Module 3 - Regression - Beta Calculation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3- Regression & Beta Calculation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query 3.1 \n",
    "Import the file 'gold.csv', which is contains the data about last 2 years price action of Indian(MCX) gold standard. Explore the dataframe. You'd see 2 unique columns - 'Pred' and 'new'.\n",
    "\n",
    "One of the 2 columns is a linear combination of the OHLC prices with varying coefficients while the other is a polynomial fucntion of the same inputs. Also, one of the 2 columns is partially filled.\n",
    "\n",
    ">Using linear regression, find the coefficients of the inputs and using the same trained model, complete the\n",
    "      entire column.\n",
    "      \n",
    ">Also, try to fit the other column as well using a new linear regression model. Check if the predicitons are \n",
    "      accurate.\n",
    "      Mention which column is a linear function and which is a polynomial function.\n",
    "      (Hint: Plotting a histogram & distplot helps in recognizing the  discrepencies in prediction, if any.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_data = pd.read_csv(r\"GOLD.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>507</td>\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>NaN</td>\n",
       "      <td>247.177322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>508</td>\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>NaN</td>\n",
       "      <td>52.201158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>509</td>\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>NaN</td>\n",
       "      <td>113.293305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>510</td>\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>NaN</td>\n",
       "      <td>48.365693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>511</td>\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>NaN</td>\n",
       "      <td>429.924911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 9 columns</p>\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",
       "507  Apr 26, 2019  31868  31851  31934  31705   9.67K    0.08%    NaN   \n",
       "508  Apr 30, 2019  31625  31800  31824  31597   6.44K   -0.76%    NaN   \n",
       "509  May 01, 2019  31563  31604  31657  31503   1.55K   -0.20%    NaN   \n",
       "510  May 02, 2019  31203  31420  31425  31160   0.48K   -1.14%    NaN   \n",
       "511  May 03, 2019  31341  31250  31500  31163   0.08K    0.44%    NaN   \n",
       "\n",
       "            new  \n",
       "0    117.570740  \n",
       "1    295.430176  \n",
       "2    132.123714  \n",
       "3    101.298064  \n",
       "4    112.153318  \n",
       "..          ...  \n",
       "507  247.177322  \n",
       "508   52.201158  \n",
       "509  113.293305  \n",
       "510   48.365693  \n",
       "511  429.924911  \n",
       "\n",
       "[512 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>406</td>\n",
       "      <td>Dec 04, 2018</td>\n",
       "      <td>30964</td>\n",
       "      <td>30874</td>\n",
       "      <td>31085</td>\n",
       "      <td>30874</td>\n",
       "      <td>0.08K</td>\n",
       "      <td>0.57%</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>301.615081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>407</td>\n",
       "      <td>Dec 05, 2018</td>\n",
       "      <td>30926</td>\n",
       "      <td>30830</td>\n",
       "      <td>30970</td>\n",
       "      <td>30825</td>\n",
       "      <td>0.04K</td>\n",
       "      <td>-0.12%</td>\n",
       "      <td>72.0</td>\n",
       "      <td>241.475101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>408</td>\n",
       "      <td>Dec 06, 2018</td>\n",
       "      <td>31005</td>\n",
       "      <td>31039</td>\n",
       "      <td>31196</td>\n",
       "      <td>30974</td>\n",
       "      <td>10.55K</td>\n",
       "      <td>0.26%</td>\n",
       "      <td>35.0</td>\n",
       "      <td>188.222186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>409</td>\n",
       "      <td>Dec 07, 2018</td>\n",
       "      <td>31440</td>\n",
       "      <td>31011</td>\n",
       "      <td>31480</td>\n",
       "      <td>30941</td>\n",
       "      <td>11.24K</td>\n",
       "      <td>1.40%</td>\n",
       "      <td>669.0</td>\n",
       "      <td>976.692705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>410</td>\n",
       "      <td>Dec 10, 2018</td>\n",
       "      <td>31895</td>\n",
       "      <td>31459</td>\n",
       "      <td>31966</td>\n",
       "      <td>31420</td>\n",
       "      <td>14.09K</td>\n",
       "      <td>1.45%</td>\n",
       "      <td>521.0</td>\n",
       "      <td>990.254297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 9 columns</p>\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",
       "406  Dec 04, 2018  30964  30874  31085  30874   0.08K    0.57%  -31.0   \n",
       "407  Dec 05, 2018  30926  30830  30970  30825   0.04K   -0.12%   72.0   \n",
       "408  Dec 06, 2018  31005  31039  31196  30974  10.55K    0.26%   35.0   \n",
       "409  Dec 07, 2018  31440  31011  31480  30941  11.24K    1.40%  669.0   \n",
       "410  Dec 10, 2018  31895  31459  31966  31420  14.09K    1.45%  521.0   \n",
       "\n",
       "            new  \n",
       "0    117.570740  \n",
       "1    295.430176  \n",
       "2    132.123714  \n",
       "3    101.298064  \n",
       "4    112.153318  \n",
       "..          ...  \n",
       "406  301.615081  \n",
       "407  241.475101  \n",
       "408  188.222186  \n",
       "409  976.692705  \n",
       "410  990.254297  \n",
       "\n",
       "[411 rows x 9 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_v2 = g_data.dropna()\n",
    "gold_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = gold_v2['Pred']\n",
    "X = gold_v2['new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.array(gold_v2[\"Pred\"])\n",
    "X = np.array(gold_v2[\"new\"])\n",
    "X = X.reshape(-1,1)\n",
    "Y = Y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_model = LinearRegression()\n",
    "reg_model.fit(X, Y)\n",
    "y_predicted = reg_model.predict(X)\n",
    "rms_value = mean_squared_error(Y, y_predicted)\n",
    "r2 = r2_score(Y, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[-0.00823083]]\n",
      "Intercept: [343.67028421]\n",
      "Root mean squared error:  73300.46132887459\n",
      "R2 score:  3.732507600850976e-05\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 395
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Slope:' ,reg_model.coef_)\n",
    "print('Intercept:', reg_model.intercept_)\n",
    "print('Root mean squared error: ', rms_value)\n",
    "print('R2 score: ', r2)\n",
    "plt.scatter(X, Y, s=10)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.plot(X, y_predicted, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>342.702580</td>\n",
       "      <td>117.570740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>341.238649</td>\n",
       "      <td>295.430176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>342.582797</td>\n",
       "      <td>132.123714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>342.836517</td>\n",
       "      <td>101.298064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>342.747169</td>\n",
       "      <td>112.153318</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>507</td>\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>341.635810</td>\n",
       "      <td>247.177322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>508</td>\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>343.240625</td>\n",
       "      <td>52.201158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>509</td>\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>342.737786</td>\n",
       "      <td>113.293305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>510</td>\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>343.272194</td>\n",
       "      <td>48.365693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>511</td>\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>340.131646</td>\n",
       "      <td>429.924911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512 rows × 9 columns</p>\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%  342.702580   \n",
       "1    May 05, 2017  28184  28136  28382  28135   0.06K    0.44%  341.238649   \n",
       "2    May 08, 2017  28119  28145  28255  28097   7.85K   -0.23%  342.582797   \n",
       "3    May 09, 2017  27981  28125  28192  27947  10.10K   -0.49%  342.836517   \n",
       "4    May 10, 2017  28007  28060  28146  27981   9.28K    0.09%  342.747169   \n",
       "..            ...    ...    ...    ...    ...     ...      ...         ...   \n",
       "507  Apr 26, 2019  31868  31851  31934  31705   9.67K    0.08%  341.635810   \n",
       "508  Apr 30, 2019  31625  31800  31824  31597   6.44K   -0.76%  343.240625   \n",
       "509  May 01, 2019  31563  31604  31657  31503   1.55K   -0.20%  342.737786   \n",
       "510  May 02, 2019  31203  31420  31425  31160   0.48K   -1.14%  343.272194   \n",
       "511  May 03, 2019  31341  31250  31500  31163   0.08K    0.44%  340.131646   \n",
       "\n",
       "            new  \n",
       "0    117.570740  \n",
       "1    295.430176  \n",
       "2    132.123714  \n",
       "3    101.298064  \n",
       "4    112.153318  \n",
       "..          ...  \n",
       "507  247.177322  \n",
       "508   52.201158  \n",
       "509  113.293305  \n",
       "510   48.365693  \n",
       "511  429.924911  \n",
       "\n",
       "[512 rows x 9 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_data = g_data[:]\n",
    "pre_data_new = pre_data['new']\n",
    "pre_data_new = pre_data_new.values.reshape(-1,1)\n",
    "na_data = (regression_model.predict(pre_data_new))\n",
    "na_data_series = pd.Series(na_data.ravel())\n",
    "sata = na_data_series.to_frame()\n",
    "g_data['Pred'] = sata\n",
    "g_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.array(g_data[\"new\"])\n",
    "X = np.array(g_data[\"Pred\"])\n",
    "X = X.reshape(-1,1)\n",
    "Y = Y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_model = LinearRegression()\n",
    "reg_model.fit(X, Y)\n",
    "y_predicted = reg_model.predict(X)\n",
    "rms_value = mean_squared_error(Y, y_predicted)\n",
    "r2 = r2_score(Y, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[-121.49444741]]\n",
      "Intercept: [41754.03127127]\n",
      "Root mean squared error:  6.5108051116976104e-24\n",
      "R2 score:  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 400
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Slope:' ,reg_model.coef_)\n",
    "print('Intercept:', reg_model.intercept_)\n",
    "print('Root mean squared error: ', rms_value)\n",
    "print('R2 score: ', r2)\n",
    "plt.scatter(X, Y, s=10)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.plot(X, y_predicted, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 380
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(g_data['Pred'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query 3.2\n",
    "Import the stock of your choosing AND the Nifty index.\n",
    "Using linear regression (OLS), calculate -\n",
    "\n",
    ">The daily Beta value for the past 3 months. (Daily= Daily returns)\n",
    "\n",
    ">The monthly Beta value. (Monthly= Monthly returns)\n",
    "\n",
    "Refrain from using the (covariance(x,y)/variance(x)) formula.\n",
    "Attempt the question using regression.(Regression Reference).\n",
    "Were the Beta values more or less than 1 ? What if it was negative ?\n",
    "Discuss. Include a brief writeup in the bottom of your jupyter notebook with your inferences from the Beta values and regression results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>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",
       "      <td>2017-05-15</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>763.45</td>\n",
       "      <td>765.00</td>\n",
       "      <td>779.00</td>\n",
       "      <td>757.05</td>\n",
       "      <td>773.90</td>\n",
       "      <td>772.80</td>\n",
       "      <td>770.09</td>\n",
       "      <td>292498</td>\n",
       "      <td>2.252491e+08</td>\n",
       "      <td>9033</td>\n",
       "      <td>55467</td>\n",
       "      <td>18.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-16</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>772.80</td>\n",
       "      <td>773.45</td>\n",
       "      <td>801.45</td>\n",
       "      <td>768.10</td>\n",
       "      <td>783.50</td>\n",
       "      <td>785.00</td>\n",
       "      <td>788.89</td>\n",
       "      <td>823956</td>\n",
       "      <td>6.500070e+08</td>\n",
       "      <td>18238</td>\n",
       "      <td>179377</td>\n",
       "      <td>21.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-17</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>785.00</td>\n",
       "      <td>786.00</td>\n",
       "      <td>804.95</td>\n",
       "      <td>778.40</td>\n",
       "      <td>785.50</td>\n",
       "      <td>783.65</td>\n",
       "      <td>792.72</td>\n",
       "      <td>598094</td>\n",
       "      <td>4.741212e+08</td>\n",
       "      <td>13968</td>\n",
       "      <td>127637</td>\n",
       "      <td>21.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>783.65</td>\n",
       "      <td>779.35</td>\n",
       "      <td>779.95</td>\n",
       "      <td>740.20</td>\n",
       "      <td>744.00</td>\n",
       "      <td>746.95</td>\n",
       "      <td>762.57</td>\n",
       "      <td>319834</td>\n",
       "      <td>2.438956e+08</td>\n",
       "      <td>7713</td>\n",
       "      <td>92200</td>\n",
       "      <td>28.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-19</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>746.95</td>\n",
       "      <td>748.00</td>\n",
       "      <td>767.50</td>\n",
       "      <td>714.00</td>\n",
       "      <td>722.00</td>\n",
       "      <td>723.10</td>\n",
       "      <td>740.30</td>\n",
       "      <td>561346</td>\n",
       "      <td>4.155619e+08</td>\n",
       "      <td>13328</td>\n",
       "      <td>137648</td>\n",
       "      <td>24.52</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-07</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>806.05</td>\n",
       "      <td>806.05</td>\n",
       "      <td>821.60</td>\n",
       "      <td>799.25</td>\n",
       "      <td>811.15</td>\n",
       "      <td>808.95</td>\n",
       "      <td>814.61</td>\n",
       "      <td>2038484</td>\n",
       "      <td>1.660574e+09</td>\n",
       "      <td>33444</td>\n",
       "      <td>321610</td>\n",
       "      <td>15.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>808.95</td>\n",
       "      <td>807.90</td>\n",
       "      <td>816.90</td>\n",
       "      <td>797.05</td>\n",
       "      <td>806.40</td>\n",
       "      <td>807.10</td>\n",
       "      <td>807.55</td>\n",
       "      <td>930010</td>\n",
       "      <td>7.510262e+08</td>\n",
       "      <td>19028</td>\n",
       "      <td>51327</td>\n",
       "      <td>5.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-09</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>807.10</td>\n",
       "      <td>801.00</td>\n",
       "      <td>811.10</td>\n",
       "      <td>798.00</td>\n",
       "      <td>805.25</td>\n",
       "      <td>805.30</td>\n",
       "      <td>804.23</td>\n",
       "      <td>425205</td>\n",
       "      <td>3.419611e+08</td>\n",
       "      <td>8608</td>\n",
       "      <td>34675</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>805.30</td>\n",
       "      <td>809.95</td>\n",
       "      <td>815.00</td>\n",
       "      <td>795.05</td>\n",
       "      <td>804.00</td>\n",
       "      <td>804.40</td>\n",
       "      <td>804.95</td>\n",
       "      <td>464539</td>\n",
       "      <td>3.739298e+08</td>\n",
       "      <td>9381</td>\n",
       "      <td>22009</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>EQ</td>\n",
       "      <td>804.40</td>\n",
       "      <td>803.00</td>\n",
       "      <td>803.00</td>\n",
       "      <td>774.75</td>\n",
       "      <td>782.00</td>\n",
       "      <td>779.35</td>\n",
       "      <td>789.32</td>\n",
       "      <td>342185</td>\n",
       "      <td>2.700928e+08</td>\n",
       "      <td>9023</td>\n",
       "      <td>29418</td>\n",
       "      <td>8.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                        \n",
       "2017-05-15  RAYMOND     EQ      763.45      765.00      779.00     757.05   \n",
       "2017-05-16  RAYMOND     EQ      772.80      773.45      801.45     768.10   \n",
       "2017-05-17  RAYMOND     EQ      785.00      786.00      804.95     778.40   \n",
       "2017-05-18  RAYMOND     EQ      783.65      779.35      779.95     740.20   \n",
       "2017-05-19  RAYMOND     EQ      746.95      748.00      767.50     714.00   \n",
       "...             ...    ...         ...         ...         ...        ...   \n",
       "2019-05-07  RAYMOND     EQ      806.05      806.05      821.60     799.25   \n",
       "2019-05-08  RAYMOND     EQ      808.95      807.90      816.90     797.05   \n",
       "2019-05-09  RAYMOND     EQ      807.10      801.00      811.10     798.00   \n",
       "2019-05-10  RAYMOND     EQ      805.30      809.95      815.00     795.05   \n",
       "2019-05-13  RAYMOND     EQ      804.40      803.00      803.00     774.75   \n",
       "\n",
       "            Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                        \n",
       "2017-05-15      773.90       772.80         770.09                 292498   \n",
       "2017-05-16      783.50       785.00         788.89                 823956   \n",
       "2017-05-17      785.50       783.65         792.72                 598094   \n",
       "2017-05-18      744.00       746.95         762.57                 319834   \n",
       "2017-05-19      722.00       723.10         740.30                 561346   \n",
       "...                ...          ...            ...                    ...   \n",
       "2019-05-07      811.15       808.95         814.61                2038484   \n",
       "2019-05-08      806.40       807.10         807.55                 930010   \n",
       "2019-05-09      805.25       805.30         804.23                 425205   \n",
       "2019-05-10      804.00       804.40         804.95                 464539   \n",
       "2019-05-13      782.00       779.35         789.32                 342185   \n",
       "\n",
       "                Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                       \n",
       "2017-05-15  2.252491e+08           9033            55467   \n",
       "2017-05-16  6.500070e+08          18238           179377   \n",
       "2017-05-17  4.741212e+08          13968           127637   \n",
       "2017-05-18  2.438956e+08           7713            92200   \n",
       "2017-05-19  4.155619e+08          13328           137648   \n",
       "...                  ...            ...              ...   \n",
       "2019-05-07  1.660574e+09          33444           321610   \n",
       "2019-05-08  7.510262e+08          19028            51327   \n",
       "2019-05-09  3.419611e+08           8608            34675   \n",
       "2019-05-10  3.739298e+08           9381            22009   \n",
       "2019-05-13  2.700928e+08           9023            29418   \n",
       "\n",
       "            % Dly Qt to Traded Qty  \n",
       "Date                                \n",
       "2017-05-15                   18.96  \n",
       "2017-05-16                   21.77  \n",
       "2017-05-17                   21.34  \n",
       "2017-05-18                   28.83  \n",
       "2017-05-19                   24.52  \n",
       "...                            ...  \n",
       "2019-05-07                   15.78  \n",
       "2019-05-08                    5.52  \n",
       "2019-05-09                    8.15  \n",
       "2019-05-10                    4.74  \n",
       "2019-05-13                    8.60  \n",
       "\n",
       "[494 rows x 14 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RAYMOND_data = pd.read_csv('RAYMOND.csv')\n",
    "RAYMOND_data['Date'] = pd.to_datetime(RAYMOND_data['Date'])\n",
    "RAYMOND_data = RAYMOND_data.sort_values('Date')\n",
    "RAYMOND_data.set_index('Date', inplace=True)\n",
    "RAYMOND_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>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",
       "      <td>2017-05-15</td>\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",
       "      <td>2017-05-16</td>\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",
       "      <td>2017-05-17</td>\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",
       "      <td>2017-05-18</td>\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",
       "      <td>2017-05-19</td>\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",
       "    <tr>\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",
       "      <td>2019-05-07</td>\n",
       "      <td>11651.50</td>\n",
       "      <td>11657.05</td>\n",
       "      <td>11484.45</td>\n",
       "      <td>11497.90</td>\n",
       "      <td>337495624</td>\n",
       "      <td>16632.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>11478.70</td>\n",
       "      <td>11479.10</td>\n",
       "      <td>11346.95</td>\n",
       "      <td>11359.45</td>\n",
       "      <td>372826025</td>\n",
       "      <td>17440.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-09</td>\n",
       "      <td>11322.40</td>\n",
       "      <td>11357.60</td>\n",
       "      <td>11255.05</td>\n",
       "      <td>11301.80</td>\n",
       "      <td>373028059</td>\n",
       "      <td>17602.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-10</td>\n",
       "      <td>11314.15</td>\n",
       "      <td>11345.80</td>\n",
       "      <td>11251.05</td>\n",
       "      <td>11278.90</td>\n",
       "      <td>387323416</td>\n",
       "      <td>18085.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-13</td>\n",
       "      <td>11258.70</td>\n",
       "      <td>11300.20</td>\n",
       "      <td>11125.60</td>\n",
       "      <td>11148.20</td>\n",
       "      <td>357586433</td>\n",
       "      <td>16722.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close  Shares Traded  \\\n",
       "Date                                                                \n",
       "2017-05-15   9433.55   9449.25   9423.10   9445.40      145163953   \n",
       "2017-05-16   9461.00   9517.20   9456.35   9512.25      171709433   \n",
       "2017-05-17   9517.60   9532.60   9486.10   9525.75      211856040   \n",
       "2017-05-18   9453.20   9489.10   9418.10   9429.45      199340647   \n",
       "2017-05-19   9469.90   9505.75   9390.75   9427.90      259861396   \n",
       "...              ...       ...       ...       ...            ...   \n",
       "2019-05-07  11651.50  11657.05  11484.45  11497.90      337495624   \n",
       "2019-05-08  11478.70  11479.10  11346.95  11359.45      372826025   \n",
       "2019-05-09  11322.40  11357.60  11255.05  11301.80      373028059   \n",
       "2019-05-10  11314.15  11345.80  11251.05  11278.90      387323416   \n",
       "2019-05-13  11258.70  11300.20  11125.60  11148.20      357586433   \n",
       "\n",
       "            Turnover (Rs. Cr)  \n",
       "Date                           \n",
       "2017-05-15            7790.97  \n",
       "2017-05-16            9209.89  \n",
       "2017-05-17           10726.23  \n",
       "2017-05-18           10782.46  \n",
       "2017-05-19           11544.77  \n",
       "...                       ...  \n",
       "2019-05-07           16632.84  \n",
       "2019-05-08           17440.01  \n",
       "2019-05-09           17602.86  \n",
       "2019-05-10           18085.19  \n",
       "2019-05-13           16722.91  \n",
       "\n",
       "[494 rows x 6 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty_data = pd.read_csv('Nifty50.csv')\n",
    "nifty_data['Date'] = pd.to_datetime(nifty_data['Date'])\n",
    "nifty_data = nifty_data.sort_values('Date')\n",
    "nifty_data.set_index('Date', inplace=True)\n",
    "nifty_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "fil_RAYMOND = RAYMOND_data[405:]\n",
    "fil_nifty = nifty_data[405:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "return_RAYMOND = fil_RAYMOND['Close Price'].pct_change()\n",
    "return_nifty = fil_nifty['Close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 561,
       "width": 1185
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "return_RAYMOND.plot()\n",
    "return_nifty.plot()\n",
    "plt.ylabel(\"Daily Return of TCS and NIFTY\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "fil_RAYMOND['pct_change'] = fil_RAYMOND['Close Price'].pct_change()\n",
    "fil_nifty['pct_change'] = fil_nifty['Close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fil_RAYMOND['pct_change'].dropna()\n",
    "Y = fil_nifty['pct_change'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>pct_change</td>    <th>  R-squared (uncentered):</th>      <td>   0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   1.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 Feb 2020</td> <th>  Prob (F-statistic):</th>           <td> 0.194</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>01:29:16</td>     <th>  Log-Likelihood:    </th>          <td>  313.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    88</td>      <th>  AIC:               </th>          <td>  -625.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    87</td>      <th>  BIC:               </th>          <td>  -623.4</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>pct_change</th> <td>    0.0491</td> <td>    0.038</td> <td>    1.309</td> <td> 0.194</td> <td>   -0.025</td> <td>    0.124</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.393</td> <th>  Durbin-Watson:     </th> <td>   1.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.498</td> <th>  Jarque-Bera (JB):  </th> <td>   1.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.186</td> <th>  Prob(JB):          </th> <td>   0.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.524</td> <th>  Cond. No.          </th> <td>    1.00</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:             pct_change   R-squared (uncentered):                   0.019\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.008\n",
       "Method:                 Least Squares   F-statistic:                              1.715\n",
       "Date:                Tue, 18 Feb 2020   Prob (F-statistic):                       0.194\n",
       "Time:                        01:29:16   Log-Likelihood:                          313.96\n",
       "No. Observations:                  88   AIC:                                     -625.9\n",
       "Df Residuals:                      87   BIC:                                     -623.4\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "pct_change     0.0491      0.038      1.309      0.194      -0.025       0.124\n",
       "==============================================================================\n",
       "Omnibus:                        1.393   Durbin-Watson:                   1.596\n",
       "Prob(Omnibus):                  0.498   Jarque-Bera (JB):                1.339\n",
       "Skew:                           0.186   Prob(JB):                        0.512\n",
       "Kurtosis:                       2.524   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import statsmodels.api as sm\n",
    "\n",
    "myModel = sm.OLS(Y,X).fit()\n",
    "myModel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            RAYMOND  NIFTY50\n",
      "Date                        \n",
      "2017-05-15   772.80  9445.40\n",
      "2017-05-16   785.00  9512.25\n",
      "2017-05-17   783.65  9525.75\n",
      "2017-05-18   746.95  9429.45\n",
      "2017-05-19   723.10  9427.90\n",
      "             RAYMOND   NIFTY50\n",
      "Date                          \n",
      "2017-05-16  0.015787  0.007078\n",
      "2017-05-17 -0.001720  0.001419\n",
      "2017-05-18 -0.046832 -0.010109\n",
      "2017-05-19 -0.031930 -0.000164\n",
      "2017-05-22 -0.009888  0.001098\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "RAYMOND = pd.read_csv('RAYMOND.csv', parse_dates=True, index_col='Date',)\n",
    "nifty50 = pd.read_csv('Nifty50.csv', parse_dates=True, index_col='Date')\n",
    "\n",
    "monthly_prices = pd.concat([RAYMOND['Close Price'], nifty50['Close']], axis=1)\n",
    "monthly_prices.columns = ['RAYMOND', 'NIFTY50']\n",
    "\n",
    "print(monthly_prices.head())\n",
    "\n",
    "monthly_returns = monthly_prices.pct_change(1)\n",
    "clean_monthly_returns = monthly_returns.dropna(axis=0)\n",
    "print(clean_monthly_returns.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                NIFTY50   R-squared:                       0.194\n",
      "Model:                            OLS   Adj. R-squared:                  0.193\n",
      "Method:                 Least Squares   F-statistic:                     118.4\n",
      "Date:                Tue, 18 Feb 2020   Prob (F-statistic):           7.64e-25\n",
      "Time:                        01:29:30   Log-Likelihood:                 1786.1\n",
      "No. Observations:                 493   AIC:                            -3568.\n",
      "Df Residuals:                     491   BIC:                            -3560.\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      0.000      1.110      0.268      -0.000       0.001\n",
      "RAYMOND        0.1386      0.013     10.879      0.000       0.114       0.164\n",
      "==============================================================================\n",
      "Omnibus:                       26.417   Durbin-Watson:                   1.866\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               39.245\n",
      "Skew:                          -0.417   Prob(JB):                     3.01e-09\n",
      "Kurtosis:                       4.102   Cond. No.                         43.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = clean_monthly_returns['RAYMOND']\n",
    "y = clean_monthly_returns['NIFTY50']\n",
    "\n",
    "X1 = sm.add_constant(X)\n",
    "model = sm.OLS(y, X1)\n",
    "\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}