Pairs Trading Example - DAL and AAL (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = pd.read_csv('DAL.csv')\n",
    "aa = pd.read_csv('AAL.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta['Date'] = pd.to_datetime(delta['Date'])\n",
    "aa['Date'] = pd.to_datetime(aa['Date'])\n",
    "\n",
    "aa.set_index('Date',inplace=True) # replace dataset index with \"Date\" column\n",
    "delta.set_index('Date',inplace=True) \n",
    "\n",
    "merged_data = pd.merge(aa,delta,left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot closing prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77ee0545f8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data['Adj Close_y'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77ee01e080>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data['Adj Close_x'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at Jul-2018  - Dec-2019 sub-sample only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77ec0e92e8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mxy3qGcxmE248awpf+9dm3ipvHPiZ1MhtrmohMcGGAX7w9A5+9vwuLjm1kBSng/q2HhrarX/1bT10+AYAVywq4omN1dz9Wjnfv3zemNvw3LZaZhWkMjHr6N0qr14yiak5KdzywLsUZSQFVJY6Ujlul+6HokYk7AF80eRMRGBNxZEA/te39/Gv9VV84YLprJgV+hRJoL7z/jkBXXfZwiJ+/OxO7l1VoQF8DPY1djItz83jnz2L3bVt/OmNSlZuqMLpsJHjdpHrdjGvOINct4ucVCenlWRxWkkWLoeNf6w7yBcumDHiI84GO9zRy9rKJm5bMW3I+5dOzebFL51Lb4gO585NdbG9RpfTq8CFPYCnJyUwMz+VNZVWedaWqhbu/M82zpuVx+fPmx7u5gRFYoKda0+fzC9f3M3e+nZKA1hlqo63r7GDORPSAZien8oPrpjH/14+96Sj3U+fPZWH1xzgvjcquOOiWaN+/pd31uE1nHCNQWYIJ9Bz3E4tI1QjEvYcOMCSKVm8u/8w/R4vv39lL8lOOz+/asGo95YYD65bNhmnw8a9r1dEuilRqd/j5eDhLiZlH526CCRVMTXXzcVzC/jr2/vGVJP//LZa8lJdzCtKH/X3GIsct4vW7n56+nXLYhWYiATw00qy6Oz18Py2Wp7ZWsPVSyaRnpwQiaYETY7bxRULi3h03cETTr6poVW3dNPvNZQcE8AD9dkV02nv6ed3r+wZ1eO7+zy8uqueC07Jj9hAYmAxzzGVKN19Hh5Zc4BH1x2MRLPUOBaxETjAt1ZuBeDjZ5REohlB96mzptDT7+XBd/ZFuilRp9JXRTIpa3QT2KdMSOOKhcXc92YlBw+P/Fi8t8sb6ez18J4QbdEQCP9yen8apaWzj9++vIezfvQydzy6iTse3TSwvP+NPQ0s/d8XaNRJz7gWUAAXkUoR2SwiG/yHE/tu/5yI7BCRrSLyf4E+aX5aIpOykmlo7+G9c/IpGsOiiPFken4qswpS2XCgOdJNiTr+s0gnj3IEDvCV985AgJ88u3PI+71ew+MbD7HiJ69w/X2rj7rv+W21JDvtLCvNHvXzj5V/AnbjwWb+5z9bWfbDF/nxszs5ZUIaP//IfLzG8OA7+zHG8ONnd1Lb2kOlnuEa10YyibnCGNPg/0JEVgAfBOYbY3pEZETlI6eVZLG/qfOENdbRKC8tkTqdiBqx/U2dOB02CtJGv0S9MD2JG8+ewm9f3ssHFxQNVDQZY3h1Vz3/98xOtlW3kphg40BTJ129HpKcdowxvLC9lnOm50Z0K9cctzVB+u2VW3HYhA/Mn8Cnz5nK7MI0AJ7cVM1Dq/ezaHLmwCChtUv34YlnY6lCuQX4oTGmBwYOPA7YjWdPYVqee2Cvk1iRl+pit2+1ngrcvsYOJmUljzn/fOvyabyys55bH3iXBz69FGPg/57ZwTsVTUzMSuIXH1mA2+Xgxr+sZcOBZpaVZrO5qoXa1h4uOCVy6ROwtpm48JR8Jmcn88kzpxy3XP8TZ5Twwr2ruf2h9bgcNnr6vbRoAI9rgQZwAzwnIga4yxhzNzADOFtEvo91JuZXjDFrjn2giNyE77zMSZOO7CcyuzBtYGQRS3JTrdV0Xq+J6qqacNvX2MnkrNGnT/xSXA7u/+QSPvyHN7n67rfp6feS43Zx5wfn8NHTJuF02AYqVdZWNrGsNJvnttZiEzgvwmsQHHYbd3+8bNj7zyzNYWpuCuX1HdyyvJTfv7JXA3icC3QS8yxjzCLgYuA2ETkHK/hnAacDXwUekSFqvowxdxtjyowxZbm5ucFq97iVl+qi32s43KmVKH4tXX30nWDxizGG/U2dQVuBm5vq4q83LGX+xAy+cuEMXv3qcj6+rASnw3q5pyf71iLsOwzA01uqWTolO6SbpAWDzSZ87rxpzCpI5eZzSwE0gMe5gEbgxpgq38c6EXkMWAIcBP5lrA1AVouIF8gB6kPV2Gjgn4iqb+8h2z36VYGxYNXueu59vYJXdtZjtwkFaYlMzEqiODOZiZnJFGcmcea0HGw26Oz1jGkC81iTspN55DPLhr1/cUkmj284xI6aVvbWd3B9lFRCXb6wmMsXWgeLpDjtNOte9HHtpAFcRFIAmzGmzff5hcCdQDuwAnhZRGYATqBh+O8UH/JSrUm4utYeZhWc5OIY9ubeBq67dzU5bhe3rSjFJsKBpk4OHO5i1e76gRNpclNdfNe3ZcGxi3hC6bSSTB58Zz+/fGE3IkfvzR0t0pMSdAQe5wIZgecDj/myIw7gQWPMMyLiBP4kIluAXuATRrfjOzICj/NKlE0HWwB44UvnDLkVb3efh00HW7j+vtV89Z8bASgJ4yZmZZOttQhPb6mhbHImeWOofomUNA3gce+kAdwYUw7MH+L2XuDaUDQqmuX5Ani8lxLua+wgO8U57D7qiQl2lkzJ4v8+fCqffXA9NiGs6wGKM5Osw0VauwcOFo426UkJWkYY58K+mVWsS3E5SHHa434EXtnQGVBK5NJTJ1Be38G2Q60Dk4zhICIsLsnkyU3VUR3A9+lCnrimATwEclNd1LWd+NzPWLevsYOlUwNb1fj58yOzC+Ut55aypCSL4szw5d6DKSM5gU0HdQQezzSAh0BeamJcj8C7+zxUt3YHtaokFOYWpTM3QjsPBoNOYqqIbGYV6/yLeeLVwcOd1klG4+BkpViWnpRAV59Ht5+NYxrAQ8BKocRvAK9sGPvGVOrk0pOsLZh1FB6/NICHQG6qi/aefjp7+yPdlIjwbw2rI/DQSvMFcK1EiV8awEMgL85rwfc1dpKa6CAjyg/pGO90BK40gIeAf1FIPAXwmpZutlRZi3cqGzsoyU4Jycnt6ggN4EoDeAjkuuNvMc9Pn9vJlX94i8Mdvb6NqTT/HWoawJUG8BDIS4u/FMrBw1109Xm49/UKDh7u0vx3GAwEcN3QKm5pAA+BrGQndpvE1WKemlbrZ717VTker9EReBgcGYHH52S50gAeEjabkON2xs0I3BhDdUsXZZMz6e239v0uydEReKg57DbcLgfNXbr3fLzSAB4ieanxczZma1c/3X1eLppbwNIp1i5/wThdR52crsaMb7qUPkQmZiWxuqKJ7j5PRA/KDYfq1i7AOlT4ux+Yw5Obqge21VWhlaY7Esa1gEbgIlIpIptFZIOIrD3mvi+LiBGRnNA0MTpdd3oJDe29PLx6f6SbEnLVLVb+uyDdxezCNL7y3plaQhgm6UkOHYHHsZGkUFYYYxYYYwZOXRWRiVgn9MR+lBqhZaXZLJmSxe9f3Ut3X2zvVVE7EMDDt5+3smgKJb6NNQf+c+AOrFPr1TG+cP50alt7+MfaA5FuSkhVt3QjcmQFqgofDeDxLdAAboDnRGSdiNwEICIfBKqMMRtD1root6w0m7LJmfzh1XLCfdrcW3sb+fbKLQFd2+/xjql9NS3d5LhdJNh1TjzcNIDHt0B/484yxiwCLgZuE5FzgG8A3z7ZA0XkJhFZKyJr6+vj68B6EeGKRcVUNXdRGeaTU/69voq/vLWPpo4Tl5j1ebx8+A9v8aVHRv93uKa1m4IoPFMyFqQnJdDd5435NJ0aWkAB3BhT5ftYBzwGnAtMATaKSCVQDLwrIsedTWWMudsYU2aMKcvNzQ1aw6PFkimZAKyuaAzr8+6tbz/q43D+uKqcDQeaeWVnHV7v6EbhNS3dFKRrAI+EdN+Zo1qJEp9OGsBFJEVEUv2fY01arjHG5BljSowxJcBBYJExpiakrY1CpbluslKcrK44HNbnLW+wtnTdWzd8AN9b384vXthNdoqTw519lDecONgPp6a1m0IN4BGh+6HEt0BG4PnA6yKyEVgNPGmMeSa0zYodIsKSkixWV4ZvBH64o3cgdTLcCNzrNXz90c0kJdj53TWLAFhbOfI/Mp29/bR09ZGvKZSI0AAe304awI0x5caY+b5/c4wx3x/imhJjTENomhj9TpuSxYGmLqpbusLyfINH0nvrO4a85oHV+1ld2cQ3L5nNkilZZKU4WTOKAF7jKyHUEXhkZPgC+Hce38oPnt5+0jkPFVu0bCAM/MvLV1c0heX5/EF7blHakCPw6pYufvT0Ds6alsOVi4sRERZPzmTdvpG3z7+JlU5iRsbswjRuPreUBLuNe1ZV8J3Ht0a6SSqMNICHwezCNNwuRxgDeDsJdmH5jDwONHUeVaFgjOGbj23B4zX84Ip5AysmTyvJpLKx86gNuHbVtvGRu96isX34PV1qBhbxaACPBKfDxtcunsW/bzuTT589lSc2HTrpxLWKHRrAw8Bus0a44Qrg5fXWiTgzClLxGuuIM7/HNx7ipR11fOW9M5k4aMOpxZOtdwmDR+FrKpt4p6KJB98ZfqFttQbwcePGs6fgctj47ct7It0UFSYawMNkyZQsdte1czgMOcry+nam5qZQmmtt6eofkTV19PI//9nGgokZXH9GyVGPmVuUhtNhO2ois7Hdauvf3tlHn8c75HPVtnaTlugg2an7okVajtvFx5ZMZuWGQ+wP87oDFRkawMNk4aQMADYcbA7p8/R5vOxr7GRqrpupOW7gSCnhnf/ZSlt3Hz/60KnYbUdvNuVy2FlQnMGafUcCuH9CrLa1h2e3Hl8heqCpk22HWinUPVDGjc+cOxW7TfjdKzoKjwc6bAqTU4szEIGNB5pZMTMvZM9zoKmTfq+hNNdNktNOUUYSe+vbeX5bLf/ecIjbz5/OzILUIR87Pd/N01uOBOrGjl4mZSVjMPz5zUrKJmfxVnkDb+1t5M29jRw8bFXVXLm4OGQ/jxqZ/LREPlI2kYfX7Odz50+nKEP/uMYyDeBh4nY5mJ7nZsOB0I7Ay30VKFN96ZPSPDfrDzTz2u4G5kxI47YV04Z9bF5qIk0dvfR5vCTYbTS295Cb6uKiOQV8/6ntnP6DFwGr9vj0qVnceNYUlpXmMCPfHdKfSY3MzctLeXjNfu56dS93fnDuwO1v7GngnlXl/PHjZTh035qYoAE8jBZMzOD5bbUYY0K2X7Y/313qS5+U5qbw2q56XA4bv/zoApyO4X9x/YcwNLT3UJieRFNHLxOzkrl66aSBk+ZPn5rNKYVp2Gy63/d4VZSRxIcWFfPwmgN8dsU08nwlnve9UcHLO+spb+hgRv7Q78JUdNE/w2G0YGImhzv72N8Uugmm8voOctxO0pOtBR7T86xf1G9eMptpeSf+pfUHcH8pYWNHLzluJ26Xg/932VxuPHsqc4vSNXhHgVuXT8PjNdz1WjkAbd19vLbLWmu3vbo1kk1TQaQj8DCaPzEdgA0HmpmcHZpDfysaOpgy6EDhyxZOIMft5D2n5J/0sYMDuNdraOroJSvFGZJ2qtCalJ3MB+dP4IF39nHL8lLe2NNAr6+SaGdNW4Rbp4JFR+BhNDM/lcQEW0jz4BWNVg24X7LTwYVzCgJK2QwO4K3dfXi8hqwUPaQhWt26Yho9/V7uWVXBM1tqyEt1MT3PzQ4N4DFDR+Bh5LDbmFeUzsYQBfD2nn7q23ooyRnd6D7HbY2269t6aPSVEPpvU9FnWp6bS+YV8te3KvEa+PDiYlq7+0a1aZkan3QEHmYLJmaw5VArvf1DL4wZi0rfFrJTRhnAXQ47GckJ1Lf3DCzi0RRKdPvsedPo6PXQ1efh4rkFzCxIpaq5S3cvjBEawMNswcRMevu9PL7xUNC/d2WjFcBLxpBfz3W7qG/roanDmsjUAB7dZhWk8b55BeSlulgyJYvZBWmA5sFjhaZQwuz82XksmZLFfz26iRSnnYvnFQbte/tH4CU5ySe5cni5qS7qjkqhaA482v3kyvm0d/fjsNuYVWhVIu2saWWJb5dMFb0CGoGLSKWIbBaRDSKy1nfbj0Vkh4hsEpHHRCQjtE2NDYkJdv50/WksmJjB5x5az3NDLFEfrYqGTvLTXGPalyQ31TcC96VQMpN1BB7tkp2OgVrwgrRE0hIdbNcReEwYSQplhTFmgTGmzPf188BcY8ypwC7g60FvXYxyuxzc/8nTmFuUzm0PvsuL22uD8n0rj6lAGQ1/CqWxo5fURMcJF/6o6CMizCpMY4fWgseEUf92GmOeM8b0+758G+tgYxWg1MQE/nzDEmYXpnHL397llVIg81wAAB8ISURBVJ11Y/6elcfUgI9GbqqLrj4P+5s6ydb8d0yaXZDKrtr2UR9ircaPQAO4AZ4TkXUictMQ998APD3UA0XkJhFZKyJr6+vrR9vOmJSelMBfbljCtDw3N/11Ha/vHv2pdK3dfTR29I66hNAvL83Kee+saSNb898xaVZhGu09/QObkanoFWgAP8sYswi4GLhNRM7x3yEi3wT6gQeGeqAx5m5jTJkxpiw3N3fMDY41GclOHrhxKVNzUrjxL2tGfejDwATmmFMoVq60qrlLK1BilH83yh01mkaJdgEFcGNMle9jHfAYsARARK4HLgWuMcbo+7FRykyxgnhGkpM/vLp32Os8XsMTmw4NeThyxRhrwP38qzEBTaHEqJn5/koUnciMdictVxCRFMBmjGnzfX4hcKeIXATcAZxrjNHjP8Yo2+1i0eQMtlcP/0v11OZqPvfQemwCZ0/P5aqyiVxwSh4uh53KBuu/YHL26EsI4ZgArqswY1KKy8Hk7GRdUh8DAqk3ywce8+2l4QAeNMY8IyJ7ABfwvO++t40xN4espXGgJDuF57bW0u/xDrlf8zNbashxO/nYkkn8c91BbnvwXTKSE7hsQRG769ooTE8kMcE+pjZkJCXgsAn9ug9KTJuZn8p2TaFEvZMGcGNMOTB/iNuHPxlAjUpJTgr9XkNVc9dxuxV293l4eWcdly0s4ksXzuT2C2bwxp4GHll7gAff2U+vx8sZpdljboPNJuS4XdS0dmsKJYbNKkzjhe21dPd5xvxHX0WOrsQcR/wTkBUNHccF8FW7G+jstfazAOuk+3Nm5HLOjFyaO3t5anMNp0xIC0o78tJ8AVxTKDFrdkEqXgO7a9uZV5we6eaoUdJVGuOIfwm8v6JksGe21PiOMjt+lJ2R7ORjSyexYGJwFsPm+soHtQolds0qtP7YaxolumkAH0dy3S5SnHYqG4+eE+7zeHlhey0XzM4nIQxnGfonMrM1Bx6zJmUlk5hgY8cJJs3V+KcBfBwRESZnpwzsKuj3TnkTLV19XORLn4TahIwkEuxCZkpCWJ5PhZ/dJszMT9Va8CinOfBxZkpOClsPtRx1m/8Mw3DtHveJZSWcOS0bl0Mnt2LZrII0nt8e2kO2VWjpCHycKclJ5uDhLvo8Rw58qG3tJtlpJy0xPH9v05MTWDxZtxqNdbMKU2nq6KW+vSfSTVGjpAF8nJmc7SslHLRPRU1rN/lpiTpKUkE1sKRe8+BRSwP4OONfCl8xKA9e19pDfppOKKrgmpbrBmBf4/FVTyo6aAAfZ/y14PsGlRL6R+BKBVOO24XTYdNdCaOYBvBxJsftPKqU0BhDbWs3BRrAVZDZbEJxRpIG8CimVSjjjIhQkpMysLtgS1cfPf3egSOxlAqmoswkDh7WvejGi87efp7YWE2i005xZhILijOw2Yaf+9IAPg6V5KSw+aBVSljbalUI6AhchUJxZjLPHRrZuaxNHb3c+3o5/1x3kB9ecSorZuWFqHXxpbffy2f+uo5Vgw52OWdGLr/4yIJhH6MBfByaluvmqc3VdPd5qGntBtBJTBUSxZlJNHb00tnbH9Bh2PsaO7j0V6/T3ttPqsvBdx7fyhm6ZmDMvF7DV/6xkVW7G/j+5XM5rSSL13c38MOnd3Dpr1YN+zjNgY9DpXlujLE2taodCOA6AlfBV5yZBMCh5sDy4G/ubaStp59/3ryM316ziP1Nndz/RmUIWxjd9ta3c7Kzbtq6+7jtwXd5fOMh7rhoJtcsncyM/FRuOGsK/7xl2QnLhwMK4CJSKSKbRWSDiKz13ZYlIs+LyG7fx8yR/GBqeP7yrj117dS2WAE8T0fgKgT8AfxAgBOZFQ0dOB02Fk7M5OzpuZw3K49fv7SH+jZdDHSsyoYOzv/pqzy7dfgU1Y6aVj74mzd4blst33jfLG45t/So+08tzuDpL5w97ONHMgJfYYxZYIwp8339NeBFY8x04EXf1yoIpuamIOIL4G3dZCYn6FtUFRLFmdYOmIFWopTXdzAlO2VgYu2bl8ymu8/D+3/9On99ex+9/d4TPr6jp39sDY4i/j59u3zoc27/9e5BLvvtG7T19PPgjUu56ZzSIUfbaYnD70k0lhTKB4E/+z7/M3DZGL6XGiQxwc7EzGT21LdT09Kj6RMVMrluF067LeBKlIqG9qPOXS3NdfPQTadTnJnEt/69hRU/eYVH1hyg33N0IN9woJlr73mHud99lr317UH9GcarBt8WBesPNB91e3efh288tpkvPbKR+cUZPPn5s1g6xDbRgQh0EtMAz4mIAe4yxtwN5Btjqn3312AdvaaCZFqem7117TgdNg3gKmRsNvGVEp58BN7v8bK/qZML5xy9K+ZpJVn84+ZlvLqrnp89v4s7Ht3E717Zw+0XTGdmfho/f2EXz2+rxemwYQxUHe6i1JcmjGX+AL79UCs9/R5cDjsHmjq59YF32VzVws3nlvKVC2cMeXxioAIN4GcZY6pEJA/rDMwdg+80xhhfcD+OiNwE3AQwadKkUTc03pTmpvD6ngbSEhOYXRCck3aUGkpxgAG8qrmLPo85agTuJyIsn5nHuTNyeWF7HT97fhdf/PtGAFJdDr78nhksnZrNVXe9RWevJ+g/w3jk3ySs1+Nl66FWFhRncO2979DU0csfP17Ge04Z+5g3oABujKnyfawTkceAJUCtiBQaY6pFpBCoG+axdwN3A5SVlZ14OlYNmJbnprffS0O77oOiQqs4M4nnt9We9Lpy3+KyqUMEcD8R4T2n5HP+rDye3VrDgcOdXFU2kYxkJ+W+1El3X3wE8Mb2XpIS7HT1ediwv5muXg/7Gjv51dULgxK8IYAALiIpgM0Y0+b7/ELgTuBx4BPAD30fVwalRQqwArhffrqmUFToFGcm09DeS1evhyTn8JPlFfVWAB9qBH4sm024eF7hUbf5v3e8jMAb2nuYluemob2H9Qea2VLVQmqigwuDFLwhsBF4PvCYb3bUATxojHlGRNYAj4jIp4B9wFVBa5ViWm7qwOf5qRrAVej4SwmrmjuZlpc67HUVDR2kJTpGfVZqcoIVbrriZATe0N5DrtvFxKwkVlc00trVz2ULi0hMCF5F2UkDuDGmHJg/xO2NwPlBa4k6SnpyAjluFw3tPRToCFyF0OBa8JMF8Cm57lHvS5/otCbr4imFMqsgjRn5bp7abNWCf3hxUVCfQ1dijmOludZbVV3Eo0Ip0FrwioaOE+a/T8Zpt2G3CZ29sV8Lboyhsb2XHLeLBROtNY5TclJYNCm46x01gI9jM/JTcdpt5Ojp8CqEct0uslKcvF3eOOw1Xb0eqpq7Asp/D0dErEm93hMv9okFrV399Hq85LidzCtKJyM5gWuWTgr6qVq6mdU4dvPyUs6blXfC7SSVGiubTfjA/Ak8uHo/LV19pCcdv/KvsjHwCcwTSUyw09UX+yPwhg6rhDDH7SLJaeeN/zqP5BNMEI+WjsDHsaKMJN2qU4XFFYuK6O338tTm6iHv9+9PP9YAnuy00xUHVSgNbUcCOECKyxGSM201gCulmFeUzrQ8N4+9WzXk/cEK4P666FjX0N4LQE7q6Cp2AqUBXCmFiHD5wiJWVzaxv/H4fVHK6zsoSEskxTW2rGuS0x4XdeCNvhRKdojnrzSAK6UAuGxhESLw2PrjR+HHbmI1WkkJ9rgoI2xo68EmjLpmPlAawJVSgDXnsmxqNv9af/C4QwisGvAgBPA4GYHXt/eSleLEHuICBA3gSqkBVywqZl9jJ+/uPzxw2+GOXg539o2pBtwvyTn2HHhP//j/A9DQ3hPy9AloAFdKDXLR3AKSEuz8a9BkZkWQSgjBl0IZwwh8S1ULC+98nm88thmPd/zujdfY3hPyCUzQAK6UGsTtcvDeOfn8Z+OhgZHuSDaxOplkp53OUY7Au/s8fPHvGzAGHnxnP59/eP1JTwCKlAbfKsxQ0wCulDrKFYuKae3u56Xt1g7RFQ0d2G3CxKzkMX9vayXm6AL4D5/ewe66dv5w3WK+8b5ZPLmpmm/9e8uY2xQKmkJRSkXEmdNyyEt18agvjVLR0MGkrGQSxnByjF+S005Pv3fE6Y+dNW3c/2Yl159RwrkzcrnpnFJuWV7K39ce4OUdQx5FEDGdvf109no0haKUCj+7zaoJf2VnHY3tPZQ3dAQlfQLWCBxGviPhun3WpOqnzpoycNsXLpjOzPxUvvavTbR09gWlfcHQ6F/EoykUpVQkXLGomH6v4fGNh6gMZgD37Qcy0kqU7dWtpLocA1vfArgcdn561Xwa2nv57n+2BvR9jj1sORT8R6nljqcALiJ2EVkvIk/4vj5fRN4VkQ0i8rqITAtdM5VS4TSzIJVTCtO4Z1UFXX2eoI/AR5oH31HTyqzC1OP2E5lblM5tK6bx2Poqnt1ac8Lv0dLVx+LvvcC197zDXt/xbqHw1l5rV8fc1HEUwIHbge2Dvv49cI0xZgHwIPDfwWyYUiqyrlhURFWztUd4MGrAYXQjcGMMO6rbmDXM4d6fXTGNUwrT+OZjm2nq6B32+7xd3khLVx+rK5u46Bev8cjaAyNrfACe21rDT57byYWn5HNKYegPIw8ogItIMXAJcM+gmw3gb2E6cCi4TVNKRdIHFkwYWEkYjFWYwMCWqiNZjXnwcBdtPf3MHiYgOh02fnrVfFq6+vj2yuGrUt4ub8TlsPHKV5ZzWkkW//3vLWyvbh3ZD3ACL+2o5fMPr+fUonR++dGFYdkGOtAR+C+AO4DBCaQbgadE5CBwHdbhxscRkZtEZK2IrK2vrx9TY5VS4ZOXmsg503NIdtqDdi5r4ihSKP4gO7tw+OPeZhemcfv503liUzVPbhp6S9y39jZSVpLJhIwkfn31QtKTEvj8Q+vHvL1tZUMHn7p/DTfcv5bizGTu+cRpJzwcOpgCOZX+UqDOGLNORJYPuuuLwPuMMe+IyFeBn2EF9aMYY+4G7gYoKysbv0unlFLHufODc9nf1Bm00eRADtx3qEO/x0tdWw/VLd3UtHRT3dI18HmO28l3PzCH7dVtiFh5+RO5+dxSnttWy3//ezNLpmRR39bDxoPNXFU2kZauPnbUtPGVC2cAkO128bOr5nPdvav53pPb+P7l80b8s3T09PObl/dw76oKEuzC1y+exSfPnILTEb7akED2hjwT+ICIvA9IBNJE5ElgljHmHd81fweeCVEblVIRMjErOSgLePySnb6T6X3Hql34i9co96309EtKsJOZnMChlm5WzMpjR00rJdkpA48djsNu46dXzueSX7/Oxb98bWBPbrsIaUnWY5eVZg9cf/b0XD5zzlTueq2cc2bk8t45BQH9DMZY1Tn/+9R2alt7uGJREV+7aBZ5aeE/fDyQU+m/DnwdwDcC/wpwGVAjIjOMMbuA93D0BKdSSh3nyAjcQ1evh/L6Di49tZAPLS6mMD2RwvQk0hId9HkMy3/8Mr99eQ/1bT3D5r+PNT0/lW9dMpv73qzk02dP5akt1qTi8pm5JCXYmVeUcdT1X75wJm/ubeS/Ht3EqcXpFKYnDfOdLS1dfdz6wDre2NPIvKJ0fnfNYhZPDu5BxSMxqrG+MaYf+DTwqIhsxMqBfzWYDVNKxZ6BKpTe/oFDD86ZnsuKmXnMKkgjPSkBEcHpsHHTOVNZU3mYysbOgAM4wHXLSnjpy8v5zLmlfOuS2dS19fDI2oOUlWQel95wOmz88qML6O338sW/bzjhCtGG9h6uvvttVlc08b3L5rLytjMjGrxhhAHcGPOKMeZS3+ePGWPmGWPmG2OWG2PKQ9NEpVSsGFxG6F+xONyhBx9dMokct3XfSAL4YGUlWbx3Tj5wdPpksKm5br77gTm8Xd7EH17dO+Q1TR29XHXXW5Q3tPPHj5dx7emTx8Vh47oSUykVNv4USmevZ6BmO8s9dABPTLDz6bOnYrcJc4tGX1P99YtnM784nYvnFg57zZWLi7n01EJ+9vwu1g/aCx2gz+Pl1gfWcfBwF3+5YSnLZ46fg8Y1gCulwsZus9IjXX0eGn0BPOcEu/Z9+uypvPTlc0+amz6RkpwUVn72rBOuJhURvn/5PArSEvn8w+tp67b2VjHG8J3Ht/J2eRM/+tA8lkzJGnU7QkEDuFIqrPyHOjT69gwZbgQOYLMJk7ODs4joZNKTEvjlRxdQdbiLb6+09la567VyHnxnPzefW8rlC4vD0o6RGNsR00opNULJvnMxmzp6cTlspIRp0UsgykqyuP38Gfz8hV14jWHlhkO8f/4E7njvzEg3bUg6AldKhVVSgn0ghZKd4jxug6pIu21FKaeVZLJywyHOnp7DT6+cPy4mLIeiI3ClVFglOa1TeTp6+k+YPokUh93Gbz62iL+vOcANZ4V3ZeVIaQBXSoWVfwTe0dMflmPHRiM/LZHPnz890s04qfH7p0UpFZOSnFYAb2i3Uihq9DSAK6XCyn+wcVNH77CLeFRgNIArpcIqyWmnsaOXrj4P2WE4diyWaQBXSoVVstNOfZtVA64plLHRAK6UCiv/oQ4w/D4oKjAawJVSYZU8aOFO9jgsI4wmGsCVUmGVNGgEPl7LCKOFBnClVFgNTqHoCHxsAg7gImIXkfUi8oTvaxGR74vILhHZLiKfD10zlVKxwn80msthOyqdokZuJCsxb8c6Ns2/Me/1wESsszG9IjJ+NslVSo1bSU5r3Dge90GJNgGNwEWkGLgEuGfQzbcAdxpjvADGmLrgN08pFWuSEqxxo9aAj12gKZRfAHcA3kG3lQIfEZG1IvK0iAy5cYCI3OS7Zm19ff0Ym6uUinb+Y9W0hHDsThrAReRSoM4Ys+6Yu1xAtzGmDPgj8KehHm+MudsYU2aMKcvNzR1zg5VS0c1fhaKLeMYukBz4mcAHROR9QCKQJiJ/Aw4C//Jd8xhwX2iaqJSKJf6JS61AGbuTjsCNMV83xhQbY0qAjwIvGWOuBf4NrPBddi6wK2StVErFDH8ZYZbWgI/ZWPYD/yHwgIh8EWgHbgxOk5RSsSzH7cTlsDEtzx3ppkQ9McaE7cnKysrM2rVrw/Z8SqnxqaWrj7REh5YRBkhE1vnmG4+iJ/IopcIuPSkh0k2ICbqUXimlopQGcKWUilIawJVSKkppAFdKqSilAVwppaKUBnCllIpSYa0DF5F6YF/YnjBycoCGSDdiHNB+0D7w0344YjR9MdkYc9xmUmEN4PFCRNYOVXQfb7QftA/8tB+OCGZfaApFKaWilAZwpZSKUhrAQ+PuSDdgnNB+0D7w0344Imh9oTlwpZSKUjoCV0qpKKUBXCmlopQG8FES3chY+ehrQR0rXK8JDeCjZ/d/or/AcU///9WxNICPRyJyiYi8APxMRM4BMHE4Eywil4nI/4t0OyJJRN4nIiuBH4vI8ki3J1L0tXBEuF8TWoUyAiJSgnWY83eACcCZwCvGmHtExGaM8UaweWEhIjbgBuBrwGTgPGPMqsi2KrxEJAHrTNizsV4LpwFFwJ+MMe9Esm3h4nvXaQM+SRy/FmCgLxKAHxDm14SOwEemFHjdGLMSuA+4B/iciGQaY7zxkErx/ZHaDSwEbgXibuRljOkDdgJXG2OexnodZACeiDYsjIzFA+whjl8LMNAXvcAu4GPhfE1oAD8BEfmwiCwddNNB4EMi4jLGdBtjXgHeBL4dkQaGyRD98KYxps0Y80cgRUQ+5bsuZl9PQ/TB/UCFiDiNMYeAVCA7Io0LIxH5vIj8UURu9N30ary9FvwG9cWnfTf9ESgP52si5jt5NEQkT0ReBX4FfN3/YjTG7ASeB37ku06APwATRSQ/1nLhw/UD0D/o828DX/K/C4lIQ0PoRH1gjPEaY3pFJBNwARsj1tAwEJHrgY8BjwLXicjXgamDLonp18Jgx/TFtf6+CPdrQgP4EIwxdcBK4CKgGvjMoLv/B7hUROb4AnY30Aa0h72hIXaCfhB/ysj3dnE7cJOIpIrIlRFqbkicoA8G/7GeDLQYY2pEpFhEzgtzM8PlfOBHxphngC8DicA1/jtj/bVwjKH64mOD7i8hDK+JuA/gx+atB42wfg1sA54DLhGRQgBjzF7gXuB3InIWcC2QB0T1iGMk/eAL3jaOvH7+C2sCZzdQEKYmB90I+8CIiMN3fxFgF5HPAU8SxX0wlEH9sB64FMAYsxZ4CygSkTMHXR4Tr4XhBNAXZ/vuD8trIu4DOJA0+Av/Wz9jTJ8xph8rx70DuH3QNT/ACuKfAmYCnzLGdIWtxaERaD983n+/McYjIqXA77GqcxYZY34d3mYH1Uj7oN936XuA9wPTgPcZYx4MW4tDQETsvo8CR/oBeAOw+ctngS1Y70om+K6fBvyO2HgtACPui0McCdRheU04Tn5JbBKR04GvAq0i8hDwoi8g+f+j/G+RG4DHgS+ISDHQC/QZY/4iIg/7Zp+j1hj6oRurLxqAzxpjdoW/9cExhj7oM8bUAg8DTxhjXohA84NGRJZhlYjuFJG7jDFtvtsdvj9Wu4GtwEdE5A1jzEERyQc6fN+iBeu1sDsS7Q+mUfZFAdDj+xaPAo8bY14MZTvjcgTuK7D/HfAvrHKwa4FMXy238b09dvmqTTzGmNew/rO2AK9iHYlEDATv5Yy+H1YB+caYligP3ssZfR+8IiLTjTFvx0DwPhf4DfAS1oj6GyJyIRz1TqMN6//dBfxErHr4TKDRd119jATvsfRFne+610IdvCFOAzhwKrDGGPMA8DesIvx2/9sjEbkTq46z0Pf1zViTV3cBp8bCi9RH+0H7wG8x8IYx5iHge0A+cLVvhI2IfA94EGuU/S2sYLXK9/WfI9Li0ImavoiLFIrvLXLToJHia8B3ReQQ1gKE7ViTks8CB7DyVt82xlT6rt8DnGGM2RPelgeX9oP2gd8Q/bATWCAiE4wxh0SkHauG+TIReRmrXPBrvkl8ROQGIMWfWohm0dwXMT0CF5EMEXkSq3b7KhFxAxhjNmCVhU0GbjXGLMealLgA6DDGfMwYs8c/gWGMeSGaf2G1H7QP/IbrB6xVhK3A/SLyKDAR2ACkGmN2+fphrxxZE+GN9uAdC30R0wEcSAGeBT7n+9xf4oMxZjWQC+zz3fQS1tLXw2CVCxlrqXAs0H7QPvA7th/8G7LtBr6EVQL4D2PM5Vi5/uX+B0rs7fcT9X0RcwFcRD4uIueKSJoxpgrr/LlHsKomloqIv+TJhVUWdqvvoecDWb7rGA//OWOh/aB94HeSflji7wdjTK8x5mVjzMO+hy4CnvF/n2jvB4i9voiJAC6WQl9+6hNYq8N+LyI5xtqzpBN4AWuy4XwAY0wPVkmYW0ReA67GKoGqi8xPMXbaD9oHfiPsh/OOeexZIrIO613KE+Fue7DFcl9EfQAXEbsxxmBtHFNljDkfuAVoYtDpz8aYN4BKYKYv95VkjNmK9R96vTHmfGPM9vD/BMGh/aB94DeKfpglIukikuK7qxz4ljHmvYMmb6NSrPdF1O4H7ptU+n9YJ+M8BaQBHzbGfMJ3vw1rZdRHjDGv+m5zY5UFnQlMwlotVhWB5geN9oP2gd8Y++EMrIncxcaYgxFoflDFS19E5QhcrEL7dVhvefZg/Uf1AStEZAkM5Ki+6/vndwlWnnMDMC8GfmHjvh+0DyxB6IeNWP0wrgNWIOKpL6K1DtwL/NQY81cAEVkITMHazvL3wGLfX9h/A+eJSInv7U83cIGxVtPFAu0H7QM/7Ycj4qYvonIEjvXX9RHf2ySw6nYnGWPux7cDmO8vbDHg8eeujDEro+k/JwDaD9oHftoPR8RNX0RlADfGdBpjesyR2tz3APW+zz8JzBaRJ4CHgHchNk+O137QPvDTfjginvoiWlMowMBEhcHaq+Bx381twDeAuUCFP7dponW2NgDaD9oHftoPR8RDX0TlCHwQL9bmQw3Aqb6/qt8CvMaY16N9YmoEtB+0D/y0H46I+b6I2jJCP7E2onnT9+8+Y8y9EW5SRGg/aB/4aT8cEet9EQsBvBi4DviZsVbUxSXtB+0DP+2HI2K9L6I+gCulVLyK9hy4UkrFLQ3gSikVpTSAK6VUlNIArpRSUUoDuFJKRSkN4CpmiYhHRDaIyFYR2SgiX/ZtYnSix5SIyMfC1UalxkIDuIplXcaYBcaYOVj7YVwMfOckjykBNICrqKB14CpmiUi7McY96OupwBogB2vD/r9iHWYL1hFqb4rI28BsoAL4M/Ar4IdYB9q6gN8aY+4K2w+h1AloAFcx69gA7rutGZiJtamR1xjTLSLTgYeMMWUishz4ijHmUt/1NwF5xpjviXX48RvAlcaYirD+MEoNIap3I1RqDBKA34jIAsADzBjmuguxNkL6sO/rdGA61ghdqYjSAK7ihi+F4gHqsHLhtcB8rLmg7uEeBnzOGPNsWBqp1AjoJKaKCyKSC/wB+I1v7+d0oNp3Mst1WIffgpVaSR300GeBW0Qkwfd9Zgw6sVypiNIRuIplSSKyAStd0o81afkz332/Ax4VkY8DzwAdvts3AR4R2QjcD/wSqzLlXd+pLfXAZeH6AZQ6EZ3EVEqpKKUpFKWUilIawJVSKkppAFdKqSilAVwppaKUBnCllIpSGsCVUipKaQBXSqko9f8BIvQiyK87CJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data.iloc[(merged_data.index > '2018-09-01') & (merged_data.index < '2019-12-01')]['Adj Close_y'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77ec152940>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data.iloc[(merged_data.index > '2018-09-01') & (merged_data.index < '2019-12-01')]['Adj Close_x'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test for cointegration: Full Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Adj Close_y</td>   <th>  R-squared:         </th> <td>   0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   8.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 07 Jan 2020</td> <th>  Prob (F-statistic):</th>  <td>0.00342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>18:13:20</td>     <th>  Log-Likelihood:    </th> <td> -623.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   252</td>      <th>  AIC:               </th> <td>   1252.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   250</td>      <th>  BIC:               </th> <td>   1259.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>   49.8092</td> <td>    1.145</td> <td>   43.520</td> <td> 0.000</td> <td>   47.555</td> <td>   52.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Adj Close_x</th> <td>    0.0826</td> <td>    0.028</td> <td>    2.956</td> <td> 0.003</td> <td>    0.028</td> <td>    0.138</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.663</td> <th>  Durbin-Watson:     </th> <td>   0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>   6.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.231</td> <th>  Prob(JB):          </th> <td>  0.0329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.340</td> <th>  Cond. No.          </th> <td>    258.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            Adj Close_y   R-squared:                       0.034\n",
       "Model:                            OLS   Adj. R-squared:                  0.030\n",
       "Method:                 Least Squares   F-statistic:                     8.736\n",
       "Date:                Tue, 07 Jan 2020   Prob (F-statistic):            0.00342\n",
       "Time:                        18:13:20   Log-Likelihood:                -623.83\n",
       "No. Observations:                 252   AIC:                             1252.\n",
       "Df Residuals:                     250   BIC:                             1259.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const          49.8092      1.145     43.520      0.000      47.555      52.063\n",
       "Adj Close_x     0.0826      0.028      2.956      0.003       0.028       0.138\n",
       "==============================================================================\n",
       "Omnibus:                       11.663   Durbin-Watson:                   0.091\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                6.827\n",
       "Skew:                           0.231   Prob(JB):                       0.0329\n",
       "Kurtosis:                       2.340   Cond. No.                         258.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data = sm.add_constant(merged_data)\n",
    "full_sample_model = sm.OLS(merged_data['Adj Close_y'],merged_data[['const','Adj Close_x']]).fit()\n",
    "full_sample_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f77ed82fd30>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_sample_model.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-2.158370274609309, 0.22169991772129616, 1, 250, {'1%': -3.456780859712, '5%': -2.8731715065600003, '10%': -2.572968544}, 629.9033245532771)\n"
     ]
    }
   ],
   "source": [
    "adf_test_result = sm.tsa.stattools.adfuller(full_sample_model.resid,maxlag=1)\n",
    "print(adf_test_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back-test simple pairs trading strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "days_in_sample = merged_data.shape[0]\n",
    "starting_date = 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate trade signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "signals = []\n",
    "\n",
    "for i in range(starting_date,days_in_sample):\n",
    "    \n",
    "    regression_data = merged_data.iloc[0:i,:]\n",
    "    current_model = sm.OLS(regression_data['Adj Close_y'],regression_data[['const','Adj Close_x']]).fit()\n",
    "    \n",
    "    current_signal = current_model.resid[-1] # get last model residual, if this is >0 - DAL is overpriced, if <0 - AAL is overpriced\n",
    "    signals = signals + [current_signal]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add trade signals to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data['TradeSignal'] = ''\n",
    "merged_data['TradeSignal'].iloc[starting_date:] = signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>const</th>\n",
       "      <th>Open_x</th>\n",
       "      <th>High_x</th>\n",
       "      <th>Low_x</th>\n",
       "      <th>Close_x</th>\n",
       "      <th>Adj Close_x</th>\n",
       "      <th>Volume_x</th>\n",
       "      <th>Open_y</th>\n",
       "      <th>High_y</th>\n",
       "      <th>Low_y</th>\n",
       "      <th>Close_y</th>\n",
       "      <th>Adj Close_y</th>\n",
       "      <th>Volume_y</th>\n",
       "      <th>TradeSignal</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",
       "      <th></th>\n",
       "      <th></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>2019-02-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.770000</td>\n",
       "      <td>36.590000</td>\n",
       "      <td>35.770000</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>36.011822</td>\n",
       "      <td>6611900</td>\n",
       "      <td>49.630001</td>\n",
       "      <td>50.490002</td>\n",
       "      <td>49.570000</td>\n",
       "      <td>49.919998</td>\n",
       "      <td>49.919998</td>\n",
       "      <td>6172900</td>\n",
       "      <td>-3.39757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.160000</td>\n",
       "      <td>36.810001</td>\n",
       "      <td>36.119999</td>\n",
       "      <td>36.779999</td>\n",
       "      <td>36.680000</td>\n",
       "      <td>9089300</td>\n",
       "      <td>49.990002</td>\n",
       "      <td>50.400002</td>\n",
       "      <td>49.560001</td>\n",
       "      <td>50.340000</td>\n",
       "      <td>50.340000</td>\n",
       "      <td>5356200</td>\n",
       "      <td>-2.91653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.810001</td>\n",
       "      <td>37.230000</td>\n",
       "      <td>36.700001</td>\n",
       "      <td>36.930000</td>\n",
       "      <td>36.930000</td>\n",
       "      <td>6003200</td>\n",
       "      <td>50.369999</td>\n",
       "      <td>51.060001</td>\n",
       "      <td>50.020000</td>\n",
       "      <td>50.540001</td>\n",
       "      <td>50.540001</td>\n",
       "      <td>8727600</td>\n",
       "      <td>-2.53564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.799999</td>\n",
       "      <td>37.090000</td>\n",
       "      <td>36.610001</td>\n",
       "      <td>36.759998</td>\n",
       "      <td>36.759998</td>\n",
       "      <td>5801800</td>\n",
       "      <td>50.169998</td>\n",
       "      <td>50.779999</td>\n",
       "      <td>49.860001</td>\n",
       "      <td>50.520000</td>\n",
       "      <td>50.520000</td>\n",
       "      <td>6224800</td>\n",
       "      <td>-2.34343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.459999</td>\n",
       "      <td>36.700001</td>\n",
       "      <td>35.939999</td>\n",
       "      <td>36.240002</td>\n",
       "      <td>36.240002</td>\n",
       "      <td>3878459</td>\n",
       "      <td>50.340000</td>\n",
       "      <td>50.668800</td>\n",
       "      <td>49.970001</td>\n",
       "      <td>50.380001</td>\n",
       "      <td>50.380001</td>\n",
       "      <td>3591066</td>\n",
       "      <td>-2.3373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            const     Open_x     High_x      Low_x    Close_x  Adj Close_x  \\\n",
       "Date                                                                         \n",
       "2019-02-01    1.0  35.770000  36.590000  35.770000  36.110001    36.011822   \n",
       "2019-02-04    1.0  36.160000  36.810001  36.119999  36.779999    36.680000   \n",
       "2019-02-05    1.0  36.810001  37.230000  36.700001  36.930000    36.930000   \n",
       "2019-02-06    1.0  36.799999  37.090000  36.610001  36.759998    36.759998   \n",
       "2019-02-07    1.0  36.459999  36.700001  35.939999  36.240002    36.240002   \n",
       "\n",
       "            Volume_x     Open_y     High_y      Low_y    Close_y  Adj Close_y  \\\n",
       "Date                                                                            \n",
       "2019-02-01   6611900  49.630001  50.490002  49.570000  49.919998    49.919998   \n",
       "2019-02-04   9089300  49.990002  50.400002  49.560001  50.340000    50.340000   \n",
       "2019-02-05   6003200  50.369999  51.060001  50.020000  50.540001    50.540001   \n",
       "2019-02-06   5801800  50.169998  50.779999  49.860001  50.520000    50.520000   \n",
       "2019-02-07   3878459  50.340000  50.668800  49.970001  50.380001    50.380001   \n",
       "\n",
       "            Volume_y TradeSignal  \n",
       "Date                              \n",
       "2019-02-01   6172900    -3.39757  \n",
       "2019-02-04   5356200    -2.91653  \n",
       "2019-02-05   8727600    -2.53564  \n",
       "2019-02-06   6224800    -2.34343  \n",
       "2019-02-07   3591066     -2.3373  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data['DAL_Return'] = merged_data['Adj Close_y'].pct_change()\n",
    "merged_data['AAL_Return'] = merged_data['Adj Close_x'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data = merged_data.iloc[30:,:] # drop first 30 observations for which trade signal is not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "DAL_short_trade = merged_data['TradeSignal'] > 0\n",
    "DAL_long_trade = merged_data['TradeSignal'] < 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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