Nifty_Exploratory_data_Analysis.ipynb

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   "source": [
    "# NIFTY Exploratory data analysis in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dB_j6LtTTO5j"
   },
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n3VfNkBBw15s"
   },
   "source": [
    "**What data are we exploring today ?**\n",
    "\n",
    "![alt text](Columns1.png \"List of columns\")\n",
    "![alt text](Columns2.png \"List of columns\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "CQDO4JCqTThV",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![alt text](SampleData.png \"Sample Data\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "markdown",
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    "id": "FPSqz1wzTXvz"
   },
   "source": [
    "## 1. Importing the required libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9eLMx1Ebwa92"
   },
   "source": [
    "Below are the libraries that are used in order to perform Exploratory data analysis in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
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    "id": "GGyDovL2QDLa"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns                       #visualisation\n",
    "import matplotlib.pyplot as plt             #visualisation\n",
    "%matplotlib inline     \n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ye6eXuohTd5Q"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Y3Z2DbKTfJt"
   },
   "source": [
    "## 2. Loading the data into the data frame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ko5zGJFCySaz"
   },
   "source": [
    "Our data is saved in csv format. Now lets import that into pandas as a dataframe and confirm that the data is same as the sample data above(taken from excel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>vixChange</th>\n",
       "      <th>netChange</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-30</td>\n",
       "      <td>2.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.15</td>\n",
       "      <td>6.95</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>3.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.58</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>3.97</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-8.82</td>\n",
       "      <td>4.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "         Date  vixChange  netChange  prevChangeNF  prevChangeBNF  NFOIChange  \\\n",
       "0  2013-12-30       2.86        8.0           0.4            0.4        4.15   \n",
       "1  2014-01-01       3.04       10.0           0.3            0.1       -2.51   \n",
       "2  2014-01-02       6.23        6.0           0.1            0.2        0.34   \n",
       "3  2014-01-03      -4.11      -32.0          -1.6           -2.2       -2.50   \n",
       "4  2014-01-06       3.97       -5.0          -0.1            0.2       -8.82   \n",
       "\n",
       "   BNFOIChange  basisChange  opengap1  closegap1  highgap1  lowgap1  \n",
       "0         6.95        -12.0       0.2       -0.6       0.3     -0.8  \n",
       "1        -0.58        -23.0       0.5        0.4       0.6      0.2  \n",
       "2         4.58        -11.0      -0.1       -1.6       0.8     -1.8  \n",
       "3        -1.20        -31.0      -0.5       -0.1       0.1     -0.9  \n",
       "4         4.86          6.0      -0.2       -0.4      -0.0     -0.8  "
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     "execution_count": 97,
     "metadata": {},
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   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "# To display the top 5 rows \n",
    "df.head(5)               "
   ]
  },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>vixChange</th>\n",
       "      <th>netChange</th>\n",
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       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>2018-12-21</td>\n",
       "      <td>11.58</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>6.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-3.35</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-26.12</td>\n",
       "      <td>-7.52</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>0.96</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-25.39</td>\n",
       "      <td>-24.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>25.65</td>\n",
       "      <td>51.26</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
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      "text/plain": [
       "            Date  vixChange  netChange  prevChangeNF  prevChangeBNF  \\\n",
       "1238  2018-12-21      11.58      -11.0          -0.1           -0.1   \n",
       "1239  2018-12-24      -1.52      -43.0          -1.9           -1.5   \n",
       "1240  2018-12-26       1.08      -24.0          -0.8           -0.6   \n",
       "1241  2018-12-27       0.96       19.0           0.6            0.9   \n",
       "1242  2018-12-28      -4.99        6.0           0.6           -0.0   \n",
       "\n",
       "      NFOIChange  BNFOIChange  basisChange  opengap1  closegap1  highgap1  \\\n",
       "1238       -1.03        -2.12          6.0      -0.2       -1.9       0.0   \n",
       "1239       -3.35        -4.06        -11.0      -0.1       -0.8       0.2   \n",
       "1240      -26.12        -7.52          4.0      -0.4        0.6       0.7   \n",
       "1241      -25.39       -24.56          0.0       1.0        0.6       1.2   \n",
       "1242       25.65        51.26         11.0       0.4        0.9       1.3   \n",
       "\n",
       "      lowgap1  \n",
       "1238     -2.0  \n",
       "1239     -1.0  \n",
       "1240     -1.4  \n",
       "1241      0.5  \n",
       "1242      0.4  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)                        # To display the botton 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JjQnr4SPzaL5"
   },
   "source": [
    "\n",
    "As we can see the data is proper(from 30th Dec 2013 to 28th Dec 2018)\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vAmC369yTpMF"
   },
   "source": [
    "## 3. Checking the types of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9ESKxikIzA1d"
   },
   "source": [
    "Before running any analysis, lets first make sure data types saved are correct in the pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "qPbKQ0noRptD",
    "outputId": "6a5aea47-ad0c-4118-8471-d91b6432b339"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "vixChange        float64\n",
       "netChange        float64\n",
       "prevChangeNF     float64\n",
       "prevChangeBNF    float64\n",
       "NFOIChange       float64\n",
       "BNFOIChange      float64\n",
       "basisChange      float64\n",
       "opengap1         float64\n",
       "closegap1        float64\n",
       "highgap1         float64\n",
       "lowgap1          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "beRJyQAezdX8"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QoHuBhXxT5E9"
   },
   "source": [
    "## 4. Dropping irrelevant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_3cy877Mze4H"
   },
   "source": [
    "Since right now, we want to look at only at next day, we drop the highgap1(next day high), lowgap1(next day low) and opengap1(next day open) columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
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    "colab_type": "code",
    "id": "uvSkK8swTr9H",
    "outputId": "1734f538-a4a1-45b9-d656-7ddc0124dc35"
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   "outputs": [
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       "      <td>2013-12-30</td>\n",
       "      <td>2.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.15</td>\n",
       "      <td>6.95</td>\n",
       "      <td>-12.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>3.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.34</td>\n",
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       "      <td>-1.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>3.97</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-8.82</td>\n",
       "      <td>4.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  vixChange  netChange  prevChangeNF  prevChangeBNF  NFOIChange  \\\n",
       "0  2013-12-30       2.86        8.0           0.4            0.4        4.15   \n",
       "1  2014-01-01       3.04       10.0           0.3            0.1       -2.51   \n",
       "2  2014-01-02       6.23        6.0           0.1            0.2        0.34   \n",
       "3  2014-01-03      -4.11      -32.0          -1.6           -2.2       -2.50   \n",
       "4  2014-01-06       3.97       -5.0          -0.1            0.2       -8.82   \n",
       "\n",
       "   BNFOIChange  basisChange  closegap1  \n",
       "0         6.95        -12.0       -0.6  \n",
       "1        -0.58        -23.0        0.4  \n",
       "2         4.58        -11.0       -1.6  \n",
       "3        -1.20        -31.0       -0.1  \n",
       "4         4.86          6.0       -0.4  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['opengap1', 'highgap1', 'lowgap1'], axis=1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "20OeQBpWz89v"
   },
   "source": [
    "We have lesser and more manageable number of columns now. Only closegap1 is our output column. Rest are input data columns.\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "caAownWdUZso"
   },
   "source": [
    "## 5. Renaming the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UABLiEcyz-2G"
   },
   "source": [
    "For the sake of our tutorial, let us rename some columns to have more descriptive names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "id": "1wHW41whURub",
    "outputId": "dec9b1b3-e344-4b33-92fd-6e9f4c03c878"
   },
   "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>vixChange</th>\n",
       "      <th>AdvDecline</th>\n",
       "      <th>NIFTY Price Change</th>\n",
       "      <th>BANKNIFTY Price Change</th>\n",
       "      <th>NFOIChange</th>\n",
       "      <th>BNFOIChange</th>\n",
       "      <th>basisChange</th>\n",
       "      <th>Next Day Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-30</td>\n",
       "      <td>2.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.15</td>\n",
       "      <td>6.95</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>3.04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-2.51</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.58</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>-4.11</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>3.97</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-8.82</td>\n",
       "      <td>4.86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  vixChange  AdvDecline  NIFTY Price Change  \\\n",
       "0  2013-12-30       2.86         8.0                 0.4   \n",
       "1  2014-01-01       3.04        10.0                 0.3   \n",
       "2  2014-01-02       6.23         6.0                 0.1   \n",
       "3  2014-01-03      -4.11       -32.0                -1.6   \n",
       "4  2014-01-06       3.97        -5.0                -0.1   \n",
       "\n",
       "   BANKNIFTY Price Change  NFOIChange  BNFOIChange  basisChange  \\\n",
       "0                     0.4        4.15         6.95        -12.0   \n",
       "1                     0.1       -2.51        -0.58        -23.0   \n",
       "2                     0.2        0.34         4.58        -11.0   \n",
       "3                    -2.2       -2.50        -1.20        -31.0   \n",
       "4                     0.2       -8.82         4.86          6.0   \n",
       "\n",
       "   Next Day Change  \n",
       "0             -0.6  \n",
       "1              0.4  \n",
       "2             -1.6  \n",
       "3             -0.1  \n",
       "4             -0.4  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={\"netChange\": \"AdvDecline\", \"prevChangeNF\": \"NIFTY Price Change\", \"prevChangeBNF\": \"BANKNIFTY Price Change\",\"closegap1\":\"Next Day Change\" })\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JkXUQtyQW3Dy"
   },
   "source": [
    "## 6. Dropping the missing or null values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K5pKvJi41YCp"
   },
   "source": [
    "Let us make sure there are no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method DataFrame.count of             Date  vixChange  AdvDecline  NIFTY Price Change  \\\n",
      "0     2013-12-30       2.86         8.0                 0.4   \n",
      "1     2014-01-01       3.04        10.0                 0.3   \n",
      "2     2014-01-02       6.23         6.0                 0.1   \n",
      "3     2014-01-03      -4.11       -32.0                -1.6   \n",
      "4     2014-01-06       3.97        -5.0                -0.1   \n",
      "...          ...        ...         ...                 ...   \n",
      "1238  2018-12-21      11.58       -11.0                -0.1   \n",
      "1239  2018-12-24      -1.52       -43.0                -1.9   \n",
      "1240  2018-12-26       1.08       -24.0                -0.8   \n",
      "1241  2018-12-27       0.96        19.0                 0.6   \n",
      "1242  2018-12-28      -4.99         6.0                 0.6   \n",
      "\n",
      "      BANKNIFTY Price Change  NFOIChange  BNFOIChange  basisChange  \\\n",
      "0                        0.4        4.15         6.95        -12.0   \n",
      "1                        0.1       -2.51        -0.58        -23.0   \n",
      "2                        0.2        0.34         4.58        -11.0   \n",
      "3                       -2.2       -2.50        -1.20        -31.0   \n",
      "4                        0.2       -8.82         4.86          6.0   \n",
      "...                      ...         ...          ...          ...   \n",
      "1238                    -0.1       -1.03        -2.12          6.0   \n",
      "1239                    -1.5       -3.35        -4.06        -11.0   \n",
      "1240                    -0.6      -26.12        -7.52          4.0   \n",
      "1241                     0.9      -25.39       -24.56          0.0   \n",
      "1242                    -0.0       25.65        51.26         11.0   \n",
      "\n",
      "      Next Day Change  \n",
      "0                -0.6  \n",
      "1                 0.4  \n",
      "2                -1.6  \n",
      "3                -0.1  \n",
      "4                -0.4  \n",
      "...               ...  \n",
      "1238             -1.9  \n",
      "1239             -0.8  \n",
      "1240              0.6  \n",
      "1241              0.6  \n",
      "1242              0.9  \n",
      "\n",
      "[1243 rows x 9 columns]>\n"
     ]
    }
   ],
   "source": [
    "print(df.count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "Tzdlg-1OWjMz",
    "outputId": "7375ab6e-1473-4346-e5b5-0c61189cc716"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date                      0\n",
      "vixChange                 0\n",
      "AdvDecline                0\n",
      "NIFTY Price Change        0\n",
      "BANKNIFTY Price Change    0\n",
      "NFOIChange                0\n",
      "BNFOIChange               0\n",
      "basisChange               0\n",
      "Next Day Change           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "KbbV0xHPWoad",
    "outputId": "17dda8ec-1282-4814-de79-8f5e1aff3a5f"
   },
   "outputs": [],
   "source": [
    "#df = df.dropna()    # Dropping the missing values.\n",
    "#df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2t7L9l2mJSoX"
   },
   "source": [
    "If we uncomment code above, we can remove the null rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "colab_type": "code",
    "id": "V-DmX1O4Wtox",
    "outputId": "2d50fc20-3535-413b-e317-75a7f94fa2a2"
   },
   "outputs": [],
   "source": [
    "#print(df.isnull().sum())   # After dropping the values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Py3sQc_ZxyU"
   },
   "source": [
    "## 7. Detecting Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1g6CJPofZzHN"
   },
   "source": [
    "Always a good idea to check outliers in the data visually as outliers can affect final analysis disproportionately\n",
    "For this, we will use box plot which use the concept of IQR(Inter quartile range). \n",
    "https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51\n",
    "\n",
    "The box covers 25th to 75th percentile of the data. Difference between 25th and 7th percentile is IQR. The two vertical lines are 1.5times IQR away from 25th and 75th percentile. Plotted points are outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CQDO4JCqTThV",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "![alt text](boxplot.png \"Box Plot Basic\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "vtxX_y6zZ2ri",
    "outputId": "e3f93522-9244-4c32-c34c-103e0834e93b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fb89390>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['vixChange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "9HtvQYVHZ8u5",
    "outputId": "3dc30a01-6fb1-41d9-dec8-0ceeead6a358"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fbad1d0>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+/ftr586dunDhgsLhcKNQSpePdl944QUlJyfrxhtvbPI5CgsL9dNPPyk1NVV9+vRR586dI2udOXNGo0aN0tdff60BAwbo008/VUVFhSSpoKBAr776arP3NGjQIO3YsSOyTn5+viZOnNjsdYB/x5Eurlp+fr6eeuqpRkex8fHxmjBhgjZt2qSsrCxlZWUpPj5e/fr1U2VlZeRxGRkZWr58uZYvX95ozZKSEh0+fFg+n0/hcFh9+vTRW2+9FTkqfeONN/Tiiy9q/fr1CoVCysnJUf/+/SVJc+fO1dSpUyVdfleBl156SWVlZc3a05AhQzRt2jRNnjxZPp9PXbt21erVq1vVxbJxbeIqYwBgiNMLAGCI6AKAIaILAIaILgAYIroAYIjoAoAhogsAhoguABj6XyRkHEiZwgxtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['AdvDecline'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fc7d350>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['NIFTY Price Change'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f3f9c10>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['BANKNIFTY Price Change'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f62c210>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['NFOIChange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e6120d0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['BNFOIChange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e612ed0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['basisChange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "Xz3MwIjbaBUr",
    "outputId": "335d1e55-55a3-4e61-8401-a414580d9d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dcd5cd0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Next Day Change'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "cFG9hck7aHUx",
    "outputId": "7c09ff98-3725-4d56-9b97-ef6b43d0e17b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n"
     ]
    }
   ],
   "source": [
    "Q1 = df['NIFTY Price Change'].quantile(0.25)\n",
    "Q3 = df['NIFTY Price Change'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c37WtYYWJuAQ"
   },
   "source": [
    "### Heat Maps\n",
    "\n",
    "Heat Maps is a type of plot which is necessary when we need to find the dependent variables. One of the best way to find the relationship between the features can be done using heat maps. In the below heat map, we can see at one glance that **Vix change has the strong impact on our output variable(\"One Day Change\")**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "id": "yhHfLVTj6nmy",
    "outputId": "50a65ae0-841a-42ec-87e6-1a99da1ea57b"
   },
   "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>vixChange</th>\n",
       "      <th>AdvDecline</th>\n",
       "      <th>NIFTY Price Change</th>\n",
       "      <th>BANKNIFTY Price Change</th>\n",
       "      <th>NFOIChange</th>\n",
       "      <th>BNFOIChange</th>\n",
       "      <th>basisChange</th>\n",
       "      <th>Next Day Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vixChange</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069701</td>\n",
       "      <td>0.074280</td>\n",
       "      <td>0.050529</td>\n",
       "      <td>0.017034</td>\n",
       "      <td>0.028477</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>-0.559466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdvDecline</th>\n",
       "      <td>0.069701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901374</td>\n",
       "      <td>0.774151</td>\n",
       "      <td>0.100542</td>\n",
       "      <td>0.052254</td>\n",
       "      <td>0.055731</td>\n",
       "      <td>0.065724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NIFTY Price Change</th>\n",
       "      <td>0.074280</td>\n",
       "      <td>0.901374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.869002</td>\n",
       "      <td>0.107594</td>\n",
       "      <td>0.062272</td>\n",
       "      <td>0.075326</td>\n",
       "      <td>0.061490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANKNIFTY Price Change</th>\n",
       "      <td>0.050529</td>\n",
       "      <td>0.774151</td>\n",
       "      <td>0.869002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089311</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.071499</td>\n",
       "      <td>0.068945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFOIChange</th>\n",
       "      <td>0.017034</td>\n",
       "      <td>0.100542</td>\n",
       "      <td>0.107594</td>\n",
       "      <td>0.089311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.779631</td>\n",
       "      <td>0.387754</td>\n",
       "      <td>0.036691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNFOIChange</th>\n",
       "      <td>0.028477</td>\n",
       "      <td>0.052254</td>\n",
       "      <td>0.062272</td>\n",
       "      <td>0.049321</td>\n",
       "      <td>0.779631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404798</td>\n",
       "      <td>0.027513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basisChange</th>\n",
       "      <td>-0.002079</td>\n",
       "      <td>0.055731</td>\n",
       "      <td>0.075326</td>\n",
       "      <td>0.071499</td>\n",
       "      <td>0.387754</td>\n",
       "      <td>0.404798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next Day Change</th>\n",
       "      <td>-0.559466</td>\n",
       "      <td>0.065724</td>\n",
       "      <td>0.061490</td>\n",
       "      <td>0.068945</td>\n",
       "      <td>0.036691</td>\n",
       "      <td>0.027513</td>\n",
       "      <td>-0.012025</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        vixChange  AdvDecline  NIFTY Price Change  \\\n",
       "vixChange                1.000000    0.069701            0.074280   \n",
       "AdvDecline               0.069701    1.000000            0.901374   \n",
       "NIFTY Price Change       0.074280    0.901374            1.000000   \n",
       "BANKNIFTY Price Change   0.050529    0.774151            0.869002   \n",
       "NFOIChange               0.017034    0.100542            0.107594   \n",
       "BNFOIChange              0.028477    0.052254            0.062272   \n",
       "basisChange             -0.002079    0.055731            0.075326   \n",
       "Next Day Change         -0.559466    0.065724            0.061490   \n",
       "\n",
       "                        BANKNIFTY Price Change  NFOIChange  BNFOIChange  \\\n",
       "vixChange                             0.050529    0.017034     0.028477   \n",
       "AdvDecline                            0.774151    0.100542     0.052254   \n",
       "NIFTY Price Change                    0.869002    0.107594     0.062272   \n",
       "BANKNIFTY Price Change                1.000000    0.089311     0.049321   \n",
       "NFOIChange                            0.089311    1.000000     0.779631   \n",
       "BNFOIChange                           0.049321    0.779631     1.000000   \n",
       "basisChange                           0.071499    0.387754     0.404798   \n",
       "Next Day Change                       0.068945    0.036691     0.027513   \n",
       "\n",
       "                        basisChange  Next Day Change  \n",
       "vixChange                 -0.002079        -0.559466  \n",
       "AdvDecline                 0.055731         0.065724  \n",
       "NIFTY Price Change         0.075326         0.061490  \n",
       "BANKNIFTY Price Change     0.071499         0.068945  \n",
       "NFOIChange                 0.387754         0.036691  \n",
       "BNFOIChange                0.404798         0.027513  \n",
       "basisChange                1.000000        -0.012025  \n",
       "Next Day Change           -0.012025         1.000000  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "c= df.corr()\n",
    "sns.heatmap(c,cmap=\"BrBG\",annot=True)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1ROThOLQfRZw"
   },
   "source": [
    "### Scatterplot\n",
    "\n",
    "We generally use scatter plots to find the correlation between two variables. Here the scatter plots are plotted between VIX Change and Next Day Change and we can see the plot below. With the plot given below, we can easily draw a trend line. These features provide a good scattering of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 401
    },
    "colab_type": "code",
    "id": "2aOfHNFefSrX",
    "outputId": "4b5286f0-5419-48bc-d2de-476aeb36f022"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation -0.56\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['vixChange'], df['Next Day Change'])\n",
    "ax.set_xlabel('vixChange')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "z = np.polyfit(df['vixChange'], df['Next Day Change'], 1)\n",
    "p = np.poly1d(z)\n",
    "ax.plot(df['vixChange'],p(df['vixChange']),\"r--\")\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['vixChange'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation 0.07\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['AdvDecline'], df['Next Day Change'])\n",
    "ax.set_xlabel('AdvDecline')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['AdvDecline'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation 0.06\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['NIFTY Price Change'], df['Next Day Change'])\n",
    "ax.set_xlabel('NIFTY Price Change')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['NIFTY Price Change'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation 0.07\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['BANKNIFTY Price Change'], df['Next Day Change'])\n",
    "ax.set_xlabel('BANKNIFTY Price Change')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['BANKNIFTY Price Change'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation 0.04\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['NFOIChange'], df['Next Day Change'])\n",
    "ax.set_xlabel('NFOIChange')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['NFOIChange'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation 0.03\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['BNFOIChange'], df['Next Day Change'])\n",
    "ax.set_xlabel('BNFOIChange')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['BNFOIChange'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation -0.01\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(df['basisChange'], df['Next Day Change'])\n",
    "ax.set_xlabel('basisChange')\n",
    "ax.set_ylabel('Next Day Change')\n",
    "plt.show()\n",
    "print(\"Correlation\",round(df['basisChange'].corr(df['Next Day Change']),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hQXy8o_gKFS5"
   },
   "source": [
    "**As we can see, VIX Change is by far the most important variable in predicting next day close vis-a-vis current day close**\n",
    "This analysis can be repeated for changes to next day Open, High and Low also relative to current day close and for days further out(3 days in the future etc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "colab": {
   "collapsed_sections": [],
   "name": "Nifty Exploratory data Analysis.ipynb",
   "provenance": []
  },
  "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": 1
}