Linear Regression (HW1).ipynb

01_linear_regression_intro.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/firmai/4689aa56920e53cbdc798e0d28926a3c/01_linear_regression_intro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "locKQ3zwjsZl"
      },
      "source": [
        "## Linear Regression - Introduction\n",
        "\n",
        "Linear regression relates a continuous response (dependent) variable to one or more predictors (features, independent variables), using the assumption that the relationship is linear in nature:\n",
        "- This essentially means that the effects of each variable on the response are additive (but we can include new variables that represent the interaction of two variables).\n",
        "\n",
        "In other words, the model assumes that the response variable can be explained or predicted by a linear combination of the features, except for random deviations from this linear relationship.\n",
        "\n",
        "**Note that I would provide the best student solution for every question as the final solution.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ywtWYaWjsZp"
      },
      "source": [
        "## Imports & Settings"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Have a look out for the <font color='orange'>orange text </font> it indicates questions that has to be answered or code that has to be writte. The number of points for each question will appear next to it, like <font color='orange'>Question 1 (4 points) </font>. **This notebook will be assessed out of 30 points.**"
      ],
      "metadata": {
        "id": "9T9laXBxrI6U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:55.794199Z",
          "start_time": "2021-04-15T19:54:55.790730Z"
        },
        "id": "oTjx_IiYjsZr"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:56.695927Z",
          "start_time": "2021-04-15T19:54:55.801810Z"
        },
        "id": "gfqosp_ojsZt"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from sklearn.linear_model import SGDRegressor\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:56.700217Z",
          "start_time": "2021-04-15T19:54:56.697453Z"
        },
        "id": "gyWtmvL3jsZu"
      },
      "outputs": [],
      "source": [
        "sns.set_style('whitegrid')\n",
        "pd.options.display.float_format = '{:,.2f}'.format"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkLyOuu1jsZu"
      },
      "source": [
        "### Simple Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7B3IKLxFjsZw"
      },
      "source": [
        "#### Generate random data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:56.926876Z",
          "start_time": "2021-04-15T19:54:56.702002Z"
        },
        "id": "GkBhq_qKjsZx",
        "outputId": "1f2a06e8-889c-4c4a-fff3-7f4fa43dccf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.linspace(-5, 50, 100)\n",
        "y = 50 + 2 * x + np.random.normal(0, 20, size=len(x))\n",
        "data = pd.DataFrame({'X': x, 'Y': y})\n",
        "ax = data.plot.scatter(x='X', y='Y', figsize=(14, 6))\n",
        "sns.despine()\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62mtRWVUvupd"
      },
      "source": [
        "Our linear model with a single independent variable on the left-hand side assumes the following form:\n",
        "\n",
        "$$y = \\beta_0 + \\beta_1 X_1 + \\epsilon$$\n",
        "\n",
        "$\\epsilon$ accounts for the deviations or errors that we will encounter when our data do not actually fit a straight line. When $\\epsilon$ materializes, that is when we run the model of this type on actual data, the errors are called **residuals**."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='orange'>Question 1: Get stock price data (4 points) </font>\n",
        "\n",
        "Have a look at the format of the random dataframe above called *data* above, you can see what it looks like by running `data.head()`  \n",
        "\n",
        "*   We want to create a new Pandas dataframe and have the S&P index **returns** in the X column, and Tesla **returns** in the y column; you can give the columns appropriate names.\n",
        "*   The data should start on the 7th of January 2020 and stop on the 21st of January 2022.\n",
        "*   The returns should be calculated on the adjusted closing price of the security.\n",
        "*   The date should be set as the index so that it doesn't appear as a feature, you can use any daily date format.\n",
        "\n",
        "\n",
        "### Some advice\n",
        "You could use multiple open source python packages like googlefinance or yfinance. There are many places where you can learn how to use the package, I can recomend Stefan Jansen's [resource](https://github.com/stefan-jansen/machine-learning-for-trading/blob/main/02_market_and_fundamental_data/03_data_providers/02_yfinance_demo.ipynb). Notice that this data might not be as accurate as those from paid providers.\n",
        "\n",
        "*Note the technique I showed below might not be the easiest solution, and you need to do some additional work to get the returns out of the package.*\n",
        "\n",
        "\n",
        "```python\n",
        "import yfinance as yf\n",
        "symbol = 'FB'\n",
        "ticker = yf.Ticker(symbol)\n",
        "```\n",
        "\n",
        "You have to learn how to download Python packages, a popular method is to use `!pip install yfinance` for example\n",
        "\n",
        "Perhaps you can even do it without a package if you know how to query and API, you can query the yahoo data directly:\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "# yahoo url template (5 years of daily data: 2015-09-21 to 2020-09-18)\n",
        "yahoo_url = 'https://query1.finance.yahoo.com/v7/finance/download/{}?period1=1442707200&period2=1600560000&interval=1d&events=history'\n",
        "# get data for 3 tickers and concatenate together\n",
        "tickers = ['AAPL', 'MSFT', '^GSPC']\n",
        "\n",
        "df = pd.DataFrame()\n",
        "for ticker in tickers:\n",
        "    url = yahoo_url.format(ticker)\n",
        "    df_tmp = pd.read_csv(url)\n",
        "    df_tmp['Ticker'] = ticker\n",
        "    df = pd.concat([df, df_tmp])\n",
        "df\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uvLc5OQ2sah-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#### Your solution for 4 points goes into this code block (there are thousands of ways to get the data you can use any method to get to the final solution):\n",
        "\n"
      ],
      "metadata": {
        "id": "y6adk7z6yJoo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ApsAlJiKjsZ0"
      },
      "source": [
        "#### Estimate a simple regression with statsmodels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avmSgvPZjsZ1"
      },
      "source": [
        "The upper part of the summary displays the dataset characteristics, namely the estimation method, the number of observations and parameters, and indicates that standard error estimates do not account for heteroskedasticity.\n",
        "\n",
        "The middle panel shows the coefficient values that closely reflect the artificial data generating process. We can confirm that the estimates displayed in the middle of the summary result can be obtained using the OLS formula derived previously:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:56.970047Z",
          "start_time": "2021-04-15T19:54:56.928301Z"
        },
        "id": "ME-AUaZ4jsZ2",
        "outputId": "ae386e7b-e8a2-4928-f9fe-a29e88fc70f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      Y   R-squared:                       0.700\n",
            "Model:                            OLS   Adj. R-squared:                  0.697\n",
            "Method:                 Least Squares   F-statistic:                     228.4\n",
            "Date:                Fri, 20 Jan 2023   Prob (F-statistic):           2.39e-27\n",
            "Time:                        20:25:30   Log-Likelihood:                -438.53\n",
            "No. Observations:                 100   AIC:                             881.1\n",
            "Df Residuals:                      98   BIC:                             886.3\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         50.5411      3.380     14.952      0.000      43.833      57.249\n",
            "X              1.8488      0.122     15.112      0.000       1.606       2.092\n",
            "==============================================================================\n",
            "Omnibus:                        0.284   Durbin-Watson:                   2.305\n",
            "Prob(Omnibus):                  0.868   Jarque-Bera (JB):                0.042\n",
            "Skew:                          -0.002   Prob(JB):                        0.979\n",
            "Kurtosis:                       3.100   Cond. No.                         47.6\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ],
      "source": [
        "## Here you can choose to assess the random dataframe, or the new return dataframe that you just created\n",
        "X = sm.add_constant(data['X'])\n",
        "model = sm.OLS(data['Y'], X).fit()\n",
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-09-03T18:56:37.274147Z",
          "start_time": "2018-09-03T18:56:37.270065Z"
        },
        "id": "rsZ2EyDUjsZ3"
      },
      "source": [
        "#### Verify calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:56.977705Z",
          "start_time": "2021-04-15T19:54:56.971237Z"
        },
        "id": "plX0AkUWjsZ3",
        "outputId": "ec76df2c-f40d-4b7c-e7f2-17a2f6563ac1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const   50.54\n",
              "X        1.85\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "beta = np.linalg.inv(X.T.dot(X)).dot(X.T.dot(y))\n",
        "pd.Series(beta, index=X.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-6daeN8jsZ4"
      },
      "source": [
        "#### Display model & residuals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.296302Z",
          "start_time": "2021-04-15T19:54:56.979198Z"
        },
        "id": "pu0x9cpPjsZ4",
        "outputId": "fb6d858c-094e-40b3-a7cd-d39bed4b10ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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z2UjOcUwocQcAAMDnooQXAKzBDDoAAABGRAkvgHhjmqb2tHSryteiTf/5tE52G7rt5gWxDuuYkKADAABgVCjhBRBrbd192l7nV5XPr0pfiz7qHGxW2bn5OTVmp+q2m2+KcYTHhgQdAAAAABCXegdC6urpV2dPvy54sEp/2tcp05QmOVK0Yp5b31jlVvG8PH15Z/a42HueBB0AAAAAEBdM01Td/oPa5vOryteinXvb1JK3THbDkCPFruvOnq9ir1snTstSin38tVQjQQcAAAAAxIz/YK+21/lV+UnZenNXryRpTt4EXXbqdBV/6UYtm5OrTEdqjCONvqgl6Bs3blRFRYVcLpdeeumlQ/7uscce01133aUdO3YoNzdXpmnqhz/8obZu3SqHw6E777xTCxcujFZoAAAAAIAYCfaHVFPfrsq6Fj371FPa19mjiYvOUrYzVSvmuVXidavIm6dp2RmxDtVyUUvQ161bpyuuuEI33njjIcc//vhjbd++XVOnTh0+tm3bNtXX1+uVV17R7t27deutt+q5556LVmgAAABJJxgMKhAIKBgM0uQNgOVqP+5Slc+vbb4W7Xq/Tb0DYaXaDXXW/FY5Gal6/qGbtXBqluw2I9ahxlTUEvTCwkI1NjZ+5vgdd9yhG264QV//+teHj5WXl+uiiy6SYRhaunSpurq6tH//fuXn50crPAAAgKTR0NCgmpoamaap+vp6FRYWyuPxxDosAOPY/q6gqur8qtt/UJ09/Vr7QKUkaV7+RF2+zKMSb55Om52rsu0/kiQtnp4dy3DjhqVr0P/3f/9X+fn5Ov744w853tzcrMmTJw//efLkyWpubh4xQe/t7VVtbW1UYkV8CAaD3GNYijEHKzHeYIWBgQG98847qqiokCSVlpbqtddeU2dnp1JSxvZRcKhDMuM2cVl5D614j4unMWlVLKN5nljEEhwI663moN74qEdvfNyj+vY+SZIxf6WW5qTqC2fk6aSpGcqb8Mn7jtmqD/a2RizWQCCgcDgcF2NhNBYsOPx+7ZYl6D09PXr44Yf12GOPReyc6enpR7wwjA+1tbXcY1iKMQcrMd5ghba2Nvl8vkMSdLvdrmnTpik3N3dM53I6nZKO/MES8c/Ke2jFe1w8jUmrYhnN81gRSzhsyrSnqbOnXz/Y3qnq99vVFworzW5T4ewc/e2yPBV73TphynmyfU7ZeqRidTqdCgQCcTEWjoVlCfoHH3ygxsZGXXjhhZKkpqYmrVu3Ts8995wKCgrU1NQ0/NimpiYVFBRYFRoAAMC45XQ6ZZrmIcdM0xz+UAwAo9XUGVSlr0WVPr+21/n11r5OSdKcg3368vKZKp6fp9Nm5SojzR7jSBOXZQn6cccdpx07dgz/edWqVdq0aZNyc3O1atUqPfXUUyorK9Pu3buVmZnJ+nMAAIAIcDgcKiwsHP6z3W5XYWEhjeIAjCjQN6DX9rZpm69FVT6/fPsPSpLcE9NVMj9PZ1x9teZPztT6/1MS40ilcDisUCiU8I0wo5agb9iwQbt27VJ7e7tKSkq0fv16XXrppYd9bGlpqbZu3arVq1crIyNDt99+e7TCAgAASDoej0cul0vd3d0qKytL6A+vAKInHDb15486Venz64knnlB9a7cyTlil9BSbTpudq8tOnaEir1vHT86UYRiSlsY6ZEmDjTBbW1slSZs3b07oRphRS9Dvvffez/37LVu2DP+/YRi65ZZbohUKAABA0rPZbLLb7STnAA6xr6NHVb4WbfP59WqdX+2BfknSgcr/VpYzVU/cs1GnzsqRIzU+y9aDwaBqamqG/xwKhVRdXa38/PyEfL+ztIs7AAAAACB2DvYO6Pv3/lTvNh9Q14wztLelW5JUMCldq44vUMl8t1bMc+sLO++WJBV53bEMd0SBQECGYWjlypXDxwzDUCAQIEEHAAAAAMSPUNjUm40dqvT5VeXz640P2tX41COyGYYuu/Vs/d2ymSr2uuXNn/hJ2XpiGWqEWVpaOnwskRthkqADAABLBINBBQIBOZ3OhJzVAIBE0TsQVkegT9c+9bq21/nVFRyQYUiLpmbpmpI5erZ8kiY6UvT4VafFOtRjNtQIs7q6WqZpyjCMhG6ESYIOAACirqGhQTU1NTIMQ6ZpJnQDHwCIN509/dqxp1VVdYNboP3hg3ZJUvaHHTp30WQVe/O0Yp5buRPSJEm/uTM1luFGnMfjUX5+vt566y0tXLgwYZNziQQdAABE2VADn6EGsaWlpQndwAfJhcoPxCPTlGrq21Tp86vS16LdjZ0KhU1NSLPr9Dkueb98pY6bnKn/941VCVm2fjQcDse4+DklQQcAAFE11MCnoqJC0mCCnsgNfJA8qPxAvDBNU/WtAVX5Wgabu/X06wsP7ZDNkE6cnq2vr5yronluneTJUVqKTVJhrEPGUSJBBwAAUTXUwOfTErmBD5IDlR+Itc5Av7bv8Q/Pkje290iSMk9crVMmZ+rrf3eyzpjrUrYzLcaRIpJI0AEAQFQNNfCRBre+sdvtCd3AB8mByg9YrT8U1g/vf0jvNh1Qz6wivdnYobApZaanaPlcl/5PyRwVe/M003Ve0pStJyMSdAAAEHUej0cul0vhcFhlZWUkOIh7VH4g2kzT1F5/tyrfa1FVnV879rRqz+M/lWEYOu8fi7V+lVcl891aMj1bKXZbrMOFRUjQAQCAJWw2m2w2G8k5EgKVH4iGtu4+ba8b3I+80teijzqDkqSZLqcuPnma/uu3mZqUkarnrz0jxpEiVkjQAQAAgMNItMoPOs5Hz9G+tr0DIb3e0P5JQu7XzpdfkClp6qlrdMZct76xyq3ieXnyuAYrM6ruZz15siNBBwAAAI4gUSo/6DgfPWN9bd9rPjDc2O21vW3q6Q8pxWboJE+2sj7crmxnqnb+848pW8dhkaADAAAACYyO88fuSDPko3lt/Qd7tb3Orz0tB9XZ069z7tsmSZrjnqDLTp2uIm+eTp+Tq0xHqlY+myFJJOc4IhJ0AAAAIIHRcf7YfN4M+eFe2wHT0O/f/kh/bO5V5Xt+vf1xlyTJOO5MFeZn6mvrTlSR163pOTQUxNiRoAMAAAAJjI7zR2+kGXKn06lw2FRvSOoJGXq0Ll17D9g1UFOrVLuhkz05umHNcSqa59aiaefJbmP7MxwbEnQAAAAggdFx/ugdqfrgg/0d+rN/QFU+vyrenaiOeWskSZ19Nl10olvnnTxby2a7NCGddAqRxYgCAAAAElyidZyPF0PVB2FTCoaklxrT5Dtg18e7Xpck5U5IU5E3X8tWf1MnT83Q7IIcXltEFQk6AAAAMA4kSsf5eBAOm3r74y5V1fn1Px+7VH/QJlPS9pYULZ02UV9eNEPFXrdOmDJJNsrWYSESdAAAACTlHtrJeM3JrKkzqEpfiyp9fm2v86u1u0+SdPzkTJ277ouanZOuf77uTOVMmhDjSJHMSNABAACSXDLuoZ2M15xsAn0Dem1vm/714Uf1XvNBdXtWSJLcE9NVMj9PxV63iua5lT/JIakktsECnyBBBwAASGLJuId2Ml7zWCRqZUEobOqtjzpV6fOr0tei1xva1R8ytf+FZ5XpSNH3/+/VKvbm6fjJmTIMytYRn0jQAQAAklgy7qGdjNc8WolWWdA7EFZnT7++8fQb2l7nV0egX5K0YMokfWXFbBV783TTjhzZDEPXlMyNcbTAyEjQAQAAklgy7qGdjNc8GolQWXCwd0A797QOriWv88uXfaqULVW/36azji9QsdetFfPcystMH/43NmbLkUBI0AEAAJJYMu6hnYzXPBqjrSywsgQ+FDb1ZmOHqnx+Vfr8euODdg2ETTlSbTp9jkuX3/hNFXvzNL9gImXrERYOhxUOhxUMBpP+Z8NKJOgAAABJLhn30E7Gax7JaCoLrCiB/7AtMLyOfPPzzyrQH1LmiWdp0dQsfa1kjoq9bp0yM0fpKfaIPi/+oqGhQa2trTIMQ5s3b477pQ7jCQk6AAAAknIP7WS85s8zUmVBtErgu4L92vFJ2XqVz6/61oAkaWqWQ3rv95qSkaat/3mnciekHeMVYjSG7rM0+AVNKBSKu6UO4xkJOgAAAABJn19ZEKnmegOhsHY3duienzyq95oPqH3acoXCpiak2XX6HJeuPGOWirx5mps3QWe+PFGSSM4tNHSfV65cOXyMJorWIUEHAAAAMOxIlQVjaa43tE49GAwqPT1dDa2BwcZuPr927GnVgd4BNT/7pCakp2jjT65Q0Ty3TvLkKC3FFtVrw8iG7nNpaenwMZooWocEHQAAAMCIRttcr6GhQdt21sjXZejuikbVBzPUdGBw+7PpORk6f8lUFXvduv3VXKXYDF1/znGWXwuObOg+V1dXH9JrgNlza5CgAwAAABiVI5XA9w2E9YcP2lXxTpM2v75XH3any5ShdJupeZOCuua8E7TqhCma6XIOd1v/kY2u6/HK4/EoPz/fsm79+AsSdAAAAFjGyi26EB1DJfCNXQOqev19Vfr82rm3Vd19IdkMyTNBOmtyv7yTQvJMCCs9NUUrF+UqN3dCrEPHGDgcDn5GY4AEHQAAAJawYosuRE9bd5+21/m1t6VbnT19OvverZKkmS6nLj55morm5enkaRNUueUVhUKh4X/H+uXRY+9xkKADAAAg6qK1RReip3cgpNcb2lXl86vS59efP+qUaUrm/JU6JX+irr54kYrn5cnjOjT5Hlq/bJqmDMNg/fIosfc4JBJ0AAAAWCBSW3QhekzTVN3+g/q4M6jOnn4t/Zf/UU9/SCk2Qyd5snXd2fNV7HXrxGlrlWI/crf1ofXLb731lhYuXMj9HQWr9x5npj5+kaADAAAg6sayRRes4z/Yq+11gzPklb4WNXf16uC0M5Q/MV2XnjpdRd48nT4nV5mO1DGd1+Fw0GdgDKzce5yZ+vhGgg4AAICoG+0WXYiusGnqQHBAd/y2VpXv+fX2x12SpGxnqlbMc6t4nltFN56p6Tl8cWIlq/Yet3qmHmNHgg4AAEZE521EwpG26EL0mKapd5oOqMrn1zZfi2rq2xU2TT1W9b5O9uTohjXHqWieW4umZcnOtmcxY9Xe41bO1OPoRC1B37hxoyoqKuRyufTSSy9Jku666y79/ve/V2pqqjwej+644w5NmjRJkvTwww9r06ZNstls+t73vqfi4uJohQYAAMaAztuIpKEtukgGomd/V1BVn5StV9X51XKgV5I0L3+i1lz8Rc2fnKmbrztHE9KZq4snVuw9btVMPY5e1H4q161bpyuuuEI33njj8LEVK1bo+uuvV0pKiu6++249/PDDuuGGG1RXV6fNmzdr8+bNam5u1lVXXaWXX35Zdrs9WuEBAIBRoPM2EP96+kLaVd+mhtaAOnv6ddrt5ZKk3Alpg2Xr3sH/pmRlSCr9/JMhpqK997hVM/U4elFL0AsLC9XY2HjIsaKiouH/X7p0qX73u99JksrLy1VWVqa0tDTNmDFDM2fO1JtvvqmTTjopWuEBAIBRoPM2EH/CYVNvf9z1ySx5i6rr29U3EFZwdomOczv15XOPV7HXrROmTJKNsnX8FStm6nH0YlbX8vzzz2vt2rWSpObmZi1ZsmT47woKCtTc3DziOXp7e1VbWxu1GBF7wWCQewxLMeZgpUQYbwMDAwqFQoccC4VC2rdv36h+V39aIBCQpLi/5vEqEAgoHA4f8fUfzf2JxD2Mp3Fg1TVHQmtgQA899bwaOvrUNbNYncGwJGlWdqrOn5+pk6ZmaNHfXSVHik1Sn9T1kd7t+iimMY/0HseYgxTZ1yQRfq8OWbBgwWGPxyRB/+lPfyq73a4LLrjgmM6Tnp5+xAvD+FBbW8s9hqUYc7BSooy3rKwsSYd23j6aNehDaxwT4ZrHI6fTqUAgcMTXfzT3JxL3MJ7GgVXXfDQCfQN6bW/b8PZnvv0H1fT880q12/TVOy9T0Ty3irxuFUyK39nPkd7jGHOQIvuaJMrv1c9jeYL+wgsvqKKiQo8//rgMY7DkpqCgQE1NTcOPaW5uVkFBgdWhAQCAw6DzNhB94bCpP3/UOZyQv97Qrv6QqfQUm06bnatLT52uR7dly5lm131/uzTW4QKIEksT9G3btunRRx/VUxcVP6EAACAASURBVE89pYyMjOHjq1at0vXXX6+rrrpKzc3Nqq+v1+LFi60MDQAAfA46bwORt6+jR1W+Fm3z+fXSpmfU3RfSxEVn6YQpk/SVFbNV7M3TqbNy5EgdbJz8dBoNlIHxLmoJ+oYNG7Rr1y61t7erpKRE69ev1yOPPKK+vj5dddVVkqQlS5botttuk9fr1dq1a3XeeefJbrfr5ptvpoM7AAAAxpWDvQPauadVlb4WVdb5tbelW5JUMCld4Xd/r8kZqfr9M3coLzM9xpECiJWoJej33nvvZ45deumlR3z8tddeq2uvvTZa4QAAAIxJMBikyzGOSShs6s3GDt3zk5/pveaDapt6ugbCphypNp0+x6XLT/OoZH6evPkTdeYrEyWJ5BxIcjHr4g4AABCvGhoaVFNTc8g+wUfTGA/J58O2gLb5WlTl82t7nV9dwQE1PfOkJqSn6Lv/drmK57l1yqwcpadQLQrgs0jQAQAAPiUYDKqmpkZbtmyRNLj3e3V1tfLz82Myk85MfnzrCvbrtnt/qveaD6hz+hlqaB3cMmpKlkPnLpqsIm+e7tqeo1S7TTeee3yMowUQ70jQAQAAPiUQCMgwDFVUVEgaTNANw1AgELA8QWYmP/4MhMLa3dihbe/5VVXn1x8/7NC+p34mu83QF289R1eeMUvF3jzNzZswvGPRvXZbjKMGkChI0AEAAD7F6XTKNM1DjpmmObxXr1XibSY/mQX7Q3pyZ4Mq32vRjj2tOtA7IMOQFk/P1rWlc/X0lknKTE/Vf1xZeNTPEQ6HFQ6HFQwGub9AEiNBBwAA+BSHw6HCwsFEyzAM2e12FRYWWp40xdNMfrLpDPTr1T1+Vdb59YcPO9TbH9I//+rPmpadofOXTFGxN09nzHUp25kmSXrpjtRjer6Ghga1trbKMAxt3ryZSgkgiZGgAwAA/BWPxyOXy6VwOKyysrKYJMTxMpOfDPpDYf3hg47B7c98fr3Z2KGwKWWmp+iksy7W/MkTtfFbKzXL5RwuW4+UoUoJafD+hkIhKiWAJEaCDgAAcBg2m002my1mSVK8zOSPR6Zpaq+/W01dQXUG+rX0X15Rd19IdpuhJdOztH6VV8Vet5bMyFaqfU1UYxmqlFi5cuXwMSolgORFgg4AABAlx9qBPR5m8seLtu4+ba/zq8rnV6WvRR91BnVw6hlyT0zXxSdPU9G8PC2f61JWxrGVq4/VUKVEaWnp8DEqJYDkRYIOAAAQBZHqwB7rmfxE1TsQ0usN7Z8k5H79+aNOmaY0yZGiFfPc+sYqt4pvOFMeV2wT4aFKierq6kPGCvcbSE4k6AAAABFGB3brmaYp3/6Dqvxkhvy1vW1q+cMrshmGSssu0XVnz1eR163F07KUEmfbnnk8HuXn57PfPQASdAAAgEijA7s1/Ad7tb3O/8me5C1q7uqVJM1xT9Clp07Xr3+3U5MyUvXc/70nxpGOzOFwMDYAkKADAABEGh3YoyPYH1JNfbsq61r07FNPaV9njyYuOkvZzlStmOtWsdetIq9b03MGX+dt96XFOGIAGBsSdAAAgAijA3tkmKapd5oOqMrn1zZfi3a936begbBS7YY6q3+rHGeqNv30Zi2aliW7LbLbnwFALJCgAwAARAEd2I/O/gNB/eC+h/Re8wG1Tl2ulgODZevz8ifq8mUeFXvdWjbbpbLtP5IkLZmRHctwkQDC4bDC4bCCwSA/h4h7JOgAAABRQgf2kfX0hbSrvk1VvhZV+vx6p+mAmp7+D6Xabbrq9vNV5B0sXZ+SlRHrUJGAGhoa1NraKsMwtHnz5qPeTQGwCgk6AAAALNXdN6CHtu5Rlc+vXfVt6hsIK81uU+HsHN147vF6bGuWJqSn6F+/dFKsQ0UCG9pNQRpcLhEKhdhNAXGPBB0AAABR1dQZVOUnM+SvN7SrPxTWnb99R8cVZOrLp89U0Sdl6xlpdknSf6bzEfVwKNUem6HdFFauXDl8jN0UEO949wMAAEBEBfoG9NreNm3ztajK55dv/0FJkntims5Y+wXNL8jU9647SwWTSJJGi1LtsRvaTaG0tHT4GLspIN6RoAMAAOCYhMOm/vxRpyp9fj3xxBOqb+1WxgmrlJ5i02mzc3XpqdNVNC9Px0/OlM22OtbhJhxKtY/O0G4K1dXVMgxDpmmymwLiHgk6AAAAxmxfR4+qfC3a5vPr1Tq/2gP9kqSuyv9WtjNVv7jnH1U4K1eOVHuMI018lGofPY/Ho/z8fAUCATmdzpi/XixTwEhI0AEAABJcMBiMegJysHdA7YE+dfb0a9U9Fdrb0i1Jys9M16rjC1TsdWvFPLcu3Xm3JKnYmxeVOJIRpdrHxuFwxEUyzDIFjAYJOgAAQAJraGhQTU3NISW8kfjQHwqberOxQ1U+vyp9fr3xQbs68k9Xqt0mT65Tl5/mUcn8PHnzJ8owjAhcCY6EUu3ExzIFjBYJOgAASc6K2VdEx9CH/i1btkiSSktLj+lDf+9AWE+/9oEqfS16dU+rOnv6ZRjSoqlZ+lrJHBVffZNOmZmj9BTK1q0Wb6XaGBuWKWC0SNABAEhi0Zp9TQbx8MXG0If+iooKSYMJ+lg+9HcF+7VjT6sqfS3644cdCvaHdNOLf9LULIfWLCxQsTdPK+a5lTshLcpXgtGIl1JtjB3LFDBaJOgAACSpSM++JpN4+WJj6EP/p33eh37TlF5vaNO29/yqqvPrjx92KBQ2NSHNrsWrLtL8gkz947dKNTdvAmXrY0DjL4yEZQoYLRJ0AACS1LHOviarePpiY+hDvzRYLmu32w/50G+aphpaA6qs8+u95gPqDPTrkp/ukGFIi6dn69rSuSr2unWSJ0dpKedaGvt4MZrGXyTwkFimgNEhQQeAJBYPJbqInbHOvmJQvH2x4fF45HK5FA6HVVZWpt6wXb/508eq9PlV6WtRY3uPJGnCiat1fKZN119+slbMcynbSdn6sRpN4y86d+PTWKaAkZCgA0CSipcSXcTOSLOvOLx4+2KjPxRWd19YHT19+uJ/vK43GzsUNqWJ6SlaPtela0rmqNibp1mu8/TOO+9owYIpMYkzFqI9cz1S4y86dwMYKxJ0AEhC8VSii9j669lX7v/IYv3Fhmma2uvvVuV7Laqq82vHnlY1u0+TIclmSN9c5VWJ160lM7KVardZElM8smLmeqTGX3TuBjBWJOgAkITirUQXsWWz2WSz2bj3Y2D1FxsDIVMvvfmRKj9p7ravY7BsfabLqYtOmqbiy76r5XNdyspIjWocicKqmeuRGn/RuRvAWJGgA0ASsrpEl7XunzX0mtA0KnFF84uNvoGwXm9oV1Vdi/68r1MHewf0zaf/oEmOFJ0x162vnzlXxfPy5HGR6B2OlTPXn9f4i87dAMaKBB0AkpCVJbqsdf+sodfENE3V19fzmkCmaapu/0Ft8/lV5WvRzr1t6ukPKcVmaM6K8+UtyNT1Xz9Di6dlKSWJy9ZHy+qZ689r/EXnbgBjQYIOAEnKihJd1rp/Fq8JhvgP9mp7nV8PPfpzvdd8QKG5JZKkOe4JuuzU6Sry5un0ObnKdJwX40gTT7zNXNO5G8BokaADQBKL9tpj1rp/Fq9J8gr2h1RT367KuhZVvufX2x93SZL8m59TVkaq7rnhGyryujU9h7L1SGDmGkAiOmKC/rWvfU233HKLpk+fbmU8AIBxJN62o4oHvCbHLlF6GpimqUBfSJ09/fr7/3hNu95vU+9AWKl2Qyd7cnTDmuNU7HVr/c4cGZK+eBrLHCKNmWsAieaICfq6det09dVX66KLLtJXv/pVpabSFRQAMDax3o4qHn36NZHEazJG8d7TYH9XUFV1flX6Brutv587eK8/7gzq8mUeFXvdWjbbpQnpf/kIZsQqWABA3Dligr527VqVlJTo3//933XJJZfowgsvlM32l6YkV111lSUBAgASG/tsf9bQa9Ld3c1rMgbxuH4/bJra+l7L8J7k7zQdkCTlTkjTinlu3bDmOhXNc2tqdkZM4gMAJJbPXYOempqqjIwM9fX1qbu7+5AEfSQbN25URUWFXC6XXnrpJUlSR0eHrrvuOu3bt0/Tpk3T/fffr6ysLJmmqR/+8IfaunWrHA6H7rzzTi1cuPDYrgwAEDfYZ/uzbDab7HY7r8kYxMP6/XDY1Nsfd6mqzq/aj7t0IDigf3hsl9LsNp06K0c3nnu8ir1unTBlkmw25sYBAGNzxAR927ZtuvPOO7Vq1Sq9+OKLysgY2ze/69at0xVXXKEbb7xx+Ngjjzyi5cuX65prrtEjjzyiRx55RDfccIO2bdum+vp6vfLKK9q9e7duvfVWPffcc0d/VQAAYNyJ1fr9ps6gKn2DM+RVPr9au/skSfmnnKuiyZlaf1Whls12KSPNHtU4AADj3xET9IceekgPPPCAvF7vUZ24sLBQjY2NhxwrLy/Xk08+KUm66KKL9Pd///e64YYbVF5erosuukiGYWjp0qXq6urS/v37lZ+ff1TPDQAAxh+rehoE+gb02vttamgNqKOnT6ffUS5Jck9MV8n8PBXNc6vI61bBpLKIPi8AAEdM0J9++umIP1lra+tw0p2Xl6fW1lZJUnNzsyZPnjz8uMmTJ6u5uXnEBL23t1e1tbURjxPxIxgMco9hqWQbc4FAQJKifs1WPU8iCQQCCofDcfGajOb+ROIeRmocOJ1OhcNhzZ8/X93d3cd8vrBpqq61T3/4OKA3PurR2/uDGghLwdnFmjspVReekqtTpmZoVk6aDMOQdEBt+w6obd8xPe0wK38+rHiPs2qs8L4S/0Yab9xnSJG9x4n0OW7BggWHPR6zfdANw/jkl9zRS09PP+KFYXyora3lHsNSyTbmhkqDo33NVj1PInE6nQoEAnHxmozm/kTiHkZqHEyYMEGSdOKJJx71OfZ19KjK16JKn1/b6/xqD/QPxjZlkq4umqpib55OnXWuHKnRL1u38ufDivc4q8YK7yvxb6TxNpp76HA4FA6HNXv2bHp2jFOR/FkeD5/jLE3QXS7XcOn6/v37lZubK0kqKChQU1PT8OOamppUUFBgZWgAAGAcO9g7oJ17WlXpa1FlnV9v/v6/JElzlp+nVccXqNjr1op5buVlpsc4UgBDGhoa1NraKsMwtHnz5rjbVhGIBksT9FWrVulXv/qVrrnmGv3qV7/SWWedNXz8qaeeUllZmXbv3q3MzEzWnwMAgKMWCpt6s7FjcD9yn19vfNCugbApR6pNy2a7tG/fq8rKSNVrN91zzBV9ACJvaFtFabAZZCgUivm2ioAVopagb9iwQbt27VJ7e7tKSkq0fv16XXPNNfrOd76jTZs2aerUqbr//vslDW6TsnXrVq1evVoZGRm6/fbboxUWAA3+0gsEAnI6nfySAzBufNgWUKXPr0d//nP5mg/KftxKSdKiaZP0tZI5Kp7n1imzcpSeYtfKJwbf+0jOgfg0tK3iypUrh49Zva0iEAtRS9Dvvffewx7/xS9+8ZljhmHolltuiVYoAD6loaFBNTU1MgxDpmlSLgYgYXUF+7VjT6uqfH5V+lpU3zrYaKjtlReUlZGqf731Oq2Y65JrImXrQKIZ2laxtLR0+JgV2yoCsRazJnEArDdULrZlyxZJg9UrlIsBSBQDobB2N3aqsb1HnT39Oum2/1EobMqZZtfyOS79wxmzVOzN01d35kiSLlgyNcYRAzhaQ9sqVldXHzKpwOcVjHck6EASGSoXq6iokDSYoFMuBiCeNbR2a5vPrypfi17d06oDwQF1z1ihGblOXV46V8Vet07y5CgtxRbrUAFEmMfjUX5+PsvykFRI0IEkMlQu9mnjoVyMNfXA+NEZ6Nere/yqrBssW/+wrUeSNC07Q+cvnqJib57OmLta2c60GEcKwAoOh4Pf7UgqJOhAEhkqF5MGez/Y7faELxdjTT2Q2PpDYf3hg47B7c98fm3/7fMyJU05ZY1On+vSNcVzVOTN0yyXk4ZuwDgTDocVDocVDAYT+rMIEEkk6ECS8Xg8crlcCofDKisrS+hfiKypBxLTnpaDqnyvRVV1fu3Y06ruvpBshrR0RrayPtyurIxU7bz5x0q1U7YOjFfscQ4cHgk6kIRsNptsNlvCJ7GsqQcSQ3t3n7bv8WtvS7c6e/p11j1bJUkzXU5ddNI0FXvztHyuS1kZqVr5ywxJIjkHxjH2OAeOjAQdQMIar2vqgUTXOxDS6w3tn2x/5tefP+qUaUrh+St1cv5EXX3RIhV73ZrpmhDrUAHEAHucA0dGgg4gYY3HNfVAIjJNU3c/+IjebT6o8Nxi7dzbpp7+kOw2Qyd7svWds+areL5bi6etVQoz40DSY49z4MhI0AEktPG0ph5IJP6DvdpeNzhDXulr0e6HHpQknfGtU3TpqdNV7M3T6XNylelIjXGkGK9oMJa42OMcODISdAAJb7ysqQfiWdg0dSA4oDt+W6sqn19vfdQlScp2pmrFXLcO5E1UVkaqtvy/lbENFEmBBmOJjz3OgcMjQQcAAJ9hmqbebT6gKp9f23x+1dS3K2yaeqzqfZ3sydENa45T0Ty3Fk3Lkt1maOXP0mMdMpIEDcbGD/Y4Bz6LBB0AAEiS9h8IqsrnH2zuVudXy4FeSdK8/Ilac/EXddzkTP3zdedoQjofHxA7NBgDMJ7xGxYAgCQV7A9p1/ttamgNqLOnX6f9sFySlDshTUXz3CryulXsdWtKVoak0s8/GWARGowBGM9I0AEASBLhsKm3P+5SVd1gY7fq+nb1DYQVnF2i49xOffnc41XsdeuEKZNksxmxDhc4LBqMARjPSNABABjH7n3wIdV+1KH0hWdrx952tXb3SZKOK8jUl0+fqeL5eTpt1rnKSLPHOFJg9GgwBmC8IkEHAGAcCfQN6LW9bdrma9Hv3/5YO3/yE0nSvC8v0vLZOTp36QIVed0qmERCg8RGgzEA4xEJOgAkoGAwyMwRJEmhsKm3Puoc3o/89YZ29YdMpafYNNPZL1e6qQy7qe+dGFBqSq/KFp7CmEHcY49zAMmKBB0AEkxDQ4NqamoOWXvJ/r/xJ5pfouzr6FGVr0XbfH69tOkZdfeFNHHRWVowZZK+smK2irxuzZ0k7dxeqfJzBhtp2Qw6XSMxsMc5gGRGgg4ACWRo/98tW7ZIkkpLS9n/Nw5F+kuUg70D2rmnVVV1fm3ztWhvS7ckKT8zXeF3f6/JGan6/TN3KC/zL3uRB4NBOl0j4bDHOYBkR4IOAAlkaP/fiooKSYMJOrOi8SUSX6KEwqb+tK9T9zz4iN7df1BtU07XQNiUI9Wm0+e4dPlpHpXMz5M3f6LO/J+JknRIci7R6RqJiT3OASQ7EnQASCBD+/9+GrOi8eVov0T5sC0wvI781T2t6uzpV9MzT2pCeoq++2+Xq9jr1ikzc5SeMvpu63S6RqJhj3MAyY4EHQASyNCsqDQ4q2S325kVjTOj/RIlFDbVGQzpn3/1Z1X6WlTfGpAkTclyaM3CAhV783TX9lyl2A3deO7xRx0Pna6RSKj8AJDsSNABIMF4PB65XC6Fw2GVlZXxwTXOHOlLlJTUNL3e0PbJLLlfNQ3tMk1Tz7/RqNPnuPQPZ8xSsTdPc/MmyDAMSdI9diOWlwIclWPtwE7lB4BkRoIOAAnIZrPJZrONmw+u423buKEvUQK9/erKX6IfVOzXjj21OtA7IMOQFk/L0oWX/Z1cKb267+ZzlJZii3XIQEREqgM7lR8AkhUJOgAgpsbTtnGdgX69usevbT6/du/rUm9/SP+y+V1Ny87Q+UumqGhens6Y61LOhDRJRaqtrSU5x7hBB3YAOHYk6ACAmEn0beP6Q2H94YOO4T3J32zsUNiUJqanaOlZF+u4yRO18VsrNcvlHC5bB8YrOrADwLEjQQcAxEyibRtnmqbe93frzn99WO82H1TrlNN1sHdANkNaOiNb31zlVYnXrSUzspVqXxPrcAFL0YEdwNE41r4V4w0JOoCYGm9rjzE2Y9k2LlZjpb27T9v3+FX1SXO3fR09anr6cTlS7frGj9ep2Jun5XNdyspItSwmIB7RgR3AWEWqb8V4QoIOIGbG09pjHJ3Rbhtn5VjpGwjr9YZ2VdW1qNLn15/2dco0pUxHilbMdevrZ87Vg5U5Sk+16YcXnxiVGIBERQd2AKNF34rDI0EHEBOJvvYYkTPStnHRHiumaapu/0Ft8/lV5WvRy7/6pfpCYWUvPlsnebJ13dnzVeR1a/G0LKXYBxu6PWyXBgYGKMcDDoMO7ABGg74Vh0eCDiAmEm3tMaLr87aNi8ZY8R/s1fa6wZL1Kp9fTV1BSdIc9wSl7dmqvIxUVd18lzIdny1bpxwv/rGeEQDiH30rDo8EHUBMjGXtMZJbJMZKsD+kmvp2Vda16Nn/7ynt6+jRxEVnKduZqhVz3Sr2ulXkdWt6jlMrX5ogSYdNzinHi398gQIAiYG+FYdHgg4gJka79jje0NTOekczVkzT1LvNB1T5nl+VdX69trdVvQNhpdoNde76rXKcqdr005u1aFqW7LbRb39GOV584wsUAEgs9K34LBJ0ADEz0trjeENTu9gZzVjZfyCoH9z3kN5rPqDWqcvVcqBXkjQvf6K+dJpHJfPdWjbbpbLtP5IkLZmRPeY4KMeLb3yBAgCJh74VhyJBBxBTn7f2OJ7Q1C72/nqsBPtD2vV+myp9g93W32k6oKan/0OpdpuuvP18FXsHS9enZGUccp5jWZ9MOV584wsUAECiI0EHgFGgqV186O4b0MNb96jS59eu+jb1DYSVZrfp1Fk5uvHc4/XY1ixNSE/Rv33ppMP++0isT6YcL37xBQoAINGRoAPAKNDULjaau4Kq9PlV6WvR6w3t6g+Fdcdv39FxBZn68ukzVeQdLFvPSLNLkv4z/ci/1iK5PplyvPjFFygAgEQWkwT98ccf13PPPSfDMDR//nzdcccd2r9/vzZs2KCOjg4tXLhQP/rRj5SWlhaL8ADgM6xuapeszegCfQN6bW/bcFLu239QkuSemK4z1n5B8wsy9b3rzlLBpLG/JqxPTh58gQIASFSWJ+jNzc164okn9Jvf/EYOh0Pf/va3tXnzZm3dulVXXnmlysrKdPPNN2vTpk26/PLLrQ4PAI7IqqZ2ydSMLhw29dZHXdrX0aPOnn4t/Zf/UV8orPQUm06bnatLT52uYm+ejp+cKcM4+5iei/XJAAAg3sVkBj0UCikYDColJUXBYFB5eXnauXOn7rnnHknSxRdfrAcffJAEHUDciXZTu2RoRrevo0dVvhZt8/n1ap1f7YF+HZy+QlOzMvSlFbNU7M3TqbNy5Ei1R/R5WZ8MAADineUJekFBgb7yla/ozDPPVHp6ulasWKGFCxdq0qRJSkkZDGfy5Mlqbm4e8Vy9vb2qra2NdsiIoWAwyD2OgkAgIElx8dpGIpZIXs9IYy7ar10gEJBpmoc0ozNNU2+99dYhM72RisOKsRDoD6up/YAO9oZVdPsrauzqlyTlZth1ytQMnTQ1WydddpVyMj75lTTQovfrWo7uuUZxPfPnz1dfX5/S0tLU3d39mcda+fMRifEWLz/PiTQmrXye0YinMQdEEuMNVkukMbdgwYLDHrc8Qe/s7FR5ebnKy8uVmZmpb3/726qsrDyqc6Wnpx/xwjA+1NbWJsw9jsSaYavWHQ8levHw2kYilkhez0hjLtqvXTAYVH19/SHHDMPQwoULDxkTkYojGtcTCpt6s7FDVT6/Kn1+vfFBuzpmlCrVbtPcydm6stitkvl58uZPlGEYEXteKf7G00giMd7i5ec5nsdkLJ9nNOJpzAGRxHiD1cbDmLM8QX/11Vc1ffp05ebmSpLOOeccvfHGG+rq6tLAwIBSUlLU1NSkgoICq0MDjlok1gwn07pjHJnVzehGMtovjT5sCww3dnt1T6s6ewZnyRdNm6SvFs9R8Vdu0ikzI1+2DgAAMJ5YnqBPnTpVu3fvVk9PjxwOh3bs2KFFixZp2bJlevnll1VWVqYXX3xRq1atsjo04KhEYs1wMqw7xuhZ1YxuJJ/3pVFXsF879rSq0teiTc8+Lf/BXk1cdJamZDm0ZmGBirx5WjHXJdfE9JjEDiSScDiscDisYDDIez4AJDnLE/QlS5ZozZo1uvjii5WSkqIFCxbob//2b7Vy5Updd911uv/++7VgwQJdeumlVocGHJWhrZs+vWZ4rFs3ReIcGF+i3YxuJH/9pVFRSal+uaVGmnxQO97v0B8/7FAobMqZZlfnH1+ROyNVLz12m+bmTYh42fpYkOgkvmS7hw0NDWptbZVhGNq8eTPVUwCQ5GLSxf1b3/qWvvWtbx1ybMaMGdq0aVMswgGOydDWTZ821q2bInEOIJICgYBaew39+pWt6glJ5dlrFQwZMt55X4tnZOva0rkq9rp1kidH52y7U5I0L39iTGOOVKKTbAliPLEyWY2H+zz0RZg0+J4fCoWongKAJBeTBB0YTyKxZjje1h0jOXUG+vXqHr8q6/za9u5+NXakK3jcOZpoN7U4e0DHZ5v6+iVna3JuZqxD/YxIJTrMZsaOlclqvNznoeqplStXDh+jegoAkhsJOhABkVgzHC/rjpE8+kNhHQgOqLOnTxf9ZLvebOxQ2JQmpqdo+VyXvnBirhwnFinPIUkhFRYWxmVyLkUm0WE2M7asSlbj6T4PVU+VlpYOH6N6CgCSGwk6ECGRWDMc63XHGN9M09Ref7cq32tRVZ1fO/a0qtl9mgxJhiF9c5VXxV63ls7IVqrdJkkKBhdYsvXfsYpEosNsZmxZlazG030eqp6qrq4+pBkj4w0AkhcJOgCMY23dfdpe5/9kT/KW/7+9e4+Pqr7zP/4+M7kMuRDITCbhFoQQlIJSLxG5xFDwto0+ird21e2q1fqoq+UnWKzV1d21hVarXfb2WM26WnXXrsoqtsZdrSBN4gUSLWvXhjIB0Mu9kgAAIABJREFUjNxymck9w+Qy5/z+iMmSihDIZM7M5PX8ywzj5DMz38mc9/eqQ+0hSVJ+dpq+dvY0FV+7TksKPMpKSz7m/+9yueIiLEQi6DCaaa9ohdVYe5/z8/Pl9XrjoiMMADD2COgAkEB6+019UN+qSl+zfvHv/6YDrUGlL1ipTFeSlhZ49Bdf8ai40KOZ7nS7S4240QYdRjPtF42wGovvc7x0hAEAxh4BHQDimGVZqmvqUuVnI+Tb97Uo2BuW02GoY/vrypqQrBf+6QEtnJ6lpM+mrSey0QYdRjPtF42wyvsMAIhVBHQAiDOBrh75u3rUfqRPi3+8VQ0dA9PWZ3vSdc2501VcmKMLZmfrincfkSSdO3OyneXGHUYzxwfeZwBALCKgA0CMC/WFP5u2PjBK/vGhDnXlLVZaslPnzpys4kKPlhV6NH0ya6UTTSyc1Q0AAKKHgA4AMcayLAV7w2o/0qc/f2qHduwLKNRnKtlp6Jz8yVp36eladsdSLZiWJafDsLtcjJFYOasbAABEDwEdAGJAU2dI79T5Vbnbr6o6vz460CZJmtF2RNedn6/iQo8WzXIrPZU/2+NBLJ3VDQAAoocrPQCwQagvrB37WlTpa1alz69dDZ2SpOz0FC2d41HRt76l0/Mytfo7JSd4JCSiWDqrGwAARA8BHQCiwDQt1TZ0qNLn17PPPKu9/m65vvQVpTgdOu+0yfr+ZWeouNCjL02ZKIfDkHR2BH4n65fjVayd1Q0AAKKDgA4AY6ShPaRKX7Oq6vyq8vkV6O6VJHVUvKqsCSl6+qf36PxZ2UpLifyfYtYvx7dYPKsbAACMPQI6AERIsLdfP/zZ4/pDY6c6ZyyRr6lLkuTJSFFxoUfFhTlaVujRN97/qSRp+eneMamD9cuJgbO6AQAYfwjoAHCKTNPSx4c6VOFrVqWvWR/Ut2r/c0/IYRi6+sEVuva86Vo2J0dn5GV+Nm09Oli/nDg4qxsAgPGFgA4AJ6G331T7kT7d+fyHeqfOr9ZgnyRp3pSJunnpLL301kRlupL03C2LbKuR9csAAADxiYAOAMfR1dOv7XsDqvT5Velr1oeftkqS0ve1aMUZuSou9GjpHI9yMlMlSW8+kmxnuZJYvwx7sCkhAACjR0DH54RCIdY8YtwKm5Z27m9T5e5mVdb59WF9q/pNS65khxbNcuv6b96oM/Iyte7OlTKM6E1bP1msX0Y0sSkhAACRQUDHMPX19aqpqRk26sZFFhLd/pagKn1+/evTT+sPhzuVNO8rMgxpwdQsffvC2Sou9OjcmZOVmuSUdL7d5Y4Y65cRDWxKCABA5BDQMWTwImvr1q2SpJKSEi6ykJA6Qn16b09AVZ9NW/8kEJQktbz5stKTDf3zQ2u1dI5H2ekpNlcKxD42JQQAIHII6BgyeJG1bds2SQMBnYssJIL+sKn1f/e4djd2KXTaMu3c36awaSktxanFs926cclpKi7M0a3vT1YwGNQVC6faXTIQN9iUEACAyCGgY8jgRdbRuMhCPLIsS/WBoCrr/Krc3az39gTke/qfJUmX3rNEt5cUqLjQo7PzJyslyWFztUB8Y1NCAAAih4COIYMXWdLA9ESn08lFFuJGv2npv353WBU+v6rqmrW/5YgkadqkCSo9a4rKczOV5UrWq3cus7lSIPGwKSEAAJFBQMcw+fn5crvdMk1TpaWlXGQhZvWFTf320zZV+pr1vwfb1d3Tr9v//UNlpCZpcYFb3y6ereLCHJ3mTpNhGHr/H1hPDowlNiUEAGD0COj4HIfDIYfDwYUWYoplWdrn7x46j/y9PQF194blMKSZS0p1ujdTa76zWF+eMUnJTqatAwAAIP4Q0AGMmVAoNKopr63dvXpnj1+P/8vT+kNjp/pmF0uSZrrTtOrsaSouzNHiAreyJpRGunQAAAAg6gjoAMZEfX29ampqhm0alZ+ff9z/p7ff1Af1rar0Nauqzq/fHWyXZUnNv3pBWROS9cja21Vc6NFMd3qUngUAAAAQPQR0ABEXCoVUU1OjrVu3Sho4sq+6ulper3fYSLplWTrSG1b7kT7d/PQObd/XomBvWE6HoXPyJ+mulXNVPNejNe9lyzCkP7tgpl1PCQAAABhzBHQAxzSa6enBYFCGYWjbtm2SBgK6YRgKBoPq7jdUVedXpc+vKp9fe7IHTg74JBDUNedO17I5Hi0ucCvTlTz0eIYRsacFAAAAxCwCOoDPOZXp6UdLS0uTZVmSJNOSfB0O1XU59eSz/6vfN3RKkrImJGvpHLf+30V3adkcj2Zkp33h45mmKdM0FQqF2LwQAAAACYuADmCYkU5P/yKWZemTtl7tTT5Nh484dCQs/UvdBDkd0nkzk/W9S+ZqWWGOzpyWJafjxEPj9fX1CgQCMgxD5eXlJ91ZAAAAAMQLAjqAYY43Pf2LAnpTZ0jv1PlVuduvqjq/mjp7JEl551+u2W6X7rhuoYrPyFNG6sn9yRnsLJAGgn84HD6pzgJgPGCGCQAAiYOADmCYo6enD7IsS2lp/zcFPdQX1o59LfqHJ/5Vuxo61DljqSQpOz1FS+d4VFzo0bI5Hk2dNLrjzwY7C5YvXz5024k6C4DxhBkmAAAkFgI6gGFcLpeKigY2bjMMQ06nU+eed572tPSoyndQlT6/dnzSot5+U02bnlemK0l/9e1v6cLCHH1pykQ5RjBtfaQGOwtKSkqGbvvjzgJgvGKGCQAAiYeADuBz8vPzlTlpstqCvXo7NFM/fm63/F29kqTTczP1zQtmqrjQowffmyyHYegvls8ZkzoGOwuqq6uHbVhH+ACYYQIAQCIioAOQJAV7+7V9X8tn68ibtXN/uyTp/X2tWjbHo+LCHC0r9Ch34v9d+DuicP5Zfn6+vF7vKR/5BiQqZpgAAJB4COjAOPa7A+2q8DWryufXB/Wt6g2bSk1y6PxZ2bruz27U3NxMff+7F0V02vqpcLlcBHPgjzDDBACAxGNLQO/o6NBf/uVfavfu3TIMQxs2bNCsWbO0Zs0aHTx4UNOmTdPGjRuVlZVlR3lAwjrYdkRVvmb5mrrUfqRPV/xjlSRp3pSJumnpaSou9KjotGy5kp2SFtlbLIATYoYJAACJxZaAvn79ehUXF+vv//7v1dvbq1AopMcff1yLFy/WbbfdprKyMpWVlWndunV2lAeMmVAoFNUL6a6efr2/J6CqOr8qfM3a29wtSUr50koty83Qd77xZS2d41FOZuqY1wJgbDDDBACAxBH1gN7Z2anq6mr95Cc/kSSlpKQoJSVFW7Zs0XPPPSdJWrVqlb75zW8S0JFQ6uvrVVNTM2wqaqSPQwqbln53sF2P/WOZ/tDUpZYpF6jftORKdmjRLLeuPz9fxYU5mpv7VRlRWD8OAAAAYOSiHtAPHDig7Oxs/eAHP9CuXbs0f/583X///QoEAvJ6vZKknJwcBQKBEz5WT0+Pamtrx7rkcScYDEpSTLy2oVAoJuoYieO9bv39/dq1a5e2bdsmSSopKdH27dvV3t6upKSkET3GF2ns6tMHh47ot4eOaOfhI+rqNdXwi+c0IdmhW//mEp0zNU1f8qYqxemQ1COz9YB2tZ768zxaLLWVExlJrcFgUKZpjur5jPT3nOg+8STRnk80xdPfOCQG2hyiifaGaIunNjdv3rxj3h71gN7f36/f//73euCBB7Rw4UL96Ec/UllZ2bD7GIYxotG91NTUL3xiOHWDOwCP5rWN1FTu2trauHmPj/e6tbS0yOfzDQvoTqdT06ZNU3Z29ogeY1BnqE/v7Qmo0udXVZ1f+/wD09anZLn01bOmallhjh5+Z7KSnQ799M+WRerpHVMk2kq0jKTWtLQ0BYPBUT2fkf6eE90nniTa84mmePobh8RAm0M00d4QbYnQ5qIe0PPy8pSXl6eFCxdKki677DKVlZXJ7XarqalJXq9XTU1Nw0IL4ks0pnIPivaa7lM1eBzS0UZ6HFJ/2NT/HGhX5We7rVe8/p8yLUvecy7R4tlu/fnimSouzFFBTvpQx9bPnI4xeR4AAAAAxk7UA3pOTo7y8vK0d+9ezZ49W++9954KCgpUUFCgzZs367bbbtPmzZu1cuXKaJeGCAiFQqqpqdHWrVslDYwUV1dXy+v1RjxAR7MjYLQGj0OSBmaIOJ3O4x6HVB/oVoXPrypfs97dE1BnqF+GIZ01LUuZ9ZXKSkvW+w8+qpQkgngkmaapcDisUCgU0x0+AAAASEy27OL+wAMP6Hvf+576+vo0Y8YM/fjHP5Zpmrrrrru0adMmTZ06VRs3brSjNIxSMBiUYRjDpnIbhqFgMBjRwBPNjoBIyc/Pl9vtlmmaKi0tHVZne7BP7+4ZmK7edqRPJT/dJkmaNmmCLj9ripbNydHSOW5NSkvR8k0Do+6E88iqr68f2vuivLw8pjt8AAAAkJhsCejz5s3Tyy+//Lnbn3nmGRuqQSSNZir3yYhWR0CkORwOORwOOZNTtGNfi6p8zarw+fXRgTaZlhSes1xf9mbolq/NV3Fhjk5zp7HbehQMdvgMCofDMd/hAwAAgMRjS0BH4jrZqdynKlodAZFiWZb2+rvV0BFSe7BPZz/0a3X19MthSAtnTNKdKwp1YaFHC2f8iZJZPx51gx0+y5cvH7otHjp8AAAAkFgI6Ii4403ljpRodQSMRmt3r97Z41fl7oHd1g+2HVHX1CVyp6fqa1+equJCjxYXeJQ1ITniv9s0TZmmyVrqERrs8CkpKRm6LZY7fAAAAJCYCOgYE4NTuccyHEajI+BkWNbAEWiP/PcuVdX59buD7bIsKdOVpCUFbt2+vEDF65Zrpjt9TOsYXEttGAZrqUdosMOnurpalmXJMIyY6/ABAABA4iOgI65FoyPgi1iWpbqmLlX6/Kr0Nav6kxaZlqUnKvbqnPxJumvlXBXP9eisaVlKitK09aPXUluWxVrqk5Cfny+v16uPP/5Y8+fP5/UCAABA1BHQgZMQ6OpRVZ1fVT6/Kn1+NXSEJEmzPem6+Mo/1em5mXpgzcXKdEV+2vpIsJZ6dFwul9LS0nitAAAAYAsCOnAcob6wPqhv1d89/qR2N3aqbdoSSdKktGQtLfCouNCjZYUeTZ+cJmm5rbVKrKUGAAAA4hkBHTiKZVna3dilys+OP9uxL6BQn6nGl55XZmqSHnz8JhUX5mjBtCw5HbF3/NnRa6kNw5BlWaylBgAAAOIEAT2OhEIhBYNBpuBGWFNnSD/a+Lh2N3SqZepiNXX2SJLmeDP0p0X5unCuR3/17mQ5HYbuXFFoc7UnNriWmraCaOHUAAAAgMggoMeJ+vp61dTUDBsVZWfuUxPqC2vHvhZV1flVsbtZuxo61fD8vyrJ6dDNGy5X8ZyBaetTJ00Y+n8eisHR8uNxuVwEJUQFpwYAAABEDgE9DgzuzL1161ZJUklJCTtznwTTtFTb0DG0sduOT1rU228qxenQeadN1j2Xna6nf5Ol9NQk/cN1Z9tdLhA3ODUAAAAgsgjocWBwZ+5t27ZJGgjo7Mx9fI0doaHjz3750i/U2dOvjAUrdXpupr55wUwVF3p0/qxspaUMfAReTOWjAJwsTg0AAACILFJJHBjcmfto7Mw9XLC3X9v3tahyt19Vdc3a3dglSfJkpKivdqtyJyRr6/MblDuR0ABECqcGAAAARBYBPQ4M7swtDYxOOZ3Ocb8zt2la+vhQhw61HVHbkT59+W9+rd6wqdQkh86fla1rzp2uZXNydEZepla8tV6SCOdAhHFqAAAAQGQR0ONEfn6+3G63TNNUaWnpuLwAPtR2RFU+vyp8zXp3T0At3b3qmr5UU7Jcum7paSou9KjotGy5kp12lwqMG5waAAAAEDkE9DjicDjkcDjGzQVwd0+/tu/v1gu+j1Xpa9ae5m5JkjczVctPz9GFhTlaev9FyslMtblSYHzj1AAAAIDIIKAjZoRNS7872K7K3c2qrPPrN+WbFDYtec6+WItmuXXd+fkqLszR3NwMGUZ8HXsGAAAAACdCQIet9rcEVekb2NjtnbqA2o/0SZIWTJuo9E8q5XKY+u3mnyo1iWnrAAAAABIbAR1R1Rnq03t7Aqr0+bXpP/5dzV09yliwUlOyXLp0fq6WFeZoaYFb7oxULf/PNAWDQcI5AAAAgHGBgI4x1R829T8H2lXpa1aVz6/f7m9T2LSUluJU28435ZmQrNeeekgFOelMWwcAAAAwrhHQEXH1gW41doTUfqRPZ//w1+oM9cswpLOmZen2kgItK/TonPzJuqTiJ5KkOd4MmysGAAAAAPsR0DFq7Uf69N4evyp8flX5/Pq0JaiuqUs0OS1FpWdOUXFhjpYUuDU5PcXuUgEAAAAgZhHQcdL6wqZ27m8b2m39f/a3ybSkjNQkXTDbrVuLZ2nZ3SWa5WHaOgAAAACMFAEdJ2RZlvb5u1Xp86vS59d/v/KCQv1hTTxzpRbOmKQ7VxSquNCjL8+YpGSnw+5yAQAAACAuEdBxTP1hS699dEhVn4Xyg21HJEn52Wly1m3T1AnJqnzpYWVNSLa5UgAAAABIDAT0KAiFQgoGg0pLS5PL5bK7nGPq7Tf1QX2rquqa9b8H29XV0687n/+tMl1JWlrg0e3LC1Rc6NFMd7qWv54uSYRzAAAAAIggAvoYq6+vV01NjQzDkGVZKioqUn5+vt1lybIs1TV1fTZtvVnb97Uo2BuW02Fo1pLLNTcvU3f/xRKdNS1LSXE+bd00TZmmqVAo9IUdJCO5DwAAAACMJQL6GAqFQqqpqdHWrVslSSUlJaqurpbX6x2TEHiikfpAV49++LePa3djp1qmLlZDR0iSNMuTrmvOna7iwhxdMDtbma6vRrw2u9TX1ysQCMgwDJWXlx+zg2Qk9wEAAACAsUZAH0PBYFCGYWjbtm2SBgK6YRgKBoMRD+jHGqn3TpmmD+pbh0bJPz7UoYbnn1SSw6Eb139Vywo9WjbHoxnZaRGtJVYMdpBIAzMGwuHw5zpIRnIfAAAAAIgGAvoYSktLk2VZw26zLEtpaZENxEeP1Lf1GZp85gqV/dtO1Qc/VqjfVJLD0DkzJ+t7l8zVz3+TpfTUJP3TDedEtIZYNNhBsnz58qHb/riDZCT3AQAAAIBoIKCPIZfLpaKiIkkDoc/pdKqoqCiiwa+pM6Q3dtbr1b3J+q/XKhS2pLzsP5HXZerKhV5dtGC6Fs12KyN14K3elDp+3vLBDpKSkpKh2/64g2Qk9wEAAACAaBg/ac0m+fn5crvdMk1TpaWlow7npmWpYnezqur8qtjdrF0NnZKktCSHTl+0UnkTTK1aEJR7gkOlpfPH9SjwYAdJdXX1sKn/R78mI7kPAAAAAEQDAT0KHA6HHA7HKYU+07RU29ChKp9ftYc71Bnq158/tUMpTofOO22y7rnsdF1YmKOM/jZ9UNNDyPwj+fn58nq9x908byT3wbGx+z0AAAAQOQT0GNTYERra2O2dOr/8Xb2SpJxzL9OyvEx99+YinT8rW2kpR799WcrLzSVkHoPL5Trh6zGS+2A4dr8HAAAAIouAHgOCvf364d8+rj80dKorf4l2N3ZJkjwZKVo2x6PiwhwtK/Qod2LpcR+HkIloYfd7AAAAIPII6DYwTUsfH+pQha9ZVT6/Pqhv1afPPSGHYejqB1fo6nMGziQ/Iy9TDodhd7ljhunR8Yvd7wEAAIDII6BHSW+/qRer96vis2nrrcE+SdIZeZm6aelpeumticp0Jem5WxbZXGl0MD06vrH7PQAAABB5BPQoCHT3ytfYqXv+8yPlZKbqK2d4VVzo0dI5HnkzB0Yb33wk2eYqo4fp0fGP3e8BAACAyLMtoIfDYV199dXKzc3VE088of3792vt2rVqa2vT/Pnz9cgjjyglJcWu8iLq27d8S7WHO3XX7Rdqbm6GDCNxp62PBNOjEwO73wMAAACRZVtAf/bZZ1VQUKCuroEN0R599FHddNNNKi0t1YMPPqhNmzbp+uuvt6u8iFr9nVvtLiGmMD06cbAxIQAAABA5Djt+aUNDg7Zt26ZrrrlG0kA4e//993XppZdKkq688kpt2bLFjtJsY5qm+vv7FQqF7C7lhEZb6+D0aKfTqaSkJDmdTqZHY1yJp887AAAAoseWEfQNGzZo3bp16u7uliS1trZq4sSJSkoaKCcvL0+NjY0nfJyenh7V1taOaa3R0NbWpkAgIEn61a9+penTp2vSpElj8ruCwaAknfLrFsla586dq97eXqWkpKi7u/tzNQWDQZmmGRPv8Whft0iKpVriyUhft1AoNKrX9kS/J5qfd8S+0bY34GTR5hBNtDdEWzy1uXnz5h3z9qgH9LffflvZ2dlasGCBtm/fPqrHSk1N/cInFi9CoZDKy8uHfrYsS4cOHdLZZ589JiPKg9PIT+V1s6PWYDAYE++xy+WSaZqaNWuW7SP9o3kPx7ORvm61tbWjem2P93ui/RlC7BttewNOFm0O0UR7Q7QlQpuLekD/8MMPtXXrVlVUVKinp0ddXV1av369Ojo61N/fr6SkJDU0NCg3NzfapdkinjZMi6daI4kj4RAp4/UzBAAAgJGJekC/++67dffdd0uStm/frqeeekqPPfaYVq9erTfeeEOlpaV65ZVXtGLFimiXZot42jAtnmqNFI6EQySNx88QAAAARs6WTeKOZd26dXr66ad18cUXq62tTddee63dJUVFPG2YFk+1RsrRI56Do56DI57AyRqPnyEAAACMnG3HrEnSokWLtGjRIknSjBkztGnTJjvLsU08nScdT7VGAiOeiLTx9hkCAADAyNka0PF/4uk86XiqdbQGRzyrq6tlGIYsy2LEM06ZpinTNBUKhWx//8bTZwgAAAAjR0AHTiDWRjxjKWjGCzb6AwAAQDyImTXoQCxzuVzKzs62PRAPBs22tjaVl5fr008/tbWeePBFG/2FQiGbKwMAAACGYwQdiBPsKH9qONoMAAAA8YKADsQJguapYaM/AAAAxAsCOhAnCJqnho3+AAAAEC8I6ECcIGieuljb6A8AAAA4FgI6EEcImqeOo80AAAAQ6wjoQJwhaAIAAACJiWPWAAAAAACIAQR0AAAAAABiAAEdAAAAAIAYQEAHAAAAACAGENABAAAAAIgBBHTELNM0FQ6HFQqF7C4FAAAAAMYcAR0xqb6+XoFAQKFQSOXl5fr000/tLgkAAAAAxhQBHTEnFAqppqZm6OdwOKzq6mpG0gEAAAAktCS7CwD+WDAYlGEYWr58+dBthmEoGAzK5XLZVxgAAAAAjCECOmJOWlqaLMtSSUnJ0G2WZSktLc3GqgAAAABgbDHFfZwxTVP9/f0xPV3c5XKpqKhITqdTDodDTqdTRUVFjJ4DAAAASGiMoI8jgxuvGYah8vJyFRUVKT8/3+6yjik/P19er1cff/yx5s+fTzgHAAAAkPAYQR8njt54zbKsuNh4zeVyKS0tjXAOAAAAYFxgBH2cYOM1IDpM05RpmgqFQny2AAAAcFII6OMEG68BYy+elpEAAAAg9jDFfZw4euO1pKQkNl4DIiwel5EAAAAgtjCCPo4MbrwWDAZZ2w1EGMtIAAAAMFoE9HHG5XIRFoAxwDISAAAAjBZT3AEgAlhGAgAAgNFiBB0AIoRlJAAAABgNAjoARBDLSAAAAHCqmOIOAAAAAEAMIKADAAAAABADCOgAAAAAAMQAAjoAAAAAADGAgA4AAAAAQAwgoAMAAAAAEAOifsza4cOHdc899ygQCMgwDH3961/XjTfeqLa2Nq1Zs0YHDx7UtGnTtHHjRmVlZUW7PAAAAAAAbBH1EXSn06l7771Xr7/+ul544QU9//zzqqurU1lZmRYvXqw333xTixcvVllZWbRLAwAAAADANlEP6F6vV/Pnz5ckZWRkaPbs2WpsbNSWLVu0atUqSdKqVav01ltvRbs0AAAAAABsY+sa9AMHDqi2tlYLFy5UIBCQ1+uVJOXk5CgQCNhZGgAAAAAAURX1NeiDuru7tXr1at13333KyMgY9m+GYcgwjBM+Rk9Pj2pra8eqRMSAUCjEe4yoos0hmmhviDbaHKKJ9oZoi6c2N2/evGPebktA7+vr0+rVq3XFFVfokksukSS53W41NTXJ6/WqqalJ2dnZJ3yc1NTUL3xiSAy1tbW8x4gq2hyiifaGaKPNIZpob4i2RGhzhmVZVjR/oWVZ+v73v6+srCzdf//9Q7c//PDDmjx5sm677TaVlZWpra1N99xzz3Efa+fOnUpNTR3rkgEAAAAAiJikpCQVFhZ+7vaoB/SamhrdcMMNmjt3rhyOgSXwa9eu1VlnnaW77rpLhw8f1tSpU7Vx40ZNmjQpmqUBAAAAAGCbqAd0AAAAAADwebbu4g4AAAAAAAYQ0AEAAAAAiAEEdAAAAAAAYgABHQAAAACAGEBABwAAAAAgBiTZXQDwRSoqKrR+/XqZpqlrr71Wt912m90lIcH84Ac/0LZt2+R2u/Xaa69Jktra2rRmzRodPHhQ06ZN08aNG5WVlWVzpUgEhw8f1j333KNAICDDMPT1r39dN954I20OY6Knp0c33HCDent7FQ6Hdemll2r16tXav3+/1q5dq7a2Ns2fP1+PPPKIUlJS7C4XCSIcDuvqq69Wbm6unnjiCdobxtSKFSuUnp4uh8Mhp9Opl19+OSG+UxlBR0wKh8N66KGH9OSTT6q8vFyvvfaa6urq7C4LCeaqq67Sk08+Oey2srIyLV68WG+++aYWL16ssrIym6pDonE6nbr33nv1+uuv64UXXtDzzz+vuro62hzGREpKip555hn98pe/1ObNm1VZWamdO3fq0Ucf1U033aRf//rXmjhxojZt2mR3qUggzz77rAoKCoZ+pr1hrD3zzDN69dVX9fLLL0tKjOs4Ajpi0kcffaSZM2dqxowZSklJUWlpqbZs2WJ3WUgwRUVFn+tV3bJli1atWiVJWrVqld5Co7x4AAAEm0lEQVR66y07SkMC8nq9mj9/viQpIyNDs2fPVmNjI20OY8IwDKWnp0uS+vv71d/fL8Mw9P777+vSSy+VJF155ZV8tyJiGhoatG3bNl1zzTWSJMuyaG+IukT4TiWgIyY1NjYqLy9v6Ofc3Fw1NjbaWBHGi0AgIK/XK0nKyclRIBCwuSIkogMHDqi2tlYLFy6kzWHMhMNhfe1rX9OSJUu0ZMkSzZgxQxMnTlRS0sAKx7y8PL5bETEbNmzQunXr5HAMxIvW1lbaG8bcLbfcoquuukovvPCCpMS4jmMNOgB8AcMwZBiG3WUgwXR3d2v16tW67777lJGRMezfaHOIJKfTqVdffVUdHR264447tHfvXrtLQoJ6++23lZ2drQULFmj79u12l4Nx4he/+IVyc3MVCAR08803a/bs2cP+PV6/UwnoiEm5ublqaGgY+rmxsVG5ubk2VoTxwu12q6mpSV6vV01NTcrOzra7JCSQvr4+rV69WldccYUuueQSSbQ5jL2JEydq0aJF2rlzpzo6OtTf36+kpCQ1NDTw3YqI+PDDD7V161ZVVFSop6dHXV1dWr9+Pe0NY2qwPbndbl188cX66KOPEuI7lSnuiElnnnmmPvnkE+3fv1+9vb0qLy/XihUr7C4L48CKFSu0efNmSdLmzZu1cuVKmytCorAsS/fff79mz56tm2++eeh22hzGQktLizo6OiRJoVBI7777rgoKCrRo0SK98cYbkqRXXnmF71ZExN13362Kigpt3bpVP/vZz3TBBRfoscceo71hzASDQXV1dQ399zvvvKPCwsKE+E41LMuy7C4COJbf/OY32rBhw9CRHbfffrvdJSHBrF27Vjt27FBra6vcbre++93v6qKLLtJdd92lw4cPa+rUqdq4caMmTZpkd6lIADU1Nbrhhhs0d+7coTWaa9eu1VlnnUWbQ8Tt2rVL9957r8LhsCzL0mWXXaY777xT+/fv15o1a9Te3q558+bp0Ucf5dgrRNT27dv11FNPDR2zRnvDWNi/f7/uuOMOSQP7bVx++eW6/fbb1draGvffqQR0AAAAAABiAFPcAQAAAACIAQR0AAAAAABiAAEdAAAAAIAYQEAHAAAAACAGENABAAAAAIgBBHQAACBJOnz4sFasWKG2tjZJUnt7u1asWKEDBw7YXBkAAOMDAR0AAEiSpkyZouuuu06PPfaYJOmxxx7TN77xDU2fPt3mygAAGB84Bx0AAAzp6+vTVVddpauvvlovvfSSNm/erOTkZLvLAgBgXCCgAwCAYSorK3Xrrbfqqaee0tKlS+0uBwCAcYMp7gAAYJiKigrl5OTI5/PZXQoAAOMKAR0AAAypra3Vu+++qxdffFE///nP1dTUZHdJAACMGwR0AAAgSbIsS3/913+t++67T1OnTtUtt9yihx9+2O6yAAAYNwjoAABAkvTiiy9qypQpQ+vOr7/+eu3du1c7duywuTIAAMYHNokDAAAAACAGMIIOAAAAAEAMIKADAAAAABADCOgAAAAAAMQAAjoAAAAAADGAgA4AAAAAQAwgoAMAAAAAEAMI6AAAAAAAxID/DxF9FF+384tCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "data['y-hat'] = model.predict()\n",
        "data['residuals'] = model.resid\n",
        "ax = data.plot.scatter(x='X', y='Y', c='darkgrey', figsize=(14,6))\n",
        "data.plot.line(x='X', y='y-hat', ax=ax);\n",
        "for _, row in data.iterrows():\n",
        "    plt.plot((row.X, row.X), (row.Y, row['y-hat']), 'k-')\n",
        "sns.despine()\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='orange'>Question 2: There is a common metric in statistics called mean squared error (MSE), define it and calculate it for the predictions above (4 points) </font>\n",
        "\n",
        "\n",
        "\n",
        "*   You can either use the prediction using returns data or the predictions of the random data, i.e., you decide what predictions to use.\n",
        "*   I only care about your definition and the methodology you use.\n",
        "\n"
      ],
      "metadata": {
        "id": "mDt2I63szA3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Write (code) your answers here to obtain all 4 points\n"
      ],
      "metadata": {
        "id": "CGs0kRTZz53H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hKSq55mjsZ5"
      },
      "source": [
        "### Multiple Regression\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6Ghn-rsjsZ5"
      },
      "source": [
        "For two independent variables, the model simply changes as follows:\n",
        "\n",
        "$$y = \\beta_0 + \\beta_1 X_1 + \\beta_2 X_2 + \\epsilon$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sw7QvQnFjsZ6"
      },
      "source": [
        "#### Generate new random data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.427534Z",
          "start_time": "2021-04-15T19:54:57.297651Z"
        },
        "id": "thYSk6gtjsZ6",
        "outputId": "d0d9a378-12c6-48a0-f44f-dbf20ed717c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Create data\n",
        "size = 25\n",
        "X_1, X_2 = np.meshgrid(np.linspace(-50, 50, size), np.linspace(-50, 50, size), indexing='ij')\n",
        "data = pd.DataFrame({'X_1': X_1.ravel(), 'X_2': X_2.ravel()})\n",
        "data['Y'] = 50 + data.X_1 + 3 * data.X_2 + np.random.normal(0, 50, size=size**2)\n",
        "\n",
        "## Plot\n",
        "three_dee = plt.figure(figsize=(15, 5)).gca(projection='3d')\n",
        "three_dee.scatter(data.X_1, data.X_2, data.Y, c='g')\n",
        "sns.despine()\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.432065Z",
          "start_time": "2021-04-15T19:54:57.428738Z"
        },
        "id": "0Bj5O3yrjsZ6"
      },
      "outputs": [],
      "source": [
        "X = data[['X_1', 'X_2']]\n",
        "y = data['Y']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4ihuYaMjsZ7"
      },
      "source": [
        "#### Estimate multiple regression model with statsmodels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAvKzgBUjsZ8"
      },
      "source": [
        "The upper right part of the panel displays the goodness-of-fit measures just discussed, alongside the F-test that rejects the hypothesis that all coefficients are zero and irrelevant. Similarly, the t-statistics indicate that intercept and both slope coefficients are, unsurprisingly, highly significant.\n",
        "\n",
        "The bottom part of the summary contains the residual diagnostics. The left panel displays skew and kurtosis that are used to test the normality hypothesis. Both the Omnibus and the Jarque—Bera test fails to reject the null hypothesis that the residuals are normally distributed. The Durbin—Watson statistic tests for serial correlation in the residuals and has a value near 2 which, given 2 parameters and 625 observations, fails to reject the hypothesis of no serial correlation.\n",
        "\n",
        "Lastly, the condition number provides evidence about multicollinearity: it is the ratio of the square roots of the largest and the smallest eigenvalue of the design matrix that contains the input data. A value above 30 suggests that the regression may have significant multicollinearity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.459228Z",
          "start_time": "2021-04-15T19:54:57.434370Z"
        },
        "id": "MgwmmKnmjsZ8",
        "outputId": "e81c69e1-fd9c-450e-82f0-f6068e0c77b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      Y   R-squared:                       0.783\n",
            "Model:                            OLS   Adj. R-squared:                  0.782\n",
            "Method:                 Least Squares   F-statistic:                     1121.\n",
            "Date:                Fri, 20 Jan 2023   Prob (F-statistic):          6.26e-207\n",
            "Time:                        20:26:06   Log-Likelihood:                -3333.1\n",
            "No. Observations:                 625   AIC:                             6672.\n",
            "Df Residuals:                     622   BIC:                             6686.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         48.5665      2.009     24.176      0.000      44.622      52.511\n",
            "X_1            1.1097      0.067     16.597      0.000       0.978       1.241\n",
            "X_2            2.9641      0.067     44.334      0.000       2.833       3.095\n",
            "==============================================================================\n",
            "Omnibus:                        9.363   Durbin-Watson:                   2.147\n",
            "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.391\n",
            "Skew:                          -0.272   Prob(JB):                      0.00913\n",
            "Kurtosis:                       3.255   Cond. No.                         30.0\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ],
      "source": [
        "X_ols = sm.add_constant(X)\n",
        "model = sm.OLS(y, X_ols).fit()\n",
        "print(model.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBKQVeGwjsZ9"
      },
      "source": [
        "#### Verify computation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.466883Z",
          "start_time": "2021-04-15T19:54:57.460886Z"
        },
        "id": "NTxJnnUIjsZ9",
        "outputId": "4cf84a1a-2297-44ed-a414-73eb3ebbabcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const   48.57\n",
              "X_1      1.11\n",
              "X_2      2.96\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "beta = np.linalg.inv(X_ols.T.dot(X_ols)).dot(X_ols.T.dot(y))\n",
        "pd.Series(beta, index=X_ols.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CyfrjfC0jsZ9"
      },
      "source": [
        "#### Save output as image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:57.634435Z",
          "start_time": "2021-04-15T19:54:57.468510Z"
        },
        "id": "-uJGbIjIjsZ-",
        "outputId": "dc8b6d3f-b0c5-47a3-c28f-1cf1a590e468",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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ejJKSEqW2xo0bBzMzM/z0009wdXU14FoQoIOPdF+7dg21tbVwdXXF9OnTIRKJ8OjRIwBAWVkZt5NIpVKUlpZCLpejsLBQ6ZKeLr+Bu7m5obi4GLdu3cJrr70GoO2S3pAhQ2BpaYlffvlF699BX6SunZaWFly7dg2tra3Iy8tT2hFtbW2V7klQtx10XV9COpO/vz+cnZ2RmZkJAPh//+//ISMjAxkZGdyAhjEGqVSKWbNmYfXq1SgrKwMAuLi4oLCwEHK5HKWlpZBKpVy71tbWePLkCRobG1FaWgpA9+OU7ziaPHkyt9x79+4pLReg44voZ9q0afjll19QU1OD8vJyPHr0CBMnTuTe59uvXhzU2Nvbo7i4GAqFAo2Njbhz5w6cnJxQUVGBx48fA2j7qev+/ftwdHTs/BXrg/S6UpOVlQWRSIRhw4bhwIEDMDU1hZ2dHT777DPEx8ejpaUFlpaW+PLLL2FiYgInJyfs2rULDx48QHR0NJycnLi2dPkN3MLCAmPGjEFzczN3WX7+/Pk4fvw4QkJCMHbsWEyePFltGxKJBAkJCairq0NjYyNEIhHWrFmjth1LS0tkZmZi+/btmDNnDtzc3Lj33nzzTSQnJyM1NRX79++Ho6Ojyu2gz/oS0tni4+OxceNGREREoF+/fi+9zxhDUlISZDIZWltbsXbtWgDA6NGjsWjRIkRERGD8+PFKx/S7776Ld955B1OnTsWIESMA6H6c8p1PXnnlFURHR6tcLkDHF9GPhYUFPvnkE+7np+TkZJiY/N/f/Kr2q1u3bmHgwIEYO3YsV+bu7o558+YhPDwcJiYmiI6Oxrhx4/DDDz9wV0FbWlrwySefaHUliOiu0wMtJRIJ4uPjub8GCSHGRyAQ4K9//etLgwxCCOlKNKMwIYQQQoxCp1+pIYQQQgjpCnSlhhBCCCFGQedBzbp16+Dj4wOg7YanOXPmvDQzrzqaslooU4kYm4sXLyI8PBxhYWE4deoUV64qUwwA9u3bh9DQUISGhiIxMbFT+hQXF4fY2Fje9188TisqKjrUF8qcIj1dZ2Y/VVRUYNmyZQgNDUVkZCSuXr3a6evTV+n19NPAgQPx008/4dmzZ7C3tzdoh8aOHYuMjAy1J1xCeovffvsNW7ZsQXp6Ovr374/w8HDMnTuXm3TuRUVFRfj3v/+N7777DhYWFrhz547B+9TQ0ID//Oc/MDc3x2+//cZN366Oo6Mjdu/e3aHlts+PU1tbi+DgYPj6+tJjraRHUCgU+PLLL3H8+HEoFAq88cYb8Pb2VnoC6nnz58/H/PnzAQCVlZWIiYmBi4sLHj16hG+//RZZWVlgjCE4OBhCoRCWlpbYuHEjJk2ahLKyMixduhS5ublduYp9hl6DGl9fX2RnZ0Mmk8HX1xcymQwAcPjwYRw/fhxA21+CixYtAtCW1ZKeno4JEyYoZbXk5ORg7969UCgU8PDwQHJyssZlt88V8PHHH+vTdUK6VFFREVxcXLhIj9mzZ+PmzZtKc1s8TyqVwtbWlpvlV9Ojz/q4ceMGZs6cCXNzc1y/fh1+fn4A+I/THTt24Pvvv4eJiYlBnmJ8PnNK3aAmLS0N//jHP2BmZgYvLy/8/ve/5/r53XffYcKECbhz5w7+9re/wcnJCTNmzMAPP/wAoC1zKikpCa6uroiPj4dUKoWZmRmioqIQExMDoO3q0V//+ldYW1ujtLQUHh4e+PTTT3nPS3zbB6DzUm/3fPYTAC77SZuYBL7sJ8YYl/00ZMgQru1Ro0ahqakJCoWCohI6gV6DmilTpiAzMxPNzc0IDAzE7du3IZFIcOTIEZw6dQrNzc0QCoXw9vZGQ0ODyqwWTRlJfGgeCtKbVFVVKV3NtLOzQ3V1NW99T09P/OlPf8KiRYvg5+eH6Ohog89nceXKFXh4eMDU1BS5ubnw8/ODRCLhzVRau3Ytli9fjvj4eIMsnzKnSE/TFdlP7XJzczFlyhQa0HQSvWMS7O3tMXDgQO51SUkJ3N3duUvZ06ZNw927dyGTyVRmtWjKSOJD2S6kN1M1wd3zbGxscObMGeTm5uL8+fOIiopCVlaWQZO6r1y5gri4OJiamnK/+d+5c0dtppIhUOYU6ek6M/sJaBs87dixA3v37u2cFSD6D2rWrFkDExMTnD9/Xm09dSdxykgixs7BwYG7ygC0XaHU9JNS//794e/vD39/f8THx+PHH3+El5eXQfojlUohFovx1ltvAQAePXqEX3/9VeNgyxAoc4r0VF2R/SSXy5GYmIikpCS88sorhl8JAqADj3Tb2NjAysqKe+3i4gKRSIT6+no8ffoUxcXFmDhxIm9Wi6aMpBczldpRtgvpTaZNm4aSkhLU1tZCJpMhPz+fN+EdAMRiMX799VcAQGNjI8rKygx6M+2VK1ewePFinDt3DufOncPSpUtx5coVjZlKfPQ5HilzivQ0nZ39xBhDcnIyBAKBwf5AIap1OKW7nZOTE2JiYrBkyRIAQEJCAndzpKqsFr5sl3aqMpUA+u2a9C6DBg1CSkoKVqxYAQBYtWoV99NHQ0OD0glu5cqVmD17NtatW4eGhgYwxhAREaF0cu2oK1euKJ2EPTw8cPz4ccTExKjNVAJUX3XV93ikzCnSk3R29lNBQQH+9a9/4f79+9zDNM//u0YMh2YUJoRoJBKJ8PXXX+Pw4cPd3RUllDlFCHmewa7UEEKMT3l5OeLj49HQ0MDd90IIIT0VXakhhBBCiFHospiEQ4cOoaGhQamMb5p4bdA05b1XR753bana3/h0RSxBT+Lr64uwsDBuivfs7Gy929JlO6urn52djf3793d5O4S027FjBzw9PSEQCDSWq4s94GuHdA29nn5qj0koLCzUOibh8OHDOp38COkIbfe352MJzp49a7AJ5nq6tLQ07omj9hmF9aHrcc1X38/PD++9916Xt0NIu4CAAOzbt0+rclNTU2zcuBFnz57Fnj17sG7dOo3tkK6h16CmPSYhJycHvr6+XHlOTg6io6MRHh6O7du3AwDy8vIgFApRWVmJFStWQCgUKj2qvWXLFgQFBSElJYUrO3z4MAQCAQQCAU6ePMmVHzhwAMHBwUhMTFQ5TTk9Utm7qdp/gLYnZcLDwxEVFYWjR49y5WlpaQgODkZYWBh27twJQPP+9iJ1sQQHDhxASEgIPvzwQwQEBHBXBp+/0hQbG8sFr/L1Mz8/H3FxcUhMTIRAIMC2bdvUrq+q9epOum5nVdtBXf2kpCR4e3u/dMVX1+XytVNVVaXUpwcPHnDv0XmDtJs5cyZsbW21Kre3t+cm3Hs+9kBdO6SLMB2tXbuW/e///i9LSEhgK1euZCdPnmSbNm1i1dXVbNGiRay+vp4xxtiqVatYXl4e9zkfHx9WU1Oj1JazszMTiURMoVCwBQsWsEePHjGxWMz8/f2ZTCZjT548YQsWLGDV1dVMLBazgIAA1tDQwH7++Wc2ceJEJhaLlfq1du1aXVeHdIPp06e/VKZu/ykvL2eMMaZQKFhgYCCrrKxkjDHm5ubGnj17xn3+ear2N1Xq6upYQEAAi4qKYv/1X//FKioqGGOMicViFhgYyBobG9m9e/eU9rfn+x8TE8OKiorU9vP69ets2rRp7O7du9wy1a2vuvUyBB8fHyYQCFh4eDgLDw9ndXV1auvrup35tgNffcYYdx7pyHL52lm1ahX75ptvGGOMPXv2TKktOm+Q54nFYhYaGqp1OWOMXb58mb311lta1yedy2AxCfrEHpibm3OTbTk5OaG6uhpSqVSnuIV2NE1576Zu/+Gb5l7VNPq64osl6K7p+AHV8QCGlpaWhqFDh2pVV9f+qNsOujDUdrhx4wY3B5aVlZXSpKF03iAdQbEHPY9BYxJ0jT0wNzfn/rtfv35K05e/qCumcSfdS9X+o26a+45Mo/88VbEE2jL0dPyA4dZLV4aIK9C0HXTRXduBEG1Q7EHPZLCYBE2xB4MGDUJdXZ3GdnWNW2hHv433bnz7D98094xnGv122u5vfLEE3TUdv6b16ky6xBW0e3E7a9oO2n4vhvp+AWD27NncgKi+vh61tbXce3TeIPpgesYe0P7W+Qw2+Z6m2IPY2FgkJCRg8ODB2L17N+/laF3jFtrRNOW9h6p4gOXLl6vcf/imuWc80+i303Z/k8vlvLEE3TEdv6b16mq6bmdN2+HF+nK5HAkJCairq0NjYyNEIhHWrFmD+fPn67RcvnYWLFiAlJQUbNiwAX//+99hZmaGXbt2cT+90XmDtNu0aRMuXLiAx48fw8vLCxs3boSfn5/K8sGDB/PGHvC1A9D+1hVo8j1CtEDT8RNCSM+n989PhBBCCCE9CV2pIYQQQohRoCs1hBBCCDEKNKghhBBCiFHQeVCTnp6OOXPmQCgUYuXKlRCLxVp9TtfgO9Lz6BOE2NGAQXXBcVlZWQgMDERQUBBycnI0tlVSUgKhUKhyKv2O0rWffPUfP36MqKgohIeHQygU4uLFiwbtJ9A2bYJQKERoaChWr16NxsZGvdrRN5j0o48+4h6jByhgs11tbS3efvttrZfZ19y6dQthYWEIDg7Ghx9+qLauuuNRl3b0aV/V8Z6bm8vt30KhEFOnTkVJSYleyyYa6DoF8fPTkOfl5bGgoCDW3Nys8XPaTltPeq7u+A6rqqpYSUkJY4wxiUTC5s2bxxhjTC6XMx8fH1ZVVcXKysqYn58fa2lp0apNVVPpd3U/+eorFAomk8kYY4zV1NSwuXPnar1e2mqPeWhtbWUfffQR+9vf/tahdnTx888/s/j4eKUyQ+5XurZlqGUbqp1169axGzdudLgdY9PS0sICAwPZzZs3GWNM47bmO750bUfX9rU5L1VUVLCAgAC9lks069DPTx4eHrC1tVUb6KcugI4v0A+gSYp6E77ARr6AQV3wBccVFRVhwoQJsLe3x8iRIzFixAiNE97x4QuinDFjhsrAVUP0k6++ubk5FxEik8mgUCi4WYsNrV+/fnjttdfwn//8BwD/98gXMMsYQ0pKCgQCAbZu3arVMs+cOdPh6AdjDtj08/PD6dOnX1rnvn4+vH37NmxtbeHu7g4AGiM++I4vde2o+/dI2/a1OS9lZWUhKChItw1AtNbhyfdGjhwJiUSC6dOn47PPPsPw4cPR1NQEgUCAoKAgeHp6IiMjA76+vkp5MzU1Ndi7dy+OHDkCS0tLJCYm4tq1a/Dw8ABAkxT1VCtWrICJSdtY+MiRI7CxsQEAFBYW4sSJE3B2dsbTp08BAF988QXS09Nx+/Ztgyw7NzcXU6ZMgYWFBaqqquDg4IBjx45h8ODBsLe3R2VlJVxcXHRuV9V+6+DggPr6eoSGhmLdunUICAhARUUFHB0dDd7P5+sDbYOZZcuWQSwWY9u2bVy5oTU1NeHy5ctYsGABV/bi9yiRSHDkyBGcOnUKzc3N3M93dnZ2aGhoQFBQEDZv3ozly5ejoKCA+8eCT2FhIUJDQ18q59uvVElNTcWlS5dgZWWFmpoaAOA9zwCqv1919fn2W12Xy9fOli1b4OXlhddffx0ymQxyuZx7z9XVFX/+859fWue+fj4sLy+HtbU13n77bdTU1GDx4sVYvny5Vp99/vjia0fTv0fatq/N8X769Gl8/vnnem8Lop7BZhQGdAuy0xSASUFzPRNfEOKLgY2Gxhcct2zZMgDAhQsX9G6bb79VFbiqaVCjaz9V1beyssKZM2dw//59bNiwAYGBgUo5aR3V2NgIoVAIAJg1axaio6O59178HvPz81UGzHp6esLU1BRz586FiYkJ5s6di9u3b2sc1FRVVancfyhgs83QoUNVDmD6+vlQLpejsLAQZ86cgY2NDRYtWgQvLy+MHj1a7edePL742vn55591DmRW1X47vuO9tLQUjY2N3FUeYngdHtRIpVI4OTnpFWSnawAm6blUBTYaiqrgOAcHB6WTf3V1NYYNG6ZVey0tLdzVD3X7rS6Bq/r0U1Mg3rhx42BmZoaffvoJrq6uWq2bNgYMGICMjAyV73Xm9wi0hYc+f2VCnb4YsCmXy9G/f3+Dt9vb2dvbY/z48Rg5ciSAtsF3aWmp2kGNquOLrx2A/98jvv1Qn/NSZmYmQkJC9N4ORLMO3VNz7Sc/3CYAACAASURBVNo11NbWwtXVVecgO00BmH39N2Rjl5qaitjYWI31GE9w3LRp0/DLL7+gpqYG5eXlePToEZfZpMr169e5n8Xu3r3LnQx1DaI0VD/56ldUVODx48cA2v4KvH//vlY/eXUWvoBZoG1weO3aNbS2tiIvL0+rgdf48ePx8OFDrZbdFwM2f/31V4wfP/6lz/T186GrqyukUimePHkChUKBn3/+WSmy5MXtw3d88bWj7t8jvv1Qn/MSDWo6n15XarKysiASiTBs2DAcOHAApqamOgfZOTg4qA3A7Ou/Ifd2EomEN2AQaPuNvP0EoY5IJOINjvvkk0+4y7zJycncPRmqPHnyBIsXL4aFhQUcHBy4Rzl1DaI0VD8LCgpU1pdKpdxfhS0tLfjkk0+0vgLVGdQFzFpaWiIzMxPbt2/HnDlz4ObmprE9b29v5Ofnw9PTU6/+MCMP2MzPz1e6x6ldXz8fWltbY/369VixYgWam5shEAgwbtw47v0Xt4+645GvHXX/Hr1In/PSrVu3MHDgQIwdO9ZwG4a8hGISSLcQCoU4dOgQbG1tu7srpAs1NjbijTfe4P5BJ8pef/117N27F0OGDOnurhDSK9GghhDSpa5evYoxY8ZQ4vkLamtrUVhYCH9//+7uCiG9Fg1qCCGEEGIUdL5ROCkpCUKhEHPnzsX8+fMhFAq5ewCAjk+LT0hnMFTcgiGnX+db7ldffQWBQIDIyMhOiUnQN97AEO2rOj/4+voq3SyrTmlpKYRCIWbMmMFN+qmp/c5YX4FAoNUjv6Rn4YshMVS5Ojt27ICnpycEAkHnrSDRPSah3e7du9nBgwf1n8uYkC5kqLgFQ02/zrfcoqIiFhUVxZqamlhNTQ3z8vJiz5496/D6P0+feIPObF+fiIGYmBhWVFTUKf3RRmhoKBOLxQZvl3QuvhgSQ5WrIxKJWFFREQsNDe3ENSQGTelWNS14amoqYmJiEBAQgE2bNiEoKIj7q4xiEkhX0Xda8xf3Q32mX1fVDt9yxWIxXFxcYGZmhqFDh8LR0VHlFYnOwBeHcPDgQSxcuBDx8fHw9/fX+wqFptiMhw8fIjIykgu71GXaek3tq4q74FtfvvIDBw4gODgYiYmJL821Q+er3oEvhsRQ5erMnDmTHozoAgZ9/IBvWnBfX19IpVKMHDkS8+fPxw8//IDp06dTTALpFrpMa65uP9Rm+vV2L7bDt9xx48bhL3/5CxoaGvD48WPcv38f1dXVnbMhnsMXhyCXy/Htt98iIyMD5eXlKiMOtKUuNuPRo0dISUnBH//4R7z66qt6TVvP176quIumpiaV69vQ0MBbfuLECZw+fRpisRhhYWFKy6DzVe/BF0NiqHLSvbrkmcrBgwdDJpNx///s2TOKSSDdQtdpzfn2Q22nX2+f5I+vnReXO3HiRERFRWHp0qVwdHTE7Nmzu2SG2ZKSEpVxCL/99hvc3d0xcOBAjBs3jpuJ1dASEhKwYMECbqIyTecHXaiKu5BKpWrX98VymUwGNzc3DBgwABMmTHhpO9D5qvfgiyExVDnpXgb9+YlPv379lP7XPk35vHnzuJkaL1y4gMWLF3dFd0gfZai4BU3Tr1tZWSlNv66KuuXGxcUhIyMD+/fvR3V1NUaMGKH3OnemW7duQSgUQigUorCwsENtbd68GSKRCEVFRVyZoc4PusZdqNKvXz+9lk26j6b98/kYks4oJ92jSwY1qlBMAulKTM9pzTs6/TpfO+qW2x6TcOPGDdTV1WHq1KmG3yAv4ItDmDJlCkQiERoaGnD//n1IpVLuM6qmj9fXlClTsHnzZnz66adQKBQazw+2traoqKjQe3l868tXPnnyZBQWFkIul+PevXtK2wGg81VPpGr/5IshMVR5O9ofuo/Bfn7imxafj52dHcUkkC6jb9yCIaZfV9WOhYUF73KTk5Px8OFDmJmZYefOnQa/StDQ0KA0IFu5ciWWL1/OG4ewZMkSREZGYvz48XByctJ474Cq9hcsWKA2NgNoe/Taw8MDf/7zn5GUlKT2/PDmm28iOTkZqamp2L9/P5qamjS2/zx18Q985dHR0YiIiOC2w/PofNU78MWQ/PDDDwYpb6dqf9i0aRMuXLiAx48fw8vLCxs3boSfn19nr3KfQ5PvEULUkslksLKyQm1tLRYtWoScnBz6OYYQ0iNR+AohRK3t27dz97p8+umnNKAhhPRYdKWGEEIIIUZB5xuF09PTMXXqVG4CvZUrVyI/P9+gnbp48SLCw8MRFhaGU6dOceWdPb27Pg4dOoSGhoaXyikugrRTFZ+gLm7BxcWFe2pj69atndKnuLg4xMbGcq8VCoVS/ElUVBT33oMHD/D6668jJCQEERERL90kS4gx4Is5UVWem5vLHaNCoRBTp05FSUmJ2uOadBFdpyA+efIkc3NzY4cPH2aMMfbee++x69evG2yKY5lMxry8vFh1dTV79uwZ8/HxYZWVlYyxzp/eXR/6TPFO+g6++AS+uAXGOn8/r6+vZz4+PiwwMJCb6r2dqviT6Ohorv+PHj1itbW1ndo/Qroa33GqTfxJRUUFCwgIYIypP65J19DrkW4fHx+cO3fupXK+6cV1UVRUBBcXF9jZ2cHKygqzZ8/GzZs32wdgSElJgUAgUPoLNi0tDcHBwQgLC8POnTu5cr5p1vPz8xEXF4fExEQIBAJs27YNZ86cUZpefdu2bdxVovj4eISHhyMqKgpHjx4FAOTl5UEoFKKyshIrVqyAUCjkHjHlm66db/vMmDFD5TTufOsF0CODvQVffAJf3EJXuHHjBmbOnImZM2fi+vXrauv+9NNPsLCw4Prv6OioNNU77YfEGPAdp5riT4C2YNqgoCAA3XtckzZ63ShsbW2NYcOG4f79+1wZ3zTr7Y9Daquqqgr29vbcazs7O26a+IaGBgQFBWHz5s1Yvnw5CgoK4O7ujtTUVFy6dAlWVlaoqakBAI3TrBcWFuLEiRNwdnbG06dPYWJigq+//hqMMfTr1w+XL1/GqlWrAACfffYZhg8fjqamJggEAgQFBcHT0xMZGRnw9fVFWlqa0s6uarp2ddtH1TTujo6OKtfr+e1Eej5N8QmActwC0Da5X2RkJPr374+PP/4Yr732mkH7dOXKFXh4eMDU1BS5ublqHysVi8XcBIOq0H5IjAHfcarN8Xv69Gl8/vnnL7X54nFNuobeTz9FRETgu+++417zTbPu6enZoQ4+/6SFqakp5s6dCxMTE8ydOxe3b9+Gu7s7XF1dsX79enh7eyMgIACA5mnWp0yZAmdnZwCAjY0NgLaZIYuLi2FmZgYnJyeu/MSJE8jOzgZjDJWVlaisrISDg4NO66Fu+6iaxt3R0VHlerWjadl7B03xCapiG77//ns4ODigqKgIH3zwAc6fP48BAwYYrE9XrlxBXFwcTE1NsWfPHrV1mYbnCGg/JMaA7zjVdPyWlpaisbGRuzrTji+OhXQ+vQc18+bNw+7du1VejusIBwcHpasSNTU1mDx5strPHDx4ECKRCGfPnsXRo0eRnp7O9ZHv0ri1tfVLZUFBQbhw4QLMzc0RGBgIoO2nqqtXr+LYsWOwtLREVFSUXtOsq8M3jTvfepHe4/n4BABcfMLo0aNVxi0A4AbM06ZNw7Bhw1BWVqY0kV9HSKVSiMVivPXWWwDagiR//fVXvPrqqyrrjx49Gg8fPjTIsgnpqfiOU3XHLwBkZmYiJCREqS2+45p0Db1jEkxNTeHu7o4ffvgBAP+04+20/e192rRpKCkpQW1tLWQyGfLz87nfM1taWnDt2jW0trYiLy8Prq6uYIxBKpVi1qxZWL16NcrKygBojmFQxdfXF5cuXUJOTg78/f0BtE08NmTIEFhaWuKXX37B3bt3lT4zaNAg1NXVaVwvTdvnRXzr1Y7uZegd+OITGE/cwpMnT9DY2Aig7SfLiooKg2Y/XblyBYsXL8a5c+dw7tw5LF26FFeuXOGt7+Ligvr6ehQUFAAAKisruenhAdoPiXHgO041xZ+8OKjhO67b0fHS+To0+V5kZCQOHToEQP2044D2v70PGjQIKSkpWLFiBQBg1apV3F+ulpaWyMzMxPbt2zFnzhy4ubmhtbUVSUlJkMlkaG1txdq1awFojmFQZciQIXBwcEBTUxPX9/nz5+P48eMICQnB2LFjX7pqFBsbi4SEBAwePBi7d++GXC7nna5d3fZ5EWNM5Xrpuj1J97K2tlYZn1BQUKAybqGsrAzJycmwsLCAqakptm7dioEDBxqsP1euXFE6CXt4eOD48eNYsmQJFi9ejOrqapiYmODs2bPclcGdO3ciJSUFdXV1MDMzw759+7ibhWk/JMaA7zgFwFt+69YtDBw4EGPHjuXaURejAtDx0hVo8j1CCCGEGIVuS+kmhBBCCDEkGtQQQgghxCjQoIYQQgghRkGv7Kc5c+ZAKBRi5cqVEIvFSu8XFBQgJCQEQqEQ9+7d420nPz8fbm5uEAqFCA4OxrFjx3Tv/XM0ZS0JBAKleWq0VVpaCqFQiBkzZqC4uLgjXSR9lC7ZT52dHfP8cScQCPCXv/wFAGU/EaJL9hPQNuWGQCBASEiI0nxPfPVJF9E1V+HkyZNs06ZNjDHG8vLyWFBQEGtubube37BhAzt16pTGdq5fv87ee+89xlhbnsasWbPYo0ePdO2O1kJDQ5lYLNb78zExMayoqMiAPSJ9ga7ZT52dHfP8cdfU1MQCAwOZRCLh3qfsJ9IX6Zr9VF5ezvz9/ZlCoWByuZz5+vqyhw8fapUVRTpXh35+8vDwgK2tLYqLi/H06VMIhUKcO3cOX3/9tcYrNc8bOnQoxowZw2Un8WU28WUh8WUtHThwAMHBwUhMTIRcLufKn0/7jo2N5a7AqMp4UoeymYgmumY/dWV2TGNjI5qamtRO407ZT6Qv0Cf7qaWlBQqFAgqFAubm5rC2ttYqK4p0rg7NUwMAI0eOhEQiwfTp05GRkYF169bB29sbCxcu1LqN8vJyAMCkSZPUZjbxZSHxZS2dOHECp0+fhlgsRlhYmMZ+qMp4UheHQNlMRBN9sp80lXdUQUEBhEIhHj58iDfffFPtPk7ZT6Qv0DX7afjw4XjjjTfg4+ODlpYWrF27FkOGDEF+fr7G4510rg4PajqioKAAoaGhkEql2LNnDywsLNRmNqnLQnrRnTt34ObmhgEDBmDChAncNNfq6JrxRNlMRBN9sp/UlRuCu7s79u3bB5lMhjfffBNBQUEvZde0Y5T9RPoAXbOfbGxscPnyZWRnZ6OpqQnLli2Dt7e3xuOddL4OD2qkUqnStNG6aD+5Xrx4EZ9++ikuXLgAgD+zSZcspOeDMNVpbm4GoF/GE2UzEU30yX7qquwYKysrzJkzByKRiHdQQ9lPpC/QNfupoaEBI0aM4DIEJ0+ejJKSEo1ZUaTzdeiemmvXrqG2thaurq4d6oS/vz+cnZ2RmZnJm9nENGQhvWjy5MkoLCyEXC7HvXv3lJ7YsLa25jJ2SktLAWjOeLK1teXu+QEom4loR9fsJ77yztDa2oqSkhK1VzEp+4n0BbpmP9nb26O4uBgKhQKNjY24c+eOVllRdLx0Pr2u1GRlZUEkEmHYsGE4cOAATE1NO9yR+Ph4bNy4ERERESozmxhPFpJEIuHNWoqOjkZERATGjx+vtGO9++67eOeddzB16lQuLFBTxtObb76J5ORkpKamYv/+/XBwcKBsJqKRrtlPYrFYbXaMIbTfU9PS0oI5c+bA29sbCoWCsp9In6VP9tO8efMQHh4OExMTREdHa6wP0PHSFSj7iRBCCCFGgWYUJoQQQohRoEENIYQQQoxCt8UkrFu3Dj4+PgDA/bb/4uR56miKPaB4A9IbdXZMAiGEGDO9bhQOCQnBH/7wB1y7dg3vvvsuzp49y90sfPr0aaxcuRJCoVBjOwMHDsRPP/2EZ8+ewd7eXp+u8Bo7diwyMjIQGxtr0HYJ6UympqbYuHEjJk2ahLKyMixduhS5ubnd3S1CCOkVOjRPzfMxCWPHjkVsbCzKy8tx5coV/M///A927dqF8ePH837e19cX2dnZkMlk8PX1hUwmAwAcPnyYe/IjLi4OixYtAtAWe5Ceno4JEyYoxR7k5ORg7969UCgU8PDwQHJyssa+tz9W9/HHH+u9/oQYmr29PTfAfz4mwdCzChNCiDHq1piEKVOmIDMzE83NzQgMDMTt27chkUhw5MgRnDp1Cs3NzRAKhfD29kZDQ4PK2AN1sQrq0KN1pKfrrJgEQggxVt0akwC0/WU6cOBA7nVJSQnc3d0xaNAgAMC0adNw9+5dyGQylbEH6mIV1KHp3UlP1pkxCYQQYqy6NSYBANasWQMTExOcP39ebT11sQd8sQqE9EZdFZNACCHGpttjEmxsbGBlZcW9dnFxgUgkQn19PZ4+fYri4mJMnDiRN/aAL1ah3YvxBu1oumrSE3VlTAIhhBibHhOT0M7JyQkxMTFYsmQJACAhIQF2dnYAoDL2wM7OTmWsQrsX4w3ap5une2pITyQSiTo9JoEQQowVxSQQQgghxCjQjMKEEEIIMQo0qCGEEEKIUaBBDSGEEEKMgl6DmgcPHuD1119HSEgIIiIiuCeRukJ2djb279/fZcvjk5SUBKFQiLlz52L+/PkQCoX4wx/+0N3dIt1oxowZBmvr0KFDaGhoMFh7hBDSF+h1o/DixYuxdu1auLu7o6KiAhYWFrC1te2M/vV4qampGDhwIN5+++3u7grpZjNmzMAPP/xgkLZ8fX3xz3/+E0OHDjVIe4QQ0hfofKXmp59+goWFBdzd3QEAjo6O3IDm8OHDEAgEEAgEOHnyJADgzJkzSunb27Ztw6lTpwAA8fHxCA8PR1RUFI4ePcrVyc/PR1xcHBITEyEQCLBt2zYAbVdHvL29X0rz5mtnxowZ2LJlC4KCgpCSksKVV1VVKX3mwYMHANoypKKjoxEeHo7t27frumm4Np7Pk9q9ezcOHToEiUSCgIAAfPjhhwgJCVG62qRuuTSfju74vndV+6e6+nz7IV87jDGkpKRAIBBg69atSu23i42N5VLj09LSEBwcjLCwMOzcuRMAkJeXB6FQiMrKSqxYsQJCoVDlPEuEEEJUYDo6f/48W7du3UvlYrGY+fv7M5lMxp48ecIWLFjAqqur2bNnz5ifnx9rbW1ljDEWGBjI6urqGGOMlZeXM8YYUygULDAwkFVWVjLGGLt+/TqbNm0au3v3LmOMcfUZY+zkyZNs06ZNSsvma8fZ2ZmJRCKmUCjYggUL2KNHjxhjjK1atYp98803jDHGnj17xqqrq1l1dTVbtGgRq6+v5+rk5eVp3B67d+9mBw8e5F43NTUxX19f9ttvvzHGGFu4cCGrqqpiYrGYTZw4kd27d481NDSwgIAAJpFINC537dq1bO3atRr7Qf6Pqu+db//kq8+Y6v1QUzuXL19mLS0tbOnSpezmzZuMMcamT5/O9S0mJoYVFRUxxhhzc3Njz549Y4wxro12Pj4+rKamphO3EiGEGB+dJ99jPL9W8WU2eXp6Yty4cSguLoaZmRmcnJxgY2MDADhx4gSys7PBGENlZSUqKyvh4OAAoC3s0tnZGQC4+nz42jE3N8fMmTMBtE3qV11dDUdHR9y4cYOboM/KygpWVlbIzs7WK0PqRWZmZvDz88PFixfx6quvYvTo0bC3t4dEIsHIkSMxbtw4AICbmxvu3LkDExMTtculjCrdqfrepVIp7/7Jt58AL++H+fn5vO2Ymppi7ty5MDExwdy5c3H79m3uiqYqrq6uWL9+Pby9vREQENBp24MQQvoKnQc1o0ePxsOHD3X6TFBQEC5cuABzc3MEBgYCaLu0f/XqVRw7dgyWlpaIiopCa2sr9xlra2ut2lbXjrm5OVevX79+Su2rYqgMqcjISPzpT3/CmDFjEBERobE+ZVcZlq7fu7r62u6H2mpubub+++DBgxCJRDh79iyOHj2K9PR0gy6LEEL6Gp3vqXFxcUF9fT0KCgoAAJWVlXj8+DFvZhPQdtPjpUuXkJOTA39/fwCATCbDkCFDYGlpiV9++QV3797VawX0aWf27NncPyD19fWora3VmCGlCxcXF9TU1ODf//43fH19uXKpVIrS0lLI5XIUFhbCxcVF43LpnhrDULd/GqqdlpYWXLt2Da2trcjLy+My0aytrfHkyRM0NjaitLQUQNsVT6lUilmzZmH16tUoKytTWs6gQYNQV1fXwbUmhJC+Ra/sp507dyIlJQV1dXUwMzPDvn371GY2DRkyBA4ODmhqauLK5s+fj+PHjyMkJARjx47F5MmT1S5TIpEgISEBdXV1aGxshEgkwpo1a3RuBwBSUlKwYcMG/P3vf4eZmRl27dqF3/3ud2ozpHQVEBCABw8eYMCAAVyZk5MTdu3ahQcPHiA6OprLr1K3XMqoMgx1+6eh2rG0tERmZia2b9+OOXPmwM3NDQDw7rvv4p133sHUqVMxYsQIAG2DmqSkJMhkMrS2tmLt2rVKy4mNjUVCQgIGDx6M3bt3cz/LEkII4UfZT53k3XffRVxcHDw9PQG0Dcri4+ORmZnZzT0jhBBCjBPNKGxgjx8/RlBQEIYOHQoPD4/u7g4hhBDSZ9CVGkIIIYQYBbpSQwghhBCjQIMaQgghhBgFGtQQQgghxCjQoIYQQgghRkGveWoOHjyIM2fOKJUFBATgwoULSmULFy6Eubn5S3WpnMqpnMqpnMr7evn7778PYlj09BMhhBBCjAJdqaFyKqdyKqdyKqcrNUaBrtQQQgghxCjQjcKEEEIIMQo0qCGEEEKIUaBBDSGEEEKMAg1qCCGEEGIUaFBDCCGEEKNAgxqiUkFBAUJCQiAUCnHv3r3u7g4OHTqEhoYG3vcFAgEkEkkX9qhv0bT9ewIXFxcIhUKEhoZi9erVaGxs1PiZ9PR0pKamvlTOt7589Xua2NhYBAUFITs7myvLyspCYGAggoKCkJOTo7ENVfUrKiqwbNkyhIaGIjIyElevXuXqt29/oVCIrVu3Gn6lDETX7fDVV19BIBAgMjISFy9eBADk5uZy6yoUCjF16lSUlJQA4N8O7edUgUDQOStG2jBCVNiwYQM7depUd3eD4+Pjw2pqanjfDw0NZWKxuAt71Ldo2v49wfTp0xljjLW2trKPPvqI/e1vf9P4mZMnT7Ldu3e/VM63vnz1e5qYmBhWVFTEvZbL5czHx4dVVVWxsrIy5ufnx1paWng/z1e/qqqKlZSUMMYYk0gkbN68edxn2rd/T6brdigqKmJRUVGsqamJ1dTUMC8vL/bs2TOlOhUVFSwgIIB7rW47iMViFhoa2vEVIbzoSo0RqaqqQnx8PMLDwxEVFYUHDx4AAA4fPgyBQACBQICTJ09y9XNychAdHY3w8HBs374dAPD06VMIhUKcO3cOX3/9dZdeqUlLS0NwcDDCwsKwc+dOAEBeXh6EQiEqKyuxYsUKCIVCVFRUAAAOHDiA4OBgJCYmQi6Xa2xf1foCQH5+PuLi4pCYmAiBQIBt27apLe9L1G3/nqpfv3547bXX8J///Ae7du3C119/rXWfe+P6aqOoqAgTJkyAvb09Ro4ciREjRuDu3bs617e3t8ekSZMAAKNGjUJTUxMUCkVXrUaH6bodxGIxXFxcYGZmhqFDh8LR0RHFxcVKdbKyshAUFNTZXSda0mtGYdIzbdmyBV5eXnj99dchk8kgl8shkUhw5MgRnDp1Cs3NzRAKhfD29gYA7N27F0eOHIGlpSUSExNx7do1eHh4ICMjA+vWrYO3tzcWLlzYZf1PTU3FpUuXYGVlhZqaGgCAp6cnMjIy4Ovri7S0NAwdOhQAIJFIcOLECZw+fRpisRhhYWFq266pqeFdXwAoLCzEiRMn4OzsjKdPn3Kf4yvvK/i2f0/W1NSEy5cvY8GCBRAIBMjIyMD777+P0aNHIyYmBq+99hrvZ3vj+mqjqqoKDg4OOHbsGAYPHgx7e3tUVlbCxcVF7/q5ubmYMmUKLCwsAAByuRyRkZHo378/Pv74Y7Xbubvouh3GjRuHv/zlL2hoaMDjx49x//59VFdXK9U5ffo0Pv/8c+51b9gOxowGNUbkxo0b+PLLLwEAVlZWsLKywoULF+Du7o5BgwYBAKZNm4a7d++ioaEBYrEYS5cuBQDU19d3+z0prq6uWL9+Pby9vREQEKC27p07d+Dm5oYBAwZgwoQJGDlypNr6P/74o9r1nTJlCpydnQEANjY2GstJz9PY2AihUAgAmDVrFqKjo2Fubo6YmBjExMTgxo0bSEpKglAoxEcffdTNve0ey5YtA4CXIm10rV9VVYUdO3Zg7969XNn3338PBwcHFBUV4YMPPsD58+cxYMAAA/XcsLTdDhMnTkRUVBSWLl0KR0dHzJ49G/379+feLy0tRWNjI3f1Cuhd28EY0aCmD5s3bx527drV3d3gHDx4ECKRCGfPnsXRo0eRnp7OW7dfv346t69ufa2trXUqJz3PgAEDkJGR8VJ5aWkpTpw4gcuXL0MgEHAD277EwcEBVVVV3Ovq6moMGzZMr/pyuRyJiYlISkrCK6+8ovQZoO0Pp2HDhqGsrAzjxo0z9Kp0iK7bAQDi4uIQFxcHAFiyZAlGjBjBvZeZmYmQkJCXlgH07O1gzOieGiMye/ZsbiBQX1+P2tpauLi4QCQSob6+Hk+fPkVxcTEmTpyI6dOnQyQS4dGjRwCAsrIypYO9qzHGIJVKMWvWLKxevRplZWVK7w8aNAh1dXXc68mTJ6OwsBByuRz37t2DVCpV235PW9/e5sXt31t89dVXWLt2LZydnfHdd9/h448/xvDhwzV+rreuL59p06bhl19+QU1NDcrLy/Ho0SNMnDiRe3/Xrl1KA36++owxJCcnQyAQwMvLi6v/5MkT7mkziUSCiooKpX/8ewpdtwMAPH78GEDblfC6ujpMpB+FSQAAIABJREFUnTqVe+/FQU1v2Q7GjK7UGJGUlBRs2LABf//732FmZoZdu3bhd7/7HWJiYrBkyRIAQEJCAuzs7AAAn332GeLj49HS0gJLS0vup6vuwBhDUlISZDIZWltbsXbtWqX3Y2NjkZCQgMGDB2P37t0YNWoUoqOjERERgfHjx8PJyUlt+3Z2dj1qfXubF7d/+1+jPd2KFSuwevVqnT/XW9eXj4WFBT755BPuZ5fk5GSYmPzf37QvDvD56hcUFOBf//oX7t+/j+PHjwMA9u/fj7KyMiQnJ8PCwgKmpqbYunUrBg4c2EVrpz1dt0N7nYcPH8LMzAw7d+7krhLfunULAwcOxNixY7m6paWlvWI7GDNK6SaE9Fnp6ekoKyvDqlWrOqV+d4mNjUVSUhJcXV27uyvkORKJBPHx8cjMzOzurhgt+vmJEEKMzODBg5GcnKw0+R7pXgUFBXj//fdha2vb3V0xanSlhhDSZ5WUlODp06eYPXt2p9QnhHQtGtQQQgghxCjQz0+EEEIIMQo0qCGEEEKIUaBBDSGEEEKMAg1qCCGEEGIU9B7U3Lp1C0KhEEKhEIWFhbxlVE7lVE7lVE7lVK66nBgWPf1ECCGEEKNAPz8RQgghxCjQoIYQQgghRoEGNYQQQggxCjSoIYQQQohRoEENIYQQQoxCjx3UHDp0CA0NDd3djS4jk8kwb948/Pd//zdXdvDgQQgEAoSEhGDPnj0a23BxceEeGdy6dStXfuvWLYSFhSE4OBgffvihxnK+dnqSiooKLFu2DKGhoYiMjMTVq1c1fiYrKwuBgYEICgpCTk6OxvKvvvoKAoEAkZGRuHjxYqesh7Y0ra+q/YfPjh074OnpCYFAwJXl5uZy37lQKMTUqVNRUlICgH8/6Wn4vkdd6+ta3hPx7Q+67Ceqvne+/VCf47G7qDq/6dN/vvOkquNLXTkxMNZD+fj4sJqamu7uRpfZuXMnW7lyJTt48CBjjLHy8nLm7+/PFAoFk8vlzNfXlz18+FBtG9OnT3+prKWlhQUGBrKbN28yxhi3TfnK+drpaaqqqlhJSQljjDGJRMLmzZuntr5cLmc+Pj6sqqqKlZWVMT8/P9bS0sJbXlRUxKKiolhTUxOrqalhXl5e7NmzZ12xaippWt8X9x91RCIRKyoqYqGhoSrfr6ioYAEBAYwx9ftJT8L3PepaX9fynopvf9B2P+H73vn2Q12Px+6k6vymT//5zpN8x5em444Yhpk+A6Gqqips2LABUqkUZmZm2LVrF373u9/h8OHDOH78OAAgLi4OixYtAgDMmDEDUVFRuHLlCl577TVuVJuWloZ//OMfMDMzg5eXF37/+98jLy8PO3bsQGVlJVasWAETExPs378fjo6OBhrG9TylpaWoqanBlClTlMpbWlqgUCjAGIO5uTmsra11bvv27duwtbWFu7s7AGDo0KFqy3sLe3t72NvbAwBGjRqFpqYmKBQKWFhYqKxfVFSECRMmcJ8ZMWIE7t69i99++01luVgshouLC8zMzDB06FA4OjqiuLgYHh4eXbOCL1C3vnz7D5+ZM2dCIpHwvp+VlYWgoCAAvWc/4ft+XVxcdKrPtz/wlfO135349gdd9hO+751vP9T1eOxpDNl/vuNL03FHDEOvn5+2bNkCLy8vnD59GocPH4aNjQ0kEgmOHDmCb7/9Ft988w1SU1NRU1MDAKivr0doaCgyMzNx5coVVFRUAABSU1Nx4sQJnDlzBm+99RYAwNPTExkZGRg2bBjS0tKQkZFh1AMaANi1axc++OADpbLhw4fjjTfegI+PDxYsWIC33noLQ4YMUduOXC5HZGQkli5dips3bwIAysvLYW1tjbfffhsRERH45ptv1JbztdOT5ebmYsqUKWpPQFVVVXBwcMCxY8eQlZUFe3t7VFZW8paPGzcOt27dQkNDA6RSKe7fv4/q6uouXCt+L66vqv2nI06fPo3Q0FAA6veTnoTve9S1vq7lPRHf/qDLfqLN98533GlzPHYnTec3bfvf286TfYVeV2pu3LiBL7/8EgBgZWUFKysrXLhwAe7u7hg0aBAAYNq0abh79y48PT1hbm6OmTNnAgCcnJxQXV0NR0dHuLq6Yv369fD29kZAQICBVql3ycnJwauvvopRo0YpldfV1eHy5cvIzs5GU1MTli1bBm9vbwwbNoy3re+//x4ODg4oKirCBx98gPPnz0Mul6OwsBBnzpyBjY0NFi1aBC8vL97y0aNHq2xnwIABnb0p9FJVVYUdO3Zg7969WtVftmwZAODChQtqyydOnIioqCgsXboUjo6OmD17Nvr372/AnuvnxfXl23/0VVpaisbGRkyaNAkA1O4nPRHf96trfV3Lewq+/UHX/UTT98533Ol6PHYHdec3Xfrfm86TfYlegxpdmZubc//dr18/tLa2Ami7EVb0/9u796gorgR/4F8UEBUlKo+IaDLiIyiy8TG+g09EpaWRuI7OwBhnzMQcEoyJi2JwjCaZxBgzs3rWTIxzVqNJduJKRIxrfE02rg+MYNAY4gOSkUd4ikjLW+7vDw71o7GqmioKbNrv5xxPwu3q27eqbl1uVzf3m5qKL774Anv37kViYmJ7NMeupKen4+jRozhx4gRKS0vRqVMneHl5wdXVFX379pU+cho2bBgyMjJUJzVeXl4AGiaU3t7eyM3NhaenJwYNGgRfX18AwPDhw5GVlaVY3r9/f9l6/P392/Iw6FJdXY3Y2FjExcVhwIABqtt6eXmhqKhI+rm4uBje3t64e/eubDnQ8BHq0qVLAQALFy5E375922AvWk5uf5X6T3h4uK7XOHToEObOnSv9rNZP7InS+dW6vVJ/UOsn9kSpP2RmZmrqJ2rnXem603I9PkhK45vW9neUcfJho2tSM27cOCQmJmLRokWoqKhAVVUVAgICsHnzZlRUVKCurg6XL1/G+vXrFesQQiAvLw9jx47FkCFDpM/wG3Xv3h1lZWV2+xm+UVauXImVK1cCaPg4rlu3bggPD8eFCxdw+fJl1NTUoL6+Ht9//73VreMtW7YAAF555RUAwO3bt+Hm5gY3Nzfk5OSgoKAAffv2hbe3N/Ly8nD79m1069YN165dg5+fn2K5Uj32RgiB+Ph4mEwmBAcH3/d48+MTFBSE69evo6SkBDU1NcjPz8fQoUNRV1cnWw4ApaWl6NWrF86fP4+ysjIEBga23w42o7S/Sv2nUfPjYMuhQ4fw17/+Vfp5xIgRsv3E3iid30at7Q9q/cSeqPUHLf1E6bwr9UNb16O9UBrftI4nHWWcfBjpmtQkJCRg3bp1+OSTT6y+KBwVFYWFCxcCAGJiYtCnTx/FOoQQiIuLg8ViQX19PVavXm31eHR0NGJiYuDh4YGtW7dKs+KHxZgxYzB58mSEh4ejU6dOWLBggdW7gKbvGoGGjw3i4+Ph6uqKzp0744033kC3bt0AAGvXrsWSJUtQV1cHk8kk1SNXnpaWpliPPUlNTcWXX36JzMxM6cvpTb9Q3vz4uLq6YtWqVdLHB/Hx8ejUqZNieeP/37x5E87Ozti8eTOcnJzaa/fuY2t/lTQ/DgCwYcMGHDt2DKWlpQgODsb69esxY8YMpKeno1u3bhg4cKC0bY8ePRT7jz1RO49A6/uDrfo7uubHR+m8X7hwQbYfZmdn6+qf7U1pnFTaL6XxRG28Vbq+lMrJWEzpJiIiIofgOG81iIiI6KHGSQ0RERE5BE5qiIiIyCFontT84x//QHR0tPTzW2+9ZfXXEnKYeaGP1uOmtL2WbCO1DCB7Y1TWj1zGjT0eB6XzqzWbSSmzhv1BX3lHobX9WvuD1qw6e6NlvNWTxcbfg+1ET7bCb3/7W3Hq1CmRnZ0tZs+eLaqrq1W3Z+aFPlqPm9z2rck2apoBZG+MyvppSbaRvRwHufOrJ5tJLrOG/cGxsp+a09p+rf1BT1advdEy3urJYuPvwfah60+616xZg3Xr1uGxxx7DypUrbS4nzcwLfbQeN7ntlTJuWpJt1DQDyN4YlfVTW1trM9vIXo6D3Pk1KpuJ/cFxsp/kaD0+evqDEVl1D5KW8VZPFht/D7YPXd+pCQgIwGOPPYbc3FzMmjXL6DaRgVqTbdQ0A8jeGJX105KMG3s+DnqymeQya9gfHCf7SY7W9mvtD3qy6hxFW2exkTa67tTU1NQgIyMD9fX1uHPnDnr27Gl0u8hgWrONmmcA2avWZv3Yyrix9+OgJ5tJLrOG/cExsp9saWn7tfYHPVl1jqCts9hIO12Tml27dmHKlCnw9fXF+++/f99qwGQ/1DJx1LKNmmcA2Rujsn5cXFxUs43s/TjoyWZSyqxhf+j42U9KtB4fQNv4cPbsWc1ZdR1de2SxkXaaJzVFRUX4r//6Lxw4cABubm6YN28eoqKiODNtZy3N9FHLxFHLNmqeAWRvjMr6uXv3rmq2kb0fB1vZTFoya9gfOn72kxKtxwfQ1h88PT1Vs+o6uubHR+jMYqO2p3lS895772HJkiXSR07Lli3De++9J510Ocy80EftuGnJ9NGabSSXAWRvjMr6Ucs2srfjoHR+1bKZtGTWsD84bvaT1uPTuE1L+4OtrLqOQMt4qyeLjb8H2wezn4iIiMghdJy3GkREREQqdH1RWI7ZbJYtT0xMROfOnY16GSIiIiJZ/PiJiIiIHAI/fiIiIiKH0OaBlraCv0ienuMmF0AHqAceduTgtYct0BKQP19q5c2VlpYiMjIS4eHhMJvNUj9RKtda/4PEQEtlesYTuYBKvf3H3ulpv5agYEA5SJYMpicwSkugpa3gL5Kn9bgpBdDZCjzsqMFrD2OgpRDy50utvLmamhphsViEEA37OmnSJHHv3j3Fcq31PygMtFSndTxRCqjU23/sndb26wkKlguSJeO1eaClWvAXKdN63JQC6Lp3764YeNiRg9cexkBLpfOlVC7HxcUFLi4uABruvtTU1KCurg6urq6K5Vrqf1AYaKlOzzgsF1Cpp/90BGr7Jac1QcHUtto10LJ58Be1TEuOm1IAnVrgYUcOXnsYAy2VzpfW82ixWDBv3jyEh4dj/fr1Ur9SKu8I/YSBli3XkvFELaBSa//pKLS0X09QsFyQLBmv3QItmwd/Ucu09LgpBdBVVVXJBh5ev37dIYLXHpZAS6WgPD0Beu7u7khOTkZmZibWrVuHWbNmwcXFRbb81KlTHaqfMNBSXUvHE7WASi39p/HuR0egp/1agoLlgmTd3NzadqceQu0SaCkX/EW2aT1ucgF05eXlsoGHHT147WELtFQ6X5mZmbrPo7+/P5ydnfHDDz9gxIgRsuUdpZ8w0NI2LeNJSwIqW9J/mpZ3FC1pv56gYKUgWTJWmwdaCoXgL1Jn67i1NIDOYrHIBh5OmTKlQwevPWyBlmpBeWrnsflxKCgogKurK3r16oWioiJkZmbCx8dHsbyjBPQx0FKd1vFEKaBSa//pKGy1v6X9B5Afh9WCZMlYbR5oqSf4i2wft5YG0KkFNirpCMFrD2OgpR7Nj0NeXh7++Mc/Amj4IuiqVavg7e2NixcvypZ3FAy0VKd1PFEKqFTqJx29/yhdF41a2n8a/7/5OKwWJEvG4orCRERE5BA6zlsNIiIiIhUMtCQiIiKHwI+fiIiIyCHw4yciIiJyCJzUEBERkUPQ9Z2anTt3Ijk52aosJCTkvlU2Z8+eDRcXl/u2ZTnLWc5ylrP8YS9//vnnQcbid2qIiIjIIfBODctZznKWs5zlvFPjEHinhoiIiBwCvyhMREREDoGTGiIiInIInNQQERGRQ+CkhoiIiBwCJzVERETkEHRNao4fP47w8HDMmzcPBw4caFUDTpw4gR07dliVjRw5slV1ErWlP//5zzCZTJg/fz6OHz+uum1paSkiIyMRHh4Os9lstX16ejrmzZuHOXPmYMWKFTa3N0JiYiLGjx8Ps9mM5557DtnZ2brrUrtO5a5rLcaNG4eamhrp5/r6eowbN87m83bt2oXKykrdr0sPp4KCAixevBhhYWGYP38+Tp8+bfM5mzZtwsSJE2Eyme57zGKxYPLkyfjb3/4GADh16hTMZrP0LzAwEBkZGYbvBwEQGlksFhEcHCyKi4tFeXm5mDZtmigsLNRajaonn3zS0PqIjHLp0iURGRkpamtrRUlJiQgODhbl5eWK29fU1AiLxSKEEKKkpERMmjRJ3Lt3T9y7d0/MmjVLfPPNN9JjatsbZf/+/WLDhg1CCCHOnDkjQkNDRV1dna662vI6jYyMFNnZ2dLP+fn5Ijw83Obzpk2bJh1LopYqKioSGRkZQgghcnJyxOTJk20+JzU1VVy6dEmEhYXd99jmzZvFc889J3bu3HnfYwUFBSIkJKT1jSZZmu/UXLp0CQEBAejTpw/c3d0xbtw4fPPNN9i2bRuioqIQEhKCDRs2IDQ0FLdu3QLQ8I7u9ddfR2hoKBISEqS64uLiMHXqVGzcuLH5RAsJCQkwmUx44403pPKm7wyjo6Nx+fJlAMDu3bsxZ84czJs3D5s3b7aqa8uWLdiyZYvW3SSSlZ2djYCAADg7O6N3797w8fGR+qEcFxcXdO/eHUDDu7eamhrU1dXhu+++Q69evTBmzBgAQO/evVW3bwsTJkxAr169pPYrXV8pKSlYunQpYmNjYTKZ8OabbwJQvk6VrmulcUDOgAEDkJ+fj1dffRWvvvoqCgoKMGDAAADA8uXLER4ejsjISOzduxcAcObMGZjNZhQWFmLJkiUwm80oKCgAoDw+fPTRRzCZTDCZTNi/f3+L2snxxDF5enriiSeeAAD069cPtbW1VncK5YwaNQq9evW6rzwrKwslJSUYPny47PMOHz6M0NDQ1jeaZGme1BQVFcHT01P6uU+fPiguLgYATJ8+HVOmTEH//v3x1FNP4eLFiwCAiooKhIWF4dChQ/i///s/abB55513EBsbe99rVFZWIjQ0FAcPHsSVK1dw4cIF1TZt27YN+/btQ3JyMn73u9/d196ioiKtu0kky9/fH+np6aisrEReXh4yMzOl/q/EYrFg3rx5CA8Px/r16+Hq6oqff/4ZPXr0wO9//3tERETg448/Vt2+rfj6+iInJ8fmdmlpaXjhhRdw6NAhvPjiiwCUr1Ol61ppHJAzYMAAFBYWIj8/HyUlJcjPz4efnx8A4LXXXsPBgwfx97//HXv27EFRUREmTpyIpKQkeHt7Y/fu3UhKSoKPjw8A+fEhJycHe/bswd///nd8/PHH2LZtG0pKSmy2k+OJ4zt16hSGDx+u+7rbsmULXnjhBcXHDx48iLCwML3NIxt0xSQ05eTkJP2/h4cHLBaL9N/y8nIADe8+R40aBQDw8/NDcXGxNODI6dy5MyZNmoROnTph0qRJ+O6776R3tHJGjBiBtWvXYurUqQgJCbF67O23327N7hFZGTp0KCIjI7Fo0SL4+Phg3Lhx6NKli+pz3N3dkZycjMzMTKxbtw6zZs1CdXU10tLSkJycjJ49e+Lpp59GcHAw+vfvL7u9i4tLO+2hvOHDh2PIkCEAgJ49ewLQfp1qGQf69++P3NxcuLq6wsXFBbm5udKdmn379uHEiRMQQqCwsBCFhYXw8vJSfF258SEjIwNjxoyR7ooFBQXh6tWrmDhxomo7OZ44tqKiImzatAnbt2/X9fyTJ0/i8ccfR79+/WQfz8rKQlVVlXRXiIyn+U6Nl5eX9I4GAEpKSqQBxcnJyepffX09AFgNyE3LW6PpLfmdO3ciKioK6enpWLJkSavrJlKzdOlSJCUlYceOHSguLkbfvn1b9Dx/f384Ozvjhx9+gKenJwYNGgRfX1+4u7tj+PDhyMrKUty+reTl5Ul3QJpq/pFXjx49Wv1aSuNAenq69AXKtLQ0AA13as6ePYvBgwdj8ODBOH36NPr374+UlBScPn0an376KZKSkvCLX/zC5niidXxoi/GK7F91dTViY2MRFxcnTaC1Sk9Px9GjRzF79mzs3bsXO3fuxMGDB6XHDx06hLlz5xrVZJKheVITFBSEjIwM3Lp1CxaLBSkpKarvzvS4d+8ezp49i/r6epw5cwYjRowA0DCw3r59G1VVVdIvACEE8vLyMHbsWKxcuRK5ublWdfEzcDJaaWkpAOD8+fMoKytDYGCg9Fjz/lZQUCBtX1RUhMzMTPj4+GDEiBHIy8vD7du3UVNTg2vXrsHPz09x+7Zw9uxZ3Lp1S/X6UqN0nWr1L//yL0hKSkJSUpJ0h2TAgAE4f/48AgMDMXz4cKSkpGDAgAGwWCx45JFH0LVrV1y/fh1Xr161qqt79+4oKyuTflYaHwICApCamoqKigrcuXMHly9fxtChQ222leOJYxJCID4+HiaTCcHBwfc93tLzvnLlShw7dgxHjhxBVFQUli1bhvDwcOlxTmranuaPn7p3746EhATpHc+LL76oeutXSU5ODmJiYlBWVoaqqiqkpqbi5ZdfxpQpU9C1a1ccOnQIb731FsaPH4/Ro0cDAJ599lksW7YMgYGB0rtjIQTi4uJgsVhQX1+P1atXW70OP/8mo8XHx+PmzZtwdnbG5s2brT6Cbd7f8vLy8Mc//hFAwyRg1apV8Pb2BgCsXbsWS5YsQV1dHUwmE/z9/XHx4kXF7Y1y+PBhpKamwtvbGx9++CE6d+4MQP76UiN3napd11r4+PigU6dOCAwMROfOnXHv3j34+vri0UcfxWeffYa5c+di4MCBGDZsmNXzoqOjERMTAw8PD2zduhV9+vSRHR/8/PwQFRWFhQsXAgBiYmLQp08fm+3ieOKYUlNT8eWXXyIzMxOfffYZAGDHjh3SGwq5875hwwYcO3YMpaWlCA4Oxvr16zFjxgzF10hPT0e3bt0wcODAttkJAsCUbiIiInIQXFGYiIiIHAInNUREROQQOKkhIiIih6BrUhMQEACz2YywsDCsXLkSVVVVul7cVsbTSy+9hJ9++kn6uaNkTt26dQu///3vDamL7M/hw4cxa9YshIaG4uTJk7q3VyrXki2lR0ZGBsxms+yqv/YiJSUFo0ePhtlshslkwvvvv9/ubWg+/sgdN7V2chzoWIzKdFOqRy0rigykJ1uhMfOlvr5evPTSS+I///M/dWU0qGXHXLt2TSxfvlz6uaNlTq1Zs0acP3/esPrIPlRXV4tp06aJoqIikZubK2bMmKGazaS0vVK51myp1miaA2Vvzp07J/7whz8IIYSora0Vs2bNEjk5Oe32+s3Hn6aaHjdb7eQ40DEYlemmVo9aVhQZp1UfPzk5OeGXv/wl/vnPfwJQzohRylgRCtkxAJCcnGy1OnBHy5yaMWOG1aJLjbjORcd26dIlDB48GJ6envD19UXfvn3vWyulJdsrlWvNljKSXKYSoHxdf/jhh5g7dy5WrFiBkJAQKW5B6Xo5efIkFixYgPDwcLz11lstbldVVRVqa2ulZeuV6lFqp9J+qWk+/uhpJ8BxoKMwKtNNrR6lrCgyVqtiEmpra/H1119brUGRlpaGffv2YciQIbhz546UsXLgwAHU1dVJt2/79OkjZcds3LgRv/nNb3DhwgVpIb+0tDSrfAxbmVN5eXnw9fWVMqdmzJghZbisWbMGISEhKCgogI+PD9555x0kJibiu+++s9oftfbI2bZtG7766iu4u7tbrbIMNCzN/pe//OW+53Cdi46tqKgIXl5e+PTTT+Hh4QFPT08UFhYiICBA0/YVFRWy5f7+/nj//fdRWVmJ0tLSFmVLGeW1117Do48+itraWphMJoSGhkprUMld1//93/+NgwcPIicnx2aWTUlJCbZv3449e/aga9euiI2NxdmzZzFhwgTF51y4cAFmsxk3b97EM888I61mrlZP83ba2i8lzccfNXLtbMRxoGPQc91ZLBYsXrwY2dnZePPNN+Hq6vpAr19qoGtSU1VVBbPZDAAYO3YsFixYID3WPCOmccVhuYwVteyYoqIiKblYjr1nTvXu3Vt24GJ2jGNYvHgxAODYsWOt2r55uZ5sKaOoZSo1v67PnTuHUaNGoUuXLvD394evr69q3d9++y2ys7OxaNEiAA2hkbaCNMeMGYMPPvgAFosFzzzzDEJDQ5Gbm6taj1xGldasKMD2+GOrnY3ZPhwHOgajMt0e5PVLDXRNatzc3JCUlCT7mBEZMQDQpUsXVFdXSz/LZU4NGzYMZWVlDzRzKjU1FV988QX27t2LxMRE6bHq6mp2Zgfk5eVl9UuquLhYdcVfpe3v3r2rWM/SpUuxdOlSAMDChQtbnC2l1b1796SPSppmKnXt2hWRkZFW14ve67rp9TJ58mTZj1zS09OlVZTXr18vvRFp5O7ujvHjxyM1NRWPPvqoYj1y7bS1X0qajz9NNT1uSu1snNRwHOg49F53TTPaRowY0W7XL8lr8z/pVstYUcuOGTRoEG7evCn93NEyp3766ScMGjTovtfgZ+kdW1BQEK5fv46SkhL8/PPPyM/Pt8oMan5+lbZXq0ctW6q1zp07J30sc/XqVfTv3x8AbGYqNRcQEIC0tDRUV1cjKysLeXl50mNy18uTTz6J1NRU5OfnAwByc3OlSZ1c9lNT9fX1yMjIgK+vr2o9crTuV6Pm44/ScVNqZyOOAx2HEZlutuqhtteq79S0hFrGilLGEwBMnToVKSkpmDhxIoCOlzmVkpIim3fDz9I7NldXV6xatUr62Cg+Ph6dOv3/9wbNz6/S9mr1qGVLtdbt27fxr//6r3B1dYWXlxdWrFgBAHjqqadUM5Wa69+/P55++mlERERg0KBBVknfctdLnz598Nprr2H58uW4d+8eunbtinfffVf1NRq/q3Lv3j2MHz8eU6dOhZOTk6Z6tO5Xo+bjj9JxU2pnI44DHYdRmW5K9WjNiiJ97Db7qaqqCr/97W/xySefwNm5zedehvv1r3+N7du345FHHnnQTSFqcyaTCX/961+tJjcdmVHjD8cBovZ1vC2gAAAV/UlEQVRltysKu7m5YcWKFdJt5o7k1q1b+N3vfseBjKiDMmL84ThA1P7s9k4NERERkRZ2e6eGiIiISAtdk5oPPvgAYWFhCAsLQ2xsrFQ+ffp0aTVfIkelJfupoKAAixcvRlhYGObPn4/Tp08DAE6dOgWz2Sz9CwwMREZGhuL2RnpQ2W3MTqL29KAy2uQyntSyoshgWnMV0tPTxa9+9StRXV0thBDiypUr0mPTpk0TJSUlrY1uILJbWrOfioqKREZGhhBCiJycHDF58uT7tikoKBAhISEt3r61HkR2W1PMTqK29iAz2uQynpSyosh4mu/U5OXloVevXtLiU3J/Innz5k3Mnz9fepcml9Uyc+ZMq4W56uvrrVbl5ToOZI+0Zj95enpKC7H169cPtbW1qKmpsdrm8OHDCA0NbfH2RmnP7LaWYHYSGeVBZrTJZTwpZUWR8TT/reLEiRPx3nvv4emnn8aMGTOwYMECqxVV8/PzkZCQgLfffhuPP/64YlbLE088gRs3bkgn32KxwN/fX6qH6ziQPdKa/dTUqVOnMHz48PtWoz148CD+9Kc/tXh7o7RndpsaZieR0ewxo00uK4qMp3lS07NnTyQnJ+PUqVM4evQoIiMjcfjwYSlnJSYmBlOmTJFWR1XKfAkKCsKVK1dw7do11NfXIygoyGpFYWajkD3Tmv1UVFSETZs2Yfv27VblWVlZqKqqku7O2NreCPaQ3dYUs5OordhTRptcVlTTKB8yhq5Vpbp06YKZM2di5syZWL58Ob799lsEBwcDADZu3Ih33nkHly5dQlBQEAD5zJdz587h6NGjqKyshJOTE65cuYJJkya1cneIjCOXSaQ1+wloyP+JjY1FXFwcBgwYYPXYoUOHMHfu3BZvb4QHkd3WFLOTqK3Zc0Zb86woMpbm79RkZ2dL35WpqqpCbm6uVfL18OHDsXHjRrz66quoqalRzGoJDAxESkoKPD098eijj+Lrr7+WJkEAPzOnB08uk0hr9pMQAvHx8TCZTNLEv6nmkxpb27c3o7LbmJ1E7cneMtrUsqLIWJrv1FRXV2PNmjWorKyEEAIRERFWnQVo+FPPCRMm4C9/+Qvi4uJks1q8vLzQqVMnjB8/Hq6urjh06BA8PDykOviZOdkjrdlPqamp+PLLL5GZmYnPPvsMALBjxw74+PggPT0d3bp1w8CBA1u0/YNgVHYbs5OoPT3IjDa5jKfevXsrZkWRsbiiMBEZjtlJRPQgcFJDRG3i9OnTeOyxx3SHXN66dQtpaWmYOXOmwS0jIkfFSQ0RERE5BM1fFE5MTJSWOK+pqUF0dLTVglxa7dq1C5WVlbqf3+jEiRPYsWNHq+shUqN1uXO12IPGuAKz2Wy1eJ1SuVF+/PFH/PrXv8bcuXMRERGBvLy8VtdpMpmQk5Oj+XmJiYkIDAyU4lWee+45pKSktLo9REZQu96NimEgg2ldgrjpEuerVq0Sf/7zn1u1pDGjFagj0brcuVrsgVLMgFr8gBEWLFggvvnmGyGEEPn5+eLWrVutrjMsLExkZ2drft7+/fvF6NGjxUcffSSEEOIPf/iDOHfuXKvbQ2QEpevdqBgGMp7ulO6tW7dCCIGXXnpJKmsacBcdHS0tGy23/PqZM2dgNptRWFiIJUuWwGw2o6CgAIDysuy7d+/GnDlzMG/ePGzevFkqj4uLswrJs7U9wD8JJX20LnfenrEHLfHDDz/A1dVVWiTPx8dHWtVb6bobOXIkXn/9dYSGhiIhIUEq//DDDzFnzhzExsYqrknTEtOmTcORI0fuK1dqD1F7UbrejYphIOPp+rOEr7/+GtnZ2YoLeMlpvvx6z549kZSUhOnTp2P37t3S6qNqy7Jv27YNX331Fdzd3VFSUiLV/c477yAxMRHfffed1WsqbQ/wT0JJP73LnTePPaiursb8+fPRpUsXvPLKK/jlL3+pWm6E7Oxs2QX91K67iooKhIWFYc2aNQgJCUFBQQFqa2uxb98+HDx4ENnZ2Zg3b57uNvXo0QPe3t7IzMxsUXuI2pPc9W5UDENL4lVIG113alxcXJCQkCCbV6Ok+fLrSjIyMqRl2T08PKRl2YGGHJi1a9ciMTGxRb9I1LZ/++23udQ66dK43Pn+/fvx8ccfo7a21uZzGmMP1q9fL5X97//+Lz7//HOsXbsWr7zyCqqqqlTLjSAU/i5A7bpzcXHBqFGj4OLiAj8/PxQXF+P777/H6NGj4ebmhsGDB1stmKdHREQEPv/88xa1h6g9qV3vixcvvm9FcDVatyftdE1qJkyYgKioKNTU1ODAgQOy2zS/JW/E8us7d+5EVFQU0tPTsWTJEsO3J9Ki6XLnapRiDxqDG4OCguDt7Y3c3FzVciP079/faqXflmiaT+Pk5IT6+nppoTGjTJ48WVqdmMgeNb3ejYphIOPp/k6Nk5MT1q1bh3fffRdlZWUAGiYut2/fRlVVFbKyslpUT/fu3aXnA8rLsgshkJeXh7Fjx2LlypU2B3pb2/M7NaSHreXOWxqT0HidAA0ftRQUFKBv376K5UYJCAhARUUFLly4AAAoLCxEaWmpahyCnGHDhiEtLQ3V1dW4cePGfX9BpfX66ty5M8aMGYOLFy9K7VRrD69fag9K17tRMQxkPP1LfaLhI6Xp06djy5Yt2LhxI5599lksW7YMgYGBLR6Io6OjERMTAw8PD2zdulVxWfb6+nrExcXBYrGgvr4eq1evBtAw8MfExKCsrAxVVVVITU3Fyy+/jKeeekp2+0b8Tg3pkZeXp7rceUtjEnJzcxEfHw9XV1d07twZb7zxBrp164YffvhBttxImzdvRkJCAsrKyuDs7IwPPvhANQ5BTr9+/bBgwQJERERg0KBB9y2wp+f6mj9/Pnbt2gVAPZ5Bb/1EWqld70bEMJDxuPgeEREROQROFYmIiMghcFJDREREDoGTGiIiInIInNQQERGRQ+CkhoiIiByCrj/p3rlzJ5KTk63KQkJCcOzYMauy2bNnw8XF5b5tWc5ylrOc5Sx/2Muff/55kLH4J91ERETkEHinhuUsZznLWc5y3qlxCLxTQ0RERA6BXxQmIiIih8BJDRERETkETmqIiIjIIXBSQ0RERA6BkxoiIiJyCJzUEBERkUPgpIaIiIgcAic1RERE5BA4qSEiIiKHwEkNEREROQROaoiIiMghcFJDREREDoGTGiIiInIInNQQERGRQ+CkhoiIiBwCJzVERETkEDipISIiIoega1KTmJiIwMBA3Lp1CwDw3HPPISUlRfU5u3btQmVlpZ6XIyIiIrJJ950aNzc3fPHFFy3e/qOPPuKkhoiIiNqM7knNtGnTcOTIEauyjz76CCaTCSaTCfv37wcAnDlzBmazGYWFhViyZAnMZjMKCgoAACdPnsSCBQsQHh6Ot956S6pn9+7dmDNnDubNm4fNmzdbvcaWLVuwZcsWvc0mIiIiB+Ws94k9evSAt7c3MjMzAQC5ubnYs2cPDhw4gLq6OpjNZkydOhUTJ05EUlISpk+fjt27d6N3794AgJKSEmzfvh179uxB165dERsbi7Nnz2LChAnYtm0bvvrqK7i7u6OkpMTqdYuKilqxu0REROSodE9qACAiIgKff/45gIZJzpgxY9C9e3cAQFBQEK5evYqJEyfKPvfbb79FdnY2Fi1aBACoqKhATk4OAGDEiBFYu3Ytpk6dipCQEKvnvf32261pMhERETmoVk1qJk+ejK1bt0p3X/Q8X+6jpJ07dyI1NRVffPEF9u7di8TExNY0k4iIiB4CrfqT7s6dO2PMmDG4ePEiysvLkZqaioqKCty5cweXL1/G0KFDpW27d++OsrIy6ecnn3wSqampyM/PB9Dw8VVRURGEEMjLy8PYsWOxcuVK5ObmWr0mv1NDREREclp1pwYA5s+fj127dqFfv36IiorCwoULAQAxMTHo06ePtF10dDRiYmLg4eGBrVu3wsvLC6+99hqWL1+Oe/fuoWvXrnj33XchhEBcXBwsFgvq6+uxevVqq9fjd2qIiIhIjpMQQjzoRhARERG1FlcUJiIiIofASQ0RERE5BN2TmujoaISGhuLEiRNS2aZNmzBx4kSYTCarbS9cuIC5c+feV67kgw8+QFhYGMLCwhAbG2v1mNFxCyaTSfpT8rauJyAgAGazWfp34cKFVr/uw06uP4wcOdKQetrjde3NiRMnsGPHjgfdDN20nse23t/Gsc9sNuPGjRs2t3fUfqVVR++HSpqf36ysLJjNZowcORKXL19+gC1rGaXzotTP9Z7HVv1eFjpFRUWJS5cuWZWlpqaKS5cuibCwsPu2z87Oli1vLj09XfzqV78S1dXVQgghrly5YvX4tGnTRElJid5m3ycsLExkZ2e3Sz1PPvlkq1+HrMn1Bz3HWWu/Mup1yVhGjw+ttW7dOnHgwIEWb89+5diU+qfc79OORGs/t6U1v5cN/fhp1KhR6NWrV6vqyMvLQ69eveDq6goAGDZsGAD1uIXly5cjPDwckZGR2Lt3r1TXyJEj8frrryM0NBQJCQlS+Ycffog5c+YgNjYW1dXVUrlSPSkpKVi6dCliY2NhMpnw5ptvqtajlVL9SuVycRRq26vFTshROg5K9SiVN32HGR0djcuXL2Pbtm2IiopCSEgINmzYgNDQUCkYVSk2Q45afwAge97l9stWPUa8rpb9Ahr+wq9pW3/88UcA8uc9OTkZGzdulJ775ptv4sCBA4r7Cyj3k7i4OEydOtWqPrV6lK4vpfZrOQ5q/UTredSyv//85z9hNptRWVmJ6upqzJ8/X2q/ErnzcufOHZjNZhw5cgT//u//bvNOTXv0K6Xt7W38UeqHWuvRch3pOQ5Kx1PudbWOMydPnsQrr7wi/bx161bs2rVLdX/ljk9OTg5CQkKwYsUKzJ071+quiZb2A/LnRa2fK51Hpdc16vepoXdqhFC+I9PSOzVlZWUiJCREREZGiv/4j/8QBQUFVo/LzXR//vlnIYQQNTU1YtasWaKwsFAIIcSQIUNEamqqqKmpEVOmTBH5+fkiOztbhISEiMrKSnHt2jUxdOhQaUaoVM+5c+dEUFCQuHr1qtRGtXqUPPHEEyI8PFz6l5eXp1i/2uvOnDlTWCwWcfv2bTFlyhRRXFysWs/o0aNFeXm5EEJI26pROg5K9SiVN32H2dhftm7dKv72t7+J119/Xfrv8ePHRXFxsXj66adFRUWFEEKIF198UZw5c8ZmW+X6g9x5V9svpXqMeF09+/Xiiy+Kjz/+WAghRHl5uSguLlY87+Xl5WLGjBmivr5eCCHErFmzpPOupT832r9/v9iwYYNVe7RcX0rt13oclPqJWnuUzovW/d2zZ4944403xKZNm8SuXbsU2yiEUL0ehRBi9erV4n/+539U62iqrfqV2vb2Nv4IIX9etNaj5TrSehzUjqfc6zZq6Z2a2tpaMX36dHH37l0hhBCzZ88WRUVFqvsrd3yys7PF0KFDxY0bN0RlZaUICQkROTk5utsvd16EUO7nzbdXel09v0+VtHqdGqP17NkTycnJOHXqFI4ePYrIyEgcPnwYPXv2VHzOvn37cOLECQghUFhYiMLCQnh5ecHFxQWjRo0CAPj5+aG4uBi5ubkYPXo03NzcMHjwYPj6+tqsBwCGDx+OIUOGSG08d+6cYj1K3NzckJSUJPtY8/qVylNSUlTjKOTqUYud0HI8lerRWr+HhwcsFov03/LyctXYDK3kzruPj4/q+TWC3Ovm5+dr3q/z58/j3XffBQC4u7vD3d0dx44dUzzv/v7+uHz5MpydneHn5yeddy39WY2W68vHx0e2/SdOnNB8HOT6ia39UqJlf3/zm9/gmWeeQX19Pf7t3/5NdduMjAxN8TB6GNGvbF1f9jT+KNFaj9brSMtxUOvPcq+rlbOzM2bMmIHjx4/j8ccfR//+/eHp6an6HKXj4+vrC39/fwDA6NGj8f3336NTp05t2n4lSv2wvLxc8+9TJXY3qQGALl26YObMmZg5cyaWL1+Ob7/9FsHBwbLbpqSk4PTp0/j000/RtWtXREZGor6+HkDDYNDIyckJ9fX1cHJy0lwP0JBt1ZRSPXo1r99WuZZ6tMROqB0HpXpaUn9dXZ30/05OTlb/GutXis3QSu682zq/RpB7XcC4/VISGhqKY8eOwcXFBbNmzQKgvT8r0Xp9qdF6HOT6id7zqOU6qqiowO3btwEAd+/eNXRQ18OofqW2vb2MP2raIz5Hy3Fo6+t6/vz5eO+99/DYY48hIiLC5vZaj09bt1/L6x47dsyw+u3uT7qzs7Px008/AQCqqqqQm5sLHx8f6fHmcQsWiwWPPPIIunbtiuvXr+Pq1auq9Q8bNgxpaWmorq7GjRs3kJeXZ2g9bS0gIEA1jqI5YSN2ojml46BUj1r9PXr0wO3bt1FVVYWsrCzV11WKzbCleX/Qul9a69G6vZ79GjdunDQgVVRU4NatW6rnffr06fjqq69w8uRJzJw5s0X721J66pFrv97zq7U9Ws+jnE2bNmHRokWIioqSvj+hROv1aEtb9Sujjn9bjz9G1qP1OtJC7XjKvW4jpfPbq1ev+75jExAQgJKSEvzjH//A9OnTVdujdnzy8vKQlZWF6upqpKWlISAgQHf7W0vpdW39PtUSj2TonZoNGzbg2LFjKC0tRXBwMNavX48ZM2ZoqqO6uhpr1qxBZWUlhBCIiIiw6nTN4xaeeuopfPbZZ5g7dy4GDhwofbFYSb9+/bBgwQJERERg0KBB8PPzAwDD6lFTVVUFs9ks/fzCCy9ovh3r5+enGkfRnLARO9Gc0nFQqket/meffRbLli1DYGAg+vbtq/q6ffr0kY3NsEUufkPLfmmtR+v2evYrISEB69atwyeffAJnZ2ds2bIFv/jFLxTP+yOPPAIvLy/U1tZKZVr7c05ODmJiYlBWVoaqqiqkpqbi5Zdf1lyPWvv1nN/mjDqPSvsLNLyx2rBhA5ycnHDkyBEcP35cmiw2p/V6tKWt+pXe66u5th5/1PqhlnoA7deRFmrHU+51G0Oflc7vM888g/j4eGzbtg07duyQ3siHhITgxx9/hJubm2p71I6zn58ftmzZgh9//BELFiyQfldpaX9FRYXseZkyZYpse5TO45QpU2Rfd8CAAaq/T7VMwHXHJERHRyMuLg4jRoxo0fY5OTlYvnw5Dh06pOfliIiIHirPPvssli5dqvs7Wg/j713dHz95eHggPj7eavE9JRcuXMDzzz/f6j/3JiIicnSlpaUIDQ1F7969MWHChAfdnA6FgZZERETkEOzui8JEREREenBSQ0RERA6BkxoiIiJyCJzUEBERkUPgpIaIiIgcAic1RERE5BA4qSEiIiKHwEkNEREROQROaoiIiMghcFJDREREDoGTGiIiInIInNQQERGRQ+CkhoiIiBwCJzVERETkEDipISIiIofASQ0RERE5BE5qiIiIyCFwUkNEREQOgZMaIiIicgic1BAREZFD4KSGiIiIHAInNUREROQQ/h+ecY0YZ3AEkQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.rc('figure', figsize=(12, 7))\n",
        "plt.text(0.01, 0.05, str(model.summary()), {'fontsize': 14}, fontproperties = 'monospace')\n",
        "plt.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.1)\n",
        "# plt.savefig('multiple_regression_summary.png', bbox_inches='tight', dpi=300);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAoVIWcbjsZ-"
      },
      "source": [
        "#### Display model & residuals"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vckwbs24jsZ-"
      },
      "source": [
        "The following diagram illustrates the hyperplane fitted by the model to the randomly generated data points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.914449Z",
          "start_time": "2021-04-15T19:54:57.635292Z"
        },
        "id": "-4lruZFhjsZ_",
        "outputId": "c81fd008-90d4-4fcc-fbb2-58d712ab5daa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "three_dee = plt.figure(figsize=(15, 5)).gca(projection='3d')\n",
        "three_dee.scatter(data.X_1, data.X_2, data.Y, c='g')\n",
        "data['y-hat'] = model.predict()\n",
        "to_plot = data.set_index(['X_1', 'X_2']).unstack().loc[:, 'y-hat']\n",
        "three_dee.plot_surface(X_1, X_2, to_plot.values, color='black', alpha=0.2, linewidth=1, antialiased=True)\n",
        "for _, row in data.iterrows():\n",
        "    plt.plot((row.X_1, row.X_1), (row.X_2, row.X_2), (row.Y, row['y-hat']), 'k-');\n",
        "three_dee.set_xlabel('$X_1$');three_dee.set_ylabel('$X_2$');three_dee.set_zlabel('$Y, \\hat{Y}$')\n",
        "sns.despine()\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kZTVg04jsZ_"
      },
      "source": [
        "Additional [diagnostic tests](https://www.statsmodels.org/dev/diagnostic.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiwM3nexjsZ_"
      },
      "source": [
        "## Stochastic Gradient Descent Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjWj8WJFjsaA"
      },
      "source": [
        "The sklearn library includes an SGDRegressor model in its linear_models module. To learn the parameters for the same model using this method, we need to first standardize the data because the gradient is sensitive to the scale."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8mzdQuPjsaB"
      },
      "source": [
        "### Prepare data\n",
        "\n",
        "The gradient is sensitive to scale and so is SGDRegressor. Use the `StandardScaler` or `scale` to adjust the features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3ZCsLCzjsaB"
      },
      "source": [
        "We use StandardScaler() for this purpose that computes the mean and the standard deviation for each input variable during the fit step, and then subtracts the mean and divides by the standard deviation during the transform step that we can conveniently conduct in a single fit_transform() command:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.920869Z",
          "start_time": "2021-04-15T19:54:58.915601Z"
        },
        "id": "_dIHt453jsaB"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "X_ = scaler.fit_transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEvd0Lq-jsaC"
      },
      "source": [
        "### Configure SGDRegressor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B7buJ7DtjsaC"
      },
      "source": [
        "Then we instantiate the SGDRegressor using the default values except for a random_state setting to facilitate replication:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.939674Z",
          "start_time": "2021-04-15T19:54:58.921999Z"
        },
        "id": "4tyBoEISjsaD"
      },
      "outputs": [],
      "source": [
        "sgd = SGDRegressor(loss='squared_loss',\n",
        "                   fit_intercept=True,\n",
        "                   shuffle=True,\n",
        "                   random_state=42,\n",
        "                   learning_rate='invscaling',\n",
        "                   eta0=0.01,\n",
        "                   power_t=0.25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1fe0KBhjsaE"
      },
      "source": [
        "### Fit Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcvWbiWdjsaE"
      },
      "source": [
        "Now we can fit the sgd model, create the in-sample predictions for both the OLS and the sgd models, and compute the root mean squared error for each:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.951053Z",
          "start_time": "2021-04-15T19:54:58.940995Z"
        },
        "id": "b3MDnK9qjsaE",
        "outputId": "ae30cfb2-04fd-44c4-bb50-06a10be5c53b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDRegressor(loss='squared_loss', random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# sgd.n_iter = np.ceil(10**6 / len(y))\n",
        "sgd.fit(X=X_, y=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_m3f5sKjsaF"
      },
      "source": [
        "As expected, both models yield the same result. We will now take on a more ambitious project using linear regression to estimate a multi-factor asset pricing model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.960700Z",
          "start_time": "2021-04-15T19:54:58.952206Z"
        },
        "id": "tnYMG9wujsaF",
        "outputId": "479f6112-5c31-415c-98b5-f09245ea01e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "X_1     997.11\n",
              "X_2   2,669.47\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "coeffs = (sgd.coef_ * scaler.scale_) + scaler.mean_\n",
        "pd.Series(coeffs, index=X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.973085Z",
          "start_time": "2021-04-15T19:54:58.962760Z"
        },
        "id": "7XeBcE0njsaF"
      },
      "outputs": [],
      "source": [
        "resids = pd.DataFrame({'sgd': y - sgd.predict(X_),\n",
        "                      'ols': y - model.predict(sm.add_constant(X))})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:58.983549Z",
          "start_time": "2021-04-15T19:54:58.974199Z"
        },
        "id": "oqRrfQz7jsaG",
        "outputId": "4b87d9bb-4f76-4172-e082-7fcaca5ca7a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sgd   50.10\n",
              "ols   50.10\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "resids.pow(2).sum().div(len(y)).pow(.5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:54:59.147588Z",
          "start_time": "2021-04-15T19:54:58.984711Z"
        },
        "id": "k_l9HUxijsaG",
        "outputId": "f0f7bf3a-d670-4e78-e951-dcbe5963ad51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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AtFNcVqu7Nparxex5QEmTMpK0+sbz6RKIXo+ABQAAAEmtxwGd3bprJUk/YdcKaEPAAgAAgNZt26sHX9hj+jjgxV9P1LfOT9E3MwexawWchIAFAADQSzldbtXUNeg3r+/Tc+UfmZqzIG+Ybr58GKEK6AQBCwAAoBcq3HFQ9z23S4ZFcnvMzXlgxljNviQ9sIUBEY6ABQAA0Iscb/To4Rcq9dS2fabnWC0WvbjwclqvAyaEbcCaPHmy+vbtK8MwZLVa9ec//1nHjx/XokWLVFtbq5SUFK1Zs0YJCQmhLhUAACAiFO44qOXFB9XSjQYWVotFq2/IJlwBJoVtwJKkp59+Wueee27bz+vWrVNubq7mz5+vdevWad26dVq8eHEIKwQAAIgMhTsO6p7Nu0yNzR16ri4b0V/pSX2VOzyJ+1ZAN4R1wPqq0tJS/c///I8kacaMGfre975HwAIAAOjAiZbrx3TM1aS0xDjd9/zu0463Wiy695rRuiyjP7tVwBmw+Hy+bmwSB8/kyZOVkJAgi8WiG264QTfccIPGjx+vnTt3SpJ8Pp8uuuiitp87U1ZWJrvdv/+vS2Njo/r06ePX70RwsYaRjfWLbKxf5GMNw9+r++q16rWPuzXnPy9J0rTzuHoR7vj9Cx+jR4/u8HnY7mD98Y9/VHJyspxOp+bNm6dhw4a1+9xischisXT5PXa7vdM/fE9VVlb6/TsRXKxhZGP9IhvrF/lYw/DldLn10u4jWvWauQYWMRbJYkj3XUt3wEjB71/4C9uAlZycLElKSkpSfn6+KioqlJSUpKNHj2rgwIE6evRou/tZAAAAvdnql97XY69UmRprs1q0+JvnacKwJKUmxnHHCvAjI9QFdOSLL76Qy+Vq++s33nhDI0aM0OTJk7V582ZJ0ubNm3XFFVeEskwAAICQc7rcmvbYdvPhypBe+NHlmp83XNlp5xCuAD8Lyx0sp9Op2267TZLk8Xh0zTXXaNKkSRo3bpwWLlyoTZs2afDgwVqzZk2IKwUAAAiNnfud+vVr+/XSew6ZvVBvj7FoYS5NLIBACsuAlZaWpueee+6U54mJiXr66adDUBEAAED4+O76HXq9yml6vNUiLbxypG6aMERHq82/YBhA94VlwAIAAEB7VY56vbj7iHYfPm46XF05eoDm5A5V5uCz244CHg1kkQAIWAAAAOHM6XLrh4Xv6O39dabnDEuK07o5F3EUEAgBAhYAAECYWrdtr37+wh7T4w1Jv55zoa4YMyhwRQE4LQIWAABAmKly1OvuP1fo7wePm54TazX0i+uzCFdAiBGwAAAAwsiSTeXauLOmW3OuHpesldeNo+U6EAYIWAAAAGFi3ba93QpXlw47VyuuG8tdKyCMELAAAABCzOlya/fhT03ft7pm3CAtvHIkwQoIQwQsAACAEHC63Kqpa9ArlQ79f9v2yrBYupxzydBEPTn7Qo4CAmGMgAUAABBkhTsO6v7nd6vF45O37amv0/HfvzRdN01IZ8cKiAAELAAAgCDZud+p1Vs/0Bt7PzE9Z9b4VC2bPjaAVQHwJwIWAABAgDldbs361Zvae+wLU+PtVqnFJy2dMkrz84YHuDoA/kTAAgAACBCny63HSz/U028dND1n4RUZ+rdRyUpNjOOuFRCBCFgAAAABsG7bXtNdAVvNGp+ihfnnBagiAMFAwAIAAPCTKke9Xq86pufLa/XOoU9NzbnlsqE6b1A/5aSdQxMLIAoQsAAAAPxgybNl2vhObbfm3DE5Q4u+yY4VEE0IWAAAAGfA6XJr5fO7tLn8SLfmzRqfSrgCohABCwAAoAecLrcK3z6kX778Qbfm5Y8eoKVXjeY4IBClCFgAAADd0Bqsnij9QM3erse3Oi+5r/73llw6AwJRjoAFAABgUuGOg7rv+V1q9pifY1ikX3/vQl0xZlDgCgMQNghYAAAAXahy1Ovhv+zRy5VHuzXPHmPokZlZhCugFyFgAQAAdMLpcmvJpnKV7vm4W/Ouv3CwvnvJUF4WDPRCBCwAAICvcLrceqL0Q21466DpOYakOZem6/bJIwhVQC9GwAIAAPhSTzsD5qSdrd/8+8UEKwAELAAAAKfLrfWv7dOvX9uvFq/P1JzrLxysryWcpUkj+mv80KQAVwggUhCwAABAr7Zu2179/IU93ZozPXuQHrn+/ABVBCCSEbAAAECvtHO/U3c9W66DnzR0a95Ppo7S/LzhAaoKQKQjYAEAgF7nu+t36PUqp+nxFkn/kTdMN18+jHtWAE6LgAUAAHqVnfudpsOV1bDoZ9dl6puZgwhWAEwhYAEAgKjndLm1+/Bnqq37Qs+VHzY1x2qxaPWsbE3PSQlwdQCiCQELAABEteKyWt21sUwtXvNzbhifqiVXjWLXCkC3EbAAAEDUqXLU68XdR/RFU4vWbd9nOlwZFmnljLGaPSE9sAUCiFoELAAAEFWWbf6nfr/jkOnxhqTp2V/Tty9MVebgBHatAJwRAhYAAIgape8d6Va4+u/v5Ch3eH9CFQC/IWABAICI5nS59ed3a1T49iEdcH5het6c3CG6JpsGFgD8i4AFAAAiVuGOg7pn8y5TYx/81lhJkrvFo8syBigjuV8gSwPQSxGwAABAxNm536k1L3+g1/d9Ymr8nNwh+g6NKwAEAQELAABEDKfLrX//7dvadbje1PjvX5qumyaks1sFIGgIWAAAIOw5XW49UfqhNrx10PScOblDtGz62ABWBQCnImABAICw5XS59Xjph3q6G8FqQF+b/jg/l10rACFBwAIAAGGnJztWknTLZUN1zzVjAlQVAHSNgAUAAMJKdzoDtpqbm67brxjB+6wAhBwBCwAAhIUqR71Wv/y+SnY5ujXvgW+N1Ww6BAIIEwQsAAAQUk6XW7cVvqsd+821XG81dnA/Pf39CexaAQgrBCwAABAyxWW1uuNPZd2ac9nwc7XwypEaPzQpQFUBQM8RsAAAQNDt3O/U5rLD+t+3D5mekzHgLD1z66XsWAEIawQsAAAQVFPX/FWVRz7v1pw7rsjQovzzAlQRAPgPAQsAAARFlaNe1//qDdU1eEzPmZTRX6tvzGHXCkDEIGABAICAqXLUq6z6uEoqPtKrH3xset63zx+sH34jg5cFA4g4BCwAABAQS54t08Z3ak2NtUgquGCwLkw/V9/MHMSOFYCIRcACAAB+5XS59Xjph6bDlSTdffUozZ80PIBVAUBwELAAAIBf7Nzv1OqtH2rHPqc8PvPzZo1PJVwBiBoELAAAcMZuWPum3t5fZ3r8oLPtmjp2kGZPSOeeFYCoQsACAAA94nS5VVPXoD+8tb9b4eqqMcn61ZzxAawMAEKHgAUAALqtuKxWi58tV5PJs4AxFkmGRUumnMdxQABRjYAFAAC65dDxJt1RXGZ6/KZbL5EtxqrUxDi6AwKIekaoC+iu7du3a8qUKcrPz9e6detCXQ4AAL3K6pfe163FNabHP/CtsRo/NEnZaecQrgD0ChG1g+XxeLRixQr97ne/U3JysmbOnKnJkycrIyMj1KUBABDVqhz1+t76Hfqovsn0nAdmjNXsCekBrAoAwk9EBayKigqlp6crLS1NkjRt2jSVlpYSsAAACIAqR71e3P2Rni8/rD2Oz03NsehE2/UlV41ixwpArxRRAcvhcGjQoEFtPycnJ6uiouK0c9xutyorK/1aR2Njo9+/E8HFGkY21i+ysX6R4aHtR7Rt/xfdmjM0waqfX5Wqc/pYdbR6n44GqDacGX4HIxvrFz5Gjx7d4fOIClg9YbfbO/3D91RlZaXfvxPBxRpGNtYvsrF+4cvpcuv/f7dG61/bryP1btPz0s+N06PXZ2v80KQAVgd/4XcwsrF+4S+iAlZycrKOHDnS9rPD4VBycnIIKwIAIDqs3bZXD76wp9vzfjJ1lObn0XYdAFpFVMAaN26cDhw4oOrqaiUnJ6ukpESPPvpoqMsCACAitb4oeM3W9/Xq+8e6NTdjwFl65tZLuWcFAF8RUQErJiZGy5Yt08033yyPx6OCggKNGDEi1GUBABBxCncc1PLiXWox957gNiMH9tX8C87WzG9cEJjCACDCRVTAkqS8vDzl5eWFugwAACKS0+XWI3/Zoz/tNP8uqxEDztKM81M0JfNrykjuxwV7ADiNiAtYAACgZwp3HNQ9m3d1aw53rACgewhYAAD0At0NV9kpZ+u38y7mjhUAdBMBCwCAKOV0ufXWXqfePfiJnt5xyPS8n1w9SvMnsWsFAD1BwAIAIMo4XW4Vvn1Ia7Z+IK/JJhbnpyXoB5cNU+7wJHatAOAMELAAAIgixWW1+vHGMjV7zc/hnhUA+A8BCwCAKHDiOOAxLfxTmbratDIkXT1ukK4a+zV2rADAzwhYAABEuMIdB7X8ud1qMXkecOOtl2j80KQAVwUAvRMBCwCACLVzv1MPv/i+/n6gzvScOblDCFcAEEAELAAAItANa9/U2/vNB6uctAT9Yma2MpL7BbAqAAABCwCACOB0ubX78Gf6rKFJJRWHTYerS4edqxXXjSVYAUCQELAAAAhzq196X4+9UmV6vEXSTRcP0byJXydYAUCQEbAAAAhTTpdbs9a+qb0ff2F6zo0XpWrxlFF0BgSAECFgAQAQhh7Y8p5+/fr+bs2ZNT5VDxVkB6giAIAZBCwAAMLM5Q9vVXWd2/T4vIz+uvfaMRwHBIAwQMACACCMzF63w3S4ijEsuv+6TM2ekB7gqgAAZhGwAAAIoSpHvV7cfUSfN7XocF2D3tjn7HLOyIF99dNrxihzcAJ3rQAgzBCwAAAIAafLrR8WvtOtd1lJ0oC+Nr105zcCUxQA4IwRsAAACCKny63Ctw/ply9/0K15yf3sunvqeZpxQVqAKgMA+AMBCwCAICnccVDLNu+SpxtzMgacpV99dzwNLAAgQhCwAAAIgu62XbdapKVXjdL8vOEBrAoA4G8ELAAAAqjKUa+5v/2baj5tNDXesEiLrhypmyYMoYEFAEQgAhYAAAHgdLm1ZFO5Svd8bGr8OX2s+tm3xil3eH+CFQBEMAIWAAB+5HS59Xjph3r6rYPdmlf6438jWAFAFCBgAQDgJ6tfel+PvVLV7XmP35hDuAKAKEHAAgDgDDldbs186nXtd5q7Z9Vqbm66br9iBOEKAKIIAQsAgB6qctTrt2/s1x//Vi2fyTnjhyToR1eOVObgBIIVAEQhAhYAAN1U5ajXwmf+oV2H603PuXL0QP3XVaN4nxUARDkCFgAAJu3c79Sy4l1674irW/Me+NZYzZ6QHqCqAADhhIAFAIAJ337ydb1b/Wm35lwyNFFPzr6Qo4AA0IsQsAAAOI2d+526/Y//0EefuU3PsUi6e+oozc8bHrjCAABhiYAFAEAnblj7pt7eX9etOT+9epS+dUEqu1YA0EsRsAAAOInT5dYf3jqgondrdKjOfNt1i6THbszR9JyUgNUGAAh/pgLWO++8o9GjR+uss85ScXGx3nvvPc2ZM0cpKfyXCAAgOjhdbj1R+qE2vHXQ9JxRyX116fD+uiD9XOUOT2LXCgAgw8yg++67T3FxcdqzZ49+97vfaciQIVq6dGmgawMAICgKdxzUhT/b2q1wNWt8iv6y6BtaNn2srskeTLgCAEgyuYMVExMji8WirVu3avbs2br++uu1adOmQNcGAEBA7dzv1OqXP9Ab+z4xPSf1nD7aMO9i3mcFAOiQqYDVt29frV27Vs8//7z+8Ic/yOv1qqWlJdC1AQAQEE6XW7PWvqm9H3/RrXl3TM7Qom+eF6CqAADRwFTAWr16tbZs2aIHHnhAAwYM0OHDh/WDH/wg0LUBAOB3hTsO6p7Nu0yPT4yz6rHvXKjMwWdzDBAA0CVTAWvAgAGaN29e28+DBw/WjBkzAlYUAACBsPql9/XYK1Wmxp57VoyWXTNGMy5IC3BVAIBoctqAdf7558tiscjn88lisbQ9b/353XffDXiBAAD4w5JN5dq4s8bU2BjDopfv/AY7VgCAbjttwPrHP/7R9teVlZXauXOnJOmiiy7SqFGjAlsZAABnqMpRr7Lq40o8y2YqXNmsFhkWix6ZmUW4AgD0iKkjgr///e/17LPPKj8/Xz6fT4sXL9asWbP0ve99L9D1AQDQbVWOevQzuUMAACAASURBVC0r3q039zlNz/nJ1FGaMCxJqYlxhCsAQI+ZCljPPvusnnnmGZ111lmSpFtuuUU33HADAQsAEHbu+OO7Ki7/yPT4ryfFqWjBREIVAMAvTAUsSbJarR3+NQAAoeZ0ubX78Kd6bOuHeufQcdPzaLsOAPA3UwHr29/+tq6//nrl5+dLkrZu3aqCgoKAFgYAgBnFZbX68bPlavb4uhz7/UvTNSSpr/rH25U7PIldKwCA35kKWPPmzdPFF1+sd955R5L04IMPasyYMQEtDACAzjhdbr2195gqP/pM617bbypcSdJNE9KVkdwvwNUBAHoz00cEMzMzlZmZGchaAADoUuGOg/rp5l0yF6n+ZU7uEMIVACDgTAcsAABCpfWOVUnFR3rG5LusJMmwSDdelKbvTxxKuAIABAUBCwAQ1gp3HNSy53bJ4+3evBsvStXiKaO4ZwUACCoCFgAgbBXuOKh7Nu8yPd5ulbyy6L5rMzX7kvQAVgYAQMcIWACAsFTlqDcdrqyGRSuuy9TYwQm8KBgAEFIELABAWDjRGdCpg06XDn3SoKJ3a7uckz96oK7LSaHlOgAgbBCwAAAh15POgD+ZOkrz84YHrCYAAHqCgAUACKnu3rOKMaT7rxur2RO4YwUACD8ELABA0DldbtXUNahvrFX3PWc+XE0bO0grZozlOCAAIGwRsAAAQeN0ufW/5Z9oY+EBxRgWuT1e+UycCyy4IEUL8obzLisAQNgLu4D1xBNPaOPGjTr33HMlSXfeeafy8vIkSWvXrtWmTZtkGIZ++tOf6vLLLw9lqQCAbiguq9WSTeVyt5xIVG4Tc6yGtGL6WFquAwAiRtgFLEmaO3eufvCDH7R7VlVVpZKSEpWUlMjhcGjevHl68cUXZbVaQ1QlAMAMp8ut3Yc/bReuTsewSLPGp+rqcV9T5uAEjgMCACJKWAasjpSWlmratGmKjY1VWlqa0tPTVVFRofPPPz/UpQEAOlFcVqulRRUyLJbThquzYq1aMuU8DRvQl1AFAIhoYRmwCgsLtXnzZo0dO1b/9V//pYSEBDkcDmVnZ7eNSU5OlsPh6PK73G63Kisr/VpfY2Oj378TwcUaRjbWL/wdb/Rox8HP9d87jsljYrzH49V5Z32uczyNOlrt1NGAV4gzwe9gZGP9IhvrFz5Gjx7d4fOQBKy5c+fq2LFjpzxfuHChvvOd7+iHP/yhLBaLHnvsMT300EN68MEHe/z3stvtnf7he6qystLv34ngYg0jG+sX3rp6p5XdKrV4JYvFIrvNkMfr06qCLOXmpAS1TvQcv4ORjfWLbKxf+AtJwNqwYYOpcddff73+4z/+Q9KJHasjR460feZwOJScnByI8gAAPeB0ufVE6Yfa8NbBTsfYYwzd+40BmnrJOElSTV2DUhPjOBIIAIgaRqgL+KqjR/91MGTr1q0aMWKEJGny5MkqKSlRU1OTqqurdeDAAWVlZYWqTADAl5wutx5+oVIXPbD1tOEqxmrRIzOzdGFKXyXF25UUb1d22jmEKwBAVAm7O1iPPPKI9uzZI0lKSUnRihUrJEkjRozQ1KlTdfXVV8tqtWrZsmV0EASAEHK63Cp8+5DWvPyBvCbG/+nmCRo/NEmVlZ8FvDYAAEIlLANWZxYsWKAFCxYEsRoAQEeKy2q1+NkyNZnpYCFpTu4QjR+aFNiiAAAIA2EXsAAA4avKUa/Xqz7Wyi2V8nT9SitdMzZZC/PPU0Zyv8AXBwBAGCBgAQBMWbKpXBt31pgaazUsWnFdpmZPSA9wVQAAhBcCFgCgU06XW7sPf6o/v1OjzeUfdTnesEi3Thqmmy8fRvMKAECvRMACAJyiylGvp7bt1eayw/J4uz4LaDUseuyGbOUO70+wAgD0agQsAEA7S54t08Z3ak2N7WMz5PNJj8zM0jXZvCgYAAACFgCgzdpte02Hq+nZX9MPLhvGi4IBADgJAQsAejmny62augb1jbXqFy+9b2rOHVdkaFH+eQGuDACAyEPAAoBeqDVU7djn1KMvf6BYq0VNHp8sXcyLMSy6n+6AAAB0ioAFAL1McVmtlmwql9frU7P3xLOmltPPmZubrsmjBypzcALHAQEAOA0CFgD0Ik6XW3dtLFOLt+PP7TGGfD6fYqyGmlq8+u4lQ3T75BGEKgAATCJgAUAv4XS59Xz54U7DlST5JP3fjy7X500emlcAANADBCwAiFInN6/4v11H9OSrH8qwnP6W1fJrxygjuV+QKgQAIPoQsAAgChXuOKjlz+2Szyt52n3S8UuDbVbpvmvH0rwCAIAzRMACgChTuOOg7tm8q8tx9hhDkvSf/5ahmyYM4TggAAB+QMACgCiyc79T9xabC1e/njNemYPPJlgBAOBHBCwAiHBOl1u7D3+m3791QFsrj3Y53h5j0SMzszRp5IDAFwcAQC9DwAKACFZcVnvatutftSBvmG6+fBi7VgAABAgBCwAiUJWjXq9XHdMDJe+ZClcWST+bMVazL6GJBQAAgUTAAoAIc8cf31Vx+Uemxs67NF0Xpicqd3h/dq0AAAgCAhYAhLnW91mlJsbp/ud36zmT4WpO7hAtnz42wNUBAICTEbAAIIwVl9VqaVGFbIYhd0uLmjynHx9jWDRrfKq+P3EoLwwGACAECFgAEKaqHPVavKlCTS1eNer0F60e/NZYpSTGKXNwAkcBAQAIIQIWAIQZp8utwrcP6YnSD9RsooHFpBFJ+s4EmlcAABAOCFgAEEYKdxzUfc/vVrPHZ2q8YZFW33B+gKsCAABmGaEuAABwwtpte3XP5l2dhqvYGENzcofIHmO0/WPNDTkcCQQAIIywgwUAIXJyd8C/7DqiB1/Y0+nYWKtF/3f7ZcpI7qc7rhjZNo9wBQBAeCFgAUAIFJfVavGzZbLIkNfnPW0LC3uMRY/MzG7rCpgUbydYAQAQpghYABBkO/c7dcefyr786fRdLBbkDdPNlw8jUAEAECEIWAAQBK3HAQvfPqiNO2tMzfnJ1FGanzc8wJUBAAB/ImABQIC1vizYkPTFafquWw2pT4xVzR6vll+bqdmX0HodAIBIQ8ACgAByutxasqlc7pau264/c8slssVYaV4BAEAEI2ABgB+1HgXsG2vV4U8bVfyPGlPhak7uEI0fmhSECgEAQCARsADAT4rLarVkU7m8XqnZa+5FwZcOO1crrhvb1iEQAABENgIWAPiB0+XWoj+VddET8IS5uekam5KgnLRzCFYAAEQZAhYA9JDT5dbuw59J8unX2/eZClf2GEO3XzGCO1YAAEQpAhYA9EBxWa3u2limFjOp6ksnXhicRbgCACCKEbAAwKTWBhbNLR79uBvhyma16EeTR+imCUMIVwAARDkCFgCYcKKBRYU8Xq/pYHXjRam6etzXlDk4gWAFAEAvQcACgC5UOepNN7BoNWt8qh4qyA5YTQAAIDwRsADgNIrLarXoma7Dlc1q0eIp5+ncs2LpDggAQC9GwAKATjhdbi3ZVKHTvdLKakiP3XC+cocncQwQAAAQsACgMzV1DbJ0MWbF9LG6JntwUOoBAADhj4AFoNc78T6rTyVZlDn47LadqNTEOHW2eWU1LFpxXaZmT0gPWp0AACD8EbAA9GrFZbX68bPlavaciFIxhvTLWTmanpOipHi7HpmZpUUby+U56ZzglaMH6uEC3mcFAABORcAC0Gu13rFqDVeS1OKVFm8q18SM/kqKt2t6ToomZvTXW3udOuZy67KM/jSwAAAAnSJgAeh1Wl8Y/GlDs6zGqbesrBZDNXUNbTtUSfF27lkBAABTCFgAeg2ny63Ctw/pyVerFGs11OTxqsVzagN2j8+r1MS4EFQIAAAiHQELQNRrDVb//coHavKceOZuORGsbFaLLD6fvvxRMYb0yMxs7lcBAIAeIWABiGrFZbVa/Gy5mjwd9wPsE2PVk7PP//Kn9l0EAQAAuouABSBqOV1u3bWxrG13qiPNXq8yBycQqgAAgF8QsABEndYmFtWffHHacGWPMbSKdusAAMCPCFgAokpxWa2WFlXIZhhyt3g6HBNjSHdcMVI3TRhCuAIAAH5FwAIQ8Vp3rPrGWrW0qEKNzV41quOtqxjDor/ccTnvsgIAAAFBwAIQ0b66Y2V85b1WfWyGPF6fbNYT//zIzCzCFQAACBgjFH/TF154QdOmTdOoUaP0z3/+s91na9euVX5+vqZMmaLXXnut7fn27ds1ZcoU5efna926dcEuGUAYOd7oUXn1cVU56tt2rOrdLWry+NTYfOrO1Qs/ulx/vOUSvflfkzU9JyUEFQMAgN4iJDtYI0eO1BNPPKHly5e3e15VVaWSkhKVlJTI4XBo3rx5evHFFyVJK1as0O9+9zslJydr5syZmjx5sjIyMkJRPoAQKi6r1ZJNhxQbUyu3xyuLr337dbvVIp/FIrvVULPXq1UF7FgBAIDgCUnAGj58eIfPS0tLNW3aNMXGxiotLU3p6emqqKiQJKWnpystLU2SNG3aNJWWlhKwgF7G6XJraVGF3B6f3J6WDsdYDItK/vMyfd7kUWpiHE0sAABAUIXVHSyHw6Hs7Oy2n5OTk+VwOCRJgwYNave8NXgB6D1q6hpkM4x2DSz62Ax5vT7ZY6zsWAEAgJALWMCaO3eujh07dsrzhQsX6sorrwzU3/YUbrdblZWVfv3OxsZGv38ngos1DH/HGz1yuJqVHG/TOX2skqQvGj1q+krrdZ/XpyeuSVFDi/fEWPtnqqz8LBQlwyR+/yIfaxjZWL/IxvqFj9GjR3f4PGABa8OGDd2ek5ycrCNHjrT97HA4lJycLEmdPu+K3W7v9A/fU5WVlX7/TgQXaxi+nC63Ct8+pCdfPaDYk+5RtTaneMSSqMXPliv2pB2rKTSuiCj8/kU+1jCysX6RjfULf2F1RHDy5Mm66667NG/ePDkcDh04cEBZWVny+Xw6cOCAqqurlZycrJKSEj366KOhLheAnxXuOKj7nt+l5i83qdwtJ44CLimq0MSM/kqKt2t6TooG+Op0Vv9U7lgBAICwE5KA9fLLL2vlypX65JNPdOutt2r06NH6zW9+oxEjRmjq1Km6+uqrZbVatWzZMlmtJ44GLVu2TDfffLM8Ho8KCgo0YsSIUJQOIEAKdxzUPZt3dfiZzTBUU9fQFqbO6WPV6LRzglkeAACAKSEJWPn5+crPz+/wswULFmjBggWnPM/Ly1NeXl6gSwMQJE6XWzV1DUpNjJMk3b/lvU7HNnu9beMAAADCWVgdEQTQOxSX1WppUYVsxok7Vrd9I0OxVouaOui8bo8xtKogi6OAAAAgIhCwAARV67usGpu9be3W//vVDyVZThm7IG+Ybr58GOEKAABEDAIWgKBwutzaffhT7fv4c8UY7cNUrNWq+ZOG6cm/VslqWNTs8Wn5tWM0e0J6iKoFAADoGQIWgIBqbbv+xCsfqtnj63BMs9ermyYM0U0ThrTdy2LXCgAARCICFoCAKS6r1ZJN5XK3dBys+tqt8nh97e5YEawAAEAkI2ABCIjWu1adhas+MYbuvzZT/zZqIKEKAABEDSPUBQCIfE6XW+XVx+V0udue1dQ1yGZ0/h8xPvkIVwAAIOqwgwXgjHy15fqqgixNz0lRamKcmr3eDufEGNIjM7MJVwAAIOoQsAD0WEct15cUVWhiRn8lxdu1qiBLS74MX00ej74/cahyh/dX5uCzCVcAACAqEbAA9FjrMcDWcCVJNsNQTV2DkuLtmp6TookZ/ekMCAAAeg0CFoAe6+gYYLPXq9TEuLafk+LtBCsAANBr0OQCQI+1HgPsYzPUzx6jPjajXct1AACA3oYdLABnhGOAAAAA/0LAAnDGOAYIAABwAkcEAQAAAMBPCFgAAAAA4CcELAAAAADwEwIWAAAAAPgJAQsAAAAA/ISABQAAAAB+QsACAAAAAD8hYAEAAACAnxCwgCjmdLlVXn1cTpc71KUAAAD0CjGhLgBAYBSX1WppUYVshqFmr1erCrI0PScl1GUBAABENXawgCjkdLm1tKhCjc1e1btb1Njs1ZKiCnayAAAAAoyABUShmroG2Yz2v942w1BNXUOIKgIAAOgdCFhAFEpNjFOz19vuWbPXq9TEuBBVBAAA0DsQsIAolBRv16qCLPWxGepnj1Efm6FVBVlKireHujQAAICoRpMLIEpNz0nRxIz+qqlrUGpiHOEKAAAgCAhYQBRLircTrAAAAIKII4IAAAAA4CcELAAAAADwEwIWAAAAAPgJAQsAAAAA/ISABQAAAAB+QsACAAAAAD8hYAEAAACAnxCwgBBwutwqrz4up8sd6lIAAADgR7xoGAiy4rJaLS2qkM0w1Oz1alVBlqbnpIS6LAAAAPgBO1hAEDldbi0tqlBjs1f17hY1Nnu1pKiCnSwAAIAoQcACgqimrkE2o/2vnc0wVFPXEKKKAAAA4E8ELCCIUhPj1Oz1tnvW7PUqNTEuRBUBAADAnwhYQBAlxdu1qiBLfWyG+tlj1MdmaFVBlpLi7aEuDQAAAH5AkwvAD5wut2rqGpSaGNdlWJqek6KJGf1NjwcAAEDkIGABZ6gnXQGT4u0EKwAAgCjEEUHgDNAVEAAAACcjYAFngK6AAAAAOBkBCzgDdAUEAADAyQhYwBmgKyAAAABORpML4AzRFRAAAACtCFiAH9AVEAAAABJHBAEAAADAbwhYAAAAAOAnBCwAAAAA8BMCFgAAAAD4SUgC1gsvvKBp06Zp1KhR+uc//9n2vKamRllZWbruuut03XXXadmyZW2f7dq1S9dee63y8/P1s5/9TD6fLxSlAwAAAECnQtJFcOTIkXriiSe0fPnyUz4bMmSIiouLT3l+3333aeXKlcrOztYtt9yi7du3Ky8vLxjlAgAAAIApIdnBGj58uIYNG2Z6/NGjR+VyuZSTkyOLxaIZM2aotLQ0gBUCAAAAQPeF3R2smpoazZgxQ9/97ne1c+dOSZLD4dCgQYPaxgwaNEgOhyNUJQIAAABAhwJ2RHDu3Lk6duzYKc8XLlyoK6+8ssM5AwcO1KuvvqrExETt2rVLt912m0pKSs6oDrfbrcrKyjP6jq9qbGz0+3ciuFjDyMb6RTbWL/KxhpGN9YtsrF/4GD16dIfPAxawNmzY0O05sbGxio2NlSSNHTtWQ4YM0f79+5WcnKwjR460jTty5IiSk5NNfafdbu/0D99TlZWVfv9OBBdrGNlYv8jG+kU+1jCysX6RjfULf2F1RPCTTz6Rx+ORJFVXV+vAgQNKS0vTwIEDFR8fr7KyMvl8Pm3evFlXXHFFiKsFAAAAgPZC0kXw5Zdf1sqVK/XJJ5/o1ltv1ejRo/Wb3/xGf//73/X4448rJiZGhmHo/vvv1znnnCNJWr58ue6++241NjZq0qRJmjRpUihKBwAAAIBOhSRg5efnKz8//5TnU6ZM0ZQpUzqcM27cOG3ZsiXQpSHCOF1u1dQ1KDUxTknx9lCXAwAAgF4uJAEL8IfislotLaqQzTDU7PVqVUGWpuekhLosAAAA9GJhdQcLMMvpcmtpUYUam72qd7eosdmrJUUVcrrcoS4NAAAAvRgBCxGppq5BNqP9v31thqGauoYQVQQAAAAQsBChUhPj1Oz1tnvW7PUqNTEuRBUBAAAABCxEqKR4u1YVZKmPzVA/e4z62AytKsii0QUAAABCiiYXiFjTc1I0MaM/XQQBAAAQNghYiGhJ8XaCFQAAAMIGRwQBAAAAwE8IWAAAAADgJwQsAAAAAPATAhYAAAAA+AkBCwAAAAD8hIAFAAAAAH5CwAIAAAAAPyFgAQAAAICfELAAAAAAwE8IWDhjTpdb5dXH5XS5Q10KAAAAEFIxoS4Aka24rFZLiypkMww1e71aVZCl6TkpoS4LAAAACAl2sNBjTpdbS4sq1NjsVb27RY3NXi0pqmAnCwAAAL0WO1joNqfLrZq6Bn3a0CSbYahR3rbPbIahmroGJcXbQ1ghAAAAEBoELHTLyUcCmzweeX3tP2/2epWaGBea4gAAAIAQI2DBtJOPBLbuWsUYkj3GUKz1X3ew2L0CAABAb0XAgmk1dQ2nHAmMs8XoydkXKCHOptTEOMIVAAAAejUCFkxLTYxTs9fb7lmz16vMwWcTrAAAAADRRRDdkBRv16qCLPWxGepnj1Efm8GRQAAAAOAk7GChW6bnpGhiRn/V1DVwJBAAAAD4CgIWui0p3k6wAgAAADrAEUEAAAAA8BMCFgAAAAD4CQELAAAAAPyEgAUAAAAAfkLAAgAAAAA/IWABAAAAgJ8QsAAAAADATwhYAAAAAOAnBCwAAAAA8BMCFgAAAAD4CQELAAAAAPyEgAUAAAAAfkLAAgAAAAA/IWABAAAAgJ8QsAAAAADATwhYAAAAAOAnBCwAAAAA8BMCFgAAAAD4CQELAAAAAPyEgAUAAAAAfkLACiKny63y6uNyutyhLgUAAABAAMSEuoDeorisVkuLKmQzDDV7vVpVkKXpOSmhLgsAAACAH7GDFQROl1tLiyrU2OxVvbtFjc1eLSmqYCcLAAAAiDIErCCoqWuQzWj/L7XNMFRT1xCiigAAAAAEAgErCFIT49Ts9bZ71uz1KjUxLkQVAQAAAAgEAlYQJMXbtaogS31shvrZY9THZmhVQZaS4u2hLg0AAACAH9HkIkim56RoYkZ/1dQ1KDUxjnAFAAAARCECVhAlxdsJVgAAAEAUC8kRwYcfflhXXXWVrr32Wt1222367LPP2j5bu3at8vPzNWXKFL322mttz7dv364pU6YoPz9f69atC0XZAAAAAHBaIQlYEydO1JYtW/T888/r61//utauXStJqqqqUklJiUpKSrR+/Xrdf//98ng88ng8WrFihdavX6+SkhJt2bJFVVVVoSgdAAAAADoVkoB12WWXKSbmxOnEnJwcHTlyRJJUWlqqadOmKTY2VmlpaUpPT1dFRYUqKiqUnp6utLQ0xcbGatq0aSotLQ1F6QAAAADQqZDfwSoqKtLUqVMlSQ6HQ9nZ2W2fJScny+FwSJIGDRrU7nlFRYWp73e73aqsrPRjxVJjY6PfvxPBxRpGNtYvsrF+kY81jGysX2Rj/cLH6NGjO3wesIA1d+5cHTt27JTnCxcu1JVXXilJeuqpp2S1WjV9+vRAlSG73d7pH76nKisr/f6dCC7WMLKxfpGN9Yt8rGFkY/0iG+sX/gIWsDZs2HDaz//85z/rr3/9qzZs2CCLxSLpxM5U63FB6cSOVnJysiR1+hwAAAAAwkVI7mBt375d69ev11NPPaW4uLi255MnT1ZJSYmamppUXV2tAwcOKCsrS+PGjdOBAwdUXV2tpqYmlZSUaPLkyaEoHQAAAAA6FZI7WCtXrlRTU5PmzZsnScrOztaKFSs0YsQITZ06VVdffbWsVquWLVsmq9UqSVq2bJluvvlmeTweFRQUaMSIEaEoHQAAAAA6FZKA9fLLL3f62YIFC7RgwYJTnufl5SkvLy+QZQEAAADAGQnJEUEAAAAAiEYELAAAAADwEwIWAAAAAPgJAQsAAAAA/ISABQAAAAB+QsACAAAAAD8hYAEAAACAn1h8vv/X3r2FRLW+cRz/TQ6Gdk4ao5jsgFKgZkIXQcexqWCcRrSIoIvELkoTpOjCoCiLSrvpQIRR4EURUVYQFlGSJnVhgWIG0YGiEVJLzU6kpe++2P+G3b8m25upNZPfz5U8awnP4jcvrsf1zowxVjfxKzU2Nmro0KFWtwEAAADgD2K325WYmPhN/Y8fsAAAAADgd2GLIAAAAACECAMWAAAAAIQIAxYAAAAAhAgDFgAAAACECAMWAAAAAIQIAxYAAAAAhAgD1gBKS0u1bNkyeb1eFRQU6M2bN4Fj5eXlcrvdWrp0qerq6gL1mzdvaunSpXK73Tp27JgVbeN/rly5Io/Ho+nTp+vevXuBektLi1JTU+Xz+eTz+bR9+/bAsebmZnm9Xrndbu3evVt8k4G1gmUosQYjzeHDhzVv3rzAuqutrQ0cC5YlwgtrKzK5XC55vV75fD5lZ2dLkl6/fq3c3FwtWbJEubm56u7utrhLfFFcXKw5c+YoMzMzUAuWlzFGu3fvltvtltfr1f37961qG/9k8EN1dXXm06dPxhhjysrKTFlZmTHGmEePHhmv12t6enrM8+fPTUZGhvn8+bP5/PmzycjIMM+fPzc9PT3G6/WaR48eWXkJg9rjx4/NkydPzJo1a0xTU1Og7vf7jcfj+e7v5OTkmIaGBtPf32/y8vJMTU3N72oX3xEsQ9Zg5Dl06JA5fvz4N/VgWSK8sLYi16JFi0xHR8dXtdLSUlNeXm6MMaa8vDxwfwPr1dfXm+bm5q/uU4LlVVNTY/Ly8kx/f79paGgwK1assKRnfI0nWAOYO3eu7Ha7JCktLU2tra2SpOrqank8HkVHR8vpdCohIUFNTU1qampSQkKCnE6noqOj5fF4VF1dbeUlDGrTpk3T1KlTf/r89vZ2vXv3TmlpabLZbMrKyiI/iwXLkDX45wiWJcILa+vPUl1draysLElSVlaWrl+/bnFH+GL27NkaNWrUV7VgeX2p22w2paWl6c2bN2pvb//tPeNrDFj/QmVlpebPny9Jamtr0/jx4wPH4uPj1dbWFrSO8NPS0qKsrCytWbNGd+/elfRtruPHjye/MMUajEynTp2S1+tVcXFxYIsLmUUGcopseXl5ys7O1pkzZyRJHR0dcjgckqRx48apo6PDyvYwgGB5cd8SnuxWNxAO1q5dq1evXn1TLyoq0uLFiyVJR48eVVRUlJYvX/6728MAfia//+dwOHTjxg2NGTNGzc3NKigoUFVV1a9uFUH8lwwRnn6U5erVq5Wfny+bzaaDBw9q37592rt3rwVdAoPL6dOnFR8fr46ODuXm5n6zK8Bms8lms1nUHf4t8gp/DFiSKioqfnj8/PnzqqmpUUVFReAFHR8fH9guKP39H4T4aPIX2QAABBtJREFU+HhJClrHrzFQft8THR2t6OhoSVJycrImTZqkp0+ffpNra2sr+f0G/yVD1mB4+tksV65cqfXr10v6cZYIH+QUub7kFBcXJ7fbraamJsXFxam9vV0Oh0Pt7e0aO3asxV3iR4LlxX1LeGKL4ABu3ryp48eP6+jRo4qJiQnUXS6Xqqqq1NvbK7/fr2fPnik1NVUpKSl69uyZ/H6/ent7VVVVJZfLZeEV4Hs6OzvV19cnSYH8nE6nHA6Hhg8frsbGRhljdPHiRWVkZFjcLb6HNRh5/vm+gOvXrysxMVFS8CwRXlhbkenDhw969+5d4Odbt24pMTFRLpdLFy9elCT+1kWAYHl9qRtj1NjYqBEjRgS2EsI6NmP4DOofcbvd6u3t1ejRoyVJM2fOVElJiaS/tw1WVlYqKipKW7du1YIFCyRJtbW12rNnj/r6+pSTk6MNGzZY1v9gd+3aNe3atUudnZ0aOXKkZsyYoRMnTujq1as6dOiQ7Ha7hgwZosLCwsCNwr1791RcXKyPHz9q/vz52rZtG4/iLRQsQ4k1GGm2bNmiBw8eSJImTpyokpKSwI1AsCwRXlhbkcfv96ugoECS1NfXp8zMTG3YsEFdXV0qKirSixcvNGHCBB04cCBwrwNrbdq0SfX19erq6lJcXJwKCwu1ePHi7+ZljFFJSYnq6uoUExOjPXv2KCUlxepLGPQYsAAAAAAgRNgiCAAAAAAhwoAFAAAAACHCgAUAAAAAIcKABQAAAAAhwoAFAAAAACHCgAUAwABcLpc6OzutbgMAEAEYsAAAAAAgROxWNwAAwO/w4cMHFRUVqbW1Vf39/crPz9ewYcO0d+9excbGKj09XX6/X+Xl5erq6tLmzZvV1tamtLQ08ZWRAICfxYAFABgU6urq5HA4dOzYMUnS27dvlZmZqZMnT8rpdGrTpk2Bc48cOaL09HRt3LhRNTU1OnfunFVtAwAiDFsEAQCDQlJSkm7fvq39+/fr7t27amlpkdPplNPplCR5PJ7AuXfu3JHP55MkLVy4UKNGjbKkZwBA5GHAAgAMClOmTNH58+eVlJSkAwcOqLq62uqWAAB/IAYsAMCg0NbWppiYGPl8PuXl5amhoUF+v18tLS2SpMuXLwfOnT17ti5duiRJqq2tVXd3tyU9AwAiD+/BAgAMCg8fPlRZWZmGDBkiu92uHTt26OXLl1q3bp1iY2OVnJwcOLegoECbN2+Wx+PRrFmzNGHCBAs7BwBEEpvho5EAAIPU+/fvNWzYMBljtHPnTk2ePFlr1661ui0AQATjCRYAYNA6e/asLly4oE+fPmnGjBlatWqV1S0BACIcT7AAAAAAIET4kAsAAAAACBEGLAAAAAAIEQYsAAAAAAgRBiwAAAAACBEGLAAAAAAIkb8A6Qo1xNI7OPwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "resids.plot.scatter(x='sgd', y='ols')\n",
        "sns.despine()\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How to build a linear factor model"
      ],
      "metadata": {
        "id": "33csJl65lVru"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Algorithmic trading strategies use linear factor models to quantify the relationship between the return of an asset and the sources of risk that represent the main drivers of these returns. Each factor risk carries a premium, and the total asset return can be expected to correspond to a weighted average of these risk premia."
      ],
      "metadata": {
        "id": "282UMofFlcj7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fh8oWGLclSfq"
      },
      "source": [
        "There are several practical applications of factor models across the portfolio management process from construction and asset selection to risk management and performance evaluation. The importance of factor models continues to grow as common risk factors are now *tradeable*:\n",
        "\n",
        "- A summary of the returns of many assets by a much smaller number of factors reduces the amount of data required to estimate the covariance matrix when optimizing a portfolio\n",
        "- An estimate of the exposure of an asset or a portfolio to these factors allows for the management of the resultant risk, for instance by entering suitable hedges when risk factors are themselves traded\n",
        "- A factor model also permits the assessment of the incremental signal content of new alpha factors\n",
        "- A factor model can also help assess whether a manager's performance relative to a benchmark is indeed due to skill in selecting assets and timing the market, or if instead, the performance can be explained by portfolio tilts towards known return drivers that can today be replicated as low-cost, passively managed funds without incurring active management fees"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmbu5zPLlSfu"
      },
      "source": [
        "## Imports & Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:07.984117Z",
          "start_time": "2021-04-15T19:55:07.982285Z"
        },
        "id": "7f70A9iSlSfv"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install linearmodels"
      ],
      "metadata": {
        "id": "BRlDPop2UHP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear (regression) models for Python. Extends statsmodels with Panel regression, instrumental variable estimators, system estimators and models for estimating asset prices:"
      ],
      "metadata": {
        "id": "5HqWzK35UL52"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:09.073976Z",
          "start_time": "2021-04-15T19:55:08.102748Z"
        },
        "id": "7U4cU1urlSfx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from statsmodels.api import OLS, add_constant\n",
        "import pandas_datareader.data as web\n",
        "\n",
        "from linearmodels.asset_pricing import LinearFactorModel\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:09.077317Z",
          "start_time": "2021-04-15T19:55:09.075045Z"
        },
        "id": "7T2Z-e4dlSfz"
      },
      "outputs": [],
      "source": [
        "sns.set_style('whitegrid')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BF2QT6GxlSf0"
      },
      "source": [
        "## Get Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Omwizr9slSf1"
      },
      "source": [
        "Fama and French make updated risk factor and research portfolio data available through their [website](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html), and you can use the `pandas_datareader` package to obtain the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLZy9TbWlSf3"
      },
      "source": [
        "### Risk Factors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4wFjbXPlSf3"
      },
      "source": [
        "In particular, we will be using the five Fama—French factors that result from sorting stocks first into three size groups and then into two for each of the remaining three firm-specific factors.\n",
        "\n",
        "Hence, the factors involve three sets of value-weighted portfolios formed as 3 x 2 sorts on size and book-to-market, size and operating profitability, and size and investment. The risk factor values computed as the average returns of the portfolios (PF) as outlined in the following table:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JPdPPqplSf4"
      },
      "source": [
        "| Label | Name                          | Description                                                                                                                                                                               |\n",
        "|-------|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
        "| SMB   | Small Minus Big               | Average return on the nine small stock portfolios minus the average return on the nine big stock portfolios                                                                               |\n",
        "| HML   | High Minus Low                | Average return on the two value portfolios minus the average return on the two growth portfolios                                                                                          |\n",
        "| RMW   | Robust minus Weak             | Average return on the two robust operating profitability portfolios minus the average return on the two weak operating profitability portfolios                                           |\n",
        "| CMA   | Conservative Minus Aggressive | Average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios                                                         |\n",
        "| Rm-Rf | Excess return on the market   | Value-weight return of all firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ at the beginning of month t with 'good' data for t minus the one-month Treasury bill rate |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qB-fIwiYlSf5"
      },
      "source": [
        "The Fama-French 5 factors are based on the 6 value-weight portfolios formed on size and book-to-market, the 6 value-weight portfolios formed on size and operating profitability, and the 6 value-weight portfolios formed on size and investment. We will look more into these factors in future lectures, so don't worry too much if you don't understand it yet."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8KjtsHFblSf6"
      },
      "source": [
        "We will use returns at a monthly frequency that we obtain for the period 2010 – 2017 as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:13.367412Z",
          "start_time": "2021-04-15T19:55:13.065455Z"
        },
        "id": "6mDC6A-VlSf6",
        "outputId": "17e5739d-b896-44ea-fe04-126f3528b873",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
            "Freq: M\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Mkt-RF  96 non-null     float64\n",
            " 1   SMB     96 non-null     float64\n",
            " 2   HML     96 non-null     float64\n",
            " 3   RMW     96 non-null     float64\n",
            " 4   CMA     96 non-null     float64\n",
            " 5   RF      96 non-null     float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 5.2 KB\n"
          ]
        }
      ],
      "source": [
        "ff_factor = 'F-F_Research_Data_5_Factors_2x3'\n",
        "ff_factor_data = web.DataReader(ff_factor, 'famafrench', start='2010-1-1', end='2017-12-31')[0]\n",
        "ff_factor_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:13.386698Z",
          "start_time": "2021-04-15T19:55:13.368623Z"
        },
        "id": "mIR2fuM3lSf7",
        "outputId": "d0182fcc-11c3-4ba8-d8ac-62bf9cdcead0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Mkt-RF   SMB   HML   RMW   CMA    RF\n",
              "count   96.00 96.00 96.00 96.00 96.00 96.00\n",
              "mean     1.16  0.06 -0.05  0.13  0.05  0.01\n",
              "std      3.58  2.30  2.20  1.58  1.41  0.02\n",
              "min     -7.89 -4.58 -4.70 -3.88 -3.24  0.00\n",
              "25%     -0.92 -1.67 -1.67 -1.08 -0.95  0.00\n",
              "50%      1.23  0.20 -0.28  0.21  0.01  0.00\n",
              "75%      3.20  1.58  1.21  1.23  0.93  0.01\n",
              "max     11.35  7.04  8.19  3.48  3.69  0.09"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Mkt-RF</th>\n",
              "      <th>SMB</th>\n",
              "      <th>HML</th>\n",
              "      <th>RMW</th>\n",
              "      <th>CMA</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>1.58</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.02</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-7.89</td>\n",
              "      <td>-4.58</td>\n",
              "      <td>-4.70</td>\n",
              "      <td>-3.88</td>\n",
              "      <td>-3.24</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.92</td>\n",
              "      <td>-1.67</td>\n",
              "      <td>-1.67</td>\n",
              "      <td>-1.08</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.23</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.20</td>\n",
              "      <td>1.58</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.01</td>\n",
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              "      <th>max</th>\n",
              "      <td>11.35</td>\n",
              "      <td>7.04</td>\n",
              "      <td>8.19</td>\n",
              "      <td>3.48</td>\n",
              "      <td>3.69</td>\n",
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          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "ff_factor_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CR8HcESklSf8"
      },
      "source": [
        "### Portfolios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjcVuZaMlSf8"
      },
      "source": [
        "Fama and French also make available numerous portfolios that we can illustrate the estimation of the factor exposures, as well as the value of the risk premia available in the market for a given time period. We will use a panel of the 17 industry portfolios at a monthly frequency.\n",
        "\n",
        "We will subtract the risk-free rate from the returns because the factor model works with excess returns:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:14.483710Z",
          "start_time": "2021-04-15T19:55:13.388307Z"
        },
        "id": "f9nNmnRzlSf8",
        "outputId": "1aacad69-fc0a-4843-dbca-9f6625d0d4fb",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
            "Freq: M\n",
            "Data columns (total 17 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Food    96 non-null     float64\n",
            " 1   Mines   96 non-null     float64\n",
            " 2   Oil     96 non-null     float64\n",
            " 3   Clths   96 non-null     float64\n",
            " 4   Durbl   96 non-null     float64\n",
            " 5   Chems   96 non-null     float64\n",
            " 6   Cnsum   96 non-null     float64\n",
            " 7   Cnstr   96 non-null     float64\n",
            " 8   Steel   96 non-null     float64\n",
            " 9   FabPr   96 non-null     float64\n",
            " 10  Machn   96 non-null     float64\n",
            " 11  Cars    96 non-null     float64\n",
            " 12  Trans   96 non-null     float64\n",
            " 13  Utils   96 non-null     float64\n",
            " 14  Rtail   96 non-null     float64\n",
            " 15  Finan   96 non-null     float64\n",
            " 16  Other   96 non-null     float64\n",
            "dtypes: float64(17)\n",
            "memory usage: 13.5 KB\n"
          ]
        }
      ],
      "source": [
        "ff_portfolio = '17_Industry_Portfolios'\n",
        "ff_portfolio_data = web.DataReader(ff_portfolio, 'famafrench', start='2010-1-1', end='2017-12-31')[0]\n",
        "ff_portfolio_data = ff_portfolio_data.sub(ff_factor_data.RF, axis=0)\n",
        "ff_portfolio_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "end_time": "2021-04-15T19:55:14.519355Z",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Food   Mines  Oil    Clths  Durbl  Chems  Cnsum  Cnstr  Steel  FabPr  \\\n",
              "count  96.00  96.00  96.00  96.00  96.00  96.00  96.00  96.00  96.00  96.00   \n",
              "mean    1.05   0.20   0.60   1.40   1.15   1.30   1.19   1.74   0.56   1.35   \n",
              "std     2.80   7.90   5.48   5.02   5.16   5.59   3.14   5.24   7.39   4.69   \n",
              "min    -5.17 -24.38 -11.68 -10.00 -13.16 -17.39  -7.15 -14.16 -20.49 -11.96   \n",
              "25%    -0.79  -5.84  -3.12  -1.87  -2.10  -1.44  -0.85  -2.41  -4.39  -1.45   \n",
              "50%     0.92  -0.44   0.98   1.16   1.23   1.44   1.46   2.17   0.66   1.48   \n",
              "75%     3.19   5.73   4.15   3.86   4.16   4.44   3.30   5.56   4.21   3.84   \n",
              "max     6.67  21.94  15.94  17.19  16.61  18.37   8.26  15.51  21.35  17.66   \n",
              "\n",
              "       Machn  Cars   Trans  Utils  Rtail  Finan  Other  \n",
              "count  96.00  96.00  96.00  96.00  96.00  96.00  96.00  \n",
              "mean    1.22   1.28   1.46   0.90   1.23   1.25   1.29  \n",
              "std     4.80   5.72   4.14   3.23   3.51   4.84   3.70  \n",
              "min    -9.07 -11.65  -8.56  -6.99  -9.18 -11.14  -7.89  \n",
              "25%    -2.06  -1.25  -0.81  -0.74  -0.95  -1.46  -1.09  \n",
              "50%     1.52   0.64   1.48   1.24   0.86   1.91   1.66  \n",
              "75%     4.58   4.80   4.24   2.96   3.37   4.10   3.48  \n",
              "max    14.75  20.86  12.98   7.84  12.44  13.41  10.77  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Food</th>\n",
              "      <th>Mines</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Clths</th>\n",
              "      <th>Durbl</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "      <td>96.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.05</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.40</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.74</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.35</td>\n",
              "      <td>1.22</td>\n",
              "      <td>1.28</td>\n",
              "      <td>1.46</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.80</td>\n",
              "      <td>7.90</td>\n",
              "      <td>5.48</td>\n",
              "      <td>5.02</td>\n",
              "      <td>5.16</td>\n",
              "      <td>5.59</td>\n",
              "      <td>3.14</td>\n",
              "      <td>5.24</td>\n",
              "      <td>7.39</td>\n",
              "      <td>4.69</td>\n",
              "      <td>4.80</td>\n",
              "      <td>5.72</td>\n",
              "      <td>4.14</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.51</td>\n",
              "      <td>4.84</td>\n",
              "      <td>3.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-5.17</td>\n",
              "      <td>-24.38</td>\n",
              "      <td>-11.68</td>\n",
              "      <td>-10.00</td>\n",
              "      <td>-13.16</td>\n",
              "      <td>-17.39</td>\n",
              "      <td>-7.15</td>\n",
              "      <td>-14.16</td>\n",
              "      <td>-20.49</td>\n",
              "      <td>-11.96</td>\n",
              "      <td>-9.07</td>\n",
              "      <td>-11.65</td>\n",
              "      <td>-8.56</td>\n",
              "      <td>-6.99</td>\n",
              "      <td>-9.18</td>\n",
              "      <td>-11.14</td>\n",
              "      <td>-7.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.79</td>\n",
              "      <td>-5.84</td>\n",
              "      <td>-3.12</td>\n",
              "      <td>-1.87</td>\n",
              "      <td>-2.10</td>\n",
              "      <td>-1.44</td>\n",
              "      <td>-0.85</td>\n",
              "      <td>-2.41</td>\n",
              "      <td>-4.39</td>\n",
              "      <td>-1.45</td>\n",
              "      <td>-2.06</td>\n",
              "      <td>-1.25</td>\n",
              "      <td>-0.81</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>-1.46</td>\n",
              "      <td>-1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.92</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>0.98</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.44</td>\n",
              "      <td>1.46</td>\n",
              "      <td>2.17</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.52</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.91</td>\n",
              "      <td>1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.19</td>\n",
              "      <td>5.73</td>\n",
              "      <td>4.15</td>\n",
              "      <td>3.86</td>\n",
              "      <td>4.16</td>\n",
              "      <td>4.44</td>\n",
              "      <td>3.30</td>\n",
              "      <td>5.56</td>\n",
              "      <td>4.21</td>\n",
              "      <td>3.84</td>\n",
              "      <td>4.58</td>\n",
              "      <td>4.80</td>\n",
              "      <td>4.24</td>\n",
              "      <td>2.96</td>\n",
              "      <td>3.37</td>\n",
              "      <td>4.10</td>\n",
              "      <td>3.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.67</td>\n",
              "      <td>21.94</td>\n",
              "      <td>15.94</td>\n",
              "      <td>17.19</td>\n",
              "      <td>16.61</td>\n",
              "      <td>18.37</td>\n",
              "      <td>8.26</td>\n",
              "      <td>15.51</td>\n",
              "      <td>21.35</td>\n",
              "      <td>17.66</td>\n",
              "      <td>14.75</td>\n",
              "      <td>20.86</td>\n",
              "      <td>12.98</td>\n",
              "      <td>7.84</td>\n",
              "      <td>12.44</td>\n",
              "      <td>13.41</td>\n",
              "      <td>10.77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "ff_portfolio_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uWxnMHslSf8"
      },
      "source": [
        "### Equity Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='orange'>Question 3: You are going to work with large files in financial machine learning applications, as such you should show that you can extract compressed files (4 points) </font>\n",
        "\n",
        "link: `https://storage.googleapis.com/public-quant/course//content/quandl-wiki-prices-us-equites.zip` (**changed**)\n",
        "\n",
        "*   Once more you can use any method and library that you please, i will simply provide you with the compressed link.\n",
        "*   The final csv file that you extract has to be renamed as ``wiki_prices.csv`` then you can succesfully run the code below.\n",
        "*   Note we don't know all the solutions, so stackoverflow and google are your friends, use them!\n",
        "*   Hint, you might want to learn how to `!wget` to download the file directly to the notebook, and how to use `!mv` to rename the file.\n",
        "*   We use the `!` command when we want to speak directly to the terminal.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "i3q1GJcPZf2G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Provide your solution here (first perform the wget command)\n"
      ],
      "metadata": {
        "id": "nVhTxPq_a3Ko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Provide your solution here (then unzip the file)\n"
      ],
      "metadata": {
        "id": "V2Zqv_Mk30qa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mv WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv wiki_prices.csv"
      ],
      "metadata": {
        "id": "dVqkbKAG3s3y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "quandl_archive = (pd.read_csv('wiki_prices.csv',\n",
        "                 parse_dates=['date'],\n",
        "                 index_col=['date', 'ticker'],\n",
        "                 infer_datetime_format=True).sort_index())\n",
        "prices = quandl_archive.adj_close.unstack().loc['2010':'2017']"
      ],
      "metadata": {
        "id": "ZAcSr-NMWjr_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "equities = pd.read_csv('https://raw.githubusercontent.com/stefan-jansen/machine-learning-for-trading/f881ea01ef8ae5a308ab75de833ba24bee27435e/data/us_equities_meta_data.csv')\n",
        "equities = equities.set_index('ticker').drop_duplicates()"
      ],
      "metadata": {
        "id": "OUKeAfu2bW5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.245270Z",
          "start_time": "2021-04-15T19:55:20.201602Z"
        },
        "id": "sjoAJQZklSf9"
      },
      "outputs": [],
      "source": [
        "sectors = equities.filter(prices.columns, axis=0).sector.to_dict()\n",
        "prices = prices.filter(sectors.keys()).dropna(how='all', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.330305Z",
          "start_time": "2021-04-15T19:55:20.246605Z"
        },
        "id": "XThhcGbrlSf-"
      },
      "outputs": [],
      "source": [
        "returns = prices.resample('M').last().pct_change().mul(100).to_period('M')\n",
        "returns = returns.dropna(how='all').dropna(axis=1)\n",
        "returns.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9axHYT9lSf-"
      },
      "source": [
        "### Align data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.336370Z",
          "start_time": "2021-04-15T19:55:20.332866Z"
        },
        "id": "faUtyS0ylSf_"
      },
      "outputs": [],
      "source": [
        "ff_factor_data = ff_factor_data.loc[returns.index]\n",
        "ff_portfolio_data = ff_portfolio_data.loc[returns.index]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.370190Z",
          "start_time": "2021-04-15T19:55:20.338297Z"
        },
        "id": "PDwBlegklSf_",
        "outputId": "6c4ea601-aad7-4f46-cf54-be76943ca8d6"
      },
      "outputs": [
        {
          "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>Mkt-RF</th>\n",
              "      <th>SMB</th>\n",
              "      <th>HML</th>\n",
              "      <th>RMW</th>\n",
              "      <th>CMA</th>\n",
              "      <th>RF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.206316</td>\n",
              "      <td>0.051053</td>\n",
              "      <td>-0.055789</td>\n",
              "      <td>0.139789</td>\n",
              "      <td>0.048842</td>\n",
              "      <td>0.012737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.568555</td>\n",
              "      <td>2.302701</td>\n",
              "      <td>2.202892</td>\n",
              "      <td>1.593750</td>\n",
              "      <td>1.416798</td>\n",
              "      <td>0.022665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-7.890000</td>\n",
              "      <td>-4.510000</td>\n",
              "      <td>-4.520000</td>\n",
              "      <td>-3.930000</td>\n",
              "      <td>-3.350000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.565000</td>\n",
              "      <td>-1.670000</td>\n",
              "      <td>-1.655000</td>\n",
              "      <td>-0.965000</td>\n",
              "      <td>-0.990000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.290000</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>-0.360000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>-0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.265000</td>\n",
              "      <td>1.555000</td>\n",
              "      <td>1.165000</td>\n",
              "      <td>1.140000</td>\n",
              "      <td>0.935000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>11.350000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>8.220000</td>\n",
              "      <td>3.530000</td>\n",
              "      <td>3.780000</td>\n",
              "      <td>0.090000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Mkt-RF        SMB        HML        RMW        CMA         RF\n",
              "count  95.000000  95.000000  95.000000  95.000000  95.000000  95.000000\n",
              "mean    1.206316   0.051053  -0.055789   0.139789   0.048842   0.012737\n",
              "std     3.568555   2.302701   2.202892   1.593750   1.416798   0.022665\n",
              "min    -7.890000  -4.510000  -4.520000  -3.930000  -3.350000   0.000000\n",
              "25%    -0.565000  -1.670000  -1.655000  -0.965000  -0.990000   0.000000\n",
              "50%     1.290000   0.150000  -0.360000   0.140000  -0.020000   0.000000\n",
              "75%     3.265000   1.555000   1.165000   1.140000   0.935000   0.010000\n",
              "max    11.350000   6.800000   8.220000   3.530000   3.780000   0.090000"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ff_factor_data.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pj605EQilSgA"
      },
      "source": [
        "### Compute excess Returns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.493870Z",
          "start_time": "2021-04-15T19:55:20.371460Z"
        },
        "id": "RDtMV3bFlSgA",
        "outputId": "cfbdce7e-15b8-43ee-9d24-7074b9ec1890"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
            "Freq: M\n",
            "Columns: 1986 entries, A to ZUMZ\n",
            "dtypes: float64(1986)\n",
            "memory usage: 1.4 MB\n"
          ]
        }
      ],
      "source": [
        "excess_returns = returns.sub(ff_factor_data.RF, axis=0)\n",
        "excess_returns.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.510527Z",
          "start_time": "2021-04-15T19:55:20.495111Z"
        },
        "id": "jcQ8Vo00lSgA"
      },
      "outputs": [],
      "source": [
        "excess_returns = excess_returns.clip(lower=np.percentile(excess_returns, 1),\n",
        "                                     upper=np.percentile(excess_returns, 99))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AZvaEqBlSgB"
      },
      "source": [
        "## Fama-Macbeth Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yu8d1yClSgB"
      },
      "source": [
        "Given data on risk factors and portfolio returns, it is useful to estimate the portfolio's exposure, that is, how much the risk factors drive portfolio returns, as well as how much the exposure to a given factor is worth, that is, the what market's risk factor premium is. The risk premium then permits to estimate the return for any portfolio provided the factor exposure is known or can be assumed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.520870Z",
          "start_time": "2021-04-15T19:55:20.511704Z"
        },
        "id": "h3ih4ZY6lSgB",
        "outputId": "bcce3da2-c252-4d2e-df55-c9de4dd737b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
            "Freq: M\n",
            "Data columns (total 17 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Food    95 non-null     float64\n",
            " 1   Mines   95 non-null     float64\n",
            " 2   Oil     95 non-null     float64\n",
            " 3   Clths   95 non-null     float64\n",
            " 4   Durbl   95 non-null     float64\n",
            " 5   Chems   95 non-null     float64\n",
            " 6   Cnsum   95 non-null     float64\n",
            " 7   Cnstr   95 non-null     float64\n",
            " 8   Steel   95 non-null     float64\n",
            " 9   FabPr   95 non-null     float64\n",
            " 10  Machn   95 non-null     float64\n",
            " 11  Cars    95 non-null     float64\n",
            " 12  Trans   95 non-null     float64\n",
            " 13  Utils   95 non-null     float64\n",
            " 14  Rtail   95 non-null     float64\n",
            " 15  Finan   95 non-null     float64\n",
            " 16  Other   95 non-null     float64\n",
            "dtypes: float64(17)\n",
            "memory usage: 13.4 KB\n"
          ]
        }
      ],
      "source": [
        "ff_portfolio_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.538124Z",
          "start_time": "2021-04-15T19:55:20.521998Z"
        },
        "id": "tYPZ7yjklSgC",
        "outputId": "f96bb893-9013-4928-ba18-0cbd77daca5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
            "Freq: M\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Mkt-RF  95 non-null     float64\n",
            " 1   SMB     95 non-null     float64\n",
            " 2   HML     95 non-null     float64\n",
            " 3   RMW     95 non-null     float64\n",
            " 4   CMA     95 non-null     float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 4.5 KB\n"
          ]
        }
      ],
      "source": [
        "ff_factor_data = ff_factor_data.drop('RF', axis=1)\n",
        "ff_factor_data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pi26oDzflSgC"
      },
      "source": [
        "To address the inference problem caused by the correlation of the residuals, Fama and MacBeth proposed a two-step methodology for a cross-sectional regression of returns on factors. The two-stage Fama—Macbeth regression is designed to estimate the premium rewarded for the exposure to a particular risk factor by the market. The two stages consist of:\n",
        "\n",
        "- First stage: N time-series regression, one for each asset or portfolio, of its excess returns on the factors to estimate the factor loadings.\n",
        "\n",
        "- Second stage: T cross-sectional regression, one for each time period, to estimate the risk premium.\n",
        "\n",
        "See corresponding section in Chapter 7 of [Machine Learning for Trading](https://www.amazon.com/Hands-Machine-Learning-Algorithmic-Trading-ebook/dp/B07JLFH7C5/ref=sr_1_2?ie=UTF8&qid=1548455634&sr=8-2&keywords=machine+learning+algorithmic+trading) for details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qpj7qHmlSgC"
      },
      "source": [
        "Now we can compute the factor risk premia as the time average and get t-statistic to assess their individual significance, using the assumption that the risk premia estimates are independent over time.\n",
        "\n",
        "If we had a very large and representative data sample on traded risk factors we could use the sample mean as a risk premium estimate. However, we typically do not have a sufficiently long history to and the margin of error around the sample mean could be quite large.\n",
        "\n",
        "The Fama—Macbeth methodology leverages the covariance of the factors with other assets to determine the factor premia. The second moment of asset returns is easier to estimate than the first moment, and obtaining more granular data improves estimation considerably, which is not true of mean estimation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9H04aLWlSgD"
      },
      "source": [
        "### Step 1: Factor Exposures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-34HrH46lSgD"
      },
      "source": [
        "We can implement the first stage to obtain the 17 factor loading estimates as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.643488Z",
          "start_time": "2021-04-15T19:55:20.539304Z"
        },
        "id": "szwi7bK3lSgD"
      },
      "outputs": [],
      "source": [
        "betas = []\n",
        "for industry in ff_portfolio_data:\n",
        "    step1 = OLS(endog=ff_portfolio_data.loc[ff_factor_data.index, industry],\n",
        "                exog=add_constant(ff_factor_data)).fit()\n",
        "    betas.append(step1.params.drop('const'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.658189Z",
          "start_time": "2021-04-15T19:55:20.646568Z"
        },
        "id": "yn5ZRFMLlSgE",
        "outputId": "d7657161-ef00-4b1c-e982-0f9ee4d07990"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 17 entries, Food  to Other\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Mkt-RF  17 non-null     float64\n",
            " 1   SMB     17 non-null     float64\n",
            " 2   HML     17 non-null     float64\n",
            " 3   RMW     17 non-null     float64\n",
            " 4   CMA     17 non-null     float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 1.3+ KB\n"
          ]
        }
      ],
      "source": [
        "betas = pd.DataFrame(betas,\n",
        "                     columns=ff_factor_data.columns,\n",
        "                     index=ff_portfolio_data.columns)\n",
        "betas.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RK6jM_h4lSgE"
      },
      "source": [
        "### Step 2: Risk Premia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBjKDzbAlSgE"
      },
      "source": [
        "For the second stage, we run 96 regressions of the period returns for the cross section of portfolios on the factor loadings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.759883Z",
          "start_time": "2021-04-15T19:55:20.659165Z"
        },
        "id": "WePW-hlilSgE"
      },
      "outputs": [],
      "source": [
        "lambdas = []\n",
        "for period in ff_portfolio_data.index:\n",
        "    step2 = OLS(endog=ff_portfolio_data.loc[period, betas.index],\n",
        "                exog=betas).fit()\n",
        "    lambdas.append(step2.params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.780931Z",
          "start_time": "2021-04-15T19:55:20.760976Z"
        },
        "id": "k2alKdFjlSgF",
        "outputId": "734b1101-ab21-479c-ae5b-7711438f5eb1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
            "Freq: M\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Mkt-RF  95 non-null     float64\n",
            " 1   SMB     95 non-null     float64\n",
            " 2   HML     95 non-null     float64\n",
            " 3   RMW     95 non-null     float64\n",
            " 4   CMA     95 non-null     float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 9.3 KB\n"
          ]
        }
      ],
      "source": [
        "lambdas = pd.DataFrame(lambdas,\n",
        "                       index=ff_portfolio_data.index,\n",
        "                       columns=betas.columns.tolist())\n",
        "lambdas.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.902217Z",
          "start_time": "2021-04-15T19:55:20.781976Z"
        },
        "id": "6LER-T_slSgF",
        "outputId": "2cb3d253-2f9a-49d7-a84b-995178799d1c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "lambdas.mean().sort_values().plot.barh(figsize=(12, 4))\n",
        "sns.despine()\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:20.908357Z",
          "start_time": "2021-04-15T19:55:20.903193Z"
        },
        "id": "gi6rHcRVlSgF",
        "outputId": "f42eb65a-c25b-4d67-9f3b-0507f57174e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Mkt-RF    0.340179\n",
              "SMB      -0.013216\n",
              "HML      -0.261636\n",
              "RMW      -0.035186\n",
              "CMA      -0.172461\n",
              "dtype: float64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "t = lambdas.mean().div(lambdas.std())\n",
        "t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItccB0n0lSgG"
      },
      "source": [
        "#### Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:21.278124Z",
          "start_time": "2021-04-15T19:55:20.909336Z"
        },
        "scrolled": false,
        "id": "rRKkB-WElSgG",
        "outputId": "68c0505c-e766-49b8-e4ae-43ffc227a175"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "window = 24  # months\n",
        "ax1 = plt.subplot2grid((1, 3), (0, 0))\n",
        "ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=2)\n",
        "lambdas.mean().sort_values().plot.barh(ax=ax1)\n",
        "lambdas.rolling(window).mean().dropna().plot(lw=1,\n",
        "                                             figsize=(14, 5),\n",
        "                                             sharey=True,\n",
        "                                             ax=ax2)\n",
        "sns.despine()\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:21.839217Z",
          "start_time": "2021-04-15T19:55:21.279210Z"
        },
        "id": "907qoHNwlSgG",
        "outputId": "889a2c57-6548-4f9a-c019-123c7b4d2ce3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 5 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "window = 24  # months\n",
        "lambdas.rolling(window).mean().dropna().plot(lw=2,\n",
        "                                             figsize=(14, 7),\n",
        "                                             subplots=True,\n",
        "                                             sharey=True)\n",
        "sns.despine()\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_zUu3UdlSgH"
      },
      "source": [
        "## Fama-Macbeth with the LinearModels library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0C7egfLlSgH"
      },
      "source": [
        "The linear_models library extends statsmodels with various models for panel data and also implements the two-stage Fama—MacBeth procedure:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:21.859424Z",
          "start_time": "2021-04-15T19:55:21.840019Z"
        },
        "id": "shAcZZG1lSgH",
        "outputId": "6e3825c3-cf51-435f-80e0-c9e7048e879a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                      LinearFactorModel Estimation Summary                      \n",
            "================================================================================\n",
            "No. Test Portfolios:                 17   R-squared:                      0.6885\n",
            "No. Factors:                          5   J-statistic:                    17.038\n",
            "No. Observations:                    95   P-value                         0.1482\n",
            "Date:                  Thu, Apr 15 2021   Distribution:                 chi2(12)\n",
            "Time:                          14:55:21                                         \n",
            "Cov. Estimator:                  robust                                         \n",
            "                                                                                \n",
            "                            Risk Premia Estimates                             \n",
            "==============================================================================\n",
            "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
            "------------------------------------------------------------------------------\n",
            "Mkt-RF         1.2208     0.4076     2.9951     0.0027      0.4219      2.0197\n",
            "SMB           -0.0523     0.7979    -0.0656     0.9477     -1.6161      1.5115\n",
            "HML           -1.0316     0.6332    -1.6292     0.1033     -2.2726      0.2094\n",
            "RMW           -0.1044     0.7738    -0.1350     0.8926     -1.6210      1.4121\n",
            "CMA           -0.6252     0.5222    -1.1973     0.2312     -1.6488      0.3983\n",
            "==============================================================================\n",
            "\n",
            "Covariance estimator:\n",
            "HeteroskedasticCovariance\n",
            "See full_summary for complete results\n"
          ]
        }
      ],
      "source": [
        "mod = LinearFactorModel(portfolios=ff_portfolio_data,\n",
        "                        factors=ff_factor_data)\n",
        "res = mod.fit()\n",
        "print(res)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:21.921756Z",
          "start_time": "2021-04-15T19:55:21.861590Z"
        },
        "scrolled": false,
        "id": "x5gOwwpelSgI",
        "outputId": "c4b4f2b0-ef3c-494e-b78c-49dd56ae0495"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                      LinearFactorModel Estimation Summary                      \n",
            "================================================================================\n",
            "No. Test Portfolios:                 17   R-squared:                      0.6885\n",
            "No. Factors:                          5   J-statistic:                    17.038\n",
            "No. Observations:                    95   P-value                         0.1482\n",
            "Date:                  Thu, Apr 15 2021   Distribution:                 chi2(12)\n",
            "Time:                          14:55:21                                         \n",
            "Cov. Estimator:                  robust                                         \n",
            "                                                                                \n",
            "                            Risk Premia Estimates                             \n",
            "==============================================================================\n",
            "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
            "------------------------------------------------------------------------------\n",
            "Mkt-RF         1.2208     0.4076     2.9951     0.0027      0.4219      2.0197\n",
            "SMB           -0.0523     0.7979    -0.0656     0.9477     -1.6161      1.5115\n",
            "HML           -1.0316     0.6332    -1.6292     0.1033     -2.2726      0.2094\n",
            "RMW           -0.1044     0.7738    -0.1350     0.8926     -1.6210      1.4121\n",
            "CMA           -0.6252     0.5222    -1.1973     0.2312     -1.6488      0.3983\n",
            "\n",
            "\n",
            "                              Food  Coefficients                              \n",
            "==============================================================================\n",
            "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
            "------------------------------------------------------------------------------\n",
            "alpha          0.1874     0.2393     0.7831     0.4336     -0.2816      0.6563\n",
            "Mkt-RF         0.6866     0.0465     14.773     0.0000      0.5955      0.7777\n",
            "SMB           -0.3100     0.1126    -2.7532     1.9941     -0.5307     -0.0893\n",
            "HML           -0.3493     0.1420    -2.4595     1.9861     -0.6277     -0.0710\n",
            "RMW            0.3075     0.1243     2.4747     0.0133      0.0640      0.5510\n",
            "CMA            0.4666     0.1636     2.8517     0.0043      0.1459      0.7873\n",
            "\n",
            "\n",
            "                              Mines Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.6490     0.5444    -1.1922     1.7668     -1.7159      0.4180\n",
            "Mkt-RF         1.2987     0.1950     6.6584     0.0000      0.9164      1.6810\n",
            "SMB            0.1805     0.3380     0.5341     0.5933     -0.4819      0.8429\n",
            "HML            0.1891     0.3305     0.5721     0.5673     -0.4587      0.8368\n",
            "RMW            0.1449     0.4295     0.3375     0.7358     -0.6968      0.9867\n",
            "CMA            0.6112     0.5507     1.1098     0.2671     -0.4682      1.6905\n",
            "\n",
            "\n",
            "                              Oil   Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.2044     0.3452     0.5922     0.5537     -0.4722      0.8811\n",
            "Mkt-RF         1.0553     0.1027     10.273     0.0000      0.8540      1.2567\n",
            "SMB            0.1554     0.1941     0.8005     0.4234     -0.2251      0.5359\n",
            "HML            0.6685     0.2048     3.2642     0.0011      0.2671      1.0698\n",
            "RMW           -0.0247     0.2326    -0.1064     1.0847     -0.4806      0.4311\n",
            "CMA            0.3117     0.2831     1.1010     0.2709     -0.2432      0.8666\n",
            "\n",
            "\n",
            "                              Clths Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.1726     0.3388     0.5093     0.6105     -0.4915      0.8367\n",
            "Mkt-RF         0.9685     0.1214     7.9776     0.0000      0.7306      1.2065\n",
            "SMB            0.3430     0.1966     1.7450     0.0810     -0.0422      0.7282\n",
            "HML           -0.1882     0.2098    -0.8969     1.6302     -0.5993      0.2230\n",
            "RMW            0.5649     0.2720     2.0767     0.0378      0.0318      1.0980\n",
            "CMA            0.0381     0.3163     0.1205     0.9040     -0.5818      0.6581\n",
            "\n",
            "\n",
            "                              Durbl Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.1564     0.3204    -0.4883     1.3747     -0.7843      0.4715\n",
            "Mkt-RF         1.1740     0.0834     14.072     0.0000      1.0105      1.3375\n",
            "SMB            0.5378     0.1194     4.5035     0.0000      0.3037      0.7719\n",
            "HML            0.0706     0.1480     0.4771     0.6333     -0.2195      0.3607\n",
            "RMW            0.5117     0.1940     2.6380     0.0083      0.1315      0.8919\n",
            "CMA           -0.1310     0.2655    -0.4936     1.3784     -0.6514      0.3893\n",
            "\n",
            "\n",
            "                              Chems Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.2048     0.3111    -0.6584     1.4897     -0.8145      0.4049\n",
            "Mkt-RF         1.3510     0.1063     12.704     0.0000      1.1426      1.5594\n",
            "SMB            0.1660     0.1489     1.1154     0.2647     -0.1257      0.4578\n",
            "HML            0.1952     0.1480     1.3189     0.1872     -0.0949      0.4852\n",
            "RMW            0.1410     0.1912     0.7374     0.4609     -0.2338      0.5158\n",
            "CMA           -0.2301     0.2633    -0.8738     1.6178     -0.7462      0.2860\n",
            "\n",
            "\n",
            "                              Cnsum Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.0380     0.3566    -0.1065     1.0848     -0.7368      0.6609\n",
            "Mkt-RF         0.7625     0.0591     12.897     0.0000      0.6466      0.8784\n",
            "SMB           -0.3327     0.1006    -3.3088     1.9991     -0.5298     -0.1356\n",
            "HML           -0.5773     0.1259    -4.5845     2.0000     -0.8241     -0.3305\n",
            "RMW           -0.0606     0.1316    -0.4603     1.3547     -0.3186      0.1974\n",
            "CMA            0.5748     0.2271     2.5306     0.0114      0.1296      1.0199\n",
            "\n",
            "\n",
            "                              Cnstr Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.6213     0.3917     1.5862     0.1127     -0.1464      1.3890\n",
            "Mkt-RF         1.1161     0.0828     13.478     0.0000      0.9538      1.2784\n",
            "SMB            0.4463     0.1337     3.3389     0.0008      0.1843      0.7083\n",
            "HML            0.0920     0.1892     0.4861     0.6269     -0.2789      0.4629\n",
            "RMW           -0.0107     0.2232    -0.0482     1.0384     -0.4482      0.4267\n",
            "CMA            0.1409     0.2425     0.5811     0.5612     -0.3344      0.6162\n",
            "\n",
            "\n",
            "                              Steel Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.3503     0.4030    -0.8692     1.6153     -1.1403      0.4396\n",
            "Mkt-RF         1.4647     0.1381     10.604     0.0000      1.1940      1.7355\n",
            "SMB            0.4104     0.2548     1.6103     0.1073     -0.0891      0.9098\n",
            "HML            0.4000     0.2653     1.5076     0.1317     -0.1200      0.9200\n",
            "RMW            0.1355     0.3342     0.4054     0.6852     -0.5196      0.7906\n",
            "CMA            0.4840     0.4192     1.1547     0.2482     -0.3376      1.3056\n",
            "\n",
            "\n",
            "                              FabPr Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.2168     0.2831     0.7659     0.4437     -0.3381      0.7718\n",
            "Mkt-RF         1.0695     0.0734     14.573     0.0000      0.9257      1.2133\n",
            "SMB            0.4602     0.0979     4.7024     0.0000      0.2684      0.6520\n",
            "HML           -0.0294     0.1111    -0.2646     1.2087     -0.2471      0.1883\n",
            "RMW            0.1531     0.1456     1.0510     0.2933     -0.1324      0.4385\n",
            "CMA            0.1865     0.1855     1.0055     0.3147     -0.1771      0.5502\n",
            "\n",
            "\n",
            "                              Machn Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.3139     0.2688    -1.1677     1.7571     -0.8409      0.2130\n",
            "Mkt-RF         1.1883     0.0582     20.424     0.0000      1.0742      1.3023\n",
            "SMB            0.1817     0.1074     1.6922     0.0906     -0.0287      0.3921\n",
            "HML            0.0384     0.1060     0.3621     0.7173     -0.1694      0.2462\n",
            "RMW            0.0540     0.1581     0.3416     0.7327     -0.2559      0.3639\n",
            "CMA           -0.3765     0.1786    -2.1079     1.9650     -0.7266     -0.0264\n",
            "\n",
            "\n",
            "                              Cars  Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.0952     0.3906    -0.2436     1.1925     -0.8607      0.6704\n",
            "Mkt-RF         1.1895     0.0996     11.949     0.0000      0.9944      1.3846\n",
            "SMB            0.5941     0.1290     4.6069     0.0000      0.3414      0.8469\n",
            "HML            0.0213     0.1757     0.1214     0.9034     -0.3231      0.3658\n",
            "RMW            0.0223     0.2201     0.1012     0.9194     -0.4091      0.4537\n",
            "CMA            0.0123     0.2993     0.0412     0.9671     -0.5743      0.5990\n",
            "\n",
            "\n",
            "                              Trans Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.4814     0.3269     1.4725     0.1409     -0.1594      1.1221\n",
            "Mkt-RF         1.0248     0.0508     20.161     0.0000      0.9251      1.1244\n",
            "SMB            0.2537     0.1030     2.4641     0.0137      0.0519      0.4556\n",
            "HML            0.0117     0.1222     0.0961     0.9235     -0.2278      0.2512\n",
            "RMW            0.3778     0.1606     2.3525     0.0186      0.0630      0.6926\n",
            "CMA            0.2634     0.2018     1.3056     0.1917     -0.1320      0.6589\n",
            "\n",
            "\n",
            "                              Utils Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.3695     0.3118     1.1849     0.2361     -0.2417      0.9807\n",
            "Mkt-RF         0.5022     0.0911     5.5126     0.0000      0.3237      0.6808\n",
            "SMB           -0.2454     0.1567    -1.5664     1.8827     -0.5525      0.0617\n",
            "HML           -0.2932     0.1772    -1.6549     1.9021     -0.6405      0.0540\n",
            "RMW            0.2424     0.1949     1.2435     0.2137     -0.1396      0.6244\n",
            "CMA            0.5207     0.2955     1.7621     0.0781     -0.0585      1.0998\n",
            "\n",
            "\n",
            "                              Rtail Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.0421     0.2681    -0.1570     1.1247     -0.5676      0.4834\n",
            "Mkt-RF         0.9087     0.0689     13.192     0.0000      0.7737      1.0437\n",
            "SMB            0.1316     0.0994     1.3235     0.1857     -0.0633      0.3265\n",
            "HML           -0.3830     0.1296    -2.9560     1.9969     -0.6370     -0.1291\n",
            "RMW            0.6884     0.1610     4.2748     0.0000      0.3728      1.0041\n",
            "CMA            0.1961     0.1741     1.1259     0.2602     -0.1452      0.5373\n",
            "\n",
            "\n",
            "                              Finan Coefficients                              \n",
            "==============================================================================\n",
            "alpha          0.3752     0.3715     1.0097     0.3126     -0.3530      1.1034\n",
            "Mkt-RF         1.0565     0.0426     24.782     0.0000      0.9730      1.1401\n",
            "SMB            0.0756     0.0856     0.8825     0.3775     -0.0923      0.2434\n",
            "HML            0.7333     0.0878     8.3550     0.0000      0.5613      0.9054\n",
            "RMW           -0.4296     0.1061    -4.0490     1.9999     -0.6376     -0.2216\n",
            "CMA           -0.5083     0.1124    -4.5216     2.0000     -0.7286     -0.2879\n",
            "\n",
            "\n",
            "                              Other Coefficients                              \n",
            "==============================================================================\n",
            "alpha         -0.1368     0.2280    -0.5998     1.4513     -0.5837      0.3102\n",
            "Mkt-RF         1.0416     0.0244     42.676     0.0000      0.9937      1.0894\n",
            "SMB           -0.1150     0.0397    -2.8966     1.9962     -0.1927     -0.0372\n",
            "HML           -0.2042     0.0379    -5.3820     2.0000     -0.2786     -0.1299\n",
            "RMW           -0.0685     0.0626    -1.0934     1.7258     -0.1912      0.0543\n",
            "CMA            0.0194     0.0657     0.2945     0.7684     -0.1095      0.1482\n",
            "==============================================================================\n",
            "\n",
            "Covariance estimator:\n",
            "HeteroskedasticCovariance\n",
            "See full_summary for complete results\n"
          ]
        }
      ],
      "source": [
        "print(res.full_summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usGUbTpJlSgI"
      },
      "source": [
        "This provides us with the same result:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:21.926652Z",
          "start_time": "2021-04-15T19:55:21.922840Z"
        },
        "id": "73_X5CVilSgI",
        "outputId": "c27328e7-6265-4469-95ac-86ac2bdb2032"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Mkt-RF    1.220797\n",
              "SMB      -0.052301\n",
              "HML      -1.031603\n",
              "RMW      -0.104427\n",
              "CMA      -0.625245\n",
              "dtype: float64"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lambdas.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "WBxROqbomtMf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RY7YG4PFmsh-"
      },
      "source": [
        "# Preparing Alpha Factors and Features to predict Stock Returns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Hm0Z_6cmsiC"
      },
      "source": [
        "## Imports & Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:39.004078Z",
          "start_time": "2021-04-15T19:55:39.002167Z"
        },
        "id": "qlBIBzGjmsiF"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:39.621179Z",
          "start_time": "2021-04-15T19:55:39.124560Z"
        },
        "id": "4e6syUM4msiK",
        "outputId": "1aa6b70e-f371-4b4a-b123-4e720e3c262b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-40-85adb7b94f49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpearsonr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspearmanr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtalib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRSI\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBBANDS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMACD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mATR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'talib'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from scipy.stats import pearsonr, spearmanr\n",
        "from talib import RSI, BBANDS, MACD, ATR"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Notice how you first have to install some libraries that don't exist in Colab by default\n",
        "\n",
        "!pip install TA-Lib-binary"
      ],
      "metadata": {
        "id": "_32IU9T_4fdp",
        "outputId": "5d4138c7-d6c8-44ec-800a-e78b7043afa0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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 TA-Lib-binary\n",
            "  Downloading TA_Lib_binary-0.4.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from TA-Lib-binary) (1.21.6)\n",
            "Installing collected packages: TA-Lib-binary\n",
            "Successfully installed TA-Lib-binary-0.4.21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from talib import RSI, BBANDS, MACD, ATR"
      ],
      "metadata": {
        "id": "6vPxnx154qXS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:39.623839Z",
          "start_time": "2021-04-15T19:55:39.622135Z"
        },
        "id": "2hYaqvHDmsiO"
      },
      "outputs": [],
      "source": [
        "MONTH = 21\n",
        "YEAR = 12 * MONTH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:39.749906Z",
          "start_time": "2021-04-15T19:55:39.748238Z"
        },
        "id": "pd1LAkK9msiQ"
      },
      "outputs": [],
      "source": [
        "START = '2013-01-01'\n",
        "END = '2017-12-31'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:40.383834Z",
          "start_time": "2021-04-15T19:55:40.382005Z"
        },
        "id": "EFm71ouEmsiT"
      },
      "outputs": [],
      "source": [
        "sns.set_style('whitegrid')\n",
        "idx = pd.IndexSlice"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVdS-gv1msiW"
      },
      "source": [
        "## Loading Quandl Wiki Stock Prices & Meta Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:44.647048Z",
          "start_time": "2021-04-15T19:55:44.645120Z"
        },
        "id": "vF16AwRKmsiZ"
      },
      "outputs": [],
      "source": [
        "ohlcv = ['adj_open', 'adj_close', 'adj_low', 'adj_high', 'adj_volume']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget \"https://storage.googleapis.com/public-quant/course//content/quandl-wiki-prices-us-equites.zip\""
      ],
      "metadata": {
        "id": "HFOhJovh5Go6",
        "outputId": "0aaf87f5-6a04-40f8-c678-2401664d4898",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-20 20:35:02--  https://open-data.s3.filebase.com/WIKI_PRICES_212b326a081eacca455e13140d7bb9db.zip\n",
            "Resolving open-data.s3.filebase.com (open-data.s3.filebase.com)... 51.81.66.104, 51.81.56.185, 51.81.107.75\n",
            "Connecting to open-data.s3.filebase.com (open-data.s3.filebase.com)|51.81.66.104|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 463198483 (442M) [application/x-zip-compressed]\n",
            "Saving to: ‘WIKI_PRICES_212b326a081eacca455e13140d7bb9db.zip’\n",
            "\n",
            "WIKI_PRICES_212b326 100%[===================>] 441.74M  3.58MB/s    in 2m 6s   \n",
            "\n",
            "2023-01-20 20:37:09 (3.51 MB/s) - ‘WIKI_PRICES_212b326a081eacca455e13140d7bb9db.zip’ saved [463198483/463198483]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## This could help you for a past problem ;)\n",
        "## Note you might have to change some names\n",
        "import zipfile\n",
        "path_to_zip_file = \"WIKI_PRICES_212b326a081eacca455e13140d7bb9db.zip\"\n",
        "directory_to_extract_to = \"\"\n",
        "with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref:\n",
        "    zip_ref.extractall(directory_to_extract_to)"
      ],
      "metadata": {
        "id": "y4GHqDL_5D-m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mv WIKI_PRICES_212b326a081eacca455e13140d7bb9db.csv wiki_prices.csv"
      ],
      "metadata": {
        "id": "2NO1unLr5JYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:55:49.239706Z",
          "start_time": "2021-04-15T19:55:44.666616Z"
        },
        "id": "4-8oQHsQmsid"
      },
      "outputs": [],
      "source": [
        "df = (pd.read_csv('wiki_prices.csv',\n",
        "                 parse_dates=['date'],\n",
        "                 index_col=['date', 'ticker'],\n",
        "                 infer_datetime_format=True)\n",
        "     .sort_index())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prices = (df\n",
        "          .loc[idx[START:END, :], ohlcv]\n",
        "          .rename(columns=lambda x: x.replace('adj_', ''))\n",
        "          .assign(volume=lambda x: x.volume.div(1000))\n",
        "          .swaplevel()\n",
        "          .sort_index())\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/stefan-jansen/machine-learning-for-trading/f881ea01ef8ae5a308ab75de833ba24bee27435e/data/us_equities_meta_data.csv')\n",
        "\n",
        "stocks = (df.set_index(\"ticker\")\n",
        "          .loc[:, ['marketcap', 'ipoyear', 'sector']])"
      ],
      "metadata": {
        "id": "VwVVq4sp5ygN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LgBQ3qlmsig"
      },
      "source": [
        "## Remove stocks with few observations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:58:51.241695Z",
          "start_time": "2021-04-15T19:55:49.240874Z"
        },
        "id": "c1u4wna5msii"
      },
      "outputs": [],
      "source": [
        "# want at least 2 years of data\n",
        "min_obs = 2 * YEAR\n",
        "\n",
        "# have this much per ticker\n",
        "nobs = prices.groupby(level='ticker').size()\n",
        "\n",
        "# keep those that exceed the limit\n",
        "keep = nobs[nobs > min_obs].index\n",
        "\n",
        "prices = prices.loc[idx[keep, :], :] ## yes this takes very long"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hfuwgtbnmsim"
      },
      "source": [
        "### Align price and meta data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T19:58:51.249517Z",
          "start_time": "2021-04-15T19:58:51.242718Z"
        },
        "id": "MzCsqSNRmsio"
      },
      "outputs": [],
      "source": [
        "stocks = stocks[~stocks.index.duplicated() & stocks.sector.notnull()]\n",
        "stocks.sector = stocks.sector.str.lower().str.replace(' ', '_')\n",
        "stocks.index.name = 'ticker'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.800393Z",
          "start_time": "2021-04-15T19:58:51.250432Z"
        },
        "id": "Spktx-A9msiq"
      },
      "outputs": [],
      "source": [
        "shared = (prices.index.get_level_values('ticker').unique()\n",
        "          .intersection(stocks.index))\n",
        "stocks = stocks.loc[shared, :]\n",
        "prices = prices.loc[idx[shared, :], :] ## yes this takes very long"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.865929Z",
          "start_time": "2021-04-15T20:00:14.801253Z"
        },
        "id": "52lPeNOJmsis",
        "outputId": "061d3437-6681-4941-c421-7df33c57f3d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count    Dtype  \n",
            "---  ------  --------------    -----  \n",
            " 0   open    2904233 non-null  float64\n",
            " 1   close   2904233 non-null  float64\n",
            " 2   low     2904233 non-null  float64\n",
            " 3   high    2904233 non-null  float64\n",
            " 4   volume  2904233 non-null  float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 122.6+ MB\n"
          ]
        }
      ],
      "source": [
        "prices.info(show_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.876541Z",
          "start_time": "2021-04-15T20:00:14.867043Z"
        },
        "scrolled": true,
        "id": "Wwt-97uomsiu",
        "outputId": "b1859655-1fbb-4701-a481-c45b684b0399",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 2348 entries, A to ZUMZ\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   marketcap  2345 non-null   float64\n",
            " 1   ipoyear    1026 non-null   float64\n",
            " 2   sector     2348 non-null   object \n",
            "dtypes: float64(2), object(1)\n",
            "memory usage: 73.4+ KB\n"
          ]
        }
      ],
      "source": [
        "stocks.info(show_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.890723Z",
          "start_time": "2021-04-15T20:00:14.878603Z"
        },
        "id": "flUSeqicmsiv",
        "outputId": "bc37e3c7-a01b-4feb-9daf-b4f86f457a86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "consumer_services        440\n",
              "finance                  393\n",
              "technology               297\n",
              "health_care              297\n",
              "capital_goods            227\n",
              "basic_industries         138\n",
              "consumer_non-durables    126\n",
              "energy                   123\n",
              "public_utilities         105\n",
              "consumer_durables         78\n",
              "miscellaneous             69\n",
              "transportation            55\n",
              "Name: sector, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "stocks.sector.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crQJ4o2Kmsix"
      },
      "source": [
        "Optional: persist intermediate results:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.898404Z",
          "start_time": "2021-04-15T20:00:14.892363Z"
        },
        "id": "aIZaI8cbmsiy"
      },
      "outputs": [],
      "source": [
        "# with pd.HDFStore('tmp.h5') as store:\n",
        "#     store.put('prices', prices)\n",
        "#     store.put('stocks', stocks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.910952Z",
          "start_time": "2021-04-15T20:00:14.899216Z"
        },
        "id": "9gMVUx_Lmsi0"
      },
      "outputs": [],
      "source": [
        "# with pd.HDFStore('tmp.h5') as store:\n",
        "#     prices = store['prices']\n",
        "#     stocks = store['stocks']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oJs5DSBmsi1"
      },
      "source": [
        "## Compute Rolling Average Dollar Volume"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:14.999567Z",
          "start_time": "2021-04-15T20:00:14.911892Z"
        },
        "id": "GMCnc3-Bmsi1"
      },
      "outputs": [],
      "source": [
        "# compute dollar volume to determine universe\n",
        "prices['dollar_vol'] = prices[['close', 'volume']].prod(axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:15.241975Z",
          "start_time": "2021-04-15T20:00:15.000621Z"
        },
        "id": "Ev1SzkOJmsi3"
      },
      "outputs": [],
      "source": [
        "prices['dollar_vol_1m'] = (prices.dollar_vol.groupby('ticker')\n",
        "                           .rolling(window=21)\n",
        "                           .mean()).values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:15.274692Z",
          "start_time": "2021-04-15T20:00:15.242815Z"
        },
        "id": "kFAu1jslmsi4",
        "outputId": "59ad8dcf-0655-44b7-e543-6741b0eb754a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))\n",
            "Data columns (total 7 columns):\n",
            " #   Column         Non-Null Count    Dtype  \n",
            "---  ------         --------------    -----  \n",
            " 0   open           2904233 non-null  float64\n",
            " 1   close          2904233 non-null  float64\n",
            " 2   low            2904233 non-null  float64\n",
            " 3   high           2904233 non-null  float64\n",
            " 4   volume         2904233 non-null  float64\n",
            " 5   dollar_vol     2904233 non-null  float64\n",
            " 6   dollar_vol_1m  2857273 non-null  float64\n",
            "dtypes: float64(7)\n",
            "memory usage: 166.9+ MB\n"
          ]
        }
      ],
      "source": [
        "prices.info(show_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:15.830659Z",
          "start_time": "2021-04-15T20:00:15.275461Z"
        },
        "id": "gAyGtRUMmsi5"
      },
      "outputs": [],
      "source": [
        "prices['dollar_vol_rank'] = (prices.groupby('date')\n",
        "                             .dollar_vol_1m\n",
        "                             .rank(ascending=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:15.867409Z",
          "start_time": "2021-04-15T20:00:15.831445Z"
        },
        "id": "DsFnarQimsi6",
        "outputId": "26b3bbb8-7a32-4a7f-9703-7e292363c749",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "MultiIndex: 2904233 entries, ('A', Timestamp('2013-01-02 00:00:00')) to ('ZUMZ', Timestamp('2017-12-29 00:00:00'))\n",
            "Data columns (total 8 columns):\n",
            " #   Column           Non-Null Count    Dtype  \n",
            "---  ------           --------------    -----  \n",
            " 0   open             2904233 non-null  float64\n",
            " 1   close            2904233 non-null  float64\n",
            " 2   low              2904233 non-null  float64\n",
            " 3   high             2904233 non-null  float64\n",
            " 4   volume           2904233 non-null  float64\n",
            " 5   dollar_vol       2904233 non-null  float64\n",
            " 6   dollar_vol_1m    2857273 non-null  float64\n",
            " 7   dollar_vol_rank  2857273 non-null  float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 189.1+ MB\n"
          ]
        }
      ],
      "source": [
        "prices.info(show_counts=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2hbYCCnmsi7"
      },
      "source": [
        "## Add some Basic Factors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrSXnyigmsi8"
      },
      "source": [
        "### Compute the Relative Strength Index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:17.415535Z",
          "start_time": "2021-04-15T20:00:15.868286Z"
        },
        "id": "zgGUdU9Vmsi8"
      },
      "outputs": [],
      "source": [
        "prices['rsi'] = prices.groupby(level='ticker').close.apply(RSI)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:23.837323Z",
          "start_time": "2021-04-15T20:00:17.416358Z"
        },
        "id": "s6JJrfRkmsi9",
        "outputId": "f1eb8b6f-922a-4364-c88a-e4cae8dd77a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = sns.distplot(prices.rsi.dropna())\n",
        "ax.axvline(30, ls='--', lw=1, c='k')\n",
        "ax.axvline(70, ls='--', lw=1, c='k')\n",
        "ax.set_title('RSI Distribution with Signal Threshold')\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gL5mxOPgmsi-"
      },
      "source": [
        "### Compute Bollinger Bands"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:23.840107Z",
          "start_time": "2021-04-15T20:00:23.838143Z"
        },
        "id": "eL6jizt9msi-"
      },
      "outputs": [],
      "source": [
        "def compute_bb(close):\n",
        "    high, mid, low = BBANDS(close, timeperiod=20)\n",
        "    return pd.DataFrame({'bb_high': high, 'bb_low': low}, index=close.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:26.383360Z",
          "start_time": "2021-04-15T20:00:23.841271Z"
        },
        "id": "M_G9qXOnmsi_"
      },
      "outputs": [],
      "source": [
        "prices = (prices.join(prices\n",
        "                      .groupby(level='ticker')\n",
        "                      .close\n",
        "                      .apply(compute_bb)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:26.439638Z",
          "start_time": "2021-04-15T20:00:26.384205Z"
        },
        "id": "EPUr2iiamsjA"
      },
      "outputs": [],
      "source": [
        "prices['bb_high'] = prices.bb_high.sub(prices.close).div(prices.bb_high).apply(np.log1p)\n",
        "prices['bb_low'] = prices.close.sub(prices.bb_low).div(prices.close).apply(np.log1p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:27.376121Z",
          "start_time": "2021-04-15T20:00:26.440442Z"
        },
        "scrolled": true,
        "id": "DQGiclvCmsjB",
        "outputId": "57680ed1-561b-479c-fa1b-2444651248e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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sW7dODz/8sCTp4Ycf1sUXX+zm4QHAdYZpvmUXEklKxCIqlJiBAQBBVMsSkuO1JitdF2N5w7GaAKAROBZgbNmyRddee6327t2rtWvX6sEHH9T111+vJ598UuvXr9dTTz2l66+/3qnDA0BdME6yjapEBwYABFl1F5Jal5C0THZgjOXpwAAQbI4torvrrrtO+vh9993n1CEBoO6UyqaikZMFGBECDAAIqNl2YDRPdmCM0oEBIOBcXUICAEFjmJYiJxlYnIyFWUICAAFVnYERO8mMpJOJRcJKxSJ0YAAIPAIMAHCQYVqKTDPEM08HBgAEUq5YVjwantoStRYtySgzMAAEHgEGADjIKE8zxDPKEE8ACKpssVzz8pGqSoBBBwaAYCPAAACHmJYl09I0u5AwxBMAgipbLCte4/KRqpZkTGMFOjAABBsBBgA4pJpPTDfE0zAtGSYhBgAETa5UViI2lw4MQ5ZlOVQVANQ/AgwAcEjZrNxkRqbZRlWSiiUCDAAImmzRUCIamdX3tCZjKpuWciw/BBBgBBgA4JDy5Kdk0fBbL7XJyU/eWEYCAMEztyUkla1UGeQJIMgIMADAIVNLSKYZ4ilJeYNP0gAgaLLFuSwhiUmSRhnkCSDACDAAwCFTS0hOOgNjsgODJSQAEDi5Oe5CItGBASDYCDAAwCGGOf0SkkSs0oFRoAMDAAInW2IJCQDMBQEGADikPOMSEmZgAEAQmaZV6cCIzW6IZyIaUTwa1hhLSAAEGAEGADikOsTzZLuQJKsdGCwhAYBAyU7uIjLbDgxJaklE6cAAEGgEGADgkJmHeFYuvwzxBIBgyRYqAcRsh3hKlUGedGAACDICDABwSHWIZ/QkQzzjLCEBgEAarwYYsxziKVXmYNCBASDICDAAwCEzDfEMh0KKRUIqEmAAs3brrbdqzZo1uvzyy6ce++pXv6oNGzboiiuu0Gc+8xmNjo56WCEwvYlCdQnJ7GZgSFIrAQaAgCPAAACHzLSERJKS0Qi7kABzcPXVV+uee+454bELL7xQjz76qH74wx/q9NNP19133+1RdcDMJorzW0JSLJsqlPjbASCYCDAAwCHmDEM8pcoykjxDPIFZW716tdra2k547KKLLlI0Wtlm8vzzz9fAwIAXpQGnNDHPJSSSNEoXBoCAinpdAAD4VXUJSeQkMzCkyqdvLCEB7PfQQw/p0ksvrem5hUJBmUzG4Yrslc/nG67mWvn53KTK+b3yxn5J0sjQoCKFkWmfe3ZXXP0D/Sc8VsoVJUl/fGNAg6clNTawz7liZykIPzu/np+fz03i/PyGAAMAHHKqJSQJlpAAtvvmN7+pSCSij370ozU9P5FIqLe31+Gq7JXJZBqu5lr5+dykyvm1L2ySdEjLerrVlopN+9xUOq2e7p4THgun89LuUcXTbers6tSyBcsdrrh2QfjZ+fX8/HxuEufXqKYLZQgwAMAh5RmGeEqV9uGRHNvhAXbZtm2bduzYoXvvvVeh0MmDQ8Br1SUk8cjsl5BUA49RtlIFEFAEGADgEGOGbVSlygwMtlEF7LFz507dc889+s53vqNUKuV1OcC0qtuoxucwAyMRDSseCWuU8BtAQBFgAIBDypX8QpFpPgmu7EJCgAHM1pYtW/Tcc89paGhIa9eu1Q033KCtW7eqWCxq06ZNkqRVq1bpH/7hHzyuFHirbLGsRDQ87YDnmYRCIbWmYhphiCeAgCLAAACHlE/RgZGIhtkKD5iDu+666y2PXXPNNR5UAszeeMFQKh6Z8/e3pqJ0YAAILLZRBQCHGKeYgRGPhWWY1lTQAQDwv4mCofQ8Aoy2ZIwAA0BgEWAAgEPKNexCIomtVAEgQCYKZaXjc2+Cbk3FNJovybQIvwEEDwEGADik2oERmWEJiSS2UgWAAMkWDaVic78Fb0vFZFrS0ETRxqoAoDEQYACAQ6aGeE7bgVENMOjAAICgyJXKSsbmMQMjWdlK9fBYwa6SAKBhEGAAgEOmhnieYgkJAQYABEeuOL8Aoy01GWCME2AACB4CDABwyKmGeE51YLATCQAExrw7MFKV+RmH6MAAEEAEGADgkKkhntPNwIixhAQAgiZbLCs5jxkYTYmowiHpCAEGgAAiwAAAhximpXBICodYQgIAqMgXy0pG596BEQ6F1JqM0YEBIJAIMADAIYZlTbt8RJLi7EICAIFiWZaypbKS8bkHGFJlK9UjzMAAEEAEGADgENOcfvmIJCUnA4wiHRgAEAiGWRnwXL3+z1Vrig4MAMFEgAEADjFMa9odSCQpGgkrEgqxhAQAAiI/eb1PzWOIpyS1JaM6MlaUZVl2lAUADYMAAwAcYpiWYpGZL7PxaFh5diEBgEAoGJXAITHPAKM1FVOuVNZo3rCjLABoGAQYAOCQsjXzEhKpshMJS0gAIBiqHRjz2YVEqgQYknRwND/vmgCgkRBgAIBDKktIZr7MJqJhlpAAQEAUypUOjOS8l5BUAoz+EQIMAMFCgAEADimfYoinVNlKlV1IACAY8pNLSOwY4ilJBwkwAAQMAQYAOKR8iiGeEh0YABAk1ev9vLdRTUYl0YEBIHgIMADAIbUM8STAAIDgKEx1YMwvwIhGwlqQjql/JGdHWQDQMKJeHPTee+/Vgw8+qFAopLPOOkt33HGHEomEF6UAgGNqGuIZjajALiQAEAjVJSTz3UZVkha3JnVgmAADQLC43oFx8OBB3X///XrooYf06KOPqlwu60c/+pHbZQCA42oZ4hmP0YEBAEFRvd4n5rkLiUSAASCYPFlCUi6Xlc/nZRiG8vm8Fi1a5EUZAOCoWmdgFA1TlmW5VBUAwCt2dmB0tyb0xnCOvx8AAsX1JSSLFy/Wddddpw996ENKJBK68MILddFFF834PYVCQZlMxqUKT5TP5z07dtDwXruD99k9pbIpq5DT4JFB9Q/0n/Q5xVxWlqQ3Bo5o/OB+dwv0CX6n3cN7DcxPoWxjB0ZbUvmSqcGJorqaWYoNIBhcDzBGRka0fft2bd++XS0tLfrc5z6nRx55RBs3bpz2exKJhHp7e12s8phMJuPZsYOG99odvM/uMR87oLaWZnV2daqn++SfkHVlB6X9WSVbWtW7/DSXK/QHfqfd8+b3mjADmJ18yVI4JMVPMeC5Ft2tSUnSgaEcAQaAwHB9CclTTz2lZcuWqaOjQ7FYTOvXr9euXbvcLgMAHFfrEhJJyhYZ5AkAflcoW0rFIgqFZv7bUIvutskAgzkYAALE9QBjyZIl+u1vf6tcrrJm7+mnn9bKlSvdLgMAHGeYla3uZpKY3EpvggADAHwvb5hKxe1pgK52YLxBgAEgQFxfQrJq1SpdcskluuqqqxSNRtXb26tPfOITbpcBAI6rqQMjVu3AMNwoCQDgoYJhKRW35/PDlmRUTfGI+oYIMAAEh+sBhiRt3rxZmzdv9uLQAOCaslVLBwZLSAAgKPKGqXTMntvvUCikpQtSLCEBECiebKMKAEFgmJZip5yBUVlCQoABAP5XMCwl4/PfQrVqaXtKB+jAABAgBBgA4BDDtBSN1DrEkyUkAOB3ecNSOmZjgEEHBoCAIcAAAIeUaxriORlgFOjAAGp16623as2aNbr88sunHhseHtamTZu0fv16bdq0SSMjIx5WCJxcwTCVsrUDI62RXEnjBUJwAMFAgAEADilbpx7iGaMDA5i1q6++Wvfcc88Jj23dulVr1qzRT3/6U61Zs0Zbt271qDpgennDsjfAWJCSJJaRAAgMAgwAcIhhWoqGZ77MhkMhxaNhZmAAs7B69Wq1tbWd8Nj27dt15ZVXSpKuvPJKPf74416UBsyoUDaVsnMJSftkgDGcte01AaCeebILCQAEQdmUYqeYgSFVlpEQYADzMzg4qEWLFkmSFi5cqMHBwZq+r1AoKJPJOFma7fL5fMPVXCs/n5sk5UqmCuOjGjwyqP6B/hmfe3ZXfMbnDHaElBsdliT9OrNXPdZRW2udLb//7Px8fn4+N4nz8xsCDABwSC1DPKXKTiQEGIB9QqGQQqFT/29PkhKJhHp7ex2uyF6ZTKbhaq6Vn89NkorlvVqyuEudXZ3q6bZmfG4qnVZPd8+0X+/s6tS7Vi5TfFufjGSb5++b3392fj4/P5+bxPk1qulCGZaQAIADLMuSaemUS0ikagcGMzCA+ejs7NShQ4ckSYcOHVJHR4fHFQEnMk1LxbK9MzDC4ZB62pPMwAAQGAQYAOAAw6x8snaqIZ4SS0gAO6xbt04PP/ywJOnhhx/WxRdf7HFFwIlypcp13s4ZGFJlDgZbqQIICgIMAHCAUZ4MME6xjapUCTAm2AIPqNmWLVt07bXXau/evVq7dq0efPBBXX/99XryySe1fv16PfXUU7r++uu9LhM4QTXASNvYgSFNBhh0YAAICGZgAIADSqYpqcYhnrGIhnMlp0sCfOOuu+466eP33Xefy5UAtctNdtol7e7AWJDSobGCCkZZiai9rw0A9YYODABwwFQHRo1LSHIsIQEAX5taQuJAB4Yk9Q/nbX1dAKhHBBgA4ACjXOnAqHUJCTMwAMDfqtd525eQLKgEGMzBABAEBBgA4IDZDPGMRyMqlk2VJkMPAID/OLWEZFl7WpKYgwEgEAgwAMABsx3iKYlBngDgY7lS5Rqfjts7gq67LalQSOqjAwNAABBgAIADZjXEczLAGCfAAADfyhUrfxfs3kY1Hg1rcUuSDgwAgUCAAQAOODbEs4YOjMmbWQIMAPCvbLHagWH/TiFLF6R0YDhr++sCQL0hwAAAB5SmhnjW3oHBEhIA8K98yZkZGFJlJxKGeAIIAgIMAHBAdYjn7JaQsBMJAPiVU7uQSJUOjIGRvMqTf3sAwK8IMADAAeXJGRiRWpaQRCs3s3RgAIB/5RzuwCiVLR0eK9j+2gBQTwgwAMABpckZGLEatlFliCcA+F+uWFY8ElKkhr8Ls7V0QUqSmIMBwPcIMADAAWyjCgA4Xq5UViJqf3ghVTowJKmPnUgA+Jy9G1EDACQd20a1liGe8dhkB0aeAAMA/CpbLCtpY4BhlE31DVU7Liqh+e7+Uf3paQumntOSiKotHbftmADgtZo6MD772c9qx44dMidvyAEAMzOmlpCc+jIbDYcVj4Q1XiTAQPBwj4GgyJXKStTQlVf765na+coR7XzliH71x2GlYhHt2j889djOV45ojM4+AD5T01X0k5/8pH74wx9q/fr1uvPOO7Vnzx6n6wKAhnZsiGdtn7al4xGWkCCQuMdAUORs7sB4swXpmIazRcdeHwDqQU1LSC644AJdcMEFGhsb06OPPqpNmzapp6dH11xzjT760Y8qFos5XScANJSpIZ41LCGRpFQ8ogm2UUUAcY+BoMgVy1Mzj5zQno7ryDi7kADwt5qvokNDQ9q2bZsefPBB9fb26i//8i+1e/duXXfddU7WBwANyZiagVHbZTYdj7ALCQKLewwEQa7kbAdGezqm4WxJlmU5dgwA8FpNHRif+cxntHfvXm3cuFHf+ta3tGjRIknSRz7yEV199dWOFggAjajagRGteQlJlCUkCCTuMRAUuWJZnQknl5DEVSybyhbLakowpx+AP9V0dfuLv/gLfeADHzjhsWKxqHg8rm3btjlSGAA0sqkhnpHwVDfGTOjAQFBxj4GgyJXKSjY5t4RkweRuI0PZIgEGAN+q6Sr6L//yL2957BOf+ITtxQCAX8x2iGdTIkqAgUDiHgNBkS2WlXByiGdTZV7M0QkGeQLwrxnj2cOHD+vgwYPK5/PavXv31Jq68fFx5XI5VwoEgEZ0/BDPWnIJdiFB0HCPgaDJl8pKOjjEs6PagUGAAcDHZgwwfvGLX2jbtm0aGBjQHXfcMfV4U1OTtmzZ4nhxANCo5jLEk11IECTcYyBILMtStmgoUePOVHORiEWUjkd0NFty7BgA4LUZA4yrrrpKV111lX7yk5/okksucasmAGh4sx/iGdFE0ZBlWQqFnLvBBeoF9xgIkmLZlGlJyZiz1/eOpriGsnRgAPCvGQOMRx55RBs3btSBAwf0H//xH2/5+qZNmxwrDAAa2fFDPGuRjkdlWWJ6PAKDewwESa5Y6bBL1Pg3Ya4WpOM6MMwSLAD+NeNdcnUNajabdaUYAPCL6k47OvMAACAASURBVBDPGhswlE5EJEnjBYMAA4HAPQaCJFeqBBhJB4d4SpUOjBffGJFpWQrTzQfAh2a8S7722mslSZ/97GddKQYA/KJkWoqGVfNykHS8cjkeLxha7GRhQJ3gHgNBkq12YDg4xFOqDPI0LWkkV5raVhUA/KSmq+jXvvY1jY+Pq1Qq6a/+6q/0vve9T4888ojTtQFAwzLKZs1bqEqVGRiS2IkEgcM9BoKguoTE6Q6MBU3sRALA32oKMJ588kk1Nzdrx44dWrp0qf73f/9X//7v/+50bQDQsEplS9FZtO9WA4xxAgwEDPcYCILqEpKEC0tIJOkoAQYAn6opwCiXKxfdHTt2aMOGDWppaXG0KABodIZpajaz2qpLSNhKFUHDPQaCIOfSEpK2VEwhiZ1IAPhWTVfRD37wg9qwYYNefPFFrVmzRkePHlUikXC6NgBoWGXTYgkJUAO77zHuvfdeXXbZZbr88su1ZcsWFQoFG6sF5ibr0hKSSDik9nRMQ9mSo8cBAK/UFGDcfPPN+u///m899NBDisViSqVS+rd/+7c5H3R0dFSbN2/Whg0bdOmll2rXrl1zfi0AqEelcmWIZ62aJgOMMQIMBIyd9xgHDx7U/fffr4ceekiPPvqoyuWyfvSjH9lcMTB7+eoSkojzO4MsSMdZQgLAt2req2/Pnj06cODAVKunJF155ZVzOuiXvvQlvf/979c3vvENFYtF5fP5Ob0OANQro2wqMqsZGNUlJAQYCB477zHK5bLy+byi0ajy+bwWLVpkV5nAnB3rwHB2CYlUmYPx8sCY48cBAC/UFGDccsstev3113X22WcrEql8ShgKheZ0czE2NqZf/vKX+spXviJJisfjisfZ5gmAv1S2Ua09wEjGwgqHCDAQPHbeYyxevFjXXXedPvShDymRSOjCCy/URRddZHfJwKy5NcRTquxEMlYwVDRMx48FAG6rKcD4/e9/r8cee0yhWXyaOJ2+vj51dHTo1ltv1UsvvaRzzjlHt912m9Lp9LxfGwDqhVE2Z7WEJBQKqSkeZRcSBI6d9xgjIyPavn27tm/frpaWFn3uc5/TI488oo0bN077PYVCQZlMZt7HdlM+n2+4mmvl13Pbf2Co8h9GUZlMRsVos/oH+mf8nrO74jM+Z7qvh0uVuS9/2H9Af7IoorGBfXMvfBb8+rOr8vP5+fncJM7Pb2oKMM4880wdPnzYljZMwzC0e/du3X777Vq1apW++MUvauvWrbrxxhun/R4vby6C9gvhJd5rd/A+u2N4ZExhqeYb1cGOkOIRSwcODvLzmSV+p93jxHtt5z3GU089pWXLlqmjo0OStH79eu3atWvGACORSKi3t3fex3ZTJpNpuJpr5ddza97/kiLhYTWnk+rt7VXfUFY93daM35NKp9XT3TPrrxvxrPSHMYVTbers6tSyBcvnXX8t/Pqzq/Lz+fn53CTOr1FNd79RU4AxNDSkyy67TOedd55isdjU49/61rdmXUh3d7e6u7u1atUqSdKGDRu0devWGb/Hy5sLv/5C1CPea3fwPrsj+fSYornRmm9UO7s61d6UVCzVzM9nlvidds+b32s7wgw77zGWLFmi3/72t8rlckomk3r66ad17rnnzrtGYL5yRVPpWMSWTqNT6WyuLM0+Ms4OPAD8p6YA44YbbrDtgAsXLlR3d7f27NmjFStW6Omnn9bKlStte30AqAeGObslJJLUnIyxCwkCx857jFWrVumSSy7RVVddpWg0qt7eXn3iE5+w7fWBucqVDCUnd5tyWjoeVToeIcAA4Es1BRh/9md/pgMHDmjfvn264IILlMvlTpgUPlu33367br75ZpVKJS1fvlx33HHHnF8LAOpRqWwpMoshnpLUnIgwxBOBY/c9xubNm7V582YbKwTmL1csK+1SgCFJC5sTOjzGVqoA/KemAOP73/++HnjgAY2MjOjxxx/XwYMH9fd///e677775nTQ3t5ebdu2bU7fCwCNoDLEc3YBRlM8qsHxrEMVAfXJ7nsMoB5li2WlYu4FGF0tCbZSBeBLNTU4/9d//Ze+973vqbm5WZJ0+umn6+jRo44WBgCNzDAtRWa7hCTBLiQIHu4xEAS5UlkpFzswupoTGi8Y/E0B4Ds13V7H43HF4/GpfxsGF0MAmIlRthSZ5bC2lmRUY3murwgW7jEQBDmXOzAWTg7yfP0oXX0A/KWmJSSrV6/Wt771LeXzeT355JP67ne/q3Xr1jldGwA0LMM0FY2d+nnHa03FNJYvybIsVybVA/WAewwEQa5UVnt6ln8U5qGrOSFJ2k+AAcBnaurAuPnmm9XR0aGzzjpLDzzwgD7wgQ/oxhtvdLo2AGhYxhyGeLYmYzItaaI49wGGQKPhHgNBkCuWlXSxA6OjOa5wSNo/SIABwF9q6sAIh8P68Ic/rA9/+MPq6OhwuiYAaHgl01Q0PLub1dZU5ZI8miupOVHT5RloeNxjIAhyJXd3IYmGw1qQjtOBAcB3ZrxDtixL//qv/6rvfOc7sixLUuVG41Of+pQ++9nPulIgADSiSgfG7L6nNVlpLx7JlbSkPeVAVUD94B4DQeL2LiRSZRnJ60M5V48JAE6b8fb63nvv1fPPP68f/OAHeu655/Tcc8/pwQcf1K5du3Tvvfe6VCIANB7DnP0Qz9ZUJcAYzZWcKAmoK9xjIEgqu5C421nX1RzX60ezMk3L1eMCgJNmDDAeeeQR/dM//ZOWL18+9djy5cv19a9/XQ8//LDjxQFAozLKpqJzmIEhSaPsRIIA4B4DQVE2LRUN0/0OjJaECoap/tG8q8cFACfNGGAYhnHS9agdHR1scwYAM5jTEpLjZmAAfsc9BoIiV6oMZnZzBoYkLZzciWTP4XFXjwsATprx9joWm367p5m+BgBBVxniObsOjLbqEpI8AQb8j3sMBEW2WAnkki4HGF1TAcaEq8cFACfNuBjvpZde0rvf/e63PG5ZlorFomNFAUCjM8rWrAOM6s4jozk+fYb/cY+BoMgXTUlS2uUlJC3JqJriEf3h0JirxwUAJ80YYGQyGbfqAADfME1LhmkpNssAIxoJqzkRpQMDgcA9BoIiW6qE0imXOzBCoZBWLGzWywMEGAD8Y5YrtAEAp1IsVz5ti87hXrU1GWUGBgD4SK5YmYHhdoAhSSsXNumlgbGprYoBoNERYACAzUrVAGOWHRhSZSvVEQIMAPCN6hBPt3chkaQVC5s1ljfUP8JOJAD8gQADAGxWKlc+6ZrtEhKpspUqS0gAwD+qHRhu70IiVTowJLGMBIBvEGAAgM2mOjAic+nAiDLEEwB8xMsOjDMmA4yXCDAA+AQBBgDYrGhUAgw6MAAA2ckOjKQHAUZrMqaetqReHhh1/dgA4AQCDACw2XxnYDDEEwD8I1/ybgmJJL2ju4UODAC+QYABADarzsCIzuEK25qKaaxgyDSZGA8AfpD1cBcSqRJgvHZ4fCpcB4BGRoABADar3iTObQlJVJYljReZgwEAflAd4pmcy97aNji7u0WlsqU9hyc8OT4A2IkAAwBsVjDmM8QzJkksIwEAn8iVykrGwgrPIdS2wzsWt0qSXmIOBgAfIMAAAJvNrwOjEmCMEGAAgC/kimWl41HPjr9yUZOi4RBbqQLwBQIMALDZfLdRlcRWqgDgE9li2ZMtVKsS0YhWLGxikCcAXyDAAACbHduFZPbfW+3AYCtVAPCHfKns2QDPqnf2tGr3GywhAdD4CDAAwGZFo7KDyFyWkLQxAwMAfCVbNDztwJCkc5e2aWA0r8NjBU/rAID5IsAAAJsd68CY+wyM0TxLSADAD3J10IFxzpI2SdKLb4x4WgcAzBcBBgDYbD4BRnOyOgODDgwA8IOcxzMwJOmdSyo7kbzIMhIADY4AAwBsVpzcRjU2hyGekXBILckoMzAAwCdypbLSHndgtKViOq0zrd8foAMDQGMjwAAAm81nG1WpsoyEbVSBuRkdHdXmzZu1YcMGXXrppdq1a5fXJSGARrJF9Q1l1TeU1VjekGlZ6hvKqhhtVt9QVoVS2fWazl3Spt+zhARAg/NuU2oA8KliuTLEMzqLD9yMsqm+oawkKRWP6NBoYerfVS2JqNrScdvqBPzoS1/6kt7//vfrG9/4horFovL5vNclIYDGCoZ2vnJEUmWm0VC2pJ2vHFH/QL96ui39ydvaXanj+L8tyztS+tEL/drdPzI1b4m/KwAaDQEGANhsLjMwciVTu147Kkkqm5b6hnJTN79Va8/q4kYTmMHY2Jh++ctf6itf+YokKR6PKx7nfzPwVqlsKh7xpun5+L8thcnljQ/9+oBWLmyWxN8VAI2HAAMAbFYy5reEJBmLaGiiaGdJQCD09fWpo6NDt956q1566SWdc845uu2225ROp6f9nkKhoEwm42KV85fP5xuu5lr55dyK0Wb1D/TLsiwVDVOF3IT6B/pllErqH+jX2V1x9Q/0z/gap3rObF8jUqr8bXpp/0Gly2OSpMGOkMYG9s3m1Kbll5/ddPx8fn4+N4nz8xsCDACwWalsKhSS5phfKBULq9+D9dFAozMMQ7t379btt9+uVatW6Ytf/KK2bt2qG2+8cdrvSSQS6u3tdbHK+ctkMg1Xc638cm59Q1n1dFuTHXmD6mhrVU/3osklJD1KpdPq6e6Z8TVO9Zy5vEb7i2MaM2NTj3V2dWrZguW1n9gM/PKzm46fz8/P5yZxfo1qulCGIZ4AYLNi2VIsElYoNPcOjBwBBjBr3d3d6u7u1qpVqyRJGzZs0O7duz2uCkE21ZEXrY9b7iXtKb0xnPO6DACYs/q4mgKAjxSN+a13TsYiKhimTMuysSrA/xYuXKju7m7t2bNHkvT0009r5cqVHleFICtOzkTyagbGmy1pT+rIeFF5QnIADYolJABgs1LZVHwen7alYpXtSwolU6n4LLYyAaDbb79dN998s0qlkpYvX6477rjD65IQYNUAo546MCSpfySvM7qaPK4GAGaPAAMAbFYqm4pF5jgAQ8cCjGzRIMAAZqm3t1fbtm3zugxAUqUjT6qnDoxKgPHGcI4AA0BDqo+rKQD4SLFsKjaPm9WmRCW0mCjS4gsAjay6dWkiVh+33K3JmFoSUeZgAGhY9XE1BQAfKZWteX3a1pSoNMdNFAy7SgIAeKAwuXVpMlo/3XRL2lN6Y4QAA0BjIsAAAJuVjPl1YDRPBhjjBBgA0NAKRqWTLlEnMzCkSoBxaLQwtbwFABpJ/VxNAcAnSmVTsejcZ2DQgQEA/pCfWkJSPx0YS9uTsiQdHM17XQoAzJpnAUa5XNaVV16pv/7rv/aqBABwxHxnYMQiYSWiYTowAKDBFUv12YEhSQeYgwGgAXl2Nb3//vvZmx2ALxUNc94T55sTUQIMAGhwecNUJBRSNDz3rjy7taViSscjDPIE0JA8CTAGBga0Y8cOffzjH/fi8ADgqFLZVHyen7Y1JaIsIQGABlcwyopHwwqF6ifACIVCDPIE0LCiXhz0y1/+sm655RZNTEzU9PxCoaBMJuNwVSeXz+c9O3bQ8F67g/fZeWMTWUXNqPL5uDKZjIrRZvUP9M/4PWd3xU94TtQyNDxRPuGxwY6Qxgb2OVZ3o+J32j2818DsFEqmknWyherxlrSl9OSrR1QqM8gTQGNxPcD42c9+po6ODp177rl69tlna/qeRCKh3t5ehys7uUwm49mxg4b32h28z86L/OSwFrSllUwm1dvbq76hrHq6rRm/J5VOq6e7Z+rfnf2mDvWPnvhYV6eWLVjuWN2Nit9p97z5vSbMAGZWMEwl6mgL1aol7UmVLUuvHZ7QGV3NXpcDADVzPRJ+/vnn9cQTT2jdunXasmWLnnnmGd18881ulwEAjpnvEE9Jak5ENFEwZFozBx8AgPqVN8p1NcCzanlHWpK0+41RjysBgNlxvQPjpptu0k033SRJevbZZ/Xtb39bd955p9tlAIBjSuX5D/FsSkRlScoVy1PbqgIAGkuhZKopUX8dGO2pmFoSUb34xojXpQDArNRfJAwADa5kWDZ0YFRCC3YiAYDGVa9LSEKhkJZ3pPUiHRgAGoynH+u9973v1Xvf+14vSwAA2xVt2oVEEjuRAEADK9TpEhJJWr4gpZ/sHtXQRFELmuJelwMANanPKyoANLCSYccMDDowAKDRVXYhqb8ODOnYHIzfvD7scSUAUDsCDACwWbFsKhYNzes1mggwAKChmZalYtms2w6MpQtSCoekXfuHvC4FAGpWn1dUAGhgdgzxTMcjCoklJADQqIqGKUl1G2AkohGtWNisXXRgAGgg9XlFBYAGVTYtmZbmvYQkHAopnYhqvFC2qTIAgJvypcr1O1GnS0gk6ZwlrfrN/mGZJlt2A2gMBBgAYKNSufKJ23wDDElqTkTowACABlWo8w4MqRJgjBUMvXZ43OtSAKAm9XtFBYAGVJwKMOY3A0OqzMFgBgYANKZjAUZ9d2BI0vPMwQDQIAgwAMBGJRs/cWtOROnAAIAGVZhcQpKM1e/t9ts60upsiuvZPUe9LgUAalK/V1QAaEBFG5eQ0IEBAI0r3wAdGKFQSO9b2amnXhuUZTEHA0D9I8AAABuVjMoNoD0zMKIqGObUXA0AQOMoGpNDPOt4BoYkrVnRqYHRvP44mPW6FAA4pfq+ogJAg5nqwLBpCYnEVqoA0IjypckOjDpeQiJJF6zslCQ99doRjysBgFOr7ysqADSYardE3IYhnscCDLZSBYBGU5jqwKjfJSSSdEZXkxa3JvT0a4NelwIAp0SAAQA2snMb1abJAIM5GADQeAqGqWg4pEh4/oG2k0KhkC5Y2aVn9jAHA0D9I8AAABvZGWBUOzDG8qV5vxYAwF2FkqlErL67L6rWrOjUkfGi/nBo3OtSAGBGBBgAYKPi5BDPuA0zMFqTUYUkjeQIMACg0eSNspJ1PsCzak11DsarzMEAUN8a46oKAA3Czm1Uo5GwmpNRAgwAaEBFw6z7HUiqlnektbwjpV+8yhwMAPUt6nUBANDIRrJFjR03o6J/JCdJGsoWFY02q28oq0Jp7kM421IxDRNgALNSLpf1sY99TIsXL9bdd9/tdTkIqHwDLSGRpLVnLtTDuw6oaJi2dBECgBMIMABgHsYKhna+cqzl9vcHRiRJv3t9RFZuSD3dlv7kbe1zfv32VEwDo4V51wkEyf3336+VK1dqfJz1/PBOwSirLRXzuoyafeCshfqvZ/frV/uO6oKVXV6XAwAnRbwKADYqm5UZGHZNnW9PxzWSKzIZHqjRwMCAduzYoY9//ONel4KAKzTQEhJJuuDtXYpFQvr5y4e9LgUApkUHBgDY6PgAw47NT9tSMZXKlrLFuS9DAYLky1/+sm655RZNTEzU9PxCoaBMJuNwVfbK5/MNV3Ot/HJuxWizcoWSyqWw+gf6px43SiX1D/Tr7K74CY+fzKmeY8drjCxOaPDIsbkXvYua9OMX3tBHzmyeeiwZsWQWTv2/J7/87Kbj5/Pz87lJnJ/fEGAAgI3s78CotB8zyBM4tZ/97Gfq6OjQueeeq2effbam70kkEurt7XW4MntlMpmGq7lWfjm3vqGsSqbU0dqsnu6eqcf7B/rV092jVDp9wuMnc6rn2PEa4URKLx48tkxxaWezfvf7AT11oDi1/GXtWV1atuBtMx5H8s/Pbjp+Pj8/n5vE+TWq6UKZxulrA4AGYEwu9YjaFGBUbyCHswQYwKk8//zzeuKJJ7Ru3Tpt2bJFzzzzjG6++Wavy0IAlcqmDNNSPNo4Qzwl6azFLZKkVw6OeVwJAJwcAQYA2Kg8uY2qnTMwJGk4V7Tl9QA/u+mmm7Rz50498cQTuuuuu/S+971Pd955p9dlIYCqy/6Ssca61V7UklBbKkaAAaBuNdZVFQDqnN1LSJriEUXDIY3QgQEADWNicnvtRIN1YIRCIZ21uEWvHhqXMRnIA0A9IcAAABtVl5DYFWCEQiG1pWIaZgYGMCvvfe97dffdd3tdBgJqYrIDo5F2Ial6Z0+LCoapPUdqG4QLAG5qvKsqANSxsmkpHJLCIXsCDElqS8cY4gkADSRbnOzAaLAlJJK0cmGz4tGwXnxj1OtSAOAtGu+qCgB1rGxatnVfVLWn4hrOMgMDABpFtjA5A6PBlpBIUjQS1jsWtyjTPypzsqsQAOoFAQYA2MiRACMd01jeYD0yADSI6gyMeAMuIZGkd/a0arxg6PWjWa9LAYATNOZVFQDqVCXAsPfS2p6KyZJ0eLxg6+sCAJzRyDMwJOkd3S2KhELa3c8yEgD1pTGvqgBQp8qmpajNHRht6Zgk6dAoAQYANILRyblF6XjU40rmJhmLaMXCJu1+Y1QWy0gA1BECDACwkeHAEpK2VCXAODiat/V1AQDOGM4WFYuEGnYJiSS9c0mrBieK+sOhca9LAYApjXtVBYA6VDYtRWzcgUSqDPGUpIN0YABAQxjOldSUaMzui6p3LW1TJBzS/3lhwOtSAGAKAQYA2MiJIZ7xaFjpeEQDdGAAQEMYzpbU1KDLR6rS8ah6u1v0v7sPqmgwRBpAfSDAAAAbORFgSFJHU1wHhnK2vy4AwH4juZKaEo23heqbvfu0BRrOlbTj5UNelwIAkggwAMBWjgYYwwQYANAIhrOlhh3gebwzF7WooymuH/y6z+tSAEASAQYA2MpwYBcSqRJgHBotqFSmjRcA6t1IrqSmeON3YETCIV1yzmI98dIhDbKVN4A6QIABADYqm6YjHRidTXGVLYtlJABQ5/KlsnKlcsMP8ay69NweGaal7/+KLgwA3iPAAAAbObeEJCFJ2nc0a/trAwDsc3SiKEkNP8SzasXCJl2wslP3P/1HugABeI4AAwBsZDgUYHQ2VbZS3T84YftrAwDsUw0w0j4Y4ll13YVnqH8kr//ze7ZUBeAtAgwAsJFTHRgtyagS0bD2DdKBAQD1zG8dGJK07uxFOqOrSf/+i72yLMvrcgAEGAEGANiobDkzxDMUCmlJe4olJABQ5/zYgREOh7TpwtP129eH9fz+Ia/LARBgrgcY/f39+vSnP62PfOQjuuyyy3Tfffe5XQIAOMapDgxJWtqe0j6WkABAXasGGM0+6sCQpI+9e5lak1Hd/fM9XpcCIMBcDzAikYg+//nP67HHHtMDDzyg7373u3r11VfdLgMAHFEJMJy5tC5tT2r/0SztuwBQx45OFBUOSUkfbKN6vKZEVP/3hWfop7sP6uWBMa/LARBQrgcYixYt0jnnnCNJam5u1ooVK3Tw4EG3ywAAR5RNZ5aQSNKSBSnlS6YOjRUceX0AwPwdzRbVmoopHHLmb4HbjLKpvqGs+oayuuScxUrFIrrzpy9PPdY3lNVItuh1mQACwtPetr6+PmUyGa1atWrG5xUKBWUyGZeqOlE+n/fs2EHDe+0O3md7FaPN6h/on/p3qWwql51Q/0C/jFJJ/QP9OrsrfsJzTqaW53S0tkiSdj6/W+cuTs2/eJ/gd9o9vNfAqR0dL6o9FfO6DNvkSqZ2vXZ06t/vOW2BHt99UOctbVNnc2WL77VndXlVHoCA8SzAmJiY0ObNm/WFL3xBzc3NMz43kUiot7fXpcpOlMlkPDt20PBeu4P32V59Q1n1dFeWdFiWJdM6orbWFvV0L1b/QL96unuUSqfV090z4+vU8pwzutKS9krNC9Xbu9yuU2h4/E67583vNWEG8FZHs0W1p/0TYLzZhWd26ek9g9rxymF97N3LvC4HQMB4sgtJqVTS5s2bdcUVV2j9+vVelAAAtjMnR1M4NcSzuy2pSDik/exEAgB16+hEUe2puNdlOKY1GdPq0zu0a/+QDrOkEYDLXA8wLMvSbbfdphUrVmjTpk1uHx4AHFOeTDAiDq17jkXCWtKe1L5BAgwAqFdDE0W1+bgDQ5I+dPYiRSNh/XT3gNelAAgY1wOMX//613rkkUf0zDPPaOPGjdq4caN+/vOfu10GANhuKsBwqANDkk7raNIf2UoVAOqSaVoayvprBsbJNCeiev/bu/TiG6N6na5AAC5yfQbGe97zHr388stuHxYAHGeYpiQpGnEuwDi7u0X/+cw+lcqmYhFPVgECAKYxkivJtOT7DgxJuujtXXpm71H9+MUBffK9zGUC4A7ufgHAJk4vIZGkdy1rU8Ew9YeD444dAwAwN4MTle1E29P+nYFRlYhFdPHZi7T3yIR2vnLE63IABAQBBgDYxI0lJOcta5ck/a5v2LFjAADmZig7GWD4fAlJ1erTO9TdmtT/98Sryhum1+UACAACDACwieHKDIy0WpJR/e7AiGPHAADMzeB4tQMjGAFGJBzSFauWaGA0rwd/T7AOwHkEGABgk2oHRtTBACMcDum8ZW16oY8AA3iz/v5+ffrTn9ZHPvIRXXbZZbrvvvu8LgkBU+3AaAtIB4YkndHVpA/3LtKDL4xoP7tkAXAYAQYA2CRXKkuSkvGIo8d519J2vTQwqoJRdvQ4QKOJRCL6/Oc/r8cee0wPPPCAvvvd7+rVV1/1uiwEyNGJYC0hqfrMh96uSFj6h0d3e10KAJ8jwAAAm0wUDElSU9zZDZ7OW9amUtnSywNjjh4HaDSLFi3SOeecI0lqbm7WihUrdPDgQY+rQpAcnSgqHY8oEXM2yK43C1sS+uSqBXo8c1A/e/mQ1+UA8DECDACwyUSx0hHRlHA2wHjX0jZJ0m9ZRgJMq6+vT5lMRqtWrfK6FATIkfGCOpv9vwPJyVzZ26YVXU36hx/upkMQgGOcvcsGgACpdmCkHPrkzSib6hvKSrLUnorpmdcG9aF3LJz6eksiqrYAbN0HnMrExIQ2b96sL3zhC2pubp7xuYVCQZlMxqXK7JHP5xuu5lo1+rm9fGBQHfGQBo8Mqn+g/y1fN0ol9Q/06+yu+Em/frxTPadeXkOSBjtCMksFXXd+i/7fxwf0xR88q0+uWjDj9zSaRv/dnImffcIQJQAAHvpJREFUz03i/PyGAAMAbJItGkrFIo7tQpIrmdr12lFJ0qLWhH69b0g7Xzky9fW1Z3URYCDwSqWSNm/erCuuuELr168/5fMTiYR6e3tdqMw+mUym4WquVaOf26Ef9OmScxaps6tTPd3WW77eP9Cvnu4epdJp9XT3zPhap3pOvbyGJLUtWKARSR88vVMXHzD1vd8d1sV/8na9fVElQPRDwN7ov5sz8fO5SZxfo5oulGEJCQDYZKJQVlPCnXXPS9tTOjSWp00XOI5lWbrtttu0YsUKbdq0yetyEDBj+ZKOThT1to4mr0txXa5k6vHdb2jnK0e05oxOJWMR3brtBf3spcPa+coRjU12KALAfBFgAIBNJoqG0g4P8Kw6o6tZpiXtPTLhyvGARvDrX/9ajzzyiJ555hlt3LhRGzdu1M9//nOvy0JA7JvcQvS0zrTHlXgrnYjqyvOXqn8krydeYqAnAHuxhAQAbJItlLWgyZ0W2dM604pFQvrDoXGd3d3qyjGBevee97xHL7/8stdlIKD2H60EGG/rCHaAIUnvXNKqd7+tXTtePqTTu9Jae1aX1yUB8Ak6MADAJhNFQ01xd5aQxCJhnd7ZpFcPjrtyPADAzOjAONFHVy3VotaEHvjl6xoYzXtdDgCfIMAAABtYlqVsoezaEhJJOnNRsw6PFzScLbp2TADAye0/OqHOprhakjGvS6kL8WhY/9efnaayaen2h3+vfImZTQDmjwADAGxQMEyVLcu1IZ6S9PbFLZKkVw/RhQEAXts3mNXb6L44QVdLQtf86XK91D+mG//7NzLNt+7MAgCzQYABADaYmJyw3uRiB8biloRaklH9gQADADy3bzCr05h/8RbvXNKqG9a9XT9+cUBffuzk2yICQK0Y4gkANsgWK62xbnZghEIhnbmoWS8NjMm0+FQLALxSMMp6YySnt3Uu87qUunTNe5ZpJFfSPb/Yq5ZkTJ/78JlelwSgQRFgAIANqh0Ybs7AkKS3L2rR8/uH9cZwztXjAgCO6RvKybJEB8Y0QqGQ/u6KczRWMPTPj78iSYQYAOaEAAMAbDAx1YHh7mX1rMXNioRCeqFvRHqvq4cGAEzazw4kMzLKpvpHcvrcxWcqWyzrnx9/RQdHc/p/PrhS4VBIktSSiKot7c5W5AAaFwEGANggW6zOwHBvCYlU6fg4c3Gzfts3zDISAPDIvsEJSdJpnU0eV1KfciVTu147Kkm66O1dGpoo6rvPva4XDozq43+6TLFIWGvP6iLAAHBKDPEEABtMFAxFwiHFo+5fVlctb9do3tBvXx92/dgAAGnf0azS8Yi6mvk/4KcSDoX00VVLtOGcbr1wYET/8eTeqQ8BAOBUCDAAwAYTxbKa4hGFJlth3dTb3ap4JKz/3X3I9WMDACpLSN7Wkfbkb0AjCoVCWnvWQn1i9XK9PpTTt36+h1lOAGpCgAEANpgoGK7Pv6iKR8N655JW/ezlQyoapic1AECQ7T0ywfyLOVi1rF3XXXiGJgr/f3t3Hh1VefcB/HtnzTZJyCQZwmJICAlFlqAsQcVKAi5JBgSBFsxhKSh6OAU8rW3t26JdbEXbt7b6Sumr1VpApYsICmgBbVR4laRAWIJhz56QScg6mfV5/0gyBEjMTJa5M5Pv5xzObHfu/T43w73P/O69z9ix+i95+PBUpdyRiMjHsYBBRNQPWqwOhHh5/IvOJo2IQGOrHf8uuiJbBiKiwehKowUXapqROnKI3FH8UkJ0KB6/ZzTiIoOx5q/5eOqfJ3hJCRF1iwUMIqJ+0Gyxe/0nVDtLitVhSIgaO/JKZMtARDQYHb5gAgDcMVovcxL/FR2mxR9zbsNj3xyNt48UI/ulz3CyrF7uWETkg1jAICLqB81W+S4hAQClQkLWxDgcKKzidcRERF50+HwNdEEq3DosXO4ofk2tVOBHD4zFttXT0WJxYP4rn+OlA2dhc/DSSCK6hgUMIqI+cjgFWm1Or/+E6o3mpQ6DAPD2l8Wy5iAiGkwOnzdhekIUVEp2q/vC7nCitK5tMNQ/r5iCu5Nj8Nt/FSHrD5/i46+qUd9ilTsiEfkAbmmJiPqo41rdEBnPwACAuIhg3JMcg7ePlPCIFRGRF5RdNeOSqQUzRkfLHcXvmW1O5BbVILeoBsdK6pEx1oCHp9+C8qutWPXGEby4/ywHqiYiFjCIiPqq2eoAANnPwACAnLR4VDda8K/TVXJHISIKeIfPc/yLgXTrsAhsyBiDiSMi8fqhS5j78mc4UcqxMYgGMxYwiIj6qMXSdgaGnGNgdLgnJRbDI4Px8sFzsNgdcschIgpoh87XICpUgxSDTu4oAStEq8LiKSPx3EMTUNtsxYOvfI7n951Bq437OKLBiAUMIqI+6jgDQ86fUe2gVEh42jgOpysasGnvV3LHISIKOPUtVpTWtaCkthmfna3BxBERKK83o7SuxfXPwi/X/e6upGj864lvYv7k4Xjlk/PIfukzHC2ukzsWEXmZ/IcLiYj8XIPZBgAIk/kMjI4B0MYNC8fC24fjz59fRLIhDHeNuXZttk6rQkSIRsaURET+rdFiR25RDS6bmlHdaMEdwRrkFtVcN83kWyJlShe47A4nGi02bJg9BmmJUXh+31dY8MohZE+MwyN3JyIqVMN9HNEgwAIGEVEfnb/ShCEhatkLGGabE0fP1wIAJg6PxKFzJjy96xRW3DEK8fpQAMDdydHs3BER9YODZ6oRolEidSSLFd7QeR8HAI99czQOFFbhgxMV+Oh0Fe5Kisb37kvmPo4owPESEiKiPrDYHTh/pQnJBh0kSZI7jotKqUBOWjzCtCr8+fOLOFvdKHckIqKAUVLbgrPVTZg5JgYaFbvTcghSK5E1cRg2ZCRjdEwYDpypxqLNh/HrPYW4bGqWOx4RDRCegUFE1AfHS+phcwikDPW9AdwiQzR49O5EvHHoEt48dBnzbxuOu5P5U39ERH318VfVCFYrkZYQJXeUQS9ap0VOWjwq6s04Vd6AVz+7iC25F5CWGIV7UmIxI1GPuMgg6EO1UCp850ADEfUOCxhERH1w+LwJKoWExOgwuaN0SRekxuq7ErHty8v4e34pgtUK/GzueCjYiSMi6pVjJVdxprIRc8YZoFXLP3gztYmLCMZDtw1HXUsSdh8vx7+LruC5vWdcr0sAIkPUMIQHIWWoDtMT9Jg5Jhojo0LkC01EHmMBg4ioDw5fMCExJtSnTyEO1iix8o4E7D5ejr/+XzGaLQ68sGgSj0QREXnoYk0z/uvdk9CHajAjUS93HLqB2eZEYUUjkmJ1SIrVocFsQ3FtCxotdjS12qELUqHFaseh8ya8d6wcADB11BA8OHk4sicMQ0SIWuYWEFFPWMAgIuqlizXNKK0zI3tinNxReqRUSJiXOgwTR0bg1U8vwuYU+N3iSVApfbfwQkTkS0xNFqx8/UsAwIo7RiGIZ1/4vPBgNcYPj3A9vmN0FFRKBYQQKKk1499FV/DhqUr817sn8cyuU0hL1OOO0XrclRSD+OgQ6LQqnxrfiohYwCAi6rVPvqoGAKQYfG/8i65IkoQVd4xCdJgWz+09g1Nl9ZgzzoDsicMwYUREzzMgIhqk8i/XYt1bx1DTZMGL30pFXYtN7kjUCzf+kkm8PhSPzExERX0rjhbXoaC0Hp+ercEmfAUACFIrEKPTIiZMixDJhgmXJSToQxGvD0FCdCiiw7S8JJPIy1jAICLqhUs1zfifj89jdEwo9GFaueO4ze5wIntiHIJUCnxwogKvtQ92NnNMNFbdlYDRMaEID1LzZ+iIiADUNlvxv59ewJ9yL2BYZBDefjQNMTotcotq5I5G/USSJAyLDMawyGBkTRyGpJgQnKlqQlVDK0xNVtQ2W2FqtuJCnQ2HSy7A4RSu96oUEqLDtDCEaxGjC0JsuBaG9ttYnRaG8CDE6rTQh3EAUaL+wgIGEZGHqhpakfPaF3A4nfj5vFtx2WSWO5LbOo4+aVRKzJ88Ag+Mj8PhCybkFl3Bp2drEBGsRlpiFNLHxmJ6gh63RIXw6BIRDSpOp8B/iuvwfkEF3jlSArPNgQW3Dcczc29FeJAapXUtckekAaQL1kClUGB4ZAiGR14b4LOisgKxsUORGBOCivpWlNWZUdNkganJipomCy7WNCHvUi2umm8+O0chAdFh2vbCRtC1gkenIkdsuBahWhWCVEqolRIvXSHqhiwFjNzcXDz77LNwOp1YtGgRHn30UTliEBF5RAiBD09V4dk9p1HXbMX2R9KgD9P4VQHjRkFqJWalxGL6qCgcL6vHxStN+OJiLT48VQWgbeyMISFqjBgSgtSRkRg/PAJJsWFI0IdCF6RicYN8DvsY5ImGVhuKTS0orWvB+SvNOF5yFXmX61DbbIVKISHjG7HISYtHQnQoGsw2NJhtsNgccscmmSgVEqJCtbhsMiNGF4QYXdBN09idTqQYwnDVbGsvblhharLA1Nx2W1LbgmMlV1HXbIXoYhlAW8EjSK1EsFqJYI0SIZq2+yEaFfRhmrbLWtovbYkND0JMmBaRIWoItPVVhACEAFRKCRqVAlqVAhqVAhqlgoUR8nteL2A4HA78/Oc/x+uvvw6DwYCFCxciPT0dSUlJ3o5CRASgbWdvcwjYHE7YHQI2Z9ttk8WO6sZWlF9txVeVDfjiYi0KSusxJjYMb66ahkkjIwPmSFyIVoUZifr2f0NQdrUVx0qvorrBgqstbaO4v3OkBG8cuuR6jyQBIWolYsODEBcRhLiIYAyPDEJseBAiQ9SICFYjMliD8GAVFO0dpsZWO+parLA7BcKDVAgPViM8SA1d0LXdkRDX5i9JgEKSoJSk9sfseFH32McIPKJ9gyA6fdPruOtwCljsDljszrZ/tmv3rXYnLHYHWqwONLba0dRqQ2OrHQ2tNpRdNaOk1ozi2hbU33C0PCE6FFPih2BIiAYpQ3UIUitRUts2fYfJt0QOdLPJj6kUCoQHa3D+SguAtktMoru41HRawhA0ttpharKgptkKU5MVZqvd9Rm2OZwwWx0w2xxotTlhtjnQZLGjpK4Ftc1WtFg9L6RJEtoLIe2FEbUKapUEm8WC8H/XQaVQQKWUoFJIUCkVUCsl13NqhQJKpQR1+2sdz3WeXqWQoO70mlIhtd2/4TWl4ubnVIq25XV+zTXQuOi4aS/OoL1QA7Q/bptAIV3L1DEfXroTeLxewCgoKEB8fDxGjhwJAMjKysKBAwe83rnYdbwc//3RVz1OZ7VaodFUdvt6d5XTbqf38A3CwyV4PH9PG+Ah4cECbHYbVKpyD+fvYZ6BXp8DOG9PP23dzd/usEOlLO3j3D372/Zu/p5O7/n6sTmdsDnEddezdkejVGBUdAi+f18ysifGQaVQoLSuJSCPxLXaBYprzYgK0SIqpK3TlZaoh1MI1DRZUNNohS5IiVabE81WO2qarKhuaMXZqiaYmi1wY3X2mqK9oKFQSK77wumESlkMSIAEQKGQIKGt2NHWb2kvfgDt2QScou0z0/nW4RRQSLiu06ZUtL3XEx5PD887V54swxAehL+umgatKvB/McFX+hj7TlZi074zcLYfCQWu7X+u+yJ+w/8V15f1Ll7v/H673Q6lqqyLeVy/jGvzETdM0WmaG5aJ65bpWa6u5tN5v9tdrs5NaHvqAgaKVqVAbLgWwyKCMWtsDIZFBGNYZHsBdkgwwrQqWGwOfHGxbsAyEAGAzSFQWNHoejwkRIMhncafmnxLJI4WX+32/Va7E8mGUNSb7TA1W9DYanft+67t8wRa7U7Y7E5YHU5YbE602hwwu24dcDoFmuFsLwjaYXcIOISA3dH2nN0pYHcI2J1O2J3C9Zyj/bmB3Of3B0kClBKgUFzqfho35tH9e7t/UbT3NyDa/hYCbbcAoJSk9v7M9QdqlArp5n6OuH5b21G4QXtBx+6wQ6ksva6o0zZt+7s6FX3aH143TwlSl/0rSZKgbM+naM+n6LQyJAmICdPiL9+ZhlCt98oKkvC0199H+/btw6effopnn30WALBz504UFBRg48aN3b7n2LFj0Gr9Z5A8IiKiwcBisSA1NVXuGC7sYxAREQWG7voYfjGIpy91joiIiChwsI9BRETkPxTeXqDBYEBl5bVLMqqqqmAwGLwdg4iIiAIM+xhERESBzesFjAkTJuDSpUsoKSmB1WrFBx98gPT0dG/HICIiogDDPgYREVFg8/olJCqVChs3bsTq1avhcDjw0EMPYcyYMd6OQURERAGGfQwiIqLA5vVBPImIiIiIiIiIPOX1S0iIiIiIiIiIiDzFAgYRERERERER+TwWMG6wd+9eZGVlYezYsThx4kS306Wnp8NoNGLevHlYsGCBFxMGDnfXdW5uLu677z7MmTMHf/rTn7yYMDBcvXoVK1euxL333ouVK1eivr6+y+m+8Y1vYN68eZg3bx4ee+wxL6f0Xz19Pq1WKzZs2IA5c+Zg0aJFKC0tlSFlYOhpXf/zn/9EWlqa63P8t7/9TYaU/u+pp57CjBkzkJ2d3eXrQgj88pe/xJw5c2A0GnHq1CkvJxx83NmOFxYW4lvf+haysrJgNBqxZ88eGZJ6zt191KpVqzBlyhSsWbPGywl7J5D3DT217ciRI5g/fz7GjRuHffv2yZCwb3pq3+uvv47MzEwYjUYsX74cZWVlMqTsnZ7a9tZbb7m+3yxZsgTnzp2TIWXvufud4cMPP0RKSsrXfv/wNewDdSLoOufOnRPnz58XOTk5oqCgoNvpZs2aJUwmkxeTBR531rXdbhcZGRmiuLhYWCwWYTQaxdmzZ72c1L9t2rRJbNmyRQghxJYtW8Tzzz/f5XSpqanejBUQ3Pl8bt26Vfz0pz8VQgjx/vvvi/Xr18sR1e+5s67/8Y9/iJ/97GcyJQwcX375pTh58qTIysrq8vVPPvlErFq1SjidTnH06FGxcOFCLyccfNzZjl+4cEFcvHhRCCFEZWWluPPOO0V9fb03Y/aKu/uoQ4cOiQMHDohHH33Um/F6JZD3De60raSkRBQWFoonn3xS7N27V6akveNO+w4fPixaWlqEEEJs27YtoP52jY2Nrvv79+8X3/nOd7wds9fc/c7Q2Ngoli5dKhYtWvS13/V8CftA1+MZGDcYPXo0EhMT5Y4xKLizrgsKChAfH4+RI0dCo9EgKysLBw4c8FLCwHDgwAE8+OCDAIAHH3wQ+/fvlzlR4HDn83nw4EHMnz8fAHDffffh8OHDEBw72WPcFnjP1KlTERER0e3rHdsUSZKQmpqKhoYGVFdXezHh4OPOdjwhIQGjRo0CABgMBkRFRaG2ttabMXvF3X3UjBkzEBoa6s1ovRbI+wZ32jZixAiMHTsWCoX/fc1wp31paWkIDg4GAKSmpqKyslKOqB5zp21hYWGu+2azGZIkeTtmr7nbT/j973+PRx55BFqtVoaUvcM+0PX8b8viQ1atWoUFCxbgnXfekTtKwKqqqsLQoUNdjw0GA6qqqmRM5H9MJhNiY2MBADExMTCZTF1OZ7FYsGDBAixevJhFDje58/msqqpCXFwcgLafeNTpdKirq/NqzkDg7rbgo48+gtFoxLp161BRUeHNiIPGjX+LoUOHcrs8wNzdjncoKCiAzWbDLbfc4o14feJp2/xBIO8bAr1f5mn7/v73v+Puu+/2RrQ+c7dt27Ztw+zZs/HCCy/gJz/5iTcj9ok77Tt16hQqKytxzz33eDld37APdD2V3AHksGLFCtTU1Nz0/IYNGzB79my35vHWW2/BYDDAZDJh5cqVSExMxNSpU/s7qt/rj3VNPfu69dyZJEndVtM//vhjGAwGlJSUYPny5UhOTvaLzi9Rh1mzZiE7OxsajQZvv/02fvjDH+LNN9+UOxaRW/pjOw4A1dXVePLJJ7Fp0yafOQLeX20j8iXvvfceTp48ia1bt8odpV89/PDDePjhh7F7925s3rwZmzZtkjtSv3A6nXjuuefw61//Wu4oA2Iw9YEGZQHjjTfe6PM8DAYDAECv12POnDkoKChgAaMLfV3XBoPhulPzqqqqXOuervm69azX61FdXY3Y2FhUV1cjKiqqy+k61uvIkSMxbdo0nD59mgWMHrjz+TQYDKioqMDQoUNht9vR2NiIIUOGeDuq33NnXXder4sWLcILL7zgtXyDyY1/i8rKSm6X+0F/bMebmpqwZs0aPPHEE0hNTR2gpJ7rj7b5k0DeNwR6v8zd9h06dAh//OMfsXXrVmg0Gm9G7DVP/3ZZWVl45plnvJCsf/TUvubmZhQVFWHZsmUAgCtXruDxxx/H5s2bMWHCBK/n9QT7QNfzjdK8n2lpaUFTU5Pr/ueff44xY8bInCowTZgwAZcuXUJJSQmsVis++OADpKenyx3Lr6Snp2Pnzp0AgJ07dyIjI+Omaerr62G1WgEAtbW1+M9//oOkpCSv5vRH7nw+09PT8e677wJoG/U6LS2NRxh7wZ113XkchoMHD2L06NHejjkodGxThBA4duwYdDqd6xIAGhjubMetVivWrl2LefPm4f777/d2xF5zp23+JpD3DYHeL3OnfadPn8bGjRuxefNm6PV6mZJ6zp22Xbp0yXX/k08+QXx8vJdT9l5P7dPpdPjiiy9w8OBBHDx4EKmpqX5RvADYB7qJzIOI+pyPPvpIzJw5U9x6661ixowZrtF3KysrxerVq4UQQhQXFwuj0SiMRqPIzMwUr7zyipyR/ZY761qIthHv7733XpGRkcF13Qu1tbVi2bJlYs6cOWL58uWirq5OCCFEQUGB+PGPfyyEECI/P19kZ2cLo9EosrOzxY4dO+SM7Fe6+ny++OKLYv/+/UIIIVpbW8V3v/tdMXv2bPHQQw+J4uJiOeP6tZ7W9W9+8xuRmZkpjEajyMnJEefOnZMzrt964oknxJ133inGjRsnZs6cKXbs2CG2b98utm/fLoQQwul0imeeeUZkZGSI7OxsvxnF3Z+5sx3fuXOnGDdunJg7d67r3+nTp+WM7RZ32iaEEEuWLBHTp08XEyZMEDNnzhS5ublyRXZLIO8bemrb8ePHxcyZM8WkSZPEtGnTRGZmppxxPdZT+5YvXy5mzJjh+n+2Zs0aOeN6pKe2/eIXvxCZmZli7ty5IicnRxQVFckZ12M9ta+znn5x0tewD3SNJIQfDHlMRERERERERIMaLyEhIiIiIiIiIp/HAgYRERERERER+TwWMIiIiIiIiIjI57GAQUREREREREQ+jwUMIiIiIiIiIvJ5LGAQERERERERkc9jAYOI3FZaWors7Oybnk9PT0dtba1b83jppZfw2muv9Xc0IiIi8kP90bf40Y9+hH379t30fFVVFdatW9fj+ydPnuzWcohIfixgEBERERFRwDEYDPjDH/4gdwwi6kcsYBCRR+x2O773ve/hgQcewLp162A2mwEAr776KoxGIxYuXIjLly+7Na/CwkIsXrwYRqMRa9euRX19PUwmExYsWAAAOHPmDFJSUlBeXg4AmD17tmt5REREFBj6o2+Rl5eHb3/728jIyHCdjdH57A6z2Yz169cjMzMTa9euxaJFi3DixAnX+3/3u99h7ty5WLx4MWpqagaopUTUVyxgEJFHLl68iKVLl2Lv3r0IDQ3F9u3bAQA6nQ67d+9GTk4OfvWrX7k1rx/84Af4/ve/j927dyM5ORkvv/wy9Ho9LBYLmpqakJeXh/HjxyMvLw9lZWXQ6/UIDg4eyOYRERGRl/VH36K6uhrbt2/Hli1b8Nvf/vam17dv346IiAjs2bMH69evx6lTp1yvtbS0YNKkSdi1axemTJmCHTt29G8DiajfsIBBRB6Ji4vD7bffDgCYO3cu8vPzAcB1hCMrKwvHjh3rcT6NjY1obGzEtGnTAADz589HXl4egLZrUfPz83HkyBE89thjyMvLQ15enmu5REREFDj6o28xe/ZsKBQKJCUldXkGRX5+PjIzMwEAycnJSElJcb2mVqsxa9YsAMD48eNRVlbW90YR0YBgAYOIPCJJ0tc+7g9TpkxBfn4+ysvLkZGRgTNnziA/Px9Tpkzp92URERGRvPqjb6HRaHq9fLVa7VqmQqGAw+Ho9byIaGCxgEFEHikvL8fRo0cBAO+//77riMnevXsBAHv27HFrNG+dTofw8HDXWRfvvfcepk6dCqCtgLFr1y7Ex8dDoVAgIiICubm5PAODiIgoAPVX3+Lr3Hbbba75nTt3DkVFRX2aHxHJgwUMIvJIQkICtm3bhgceeAANDQ1YsmQJAKC+vh5GoxFvvvkmnnrqKbfmtWnTJjz//PMwGo0oLCzE2rVrAQAjRoyAEMJV0Lj99tuh0+kQERExMI0iIiIi2fRn36I7S5cuRV1dHTIzM/Hiiy8iKSkJOp2uP+ITkRdJQgghdwgiIiIiIqKB4nA4YLfbodVqUVxcjBUrVmDfvn19uvSEiLxPJXcAIiIiIiKigWQ2m7Fs2TLY7XYIIfD000+zeEHkh3gGBhENiM2bN7t+h73D/fffj8cff1ymREREROTP2LcgIhYwiIiIiIiIiMjncRBPIiIiIiIiIvJ5LGAQERERERERkc9jAYOIiIiIiIiIfB4LGERERERERETk8/4f4Vm9FaSmVzcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n",
        "sns.distplot(prices.loc[prices.dollar_vol_rank<100, 'bb_low'].dropna(), ax=axes[0])\n",
        "sns.distplot(prices.loc[prices.dollar_vol_rank<100, 'bb_high'].dropna(), ax=axes[1])\n",
        "plt.tight_layout();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGWv5bmomsjC"
      },
      "source": [
        "### Compute Average True Range"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:27.379077Z",
          "start_time": "2021-04-15T20:00:27.377064Z"
        },
        "id": "HZVCqkH_msjF"
      },
      "outputs": [],
      "source": [
        "def compute_atr(stock_data):\n",
        "    df = ATR(stock_data.high, stock_data.low,\n",
        "             stock_data.close, timeperiod=14)\n",
        "    return df.sub(df.mean()).div(df.std())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:29.206072Z",
          "start_time": "2021-04-15T20:00:27.381072Z"
        },
        "id": "zU702EIcmsjG"
      },
      "outputs": [],
      "source": [
        "prices['atr'] = (prices.groupby('ticker', group_keys=False)\n",
        "                 .apply(compute_atr))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:29.564498Z",
          "start_time": "2021-04-15T20:00:29.207138Z"
        },
        "id": "mW-n7Dv_msjH",
        "outputId": "7282db8e-b54f-4a32-b16e-35442cf1f1cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.distplot(prices[prices.dollar_vol_rank<50].atr.dropna());"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LnNTNyY3msjJ"
      },
      "source": [
        "### Compute Moving Average Convergence/Divergence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:29.567238Z",
          "start_time": "2021-04-15T20:00:29.565333Z"
        },
        "id": "oOMhyEZkmsjK"
      },
      "outputs": [],
      "source": [
        "def compute_macd(close):\n",
        "    macd = MACD(close)[0]\n",
        "    return (macd - np.mean(macd))/np.std(macd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:31.732532Z",
          "start_time": "2021-04-15T20:00:29.568085Z"
        },
        "id": "ktpsSSuPmsjL"
      },
      "outputs": [],
      "source": [
        "prices['macd'] = (prices\n",
        "                  .groupby('ticker', group_keys=False)\n",
        "                  .close\n",
        "                  .apply(compute_macd))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-04-15T20:00:31.786384Z",
          "start_time": "2021-04-15T20:00:31.733339Z"
        },
        "id": "SyeyAP-6msjN",
        "outputId": "654a3ca2-d5c9-492b-df87-fc39bed140ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2,826,749.0\n",
              "mean             0.0\n",
              "std              1.0\n",
              "min            -10.5\n",
              "0.1%            -4.1\n",
              "1%              -2.6\n",
              "2%              -2.2\n",
              "3%              -2.0\n",
              "4%              -1.8\n",
              "5%              -1.6\n",
              "50%              0.0\n",
              "95%              1.6\n",
              "96%              1.7\n",
              "97%              1.9\n",
              "98%              2.1\n",
              "99%              2.6\n",
              "99.9%            4.0\n",
              "max              8.7\n",
              "Name: macd, dtype: object"
            ]
          },
          "metad

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