{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "yfinance package to download stock data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cyrus723/my-first-binder/blob/main/yfinance_package_to_download_stock_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iclv4RDI1B3r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9cd9dd3-bd87-42d4-9d18-ff011ac8ade4"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 12.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXQJ-yX7UIAM"
      },
      "source": [
        "# Importing the modules\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import math\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsIT-Q1x-dXc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "d3b520c2-2f5f-470e-8030-6a91f6ed95c7"
      },
      "source": [
        "dataset = yf.download(\"TSLA\", start=\"2017-01-01\", end=\"2022-05-19\")\n",
        "dataset"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2017-01-03   42.972000   44.066002   42.192001   43.397999   43.397999   \n",
              "2017-01-04   42.950001   45.599998   42.862000   45.397999   45.397999   \n",
              "2017-01-05   45.284000   45.495998   44.389999   45.349998   45.349998   \n",
              "2017-01-06   45.386002   46.062000   45.090000   45.801998   45.801998   \n",
              "2017-01-09   45.793999   46.383999   45.599998   46.256001   46.256001   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "2022-05-12  701.000000  759.659973  680.000000  728.000000  728.000000   \n",
              "2022-05-13  773.479980  787.349976  751.570007  769.590027  769.590027   \n",
              "2022-05-16  767.159973  769.760010  719.090027  724.369995  724.369995   \n",
              "2022-05-17  747.359985  764.479980  728.849976  761.609985  761.609985   \n",
              "2022-05-18  744.520020  760.500000  700.809998  709.809998  709.809998   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2017-01-03  29616500  \n",
              "2017-01-04  56067500  \n",
              "2017-01-05  29558500  \n",
              "2017-01-06  27639500  \n",
              "2017-01-09  19897500  \n",
              "...              ...  \n",
              "2022-05-12  46771000  \n",
              "2022-05-13  30651800  \n",
              "2022-05-16  28699500  \n",
              "2022-05-17  26745400  \n",
              "2022-05-18  29270600  \n",
              "\n",
              "[1354 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52c11577-46a6-4b00-8c14-30f9ef3f82e3\">\n",
              "    <div 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>Adj Close</th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>42.972000</td>\n",
              "      <td>44.066002</td>\n",
              "      <td>42.192001</td>\n",
              "      <td>43.397999</td>\n",
              "      <td>43.397999</td>\n",
              "      <td>29616500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>42.950001</td>\n",
              "      <td>45.599998</td>\n",
              "      <td>42.862000</td>\n",
              "      <td>45.397999</td>\n",
              "      <td>45.397999</td>\n",
              "      <td>56067500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-05</th>\n",
              "      <td>45.284000</td>\n",
              "      <td>45.495998</td>\n",
              "      <td>44.389999</td>\n",
              "      <td>45.349998</td>\n",
              "      <td>45.349998</td>\n",
              "      <td>29558500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-06</th>\n",
              "      <td>45.386002</td>\n",
              "      <td>46.062000</td>\n",
              "      <td>45.090000</td>\n",
              "      <td>45.801998</td>\n",
              "      <td>45.801998</td>\n",
              "      <td>27639500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-09</th>\n",
              "      <td>45.793999</td>\n",
              "      <td>46.383999</td>\n",
              "      <td>45.599998</td>\n",
              "      <td>46.256001</td>\n",
              "      <td>46.256001</td>\n",
              "      <td>19897500</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-12</th>\n",
              "      <td>701.000000</td>\n",
              "      <td>759.659973</td>\n",
              "      <td>680.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>728.000000</td>\n",
              "      <td>46771000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-13</th>\n",
              "      <td>773.479980</td>\n",
              "      <td>787.349976</td>\n",
              "      <td>751.570007</td>\n",
              "      <td>769.590027</td>\n",
              "      <td>769.590027</td>\n",
              "      <td>30651800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-16</th>\n",
              "      <td>767.159973</td>\n",
              "      <td>769.760010</td>\n",
              "      <td>719.090027</td>\n",
              "      <td>724.369995</td>\n",
              "      <td>724.369995</td>\n",
              "      <td>28699500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-17</th>\n",
              "      <td>747.359985</td>\n",
              "      <td>764.479980</td>\n",
              "      <td>728.849976</td>\n",
              "      <td>761.609985</td>\n",
              "      <td>761.609985</td>\n",
              "      <td>26745400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-18</th>\n",
              "      <td>744.520020</td>\n",
              "      <td>760.500000</td>\n",
              "      <td>700.809998</td>\n",
              "      <td>709.809998</td>\n",
              "      <td>709.809998</td>\n",
              "      <td>29270600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1354 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52c11577-46a6-4b00-8c14-30f9ef3f82e3')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-52c11577-46a6-4b00-8c14-30f9ef3f82e3 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-52c11577-46a6-4b00-8c14-30f9ef3f82e3');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3HsPzNKUIAT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "413b6201-211c-4d97-d0a7-3fa9c212038e"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1354 entries, 2017-01-03 to 2022-05-18\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Open       1354 non-null   float64\n",
            " 1   High       1354 non-null   float64\n",
            " 2   Low        1354 non-null   float64\n",
            " 3   Close      1354 non-null   float64\n",
            " 4   Adj Close  1354 non-null   float64\n",
            " 5   Volume     1354 non-null   int64  \n",
            "dtypes: float64(5), int64(1)\n",
            "memory usage: 74.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vm8yEy1VOrAD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "a1ac3b68-607b-40de-8948-cddc5853ea39"
      },
      "source": [
        "(dataset['Adj Close'] / dataset['Adj Close'].iloc[0] * 100).plot(figsize = (15, 6));\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MxcUTtkm-DrS"
      },
      "source": [
        "#Let's do the multiple stocks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlLOkS2-9RUj",
        "outputId": "96b4b280-72c4-4c25-db5d-7b518d3e84d9"
      },
      "source": [
        "tickers = ['SBUX', 'WMT', 'AMZN', 'HD']\n",
        "mydata = pd.DataFrame()\n",
        "for t in tickers:\n",
        "    mydata[t] = yf.download(t, start=\"2000-01-01\", end=\"2022-05-31\")['Adj Close']"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "mRwoDn6f-4e6",
        "outputId": "4c87ab42-8961-4745-9a85-262ff11b2f12"
      },
      "source": [
        "mydata.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SBUX        WMT     AMZN         HD\n",
              "Date                                               \n",
              "2000-01-03  2.503720  44.580296  89.3750  41.701786\n",
              "2000-01-04  2.424388  42.912197  81.9375  39.502750\n",
              "2000-01-05  2.456121  42.036438  69.7500  40.302402\n",
              "2000-01-06  2.544973  42.495159  65.5625  38.383224\n",
              "2000-01-07  2.532281  45.706284  69.5625  40.622269"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36cdadcd-10e0-41d9-9703-0e8a72173562\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>2.503720</td>\n",
              "      <td>44.580296</td>\n",
              "      <td>89.3750</td>\n",
              "      <td>41.701786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>2.424388</td>\n",
              "      <td>42.912197</td>\n",
              "      <td>81.9375</td>\n",
              "      <td>39.502750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>2.456121</td>\n",
              "      <td>42.036438</td>\n",
              "      <td>69.7500</td>\n",
              "      <td>40.302402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>2.544973</td>\n",
              "      <td>42.495159</td>\n",
              "      <td>65.5625</td>\n",
              "      <td>38.383224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>2.532281</td>\n",
              "      <td>45.706284</td>\n",
              "      <td>69.5625</td>\n",
              "      <td>40.622269</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36cdadcd-10e0-41d9-9703-0e8a72173562')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-36cdadcd-10e0-41d9-9703-0e8a72173562 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-36cdadcd-10e0-41d9-9703-0e8a72173562');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1MGDcKGmIwhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mydata.plot(figsize=(10, 12), subplots=True);  "
      ],
      "metadata": {
        "id": "LH2fhPgYHlw-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "64034ebf-111d-4989-bc82-72102be5d40c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mydata.describe().round(2)  "
      ],
      "metadata": {
        "id": "EmjmwYoTHlum",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1e376368-0cd0-45c2-e630-4afa0a821410"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          SBUX      WMT     AMZN       HD\n",
              "count  5632.00  5632.00  5632.00  5632.00\n",
              "mean     30.75    59.33   642.96    84.04\n",
              "std      30.04    32.15   965.07    88.83\n",
              "min       2.42    28.96     5.97    13.01\n",
              "25%       7.52    36.35    43.15    24.42\n",
              "50%      15.18    42.04   179.33    32.53\n",
              "75%      50.42    66.65   769.87   118.19\n",
              "max     123.53   159.29  3731.41   413.68"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38953271-340a-4288-ab36-dcbde7e743c4\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5632.00</td>\n",
              "      <td>5632.00</td>\n",
              "      <td>5632.00</td>\n",
              "      <td>5632.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>30.75</td>\n",
              "      <td>59.33</td>\n",
              "      <td>642.96</td>\n",
              "      <td>84.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>30.04</td>\n",
              "      <td>32.15</td>\n",
              "      <td>965.07</td>\n",
              "      <td>88.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.42</td>\n",
              "      <td>28.96</td>\n",
              "      <td>5.97</td>\n",
              "      <td>13.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.52</td>\n",
              "      <td>36.35</td>\n",
              "      <td>43.15</td>\n",
              "      <td>24.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.18</td>\n",
              "      <td>42.04</td>\n",
              "      <td>179.33</td>\n",
              "      <td>32.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>50.42</td>\n",
              "      <td>66.65</td>\n",
              "      <td>769.87</td>\n",
              "      <td>118.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>123.53</td>\n",
              "      <td>159.29</td>\n",
              "      <td>3731.41</td>\n",
              "      <td>413.68</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38953271-340a-4288-ab36-dcbde7e743c4')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-38953271-340a-4288-ab36-dcbde7e743c4 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-38953271-340a-4288-ab36-dcbde7e743c4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mydata.pct_change().describe().round(4) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Zkoog8A1JvQ5",
        "outputId": "0da3732f-5785-4f58-dc72-6027c0ab9408"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            SBUX        WMT       AMZN         HD\n",
              "count  5631.0000  5631.0000  5631.0000  5631.0000\n",
              "mean      0.0008     0.0003     0.0011     0.0005\n",
              "std       0.0213     0.0150     0.0320     0.0194\n",
              "min      -0.1842    -0.1138    -0.2477    -0.2874\n",
              "25%      -0.0090    -0.0067    -0.0119    -0.0081\n",
              "50%       0.0003     0.0002     0.0005     0.0005\n",
              "75%       0.0100     0.0070     0.0135     0.0092\n",
              "max       0.1838     0.1171     0.3447     0.1407"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32184c76-7859-4fba-92bb-9164b66d50d9\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5631.0000</td>\n",
              "      <td>5631.0000</td>\n",
              "      <td>5631.0000</td>\n",
              "      <td>5631.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.0008</td>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0213</td>\n",
              "      <td>0.0150</td>\n",
              "      <td>0.0320</td>\n",
              "      <td>0.0194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.1842</td>\n",
              "      <td>-0.1138</td>\n",
              "      <td>-0.2477</td>\n",
              "      <td>-0.2874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.0090</td>\n",
              "      <td>-0.0067</td>\n",
              "      <td>-0.0119</td>\n",
              "      <td>-0.0081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0003</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>0.0005</td>\n",
              "      <td>0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.0070</td>\n",
              "      <td>0.0135</td>\n",
              "      <td>0.0092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.1838</td>\n",
              "      <td>0.1171</td>\n",
              "      <td>0.3447</td>\n",
              "      <td>0.1407</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32184c76-7859-4fba-92bb-9164b66d50d9')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-32184c76-7859-4fba-92bb-9164b66d50d9 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-32184c76-7859-4fba-92bb-9164b66d50d9');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mydata.pct_change().mean().plot(kind='bar', figsize=(10, 6));  "
      ],
      "metadata": {
        "id": "gVBLtJjLHlpL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "8b70a012-21c5-46c4-dde8-55aaa713b852"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mydata.pct_change().std().plot(kind='bar', figsize=(10, 6));  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "4vg5pJiFJbfZ",
        "outputId": "b53d9175-d39b-4930-ff34-df3735bb18e3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret = np.log(mydata / mydata.shift(1))  \n",
        "ret.describe()"
      ],
      "metadata": {
        "id": "KadQFkrRHlms",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "612e5816-25f4-4ef5-ba35-795b925c0430"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              SBUX          WMT         AMZN           HD\n",
              "count  5631.000000  5631.000000  5631.000000  5631.000000\n",
              "mean      0.000596     0.000174     0.000564     0.000343\n",
              "std       0.021199     0.014969     0.031585     0.019546\n",
              "min      -0.203626    -0.120765    -0.284568    -0.338773\n",
              "25%      -0.009081    -0.006747    -0.012017    -0.008143\n",
              "50%       0.000303     0.000241     0.000462     0.000536\n",
              "75%       0.009962     0.006937     0.013455     0.009195\n",
              "max       0.168728     0.110723     0.296181     0.131612"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a8ec6c5-aba8-4853-9042-74941502efc1\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5631.000000</td>\n",
              "      <td>5631.000000</td>\n",
              "      <td>5631.000000</td>\n",
              "      <td>5631.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.000596</td>\n",
              "      <td>0.000174</td>\n",
              "      <td>0.000564</td>\n",
              "      <td>0.000343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.021199</td>\n",
              "      <td>0.014969</td>\n",
              "      <td>0.031585</td>\n",
              "      <td>0.019546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.203626</td>\n",
              "      <td>-0.120765</td>\n",
              "      <td>-0.284568</td>\n",
              "      <td>-0.338773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.009081</td>\n",
              "      <td>-0.006747</td>\n",
              "      <td>-0.012017</td>\n",
              "      <td>-0.008143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000303</td>\n",
              "      <td>0.000241</td>\n",
              "      <td>0.000462</td>\n",
              "      <td>0.000536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.009962</td>\n",
              "      <td>0.006937</td>\n",
              "      <td>0.013455</td>\n",
              "      <td>0.009195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.168728</td>\n",
              "      <td>0.110723</td>\n",
              "      <td>0.296181</td>\n",
              "      <td>0.131612</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a8ec6c5-aba8-4853-9042-74941502efc1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9a8ec6c5-aba8-4853-9042-74941502efc1 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-9a8ec6c5-aba8-4853-9042-74941502efc1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret.mean().plot(kind='bar', figsize=(10, 6));  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "QiT0eDgAKvF7",
        "outputId": "881a6e9d-27d8-49e2-cb7c-ebe078821e47"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret.std().plot(kind='bar', figsize=(10, 6));  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "KQi_XczOK5iD",
        "outputId": "0f2f4b4c-4528-4d4d-9338-fb1e095eb641"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret.cumsum().apply(np.exp).plot(figsize=(10, 6));  "
      ],
      "metadata": {
        "id": "FWQpW6HFHlj-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "0c2eebf3-188c-41b9-f401-b430bb34f01a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "Q5Ig-r2IIweQ"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ret.plot(subplots=True, figsize=(10, 12));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "tfA-LhNGIwcl",
        "outputId": "987decb2-9103-448a-b8ff-bb0b2932615f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x864 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ret.plot(subplots=False, figsize=(15, 10));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "sVO9opsnIwZZ",
        "outputId": "88317a62-cf2b-457d-ebd1-0b2a285bed4c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NjxWURi5IwWm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lsbctnPlIwT7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "LxwAVideIwRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wSEOUw3MIwOo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qPhH7xXmIwLz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "M-djJsRsIwJm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OU9wQK4Z_NA5"
      },
      "source": [
        "### Oops! AMZN was not a public firm in 1995. Let's choose another one instead."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQtSAZwK_cb0",
        "outputId": "cb8f428a-a7c4-4f58-9e46-6d58f482ecef"
      },
      "source": [
        "tickers = ['SBUX', 'WMT', 'ORCL', 'HD']\n",
        "mydata = pd.DataFrame()\n",
        "for t in tickers:\n",
        "    mydata[t] = yf.download(t, start=\"1995-01-01\", end=\"2020-12-31\")['Adj Close']"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "cvE-kakn_0lx",
        "outputId": "9fc3211b-2ac5-4850-910e-1e5f626b9d88"
      },
      "source": [
        "mydata"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  SBUX         WMT       ORCL          HD\n",
              "Date                                                     \n",
              "1995-01-03    0.679083    6.736091   1.778506    6.333531\n",
              "1995-01-04    0.666390    6.978107   1.794061    6.298632\n",
              "1995-01-05    0.648936    7.058782   1.757766    6.263737\n",
              "1995-01-06    0.628311    7.099112   1.778506    6.246289\n",
              "1995-01-09    0.647349    7.058782   1.830358    6.333531\n",
              "...                ...         ...        ...         ...\n",
              "2020-12-23   99.195160  139.926331  63.821392  262.703430\n",
              "2020-12-24   99.146561  140.199890  63.489101  263.784241\n",
              "2020-12-28  101.411163  141.880325  63.401131  262.158234\n",
              "2020-12-29  102.664948  140.981476  63.000408  259.178802\n",
              "2020-12-30  102.995415  140.864227  62.941772  258.273346\n",
              "\n",
              "[6546 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8811c5f9-755b-4fca-a56e-b2aa8a971fee\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1995-01-03</th>\n",
              "      <td>0.679083</td>\n",
              "      <td>6.736091</td>\n",
              "      <td>1.778506</td>\n",
              "      <td>6.333531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-04</th>\n",
              "      <td>0.666390</td>\n",
              "      <td>6.978107</td>\n",
              "      <td>1.794061</td>\n",
              "      <td>6.298632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-05</th>\n",
              "      <td>0.648936</td>\n",
              "      <td>7.058782</td>\n",
              "      <td>1.757766</td>\n",
              "      <td>6.263737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-06</th>\n",
              "      <td>0.628311</td>\n",
              "      <td>7.099112</td>\n",
              "      <td>1.778506</td>\n",
              "      <td>6.246289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-09</th>\n",
              "      <td>0.647349</td>\n",
              "      <td>7.058782</td>\n",
              "      <td>1.830358</td>\n",
              "      <td>6.333531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>99.195160</td>\n",
              "      <td>139.926331</td>\n",
              "      <td>63.821392</td>\n",
              "      <td>262.703430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>99.146561</td>\n",
              "      <td>140.199890</td>\n",
              "      <td>63.489101</td>\n",
              "      <td>263.784241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>101.411163</td>\n",
              "      <td>141.880325</td>\n",
              "      <td>63.401131</td>\n",
              "      <td>262.158234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>102.664948</td>\n",
              "      <td>140.981476</td>\n",
              "      <td>63.000408</td>\n",
              "      <td>259.178802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>102.995415</td>\n",
              "      <td>140.864227</td>\n",
              "      <td>62.941772</td>\n",
              "      <td>258.273346</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6546 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8811c5f9-755b-4fca-a56e-b2aa8a971fee')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8811c5f9-755b-4fca-a56e-b2aa8a971fee 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-8811c5f9-755b-4fca-a56e-b2aa8a971fee');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVIWuWfRA7Lo"
      },
      "source": [
        "### What if we invest $1 in 1995?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "dHqwfZPC_0sf",
        "outputId": "82b0995b-0e4c-4ae1-b812-804cb05c1e5a"
      },
      "source": [
        "(mydata / mydata.iloc[0] * 1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  SBUX        WMT       ORCL         HD\n",
              "Date                                                   \n",
              "1995-01-03    1.000000   1.000000   1.000000   1.000000\n",
              "1995-01-04    0.981308   1.035928   1.008746   0.994490\n",
              "1995-01-05    0.955607   1.047905   0.988338   0.988980\n",
              "1995-01-06    0.925234   1.053892   1.000000   0.986225\n",
              "1995-01-09    0.953270   1.047905   1.029155   1.000000\n",
              "...                ...        ...        ...        ...\n",
              "2020-12-23  146.072220  20.772630  35.884833  41.478195\n",
              "2020-12-24  146.000654  20.813241  35.697996  41.648844\n",
              "2020-12-28  149.335449  21.062709  35.648533  41.392114\n",
              "2020-12-29  151.181739  20.929271  35.423219  40.921692\n",
              "2020-12-30  151.668377  20.911865  35.390249  40.778730\n",
              "\n",
              "[6546 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18237c69-faba-4750-8ba4-b3b3bc5107ad\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1995-01-03</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-04</th>\n",
              "      <td>0.981308</td>\n",
              "      <td>1.035928</td>\n",
              "      <td>1.008746</td>\n",
              "      <td>0.994490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-05</th>\n",
              "      <td>0.955607</td>\n",
              "      <td>1.047905</td>\n",
              "      <td>0.988338</td>\n",
              "      <td>0.988980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-06</th>\n",
              "      <td>0.925234</td>\n",
              "      <td>1.053892</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995-01-09</th>\n",
              "      <td>0.953270</td>\n",
              "      <td>1.047905</td>\n",
              "      <td>1.029155</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>146.072220</td>\n",
              "      <td>20.772630</td>\n",
              "      <td>35.884833</td>\n",
              "      <td>41.478195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>146.000654</td>\n",
              "      <td>20.813241</td>\n",
              "      <td>35.697996</td>\n",
              "      <td>41.648844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>149.335449</td>\n",
              "      <td>21.062709</td>\n",
              "      <td>35.648533</td>\n",
              "      <td>41.392114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>151.181739</td>\n",
              "      <td>20.929271</td>\n",
              "      <td>35.423219</td>\n",
              "      <td>40.921692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>151.668377</td>\n",
              "      <td>20.911865</td>\n",
              "      <td>35.390249</td>\n",
              "      <td>40.778730</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6546 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18237c69-faba-4750-8ba4-b3b3bc5107ad')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-18237c69-faba-4750-8ba4-b3b3bc5107ad 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-18237c69-faba-4750-8ba4-b3b3bc5107ad');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_QYfUjkBMD_"
      },
      "source": [
        "### What if we invest $100?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ESCGsZ9vmA-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "b39cc81d-8bd2-416e-dec1-2a295fbea833"
      },
      "source": [
        "(mydata / mydata.iloc[0] * 100).plot(figsize = (15, 6));\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljHrDMgiavrj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFHGo7zpavyS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNAe1wgJawi3",
        "outputId": "374912a3-9e9f-4109-a03c-04895812735e"
      },
      "source": [
        "tickers = ['SBUX', 'WMT', 'ORCL', 'HD']\n",
        "mydata = pd.DataFrame()\n",
        "for t in tickers:\n",
        "    mydata[t] = yf.download(t, start=\"2000-01-01\", end=\"2020-12-31\", interval=\"1wk\")['Adj Close']"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xihuAZpKav0r",
        "outputId": "47999264-64ed-47c1-8edf-e2d9c335776c"
      },
      "source": [
        "mydata[\"HD\"].pct_change().plot.hist(alpha=0.9, bins=50)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb75299e710>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JoVuwY7pOK-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8Tpzvgppqda",
        "outputId": "44388c37-3760-4d01-f019-321e7282cfbb"
      },
      "source": [
        "tickers = ['SBUX', 'WMT', 'ORCL', 'HD']\n",
        "mydata = pd.DataFrame()\n",
        "for t in tickers:\n",
        "    mydata[t] = yf.download(t, start=\"2000-01-01\", end=\"2020-12-31\", interval=\"1d\")['Adj Close']"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "oLI5-2rApzHH",
        "outputId": "50d66b92-3ff6-48bf-b7c6-76e23aec8b40"
      },
      "source": [
        "mydata"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  SBUX         WMT       ORCL          HD\n",
              "Date                                                     \n",
              "2000-01-03    2.503720   44.580292  24.806074   41.701782\n",
              "2000-01-04    2.424388   42.912189  22.614220   39.502758\n",
              "2000-01-05    2.456121   42.036449  21.419847   40.302418\n",
              "2000-01-06    2.544973   42.495148  20.159863   38.383232\n",
              "2000-01-07    2.532281   45.706284  21.708597   40.622265\n",
              "...                ...         ...        ...         ...\n",
              "2020-12-23   99.195160  139.926346  63.821404  262.703430\n",
              "2020-12-24   99.146568  140.199890  63.489098  263.784210\n",
              "2020-12-28  101.411163  141.880325  63.401127  262.158203\n",
              "2020-12-29  102.664940  140.981476  63.000412  259.178802\n",
              "2020-12-30  102.995407  140.864227  62.941772  258.273315\n",
              "\n",
              "[5283 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ee37c69f-c3e7-4f4d-bad3-aefe30b8ff8c\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>2.503720</td>\n",
              "      <td>44.580292</td>\n",
              "      <td>24.806074</td>\n",
              "      <td>41.701782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>2.424388</td>\n",
              "      <td>42.912189</td>\n",
              "      <td>22.614220</td>\n",
              "      <td>39.502758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>2.456121</td>\n",
              "      <td>42.036449</td>\n",
              "      <td>21.419847</td>\n",
              "      <td>40.302418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>2.544973</td>\n",
              "      <td>42.495148</td>\n",
              "      <td>20.159863</td>\n",
              "      <td>38.383232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>2.532281</td>\n",
              "      <td>45.706284</td>\n",
              "      <td>21.708597</td>\n",
              "      <td>40.622265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-23</th>\n",
              "      <td>99.195160</td>\n",
              "      <td>139.926346</td>\n",
              "      <td>63.821404</td>\n",
              "      <td>262.703430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-24</th>\n",
              "      <td>99.146568</td>\n",
              "      <td>140.199890</td>\n",
              "      <td>63.489098</td>\n",
              "      <td>263.784210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>101.411163</td>\n",
              "      <td>141.880325</td>\n",
              "      <td>63.401127</td>\n",
              "      <td>262.158203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>102.664940</td>\n",
              "      <td>140.981476</td>\n",
              "      <td>63.000412</td>\n",
              "      <td>259.178802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>102.995407</td>\n",
              "      <td>140.864227</td>\n",
              "      <td>62.941772</td>\n",
              "      <td>258.273315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5283 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee37c69f-c3e7-4f4d-bad3-aefe30b8ff8c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ee37c69f-c3e7-4f4d-bad3-aefe30b8ff8c 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-ee37c69f-c3e7-4f4d-bad3-aefe30b8ff8c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5z1xkg4Dqtyr"
      },
      "source": [
        "### resmaple of Pandas ###\n",
        "https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lssrduVAav4L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "09976434-7ad2-4e00-de63-7bcff18d75e2"
      },
      "source": [
        "mydata_m=mydata.resample('BM').last()\n",
        "mydata_m"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  SBUX         WMT       ORCL          HD\n",
              "Date                                                     \n",
              "2000-01-31    3.249441   36.531647  20.980167   36.224167\n",
              "2000-02-29    3.566770   32.528210  31.184801   36.783936\n",
              "2000-03-31    4.550488   37.746773  32.786026   41.292625\n",
              "2000-04-28    3.070152   36.995167  33.573517   36.171055\n",
              "2000-05-31    3.452532   38.498367  30.187281   31.369606\n",
              "...                ...         ...        ...         ...\n",
              "2020-08-31   81.713600  135.162766  55.698818  274.579224\n",
              "2020-09-30   83.116272  136.194595  58.112888  268.928955\n",
              "2020-10-30   84.122337  135.065430  54.839489  258.276794\n",
              "2020-11-30   95.268570  148.732544  56.413033  268.638489\n",
              "2020-12-31  102.995407  140.864227  62.941772  258.273315\n",
              "\n",
              "[252 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb2d3d12-e7db-4fed-a280-c3db230baee1\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-31</th>\n",
              "      <td>3.249441</td>\n",
              "      <td>36.531647</td>\n",
              "      <td>20.980167</td>\n",
              "      <td>36.224167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-29</th>\n",
              "      <td>3.566770</td>\n",
              "      <td>32.528210</td>\n",
              "      <td>31.184801</td>\n",
              "      <td>36.783936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-31</th>\n",
              "      <td>4.550488</td>\n",
              "      <td>37.746773</td>\n",
              "      <td>32.786026</td>\n",
              "      <td>41.292625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-28</th>\n",
              "      <td>3.070152</td>\n",
              "      <td>36.995167</td>\n",
              "      <td>33.573517</td>\n",
              "      <td>36.171055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-31</th>\n",
              "      <td>3.452532</td>\n",
              "      <td>38.498367</td>\n",
              "      <td>30.187281</td>\n",
              "      <td>31.369606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>81.713600</td>\n",
              "      <td>135.162766</td>\n",
              "      <td>55.698818</td>\n",
              "      <td>274.579224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>83.116272</td>\n",
              "      <td>136.194595</td>\n",
              "      <td>58.112888</td>\n",
              "      <td>268.928955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-30</th>\n",
              "      <td>84.122337</td>\n",
              "      <td>135.065430</td>\n",
              "      <td>54.839489</td>\n",
              "      <td>258.276794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-30</th>\n",
              "      <td>95.268570</td>\n",
              "      <td>148.732544</td>\n",
              "      <td>56.413033</td>\n",
              "      <td>268.638489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>102.995407</td>\n",
              "      <td>140.864227</td>\n",
              "      <td>62.941772</td>\n",
              "      <td>258.273315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb2d3d12-e7db-4fed-a280-c3db230baee1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fb2d3d12-e7db-4fed-a280-c3db230baee1 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-fb2d3d12-e7db-4fed-a280-c3db230baee1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iE7jZsp8_sWc"
      },
      "source": [
        "returns=mydata_m.pct_change()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "6Ztxqfk8U5-v",
        "outputId": "e31f0449-7d87-44ad-9d5f-a28ff79b9cd2"
      },
      "source": [
        "returns"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SBUX       WMT      ORCL        HD\n",
              "Date                                              \n",
              "2000-01-31       NaN       NaN       NaN       NaN\n",
              "2000-02-29  0.097656 -0.109588  0.486394  0.015453\n",
              "2000-03-31  0.275801  0.160432  0.051346  0.122572\n",
              "2000-04-28 -0.325314 -0.019912  0.024019 -0.124031\n",
              "2000-05-31  0.124548  0.040632 -0.100860 -0.132743\n",
              "...              ...       ...       ...       ...\n",
              "2020-08-31  0.109755  0.077441  0.031921  0.073637\n",
              "2020-09-30  0.017166  0.007634  0.043341 -0.020578\n",
              "2020-10-30  0.012104 -0.008291 -0.056328 -0.039610\n",
              "2020-11-30  0.132500  0.101189  0.028694  0.040119\n",
              "2020-12-31  0.081106 -0.052902  0.115731 -0.038584\n",
              "\n",
              "[252 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c58aa6c6-3ed7-43ce-9c9c-4817ba4ed920\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-29</th>\n",
              "      <td>0.097656</td>\n",
              "      <td>-0.109588</td>\n",
              "      <td>0.486394</td>\n",
              "      <td>0.015453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-31</th>\n",
              "      <td>0.275801</td>\n",
              "      <td>0.160432</td>\n",
              "      <td>0.051346</td>\n",
              "      <td>0.122572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-28</th>\n",
              "      <td>-0.325314</td>\n",
              "      <td>-0.019912</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>-0.124031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-31</th>\n",
              "      <td>0.124548</td>\n",
              "      <td>0.040632</td>\n",
              "      <td>-0.100860</td>\n",
              "      <td>-0.132743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>0.109755</td>\n",
              "      <td>0.077441</td>\n",
              "      <td>0.031921</td>\n",
              "      <td>0.073637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>0.017166</td>\n",
              "      <td>0.007634</td>\n",
              "      <td>0.043341</td>\n",
              "      <td>-0.020578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-30</th>\n",
              "      <td>0.012104</td>\n",
              "      <td>-0.008291</td>\n",
              "      <td>-0.056328</td>\n",
              "      <td>-0.039610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-30</th>\n",
              "      <td>0.132500</td>\n",
              "      <td>0.101189</td>\n",
              "      <td>0.028694</td>\n",
              "      <td>0.040119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>0.081106</td>\n",
              "      <td>-0.052902</td>\n",
              "      <td>0.115731</td>\n",
              "      <td>-0.038584</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c58aa6c6-3ed7-43ce-9c9c-4817ba4ed920')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c58aa6c6-3ed7-43ce-9c9c-4817ba4ed920 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-c58aa6c6-3ed7-43ce-9c9c-4817ba4ed920');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKNYVSijUNmm"
      },
      "source": [
        "returns2=returns.dropna()"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "5RoMzVxyT4Yd",
        "outputId": "5e5b0d77-3ac5-424d-cb00-9a8d4d390d7a"
      },
      "source": [
        "returns2"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                SBUX       WMT      ORCL        HD\n",
              "Date                                              \n",
              "2000-02-29  0.097656 -0.109588  0.486394  0.015453\n",
              "2000-03-31  0.275801  0.160432  0.051346  0.122572\n",
              "2000-04-28 -0.325314 -0.019912  0.024019 -0.124031\n",
              "2000-05-31  0.124548  0.040632 -0.100860 -0.132743\n",
              "2000-06-30  0.123162  0.001092  0.169565  0.020020\n",
              "...              ...       ...       ...       ...\n",
              "2020-08-31  0.109755  0.077441  0.031921  0.073637\n",
              "2020-09-30  0.017166  0.007634  0.043341 -0.020578\n",
              "2020-10-30  0.012104 -0.008291 -0.056328 -0.039610\n",
              "2020-11-30  0.132500  0.101189  0.028694  0.040119\n",
              "2020-12-31  0.081106 -0.052902  0.115731 -0.038584\n",
              "\n",
              "[251 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7751bf5e-ad81-465f-bc7b-e0be78083697\">\n",
              "    <div 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>SBUX</th>\n",
              "      <th>WMT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>HD</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-02-29</th>\n",
              "      <td>0.097656</td>\n",
              "      <td>-0.109588</td>\n",
              "      <td>0.486394</td>\n",
              "      <td>0.015453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-31</th>\n",
              "      <td>0.275801</td>\n",
              "      <td>0.160432</td>\n",
              "      <td>0.051346</td>\n",
              "      <td>0.122572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-28</th>\n",
              "      <td>-0.325314</td>\n",
              "      <td>-0.019912</td>\n",
              "      <td>0.024019</td>\n",
              "      <td>-0.124031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-31</th>\n",
              "      <td>0.124548</td>\n",
              "      <td>0.040632</td>\n",
              "      <td>-0.100860</td>\n",
              "      <td>-0.132743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-06-30</th>\n",
              "      <td>0.123162</td>\n",
              "      <td>0.001092</td>\n",
              "      <td>0.169565</td>\n",
              "      <td>0.020020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>0.109755</td>\n",
              "      <td>0.077441</td>\n",
              "      <td>0.031921</td>\n",
              "      <td>0.073637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-30</th>\n",
              "      <td>0.017166</td>\n",
              "      <td>0.007634</td>\n",
              "      <td>0.043341</td>\n",
              "      <td>-0.020578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-30</th>\n",
              "      <td>0.012104</td>\n",
              "      <td>-0.008291</td>\n",
              "      <td>-0.056328</td>\n",
              "      <td>-0.039610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-30</th>\n",
              "      <td>0.132500</td>\n",
              "      <td>0.101189</td>\n",
              "      <td>0.028694</td>\n",
              "      <td>0.040119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>0.081106</td>\n",
              "      <td>-0.052902</td>\n",
              "      <td>0.115731</td>\n",
              "      <td>-0.038584</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>251 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7751bf5e-ad81-465f-bc7b-e0be78083697')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7751bf5e-ad81-465f-bc7b-e0be78083697 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-7751bf5e-ad81-465f-bc7b-e0be78083697');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzKlKooaV-Lg"
      },
      "source": [
        "sbux_stock = returns2[\"SBUX\"]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHXXSQd0WQkc",
        "outputId": "28b97a09-2b0a-4243-aba2-23e7df63a125"
      },
      "source": [
        "sbux_stock"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2000-02-29    0.097656\n",
              "2000-03-31    0.275801\n",
              "2000-04-28   -0.325314\n",
              "2000-05-31    0.124548\n",
              "2000-06-30    0.123162\n",
              "                ...   \n",
              "2020-08-31    0.109755\n",
              "2020-09-30    0.017166\n",
              "2020-10-30    0.012104\n",
              "2020-11-30    0.132500\n",
              "2020-12-31    0.081106\n",
              "Freq: BM, Name: SBUX, Length: 251, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "9UTBo9PmWVA4",
        "outputId": "81d9e70f-69fe-43dd-c562-35fd0a776c58"
      },
      "source": [
        "plt.hist(sbux_stock,bins=25)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 2.,  0.,  0.,  0.,  2.,  0.,  2.,  4.,  5., 13., 16., 26., 27.,\n",
              "        40., 34., 28., 17., 15.,  9.,  5.,  1.,  1.,  1.,  2.,  1.]),\n",
              " array([-0.3253137 , -0.30023997, -0.27516623, -0.25009249, -0.22501875,\n",
              "        -0.19994501, -0.17487127, -0.14979753, -0.12472379, -0.09965006,\n",
              "        -0.07457632, -0.04950258, -0.02442884,  0.0006449 ,  0.02571864,\n",
              "         0.05079238,  0.07586612,  0.10093986,  0.12601359,  0.15108733,\n",
              "         0.17616107,  0.20123481,  0.22630855,  0.25138229,  0.27645603,\n",
              "         0.30152977]),\n",
              " <a list of 25 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8reFtDksWbqU",
        "outputId": "91d33f70-d6a1-4864-c13e-45e00ce97271"
      },
      "source": [
        "p=np.percentile(sbux_stock, 50)\n",
        "p"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020890229606218558"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBHMvToqds8I",
        "outputId": "0c8ffc9f-6c99-4d98-abde-8f3ac25e1901"
      },
      "source": [
        "sbux_stock.median()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020890229606218558"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XcYMWb_-eXIy",
        "outputId": "3691feff-7aff-4e51-915e-bb3d94601cf9"
      },
      "source": [
        "p=np.percentile(sbux_stock, 95)\n",
        "p"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14582886300958497"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxAYdO45eY6B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}