2_Creating_a_Time_Series_object.ipynb

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  "metadata": {
    "colab": {
      "name": "2. Creating a Time Series object.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMOBt0DV9Q8KREyS7Jz85QD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tanaymukherjee/Time-Series-Modeling/blob/master/2_Creating_a_Time_Series_object.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rT1378K9iapT",
        "colab_type": "text"
      },
      "source": [
        "# Time Series Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mM1WVU2Iie0K",
        "colab_type": "text"
      },
      "source": [
        "### Definition:\n",
        "A sequence of information which attaches a time period to each value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PfyEkXFujSyW",
        "colab_type": "text"
      },
      "source": [
        "### Common Objective:\n",
        "1. Determining the stability of financial markets and the efficiency portfolios.\n",
        "2. Weather forcasting based on past records."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4z-_nucZluTz",
        "colab_type": "text"
      },
      "source": [
        "#### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcw1FIsbiW-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats\n",
        "import pylab"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAJMSXfgmvue",
        "colab_type": "text"
      },
      "source": [
        "#### Mounting Google colab with personal GDrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB-t7ZDCmRoR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "11a4a8ac-15b0-4123-f3a2-1eeef8d28565"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1cVsLKCm155",
        "colab_type": "text"
      },
      "source": [
        "#### Checking the content in my GDrive witihin the working directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8kooE9umgLn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "51b27303-37a4-46ea-cbb7-9083ef7e9c87"
      },
      "source": [
        "!ls -ahl \"/content/gdrive/My Drive/Colab Notebooks/Data\""
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 291K\n",
            "-rw------- 1 root root 291K May 31 02:02 Index2018.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAtGYiuknPlx",
        "colab_type": "text"
      },
      "source": [
        "#### Read the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLKsgBinl0j6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(\"/content/gdrive/My Drive/Colab Notebooks/Data/Index2018.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMVAtJi7nmPy",
        "colab_type": "text"
      },
      "source": [
        "#### View the dataset summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjdaO_ben1O2",
        "colab_type": "text"
      },
      "source": [
        "Date column includes the timen period where as the other features tell us the stability of market.\n",
        "\n",
        "1. SPX is US stock exchange by S&P 500\n",
        "2. DAX is German stock exchange by  DAX 30\n",
        "3. FTSE is UK stock exchange by FTSE 100\n",
        "4. NIKKEI is Japan stock exchange by NIKKEI 225"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boxy1I1gnRn6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "7f19bdd8-33de-4871-913e-52536d5a9d5a"
      },
      "source": [
        "df = data.copy()\n",
        "df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>date</th>\n",
              "      <th>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07/01/1994</td>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10/01/1994</td>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11/01/1994</td>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12/01/1994</td>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13/01/1994</td>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date     spx      dax     ftse    nikkei\n",
              "0  07/01/1994  469.90  2224.95  3445.98  18124.01\n",
              "1  10/01/1994  475.27  2225.00  3440.58  18443.44\n",
              "2  11/01/1994  474.13  2228.10  3413.77  18485.25\n",
              "3  12/01/1994  474.17  2182.06  3372.02  18793.88\n",
              "4  13/01/1994  472.47  2142.37  3360.01  18577.26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2cA82j5ojvJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "cb16bc07-0998-4408-d88f-1382a60afaa4"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "      <td>6269.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1288.127542</td>\n",
              "      <td>6080.063363</td>\n",
              "      <td>5422.713545</td>\n",
              "      <td>14597.055700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>487.586473</td>\n",
              "      <td>2754.361032</td>\n",
              "      <td>1145.572428</td>\n",
              "      <td>4043.122953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>438.920000</td>\n",
              "      <td>1911.700000</td>\n",
              "      <td>2876.600000</td>\n",
              "      <td>7054.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>990.671905</td>\n",
              "      <td>4069.350000</td>\n",
              "      <td>4486.100000</td>\n",
              "      <td>10709.290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1233.420000</td>\n",
              "      <td>5773.340000</td>\n",
              "      <td>5662.430000</td>\n",
              "      <td>15028.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1459.987747</td>\n",
              "      <td>7443.070000</td>\n",
              "      <td>6304.250000</td>\n",
              "      <td>17860.470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.867839</td>\n",
              "      <td>13559.600000</td>\n",
              "      <td>7778.637689</td>\n",
              "      <td>24124.150000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx           dax         ftse        nikkei\n",
              "count  6269.000000   6269.000000  6269.000000   6269.000000\n",
              "mean   1288.127542   6080.063363  5422.713545  14597.055700\n",
              "std     487.586473   2754.361032  1145.572428   4043.122953\n",
              "min     438.920000   1911.700000  2876.600000   7054.980000\n",
              "25%     990.671905   4069.350000  4486.100000  10709.290000\n",
              "50%    1233.420000   5773.340000  5662.430000  15028.170000\n",
              "75%    1459.987747   7443.070000  6304.250000  17860.470000\n",
              "max    2872.867839  13559.600000  7778.637689  24124.150000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzDKcb82o7Nv",
        "colab_type": "text"
      },
      "source": [
        "#### Check for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6E_o3hQo6IL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "15762b1f-57d8-459d-c834-11c4ccc1d36f"
      },
      "source": [
        "df.isna()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\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>date</th>\n",
              "      <th>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6264</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6265</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6266</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6267</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6268</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6269 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       date    spx    dax   ftse  nikkei\n",
              "0     False  False  False  False   False\n",
              "1     False  False  False  False   False\n",
              "2     False  False  False  False   False\n",
              "3     False  False  False  False   False\n",
              "4     False  False  False  False   False\n",
              "...     ...    ...    ...    ...     ...\n",
              "6264  False  False  False  False   False\n",
              "6265  False  False  False  False   False\n",
              "6266  False  False  False  False   False\n",
              "6267  False  False  False  False   False\n",
              "6268  False  False  False  False   False\n",
              "\n",
              "[6269 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igeONp9RpGTx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "922f6a64-9183-4818-966c-1db6cd9aed62"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date      0\n",
              "spx       0\n",
              "dax       0\n",
              "ftse      0\n",
              "nikkei    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KKAWTkXFpZ3w",
        "colab_type": "text"
      },
      "source": [
        "#### Plotting the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lS2_ruTmpZLi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "0c56ade2-e143-4e9e-fe05-1f69aa0d49ad"
      },
      "source": [
        "df.spx.plot(title = \"S&P 500 Prices\", figsize=(25,5))\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ck3aZCnRqOYC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "ec7c80dc-3288-4b35-f36d-06633ba56a96"
      },
      "source": [
        "df.ftse.plot(title = \"FTSE 100 Prices\", figsize=(25,5))\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd4daqdoqsdB",
        "colab_type": "text"
      },
      "source": [
        "##### Plotting all the stock markerts together"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsQKS3KrqbCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "3c857f29-ce00-4832-c698-5c01c2517ab0"
      },
      "source": [
        "df.spx.plot(title = \"S&P 500 Prices\", figsize=(25,5))\n",
        "df.dax.plot(title = \"DAX 30 Prices\", figsize=(25,5))\n",
        "df.ftse.plot(title = \"FTSE 100 Prices\", figsize=(25,5))\n",
        "df.nikkei.plot(title = \"NIKKEI 225 Prices\", figsize=(25,5))\n",
        "plt.title(\"S&P v/s DAX v/s FTSE v/s NIKKEI\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZUv2xZBrM7z",
        "colab_type": "text"
      },
      "source": [
        "### QQ Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ym03y7sJrMZL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "765b19bc-5d83-4dbb-fa70-392e906e0db4"
      },
      "source": [
        "scipy.stats.probplot(df.spx, plot = pylab)\n",
        "pylab.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePuOgwijtKPz",
        "colab_type": "text"
      },
      "source": [
        "#### From text to date"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrCz5mXxtNVv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "c11a870c-55ca-442d-c41a-0e8ed19dc259"
      },
      "source": [
        "df.date = pd.to_datetime(df.date, dayfirst=True)\n",
        "df.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1994-01-07</td>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1994-01-10</td>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1994-01-11</td>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1994-01-12</td>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1994-01-13</td>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date     spx      dax     ftse    nikkei\n",
              "0 1994-01-07  469.90  2224.95  3445.98  18124.01\n",
              "1 1994-01-10  475.27  2225.00  3440.58  18443.44\n",
              "2 1994-01-11  474.13  2228.10  3413.77  18485.25\n",
              "3 1994-01-12  474.17  2182.06  3372.02  18793.88\n",
              "4 1994-01-13  472.47  2142.37  3360.01  18577.26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtGPku38tnwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "31fc75f3-f3e6-4922-f43c-5e784653a269"
      },
      "source": [
        "df.date.describe()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                    6269\n",
              "unique                   6269\n",
              "top       1999-08-23 00:00:00\n",
              "freq                        1\n",
              "first     1994-01-07 00:00:00\n",
              "last      2018-01-29 00:00:00\n",
              "Name: date, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOXy7GvyvVKB",
        "colab_type": "text"
      },
      "source": [
        "#### Setting the index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-0SofZxvWte",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.set_index(\"date\", inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzKsX9S5vnuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "a0ee5a81-bc9a-4d78-bc3d-769f3b949fd8"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1994-01-07</th>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-10</th>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-11</th>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-12</th>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-13</th>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx      dax     ftse    nikkei\n",
              "date                                          \n",
              "1994-01-07  469.90  2224.95  3445.98  18124.01\n",
              "1994-01-10  475.27  2225.00  3440.58  18443.44\n",
              "1994-01-11  474.13  2228.10  3413.77  18485.25\n",
              "1994-01-12  474.17  2182.06  3372.02  18793.88\n",
              "1994-01-13  472.47  2142.37  3360.01  18577.26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7jVB07LvslR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "ee3f6306-9c36-496b-f0f4-ccc859740737"
      },
      "source": [
        "df.date.describe()\n",
        "\n",
        "# it throws an error because once date becomes an index, it is no longer a seperate attribute in the dataframe."
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-30248fe5d819>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# it throws an error because once date becomes an index, it is no longer a seperate attribute in the dataframe.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5273\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'date'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZT8EXlxwGFu",
        "colab_type": "text"
      },
      "source": [
        "#### Setting the frequency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzgCE6yEwFlD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "385f5f00-b85b-4387-f08e-74094b0f6618"
      },
      "source": [
        "# When we wish to define the frequency by day\n",
        "df = df.asfreq('d')\n",
        "df.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1994-01-07</th>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-09</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-10</th>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-11</th>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx      dax     ftse    nikkei\n",
              "date                                          \n",
              "1994-01-07  469.90  2224.95  3445.98  18124.01\n",
              "1994-01-08     NaN      NaN      NaN       NaN\n",
              "1994-01-09     NaN      NaN      NaN       NaN\n",
              "1994-01-10  475.27  2225.00  3440.58  18443.44\n",
              "1994-01-11  474.13  2228.10  3413.77  18485.25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GL7DiS-qwm34",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "1f4e423e-f04d-4235-bf1c-5b8542f89c7d"
      },
      "source": [
        "# What if we wish to not include the weekends because the stock markets are not operative?\n",
        "\n",
        "df = df.asfreq('b')\n",
        "df.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1994-01-07</th>\n",
              "      <td>469.90</td>\n",
              "      <td>2224.95</td>\n",
              "      <td>3445.98</td>\n",
              "      <td>18124.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-10</th>\n",
              "      <td>475.27</td>\n",
              "      <td>2225.00</td>\n",
              "      <td>3440.58</td>\n",
              "      <td>18443.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-11</th>\n",
              "      <td>474.13</td>\n",
              "      <td>2228.10</td>\n",
              "      <td>3413.77</td>\n",
              "      <td>18485.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-12</th>\n",
              "      <td>474.17</td>\n",
              "      <td>2182.06</td>\n",
              "      <td>3372.02</td>\n",
              "      <td>18793.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-13</th>\n",
              "      <td>472.47</td>\n",
              "      <td>2142.37</td>\n",
              "      <td>3360.01</td>\n",
              "      <td>18577.26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx      dax     ftse    nikkei\n",
              "date                                          \n",
              "1994-01-07  469.90  2224.95  3445.98  18124.01\n",
              "1994-01-10  475.27  2225.00  3440.58  18443.44\n",
              "1994-01-11  474.13  2228.10  3413.77  18485.25\n",
              "1994-01-12  474.17  2182.06  3372.02  18793.88\n",
              "1994-01-13  472.47  2142.37  3360.01  18577.26"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EfKxwfTwZTv",
        "colab_type": "text"
      },
      "source": [
        "#### Handling missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvBx6ITrxDX_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "979aee6c-a097-4eb8-a2a4-27758ac4b578"
      },
      "source": [
        "df.isna()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1994-01-07</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-10</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-11</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-12</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994-01-13</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-23</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-24</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-25</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-26</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-29</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6277 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              spx    dax   ftse  nikkei\n",
              "date                                   \n",
              "1994-01-07  False  False  False   False\n",
              "1994-01-10  False  False  False   False\n",
              "1994-01-11  False  False  False   False\n",
              "1994-01-12  False  False  False   False\n",
              "1994-01-13  False  False  False   False\n",
              "...           ...    ...    ...     ...\n",
              "2018-01-23  False  False  False   False\n",
              "2018-01-24  False  False  False   False\n",
              "2018-01-25  False  False  False   False\n",
              "2018-01-26  False  False  False   False\n",
              "2018-01-29  False  False  False   False\n",
              "\n",
              "[6277 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3AovDOzwb62",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "f39a1830-f1e2-48b9-a077-dddd376beb5e"
      },
      "source": [
        "df.isna().sum()\n",
        "\n",
        "# We see there are 8 missing values for each stock market.\n",
        "# Setting the frequency to business days thus generated these 8 missing values."
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "spx       8\n",
              "dax       8\n",
              "ftse      8\n",
              "nikkei    8\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZoTYENtxQzi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Filling data\n",
        "# 1. Front filling\n",
        "# 2. Back filling\n",
        "# 3. Assign the same value\n",
        "\n",
        "# Front fill the spx\n",
        "df.spx = df.spx.fillna(method = \"ffill\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUbLmSjlxxLR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "d534c1c6-7628-4a10-b3b8-ab53b682ea4c"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "spx       0\n",
              "dax       8\n",
              "ftse      8\n",
              "nikkei    8\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRLNBFcQx1wd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Back fill the ftse\n",
        "df.ftse = df.ftse.fillna(method = \"bfill\")\n",
        "\n",
        "# Average fill the dax and nikkei\n",
        "df.dax = df.dax.fillna(value = df.dax.mean())\n",
        "df.nikkei = df.nikkei.fillna(value = df.nikkei.mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJOObLlhyQRp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "b29b54d6-2492-42ef-9030-d2c1665c3a9e"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "spx       0\n",
              "dax       0\n",
              "ftse      0\n",
              "nikkei    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghNQGV8tyjUp",
        "colab_type": "text"
      },
      "source": [
        "#### Simplifying the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IatLdlZdym__",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['market_value'] = df.spx"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHCM4DZ3zJSZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "1571faf4-7786-4e18-b1ba-61aa5d000a31"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>spx</th>\n",
              "      <th>dax</th>\n",
              "      <th>ftse</th>\n",
              "      <th>nikkei</th>\n",
              "      <th>market_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6277.000000</td>\n",
              "      <td>6277.000000</td>\n",
              "      <td>6277.000000</td>\n",
              "      <td>6277.000000</td>\n",
              "      <td>6277.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1288.642547</td>\n",
              "      <td>6080.063363</td>\n",
              "      <td>5423.690398</td>\n",
              "      <td>14597.055700</td>\n",
              "      <td>1288.642547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>487.868210</td>\n",
              "      <td>2752.604984</td>\n",
              "      <td>1145.568370</td>\n",
              "      <td>4040.545253</td>\n",
              "      <td>487.868210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>438.920000</td>\n",
              "      <td>1911.700000</td>\n",
              "      <td>2876.600000</td>\n",
              "      <td>7054.980000</td>\n",
              "      <td>438.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>992.715221</td>\n",
              "      <td>4070.460000</td>\n",
              "      <td>4487.880000</td>\n",
              "      <td>10711.090000</td>\n",
              "      <td>992.715221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1233.761241</td>\n",
              "      <td>5774.380000</td>\n",
              "      <td>5663.300000</td>\n",
              "      <td>15014.040000</td>\n",
              "      <td>1233.761241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1460.250000</td>\n",
              "      <td>7442.660000</td>\n",
              "      <td>6304.630175</td>\n",
              "      <td>17858.420000</td>\n",
              "      <td>1460.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.867839</td>\n",
              "      <td>13559.600000</td>\n",
              "      <td>7778.637689</td>\n",
              "      <td>24124.150000</td>\n",
              "      <td>2872.867839</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               spx           dax         ftse        nikkei  market_value\n",
              "count  6277.000000   6277.000000  6277.000000   6277.000000   6277.000000\n",
              "mean   1288.642547   6080.063363  5423.690398  14597.055700   1288.642547\n",
              "std     487.868210   2752.604984  1145.568370   4040.545253    487.868210\n",
              "min     438.920000   1911.700000  2876.600000   7054.980000    438.920000\n",
              "25%     992.715221   4070.460000  4487.880000  10711.090000    992.715221\n",
              "50%    1233.761241   5774.380000  5663.300000  15014.040000   1233.761241\n",
              "75%    1460.250000   7442.660000  6304.630175  17858.420000   1460.250000\n",
              "max    2872.867839  13559.600000  7778.637689  24124.150000   2872.867839"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqvojR72zA34",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del df['spx'], df['ftse'], df['dax'], df['nikkei']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydOqnKWFzZho",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "d1ed7053-976f-407a-9189-ea996c53f533"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>market_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6277.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1288.642547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>487.868210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>438.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>992.715221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1233.761241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1460.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.867839</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       market_value\n",
              "count   6277.000000\n",
              "mean    1288.642547\n",
              "std      487.868210\n",
              "min      438.920000\n",
              "25%      992.715221\n",
              "50%     1233.761241\n",
              "75%     1460.250000\n",
              "max     2872.867839"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OURsxumOzmqQ",
        "colab_type": "text"
      },
      "source": [
        "#### Splitting the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l9kwhgT40Iln",
        "colab_type": "text"
      },
      "source": [
        "In timeseries we must never randomize the training and test set. It must be in the same sequence.\n",
        "\n",
        "We will do a 80:20 split in this case"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EurlRp3qzqZ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "size = int(len(df)*0.8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaEwGZ0C0SVv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = df.iloc[:size]\n",
        "df_test = df.iloc[size:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFZhdkXO0sBe",
        "colab_type": "text"
      },
      "source": [
        "##### Verify the data split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1E8c6glM0qp4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "ca4bc1ed-0356-4626-8c9d-064d78d3d86c"
      },
      "source": [
        "df_train.tail()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>market_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-04-01</th>\n",
              "      <td>1562.173837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-02</th>\n",
              "      <td>1570.252238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-03</th>\n",
              "      <td>1553.686978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-04</th>\n",
              "      <td>1559.979316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-05</th>\n",
              "      <td>1553.278930</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            market_value\n",
              "date                    \n",
              "2013-04-01   1562.173837\n",
              "2013-04-02   1570.252238\n",
              "2013-04-03   1553.686978\n",
              "2013-04-04   1559.979316\n",
              "2013-04-05   1553.278930"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6tl1Vhu06JW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "956e076d-f820-4207-c1b1-30c23445805e"
      },
      "source": [
        "df_test.head()\n",
        "\n",
        "# We see the sequence has been properly maintained. The couple of days missing are the weekends."
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>market_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-04-08</th>\n",
              "      <td>1563.071269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-09</th>\n",
              "      <td>1568.607909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-10</th>\n",
              "      <td>1587.731827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-11</th>\n",
              "      <td>1593.369863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-04-12</th>\n",
              "      <td>1588.854623</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            market_value\n",
              "date                    \n",
              "2013-04-08   1563.071269\n",
              "2013-04-09   1568.607909\n",
              "2013-04-10   1587.731827\n",
              "2013-04-11   1593.369863\n",
              "2013-04-12   1588.854623"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    }
  ]
}