python/notebooks/ch10_robust_optimization_factor.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": 14,
   "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",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.optimize import brentq\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": 15,
   "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": 16,
   "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 = [\"SPY\", \"IWM\"]\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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "stock_data/daily_adjusted_SPY.csv\n",
      "stock_data/daily_adjusted_IWM.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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# |x| <= t\n",
    "def absval(M, x, t):\n",
    "    M.constraint(t + x >= 0)\n",
    "    M.constraint(t - x >= 0)\n",
    "    \n",
    "    \n",
    "def sqrtm_symm(m):\n",
    "    e, v = np.linalg.eigh(m)\n",
    "    sqrt_e = np.sqrt(e)\n",
    "    sqrt_m = np.dot(v, np.dot(np.diag(sqrt_e), v.T))\n",
    "    return sqrt_m\n",
    "\n",
    "    \n",
    "def EfficientFrontier(N, mu0, gamma, beta0, Gmx, rho, diag_S_theta_upper, Q0, Nmx, zeta, deltas):\n",
    "\n",
    "    with Model(\"Case study\") as M:\n",
    "        # Settings\n",
    "        #M.setLogHandler(sys.stdout)\n",
    "        \n",
    "        # Get number of factors\n",
    "        K = Q0.shape[0]\n",
    "        \n",
    "        # Variables \n",
    "        # The variable x is the fraction of holdings in each security. \n",
    "        # It is restricted to be positive, which imposes the constraint of no short-selling.   \n",
    "        x = M.variable(\"x\", N, Domain.greaterThan(0.0))\n",
    "        z = M.variable(\"z\", N, Domain.greaterThan(0.0))\n",
    "        \n",
    "        # Constrain absolute value\n",
    "        absval(M, x, z)\n",
    "        \n",
    "        # The variable t1 and t2 models the factor and specific portfolio variance terms.\n",
    "        t1 = M.variable(\"t1\", 1, Domain.greaterThan(0.0))\n",
    "        t2 = M.variable(\"t2\", 1, Domain.greaterThan(0.0))\n",
    "        \n",
    "        # The variables tau, s, u help modeling the factor risk.\n",
    "        tau = M.variable(\"tau\", 1, Domain.greaterThan(0.0))\n",
    "        s = M.variable(\"s\", 1, Domain.greaterThan(0.0))\n",
    "        u = M.variable(\"u\", K, Domain.greaterThan(0.0))\n",
    "    \n",
    "        # Budget constraint\n",
    "        M.constraint('budget', Expr.sum(x), Domain.equalsTo(1.0))\n",
    "        \n",
    "        # Objective (variance minimization)\n",
    "        delta = M.parameter()\n",
    "        wc_return = x.T @ mu0 - z.T @ gamma\n",
    "        M.objective('obj', ObjectiveSense.Maximize, wc_return - delta * (t1 + t2))\n",
    "                       \n",
    "        # Risk constraint (specific)\n",
    "        M.constraint('spec-risk', Expr.vstack(t2, 0.5, Expr.mulElm(np.sqrt(diag_S_theta_upper), x)), Domain.inRotatedQCone())\n",
    "                    \n",
    "        # Risk constraint (factor)\n",
    "        siG = sqrtm_symm(np.linalg.inv(Gmx))            \n",
    "        H = siG @ (Q0 + zeta * Nmx) @ siG \n",
    "        lam, V = np.linalg.eigh(H)\n",
    "        w = (V.T @ sqrtm_symm(H) @ sqrtm_symm(Gmx) @ beta0.T) @ x\n",
    "        M.constraint('fact-risk-1', t1 >= tau + Expr.sum(u))\n",
    "        M.constraint('fact-risk-2', s <= 1.0 / lam[-1])\n",
    "        M.constraint('fact-risk-3', Expr.vstack(s, 0.5 * tau, z.T @ rho), Domain.inRotatedQCone())\n",
    "        col1 = Expr.constTerm(K, 1.0) - Expr.mulElm(Expr.repeat(s, K, 0), lam)\n",
    "        M.constraint('fact-risk-4', Expr.hstack(col1, 0.5 * u, w), Domain.inRotatedQCone())\n",
    "    \n",
    "        # Create DataFrame to store the results. Last security names (the factors) are removed.\n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\", \"zdiff\"] + df_prices.columns[:-K].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 = mu0 @ x.level() - gamma @ z.level()\n",
    "            portfolio_risk = np.sqrt((t1.level() + t2.level())[0])\n",
    "            zdiff = np.sum(np.abs(x.level()) - z.level())\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk, zdiff] + 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": [
    "## Define the factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a function to make scenarios. The input is the expected return and covariance of yearly logarithmic returns. The reason for this is that it is easier to generate logarithmic return scenarios from normal distribution than generating linear return scenarios from lognormal distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scenarios(m_log, S_log, factor_num):\n",
    "    \"\"\"\n",
    "    It is assumed that the last factor_num coordinates correspond to the factors.\n",
    "    \"\"\"\n",
    "    if factor_num < 1: \n",
    "        raise Exception(\"Does not make sense to compute a factor model without factors!\")\n",
    "    \n",
    "    # Generate logarithmic return scenarios\n",
    "    scenarios_log = np.random.default_rng().multivariate_normal(m_log, S_log, 100000)\n",
    "    \n",
    "    # Convert logarithmic return scenarios to linear return scenarios \n",
    "    scenarios = np.exp(scenarios_log) - 1\n",
    "    \n",
    "    R = scenarios[:, :-factor_num]\n",
    "    F = scenarios[:, -factor_num:]\n",
    "    \n",
    "    return R, F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define a function that computes the factor model\n",
    "$$\n",
    "R_t = \\mu + \\beta F_{t} + \\theta_t.\n",
    "$$\n",
    "\n",
    "The function can handle any number of factors, and returns estimates $\\EMean$, $\\EBeta$, $\\ECov_F$, $\\ECov_\\theta$, and the factor return matrix. The factors are assumed to be at the last coordinates of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factor_model(R, F):\n",
    "    \"\"\"\n",
    "    It is assumed that the last factor_num coordinates correspond to the factors.\n",
    "    \"\"\"\n",
    "    factor_num = F.shape[1]\n",
    "    \n",
    "    # Do linear regression \n",
    "    params = []\n",
    "    resid = []\n",
    "    X = F\n",
    "    X = sm.add_constant(X, prepend=True)\n",
    "    \n",
    "    for k in range(N):\n",
    "        y = R[:, k]\n",
    "        model = sm.OLS(y, X, hasconst=True).fit()        \n",
    "        resid.append(model.resid)        \n",
    "        params.append(model.params)\n",
    "    resid = np.array(resid)\n",
    "    params = np.array(params)\n",
    "    \n",
    "\n",
    "    # Get parameter estimates\n",
    "    mu = params[:, 0]\n",
    "    B = params[:, 1:]\n",
    "    \n",
    "    S_F = np.atleast_2d(np.cov(X[:, 1:].T))  # MLE computed from data\n",
    "    S_theta = np.cov(resid, ddof=factor_num + 1)   \n",
    "    diag_S_theta = np.diag(S_theta)\n",
    "    \n",
    "    return mu, B, S_F, diag_S_theta, X "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define functions that will compute the parametrization for the uncertainty sets of the factor model parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unc_mu(mu_est, diag_S_theta_est, A, omega):\n",
    "    K = A.shape[1] - 1\n",
    "    T = A.shape[0]\n",
    "    iAA = np.linalg.inv(A.T @ A)\n",
    "    c = stats.f.ppf(omega, K + 1, T - K - 1)\n",
    "\n",
    "    # Parametrization\n",
    "    mu0 = mu_est\n",
    "    gamma = np.sqrt(diag_S_theta_est) * np.sqrt((K + 1) * iAA[0, 0] * c)\n",
    "    \n",
    "    return mu0, gamma\n",
    "\n",
    "def unc_beta(beta_est, diag_S_theta_est, A, omega):\n",
    "    K = A.shape[1] - 1\n",
    "    T = A.shape[0]\n",
    "    F = A[:,1:].T\n",
    "    F1 = F @ np.ones((T, 1))\n",
    "    c = stats.f.ppf(omega, K + 1, T - K - 1)\n",
    "\n",
    "    # Parametrization\n",
    "    beta0 = beta_est\n",
    "    Q = np.array([[0, 1, 0], [0, 0, 1]])\n",
    "    iAA = np.linalg.inv(A.T @ A)\n",
    "    Gmx = F @ F.T - F1 @ F1.T / T\n",
    "    rho = np.sqrt(diag_S_theta_est) * np.sqrt((K + 1) * c)\n",
    "    \n",
    "    return beta0, Gmx, rho\n",
    "\n",
    "def unc_d(diag_S_theta_est, percent):    \n",
    "    # Here we just add a percentage to the estimated error variance, to get an upper bound estimate. \n",
    "    diag_S_theta_upper = diag_S_theta_est * (1.0 + percent)\n",
    "    return diag_S_theta_upper\n",
    "\n",
    "def unc_q(S_F, A, omega):\n",
    "    T = A.shape[0]\n",
    "      \n",
    "    \n",
    "    def fun(eta, T, omega):\n",
    "        return stats.gamma.cdf(1 + eta, (T + 1) / 2, scale=2 / (T - 1)) - \\\n",
    "               stats.gamma.cdf(1 - eta, (T + 1) / 2, scale=2 / (T - 1)) - \\\n",
    "               omega\n",
    "    \n",
    "\n",
    "    eta = brentq(fun, 0, 1, args=(T, omega))\n",
    "    \n",
    "    # Parametrization\n",
    "    Q0 = S_F\n",
    "    Nmx = S_F\n",
    "        \n",
    "    zeta = eta / (1 - eta)\n",
    "    return Q0, Nmx, zeta"
   ]
  },
  {
   "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": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Number of factors\n",
    "fnum = len(list_factors)\n",
    "\n",
    "# Number of securities (We subtract fnum to account for factors at the end of the price data)\n",
    "N = df_prices.shape[1] - fnum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compute the same using the factor model. First we compute logarithmic return statistics and use them to compute the factor exposures and covariances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "R, F = scenarios(m_log, S_log, fnum)\n",
    "\n",
    "# Center factors, so we have the same model as in the article (Goldfarb--Iyengar 2003). \n",
    "F -= F.mean(axis=0)\n",
    "\n",
    "# Compute factor model\n",
    "mu, B, S_F, diag_S_theta, X = factor_model(R, F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the parameters of the uncertainty sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncertainty set parameters\n",
    "omega = 0.95\n",
    "percent = 0.2\n",
    "mu0, gamma = unc_mu(mu, diag_S_theta, X, omega)\n",
    "beta0, Gmx, rho = unc_beta(B, diag_S_theta, X, omega)\n",
    "diag_S_theta_upper = unc_d(diag_S_theta, percent)\n",
    "Q0, Nmx, zeta = unc_q(S_F, X, omega)\n",
    "\n",
    "# To get back the non_robust case, we have to zero the bounds\n",
    "gamma_z = np.zeros(N)\n",
    "rho_z = np.zeros(N)\n",
    "zeta_z = 0.0"
   ]
  },
  {
   "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 factor model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>zdiff</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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       "      <th>0</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>-1.963011</td>\n",
       "      <td>0.166737</td>\n",
       "      <td>0.206385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.166168e-01</td>\n",
       "      <td>1.473706e-01</td>\n",
       "      <td>2.789160e-01</td>\n",
       "      <td>2.004959e-01</td>\n",
       "      <td>8.926464e-09</td>\n",
       "      <td>6.577475e-02</td>\n",
       "      <td>5.658599e-02</td>\n",
       "      <td>1.342400e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.759640</td>\n",
       "      <td>-1.312790</td>\n",
       "      <td>0.173682</td>\n",
       "      <td>0.206795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.086712e-01</td>\n",
       "      <td>1.431518e-01</td>\n",
       "      <td>2.767043e-01</td>\n",
       "      <td>1.898222e-01</td>\n",
       "      <td>2.063415e-08</td>\n",
       "      <td>8.340493e-02</td>\n",
       "      <td>6.552252e-02</td>\n",
       "      <td>1.327230e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.164651</td>\n",
       "      <td>-0.858179</td>\n",
       "      <td>0.183676</td>\n",
       "      <td>0.207641</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.724220e-02</td>\n",
       "      <td>1.370804e-01</td>\n",
       "      <td>2.735150e-01</td>\n",
       "      <td>1.744604e-01</td>\n",
       "      <td>3.521223e-08</td>\n",
       "      <td>1.087845e-01</td>\n",
       "      <td>7.837979e-02</td>\n",
       "      <td>1.305377e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.799091</td>\n",
       "      <td>-0.538423</td>\n",
       "      <td>0.198055</td>\n",
       "      <td>0.209381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.080884e-02</td>\n",
       "      <td>1.283463e-01</td>\n",
       "      <td>2.689221e-01</td>\n",
       "      <td>1.523550e-01</td>\n",
       "      <td>3.474531e-08</td>\n",
       "      <td>1.453084e-01</td>\n",
       "      <td>9.686674e-02</td>\n",
       "      <td>1.273926e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.678607</td>\n",
       "      <td>-0.310775</td>\n",
       "      <td>0.219299</td>\n",
       "      <td>0.213046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.689619e-02</td>\n",
       "      <td>1.153949e-01</td>\n",
       "      <td>2.619704e-01</td>\n",
       "      <td>1.202415e-01</td>\n",
       "      <td>3.024147e-03</td>\n",
       "      <td>1.969154e-01</td>\n",
       "      <td>1.230439e-01</td>\n",
       "      <td>1.225136e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.118884</td>\n",
       "      <td>-0.143863</td>\n",
       "      <td>0.254343</td>\n",
       "      <td>0.221465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.035281e-02</td>\n",
       "      <td>9.370146e-02</td>\n",
       "      <td>2.491199e-01</td>\n",
       "      <td>7.143395e-02</td>\n",
       "      <td>3.111944e-02</td>\n",
       "      <td>2.641215e-01</td>\n",
       "      <td>1.574606e-01</td>\n",
       "      <td>1.126904e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.644189</td>\n",
       "      <td>-0.015299</td>\n",
       "      <td>0.298891</td>\n",
       "      <td>0.235936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.299772e-08</td>\n",
       "      <td>5.702187e-02</td>\n",
       "      <td>2.234612e-01</td>\n",
       "      <td>7.531158e-07</td>\n",
       "      <td>6.938467e-02</td>\n",
       "      <td>3.535480e-01</td>\n",
       "      <td>2.029646e-01</td>\n",
       "      <td>9.361888e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.923800</td>\n",
       "      <td>0.086856</td>\n",
       "      <td>0.338987</td>\n",
       "      <td>0.253489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.492498e-08</td>\n",
       "      <td>2.092341e-07</td>\n",
       "      <td>1.473377e-01</td>\n",
       "      <td>2.308300e-08</td>\n",
       "      <td>1.153779e-01</td>\n",
       "      <td>4.545953e-01</td>\n",
       "      <td>2.432316e-01</td>\n",
       "      <td>3.945738e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.727797</td>\n",
       "      <td>0.170403</td>\n",
       "      <td>0.379125</td>\n",
       "      <td>0.276616</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.590732e-09</td>\n",
       "      <td>2.339015e-08</td>\n",
       "      <td>4.524015e-07</td>\n",
       "      <td>1.137332e-08</td>\n",
       "      <td>1.685260e-01</td>\n",
       "      <td>5.615656e-01</td>\n",
       "      <td>2.699078e-01</td>\n",
       "      <td>5.726565e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.896345</td>\n",
       "      <td>0.234958</td>\n",
       "      <td>0.385263</td>\n",
       "      <td>0.281532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.588257e-10</td>\n",
       "      <td>2.240931e-10</td>\n",
       "      <td>3.481956e-10</td>\n",
       "      <td>1.419420e-10</td>\n",
       "      <td>2.054034e-01</td>\n",
       "      <td>5.925224e-01</td>\n",
       "      <td>2.020743e-01</td>\n",
       "      <td>3.069764e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.318325</td>\n",
       "      <td>0.282118</td>\n",
       "      <td>0.394093</td>\n",
       "      <td>0.291440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.742613e-09</td>\n",
       "      <td>3.665235e-09</td>\n",
       "      <td>5.338247e-09</td>\n",
       "      <td>2.207221e-09</td>\n",
       "      <td>2.584473e-01</td>\n",
       "      <td>6.370531e-01</td>\n",
       "      <td>1.044995e-01</td>\n",
       "      <td>5.065399e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.916490</td>\n",
       "      <td>0.318071</td>\n",
       "      <td>0.403897</td>\n",
       "      <td>0.306018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.879743e-10</td>\n",
       "      <td>1.032590e-09</td>\n",
       "      <td>1.358933e-09</td>\n",
       "      <td>6.628006e-10</td>\n",
       "      <td>3.250563e-01</td>\n",
       "      <td>6.749436e-01</td>\n",
       "      <td>3.749047e-08</td>\n",
       "      <td>1.336757e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.637137</td>\n",
       "      <td>0.344530</td>\n",
       "      <td>0.405855</td>\n",
       "      <td>0.310243</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.533820e-09</td>\n",
       "      <td>7.327005e-09</td>\n",
       "      <td>9.234237e-09</td>\n",
       "      <td>4.694245e-09</td>\n",
       "      <td>3.802092e-01</td>\n",
       "      <td>6.197907e-01</td>\n",
       "      <td>7.892768e-08</td>\n",
       "      <td>9.391718e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.442933</td>\n",
       "      <td>0.363651</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>0.318809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.835981e-10</td>\n",
       "      <td>4.379486e-10</td>\n",
       "      <td>5.527641e-10</td>\n",
       "      <td>2.790588e-10</td>\n",
       "      <td>4.595503e-01</td>\n",
       "      <td>5.404497e-01</td>\n",
       "      <td>4.227954e-09</td>\n",
       "      <td>5.620060e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.307924</td>\n",
       "      <td>0.377991</td>\n",
       "      <td>0.412720</td>\n",
       "      <td>0.335838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.024964e-10</td>\n",
       "      <td>2.093749e-10</td>\n",
       "      <td>2.645421e-10</td>\n",
       "      <td>1.258830e-10</td>\n",
       "      <td>5.736666e-01</td>\n",
       "      <td>4.263334e-01</td>\n",
       "      <td>1.513995e-09</td>\n",
       "      <td>2.717500e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.214067</td>\n",
       "      <td>0.389464</td>\n",
       "      <td>0.418546</td>\n",
       "      <td>0.368585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.072704e-10</td>\n",
       "      <td>1.847760e-10</td>\n",
       "      <td>2.349052e-10</td>\n",
       "      <td>1.018436e-10</td>\n",
       "      <td>7.378251e-01</td>\n",
       "      <td>2.621749e-01</td>\n",
       "      <td>1.277006e-09</td>\n",
       "      <td>2.444856e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.148818</td>\n",
       "      <td>0.399606</td>\n",
       "      <td>0.426929</td>\n",
       "      <td>0.428482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.757861e-10</td>\n",
       "      <td>3.109929e-10</td>\n",
       "      <td>3.894916e-10</td>\n",
       "      <td>1.541796e-10</td>\n",
       "      <td>9.740096e-01</td>\n",
       "      <td>2.599038e-02</td>\n",
       "      <td>2.013008e-09</td>\n",
       "      <td>4.142489e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.103457</td>\n",
       "      <td>0.408205</td>\n",
       "      <td>0.427851</td>\n",
       "      <td>0.435775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.960653e-09</td>\n",
       "      <td>1.423931e-09</td>\n",
       "      <td>1.808491e-09</td>\n",
       "      <td>6.650234e-10</td>\n",
       "      <td>9.999999e-01</td>\n",
       "      <td>5.105296e-08</td>\n",
       "      <td>8.738149e-09</td>\n",
       "      <td>1.921770e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.071922</td>\n",
       "      <td>0.414193</td>\n",
       "      <td>0.427851</td>\n",
       "      <td>0.435775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.078207e-10</td>\n",
       "      <td>3.458236e-10</td>\n",
       "      <td>4.457228e-10</td>\n",
       "      <td>1.407267e-10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.039306e-08</td>\n",
       "      <td>2.197704e-09</td>\n",
       "      <td>4.777987e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.418356</td>\n",
       "      <td>0.427851</td>\n",
       "      <td>0.435775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.611973e-10</td>\n",
       "      <td>1.963533e-10</td>\n",
       "      <td>2.560732e-10</td>\n",
       "      <td>7.436289e-11</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.173510e-09</td>\n",
       "      <td>1.280648e-09</td>\n",
       "      <td>2.737299e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        delta       obj    return      risk  zdiff            PM  \\\n",
       "0   50.000000 -1.963011  0.166737  0.206385    0.0  1.166168e-01   \n",
       "1   34.759640 -1.312790  0.173682  0.206795    0.0  1.086712e-01   \n",
       "2   24.164651 -0.858179  0.183676  0.207641    0.0  9.724220e-02   \n",
       "3   16.799091 -0.538423  0.198055  0.209381    0.0  8.080884e-02   \n",
       "4   11.678607 -0.310775  0.219299  0.213046    0.0  5.689619e-02   \n",
       "5    8.118884 -0.143863  0.254343  0.221465    0.0  2.035281e-02   \n",
       "6    5.644189 -0.015299  0.298891  0.235936    0.0  4.299772e-08   \n",
       "7    3.923800  0.086856  0.338987  0.253489    0.0  1.492498e-08   \n",
       "8    2.727797  0.170403  0.379125  0.276616    0.0  9.590732e-09   \n",
       "9    1.896345  0.234958  0.385263  0.281532    0.0  1.588257e-10   \n",
       "10   1.318325  0.282118  0.394093  0.291440    0.0  2.742613e-09   \n",
       "11   0.916490  0.318071  0.403897  0.306018    0.0  9.879743e-10   \n",
       "12   0.637137  0.344530  0.405855  0.310243    0.0  8.533820e-09   \n",
       "13   0.442933  0.363651  0.408670  0.318809    0.0  5.835981e-10   \n",
       "14   0.307924  0.377991  0.412720  0.335838    0.0  3.024964e-10   \n",
       "15   0.214067  0.389464  0.418546  0.368585    0.0  3.072704e-10   \n",
       "16   0.148818  0.399606  0.426929  0.428482    0.0  5.757861e-10   \n",
       "17   0.103457  0.408205  0.427851  0.435775    0.0  2.960653e-09   \n",
       "18   0.071922  0.414193  0.427851  0.435775    0.0  8.078207e-10   \n",
       "19   0.050000  0.418356  0.427851  0.435775    0.0  4.611973e-10   \n",
       "\n",
       "             LMT           MCD           MMM          AAPL          MSFT  \\\n",
       "0   1.473706e-01  2.789160e-01  2.004959e-01  8.926464e-09  6.577475e-02   \n",
       "1   1.431518e-01  2.767043e-01  1.898222e-01  2.063415e-08  8.340493e-02   \n",
       "2   1.370804e-01  2.735150e-01  1.744604e-01  3.521223e-08  1.087845e-01   \n",
       "3   1.283463e-01  2.689221e-01  1.523550e-01  3.474531e-08  1.453084e-01   \n",
       "4   1.153949e-01  2.619704e-01  1.202415e-01  3.024147e-03  1.969154e-01   \n",
       "5   9.370146e-02  2.491199e-01  7.143395e-02  3.111944e-02  2.641215e-01   \n",
       "6   5.702187e-02  2.234612e-01  7.531158e-07  6.938467e-02  3.535480e-01   \n",
       "7   2.092341e-07  1.473377e-01  2.308300e-08  1.153779e-01  4.545953e-01   \n",
       "8   2.339015e-08  4.524015e-07  1.137332e-08  1.685260e-01  5.615656e-01   \n",
       "9   2.240931e-10  3.481956e-10  1.419420e-10  2.054034e-01  5.925224e-01   \n",
       "10  3.665235e-09  5.338247e-09  2.207221e-09  2.584473e-01  6.370531e-01   \n",
       "11  1.032590e-09  1.358933e-09  6.628006e-10  3.250563e-01  6.749436e-01   \n",
       "12  7.327005e-09  9.234237e-09  4.694245e-09  3.802092e-01  6.197907e-01   \n",
       "13  4.379486e-10  5.527641e-10  2.790588e-10  4.595503e-01  5.404497e-01   \n",
       "14  2.093749e-10  2.645421e-10  1.258830e-10  5.736666e-01  4.263334e-01   \n",
       "15  1.847760e-10  2.349052e-10  1.018436e-10  7.378251e-01  2.621749e-01   \n",
       "16  3.109929e-10  3.894916e-10  1.541796e-10  9.740096e-01  2.599038e-02   \n",
       "17  1.423931e-09  1.808491e-09  6.650234e-10  9.999999e-01  5.105296e-08   \n",
       "18  3.458236e-10  4.457228e-10  1.407267e-10  1.000000e+00  1.039306e-08   \n",
       "19  1.963533e-10  2.560732e-10  7.436289e-11  1.000000e+00  6.173510e-09   \n",
       "\n",
       "             TXN          CSCO  \n",
       "0   5.658599e-02  1.342400e-01  \n",
       "1   6.552252e-02  1.327230e-01  \n",
       "2   7.837979e-02  1.305377e-01  \n",
       "3   9.686674e-02  1.273926e-01  \n",
       "4   1.230439e-01  1.225136e-01  \n",
       "5   1.574606e-01  1.126904e-01  \n",
       "6   2.029646e-01  9.361888e-02  \n",
       "7   2.432316e-01  3.945738e-02  \n",
       "8   2.699078e-01  5.726565e-08  \n",
       "9   2.020743e-01  3.069764e-10  \n",
       "10  1.044995e-01  5.065399e-09  \n",
       "11  3.749047e-08  1.336757e-09  \n",
       "12  7.892768e-08  9.391718e-09  \n",
       "13  4.227954e-09  5.620060e-10  \n",
       "14  1.513995e-09  2.717500e-10  \n",
       "15  1.277006e-09  2.444856e-10  \n",
       "16  2.013008e-09  4.142489e-10  \n",
       "17  8.738149e-09  1.921770e-09  \n",
       "18  2.197704e-09  4.777987e-10  \n",
       "19  1.280648e-09  2.737299e-10  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute efficient frontier with and without factor model\n",
    "deltas = np.logspace(start=-1, stop=2, num=20)[::-1] / 2\n",
    "df_result_orig = EfficientFrontier(N, mu0, gamma_z, beta0, Gmx, rho_z, diag_S_theta_upper, Q0, Nmx, zeta_z, deltas)\n",
    "df_result_robust = EfficientFrontier(N, mu0, gamma, beta0, Gmx, rho, diag_S_theta_upper, Q0, Nmx, zeta, deltas)\n",
    "df_result_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Set small negatives to zero to make plotting work\n",
    "mask = df_result_orig < 0\n",
    "mask.iloc[:, :-8] = False\n",
    "df_result_orig[mask] = 0\n",
    "\n",
    "# Set small negatives to zero to make plotting work\n",
    "mask = df_result_robust < 0\n",
    "mask.iloc[:, :-8] = False\n",
    "df_result_robust[mask] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the efficient frontier for both cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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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_robust.plot(x=\"risk\", y=\"return\", style=\"-o\", xlabel=\"portfolio risk (std. dev.)\", ylabel=\"portfolio return\", grid=True)\n",
    "df_result_orig.plot(ax=ax, x=\"risk\", y=\"return\", style=\"-o\", xlabel=\"portfolio risk (std. dev.)\", ylabel=\"portfolio return\", grid=True)   \n",
    "ax.legend([\"robust return\", \"return\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the portfolio composition for both cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot portfolio composition\n",
    "my_cmap = LinearSegmentedColormap.from_list(\"non-extreme gray\", [\"#111111\", \"#eeeeee\"], N=256, gamma=1.0)\n",
    "ax1 = df_result_robust.set_index('risk').iloc[:, 4:].plot.area(colormap=my_cmap, xlabel='portfolio risk (std. dev.)', ylabel=\"x\")\n",
    "ax1.grid(which='both', axis='x', linestyle=':', color='k', linewidth=1)\n",
    "ax2 = df_result_orig.set_index('risk').iloc[:, 4:].plot.area(colormap=my_cmap, xlabel='portfolio risk (std. dev.)', ylabel=\"x\") \n",
    "ax2.grid(which='both', axis='x', linestyle=':', color='k', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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