python/notebooks/ch8_evar_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": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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% EVaR of the portfolio loss distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Confidence level\n",
    "alpha = 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Primal CVaR formula (for testing)\n",
    "def CVaR(alpha, p, q):\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",
    "    \n",
    "    # Starting index \n",
    "    i_alpha = np.sort(np.nonzero(np.cumsum(p) >= alpha)[0])[0]\n",
    "\n",
    "    # Weight of VaR component in CVaR\n",
    "    lambda_alpha = (sum(p[:(i_alpha + 1)]) - alpha) / (1 - alpha)    \n",
    "    \n",
    "    # CVaR\n",
    "    sort_idx = np.argsort(q)\n",
    "    sorted_q = q[sort_idx]\n",
    "    sorted_p = p[sort_idx]\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 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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s * log(sum_i(p_i * exp(x_i / s))) <= t\n",
    "def persplogsumexp(M, x, s, p, t):\n",
    "    n = int(x.getSize())\n",
    "    u = M.variable(n)\n",
    "    M.constraint(Expr.hstack(u, Var.repeat(s, n), x - Var.repeat(t, n)), Domain.inPExpCone())\n",
    "    M.constraint(u.T @ p <= s)\n",
    "    \n",
    "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), Domain.equalsTo(1.0))\n",
    "        \n",
    "        # Auxiliary variables.\n",
    "        z = M.variable(\"z\", 1, Domain.unbounded())\n",
    "        t = M.variable(\"t\", 1, Domain.greaterThan(0.0))\n",
    "        \n",
    "        # Constraint modeling perspective of log-sum-exp\n",
    "        persplogsumexp(M, -R * x, t, p, z)\n",
    "        \n",
    "        # Objective\n",
    "        delta = M.parameter()\n",
    "        evar_term = z - t * np.log(1.0 - alpha)\n",
    "        M.objective('obj', ObjectiveSense.Maximize, x.T @ m - delta * evar_term)\n",
    "        \n",
    "        # Create DataFrame to store the results.\n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\", \"evar\"] + 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 = z.level()[0] - t.level()[0] * np.log(1.0 - alpha)\n",
    "            cvar = CVaR(alpha, p, -R @ x.level())\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk, cvar] + 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",
    "            print(f\"Relative difference between EVaR and CVaR (%): {(cvar - portfolio_risk) / portfolio_risk * 100}\")\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": 19,
   "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": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative difference between EVaR and CVaR (%): -33.42378204398512\n",
      "Relative difference between EVaR and CVaR (%): -33.45646972266584\n",
      "Relative difference between EVaR and CVaR (%): -33.50204787771225\n",
      "Relative difference between EVaR and CVaR (%): -33.56488606024007\n",
      "Relative difference between EVaR and CVaR (%): -33.65252039086718\n",
      "Relative difference between EVaR and CVaR (%): -33.77210072922806\n",
      "Relative difference between EVaR and CVaR (%): -33.90833145658367\n",
      "Relative difference between EVaR and CVaR (%): -34.06647761773668\n",
      "Relative difference between EVaR and CVaR (%): -34.228864613910865\n",
      "Relative difference between EVaR and CVaR (%): -34.32076597469258\n",
      "Relative difference between EVaR and CVaR (%): -34.26903206118584\n",
      "Relative difference between EVaR and CVaR (%): -34.0913142099972\n",
      "Relative difference between EVaR and CVaR (%): -33.706820776915826\n",
      "Relative difference between EVaR and CVaR (%): -32.87550621993\n",
      "Relative difference between EVaR and CVaR (%): -32.13821514072266\n",
      "Relative difference between EVaR and CVaR (%): -31.414840069754284\n",
      "Relative difference between EVaR and CVaR (%): -29.94969115976413\n",
      "Relative difference between EVaR and CVaR (%): -27.216047490936095\n",
      "Relative difference between EVaR and CVaR (%): -23.044884017560292\n",
      "Relative difference between EVaR and CVaR (%): -20.845617388377192\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": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<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>evar</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>100.000000</td>\n",
       "      <td>-14.809388</td>\n",
       "      <td>0.347111</td>\n",
       "      <td>0.151565</td>\n",
       "      <td>0.100906</td>\n",
       "      <td>-2.712381e-10</td>\n",
       "      <td>1.494921e-02</td>\n",
       "      <td>1.363686e-01</td>\n",
       "      <td>2.281447e-10</td>\n",
       "      <td>0.090220</td>\n",
       "      <td>5.360870e-01</td>\n",
       "      <td>2.223747e-01</td>\n",
       "      <td>3.692416e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.519280</td>\n",
       "      <td>-10.189479</td>\n",
       "      <td>0.347760</td>\n",
       "      <td>0.151573</td>\n",
       "      <td>0.100862</td>\n",
       "      <td>-1.494799e-10</td>\n",
       "      <td>1.380761e-02</td>\n",
       "      <td>1.347412e-01</td>\n",
       "      <td>8.227581e-10</td>\n",
       "      <td>0.090979</td>\n",
       "      <td>5.380318e-01</td>\n",
       "      <td>2.224405e-01</td>\n",
       "      <td>2.026158e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48.329302</td>\n",
       "      <td>-6.977512</td>\n",
       "      <td>0.348694</td>\n",
       "      <td>0.151589</td>\n",
       "      <td>0.100804</td>\n",
       "      <td>-2.945664e-10</td>\n",
       "      <td>1.216126e-02</td>\n",
       "      <td>1.323962e-01</td>\n",
       "      <td>-2.890110e-10</td>\n",
       "      <td>0.092075</td>\n",
       "      <td>5.408365e-01</td>\n",
       "      <td>2.225308e-01</td>\n",
       "      <td>5.209527e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.598183</td>\n",
       "      <td>-4.744225</td>\n",
       "      <td>0.350042</td>\n",
       "      <td>0.151623</td>\n",
       "      <td>0.100731</td>\n",
       "      <td>-1.268907e-10</td>\n",
       "      <td>9.786780e-03</td>\n",
       "      <td>1.290179e-01</td>\n",
       "      <td>-1.236874e-10</td>\n",
       "      <td>0.093662</td>\n",
       "      <td>5.448811e-01</td>\n",
       "      <td>2.226519e-01</td>\n",
       "      <td>-5.141085e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.357215</td>\n",
       "      <td>-3.191162</td>\n",
       "      <td>0.351989</td>\n",
       "      <td>0.151694</td>\n",
       "      <td>0.100645</td>\n",
       "      <td>9.499372e-11</td>\n",
       "      <td>6.357014e-03</td>\n",
       "      <td>1.241467e-01</td>\n",
       "      <td>1.313828e-09</td>\n",
       "      <td>0.095965</td>\n",
       "      <td>5.507230e-01</td>\n",
       "      <td>2.228084e-01</td>\n",
       "      <td>7.211346e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.237767</td>\n",
       "      <td>-2.110756</td>\n",
       "      <td>0.354809</td>\n",
       "      <td>0.151841</td>\n",
       "      <td>0.100561</td>\n",
       "      <td>-3.079113e-10</td>\n",
       "      <td>1.390277e-03</td>\n",
       "      <td>1.171098e-01</td>\n",
       "      <td>-2.952811e-10</td>\n",
       "      <td>0.099325</td>\n",
       "      <td>5.591803e-01</td>\n",
       "      <td>2.229945e-01</td>\n",
       "      <td>-1.388656e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.288379</td>\n",
       "      <td>-1.358693</td>\n",
       "      <td>0.358261</td>\n",
       "      <td>0.152099</td>\n",
       "      <td>0.100525</td>\n",
       "      <td>-4.289182e-10</td>\n",
       "      <td>1.324001e-07</td>\n",
       "      <td>1.038537e-01</td>\n",
       "      <td>-3.300871e-10</td>\n",
       "      <td>0.104016</td>\n",
       "      <td>5.700917e-01</td>\n",
       "      <td>2.220389e-01</td>\n",
       "      <td>4.122753e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.847600</td>\n",
       "      <td>-0.834635</td>\n",
       "      <td>0.363036</td>\n",
       "      <td>0.152616</td>\n",
       "      <td>0.100625</td>\n",
       "      <td>-7.075282e-10</td>\n",
       "      <td>1.695273e-08</td>\n",
       "      <td>8.366961e-02</td>\n",
       "      <td>-2.133383e-10</td>\n",
       "      <td>0.110821</td>\n",
       "      <td>5.854145e-01</td>\n",
       "      <td>2.200944e-01</td>\n",
       "      <td>4.496131e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.455595</td>\n",
       "      <td>-0.468526</td>\n",
       "      <td>0.369963</td>\n",
       "      <td>0.153693</td>\n",
       "      <td>0.101086</td>\n",
       "      <td>-6.042409e-10</td>\n",
       "      <td>5.839406e-08</td>\n",
       "      <td>5.453391e-02</td>\n",
       "      <td>1.396929e-10</td>\n",
       "      <td>0.120911</td>\n",
       "      <td>6.076218e-01</td>\n",
       "      <td>2.169333e-01</td>\n",
       "      <td>1.057438e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.792690</td>\n",
       "      <td>-0.211414</td>\n",
       "      <td>0.380061</td>\n",
       "      <td>0.155952</td>\n",
       "      <td>0.102428</td>\n",
       "      <td>4.562780e-09</td>\n",
       "      <td>1.048043e-07</td>\n",
       "      <td>1.233776e-02</td>\n",
       "      <td>9.746963e-09</td>\n",
       "      <td>0.135994</td>\n",
       "      <td>6.399893e-01</td>\n",
       "      <td>2.116789e-01</td>\n",
       "      <td>7.122438e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.636651</td>\n",
       "      <td>-0.030093</td>\n",
       "      <td>0.384901</td>\n",
       "      <td>0.157394</td>\n",
       "      <td>0.103457</td>\n",
       "      <td>-2.675437e-10</td>\n",
       "      <td>8.275791e-09</td>\n",
       "      <td>1.358343e-07</td>\n",
       "      <td>7.311167e-10</td>\n",
       "      <td>0.153269</td>\n",
       "      <td>6.565735e-01</td>\n",
       "      <td>1.901573e-01</td>\n",
       "      <td>7.955828e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.832981</td>\n",
       "      <td>0.096914</td>\n",
       "      <td>0.388271</td>\n",
       "      <td>0.158953</td>\n",
       "      <td>0.104764</td>\n",
       "      <td>1.299553e-09</td>\n",
       "      <td>4.993040e-08</td>\n",
       "      <td>4.729194e-07</td>\n",
       "      <td>2.645259e-09</td>\n",
       "      <td>0.176696</td>\n",
       "      <td>6.686228e-01</td>\n",
       "      <td>1.546808e-01</td>\n",
       "      <td>3.657430e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.274275</td>\n",
       "      <td>0.186450</td>\n",
       "      <td>0.393112</td>\n",
       "      <td>0.162180</td>\n",
       "      <td>0.107514</td>\n",
       "      <td>-2.211452e-10</td>\n",
       "      <td>6.756127e-10</td>\n",
       "      <td>4.488115e-08</td>\n",
       "      <td>9.512924e-10</td>\n",
       "      <td>0.211567</td>\n",
       "      <td>6.840754e-01</td>\n",
       "      <td>1.043575e-01</td>\n",
       "      <td>4.037568e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.885867</td>\n",
       "      <td>0.250502</td>\n",
       "      <td>0.400035</td>\n",
       "      <td>0.168798</td>\n",
       "      <td>0.113305</td>\n",
       "      <td>2.755646e-09</td>\n",
       "      <td>-1.266690e-11</td>\n",
       "      <td>2.424045e-08</td>\n",
       "      <td>3.777747e-10</td>\n",
       "      <td>0.264047</td>\n",
       "      <td>7.022571e-01</td>\n",
       "      <td>3.369572e-02</td>\n",
       "      <td>2.769004e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.615848</td>\n",
       "      <td>0.297040</td>\n",
       "      <td>0.404439</td>\n",
       "      <td>0.174391</td>\n",
       "      <td>0.118345</td>\n",
       "      <td>-2.052848e-11</td>\n",
       "      <td>-1.011557e-11</td>\n",
       "      <td>1.175816e-11</td>\n",
       "      <td>-1.748460e-11</td>\n",
       "      <td>0.319658</td>\n",
       "      <td>6.803416e-01</td>\n",
       "      <td>8.585560e-10</td>\n",
       "      <td>-6.242794e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.428133</td>\n",
       "      <td>0.330133</td>\n",
       "      <td>0.406807</td>\n",
       "      <td>0.179091</td>\n",
       "      <td>0.122830</td>\n",
       "      <td>5.774546e-10</td>\n",
       "      <td>3.875773e-10</td>\n",
       "      <td>2.384531e-08</td>\n",
       "      <td>1.150304e-09</td>\n",
       "      <td>0.385380</td>\n",
       "      <td>6.146204e-01</td>\n",
       "      <td>2.126529e-08</td>\n",
       "      <td>2.074802e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.297635</td>\n",
       "      <td>0.354048</td>\n",
       "      <td>0.410417</td>\n",
       "      <td>0.189392</td>\n",
       "      <td>0.132669</td>\n",
       "      <td>6.395015e-10</td>\n",
       "      <td>6.525280e-10</td>\n",
       "      <td>1.226104e-09</td>\n",
       "      <td>2.599322e-09</td>\n",
       "      <td>0.485545</td>\n",
       "      <td>5.144548e-01</td>\n",
       "      <td>3.869882e-08</td>\n",
       "      <td>3.423996e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.206914</td>\n",
       "      <td>0.372056</td>\n",
       "      <td>0.415926</td>\n",
       "      <td>0.212022</td>\n",
       "      <td>0.154318</td>\n",
       "      <td>6.981106e-10</td>\n",
       "      <td>1.975800e-09</td>\n",
       "      <td>5.781582e-09</td>\n",
       "      <td>3.504131e-10</td>\n",
       "      <td>0.638413</td>\n",
       "      <td>3.615865e-01</td>\n",
       "      <td>1.405329e-08</td>\n",
       "      <td>2.153144e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.143845</td>\n",
       "      <td>0.386679</td>\n",
       "      <td>0.424172</td>\n",
       "      <td>0.260645</td>\n",
       "      <td>0.200580</td>\n",
       "      <td>2.219064e-09</td>\n",
       "      <td>6.181270e-09</td>\n",
       "      <td>1.355847e-08</td>\n",
       "      <td>4.917751e-09</td>\n",
       "      <td>0.867205</td>\n",
       "      <td>1.327948e-01</td>\n",
       "      <td>1.368773e-08</td>\n",
       "      <td>2.201932e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.399278</td>\n",
       "      <td>0.428958</td>\n",
       "      <td>0.296801</td>\n",
       "      <td>0.234931</td>\n",
       "      <td>-1.725104e-12</td>\n",
       "      <td>1.257411e-11</td>\n",
       "      <td>2.114564e-11</td>\n",
       "      <td>-4.253230e-13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.343903e-08</td>\n",
       "      <td>1.405113e-10</td>\n",
       "      <td>4.463059e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         delta        obj    return      risk      evar            PM  \\\n",
       "0   100.000000 -14.809388  0.347111  0.151565  0.100906 -2.712381e-10   \n",
       "1    69.519280 -10.189479  0.347760  0.151573  0.100862 -1.494799e-10   \n",
       "2    48.329302  -6.977512  0.348694  0.151589  0.100804 -2.945664e-10   \n",
       "3    33.598183  -4.744225  0.350042  0.151623  0.100731 -1.268907e-10   \n",
       "4    23.357215  -3.191162  0.351989  0.151694  0.100645  9.499372e-11   \n",
       "5    16.237767  -2.110756  0.354809  0.151841  0.100561 -3.079113e-10   \n",
       "6    11.288379  -1.358693  0.358261  0.152099  0.100525 -4.289182e-10   \n",
       "7     7.847600  -0.834635  0.363036  0.152616  0.100625 -7.075282e-10   \n",
       "8     5.455595  -0.468526  0.369963  0.153693  0.101086 -6.042409e-10   \n",
       "9     3.792690  -0.211414  0.380061  0.155952  0.102428  4.562780e-09   \n",
       "10    2.636651  -0.030093  0.384901  0.157394  0.103457 -2.675437e-10   \n",
       "11    1.832981   0.096914  0.388271  0.158953  0.104764  1.299553e-09   \n",
       "12    1.274275   0.186450  0.393112  0.162180  0.107514 -2.211452e-10   \n",
       "13    0.885867   0.250502  0.400035  0.168798  0.113305  2.755646e-09   \n",
       "14    0.615848   0.297040  0.404439  0.174391  0.118345 -2.052848e-11   \n",
       "15    0.428133   0.330133  0.406807  0.179091  0.122830  5.774546e-10   \n",
       "16    0.297635   0.354048  0.410417  0.189392  0.132669  6.395015e-10   \n",
       "17    0.206914   0.372056  0.415926  0.212022  0.154318  6.981106e-10   \n",
       "18    0.143845   0.386679  0.424172  0.260645  0.200580  2.219064e-09   \n",
       "19    0.100000   0.399278  0.428958  0.296801  0.234931 -1.725104e-12   \n",
       "\n",
       "             LMT           MCD           MMM      AAPL          MSFT  \\\n",
       "0   1.494921e-02  1.363686e-01  2.281447e-10  0.090220  5.360870e-01   \n",
       "1   1.380761e-02  1.347412e-01  8.227581e-10  0.090979  5.380318e-01   \n",
       "2   1.216126e-02  1.323962e-01 -2.890110e-10  0.092075  5.408365e-01   \n",
       "3   9.786780e-03  1.290179e-01 -1.236874e-10  0.093662  5.448811e-01   \n",
       "4   6.357014e-03  1.241467e-01  1.313828e-09  0.095965  5.507230e-01   \n",
       "5   1.390277e-03  1.171098e-01 -2.952811e-10  0.099325  5.591803e-01   \n",
       "6   1.324001e-07  1.038537e-01 -3.300871e-10  0.104016  5.700917e-01   \n",
       "7   1.695273e-08  8.366961e-02 -2.133383e-10  0.110821  5.854145e-01   \n",
       "8   5.839406e-08  5.453391e-02  1.396929e-10  0.120911  6.076218e-01   \n",
       "9   1.048043e-07  1.233776e-02  9.746963e-09  0.135994  6.399893e-01   \n",
       "10  8.275791e-09  1.358343e-07  7.311167e-10  0.153269  6.565735e-01   \n",
       "11  4.993040e-08  4.729194e-07  2.645259e-09  0.176696  6.686228e-01   \n",
       "12  6.756127e-10  4.488115e-08  9.512924e-10  0.211567  6.840754e-01   \n",
       "13 -1.266690e-11  2.424045e-08  3.777747e-10  0.264047  7.022571e-01   \n",
       "14 -1.011557e-11  1.175816e-11 -1.748460e-11  0.319658  6.803416e-01   \n",
       "15  3.875773e-10  2.384531e-08  1.150304e-09  0.385380  6.146204e-01   \n",
       "16  6.525280e-10  1.226104e-09  2.599322e-09  0.485545  5.144548e-01   \n",
       "17  1.975800e-09  5.781582e-09  3.504131e-10  0.638413  3.615865e-01   \n",
       "18  6.181270e-09  1.355847e-08  4.917751e-09  0.867205  1.327948e-01   \n",
       "19  1.257411e-11  2.114564e-11 -4.253230e-13  1.000000  1.343903e-08   \n",
       "\n",
       "             TXN          CSCO  \n",
       "0   2.223747e-01  3.692416e-08  \n",
       "1   2.224405e-01  2.026158e-08  \n",
       "2   2.225308e-01  5.209527e-10  \n",
       "3   2.226519e-01 -5.141085e-11  \n",
       "4   2.228084e-01  7.211346e-08  \n",
       "5   2.229945e-01 -1.388656e-10  \n",
       "6   2.220389e-01  4.122753e-09  \n",
       "7   2.200944e-01  4.496131e-09  \n",
       "8   2.169333e-01  1.057438e-08  \n",
       "9   2.116789e-01  7.122438e-08  \n",
       "10  1.901573e-01  7.955828e-09  \n",
       "11  1.546808e-01  3.657430e-08  \n",
       "12  1.043575e-01  4.037568e-09  \n",
       "13  3.369572e-02  2.769004e-09  \n",
       "14  8.585560e-10 -6.242794e-12  \n",
       "15  2.126529e-08  2.074802e-09  \n",
       "16  3.869882e-08  3.423996e-09  \n",
       "17  1.405329e-08  2.153144e-09  \n",
       "18  1.368773e-08  2.201932e-09  \n",
       "19  1.405113e-10  4.463059e-12  "
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fyWDa++uSPK+19mcLNTDk1qF1gTwAADDRBPIAAABMmk/vZf+nuq8nVdWaocdnpvf+cmvtpvlObq1tz+6pwxeaEnwxU2eP2qhfw74+f7L3KeOH9y+nhutaa9uWcf7HM7jXeZL8RlV9qqp+saqeOOv7Y28ekeTSJA/LYPT/01prf7eMumYcNbS+lED+3a21WsTy0blO7m6JMPM98ryqmj1d/3BIf+FcbVTVo6rqf1fVNzIIwb+Y5OrsHp1/XnfoQRnMBLAvWpL3Z3CLgMUafh8Pm/coAACACSCQBwAAYNLsbVrvzd3Xyp4h4EzwuZhpwW+cdc5cVnI084xRv4Z9ff7F1HDj0PpyaljW+9xa25HkmUn+qXvocUnemEFQ/62q+lBVvaCqVu2lqY3ZfbuAl3ezMYzC8IcNDh1Rm4s1c1/4wzM0rXx3X/sXdpvfSPLXs0+sqpcl+WwGsw4sZhT63l7bWUke1S1nJHlpkisz6Me/nOS3FvEccz3XjiWcBwAAsOIE8gAAAEyaee9bvULnz5hzuvYVMqrXsBwrVcOy3+fW2uczCHqfm8FU/1/sdh2aQRD8viSfrKr7LdDMxUnu7NbfXlWPXG5dneGZDkb54YnF+D8ZTA+f7Dki/klJjp85ZvatCbrp7H83yeoMPpjx6gxuZ3B0kjUzo/OTvGz4tL3Ucm1r7epuuby19q4kj0/y4W7/K6rquYt8XcPv47cWeQ4AAMBYCOQBAACYNBsWub9lz9HVt87av5CZEb+3LnjUyhv3axhua281DI+aHvv72Frb2Vq7qLX2stbaiRmMdn9pkiu6Q05Lcv4CTVye5BlJtia5X5K/raqHj6C04UB+X6d13yettW9m8EGDJHl6Vc38mw6H8+/Jvb04gzB+Z5Int9be1Fr7bGvt1m5GghnL+oBB19aLk8zcv/5Ni7gXfbLn+/i15dQAAADQN4E8AAAAk+Zxi9x/XXcv9RlXd18fUlXHzHdyF/g9dtY5+2rUo8jH8Rrmev4k+e69HPv4ec6bCK21b3SjsM/IYOr1JPnBqpp3avXW2scymP7+rgw+cPCRqjpxmXXcneS6bvOk5bS1j2YC91VJfrSq1iZ5fvfY51pr/zjHOScP7f/nBdr+ruUW11r7RpLf7DZPyJ6j7ucz8z5+ubW2dbk1AAAA9EkgDwAAwKT5sfl2VNXjkpzSbf7NrN0z25XBfa/n8/wkR87TxlIN3x/8kGW2lYznNQy7IrunAP+xqprzukFVrc/gnutJ8vkuVJ1I3Sjsj3Wbq5PcZy/H/12SZ2fwb/vtGYTyD11mGZd0X/f2YZM+/Hl2/5ueneRZ2f29M9fo+GTwPiXJYfM1WlXf3rU1Cm9Ncke3/pqqWr3Qwdn9QYBPjuj5AQAAeiOQBwAAYNI8q6o2zn6wqg7P7inHd+Xe049flORfu/VfqqpHzdHGA5O8qdvcmuRdy6x1OIhebmibjOc1/LtuNPc7u81TkvzKHM9fSd6e5L7dQ28f1fPvi6o6s6oetsD+NUme3G3ekT2nkJ9Ta+2vkzwnyd1JjssglD9hGWXOBPL3raqHLKOdJWutbUvyR93mdyb5/7r1nUneN89pMyP6T6yqJ8zeWVXrMrg//byzDSyxxlszuGd9Mri3/dnzHdv9O8x87314vuMAAAAmhUAeAACASfOZJP+nqn67qp5aVadV1Uu6x2emaf/t2VNtd9PX/0QG08gfkeTSqvqVqnpCVX13Vf1s18b9u1N+vrV28zJr/YfsHiX/hqp6elWdVFUP65YlBZZjeg2z/WqS67v186rqj6vqGVX1nVX1Q0n+Lsl/6vZfluT3Rvz8S/W0JF+oqo9W1aur6qyu1id23zeXZBBEJ8n/aq3ds5hGW2sXJ3leku1JHpjk76rq+H2s8YNJZu69/rRFnnOfqjplEcsjFtHW8Ej4menoP9xa2zzP8Rd2Xw9K8oGqem1VPamqHl9VL09yZZKnJLl0ka9lMd6c3X3pNfPNzpDd7989Sf5yhM8PAADQi71NAQYAAAArbWOSv03yim6Z7U+SnDvXia21D3Qh7PlJ1mcQLv/qrMN2JvmV1to7lltoa+32qnpbkl/IIPSdPWL3qUk+usQ2V/Q1zPH8t1fV05L8VZLvSPJD3TLbpUme1VrbOeoa9sFBGYyCf/ICx/xZkl9cSqOttQ9W1fMz+J47PoOR8k9urX1tie18s6r+PIP38QXZPQvBQp7dLXuzJXufhv/jVXV9BvdonzHfdPVprX26ql6X5PVd2/9tjsPenOTqJE9cRI171Vq7sar+dwZ9/qQkP5zkD+Y49AXd14tba3ud7QAAAGDcjJAHAABgorTWvpzktCRvTPJPGUzLviXJ3yd5UWvt+QuNcm6tvTuDIPk3u/PvTHJXki8l+f0kj22t/doIS35Nkv+cwUjsWzMIy5dlDK9h9vN/Jcljkrwyg/uv35LBCO/NST6UwZTiT+qmGh+3N2UQdL8jyeVJ/iWDkdbbknwlyaYkP9hae05r7a6lNt5a+4sMPiSyI8lDMgjlH7APdc7MJPDkqrr/gkf2YziAvy2DDyjM9oUlLQAAAdNJREFUq7X2q0mekcGHTP4tg5kCvp7k/yb5D621n++hxl/P7pkEXtvdHuHfVdVxSZ7Ubf5OD88PAAAwctVaG3cNAAAAHOCq6rwkr0uS1lotfDRMny5cviqDKeN/ubU216hzFlBVv5zkDRl8SOXk5qIWAAAwBYyQBwAAAOhZFx7PTJn/qqo6bJz1TJuqOjzJq7rN1wvjAQCAaSGQBwAAAFgB3fT3lyS5b5KfGnM50+ankhyd5FMZ3IYAAABgKqwedwEAAAAAB5CfyuCe93eMu5Apc3uS1yf5v0bHAwAA00QgDwAAALBCWmtXZXAveZagtfY7464BAABgX5iyHgAAAAAAAAB6UGb5AgAAAAAAAIDRM0IeAAAAAAAAAHogkAcAAAAAAACAHgjkAQAAAAAAAKAHAnkAAAAAAAAA6IFAHgAAAAAAAAB6IJAHAAAAAAAAgB4I5AEAAAAAAACgBwJ5AAAAAAAAAOiBQB4AAAAAAAAAeiCQBwAAAAAAAIAeCOQBAAAAAAAAoAcCeQAAAAAAAADogUAeAAAAAAAAAHogkAcAAAAAAACAHgjkAQAAAAAAAKAHAnkAAAAAAAAA6IFAHgAAAAAAAAB6IJAHAAAAAAAAgB78/7wBXLrGgcv+AAAAAElFTkSuQmCC\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 (EVaR)\", ylabel=\"portfolio return\", grid=True)   \n",
    "ax.legend([\"return\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the portfolio composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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[:, 4:].plot.area(colormap=my_cmap, xlabel='portfolio risk (EVaR)', 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
}