Module-3(ML-intern).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Pred</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>May 04, 2017</td>\n",
       "      <td>28060</td>\n",
       "      <td>28400</td>\n",
       "      <td>28482</td>\n",
       "      <td>28025</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>-1.79%</td>\n",
       "      <td>738.0</td>\n",
       "      <td>117.570740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>May 05, 2017</td>\n",
       "      <td>28184</td>\n",
       "      <td>28136</td>\n",
       "      <td>28382</td>\n",
       "      <td>28135</td>\n",
       "      <td>0.06K</td>\n",
       "      <td>0.44%</td>\n",
       "      <td>-146.0</td>\n",
       "      <td>295.430176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>May 08, 2017</td>\n",
       "      <td>28119</td>\n",
       "      <td>28145</td>\n",
       "      <td>28255</td>\n",
       "      <td>28097</td>\n",
       "      <td>7.85K</td>\n",
       "      <td>-0.23%</td>\n",
       "      <td>30.0</td>\n",
       "      <td>132.123714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>May 09, 2017</td>\n",
       "      <td>27981</td>\n",
       "      <td>28125</td>\n",
       "      <td>28192</td>\n",
       "      <td>27947</td>\n",
       "      <td>10.10K</td>\n",
       "      <td>-0.49%</td>\n",
       "      <td>357.0</td>\n",
       "      <td>101.298064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May 10, 2017</td>\n",
       "      <td>28007</td>\n",
       "      <td>28060</td>\n",
       "      <td>28146</td>\n",
       "      <td>27981</td>\n",
       "      <td>9.28K</td>\n",
       "      <td>0.09%</td>\n",
       "      <td>124.0</td>\n",
       "      <td>112.153318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Price   Open   High    Low    Vol. Change %   Pred  \\\n",
       "0  May 04, 2017  28060  28400  28482  28025   0.08K   -1.79%  738.0   \n",
       "1  May 05, 2017  28184  28136  28382  28135   0.06K    0.44% -146.0   \n",
       "2  May 08, 2017  28119  28145  28255  28097   7.85K   -0.23%   30.0   \n",
       "3  May 09, 2017  27981  28125  28192  27947  10.10K   -0.49%  357.0   \n",
       "4  May 10, 2017  28007  28060  28146  27981   9.28K    0.09%  124.0   \n",
       "\n",
       "          new  \n",
       "0  117.570740  \n",
       "1  295.430176  \n",
       "2  132.123714  \n",
       "3  101.298064  \n",
       "4  112.153318  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/home/deepak/Documents/ML-Intern/GOLD.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = ['Price','Open','High','Low']\n",
    "y = 'Pred'\n",
    "train = df[-np.isnan(df['Pred'])]\n",
    "test = df[np.isnan(df['Pred'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression().fit(train[X],train[y])\n",
    "score = model.score(test[X],test[y])\n",
    "coef = model.coef_\n",
    "intercept = model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Pred</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Dec 11, 2018</td>\n",
       "      <td>31812</td>\n",
       "      <td>31850</td>\n",
       "      <td>31850</td>\n",
       "      <td>31618</td>\n",
       "      <td>10.53K</td>\n",
       "      <td>-0.26%</td>\n",
       "      <td>852.0</td>\n",
       "      <td>195.423493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Dec 12, 2018</td>\n",
       "      <td>31626</td>\n",
       "      <td>31749</td>\n",
       "      <td>31749</td>\n",
       "      <td>31582</td>\n",
       "      <td>7.57K</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>422.0</td>\n",
       "      <td>44.232664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Dec 13, 2018</td>\n",
       "      <td>31414</td>\n",
       "      <td>31550</td>\n",
       "      <td>31600</td>\n",
       "      <td>31337</td>\n",
       "      <td>8.43K</td>\n",
       "      <td>-0.67%</td>\n",
       "      <td>530.0</td>\n",
       "      <td>127.646233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Dec 14, 2018</td>\n",
       "      <td>31437</td>\n",
       "      <td>31440</td>\n",
       "      <td>31514</td>\n",
       "      <td>31384</td>\n",
       "      <td>6.75K</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>144.0</td>\n",
       "      <td>127.219539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Dec 17, 2018</td>\n",
       "      <td>31501</td>\n",
       "      <td>31369</td>\n",
       "      <td>31530</td>\n",
       "      <td>31291</td>\n",
       "      <td>5.97K</td>\n",
       "      <td>0.20%</td>\n",
       "      <td>415.0</td>\n",
       "      <td>372.603976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>Apr 26, 2019</td>\n",
       "      <td>31868</td>\n",
       "      <td>31851</td>\n",
       "      <td>31934</td>\n",
       "      <td>31705</td>\n",
       "      <td>9.67K</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>535.0</td>\n",
       "      <td>247.177322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Apr 30, 2019</td>\n",
       "      <td>31625</td>\n",
       "      <td>31800</td>\n",
       "      <td>31824</td>\n",
       "      <td>31597</td>\n",
       "      <td>6.44K</td>\n",
       "      <td>-0.76%</td>\n",
       "      <td>438.0</td>\n",
       "      <td>52.201158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>May 01, 2019</td>\n",
       "      <td>31563</td>\n",
       "      <td>31604</td>\n",
       "      <td>31657</td>\n",
       "      <td>31503</td>\n",
       "      <td>1.55K</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>269.0</td>\n",
       "      <td>113.293305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>May 02, 2019</td>\n",
       "      <td>31203</td>\n",
       "      <td>31420</td>\n",
       "      <td>31425</td>\n",
       "      <td>31160</td>\n",
       "      <td>0.48K</td>\n",
       "      <td>-1.14%</td>\n",
       "      <td>601.0</td>\n",
       "      <td>48.365693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>May 03, 2019</td>\n",
       "      <td>31341</td>\n",
       "      <td>31250</td>\n",
       "      <td>31500</td>\n",
       "      <td>31163</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.44%</td>\n",
       "      <td>280.0</td>\n",
       "      <td>429.924911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Price   Open   High    Low    Vol. Change %   Pred  \\\n",
       "411  Dec 11, 2018  31812  31850  31850  31618  10.53K   -0.26%  852.0   \n",
       "412  Dec 12, 2018  31626  31749  31749  31582   7.57K   -0.58%  422.0   \n",
       "413  Dec 13, 2018  31414  31550  31600  31337   8.43K   -0.67%  530.0   \n",
       "414  Dec 14, 2018  31437  31440  31514  31384   6.75K    0.07%  144.0   \n",
       "415  Dec 17, 2018  31501  31369  31530  31291   5.97K    0.20%  415.0   \n",
       "..            ...    ...    ...    ...    ...     ...      ...    ...   \n",
       "507  Apr 26, 2019  31868  31851  31934  31705   9.67K    0.08%  535.0   \n",
       "508  Apr 30, 2019  31625  31800  31824  31597   6.44K   -0.76%  438.0   \n",
       "509  May 01, 2019  31563  31604  31657  31503   1.55K   -0.20%  269.0   \n",
       "510  May 02, 2019  31203  31420  31425  31160   0.48K   -1.14%  601.0   \n",
       "511  May 03, 2019  31341  31250  31500  31163   0.08K    0.44%  280.0   \n",
       "\n",
       "            new  \n",
       "411  195.423493  \n",
       "412   44.232664  \n",
       "413  127.646233  \n",
       "414  127.219539  \n",
       "415  372.603976  \n",
       "..          ...  \n",
       "507  247.177322  \n",
       "508   52.201158  \n",
       "509  113.293305  \n",
       "510   48.365693  \n",
       "511  429.924911  \n",
       "\n",
       "[101 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Pred'] = model.predict(test[X])\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fede8983af0>"
      ]
     },
     "execution_count": 21,
     "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": [
    "sns.distplot(df['Price'])\n",
    "sns.distplot(df['Open'])\n",
    "sns.distplot(df['High'])\n",
    "sns.distplot(df['Low'])\n",
    "sns.distplot(train['Pred'],label='train')\n",
    "sns.distplot(test['Pred'], label='test')\n",
    "plt.legend(loc = 'upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression \"New\"\n",
    "model_new = LinearRegression().fit(train[X],train[y])\n",
    "score = model_new.score(test[X],test[y])\n",
    "coef = model_new.coef_\n",
    "intercept = model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  3., -1., -4.])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999683016109174"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(2)\n",
    "model_poly = poly.fit_transform(train[X],test[X])\n",
    "new_model_poly = LinearRegression().fit(train[X], train['new'])\n",
    "new_model_poly.score(test[X],test['new'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc['new_linear'] = model_new.predict( test[X])\n",
    "test.loc['new_poly']   = new_model_poly.predict(test[X])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Pred</th>\n",
       "      <th>new</th>\n",
       "      <th>new_linear</th>\n",
       "      <th>new_poly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Dec 11, 2018</td>\n",
       "      <td>31812</td>\n",
       "      <td>31850</td>\n",
       "      <td>31850</td>\n",
       "      <td>31618</td>\n",
       "      <td>10.53K</td>\n",
       "      <td>-0.26%</td>\n",
       "      <td>852.0</td>\n",
       "      <td>195.423493</td>\n",
       "      <td>852.0</td>\n",
       "      <td>195.871760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Dec 12, 2018</td>\n",
       "      <td>31626</td>\n",
       "      <td>31749</td>\n",
       "      <td>31749</td>\n",
       "      <td>31582</td>\n",
       "      <td>7.57K</td>\n",
       "      <td>-0.58%</td>\n",
       "      <td>422.0</td>\n",
       "      <td>44.232664</td>\n",
       "      <td>422.0</td>\n",
       "      <td>43.810450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Dec 13, 2018</td>\n",
       "      <td>31414</td>\n",
       "      <td>31550</td>\n",
       "      <td>31600</td>\n",
       "      <td>31337</td>\n",
       "      <td>8.43K</td>\n",
       "      <td>-0.67%</td>\n",
       "      <td>530.0</td>\n",
       "      <td>127.646233</td>\n",
       "      <td>530.0</td>\n",
       "      <td>127.645552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Dec 14, 2018</td>\n",
       "      <td>31437</td>\n",
       "      <td>31440</td>\n",
       "      <td>31514</td>\n",
       "      <td>31384</td>\n",
       "      <td>6.75K</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>144.0</td>\n",
       "      <td>127.219539</td>\n",
       "      <td>144.0</td>\n",
       "      <td>126.848503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Dec 17, 2018</td>\n",
       "      <td>31501</td>\n",
       "      <td>31369</td>\n",
       "      <td>31530</td>\n",
       "      <td>31291</td>\n",
       "      <td>5.97K</td>\n",
       "      <td>0.20%</td>\n",
       "      <td>415.0</td>\n",
       "      <td>372.603976</td>\n",
       "      <td>415.0</td>\n",
       "      <td>373.255871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Price   Open   High    Low    Vol. Change %   Pred  \\\n",
       "411  Dec 11, 2018  31812  31850  31850  31618  10.53K   -0.26%  852.0   \n",
       "412  Dec 12, 2018  31626  31749  31749  31582   7.57K   -0.58%  422.0   \n",
       "413  Dec 13, 2018  31414  31550  31600  31337   8.43K   -0.67%  530.0   \n",
       "414  Dec 14, 2018  31437  31440  31514  31384   6.75K    0.07%  144.0   \n",
       "415  Dec 17, 2018  31501  31369  31530  31291   5.97K    0.20%  415.0   \n",
       "\n",
       "            new  new_linear    new_poly  \n",
       "411  195.423493       852.0  195.871760  \n",
       "412   44.232664       422.0   43.810450  \n",
       "413  127.646233       530.0  127.645552  \n",
       "414  127.219539       144.0  126.848503  \n",
       "415  372.603976       415.0  373.255871  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()\n",
    "# Hence 'Pred' was Linear and 'new' was Polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</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>2017-05-15</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>30.25</td>\n",
       "      <td>30.40</td>\n",
       "      <td>30.50</td>\n",
       "      <td>30.10</td>\n",
       "      <td>30.35</td>\n",
       "      <td>30.25</td>\n",
       "      <td>30.28</td>\n",
       "      <td>2666848</td>\n",
       "      <td>8.074250e+07</td>\n",
       "      <td>4292</td>\n",
       "      <td>861729</td>\n",
       "      <td>32.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-16</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>30.25</td>\n",
       "      <td>30.35</td>\n",
       "      <td>33.65</td>\n",
       "      <td>30.35</td>\n",
       "      <td>32.80</td>\n",
       "      <td>32.85</td>\n",
       "      <td>32.59</td>\n",
       "      <td>39322702</td>\n",
       "      <td>1.281594e+09</td>\n",
       "      <td>57037</td>\n",
       "      <td>5217922</td>\n",
       "      <td>13.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-17</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>32.85</td>\n",
       "      <td>32.95</td>\n",
       "      <td>33.75</td>\n",
       "      <td>32.45</td>\n",
       "      <td>33.00</td>\n",
       "      <td>33.10</td>\n",
       "      <td>33.13</td>\n",
       "      <td>27368209</td>\n",
       "      <td>9.067376e+08</td>\n",
       "      <td>38034</td>\n",
       "      <td>3760475</td>\n",
       "      <td>13.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>33.10</td>\n",
       "      <td>32.80</td>\n",
       "      <td>32.85</td>\n",
       "      <td>31.70</td>\n",
       "      <td>31.90</td>\n",
       "      <td>31.90</td>\n",
       "      <td>32.37</td>\n",
       "      <td>9833756</td>\n",
       "      <td>3.182696e+08</td>\n",
       "      <td>15013</td>\n",
       "      <td>2046350</td>\n",
       "      <td>20.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-19</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>31.90</td>\n",
       "      <td>33.25</td>\n",
       "      <td>33.25</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.45</td>\n",
       "      <td>32.40</td>\n",
       "      <td>32.55</td>\n",
       "      <td>12551755</td>\n",
       "      <td>4.085741e+08</td>\n",
       "      <td>20421</td>\n",
       "      <td>2456282</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                           \n",
       "2017-05-15  ADANIPOWER     EQ       30.25       30.40       30.50      30.10   \n",
       "2017-05-16  ADANIPOWER     EQ       30.25       30.35       33.65      30.35   \n",
       "2017-05-17  ADANIPOWER     EQ       32.85       32.95       33.75      32.45   \n",
       "2017-05-18  ADANIPOWER     EQ       33.10       32.80       32.85      31.70   \n",
       "2017-05-19  ADANIPOWER     EQ       31.90       33.25       33.25      32.00   \n",
       "\n",
       "            Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                        \n",
       "2017-05-15       30.35        30.25          30.28                2666848   \n",
       "2017-05-16       32.80        32.85          32.59               39322702   \n",
       "2017-05-17       33.00        33.10          33.13               27368209   \n",
       "2017-05-18       31.90        31.90          32.37                9833756   \n",
       "2017-05-19       32.45        32.40          32.55               12551755   \n",
       "\n",
       "                Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                       \n",
       "2017-05-15  8.074250e+07           4292           861729   \n",
       "2017-05-16  1.281594e+09          57037          5217922   \n",
       "2017-05-17  9.067376e+08          38034          3760475   \n",
       "2017-05-18  3.182696e+08          15013          2046350   \n",
       "2017-05-19  4.085741e+08          20421          2456282   \n",
       "\n",
       "            % Dly Qt to Traded Qty  \n",
       "Date                                \n",
       "2017-05-15                   32.31  \n",
       "2017-05-16                   13.27  \n",
       "2017-05-17                   13.74  \n",
       "2017-05-18                   20.81  \n",
       "2017-05-19                   19.57  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 3.2\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from sklearn.model_selection import train_test_split\n",
    "nifty = pd.read_csv('/home/deepak/Documents/ML-Intern/Nifty50/Nifty50.csv', parse_dates=['Date'],index_col = 'Date')\n",
    "adani = pd.read_csv('/home/deepak/Documents/ML-Intern/Mid_Cap/ADANIPOWER.csv', parse_dates=['Date'],index_col='Date')\n",
    "adani.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Shares Traded</th>\n",
       "      <th>Turnover (Rs. Cr)</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>15-May-2017</th>\n",
       "      <td>9433.55</td>\n",
       "      <td>9449.25</td>\n",
       "      <td>9423.10</td>\n",
       "      <td>9445.40</td>\n",
       "      <td>145163953</td>\n",
       "      <td>7790.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-May-2017</th>\n",
       "      <td>9461.00</td>\n",
       "      <td>9517.20</td>\n",
       "      <td>9456.35</td>\n",
       "      <td>9512.25</td>\n",
       "      <td>171709433</td>\n",
       "      <td>9209.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-May-2017</th>\n",
       "      <td>9517.60</td>\n",
       "      <td>9532.60</td>\n",
       "      <td>9486.10</td>\n",
       "      <td>9525.75</td>\n",
       "      <td>211856040</td>\n",
       "      <td>10726.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-May-2017</th>\n",
       "      <td>9453.20</td>\n",
       "      <td>9489.10</td>\n",
       "      <td>9418.10</td>\n",
       "      <td>9429.45</td>\n",
       "      <td>199340647</td>\n",
       "      <td>10782.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-May-2017</th>\n",
       "      <td>9469.90</td>\n",
       "      <td>9505.75</td>\n",
       "      <td>9390.75</td>\n",
       "      <td>9427.90</td>\n",
       "      <td>259861396</td>\n",
       "      <td>11544.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open     High      Low    Close  Shares Traded  \\\n",
       "Date                                                             \n",
       "15-May-2017  9433.55  9449.25  9423.10  9445.40      145163953   \n",
       "16-May-2017  9461.00  9517.20  9456.35  9512.25      171709433   \n",
       "17-May-2017  9517.60  9532.60  9486.10  9525.75      211856040   \n",
       "18-May-2017  9453.20  9489.10  9418.10  9429.45      199340647   \n",
       "19-May-2017  9469.90  9505.75  9390.75  9427.90      259861396   \n",
       "\n",
       "             Turnover (Rs. Cr)  \n",
       "Date                            \n",
       "15-May-2017            7790.97  \n",
       "16-May-2017            9209.89  \n",
       "17-May-2017           10726.23  \n",
       "18-May-2017           10782.46  \n",
       "19-May-2017           11544.77  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This computes the linear regression correleation coefficent of the pct_change() between days.\n",
    "\n",
    "# Beta is 1.3592 for ADANIPOWER vs NIFTY50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADANIPOWER</th>\n",
       "      <th>NIFTY50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15-May-2017</th>\n",
       "      <td>30.25</td>\n",
       "      <td>9445.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-May-2017</th>\n",
       "      <td>32.85</td>\n",
       "      <td>9512.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-May-2017</th>\n",
       "      <td>33.10</td>\n",
       "      <td>9525.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-May-2017</th>\n",
       "      <td>31.90</td>\n",
       "      <td>9429.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-May-2017</th>\n",
       "      <td>32.40</td>\n",
       "      <td>9427.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ADANIPOWER  NIFTY50\n",
       "Date                            \n",
       "15-May-2017       30.25  9445.40\n",
       "16-May-2017       32.85  9512.25\n",
       "17-May-2017       33.10  9525.75\n",
       "18-May-2017       31.90  9429.45\n",
       "19-May-2017       32.40  9427.90"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pd.concat([ adani['Close Price'], nifty['Close']], axis=1)\n",
    "prices.columns = ['ADANIPOWER', 'NIFTY50']\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>ADANIPOWER</th>\n",
       "      <th>NIFTY50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16-May-2017</th>\n",
       "      <td>0.085950</td>\n",
       "      <td>0.007078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-May-2017</th>\n",
       "      <td>0.007610</td>\n",
       "      <td>0.001419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-May-2017</th>\n",
       "      <td>-0.036254</td>\n",
       "      <td>-0.010109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-May-2017</th>\n",
       "      <td>0.015674</td>\n",
       "      <td>-0.000164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22-May-2017</th>\n",
       "      <td>-0.029321</td>\n",
       "      <td>0.001098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ADANIPOWER   NIFTY50\n",
       "Date                             \n",
       "16-May-2017    0.085950  0.007078\n",
       "17-May-2017    0.007610  0.001419\n",
       "18-May-2017   -0.036254 -0.010109\n",
       "19-May-2017    0.015674 -0.000164\n",
       "22-May-2017   -0.029321  0.001098"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_changes = prices.pct_change().dropna(axis=0)\n",
    "pct_changes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Beta can also be calculated using the linear regression covarience coefficent.\n",
    "\n",
    "#Beta of ADANIPOWER vs NIFTY50 is 1.3592"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>ADANIPOWER</td>    <th>  R-squared:         </th> <td>   0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   138.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 17 Jul 2020</td> <th>  Prob (F-statistic):</th> <td>2.58e-28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:40:21</td>     <th>  Log-Likelihood:    </th> <td>  968.43</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   493</td>      <th>  AIC:               </th> <td>  -1933.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   491</td>      <th>  BIC:               </th> <td>  -1924.</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>    0.0003</td> <td>    0.002</td> <td>    0.199</td> <td> 0.842</td> <td>   -0.003</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NIFTY50</th> <td>    2.5025</td> <td>    0.213</td> <td>   11.763</td> <td> 0.000</td> <td>    2.084</td> <td>    2.920</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>136.836</td> <th>  Durbin-Watson:     </th> <td>   1.775</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1006.537</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.994</td>  <th>  Prob(JB):          </th> <td>2.71e-219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 9.712</td>  <th>  Cond. No.          </th> <td>    139.</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:             ADANIPOWER   R-squared:                       0.220\n",
       "Model:                            OLS   Adj. R-squared:                  0.218\n",
       "Method:                 Least Squares   F-statistic:                     138.4\n",
       "Date:                Fri, 17 Jul 2020   Prob (F-statistic):           2.58e-28\n",
       "Time:                        00:40:21   Log-Likelihood:                 968.43\n",
       "No. Observations:                 493   AIC:                            -1933.\n",
       "Df Residuals:                     491   BIC:                            -1924.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0003      0.002      0.199      0.842      -0.003       0.003\n",
       "NIFTY50        2.5025      0.213     11.763      0.000       2.084       2.920\n",
       "==============================================================================\n",
       "Omnibus:                      136.836   Durbin-Watson:                   1.775\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1006.537\n",
       "Skew:                           0.994   Prob(JB):                    2.71e-219\n",
       "Kurtosis:                       9.712   Cond. No.                         139.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Px  = pct_changes['NIFTY50']\n",
    "Py = pct_changes['ADANIPOWER']\n",
    "Px1 = sm.add_constant(Px)\n",
    "model = OLS( Py, Px1 )\n",
    "model.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 494 entries, 2017-05-15 to 2019-05-13\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Open               494 non-null    float64\n",
      " 1   High               494 non-null    float64\n",
      " 2   Low                494 non-null    float64\n",
      " 3   Close              494 non-null    float64\n",
      " 4   Shares Traded      494 non-null    int64  \n",
      " 5   Turnover (Rs. Cr)  494 non-null    float64\n",
      " 6   month              494 non-null    object \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 30.9+ KB\n"
     ]
    }
   ],
   "source": [
    "## Monthly\n",
    "nifty['month'] = nifty.index.map(lambda date: f\"{date.year}-{date.month}\")\n",
    "adani['month'] = adani.index.map(lambda date: f\"{date.year}-{date.month}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we groupby year+month, taking the last price of each month rather than each day. Then recompute the linear regression correleation coefficient of the pct_change() between the index and the stock.\n",
    "\n",
    "# Monthly Beta is 1.7670"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>2017-10</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>33.45</td>\n",
       "      <td>33.60</td>\n",
       "      <td>33.60</td>\n",
       "      <td>31.95</td>\n",
       "      <td>32.85</td>\n",
       "      <td>32.75</td>\n",
       "      <td>32.68</td>\n",
       "      <td>12858464</td>\n",
       "      <td>4.201582e+08</td>\n",
       "      <td>16309</td>\n",
       "      <td>4094794</td>\n",
       "      <td>31.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>36.00</td>\n",
       "      <td>35.85</td>\n",
       "      <td>36.05</td>\n",
       "      <td>35.20</td>\n",
       "      <td>35.50</td>\n",
       "      <td>35.50</td>\n",
       "      <td>35.57</td>\n",
       "      <td>6720465</td>\n",
       "      <td>2.390793e+08</td>\n",
       "      <td>11767</td>\n",
       "      <td>1897902</td>\n",
       "      <td>28.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>40.70</td>\n",
       "      <td>40.80</td>\n",
       "      <td>42.10</td>\n",
       "      <td>40.60</td>\n",
       "      <td>41.60</td>\n",
       "      <td>41.70</td>\n",
       "      <td>41.34</td>\n",
       "      <td>13477195</td>\n",
       "      <td>5.572042e+08</td>\n",
       "      <td>19564</td>\n",
       "      <td>3179942</td>\n",
       "      <td>23.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-5</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>27.35</td>\n",
       "      <td>27.60</td>\n",
       "      <td>28.15</td>\n",
       "      <td>27.15</td>\n",
       "      <td>27.90</td>\n",
       "      <td>27.70</td>\n",
       "      <td>27.57</td>\n",
       "      <td>12954263</td>\n",
       "      <td>3.571181e+08</td>\n",
       "      <td>15130</td>\n",
       "      <td>3140616</td>\n",
       "      <td>24.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-6</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>EQ</td>\n",
       "      <td>29.10</td>\n",
       "      <td>29.20</td>\n",
       "      <td>30.10</td>\n",
       "      <td>28.70</td>\n",
       "      <td>29.70</td>\n",
       "      <td>29.80</td>\n",
       "      <td>29.54</td>\n",
       "      <td>11179263</td>\n",
       "      <td>3.301910e+08</td>\n",
       "      <td>14577</td>\n",
       "      <td>3265596</td>\n",
       "      <td>29.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "month                                                                       \n",
       "2017-10  ADANIPOWER     EQ       33.45       33.60       33.60      31.95   \n",
       "2017-11  ADANIPOWER     EQ       36.00       35.85       36.05      35.20   \n",
       "2017-12  ADANIPOWER     EQ       40.70       40.80       42.10      40.60   \n",
       "2017-5   ADANIPOWER     EQ       27.35       27.60       28.15      27.15   \n",
       "2017-6   ADANIPOWER     EQ       29.10       29.20       30.10      28.70   \n",
       "\n",
       "         Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "month                                                                    \n",
       "2017-10       32.85        32.75          32.68               12858464   \n",
       "2017-11       35.50        35.50          35.57                6720465   \n",
       "2017-12       41.60        41.70          41.34               13477195   \n",
       "2017-5        27.90        27.70          27.57               12954263   \n",
       "2017-6        29.70        29.80          29.54               11179263   \n",
       "\n",
       "             Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "month                                                                          \n",
       "2017-10  4.201582e+08          16309          4094794                   31.85  \n",
       "2017-11  2.390793e+08          11767          1897902                   28.24  \n",
       "2017-12  5.572042e+08          19564          3179942                   23.59  \n",
       "2017-5   3.571181e+08          15130          3140616                   24.24  \n",
       "2017-6   3.301910e+08          14577          3265596                   29.21  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty_monthly = nifty.groupby('month').last()\n",
    "adani_monthly = adani.groupby('month').last()\n",
    "adani_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>ADANIPOWER_M</th>\n",
       "      <th>NIFTY50_M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>0.083969</td>\n",
       "      <td>-0.010522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>0.174648</td>\n",
       "      <td>0.029741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-5</th>\n",
       "      <td>-0.335731</td>\n",
       "      <td>-0.086362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-6</th>\n",
       "      <td>0.075812</td>\n",
       "      <td>-0.010430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-7</th>\n",
       "      <td>0.124161</td>\n",
       "      <td>0.058419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ADANIPOWER_M  NIFTY50_M\n",
       "month                           \n",
       "2017-11      0.083969  -0.010522\n",
       "2017-12      0.174648   0.029741\n",
       "2017-5      -0.335731  -0.086362\n",
       "2017-6       0.075812  -0.010430\n",
       "2017-7       0.124161   0.058419"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_monthly = pd.concat([ adani_monthly['Close Price'], nifty_monthly['Close']], axis=1)\n",
    "price_monthly.columns = ['ADANIPOWER_M', 'NIFTY50_M']\n",
    "monthly_pct = price_monthly.pct_change().dropna(axis=0)\n",
    "monthly_pct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>ADANIPOWER_M</td>   <th>  R-squared:         </th> <td>   0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 17 Jul 2020</td> <th>  Prob (F-statistic):</th>  <td>0.0281</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:51:15</td>     <th>  Log-Likelihood:    </th> <td> -4.0772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    24</td>      <th>  AIC:               </th> <td>   12.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    22</td>      <th>  BIC:               </th> <td>   14.51</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>    0.0358</td> <td>    0.061</td> <td>    0.584</td> <td> 0.565</td> <td>   -0.091</td> <td>    0.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NIFTY50_M</th> <td>    2.9465</td> <td>    1.253</td> <td>    2.352</td> <td> 0.028</td> <td>    0.348</td> <td>    5.545</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>20.195</td> <th>  Durbin-Watson:     </th> <td>   2.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  24.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.912</td> <th>  Prob(JB):          </th> <td>4.96e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.132</td> <th>  Cond. No.          </th> <td>    20.5</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:           ADANIPOWER_M   R-squared:                       0.201\n",
       "Model:                            OLS   Adj. R-squared:                  0.165\n",
       "Method:                 Least Squares   F-statistic:                     5.531\n",
       "Date:                Fri, 17 Jul 2020   Prob (F-statistic):             0.0281\n",
       "Time:                        00:51:15   Log-Likelihood:                -4.0772\n",
       "No. Observations:                  24   AIC:                             12.15\n",
       "Df Residuals:                      22   BIC:                             14.51\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0358      0.061      0.584      0.565      -0.091       0.163\n",
       "NIFTY50_M      2.9465      1.253      2.352      0.028       0.348       5.545\n",
       "==============================================================================\n",
       "Omnibus:                       20.195   Durbin-Watson:                   2.105\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               24.428\n",
       "Skew:                           1.912   Prob(JB):                     4.96e-06\n",
       "Kurtosis:                       6.132   Cond. No.                         20.5\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MPx  = monthly_pct['NIFTY50_M']\n",
    "MPy = monthly_pct['ADANIPOWER_M']\n",
    "MPx1 = sm.add_constant(MPx)\n",
    "model_M = OLS( MPy, MPx1 )\n",
    "model_M.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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