PairsTrading.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an illustrative example of a Pairs Trading involving Coca-Cola (KO) and PepsiCo (PEP), despite being fundamentaly different companies, for example, KO has half the sales of PEP but has a higher Net Income however they both manufacture, distribute and sell soft beverages.\n",
    "\n",
    "But the market already knows past information if any new information is released, specially if affects both companies like for example new regulations affecting the beverage market, its expected that it will affect both companies in the same way therefore the price of each should move in the same direction.\n",
    "\n",
    "This strategy falls on the expectation that when prices depart from their historic equilibrium (rolling correlation), the one that valued less in the last week will catchup during the trade session, being the positions open at open price and closed at close price (slippage can be factored in but its not accounted on this version of the script), if the position doesn't return profit its hold for another day and then liquidated at close T+1 no matter its net return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# author: Leonardo Filipe\n",
    "# website: https://www.leonardofilipe.com\n",
    "# contact: contact[at]leonardofilipe.com\n",
    "import io\n",
    "import re\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "\n",
    "def getdata(tickers,start,end,frequency):\n",
    "    OHLC = {}\n",
    "    cookie = ''\n",
    "    crumb = ''\n",
    "    res = requests.get('https://finance.yahoo.com/quote/SPY/history')\n",
    "    cookie = res.cookies['B']\n",
    "    pattern = re.compile('.*\"CrumbStore\":\\{\"crumb\":\"(?P<crumb>[^\"]+)\"\\}')\n",
    "    for line in res.text.splitlines():\n",
    "        m = pattern.match(line)\n",
    "        if m is not None:\n",
    "            crumb = m.groupdict()['crumb']\n",
    "    for ticker in tickers:\n",
    "        url_str = \"https://query1.finance.yahoo.com/v7/finance/download/%s\"\n",
    "        url_str += \"?period1=%s&period2=%s&interval=%s&events=history&crumb=%s\"\n",
    "        url = url_str % (ticker, start, end, frequency, crumb)\n",
    "        res = requests.get(url, cookies={'B': cookie}).text\n",
    "        OHLC[ticker] = pd.read_csv(io.StringIO(res), index_col=0,\n",
    "                                   error_bad_lines=False).replace('null', np.nan).dropna()\n",
    "        OHLC[ticker].index = pd.to_datetime(OHLC[ticker].index)\n",
    "        OHLC[ticker] = OHLC[ticker].apply(pd.to_numeric)\n",
    "    return OHLC\n",
    "\n",
    "tickers = ['PEP','KO']\n",
    "\n",
    "data = None\n",
    "while data is None:\n",
    "    try:\n",
    "        data = getdata(tickers,'946610000','1538960000','1d')\n",
    "    except:\n",
    "         pass\n",
    "        \n",
    "PEP = data['PEP']\n",
    "KO = data['KO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>112.150002</td>\n",
       "      <td>112.480003</td>\n",
       "      <td>110.669998</td>\n",
       "      <td>110.709999</td>\n",
       "      <td>110.709999</td>\n",
       "      <td>5064700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02</th>\n",
       "      <td>110.449997</td>\n",
       "      <td>111.110001</td>\n",
       "      <td>107.699997</td>\n",
       "      <td>108.720001</td>\n",
       "      <td>108.720001</td>\n",
       "      <td>6702200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-03</th>\n",
       "      <td>109.209999</td>\n",
       "      <td>109.709999</td>\n",
       "      <td>107.169998</td>\n",
       "      <td>107.519997</td>\n",
       "      <td>107.519997</td>\n",
       "      <td>6107500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-04</th>\n",
       "      <td>106.849998</td>\n",
       "      <td>107.400002</td>\n",
       "      <td>105.800003</td>\n",
       "      <td>106.610001</td>\n",
       "      <td>106.610001</td>\n",
       "      <td>4930300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-05</th>\n",
       "      <td>106.250000</td>\n",
       "      <td>107.010002</td>\n",
       "      <td>105.980003</td>\n",
       "      <td>106.489998</td>\n",
       "      <td>106.489998</td>\n",
       "      <td>3438100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2018-10-01  112.150002  112.480003  110.669998  110.709999  110.709999   \n",
       "2018-10-02  110.449997  111.110001  107.699997  108.720001  108.720001   \n",
       "2018-10-03  109.209999  109.709999  107.169998  107.519997  107.519997   \n",
       "2018-10-04  106.849998  107.400002  105.800003  106.610001  106.610001   \n",
       "2018-10-05  106.250000  107.010002  105.980003  106.489998  106.489998   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2018-10-01  5064700  \n",
       "2018-10-02  6702200  \n",
       "2018-10-03  6107500  \n",
       "2018-10-04  4930300  \n",
       "2018-10-05  3438100  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PEP.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ReturnPEP = PEP['Close']/PEP['Close'].shift(1)-1\n",
    "BuyHoldPEP = PEP['Adj Close']/float(PEP['Adj Close'][:1])-1\n",
    "\n",
    "ReturnKO = KO['Close']/KO['Close'].shift(1)-1\n",
    "BuyHoldKO = KO['Adj Close']/float(KO['Adj Close'][:1])-1\n",
    "\n",
    "ReturnBoth = ((ReturnPEP+ReturnKO)/2)\n",
    "BuyHoldBoth = (BuyHoldPEP*0.5+BuyHoldKO*0.5).fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pairs = pd.DataFrame({'TPEP':(PEP['Close']/PEP['Close'].shift(5)-1).shift(1),\n",
    "                      'TKO':(KO['Close']/KO['Close'].shift(5)-1).shift(1)})\n",
    "\n",
    "pairs['Target'] = pairs.min(axis=1)\n",
    "\n",
    "pairs['PEP'] = PEP['Close']/PEP['Open']-1\n",
    "pairs['PEP'][pairs['PEP'] < 0]  = PEP['Close'].shift(-1)/PEP['Open']-1\n",
    "\n",
    "pairs['KO'] = KO['Close']/KO['Open']-1\n",
    "pairs['KO'][pairs['KO'] < 0] = KO['Close'].shift(-1)/KO['Open']-1\n",
    "\n",
    "pairs['Correlation'] = ((PEP['Close']/PEP['Close'].shift(7)-1).rolling(window=13)\n",
    "                        .corr((KO['Close']/KO['Close'].shift(7)-1))).shift(1)\n",
    "pairs = pairs.dropna()\n",
    "\n",
    "signalPEP = pairs['PEP'].loc[pairs['Target'].isin(pairs['TPEP'])][pairs['Correlation'] < 0.9]\n",
    "signalKO = pairs['KO'].loc[pairs['Target'].isin(pairs['TKO'])][pairs['Correlation'] < 0.9]\n",
    "\n",
    "returns = signalPEP.add(signalKO,fill_value=0)\n",
    "cumulret = returns.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean return = 0.13 %\n",
      "Standard deviaton = 1.44 %\n",
      "=========================\n",
      "Worst return = -12.76 %\n",
      "Best return = 12.39 %\n",
      "=========================\n",
      "Lower quantile = -0.44 %\n",
      "Median return = 0.26 %\n",
      "Upper quantile = 0.78 %\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,6))\n",
    "\n",
    "hist1, bins1 = np.histogram(returns, bins=50)\n",
    "width1 = 0.7 * (bins1[1] - bins1[0])\n",
    "center1 = (bins1[:-1] + bins1[1:]) / 2\n",
    "\n",
    "hist2, bins2 = np.histogram(ReturnBoth.dropna(), bins=50)\n",
    "width2 = 0.7 * (bins2[1] - bins2[0])\n",
    "center2 = (bins2[:-1] + bins2[1:]) / 2\n",
    "\n",
    "ax.bar(center1, hist1, align='center', width=width1, label='Pairs Trading')\n",
    "ax.bar(center2, hist2, align='center', width=width2, label='50/50 Returns')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print('Mean return =',round((returns.mean())*100,2),\"%\")\n",
    "print('Standard deviaton =',round((returns.std())*100,2),\"%\")\n",
    "print(\"=========================\")\n",
    "print('Worst return =',round((min(returns))*100,2),\"%\")\n",
    "print('Best return =',round((max(returns))*100,2),\"%\")\n",
    "print(\"=========================\")\n",
    "print('Lower quantile =',round((returns.quantile(q=0.25))*100,2),\"%\")\n",
    "print('Median return =',round((returns.quantile(q=0.5))*100,2),\"%\")\n",
    "print('Upper quantile =',round((returns.quantile(q=0.75))*100,2),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "maxdd = round(max(np.maximum.accumulate(cumulret)-cumulret)*100,2)\n",
    "\n",
    "mask = returns<0\n",
    "diffs = np.diff(mask.astype(int))\n",
    "start_mask = np.append(True,diffs==1) \n",
    "mask1 = mask & ~(start_mask & np.append(diffs==-1,True))\n",
    "id = (start_mask & mask1).cumsum()\n",
    "out = np.bincount(id[mask1]-1,returns[mask1])\n",
    "badd = round(max(-out)*100,2)\n",
    "\n",
    "executionrate = round((len(returns)/len(ReturnBoth))*100,2)\n",
    "\n",
    "spositive = returns[returns > 0]\n",
    "snegative = -returns[returns < 0]\n",
    "winrate = round((len(spositive)/(len(spositive)+len(snegative)))*100,2)\n",
    "\n",
    "beta = round(returns.corr(ReturnBoth),2)\n",
    "\n",
    "sharpe = round((float(cumulret[-1:]))/cumulret.std(),2)\n",
    "\n",
    "tret = round((float(cumulret[-1:]))*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative Return =  519.52 %\n",
      "=========================\n",
      "Execution Rate =  85.05 %\n",
      "Win Rate =  66.86 %\n",
      "=========================\n",
      "Maximum Loss =  58.79 %\n",
      "Maximum Consecutive Loss =  39.33 %\n",
      "=========================\n",
      "Beta =  0.55\n",
      "Sharpe =  3.86\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "plt.plot(BuyHoldBoth*100, label='Buy & Hold 50/50 KO&PEP')\n",
    "plt.plot(cumulret*100, label='Pairs Trading', color='coral')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Returns (in %)')\n",
    "plt.margins(x=0.005,y=0.02)\n",
    "plt.axhline(y=0, xmin=0, xmax=1, linestyle='--', color='k')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"Cumulative Return = \",tret,\"%\")\n",
    "print(\"=========================\")\n",
    "print(\"Execution Rate = \",executionrate,\"%\")\n",
    "print(\"Win Rate = \",winrate,\"%\")\n",
    "print(\"=========================\")\n",
    "print(\"Maximum Loss = \",maxdd,\"%\")\n",
    "print(\"Maximum Consecutive Loss = \",badd,\"%\")\n",
    "print(\"=========================\")\n",
    "print(\"Beta = \",beta)\n",
    "print(\"Sharpe = \",sharpe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}