{
 "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": 57,
   "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",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "from mosek.fusion import *\n",
    "\n",
    "from notebook.services.config import ConfigManager\n",
    "\n",
    "from portfolio_tools import data_download, DataReader, compute_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 59,
   "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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "investment_start = \"2016-03-18\"\n",
    "investment_end = \"2021-03-18\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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(read_volume=True)\n",
    "df_prices, df_volumes = dr.get_period(start_date=investment_start, end_date=investment_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also create the benchmark. It is the so called $\\frac{1}{n}$ portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create benchmark\n",
    "df_prices['bm'] = df_prices.iloc[:-2, 0:8].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run the optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the optimization model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we implement the optimization model in Fusion API. We create it inside a function so we can call it later.\n",
    "\n",
    "The parameters:\n",
    "- `a`: The vector of alphas. (Used instead of mean returns.)\n",
    "- `B`: The vector of betas ().\n",
    "- `xbm`: The benchmark portfolio.\n",
    "- `uh`/`ub`: Upper bound on active holdings and active beta.\n",
    "- `lh`/`lb`: Lower bound on active holdings and active beta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EfficientFrontier(N, a, B, G, xbm, deltas, uh, ub, lh, lb):\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 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",
    "        \n",
    "        # Active holdings\n",
    "        xa = Expr.sub(x, xbm) \n",
    "        \n",
    "        # The variable s models the portfolio variance term in the objective.\n",
    "        s = M.variable(\"s\", 1, Domain.unbounded())\n",
    "    \n",
    "        # Budget constraint\n",
    "        M.constraint('budget_x', Expr.sum(x), Domain.equalsTo(1))\n",
    "        \n",
    "        # Constraint on active holdings\n",
    "        M.constraint('bound-h', xa, Domain.inRange(lh, uh))\n",
    "        \n",
    "        # Constraint on portfolio active beta \n",
    "        port_act_beta = Expr.sub(Expr.dot(B, x), 1)\n",
    "        M.constraint('bound-b', port_act_beta, Domain.inRange(lb, ub))\n",
    "        \n",
    "        # Conic constraint for the portfolio variance\n",
    "        M.constraint('risk', Expr.vstack(s, 1, Expr.mul(G.T, xa)), Domain.inRotatedQCone())\n",
    "        \n",
    "        # Objective (quadratic utility version)\n",
    "        delta = M.parameter()\n",
    "        M.objective('obj', ObjectiveSense.Maximize, Expr.sub(Expr.dot(a, x), Expr.mul(delta, s)))\n",
    "    \n",
    "        # Create DataFrame to store the results. Last security name (the SPY ETF) is removed.\n",
    "        columns = [\"delta\", \"obj\", \"return\", \"risk\"] + df_prices.columns[:-1].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 = a @ x.level()\n",
    "            portfolio_risk = np.sqrt(2 * s.level()[0])\n",
    "            row = pd.Series([d, M.primalObjValue(), portfolio_return, portfolio_risk] + list(x.level()), index=columns)\n",
    "            df_result = pd.concat([df_result, row.to_frame().T], ignore_index=True)\n",
    "\n",
    "        return df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define a function that computes the factor model\n",
    "$$\n",
    "R_t = \\alpha + \\beta R_{F,t} + \\varepsilon_t.\n",
    "$$\n",
    "It can handle any number of factors, and returns estimates $\\EBeta$, $\\ECov_F$, and $\\ECov_\\theta$. The factors are assumed to be at the last coordinates of the data. \n",
    "\n",
    "The input of the function is the expected return and covariance of yearly logarithmic returns. The reason is that it is easier to generate logarithmic return scenarios from normal distribution instead of generating linear return scenarios from lognormal distribution.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def factor_model(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_lin = np.exp(scenarios_log) - 1\n",
    "    \n",
    "    # Do linear regression \n",
    "    params = []\n",
    "    resid = []\n",
    "    X = scenarios_lin[:, -factor_num:]\n",
    "    X = sm.add_constant(X, prepend=False)\n",
    "    \n",
    "    for k in range(N):\n",
    "        y = scenarios_lin[:, 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",
    "    # Get parameter estimates\n",
    "    a = params[:, 1]\n",
    "    B = params[:, 0:factor_num]\n",
    "    S_F = np.atleast_2d(np.cov(X[:, 0:factor_num].T))\n",
    "    S_theta = np.cov(resid)\n",
    "    S_theta = np.diag(np.diag(S_theta))\n",
    "    \n",
    "    return a, B, S_F, S_theta "
   ]
  },
  {
   "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": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Number of factors\n",
    "fnum = 1\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\n",
    "\n",
    "# Get optimization parameters\n",
    "m, S = compute_inputs(df_prices, security_num=N)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we compute the matrix $G$ such that $\\ECov=GG^\\mathsf{T}$, this is the input of the conic form of the optimization problem. Here we use Cholesky factorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = np.linalg.cholesky(S)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we compute the estimates $\\EAlpha$ and $\\EBeta$ 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": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_log, S_log = compute_inputs(df_prices, return_log=True)\n",
    "a, B, _, _ = factor_model(m_log, S_log, fnum)"
   ]
  },
  {
   "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: $\\delta = 10^{-0.5},\\dots,10^{2}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "xbm = np.ones(N) / N\n",
    "uh = np.ones(N) * 0.5\n",
    "lh = -np.ones(N) * 0.5\n",
    "ub = 0.5\n",
    "lb = -0.5\n",
    "\n",
    "deltas = np.logspace(start=-0.5, stop=2, num=20)[::-1]\n",
    "\n",
    "df_result = EfficientFrontier(N, a, B, G, xbm, deltas, uh, ub, lh, lb)\n",
    "mask = df_result < 0\n",
    "mask.iloc[:, :2] = False\n",
    "df_result[mask] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the efficient frontier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gfWKSJUl2qLUu6tPnuWl8YCFpfK+fOcR8O9Zab++1v2sG+P6WUnZM8uM0VjFYmuSFtdYfDjLmmt/B99Va39un7cSs+4iG42ut5/bpMzPJRUn2T3Jfku373sVeSrksjVxameTJtdbf9Wnfrvm1r/kAx3p/ToN8LTsluS7JJmkUyU8a6I76Xqtb9CTZt9Z6fa+2XbPuoybOTnJiHeAi1Aj7HpPke83dn6bxc17Zp8/JaXyAIUnOq7W+pE/7iVn35/HqWut/950LAABgonnGPQAAAGP1vb5F+ySptS5O8prmbkeS1/bp8rw0Ct5J8q8DLWtda701yd83dzdJ8qoh4uhJo8g4kXfLjvfXMCLNgu9Jzd1rkrx3gPlrklPTKAgnjTvwJ9LrByuiJ0mtdclgRftme03jju7uNJaaf9oA3d7RfF+a5EXrme/2wdrWKKU8Kslv0yjaP5jk6YMV7Ufom32L9s2YVqSxQkCSbJ3k0X3iOTyNon2SfLpv0b45xt1J3jwOMb4xjd/TOzP0MvjvSXJHGrl9whDjPZjG4wWGc+fI+vq+vvm+Ko0PWazs26HWemYaK14kyfNLKY8YYr6fKdoDAACThcI9AAAAY/X5wRpqrX/I2qW7+xZc1+zXNO6oHszX01iOe6AxevvtUHfQbyDj/TWM1Lw0lhJPki8MtgR+rfWhNJZmT5JHr6eYOZ5uq7X+eiQnlFKml1J2LKXsW0rZv5SyfxpLn6/54MFBffpvncZz65PGHdZDLY0+nPkPSWOliJ2TLEzypFrrb8cyZi9fGqLt4l7bu/dpe2qv7S8OMcb3s/b7NFrPbb5/t9a6fLBOzYL+Bc3dI4YY77vND/EMx6B9SynT0njkQ5L8pNZ62xDjnNl8n5bkSUP0G+rnAQAAMKEU7gEAABirP66n/Q/N971KKTN6Hd+/+X5L827hATXvqr20zzkDuWI9cWwI4/01jHb+JPn9evr2bh/PGIYyrJ9Js1j/+lLKhWk8cuC2NFYQuLLXa7tm9236nH5wktLc/tUY4z0yyS+ac92S5Am11svHOGZv1w3Rdn+v7dl92tb8vFak8cz5ATU/uHHZqCJLUkqZk2TP5u4ppZQ61CvJC5t9tx9i2JHk5VB9d09jJYBk/H7XW/FnBgAAwICmtToAAAAA2t6gBeumhc33kmTLXvtb9WkfyoI+5wzkgWGMM97G+2sY7fzDiWFBr+3xjGEo6/2ZlFK2SuNZ8vOGOeasPvu9C/mDLrk/TH/ba/sltdYbxzheX0uHaOvptd3Zp23L5vv9g62q0Ms9I45qre3W32VAmwzRNpK8HKrvhvhdb8WfGQAAAANSuAcAAGCs1vfs6rKe9uE8+3p9YySNZ6C3ynh9DRsyhg09/0CG8zP5RNYW7b+VxiMHrkjjAyHL1zzvvJTylyQ7ZeivYzg/h6F8O8kxaVwvObeU8sRa61g/DNBOen9g4D+SfG6Y5/V71nwvI8nL4fYd6895pPMBAABscAr3AAAAjNXcNJY2H8yau3hr1r3Ddc3S4EMts917jt7nTBat/hp6j7V9kuuHMf94xzBqpZTNk7ykufvlWusrhui+5SDH7+21/cgxhvStJF9Ncm6SvZL8vJTypFrrgiHP2vDW5M1WpZTO9dx1v+0Y5rmv1/YmtdZBl+Vvgb6/60Pp3T4pftcBAADWxzPuAQAAGKvDhtl+Q/NZ72usKQruWkoZdInuUsr0JIf0OWe0xutO3TVa8TUMNH+SPHY9fR8zyHnDNd7fu6RRHJ/e3P7qYJ1KKXsn2WyQ5kuzNrajxhpQrfWrSU5IY+n6vZP8dKif7QS5uvk+M8kBg3UqpXQmOXi0k9Ra70lyR3P3aaWUVqzSMJibsvZRAxv6dx0AAGDCKdwDAAAwVq8crKGUMj/J/s3d/+vTvGa/ZN1ni/f1wiRzBhljpJb32p45xrGS1nwNvV2c5MHm9iubhdt+Simzk7y4uXvNKJd/X/O9G4/v2xq9VwIc6jnprx2sodZ6f5LfNXdfXEoZ6133qbV+OcmJaRTvH51G8X6bsY47Bj/ttX3CEP2OSbL1GOf6TvN99zR+byeFWuvqJL9s7v5/pZSdhuh+UvO9O8kvNmRcAAAA40XhHgAAgLF6TinlxX0PllI2S3JGc7cnyel9uvxvkjub2+8qpRw0wBg7JflIc3dpks+PMdbeBes9xjhW0pqv4a9qrSuSnNXc3S/JewaYvyT5VJI1hedPjXK6Nd+77ZofBBgPN2bt3fIDFqRLKc9K8nfrGeeDzfdNkny9lDJnsI6llB2HE1it9Zw0PozRk8aHT35aShlrUXxUaq0XJLmiufv6Usrj+vYppWyb5OPjMN2Hk6xobn+2+eGbQZVSnllKOXAc5h2OTzffpyf571LKjAHi+dskT2/ufmOUH1IBAACYcAr3AAAAjNVFSb5cSvl0KeXJpZR5pZRXNY+vWR7+07XWK3qfVGtdleQ1aRRuZyf5TSnln0opjy+lPLaU8ubmGGvuoH5rrbX388xH49KsvXP8X0opTy+lPKqUsmfzNWskg7Xoa+jrn9NYRjxJ/rGU8s1SyrNKKYeWUl6Q5GdZWxS/IGs/TDFSa+5q70ijoHt4KWWvNd+70QxYa70vyQ+au88spfyolPK85u/QM0opZ6Xx3PmbktwzxDjfTfK55u7jklxTSnlnKeWoUsrBpZSnlVLeUUq5JMn7RxDfF5OcnMbP98AkPymlbDnSr3OcvD6NDxHMSPJ/pZT3l1KeUEo5rJTyujRWX9gpyWXN/qN6tEGt9easXeFgqyS/LaWcVUo5tvk79ZhSyvNLKf9eSrkxyfeT7DyGr2sksX0/ydebu09L8vtSynHN35enNX9f1nyQ5f4kp01EXAAAAONh2vq7AAAAwJBenMZS3qc2X319I4MU0Gqt328W+U9P4xnm72u+eutO8o+11v8aa6C11sWllP9M8rYkhyY5v0+XJ2eES2tP9NcwwPyLSylPTfLDJPskeV7z1ddvkzyn1to9yql+luTCJIcneXnz1dton4f+uiS/SaP4e3Tz1dtfkhybtQX+wZySZFkaBe5HJvm3QfpdMcjxAdVa/7v5CILT0/ggyk9KKU+rtT44knHGqtb6m+bd5GcmmZXk3c3XGqvT+F4elcZz7pf3HWMEc32hlLIsjQ95bJ7k1c3XQHqSLBntXKNwQhrXs56Xxtd5zgB97kxyTK31jgmMCwAAYEzccQ8AAMCYNO/QnZdGofTaNJaDX5TkV0mOq7W+sPl86sHO/2IaBedPNM9fkkYB9s9pFCkPqbV+YBxDfkcad1H/Oo27ckdbyP6rFnwNfee/JclBSd6QxnPA70uyKsnCJD9KcnySo5rPgx/tHD1pLEH+/iSXJ3k4o7yru8+4t6XxIYoPJ7k+jWXaFzXneF+Sg2ut1wxjnO5a698lmZ9Gwfn6NH4OS5PckEbh/+Qkbx5FjGemURSvafyu/3io5fg3lObv2fwkX0qjOL0yyR1JzkvyhFrrWWkU2pPG93Asc30tya5p5Msvktydxu/U0jRWQPhuGh/I2bXW+vOxzDXCuJbXWp+f5DlJvpm134cHkvw+yTuT7F1rvWyiYgIAABgPpdYx/x8bAACAKaaU8t40n6deax3tndbAOGsuX79HknNrrce3Oh4AAACGxx33AAAAABuBUsphaRTtk8ZjDQAAAGgTCvcAAAAAbaCUsucQbVun8ViGpPG4ga9NSFAAAACMi2mtDgAAAACAYflJKeXmJP+b5Io0nmO/ZZLHJzk1ySOa/d5fa723NSECAAAwGgr3AAAAAO2hJHly8zWYzyT5t4kJBwAAgPGicA8AAADQHl6Z5NlJnpjG3fXbJFmdZEGS3yQ5o9b6u9aFBwAAwGiVWmurYwAAAAAAAACAKauj1QEAAAAAAAAAwFSmcA8AAAAAAAAALaRwDwAAAAAAAAAtpHAPAAAAAAAAAC2kcA8AAAAAAAAALaRwDwAAAAAAAAAtpHAPAAAAAAAAAC2kcA8AAAAAAAAALaRwDwAAAAAAAAAtpHAPAAAAAAAAAC2kcA8AAAAAAAAALaRwDwAAAAAAAAAtpHAPAAAAAAAAAC2kcA8AAAAAAAAALaRwDwAAAAAAAAAtpHAPAAAAAAAAAC2kcA8AAAAAAAAALaRwDwAAAAAAAAAt9P8DI9/v24aAw2YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Efficient frontier\n",
    "ax = df_result.plot(x=\"risk\", y=\"return\", style=\"-o\", xlabel=\"portfolio tracking error\", ylabel=\"portfolio alpha\", grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the portfolio composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
    "ax1 = df_result.set_index('risk').iloc[:, 3:].plot.area(logx=False, colormap=my_cmap, \n",
    "                                                        xlabel='portfolio risk (std. dev.)', ylabel=\"x\")\n",
    "ax1.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
}