optimization_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6487c668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#Import required libraries\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import gurobipy as gp\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6975b1",
   "metadata": {},
   "source": [
    "### Loading the 2019 and 2020 file and initializing variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbb7a67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the required datasets\n",
    "df_2019 = pd.read_csv('https://raw.githubusercontent.com/KshitijMahajan1998/Optimization/main/stocks2019.csv')\n",
    "df_2020 = pd.read_csv('https://raw.githubusercontent.com/KshitijMahajan1998/Optimization/main/stocks2020.csv')\n",
    "\n",
    "#setting the time limit \n",
    "time = 3600\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c5df350",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the number of stocks in the data  \n",
    "num_stocks = len(df_2019.columns) - 2\n",
    "\n",
    "# creating array for m values \n",
    "m_values = [5,10,20,30,40,50,60,70,80,90,100]\n",
    "m = [x for x in m_values if x <= num_stocks]\n",
    "\n",
    "#renaming the first column to date \n",
    "df_2019.rename(columns = {list(df_2019)[0] : 'Date'}, inplace = True)\n",
    "df_2020.rename(columns = {list(df_2020)[0] : 'Date'}, inplace = True)\n",
    "\n",
    "# Convert the 'Date' column into a datetime object\n",
    "df_2019['Date'] = pd.to_datetime(df_2019['Date'])\n",
    "df_2020['Date'] = pd.to_datetime(df_2020['Date'])\n",
    "\n",
    "# Set 'Date' as the index\n",
    "df_2019.set_index('Date', inplace=True)\n",
    "df_2020.set_index('Date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa3d369",
   "metadata": {},
   "source": [
    "### Calculating the stock return similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9910bfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dataframe containing daily returns of stocks \n",
    "df_2019_returns = df_2019/df_2019.shift(1) - 1\n",
    "\n",
    "#calculating correlation between different stocks and creating the matrix\n",
    "df_corr = df_2019_returns.corr().iloc[1:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb67cfe1",
   "metadata": {},
   "source": [
    "## 1. Stock Allocation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da66f36",
   "metadata": {},
   "source": [
    "#### Objective Function\n",
    "$$\\underset{x,y}{max}\\sum_{i=1}^{n}\\sum_{j=1}^{n}\\rho _{ij}x_{ij}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b8d145de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating objective array \n",
    "N = len(df_corr)\n",
    "obj = np.zeros(N**2 + N) # N**2 + N represents number of decision variables \n",
    "\n",
    "#filling in the values in objective array \n",
    "index = 0 \n",
    "for i in df_corr.columns:\n",
    "    for j in df_corr.columns:\n",
    "        obj[index] = df_corr.loc[i,j]\n",
    "        index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f8ccdf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing constraints, and directions\n",
    "A = np.zeros((1 + N + N**2,N**2 + N))\n",
    "b = np.zeros(1 + N + N**2)\n",
    "direction = np.array(['']*(1 + N + N**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40aa45bc",
   "metadata": {},
   "source": [
    "#### Constraint 1 - Limiting the number of selected stocks to m\n",
    "$$\\sum_{j=1}^{n} y_{j} = m$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0af9aa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1st constraint -- limiting the number of selected stocks to m \n",
    "A[0,N**2:] = 1\n",
    "direction[0] = '='"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2f9978",
   "metadata": {},
   "source": [
    "#### Constraint 2 - Limiting the similar stock count to 1\n",
    "$$\\sum_{j=1}^{n} x_{ij} = 1 \\: for\\: i = 1,2,....n$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cc50841",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2nd constraint -- limiting the similar stock count to 1 \n",
    "row = 1 \n",
    "ind_vec = np.array(range(N))\n",
    "for i in range(N):\n",
    "    A[row, i*N + ind_vec] = 1\n",
    "    b[row] = 1 \n",
    "    direction[row] = '='\n",
    "    row += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0156806e",
   "metadata": {},
   "source": [
    "#### Constraint 3 - Making sure best stock representation is in fund\n",
    "$$x_{ij} \\leq y_{ij} \\:for\\: i,j = 1,2....n$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "058e1c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "#3rd constraint -- making sure best stock representation is present in fund \n",
    "A[N+1:,0:(N**2)] = np.diag(np.ones(N*N))\n",
    "\n",
    "for i in range(N):\n",
    "    A[(N+1+(i*N)):(N+1+(i*N)+100),N**2:] = -(np.diag(np.ones(N)))\n",
    "\n",
    "b[row : row + N*N] = 0\n",
    "direction[row : row + N*N] = '<'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3175d4a5",
   "metadata": {},
   "source": [
    "### Performing stock selection for m = 5,10,...100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4822cdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing a variable to save optimal decision variable values \n",
    "stock_selection = {}\n",
    "\n",
    "for i in m:\n",
    "    b[0] = i\n",
    "    tspMod = gp.Model()\n",
    "    tspMod_x = tspMod.addMVar(N**2 + N,vtype=['B']*(N**2 + N)) \n",
    "    tspMod_con = tspMod.addMConstrs(A, tspMod_x, direction, b)\n",
    "    tspMod.setMObjective(None,obj,0,sense=gp.GRB.MAXIMIZE)\n",
    "    tspMod.Params.OutputFlag = 0 # tell gurobi to shut up!!\n",
    "    tspMod.optimize()\n",
    "    stock_selection[i] = tspMod.x[N**2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a0812a",
   "metadata": {},
   "source": [
    "## 2. Calculating Portfolio Weights for m=5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ff9eac",
   "metadata": {},
   "source": [
    "#### Filtering for the selected stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38fc48e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0., -0.,  0.,  0., -0., -0.,  0.,  0.,  0., -0.,  0.,  0., -0.,\n",
       "       -0.,  0., -0.,  0.,  0., -0., -0., -0., -0.,  0.,  0., -0., -0.,\n",
       "       -0., -0., -0.,  0., -0.,  0.,  0., -0., -0.,  0., -0.,  0., -0.,\n",
       "       -0., -0., -0., -0., -0., -0., -0., -0., -0., -0., -0., -0., -0.,\n",
       "       -0., -0., -0.,  0.,  1., -0., -0.,  1., -0., -0., -0.,  1., -0.,\n",
       "       -0.,  0., -0., -0., -0., -0., -0., -0., -0., -0., -0.,  0., -0.,\n",
       "       -0., -0., -0., -0., -0., -0., -0., -0.,  0., -0., -0., -0., -0.,\n",
       "       -0., -0., -0.,  1., -0., -0., -0.,  1., -0.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identifying the selected stocks \n",
    "selected_stocks = np.array(stock_selection[5])\n",
    "selected_stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f67596",
   "metadata": {},
   "source": [
    "#### Objective Function\n",
    "$$\\underset{w}{min} \\sum_{t=1}^{T}\\left | q_{t} - \\sum_{i=1}^{m}w_{i}r_{it} \\right |$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2c54185",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the objective array\n",
    "N = len(df_2019) - 1\n",
    "obj = np.zeros(sum(selected_stocks.astype(int)) + N)\n",
    "obj[0:N] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "986484f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating constraints \n",
    "n_var = sum(selected_stocks.astype(int)) + N\n",
    "A = np.zeros((N*2+1, n_var))\n",
    "b = np.zeros(N*2+1)\n",
    "direction = np.array(['']*(N*2+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c7a366",
   "metadata": {},
   "source": [
    "#### Constraint 1 - Sum of weights of all the selected stocks = 1 \n",
    "$$ \\sum_{i=1}^{m}w_{i} = 1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ef001fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#constraint 1\n",
    "A[0,N:] = 1 \n",
    "b[0] = 1\n",
    "direction[0] = '='"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db6d95b",
   "metadata": {},
   "source": [
    "#### Remaining Constraints - Convert the objective function to linear program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de50aa83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remaining constraints \n",
    "q = df_2019_returns[['NDX']]\n",
    "r = df_2019_returns[df_2019_returns.columns[1:].values[selected_stocks.astype(bool)]]\n",
    "\n",
    "A[1:N+1,0:N] = np.diag(np.ones(N))\n",
    "A[N+1:,0:N] = -(np.diag(np.ones(N)))\n",
    "for i in range(N):\n",
    "    A[i+1,N:] = r.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    b[i+1] = q.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    direction[i+1] = '>'\n",
    "    A[N+1+i, N:] = r.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    b[N+1+i] = q.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    direction[N+1+i] = '<' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9aa6a531",
   "metadata": {},
   "outputs": [],
   "source": [
    "tspMod = gp.Model()\n",
    "tspMod_x = tspMod.addMVar(int(n_var)) \n",
    "tspMod_con = tspMod.addMConstrs(A, tspMod_x, direction, b)\n",
    "tspMod.setMObjective(None,obj,0,sense=gp.GRB.MINIMIZE)\n",
    "\n",
    "tspMod.Params.OutputFlag = 0 # tell gurobi to shut up!!\n",
    "tspMod.optimize()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e70b50",
   "metadata": {},
   "source": [
    "#### Comparing fund performance with 2020 data based on the below formula\n",
    "$$ \\sum_{t=1}^{T}\\left | q_{t} - \\sum_{i=1}^{5}w_{i}r_{it} \\right |$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0a5a4d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance score of this fund (m = 5) based on the above equation is 0.87.\n"
     ]
    }
   ],
   "source": [
    "#calculating 2020 returns matrix \n",
    "df_2020_returns = df_2020/df_2020.shift(1) - 1 \n",
    "\n",
    "#identifying the selected stocks \n",
    "selected_stocks = np.array(stock_selection[5])\n",
    "\n",
    "#filtering df_2020 for these stocks \n",
    "df_2020_m = df_2020_returns[df_2020_returns.columns[1:].values[selected_stocks.astype(bool)]]\n",
    "df_2020_index = df_2020_returns[['NDX']]\n",
    "df_2019_index = df_2019_returns[['NDX']]\n",
    "\n",
    "# calculating performance score \n",
    "for i in range(5):\n",
    "    df_2020_m.iloc[:,i] = df_2020_m.iloc[:,i]*tspMod.x[N+i]\n",
    "\n",
    "df_2020_m['return_sum'] = df_2020_m.sum(axis = 1)\n",
    "df_2020_m = df_2020_m.merge(df_2020_index, left_index = True, right_index = True)\n",
    "df_2020_m['return_diff'] = np.abs(df_2020_m['NDX'] - df_2020_m['return_sum'])\n",
    "print('Performance score of this fund (m = 5) based on the above equation is {}.'.format(round(df_2020_m['return_diff'].sum(),2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c843bab0",
   "metadata": {},
   "source": [
    "#### Tracking daily 2020 returns of index vs our fund with m = 5 stocks selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "483da5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tracking daily 2020 returns of index vs our fund with 5 selected stocks ')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tracking daily returns of 2020 index vs our fund with m = 5 stocks selected \n",
    "figure(figsize=(15, 5))\n",
    "df_2020_m['return_sum'].plot(label = 'Fund Performance')\n",
    "df_2020_m['NDX'].plot(label = 'Index Performance')\n",
    "legend()\n",
    "xlabel('Time Period')\n",
    "ylabel('Daily Returns')\n",
    "title('Tracking daily 2020 returns of index vs our fund with 5 selected stocks ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829f5271",
   "metadata": {},
   "source": [
    "## 3. Replicating the same task for m = 10,20.....100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00701714",
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a dataframe to store performance values \n",
    "df_performance = pd.DataFrame(columns = ['2019','2020'], index = m)\n",
    "\n",
    "#calculating the performing score \n",
    "for i in m:    \n",
    "    \n",
    "    #filtering for selected stocks \n",
    "    selected_stocks = np.array(stock_selection[i])\n",
    "    \n",
    "    #creating objective array \n",
    "    obj = np.zeros(sum(selected_stocks.astype(int)) + N)\n",
    "    obj[0:N] = 1\n",
    "    \n",
    "    #creating constraints \n",
    "    n_var = sum(selected_stocks.astype(int)) + N\n",
    "    A = np.zeros((N*2+1, n_var))\n",
    "    b = np.zeros(N*2+1)\n",
    "    direction = np.array(['']*(N*2+1))\n",
    "    \n",
    "    #constraint 1 \n",
    "    A[0,N:] = 1 \n",
    "    b[0] = 1\n",
    "    direction[0] = '='\n",
    "    \n",
    "    #remaining constraints \n",
    "    A[1:N+1,0:N] = np.diag(np.ones(N))\n",
    "    A[N+1:,0:N] = -(np.diag(np.ones(N)))\n",
    "    r = df_2019_returns[df_2019_returns.columns[1:].values[selected_stocks.astype(bool)]]\n",
    "    for j in range(N):\n",
    "        A[j+1,N:] = r.reset_index().drop(columns = 'Date').iloc[j+1].values\n",
    "        b[j+1] = q.reset_index().drop(columns = 'Date').iloc[j+1].values\n",
    "        direction[j+1] = '>'\n",
    "        A[N+1+j, N:] = r.reset_index().drop(columns = 'Date').iloc[j+1].values\n",
    "        b[N+1+j] = q.reset_index().drop(columns = 'Date').iloc[j+1].values\n",
    "        direction[N+1+j] = '<' \n",
    "    \n",
    "    #solving the optimization problem \n",
    "    tspMod = gp.Model()\n",
    "    tspMod_x = tspMod.addMVar(int(n_var)) \n",
    "    tspMod_con = tspMod.addMConstrs(A, tspMod_x, direction, b)\n",
    "    tspMod.setMObjective(None,obj,0,sense=gp.GRB.MINIMIZE)\n",
    "    tspMod.Params.OutputFlag = 0 # tell gurobi to shut up!!\n",
    "    tspMod.optimize()\n",
    "    \n",
    "     #calculating performance on 2020 data  \n",
    "    \n",
    "    selected_stocks = np.array(stock_selection[i])\n",
    "    df_2020_m = df_2020_returns[df_2020_returns.columns[1:].values[selected_stocks.astype(bool)]]\n",
    "    for j in range(i):\n",
    "        df_2020_m.iloc[:,j] = df_2020_m.iloc[:,j]*tspMod.x[N+j]\n",
    "    df_2020_m['return_sum'] = df_2020_m.sum(axis = 1)\n",
    "    df_2020_m = df_2020_m.merge(df_2020_index, left_index = True, right_index = True)\n",
    "    df_2020_m['return_diff'] = np.abs(df_2020_m['NDX'] - df_2020_m['return_sum'])\n",
    "    df_performance.loc[i,'2020'] = df_2020_m['return_diff'].sum()\n",
    "    \n",
    "    #calculating performance on 2019 data \n",
    "    df_2019_m = df_2019_returns[df_2019_returns.columns[1:].values[selected_stocks.astype(bool)]]\n",
    "    for j in range(i):\n",
    "        df_2019_m.iloc[:,j] = df_2019_m.iloc[:,j]*tspMod.x[N+j]\n",
    "    df_2019_m['return_sum'] = df_2019_m.sum(axis = 1)\n",
    "    df_2019_m = df_2019_m.merge(df_2019_index, left_index = True, right_index = True)\n",
    "    df_2019_m['return_diff'] = np.abs(df_2019_m['NDX'] - df_2019_m['return_sum'])\n",
    "    df_performance.loc[i,'2019'] = df_2019_m['return_diff'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546b218b",
   "metadata": {},
   "source": [
    "#### Tracking # of stocks in fund (m) vs. the fund performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa8958ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fund Performance vs. # of selected stocks (m)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tracking m vs. fund performance \n",
    "figure(figsize=(15, 5))\n",
    "plot(df_performance.index,df_performance['2019'], marker = '.', label = '2019')\n",
    "plot(df_performance.index,df_performance['2020'], marker = '.', label = '2020')\n",
    "grid(linestyle = '--', linewidth = 0.5)\n",
    "legend()\n",
    "xlabel('Number of selected stocks (m)')\n",
    "ylabel('Tracking Error')\n",
    "title('Fund Performance vs. # of selected stocks (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ea8425b",
   "metadata": {},
   "source": [
    "## 4. Directly solving MIP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f486fdf",
   "metadata": {},
   "source": [
    "#### Objective Function\n",
    "$$\\underset{w}{min} \\sum_{t=1}^{T}\\left | q_{t} - \\sum_{i=1}^{n}w_{i}r_{it} \\right |$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6103c4cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the objective array \n",
    "N = len(df_2019) - 1 # number of time periods \n",
    "n = len(df_corr) #number of stocks \n",
    "obj = np.zeros(N + n + n) #function to be minimized + weights of stocks (w) + selection variable for each stock (y)\n",
    "obj[0:N] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5dc3e3d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating constraints \n",
    "n_var = N + n + n\n",
    "A = np.zeros((n+1+1+N*2, n_var))\n",
    "b = np.zeros(n+1+1+N*2)\n",
    "direction = np.array(['']*(n+1+1+N*2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c049334",
   "metadata": {},
   "source": [
    "#### Constraint 1 \n",
    "$$\\sum_{j=1}^{n} y_{j} = m$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63ece826",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1st constraint -- limiting the number of selected stocks to m \n",
    "A[0,N+n:] = 1 \n",
    "direction[0] = '='"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18213dae",
   "metadata": {},
   "source": [
    "#### Constraint 2 - Wi = 0 if yi = 0\n",
    "$$w_{i} \\leq y_{i}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89c5bc30",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2nd constraint - wi = 0 if yi = 0, big M = 1 \n",
    "A[1:n+1,N:N+n] = np.diag(np.ones(n))\n",
    "A[1:n+1,N+n:] = -(np.diag(np.ones(n)))\n",
    "direction[1:n+1] = '<'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6bab59",
   "metadata": {},
   "source": [
    "#### Constraint 3 - Sum of weights = 1 \n",
    "$$ \\sum_{i=1}^{m}w_{i} = 1$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9d29b946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3rd constraint - sum of w = 1 \n",
    "A[n+1,N:N+n] = 1\n",
    "b[n+1] = 1\n",
    "direction[n+1] = '='"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "377586a2",
   "metadata": {},
   "source": [
    "#### Remaining Constraints - Convert the objective function to linear program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "693f5004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4th Constraint - converting to LP \n",
    "A[n+1+1:n+1+1+N,0:N] = np.diag(np.ones(N)) #coefficient of y \n",
    "A[n+1+1+N:,0:N] = -(np.diag(np.ones(N))) #coefficient of y \n",
    "\n",
    "for i in range(N):\n",
    "    A[i+n+1+1,N:N+n] = df_2019_returns.reset_index().drop(columns = ['Date', 'NDX']).iloc[i+1]\n",
    "    b[i+n+1+1] = q.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    direction[i+n+1+1] = '>'\n",
    "    A[i+n+1+1+N,N:N+n] = df_2019_returns.reset_index().drop(columns = ['Date', 'NDX']).iloc[i+1]\n",
    "    b[i+n+1+1+N] = q.reset_index().drop(columns = 'Date').iloc[i+1].values\n",
    "    direction[i+n+1+1+N] = '<'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad235b8",
   "metadata": {},
   "source": [
    "#### Solving the MIP!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1ed01af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initializing variable to store the objective value \n",
    "objvalues = []\n",
    "\n",
    "#solving the MIP for each value of m\n",
    "for i in m:\n",
    "    b[0] = i\n",
    "    tspMod = gp.Model()\n",
    "    tspMod_x = tspMod.addMVar(len(obj), vtype = ['C']*(N+n) + ['B']*n) \n",
    "    tspMod_con = tspMod.addMConstrs(A, tspMod_x, direction, b)\n",
    "    tspMod.setMObjective(None,obj,0,sense=gp.GRB.MINIMIZE)\n",
    "\n",
    "    tspMod.Params.OutputFlag = 0 # tell gurobi to shut up!!\n",
    "    tspMod.params.TimeLimit = time\n",
    "    tspMod.optimize()\n",
    "    objvalues.append(tspMod.objval)\n",
    "    pd.DataFrame(tspMod.x).to_excel('obj_values_{}.xlsx'.format(i))   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66fc86c3",
   "metadata": {},
   "source": [
    "#### Tracking Performance Score with 2019 and 2020 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0fadc5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_performance_mip = pd.DataFrame(columns = ['2019','2020'], index = m)\n",
    "\n",
    "for i in m:\n",
    "    \n",
    "    #creating a copy of 2019 daily returns df \n",
    "    df_returns_m = df_2019_returns.copy()\n",
    "    \n",
    "    #creating a copy of 2020 daily returns df \n",
    "    df_returns_m_2020 = df_2020_returns.copy()\n",
    "    \n",
    "    #loading the objective values from excel \n",
    "    df = np.array(pd.read_excel('obj_values_{}.xlsx'.format(i)).iloc[:,1])[N:N+n]\n",
    "     \n",
    "    #calculating performance on 2019 data \n",
    "    for j in range(n):\n",
    "        df_returns_m.iloc[:,j+1] = df_returns_m.iloc[:,j+1]*df[j]\n",
    "        \n",
    "    df_returns_m['return_sum'] = df_returns_m.iloc[:,1:].sum(axis = 1)\n",
    "    df_returns_m['return_diff'] = np.abs(df_returns_m['NDX'] - df_returns_m['return_sum'])\n",
    "    df_performance_mip.loc[i,'2019'] = df_returns_m['return_diff'].sum()\n",
    "\n",
    "    #calculating performance on 2020 data \n",
    "    for j in range(n):\n",
    "        df_returns_m_2020.iloc[:,j+1] = df_returns_m_2020.iloc[:,j+1]*df[j]\n",
    "        \n",
    "    df_returns_m_2020['return_sum'] = df_returns_m_2020.iloc[:,1:].sum(axis = 1)\n",
    "    df_returns_m_2020['return_diff'] = np.abs(df_returns_m_2020['NDX'] - df_returns_m_2020['return_sum'])\n",
    "    df_performance_mip.loc[i,'2020'] = df_returns_m_2020['return_diff'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8784762c",
   "metadata": {},
   "source": [
    "## Comparing the fund performance vs. number of stocks (m) for - \n",
    "### (i) Solving 1 IP and 1 LP   \n",
    "### (ii) Solving 1 MIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c71aa82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = subplots(nrows=1,ncols=2,sharey=True,figsize=(15, 5))\n",
    "\n",
    "ax1.plot(df_performance.index,df_performance['2019'], marker = '.', label = '2019')\n",
    "ax1.plot(df_performance.index,df_performance['2020'], marker = '.', label = '2020')\n",
    "ax1.legend()\n",
    "ax1.set_xlabel('No. selected stocks (m)')\n",
    "ax1.set_ylabel('Tracking Error')\n",
    "ax1.set_title('Fund Performance vs. # of selected stocks (m) - Method (i)')\n",
    "ax1.grid(linestyle = '--', linewidth = 0.5)\n",
    "ax2.plot(df_performance_mip.index,df_performance_mip['2019'], marker = '.', label = '2019')\n",
    "ax2.plot(df_performance_mip.index,df_performance_mip['2020'], marker = '.', label = '2020')\n",
    "ax2.legend()\n",
    "ax2.set_xlabel('No. of selected stocks (m)')\n",
    "ax2.set_ylabel('Tracking Error')\n",
    "ax2.set_title('Fund Performance vs. # of selected stocks (m) - Method (ii)')\n",
    "ax2.grid(linestyle = '--', linewidth = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10a6ac34",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}