{
 "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": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import re\n",
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\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": 44,
   "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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()\n",
    "df_prices, _ = 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": [
    "Below we implement the optimization model in Fusion API. We create it inside a function so we can call it later.\n",
    "\n",
    "In this model, the gross exposure is kept on a constant level. This can be modeled by separating the portfolio variable to positive and negative part, and constraining their sum. The tradeoff is that modeling positive and negative parts requires us to assign each part a binary vector. Thus the problem becomes mixed integer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = xp - xm\n",
    "# NOTE: Uses integer variables!\n",
    "def posneg(M, x, bigm_p, bigm_m=None):\n",
    "    bigm_m = bigm_p if bigm_m is None else bigm_m\n",
    "    \n",
    "    # Positive and negative part of x\n",
    "    xp = M.variable(\"_xp\", N, Domain.greaterThan(0.0))\n",
    "    xm = M.variable(\"_xm\", N, Domain.greaterThan(0.0))\n",
    "    \n",
    "    # Binary variables\n",
    "    yp = M.variable(\"_yp\", N, Domain.binary())\n",
    "    ym = M.variable(\"_ym\", N, Domain.binary())\n",
    "    \n",
    "    # Constraint assigning xp and xm to be the positive and negative part of x.\n",
    "    M.constraint('_pos-neg-part', Expr.sub(x, Expr.sub(xp, xm)), Domain.equalsTo(0.0))\n",
    "    \n",
    "    # Constraints making sure xp and xm are never both positive.\n",
    "    M.constraint('_ubound-p', Expr.sub(xp, Expr.mul(bigm_p, yp)), Domain.lessThan(0.0))\n",
    "    M.constraint('_ubound-m', Expr.sub(xm, Expr.mul(bigm_m, ym)), Domain.lessThan(0.0))\n",
    "    M.constraint('_exclusion', Expr.add(yp, ym), Domain.lessThan(1.0))\n",
    "    \n",
    "    return xp, xm, yp, ym\n",
    "\n",
    "# L <= ||x||_1 <= U \n",
    "# NOTE: Uses integer variables!\n",
    "def norm1_mio(M, x, bigm, domain):\n",
    "    xp, xm, _, _ = posneg(M, x, bigm)\n",
    "\n",
    "    # Gross exposure constraint (forces 2 times the initial capital)\n",
    "    M.constraint('gross_exp', Expr.sum(Expr.add(xp, xm)), domain)   \n",
    "    \n",
    "def EfficientFrontier(N, m, G, deltas):\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 relative to the initial capital. \n",
    "        # It is a free variable, allowing long and short positions.   \n",
    "        x = M.variable(\"x\", N, Domain.unbounded())\n",
    "        \n",
    "        # The variable s models the portfolio variance term in the objective.\n",
    "        s = M.variable(\"s\", 1, Domain.unbounded())\n",
    "        \n",
    "        # Gross exposure constraint\n",
    "        norm1_mio(M, x, 2.0, Domain.equalsTo(2.0))\n",
    "                \n",
    "        # Dollar neutrality constraint\n",
    "        M.constraint('neutrality', Expr.sum(x), Domain.equalsTo(0.0))\n",
    "        \n",
    "        # Objective (quadratic utility version)\n",
    "        delta = M.parameter()\n",
    "        M.objective('obj', ObjectiveSense.Maximize, Expr.sub(Expr.dot(m, x), Expr.mul(delta, s)))\n",
    "\n",
    "        # Conic constraint for the portfolio variance \n",
    "        M.constraint('risk', Expr.vstack(s, 0.5, Expr.mul(G.transpose(), x)), Domain.inRotatedQCone())\n",
    "        \n",
    "        # Create DataFrame to store the results. Last security name (the SPY) is removed.\n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\", \"g. exp.\"] + df_prices.columns.tolist()\n",
    "        df_result = pd.DataFrame(columns=columns)\n",
    "        for d in deltas:\n",
    "            # Update parameter\n",
    "            delta.setValue(d)\n",
    "            \n",
    "            # Solve optimization\n",
    "            M.solve()\n",
    "            \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 = m @ x.level()\n",
    "            portfolio_risk = np.sqrt(s.level()[0])\n",
    "            gross_exp = sum(np.absolute(x.level()))\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk, gross_exp] + list(x.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": [
    "## 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": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Number of securities\n",
    "N = df_prices.shape[1]\n",
    "\n",
    "# Get optimization parameters\n",
    "m, S = compute_inputs(df_prices)"
   ]
  },
  {
   "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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = np.linalg.cholesky(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call the optimizer function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run the optimization for a range of risk aversion parameter values: $\\delta = 10^{-1},\\dots,10^{1.5}$. We compute the efficient frontier this way both with and without using shrinkage estimation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Compute efficient frontier with and without shrinkage\n",
    "deltas = np.logspace(start=-1, stop=1.5, num=20)[::-1]\n",
    "df_result = EfficientFrontier(N, m, G, deltas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>delta</th>\n",
       "      <th>obj</th>\n",
       "      <th>return</th>\n",
       "      <th>risk</th>\n",
       "      <th>g. exp.</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>31.622777</td>\n",
       "      <td>-0.464990</td>\n",
       "      <td>0.062518</td>\n",
       "      <td>0.129156</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.173130</td>\n",
       "      <td>2.474678e-01</td>\n",
       "      <td>-3.372581e-01</td>\n",
       "      <td>-2.568931e-01</td>\n",
       "      <td>-0.163862</td>\n",
       "      <td>3.121972e-01</td>\n",
       "      <td>2.672053e-01</td>\n",
       "      <td>-2.419872e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.357215</td>\n",
       "      <td>-0.326053</td>\n",
       "      <td>0.070600</td>\n",
       "      <td>0.130315</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.159688</td>\n",
       "      <td>2.432988e-01</td>\n",
       "      <td>-3.337110e-01</td>\n",
       "      <td>-2.678514e-01</td>\n",
       "      <td>-0.155205</td>\n",
       "      <td>3.249477e-01</td>\n",
       "      <td>2.720653e-01</td>\n",
       "      <td>-2.432330e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.252105</td>\n",
       "      <td>-0.220946</td>\n",
       "      <td>0.081541</td>\n",
       "      <td>0.132414</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.141491</td>\n",
       "      <td>2.376549e-01</td>\n",
       "      <td>-3.289090e-01</td>\n",
       "      <td>-2.826883e-01</td>\n",
       "      <td>-0.143483</td>\n",
       "      <td>3.422096e-01</td>\n",
       "      <td>2.786448e-01</td>\n",
       "      <td>-2.449195e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.742750</td>\n",
       "      <td>-0.139946</td>\n",
       "      <td>0.096354</td>\n",
       "      <td>0.136176</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.116853</td>\n",
       "      <td>2.300138e-01</td>\n",
       "      <td>-3.224075e-01</td>\n",
       "      <td>-3.027758e-01</td>\n",
       "      <td>-0.127614</td>\n",
       "      <td>3.655803e-01</td>\n",
       "      <td>2.875527e-01</td>\n",
       "      <td>-2.472028e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.412050</td>\n",
       "      <td>-0.067613</td>\n",
       "      <td>0.169392</td>\n",
       "      <td>0.158685</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.185170</td>\n",
       "      <td>3.159332e-01</td>\n",
       "      <td>-2.750160e-01</td>\n",
       "      <td>-2.899102e-01</td>\n",
       "      <td>0.090165</td>\n",
       "      <td>3.390421e-01</td>\n",
       "      <td>2.548602e-01</td>\n",
       "      <td>-2.499033e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.951928</td>\n",
       "      <td>-0.003511</td>\n",
       "      <td>0.185865</td>\n",
       "      <td>0.165048</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.208575</td>\n",
       "      <td>2.678201e-01</td>\n",
       "      <td>-2.413193e-01</td>\n",
       "      <td>-3.146996e-01</td>\n",
       "      <td>0.106035</td>\n",
       "      <td>3.684833e-01</td>\n",
       "      <td>2.576615e-01</td>\n",
       "      <td>-2.354058e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.134833</td>\n",
       "      <td>0.048903</td>\n",
       "      <td>0.208168</td>\n",
       "      <td>0.176116</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.240260</td>\n",
       "      <td>2.026776e-01</td>\n",
       "      <td>-1.956946e-01</td>\n",
       "      <td>-3.482634e-01</td>\n",
       "      <td>0.127514</td>\n",
       "      <td>4.083526e-01</td>\n",
       "      <td>2.614562e-01</td>\n",
       "      <td>-2.157824e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.792690</td>\n",
       "      <td>0.094477</td>\n",
       "      <td>0.238362</td>\n",
       "      <td>0.194775</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.283159</td>\n",
       "      <td>1.144903e-01</td>\n",
       "      <td>-1.339346e-01</td>\n",
       "      <td>-3.936950e-01</td>\n",
       "      <td>0.156605</td>\n",
       "      <td>4.623162e-01</td>\n",
       "      <td>2.665886e-01</td>\n",
       "      <td>-1.892116e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.801357</td>\n",
       "      <td>0.137483</td>\n",
       "      <td>0.277953</td>\n",
       "      <td>0.223928</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.338266</td>\n",
       "      <td>-2.157196e-02</td>\n",
       "      <td>-4.161092e-02</td>\n",
       "      <td>-4.504126e-01</td>\n",
       "      <td>0.195031</td>\n",
       "      <td>5.335729e-01</td>\n",
       "      <td>2.713965e-01</td>\n",
       "      <td>-1.481390e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.069138</td>\n",
       "      <td>0.177107</td>\n",
       "      <td>0.298427</td>\n",
       "      <td>0.242143</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.415078</td>\n",
       "      <td>1.225415e-08</td>\n",
       "      <td>1.700785e-08</td>\n",
       "      <td>-5.158707e-01</td>\n",
       "      <td>0.246381</td>\n",
       "      <td>5.503481e-01</td>\n",
       "      <td>2.032710e-01</td>\n",
       "      <td>-6.905105e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.528307</td>\n",
       "      <td>0.211422</td>\n",
       "      <td>0.315448</td>\n",
       "      <td>0.260895</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.476221</td>\n",
       "      <td>-2.155736e-09</td>\n",
       "      <td>-1.776444e-09</td>\n",
       "      <td>-5.237788e-01</td>\n",
       "      <td>0.309753</td>\n",
       "      <td>5.874734e-01</td>\n",
       "      <td>1.027733e-01</td>\n",
       "      <td>8.620232e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.128838</td>\n",
       "      <td>0.240334</td>\n",
       "      <td>0.326192</td>\n",
       "      <td>0.275787</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.523016</td>\n",
       "      <td>-1.386673e-08</td>\n",
       "      <td>-1.262417e-08</td>\n",
       "      <td>-4.769844e-01</td>\n",
       "      <td>0.381652</td>\n",
       "      <td>6.183479e-01</td>\n",
       "      <td>9.418158e-08</td>\n",
       "      <td>-3.164627e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.833782</td>\n",
       "      <td>0.263130</td>\n",
       "      <td>0.328904</td>\n",
       "      <td>0.280867</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.559580</td>\n",
       "      <td>-3.949152e-08</td>\n",
       "      <td>-3.949057e-08</td>\n",
       "      <td>-4.404201e-01</td>\n",
       "      <td>0.438813</td>\n",
       "      <td>5.611868e-01</td>\n",
       "      <td>9.387767e-08</td>\n",
       "      <td>-1.070953e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.615848</td>\n",
       "      <td>0.280802</td>\n",
       "      <td>0.332579</td>\n",
       "      <td>0.289955</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.609112</td>\n",
       "      <td>-1.073465e-08</td>\n",
       "      <td>-9.791930e-09</td>\n",
       "      <td>-3.908876e-01</td>\n",
       "      <td>0.516269</td>\n",
       "      <td>4.837309e-01</td>\n",
       "      <td>1.521013e-08</td>\n",
       "      <td>-3.706820e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.454878</td>\n",
       "      <td>0.294985</td>\n",
       "      <td>0.337550</td>\n",
       "      <td>0.305899</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.676097</td>\n",
       "      <td>-2.586948e-10</td>\n",
       "      <td>-2.543143e-10</td>\n",
       "      <td>-3.239028e-01</td>\n",
       "      <td>0.621050</td>\n",
       "      <td>3.789498e-01</td>\n",
       "      <td>-5.996953e-11</td>\n",
       "      <td>-1.062450e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.335982</td>\n",
       "      <td>0.306991</td>\n",
       "      <td>0.344283</td>\n",
       "      <td>0.333161</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.766839</td>\n",
       "      <td>-2.405226e-09</td>\n",
       "      <td>-2.331970e-09</td>\n",
       "      <td>-2.331608e-01</td>\n",
       "      <td>0.762972</td>\n",
       "      <td>2.370280e-01</td>\n",
       "      <td>-8.019062e-10</td>\n",
       "      <td>-1.050058e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.248163</td>\n",
       "      <td>0.317929</td>\n",
       "      <td>0.353399</td>\n",
       "      <td>0.378062</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.889698</td>\n",
       "      <td>-6.343712e-09</td>\n",
       "      <td>-6.401940e-09</td>\n",
       "      <td>-1.103019e-01</td>\n",
       "      <td>0.955109</td>\n",
       "      <td>4.489092e-02</td>\n",
       "      <td>-1.726304e-09</td>\n",
       "      <td>-2.656342e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.183298</td>\n",
       "      <td>0.327910</td>\n",
       "      <td>0.356984</td>\n",
       "      <td>0.398270</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-4.364673e-09</td>\n",
       "      <td>-4.519869e-09</td>\n",
       "      <td>-4.854710e-09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.363003e-08</td>\n",
       "      <td>-1.498877e-09</td>\n",
       "      <td>-1.937569e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.135388</td>\n",
       "      <td>0.335509</td>\n",
       "      <td>0.356984</td>\n",
       "      <td>0.398270</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-4.364673e-09</td>\n",
       "      <td>-4.519869e-09</td>\n",
       "      <td>-4.854710e-09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.363003e-08</td>\n",
       "      <td>-1.498877e-09</td>\n",
       "      <td>-1.937569e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.341122</td>\n",
       "      <td>0.356984</td>\n",
       "      <td>0.398270</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-4.364673e-09</td>\n",
       "      <td>-4.519869e-09</td>\n",
       "      <td>-4.854710e-09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.363003e-08</td>\n",
       "      <td>-1.498877e-09</td>\n",
       "      <td>-1.937569e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        delta       obj    return      risk  g. exp.        PM           LMT  \\\n",
       "0   31.622777 -0.464990  0.062518  0.129156      2.0  0.173130  2.474678e-01   \n",
       "1   23.357215 -0.326053  0.070600  0.130315      2.0  0.159688  2.432988e-01   \n",
       "2   17.252105 -0.220946  0.081541  0.132414      2.0  0.141491  2.376549e-01   \n",
       "3   12.742750 -0.139946  0.096354  0.136176      2.0  0.116853  2.300138e-01   \n",
       "4    9.412050 -0.067613  0.169392  0.158685      2.0 -0.185170  3.159332e-01   \n",
       "5    6.951928 -0.003511  0.185865  0.165048      2.0 -0.208575  2.678201e-01   \n",
       "6    5.134833  0.048903  0.208168  0.176116      2.0 -0.240260  2.026776e-01   \n",
       "7    3.792690  0.094477  0.238362  0.194775      2.0 -0.283159  1.144903e-01   \n",
       "8    2.801357  0.137483  0.277953  0.223928      2.0 -0.338266 -2.157196e-02   \n",
       "9    2.069138  0.177107  0.298427  0.242143      2.0 -0.415078  1.225415e-08   \n",
       "10   1.528307  0.211422  0.315448  0.260895      2.0 -0.476221 -2.155736e-09   \n",
       "11   1.128838  0.240334  0.326192  0.275787      2.0 -0.523016 -1.386673e-08   \n",
       "12   0.833782  0.263130  0.328904  0.280867      2.0 -0.559580 -3.949152e-08   \n",
       "13   0.615848  0.280802  0.332579  0.289955      2.0 -0.609112 -1.073465e-08   \n",
       "14   0.454878  0.294985  0.337550  0.305899      2.0 -0.676097 -2.586948e-10   \n",
       "15   0.335982  0.306991  0.344283  0.333161      2.0 -0.766839 -2.405226e-09   \n",
       "16   0.248163  0.317929  0.353399  0.378062      2.0 -0.889698 -6.343712e-09   \n",
       "17   0.183298  0.327910  0.356984  0.398270      2.0 -1.000000 -4.364673e-09   \n",
       "18   0.135388  0.335509  0.356984  0.398270      2.0 -1.000000 -4.364673e-09   \n",
       "19   0.100000  0.341122  0.356984  0.398270      2.0 -1.000000 -4.364673e-09   \n",
       "\n",
       "             MCD           MMM      AAPL          MSFT           TXN  \\\n",
       "0  -3.372581e-01 -2.568931e-01 -0.163862  3.121972e-01  2.672053e-01   \n",
       "1  -3.337110e-01 -2.678514e-01 -0.155205  3.249477e-01  2.720653e-01   \n",
       "2  -3.289090e-01 -2.826883e-01 -0.143483  3.422096e-01  2.786448e-01   \n",
       "3  -3.224075e-01 -3.027758e-01 -0.127614  3.655803e-01  2.875527e-01   \n",
       "4  -2.750160e-01 -2.899102e-01  0.090165  3.390421e-01  2.548602e-01   \n",
       "5  -2.413193e-01 -3.146996e-01  0.106035  3.684833e-01  2.576615e-01   \n",
       "6  -1.956946e-01 -3.482634e-01  0.127514  4.083526e-01  2.614562e-01   \n",
       "7  -1.339346e-01 -3.936950e-01  0.156605  4.623162e-01  2.665886e-01   \n",
       "8  -4.161092e-02 -4.504126e-01  0.195031  5.335729e-01  2.713965e-01   \n",
       "9   1.700785e-08 -5.158707e-01  0.246381  5.503481e-01  2.032710e-01   \n",
       "10 -1.776444e-09 -5.237788e-01  0.309753  5.874734e-01  1.027733e-01   \n",
       "11 -1.262417e-08 -4.769844e-01  0.381652  6.183479e-01  9.418158e-08   \n",
       "12 -3.949057e-08 -4.404201e-01  0.438813  5.611868e-01  9.387767e-08   \n",
       "13 -9.791930e-09 -3.908876e-01  0.516269  4.837309e-01  1.521013e-08   \n",
       "14 -2.543143e-10 -3.239028e-01  0.621050  3.789498e-01 -5.996953e-11   \n",
       "15 -2.331970e-09 -2.331608e-01  0.762972  2.370280e-01 -8.019062e-10   \n",
       "16 -6.401940e-09 -1.103019e-01  0.955109  4.489092e-02 -1.726304e-09   \n",
       "17 -4.519869e-09 -4.854710e-09  1.000000  4.363003e-08 -1.498877e-09   \n",
       "18 -4.519869e-09 -4.854710e-09  1.000000  4.363003e-08 -1.498877e-09   \n",
       "19 -4.519869e-09 -4.854710e-09  1.000000  4.363003e-08 -1.498877e-09   \n",
       "\n",
       "            CSCO  \n",
       "0  -2.419872e-01  \n",
       "1  -2.432330e-01  \n",
       "2  -2.449195e-01  \n",
       "3  -2.472028e-01  \n",
       "4  -2.499033e-01  \n",
       "5  -2.354058e-01  \n",
       "6  -2.157824e-01  \n",
       "7  -1.892116e-01  \n",
       "8  -1.481390e-01  \n",
       "9  -6.905105e-02  \n",
       "10  8.620232e-10  \n",
       "11 -3.164627e-09  \n",
       "12 -1.070953e-08  \n",
       "13 -3.706820e-09  \n",
       "14 -1.062450e-10  \n",
       "15 -1.050058e-09  \n",
       "16 -2.656342e-09  \n",
       "17 -1.937569e-09  \n",
       "18 -1.937569e-09  \n",
       "19 -1.937569e-09  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the efficient frontier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_result.plot(x=\"risk\", y=\"return\", style=\"-o\", \n",
    "                    xlabel=\"portfolio risk (std. dev.)\", ylabel=\"portfolio return\", grid=True)   \n",
    "ax.legend([\"return\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the portfolio composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "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": [
    "my_cmap = LinearSegmentedColormap.from_list(\"non-extreme gray\", [\"#111111\", \"#eeeeee\"], N=256, gamma=1.0)\n",
    "ax = plt.gca()\n",
    "ax.set_xticks(df_result['risk'])\n",
    "df_result.set_index('risk').iloc[:, 4:].plot.bar(ax=ax, colormap=my_cmap, xlabel='portfolio risk (std. dev.)', ylabel=\"x\", stacked=True, width=1.0) \n",
    "ax.set_ylim([-1, 1])\n",
    "ax.grid(which='both', axis='x', linestyle=':', color='k', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}