python/notebooks/ch5_factor_model_large.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": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import re\n",
    "import glob\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "# Define the optimization model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a function to randomize factor models, i. e., large random covariance matrices with only a few significant eigenvalues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_factor_model(N, K, T):\n",
    "    # Generate K + N zero mean factors, with block covariance: \n",
    "    # - K x K weighted diagonal block for the factors, \n",
    "    # - N x N white noise (uncorrelated to the factors)\n",
    "    S_F = np.diag(np.sqrt(range(1, K + 1)))\n",
    "    Cov = np.block([\n",
    "        [S_F,              np.zeros((K, N))],\n",
    "        [np.zeros((N, K)), np.eye(N)]\n",
    "    ])\n",
    "    Y = np.random.default_rng(seed=1).multivariate_normal(np.zeros(K + N), Cov, T).T\n",
    "    Z_F = Y[:K, :]\n",
    "    \n",
    "    # Generate random factor model parameters\n",
    "    B = np.random.default_rng(seed=2).normal(size=(N, K))\n",
    "    a = np.random.default_rng(seed=3).normal(loc=1, size=(N, 1))\n",
    "    e = Y[K:, :]\n",
    "\n",
    "    # Generate N time-series from the factors\n",
    "    Z = a + B @ Z_F + e\n",
    "    \n",
    "    # Residual covariance\n",
    "    S_theta = np.cov(e)\n",
    "    diag_S_theta = np.diag(S_theta)\n",
    "\n",
    "    # Optimization parameters\n",
    "    m = np.mean(Z, axis=1)\n",
    "    S = np.cov(Z)\n",
    "    #print(np.linalg.eigvalsh(np.corrcoef(Z))[-20:])\n",
    "    \n",
    "    return m, S, B, S_F, diag_S_theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the optimization model in MOSEK Fusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solve optimization\n",
    "def Markowitz(N, m, G, gamma2):\n",
    "    with Model(\"markowitz\") as M:\n",
    "        # Settings\n",
    "        #M.setLogHandler(sys.stdout) \n",
    "\n",
    "        # Decision variable (fraction of holdings in each security)\n",
    "        # The variable x is restricted to be positive, which imposes the constraint of no short-selling.   \n",
    "        x = M.variable(\"x\", N, Domain.greaterThan(0.0))\n",
    "\n",
    "        # Budget constraint\n",
    "        M.constraint('budget', Expr.sum(x) == 1.0)\n",
    "\n",
    "        # Objective \n",
    "        M.objective('obj', ObjectiveSense.Maximize, x.T @ m)\n",
    "\n",
    "        # Imposes a bound on the risk\n",
    "        if isinstance(G, tuple):\n",
    "            G_factor = G[0]\n",
    "            g_specific = G[1]\n",
    "            \n",
    "            factor_risk = G_factor.T @ x \n",
    "            specific_risk = Expr.mulElm(g_specific, x)\n",
    "            total_risk = Expr.vstack(factor_risk, specific_risk)\n",
    "            \n",
    "            M.constraint('risk', Expr.vstack(gamma2**0.5, total_risk), Domain.inQCone())\n",
    "        else:\n",
    "            M.constraint('risk', Expr.vstack(gamma2**0.5, G.T @ x), Domain.inQCone())\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",
    "        returns = M.primalObjValue()\n",
    "        portfolio = x.level()\n",
    "        time = M.getSolverDoubleInfo(\"optimizerTime\")\n",
    "        \n",
    "        return returns, time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters are the number of factors $K$ and the risk limit $\\gamma^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk limit\n",
    "gamma2 = 0.1\n",
    "\n",
    "# Number of factors\n",
    "K = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve the optimization problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we generate random factor structured covariance matrices of different sizes, and solve the portfolio optimization both when we utilize the factor structure and when we do Cholesky factorization on it instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate runtime data \n",
    "# NOTE: This can have a long runtime, depending on the range given for n below!\n",
    "list_runtimes_orig = []\n",
    "list_runtimes_factor = []\n",
    "for n in range(5, 13):\n",
    "    N = 2**n\n",
    "    T = 10000\n",
    "    m, S, B, S_F, diag_S_theta = random_factor_model(N, K, T)\n",
    "\n",
    "    F = np.linalg.cholesky(S_F)\n",
    "    G_factor = B @ F\n",
    "    g_specific = np.sqrt(diag_S_theta)\n",
    "    \n",
    "    G_orig = np.linalg.cholesky(S)\n",
    "        \n",
    "    optimum_orig, runtime_orig = Markowitz(N, m, G_orig, gamma2)\n",
    "    optimum_factor, runtime_factor = Markowitz(N, m, (G_factor, g_specific), gamma2)\n",
    "    list_runtimes_orig.append((N, runtime_orig))\n",
    "    list_runtimes_factor.append((N, runtime_factor))\n",
    "    \n",
    "tup_N_orig, tup_time_orig = list(zip(*list_runtimes_orig))\n",
    "tup_N_factor, tup_time_factor = list(zip(*list_runtimes_factor))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc64a0d0160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Runtime plot\n",
    "plt.plot(tup_N_orig, tup_time_orig, \"-o\")\n",
    "plt.plot(tup_N_factor, tup_time_factor, \"-o\")\n",
    "plt.xlabel(\"N\")\n",
    "plt.ylabel(\"runtime (s)\")\n",
    "ax = plt.gca()\n",
    "ax.set_xscale('log', base=2)\n",
    "ax.set_yscale('log')\n",
    "ax.grid()\n",
    "legend = [\"Cholesky\", \"factor model\"]\n",
    "plt.legend(legend)"
   ]
  }
 ],
 "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
}