python/notebooks/ch8_cvar_risk_measure.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": 32,
   "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",
    "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": 33,
   "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: 3.4.3\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": 34,
   "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": [
    "First we generate return scenario data using Monte Carlo method based on the moments of historical prices.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of scenarios\n",
    "T = 99999\n",
    "\n",
    "# Mean and covariance of historical log-returns.  \n",
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "\n",
    "# Generate logarithmic return scenarios assuming normal distribution\n",
    "scenarios_log = np.random.default_rng().multivariate_normal(m_log, S_log, T)\n",
    "    \n",
    "# Convert logarithmic return scenarios to linear return scenarios \n",
    "scenarios_lin = np.exp(scenarios_log) - 1\n",
    "\n",
    "# Scenario probabilities\n",
    "p = np.ones(T) / T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to optimize the 95% CVaR of the portfolio loss distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Confidence level\n",
    "alpha = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Primal CVaR formula (for testing)\n",
    "def CVaR(alpha, p, q):\n",
    "    \"\"\"\n",
    "    Computes CVaR using primal formula. \n",
    "    NOTE: Inputs p and q should be numpy arrays.\n",
    "    \"\"\"\n",
    "    # We need to be careful that math index starts from 1 but numpy starts from 0 (matters in formulas like ceil(alpha * T))\n",
    "    T = q.shape[0]\n",
    "    sort_idx = np.argsort(q)\n",
    "    sorted_q = q[sort_idx]\n",
    "    sorted_p = p[sort_idx]\n",
    "    \n",
    "    # Starting index \n",
    "    i_alpha = np.sort(np.nonzero(np.cumsum(sorted_p) >= alpha)[0])[0]\n",
    "\n",
    "    # Weight of VaR component in CVaR\n",
    "    lambda_alpha = (np.sum(sorted_p[:(i_alpha + 1)]) - alpha) / (1 - alpha)    \n",
    "    \n",
    "    # CVaR\n",
    "    var = sorted_q[i_alpha]\n",
    "    cvar = lambda_alpha * sorted_q[i_alpha] + np.dot(sorted_p[(i_alpha + 1):], sorted_q[(i_alpha + 1):]) / (1 - alpha)\n",
    "    \n",
    "    return var, cvar"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EfficientFrontier(N, T, m, R, p, alpha, 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 constrained to take only positive values. \n",
    "        x = M.variable(\"x\", N, Domain.greaterThan(0.0))\n",
    "        \n",
    "        # Budget constraint\n",
    "        M.constraint('budget', Expr.sum(x) == 1)\n",
    "        \n",
    "        # Auxiliary variables.\n",
    "        t = M.variable(\"t\", 1, Domain.unbounded())\n",
    "        u = M.variable(\"u\", T, Domain.unbounded())\n",
    "        \n",
    "        # Constraint modeling maximum\n",
    "        M.constraint(u >= - R * x - Var.repeat(t, T))\n",
    "        M.constraint(u >= 0)\n",
    "        \n",
    "        # Objective\n",
    "        delta = M.parameter()\n",
    "        cvar_term = t + u.T @ p / (1-alpha)\n",
    "        M.objective('obj', ObjectiveSense.Maximize, x.T @ m - delta * cvar_term)\n",
    "        \n",
    "        # Create DataFrame to store the results.\n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\"] + 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",
    "            # 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 = t.level()[0] + 1/(1-alpha) * np.dot(p, u.level())\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk] + list(x.level()), index=columns)\n",
    "            df_result = pd.concat([df_result, pd.DataFrame([row])], ignore_index=True)\n",
    "            \n",
    "            # Check CVaR value using primal formula \n",
    "            var, cvar = CVaR(alpha, p, -R @ x.level())\n",
    "            print(f\"\"\"Relative difference between primal and dual Var and CVaR (%): \n",
    "                      {(var - t.level()[0]) / t.level()[0] * 100:.6f}, \n",
    "                      {(cvar - portfolio_risk) / portfolio_risk * 100:.6f}\"\"\")\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": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Number of securities\n",
    "N = df_prices.shape[1]  \n",
    "\n",
    "# Get optimization parameters\n",
    "m, _ = compute_inputs(df_prices)"
   ]
  },
  {
   "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: $\\delta = 10^{-1},\\dots,10^{2}$. We compute the efficient frontier this way both with and without using shrinkage estimation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative difference between primal and dual CVaR (%): 1.4161786599716532e-10\n",
      "Relative difference between primal and dual VaR (%): (0.027736024649563776, 0.027736020959021005)\n",
      "Relative difference between primal and dual VaR (%): 1.3305956092774767e-05\n",
      "Relative difference between primal and dual CVaR (%): 8.15138839549319e-09\n",
      "Relative difference between primal and dual VaR (%): (0.027782504118262386, 0.027782503676069256)\n",
      "Relative difference between primal and dual VaR (%): 1.5916244788122963e-06\n",
      "Relative difference between primal and dual CVaR (%): 9.349080447210802e-10\n",
      "Relative difference between primal and dual VaR (%): (0.027640378059852443, 0.02764037080030987)\n",
      "Relative difference between primal and dual VaR (%): 2.6264273456005898e-05\n",
      "Relative difference between primal and dual CVaR (%): -8.446379175621044e-11\n",
      "Relative difference between primal and dual VaR (%): (0.027244346684367447, 0.027244346665335425)\n",
      "Relative difference between primal and dual VaR (%): 6.985677696569969e-08\n",
      "Relative difference between primal and dual CVaR (%): -9.670229848842551e-14\n",
      "Relative difference between primal and dual VaR (%): (0.027061830991095652, 0.02706183099109565)\n",
      "Relative difference between primal and dual VaR (%): 1.2820444237846255e-14\n",
      "Relative difference between primal and dual CVaR (%): -9.656064744700007e-14\n",
      "Relative difference between primal and dual VaR (%): (0.02697434371773732, 0.026974343717737315)\n",
      "Relative difference between primal and dual VaR (%): 1.2862025442614332e-14\n",
      "Relative difference between primal and dual CVaR (%): -7.847225581229282e-09\n",
      "Relative difference between primal and dual VaR (%): (0.026610510015199998, 0.026610479022667683)\n",
      "Relative difference between primal and dual VaR (%): 0.00011646739725518695\n",
      "Relative difference between primal and dual CVaR (%): -1.2306335246770936e-13\n",
      "Relative difference between primal and dual VaR (%): (0.026318601435351016, 0.02631860143535102)\n",
      "Relative difference between primal and dual VaR (%): -1.318248980849518e-14\n",
      "Relative difference between primal and dual CVaR (%): 8.932381736117497e-11\n",
      "Relative difference between primal and dual VaR (%): (0.02608483968139401, 0.02608483966623499)\n",
      "Relative difference between primal and dual VaR (%): 5.811429192844035e-08\n",
      "Relative difference between primal and dual CVaR (%): -1.0855046757503662e-13\n",
      "Relative difference between primal and dual VaR (%): (0.026109034506811207, 0.026109034506810798)\n",
      "Relative difference between primal and dual VaR (%): 1.568019454046575e-12\n",
      "Relative difference between primal and dual CVaR (%): -3.3671708057422144e-13\n",
      "Relative difference between primal and dual VaR (%): (0.026391756127036592, 0.026391756126147175)\n",
      "Relative difference between primal and dual VaR (%): 3.370056194861539e-09\n",
      "Relative difference between primal and dual CVaR (%): 8.131853565211792e-12\n",
      "Relative difference between primal and dual VaR (%): (0.02835914792690567, 0.028359147908546285)\n",
      "Relative difference between primal and dual VaR (%): 6.473885247401805e-08\n",
      "Relative difference between primal and dual CVaR (%): -7.700017951949094e-10\n",
      "Relative difference between primal and dual VaR (%): (0.03083294001833356, 0.030832943755536232)\n",
      "Relative difference between primal and dual VaR (%): -1.2120810461557004e-05\n",
      "Relative difference between primal and dual CVaR (%): -1.3003779112892375e-11\n",
      "Relative difference between primal and dual VaR (%): (0.0352050685018132, 0.03520506850464213)\n",
      "Relative difference between primal and dual VaR (%): -8.035580254867914e-09\n",
      "Relative difference between primal and dual CVaR (%): 6.926891733814434e-13\n",
      "Relative difference between primal and dual VaR (%): (0.03602093141411038, 0.03602093140898131)\n",
      "Relative difference between primal and dual VaR (%): 1.423912053266804e-08\n",
      "Relative difference between primal and dual CVaR (%): -1.594610719751977e-08\n",
      "Relative difference between primal and dual VaR (%): (0.03921916192379876, 0.03921926269488027)\n",
      "Relative difference between primal and dual VaR (%): -0.00025694282498643997\n",
      "Relative difference between primal and dual CVaR (%): -1.6845650698718496e-13\n",
      "Relative difference between primal and dual VaR (%): (0.0486096008879829, 0.0486096008879829)\n",
      "Relative difference between primal and dual VaR (%): 0.0\n",
      "Relative difference between primal and dual CVaR (%): -2.126398348456684e-13\n",
      "Relative difference between primal and dual VaR (%): (0.07129700005939439, 0.07129700005939435)\n",
      "Relative difference between primal and dual VaR (%): 5.839427099143087e-14\n",
      "Relative difference between primal and dual CVaR (%): -6.286670119365858e-11\n",
      "Relative difference between primal and dual VaR (%): (0.12183111805677034, 0.12183111797818938)\n",
      "Relative difference between primal and dual VaR (%): 6.449990978215029e-08\n",
      "Relative difference between primal and dual CVaR (%): 6.043802806539741e-08\n",
      "Relative difference between primal and dual VaR (%): (0.1402178351621846, 0.1402318789321507)\n",
      "Relative difference between primal and dual VaR (%): -0.010014677171142478\n"
     ]
    }
   ],
   "source": [
    "# Compute efficient frontier with and without shrinkage\n",
    "deltas = np.logspace(start=-1, stop=2, num=20)[::-1]\n",
    "df_result = EfficientFrontier(N, T, m, scenarios_lin, p, alpha, deltas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <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>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",
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       "      <td>0.364457</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>69.519280</td>\n",
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       "      <td>0.100356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104977</td>\n",
       "      <td>0.601108</td>\n",
       "      <td>0.221721</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.068695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105930</td>\n",
       "      <td>0.603444</td>\n",
       "      <td>0.221931</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.598183</td>\n",
       "      <td>-3.005991</td>\n",
       "      <td>0.367486</td>\n",
       "      <td>0.100407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.062622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109659</td>\n",
       "      <td>0.606404</td>\n",
       "      <td>0.221315</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.357215</td>\n",
       "      <td>-1.977525</td>\n",
       "      <td>0.368877</td>\n",
       "      <td>0.100457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111215</td>\n",
       "      <td>0.611089</td>\n",
       "      <td>0.221161</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.237767</td>\n",
       "      <td>-1.261894</td>\n",
       "      <td>0.371701</td>\n",
       "      <td>0.100605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115894</td>\n",
       "      <td>0.619923</td>\n",
       "      <td>0.219214</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.288379</td>\n",
       "      <td>-0.763400</td>\n",
       "      <td>0.375156</td>\n",
       "      <td>0.100861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120586</td>\n",
       "      <td>0.631022</td>\n",
       "      <td>0.218189</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.847600</td>\n",
       "      <td>-0.415547</td>\n",
       "      <td>0.380925</td>\n",
       "      <td>0.101493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129747</td>\n",
       "      <td>0.647615</td>\n",
       "      <td>0.217071</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.455595</td>\n",
       "      <td>-0.172264</td>\n",
       "      <td>0.383513</td>\n",
       "      <td>0.101873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.656094</td>\n",
       "      <td>0.203259</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.792690</td>\n",
       "      <td>-0.002597</td>\n",
       "      <td>0.385309</td>\n",
       "      <td>0.102277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151508</td>\n",
       "      <td>0.664952</td>\n",
       "      <td>0.183540</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.636651</td>\n",
       "      <td>0.116030</td>\n",
       "      <td>0.387704</td>\n",
       "      <td>0.103037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166825</td>\n",
       "      <td>0.675518</td>\n",
       "      <td>0.157657</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.832981</td>\n",
       "      <td>0.199454</td>\n",
       "      <td>0.391836</td>\n",
       "      <td>0.104956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196281</td>\n",
       "      <td>0.689182</td>\n",
       "      <td>0.114537</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.274275</td>\n",
       "      <td>0.258896</td>\n",
       "      <td>0.396843</td>\n",
       "      <td>0.108256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.231437</td>\n",
       "      <td>0.706544</td>\n",
       "      <td>0.062019</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.885867</td>\n",
       "      <td>0.302062</td>\n",
       "      <td>0.403032</td>\n",
       "      <td>0.113978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.280614</td>\n",
       "      <td>0.719386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.615848</td>\n",
       "      <td>0.333074</td>\n",
       "      <td>0.404636</td>\n",
       "      <td>0.116201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.325144</td>\n",
       "      <td>0.674856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.428133</td>\n",
       "      <td>0.355268</td>\n",
       "      <td>0.407087</td>\n",
       "      <td>0.121035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.393136</td>\n",
       "      <td>0.606864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.297635</td>\n",
       "      <td>0.371645</td>\n",
       "      <td>0.410877</td>\n",
       "      <td>0.131811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.498296</td>\n",
       "      <td>0.501704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.206914</td>\n",
       "      <td>0.384508</td>\n",
       "      <td>0.416918</td>\n",
       "      <td>0.156634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.665914</td>\n",
       "      <td>0.334086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.143845</td>\n",
       "      <td>0.395782</td>\n",
       "      <td>0.426049</td>\n",
       "      <td>0.210418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.919297</td>\n",
       "      <td>0.080703</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.405799</td>\n",
       "      <td>0.428958</td>\n",
       "      <td>0.231588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         delta       obj    return      risk   PM  LMT       MCD  MMM  \\\n",
       "0   100.000000 -9.670191  0.364457  0.100346  0.0  0.0  0.075496  0.0   \n",
       "1    69.519280 -6.611442  0.365206  0.100356  0.0  0.0  0.072194  0.0   \n",
       "2    48.329302 -4.484791  0.365987  0.100369  0.0  0.0  0.068695  0.0   \n",
       "3    33.598183 -3.005991  0.367486  0.100407  0.0  0.0  0.062622  0.0   \n",
       "4    23.357215 -1.977525  0.368877  0.100457  0.0  0.0  0.056535  0.0   \n",
       "5    16.237767 -1.261894  0.371701  0.100605  0.0  0.0  0.044969  0.0   \n",
       "6    11.288379 -0.763400  0.375156  0.100861  0.0  0.0  0.030203  0.0   \n",
       "7     7.847600 -0.415547  0.380925  0.101493  0.0  0.0  0.005567  0.0   \n",
       "8     5.455595 -0.172264  0.383513  0.101873  0.0  0.0  0.000000  0.0   \n",
       "9     3.792690 -0.002597  0.385309  0.102277  0.0  0.0  0.000000  0.0   \n",
       "10    2.636651  0.116030  0.387704  0.103037  0.0  0.0  0.000000  0.0   \n",
       "11    1.832981  0.199454  0.391836  0.104956  0.0  0.0  0.000000  0.0   \n",
       "12    1.274275  0.258896  0.396843  0.108256  0.0  0.0  0.000000  0.0   \n",
       "13    0.885867  0.302062  0.403032  0.113978  0.0  0.0  0.000000  0.0   \n",
       "14    0.615848  0.333074  0.404636  0.116201  0.0  0.0  0.000000  0.0   \n",
       "15    0.428133  0.355268  0.407087  0.121035  0.0  0.0  0.000000  0.0   \n",
       "16    0.297635  0.371645  0.410877  0.131811  0.0  0.0  0.000000  0.0   \n",
       "17    0.206914  0.384508  0.416918  0.156634  0.0  0.0  0.000000  0.0   \n",
       "18    0.143845  0.395782  0.426049  0.210418  0.0  0.0  0.000000  0.0   \n",
       "19    0.100000  0.405799  0.428958  0.231588  0.0  0.0  0.000000  0.0   \n",
       "\n",
       "        AAPL      MSFT       TXN  CSCO  \n",
       "0   0.103786  0.599174  0.221543   0.0  \n",
       "1   0.104977  0.601108  0.221721   0.0  \n",
       "2   0.105930  0.603444  0.221931   0.0  \n",
       "3   0.109659  0.606404  0.221315   0.0  \n",
       "4   0.111215  0.611089  0.221161   0.0  \n",
       "5   0.115894  0.619923  0.219214   0.0  \n",
       "6   0.120586  0.631022  0.218189   0.0  \n",
       "7   0.129747  0.647615  0.217071   0.0  \n",
       "8   0.140647  0.656094  0.203259   0.0  \n",
       "9   0.151508  0.664952  0.183540   0.0  \n",
       "10  0.166825  0.675518  0.157657   0.0  \n",
       "11  0.196281  0.689182  0.114537   0.0  \n",
       "12  0.231437  0.706544  0.062019   0.0  \n",
       "13  0.280614  0.719386  0.000000   0.0  \n",
       "14  0.325144  0.674856  0.000000   0.0  \n",
       "15  0.393136  0.606864  0.000000   0.0  \n",
       "16  0.498296  0.501704  0.000000   0.0  \n",
       "17  0.665914  0.334086  0.000000   0.0  \n",
       "18  0.919297  0.080703  0.000000   0.0  \n",
       "19  1.000000  0.000000  0.000000   0.0  "
      ]
     },
     "execution_count": 43,
     "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": 44,
   "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": [
    "ax = df_result.plot(x=\"risk\", y=\"return\", style=\"-o\", \n",
    "                    xlabel=\"portfolio risk (CVaR)\", ylabel=\"portfolio return\", grid=True)   \n",
    "ax.legend([\"return\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the portfolio composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round small values to 0 to make plotting work\n",
    "mask = np.absolute(df_result) < 1e-7\n",
    "mask.iloc[:, :-8] = False\n",
    "df_result[mask] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": [
    "my_cmap = LinearSegmentedColormap.from_list(\"non-extreme gray\", [\"#111111\", \"#eeeeee\"], N=256, gamma=1.0)\n",
    "ax = df_result.set_index('risk').iloc[:, 3:].plot.area(colormap=my_cmap, xlabel='portfolio risk (CVaR)', ylabel=\"x\") \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
}