Normal Distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ea6caad",
   "metadata": {},
   "source": [
    "## Normal Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be856782",
   "metadata": {},
   "source": [
    "- A normal distribution also known as a Gaussian distribution.\n",
    "- It is a probability distribution that describes a continuous random variable whose values tend to cluster around a central      value with fewer values located farther away from the center. \n",
    "- The distribution is bell-shaped and symmetrical around the mean and it is characterized by two parameters the mean which is     the central value around which the distribution is centered and the standard deviation which determines the spread or           variability of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b9caf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9139bc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37814937265194704"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - stats.norm.cdf(720,711,29)   #to find P(X>720),mean=11,sd=29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f12f8d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8413447460685429"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.cdf(70,60,10)     #to find P(X<70),mean=60,sd=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67346162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02275013194817921"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - stats.norm.cdf(60,50,5)  #to find P(X>60),mean=50,sd=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0da10163",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3dbfbb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings                        #to ignore warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0edd342",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Close</th>\n",
       "      <th>Total Trade Quantity</th>\n",
       "      <th>Turnover (Lacs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1121.00</td>\n",
       "      <td>1151.00</td>\n",
       "      <td>1121.00</td>\n",
       "      <td>1134.00</td>\n",
       "      <td>1135.60</td>\n",
       "      <td>101651.0</td>\n",
       "      <td>1157.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>1146.80</td>\n",
       "      <td>1149.00</td>\n",
       "      <td>1128.75</td>\n",
       "      <td>1135.00</td>\n",
       "      <td>1134.60</td>\n",
       "      <td>59504.0</td>\n",
       "      <td>676.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>1140.00</td>\n",
       "      <td>1164.25</td>\n",
       "      <td>1130.05</td>\n",
       "      <td>1137.00</td>\n",
       "      <td>1139.60</td>\n",
       "      <td>128908.0</td>\n",
       "      <td>1482.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>1142.00</td>\n",
       "      <td>1159.40</td>\n",
       "      <td>1119.20</td>\n",
       "      <td>1141.00</td>\n",
       "      <td>1144.15</td>\n",
       "      <td>117871.0</td>\n",
       "      <td>1352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>1156.00</td>\n",
       "      <td>1172.00</td>\n",
       "      <td>1140.00</td>\n",
       "      <td>1141.20</td>\n",
       "      <td>1144.05</td>\n",
       "      <td>170063.0</td>\n",
       "      <td>1971.42</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>965.00</td>\n",
       "      <td>965.05</td>\n",
       "      <td>935.00</td>\n",
       "      <td>950.10</td>\n",
       "      <td>950.25</td>\n",
       "      <td>398696.0</td>\n",
       "      <td>3783.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>960.70</td>\n",
       "      <td>989.00</td>\n",
       "      <td>952.35</td>\n",
       "      <td>974.00</td>\n",
       "      <td>975.70</td>\n",
       "      <td>808561.0</td>\n",
       "      <td>7885.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>980.75</td>\n",
       "      <td>985.00</td>\n",
       "      <td>970.15</td>\n",
       "      <td>977.00</td>\n",
       "      <td>974.40</td>\n",
       "      <td>367041.0</td>\n",
       "      <td>3592.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>977.10</td>\n",
       "      <td>997.95</td>\n",
       "      <td>974.55</td>\n",
       "      <td>985.15</td>\n",
       "      <td>986.05</td>\n",
       "      <td>555233.0</td>\n",
       "      <td>5489.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>986.00</td>\n",
       "      <td>1006.95</td>\n",
       "      <td>985.90</td>\n",
       "      <td>1004.00</td>\n",
       "      <td>1000.60</td>\n",
       "      <td>460675.0</td>\n",
       "      <td>4606.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date     Open     High      Low     Last    Close  \\\n",
       "0     2010-01-04  1121.00  1151.00  1121.00  1134.00  1135.60   \n",
       "1     2010-01-05  1146.80  1149.00  1128.75  1135.00  1134.60   \n",
       "2     2010-01-06  1140.00  1164.25  1130.05  1137.00  1139.60   \n",
       "3     2010-01-07  1142.00  1159.40  1119.20  1141.00  1144.15   \n",
       "4     2010-01-08  1156.00  1172.00  1140.00  1141.20  1144.05   \n",
       "...          ...      ...      ...      ...      ...      ...   \n",
       "1734  2016-12-26   965.00   965.05   935.00   950.10   950.25   \n",
       "1735  2016-12-27   960.70   989.00   952.35   974.00   975.70   \n",
       "1736  2016-12-28   980.75   985.00   970.15   977.00   974.40   \n",
       "1737  2016-12-29   977.10   997.95   974.55   985.15   986.05   \n",
       "1738  2016-12-30   986.00  1006.95   985.90  1004.00  1000.60   \n",
       "\n",
       "      Total Trade Quantity  Turnover (Lacs)  \n",
       "0                 101651.0          1157.18  \n",
       "1                  59504.0           676.47  \n",
       "2                 128908.0          1482.84  \n",
       "3                 117871.0          1352.98  \n",
       "4                 170063.0          1971.42  \n",
       "...                    ...              ...  \n",
       "1734              398696.0          3783.63  \n",
       "1735              808561.0          7885.14  \n",
       "1736              367041.0          3592.49  \n",
       "1737              555233.0          5489.14  \n",
       "1738              460675.0          4606.48  \n",
       "\n",
       "[1739 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df = pd.read_csv(\"BEML.csv\")\n",
    "beml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39f76814",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Low</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1613.00</td>\n",
       "      <td>1629.10</td>\n",
       "      <td>1602.00</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>1625.65</td>\n",
       "      <td>9365.0</td>\n",
       "      <td>151.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>1639.95</td>\n",
       "      <td>1639.95</td>\n",
       "      <td>1611.05</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>1616.80</td>\n",
       "      <td>38148.0</td>\n",
       "      <td>622.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>1618.00</td>\n",
       "      <td>1644.00</td>\n",
       "      <td>1617.00</td>\n",
       "      <td>1639.0</td>\n",
       "      <td>1638.50</td>\n",
       "      <td>36519.0</td>\n",
       "      <td>595.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>1645.00</td>\n",
       "      <td>1654.00</td>\n",
       "      <td>1636.00</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1648.70</td>\n",
       "      <td>12809.0</td>\n",
       "      <td>211.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>1650.00</td>\n",
       "      <td>1626.55</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1639.80</td>\n",
       "      <td>28035.0</td>\n",
       "      <td>459.11</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>2703.00</td>\n",
       "      <td>2740.00</td>\n",
       "      <td>2677.00</td>\n",
       "      <td>2715.0</td>\n",
       "      <td>2723.50</td>\n",
       "      <td>3953.0</td>\n",
       "      <td>107.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>2722.95</td>\n",
       "      <td>2725.00</td>\n",
       "      <td>2683.00</td>\n",
       "      <td>2692.0</td>\n",
       "      <td>2701.75</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>286.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>2701.75</td>\n",
       "      <td>2718.00</td>\n",
       "      <td>2690.00</td>\n",
       "      <td>2698.0</td>\n",
       "      <td>2702.15</td>\n",
       "      <td>6050.0</td>\n",
       "      <td>163.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>2702.05</td>\n",
       "      <td>2739.00</td>\n",
       "      <td>2691.95</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>2727.90</td>\n",
       "      <td>7649.0</td>\n",
       "      <td>207.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>2730.00</td>\n",
       "      <td>2740.45</td>\n",
       "      <td>2705.00</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>2729.80</td>\n",
       "      <td>6513.0</td>\n",
       "      <td>177.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            Date     Open     High      Low    Last    Close  \\\n",
       "0     2010-01-04  1613.00  1629.10  1602.00  1629.0  1625.65   \n",
       "1     2010-01-05  1639.95  1639.95  1611.05  1620.0  1616.80   \n",
       "2     2010-01-06  1618.00  1644.00  1617.00  1639.0  1638.50   \n",
       "3     2010-01-07  1645.00  1654.00  1636.00  1648.0  1648.70   \n",
       "4     2010-01-08  1650.00  1650.00  1626.55  1640.0  1639.80   \n",
       "...          ...      ...      ...      ...     ...      ...   \n",
       "1734  2016-12-26  2703.00  2740.00  2677.00  2715.0  2723.50   \n",
       "1735  2016-12-27  2722.95  2725.00  2683.00  2692.0  2701.75   \n",
       "1736  2016-12-28  2701.75  2718.00  2690.00  2698.0  2702.15   \n",
       "1737  2016-12-29  2702.05  2739.00  2691.95  2710.0  2727.90   \n",
       "1738  2016-12-30  2730.00  2740.45  2705.00  2730.0  2729.80   \n",
       "\n",
       "      Total Trade Quantity  Turnover (Lacs)  \n",
       "0                   9365.0           151.74  \n",
       "1                  38148.0           622.58  \n",
       "2                  36519.0           595.09  \n",
       "3                  12809.0           211.00  \n",
       "4                  28035.0           459.11  \n",
       "...                    ...              ...  \n",
       "1734                3953.0           107.15  \n",
       "1735               10600.0           286.10  \n",
       "1736                6050.0           163.44  \n",
       "1737                7649.0           207.87  \n",
       "1738                6513.0           177.65  \n",
       "\n",
       "[1739 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glaxo_df = pd.read_csv(\"GLAXO.csv\")\n",
    "glaxo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "821a2ed4",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Close</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1135.60</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>1134.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>1139.60</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>1144.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>1144.05</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>950.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>975.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>974.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>986.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>1000.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Close\n",
       "0     2010-01-04  1135.60\n",
       "1     2010-01-05  1134.60\n",
       "2     2010-01-06  1139.60\n",
       "3     2010-01-07  1144.15\n",
       "4     2010-01-08  1144.05\n",
       "...          ...      ...\n",
       "1734  2016-12-26   950.25\n",
       "1735  2016-12-27   975.70\n",
       "1736  2016-12-28   974.40\n",
       "1737  2016-12-29   986.05\n",
       "1738  2016-12-30  1000.60\n",
       "\n",
       "[1739 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df = beml_df[['Date','Close']]\n",
    "beml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83fa6d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>1625.65</td>\n",
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       "      <td>2010-01-05</td>\n",
       "      <td>1616.80</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>1638.50</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>1648.70</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>1639.80</td>\n",
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       "      <th>1734</th>\n",
       "      <td>2016-12-26</td>\n",
       "      <td>2723.50</td>\n",
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       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>2701.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1736</th>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>2702.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>2727.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738</th>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>2729.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date    Close\n",
       "0     2010-01-04  1625.65\n",
       "1     2010-01-05  1616.80\n",
       "2     2010-01-06  1638.50\n",
       "3     2010-01-07  1648.70\n",
       "4     2010-01-08  1639.80\n",
       "...          ...      ...\n",
       "1734  2016-12-26  2723.50\n",
       "1735  2016-12-27  2701.75\n",
       "1736  2016-12-28  2702.15\n",
       "1737  2016-12-29  2727.90\n",
       "1738  2016-12-30  2729.80\n",
       "\n",
       "[1739 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glaxo_df = glaxo_df[['Date','Close']]\n",
    "glaxo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee992f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making date column into datetime datatype\n",
    "glaxo_df['Date'] = pd.to_datetime(glaxo_df['Date'])\n",
    "\n",
    "beml_df['Date'] = pd.to_datetime(beml_df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4405a4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date     datetime64[ns]\n",
       "Close           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glaxo_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7217c82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date     datetime64[ns]\n",
       "Close           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b6996737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>1625.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1616.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1638.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1648.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1639.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>2723.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>2701.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>2702.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>2727.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>2729.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close\n",
       "Date               \n",
       "2010-01-04  1625.65\n",
       "2010-01-05  1616.80\n",
       "2010-01-06  1638.50\n",
       "2010-01-07  1648.70\n",
       "2010-01-08  1639.80\n",
       "...             ...\n",
       "2016-12-26  2723.50\n",
       "2016-12-27  2701.75\n",
       "2016-12-28  2702.15\n",
       "2016-12-29  2727.90\n",
       "2016-12-30  2729.80\n",
       "\n",
       "[1739 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making date column as index\n",
    "glaxo_df = glaxo_df.set_index(pd.DatetimeIndex(glaxo_df['Date'])).drop('Date',axis=1)\n",
    "glaxo_df                                                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d03e5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>1135.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1134.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1139.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1144.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1144.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>950.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>975.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>974.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>986.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>1000.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close\n",
       "Date               \n",
       "2010-01-04  1135.60\n",
       "2010-01-05  1134.60\n",
       "2010-01-06  1139.60\n",
       "2010-01-07  1144.15\n",
       "2010-01-08  1144.05\n",
       "...             ...\n",
       "2016-12-26   950.25\n",
       "2016-12-27   975.70\n",
       "2016-12-28   974.40\n",
       "2016-12-29   986.05\n",
       "2016-12-30  1000.60\n",
       "\n",
       "[1739 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df = beml_df.set_index(pd.DatetimeIndex(beml_df['Date'])).drop('Date',axis=1)\n",
    "beml_df   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4771fe39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close')"
      ]
     },
     "execution_count": 19,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "## matplotlib inline\n",
    "plt.plot(glaxo_df.Close)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4e00276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Close')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(beml_df.Close)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7f234e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "glaxo_df['gain'] = glaxo_df.Close.pct_change(periods=1)\n",
    "beml_df['gain'] = beml_df.Close.pct_change(periods=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b5cc822",
   "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>Close</th>\n",
       "      <th>gain</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>2010-01-04</th>\n",
       "      <td>1625.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1616.80</td>\n",
       "      <td>-0.005444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1638.50</td>\n",
       "      <td>0.013422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1648.70</td>\n",
       "      <td>0.006225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1639.80</td>\n",
       "      <td>-0.005398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>2723.50</td>\n",
       "      <td>-0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>2701.75</td>\n",
       "      <td>-0.007986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>2702.15</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>2727.90</td>\n",
       "      <td>0.009529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>2729.80</td>\n",
       "      <td>0.000697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close      gain\n",
       "Date                         \n",
       "2010-01-04  1625.65       NaN\n",
       "2010-01-05  1616.80 -0.005444\n",
       "2010-01-06  1638.50  0.013422\n",
       "2010-01-07  1648.70  0.006225\n",
       "2010-01-08  1639.80 -0.005398\n",
       "...             ...       ...\n",
       "2016-12-26  2723.50 -0.001283\n",
       "2016-12-27  2701.75 -0.007986\n",
       "2016-12-28  2702.15  0.000148\n",
       "2016-12-29  2727.90  0.009529\n",
       "2016-12-30  2729.80  0.000697\n",
       "\n",
       "[1739 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glaxo_df   #  Calculating gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c48176a8",
   "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>Close</th>\n",
       "      <th>gain</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>2010-01-04</th>\n",
       "      <td>1135.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1134.60</td>\n",
       "      <td>-0.000881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1139.60</td>\n",
       "      <td>0.004407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1144.15</td>\n",
       "      <td>0.003993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1144.05</td>\n",
       "      <td>-0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>950.25</td>\n",
       "      <td>-0.021924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>975.70</td>\n",
       "      <td>0.026782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>974.40</td>\n",
       "      <td>-0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>986.05</td>\n",
       "      <td>0.011956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>1000.60</td>\n",
       "      <td>0.014756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1739 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close      gain\n",
       "Date                         \n",
       "2010-01-04  1135.60       NaN\n",
       "2010-01-05  1134.60 -0.000881\n",
       "2010-01-06  1139.60  0.004407\n",
       "2010-01-07  1144.15  0.003993\n",
       "2010-01-08  1144.05 -0.000087\n",
       "...             ...       ...\n",
       "2016-12-26   950.25 -0.021924\n",
       "2016-12-27   975.70  0.026782\n",
       "2016-12-28   974.40 -0.001332\n",
       "2016-12-29   986.05  0.011956\n",
       "2016-12-30  1000.60  0.014756\n",
       "\n",
       "[1739 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da6a18b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Close</th>\n",
       "      <th>gain</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>2010-01-05</th>\n",
       "      <td>1616.80</td>\n",
       "      <td>-0.005444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1638.50</td>\n",
       "      <td>0.013422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1648.70</td>\n",
       "      <td>0.006225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1639.80</td>\n",
       "      <td>-0.005398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>1629.45</td>\n",
       "      <td>-0.006312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>2723.50</td>\n",
       "      <td>-0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>2701.75</td>\n",
       "      <td>-0.007986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>2702.15</td>\n",
       "      <td>0.000148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>2727.90</td>\n",
       "      <td>0.009529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>2729.80</td>\n",
       "      <td>0.000697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1738 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close      gain\n",
       "Date                         \n",
       "2010-01-05  1616.80 -0.005444\n",
       "2010-01-06  1638.50  0.013422\n",
       "2010-01-07  1648.70  0.006225\n",
       "2010-01-08  1639.80 -0.005398\n",
       "2010-01-11  1629.45 -0.006312\n",
       "...             ...       ...\n",
       "2016-12-26  2723.50 -0.001283\n",
       "2016-12-27  2701.75 -0.007986\n",
       "2016-12-28  2702.15  0.000148\n",
       "2016-12-29  2727.90  0.009529\n",
       "2016-12-30  2729.80  0.000697\n",
       "\n",
       "[1738 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop first row since it is NaN\n",
    "glaxo_df = glaxo_df.dropna()\n",
    "glaxo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "de766eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Close</th>\n",
       "      <th>gain</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>2010-01-05</th>\n",
       "      <td>1134.60</td>\n",
       "      <td>-0.000881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>1139.60</td>\n",
       "      <td>0.004407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>1144.15</td>\n",
       "      <td>0.003993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>1144.05</td>\n",
       "      <td>-0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>1137.00</td>\n",
       "      <td>-0.006162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-26</th>\n",
       "      <td>950.25</td>\n",
       "      <td>-0.021924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>975.70</td>\n",
       "      <td>0.026782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>974.40</td>\n",
       "      <td>-0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>986.05</td>\n",
       "      <td>0.011956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>1000.60</td>\n",
       "      <td>0.014756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1738 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Close      gain\n",
       "Date                         \n",
       "2010-01-05  1134.60 -0.000881\n",
       "2010-01-06  1139.60  0.004407\n",
       "2010-01-07  1144.15  0.003993\n",
       "2010-01-08  1144.05 -0.000087\n",
       "2010-01-11  1137.00 -0.006162\n",
       "...             ...       ...\n",
       "2016-12-26   950.25 -0.021924\n",
       "2016-12-27   975.70  0.026782\n",
       "2016-12-28   974.40 -0.001332\n",
       "2016-12-29   986.05  0.011956\n",
       "2016-12-30  1000.60  0.014756\n",
       "\n",
       "[1738 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beml_df = beml_df.dropna()\n",
    "beml_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "51f7d4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plotting the gains\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7661dfd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'gain')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(glaxo_df.gain)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('gain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "403aba6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'gain')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(beml_df.gain)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('gain')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5afef4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b01603c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1af2d1435e0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.suptitle('Distribution',fontsize=15)\n",
    "\n",
    "sn.distplot(glaxo_df.gain,label='Glaxo')\n",
    "\n",
    "plt.xlabel('gain')\n",
    "plt.ylabel('density')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sn.distplot(beml_df.gain,label='Beml')\n",
    "\n",
    "plt.xlabel('gain')\n",
    "plt.ylabel('density')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d571d2d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.0004\n",
      "Standard Deviation: 0.0134\n"
     ]
    }
   ],
   "source": [
    "print('Mean:',round(glaxo_df.gain.mean(),4))\n",
    "print('Standard Deviation:',round(glaxo_df.gain.std(),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e019c910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.0003\n",
      "Standard Deviation: 0.0264\n"
     ]
    }
   ],
   "source": [
    "print('Mean:',round(beml_df.gain.mean(),4))\n",
    "print('Standard Deviation:',round(beml_df.gain.std(),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "643c0600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06395593743937553"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import scipy\n",
    "from scipy import stats\n",
    "\n",
    "#Probability of making 2% loss or higher in Glaxo\n",
    "stats.norm.cdf(-0.02, 0.0004,0.0134)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "536ad1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07177658173190637"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Probability of making 2% gain or higher in Glaxo\n",
    "1 - stats.norm.cdf(0.02,0.0004, 0.0134)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "09f825b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3055164867412168"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Probability of making 2% loss or higher in Beml\n",
    "stats.norm.cdf(-0.02,0.0007,0.0407)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "df253e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3176781211102836"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Probability of making 2% gain or higher in Beml\n",
    "1 - stats.norm.cdf(0.02,0.0007,0.0407)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a3af52d",
   "metadata": {},
   "source": [
    "## PDF(Probability Density Function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d9856202",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Define the parameters of the normal distribution\n",
    "mu = 0 # mean\n",
    "sigma = 1 # standard deviation\n",
    "\n",
    "# Generate a sample of random values from the normal distribution\n",
    "sample_size = 1000\n",
    "sample = np.random.normal(mu, sigma, sample_size)\n",
    "\n",
    "# Calculate the mean and standard deviation of the sample\n",
    "sample_mean = np.mean(sample)\n",
    "sample_std = np.std(sample)\n",
    "\n",
    "# Calculate the probability density function (PDF) of the normal distribution\n",
    "x_values = np.linspace(-4, 4, 100) # x-values for the PDF plot\n",
    "pdf_values = norm.pdf(x_values, mu, sigma) # PDF values for the x-values\n",
    "\n",
    "# Plot the PDF of the normal distribution and the histogram of the sample\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(sample, bins=30, density=True, alpha=0.5, label='Sample')\n",
    "plt.plot(x_values, pdf_values, label='Normal Distribution')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3382251d",
   "metadata": {},
   "source": [
    "## Cdf(Cumulative distribution function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "352c3abf",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Define the parameters of the normal distribution\n",
    "mu = 0 # mean\n",
    "sigma = 1 # standard deviation\n",
    "\n",
    "# Generate a sample of random values from the normal distribution\n",
    "sample_size = 1000\n",
    "sample = np.random.normal(mu, sigma, sample_size)\n",
    "\n",
    "# Calculate the mean and standard deviation of the sample\n",
    "sample_mean = np.mean(sample)\n",
    "sample_std = np.std(sample)\n",
    "\n",
    "# Calculate the cumulative distribution function (CDF) of the normal distribution\n",
    "x_values = np.linspace(-4, 4, 100) # x-values for the CDF plot\n",
    "cdf_values = norm.cdf(x_values, mu, sigma) # CDF values for the x-values\n",
    "\n",
    "# Plot the CDF of the normal distribution and the empirical CDF of the sample\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_values, cdf_values, label='Normal Distribution')\n",
    "plt.hist(sample, bins=30, density=True, cumulative=True, alpha=0.5, label='Empirical CDF')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}