high']\n",
        "        low = row_to_check['low']\n",
        "\n",
        "        if low < tp < high:\n",
        "            pnl[i] = 1\n",
        "            break\n",
        "        elif low < sl < high:\n",
        "            pnl[i] = -1\n",
        "            break\n",
        "        else:\n",
        "            j += 1\n",
        "\n",
        "raw_data['pnl'] = pnl"
      ],
      "metadata": {
        "id": "FBAIVCj5U9L0"
      },
      "execution_count": 264,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sum = [0]\n",
        "for i in range(len(raw_data)-500):\n",
        "    sum.append(sum[i] + raw_data['pnl'][i])"
      ],
      "metadata": {
        "id": "TudVqjNMVIif"
      },
      "execution_count": 265,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(sum)\n",
        "plt.title('3 Consecutve Candles Strategy (Risk/Reward = 1)')\n",
        "plt.xlabel('Total PNL')\n",
        "plt.ylabel('# of Candle')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "km2MCF0tVNpt",
        "outputId": "8749ea5a-e0c3-457a-8a45-8ac1da354722"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FIvcdnalVcri"
      },
      "execution_count": 227,
      "outputs": []
    }
  ]
}