Portfolio Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff9e5dbc",
   "metadata": {},
   "source": [
    "# Portfolio Optimization "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b80035",
   "metadata": {},
   "source": [
    "## Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "625b38dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (0.2.32)\n",
      "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: lxml>=4.9.1 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (4.9.1)\n",
      "Requirement already satisfied: peewee>=3.16.2 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (3.17.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (2.3.9)\n",
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      "Requirement already satisfied: appdirs>=1.4.4 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: pytz>=2022.5 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (2023.3.post1)\n",
      "Requirement already satisfied: requests>=2.31 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from yfinance) (2.31.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.3.1)\n",
      "Requirement already satisfied: webencodings in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
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      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2022.9.14)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (1.26.11)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (3.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2c4c26f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datetime in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (5.3)\n",
      "Requirement already satisfied: zope.interface in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from datetime) (5.4.0)\n",
      "Requirement already satisfied: pytz in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from datetime) (2023.3.post1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (from zope.interface->datetime) (63.4.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c395239d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: timedelta in c:\\users\\hardik vaibhav\\anaconda3\\lib\\site-packages (2020.12.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c0e6ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf \n",
    "#This library is used to pick stock prices from website of Yahoo Finance\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "#datatime allows us select a certain time range\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "#NumPy and SciPy will allow us to use certain statistical methods that we need"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdfb85b",
   "metadata": {},
   "source": [
    "# Section 1- Define Tickers and Time Range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144ab8c5",
   "metadata": {},
   "source": [
    "## Define the list of tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f4cb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = ['HDB','RELIANCE.NS','TCS.NS','SBIN.NS', 'AXB.IL']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8088d45e",
   "metadata": {},
   "source": [
    "## Set the end date to today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "509f85ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "end_date = datetime.today()\n",
    "print(end_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb4ebcc2",
   "metadata": {},
   "source": [
    "## Set the start date to 5 years ago"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "527fdacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-11-30 09:21:31.764510\n"
     ]
    }
   ],
   "source": [
    "start_date = end_date - timedelta(days = 5*365)\n",
    "print(start_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305044d4",
   "metadata": {},
   "source": [
    "# Section 2 - Download Adjusted Closed Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1600c90",
   "metadata": {},
   "source": [
    "## Create an empty DataFrame to store the adjusted close prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "705cc3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_close_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "194f96e1",
   "metadata": {},
   "source": [
    "## Download the close prices for each ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e9bf911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "for ticker in tickers:\n",
    "    data = yf.download(ticker, start = start_date,end = end_date)\n",
    "    adj_close_df[ticker] = data['Adj Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3603032a",
   "metadata": {},
   "source": [
    "# Display the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b89f004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  HDB  RELIANCE.NS       TCS.NS     SBIN.NS     AXB.IL\n",
      "Date                                                                  \n",
      "2018-11-30  49.095390  1134.802368  1765.933472  272.418213  44.180496\n",
      "2018-12-03  48.495129  1124.013916  1778.629272  274.475800  44.130688\n",
      "2018-12-04  48.064297  1119.980225  1804.154907  270.408447  43.931450\n",
      "2018-12-06  49.308380  1092.133911  1787.870483  263.804993  41.739864\n",
      "2018-12-07  48.630669  1101.999268  1790.113770  263.565704  41.590435\n",
      "...               ...          ...          ...         ...        ...\n",
      "2023-11-21  58.849998  2378.899902  3510.199951  561.500000  59.200001\n",
      "2023-11-22  58.639999  2388.199951  3530.149902  558.950012  59.700001\n",
      "2023-11-24  59.500000  2393.899902  3457.100098  560.349976  60.599998\n",
      "2023-11-27  58.889999          NaN          NaN         NaN  60.200001\n",
      "2023-11-28  59.349998  2394.399902  3470.149902  564.450012  61.700001\n",
      "\n",
      "[1256 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(adj_close_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42513038",
   "metadata": {},
   "source": [
    "# Section 3 - Calculate Lognormal Returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030cae0d",
   "metadata": {},
   "source": [
    "## Calculate the lognormal returns for each ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07e853e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_returns = np.log(adj_close_df/adj_close_df.shift(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c66cc07",
   "metadata": {},
   "source": [
    "## Drop any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4adbded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 HDB  RELIANCE.NS    TCS.NS   SBIN.NS    AXB.IL\n",
      "Date                                                           \n",
      "2018-12-03 -0.012302    -0.009552  0.007164  0.007525 -0.001128\n",
      "2018-12-04 -0.008924    -0.003595  0.014249 -0.014930 -0.004525\n",
      "2018-12-06  0.025554    -0.025178 -0.009067 -0.024723 -0.051174\n",
      "2018-12-07 -0.013840     0.008993  0.001254 -0.000907 -0.003586\n",
      "2018-12-10 -0.052940    -0.039168 -0.009771 -0.004367 -0.037833\n",
      "...              ...          ...       ...       ...       ...\n",
      "2023-11-17  0.006038    -0.002184  0.001314 -0.037645 -0.026404\n",
      "2023-11-20  0.002234    -0.002636  0.004885  0.001242 -0.011775\n",
      "2023-11-21  0.009904     0.012499 -0.002674 -0.003999  0.001691\n",
      "2023-11-22 -0.003575     0.003902  0.005667 -0.004552  0.008410\n",
      "2023-11-24  0.014559     0.002384 -0.020910  0.002501  0.014963\n",
      "\n",
      "[1088 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "log_returns = log_returns.dropna()\n",
    "print(log_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eac6e18",
   "metadata": {},
   "source": [
    "# Section 4 - Calculate Covariance Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f66380",
   "metadata": {},
   "source": [
    "## Calculate the covariance matrix using annualized returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4f8e22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  HDB  RELIANCE.NS    TCS.NS   SBIN.NS    AXB.IL\n",
      "HDB          0.117736     0.039472  0.019102  0.054027  0.080411\n",
      "RELIANCE.NS  0.039472     0.093286  0.027346  0.041434  0.047385\n",
      "TCS.NS       0.019102     0.027346  0.063284  0.021609  0.025122\n",
      "SBIN.NS      0.054027     0.041434  0.021609  0.118086  0.088282\n",
      "AXB.IL       0.080411     0.047385  0.025122  0.088282  0.182469\n"
     ]
    }
   ],
   "source": [
    "cov_matrix = log_returns.cov()*252\n",
    "print(cov_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0235d0e",
   "metadata": {},
   "source": [
    "# Section 4 - Define Portfolio Performance Metrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f972aeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27f21a8b",
   "metadata": {},
   "source": [
    "## Calculate the portfolio standard deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a2d64f",
   "metadata": {},
   "source": [
    "This line of code calculates the portfolio variance, which is a measure of risk associated with a portfolio of assets.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30f5b896",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_deviation(weights,cov_matrix):\n",
    "    variance = weights.T @ cov_matrix @ weights\n",
    "    return np.sqrt(variance)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472a3f2d",
   "metadata": {},
   "source": [
    "## Calculate the expected return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80373a7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e3f462ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def expected_return(weights,log_returns):\n",
    "    return np.sum(log_returns.mean()*weights)*252"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289f9622",
   "metadata": {},
   "source": [
    "## Calculate the Sharpe Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8fcfebba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sharpe_ratio(weights,log_returns, cov_matrix, risk_free_rate):\n",
    "    return(expected_return(weights,log_returns)- risk_free_rate) / standard_deviation(weights,cov_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e30fc1a",
   "metadata": {},
   "source": [
    "# Section 5 - Portfolio Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a83f6da",
   "metadata": {},
   "source": [
    "## Set the Risk-free rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46144ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_free_rate = 0.07 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d39045",
   "metadata": {},
   "source": [
    "## Define the function to minimize (negative Sharpe Ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e660ab4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neg_sharpe_ratio(weights,log_returns, cov_matrix, risk_free_rate):\n",
    "    return -sharpe_ratio(weights,log_returns, cov_matrix, risk_free_rate)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64574a51",
   "metadata": {},
   "source": [
    "## Set the Constraints and Bounds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8d316a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "constraints = {'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1}\n",
    "bounds = [(0, 0.4) for _ in range(len(tickers))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2478cb40",
   "metadata": {},
   "source": [
    "## Set the Initial Weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ad0dc23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2 0.2 0.2 0.2 0.2]\n"
     ]
    }
   ],
   "source": [
    "initial_weights = np.array([1/len(tickers)]*len(tickers))\n",
    "print (initial_weights) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb503206",
   "metadata": {},
   "source": [
    "##  Optimize the weights to maximize sharpe ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "68070281",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_results = minimize(neg_sharpe_ratio,initial_weights, args =(log_returns, cov_matrix, risk_free_rate), method = 'SLSQP', constraints = constraints, bounds = bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e306d1",
   "metadata": {},
   "source": [
    "## Get the Optimal Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2f09fbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimal_weights = optimized_results.x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a23ab74c",
   "metadata": {},
   "source": [
    "## Section 7 - Analyze the optimal Portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13a0ef6",
   "metadata": {},
   "source": [
    "## Display the analytics of the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "588f331c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Weights\n",
      "HDB: 0.0000\n",
      "RELIANCE.NS: 0.2000\n",
      "TCS.NS: 0.4000\n",
      "SBIN.NS: 0.4000\n",
      "AXB.IL: 0.0000\n",
      "Expected Annual Return: 0.1571\n",
      "Expected Volatility: 0.2251\n",
      "Sharpe Ratio: 0.3868\n"
     ]
    }
   ],
   "source": [
    "print('Optimal Weights')\n",
    "for ticker, weight in zip(tickers,optimal_weights):\n",
    "    print(f\"{ticker}: {weight:.4f}\")\n",
    "\n",
    "optimal_portfolio_return = expected_return(optimal_weights, log_returns)\n",
    "optimal_portfolio_volatility = standard_deviation(optimal_weights, cov_matrix)\n",
    "optimal_sharpe_ratio = sharpe_ratio(optimal_weights, log_returns, cov_matrix, risk_free_rate)\n",
    "\n",
    "print(f\"Expected Annual Return: {optimal_portfolio_return:.4f}\")\n",
    "print(f\"Expected Volatility: {optimal_portfolio_volatility:.4f}\")\n",
    "print(f\"Sharpe Ratio: {optimal_sharpe_ratio:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545f848f",
   "metadata": {},
   "source": [
    "## Display the final portfolio as a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d415418a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(tickers, optimal_weights)\n",
    "\n",
    "plt.xlabel('Assets')\n",
    "plt.ylabel('Optimal Weights')\n",
    "plt.title('Optimal Portfolio Weights')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55619fd",
   "metadata": {},
   "source": [
    "## Happy Coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b74782cb",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}