SCL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdf492d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "80602cf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stock_data(ticker, start_date, end_date):\n",
    "    stock = yf.Ticker(ticker)\n",
    "    df = stock.history(start=start_date, end=end_date)\n",
    "    return df['Close']\n",
    "\n",
    "def calculate_returns(prices):\n",
    "    returns = prices.pct_change().dropna()\n",
    "    return returns\n",
    "\n",
    "def calculate_alpha_beta(stock_returns, market_returns):\n",
    "    slope, intercept, r_value, p_value, std_err = linregress(market_returns, stock_returns)\n",
    "    return intercept, slope\n",
    "\n",
    "def get_risk_free_rate(start_date, end_date, stock_ticker):\n",
    "    if stock_ticker.endswith(\".L\"):\n",
    "        rf_ticker = \"^IRX\"  # UK 3-Month Treasury Bill\n",
    "    else:\n",
    "        rf_ticker = \"^TNX\"  # US 10-Year Treasury Note\n",
    "\n",
    "    rf = yf.Ticker(rf_ticker)\n",
    "    rf_data = rf.history(start=start_date, end=end_date)\n",
    "    \n",
    "    if rf_ticker == \"^IRX\":\n",
    "        # IRX is in percentage terms, so divide by 100, then annualize by dividing by 12 (monthly rate)\n",
    "        rf_data['Daily Rate'] = rf_data['Close'] / 100 / 252\n",
    "    else:\n",
    "        # TNX is in percentage terms, so divide by 100, then annualize by dividing by 252 (daily rate)\n",
    "        rf_data['Daily Rate'] = rf_data['Close'] / 100 / 252\n",
    "    \n",
    "    return rf_data['Daily Rate']\n",
    "\n",
    "def get_market_index(stock_ticker):\n",
    "    if stock_ticker.endswith(\".L\"):\n",
    "        return \"^FTSE\"\n",
    "    else:\n",
    "        return \"^GSPC\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1bbc445",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the stock ticker symbol: AZN.L\n",
      "Enter the end date (YYYYMMDD): 20240725\n",
      "Enter the number of years: 5\n"
     ]
    }
   ],
   "source": [
    "# User input\n",
    "ticker = input(\"Enter the stock ticker symbol: \")\n",
    "market_index = '^GSPC'  # S&P 500 index\n",
    "end_date = input(\"Enter the end date (YYYYMMDD): \")\n",
    "years = int(input(\"Enter the number of years: \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "259fddec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Combined Data (with aligned dates):\n",
      "            Stock Returns  Market Returns  Risk-Free Rate\n",
      "2019-07-26       0.008029        0.008012        0.000082\n",
      "2019-07-29       0.039826        0.018214        0.000081\n",
      "2019-07-30      -0.007242       -0.005178        0.000081\n",
      "2019-07-31      -0.009400       -0.007846        0.000081\n",
      "2019-08-01       0.019686       -0.000250        0.000081\n",
      "\n",
      "The SCL regression equation for AZN.L is:\n",
      "Excess Stock Return (ri - rf) = 0.0005 + 0.6278 * Excess Market Return (rm - rf)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    end = datetime.strptime(end_date, \"%Y%m%d\")\n",
    "    start = end - relativedelta(years=years)\n",
    "except ValueError:\n",
    "    print(\"Invalid date format. Please use YYYYMMDD.\")\n",
    "else:\n",
    "    start_date_yf = start.strftime(\"%Y-%m-%d\")\n",
    "    end_date_yf = end.strftime(\"%Y-%m-%d\")\n",
    "\n",
    "    # Fetch stock data\n",
    "    stock_prices = get_stock_data(ticker, start_date_yf, end_date_yf)\n",
    "    \n",
    "    # Determine and fetch market index data\n",
    "    market_index = get_market_index(ticker)\n",
    "    market_prices = get_stock_data(market_index, start_date_yf, end_date_yf)\n",
    "    \n",
    "    # Fetch risk-free rate data\n",
    "    rf_data = get_risk_free_rate(start_date_yf, end_date_yf, ticker)\n",
    "\n",
    "    # Calculate returns\n",
    "    stock_returns = calculate_returns(stock_prices)\n",
    "    market_returns = calculate_returns(market_prices)\n",
    "\n",
    "    # Remove time component from index\n",
    "    stock_returns.index = stock_returns.index.date\n",
    "    market_returns.index = market_returns.index.date\n",
    "    rf_data.index = rf_data.index.date\n",
    "\n",
    "    # Create a DataFrame to hold all the data with a common date index\n",
    "    combined_data = pd.DataFrame(index=stock_returns.index)\n",
    "    combined_data['Stock Returns'] = stock_returns\n",
    "    combined_data['Market Returns'] = market_returns\n",
    "    combined_data['Risk-Free Rate'] = rf_data.reindex(combined_data.index)\n",
    "\n",
    "    # Print combined data to debug alignment\n",
    "    print(\"\\nCombined Data (with aligned dates):\")\n",
    "    print(combined_data.head())\n",
    "\n",
    "    # Drop rows with any NaN values\n",
    "    combined_data = combined_data.dropna()\n",
    "\n",
    "    if combined_data.empty:\n",
    "        print(\"No overlapping data between stock returns, market returns, and risk-free rate.\")\n",
    "    else:\n",
    "        # Adjust returns by the risk-free rate\n",
    "        excess_stock_returns = combined_data['Stock Returns'] - combined_data['Risk-Free Rate']\n",
    "        excess_market_returns = combined_data['Market Returns'] - combined_data['Risk-Free Rate']\n",
    "\n",
    "        # Calculate alpha and beta\n",
    "        alpha, beta = calculate_alpha_beta(excess_stock_returns, excess_market_returns)\n",
    "        \n",
    "        print(f\"\\nThe SCL regression equation for {ticker} is:\")\n",
    "        print(f\"Excess Stock Return (ri - rf) = {alpha:.4f} + {beta:.4f} * Excess Market Return (rm - rf)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "593d4e89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Average Annual Return for AZN.L: 17.14%\n",
      "Annual Variance for AZN.L: 6.15%\n",
      "\n",
      "Average Annual Return for ^FTSE: 3.26%\n",
      "Annual Variance for ^FTSE: 3.09%\n"
     ]
    }
   ],
   "source": [

    "def calculate_annual_metrics(daily_returns):\n",
    "    average_annual_return = np.mean(daily_returns) * 252\n",
    "    annual_variance = np.var(daily_returns) * 252\n",
    "    return average_annual_return, annual_variance\n",
    "\n",
    "# Calculate annual metrics for the stock\n",
    "stock_annual_return, stock_annual_variance = calculate_annual_metrics(stock_returns)\n",
    "\n",
    "# Calculate annual metrics for the market index\n",
    "market_annual_return, market_annual_variance = calculate_annual_metrics(market_returns)\n",
    "\n",
    "# Print the results\n",
    "print(f\"\\nAverage Annual Return for {ticker}: {stock_annual_return:.2%}\")\n",
    "print(f\"Annual Variance for {ticker}: {stock_annual_variance:.2%}\")\n",
    "\n",
    "print(f\"\\nAverage Annual Return for {market_index}: {market_annual_return:.2%}\")\n",
    "print(f\"Annual Variance for {market_index}: {market_annual_variance:.2%}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b391b147",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_rf_daily = rf_data.mean()  # Average daily risk-free rate\n",
    "\n",
    "# Create scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(excess_market_returns, excess_stock_returns, label='Returns', color='blue', s=10)\n",
    "\n",
    "# Calculate the SCL\n",
    "x = np.linspace(excess_stock_returns.min(), excess_stock_returns.max(), 100)\n",
    "y = alpha + beta * (x - average_rf_daily)  # Use the average weekly risk-free rate\n",
    "\n",
    "# Plot the SCL\n",
    "plt.plot(x, y, color='red', label=f'SCL: α={alpha:.2f}, β={beta:.2f}')\n",
    "\n",
    "# Chart details\n",
    "plt.xlabel('Excess Market Returns')\n",
    "plt.ylabel('Excess Stock Returns')\n",
    "\n",
    "# Set the limits to center (0,0)\n",
    "plt.xlim(left=-excess_market_returns.abs().max(), right=excess_market_returns.abs().max())\n",
    "plt.ylim(bottom=-excess_stock_returns.abs().max(), top=excess_stock_returns.abs().max())\n",
    "\n",
    "# Draw the x and y axes at (0, 0)\n",
    "plt.axhline(0, color='black', lw=1)  # X-axis\n",
    "plt.axvline(0, color='black', lw=1)  # Y-axis\n",
    "\n",
    "# Remove grid lines\n",
    "plt.grid(False)\n",
    "\n",
    "# Move the legend to the upper left corner\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.title(f'{ticker} vs {market_index} Returns and SCL')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}