{
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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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    "colab": {
      "name": "CAPM-Capital-Asset-Pricing-Model1.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cyrus723/my-first-binder/blob/main/CAPM_Capital_Asset_Pricing_Model1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSBv_Oz_mwUT"
      },
      "source": [
        "# CAPM - Capital Asset Pricing Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "IFH0XGudmwUf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d09e52e-de4b-4bc0-92ff-2931c93e60b8"
      },
      "source": [
        "pip install yfinance"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.2.12-py2.py3-none-any.whl (59 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.2/59.2 KB\u001b[0m \u001b[31m837.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: lxml>=4.9.1 in /usr/local/lib/python3.9/dist-packages (from yfinance) (4.9.2)\n",
            "Collecting beautifulsoup4>=4.11.1\n",
            "  Downloading beautifulsoup4-4.11.2-py3-none-any.whl (129 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.4/129.4 KB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.9/dist-packages (from yfinance) (0.0.11)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.2-py3-none-any.whl (62 kB)\n",
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            "\u001b[?25hCollecting frozendict>=2.3.4\n",
            "  Downloading frozendict-2.3.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (112 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.8/112.8 KB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cryptography>=3.3.2\n",
            "  Downloading cryptography-39.0.2-cp36-abi3-manylinux_2_28_x86_64.whl (4.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting html5lib>=1.1\n",
            "  Downloading html5lib-1.1-py2.py3-none-any.whl (112 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.2/112.2 KB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.9/dist-packages (from yfinance) (2022.7.1)\n",
            "Collecting appdirs>=1.4.4\n",
            "  Downloading appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.9/dist-packages (from yfinance) (1.4.4)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.4)\n",
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            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.9/dist-packages (from html5lib>=1.1->yfinance) (1.15.0)\n",
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            "Collecting charset-normalizer<4,>=2\n",
            "  Downloading charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (199 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.2/199.2 KB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.21)\n",
            "Installing collected packages: appdirs, html5lib, frozendict, charset-normalizer, beautifulsoup4, requests, cryptography, yfinance\n",
            "  Attempting uninstall: html5lib\n",
            "    Found existing installation: html5lib 1.0.1\n",
            "    Uninstalling html5lib-1.0.1:\n",
            "      Successfully uninstalled html5lib-1.0.1\n",
            "  Attempting uninstall: beautifulsoup4\n",
            "    Found existing installation: beautifulsoup4 4.9.3\n",
            "    Uninstalling beautifulsoup4-4.9.3:\n",
            "      Successfully uninstalled beautifulsoup4-4.9.3\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.25.1\n",
            "    Uninstalling requests-2.25.1:\n",
            "      Successfully uninstalled requests-2.25.1\n",
            "Successfully installed appdirs-1.4.4 beautifulsoup4-4.11.2 charset-normalizer-3.1.0 cryptography-39.0.2 frozendict-2.3.5 html5lib-1.1 requests-2.28.2 yfinance-0.2.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "nySQT5GBmwUg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8decb23-ae4d-4647-a2f1-3acea936a50a"
      },
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm   \n",
        "import datetime as dt\n",
        "\n",
        "import urllib.request       # handle online files\n",
        "import zipfile            \n",
        "import os                \n",
        "\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from pylab import mpl, plt\n",
        "\n",
        "plt.style.use('seaborn')\n",
        "mpl.rcParams['font.family'] = 'DejaVu Sans'\n",
        "mpl.rcParams[\"savefig.dpi\"] = 500\n",
        "np.set_printoptions(precision=5, suppress=True, formatter={\"float\": lambda x: f\"{x:6.3f}\"})\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-4039a680b4e1>:20: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use('seaborn')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4f7t_LPmwUh"
      },
      "source": [
        "#help(stats.linregress)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-WJe7qxmwUl"
      },
      "source": [
        "def get_prices(tickers, freq_p, st_day, end_day):\n",
        "    mystock = pd.DataFrame()\n",
        "    for t in tickers:\n",
        "        mystock[t] = yf.download(t, start=st_day, end=end_day, interval=freq_p)['Adj Close']\n",
        "    return mystock"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tic=['TSLA', 'SPY']\n",
        "prices= get_prices(tic, freq_p='1wk', st_day=\"2011-01-01\", end_day=\"2022-05-31\")  # id , 1wk, 1mo\n",
        "print(prices)\n",
        "\n",
        "prices.rename(columns={\"TSLA\": \"Your_Stock\", \"SPY\":\"INDEX\"}, inplace=True)\n",
        "\n",
        "prices.info()\n",
        "prices.tail(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ctHO6RAQxX7j",
        "outputId": "7940b6dc-7fa8-4115-edaf-66714f6436a4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "                  TSLA         SPY\n",
            "Date                              \n",
            "2011-01-01    1.882667  101.180954\n",
            "2011-01-08    1.716667  102.899910\n",
            "2011-01-15    1.536000  102.159821\n",
            "2011-01-22    1.600667  101.642517\n",
            "2011-01-29    1.564000  104.372192\n",
            "...                ...         ...\n",
            "2022-04-30  288.549988  406.030701\n",
            "2022-05-07  256.529999  396.534882\n",
            "2022-05-14  221.300003  384.600952\n",
            "2022-05-21  253.210007  409.900116\n",
            "2022-05-28  252.753326  407.600189\n",
            "\n",
            "[596 rows x 2 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 596 entries, 2011-01-01 to 2022-05-28\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Your_Stock  596 non-null    float64\n",
            " 1   INDEX       596 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 14.0 KB\n"
          ]
        },
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              "            Your_Stock       INDEX\n",
              "Date                              \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80d4b065-8c3b-4a3d-b6e2-e9c57f81d537 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80d4b065-8c3b-4a3d-b6e2-e9c57f81d537');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrQgItnKmwUp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1888c939-86e4-4e78-d51c-e6e51c5193d3"
      },
      "source": [
        "print(prices.isnull().sum())\n",
        "prices.dropna(how='any', inplace=True)\n",
        "print(prices.isnull().sum())\n",
        "prices.info()\n",
        "print(prices)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your_Stock    0\n",
            "INDEX         0\n",
            "dtype: int64\n",
            "Your_Stock    0\n",
            "INDEX         0\n",
            "dtype: int64\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 596 entries, 2011-01-01 to 2022-05-28\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Your_Stock  596 non-null    float64\n",
            " 1   INDEX       596 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 14.0 KB\n",
            "            Your_Stock       INDEX\n",
            "Date                              \n",
            "2011-01-01    1.882667  101.180954\n",
            "2011-01-08    1.716667  102.899910\n",
            "2011-01-15    1.536000  102.159821\n",
            "2011-01-22    1.600667  101.642517\n",
            "2011-01-29    1.564000  104.372192\n",
            "...                ...         ...\n",
            "2022-04-30  288.549988  406.030701\n",
            "2022-05-07  256.529999  396.534882\n",
            "2022-05-14  221.300003  384.600952\n",
            "2022-05-21  253.210007  409.900116\n",
            "2022-05-28  252.753326  407.600189\n",
            "\n",
            "[596 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mivqtbW9mwUw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "cb3c27fb-02f7-4ade-b8ca-8f47f1abc6aa"
      },
      "source": [
        "prices.plot(figsize=(10,6))\n",
        "plt.legend()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6d43cc6100>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(prices / prices.iloc[0] * 1).plot(figsize=(10, 6), subplots=False)  #cumulative returns\n",
        "plt.title('Cumulative Return')\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "U1MhYpXJ5S8M",
        "outputId": "9a241904-1a18-48ea-92cc-760f62bd0c68"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prices.pct_change(1).dropna()\n",
        "plt.scatter(prices['Your_Stock'], prices['INDEX'], color = 'red', alpha=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "TnWNwpr-5S5L",
        "outputId": "4f32e9e4-1567-4671-ddf1-08c4e8cb0a3d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6d0c53ff40>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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4UTk1gbp5c62sLArX1o4/cSIs7NntJKD3cPEi8fMVcspLZW+HL+08Pl3LueKH1Ei6XhFxF4Ty2K5zoZVbKhw6n6fYfxI1WrZIKc7gentJHn4kZKaUTVSqsdHV+iyl+uN6aQE3MV7Z09ykqFepHynpAiJ95TKwzGbNnQ8kPl0KUUHa/OLwxbTeaqwfJiLuwu2NczA3FxYMuTgtHVDWFMMVUdeuohfmoJi2YIujTT1iFVb9hBVGJXy6vZxK9Wl6eoAgZOrG9dXGz+7kKdToCPS0o6/evLW5cym0sl/xae/Rw5eInv1WKG+ggK4ekvvuDz/vYaYgSqmBXSPiLtyelMR1YgJ17SrRc9+CtnZ8bxYK89DSGUTMp+GOpUV8kqDjDHT3hPTDjR5xoYA7cQI9Ga2mSa7WY9+wEKhSfZrouWfDqlOt0UsLq7Vk9M0R1MULMNmGau0Kk7QQSgMUVuCBB/c1Pq0vDxM992z4BtLUFK41P0f83LMkqdgeOrUcPqpRRNyF25KSuKrxMfTKEr6rJ5QQyE9DawNqbAx/IotfXCRamA8hkq5uVCYTRBVwfX3rPeJMJky+lqVJoqO1bwIlgd2kaqSencU7F3LWS632tIaZPGphARr02kOntG92drUW+77Ep51Dj4+HkrpR2TFaw+wMenxcUhCPCVWLuzEmAr4OXLfWvskYcxZ4HOgFngR+xlq7YoxpBD4OvAyYBH7SWju875YLwm4piavWa42X+/pwgMrnYfA8fvoarrEJlVkKi5HK661oHR4Cfdn1HnG5wCqFmphY7bDku7rRVy6v1VLfGEJJkhACKiShiFdZH1XliqHWTJKkq0XTfaXmzsUixPH+xKcLBVhJC35F6wVcOQeFZUlBPCbs5PH7y8B3yj7/HvABa+05IAe8M93+TiCXbv9AOk4QaoeSuKaCCgRvOJvFnz4N589TfNWrKbz29bhz5yk++BC+r3ddrFklCa6z8xYP1t1xBtfdE+L0kxMhnbK3Dzc0hM5NhbrslUIocQw6wmcyEDes2+XjDO7kSWhvX7O/6HBtHfi+7FpJ3TRkkjzyKMmDD5M88ujOY+SZDDQ0VgzxeK3DylhJQTwWVCXuxpgh4IeBP00/K+D1wCfTIR8D3py+fyz9TLr/B9LxglAblMQ1FdRyfBxDSwvEGWhrg6Zm/KlBXHtnaPZcEtaurlCmdyPpcn/fP0DRvBRnXhrCNGnFyNC/k7QWetnErVa49nboTAuQ+XSfc9DdA53d4aFzz70Uz92N62hHT0+jJ8aIv/k0evhSSJeEtfj0bkInWuOy2WCLL59YdtDegctmJSRzTKg2LPNHwG8AqetALzBtrU3TB7gGDKbvB4GrANbaxBiTT8dP7IfBgrBnysInrrMz9DBVac2Vzi4gFd84Xh1XaiUX4uhpo4qNTShKFAqADxOiG0kzVyqFUIr33re6zRUKa9ky2RPher1t8MIV1M0R1NwsPp3YRal9rbeyusp1Y7bMvfdJCuIxYltxN8a8CRiz1j5pjHndQRjR3d1CvDE/t0qy2fbtBx0BtWhXLdoER2RX3wNw6RJ0NMHVq2Fisr0LTp+G3l56S7VfSuPGx9O+oE2QzcJWtWFcK1xvXy3/u46kEU6lpXhPPFh1nntpX/cdd8C//EuIwW/0oJNF6G3dH8/6xEPw8geqynOX36vqOUybqvHcXwP8iDHmjUAT0AF8EOgyxsSp9z4EXE/HXwdOA9eMMTHQSZhY3ZRcbmFXxmez7YyPz+7q2IOkFu2qRZvgiO1qz0JrLwydWyek2f7O9TaVxpWL8MRc5XOmwqxpQk/mbs1c6e7BTc5XOHBlC0PDvmy2nfGb08Szy7BcoYHGygrJjamDmeycSypult+r6jkIm7Z6WGz7iLfWvsdaO2StPQO8Ffictfangc8DP54Oezvw9+n7T6WfSfd/zlq7SSsXQThiSvHpON46Tr1dHDtd+BM/9Q3iZ55GT03C/AIU0kbTSXGtDO9ekHorQpXsJc/9N4HHjTH/GXgK+Ei6/SPAXxhjLgBThAeCIBxfKoVONlBpURJRC66zCzc4tH8rK6XeilAlOxJ3a+0XgC+k7y8Cr6gwZgn4iX2wTRCOlkolAipVWdxYmyZJ0kwcjc7lwiTnPoqu1FsRqkFWqArCJlTyxitmpRQKUEzQY6Pblh2o5lvAtuym3sp+XFc4Voi4C0IldlJlMZNBj42h5ufCxGx57ZdStku13wJ2QjX1Vg7iusKxQB7hglCJ1RIBFShVWSzD+zQc48tyBxT4ULxg/beAhobVypL68vD+2Luxg1TKgV9XqFnEcxeESlSbleI9+sIL6FwONZ2DhQVoacVnT+C6uvC9oaH1gfUC3coz935n15XQTV0h4i4IlagyK0VfHkbPzuAbGmDgVFiyX0hwnR34U6dCoS84sF6gW84LDJyq7roSuqlL5PEsCOWUhTdKRcBIipVz1Vfj8nEoW+BcKGPQ0ICemQ3je3vT/PgDyE0vq265/pxpDZsoquq6a8LuQk0dCd3UBeK5CwKs9147Golnlle9102zUspK97qBgeAplbJlXBHX3r7q/R5Ibvp23ZeKxe2vWywSffsZ9Nz8mtfe2Ynf2IxEQjbHDhF3QWBDeKOxEeJkfdpjpbBJeVxeqfVNOlC4O+8KHn8mczC56VXMC2x3Xf3iBfT0dPj5Sj1fZ/M4CPXrV1bQN0ckZHMMEXEXhN02l64Ul9c6hEMWFom/+fQtgrivvUCrnBfYNCfeOXQ+j4/j9R1eVWhiUuzLom9cR+ent8/1F2oO+X4lCDtMe1y3u0JcnoVFaGmunH64l1rrVV6/Yg2b8uuW5hWWlwG/Nl9QTlIINeNzuVttLcX0Nx4j1BTiuQvCXopxbVwtGkXBY99Y673St4DDXq26MSsGhRq9iRscWj9foDV0deNO34F+9ltsmW0j1Cwi7oKwH8W4Sp7x8vL26YcNDUeyWrVS2qRSGm7cwA0NlTX11rjevrRmvFSgPK5IWEYQ2BDeWF7efYneKArncBWqXKeCeCSrVTdJm3SDgyjvYaWQCnuE6+0LP3ep49TG8ItUoDwWiOcuCLAW3hg6Da0RyXyxcielzSgLeejxUdTcPK67G5+2wVsVROfQIyPBKy7noFerbpY2qRSuv5/k/gfCdTeEdaQC5fFFxF0QYMs892pCJeXeuHvJHeiREVRuCgoF/OAQrqcHvCf+168RPf9daGxazSdfPf9BrlZ9yR1bh1g2m+TdTQVKoSaQ/0uCQKU89wg9OYF+4fnts0I2hjzSnHd3z7347AmShx4OTaync9DUiE9zyvVsHjUyUmbEAa5Whb2FWPY5y0c4eMRzF4QkWR8q8R594wbkp4kKBYr5PC6b3dyL3yzkoXV4WBQK6/PoO7tgJh+EN5+neHIA8Ae7WrVQkBDLbYaIu3D7UgrFjIysC5Uw14SayYfJ0dTT3XLhznaplLBOeNeVKlhehqUl3MDAga9WlRDL7cW24m6MaQK+CDSm4z9prX2fMeajwGuBfDr0Hdbap40xCvgg8EZgId3+jYMwXhD2wmooJg2VqEij8zkYnYPWrnRQlLbMU5tPeG6XSrmxcFh5qYKlZZLvecXOJm8r/jA7SOespsmHcOyp5jdqGXi9tXbOGJMBvmSM+R/pvl+31n5yw/gfAs6n/14JfCh9FW4HjkuBqY0lB0qhEuchn4eWTsAHT15vP+G5Zchjs8JhBC9+z8JejQ3Cbce2v1XWWg/MpR8z6b8KSbyrPAZ8PD3uK8aYLmPMgLV2ZItjhOPOQdYEP4gHxoYY9WqoJM1wYXkF19cXsllKbDXhuU3I41CEV8IuQhlVuQzGmAh4EjgH/LG19qvGmP8TeL8x5j8CTwC/Za1dBgaBq2WHX0u3bSru3d0txBuLNlVJNtu+q+MOmlq060BtungRWIZsx9o2twxzE3Dnnbuzy3u4dAnGx6FYDDHwbBbOnt2HB0YrXG9f7zX3nA8Pkou9dLzkJWGB0ep4B7290N+5+2ueeHDPD6rb7vdqD9SiXYdpU1Xibq0tAg8bY7qAvzPG3A+8B7gJNAAfBn4T+E+7MSKXW9jNYWSz7YyPz+7q2IOkFu06UJucI37+8q1VFQEmL5O0bp4FspVdevjShlBGAvkruMm5falIqHUzevLWGHX3y1/O5NQ8enyDl93WB/t2D1d2fMRt93u1B2rRroOwaauHxY6CfdbaaWPM54E3WGt/P928bIz5c+DX0s/XgdNlhw2l24R6pYo0PDKZtdfSMVvldO+2DO8O2CxUwp134jrmJLwhHGuqyZbJAoVU2JuBHwR+rxRHT7Nj3gx8Oz3kU8C7jTGPEyZS8xJvr3O2SsNTGn39WigdWyigblxDAe7UIGQa4O47oK3v1jBLNQ+MvWZ8bBajLtkiWSXCMaYaz30A+Fgad9fAJ6y1/2CM+Vwq/Ap4GvjFdPynCWmQFwipkD+771YLtcVmaXhJEWZn0IAaGyX6+teIJsbxEBYFvewV0NOKrhRm2UsZ3l3YLyIu1BvVZMs8AzxSYfvrNxnvgXft3TThOLEuxFFMUGOjKOchn0Pn86ipqeC9NzaiAD2VgxdfgK4WdGPHrWGW/SjDKwi3MfIXIuwPaYgjeeRRXFc3fuAUrv8kquhQc3OoyQmYnVkbjofZWZiagqRQsfFD1V2GBEG4BSk/IOw7emYGNT6GzuXQVy+jx0ZRi0uwsoL3HpTCa43SOo2t+8phFsnbFoRdI38pwv5SKKCuXUNPhUwX19OLjzNQWAkeejEB7/HNLaGdWxzj+vu3Fm2pSCgIO0Y8d2H/VoB6j756hei5Z1BRDErjW1tDjHx2Bp0k+MYmvIqgpQWam0Pa4dmtFzkJgrBzRNzrmXLRrsQ+lwwI55rAt7WjlpbAO9T8HL6nF9fZSXF0DH/2TlScwXV0UHzwYXjlQzAxt+25BUHYGSLuB8VRFtCqINqV8sm37NxTaQXoVj9TadFRWlqWXA61MB9y2ptbKH7/a3GvfA3JffevD7PstYyAIAgVEXHfb7yHixfDcvz9LqBVJZVEm8nJ9fnkO1kBWo2Hny460hMTKKWhpwff24svFlcfCu7ECWht3f4bhSAIe0bEfTt26IHrS5cgmVvrwsOGPpYH7c1vJdrjZaK9gxWgVXn4mQygIDeF7+mGnIL5ORTglcJHGjd0OtSL2eYbhSAIe0fEfTN2Go/2Hn3pIvG/fBk6monmC+saIMff/hZufBzwB+vNl0TbaUiS1UYTwHrRrnYF6JYPi3Fc/8lwPqVgbg598UVUJoNXClrbcJ2duJ4efPYE+uKL6Pm5rb9RCIKwL4i4b0JFb3VyAlZWcOfO3+J568vD6PExlFKhVOxSET2bxxHqM6jpHPSfXC0ju2Vsey/EMWr0Jnpufu2h1NkJXefWi3a1K0Arefjeh56jk+PEK8vQ1AyLi9DWiu/qQi0soryDuTl8Wxv+1CAUEnQ+Dw0bQjEbv1EIgrAviLhXYqO3WhKzzRoml8Y3NITc7RJKo3O50NkkzqyvHb6P1Q3L0VevhJi3YlXI9Wwerl7FDd218wYSFTx8PTISeozGDdDSCkB05TK+uxt/13lcfjosTIqj8Fp0uM5OdH66stH7VQhMEIRVRNwrUSiEBTfOQRyjb45s3TC55N3GDaFdmyur1Z2soIoed/LkWnikxH6LWvqQcQOn0NeuwtwcCo9HhW8cg0Prx1ezAnSjh+8c5KdBK1xH2oJupQB4yE/j7jZrzZ+TBO+KuPZ23F3n0E8/Vdnu/S4EJgiCiPstFApo+x30898NC3FQMDUBJ/rD/koNk8u8WzcwAAvT+PwIyjm80vi+rvXt2krst6itrKCuX0MvLIQHhweWFlFNzTA9Tfz1f8UNDNwa69+mKuI6D395EVUo4Pqyaz9THIOOUK4IxeJa8+ckAdRqGEsKgQnC4SHiXsI54n/+MtGTX0PPzuLnZlFt7bizd6LnZvE6wvf1rm+YnBRgbg7a2tYL19AQrrkzeMvZEyEEk5sKaZKlSU7Yd1HTN66j5mbDAyPSqPFx1Nwsvq0dugehqTEI9CbzBptS7uEvL0OmcX3sXCtcZycqN7UWeiqVFujuWb1OpTAQpQ5HgiDsKyLuKfE/f5lo+BJ6fgGamlBNTSGT48UXQrx8Jo87czZ4q6UY/NRkuIFxBtfTg+vqRk9NBQF0Htd/Mgia9+hr19CXL4VQRRzj7jiLe+iWSsq7x7lQUre7B2byoBRqbi6EkmZnoP2lqJs30fk8OlnBbZw3qAatobkZl83e4oH7/n58e0eIsScrleP3lcJA/Z372LpOEIQSIu7OwcJC8NjnF9A3b0Ccwbe0hEU4hQLunntRs7P47AlQCn3jBiqfw/X0QlMTAHo6h+vuIXnkUehqIpleWhU/fXkYWltw995f5rl79JXL+5ctk8b93cBAiHlPTsDKcpjkbW6GYsjeIdIoF4Xr7zJjZ9OJ2EfOhG8n2+XyS3MMQThwbl9xL8tj1y9YYmtDtkccoyKNWlqAaVJP9QQ6TsMQS0swNRmqHZbH0cuzXxobQaeTqhszb1bDGWrn2TJbLagqxf2VCjHvE/0oH67niyEtERWOKVVj3HXGzlYTsUqJcAtCDVBND9Um4ItAYzr+k9ba9xljzgKPA73Ak8DPWGtXjDGNwMeBlwGTwE9aa4cPyP5doy8PoyfGg1ivLOMbGlDLi/hCktY80aiFBVxrKzQ2ktz/QBCzublw01KPfR2l7Jdy9qMXaDULqjZOWMYRrq8Pnc9BWxv41C7nQkZPSYz3krEjHrgg1CzVuGvLwOuttQ8BDwNvMMa8Cvg94APW2nNADnhnOv6dQC7d/oF0XG2RJMSfe4L4s/9I5rOfIf7KV/CLC+A8Ko5xjU1452BlGdfbh+tLY9NaB6GMN8lwqZT9st1K0ChKY/RuU3PXLahqaIA4QuemQrinjI2di3z2BMWhO3AnB6BYxBeL+I7OkNGzlc2CIBx7qumh6oFSTdZM+s8Drwf+j3T7x4DfBj4EPJa+B/gk8F+MMSo9T00Q/68vEo1cRy0uoGZmUbN58Aq3MA9d3dDQgGttw/f1sfKOd66uKgV2ntK32fhiERYXib/59NblDXZS4GuzcIlzMDWIuzyyfiGVpCEKQt1SVczdGBMRQi/ngD8GXgSmrbVJOuQaMJi+HwSuAlhrE2NMnhC6mdjs/N3dLcQbxatKstn2nR2QJJAfg+kJyOeDyLokjRU3wQP3wqtfHcIu/f0w2HvrOfoegEuXYHw8HB9FkM3C2bOrwrxql3PQfjdcuwaTk2G8UjCzCAM9671mtwxzE3BnWfOK5WXoaKwc/lhehq6m6kIjJzrobmzc0uajYsf/Dw+BWrQJatOuWrQJatOuw7SpKnG31haBh40xXcDfAS/dTyNyuYVdHZfNtjO+0zS6mRmaLlwmvn4DNTufeq0+eM+zCxSmZkgWHa6tNeRfb3b+9iy09q73kNOmE9lsO+NjM7fGybu6wCfoqWn0C8+j4oZ1xcUAmLxM0lrmTTtHPLMMcXKrDUkxzcpZuXVfpXu1hc1Hxa7+Hx4wtWgT1KZdtWgT1KZdB2HTVg+LHX0ft9ZOA58HvhfoMsaUHg5DwPX0/XXgNEC6v5MwsVobNDain7eo2TlYWUbNzYauQTqi2NhI8srvDZOnZ6rwaLfo7VkpTh5/9ztEL7wAWqFQof55PocaGVk7cOOkbBrWuSUmv9uQivQjFYTbgm3/wo0x2dRjxxjTDPwg8B2CyP94OuztwN+n7z+Vfibd/7lairfrSxdxipAW2NoSqhamJXB9X08Qvr1mgJTi5Bti8szOoGdm0KOjqKtX0Vcuo69cQb94AYqpeFeY4Nw4UUpSxHX3rF8gJAiCUEY1YZkB4GNp3F0Dn7DW/oMx5jngcWPMfwaeAj6Sjv8I8BfGmAvAFPDWA7B75zgH8/PoK8OowUGKuRzx+Dh4B0rjujqg/1RYmLRXr7ZS+mOSoJyDiXFUsQidnTAfmnrouVn8tau406cre+PVFPgSBEEoo5psmWeAW9bJW2svAq+osH0J+Il9sW4/KDXR+MITRFeGUSPXUSM3Ua3tFO86h1qcAxWBd7iGRtz5u3d2fufCxCashTtK6Y+urJZMHOOBaH4BdzKD70vrqczNBtGfyeM6H9jaG5e8ckEQqqTuV6jqy8PEX/gckf0uamoCPT0NiwuohQVQCn/iJB6Pa2rC33tf9Zkj3qOHLxE/+22YnkJ5cN3dFO97AHofgMVFouFLIRMnbsB1d0NLSygFkF7DZ7PQ00OxpRXf3x9K8kq7OUEQ9oH6Fnfn0NevE/9/f0Xm2lXUSgGKSVqGtw+/sBDCIHGMPzUYFvdUuVpTXx4mfu5Z1PxcSKEE9MI8PPcszE+FzkvTOfRMHlAwMUbxwUfwd0WhONlql6S0HHDRyWIiQRD2jfoW90KB6G/+msyliyH0AaGsQJLA5CSqtY1i/0kYHMINDlYvsM6hx8dDtcWoLK6udGgl9y/D6PZe6O/HZbMhr1wr1NwMxfsexOemVhuBoJUsJhIEYd+pe3HPfPUrofQthIqFWofGEivLYSL1nnuho2NnAlsoQGE5PDCiDYuvlhZhdBSaO0KT6ngt+0XnchTSjkx6cjLUg69UGlcQBGGP1LW4x//4GfTMDBQSwId4drEIKkxyOtIqi0lxZwKbyUCmEa81qxFy72FiInj01y4Tj+fwLS0h1t7WHsJACsl8EQThUKhfcU8S9M0bqMX5kO4IQYBLeI/v6iV52fdAe/vOBFbr0LBibBTm0hVno6PoxXm8K6bni1GFZfTMDD5tou3PnF2L50vmiyAIB0j9uowLC6ipqZCOWAkPeEf85Nd35Tm7O86Q3HMvzM+jnv8u8ZNfQ3/3u+jRm9DZiV9cwjuPmp0Nwu6KYbx46YIgHAL167k3NYWeopuKuyMefhH+20dJXvN91XnRG5tlaE3xoUdQHZ0or6Ahg755M2TfdHaifEixdCdP4bq7Q6qjIAjCIVC/4l4swtg4KA+NzbC8uH5/nIG4kWh4mIbH/5KVt/+Hzc9VqVlGd9ovNYrQKyuhiqRW+EjD4iJ09+Kcww+conj/A+GbQnnpYEEQhAOkfmMEy8tEi/P4xua1mHs5SQE1P4OezRM99eTaKtMKVGyWMT6GunE9rEDF49vawmtzS1pG2KGcw3V1h0lUSXUUBOEQqVu10RdeAA1+4BS0tFZc+ammcjA3S/zd54j/56fXT7iWqFQEDKChATU7A1FIrfR9ffiWNmjvCCGeosM3N+NPDkiRL0EQDp36DMs4hy4UcNkTKDcKzc2wvASLG+rGF1ZgZgY9NkrD3/41enmZlR9/y3oh36wHqtYhK6aQ4Do70bN5fDaL7+mBgT4KLd24zk7cufPisQuCcOjUp+oUChBHFO97AHfiBK6pKYRUKvQyVYUCxYJDF4pE9rvEX/rS+gFb9EB1p4Zw2RP47AlcUwt+ZQXf0gJDQ7hsNhQhE2EXBOEIqE/PPZMBpfHmHpIb14mamsJK0jiCleItw6PcJP7CC7iODvSLz4c2e6Veo6UeqJOTt5YM6OsLC5LOnIEHHgzXKBbhVA9ucv5wf2ZBEIQy6lPctYalJdTcDLzq1awMnCLzib8iWlgMTTrw6+LramUZxkbRw5fQPb2wsBBKEkAY5z165MYt1R9X4+jlC5LiWLx1QRCOnPoUd+egqQnX2Y3O51Gzs/iuHvzkJKpQqDhxqhYX0NM5ohfsupRFfXkYPZ3DnT4Ng4MhO0brMEEr5XkFQahR6tPFLBTAO/ypUxTPncM3ZCi+7FFcTx8h4bwC3qMnJ/Dz88Fzh1szZbROUyHjtTCNIAhCDbKt526MOQ18HOgnKOOHrbUfNMb8NvBzwHg69L3W2k+nx7wHeCdQBH7JWvuZA7B9c8onQVdWQjPqjq7wKKuU7liikKCXl9dEe7NMGVhrZC31YQRBqEGqCcskwK9aa79hjGkHnjTG/FO67wPW2t8vH2yMuZfQN/U+4BTwWWPM3dbaW2cyD4rSJGhuCpqa8FFEdPUqulDY+rhiEsS/VNN9i0yZSo2sBUEQaoVtwzLW2hFr7TfS97PAd4DBLQ55DHjcWrtsrb0EXKBCr9WDxt1xBtfdE+ZOe3phfAyXbCPu3uOGTq959+lD4pbwizTXEAShxtnRhKox5gyhWfZXgdcA7zbGvA34OsG7zxGE/ytlh11j64cB3d0txPEmHvI2ZLPtm+888WAQ5kfuhZEr8OwzW5+soYHMow/RenZgLRWy5z54/nmYng6iH0WQzcLZs1tOqG5p1xFRizZBbdpVizZBbdpVizZBbdp1mDZVLe7GmDbgb4BfsdbOGGM+BPwOIQ7/O8AfAFtU39qcXG5h+0EVyGbbGR+f3X6ga6Bh4DRNSqOV2jTu7lEsqQzLU8GedcXCUGHF6R3ngsBPzO3drkOkFm2C2rSrFm2C2rSrFm2C2rTrIGza6mFRlbgbYzIEYf9La+3fAlhrR8v2/wnwD+nH68DpssOH0m1HRxyH5hneh2yXzYqEZTIwv4i+dCm0wisVC0snVPX8HFy9gjtz9vBsFwRB2AXbBo2NMQr4CPAda+0flm0fKBv2o8C30/efAt5qjGk0xpwFzgNf2z+Td4FzuDNnKba1w2bZi83N0N6BXpxDX7+GHh0NOe3l8XatJQVSEIRjQTWe+2uAnwG+ZYx5Ot32XuCnjDEPE8Iyw8AvAFhrnzXGfAJ4jpBp865DzZSpRKEArS24O+6EkRtQaV5VR6s1aPT1K7C4hGoMfVLp7MINDIQYu6RACoJwDNhW3K21XwIqzRx+eotj3g+8fw927S+ZDMQZin29oDf5kYsJvqsTh0IVEmhsgkwcfvCZPBpwp05JCqQgCMeC2yOXT2tcewdRYyNOUznLpejwnV2hNV5fFtfdvdbkQ2vIT0OSSAqkIAjHgvqsLVMBNzSEdwkqk4HW1tAKr7gWLfIKfENDEPn+k6AVDtD5PLgiqljEtXdI0w1BEI4Ft424E0VhUVNbK34xRi0vhwqR3oHWqOYWfEsremkRkhBT96dOUTw5kLbSIzTekGJhgiAcA26f+EJjI/6Bh3BNrSitQnpkHIVG2VEECqLxCbzzq2IOhNrtcYTLZiUcIwjCseH2USutSR59OSsPPIiPIkCFsExSCNkvSZHo4gVYWMT19EBShJUVSIrSA1UQhGPH7ROWAdyZsxTe/UtEF1+gYXxsvYfuiuAS1PQkxBmSRx4Nop/JiMcuCMKx4/ZSLaWgpQ3V2Q0NjSEcgwrivbAIU9OolQL65o0wvrFRhF0QhGPJ7aVczqFv3kDlJkN532IR8OmKU49amEOP3EC9+OLmJQoEQRCOAbeXuBcKKPtd9EqIsVdCj48SP/Uk8be+iR6+tHVzD0EQhBrl9hF350LT7JkZ9PhY8NwroDyrtWN0bgp9efjwbBQEQdgn6n9C1Xv0pYvEX3iC6PIw6oknYDpXeWwc41ta8G1t4XNaKMy95A6JvQuCcKyoe3HXl4eJP/8E8ZP/SnThBfSVS+tWpq6jqYliRydk+yHTELZJoTBBEI4h9e2OOoceHSV+8mtEVy6jZvOhEFilVaY6wjc0UDR3UzQvDYuX0u1SKEwQhONGfXvuhQLMzqInp1ALC6il5eC1b5wkjWN8FJGYe0je/Bb8qVNhu/RKFQThmFLf4p7JQKShmKCnJmF2FoqlRhsqeOfNzfiuLlYGBin8X78C3b3hoaAjXG+vrEwVBOFYUt/irjVuYBDGx2BqMqQ/ulK83YOKoKGBwtAQ7rWvJ3n190NTk6xMFQTh2FO/4u49evgSmb/9JExNBWEvJiHerjVojW9rp3j/A7jvfy3+3N1B2LWWyVNBEI4924q7MeY08HGgn9BS78PW2g8aY3qAvwbOENrsvcVam0t7rn4QeCOwALzDWvuNgzF/c/TwJTL/9Bnif/5fxEUHjQ1QUCEsozXEGXxLM+70Hbi7zpPcd7946oIg1A3VqFkC/Kq19l7gVcC7jDH3Ar8FPGGtPQ88kX4G+CFCU+zzwM8DH9p3q7fDOeJnvkn0tX8hyk3ivYfG5tCko7sL338Cd/o07sxdFF7zfSQPPIg7c/bQzRQEQTgothV3a+1IyfO21s4C3wEGgceAj6XDPga8OX3/GPBxa6231n4F6DLGDOy34VuytET01a8Q3biBWikQ6scUQ7sl54LX3t5B4eXfQ+HNPxaEXZpwCIJQR+wo5m6MOQM8AnwV6LfWjqS7bhLCNhCE/2rZYdfSbSNsQnd3C3Ec7cSUVbLZ9ls3fvsyLORheRGSFZibTTNgQqydpiZ4xctoevcvwqmeXV13V3YdMbVoE9SmXbVoE9SmXbVoE9SmXYdpU9XiboxpA/4G+BVr7YwxZnWftdYbY3ZdYSuXW9jVcdlsO+Pjs+s3Okd84SoNE1NkpnKom6NrdWTSmjFJpolC3wDLHSdg4/H7QEW7jphatAlq065atAlq065atAlq066DsGmrh0VVM4jGmAxB2P/SWvu36ebRUrglfR1Lt18HTpcdPpRuOxwKhZDCPp2HqVzlAmGNDeirl8NYQRCEOmRbcU+zXz4CfMda+4dluz4FvD19/3bg78u2v80Yo4wxrwLyZeGbgyeTgeUVmM6hlip/I4jy0+hcbvMCYoIgCMecasIyrwF+BviWMebpdNt7gd8FPmGMeSdwGXhLuu/ThDTIC4RUyJ/dT4O3RWtcRwc6n9u0FruancUvrUBzy6GaJgiCcFhsK+7W2i8Bm6WS/ECF8R541x7t2hNu6DRbTgAszONmptCjN3FtbZIpIwhC3VGfq3aiCHqzWw7JvHCBzJ9+GH3p4iEZJQiCcHjUp7g3N4cGG1sQTYzR8NnPEH/2H1ezaARBEOqF+hX3xflth0XXrxF98ylYXDwEowRBEA6P+hT3lRXU6Nj244oJ0Y3rsLx88DYJgiAcIvUp7mOjRLnJqoY6raXTkiAIdUd9insmg6oq1KIonnspxPVb+VgQhNuT+hT3qDqxTtra8C9/mdRvFwSh7qhPcdca19q67TDV1ASLS1LHXRCEuqM+VS2O8Y1N2w5TiwuokeuQVKg/IwiCcIypP3H3nvgrX0JXIdjKudA4e27uEAwTBEE4POpO3PXFF4m/9W1UHG8bblFFh0+Kki0jCELdUV/inrbXU0sLuPaO7WvGaI0bOg3NzYdjnyAIwiFRX+K+vAyzecg0QGcXFItbDi90tJP82x+UCVVBEOqOulM1pTQ+SdJyv1t77smrvx931/nDMUwQBOEQqS9xb2zEdXWhdBS8eL25uLueXvz9D4Z+qoIgCHVGfYm71hTvvAv17DNEL9gtqz0Wu7uhseEQjRMEQTg86kfcvUe/eIHM7/4/ZL7xJGp2ZvOxDQ347l5UpkGKhgmCUJfUjbjry8NkPvnXNF2wId6+mdeuFK6rB3X6NN7J4iVBEOqTbYuwGGP+DHgTMGatvT/d9tvAzwHj6bD3Wms/ne57D/BOoAj8krX2Mwdg93qcQ4+MEH3zqVAwbGGLWu5NTfgTWXycgb5+qSsjCEJdUk2FrY8C/wX4+IbtH7DW/n75BmPMvcBbgfuAU8BnjTF3W2u3zkncK4UCzOaJ8nn8/DyqsIlHHsf45hZ8ezvFM2dJHnpY0iAFQahLtlU2a+0Xgakqz/cY8Li1dtlaewm4ALxiD/ZVRyYDDY34ogvle/0mIZk4Q7Gnl8IrvpfCj/0E7szZAzdNEAThKNhLIfN3G2PeBnwd+FVrbQ4YBL5SNuZaum1LurtbiONoV0Zks+3hzb3nINsDI9cqr0xtboaBATK/9Rs0v+1tB15yYNWuGqIWbYLatKsWbYLatKsWbYLatOswbdqtuH8I+B3Ap69/APyH3RqRyy3s6rhstp3x8dnwoecUjf2DNK18HVVpZarSJO2dLJx9KW56CVjarbk7s6tGqEWboDbtqkWboDbtqkWboDbtOgibtnpY7ErcrbWjpffGmD8B/iH9eB04XTZ0KN12sHhP/M9fRs/kcTqm0ncAr1VIe4ylSJggCPXPrmYTjTEDZR9/FPh2+v5TwFuNMY3GmLPAeeBrezNxe/SLF8j84/8g+tYzRAuVy/eqKAYF+uaNLRc3CYIg1APVpEL+FfA6oM8Ycw14H/A6Y8zDhLDMMPALANbaZ40xnwCeAxLgXQeeKVMsknn8vxN9+UvEI9dhYUOIRymIIohitFehMUehICmQgiDUNduKu7X2pyps/sgW498PvH8vRlWN98Sf/Szx5z9LNDVeuaNSHIdQTEMG19QATY1Sv10QhLpnL9kyR4v38LnP0fCpvyG+fhW1tBQ88o04D5kY19pK8eyduBP9ktsuCELdc2zFXb/wAvz+75N56ilULpeW+K1AQwO+u5vk7rtx3/Mq3Lm7D9dQQRCEI+B4irtzNP7J/wvWolYKIa5eLu5KhXBMppHk7BkK/+6N+PvuJ7n/wRB/FwRBqHOOp7jPzhJ9+xmYmIDFpfXZL1pDcwvFEyfwg0MkL38lxX/zb3H9/bg7zhyZyYIgCIfJ8RT3fI5o+FLIW98YjnEO19FJ8dGXUbz7Hgpv+GGcMRJnFwThtuJ4inumEZYW0tK+t2ZaJq94VWihd/Yu3Etfun2jbEEQhDrjeIr7fLqEt1J2jNYUHn2U4mu+H3f2ThF2QRBuS46nuHd2b5od4zMZCj/6E3Dy5CEbJQiCUDscz0C096G8b6VdKImvC4Jw23M8VXBhHrVcuaqjWl7auhOTIAjCbcDxFPexMTaLpKt0vyAIwu3M8RT369tUEd5uvyAIQp1zPMW9vWNv+wVBEOqc4ynuheVt9q8cjh2CIAg1yrEUd22f23rAdO5wDBEEQahRjp+4O0fmS1/eekxr2+HYIgiCUKMcP3GfnSVz8cLWYxZ313BbEAShXqimzd6fAW8Cxqy196fbeoC/Bs4Q2uy9xVqbM8Yo4IPAG4EF4B3W2m/sq8X5afRMfusxHZ37eklBEITjRjWe+0eBN2zY9lvAE9ba88AT6WeAHyI0xT4P/Dzwof0xs4zWNjZpy7HGJqtXBUEQbhe2FXdr7ReBqQ2bHwM+lr7/GPDmsu0ft9Z6a+1XgC5jzMA+2RqIIpLu3q3HFCqvXhUEQbhd2G3hsH5r7Uj6/ibQn74fBK6WjbuWbhthC7q7W4jjKjskdTfDG/8dfCh8KajU6jr7fa+EbHt15zsgskd8/UrUok1Qm3bVok1Qm3bVok1Qm3Ydpk17rgpprfXGmG0jJVuRy+1sArSxoY1mgrBvLPrrgXxBw/jsXkzaE9lsO+NHeP1K1KJNUJt21aJNUJt21aJNUJt2HYRNWz0sdpstM1oKt6SvpWIu14HTZeOG0m37R5KAYtO4u+vohMamfb2kIAjCcWO34v4p4O3p+7cDf1+2/W3GGGWMeRWQLwvf7A9zc0RXhlHNzdDQsNaMQynIZKCtHTapGCkIgnC7UE0q5F8BrwP6jDHXgPcBvwt8whjzTuAy8JZ0+KcJaZAXCKmQP7vvFkcRFFbwLa2QFMJn74O4RxG+vx8aGvf9soIgCMeJbcXdWvtTm+z6gQpjPfCuvRq1JXGMHxjCd3aFPqqZBkKQRuEbGiicfyl0dR2oCYIgCLXO8Vuh2thI8RWvpPDAg9Daio9jiDO4tlYKd56j+GNvgfh4dg8UBEHYL46fCmpN8sCDIQxz/k4Kz18El0BPL8krX03y2tcetYWCIAhHzvETd8CdOUuiFNxzFys3xsGDGxzC3XVubYJVEAThNuZYijtK4c6chd5WkhtTIUtGmmILgiCscjzFvYTW0CiZMYIgCBsRd1cQBKEOEXEXBEGoQ0TcBUEQ6hARd0EQhDpEeb+ngo6CIAhCDSKeuyAIQh0i4i4IglCHiLgLgiDUISLugiAIdYiIuyAIQh0i4i4IglCHiLgLgiDUIce2cJgx5g3AB4EI+FNr7e8ekR3DwCxQBBJr7cuNMT3AXwNngGHgLdba3AHb8WfAm4Axa+396baKdhhjFOHevZHQDvEd1tpvHJJNvw38HDCeDnuvtfbT6b73AO8k3MtfstZ+Zr9tSq9zGvg40E9o4/Vha+0Hj/J+bWHTb3OE98sY0wR8EWgk6MUnrbXvM8acBR4HeoEngZ+x1q4YYxrTn+NlwCTwk9ba4UOy6aPAa4F8OvQd1tqnD+v3PbUtAr4OXLfWvuko79Ox9NzTG/jHwA8B9wI/ZYy59whN+jfW2oettS9PP/8W8IS19jzwRPr5oPko8IYN2zaz44eA8+m/nwc+dIg2AXwgvV8PlwnVvcBbgfvSY/5r+v/5IEiAX7XW3gu8CnhXev2jvF+b2QRHe7+Wgddbax8CHgbeYIx5FfB7qV3ngBzhIUP6mku3fyAdd1g2Afx62b16Ot12WL/vAL8MfKfs85Hdp2Mp7sArgAvW2ovW2hXCk/GxI7apnMeAj6XvPwa8+aAvaK39IjBVpR2PAR+31npr7VeALmPMwCHZtBmPAY9ba5ettZcITdZfsd82pXaNlDw3a+0s4Y9xkCO8X1vYtBmHcr/Sn3ku/ZhJ/3ng9cAn0+0b71XpHn4S+IHUcz4MmzbjUH7fjTFDwA8Df5p+VhzhfTqu4j4IXC37fI2t/xAOEg/8ozHmSWPMz6fb+q21I+n7m4Sv2kfBZnYc9f17tzHmGWPMnxljuo/SJmPMGeAR4KvUyP3aYBMc8f0yxkTGmKeBMeCfgBeBaWttUuHaq3al+/OEkMSB2mStLd2r96f36gNp6GOdTRXs3U/+CPgNwKWfeznC+3Rcxb2W+D5r7aOEr37vMsb8b+U7rbWerb2KQ6FW7CB8Jb6L8HV6BPiDozLEGNMG/A3wK9bamfJ9R3W/Kth05PfLWlu01j4MDBG+Hbz0sG3YyEabjDH3A+8h2PY9QA/wm4dljzGmNLf05GFdczuOq7hfB06XfR5Ktx061trr6esY8HeEX/7R0te+9HXsKGzbwo4ju3/W2tH0D9MBf8JaKOFQbTLGZAgi+pfW2r9NNx/p/apkU63cr9SWaeDzwPcSQhulhIzya6/ale7vJEwYHrRNb0hDW95auwz8OYd7r14D/EiaYPE4IRzzQY7wPh1Xcf9X4Lwx5qwxpoEwsfSpwzbCGNNqjGkvvQf+d+DbqS1vT4e9Hfj7w7YtZTM7PgW8zRij0omofFk44kDZEOv8UcL9Ktn0VmNMY5phcB742gHZoICPAN+x1v5h2a4ju1+b2XTU98sYkzXGdKXvm4EfJMwHfB748XTYxntVuoc/Dnwu/RZ00DZ9t+zBrAix7fJ7daD//6y177HWDllrzxD06HPW2p/mCO/TsUyFtNYmxph3A58hpEL+mbX22SMwpR/4O2MMhHv53621/9MY86/AJ4wx7wQuA285aEOMMX8FvA7oM8ZcA94H/O4mdnyakBZ2gZAa9rOHaNPrjDEPE0Iew8AvAFhrnzXGfAJ4jpA58i5rbfEg7CJ4WT8DfCuN2wK8l6O9X5vZ9FNHfL8GgI+lmTga+IS19h+MMc8Bjxtj/jPwFOHBRPr6F8aYC4TJ9Lceok2fM8ZkAQU8DfxiOv5Qft834Tc5ovsk9dwFQRDqkOMalhEEQRC2QMRdEAShDhFxFwRBqENE3AVBEOoQEXdBEIQ6RMRdEAShDhFxFwRBqEP+f4TITQIKO7k2AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hics-JmemwUz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "e974db1d-4da6-4654-be72-fa02ed3ea728"
      },
      "source": [
        "returns = prices.pct_change(1).dropna()\n",
        "\n",
        "plt.hist(returns['INDEX'], label='INDEX', bins=100) \n",
        "plt.hist(returns['Your_Stock'], label='Stock', bins=100)\n",
        "  \n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Overlapping')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "returns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "GH9qh0h59udQ",
        "outputId": "3141b7c6-361f-4ea8-c144-c6049192f40a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Your_Stock     INDEX\n",
              "Date                            \n",
              "2011-01-08   -0.088173  0.016989\n",
              "2011-01-15   -0.105243 -0.007192\n",
              "2011-01-22    0.042101 -0.005064\n",
              "2011-01-29   -0.022907  0.026856\n",
              "2011-02-05   -0.008951  0.014945\n",
              "...                ...       ...\n",
              "2022-04-30   -0.005868 -0.001602\n",
              "2022-05-07   -0.110969 -0.023387\n",
              "2022-05-14   -0.137333 -0.030096\n",
              "2022-05-21    0.144193  0.065780\n",
              "2022-05-28   -0.001804 -0.005611\n",
              "\n",
              "[595 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fbe21489-45ce-411f-8f3c-bdd4185c48b2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Your_Stock</th>\n",
              "      <th>INDEX</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2011-01-08</th>\n",
              "      <td>-0.088173</td>\n",
              "      <td>0.016989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-15</th>\n",
              "      <td>-0.105243</td>\n",
              "      <td>-0.007192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-22</th>\n",
              "      <td>0.042101</td>\n",
              "      <td>-0.005064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-29</th>\n",
              "      <td>-0.022907</td>\n",
              "      <td>0.026856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-02-05</th>\n",
              "      <td>-0.008951</td>\n",
              "      <td>0.014945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-30</th>\n",
              "      <td>-0.005868</td>\n",
              "      <td>-0.001602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-07</th>\n",
              "      <td>-0.110969</td>\n",
              "      <td>-0.023387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-14</th>\n",
              "      <td>-0.137333</td>\n",
              "      <td>-0.030096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-21</th>\n",
              "      <td>0.144193</td>\n",
              "      <td>0.065780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-05-28</th>\n",
              "      <td>-0.001804</td>\n",
              "      <td>-0.005611</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>595 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbe21489-45ce-411f-8f3c-bdd4185c48b2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fbe21489-45ce-411f-8f3c-bdd4185c48b2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fbe21489-45ce-411f-8f3c-bdd4185c48b2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#using scipy"
      ],
      "metadata": {
        "id": "fglKONGneVBW"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srnu6WvomwU0"
      },
      "source": [
        "beta,alpha,r_value,p_value,std_err = stats.linregress(returns['INDEX'],returns[\"Your_Stock\"])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1Lzl9kQmwU1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3bcc6e1-6066-49aa-877f-1b65f5793839"
      },
      "source": [
        "print(beta.round(4))\n",
        "print(alpha.round(4))\n",
        "print(r_value.round(2))\n",
        "print(p_value.round(4))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5397\n",
            "0.0071\n",
            "0.45\n",
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#using statsmodels"
      ],
      "metadata": {
        "id": "TgesteAwedyG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcLnmw7JmwU2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ddefded-0902-4291-8793-4689b1e93900"
      },
      "source": [
        "formula = 'Your_Stock ~ INDEX'\n",
        "results = smf.ols(formula, returns).fit()\n",
        "print(results.summary())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:             Your_Stock   R-squared:                       0.205\n",
            "Model:                            OLS   Adj. R-squared:                  0.204\n",
            "Method:                 Least Squares   F-statistic:                     152.8\n",
            "Date:                Thu, 16 Mar 2023   Prob (F-statistic):           2.18e-31\n",
            "Time:                        23:16:11   Log-Likelihood:                 756.72\n",
            "No. Observations:                 595   AIC:                            -1509.\n",
            "Df Residuals:                     593   BIC:                            -1501.\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      0.0071      0.003      2.527      0.012       0.002       0.013\n",
            "INDEX          1.5397      0.125     12.360      0.000       1.295       1.784\n",
            "==============================================================================\n",
            "Omnibus:                      110.210   Durbin-Watson:                   1.918\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              341.677\n",
            "Skew:                           0.874   Prob(JB):                     6.40e-75\n",
            "Kurtosis:                       6.275   Cond. No.                         44.7\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#using scikit-learn\n",
        "You need to take a look at the shape of the data you are feeding into .fit().\n",
        "\n",
        "Here x.shape = (10,) but we need it to be (10, 1), see sklearn. Same goes for y. So we reshape:"
      ],
      "metadata": {
        "id": "8Smo3CZNCT-_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = returns.loc[:, ['INDEX']].values"
      ],
      "metadata": {
        "id": "4JaPe7H7_o3j"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = returns.loc[:, ['Your_Stock']].values"
      ],
      "metadata": {
        "id": "w9V4AufwHUfD"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHAQJ9zqAhYc",
        "outputId": "38a5e804-0ed0-4dd8-f080-31e21e597c12"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9NriPRzAwAB",
        "outputId": "eda1b7d2-028a-499e-b956-84ba685eb7aa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpXm-LKjAv9O",
        "outputId": "44ad4692-9b19-41b3-9cdd-c7903ead37da"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('float64')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "id": "D32vbocuAhTD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "134a3718-34b9-400d-fa08-e22ce6a08746"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = LinearRegression(fit_intercept=True).fit(X,y)"
      ],
      "metadata": {
        "id": "LJm98NZjAhPD"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict(X[0].reshape(-1,1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUcVMJ87GHah",
        "outputId": "de68e5dc-8a42-4d16-f249-1ffcc9250719"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.033]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict(X[0:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzZ8c7OyGHXH",
        "outputId": "9a6fa791-999b-4767-fbff-770ea2205f79"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.033],\n",
              "       [-0.004],\n",
              "       [-0.001],\n",
              "       [ 0.048],\n",
              "       [ 0.030],\n",
              "       [ 0.024],\n",
              "       [-0.018],\n",
              "       [ 0.009],\n",
              "       [-0.012],\n",
              "       [-0.029]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = reg.score(X, y)\n",
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaI6NNfYGHUZ",
        "outputId": "0a34e3f0-645f-4d00-d8d4-c0d3464f4a59"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2048562366630433\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqF2UwSl_Qsa",
        "outputId": "f14c402b-644e-47ae-94d4-0e3f9043edaf"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.540]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lksDjxVw_Qp_",
        "outputId": "68571707-135b-4bb1-aec4-c9e405157688"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.007])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = reg.coef_[0,0]\n",
        "b = reg.intercept_[0]"
      ],
      "metadata": {
        "id": "yz0iksty_Qno"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nofj5AJ9Eeir",
        "outputId": "0ac01b3c-869e-4aab-d435-ab005659c8df"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.5397127262862014"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZ2BnuhXEefu",
        "outputId": "77b843aa-0185-4a58-f0a7-8674b061914f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.007084853239936928"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# following slope intercept form \n",
        "print(\"formula: y = {:.4f}X + {:.4f}\".format(m, b) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvDRXsafEecm",
        "outputId": "270b09ba-7a33-4c5a-866a-e65b39eca59c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "formula: y = 1.5397X + 0.0071\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import flatten_structured_array\n",
        "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10,7));\n",
        "\n",
        "ax.scatter(X, y, color='blue', s=10);\n",
        "ax.plot(X, reg.predict(X), color='red',linewidth=2);\n",
        "ax.grid(True,\n",
        "        axis = 'both',\n",
        "        zorder = 0,\n",
        "        linestyle = ':',\n",
        "        color = 'y')\n",
        "ax.tick_params(labelsize = 20)\n",
        "ax.set_xlabel('INDEX', fontsize = 24)\n",
        "ax.set_ylabel('Your_Stock', fontsize = 24)\n",
        "ax.set_title(\"Linear Regression Line with Intercept y = {:.4f} + {:.4f}x (R2 = {:.2f})\".format(b, m, score), fontsize = 16 )\n",
        "fig.tight_layout()\n",
        "#fig.savefig('images/linearregression', dpi = 300)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "8VRzrUmd_Qim",
        "outputId": "439328d9-7e38-4174-c5d3-88789fe2c773"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}