python/notebooks/ch7_fund_of_funds_mvo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LaTeX macros (hidden cell)\n",
    "$\n",
    "\\newcommand{\\Q}{\\mathcal{Q}}\n",
    "\\newcommand{\\ECov}{\\boldsymbol{\\Sigma}}\n",
    "\\newcommand{\\EMean}{\\boldsymbol{\\mu}}\n",
    "\\newcommand{\\EAlpha}{\\boldsymbol{\\alpha}}\n",
    "\\newcommand{\\EBeta}{\\boldsymbol{\\beta}}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import os,sys\n",
    "import re\n",
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "from mosek.fusion import *\n",
    "\n",
    "from notebook.services.config import ConfigManager\n",
    "\n",
    "from portfolio_tools import data_download, DataReader, compute_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.7 (default, Sep 16 2021, 13:09:58) \n",
      "[GCC 7.5.0]\n",
      "matplotlib: 3.7.2\n"
     ]
    }
   ],
   "source": [
    "# Version checks\n",
    "print(sys.version)\n",
    "print('matplotlib: {}'.format(matplotlib.__version__))\n",
    "\n",
    "# Jupyter configuration\n",
    "c = ConfigManager()\n",
    "c.update('notebook', {\"CodeCell\": {\"cm_config\": {\"autoCloseBrackets\": False}}})  \n",
    "\n",
    "# Numpy options\n",
    "np.set_printoptions(precision=5, linewidth=120, suppress=True)\n",
    "\n",
    "# Pandas options\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Matplotlib options\n",
    "plt.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.rcParams['figure.dpi'] = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare input data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we load the raw data that will be used to compute the optimization input variables, the vector $\\EMean$ of expected returns and the covariance matrix $\\ECov$. The data consists of daily stock prices of $8$ stocks from the US market. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data downloading:\n",
    "# If the user has an API key for alphavantage.co, then this code part will download the data. \n",
    "# The code can be modified to download from other sources. To be able to run the examples, \n",
    "# and reproduce results in the cookbook, the files have to have the following format and content:\n",
    "# - File name pattern: \"daily_adjusted_[TICKER].csv\", where TICKER is the symbol of a stock. \n",
    "# - The file contains at least columns \"timestamp\", \"adjusted_close\", and \"volume\".\n",
    "# - The data is daily price/volume, covering at least the period from 2016-03-18 until 2021-03-18, \n",
    "# - Files are for the stocks PM, LMT, MCD, MMM, AAPL, MSFT, TXN, CSCO.\n",
    "list_stocks = [\"PM\", \"LMT\", \"MCD\", \"MMM\", \"AAPL\", \"MSFT\", \"TXN\", \"CSCO\"]\n",
    "list_factors = []\n",
    "alphaToken = None\n",
    " \n",
    "list_tickers = list_stocks + list_factors\n",
    "if alphaToken is not None:\n",
    "    data_download(list_tickers, alphaToken)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the daily stock price data from the downloaded CSV files. The data is adjusted for splits and dividends. Then a selected time period is taken from the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found data files: \n",
      "stock_data/daily_adjusted_AAPL.csv\n",
      "stock_data/daily_adjusted_PM.csv\n",
      "stock_data/daily_adjusted_CSCO.csv\n",
      "stock_data/daily_adjusted_TXN.csv\n",
      "stock_data/daily_adjusted_MMM.csv\n",
      "stock_data/daily_adjusted_IWM.csv\n",
      "stock_data/daily_adjusted_MCD.csv\n",
      "stock_data/daily_adjusted_SPY.csv\n",
      "stock_data/daily_adjusted_MSFT.csv\n",
      "stock_data/daily_adjusted_LMT.csv\n",
      "\n",
      "Using data files: \n",
      "stock_data/daily_adjusted_PM.csv\n",
      "stock_data/daily_adjusted_LMT.csv\n",
      "stock_data/daily_adjusted_MCD.csv\n",
      "stock_data/daily_adjusted_MMM.csv\n",
      "stock_data/daily_adjusted_AAPL.csv\n",
      "stock_data/daily_adjusted_MSFT.csv\n",
      "stock_data/daily_adjusted_TXN.csv\n",
      "stock_data/daily_adjusted_CSCO.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The files are in \"stock_data\" folder, named as \"daily_adjusted_[TICKER].csv\"\n",
    "dr = DataReader(folder_path=\"stock_data\", symbol_list=list_tickers)\n",
    "dr.read_data(read_volume=True)\n",
    "df_prices, df_volumes = dr.get_period(start_date=investment_start, end_date=investment_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the optimization model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will solve a fund of funds problem. Suppose there are fund managers optimizing their funds wrt. specific benchmarks, and there is an overall benchmark, against which all funds are optimized together. This can be modeled as the following optimization problem: \n",
    "\n",
    "$$\n",
    "    \\begin{array}{lrcl}\n",
    "    \\mbox{minimize}     & (\\mathbf{x}_\\mathrm{o}-\\mathbf{x}_{\\mathrm{bm},\\mathrm{o}})^\\mathsf{T}\\ECov(\\mathbf{x}_{\\mathrm{o}}-\\mathbf{x}_{\\mathrm{bm},\\mathrm{o}})  &          &\\\\\n",
    "      \\mbox{subject to} & \\mathbf{x}_\\mathrm{o}                          & =        & \\sum_i f_i \\mathbf{x}_i\\\\\n",
    "                        & (\\mathbf{x}_i-\\mathbf{x}_{\\mathrm{bm},i})^\\mathsf{T}\\ECov(\\mathbf{x}_{i}-\\mathbf{x}_{\\mathrm{bm},i})  & \\leq     &\\sigma_{\\mathrm{max},i}^2\\quad i=1,\\dots,K\\\\\n",
    "                        & \\alpha_i^\\mathsf{T}\\mathbf{x}_i                & \\geq     & \\alpha_{\\mathrm{min},i}\\quad i=1,\\dots,K\\\\ \n",
    "                        & \\mathbf{1}^\\mathsf{T}\\mathbf{x}_i              & =        & 1,\\quad i=1,\\dots,K\\\\\n",
    "                        & \\mathbf{x}_i                                   & \\geq     & 0,\\quad i=1,\\dots,K\\\\\n",
    "    \\end{array}\n",
    "$$\n",
    "\n",
    "The objective is the squared tracking error of the overall fund. $\\mathbf{x}_\\mathrm{o}$ is the overall fund portfolio, and $\\mathrm{x}_i$ is the portfolio of fund $i$. Likewise, $\\mathbf{x}_{\\mathrm{bm},\\mathrm{o}}$ is the the overall benchmark, and $\\mathbf{x}_{\\mathrm{bm},i}$ is the benchmark of fund $i$. $f_i$ is the weight of fund $i$, and has to satisfy $f_i\\geq 0$, $\\sum_if_i=1$. $\\sigma_{\\mathrm{max},i}^2$ is the squared tracking error upper bound for fund $i$, and $\\alpha_{\\mathrm{min},i}$ is the portfolio alpha lower bound for fund $i$.\n",
    "\n",
    "Then we rewrite the above problem into conic form, and implement it in Fusion API:\n",
    "\n",
    "$$\n",
    "    \\begin{array}{lrcl}\n",
    "    \\mbox{minimize}     & t_\\mathrm{o}                                   &          &\\\\\n",
    "    \\mbox{subject to}   & (t_\\mathrm{o}, 0.5, \\mathbf{G}^\\mathrm{T}(\\mathbf{x}_{\\mathrm{o}}-\\mathbf{x}_{\\mathrm{bm},\\mathrm{o}}))  & \\in     &\\Q_\\mathrm{r}^{N+2}\\\\\n",
    "                        & \\mathbf{x}_\\mathrm{o}                          & =        & \\sum_i f_i \\mathbf{x}_i\\\\\n",
    "                        & (\\sigma_{\\mathrm{max},i}^2, 0.5, \\mathbf{G}^\\mathrm{T}(\\mathbf{x}_i-\\mathbf{x}_{\\mathrm{bm},i}))  & \\in     &\\Q_\\mathrm{r}^{N+2},\\quad i=1,\\dots,K\\\\\n",
    "                        & \\alpha_i^\\mathsf{T}\\mathbf{x}_i                & \\geq     & \\alpha_{\\mathrm{min},i}\\quad i=1,\\dots,K\\\\ \n",
    "                        & \\mathbf{1}^\\mathsf{T}\\mathbf{x}_i              & =        & 1,\\quad i=1,\\dots,K\\\\\n",
    "                        & \\mathbf{x}_i                                   & \\geq     & 0,\\quad i=1,\\dots,K\\\\\n",
    "    \\end{array}\n",
    "$$\n",
    "\n",
    "We create it inside a function so we can call it later.\n",
    "\n",
    "Below we implement the optimization model in Fusion API. We create it inside a function so we can call it later.\n",
    "\n",
    "The parameters:\n",
    "- `a`: The vectors of alphas for each fund.\n",
    "- `ao`: The vector of alphas for the overall fund.\n",
    "- `a_min`: The minimum required portfolio alpha for each fund.\n",
    "- `s2_max`: The maximum tracking error for each fund.\n",
    "- `f`: The weigth of each fund portfolio in the overall portfolio.\n",
    "- `xobm`: The overall benchmark portfolio.\n",
    "- `XFbm`: The benchmark portfolio for each fund."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EfficientFrontier(N, K, a, ao, a_min, s2_max, G, f, xobm, Xfbm):\n",
    "\n",
    "    with Model(\"Case study\") as M:\n",
    "        # Settings\n",
    "        #M.setLogHandler(sys.stdout)\n",
    "        \n",
    "        # Variables \n",
    "        # The variable x is the fraction of holdings in each security. \n",
    "        # It is restricted to be positive, which imposes the constraint of no short-selling. \n",
    "        xo = M.variable(\"xo\", N, Domain.greaterThan(0.0))\n",
    "        Xf = M.variable(\"Xf\", [N, K], Domain.greaterThan(0.0))\n",
    "        \n",
    "        # Active holdings\n",
    "        xoa = Expr.sub(xo, xobm)\n",
    "        Xfa = Expr.sub(Xf, Xfbm) \n",
    "        \n",
    "        # The variable teo models the overall tracking error in the objective.\n",
    "        te2o = M.variable(\"teo\", 1, Domain.unbounded())\n",
    "    \n",
    "        # Relate overall portfolio to fund portfolios\n",
    "        M.constraint(\"combine\", Expr.sub(xo, Expr.mul(Xf, f)), Domain.equalsTo(0.0))\n",
    "    \n",
    "        # Budget constraint for each fund\n",
    "        M.constraint('budget_f', Expr.sum(Xf, 0), Domain.equalsTo(np.ones(K)))\n",
    "        \n",
    "        # Conic constraint for the fund sq. tracking errors\n",
    "        sigma2 = M.parameter()\n",
    "        for i in range(K):\n",
    "            idx = [[j, i] for j in range(N)]\n",
    "            M.constraint(f'fund_te2_{i}', Expr.vstack(sigma2, 0.5, Expr.mul(G.T, Xfa.pick(idx))), Domain.inRotatedQCone())\n",
    "        \n",
    "        # Conic constraint for the overall sq. tracking error\n",
    "        M.constraint('overall_te2', Expr.vstack(te2o, 0.5, Expr.mul(G.T, xoa)), Domain.inRotatedQCone())\n",
    "        \n",
    "        # Alpha constraint for each fund.\n",
    "        for i in range(K):\n",
    "            idx = [[j, i] for j in range(N)]\n",
    "            M.constraint(f'fund_alpha_{i}', Expr.dot(a[:, i], Xf.pick(idx)), Domain.greaterThan(a_min[i]))\n",
    "        \n",
    "        # Objective\n",
    "        M.objective('obj', ObjectiveSense.Minimize, te2o)\n",
    "    \n",
    "        # Create DataFrame to store the results. Last security name (the SPY ETF) is removed.\n",
    "        columns = [\"s2\", \"obj\", \"return\", \"te_o\", \"te_1\", \"te_2\"] + df_prices.columns[:-1].tolist()\n",
    "        df_result = pd.DataFrame(columns=columns)\n",
    "        for s2 in s2_max:\n",
    "            # Update parameter\n",
    "            sigma2.setValue(s2) \n",
    "            \n",
    "            # Solve optimization\n",
    "            M.solve()\n",
    "            # Check if the solution is an optimal point\n",
    "            solsta = M.getPrimalSolutionStatus()\n",
    "            if (solsta != SolutionStatus.Optimal):\n",
    "                # See https://docs.mosek.com/latest/pythonfusion/accessing-solution.html about handling solution statuses.\n",
    "                raise Exception(\"Unexpected solution status!\")\n",
    "\n",
    "            # Save results\n",
    "            portfolio_return = ao @ xo.level()\n",
    "            overall_te2 = te2o.level()[0]\n",
    "            r1 = G.T @ (Xf.level().reshape(N, K)[:, 0] - Xfbm[:, 0])\n",
    "            fund_te2_1 = np.dot(r1, r1)\n",
    "            r2 = G.T @ (Xf.level().reshape(N, K)[:, 1] - Xfbm[:, 1])\n",
    "            fund_te2_2 = np.dot(r2, r2)\n",
    "            row = pd.Series([s2, M.primalObjValue(), portfolio_return, overall_te2, fund_te2_1, fund_te2_2] + list(xo.level()), index=columns)\n",
    "            df_result = pd.concat([df_result, pd.DataFrame([row])], ignore_index=True)\n",
    "\n",
    "        return df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define a function that computes the factor model\n",
    "$$\n",
    "R_t = \\alpha + \\beta R_{F,t} + \\varepsilon_t.\n",
    "$$\n",
    "It can handle any number of factors, and returns estimates $\\EBeta$, $\\ECov_F$, and $\\ECov_\\theta$. The factors are assumed to be at the last coordinates of the data. \n",
    "\n",
    "The input of the function is the expected return and covariance of yearly logarithmic returns. The reason is that it is easier to generate logarithmic return scenarios from normal distribution instead of generating linear return scenarios from lognormal distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factor_model(m_log, S_log, factor_num):\n",
    "    \"\"\"\n",
    "    It is assumed that the last factor_num coordinates correspond to the factors.\n",
    "    \"\"\"\n",
    "    if factor_num < 1: \n",
    "        raise Exception(\"Does not make sense to compute a factor model without factors!\")\n",
    "    \n",
    "    # Generate logarithmic return scenarios\n",
    "    scenarios_log = np.random.default_rng().multivariate_normal(m_log, S_log, 100000)\n",
    "    \n",
    "    # Convert logarithmic return scenarios to linear return scenarios \n",
    "    scenarios_lin = np.exp(scenarios_log) - 1\n",
    "    \n",
    "    # Do linear regression \n",
    "    params = []\n",
    "    resid = []\n",
    "    X = scenarios_lin[:, -factor_num:]\n",
    "    X = sm.add_constant(X, prepend=False)\n",
    "    \n",
    "    for k in range(N):\n",
    "        y = scenarios_lin[:, k]\n",
    "        model = sm.OLS(y, X, hasconst=True).fit()\n",
    "        resid.append(model.resid)\n",
    "        params.append(model.params)\n",
    "    resid = np.array(resid)\n",
    "    params = np.array(params)\n",
    "    \n",
    "    # Get parameter estimates\n",
    "    a = params[:, 1]\n",
    "    B = params[:, 0:factor_num]\n",
    "    S_F = np.atleast_2d(np.cov(X[:, 0:factor_num].T))\n",
    "    S_theta = np.cov(resid)\n",
    "    S_theta = np.diag(np.diag(S_theta))\n",
    "    \n",
    "    return a, B, S_F, S_theta "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute optimization input variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the loaded daily price data to compute the corresponding yearly mean return and covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of securities (We subtract fnum to account for factors at the end of the price data)\n",
    "N = 8\n",
    "K = 2\n",
    "\n",
    "# Get optimization parameters\n",
    "m, S = compute_inputs(df_prices, security_num=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create three benchmarks, one for each fund, and an overall benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create benchmarks\n",
    "# - Benchmark for fund 1 \n",
    "w1 = np.diag(S)\n",
    "w1 = w1 / sum(w1)\n",
    "bm_1 = df_prices.iloc[:-2, 0:8].dot(w1)\n",
    "\n",
    "# - Benchmark for fund 2\n",
    "w2 = np.diag(S)**2\n",
    "w2 = w2 / sum(w2)\n",
    "bm_2 = df_prices.iloc[:-2, 0:8].dot(w2)\n",
    "\n",
    "# - Overall benchmark\n",
    "wo = (1.0 / np.diag(S))\n",
    "wo = wo / sum(wo)\n",
    "bm_o = df_prices.iloc[:-2, 0:8].dot(wo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compute the matrix $G$ such that $\\ECov=GG^\\mathsf{T}$, this is the input of the conic form of the optimization problem. Here we use Cholesky factorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = np.linalg.cholesky(S)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compute the estimates $\\EAlpha$ and $\\EBeta$ using the factor model, for each benchmark. First we compute logarithmic return statistics and use them to compute the factor exposures and covariances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prices['bm'] = bm_1\n",
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "a_1, _, _, _ = factor_model(m_log, S_log, 1)\n",
    "\n",
    "df_prices['bm'] = bm_2\n",
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "a_2, _, _, _ = factor_model(m_log, S_log, 1)\n",
    "\n",
    "df_prices['bm'] = bm_o\n",
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "a_3, _, _, _ = factor_model(m_log, S_log, 1)\n",
    "\n",
    "a = np.vstack([a_1, a_2]).T\n",
    "ao = a_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also define the benchmark weights for the funds, and the overall benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfbm = np.vstack([w1, w2]).T\n",
    "xobm = wo\n",
    "\n",
    "# Fund weights\n",
    "f = [0.5, 0.5]\n",
    "\n",
    "# Alpha lower bounds\n",
    "a_min = [0.05, 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call the optimizer function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the optimization for a range of tracking error limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Tracking error upper bounds (in percent)\n",
    "s2_max = np.linspace(start=1, stop=0.1, num=10) / 100\n",
    "\n",
    "df_result = EfficientFrontier(N, K, a, ao, a_min, s2_max, G, f, xobm, Xfbm)\n",
    "mask = df_result < 0\n",
    "mask.iloc[:, :2] = False\n",
    "df_result[mask] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the squared tracking errors of the funds in function of the tracking error limit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>s2</th>\n",
       "      <th>obj</th>\n",
       "      <th>return</th>\n",
       "      <th>te_o</th>\n",
       "      <th>te_1</th>\n",
       "      <th>te_2</th>\n",
       "      <th>PM</th>\n",
       "      <th>LMT</th>\n",
       "      <th>MCD</th>\n",
       "      <th>MMM</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>TXN</th>\n",
       "      <th>CSCO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.093003</td>\n",
       "      <td>0.001884</td>\n",
       "      <td>0.003703</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.044036</td>\n",
       "      <td>0.102734</td>\n",
       "      <td>0.192379</td>\n",
       "      <td>0.062397</td>\n",
       "      <td>0.078985</td>\n",
       "      <td>0.228180</td>\n",
       "      <td>0.165808</td>\n",
       "      <td>0.125481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.093437</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.004370</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.043458</td>\n",
       "      <td>0.102432</td>\n",
       "      <td>0.192544</td>\n",
       "      <td>0.061605</td>\n",
       "      <td>0.079146</td>\n",
       "      <td>0.229107</td>\n",
       "      <td>0.166272</td>\n",
       "      <td>0.125436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.001951</td>\n",
       "      <td>0.093898</td>\n",
       "      <td>0.001951</td>\n",
       "      <td>0.005142</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.042831</td>\n",
       "      <td>0.102125</td>\n",
       "      <td>0.192737</td>\n",
       "      <td>0.060755</td>\n",
       "      <td>0.079310</td>\n",
       "      <td>0.230093</td>\n",
       "      <td>0.166765</td>\n",
       "      <td>0.125385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.007</td>\n",
       "      <td>0.001988</td>\n",
       "      <td>0.094410</td>\n",
       "      <td>0.001988</td>\n",
       "      <td>0.005931</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>0.101833</td>\n",
       "      <td>0.192939</td>\n",
       "      <td>0.059923</td>\n",
       "      <td>0.080024</td>\n",
       "      <td>0.230799</td>\n",
       "      <td>0.167092</td>\n",
       "      <td>0.125316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.095282</td>\n",
       "      <td>0.002036</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.043524</td>\n",
       "      <td>0.101328</td>\n",
       "      <td>0.189254</td>\n",
       "      <td>0.059700</td>\n",
       "      <td>0.087500</td>\n",
       "      <td>0.228238</td>\n",
       "      <td>0.166165</td>\n",
       "      <td>0.124293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>0.096745</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.049023</td>\n",
       "      <td>0.100206</td>\n",
       "      <td>0.179244</td>\n",
       "      <td>0.060148</td>\n",
       "      <td>0.106671</td>\n",
       "      <td>0.219698</td>\n",
       "      <td>0.163110</td>\n",
       "      <td>0.121901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.098373</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.055574</td>\n",
       "      <td>0.098766</td>\n",
       "      <td>0.167765</td>\n",
       "      <td>0.060563</td>\n",
       "      <td>0.128138</td>\n",
       "      <td>0.210113</td>\n",
       "      <td>0.159798</td>\n",
       "      <td>0.119282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.100223</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.063121</td>\n",
       "      <td>0.097105</td>\n",
       "      <td>0.154651</td>\n",
       "      <td>0.061002</td>\n",
       "      <td>0.152573</td>\n",
       "      <td>0.199199</td>\n",
       "      <td>0.156054</td>\n",
       "      <td>0.116295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>0.102425</td>\n",
       "      <td>0.003497</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.072137</td>\n",
       "      <td>0.095111</td>\n",
       "      <td>0.139027</td>\n",
       "      <td>0.061510</td>\n",
       "      <td>0.181652</td>\n",
       "      <td>0.186214</td>\n",
       "      <td>0.151607</td>\n",
       "      <td>0.112742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.105326</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.083985</td>\n",
       "      <td>0.092474</td>\n",
       "      <td>0.118496</td>\n",
       "      <td>0.062154</td>\n",
       "      <td>0.219865</td>\n",
       "      <td>0.169160</td>\n",
       "      <td>0.145778</td>\n",
       "      <td>0.108087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      s2       obj    return      te_o      te_1   te_2        PM       LMT  \\\n",
       "0  0.010  0.001884  0.093003  0.001884  0.003703  0.010  0.044036  0.102734   \n",
       "1  0.009  0.001916  0.093437  0.001916  0.004370  0.009  0.043458  0.102432   \n",
       "2  0.008  0.001951  0.093898  0.001951  0.005142  0.008  0.042831  0.102125   \n",
       "3  0.007  0.001988  0.094410  0.001988  0.005931  0.007  0.042075  0.101833   \n",
       "4  0.006  0.002036  0.095282  0.002036  0.006000  0.006  0.043524  0.101328   \n",
       "5  0.005  0.002160  0.096745  0.002160  0.005000  0.005  0.049023  0.100206   \n",
       "6  0.004  0.002404  0.098373  0.002404  0.004000  0.004  0.055574  0.098766   \n",
       "7  0.003  0.002817  0.100223  0.002817  0.003000  0.003  0.063121  0.097105   \n",
       "8  0.002  0.003497  0.102425  0.003497  0.002000  0.002  0.072137  0.095111   \n",
       "9  0.001  0.004704  0.105326  0.004704  0.001000  0.001  0.083985  0.092474   \n",
       "\n",
       "        MCD       MMM      AAPL      MSFT       TXN      CSCO  \n",
       "0  0.192379  0.062397  0.078985  0.228180  0.165808  0.125481  \n",
       "1  0.192544  0.061605  0.079146  0.229107  0.166272  0.125436  \n",
       "2  0.192737  0.060755  0.079310  0.230093  0.166765  0.125385  \n",
       "3  0.192939  0.059923  0.080024  0.230799  0.167092  0.125316  \n",
       "4  0.189254  0.059700  0.087500  0.228238  0.166165  0.124293  \n",
       "5  0.179244  0.060148  0.106671  0.219698  0.163110  0.121901  \n",
       "6  0.167765  0.060563  0.128138  0.210113  0.159798  0.119282  \n",
       "7  0.154651  0.061002  0.152573  0.199199  0.156054  0.116295  \n",
       "8  0.139027  0.061510  0.181652  0.186214  0.151607  0.112742  \n",
       "9  0.118496  0.062154  0.219865  0.169160  0.145778  0.108087  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Efficient frontier\n",
    "ax = df_result.plot(x=\"s2\", y=\"te_o\", style=\"-o\", xlabel=\"risk limit\", ylabel=\"portfolio tracking error\", grid=True)\n",
    "df_result.plot(ax=ax, x=\"s2\", y=\"te_1\", style=\"-o\", xlabel=\"risk limit\", ylabel=\"portfolio tracking error\", grid=True) \n",
    "df_result.plot(ax=ax, x=\"s2\", y=\"te_2\", style=\"-o\", xlabel=\"risk limit\", ylabel=\"portfolio tracking error\", grid=True)\n",
    "ax.legend([\"overall portfolio\", \"fund 1\", \"fund_2\"]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}