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",
    "import mosek.fusion.pythonic   # Requires MOSEK >= 10.2\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 = xo - xobm\n",
    "        Xfa = 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\", xo == Xf @ f)\n",
    "    \n",
    "        # Budget constraint for each fund\n",
    "        M.constraint('budget_f', Expr.sum(Xf, 0) == np.ones(K))\n",
    "        \n",
    "        # Conic constraint for the fund sq. tracking errors\n",
    "        sigma2 = M.parameter()\n",
    "        for i in range(K):\n",
    "            M.constraint(f'fund_te2_{i}', \n",
    "                         Expr.flatten(Expr.vstack(sigma2, 0.5, G.T @ Xfa[:, i])),\n",
    "                         Domain.inRotatedQCone())\n",
    "        \n",
    "        # Conic constraint for the overall sq. tracking error\n",
    "        M.constraint('overall_te2', Expr.vstack(te2o, 0.5, G.T @ xoa), Domain.inRotatedQCone())\n",
    "        \n",
    "        # Alpha constraint for each fund.\n",
    "        for i in range(K):\n",
    "            M.constraint(f'fund_alpha_{i}', Xf[:, i].T @ a[:, i]>= 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": [
    {
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       "      <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": {
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      "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
}