covid_fin.ipynb

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  "metadata": {
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      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PragyaGupta2912/Financial-Impact-of-Covid-19/blob/main/covid_fin.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBsz77b5iRIv"
      },
      "source": [
        "\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_k0-AHsjPPE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b70a1cae-1e38-41f4-ce6f-98a44d6bf349"
      },
      "source": [
        "!pip install yfinance"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.67)\n",
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            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFc-xHWkjrWt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3491e805-4154-4174-916d-9a122d3ea2db"
      },
      "source": [
        "!pip install --upgrade pandas-datareader"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.7/dist-packages (0.9.0)\n",
            "Collecting pandas-datareader\n",
            "  Downloading pandas_datareader-0.10.0-py3-none-any.whl (109 kB)\n",
            "\u001b[?25l\r\u001b[K     |███                             | 10 kB 19.6 MB/s eta 0:00:01\r\u001b[K     |██████                          | 20 kB 21.2 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 30 kB 22.8 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 40 kB 23.9 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 51 kB 20.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 61 kB 12.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 71 kB 12.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 81 kB 13.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 92 kB 13.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 102 kB 14.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 109 kB 14.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (1.1.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (2.23.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from pandas-datareader) (4.6.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->pandas-datareader) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas-datareader) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
            "Installing collected packages: pandas-datareader\n",
            "  Attempting uninstall: pandas-datareader\n",
            "    Found existing installation: pandas-datareader 0.9.0\n",
            "    Uninstalling pandas-datareader-0.9.0:\n",
            "      Successfully uninstalled pandas-datareader-0.9.0\n",
            "Successfully installed pandas-datareader-0.10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhKah1UMj2y7"
      },
      "source": [
        "from datetime import *\n",
        "import pandas_datareader.data as data\n",
        "\n",
        "import datetime as datetime\n",
        "import math\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pandas_datareader.data as data\n",
        "from pandas.plotting import scatter_matrix\n",
        "from pandas import Series, DataFrame\n",
        "import copy\n",
        "\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "from sklearn import preprocessing\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "import matplotlib as mpl\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KO2C68T_mVUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "b15d41b5-a795-4cd9-bbb6-84034d242cce"
      },
      "source": [
        "# setting the timeframe for our data\n",
        "past = datetime.datetime(2020, 1, 1)\n",
        "present = datetime.datetime(2021, 1, 1)\n",
        "\n",
        "# Yahoo API google finance data\n",
        "google2020 = data.DataReader(\"GOOG\", 'yahoo', start=past, end=present)\n",
        "google2020.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2020-12-24</th>\n",
              "      <td>1746.000000</td>\n",
              "      <td>1729.109985</td>\n",
              "      <td>1735.000000</td>\n",
              "      <td>1738.849976</td>\n",
              "      <td>346800</td>\n",
              "      <td>1738.849976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>1790.728027</td>\n",
              "      <td>1746.334961</td>\n",
              "      <td>1751.635010</td>\n",
              "      <td>1776.089966</td>\n",
              "      <td>1393000</td>\n",
              "      <td>1776.089966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>1792.439941</td>\n",
              "      <td>1756.089966</td>\n",
              "      <td>1787.790039</td>\n",
              "      <td>1758.719971</td>\n",
              "      <td>1299400</td>\n",
              "      <td>1758.719971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>1765.094971</td>\n",
              "      <td>1725.599976</td>\n",
              "      <td>1762.010010</td>\n",
              "      <td>1739.520020</td>\n",
              "      <td>1306100</td>\n",
              "      <td>1739.520020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-31</th>\n",
              "      <td>1758.930054</td>\n",
              "      <td>1735.420044</td>\n",
              "      <td>1735.420044</td>\n",
              "      <td>1751.880005</td>\n",
              "      <td>1011900</td>\n",
              "      <td>1751.880005</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   High          Low  ...   Volume    Adj Close\n",
              "Date                                  ...                      \n",
              "2020-12-24  1746.000000  1729.109985  ...   346800  1738.849976\n",
              "2020-12-28  1790.728027  1746.334961  ...  1393000  1776.089966\n",
              "2020-12-29  1792.439941  1756.089966  ...  1299400  1758.719971\n",
              "2020-12-30  1765.094971  1725.599976  ...  1306100  1739.520020\n",
              "2020-12-31  1758.930054  1735.420044  ...  1011900  1751.880005\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ba4ft72MmaXH"
      },
      "source": [
        "# moving average mean of closing data for past 6 months\n",
        "googleClose2020 = google2020['Adj Close']\n",
        "#avgMean = googleClose.rolling(window=180).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "wbH_4mbYmy86",
        "outputId": "33231f9b-cb26-4118-faa3-97f7ff27f6b7"
      },
      "source": [
        "# matplotlib plot of Google data\n",
        "mpl.rc('figure', figsize=(8, 7))\n",
        "mpl.__version__\n",
        "\n",
        "\n",
        "style.use('ggplot')\n",
        "googleClose2020.plot(label='GOOG')\n",
        "#avgMean.plot(label='avgMean')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd70c26eed0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "YiOblIdVTUQj",
        "outputId": "dc53e231-968d-497b-8033-72a951cb98d3"
      },
      "source": [
        "past = datetime.datetime(2015, 1, 1)\n",
        "present = datetime.datetime(2020, 4, 1)\n",
        "\n",
        "# Yahoo API google finance data\n",
        "google = data.DataReader(\"GOOG\", 'yahoo', start=past, end=present)\n",
        "google.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2020-03-26</th>\n",
              "      <td>1169.969971</td>\n",
              "      <td>1093.530029</td>\n",
              "      <td>1111.800049</td>\n",
              "      <td>1161.750000</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>1161.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>1150.670044</td>\n",
              "      <td>1105.910034</td>\n",
              "      <td>1125.670044</td>\n",
              "      <td>1110.709961</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>1110.709961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>1151.630005</td>\n",
              "      <td>1096.479980</td>\n",
              "      <td>1125.040039</td>\n",
              "      <td>1146.819946</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>1146.819946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>1175.310059</td>\n",
              "      <td>1138.140015</td>\n",
              "      <td>1147.300049</td>\n",
              "      <td>1162.810059</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>1162.810059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1129.689941</td>\n",
              "      <td>1097.449951</td>\n",
              "      <td>1122.000000</td>\n",
              "      <td>1105.619995</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>1105.619995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   High          Low  ...     Volume    Adj Close\n",
              "Date                                  ...                        \n",
              "2020-03-26  1169.969971  1093.530029  ...  3571700.0  1161.750000\n",
              "2020-03-27  1150.670044  1105.910034  ...  3208500.0  1110.709961\n",
              "2020-03-30  1151.630005  1096.479980  ...  2574100.0  1146.819946\n",
              "2020-03-31  1175.310059  1138.140015  ...  2486400.0  1162.810059\n",
              "2020-04-01  1129.689941  1097.449951  ...  2344200.0  1105.619995\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r59Ia4kEPE14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "4f2e585e-121d-4ad4-c550-beb4e9d90421"
      },
      "source": [
        "googleClose = google['Adj Close']\n",
        "\n",
        "mpl.rc('figure', figsize=(8, 7))\n",
        "mpl.__version__\n",
        "\n",
        "\n",
        "style.use('ggplot')\n",
        "googleClose.plot(label='GOOG')\n",
        "#avgMean.plot(label='avgMean')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd70c379dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtgKln9rnD2m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "99619b17-f706-4014-9f11-452c1d0bc466"
      },
      "source": [
        "# returns\n",
        "returns = googleClose / googleClose.shift(1) - 1\n",
        "returns.plot(label='return')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd70c38fdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "g9TNhd1An6zg",
        "outputId": "b26734e9-6a0d-4d4b-83bd-4b394f2182c3"
      },
      "source": [
        "# Yahoo finance API data \n",
        "past = datetime.datetime(2015, 1, 1)\n",
        "present = datetime.datetime(2020, 4, 1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Attributes</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">High</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Low</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>2020-03-26</th>\n",
              "      <td>63.815430</td>\n",
              "      <td>64.549179</td>\n",
              "      <td>1161.750000</td>\n",
              "      <td>98.654610</td>\n",
              "      <td>153.607895</td>\n",
              "      <td>64.610001</td>\n",
              "      <td>64.959999</td>\n",
              "      <td>1161.750000</td>\n",
              "      <td>107.925430</td>\n",
              "      <td>156.110001</td>\n",
              "      <td>64.669998</td>\n",
              "      <td>66.400002</td>\n",
              "      <td>1169.969971</td>\n",
              "      <td>108.173996</td>\n",
              "      <td>156.660004</td>\n",
              "      <td>61.590000</td>\n",
              "      <td>60.080002</td>\n",
              "      <td>1093.530029</td>\n",
              "      <td>100.927345</td>\n",
              "      <td>148.369995</td>\n",
              "      <td>61.630001</td>\n",
              "      <td>61.040001</td>\n",
              "      <td>1111.800049</td>\n",
              "      <td>102.208412</td>\n",
              "      <td>148.399994</td>\n",
              "      <td>252087200.0</td>\n",
              "      <td>15397613.0</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>7482561.0</td>\n",
              "      <td>64568100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>61.173321</td>\n",
              "      <td>60.574471</td>\n",
              "      <td>1110.709961</td>\n",
              "      <td>94.407455</td>\n",
              "      <td>147.300644</td>\n",
              "      <td>61.935001</td>\n",
              "      <td>60.959999</td>\n",
              "      <td>1110.709961</td>\n",
              "      <td>103.279160</td>\n",
              "      <td>149.699997</td>\n",
              "      <td>63.967499</td>\n",
              "      <td>62.959999</td>\n",
              "      <td>1150.670044</td>\n",
              "      <td>106.596558</td>\n",
              "      <td>154.889999</td>\n",
              "      <td>61.762501</td>\n",
              "      <td>60.320000</td>\n",
              "      <td>1105.910034</td>\n",
              "      <td>102.915871</td>\n",
              "      <td>149.199997</td>\n",
              "      <td>63.187500</td>\n",
              "      <td>61.439999</td>\n",
              "      <td>1125.670044</td>\n",
              "      <td>103.804970</td>\n",
              "      <td>151.750000</td>\n",
              "      <td>204216800.0</td>\n",
              "      <td>11662375.0</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>6718458.0</td>\n",
              "      <td>57042300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>62.919079</td>\n",
              "      <td>62.720810</td>\n",
              "      <td>1146.819946</td>\n",
              "      <td>98.689575</td>\n",
              "      <td>157.661865</td>\n",
              "      <td>63.702499</td>\n",
              "      <td>63.119999</td>\n",
              "      <td>1146.819946</td>\n",
              "      <td>107.963669</td>\n",
              "      <td>160.229996</td>\n",
              "      <td>63.880001</td>\n",
              "      <td>63.520000</td>\n",
              "      <td>1151.630005</td>\n",
              "      <td>108.470360</td>\n",
              "      <td>160.600006</td>\n",
              "      <td>62.349998</td>\n",
              "      <td>58.799999</td>\n",
              "      <td>1096.479980</td>\n",
              "      <td>103.068832</td>\n",
              "      <td>150.009995</td>\n",
              "      <td>62.685001</td>\n",
              "      <td>60.320000</td>\n",
              "      <td>1125.040039</td>\n",
              "      <td>103.336517</td>\n",
              "      <td>152.440002</td>\n",
              "      <td>167976400.0</td>\n",
              "      <td>10856275.0</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>5820467.0</td>\n",
              "      <td>63420300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>62.790680</td>\n",
              "      <td>63.118279</td>\n",
              "      <td>1162.810059</td>\n",
              "      <td>96.941780</td>\n",
              "      <td>155.182251</td>\n",
              "      <td>63.572498</td>\n",
              "      <td>63.520000</td>\n",
              "      <td>1162.810059</td>\n",
              "      <td>106.051628</td>\n",
              "      <td>157.710007</td>\n",
              "      <td>65.622498</td>\n",
              "      <td>65.440002</td>\n",
              "      <td>1175.310059</td>\n",
              "      <td>108.804970</td>\n",
              "      <td>164.779999</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>62.560001</td>\n",
              "      <td>1138.140015</td>\n",
              "      <td>105.325050</td>\n",
              "      <td>156.559998</td>\n",
              "      <td>63.900002</td>\n",
              "      <td>62.959999</td>\n",
              "      <td>1147.300049</td>\n",
              "      <td>107.074570</td>\n",
              "      <td>159.399994</td>\n",
              "      <td>197002000.0</td>\n",
              "      <td>15143738.0</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>6635092.0</td>\n",
              "      <td>77927200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>59.486820</td>\n",
              "      <td>55.963814</td>\n",
              "      <td>1105.619995</td>\n",
              "      <td>91.881889</td>\n",
              "      <td>149.672028</td>\n",
              "      <td>60.227501</td>\n",
              "      <td>56.320000</td>\n",
              "      <td>1105.619995</td>\n",
              "      <td>100.516251</td>\n",
              "      <td>152.110001</td>\n",
              "      <td>62.180000</td>\n",
              "      <td>60.400002</td>\n",
              "      <td>1129.689941</td>\n",
              "      <td>105.086044</td>\n",
              "      <td>157.750000</td>\n",
              "      <td>59.782501</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>1097.449951</td>\n",
              "      <td>99.923515</td>\n",
              "      <td>150.820007</td>\n",
              "      <td>61.625000</td>\n",
              "      <td>60.160000</td>\n",
              "      <td>1122.000000</td>\n",
              "      <td>101.682602</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>176218400.0</td>\n",
              "      <td>12444613.0</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>6394093.0</td>\n",
              "      <td>57969900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Attributes  Adj Close                          ...     Volume                       \n",
              "Symbols          AAPL         GE         GOOG  ...       GOOG        IBM        MSFT\n",
              "Date                                           ...                                  \n",
              "2020-03-26  63.815430  64.549179  1161.750000  ...  3571700.0  7482561.0  64568100.0\n",
              "2020-03-27  61.173321  60.574471  1110.709961  ...  3208500.0  6718458.0  57042300.0\n",
              "2020-03-30  62.919079  62.720810  1146.819946  ...  2574100.0  5820467.0  63420300.0\n",
              "2020-03-31  62.790680  63.118279  1162.810059  ...  2486400.0  6635092.0  77927200.0\n",
              "2020-04-01  59.486820  55.963814  1105.619995  ...  2344200.0  6394093.0  57969900.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "KBb9tWRl_QXK",
        "outputId": "e83b5c83-57a1-43fa-bbdf-95b5774092db"
      },
      "source": [
        "df = data.DataReader(['AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],'yahoo',start=past,end=present)['Adj Close']\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>63.815422</td>\n",
              "      <td>64.549171</td>\n",
              "      <td>1161.750000</td>\n",
              "      <td>98.654617</td>\n",
              "      <td>153.607910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>61.173321</td>\n",
              "      <td>60.574467</td>\n",
              "      <td>1110.709961</td>\n",
              "      <td>94.407463</td>\n",
              "      <td>147.300629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>62.919083</td>\n",
              "      <td>62.720810</td>\n",
              "      <td>1146.819946</td>\n",
              "      <td>98.689568</td>\n",
              "      <td>157.661865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>62.790676</td>\n",
              "      <td>63.118290</td>\n",
              "      <td>1162.810059</td>\n",
              "      <td>96.941780</td>\n",
              "      <td>155.182281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>59.486820</td>\n",
              "      <td>55.963818</td>\n",
              "      <td>1105.619995</td>\n",
              "      <td>91.881889</td>\n",
              "      <td>149.672012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols          AAPL         GE         GOOG        IBM        MSFT\n",
              "Date                                                                \n",
              "2020-03-26  63.815422  64.549171  1161.750000  98.654617  153.607910\n",
              "2020-03-27  61.173321  60.574467  1110.709961  94.407463  147.300629\n",
              "2020-03-30  62.919083  62.720810  1146.819946  98.689568  157.661865\n",
              "2020-03-31  62.790676  63.118290  1162.810059  96.941780  155.182281\n",
              "2020-04-01  59.486820  55.963818  1105.619995  91.881889  149.672012"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "JDWqEpXIoKlX",
        "outputId": "259fcdd8-f956-40ad-dc33-15b024df343b"
      },
      "source": [
        "\n",
        "# correlation plot\n",
        "correlation = df.pct_change()\n",
        "corr = correlation.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</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>AAPL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.426416</td>\n",
              "      <td>0.612287</td>\n",
              "      <td>0.520649</td>\n",
              "      <td>0.675125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>0.426416</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.387735</td>\n",
              "      <td>0.486742</td>\n",
              "      <td>0.429476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>0.612287</td>\n",
              "      <td>0.387735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511889</td>\n",
              "      <td>0.721428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBM</th>\n",
              "      <td>0.520649</td>\n",
              "      <td>0.486742</td>\n",
              "      <td>0.511889</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.588914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.675125</td>\n",
              "      <td>0.429476</td>\n",
              "      <td>0.721428</td>\n",
              "      <td>0.588914</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols      AAPL        GE      GOOG       IBM      MSFT\n",
              "Symbols                                                  \n",
              "AAPL     1.000000  0.426416  0.612287  0.520649  0.675125\n",
              "GE       0.426416  1.000000  0.387735  0.486742  0.429476\n",
              "GOOG     0.612287  0.387735  1.000000  0.511889  0.721428\n",
              "IBM      0.520649  0.486742  0.511889  1.000000  0.588914\n",
              "MSFT     0.675125  0.429476  0.721428  0.588914  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "BmQmUGeyo2nq",
        "outputId": "421fac9b-16eb-4cce-d2d7-740defa3cda2"
      },
      "source": [
        "plt.scatter(correlation.GOOG, correlation.MSFT)\n",
        "plt.xlabel('Google Returns')\n",
        "plt.ylabel('Microsoft Returns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Microsoft Returns')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "3F7iQWK5WAVj",
        "outputId": "bb68e82f-059d-47f9-9423-38ede7f0ca1c"
      },
      "source": [
        "plt.scatter(correlation.GE, correlation.AAPL)\n",
        "plt.xlabel('GE Returns')\n",
        "plt.ylabel('Apple Returns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Apple Returns')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAKrR7Smo6gA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "6dc667bb-c4f0-495d-8d07-3cdb5fbce5f8"
      },
      "source": [
        "plt.imshow(corr, cmap='hot', interpolation='none')\n",
        "plt.colorbar()\n",
        "plt.xticks(range(len(corr)), corr.columns)\n",
        "plt.yticks(range(len(corr)), corr.columns);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "HJP_GLPQi8i7",
        "outputId": "540c7cab-3289-4f3e-8b4f-72b12d0688eb"
      },
      "source": [
        "plt.scatter(correlation.mean(), correlation.std())\n",
        "plt.xlabel('Expected returns')\n",
        "plt.ylabel('Risk')\n",
        "for label, x, y in zip(correlation.columns, correlation.mean(), correlation.std()):\n",
        "    plt.annotate(\n",
        "        label, \n",
        "        xy = (x, y), xytext = (20, -20),\n",
        "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
        "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
        "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "e8CUtjWDj4RK",
        "outputId": "7f9c037d-abef-410a-d47c-98a0b67a3356"
      },
      "source": [
        "\n",
        "google.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2020-03-26</th>\n",
              "      <td>1169.969971</td>\n",
              "      <td>1093.530029</td>\n",
              "      <td>1111.800049</td>\n",
              "      <td>1161.750000</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>1161.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>1150.670044</td>\n",
              "      <td>1105.910034</td>\n",
              "      <td>1125.670044</td>\n",
              "      <td>1110.709961</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>1110.709961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>1151.630005</td>\n",
              "      <td>1096.479980</td>\n",
              "      <td>1125.040039</td>\n",
              "      <td>1146.819946</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>1146.819946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>1175.310059</td>\n",
              "      <td>1138.140015</td>\n",
              "      <td>1147.300049</td>\n",
              "      <td>1162.810059</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>1162.810059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1129.689941</td>\n",
              "      <td>1097.449951</td>\n",
              "      <td>1122.000000</td>\n",
              "      <td>1105.619995</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>1105.619995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   High          Low  ...     Volume    Adj Close\n",
              "Date                                  ...                        \n",
              "2020-03-26  1169.969971  1093.530029  ...  3571700.0  1161.750000\n",
              "2020-03-27  1150.670044  1105.910034  ...  3208500.0  1110.709961\n",
              "2020-03-30  1151.630005  1096.479980  ...  2574100.0  1146.819946\n",
              "2020-03-31  1175.310059  1138.140015  ...  2486400.0  1162.810059\n",
              "2020-04-01  1129.689941  1097.449951  ...  2344200.0  1105.619995\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "AKgSm8OXj-rg",
        "outputId": "ec97d494-ab4f-431b-f80b-cd784bc90b44"
      },
      "source": [
        "dfFeat = google.loc[:,['Adj Close','Volume']]\n",
        "dfFeat['HL_PCT'] = (google['High'] - google['Low']) / google['Close'] * 100.0\n",
        "dfFeat['PCT_change'] = (google['Close'] - google['Open']) / google['Open'] * 100.0\n",
        "dfFeat.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>HL_PCT</th>\n",
              "      <th>PCT_change</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>2020-03-26</th>\n",
              "      <td>1161.750000</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>6.579724</td>\n",
              "      <td>4.492710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>1110.709961</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>4.029856</td>\n",
              "      <td>-1.328994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>1146.819946</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>4.808952</td>\n",
              "      <td>1.935923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>1162.810059</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>3.196571</td>\n",
              "      <td>1.351870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1105.619995</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>2.916010</td>\n",
              "      <td>-1.459893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Adj Close     Volume    HL_PCT  PCT_change\n",
              "Date                                                    \n",
              "2020-03-26  1161.750000  3571700.0  6.579724    4.492710\n",
              "2020-03-27  1110.709961  3208500.0  4.029856   -1.328994\n",
              "2020-03-30  1146.819946  2574100.0  4.808952    1.935923\n",
              "2020-03-31  1162.810059  2486400.0  3.196571    1.351870\n",
              "2020-04-01  1105.619995  2344200.0  2.916010   -1.459893"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "JG52hWNfkTmK",
        "outputId": "400be068-e27e-4763-846d-1d0aa82f491a"
      },
      "source": [
        "dfFeat.fillna(value=-99999, inplace=True)\n",
        "# forecasting 20% of data and predicting the AdjCllose\n",
        "forecast_col = 'Adj Close'\n",
        "forecast_out = int(math.ceil(0.2 * len(dfFeat)))\n",
        "dfFeat['label'] = dfFeat[forecast_col].shift(forecast_out)\n",
        "dfFeat_noDrop = dfFeat\n",
        "dfFeat.dropna(inplace=True)\n",
        "dfFeat.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>HL_PCT</th>\n",
              "      <th>PCT_change</th>\n",
              "      <th>label</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>1161.750000</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>6.579724</td>\n",
              "      <td>4.492710</td>\n",
              "      <td>1142.319946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>1110.709961</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>4.029856</td>\n",
              "      <td>-1.328994</td>\n",
              "      <td>1175.760010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>1146.819946</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>4.808952</td>\n",
              "      <td>1.935923</td>\n",
              "      <td>1193.199951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>1162.810059</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>3.196571</td>\n",
              "      <td>1.351870</td>\n",
              "      <td>1193.319946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1105.619995</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>2.916010</td>\n",
              "      <td>-1.459893</td>\n",
              "      <td>1185.550049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Adj Close     Volume    HL_PCT  PCT_change        label\n",
              "Date                                                                 \n",
              "2020-03-26  1161.750000  3571700.0  6.579724    4.492710  1142.319946\n",
              "2020-03-27  1110.709961  3208500.0  4.029856   -1.328994  1175.760010\n",
              "2020-03-30  1146.819946  2574100.0  4.808952    1.935923  1193.199951\n",
              "2020-03-31  1162.810059  2486400.0  3.196571    1.351870  1193.319946\n",
              "2020-04-01  1105.619995  2344200.0  2.916010   -1.459893  1185.550049"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8kTpKgekWqa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04997b15-3dd9-4b4c-8b63-6302b7c0bbc7"
      },
      "source": [
        "forecast_out"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "265"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUI7E4dqOJB3"
      },
      "source": [
        "X = np.array(dfFeat.drop(['label'],1))\n",
        "y = np.array(dfFeat['label'])\n",
        "X = preprocessing.scale(X)\n",
        "X_late = X[-forecast_out:]\n",
        "y = np.array(dfFeat['label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hz4_QRNKHIIw"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlDsovC-HKo0",
        "outputId": "70075809-1206-4cf7-cf0e-1bb1399e09fd"
      },
      "source": [
        "# Linear regression\n",
        "linearReg = LinearRegression()\n",
        "linearReg.fit(X_train, y_train)\n",
        "\n",
        "y_predict = linearReg.predict(X_test)\n",
        "accuracy = linearReg.score(X_test, y_test)\n",
        "\n",
        "print(\"Prediction Accuracy: %.1f%%\" % (accuracy * 100.0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction Accuracy: 74.9%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0bfMoHTHQX3"
      },
      "source": [
        "forecastStock = linearReg.predict(X_late)\n",
        "dfFeat['Forecast'] = np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "3jTLjnolHTEr",
        "outputId": "74612b73-f700-41cf-c1f5-57511b13af75"
      },
      "source": [
        "dfFeat.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>HL_PCT</th>\n",
              "      <th>PCT_change</th>\n",
              "      <th>label</th>\n",
              "      <th>Forecast</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>2020-03-26</th>\n",
              "      <td>1161.750000</td>\n",
              "      <td>3571700.0</td>\n",
              "      <td>6.579724</td>\n",
              "      <td>4.492710</td>\n",
              "      <td>1142.319946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-27</th>\n",
              "      <td>1110.709961</td>\n",
              "      <td>3208500.0</td>\n",
              "      <td>4.029856</td>\n",
              "      <td>-1.328994</td>\n",
              "      <td>1175.760010</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-30</th>\n",
              "      <td>1146.819946</td>\n",
              "      <td>2574100.0</td>\n",
              "      <td>4.808952</td>\n",
              "      <td>1.935923</td>\n",
              "      <td>1193.199951</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>1162.810059</td>\n",
              "      <td>2486400.0</td>\n",
              "      <td>3.196571</td>\n",
              "      <td>1.351870</td>\n",
              "      <td>1193.319946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1105.619995</td>\n",
              "      <td>2344200.0</td>\n",
              "      <td>2.916010</td>\n",
              "      <td>-1.459893</td>\n",
              "      <td>1185.550049</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Adj Close     Volume    HL_PCT  PCT_change        label  Forecast\n",
              "Date                                                                           \n",
              "2020-03-26  1161.750000  3571700.0  6.579724    4.492710  1142.319946       NaN\n",
              "2020-03-27  1110.709961  3208500.0  4.029856   -1.328994  1175.760010       NaN\n",
              "2020-03-30  1146.819946  2574100.0  4.808952    1.935923  1193.199951       NaN\n",
              "2020-03-31  1162.810059  2486400.0  3.196571    1.351870  1193.319946       NaN\n",
              "2020-04-01  1105.619995  2344200.0  2.916010   -1.459893  1185.550049       NaN"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QtJyZd3EHV22",
        "outputId": "4d417fd5-3a82-4b5f-fc5b-b1f1f5a1abe9"
      },
      "source": [
        "dfFeat.iloc[-1].name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2020-04-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "9stjzZ37HYr6",
        "outputId": "908e3b10-4f17-45ca-f166-c007c9c090e6"
      },
      "source": [
        "dateRecent = dfFeat.iloc[-1].name\n",
        "nextCalc = dateRecent + datetime.timedelta(days=1)\n",
        "\n",
        "for i in forecastStock:\n",
        "    datePred = nextCalc\n",
        "    nextCalc += datetime.timedelta(days=1)\n",
        "    dfFeat.loc[nextCalc] = [np.nan for _ in range(len(dfFeat.columns)-1)]+[i]\n",
        "dfFeat['Adj Close'].tail(500).plot()\n",
        "dfFeat['Forecast'].tail(500).plot()\n",
        "plt.legend(loc=2)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R8YicQWHeuo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "91ad70c1-b8db-49ac-e829-6a8e90304749"
      },
      "source": [
        "# setting the timeframe for our data gathering\n",
        "past = datetime.datetime(2020, 1, 1)\n",
        "present = datetime.datetime(2020, 5, 1)\n",
        "\n",
        "# Yahoo API google finance data\n",
        "google = data.DataReader(\"GOOG\", 'yahoo', start=past, end=present)\n",
        "google.tail()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2020-04-27</th>\n",
              "      <td>1296.150024</td>\n",
              "      <td>1269.000000</td>\n",
              "      <td>1296.000000</td>\n",
              "      <td>1275.880005</td>\n",
              "      <td>1600600</td>\n",
              "      <td>1275.880005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-28</th>\n",
              "      <td>1288.050049</td>\n",
              "      <td>1232.199951</td>\n",
              "      <td>1287.930054</td>\n",
              "      <td>1233.670044</td>\n",
              "      <td>2951300</td>\n",
              "      <td>1233.670044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>1359.989990</td>\n",
              "      <td>1325.339966</td>\n",
              "      <td>1341.459961</td>\n",
              "      <td>1341.479980</td>\n",
              "      <td>3793600</td>\n",
              "      <td>1341.479980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>1352.819946</td>\n",
              "      <td>1322.489990</td>\n",
              "      <td>1324.880005</td>\n",
              "      <td>1348.660034</td>\n",
              "      <td>2668900</td>\n",
              "      <td>1348.660034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>1352.069946</td>\n",
              "      <td>1311.000000</td>\n",
              "      <td>1328.500000</td>\n",
              "      <td>1320.609985</td>\n",
              "      <td>2072500</td>\n",
              "      <td>1320.609985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   High          Low  ...   Volume    Adj Close\n",
              "Date                                  ...                      \n",
              "2020-04-27  1296.150024  1269.000000  ...  1600600  1275.880005\n",
              "2020-04-28  1288.050049  1232.199951  ...  2951300  1233.670044\n",
              "2020-04-29  1359.989990  1325.339966  ...  3793600  1341.479980\n",
              "2020-04-30  1352.819946  1322.489990  ...  2668900  1348.660034\n",
              "2020-05-01  1352.069946  1311.000000  ...  2072500  1320.609985\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1vBSCnWILKP"
      },
      "source": [
        "# moving average mean of closing data for past 6 months\n",
        "googleClose = google['Adj Close']\n",
        "#vgMean = googleClose.rolling(window=180).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "n7WOM-i5IY3H",
        "outputId": "bd3a2676-828c-464d-d626-ebc27e367c5f"
      },
      "source": [
        "# matplotlib plot of Google data\n",
        "mpl.rc('figure', figsize=(8, 7))\n",
        "mpl.__version__\n",
        "\n",
        "\n",
        "style.use('ggplot')\n",
        "googleClose.plot(label='GOOG')\n",
        "#avgMean.plot(label='avgMean')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd70a863b90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "lI-HYloaIeIV",
        "outputId": "c803dae9-0377-4ff6-f4f8-e1d5e53fcffd"
      },
      "source": [
        "# returns\n",
        "returns = googleClose / googleClose.shift(1) - 1\n",
        "returns.plot(label='return')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd70aa78650>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "PqPPY9GcIjxn",
        "outputId": "cf22efba-e696-4a5c-805c-f74c04ca1d3f"
      },
      "source": [
        "df = data.DataReader(['AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],'yahoo',start=past,end=present)\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Attributes</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">High</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Low</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>2020-04-27</th>\n",
              "      <td>69.921890</td>\n",
              "      <td>51.114677</td>\n",
              "      <td>1275.880005</td>\n",
              "      <td>110.041534</td>\n",
              "      <td>171.260361</td>\n",
              "      <td>70.792503</td>\n",
              "      <td>51.439999</td>\n",
              "      <td>1275.880005</td>\n",
              "      <td>120.382408</td>\n",
              "      <td>174.050003</td>\n",
              "      <td>71.135002</td>\n",
              "      <td>51.759998</td>\n",
              "      <td>1296.150024</td>\n",
              "      <td>121.405357</td>\n",
              "      <td>176.899994</td>\n",
              "      <td>69.987503</td>\n",
              "      <td>50.639999</td>\n",
              "      <td>1269.000000</td>\n",
              "      <td>119.952202</td>\n",
              "      <td>173.300003</td>\n",
              "      <td>70.449997</td>\n",
              "      <td>51.040001</td>\n",
              "      <td>1296.000000</td>\n",
              "      <td>120.038239</td>\n",
              "      <td>176.589996</td>\n",
              "      <td>117087600.0</td>\n",
              "      <td>13379775.0</td>\n",
              "      <td>1600600.0</td>\n",
              "      <td>5150295.0</td>\n",
              "      <td>33194400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-28</th>\n",
              "      <td>68.788490</td>\n",
              "      <td>54.055962</td>\n",
              "      <td>1233.670044</td>\n",
              "      <td>110.347404</td>\n",
              "      <td>167.088333</td>\n",
              "      <td>69.644997</td>\n",
              "      <td>54.400002</td>\n",
              "      <td>1233.670044</td>\n",
              "      <td>120.717018</td>\n",
              "      <td>169.809998</td>\n",
              "      <td>71.457497</td>\n",
              "      <td>55.360001</td>\n",
              "      <td>1288.050049</td>\n",
              "      <td>122.562141</td>\n",
              "      <td>175.669998</td>\n",
              "      <td>69.550003</td>\n",
              "      <td>52.560001</td>\n",
              "      <td>1232.199951</td>\n",
              "      <td>119.866158</td>\n",
              "      <td>169.389999</td>\n",
              "      <td>71.269997</td>\n",
              "      <td>52.560001</td>\n",
              "      <td>1287.930054</td>\n",
              "      <td>122.399620</td>\n",
              "      <td>175.589996</td>\n",
              "      <td>112004800.0</td>\n",
              "      <td>17583575.0</td>\n",
              "      <td>2951300.0</td>\n",
              "      <td>4997893.0</td>\n",
              "      <td>34392700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>71.047867</td>\n",
              "      <td>52.307091</td>\n",
              "      <td>1341.479980</td>\n",
              "      <td>112.462242</td>\n",
              "      <td>174.586182</td>\n",
              "      <td>71.932503</td>\n",
              "      <td>52.639999</td>\n",
              "      <td>1341.479980</td>\n",
              "      <td>123.030594</td>\n",
              "      <td>177.429993</td>\n",
              "      <td>72.417503</td>\n",
              "      <td>55.200001</td>\n",
              "      <td>1359.989990</td>\n",
              "      <td>123.623329</td>\n",
              "      <td>177.679993</td>\n",
              "      <td>70.972504</td>\n",
              "      <td>52.160000</td>\n",
              "      <td>1325.339966</td>\n",
              "      <td>121.921608</td>\n",
              "      <td>171.880005</td>\n",
              "      <td>71.182503</td>\n",
              "      <td>53.439999</td>\n",
              "      <td>1341.459961</td>\n",
              "      <td>123.212234</td>\n",
              "      <td>173.220001</td>\n",
              "      <td>137280800.0</td>\n",
              "      <td>28153500.0</td>\n",
              "      <td>3793600.0</td>\n",
              "      <td>5499450.0</td>\n",
              "      <td>51286600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>72.546700</td>\n",
              "      <td>54.055962</td>\n",
              "      <td>1348.660034</td>\n",
              "      <td>109.726936</td>\n",
              "      <td>176.337646</td>\n",
              "      <td>73.449997</td>\n",
              "      <td>54.400002</td>\n",
              "      <td>1348.660034</td>\n",
              "      <td>120.038239</td>\n",
              "      <td>179.210007</td>\n",
              "      <td>73.632500</td>\n",
              "      <td>54.560001</td>\n",
              "      <td>1352.819946</td>\n",
              "      <td>121.673042</td>\n",
              "      <td>180.399994</td>\n",
              "      <td>72.087502</td>\n",
              "      <td>51.279999</td>\n",
              "      <td>1322.489990</td>\n",
              "      <td>119.713196</td>\n",
              "      <td>176.229996</td>\n",
              "      <td>72.489998</td>\n",
              "      <td>52.480000</td>\n",
              "      <td>1324.880005</td>\n",
              "      <td>120.956024</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>183064000.0</td>\n",
              "      <td>21963888.0</td>\n",
              "      <td>2668900.0</td>\n",
              "      <td>6941047.0</td>\n",
              "      <td>53875900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>71.378754</td>\n",
              "      <td>51.671139</td>\n",
              "      <td>1320.609985</td>\n",
              "      <td>106.502243</td>\n",
              "      <td>171.772049</td>\n",
              "      <td>72.267502</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>1320.609985</td>\n",
              "      <td>116.510513</td>\n",
              "      <td>174.570007</td>\n",
              "      <td>74.750000</td>\n",
              "      <td>53.919998</td>\n",
              "      <td>1352.069946</td>\n",
              "      <td>118.040154</td>\n",
              "      <td>178.639999</td>\n",
              "      <td>71.462502</td>\n",
              "      <td>51.279999</td>\n",
              "      <td>1311.000000</td>\n",
              "      <td>116.051628</td>\n",
              "      <td>174.009995</td>\n",
              "      <td>71.562500</td>\n",
              "      <td>53.360001</td>\n",
              "      <td>1328.500000</td>\n",
              "      <td>117.772469</td>\n",
              "      <td>175.800003</td>\n",
              "      <td>240616800.0</td>\n",
              "      <td>15047063.0</td>\n",
              "      <td>2072500.0</td>\n",
              "      <td>5152073.0</td>\n",
              "      <td>39370500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Attributes  Adj Close                          ...     Volume                       \n",
              "Symbols          AAPL         GE         GOOG  ...       GOOG        IBM        MSFT\n",
              "Date                                           ...                                  \n",
              "2020-04-27  69.921890  51.114677  1275.880005  ...  1600600.0  5150295.0  33194400.0\n",
              "2020-04-28  68.788490  54.055962  1233.670044  ...  2951300.0  4997893.0  34392700.0\n",
              "2020-04-29  71.047867  52.307091  1341.479980  ...  3793600.0  5499450.0  51286600.0\n",
              "2020-04-30  72.546700  54.055962  1348.660034  ...  2668900.0  6941047.0  53875900.0\n",
              "2020-05-01  71.378754  51.671139  1320.609985  ...  2072500.0  5152073.0  39370500.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "EzOXherFIkvh",
        "outputId": "875105eb-132d-456c-d9e5-5ea49902d6a3"
      },
      "source": [
        "\n",
        "df = data.DataReader(['AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],'yahoo',\n",
        "                     start=past,end=present)['Adj Close']\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-04-27</th>\n",
              "      <td>69.921890</td>\n",
              "      <td>51.114677</td>\n",
              "      <td>1275.880005</td>\n",
              "      <td>110.041527</td>\n",
              "      <td>171.260376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-28</th>\n",
              "      <td>68.788498</td>\n",
              "      <td>54.055958</td>\n",
              "      <td>1233.670044</td>\n",
              "      <td>110.347404</td>\n",
              "      <td>167.088303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-29</th>\n",
              "      <td>71.047882</td>\n",
              "      <td>52.307083</td>\n",
              "      <td>1341.479980</td>\n",
              "      <td>112.462250</td>\n",
              "      <td>174.586197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-30</th>\n",
              "      <td>72.546700</td>\n",
              "      <td>54.055958</td>\n",
              "      <td>1348.660034</td>\n",
              "      <td>109.726929</td>\n",
              "      <td>176.337677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>71.378754</td>\n",
              "      <td>51.671135</td>\n",
              "      <td>1320.609985</td>\n",
              "      <td>106.502243</td>\n",
              "      <td>171.772034</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols          AAPL         GE         GOOG         IBM        MSFT\n",
              "Date                                                                 \n",
              "2020-04-27  69.921890  51.114677  1275.880005  110.041527  171.260376\n",
              "2020-04-28  68.788498  54.055958  1233.670044  110.347404  167.088303\n",
              "2020-04-29  71.047882  52.307083  1341.479980  112.462250  174.586197\n",
              "2020-04-30  72.546700  54.055958  1348.660034  109.726929  176.337677\n",
              "2020-05-01  71.378754  51.671135  1320.609985  106.502243  171.772034"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "icKWuJilIsYC",
        "outputId": "42b18f66-5693-4530-9c25-e5883b0af386"
      },
      "source": [
        "# correlation plot\n",
        "correlation = df.pct_change()\n",
        "corr = correlation.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</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>AAPL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730366</td>\n",
              "      <td>0.898244</td>\n",
              "      <td>0.805050</td>\n",
              "      <td>0.933509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>0.730366</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.677500</td>\n",
              "      <td>0.727004</td>\n",
              "      <td>0.716911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>0.898244</td>\n",
              "      <td>0.677500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.797066</td>\n",
              "      <td>0.912285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBM</th>\n",
              "      <td>0.805050</td>\n",
              "      <td>0.727004</td>\n",
              "      <td>0.797066</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.798018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.933509</td>\n",
              "      <td>0.716911</td>\n",
              "      <td>0.912285</td>\n",
              "      <td>0.798018</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols      AAPL        GE      GOOG       IBM      MSFT\n",
              "Symbols                                                  \n",
              "AAPL     1.000000  0.730366  0.898244  0.805050  0.933509\n",
              "GE       0.730366  1.000000  0.677500  0.727004  0.716911\n",
              "GOOG     0.898244  0.677500  1.000000  0.797066  0.912285\n",
              "IBM      0.805050  0.727004  0.797066  1.000000  0.798018\n",
              "MSFT     0.933509  0.716911  0.912285  0.798018  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "Fr2L7kdNIvqM",
        "outputId": "40580fd1-e645-4636-d83b-5c86fa91caf2"
      },
      "source": [
        "plt.scatter(correlation.GOOG, correlation.MSFT)\n",
        "plt.xlabel('Google Returns')\n",
        "plt.ylabel('Microsoft Returns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Microsoft Returns')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Es9X1DVvIz0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "ed7f350a-488a-487d-e660-7b4c993fa30a"
      },
      "source": [
        "plt.imshow(corr, cmap='hot', interpolation='none')\n",
        "plt.colorbar()\n",
        "plt.xticks(range(len(corr)), corr.columns)\n",
        "plt.yticks(range(len(corr)), corr.columns);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgeJuI-MI2n7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "7f965ec1-8686-40eb-d4f5-84c0c78d81ed"
      },
      "source": [
        "plt.scatter(correlation.mean(), correlation.std())\n",
        "plt.xlabel('Expected returns')\n",
        "plt.ylabel('Risk')\n",
        "for label, x, y in zip(correlation.columns, correlation.mean(), correlation.std()):\n",
        "    plt.annotate(\n",
        "        label, \n",
        "        xy = (x, y), xytext = (20, -20),\n",
        "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
        "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
        "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AH/zgB+Tl5QHQ2NhIt27dAEhJSaGxsdF2XiUlJZSUlAAwbdo0UlNTj7DSb7hcrqCOF2nRVE801QKqpyOLplpA9XR0oagnbNckHK758+czZswYYmNj2+174IEH8Hg8NDY28uCDD5KVlUXfvn3b9DEMA8MwbMfOy8sLBAvYfd1DsKSmpgZ1vEiLpnqiqRZQPR1ZNNUCqqejO9R6srKyDtgnLCHB4/FQV1cX2K6rq8Pj8dj28Xq9+P1+mpubSUxMpLy8nLfffpvi4mK2b9+OYRi43W5Gjx4dGCM5OZlBgwZRXl5O3759SU5OpqGhgW7dutHQ0NDumgURERE5sLCEhJycHKqqqqiursbj8bBixQpuueWWNn0GDhzIkiVL6NOnDytXrqRfv34YhsGUKVMCfebPn09sbCyjR49m586dWJZF165d2blzJ2vWrOHiiy8GIDc3l6VLl5Kfn8/SpUsZNGhQOMoUERGJKmEJCU6nkwkTJjB16lRM02T48OFkZ2czb948cnJyyM3NZcSIERQVFTFp0iQSEhIoKCjY75iNjY088sgjAPj9fr7//e/Tv39/APLz85kxYwaLFy8O3AIpIiIih8aw9t5CIFRWVgZtrKN9rasji6ZaQPV0ZNFUC6ieji4U1yToiYsiIiJiSyFBREREbCkkiIiIiC2FBBEREbGlkCAiIiK2FBJERETElkKCiIiI2FJIEBEREVsKCSIiImJLIUFERERsKSSIiIiILYUEERERsaWQICIiIrYUEkRERMSWQoKIiIjYUkgQERERWwoJIiIiYkshQURERGwpJIiIiIgthQQRERGxpZAgIiIithQSRERExJZCgoiIiNhSSBARERFbCgkiIiJiSyFBREREbCkkiIiIiC2FBBEREbGlkCAiIiK2FBJERETElkKCiIiI2FJIEBEREVsKCSIiImJLIUFERERsKSSIiIiILYUEERERsaWQICIiIrYUEkRERMTWQYeEFStW2LbPnz8/aJMRERGRjuOgQ8Jf/vIX3nvvvXZtq1evDvqkREREJPIOOiTceeed/OlPf+Kjjz4CYO7cuaxZs4Z77703ZJMTERGRyHEdbMcePXpw2223MX36dE488URqa2u59957iYuLC+X8REREJEL2GxLWrl3brm348OGUlJRw7bXXUlFRAcApp5wSmtmJiIhIxOw3JDz55JO27TExMfz5z38GwDAMioqKDnigsrIy5syZg2majBw5kvz8/Db7W1tbKSoqoqKigsTERAoKCkhPTw/sr62tpbCwkLFjx3L++edTW1vLrFmz2Lp1K4ZhkJeXx3nnnQfsvpjyjTfeICkpCYDx48dzxhlnHHCOIiIi8o39hoRZs2YF5SCmaTJ79mzuuecevF4vd911F7m5ufTo0SPQZ/HixcTHxzNz5kyWL19OcXExhYWFgf1z585lwIABgW2n08kVV1xBr1692LFjB3feeSennXZaYMwxY8Zw/vnnB2X+IiIiR6PDfk7C2rVrWbdu3UH1LS8vJzMzk4yMDFwuF0OGDKG0tLRNn9WrVzNs2DAABg8ezNq1a7EsC4BVq1aRnp7eJlR069aNXr16AdC1a1e6d+9OfX394ZYjIiIi33HQFy7ed999jB8/npNOOomFCxfy6quv4nA4+OEPf8iFF16438/W19fj9XoD216vlw0bNuyzj9PpJC4ujqamJtxuN4sWLWLy5Mm8/PLLtuNXV1fz6aefcsIJJwTaXnvtNZYtW0avXr248sorSUhIaPe5kpISSkpKAJg2bRqpqakH949xEFwuV1DHi7RoqieaagHV05FFUy2gejq6UNRz0CHhiy++oE+fPgC88cYb3HfffcTGxjJ58uQDhoQjMX/+fMaMGUNsbKzt/p07d/Loo49y1VVXBe60GDVqFBdffDEA8+bN49lnn+XGG29s99m8vDzy8vIC27W1tUGbd2pqalDHi7RoqieaagHV05FFUy2gejq6Q60nKyvrgH0OOiTsPfW/efNmgMCp/+3btx/wsx6Ph7q6usB2XV0dHo/Hto/X68Xv99Pc3ExiYiLl5eW8/fbbFBcXs337dgzDwO12M3r0aHw+H48++ijnnHMOZ511VmCslJSUwN9HjhzJww8/fLBlioiIyB4HHRJOPPFEnnnmGRoaGhg0aBCwOzAkJiYe8LM5OTlUVVVRXV2Nx+NhxYoV3HLLLW36DBw4kCVLltCnTx9WrlxJv379MAyDKVOmBPrMnz+f2NhYRo8ejWVZ/OEPf6B79+78+Mc/bjNWQ0MD3bp1A3Zfz5CdnX2wZYqIiMgeBx0SbrrpJl555RWSkpICdw1UVlYGbjvcH6fTyYQJE5g6dSqmaTJ8+HCys7OZN28eOTk55ObmMmLECIqKipg0aRIJCQkUFBTsd8yPP/6YZcuW0bNnT26//Xbgm1sdn3/+eTZt2oRhGKSlpTFx4sSDLVNERET2MKy96whCZWVl0MY62te6OrJoqgVUT0cWTbWA6unown5NwksvvRS4KHHevHn77HfJJZcc9KRERESkc9hvSPjuxYYiIiJy9NhvSLj22msDf7e7hfCzzz7j73//e/BnJSIiIhF3wAsXd+3axYIFC9i0aRPHHHMMY8eOpampiWeffZYPPviAc889NxzzFBERkTA7YEiYPXs2n376KaeffjplZWV8/vnnVFZWMnToUK677rrAS5REREQkuhwwJLz//vv89re/JTk5mR/96EfceOON3H///Zx88snhmJ+IiIhEyAFf8LRz506Sk5OB3e9ciI2NVUAQERE5ChzwTILf72ft2rVt2r67fcoppwR3ViIiIhJxBwwJycnJPPnkk4HthISENtuGYVBUVBSa2YmIiEjEHDAkzJo1KxzzEBERkQ7mgNckiIiIyNFJIUFERERsKSSIiIiILYUEERERsaWQICIiIrYUEkRERMSWQoKIiIjYUkgQERERWwoJIiIiYkshQURERGwpJIiIiIgthQQRERGxpZAgIiIithQSRERExJZCgoiIiNhSSBARERFbCgkiIiJiSyFBREREbCkkiIiIiC2FBBEREbGlkCAiIiK2XJGegIiIiOybWbMZFhVjba3HSPHABZfhSMsMy7EVEkRERDoos2Yz1ox7oWYzABZAxceYhVPCEhS03CAiItJRLSoOBISAPWcWwkEhQUREpIOyttYfUnuwKSSIiIh0UEaK5zsNhn17iCgkiIiIdFQXXAZ7rj0wehxH14efxjjh5N3tYaALF0VERDooR1omZuEUWFSMERtH65rVxNw6BdMdi2VZIT++QoKIiEgHYBgtGEYLe+5hCHBkxMPEiTgcu5caTLMFp8O35+9moJ/f78DhaDro4/l8WzGMeBwOF8aeZYzvUkgQERGJoJiYL3C5NuBwlNuGhL32fpHv6wxCTExX4uJ2HPRxHY4kLCsZn+8kDON0XC5vuz4KCSIiIhHidn+K2/0KhtF6wL6GYWBZFvv4pR/DcO0JGQfH4dgFVON0VuP3V+DzXYjL1faCSF24KCIiEgGG4ScmZvlBBQTY9xmEYHA6v8Sy1rU7hkKCiIhIBDidNRhGdaSnEWAYH7cLCWFbbigrK2POnDmYpsnIkSPJz89vs7+1tZWioiIqKipITEykoKCA9PT0wP7a2loKCwsZO3Ys559//n7HrK6u5vHHH6epqYlevXoxadIkXC6trIiISMfhcGzHMEJ/h8LBcjgasCwf4P6mLRwHNk2T2bNnc/fddzNjxgyWL1/Ol19+2abP4sWLiY+PZ+bMmYwZM4bi4raPnJw7dy4DBgw4qDGff/55xowZw8yZM4mPj2fx4sWhL1JEROSQmAfuElYm4GvTEpZfr8vLy8nMzCQjIwOAIUOGUFpaSo8ePQJ9Vq9ezdixYwEYPHgwzzzzzJ4LNAxWrVpFeno6Xbp0OeCY3bt358MPP+T//u//ABg2bBgvvPACo0aNCkepIiIiRywp6THGjTuJp58+DwCfz6R376fIzc3khRd+SnX1dm666T989VUTra0mPXsm8a9/Xc5nnzUyaNCf6d37mwsQf/zjE/jHP8oB+PDDWvr1SwXgF78YyC23nLXfeYQlJNTX1+P1fnNrhdfrZcOGDfvs43Q6iYuLo6mpCbfbzaJFi5g8eTIvv/zyAcdsamoiLi4Op9MJgMfjob7e/hnXJSUllJSUADBt2jRSU1ODUzDgcrmCOl6kRVM90VQLqJ6OLJpqAdUTbH5/FW53nO2++PgYPv64AcOIoWvXGP71r3J69EgKfD8+/PCb/PCHvbnlljMBWLNmCw6Hg65du5KT4+G9965rM94DD4wEIDn54cC+vd+T+9PhF+rnz5/PmDFjiI2NDfrYeXl55OXlBbZra2uDNnZqampQx4u0aKonmmoB1dORRVMtoHqCze3ehs/XvM/9eXnHsmDBh+Tn96G4+H0uvLA3K1Z8RXNzM19+2ci553anuXn35084IRHTNNmxYwemaQba7ezdl5iYeMA5hiUkeDwe6urqAtt1dXV4PB7bPl6vF7/fT3NzM4mJiZSXl/P2229TXFzM9u3bMQwDt9tNr169bMdMTEykubkZv9+P0+mkvr6+3bFEREQ6uosuOpGHH17J6NG9+PDDWq644hRWrPgKgGuv7c/VV7/KH/9YxrBhx3L55f3Iydl9VuLTT7dy9tnPAXDWWVk89tjIw55DWEJCTk4OVVVVVFdX4/F4WLFiBbfcckubPgMHDmTJkiX06dOHlStX0q9fPwzDYMqUKYE+8+fPJzY2ltGjR+P3+23HNAyDfv36sXLlSs4++2yWLFlCbm5uOMoUEREJmlNOSePzz7/mxRfXM2rU8W325eUdx/vvT6CkZBOvv76J73//edas2b2McPzxKSxffkVQ5hCWkOB0OpkwYQJTp07FNE2GDx9OdnY28+bNIycnh9zcXEaMGEFRURGTJk0iISGBgoKCwxoT4LLLLuPxxx/nb3/7G8cffzwjRowIR5kiIiJB9aMf9eLXv17GP/85lvr6nW32eTxdGTfuZMaNO5mxYxfw3/9+zsknpwT1+GG7JuGMM87gjDPOaNN2ySWXBP7udru59dZb9zvGuHHjDjgmQEZGBg899NARzFZERCTyrrjiFJKTu9CvXxr//e8XgfalSz9n0KBjiIuLoamphU8/bSQ7Oznox+/wFy6KiIgcrbp3T+SGG9r/MlxWtoXbbluMy+XANC1+/vNTGDQoi48+qgrq8Q0rHC+k7iQqKyuDNlakr5oNtmiqJ5pqAdXTkUVTLaB6gs3tLqdLl0VBGy8uLm6/dzV813fvbjDNOCxrEk5n10Cb3t0gIiISER3rZL5lOYGYNm0KCSIiIhHg9ydjWR0nKFhWNg5H2wcsKSSIiIhEgN/fDdM8/sAdw8CywLJOwDCMNu0KCSIiIhGya9c5mGb6gTuGkGWBz3cWTucp7fZ1nPMcIiIiRxm/vxs7dlyIy/UVTmclsB3DOLy3Q/p8XfH7dxxC/xQsywMci9N5HA6Hu10fhQQREQk5s2YzLCrG2lqPkeKBCy7DkZYZ6Wl1CKYZT0tLH6DPEY3TtauX5ua6A3fcIzn5GIB2SwzfppAgIiIhZdZsxppxL9RsBsACqPgYs3CKgkIQ7e/L/nD765oEEREJrUXFgYAQsOfMgnRsCgkiIhJS1tb6Q2qXjkMhQUREQspI8RxSu3QcCgkiIhJaF1wG37r2oMt1v4Jjsne3S4emCxdFRCSkHGmZmIVTvrm7ISubLvc+jj/GjV4f1LEpJIiISMg50jLhF78EwHK58Pl8uJxOTNPENA/vuQASegoJIiISJH5iYqpxOisxjB2A/Ze/w+EgJmbvPoM9N0UeJiemGYffn4XPl4ZW0YNLIUFERI6YYewiNnYpTudaDGP/X/oOhyPoZw8sy4Hfn8uOHd9DX23Bo8glIiJHrEuXMlyuDw4YEICQLC8YhonTuYouXT4K+thHM4UEERE5IobRitP5YaSngWGA07meI1u+kG9TSBARkSPidDZiGA2RngYADsdmHI5tkZ5G1FBIEBGRI+TjEF8bEEJ+DMMf6UlEDYUEERE5Qvs/vf+Pf5STlPQYn3zS9jHMa9ZUk5T0GK+//mmb9pSUGZx99nOcddZcrrzyFZqbWwE45j1fmMQAACAASURBVJiZQZmPHDyFBBERCakXX1zP977XnRdeWG/b/uKLH7dp79rVxfLlV/D22z8nJsbJM8+sCed05VsUEkREJGS2bWvhrbe+oqhoFH//+zdhwLIsFi7cwJNP/pA33/yMnTt9tp8fMqQ7GzduDdd05TsUEkREJGRefXUjeXnH0bt3NzyeWN57bwsAb79dybHHJtGrVwrf/34PXnutot1nfT6T11/fRL9+qeGetuyhkCAiIiHz4ovrueiikwC46KITefHF3UsOL7ywnosuOhGAiy8+qc2Sw44dPs4++zmGDi2mR49ErrzylPBPXAA9lkpEREKkvn4Hy5Z9wbp1tRiGgd9vYhgGU6acw8svl/PPf27kkUdWYVkW9fU7aWpqITHRHbgmQSJPIUFEREJi0aIN/OxnJ/O73/0g0PajH81j+vS36dcvlYULLwq0X3fdv3nllXIuvbRvJKYq+6DlBhERCYkXX1zPj398Qpu288/vzWeffc1PftK2/YILegeWIvalubmVk076Y+BPUdE7QZ+ztKUzCSIiEhKvvjquXdsNN5xh2/e883I477wcAKqqJtn2aWy8NXiTk4OiMwkiInKEOtJXiUHHmk/npn9JERE5IpYVi2XFRHoawN65dI30NKKGQoKIiBwRvz8Z0zw20tMAwDR7Y5ruSE8jaigkiIjIEWttHYBlRfbL2TTjaW3V3RHBpAsXRUTkiLW29gQuwOX6AKezAmgJy5shLQsgFr8/B5/vdHy+zNAf9CiikCAiIkHR2tqT1taeOJ1NGMYuwAzDUR2YZiymmRCGYx19FBJERCSo/P5EIDHS05Ag0DUJIiIiYkshQURERGwpJIiIiIgthQQRERGxpZAgIiIithQSRERExFbYboEsKytjzpw5mKbJyJEjyc/Pb7O/tbWVoqIiKioqSExMpKCggPT0dMrLy3nqqacC/caOHcuZZ55JZWUlM2bMCLRXV1czbtw4xowZw/z583njjTdISkoCYPz48Zxxhv2bx0RERMReWEKCaZrMnj2be+65B6/Xy1133UVubi49evQI9Fm8eDHx8fHMnDmT5cuXU1xcTGFhIdnZ2UybNg2n00lDQwO33347AwcOJCsri+nTpwfGv+666zjzzDMD440ZM4bzzz8/HOWJiIhEpbAsN5SXl5OZmUlGRgYul4shQ4ZQWlraps/q1asZNmwYAIMHD2bt2rVYlkWXLl1wOp3A7rMNhs1zPj/44AMyMzNJS0sLeS0iIiJHi7CcSaivr8fr9Qa2vV4vGzZs2Gcfp9NJXFwcTU1NJCUlsWHDBp588klqamqYNGlSIDTstXz5cs4+++w2ba+99hrLli2jV69eXHnllSQktH9kZ0lJCSUlJQBMmzaN1NTUoNQL4HK5gjpepEVTPdFUC6iejiyaagHV09GFop5O8Vjm3r1789hjj/Hll18ya9Ys+vfvj9u9+21jPp+Pd955h0svvTTQf9SoUVx88cUAzJs3j2effZYbb7yx3bh5eXnk5eUFtmtra4M259TU1KCOF2nRVE801QKqpyOLplpA9XR0h1pPVlbWAfuEZbnB4/FQV1cX2K6rq8Pj8eyzj9/vp7m5mcTEts/+7tGjB7GxsXzxxReBtvfee4/jjz+elJSUQFtKSgoOhwOHw8HIkSPZuHFjKMoSERGJamEJCTk5OVRVVVFdXY3P52PFihXk5ua26TNw4ECWLFkCwMqVK+nXrx+GYVBdXY3f7wegpqaGysrKNtce2C01NDQ0BP6+atUqsrOzQ1SZiIhI9ArLcoPT6WTChAlMnToV0zQZPnw42dnZzJs3j5ycHHJzcxkxYgRFRUVMmjSJhIQECgoKAFi/fj0LFy7E6XTicDi45pprArc27ty5kzVr1jBx4sQ2x3v++efZtGkThmGQlpbWbr+IiIgcmGFZlhXpSXQUlZWVQRvraF/r6siiqRZQPR1ZNNUCqqej67TXJIiIiEjno5AgIiIithQSRERExFaneE6CiIjsm1mzGRYVY22tx0jxwAWX4UjLjPS0JAooJIiIdGJmzWasGfdCzWYALICKjzELpygoyBHTcoOISGe2qDgQEAL2nFkQOVIKCSIinZi1tf6Q2kUOhUKCiEgnZqS0fcS9+4obceSc1K5d5HDomgQRkc7sgsug4uNvlhwcTlwjf4zRfzB+w0DPy5MjoTMJIiKdmCMtE6NwCsZZQ+HEU7EcDnw5J+OLceNwOHA6nZGeonRiOpMgItLJOdIycUy8GadzMw7HLmKow7K+eTzv4ecEJ5aViM+XhmXp6+JopJ+6iEinZtGly/u4XKswjCYMAxwOB6ZpBmd0y8DtTqW1dSiW5Q3KmNJ5KCSIiHRiXbp8REzMYgzjm2sPghUQAAzDwjBqiIl5BdP0AglBG1s6Pl2TICLSSRmGicv1fpuAECoOxy4MY0PIjyMdi0KCiEgn5XQ2YBhVYTuew/EJhhG2w0kHoJAgItJptYTlLMJehrELaA3b8STyFBJERDqp/f1Wf8wxMwH47LNG0tN/x9lnP8eQIc+Sl/dXNmzY/TTG//73C5KSHmPu3A8Cn1uzppqkpMf4/e9X24xq7fkjRwuFBBGRKHf88SksX34FK1ZcyaWX9uORR1YF9vXt6+Wllz4JbL/44npOPTUtEtOUDkghQUTkKNLUtIuUlC6B7ezsJHbt8lFdvR3Lsigp2cQPfnBc5CYoHYpugRQRiXKffrqVs89+jqamFnbs8LF48fg2+y+4oDcLFnzC6aenc/rpGbjdekqj7KYzCSIiUW7vcsOaNdcwbdowbrnl9Tb7L7zwRBYu/IQXXljPxRefGKFZSkekkCAichQ577xerFjxVZu2jIx4YmKcvPnm5wwb1jNCM5OOSMsNIiJHkbfequT445Pbtf/610OoqWnG6dTvjvINhQQRkSi395oEy7KIiXEyc+aodn3OOisrAjOTjk4hQUQkClVVTQLg2GOTqa7+P9s+55yTzTnnZLdrv/vuISGdm3QeOq8kItJJWWF/rpGx548cLRQSREQ6LTeWFb4vbcvqAsSE7XgSeQoJIiKdlN/fDcvKCNvxTLN3BM5eSCQpJIiIdFKW5cDnOz0sZxMsy41lnRDy40jHopAgItKJ7drVF59vOKYZH5LxLQtM00tLy09wOI4PyTGk49LdDSIinZqDnTsH4HCchMu1BdgBmEEa24llJeLzZWBZLoz9vXZSopJCgohIFDDNrrS0HBfpaUiU0XKDiIiI2FJIEBEREVsKCSIiImJLIUFERERsKSSIiIiILYUEERERsaWQICIiIrYUEkRERMSWQoKIiIjYUkgQERERWwoJIiIiYits724oKytjzpw5mKbJyJEjyc/Pb7O/tbWVoqIiKioqSExMpKCggPT0dMrLy3nqqacC/caOHcuZZ54JwE033URsbCwOhwOn08m0adMA2LZtGzNmzKCmpoa0tDQKCwtJSEgIV6kiIiJRISwhwTRNZs+ezT333IPX6+Wuu+4iNzeXHj16BPosXryY+Ph4Zs6cyfLlyykuLqawsJDs7GymTZuG0+mkoaGB22+/nYEDB+J0OgG47777SEpKanO8hQsXcuqpp5Kfn8/ChQtZuHAhl19+eThKFRERiRphWW4oLy8nMzOTjIwMXC4XQ4YMobS0tE2f1atXM2zYMAAGDx7M2rVrsSyLLl26BAJBa2vrQb2qtLS0lKFDhwIwdOjQdscSERGRAwvLmYT6+nq8Xm9g2+v1smHDhn32cTqdxMXF0dTURFJSEhs2bODJJ5+kpqaGSZMmBUIDwNSpUwH4wQ9+QF5eHgCNjY1069YNgJSUFBobG23nVVJSQklJCQDTpk0jNTU1SBWDy+UK6niRFk31RFMtoHo6smiqBVRPRxeKesJ2TcKR6N27N4899hhffvkls2bNon///rjdbh544AE8Hg+NjY08+OCDZGVl0bdv3zafNQxjn2cf8vLyAsECoLa2NmhzTk1NDep4kRZN9URTLaB6OrJoqgVUT0d3qPVkZWUdsE9Ylhs8Hg91dXWB7bq6Ojwezz77+P1+mpubSUxMbNOnR48exMbG8sUXXwQ+A5CcnMygQYMoLy8PbDc0NADQ0NDQ7poFERERObCwhIScnByqqqqorq7G5/OxYsUKcnNz2/QZOHAgS5YsAWDlypX069cPwzCorq7G7/cDUFNTQ2VlJWlpaezcuZMdO3YAsHPnTtasWUPPnj0ByM3NZenSpQAsXbqUQYMGhaNMERGRqBKW5Qan08mECROYOnUqpmkyfPhwsrOzmTdvHjk5OeTm5jJixAiKioqYNGkSCQkJFBQUALB+/XoWLlyI0+nE4XBwzTXXkJSUxJYtW3jkkUeA3Wcevv/979O/f38A8vPzmTFjBosXLw7cAikiIiKHxrAsy4r0JDqKysrKoI11tK91dWTRVAuono4smmoB1dPRddprEkRERKTzUUgQERERWwoJIiIiYkshQURERGwpJIiIiIgthQQRERGxpZAgIiIithQSRERExJZCgoiIiNjqFG+BFBGRjsus2QyLirG21mOkeOCCy3CkZUZ6WhIECgkiInLYzJrNWDPuhZrNAFgAFR9jFk5RUIgCWm4QEZHDt6g4EBAC9pxZkM5PIUFERA6btbX+kNqlc1FIEBGRw2akeL7Z6NIFZ//B7dul01JIEBGRw3fBZbDn2gNH9+Nwj70Ko88pu9ul09OFiyIictgcaZmYhVNgUTGOnr3Y9d/XcRfej8/ljvTUJAgUEkREpB2n82uczhqghT33LOD3J+B2b2vfuTtw449wOp34/X6gnK6Bvx8JB9AFny8D04w7wrHkcCgkiIhIgMOxky5dluN0rsMwWtrsc7vj8Pma9/NZB6ZpBrZdQfqGcbu74vMNZOfOXMAZnEHloOiaBBER2cOiS5dluFxl7QLCQX3askIwJzCMHbhc/yM29t2QjC/7ppAgIiIAOJ31OJ0fHfbnQxUSAAwDnM4yHI5DDy9y+BQSREQEAKezGsPwRXoa+2QYe6+TkHBRSBAREYDDWmIIJ8MAw9gV6WkcVXThooiI7LHv5YLq6u3cc89rrFz5JSkpXYiJcVJQkMtPftKbt976irvuWkJT0+6QcfPNA7n66tMCn50zZw1FRe8AkJjo5qGHhvG973UHwOczmTp1BQsWfEJ8fAwA+fl9uP32sw55jhJ8CgkiIrJflmUxfvzLXHVVf/74xx8C8PnnX/PPf25ky5btXHPNP/nLX86nf/8M6up28NOf/p1jjklg9Ohe/OtfFcyZs4b//OdneL1dKSvbwqWXvsybb15KRkY8DzywnC1btrNy5ZXExrpoamph5szVEa5Y9tJyg4iI7NfSpV/gdju47rqBgbaePZO4/voB/PGPZVx6aV/6988AwOvtypQp5zJjRikAjz9eygMPnIvX2xWA/v0zGD++L3/8YxnNza38+c8fMH36CGJjd//Ompjo5u67h4S5QtkXhQQREdmvjz6q5fTTM2z3rV9fx4ABbfcNGJDBRx/VBj67N0DsdcYZGaxfX0dFxVaysxNJTNTTGTsqLTeIiMghufXWN1i58itiYpz06JEYtHGff34tTz75HvX1O3j99fFBHVsOj84kiIjIfp18cirvv78lsP3YYyN55ZWx1NXt4MQTvbz33pY2/cvKtnDyyakAnHSSl7Kytvvfe28LJ53kpVevFL74oilwwePll5/C8uVXkJTUBb/fRCJPIUFERPZr6NBsdu7084c/vBNoa25uBWDixNP5y1/WsWZNNQB1dTu4997/UlCQC0BBwSDuvfe/1NXtAGDNmmr+8pd1XHvt6cTFxXDlladw222L2blz9/MZ/H6TlpYjfeeDBIthhfIRWSIi0mm0tv6PmJgS231VVU0UFr7G229/RVpaHPHxbq6/fiCXXHIKy5Z9xi9/+R+amnZhWVBQcBY33DAo8Nknnyzl8cffxjAgMbELjz46inPPPXbPMf1MnvwmL764jsTELnTt6mLMmN7cfvvZuN3t39PQ2noRMTGnhuYfQNpRSAiRO++8k2nTpkV6GkETTfVEUy2gejqyzlbL/kICQElJCXl5eWGcUXvBDAmd7edzIKGoR8sNIiKyhxHpCRyEzjDH6KGQICIie3SJ9AT2a/d5b90uGU7O+++///5ITyJa9erVK9JTCKpoqieaagHV05F1plosy8Iw3scw9r0K3a1btzDOqC3LSsQwhuFwBC/MdKafz8EIdj26JkFERAAwTT+m+RIu14eRnko7lgU+3zm4XCMwDC05hIsepiQiIgA4HE4sazStrU5cro8wjNZITwkA04zF7x+I0/l9BYQw05kEERFpw7Is/P5aLKsKiOSrmQ2gK4aRhdOZooAQAQoJh2jbtm3MmDGDmpoa0tLSKCwsJCEhoV2/JUuW8NJLLwFw4YUXMmzYMAAqKiqYNWsWLS0tDBgwgKuvvjrwP/x//etfvPbaazgcDs444wwuv/zyTlnL/PnzeeONN0hKSgJg/PjxnHHGGSGtJZT17PXKK6/w3HPP8fTTTwdq64z1/O1vf2P16tUYhkFycjI33ngjHo+nU9by3HPP8c477+ByucjIyODGG28kPj4+pLWEsp633nqLF154ga+++or/9//+Hzk5OSGto6ysjDlz5mCaJiNHjiQ/P7/N/tbWVoqKiqioqCAxMZGCggLS09MBWLBgAYsXL8bhcHD11VfTv3//gxqzM9XyxBNP8O6775KcnMyjjz4aljpCVU9tbS2zZs1i69atGIZBXl4e55133oEnYskhee6556wFCxZYlmVZCxYssJ577rl2fZqamqybbrrJampqavN3y7KsO++80/r4448t0zStqVOnWu+++65lWZb1wQcfWFOmTLFaWlosy7KsrVu3dtpa5s2bZy1atCjk8/+uUNVjWZZVU1NjPfjgg9YNN9xgNTY2dup6tm/fHvj8q6++aj311FOdtpaysjLL5/MFjmE3bmeq54svvrC++uor67777rPKy8tDWoPf77duvvlma/PmzVZra6t12223WV988UWbPv/+978D//v43//+Zz322GOBed52221WS0uLtWXLFuvmm2+2/H7/QY3ZWWqxLMv68MMPrY0bN1q33npryGsIdT319fXWxo0bLcuyrObmZuuWW245qJ+NboE8RKWlpQwdOhSAoUOHUlpa2q5PWVkZp512GgkJCSQkJHDaaadRVlZGQ0MDO3bsoE+fPhiGwbnnnhv4/H/+8x8uuOACYmJiAEhOTu60tURKKOuZO3cul112WVhPd4aqnri4uMDnd+3aFZaaQlXL6aefjtO5+6l8ffr0ob6+PuS1hLKeHj16kJWVFZYaysvLyczMJCMjA5fLxZAhQ9rVsXr16sDZj8GDB7N27Vosy6K0tJQhQ4YQExNDeno6mZmZlJeXH9SYnaUWgL59+9qeIQq1UNTTrVu3wJ0PXbt2pXv37gf134suXDxEjY2NgVuAUlJSaGxsbNenvr4er9cb2PZ4PNTX17dr93q9gR9SVVUV69ev529/+xsxMTFcccUVnHDCCZ2yFoDXXnuNZcuW0atXL6688sqw/IcWqnpKS0vxeDwcd9xxoS3gO0L58/nrX//KsmXLiIuL47777gthFbuFspa9Fi9ezJAhQ0Iw+/bCUU+o2c1jw4YN++zjdDqJi4ujqamJ+vp6evfuHei3t7a94+xvzFAIVS2REup6qqur+fTTTw/qO0YhwcYDDzzA1q1b27X/7Gc/a7NtGEbQfgszTZNt27YxdepUNm7cyIwZMygqKjri8SNRy6hRo7j44osBmDdvHs8++yw33nhjUMYOdz27du1iwYIF3HPPPUc8lp1I/Hxg93Ui48ePZ8GCBfz73/9m3LhxRzxmpGoBeOmll3A6nZxzzjlBGzOS9YiEys6dO3n00Ue56qqr2pxV3BeFBBuTJ0/e577k5GQaGhro1q0bDQ0NtheweTwe1q1bF9iur6+nb9++eDwe6urqAu11dXWBC8Y8Hg9nnnkmhmFwwgkn4HA4aGpqOuIL5CJRS0pKSqB95MiRPPzww0dUw7eFu54tW7ZQXV3N7bffHmi/4447eOihh9rU2Vnq+a5zzjmHhx56KCghIVK1LFmyhHfeeYd77703qF/Wkf7ZhNrBzGNvH6/Xi9/vp7m5mcTExHafra+vD3w2ErWFqpZICVU9Pp+PRx99lHPOOYezzjrroOaiaxIOUW5uLkuXLgVg6dKlDBo0qF2f/v378/7777Nt2za2bdvG+++/T//+/enWrRtdu3blk08+wbIsli1bRm7u7tepDho0iA8/3P0Ak8rKSnw+H4mJiZ2yloaGhsDnV61aRXZ2dkjrCGU9PXv25Omnn2bWrFnMmjULr9fLww8/HJSAEIl6YPfS1l6lpaVhWQMPVS1lZWUsWrSIO+64gy5dwvdI4VDVE045OTlUVVVRXV2Nz+djxYoV7eYxcOBAlixZAsDKlSvp168fhmGQm5vLihUraG1tpbq6mqqqKk444YSDGrOz1BJJoajHsiz+8Ic/0L17d3784x8f9Fx0C+QhampqYsaMGdTW1ra59Wnjxo28/vrrXH/99cDu9dEFCxYAu299Gj58OAAbN27kiSeeoKWlhf79+zNhwgQMw8Dn8/HEE0/w2Wef4XK5uOKKKzjllFM6ZS0zZ85k06ZNGIZBWloaEydODMujXENVz7fddNNNPPTQQ2G5BTJU9TzyyCNUVVVhGAapqalMnDgx5L85haqWSZMm4fP5Ate89O7dm4kTJ4a0llDWs2rVKp555hm+/vpr4uPjOe644/j1r38dsjreffdd5s6di2maDB8+nAsvvJB58+aRk5NDbm4uLS0tFBUV8emnn5KQkEBBQQEZGRnA7iWeN998E4fDwVVXXcWAAQP2OWY4hKKWxx9/nHXr1tHU1ERycjLjxo1jxIgRnbKe9evXc++999KzZ8/A/68dzO3pCgkiIiJiS8sNIiIiYkshQURERGwpJIiIiIgthQQRERGxpZAgIiIithQSRKRDWLJkyX4fYCQi4acnLop0UjfddBNbt27F4fgm6w8bNoxrrrkmIvO5//77Oeeccxg5cmREjv9d48aN4/e//z2ZmZmRnopIp6WQINKJ3XHHHZx22mmRnkbY+f3+wNsfQ8U0zTYBTORopJAgEoX+9Kc/0djYyG233QbA888/T0VFBZMnT2bdunXMnDmTUaNG8eqrrxIbG8vPfvazwMuRWltb+etf/8pbb72Fz+dj0KBBXHXVVbjdbmD3o5znz59PdXU1SUlJXHPNNXz00Ud89NFHbNiwgT//+c+BMxpfffUVzzzzDBUVFSQlJXHJJZcE3tTY1NTEE088wbp168jKyuL000/fZz3V1dXcfPPNXH/99bzwwgukp6fzm9/8hsWLF/PKK6+wdetWTjjhBCZOnEhaWlrgzZZ737lxww030NLSwhtvvMEDDzwQGPfbZxtmzZqF2+2mtraWdevWcfvtt/PUU0/xwx/+kGXLllFTU0P//v256aabcLvdfP311zzxxBOsX78ewzDIzs7m/vvvV7CQqKKQIBKFrrzySn71q1+xZMkSMjIyePPNN/ntb38beBzr1q1baWpq4g9/+AMbNmzgoYceIicnh6ysLIqLi9myZQvTp0/H6XTyu9/9jhdffJFLL72U8vJyioqK+OUvf8kpp5zC1q1b2bFjB/379+fjjz9us9ywc+dOHnzwQcaNG8fdd9/N559/zoMPPkjPnj3p0aMHs2fPJiYmhqeeeorq6mqmTp1Kenr6futat24dM2bMwOFwUFpayoIFC7jjjjs45phjWLhwIb/73e948MEH+c1vfsO4ceOYPn16YLlh73Pu9+d///sfd911F3fccQc+nw+At956i7vvvhu3283kyZNZsmQJo0aN4h//+Acej4enn34agA0bNuhtkBJ1FHlFOrHp06dz1VVXBf6UlJQA0KVLF26++Wbmzp3LzJkzufrqq9u8nx7gkksuISYmhr59+zJgwABWrFiBZVm88cYb/PznPychIYGuXbty4YUXsnz5cmD3uwiGDx/OaaedhsPhwOPx0L17d9u5vfvuu6SlpTF8+HCcTifHH388Z511Fm+99RamafL2229zySWXEBsbS8+ePRk6dOgB6x07diyxsbG43W5ef/11fvrTn9KjRw+cTic//elP2bRpEzU1NYf97zlo0CBOOukkHA5H4MzJj370IzweDwkJCQwcOJBNmzYB4HQ62bp1K7W1tbhcLk4++WSFBIk6OpMg0ondfvvt+7wmoXfv3mRkZNDY2Bg4xb9XfHw8sbGxge20tDQaGhr4+uuv2bVrF3feeWdgn2VZmKYJ7H5l7d6X3xxITU0NGzZs4Kqrrgq0+f1+zj33XL7++mv8fn+b4JKWlsb/b+9+Qtn/4wCOP39THxlabVxIU5xkUmo1pYQ5OPiXi9SUZUpcXDnhwEVqTU6Ym80BJ+WCixMprV0cZGPDfPInmlnb76Dv8tUnfB1+Xz+9HrdPn0+v9+t9e71f7/endzAYfDfm6++vrq5YXFxkeXn5t1xVVaWwsPBTOb4X/5fXN34qioKqqgC0trbi9/uZnJwEoKmpifb29i+NK8R3JUWCED/U5uYmz8/PGI1G1tfX6ejoyLx7eHggHo9nCoVYLEZJSQn5+fkoisLMzIzmzZAmk4loNKo53ttVtMlkoqKiQvO3xlQqRVZWFtfX15lORCwW+3BOr8coKCigs7Mzc5biI9nZ2SQSiczzzc3Nh3N4T05ODg6HA4fDwenpKePj45SVlWGxWD4dQ4jvTrYbhPiBzs/PWVlZYXh4mKGhITY2NjJt8l98Ph/JZJJgMMjBwQE2mw2dTkdjYyNLS0vc3t4CoKoqh4eHADQ0NLC9vc3R0RGpVApVVTk7OwPAYDBwcXGRiV9TU0MkEmF3d5dkMkkymeT4+JhwOIxO7tqXKgAAAZVJREFUp8NqteL3+3l6eiIcDrOzs/NHc7Tb7aytrREKhQB4fHxkb28v8/5tPmazmVAoxMnJCYlEAp/P90fjvbW/v080GiWdTqPX69HpdLLdIH4c6SQI8T82PT3922n6qqoqRkZGcLvdtLW1UVpaCrzcG+92u5mamgJeWuh5eXkMDAygKAr9/f2ZFX1PTw+rq6uMjo5yf3+P0WjEbrdTXV1NeXk5g4ODeL1eLi8vMRgMOJ1OiouLaWlpwePxsLW1RV1dHX19fYyNjeH1evF6vaTTacxmM729vQA4nU7m5uZwuVwUFRVRX19PIBD49NytVivxeJzZ2VlisRh6vR6LxYLNZgNezi94PB4SiQQul4va2lq6urqYmJhAURS6u7szZzi+IhKJsLCwwN3dHbm5uTQ3N1NZWfnleEJ8R/+k0+n0305CCPHfCQQCuN1u5ufn/3YqQohvTrYbhBBCCKFJigQhhBBCaJLtBiGEEEJokk6CEEIIITRJkSCEEEIITVIkCCGEEEKTFAlCCCGE0CRFghBCCCE0/Qvvx1VCJ9mYVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDMkPUXVfeCK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "6779c21b-33f0-4088-d14f-00be6b3118be"
      },
      "source": [
        "# setting the timeframe for our data gathering\n",
        "past = datetime.datetime(2020, 11, 1)\n",
        "present = datetime.datetime(2021, 11, 19)\n",
        "\n",
        "# Yahoo API google finance data\n",
        "google = data.DataReader(\"GOOG\", 'yahoo', start=past, end=present)\n",
        "google.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</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>2021-11-15</th>\n",
              "      <td>3009.540039</td>\n",
              "      <td>2973.050049</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>2987.760010</td>\n",
              "      <td>812400</td>\n",
              "      <td>2987.760010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-16</th>\n",
              "      <td>2996.649902</td>\n",
              "      <td>2967.000000</td>\n",
              "      <td>2983.409912</td>\n",
              "      <td>2981.520020</td>\n",
              "      <td>862700</td>\n",
              "      <td>2981.520020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-17</th>\n",
              "      <td>2992.520020</td>\n",
              "      <td>2971.260010</td>\n",
              "      <td>2984.580078</td>\n",
              "      <td>2981.239990</td>\n",
              "      <td>764500</td>\n",
              "      <td>2981.239990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-18</th>\n",
              "      <td>3032.199951</td>\n",
              "      <td>2979.969971</td>\n",
              "      <td>2982.919922</td>\n",
              "      <td>3014.179932</td>\n",
              "      <td>1332900</td>\n",
              "      <td>3014.179932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-19</th>\n",
              "      <td>3037.000000</td>\n",
              "      <td>2997.750000</td>\n",
              "      <td>3020.000000</td>\n",
              "      <td>2999.050049</td>\n",
              "      <td>988300</td>\n",
              "      <td>2999.050049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   High          Low  ...   Volume    Adj Close\n",
              "Date                                  ...                      \n",
              "2021-11-15  3009.540039  2973.050049  ...   812400  2987.760010\n",
              "2021-11-16  2996.649902  2967.000000  ...   862700  2981.520020\n",
              "2021-11-17  2992.520020  2971.260010  ...   764500  2981.239990\n",
              "2021-11-18  3032.199951  2979.969971  ...  1332900  3014.179932\n",
              "2021-11-19  3037.000000  2997.750000  ...   988300  2999.050049\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8novK7cTA1Nw"
      },
      "source": [
        "# moving average mean of closing data for past 6 months\n",
        "googleClose = google['Adj Close']\n",
        "#vgMean = googleClose.rolling(window=180).mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "V4mV-MD1BBON",
        "outputId": "379daaac-1b19-4e70-bffc-65a71eced177"
      },
      "source": [
        "# matplotlib plot of Google data\n",
        "mpl.rc('figure', figsize=(8, 7))\n",
        "mpl.__version__\n",
        "\n",
        "\n",
        "style.use('ggplot')\n",
        "googleClose.plot(label='GOOG')\n",
        "#avgMean.plot(label='avgMean')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd70a863c50>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "JUj5uZSeBFDa",
        "outputId": "7a1573f5-3a6a-4646-f330-dce755bdaac6"
      },
      "source": [
        "# returns\n",
        "returns = googleClose / googleClose.shift(1) - 1\n",
        "returns.plot(label='return')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd708508f90>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "wOzFfC3dBmwm",
        "outputId": "2ac99681-226a-4758-9d42-57dc5ebe8d99"
      },
      "source": [
        "df = data.DataReader(['AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],'yahoo',start=past,end=present)\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>Attributes</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">High</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Low</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>2021-11-15</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>106.669998</td>\n",
              "      <td>2987.760010</td>\n",
              "      <td>118.870003</td>\n",
              "      <td>335.456299</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>106.669998</td>\n",
              "      <td>2987.760010</td>\n",
              "      <td>118.870003</td>\n",
              "      <td>336.070007</td>\n",
              "      <td>151.880005</td>\n",
              "      <td>108.669998</td>\n",
              "      <td>3009.540039</td>\n",
              "      <td>120.160004</td>\n",
              "      <td>337.880005</td>\n",
              "      <td>149.429993</td>\n",
              "      <td>106.199997</td>\n",
              "      <td>2973.050049</td>\n",
              "      <td>118.309998</td>\n",
              "      <td>334.029999</td>\n",
              "      <td>150.369995</td>\n",
              "      <td>108.029999</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>119.540001</td>\n",
              "      <td>337.540009</td>\n",
              "      <td>59222800.0</td>\n",
              "      <td>6124900.0</td>\n",
              "      <td>812400.0</td>\n",
              "      <td>5046300.0</td>\n",
              "      <td>16723000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-16</th>\n",
              "      <td>151.000000</td>\n",
              "      <td>103.349998</td>\n",
              "      <td>2981.520020</td>\n",
              "      <td>118.459999</td>\n",
              "      <td>338.890015</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>103.349998</td>\n",
              "      <td>2981.520020</td>\n",
              "      <td>118.459999</td>\n",
              "      <td>339.510010</td>\n",
              "      <td>151.490005</td>\n",
              "      <td>106.209999</td>\n",
              "      <td>2996.649902</td>\n",
              "      <td>119.900002</td>\n",
              "      <td>340.670013</td>\n",
              "      <td>149.339996</td>\n",
              "      <td>102.820000</td>\n",
              "      <td>2967.000000</td>\n",
              "      <td>118.419998</td>\n",
              "      <td>335.510010</td>\n",
              "      <td>149.940002</td>\n",
              "      <td>106.150002</td>\n",
              "      <td>2983.409912</td>\n",
              "      <td>118.919998</td>\n",
              "      <td>335.679993</td>\n",
              "      <td>59256200.0</td>\n",
              "      <td>11997700.0</td>\n",
              "      <td>862700.0</td>\n",
              "      <td>4750800.0</td>\n",
              "      <td>20886800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-17</th>\n",
              "      <td>153.490005</td>\n",
              "      <td>101.989998</td>\n",
              "      <td>2981.239990</td>\n",
              "      <td>118.059998</td>\n",
              "      <td>339.119995</td>\n",
              "      <td>153.490005</td>\n",
              "      <td>101.989998</td>\n",
              "      <td>2981.239990</td>\n",
              "      <td>118.059998</td>\n",
              "      <td>339.119995</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>103.879997</td>\n",
              "      <td>2992.520020</td>\n",
              "      <td>119.330002</td>\n",
              "      <td>342.190002</td>\n",
              "      <td>150.990005</td>\n",
              "      <td>101.419998</td>\n",
              "      <td>2971.260010</td>\n",
              "      <td>117.779999</td>\n",
              "      <td>338.000000</td>\n",
              "      <td>151.000000</td>\n",
              "      <td>103.699997</td>\n",
              "      <td>2984.580078</td>\n",
              "      <td>118.379997</td>\n",
              "      <td>338.940002</td>\n",
              "      <td>88807000.0</td>\n",
              "      <td>8307600.0</td>\n",
              "      <td>764500.0</td>\n",
              "      <td>4043300.0</td>\n",
              "      <td>19053400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-18</th>\n",
              "      <td>157.869995</td>\n",
              "      <td>100.669998</td>\n",
              "      <td>3014.179932</td>\n",
              "      <td>116.660004</td>\n",
              "      <td>341.269989</td>\n",
              "      <td>157.869995</td>\n",
              "      <td>100.669998</td>\n",
              "      <td>3014.179932</td>\n",
              "      <td>116.660004</td>\n",
              "      <td>341.269989</td>\n",
              "      <td>158.669998</td>\n",
              "      <td>101.800003</td>\n",
              "      <td>3032.199951</td>\n",
              "      <td>118.360001</td>\n",
              "      <td>342.450012</td>\n",
              "      <td>153.050003</td>\n",
              "      <td>99.180000</td>\n",
              "      <td>2979.969971</td>\n",
              "      <td>116.309998</td>\n",
              "      <td>337.119995</td>\n",
              "      <td>153.710007</td>\n",
              "      <td>101.430000</td>\n",
              "      <td>2982.919922</td>\n",
              "      <td>118.360001</td>\n",
              "      <td>338.179993</td>\n",
              "      <td>137827700.0</td>\n",
              "      <td>8530900.0</td>\n",
              "      <td>1332900.0</td>\n",
              "      <td>5046900.0</td>\n",
              "      <td>22463500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-19</th>\n",
              "      <td>160.550003</td>\n",
              "      <td>99.959999</td>\n",
              "      <td>2999.050049</td>\n",
              "      <td>116.050003</td>\n",
              "      <td>343.109985</td>\n",
              "      <td>160.550003</td>\n",
              "      <td>99.959999</td>\n",
              "      <td>2999.050049</td>\n",
              "      <td>116.050003</td>\n",
              "      <td>343.109985</td>\n",
              "      <td>161.020004</td>\n",
              "      <td>100.739998</td>\n",
              "      <td>3037.000000</td>\n",
              "      <td>116.559998</td>\n",
              "      <td>345.100006</td>\n",
              "      <td>156.529999</td>\n",
              "      <td>99.300003</td>\n",
              "      <td>2997.750000</td>\n",
              "      <td>115.269997</td>\n",
              "      <td>342.200012</td>\n",
              "      <td>157.649994</td>\n",
              "      <td>99.800003</td>\n",
              "      <td>3020.000000</td>\n",
              "      <td>116.489998</td>\n",
              "      <td>342.640015</td>\n",
              "      <td>117147500.0</td>\n",
              "      <td>6603300.0</td>\n",
              "      <td>988300.0</td>\n",
              "      <td>5380200.0</td>\n",
              "      <td>21942200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Attributes   Adj Close              ...     Volume            \n",
              "Symbols           AAPL          GE  ...        IBM        MSFT\n",
              "Date                                ...                       \n",
              "2021-11-15  150.000000  106.669998  ...  5046300.0  16723000.0\n",
              "2021-11-16  151.000000  103.349998  ...  4750800.0  20886800.0\n",
              "2021-11-17  153.490005  101.989998  ...  4043300.0  19053400.0\n",
              "2021-11-18  157.869995  100.669998  ...  5046900.0  22463500.0\n",
              "2021-11-19  160.550003   99.959999  ...  5380200.0  21942200.0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "pXK2EBQqBvmL",
        "outputId": "213ad23b-879e-4540-de94-90cfccc0db7d"
      },
      "source": [
        "df = data.DataReader(['AAPL', 'GE', 'GOOG', 'IBM', 'MSFT'],'yahoo',\n",
        "                     start=past,end=present)['Adj Close']\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-11-15</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>106.669998</td>\n",
              "      <td>2987.760010</td>\n",
              "      <td>118.870003</td>\n",
              "      <td>335.456299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-16</th>\n",
              "      <td>151.000000</td>\n",
              "      <td>103.349998</td>\n",
              "      <td>2981.520020</td>\n",
              "      <td>118.459999</td>\n",
              "      <td>338.890015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-17</th>\n",
              "      <td>153.490005</td>\n",
              "      <td>101.989998</td>\n",
              "      <td>2981.239990</td>\n",
              "      <td>118.059998</td>\n",
              "      <td>339.119995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-18</th>\n",
              "      <td>157.869995</td>\n",
              "      <td>100.669998</td>\n",
              "      <td>3014.179932</td>\n",
              "      <td>116.660004</td>\n",
              "      <td>341.269989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-11-19</th>\n",
              "      <td>160.550003</td>\n",
              "      <td>99.959999</td>\n",
              "      <td>2999.050049</td>\n",
              "      <td>116.050003</td>\n",
              "      <td>343.109985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols           AAPL          GE         GOOG         IBM        MSFT\n",
              "Date                                                                   \n",
              "2021-11-15  150.000000  106.669998  2987.760010  118.870003  335.456299\n",
              "2021-11-16  151.000000  103.349998  2981.520020  118.459999  338.890015\n",
              "2021-11-17  153.490005  101.989998  2981.239990  118.059998  339.119995\n",
              "2021-11-18  157.869995  100.669998  3014.179932  116.660004  341.269989\n",
              "2021-11-19  160.550003   99.959999  2999.050049  116.050003  343.109985"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "_S_a7fTvB1lH",
        "outputId": "9050372c-c4ae-4d3d-fc32-03dc59249c1e"
      },
      "source": [
        "# correlation plot\n",
        "correlation = df.pct_change()\n",
        "corr = correlation.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GE</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</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>AAPL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.054233</td>\n",
              "      <td>0.537254</td>\n",
              "      <td>0.032301</td>\n",
              "      <td>0.657898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GE</th>\n",
              "      <td>0.054233</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.018518</td>\n",
              "      <td>0.412322</td>\n",
              "      <td>-0.112048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GOOG</th>\n",
              "      <td>0.537254</td>\n",
              "      <td>0.018518</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.052586</td>\n",
              "      <td>0.697746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IBM</th>\n",
              "      <td>0.032301</td>\n",
              "      <td>0.412322</td>\n",
              "      <td>0.052586</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.016125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.657898</td>\n",
              "      <td>-0.112048</td>\n",
              "      <td>0.697746</td>\n",
              "      <td>-0.016125</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Symbols      AAPL        GE      GOOG       IBM      MSFT\n",
              "Symbols                                                  \n",
              "AAPL     1.000000  0.054233  0.537254  0.032301  0.657898\n",
              "GE       0.054233  1.000000  0.018518  0.412322 -0.112048\n",
              "GOOG     0.537254  0.018518  1.000000  0.052586  0.697746\n",
              "IBM      0.032301  0.412322  0.052586  1.000000 -0.016125\n",
              "MSFT     0.657898 -0.112048  0.697746 -0.016125  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "gOYbF875CCcz",
        "outputId": "07b30c80-4391-410e-8839-e9c5b6b0306e"
      },
      "source": [
        "plt.scatter(correlation.GOOG, correlation.MSFT)\n",
        "plt.xlabel('Google Returns')\n",
        "plt.ylabel('Microsoft Returns')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Microsoft Returns')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "BCfDZuRMCHER",
        "outputId": "91e89593-81a4-409e-b53f-e05ac9693c3c"
      },
      "source": [
        "plt.imshow(corr, cmap='hot', interpolation='none')\n",
        "plt.colorbar()\n",
        "plt.xticks(range(len(corr)), corr.columns)\n",
        "plt.yticks(range(len(corr)), corr.columns);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "mHC8LVZECLen",
        "outputId": "7444f39c-0f5c-440d-9ffb-db6ae63a9a4c"
      },
      "source": [
        "plt.scatter(correlation.mean(), correlation.std())\n",
        "plt.xlabel('Expected returns')\n",
        "plt.ylabel('Risk')\n",
        "for label, x, y in zip(correlation.columns, correlation.mean(), correlation.std()):\n",
        "    plt.annotate(\n",
        "        label, \n",
        "        xy = (x, y), xytext = (20, -20),\n",
        "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
        "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
        "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x504 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FqwoGeOiXmMq"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}