find_safer_stocks_for_bear_market.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df=pd.read_csv('GSPX_holdings_12.12.2024.csv',skiprows=2)\n",
    "tickrs=df['Ticker'].to_list()\n",
    "\n",
    "missing_stocks_yfinance=['BRKB','GEV','KVUE','GEHC','SW','VLTO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAPL': 2.1903386949757118,\n",
       " 'NVDA': 2.560284677940616,\n",
       " 'MSFT': 1.407335447828631,\n",
       " 'GOOG': 1.2288266737635405,\n",
       " 'GOOGL': 1.230613467342892,\n",
       " 'AMZN': 2.007286109443888,\n",
       " 'META': 1.345179314062828,\n",
       " 'TSLA': 1.86848189565212}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find stocks which are less affected by bear markets\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "#stocks=['AAPL','NVDA','MSFT','GOOG','GOOGL','AMZN','META','TSLA']\n",
    "spx=yf.Ticker('^GSPC').history(period=\"max\")\n",
    "spx['daily_return']=spx['Close'].pct_change()\n",
    "spx.dropna(inplace=True)\n",
    "\n",
    "\n",
    "bear_markets=[('2000-08-29','2002-09-03'),('2007-10-09','2009-03-09'),('2020-02-19','2020-03-23'),('2021-12-29','2022-10-04')]\n",
    "\n",
    "def get_bear_market_info(bear_markets, stock):\n",
    "    selected_dfs = []\n",
    "    for bear_market in bear_markets:\n",
    "        start,end=bear_market\n",
    "        data=stock[(stock.index>=start) & (stock.index<=end)]\n",
    "        selected_dfs.append(data)\n",
    "    \n",
    "    return pd.concat(selected_dfs) if selected_dfs else pd.DataFrame()\n",
    "\n",
    "spx_bear=get_bear_market_info(bear_markets,spx)\n",
    "\n",
    "def get_beta(tikr='AAPL',spx=spx_bear):\n",
    "    try:\n",
    "        data=yf.Ticker(tikr).history(period=\"max\")\n",
    "    except:\n",
    "        print(f'Ticker {tikr} not found')\n",
    "        return None\n",
    "    data=get_bear_market_info(bear_markets,data)\n",
    "    cutoff_date=max(data.index[0],spx.index[0])\n",
    "    _spx=spx[spx.index>cutoff_date]\n",
    "    data['daily_return']=data['Close'].pct_change()\n",
    "    data.dropna(inplace=True)\n",
    "    data=data[data.index>cutoff_date]\n",
    "    beta = np.cov(data['daily_return'],_spx['daily_return'])[0,1]/np.var(_spx['daily_return'])\n",
    "    return beta\n",
    "\n",
    "betas = [get_beta(tikr) for tikr in tickrs]\n",
    "sorter = np.argsort(betas)\n",
    "sorted_stocks = [tickrs[i] for i in sorter]\n",
    "sorted_betas = [betas[i] for i in sorter]\n",
    "\n",
    "df=pd.DataFrame({'Stock':sorted_stocks,'Beta':sorted_betas})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the S&P 500 daily returns during bear markets\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "spx['Close'].plot(color='blue')\n",
    "spx_bear['Close'].plot(color='red',linestyle='',marker='.',markersize=1)\n",
    "plt.title(f'S&P 500')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['-0.44', '-0.60', '-0.34', '-0.38', '-0.38', '-0.35', '-0.45', '-0.42']\n"
     ]
    }
   ],
   "source": [
    "# Find stocks which are less affected by bear markets\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "stocks=['AAPL','NVDA','MSFT','GOOG','GOOGL','AMZN','META','TSLA']\n",
    "spx=yf.Ticker('^GSPC').history(start=bear_markets[0][0], end=bear_markets[-1][-1])\n",
    "spx['daily_return']=spx['Close'].pct_change()\n",
    "spx.dropna(inplace=True)\n",
    "\n",
    "\n",
    "bear_markets=[('2000-08-29','2002-09-03'),('2007-10-09','2009-03-09'),('2020-02-19','2020-03-23'),('2021-12-29','2022-10-04')]\n",
    "\n",
    "def get_bear_market_returns(bear_markets, tikr):\n",
    "    try:\n",
    "        data=yf.Ticker(tikr).history(period=\"max\")\n",
    "    except:\n",
    "        print(f'Ticker {tikr} not found')\n",
    "        return None\n",
    "    bear_returns = []\n",
    "    for bear_market in bear_markets:\n",
    "        start,end=bear_market\n",
    "        _data=data[(data.index>=start) & (data.index<=end)]\n",
    "        if _data.empty:\n",
    "            continue\n",
    "        bear_returns.append((_data['Close'].iloc[-1]-_data['Close'].iloc[0])/_data['Close'].iloc[0])\n",
    "    \n",
    "    return np.average(bear_returns)# if bear_returns else None\n",
    "\n",
    "bear_returns = [get_bear_market_returns(bear_markets, tikr) for tikr in stocks]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'stocks':tickrs,'bear_returns':bear_returns})\n",
    "df.sort_values(by='bear_returns',ascending=False,inplace=True)\n",
    "df.to_csv('bear_returns.csv',index=False)\n",
    "df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stocks</th>\n",
       "      <th>bear_returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CEG</td>\n",
       "      <td>1.143218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TSCO</td>\n",
       "      <td>0.830696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZO</td>\n",
       "      <td>0.538697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NVR</td>\n",
       "      <td>0.516403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VTR</td>\n",
       "      <td>0.427086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>MLIFT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>AMTM</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>MXN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>ESZ4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>510 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    stocks  bear_returns\n",
       "0      CEG      1.143218\n",
       "1     TSCO      0.830696\n",
       "2      AZO      0.538697\n",
       "3      NVR      0.516403\n",
       "4      VTR      0.427086\n",
       "..     ...           ...\n",
       "505  MLIFT           NaN\n",
       "506   AMTM           NaN\n",
       "507    MXN           NaN\n",
       "508   ESZ4           NaN\n",
       "509                  NaN\n",
       "\n",
       "[510 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('bear_returns.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5164030517709728"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_bear_market_returns(bear_markets,'NVR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPX: -38.36%\n",
      "['AAPL: -43.91%', 'NVDA: -60.39%', 'MSFT: -34.02%', 'GOOG: -37.87%', 'GOOGL: -38.09%', 'AMZN: -35.11%', 'META: -45.50%', 'TSLA: -41.88%']\n"
     ]
    }
   ],
   "source": [
    "print(f\"SPX: {get_bear_market_returns(bear_markets, '^GSPC')*100:.2f}%\")\n",
    "print([f'{stock}: {x*100:.2f}%' for stock,x in zip(stocks,bear_returns)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}