ARFIMA_Models.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtWj0ASEFHJS"
      },
      "source": [
        "Sources: Prado's Advances in Financial Machine learning"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install quandl"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XL4_qX1HXP5c",
        "outputId": "78a8e19a-e8d0-4a43-ffea-36018afb2222"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: quandl in /usr/local/lib/python3.10/dist-packages (3.7.0)\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.10/dist-packages (from quandl) (2.0.3)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.10/dist-packages (from quandl) (1.25.2)\n",
            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.10/dist-packages (from quandl) (2.31.0)\n",
            "Requirement already satisfied: inflection>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from quandl) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from quandl) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from quandl) (1.16.0)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.10/dist-packages (from quandl) (10.1.0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.14->quandl) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.14->quandl) (2024.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.7.0->quandl) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.7.0->quandl) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.7.0->quandl) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.7.0->quandl) (2024.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "scrolled": true,
        "id": "wufdp-3EFHJU"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import quandl\n",
        "import statsmodels.tsa.stattools as st\n",
        "\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.tsa.api as smt\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "#plotting libraries\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "#Initialize figure size (in inches)\n",
        "plt.rcParams['figure.figsize'] = [10,5]\n",
        "\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "5JsYoqS5FHJX"
      },
      "outputs": [],
      "source": [
        "def tsplot(y, lags=None, title='', figsize=(14, 8)):\n",
        "    '''Examine the patterns of ACF and PACF, along with the time series plot and histogram.\n",
        "\n",
        "    Original source: https://tomaugspurger.github.io/modern-7-timeseries.html\n",
        "    '''\n",
        "    fig = plt.figure(figsize=figsize)\n",
        "    layout = (2, 2)\n",
        "    ts_ax   = plt.subplot2grid(layout, (0, 0))\n",
        "    hist_ax = plt.subplot2grid(layout, (0, 1))\n",
        "    acf_ax  = plt.subplot2grid(layout, (1, 0))\n",
        "    pacf_ax = plt.subplot2grid(layout, (1, 1))\n",
        "\n",
        "    y.plot(ax=ts_ax)\n",
        "    ts_ax.set_title(title)\n",
        "    y.plot(ax=hist_ax, kind='hist', bins=25)\n",
        "    hist_ax.set_title('Histogram')\n",
        "    smt.graphics.plot_acf(y, lags=lags, ax=acf_ax)\n",
        "    smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax)\n",
        "    [ax.set_xlim(0) for ax in [acf_ax, pacf_ax]]\n",
        "    sns.despine()\n",
        "    plt.tight_layout()\n",
        "    return ts_ax, acf_ax, pacf_ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FNxeryJ2FHJY"
      },
      "outputs": [],
      "source": [
        "def adf(series):\n",
        "    adf_result = st.adfuller(series, store = True)\n",
        "    print(\"ADF Test Results: \")\n",
        "    print(\"Test Statistic: %.4f\" % adf_result[0])\n",
        "    print(\"p-value: %.10f\" % adf_result[1])\n",
        "    print(\"Critical Values: \")\n",
        "    for key, value in adf_result[2].items():\n",
        "        print('\\t%s: %0.3f' % (key, value))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "UxZwlx9AFHJZ"
      },
      "outputs": [],
      "source": [
        "def kpss(series):\n",
        "    kpss_result = st.kpss(series, store=True)\n",
        "    print(\"KPSS Test Results: \")\n",
        "    print(\"Test Statistic: %.4f\" % kpss_result[0])\n",
        "    print(\"p-value: %.10f\" % kpss_result[1])\n",
        "    print(\"Critical Values: \")\n",
        "    for key, value in kpss_result[2].items():\n",
        "        print('\\t%s: %0.3f' % (key, value))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3rTjqxaFHJZ"
      },
      "source": [
        "========================================================================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfA72NTSFHJZ"
      },
      "source": [
        "# Load TSLA from quandl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnW6mBgDFHJa"
      },
      "source": [
        "* Quandl unifies financial and economic datasets from hundreds of publishers on a single user-friendly platform.\n",
        "\n",
        "* Most datasets on Quandl are available directly in Python, using the Quandl Python module.\n",
        "\n",
        "* The Quandl Python package is free to use and grants access to all free datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mAPcBib0FHJa"
      },
      "outputs": [],
      "source": [
        "#set API key (saved locally on machine)\n",
        "quandl.ApiConfig.api_key = os.environ.get('quandl_api_key')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMhBOrp1FHJb"
      },
      "source": [
        "* If you are not familiar with quandl database and its functions, try using dir and help functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwxunfdbFHJb",
        "outputId": "9879d135-414b-4508-a159-c96bdd0a4f50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function get in module quandl.get:\n",
            "\n",
            "get(dataset, **kwargs)\n",
            "    Return dataframe of requested dataset from Quandl.\n",
            "    :param dataset: str or list, depending on single dataset usage or multiset usage\n",
            "            Dataset codes are available on the Quandl website\n",
            "    :param str api_key: Downloads are limited to 50 unless api_key is specified\n",
            "    :param str start_date, end_date: Optional datefilers, otherwise entire\n",
            "           dataset is returned\n",
            "    :param str collapse: Options are daily, weekly, monthly, quarterly, annual\n",
            "    :param str transform: options are diff, rdiff, cumul, and normalize\n",
            "    :param int rows: Number of rows which will be returned\n",
            "    :param str order: options are asc, desc. Default: `asc`\n",
            "    :param str returns: specify what format you wish your dataset returned as,\n",
            "        either `numpy` for a numpy ndarray or `pandas`. Default: `pandas`\n",
            "    :returns: :class:`pandas.DataFrame` or :class:`numpy.ndarray`\n",
            "    Note that Pandas expects timeseries data to be sorted ascending for most\n",
            "    timeseries functionality to work.\n",
            "    Any other `kwargs` passed to `get` are sent as field/value params to Quandl\n",
            "    with no interference.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# documentation of quandl.get\n",
        "help(quandl.get)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3qTDnIeFHJc"
      },
      "source": [
        "* Let us extract the stock Tesla (TSLA) from quandl."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "N0AOBG3_FHJc",
        "outputId": "32c24b2e-6039-4b63-a7dc-c12382e44770"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Open  High  Low  Close     Volume  Ex-Dividend  Split Ratio  \\\n",
              "Date                                                                      \n",
              "2010-06-29  19.0  25.0 17.5   23.9 18766300.0          0.0          1.0   \n",
              "2010-06-30  25.8  30.4 23.3   23.8 17187100.0          0.0          1.0   \n",
              "2010-07-01  25.0  25.9 20.3   22.0  8218800.0          0.0          1.0   \n",
              "2010-07-02  23.0  23.1 18.7   19.2  5139800.0          0.0          1.0   \n",
              "2010-07-06  20.0  20.0 15.8   16.1  6866900.0          0.0          1.0   \n",
              "\n",
              "            Adj. Open  Adj. High  Adj. Low  Adj. Close  Adj. Volume  \n",
              "Date                                                                 \n",
              "2010-06-29       19.0       25.0      17.5        23.9   18766300.0  \n",
              "2010-06-30       25.8       30.4      23.3        23.8   17187100.0  \n",
              "2010-07-01       25.0       25.9      20.3        22.0    8218800.0  \n",
              "2010-07-02       23.0       23.1      18.7        19.2    5139800.0  \n",
              "2010-07-06       20.0       20.0      15.8        16.1    6866900.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bad2faf2-0c2c-4651-854e-83b263f3138a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Ex-Dividend</th>\n",
              "      <th>Split Ratio</th>\n",
              "      <th>Adj. Open</th>\n",
              "      <th>Adj. High</th>\n",
              "      <th>Adj. Low</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Adj. Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-06-29</th>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>23.9</td>\n",
              "      <td>18766300.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>23.9</td>\n",
              "      <td>18766300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-06-30</th>\n",
              "      <td>25.8</td>\n",
              "      <td>30.4</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.8</td>\n",
              "      <td>17187100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>25.8</td>\n",
              "      <td>30.4</td>\n",
              "      <td>23.3</td>\n",
              "      <td>23.8</td>\n",
              "      <td>17187100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-01</th>\n",
              "      <td>25.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>20.3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>8218800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>25.9</td>\n",
              "      <td>20.3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>8218800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-02</th>\n",
              "      <td>23.0</td>\n",
              "      <td>23.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>19.2</td>\n",
              "      <td>5139800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>23.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>19.2</td>\n",
              "      <td>5139800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-07-06</th>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>15.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6866900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>15.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>6866900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bad2faf2-0c2c-4651-854e-83b263f3138a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bad2faf2-0c2c-4651-854e-83b263f3138a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bad2faf2-0c2c-4651-854e-83b263f3138a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a3bb3295-3985-4f5d-836c-797f1112f4fe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a3bb3295-3985-4f5d-836c-797f1112f4fe')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a3bb3295-3985-4f5d-836c-797f1112f4fe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tsla",
              "summary": "{\n  \"name\": \"tsla\",\n  \"rows\": 1949,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-06-29 00:00:00\",\n        \"max\": \"2018-03-27 00:00:00\",\n        \"num_unique_values\": 1949,\n        \"samples\": [\n          \"2016-11-23 00:00:00\",\n          \"2016-01-28 00:00:00\",\n          \"2014-05-13 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.10179320423333,\n        \"min\": 16.14,\n        \"max\": 386.69,\n        \"num_unique_values\": 1713,\n        \"samples\": [\n          183.56,\n          245.77,\n          341.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.58725796284469,\n        \"min\": 16.63,\n        \"max\": 389.61,\n        \"num_unique_values\": 1746,\n        \"samples\": [\n          38.09,\n          34.41,\n          127.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.42711370749286,\n        \"min\": 14.98,\n        \"max\": 379.345,\n        \"num_unique_values\": 1749,\n        \"samples\": [\n          163.96,\n          221.3715,\n          228.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.04131616877629,\n        \"min\": 15.8,\n        \"max\": 385.0,\n        \"num_unique_values\": 1787,\n        \"samples\": [\n          37.51,\n          267.2,\n          235.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4185588.27296965,\n        \"min\": 118500.0,\n        \"max\": 37163900.0,\n        \"num_unique_values\": 1937,\n        \"samples\": [\n          1030300.0,\n          8029300.0,\n          8278400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Dividend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Split Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.10179320423333,\n        \"min\": 16.14,\n        \"max\": 386.69,\n        \"num_unique_values\": 1713,\n        \"samples\": [\n          183.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.58725796284469,\n        \"min\": 16.63,\n        \"max\": 389.61,\n        \"num_unique_values\": 1746,\n        \"samples\": [\n          38.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.42711370749286,\n        \"min\": 14.98,\n        \"max\": 379.345,\n        \"num_unique_values\": 1749,\n        \"samples\": [\n          163.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.04131616877629,\n        \"min\": 15.8,\n        \"max\": 385.0,\n        \"num_unique_values\": 1787,\n        \"samples\": [\n          37.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4185588.27296965,\n        \"min\": 118500.0,\n        \"max\": 37163900.0,\n        \"num_unique_values\": 1937,\n        \"samples\": [\n          1030300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "tsla = quandl.get('WIKI/TSLA')\n",
        "tsla.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFu5vKTEFHJd",
        "outputId": "789da331-be83-4b07-e8e5-f62eba2c4e1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1949 entries, 2010-06-29 to 2018-03-27\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Open         1949 non-null   float64\n",
            " 1   High         1949 non-null   float64\n",
            " 2   Low          1949 non-null   float64\n",
            " 3   Close        1949 non-null   float64\n",
            " 4   Volume       1949 non-null   float64\n",
            " 5   Ex-Dividend  1949 non-null   float64\n",
            " 6   Split Ratio  1949 non-null   float64\n",
            " 7   Adj. Open    1949 non-null   float64\n",
            " 8   Adj. High    1949 non-null   float64\n",
            " 9   Adj. Low     1949 non-null   float64\n",
            " 10  Adj. Close   1949 non-null   float64\n",
            " 11  Adj. Volume  1949 non-null   float64\n",
            "dtypes: float64(12)\n",
            "memory usage: 197.9 KB\n"
          ]
        }
      ],
      "source": [
        "tsla.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MkHHUvglFHJd"
      },
      "source": [
        "* No missing value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "n7Sz-mUXFHJd",
        "outputId": "5fbb52fe-fe72-4bc0-9c3e-418944a29b76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Open   High    Low  Close     Volume  Ex-Dividend  Split Ratio  \\\n",
              "count 1949.0 1949.0 1949.0 1949.0     1949.0       1949.0       1949.0   \n",
              "mean   158.7  161.3  155.9  158.6  4548989.3          0.0          1.0   \n",
              "std    111.1  112.6  109.4  111.0  4185588.3          0.0          0.0   \n",
              "min     16.1   16.6   15.0   15.8   118500.0          0.0          1.0   \n",
              "25%     31.6   32.2   30.9   31.6  1350400.0          0.0          1.0   \n",
              "50%    192.1  195.1  189.0  192.7  3715161.0          0.0          1.0   \n",
              "75%    240.5  244.1  235.7  240.2  6076998.0          0.0          1.0   \n",
              "max    386.7  389.6  379.3  385.0 37163900.0          0.0          1.0   \n",
              "\n",
              "       Adj. Open  Adj. High  Adj. Low  Adj. Close  Adj. Volume  \n",
              "count     1949.0     1949.0    1949.0      1949.0       1949.0  \n",
              "mean       158.7      161.3     155.9       158.6    4548989.3  \n",
              "std        111.1      112.6     109.4       111.0    4185588.3  \n",
              "min         16.1       16.6      15.0        15.8     118500.0  \n",
              "25%         31.6       32.2      30.9        31.6    1350400.0  \n",
              "50%        192.1      195.1     189.0       192.7    3715161.0  \n",
              "75%        240.5      244.1     235.7       240.2    6076998.0  \n",
              "max        386.7      389.6     379.3       385.0   37163900.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad8b665b-1a6e-46ef-989c-ed0449cdfbc4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Ex-Dividend</th>\n",
              "      <th>Split Ratio</th>\n",
              "      <th>Adj. Open</th>\n",
              "      <th>Adj. High</th>\n",
              "      <th>Adj. Low</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Adj. Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>1949.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>158.7</td>\n",
              "      <td>161.3</td>\n",
              "      <td>155.9</td>\n",
              "      <td>158.6</td>\n",
              "      <td>4548989.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>158.7</td>\n",
              "      <td>161.3</td>\n",
              "      <td>155.9</td>\n",
              "      <td>158.6</td>\n",
              "      <td>4548989.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>111.1</td>\n",
              "      <td>112.6</td>\n",
              "      <td>109.4</td>\n",
              "      <td>111.0</td>\n",
              "      <td>4185588.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>111.1</td>\n",
              "      <td>112.6</td>\n",
              "      <td>109.4</td>\n",
              "      <td>111.0</td>\n",
              "      <td>4185588.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.1</td>\n",
              "      <td>16.6</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.8</td>\n",
              "      <td>118500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.1</td>\n",
              "      <td>16.6</td>\n",
              "      <td>15.0</td>\n",
              "      <td>15.8</td>\n",
              "      <td>118500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.6</td>\n",
              "      <td>32.2</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31.6</td>\n",
              "      <td>1350400.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.6</td>\n",
              "      <td>32.2</td>\n",
              "      <td>30.9</td>\n",
              "      <td>31.6</td>\n",
              "      <td>1350400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>192.1</td>\n",
              "      <td>195.1</td>\n",
              "      <td>189.0</td>\n",
              "      <td>192.7</td>\n",
              "      <td>3715161.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>192.1</td>\n",
              "      <td>195.1</td>\n",
              "      <td>189.0</td>\n",
              "      <td>192.7</td>\n",
              "      <td>3715161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>240.5</td>\n",
              "      <td>244.1</td>\n",
              "      <td>235.7</td>\n",
              "      <td>240.2</td>\n",
              "      <td>6076998.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>240.5</td>\n",
              "      <td>244.1</td>\n",
              "      <td>235.7</td>\n",
              "      <td>240.2</td>\n",
              "      <td>6076998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>386.7</td>\n",
              "      <td>389.6</td>\n",
              "      <td>379.3</td>\n",
              "      <td>385.0</td>\n",
              "      <td>37163900.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>386.7</td>\n",
              "      <td>389.6</td>\n",
              "      <td>379.3</td>\n",
              "      <td>385.0</td>\n",
              "      <td>37163900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad8b665b-1a6e-46ef-989c-ed0449cdfbc4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad8b665b-1a6e-46ef-989c-ed0449cdfbc4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad8b665b-1a6e-46ef-989c-ed0449cdfbc4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-043e4853-a304-4615-8453-fc00d895b5c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-043e4853-a304-4615-8453-fc00d895b5c2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-043e4853-a304-4615-8453-fc00d895b5c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"tsla\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.6946453257428,\n        \"min\": 16.14,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          158.68648152898922,\n          192.05,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.1333298893719,\n        \"min\": 16.63,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          161.31598368394046,\n          195.12,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 643.3815366398927,\n        \"min\": 14.98,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          155.8635832734736,\n          189.0,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.7090519709782,\n        \"min\": 15.8,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          158.64113288866085,\n          192.69,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12325979.759336624,\n        \"min\": 1949.0,\n        \"max\": 37163900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4548989.281169831,\n          3715161.0,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Dividend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 689.0755582662906,\n        \"min\": 0.0,\n        \"max\": 1949.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Split Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 688.7726013921949,\n        \"min\": 0.0,\n        \"max\": 1949.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1949.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.6946453257428,\n        \"min\": 16.14,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          158.68648152898922,\n          192.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.1333298893719,\n        \"min\": 16.63,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          161.31598368394046,\n          195.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 643.3815366398927,\n        \"min\": 14.98,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          155.8635832734736,\n          189.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 642.7090519709782,\n        \"min\": 15.8,\n        \"max\": 1949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          158.64113288866085,\n          192.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12325979.759336624,\n        \"min\": 1949.0,\n        \"max\": 37163900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4548989.281169831,\n          3715161.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tsla.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFhrrtErFHJe"
      },
      "source": [
        "* Note that all descriptive statistics of Open, High, Low, Close and Volume are the same as Adj. Open, Adj. High, Adj. Low, Adj. Close and Adj. Volume respectively."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "2jpYb4lvFHJe",
        "outputId": "89a0e604-b521-42df-baa0-62b73ee92a2a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tsplot(tsla['High'], title = 'Tesla High Price');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaNhZunDFHJf"
      },
      "source": [
        "* ACF and PACF of Tesla resemble AR(2) model. This might suggest that fitting ARMA$(2,0)$ is a good guess."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "3028vBajFHJf",
        "outputId": "e5bdcc9a-ab10-4194-85e2-8ffd0633edbf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#extract close price values to get numpy array of values\n",
        "price_list = tsla['Adj. Close'].values\n",
        "\n",
        "#shift price list by one down and replace first entry with na value\n",
        "shifted_prices = np.roll(price_list, shift=1, axis=0)\n",
        "shifted_prices[:1] = np.nan\n",
        "\n",
        "#calculate returns numpy array\n",
        "tsla_returns = ((price_list - shifted_prices)/shifted_prices)*100\n",
        "\n",
        "#plot graph of returns below\n",
        "plt.ylabel(\"% change\")\n",
        "plt.yticks(np.arange(-20,21,2))\n",
        "plt.ylim(-22,22)\n",
        "\n",
        "plt.xlabel(\"Date\")\n",
        "plt.title(\"TSLA Returns\")\n",
        "plt.tight_layout()\n",
        "plt.plot(tsla.index, tsla_returns);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8q7ELnV9FHJf"
      },
      "source": [
        "========================================================================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CIjax5TeFHJh"
      },
      "source": [
        "# Motivation of Fractional Differencing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7fYGNcKFHJi"
      },
      "source": [
        "* Returns are stationary but memoryless while price series have memory but non-stationary.\n",
        "\n",
        "* $\\textbf{Integer differencing wipe out all memories.}$\n",
        "\n",
        "* There is a trade-off between stationarity and memory.\n",
        "\n",
        "Recall that $L$ is the lag operator.\n",
        "For a real number $d,$ we have\n",
        "\n",
        "$$\\begin{align*}\n",
        "(1-L)^d & = \\sum_{k=0}^\\infty \\binom{d}{k} (-L)^k \\\\\n",
        "& = 1 - dL + \\frac{d(d-1)}{2!} L^2 - \\frac{d(d-1)(d-2)}{3!} L^3 + \\dots\n",
        "\\end{align*}$$\n",
        "\n",
        "The arithmetic series consists of\n",
        "\n",
        "$$\\tilde{X}_t = \\sum_{k=0}^\\infty \\omega_k X_{t-k}$$\n",
        "\n",
        "with\n",
        "\n",
        "$$\\omega = \\left\\{1 ,-d, \\frac{d(d-1)}{2!}, -\\frac{d(d-1)(d-2)}{3!}, \\dots,(-1)^k \\prod_{i=0}^{k-1} \\frac{(d-i)}{k!},\\dots \\right\\}$$\n",
        "\n",
        "and\n",
        "\n",
        "$$X = {X_t, X_{t-1}, X_{t-2},..., X_{t-k},...}$$\n",
        "\n",
        "If $d$ is an integer, then\n",
        "\n",
        "$$\\prod_{i=0}^{k-1} \\frac{(d-i)}{k!} = 0 \\quad \\text{for all } k>d.$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myS0efXWFHJi"
      },
      "source": [
        "========================================================================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLQEHhMTFHJi"
      },
      "source": [
        "# Fractional Differencing Part 1: Finding weights & Formula"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0iWBVmZhFHJj"
      },
      "source": [
        "* For $k\\geq 1,$ if $w_0 = 1,$ the weights can be generated iteratively as:\n",
        "\n",
        "$$w_k = - w_{k-1} \\frac{d-k+1}{k}.$$\n",
        "\n",
        "* The following findWeights_FFD and fracDiff functions are similar to functions Snippet 5.1 (page $79$) and Snippet 5.2 (page $82$) in Prado's book."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "xqarSXymFHJj"
      },
      "outputs": [],
      "source": [
        "\"\"\"Computes the weights for our fractionally differenced features up to a given threshold\n",
        "   requirement for fixed-window fractional differencing.\n",
        "    Args:\n",
        "        d: A float representing the differencing factor\n",
        "        length: An int length of series to be differenced\n",
        "        threshold: A float representing the minimum threshold to include weights for\n",
        "    Returns:\n",
        "        A numpy array containing the weights to be applied to our time series\n",
        "\"\"\"\n",
        "def findWeights_FFD(d, length, threshold):\n",
        "    #set first weight to be 1 and k to be 1\n",
        "    w, k = [1.], 1\n",
        "    w_curr = 1\n",
        "\n",
        "    #while we still have more weights to process, do the following:\n",
        "    while(k < length):\n",
        "\n",
        "        w_curr = (-w[-1]*(d-k+1))/k\n",
        "\n",
        "        #if the current weight is below threshold, exit loop\n",
        "        if(abs(w_curr) <= threshold):\n",
        "\n",
        "            break\n",
        "\n",
        "        #append coefficient to list if it passes above threshold condition\n",
        "        w.append(w_curr)\n",
        "\n",
        "        #increment k\n",
        "        k += 1\n",
        "\n",
        "    #make sure to convert it into a numpy array and reshape from a single row to a single\n",
        "    #column so they can be applied to time-series values easier\n",
        "    w = np.array(w[::-1]).reshape(-1,1)\n",
        "\n",
        "    return w"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Pp1ea7tVFHJk"
      },
      "outputs": [],
      "source": [
        "\"\"\"Computes fractionally differenced series\n",
        "    Args:\n",
        "        d: A float representing the differencing factor (any positive fractional)\n",
        "        series: A pandas dataframe with one or more columns of time-series values to be differenced\n",
        "        threshold: Threshold value past which we ignore weights\n",
        "            (cutoff weight for window)\n",
        "    Returns:\n",
        "        diff_series: A numpy array of differenced series by d.\n",
        "\"\"\"\n",
        "def fracDiff(series, d, threshold = 1e-5):\n",
        "    #compute weights using function above\n",
        "    weights = findWeights_FFD(d, len(series), threshold)\n",
        "    width = len(weights) - 1\n",
        "\n",
        "    df = {}\n",
        "    #for each series to be differenced, apply weights to appropriate prices and save\n",
        "    for name in series.columns:\n",
        "\n",
        "        #forward fill through unavailable prices and create a temporary series to hold values\n",
        "        curr_series = series[[name]].fillna(method='ffill').dropna()\n",
        "        df_temp = pd.Series()\n",
        "\n",
        "        #loop through all values that fall into range to be fractionally differenced\n",
        "        for iloc1 in range(width, curr_series.shape[0]):\n",
        "\n",
        "            #set values for first and last time-series point to be used in current pass of fractional\n",
        "                #difference\n",
        "            loc0 = curr_series.index[iloc1-width]\n",
        "            loc1 = curr_series.index[iloc1]\n",
        "\n",
        "            #make sure current value is valid\n",
        "            if not np.isfinite(curr_series.loc[loc1,name]):\n",
        "                continue\n",
        "\n",
        "            #dot product of weights with values from first and last indices\n",
        "            df_temp[loc1]= np.dot(weights.T, curr_series.loc[loc0:loc1])[0,0]\n",
        "\n",
        "        df[name] = df_temp.copy(deep=True)\n",
        "    df = pd.concat(df, axis=1)\n",
        "\n",
        "    return df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4PfBeKZFHJk"
      },
      "source": [
        "========================================================================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEXV3vxjFHJl"
      },
      "source": [
        "# Fractional Differencing Part 2: Test-Run/Sanity Check"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "SZFy_plxFHJm",
        "outputId": "8d1f201e-1e55-42bd-981b-fd0a98e6a582"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Adj. Close\n",
              "2014-03-05        22.5\n",
              "2014-03-06        22.3\n",
              "2014-03-07        14.7\n",
              "2014-03-10         9.9\n",
              "2014-03-11         9.4\n",
              "...                ...\n",
              "2018-03-21         5.8\n",
              "2018-03-22        -3.3\n",
              "2018-03-23        -7.1\n",
              "2018-03-26         0.4\n",
              "2018-03-27       -24.3\n",
              "\n",
              "[1023 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3278242b-86e8-47b5-bff2-cf8ebc395614\" class=\"colab-df-container\">\n",
              "    <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>Adj. Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-03-05</th>\n",
              "      <td>22.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-06</th>\n",
              "      <td>22.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-07</th>\n",
              "      <td>14.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-10</th>\n",
              "      <td>9.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-03-11</th>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-21</th>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-22</th>\n",
              "      <td>-3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-23</th>\n",
              "      <td>-7.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-26</th>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-03-27</th>\n",
              "      <td>-24.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1023 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3278242b-86e8-47b5-bff2-cf8ebc395614')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3278242b-86e8-47b5-bff2-cf8ebc395614 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3278242b-86e8-47b5-bff2-cf8ebc395614');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0c3acb62-8475-4519-b0d7-240b79d63008\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c3acb62-8475-4519-b0d7-240b79d63008')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c3acb62-8475-4519-b0d7-240b79d63008 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_dc3bb907-8c61-4cc1-ac3f-7b9c1a4a19e3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_result')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_dc3bb907-8c61-4cc1-ac3f-7b9c1a4a19e3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_result');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_result",
              "summary": "{\n  \"name\": \"df_result\",\n  \"rows\": 1023,\n  \"fields\": [\n    {\n      \"column\": \"Adj. Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.61837696176338,\n        \"min\": -26.03050527949938,\n        \"max\": 36.797590826497924,\n        \"num_unique_values\": 1023,\n        \"samples\": [\n          24.240510098883618,\n          12.745730117607053,\n          6.239747765598281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "test_series = tsla[['Adj. Close']]\n",
        "df_result = fracDiff(test_series, 0.5, 1e-5)\n",
        "\n",
        "df_result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "GVc0GG2sFHJn",
        "outputId": "7780184b-f1c4-4279-cdc0-4ad5c526311a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Date')\n",
        "ax1.set_ylabel('Differenced Prices', color=color)\n",
        "ax1.plot(df_result.index, df_result['Adj. Close'], color=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('Prices', color = color)\n",
        "ax2.plot(df_result.index, tsla['Adj. Close'].values[-(df_result.shape[0]):], color=color)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JdGZDXpFHJq"
      },
      "source": [
        "========================================================================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5m96PsvFHJq"
      },
      "source": [
        "# The ADF and KPSS Tests for stationarity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpkMugwZFHJr"
      },
      "source": [
        "1. $\\textbf{ADF Test}$: This test is used to detect the presence of a unit-root.\n",
        "\n",
        "2. $\\textbf{KPSS Test}$: This test is used to detect the presence of trend-stationarity around a deterministic trend.\n",
        "\n",
        "\n",
        "* These two tests are meant to complement each other. By testing both the unit-root and trend-stationarity, it gives us a better idea of where we can categorize the stochastic behaviour of our data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqmHu-CiFHJs",
        "outputId": "9d1e3f38-bb2a-4756-d79f-866bf791b195"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Test Results: \n",
            "Test Statistic: -6.8293\n",
            "p-value: 0.0000000019\n",
            "Critical Values: \n",
            "\t1%: -3.437\n",
            "\t5%: -2.864\n",
            "\t10%: -2.568\n"
          ]
        }
      ],
      "source": [
        "#Running the adf test and displaying results\n",
        "adf(df_result['Adj. Close'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeSURisoFHJt"
      },
      "source": [
        "This is definitely a good thing since such a low p-value is basically telling us there is no unit root (and hence no non-stationary trend in our data)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pGnaciJFHJu",
        "outputId": "a30b0926-32d8-47a5-98ce-2dae6ba80ba6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KPSS Test Results: \n",
            "Test Statistic: 0.3461\n",
            "p-value: 0.1000000000\n",
            "Critical Values: \n",
            "\t10%: 0.347\n",
            "\t5%: 0.463\n",
            "\t2.5%: 0.574\n",
            "\t1%: 0.739\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-ed299b89e4f0>:2: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
            "look-up table. The actual p-value is greater than the p-value returned.\n",
            "\n",
            "  kpss_result = st.kpss(series, store=True)\n"
          ]
        }
      ],
      "source": [
        "#Running the kpss test and displaying results\n",
        "kpss(df_result['Adj. Close'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnmNpf64FHJv"
      },
      "source": [
        "Although at least 10% doesn't sound like a whole lot, it definitely is in statistics. This test also further shows that our series is in-fact very likely to be trend-stationary."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrEIEJoBFHKR"
      },
      "source": [
        "# Varying Starting Conditions: How do test results fare?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rNSNCs8sFHKR"
      },
      "source": [
        "Are you curious on how our test results change as our differencing parameter changes? So, below, let us look at how changing our threshold value (for fractional differencing coefficients) and differencing factors change the results of the adf test results.\n",
        "\n",
        "I also decide to take a look at how these parameters change the number of data points we are able to difference (or the length of the differenced series)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "y-EdbCiZFHKS",
        "outputId": "eacd2ea9-4157-45eb-f8c6-cffbb0d7d51f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         0.00100  0.00090  0.00070  0.00050  0.00030  0.00010  0.00009  \\\n",
              "0.80000     1932     1930     1928     1923     1915     1886     1882   \n",
              "0.75000     1928     1927     1923     1918     1907     1871     1866   \n",
              "0.70000     1924     1923     1919     1912     1899     1853     1847   \n",
              "0.65000     1920     1919     1913     1905     1890     1833     1825   \n",
              "0.60000     1916     1914     1908     1898     1879     1810     1800   \n",
              "...          ...      ...      ...      ...      ...      ...      ...   \n",
              "0.40000     1895     1891     1879     1860     1821     1668     1646   \n",
              "0.35000     1889     1884     1871     1849     1802     1618     1591   \n",
              "0.30000     1884     1878     1863     1837     1783     1562     1530   \n",
              "0.25000     1879     1873     1856     1827     1765     1505     1466   \n",
              "0.20000     1877     1870     1851     1820     1751     1453     1407   \n",
              "\n",
              "         0.00007  0.00005  0.00003  \n",
              "0.80000     1872     1856     1826  \n",
              "0.75000     1853     1833     1793  \n",
              "0.70000     1831     1805     1754  \n",
              "0.65000     1805     1772     1708  \n",
              "0.60000     1775     1734     1653  \n",
              "...          ...      ...      ...  \n",
              "0.40000     1586     1488     1284  \n",
              "0.35000     1518     1395     1141  \n",
              "0.30000     1440     1290      973  \n",
              "0.25000     1358     1176      785  \n",
              "0.20000     1281     1065      596  \n",
              "\n",
              "[13 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45df06d0-b75e-42e3-a49b-465b82d1fca0\" class=\"colab-df-container\">\n",
              "    <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>0.00100</th>\n",
              "      <th>0.00090</th>\n",
              "      <th>0.00070</th>\n",
              "      <th>0.00050</th>\n",
              "      <th>0.00030</th>\n",
              "      <th>0.00010</th>\n",
              "      <th>0.00009</th>\n",
              "      <th>0.00007</th>\n",
              "      <th>0.00005</th>\n",
              "      <th>0.00003</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.80000</th>\n",
              "      <td>1932</td>\n",
              "      <td>1930</td>\n",
              "      <td>1928</td>\n",
              "      <td>1923</td>\n",
              "      <td>1915</td>\n",
              "      <td>1886</td>\n",
              "      <td>1882</td>\n",
              "      <td>1872</td>\n",
              "      <td>1856</td>\n",
              "      <td>1826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.75000</th>\n",
              "      <td>1928</td>\n",
              "      <td>1927</td>\n",
              "      <td>1923</td>\n",
              "      <td>1918</td>\n",
              "      <td>1907</td>\n",
              "      <td>1871</td>\n",
              "      <td>1866</td>\n",
              "      <td>1853</td>\n",
              "      <td>1833</td>\n",
              "      <td>1793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.70000</th>\n",
              "      <td>1924</td>\n",
              "      <td>1923</td>\n",
              "      <td>1919</td>\n",
              "      <td>1912</td>\n",
              "      <td>1899</td>\n",
              "      <td>1853</td>\n",
              "      <td>1847</td>\n",
              "      <td>1831</td>\n",
              "      <td>1805</td>\n",
              "      <td>1754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.65000</th>\n",
              "      <td>1920</td>\n",
              "      <td>1919</td>\n",
              "      <td>1913</td>\n",
              "      <td>1905</td>\n",
              "      <td>1890</td>\n",
              "      <td>1833</td>\n",
              "      <td>1825</td>\n",
              "      <td>1805</td>\n",
              "      <td>1772</td>\n",
              "      <td>1708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.60000</th>\n",
              "      <td>1916</td>\n",
              "      <td>1914</td>\n",
              "      <td>1908</td>\n",
              "      <td>1898</td>\n",
              "      <td>1879</td>\n",
              "      <td>1810</td>\n",
              "      <td>1800</td>\n",
              "      <td>1775</td>\n",
              "      <td>1734</td>\n",
              "      <td>1653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.40000</th>\n",
              "      <td>1895</td>\n",
              "      <td>1891</td>\n",
              "      <td>1879</td>\n",
              "      <td>1860</td>\n",
              "      <td>1821</td>\n",
              "      <td>1668</td>\n",
              "      <td>1646</td>\n",
              "      <td>1586</td>\n",
              "      <td>1488</td>\n",
              "      <td>1284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.35000</th>\n",
              "      <td>1889</td>\n",
              "      <td>1884</td>\n",
              "      <td>1871</td>\n",
              "      <td>1849</td>\n",
              "      <td>1802</td>\n",
              "      <td>1618</td>\n",
              "      <td>1591</td>\n",
              "      <td>1518</td>\n",
              "      <td>1395</td>\n",
              "      <td>1141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.30000</th>\n",
              "      <td>1884</td>\n",
              "      <td>1878</td>\n",
              "      <td>1863</td>\n",
              "      <td>1837</td>\n",
              "      <td>1783</td>\n",
              "      <td>1562</td>\n",
              "      <td>1530</td>\n",
              "      <td>1440</td>\n",
              "      <td>1290</td>\n",
              "      <td>973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.25000</th>\n",
              "      <td>1879</td>\n",
              "      <td>1873</td>\n",
              "      <td>1856</td>\n",
              "      <td>1827</td>\n",
              "      <td>1765</td>\n",
              "      <td>1505</td>\n",
              "      <td>1466</td>\n",
              "      <td>1358</td>\n",
              "      <td>1176</td>\n",
              "      <td>785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.20000</th>\n",
              "      <td>1877</td>\n",
              "      <td>1870</td>\n",
              "      <td>1851</td>\n",
              "      <td>1820</td>\n",
              "      <td>1751</td>\n",
              "      <td>1453</td>\n",
              "      <td>1407</td>\n",
              "      <td>1281</td>\n",
              "      <td>1065</td>\n",
              "      <td>596</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45df06d0-b75e-42e3-a49b-465b82d1fca0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-45df06d0-b75e-42e3-a49b-465b82d1fca0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-45df06d0-b75e-42e3-a49b-465b82d1fca0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a48577cb-c9cc-49e2-affa-fbcff6d20b33\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a48577cb-c9cc-49e2-affa-fbcff6d20b33')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a48577cb-c9cc-49e2-affa-fbcff6d20b33 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fb0f630f-cc83-4e17-bb8b-1114ea78b3bd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lengths_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fb0f630f-cc83-4e17-bb8b-1114ea78b3bd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lengths_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lengths_df",
              "summary": "{\n  \"name\": \"lengths_df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": 0.001,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 1877,\n        \"max\": 1932,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1879,\n          1889,\n          1932\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0.0009,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 1870,\n        \"max\": 1930,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1873,\n          1884,\n          1930\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0.0007,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 1851,\n        \"max\": 1928,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1856,\n          1871,\n          1928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0.0005,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 1820,\n        \"max\": 1923,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1827,\n          1849,\n          1923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0.0003,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55,\n        \"min\": 1751,\n        \"max\": 1915,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1765,\n          1802,\n          1915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0.0001,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 144,\n        \"min\": 1453,\n        \"max\": 1886,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1505,\n          1618,\n          1886\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9e-05,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157,\n        \"min\": 1407,\n        \"max\": 1882,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1466,\n          1591,\n          1882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 7e-05,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195,\n        \"min\": 1281,\n        \"max\": 1872,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1358,\n          1518,\n          1872\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 5e-05,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260,\n        \"min\": 1065,\n        \"max\": 1856,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1176,\n          1395,\n          1856\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3e-05,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 403,\n        \"min\": 596,\n        \"max\": 1826,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          785,\n          1141,\n          1826\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Please wait for at least 5 minutes for the cell to run completely\n",
        "\n",
        "pd.options.display.float_format = '{:.5f}'.format\n",
        "\n",
        "#the 130 combinations to test, varying values of our thresholds and differencing factors\n",
        "thresh_values = [1e-3, 9e-4, 7e-4, 5e-4, 3e-4, 1e-4, 9e-5, 7e-5, 5e-5, 3e-5]\n",
        "diff_values = [0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2]\n",
        "\n",
        "#dataframe to hold results\n",
        "pval_hyperparam_df = pd.DataFrame(0.1, columns = thresh_values, index=diff_values)\n",
        "\n",
        "#dataframe to hold lengths\n",
        "lengths_df = pd.DataFrame(1, columns = thresh_values, index = diff_values)\n",
        "\n",
        "#to keep track of column and row indices when placing results in dataframe\n",
        "col = 0\n",
        "row = 0\n",
        "\n",
        "#cycle through each combination of threshold values and diff values\n",
        "    # and generate adf test-statistics for each combination\n",
        "\n",
        "for i in thresh_values:\n",
        "    for j in diff_values:\n",
        "\n",
        "        #fractionally difference series and store series lengths\n",
        "        test_diff_series = fracDiff(test_series, j, i)\n",
        "        lengths_df.iat[row,col] = test_diff_series.shape[0]\n",
        "\n",
        "        #run and collect adf fuller test\n",
        "        test_adf_result = st.adfuller(test_diff_series['Adj. Close'], regression = 'ctt', store = True)\n",
        "\n",
        "        pval_hyperparam_df.iat[row, col] = test_adf_result[0]\n",
        "\n",
        "        row += 1\n",
        "    row = 0\n",
        "    col += 1\n",
        "\n",
        "#display our results\n",
        "(pval_hyperparam_df)\n",
        "display(lengths_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "z-usZqnNFHKT",
        "outputId": "d441c522-f510-4f84-c709-2119ef863bf3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Heatmap plot just so we get a better idea of how these changes are happening\n",
        "heatmap = sns.heatmap(pval_hyperparam_df, vmin=0, vmax=-12, annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4h7h8upOFHKT"
      },
      "source": [
        "We see that higher amounts of differencing do typically lead to much more negative test-statistics (which translate to very low p-values). All in all, this basically tells us that as we increase our differencing factor our series becomes more stationary. This would make sense since we typically take first differences (d=1) to get returns which are considered stationary.\n",
        "\n",
        "What this chart highlights is the fact that we don't actually have to overly difference a series to get trend-stationarity in the underlying series.\n",
        "\n",
        "Also notice that lowering our threshold, holding the differencing-factor constant, lowers the number of observations we can difference (and this is much more pronounced for lower differencing factors since the values converge much quicker). The same can be said for holding our threshold constant and decreasing our differencing factor."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9IoNDGlFHKU"
      },
      "source": [
        "# Pitfalls, Limitations, Conclusions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uCktv8MwFHKU"
      },
      "source": [
        "So, why do we need to be careful about what we've been studying here? Well, for the ADF test in particular, testing for a unit-root and testing for trend-stationarity aren't exactly the same thing. The weird thing is, it is possible for a time series to be non-stationary but have no unit root.\n",
        "\n",
        "Trend-stationary tests (like the KPSS test) are really looking for time-series convergence around a certain mean, and this mean can grow or shrink over time. Unit root processes however, assume that shocks to our series have a permanent impact.\n",
        "\n",
        "Also, in the presence of time-varying variance (which is the case in some of the computed examples above), our results becomes less robust (although this can be easily fixed by conducting other unit root tests such as the Phillips-Peron test that is more robust to heteroskedasticity)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GOIqHsnFHKU"
      },
      "source": [
        "So, what exactly has this series of posts shown? Virtually all finance papers apply integer differencing (a factor of 1) and is usually overkill for most financial time-series. This over-differencing causes most if not all memory to be lost, leaving us very little with regard to information in using prices in statistical models.\n",
        "\n",
        "I'll conclude with a thought that radically shifted my perspective on quantitative trading:\n",
        "\n",
        "It's important to think about quantitative trading from a statistical framework. The price path you see in a chart is simply a realized price path, generated from an underlying distribution (that we, unfortunately, don't know much about). Going forward, under repeated sampling, we have to assume that other price paths are also very likely.\n",
        "\n",
        "Thinking about equity curves and time-series paths in this manner definitely make things more confusing but solidify why statistical ideas, although complex on the surface, are so useful and powerful in giving quants an edge."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}