1-identify-pairs.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 244,
   "id": "e0d8dd16",
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     "end_time": "2022-11-01T17:35:59.207089Z",
     "start_time": "2022-11-01T17:35:59.185086Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "from statsmodels.tsa.stattools import coint\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c93d9b",
   "metadata": {},
   "source": [
    "### Find Tickers\n",
    "We would like to identify cointegrated pairs.\n",
    "One way to find them is to look into some specific sectors.\n",
    "For exp: 'utilities.csv' includes a list of active utilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "c8914871",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:59.331087Z",
     "start_time": "2022-11-01T17:35:59.217083Z"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Description</th>\n",
       "      <th>Price</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Change</th>\n",
       "      <th>Technical Rating</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Volume*Price</th>\n",
       "      <th>Market Capitalization</th>\n",
       "      <th>Price to Earnings Ratio (TTM)</th>\n",
       "      <th>Basic EPS (TTM)</th>\n",
       "      <th>Number of Employees</th>\n",
       "      <th>Sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PCG</td>\n",
       "      <td>Pacific Gas &amp; Electric Co.</td>\n",
       "      <td>13.83</td>\n",
       "      <td>-3.354298</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>Buy</td>\n",
       "      <td>24222506</td>\n",
       "      <td>3.349973e+08</td>\n",
       "      <td>2.748945e+10</td>\n",
       "      <td>185.844156</td>\n",
       "      <td>0.1064</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RUN</td>\n",
       "      <td>Sunrun Inc.</td>\n",
       "      <td>21.21</td>\n",
       "      <td>-7.621951</td>\n",
       "      <td>-1.75</td>\n",
       "      <td>Strong Sell</td>\n",
       "      <td>10480596</td>\n",
       "      <td>2.222934e+08</td>\n",
       "      <td>4.498730e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.5481</td>\n",
       "      <td>11383.0</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NEE</td>\n",
       "      <td>NextEra Energy, Inc.</td>\n",
       "      <td>71.77</td>\n",
       "      <td>-1.805993</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>Sell</td>\n",
       "      <td>8735692</td>\n",
       "      <td>6.269606e+08</td>\n",
       "      <td>1.425650e+11</td>\n",
       "      <td>55.960493</td>\n",
       "      <td>1.3138</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXC</td>\n",
       "      <td>Exelon Corporation</td>\n",
       "      <td>36.59</td>\n",
       "      <td>-1.719044</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>Sell</td>\n",
       "      <td>6830065</td>\n",
       "      <td>2.499121e+08</td>\n",
       "      <td>3.628839e+10</td>\n",
       "      <td>13.743079</td>\n",
       "      <td>2.7108</td>\n",
       "      <td>31518.0</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FE</td>\n",
       "      <td>FirstEnergy Corp.</td>\n",
       "      <td>36.17</td>\n",
       "      <td>-2.559267</td>\n",
       "      <td>-0.95</td>\n",
       "      <td>Strong Sell</td>\n",
       "      <td>6503663</td>\n",
       "      <td>2.352375e+08</td>\n",
       "      <td>2.066737e+10</td>\n",
       "      <td>15.636716</td>\n",
       "      <td>2.3782</td>\n",
       "      <td>12395.0</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker                 Description  Price  Change %  Change  \\\n",
       "0    PCG  Pacific Gas & Electric Co.  13.83 -3.354298   -0.48   \n",
       "1    RUN                 Sunrun Inc.  21.21 -7.621951   -1.75   \n",
       "2    NEE        NextEra Energy, Inc.  71.77 -1.805993   -1.32   \n",
       "3    EXC          Exelon Corporation  36.59 -1.719044   -0.64   \n",
       "4     FE           FirstEnergy Corp.  36.17 -2.559267   -0.95   \n",
       "\n",
       "  Technical Rating    Volume  Volume*Price  Market Capitalization  \\\n",
       "0              Buy  24222506  3.349973e+08           2.748945e+10   \n",
       "1      Strong Sell  10480596  2.222934e+08           4.498730e+09   \n",
       "2             Sell   8735692  6.269606e+08           1.425650e+11   \n",
       "3             Sell   6830065  2.499121e+08           3.628839e+10   \n",
       "4      Strong Sell   6503663  2.352375e+08           2.066737e+10   \n",
       "\n",
       "   Price to Earnings Ratio (TTM)  Basic EPS (TTM)  Number of Employees  \\\n",
       "0                     185.844156           0.1064              26000.0   \n",
       "1                            NaN          -0.5481              11383.0   \n",
       "2                      55.960493           1.3138              15000.0   \n",
       "3                      13.743079           2.7108              31518.0   \n",
       "4                      15.636716           2.3782              12395.0   \n",
       "\n",
       "      Sector  \n",
       "0  Utilities  \n",
       "1  Utilities  \n",
       "2  Utilities  \n",
       "3  Utilities  \n",
       "4  Utilities  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename='utilities.csv'\n",
    "# filename='minerals.csv'\n",
    "# filename='reit.csv'\n",
    "# filename='etf.csv'\n",
    "# filename='banks.csv'\n",
    "df = pd.read_csv(filename)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c46bdb",
   "metadata": {},
   "source": [
    "We want to trade with stocks with adequate demand from buyers and sellers, we can eliminate others by defining a cap for the trade volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "050ed489",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:59.363093Z",
     "start_time": "2022-11-01T17:35:59.337085Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PCG',\n",
       " 'RUN',\n",
       " 'NEE',\n",
       " 'EXC',\n",
       " 'FE',\n",
       " 'PPL',\n",
       " 'NI',\n",
       " 'NOVA',\n",
       " 'CNP',\n",
       " 'SO',\n",
       " 'DUK',\n",
       " 'D',\n",
       " 'AES',\n",
       " 'AEP',\n",
       " 'XEL',\n",
       " 'ED',\n",
       " 'EIX',\n",
       " 'DTE',\n",
       " 'CLNE',\n",
       " 'VST',\n",
       " 'LNT',\n",
       " 'CMS',\n",
       " 'ES',\n",
       " 'PEG',\n",
       " 'WEC',\n",
       " 'SRE',\n",
       " 'CEG',\n",
       " 'NRG',\n",
       " 'RNW',\n",
       " 'AGR',\n",
       " 'OGE',\n",
       " 'AEE',\n",
       " 'WTRG',\n",
       " 'AWK',\n",
       " 'UGI',\n",
       " 'ETR',\n",
       " 'AMPS']"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Define a volume cap\n",
    "volume_cap = 1000000\n",
    "\n",
    "df = df[['Ticker','Description','Volume','Price']].sort_values(by='Volume', ascending=False)\n",
    "high_volume = df.Volume>volume_cap\n",
    "df_h = df[high_volume]\n",
    "tickers = df_h.Ticker.tolist()\n",
    "tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "66b7e3c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:35:59.441610Z",
     "start_time": "2022-11-01T17:35:59.386086Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RUN',\n",
       " 'NEE',\n",
       " 'EXC',\n",
       " 'FE',\n",
       " 'PPL',\n",
       " 'NI',\n",
       " 'CNP',\n",
       " 'SO',\n",
       " 'DUK',\n",
       " 'D',\n",
       " 'AES',\n",
       " 'AEP',\n",
       " 'XEL',\n",
       " 'ED',\n",
       " 'EIX',\n",
       " 'VST',\n",
       " 'LNT',\n",
       " 'CMS',\n",
       " 'ES',\n",
       " 'PEG',\n",
       " 'WEC',\n",
       " 'CEG',\n",
       " 'NRG',\n",
       " 'AGR',\n",
       " 'OGE',\n",
       " 'AEE',\n",
       " 'WTRG',\n",
       " 'UGI']"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Define a price interval if needed\n",
    "price_low = 20 \n",
    "price_high = 100\n",
    "\n",
    "query=\"Price>\"+str(price_low)+\" & Price<\"+str(price_high)\n",
    "df_h_p = df_h.query(query)\n",
    "tickers = df_h_p.Ticker.tolist()\n",
    "tickers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c7087d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-17T20:23:09.658974Z",
     "start_time": "2022-10-17T20:23:09.641937Z"
    }
   },
   "source": [
    "### Download Price Data\n",
    "We will use yfinance (https://pypi.org/project/yfinance/) to downloed the closing prices of stocks.\n",
    "More information is available on the next notebook which we will download and save a more detailed stock data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "dc2f71c8",
   "metadata": {
    "ExecuteTime": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  28 of 28 completed\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 253 entries, 2021-11-01 00:00:00-04:00 to 2022-11-01 00:00:00-04:00\n",
      "Data columns (total 28 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AEE     253 non-null    float64\n",
      " 1   AEP     253 non-null    float64\n",
      " 2   AES     253 non-null    float64\n",
      " 3   AGR     253 non-null    float64\n",
      " 4   CEG     199 non-null    float64\n",
      " 5   CMS     253 non-null    float64\n",
      " 6   CNP     253 non-null    float64\n",
      " 7   D       253 non-null    float64\n",
      " 8   DUK     253 non-null    float64\n",
      " 9   ED      253 non-null    float64\n",
      " 10  EIX     253 non-null    float64\n",
      " 11  ES      253 non-null    float64\n",
      " 12  EXC     253 non-null    float64\n",
      " 13  FE      253 non-null    float64\n",
      " 14  LNT     253 non-null    float64\n",
      " 15  NEE     253 non-null    float64\n",
      " 16  NI      253 non-null    float64\n",
      " 17  NRG     253 non-null    float64\n",
      " 18  OGE     253 non-null    float64\n",
      " 19  PEG     253 non-null    float64\n",
      " 20  PPL     253 non-null    float64\n",
      " 21  RUN     253 non-null    float64\n",
      " 22  SO      253 non-null    float64\n",
      " 23  UGI     253 non-null    float64\n",
      " 24  VST     253 non-null    float64\n",
      " 25  WEC     253 non-null    float64\n",
      " 26  WTRG    253 non-null    float64\n",
      " 27  XEL     253 non-null    float64\n",
      "dtypes: float64(28)\n",
      "memory usage: 57.3 KB\n"
     ]
    }
   ],
   "source": [
    "period = '1y' #valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max\n",
    "interval = '1d' #valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo\n",
    "df = yf.download(tickers, period=period, interval=interval)[\"Close\"]\n",
    "\n",
    "# Enable to use start & end dates:\n",
    "# start = datetime.datetime(2018, 1, 1)\n",
    "# end = datetime.datetime(2022, 1, 1)\n",
    "#df = yf.download(tickers, start, end)[\"Close\"]\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfacef73",
   "metadata": {},
   "source": [
    "Check for null values and eliminate them:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03fae61d",
   "metadata": {},
   "source": [
    "It is important to check the dataframe for Na rows & columns before moving forward. Downloaded data may not be consistent all the time, so better doing it manually before proceeding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "830633e6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:02.694607Z",
     "start_time": "2022-11-01T17:36:02.503537Z"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AEE</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>AGR</th>\n",
       "      <th>CEG</th>\n",
       "      <th>CMS</th>\n",
       "      <th>CNP</th>\n",
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       "      <th>DUK</th>\n",
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       "      <th>OGE</th>\n",
       "      <th>PEG</th>\n",
       "      <th>PPL</th>\n",
       "      <th>RUN</th>\n",
       "      <th>SO</th>\n",
       "      <th>UGI</th>\n",
       "      <th>VST</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WTRG</th>\n",
       "      <th>XEL</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-01 00:00:00-04:00</th>\n",
       "      <td>84.279999</td>\n",
       "      <td>85.129997</td>\n",
       "      <td>25.370001</td>\n",
       "      <td>52.580002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.290001</td>\n",
       "      <td>26.290001</td>\n",
       "      <td>75.410004</td>\n",
       "      <td>102.309998</td>\n",
       "      <td>76.110001</td>\n",
       "      <td>...</td>\n",
       "      <td>34.669998</td>\n",
       "      <td>64.360001</td>\n",
       "      <td>29.299999</td>\n",
       "      <td>58.189999</td>\n",
       "      <td>62.480000</td>\n",
       "      <td>44.389999</td>\n",
       "      <td>20.010000</td>\n",
       "      <td>90.339996</td>\n",
       "      <td>47.049999</td>\n",
       "      <td>64.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02 00:00:00-04:00</th>\n",
       "      <td>84.330002</td>\n",
       "      <td>85.300003</td>\n",
       "      <td>25.370001</td>\n",
       "      <td>52.619999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.439999</td>\n",
       "      <td>26.190001</td>\n",
       "      <td>75.489998</td>\n",
       "      <td>102.449997</td>\n",
       "      <td>76.440002</td>\n",
       "      <td>...</td>\n",
       "      <td>34.480000</td>\n",
       "      <td>63.980000</td>\n",
       "      <td>29.240000</td>\n",
       "      <td>57.720001</td>\n",
       "      <td>62.490002</td>\n",
       "      <td>44.349998</td>\n",
       "      <td>19.870001</td>\n",
       "      <td>90.889999</td>\n",
       "      <td>47.270000</td>\n",
       "      <td>64.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-03 00:00:00-04:00</th>\n",
       "      <td>84.190002</td>\n",
       "      <td>84.930000</td>\n",
       "      <td>25.299999</td>\n",
       "      <td>51.599998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.150002</td>\n",
       "      <td>26.290001</td>\n",
       "      <td>75.480003</td>\n",
       "      <td>102.540001</td>\n",
       "      <td>76.980003</td>\n",
       "      <td>...</td>\n",
       "      <td>34.590000</td>\n",
       "      <td>63.369999</td>\n",
       "      <td>29.309999</td>\n",
       "      <td>58.310001</td>\n",
       "      <td>62.549999</td>\n",
       "      <td>44.520000</td>\n",
       "      <td>19.680000</td>\n",
       "      <td>89.870003</td>\n",
       "      <td>47.259998</td>\n",
       "      <td>63.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-04 00:00:00-04:00</th>\n",
       "      <td>84.370003</td>\n",
       "      <td>83.889999</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>51.160000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.020000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>75.440002</td>\n",
       "      <td>101.019997</td>\n",
       "      <td>76.279999</td>\n",
       "      <td>...</td>\n",
       "      <td>34.160000</td>\n",
       "      <td>63.680000</td>\n",
       "      <td>28.889999</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>44.230000</td>\n",
       "      <td>19.209999</td>\n",
       "      <td>89.720001</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>63.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-05 00:00:00-04:00</th>\n",
       "      <td>85.500000</td>\n",
       "      <td>85.040001</td>\n",
       "      <td>24.990000</td>\n",
       "      <td>51.450001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.520000</td>\n",
       "      <td>26.670000</td>\n",
       "      <td>76.529999</td>\n",
       "      <td>101.680000</td>\n",
       "      <td>78.209999</td>\n",
       "      <td>...</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>64.190002</td>\n",
       "      <td>28.719999</td>\n",
       "      <td>56.189999</td>\n",
       "      <td>63.099998</td>\n",
       "      <td>44.639999</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>90.870003</td>\n",
       "      <td>47.630001</td>\n",
       "      <td>63.799999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 AEE        AEP        AES        AGR  CEG  \\\n",
       "Date                                                                         \n",
       "2021-11-01 00:00:00-04:00  84.279999  85.129997  25.370001  52.580002  NaN   \n",
       "2021-11-02 00:00:00-04:00  84.330002  85.300003  25.370001  52.619999  NaN   \n",
       "2021-11-03 00:00:00-04:00  84.190002  84.930000  25.299999  51.599998  NaN   \n",
       "2021-11-04 00:00:00-04:00  84.370003  83.889999  25.120001  51.160000  NaN   \n",
       "2021-11-05 00:00:00-04:00  85.500000  85.040001  24.990000  51.450001  NaN   \n",
       "\n",
       "                                 CMS        CNP          D         DUK  \\\n",
       "Date                                                                     \n",
       "2021-11-01 00:00:00-04:00  60.290001  26.290001  75.410004  102.309998   \n",
       "2021-11-02 00:00:00-04:00  60.439999  26.190001  75.489998  102.449997   \n",
       "2021-11-03 00:00:00-04:00  60.150002  26.290001  75.480003  102.540001   \n",
       "2021-11-04 00:00:00-04:00  60.020000  26.500000  75.440002  101.019997   \n",
       "2021-11-05 00:00:00-04:00  60.520000  26.670000  76.529999  101.680000   \n",
       "\n",
       "                                  ED  ...        OGE        PEG        PPL  \\\n",
       "Date                                  ...                                    \n",
       "2021-11-01 00:00:00-04:00  76.110001  ...  34.669998  64.360001  29.299999   \n",
       "2021-11-02 00:00:00-04:00  76.440002  ...  34.480000  63.980000  29.240000   \n",
       "2021-11-03 00:00:00-04:00  76.980003  ...  34.590000  63.369999  29.309999   \n",
       "2021-11-04 00:00:00-04:00  76.279999  ...  34.160000  63.680000  28.889999   \n",
       "2021-11-05 00:00:00-04:00  78.209999  ...  34.599998  64.190002  28.719999   \n",
       "\n",
       "                                 RUN         SO        UGI        VST  \\\n",
       "Date                                                                    \n",
       "2021-11-01 00:00:00-04:00  58.189999  62.480000  44.389999  20.010000   \n",
       "2021-11-02 00:00:00-04:00  57.720001  62.490002  44.349998  19.870001   \n",
       "2021-11-03 00:00:00-04:00  58.310001  62.549999  44.520000  19.680000   \n",
       "2021-11-04 00:00:00-04:00  57.750000  62.500000  44.230000  19.209999   \n",
       "2021-11-05 00:00:00-04:00  56.189999  63.099998  44.639999  20.500000   \n",
       "\n",
       "                                 WEC       WTRG        XEL  \n",
       "Date                                                        \n",
       "2021-11-01 00:00:00-04:00  90.339996  47.049999  64.500000  \n",
       "2021-11-02 00:00:00-04:00  90.889999  47.270000  64.709999  \n",
       "2021-11-03 00:00:00-04:00  89.870003  47.259998  63.500000  \n",
       "2021-11-04 00:00:00-04:00  89.720001  47.000000  63.070000  \n",
       "2021-11-05 00:00:00-04:00  90.870003  47.630001  63.799999  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "872d77e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:02.822612Z",
     "start_time": "2022-11-01T17:36:02.701711Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have 27 tickers left\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 253 entries, 2021-11-01 00:00:00-04:00 to 2022-11-01 00:00:00-04:00\n",
      "Data columns (total 27 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AEE     253 non-null    float64\n",
      " 1   AEP     253 non-null    float64\n",
      " 2   AES     253 non-null    float64\n",
      " 3   AGR     253 non-null    float64\n",
      " 4   CMS     253 non-null    float64\n",
      " 5   CNP     253 non-null    float64\n",
      " 6   D       253 non-null    float64\n",
      " 7   DUK     253 non-null    float64\n",
      " 8   ED      253 non-null    float64\n",
      " 9   EIX     253 non-null    float64\n",
      " 10  ES      253 non-null    float64\n",
      " 11  EXC     253 non-null    float64\n",
      " 12  FE      253 non-null    float64\n",
      " 13  LNT     253 non-null    float64\n",
      " 14  NEE     253 non-null    float64\n",
      " 15  NI      253 non-null    float64\n",
      " 16  NRG     253 non-null    float64\n",
      " 17  OGE     253 non-null    float64\n",
      " 18  PEG     253 non-null    float64\n",
      " 19  PPL     253 non-null    float64\n",
      " 20  RUN     253 non-null    float64\n",
      " 21  SO      253 non-null    float64\n",
      " 22  UGI     253 non-null    float64\n",
      " 23  VST     253 non-null    float64\n",
      " 24  WEC     253 non-null    float64\n",
      " 25  WTRG    253 non-null    float64\n",
      " 26  XEL     253 non-null    float64\n",
      "dtypes: float64(27)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(axis=0, how='all') # check if necessary\n",
    "df = df.dropna(axis=1) # check if necessary\n",
    "new_tickers = df.columns.tolist()\n",
    "print(f\"we have {len(new_tickers)} tickers left\")\n",
    "df.to_pickle(\"close_prices.pkl\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c3bea8",
   "metadata": {},
   "source": [
    "### Find Cointegrated Pairs\n",
    "\n",
    "We will perfom cointegration tests for all of the pairs that we can produce from the available tickers. We still need to perform more detailed tests for stationarity in the following notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "29213b5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:02.856612Z",
     "start_time": "2022-11-01T17:36:02.826609Z"
    }
   },
   "outputs": [],
   "source": [
    "# let's define a function to loop through the tickers and do a cointegration test for each combination\n",
    "def cointegration_test(data, p_value=0.05):\n",
    "    n = data.shape[1]\n",
    "    # create matrix to be filled later\n",
    "    stats = np.zeros((n, n))\n",
    "    pvalues = np.ones((n, n))\n",
    "    keys = data.keys() # get the info axis, returns columns if object is a dataframe\n",
    "    pairs = []\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            result = coint(data[keys[i]], data[keys[j]])\n",
    "            test_stat = result[0]\n",
    "            pvalue = result[1]\n",
    "            stats[i, j] = test_stat\n",
    "            pvalues[i, j] = pvalue\n",
    "            \n",
    "            if pvalue < p_value: # pick only statistically significant ones\n",
    "                pairs.append((keys[i], keys[j]))\n",
    "                \n",
    "    return stats, pvalues, pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aad911b",
   "metadata": {},
   "source": [
    "See the result on a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "4dc9df43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:12.501143Z",
     "start_time": "2022-11-01T17:36:02.862608Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('AEE', 'DUK'), ('AEP', 'VST'), ('CMS', 'DUK'), ('CNP', 'VST'), ('ED', 'VST'), ('OGE', 'XEL'), ('RUN', 'WTRG'), ('VST', 'WEC')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_value = 0.05  # A p-value less than 0.05 is typically considered to be statistically significant\n",
    "\n",
    "scores, pvalues, pairs = cointegration_test(df,p_value)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "seaborn.heatmap(\n",
    "    pvalues,\n",
    "    xticklabels=new_tickers,\n",
    "    yticklabels=new_tickers,\n",
    "    cmap=\"ocean\",\n",
    "    mask=(pvalues >= 0.05),\n",
    "    square=True\n",
    ")\n",
    "print(pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa61da6c",
   "metadata": {},
   "source": [
    "### Filter For More Significant Pairs\n",
    "Filter more to get more significant pairs, and eliminate empty cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "225dd586",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:12.881152Z",
     "start_time": "2022-11-01T17:36:12.509157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_2e14e_row0_col0, #T_2e14e_row1_col1, #T_2e14e_row2_col0, #T_2e14e_row3_col1, #T_2e14e_row4_col1, #T_2e14e_row5_col4, #T_2e14e_row6_col3, #T_2e14e_row7_col2 {\n",
       "  background-color: #c78f2e;\n",
       "}\n",
       "#T_2e14e_row0_col1, #T_2e14e_row0_col2, #T_2e14e_row0_col3, #T_2e14e_row0_col4, #T_2e14e_row1_col0, #T_2e14e_row1_col2, #T_2e14e_row1_col3, #T_2e14e_row1_col4, #T_2e14e_row2_col1, #T_2e14e_row2_col2, #T_2e14e_row2_col3, #T_2e14e_row2_col4, #T_2e14e_row3_col0, #T_2e14e_row3_col2, #T_2e14e_row3_col3, #T_2e14e_row3_col4, #T_2e14e_row4_col0, #T_2e14e_row4_col2, #T_2e14e_row4_col3, #T_2e14e_row4_col4, #T_2e14e_row5_col0, #T_2e14e_row5_col1, #T_2e14e_row5_col2, #T_2e14e_row5_col3, #T_2e14e_row6_col0, #T_2e14e_row6_col1, #T_2e14e_row6_col2, #T_2e14e_row6_col4, #T_2e14e_row7_col0, #T_2e14e_row7_col1, #T_2e14e_row7_col3, #T_2e14e_row7_col4 {\n",
       "  color: white;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2e14e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >DUK</th>\n",
       "      <th class=\"col_heading level0 col1\" >VST</th>\n",
       "      <th class=\"col_heading level0 col2\" >WEC</th>\n",
       "      <th class=\"col_heading level0 col3\" >WTRG</th>\n",
       "      <th class=\"col_heading level0 col4\" >XEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row0\" class=\"row_heading level0 row0\" >AEE</th>\n",
       "      <td id=\"T_2e14e_row0_col0\" class=\"data row0 col0\" >0.032</td>\n",
       "      <td id=\"T_2e14e_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_2e14e_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row1\" class=\"row_heading level0 row1\" >AEP</th>\n",
       "      <td id=\"T_2e14e_row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row1_col1\" class=\"data row1 col1\" >0.031</td>\n",
       "      <td id=\"T_2e14e_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row2\" class=\"row_heading level0 row2\" >CMS</th>\n",
       "      <td id=\"T_2e14e_row2_col0\" class=\"data row2 col0\" >0.001</td>\n",
       "      <td id=\"T_2e14e_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "      <td id=\"T_2e14e_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row3\" class=\"row_heading level0 row3\" >CNP</th>\n",
       "      <td id=\"T_2e14e_row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row3_col1\" class=\"data row3 col1\" >0.017</td>\n",
       "      <td id=\"T_2e14e_row3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row4\" class=\"row_heading level0 row4\" >ED</th>\n",
       "      <td id=\"T_2e14e_row4_col0\" class=\"data row4 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row4_col1\" class=\"data row4 col1\" >0.003</td>\n",
       "      <td id=\"T_2e14e_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row5\" class=\"row_heading level0 row5\" >OGE</th>\n",
       "      <td id=\"T_2e14e_row5_col0\" class=\"data row5 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row5_col1\" class=\"data row5 col1\" >nan</td>\n",
       "      <td id=\"T_2e14e_row5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row5_col4\" class=\"data row5 col4\" >0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row6\" class=\"row_heading level0 row6\" >RUN</th>\n",
       "      <td id=\"T_2e14e_row6_col0\" class=\"data row6 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row6_col1\" class=\"data row6 col1\" >nan</td>\n",
       "      <td id=\"T_2e14e_row6_col2\" class=\"data row6 col2\" >nan</td>\n",
       "      <td id=\"T_2e14e_row6_col3\" class=\"data row6 col3\" >0.047</td>\n",
       "      <td id=\"T_2e14e_row6_col4\" class=\"data row6 col4\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e14e_level0_row7\" class=\"row_heading level0 row7\" >VST</th>\n",
       "      <td id=\"T_2e14e_row7_col0\" class=\"data row7 col0\" >nan</td>\n",
       "      <td id=\"T_2e14e_row7_col1\" class=\"data row7 col1\" >nan</td>\n",
       "      <td id=\"T_2e14e_row7_col2\" class=\"data row7 col2\" >0.046</td>\n",
       "      <td id=\"T_2e14e_row7_col3\" class=\"data row7 col3\" >nan</td>\n",
       "      <td id=\"T_2e14e_row7_col4\" class=\"data row7 col4\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x291c7b566d0>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_matrix = (pvalues >= 0.05) # possible to increase the level of significance\n",
    "\n",
    "mask_df = pd.DataFrame(mask_matrix, index= new_tickers, columns = new_tickers)\n",
    "scores_df = pd.DataFrame(scores, index= new_tickers, columns = new_tickers)\n",
    "pvalues_df = pd.DataFrame(pvalues, index= new_tickers, columns = new_tickers)\n",
    "\n",
    "df_f = pvalues_df[~mask_df].dropna(how='all', axis=1)\n",
    "df_f = df_f.dropna(how='all')\n",
    "\n",
    "def gold_apply(x):\n",
    "    color = {True: \"background-color: #c78f2e\", False: \"color : white\"}\n",
    "    is_color = (x > 0).replace(color)\n",
    "    return is_color\n",
    "\n",
    "# Another way to format table is applymap\n",
    "# def color_map(x):\n",
    "#     return f\"color: {'red' if x > 0 else 'white'}\"\n",
    "\n",
    "# df_f.style.applymap(color_map)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "seaborn.heatmap(\n",
    "    df_f,\n",
    "    xticklabels=df_f.columns,\n",
    "    yticklabels=df_f.index,\n",
    "    cmap=\"summer\",\n",
    "    square=True\n",
    ")\n",
    "df_f.style.format(precision=3)\\\n",
    ".apply(gold_apply)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6091be26",
   "metadata": {},
   "source": [
    "### EXERCISE: Get List/Dic of Pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "95aefcbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:12.912143Z",
     "start_time": "2022-11-01T17:36:12.884144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('AEE', 'DUK'),\n",
       " ('AEP', 'VST'),\n",
       " ('CMS', 'DUK'),\n",
       " ('CNP', 'VST'),\n",
       " ('ED', 'VST'),\n",
       " ('OGE', 'XEL'),\n",
       " ('RUN', 'WTRG'),\n",
       " ('VST', 'WEC')]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_list = df_f.index.tolist()\n",
    "y_list = df_f.columns.tolist()\n",
    "\n",
    "new_pairs_list = [] # list of pairs as tuples (A,B)\n",
    "\n",
    "for i in range(len(x_list)):\n",
    "    for j in range(len(y_list)):\n",
    "        if df_f.iloc[i,j]>0:\n",
    "            new_pairs_list.append((x_list[i], y_list[j]))\n",
    "\n",
    "new_pairs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "f13070ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:12.982146Z",
     "start_time": "2022-11-01T17:36:12.919159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['DUK-AEE', 'DUK-CMS'],\n",
       " ['VST-AEP', 'VST-CNP', 'VST-ED'],\n",
       " ['WEC-VST'],\n",
       " ['WTRG-RUN'],\n",
       " ['XEL-OGE']]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pairs_str =[] # list of pairs as string A-B\n",
    "\n",
    "for column in df_f:\n",
    "    toprint = df_f.index[df_f[column]>0].tolist()\n",
    "    new_pairs_str.append([column + \"-\" + s for s in toprint])\n",
    "    \n",
    "new_pairs_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "6b3f6469",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:13.053902Z",
     "start_time": "2022-11-01T17:36:13.007145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DUK': ['AEE', 'CMS'],\n",
       " 'VST': ['AEP', 'CNP', 'ED'],\n",
       " 'WEC': ['VST'],\n",
       " 'WTRG': ['RUN'],\n",
       " 'XEL': ['OGE']}"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pairs_dic= {} # list of pairs as dictionary {'A':['B','C']}\n",
    "\n",
    "for column in df_f:\n",
    "    new_pairs_dic[column] = df_f.index[df_f[column]>0].tolist()\n",
    "\n",
    "new_pairs_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6680d767",
   "metadata": {},
   "source": [
    "### Save to file\n",
    "Save dataframa as pickle to use for the next steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "4a323e8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-01T17:36:13.132908Z",
     "start_time": "2022-11-01T17:36:13.083901Z"
    }
   },
   "outputs": [],
   "source": [
    "df_f.to_pickle(\"pairs_to_download.pkl\")"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}