968MomentumPortfolioTesting.ipynb

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      "Enter the year for analysis:  2023\n"
     ]
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   "source": [
    "#Code to backtest momentum trading algorithm for the year 2022, 2023 and 2024\n",
    "\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "\n",
    "RISK_FREE_RATE = 0.02\n",
    "\n",
    "def get_sp500_tickers():\n",
    "    table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "    tickers = table[0]['Symbol'].tolist()\n",
    "    return tickers\n",
    "\n",
    "def fetch_and_analyze_data(ticker, start_date, end_date):\n",
    "    try:\n",
    "        adjusted_ticker = ticker.replace('.', '-')\n",
    "        stock = yf.Ticker(adjusted_ticker)\n",
    "        stock_data = stock.history(start=start_date, end=end_date)\n",
    "\n",
    "        if not stock_data.empty:\n",
    "            stock_data.index = stock_data.index.tz_localize(None)\n",
    "            \n",
    "            start_price = stock_data.iloc[0]['Close']\n",
    "            end_price = stock_data.iloc[-1]['Close']\n",
    "            annual_return = (end_price - start_price) / start_price * 100\n",
    "            \n",
    "            stock_data['Daily Return'] = stock_data['Close'].pct_change()\n",
    "            std_dev = stock_data['Daily Return'].std() * np.sqrt(252)\n",
    "            sharpe_ratio = (annual_return / 100 - RISK_FREE_RATE) / std_dev\n",
    "            \n",
    "            pe_ratio = stock.info.get('trailingPE', None)\n",
    "            stock_name = stock.info.get('longName', None)\n",
    "\n",
    "            stock_data['up_days'] = stock_data['Daily Return'] > 0\n",
    "            stock_data['down_days'] = stock_data['Daily Return'] < 0\n",
    "\n",
    "            up_days_percentage = stock_data['up_days'].sum() / len(stock_data) * 100\n",
    "            down_days_percentage = stock_data['down_days'].sum() / len(stock_data) * 100\n",
    "\n",
    "            stock_data['Momentum Health Indicator'] = up_days_percentage - down_days_percentage\n",
    "            stock_data.drop(columns=['up_days', 'down_days'], inplace=True)\n",
    "            \n",
    "            expected_return = stock_data['Daily Return'].mean()\n",
    "            var_95 = expected_return - 1.96 * std_dev\n",
    "            stock_data['VAR'] = var_95\n",
    "\n",
    "            return {\n",
    "                'Ticker': ticker, \n",
    "                'Stock Name': stock_name,\n",
    "                'Return': annual_return, \n",
    "                'Std Dev': std_dev, \n",
    "                'Sharpe Ratio': sharpe_ratio, \n",
    "                'P/E Ratio': pe_ratio, \n",
    "                'Momentum Health Indicator': stock_data['Momentum Health Indicator'].iloc[-1], \n",
    "                'VAR': stock_data['VAR'].iloc[-1]\n",
    "            }, stock_data\n",
    "        else:\n",
    "            return None, None\n",
    "    except Exception as e:\n",
    "        print(f\"Skipping {ticker} due to error: {e}\")\n",
    "        return None, None\n",
    "\n",
    "def fetch_future_returns(ticker, start_date, end_date):\n",
    "    try:\n",
    "        adjusted_ticker = ticker.replace('.', '-')\n",
    "        stock_data = yf.Ticker(adjusted_ticker).history(start=start_date, end=end_date)\n",
    "        if not stock_data.empty:\n",
    "            stock_data.index = stock_data.index.tz_localize(None)\n",
    "            start_price = stock_data.iloc[0]['Close']\n",
    "            end_price = stock_data.iloc[-1]['Close']\n",
    "            annual_return = (end_price - start_price) / start_price * 100\n",
    "            return annual_return\n",
    "        else:\n",
    "            return None\n",
    "    except Exception as e:\n",
    "        print(f\"Skipping {ticker} for future returns due to error: {e}\")\n",
    "        return None\n",
    "\n",
    "def process_year(year, start_date, end_date, sp500_tickers):\n",
    "    returns_data = []\n",
    "    start_year = pd.to_datetime(start_date).year \n",
    "    next_year_start_date = pd.to_datetime(start_date).replace(year=start_year + 1) \n",
    "    next_year_end_date = pd.to_datetime(end_date).replace(year=start_year + 1)\n",
    "\n",
    "    for ticker in sp500_tickers:\n",
    "        result, stock_data = fetch_and_analyze_data(ticker, start_date, end_date)\n",
    "        if result and stock_data is not None:\n",
    "            returns_data.append(result)\n",
    "    \n",
    "    returns_df = pd.DataFrame(returns_data)\n",
    "    returns_df['P/E Ratio'] = pd.to_numeric(returns_df['P/E Ratio'], errors='coerce')\n",
    "    returns_df = returns_df.dropna(subset=['P/E Ratio'])\n",
    "    returns_df = returns_df[returns_df['P/E Ratio'] < 1000]\n",
    "\n",
    "    average_pe_ratio = returns_df['P/E Ratio'].mean()\n",
    "    print(f\"Average PE Ratio for {year}: {average_pe_ratio}\\n\")\n",
    "\n",
    "    filtered_df = returns_df[(returns_df['P/E Ratio'] < average_pe_ratio) & (returns_df['Momentum Health Indicator'] > 10)]\n",
    "    filtered_df_sorted = filtered_df.sort_values(by='Return', ascending=False)\n",
    "    top_10_stocks = filtered_df_sorted.head(10)\n",
    "    \n",
    "    top_10_stocks['Next Year Return'] = top_10_stocks['Ticker'].apply(lambda x: fetch_future_returns(x, next_year_start_date.strftime('%Y-%m-%d'), next_year_end_date.strftime('%Y-%m-%d')))\n",
    "    \n",
    "    # Round values to 1 decimal place\n",
    "    top_10_stocks = top_10_stocks.round(1)\n",
    "    \n",
    "    # Print the top 10 stocks and their metrics\n",
    "    print(top_10_stocks)\n",
    "    print(f\"\\nAverage Return for Next Year: {top_10_stocks['Next Year Return'].mean():.1f}\\n\")\n",
    "    \n",
    "    # Save to Excel\n",
    "    top_10_stocks.to_excel('top_10_stocks.xlsx', index=False)\n",
    "    \n",
    "    return top_10_stocks\n",
    "\n",
    "def calculate_dates(year):\n",
    "    start_date = f\"{year}-01-01\"\n",
    "    end_date = f\"{year}-12-31\"\n",
    "    return start_date, end_date\n",
    "\n",
    "def main():\n",
    "    sp500_tickers = get_sp500_tickers()\n",
    "    year = int(input(\"Enter the year for analysis: \"))\n",
    "    start_date, end_date = calculate_dates(year)\n",
    "    top_10_stocks = process_year(year, start_date, end_date, sp500_tickers)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
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