py_mlfactor.ipynb

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  "metadata": {
    "colab": {
      "name": "py-mlfactor.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Eire9PNPHV84V51fkKAoITcXKpv582qu",
      "authorship_tag": "ABX9TyP6V07Btxwjm/oW8AQiQ+f5"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLmdeyd_zlvI"
      },
      "source": [
        "# **Machine Learning for Factor Investing**\n",
        "\n",
        "This notebook implements and expands on the ideas presented in [Coqueret and Guida (2021)](http://www.mlfactor.com/), please refer to their book as it provides a really good, data-oriented introduction to quantitative equity investing. My goal in this exercise is for nothing more than my own practice and learning, and all credit goes to the original authors.\n",
        "\n",
        "I recommend running this notebook on [Google Colab](https://colab.research.google.com/), where you can up and running in only a few seconds. You might just need to download the [original data](https://github.com/shokru/mlfactor.github.io/blob/master/material/data_ml.RData) to your Google Drive."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRzqJgh50Gea"
      },
      "source": [
        "### **Chapter 0.** Importing stuff and setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ozdQi6moAfN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fcbbf56-dd51-41b0-9601-3e215ad964c5"
      },
      "source": [
        "# Basic stuff\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "from calendar import monthrange\n",
        "from dateutil.relativedelta import relativedelta\n",
        "\n",
        "# Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.style.use('seaborn')\n",
        "plt.rcParams['patch.facecolor'] = 'white' # This is helpful if you're using Colab in dark mode\n",
        "plt.rcParams['figure.figsize'] = 15, 7\n",
        "import graphviz\n",
        "\n",
        "# Reading Rdata - need to install in Collab\n",
        "!pip install pyreadr\n",
        "import pyreadr\n",
        "\n",
        "# Regression type stuff\n",
        "from statsmodels.formula.api import ols\n",
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "from statsmodels.distributions.empirical_distribution import ECDF\n",
        "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n",
        "\n",
        "# Trees\n",
        "from sklearn import tree\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, AdaBoostClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# Deep learning stuff\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyreadr in /usr/local/lib/python3.6/dist-packages (0.4.0)\n",
            "Requirement already satisfied: pandas>0.24.0 in /usr/local/lib/python3.6/dist-packages (from pyreadr) (1.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>0.24.0->pyreadr) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas>0.24.0->pyreadr) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>0.24.0->pyreadr) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>0.24.0->pyreadr) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfBNEtb00R1a"
      },
      "source": [
        "### **Chapter 1.** Notations and data\n",
        "\n",
        "Mostly ingesting the data and some quick and dirty data cleaning as well as initial exploration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "N-XJABFnoqO4",
        "outputId": "35c47a96-47b2-4893-a485-4a23d1e1fc32"
      },
      "source": [
        "Rdata = pyreadr.read_r('/content/drive/MyDrive/data/data_ml.RData')\n",
        "\n",
        "data = Rdata['data_ml']\n",
        "data['date'] = pd.to_datetime(data['date'], format = '%Y-%m-%d')\n",
        "\n",
        "data.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>stock_id</th>\n",
              "      <th>date</th>\n",
              "      <th>Advt_12M_Usd</th>\n",
              "      <th>Advt_3M_Usd</th>\n",
              "      <th>Advt_6M_Usd</th>\n",
              "      <th>Asset_Turnover</th>\n",
              "      <th>Bb_Yld</th>\n",
              "      <th>Bv</th>\n",
              "      <th>Capex_Ps_Cf</th>\n",
              "      <th>Capex_Sales</th>\n",
              "      <th>Cash_Div_Cf</th>\n",
              "      <th>Cash_Per_Share</th>\n",
              "      <th>Cf_Sales</th>\n",
              "      <th>Debtequity</th>\n",
              "      <th>Div_Yld</th>\n",
              "      <th>Dps</th>\n",
              "      <th>Ebit_Bv</th>\n",
              "      <th>Ebit_Noa</th>\n",
              "      <th>Ebit_Oa</th>\n",
              "      <th>Ebit_Ta</th>\n",
              "      <th>Ebitda_Margin</th>\n",
              "      <th>Eps</th>\n",
              "      <th>Eps_Basic</th>\n",
              "      <th>Eps_Basic_Gr</th>\n",
              "      <th>Eps_Contin_Oper</th>\n",
              "      <th>Eps_Dil</th>\n",
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              "      <th>Ev_Ebitda</th>\n",
              "      <th>Fa_Ci</th>\n",
              "      <th>Fcf</th>\n",
              "      <th>Fcf_Bv</th>\n",
              "      <th>Fcf_Ce</th>\n",
              "      <th>Fcf_Margin</th>\n",
              "      <th>Fcf_Noa</th>\n",
              "      <th>Fcf_Oa</th>\n",
              "      <th>Fcf_Ta</th>\n",
              "      <th>Fcf_Tbv</th>\n",
              "      <th>Fcf_Toa</th>\n",
              "      <th>Fcf_Yld</th>\n",
              "      <th>Free_Ps_Cf</th>\n",
              "      <th>...</th>\n",
              "      <th>Oa</th>\n",
              "      <th>Ocf</th>\n",
              "      <th>Ocf_Bv</th>\n",
              "      <th>Ocf_Ce</th>\n",
              "      <th>Ocf_Margin</th>\n",
              "      <th>Ocf_Noa</th>\n",
              "      <th>Ocf_Oa</th>\n",
              "      <th>Ocf_Ta</th>\n",
              "      <th>Ocf_Tbv</th>\n",
              "      <th>Ocf_Toa</th>\n",
              "      <th>Op_Margin</th>\n",
              "      <th>Op_Prt_Margin</th>\n",
              "      <th>Oper_Ps_Net_Cf</th>\n",
              "      <th>Pb</th>\n",
              "      <th>Pe</th>\n",
              "      <th>Ptx_Mgn</th>\n",
              "      <th>Recurring_Earning_Total_Assets</th>\n",
              "      <th>Return_On_Capital</th>\n",
              "      <th>Rev</th>\n",
              "      <th>Roa</th>\n",
              "      <th>Roc</th>\n",
              "      <th>Roce</th>\n",
              "      <th>Roe</th>\n",
              "      <th>Sales_Ps</th>\n",
              "      <th>Share_Turn_12M</th>\n",
              "      <th>Share_Turn_3M</th>\n",
              "      <th>Share_Turn_6M</th>\n",
              "      <th>Ta</th>\n",
              "      <th>Tev_Less_Mktcap</th>\n",
              "      <th>Tot_Debt_Rev</th>\n",
              "      <th>Total_Capital</th>\n",
              "      <th>Total_Debt</th>\n",
              "      <th>Total_Debt_Capital</th>\n",
              "      <th>Total_Liabilities_Total_Assets</th>\n",
              "      <th>Vol1Y_Usd</th>\n",
              "      <th>Vol3Y_Usd</th>\n",
              "      <th>R1M_Usd</th>\n",
              "      <th>R3M_Usd</th>\n",
              "      <th>R6M_Usd</th>\n",
              "      <th>R12M_Usd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>2006-12-31</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.16</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.104</td>\n",
              "      <td>0.344</td>\n",
              "      <td>-0.041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
              "      <td>2007-01-31</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.16</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.039</td>\n",
              "      <td>0.091</td>\n",
              "      <td>-0.011</td>\n",
              "      <td>-0.253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>2007-02-28</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.16</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-0.012</td>\n",
              "      <td>0.101</td>\n",
              "      <td>0.118</td>\n",
              "      <td>-0.366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17</td>\n",
              "      <td>2015-03-31</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.18</td>\n",
              "      <td>...</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.174</td>\n",
              "      <td>-0.144</td>\n",
              "      <td>-0.518</td>\n",
              "      <td>-0.376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>2015-04-30</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.18</td>\n",
              "      <td>...</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.99</td>\n",
              "      <td>-0.106</td>\n",
              "      <td>-0.576</td>\n",
              "      <td>-0.535</td>\n",
              "      <td>-0.113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 99 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   stock_id       date  Advt_12M_Usd  ...  R3M_Usd  R6M_Usd  R12M_Usd\n",
              "0        13 2006-12-31          0.25  ...    0.104    0.344    -0.041\n",
              "1        13 2007-01-31          0.25  ...    0.091   -0.011    -0.253\n",
              "2        13 2007-02-28          0.26  ...    0.101    0.118    -0.366\n",
              "3        17 2015-03-31          0.73  ...   -0.144   -0.518    -0.376\n",
              "4        17 2015-04-30          0.72  ...   -0.576   -0.535    -0.113\n",
              "\n",
              "[5 rows x 99 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "qeu6Ijr3pGuZ",
        "outputId": "032a59fc-5d15-44e2-fb24-98d3074a39e1"
      },
      "source": [
        "data.groupby('date')['stock_id'].count().plot(ylabel='n_assets',title='Number of distinct assets through time');"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkoqZEVzqwQv"
      },
      "source": [
        "features = data.columns.to_list()[2:-4]\n",
        "features_short = [\"Div_Yld\", \"Eps\", \"Mkt_Cap_12M_Usd\", \"Mom_11M_Usd\", \"Ocf\", \"Pb\", \"Vol1Y_Usd\"]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "ekWGnP5YrKrD",
        "outputId": "cde2c994-a706-424d-af30-de20bd3b5193"
      },
      "source": [
        "data.loc[(data['date']=='2000-2-29'),'Div_Yld'].hist(bins=100); \r\n",
        "# shows that for any given point in time, the features have been normalized\r\n",
        "# so you should expect a rough rectangle "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-J6A8wCr9qX"
      },
      "source": [
        "data['R1M_Usd_C'] = data.groupby('date')['R1M_Usd'].apply(lambda x: (x > x.median()))\n",
        "data['R12M_Usd_C'] = data.groupby('date')['R12M_Usd'].apply(lambda x: (x > x.median()))\n",
        "# this creates dummy variables indicated whether the return of a given stock was higher\n",
        "# than the median cross-section return. this will be used as the Y variable in\n",
        "# categorical prediction problems afterwards. i.e. we'll try predict which stocks will perform relatively better"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rx3GCgILu7jw"
      },
      "source": [
        "separation_date = '2014-1-15'\n",
        "separation_mask = (data['date'] < separation_date)\n",
        "\n",
        "training_sample = data.loc[separation_mask]\n",
        "testing_sample = data.loc[~separation_mask]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfIIS69hvxNx"
      },
      "source": [
        "stock_ids = data['stock_id'].unique().tolist()\n",
        "\n",
        "max_dates = data.groupby('stock_id')['date'].count().max()\n",
        "stocks_with_max_dates = data.groupby('stock_id')['date'].count() == max_dates\n",
        "stock_ids_short = stocks_with_max_dates.where(stocks_with_max_dates).dropna().index.tolist() # these are stocks who have data for all timestamps\n",
        "\n",
        "returns = data[data['stock_id'].isin(stock_ids_short)][['date','stock_id','R1M_Usd']]\n",
        "returns = returns.pivot(index='date',columns='stock_id');"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hy-9J7IF0kaq"
      },
      "source": [
        "### **Chapter 2.** Introduction\n",
        "\n",
        "There is no code in this section :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14J11TVD0kc-"
      },
      "source": [
        "### **Chapter 3.** Factor investing and asset pricing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "3ScBMoF11QwF",
        "outputId": "192b9c73-619b-4ab2-ab72-9ec688145b4b"
      },
      "source": [
        "# we create a simple size factor where a stock is \"large\" if it's larger than the cross-section median stock\n",
        "# and small if not. one would expect based on the size factor framework that small stocks outperform large\n",
        "# stocks.\n",
        "\n",
        "data['size'] = data.groupby('date')['Mkt_Cap_12M_Usd'].apply(lambda x: (x > x.median())).replace({True:'Large',False:'Small'})\n",
        "data['year'] = data['date'].dt.year\n",
        "\n",
        "return_by_size = data.groupby(['year','size'])['R1M_Usd'].mean().reset_index()\n",
        "\n",
        "ax = sns.barplot(x='year',y='R1M_Usd',hue='size',data=return_by_size)\n",
        "ax.set(xlabel = '', ylabel = 'Average return');\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\");"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8QySJJZX5d7Z",
        "outputId": "50dd8a14-9220-4287-9b58-cc95495b916d"
      },
      "source": [
        "# below we download the factor data made available by ken french (yeah, fama's buddy)\n",
        "# his website is a goldmine of factor-related data, it's highly advisable to do some digging\n",
        "ff_url = 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip'\n",
        "\n",
        "ff_factors = pd.read_csv(ff_url, compression='zip', skiprows=3)\n",
        "ff_factors.rename({'Unnamed: 0':'date','Mkt-RF':'MKT_RF'}, axis = 1, inplace=True)\n",
        "ff_factors = ff_factors.loc[ff_factors['date'].str.strip(' ').str.len() == 6]\n",
        "\n",
        "def last_day_of_month(date_value):\n",
        "    return date_value.replace(day = monthrange(date_value.year, date_value.month)[1])\n",
        "\n",
        "ff_factors['date'] = pd.to_datetime(ff_factors['date'], format = '%Y%m').apply(last_day_of_month)\n",
        "ff_factors[ff_factors.columns[1:]] = ff_factors[ff_factors.columns[1:]].apply(pd.to_numeric) / 100\n",
        "\n",
        "ff_factors.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</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>0</th>\n",
              "      <td>1963-07-31</td>\n",
              "      <td>-0.0039</td>\n",
              "      <td>-0.0045</td>\n",
              "      <td>-0.0094</td>\n",
              "      <td>0.0066</td>\n",
              "      <td>-0.0115</td>\n",
              "      <td>0.0027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1963-08-31</td>\n",
              "      <td>0.0507</td>\n",
              "      <td>-0.0082</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>0.0040</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>0.0025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1963-09-30</td>\n",
              "      <td>-0.0157</td>\n",
              "      <td>-0.0048</td>\n",
              "      <td>0.0017</td>\n",
              "      <td>-0.0076</td>\n",
              "      <td>0.0024</td>\n",
              "      <td>0.0027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1963-10-31</td>\n",
              "      <td>0.0253</td>\n",
              "      <td>-0.0130</td>\n",
              "      <td>-0.0004</td>\n",
              "      <td>0.0275</td>\n",
              "      <td>-0.0224</td>\n",
              "      <td>0.0029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1963-11-30</td>\n",
              "      <td>-0.0085</td>\n",
              "      <td>-0.0085</td>\n",
              "      <td>0.0170</td>\n",
              "      <td>-0.0045</td>\n",
              "      <td>0.0222</td>\n",
              "      <td>0.0027</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  MKT_RF     SMB     HML     RMW     CMA      RF\n",
              "0 1963-07-31 -0.0039 -0.0045 -0.0094  0.0066 -0.0115  0.0027\n",
              "1 1963-08-31  0.0507 -0.0082  0.0182  0.0040 -0.0040  0.0025\n",
              "2 1963-09-30 -0.0157 -0.0048  0.0017 -0.0076  0.0024  0.0027\n",
              "3 1963-10-31  0.0253 -0.0130 -0.0004  0.0275 -0.0224  0.0029\n",
              "4 1963-11-30 -0.0085 -0.0085  0.0170 -0.0045  0.0222  0.0027"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "pOlnNeZ3ueRd",
        "outputId": "36767646-07da-46fb-e6ee-ff3aaf7df913"
      },
      "source": [
        "temp_factors = ff_factors.copy()\n",
        "\n",
        "temp_factors['date'] = temp_factors['date'].dt.year\n",
        "temp_factors = pd.melt(temp_factors, id_vars = 'date')\n",
        "temp_factors = temp_factors.groupby(['date','variable']).mean().reset_index()\n",
        "\n",
        "plt = sns.lineplot(x='date',y='value',hue='variable', data = temp_factors);\n",
        "plt.legend(bbox_to_anchor=(1.05, 0.7), loc=2, borderaxespad=0.);\n",
        "plt.set_title('Average returns over time of common factors');\n",
        "# replicating this from the book for completeness only, but i think it's a pretty messy chart \n",
        "# it's hard to take much insight from it"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "98Fhent-pb3y",
        "outputId": "05dbe957-7313-4199-9a4f-ab736885fcac"
      },
      "source": [
        "# let's see how factors cumulative performance over time\r\n",
        "# but wrap that in a function that allows you to choose the start period (as that influences cumulative performance a lot)\r\n",
        "\r\n",
        "def plot_cumulative_performance(df, start_date = None):\r\n",
        "\r\n",
        "  # this function will plot cumulative performance for any wide dataframe of returns (e.g. index is date, columns are assets/factor)\r\n",
        "  # optional: you can pass the start date in %m/%d/%y format e.g. '1/1/1995', '12/15/2000'\r\n",
        "  # if you don't pass a start date, it will use the whole sample\r\n",
        "\r\n",
        "  cumul_returns = (1+df.set_index('date')).cumprod()\r\n",
        "\r\n",
        "  if start_date is None:\r\n",
        "    start_date = cumul_returns.index.min()\r\n",
        "  else:\r\n",
        "    start_date = datetime.strptime(start_date, '%m/%d/%Y')\r\n",
        "    cumul_returns = cumul_returns.loc[cumul_returns.index >= start_date]\r\n",
        "\r\n",
        "  first_line = pd.DataFrame([[1. for col in cumul_returns.columns]],\r\n",
        "                            columns = cumul_returns.columns,\r\n",
        "                            index = [start_date - relativedelta(months=1)])\r\n",
        "\r\n",
        "  cumul_returns = pd.concat([first_line,cumul_returns])\r\n",
        "\r\n",
        "  return cumul_returns.plot(title = f'Cumulative factor performance since {start_date.strftime(\"%B %Y\")}')\r\n",
        "\r\n",
        "plot_cumulative_factor_performance(ff_factors);"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiNPjtKWbAxe"
      },
      "source": [
        "Below we perform **Fama-Macbeth regressions**, which is the standard way of validating a factor's risk premium in the cross-section of stock returns. As we will see, this involves a two-step process of:\r\n",
        "\r\n",
        "*   Time-series regression: regress each asset's returns on factors, i.e. one regression per asset. Store the coefficients.\r\n",
        "*   Cross-section regression: regress each asset's returns on coefficients obtained in previous step, i.e. one regression per time period.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "skLl8esqxUeU",
        "outputId": "7bb66fe6-fef0-4a9f-d1d6-98e9c13dcb63"
      },
      "source": [
        "# merging and cleaning up the data before we run the regressions\n",
        "data_fm = data[['date','stock_id','R1M_Usd']][data['stock_id'].isin(stock_ids_short)]\n",
        "data_fm = data_fm.merge(ff_factors, on = 'date')\n",
        "data_fm['R1M_Usd'] = data_fm.groupby('stock_id')['R1M_Usd'].shift(1)\n",
        "data_fm.dropna(inplace=True)\n",
        "\n",
        "# running time series regressions\n",
        "\n",
        "reg_output = {}\n",
        "\n",
        "for k,g in data_fm.groupby('stock_id'):\n",
        "  model = ols('R1M_Usd ~ MKT_RF + SMB + HML + RMW + CMA', data=g)\n",
        "  results = model.fit()\n",
        "\n",
        "  reg_output[k] = results.params\n",
        "\n",
        "betas = pd.DataFrame.from_dict(reg_output).T\n",
        "betas.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Intercept</th>\n",
              "      <th>MKT_RF</th>\n",
              "      <th>SMB</th>\n",
              "      <th>HML</th>\n",
              "      <th>RMW</th>\n",
              "      <th>CMA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.002153</td>\n",
              "      <td>0.858778</td>\n",
              "      <td>0.854282</td>\n",
              "      <td>0.758870</td>\n",
              "      <td>0.331286</td>\n",
              "      <td>-0.305538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.003498</td>\n",
              "      <td>0.288709</td>\n",
              "      <td>0.326704</td>\n",
              "      <td>-0.129584</td>\n",
              "      <td>0.631775</td>\n",
              "      <td>0.396834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.006208</td>\n",
              "      <td>0.365521</td>\n",
              "      <td>0.654501</td>\n",
              "      <td>0.236258</td>\n",
              "      <td>0.256116</td>\n",
              "      <td>0.141819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.003280</td>\n",
              "      <td>0.837283</td>\n",
              "      <td>0.597429</td>\n",
              "      <td>1.051493</td>\n",
              "      <td>0.111087</td>\n",
              "      <td>0.116409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.000342</td>\n",
              "      <td>1.187396</td>\n",
              "      <td>-0.145918</td>\n",
              "      <td>1.363226</td>\n",
              "      <td>0.122050</td>\n",
              "      <td>-0.339385</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Intercept    MKT_RF       SMB       HML       RMW       CMA\n",
              "3   -0.002153  0.858778  0.854282  0.758870  0.331286 -0.305538\n",
              "4    0.003498  0.288709  0.326704 -0.129584  0.631775  0.396834\n",
              "7    0.006208  0.365521  0.654501  0.236258  0.256116  0.141819\n",
              "9    0.003280  0.837283  0.597429  1.051493  0.111087  0.116409\n",
              "16   0.000342  1.187396 -0.145918  1.363226  0.122050 -0.339385"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ElUn6HOg2qCi",
        "outputId": "2eccd612-1a45-4463-e6ed-20677ed06cd9"
      },
      "source": [
        "# prepping coeficient data to run second round of regressions\n",
        "loadings = betas.drop('Intercept', axis = 1).reset_index(drop=True)\n",
        "ret = returns.T.reset_index(drop=True)\n",
        "fm_data = pd.concat([loadings,ret], axis=1)\n",
        "fm_data = pd.melt(fm_data,id_vars=['MKT_RF','SMB','HML','RMW','CMA'])\n",
        "\n",
        "# running cross section regressions\n",
        "\n",
        "reg_output_2 = {}\n",
        "\n",
        "for k,g in fm_data.groupby('variable'):\n",
        "  model = ols('value ~ MKT_RF + SMB + HML + RMW + CMA', data=g)\n",
        "  results = model.fit()\n",
        "\n",
        "  reg_output_2[k] = results.params\n",
        "\n",
        "# refer to the mlfactor book or the fama-macbeth literature for more info on what the gammas stand for\n",
        "# but you can think of them as an estimate of a given factor's risk premium at a point in time\n",
        "gammas = pd.DataFrame.from_dict(reg_output_2).T.reset_index().rename({'index':'date'}, axis = 1)\n",
        "gammas.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Intercept</th>\n",
              "      <th>MKT_RF</th>\n",
              "      <th>SMB</th>\n",
              "      <th>HML</th>\n",
              "      <th>RMW</th>\n",
              "      <th>CMA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1998-11-30</td>\n",
              "      <td>0.021426</td>\n",
              "      <td>0.080686</td>\n",
              "      <td>-0.033403</td>\n",
              "      <td>-0.049437</td>\n",
              "      <td>-0.021475</td>\n",
              "      <td>-0.040430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1998-12-31</td>\n",
              "      <td>0.012475</td>\n",
              "      <td>0.021139</td>\n",
              "      <td>0.002156</td>\n",
              "      <td>-0.044074</td>\n",
              "      <td>-0.055624</td>\n",
              "      <td>-0.058398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1999-01-31</td>\n",
              "      <td>-0.026566</td>\n",
              "      <td>-0.043738</td>\n",
              "      <td>-0.014593</td>\n",
              "      <td>0.035471</td>\n",
              "      <td>0.009667</td>\n",
              "      <td>0.034382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1999-02-28</td>\n",
              "      <td>-0.034912</td>\n",
              "      <td>0.046694</td>\n",
              "      <td>-0.020990</td>\n",
              "      <td>0.001394</td>\n",
              "      <td>-0.019188</td>\n",
              "      <td>-0.014189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1999-03-31</td>\n",
              "      <td>-0.001502</td>\n",
              "      <td>0.096012</td>\n",
              "      <td>0.007468</td>\n",
              "      <td>0.000645</td>\n",
              "      <td>-0.024486</td>\n",
              "      <td>-0.018530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  Intercept    MKT_RF       SMB       HML       RMW       CMA\n",
              "0 1998-11-30   0.021426  0.080686 -0.033403 -0.049437 -0.021475 -0.040430\n",
              "1 1998-12-31   0.012475  0.021139  0.002156 -0.044074 -0.055624 -0.058398\n",
              "2 1999-01-31  -0.026566 -0.043738 -0.014593  0.035471  0.009667  0.034382\n",
              "3 1999-02-28  -0.034912  0.046694 -0.020990  0.001394 -0.019188 -0.014189\n",
              "4 1999-03-31  -0.001502  0.096012  0.007468  0.000645 -0.024486 -0.018530"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "qwoBZ1TX5wE0",
        "outputId": "7e5ec21f-7fa0-45f7-9079-e22d217165df"
      },
      "source": [
        "# since we get one estimate of that risk premium for each time step, we can plot how it evolves over time\n",
        "x = pd.melt(gammas.drop('Intercept',axis=1), id_vars='date')\n",
        "\n",
        "g = sns.FacetGrid(x, col='variable');\n",
        "g.map(sns.lineplot, 'date', 'value');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aj4S2POPfHHp"
      },
      "source": [
        "Below we deploy the factor competition strategy outlined in the book. The main idea here is to regress a factor on the remaining factors and test whether the coefficient is significant. A significant coefficient means that the factors on the right-hand side don't completely explain the factor on the left-hand side, which naturally means the latter is useful.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PWROYGUb9A4T",
        "outputId": "89d93729-9df0-4361-d5b0-3e6a6de47515"
      },
      "source": [
        "factor_comp = pd.melt(ff_factors.drop('RF',axis=1),id_vars='date')\n",
        "\n",
        "factor_comp = factor_comp.merge(ff_factors.drop('RF',axis=1), on = 'date')\n",
        "\n",
        "factor_comp_coefs = {}\n",
        "factor_comp_tstats = {}\n",
        "\n",
        "for k,g in factor_comp.groupby('variable'):\n",
        "\n",
        "  reg_data = g.drop([k,'date','variable'],axis=1)\n",
        "  formula = 'value ~ ' + ' + '.join(reg_data.columns.values[1:].tolist())\n",
        "  \n",
        "  model = ols(formula, data=reg_data)\n",
        "  results = model.fit()\n",
        "\n",
        "  factor_comp_coefs[k] = results.params\n",
        "  factor_comp_tstats[k] = results.tvalues\n",
        "\n",
        "alphas = pd.DataFrame.from_dict(factor_comp_coefs).T\n",
        "alphas_tstats = pd.DataFrame.from_dict(factor_comp_tstats).T\n",
        "\n",
        "alphas_tstats"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CMA</th>\n",
              "      <th>HML</th>\n",
              "      <th>Intercept</th>\n",
              "      <th>MKT_RF</th>\n",
              "      <th>RMW</th>\n",
              "      <th>SMB</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CMA</th>\n",
              "      <td>NaN</td>\n",
              "      <td>23.701945</td>\n",
              "      <td>4.701035</td>\n",
              "      <td>-8.866156</td>\n",
              "      <td>-5.132745</td>\n",
              "      <td>-2.078712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HML</th>\n",
              "      <td>23.701945</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.072137</td>\n",
              "      <td>1.764050</td>\n",
              "      <td>4.285108</td>\n",
              "      <td>2.261494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MKT_RF</th>\n",
              "      <td>-8.866156</td>\n",
              "      <td>1.764050</td>\n",
              "      <td>5.106375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-4.242858</td>\n",
              "      <td>5.467244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMW</th>\n",
              "      <td>-5.132745</td>\n",
              "      <td>4.285108</td>\n",
              "      <td>4.879387</td>\n",
              "      <td>-4.242858</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-8.555502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SMB</th>\n",
              "      <td>-2.078712</td>\n",
              "      <td>2.261494</td>\n",
              "      <td>2.457764</td>\n",
              "      <td>5.467244</td>\n",
              "      <td>-8.555502</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              CMA        HML  Intercept    MKT_RF       RMW       SMB\n",
              "CMA           NaN  23.701945   4.701035 -8.866156 -5.132745 -2.078712\n",
              "HML     23.701945        NaN  -1.072137  1.764050  4.285108  2.261494\n",
              "MKT_RF  -8.866156   1.764050   5.106375       NaN -4.242858  5.467244\n",
              "RMW     -5.132745   4.285108   4.879387 -4.242858       NaN -8.555502\n",
              "SMB     -2.078712   2.261494   2.457764  5.467244 -8.555502       NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6gGxAQt0EYMO",
        "outputId": "c108556d-accd-4637-ee7a-51db6bc9bcf8"
      },
      "source": [
        "alphas_table = alphas.round(3).applymap(str)\n",
        "\n",
        "prob99 = 2.58\n",
        "prob95 = 1.96\n",
        "\n",
        "alphas_table[alphas_tstats >= prob99] = alphas_table[alphas_tstats >= prob99] + ' (**)'\n",
        "alphas_table[alphas_tstats.apply(lambda x: x.between(prob95, prob99))] = alphas_table[alphas_tstats.apply(lambda x: x.between(prob95, prob99))] + ' (*)'\n",
        "\n",
        "factors = factor_comp.columns[3:].tolist()\n",
        "\n",
        "alphas_table = alphas_table[['Intercept'] + factors].reindex(factors)\n",
        "\n",
        "alphas_table"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Intercept</th>\n",
              "      <th>MKT_RF</th>\n",
              "      <th>SMB</th>\n",
              "      <th>HML</th>\n",
              "      <th>RMW</th>\n",
              "      <th>CMA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MKT_RF</th>\n",
              "      <td>0.008 (**)</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.292 (**)</td>\n",
              "      <td>0.128</td>\n",
              "      <td>-0.317</td>\n",
              "      <td>-0.929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SMB</th>\n",
              "      <td>0.003 (*)</td>\n",
              "      <td>0.143 (**)</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.114 (*)</td>\n",
              "      <td>-0.431</td>\n",
              "      <td>-0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HML</th>\n",
              "      <td>-0.001</td>\n",
              "      <td>0.035</td>\n",
              "      <td>0.065 (*)</td>\n",
              "      <td>nan</td>\n",
              "      <td>0.169 (**)</td>\n",
              "      <td>1.024 (**)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RMW</th>\n",
              "      <td>0.004 (**)</td>\n",
              "      <td>-0.081</td>\n",
              "      <td>-0.224</td>\n",
              "      <td>0.155 (**)</td>\n",
              "      <td>nan</td>\n",
              "      <td>-0.281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CMA</th>\n",
              "      <td>0.003 (**)</td>\n",
              "      <td>-0.111</td>\n",
              "      <td>-0.039</td>\n",
              "      <td>0.44 (**)</td>\n",
              "      <td>-0.132</td>\n",
              "      <td>nan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Intercept      MKT_RF         SMB         HML         RMW         CMA\n",
              "MKT_RF  0.008 (**)         nan  0.292 (**)       0.128      -0.317      -0.929\n",
              "SMB      0.003 (*)  0.143 (**)         nan   0.114 (*)      -0.431       -0.16\n",
              "HML         -0.001       0.035   0.065 (*)         nan  0.169 (**)  1.024 (**)\n",
              "RMW     0.004 (**)      -0.081      -0.224  0.155 (**)         nan      -0.281\n",
              "CMA     0.003 (**)      -0.111      -0.039   0.44 (**)      -0.132         nan"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vyiXTi0gOf3"
      },
      "source": [
        "Below we explore factor time series momentum by looking at the partial auto-correlation functions. The shaded lines are confidence intervals, which the *statsmodels* library handily calculates for us by default (at 5% confidence levels).\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "wk5ClzvnKrV2",
        "outputId": "394dd83c-0826-4aff-990e-f6c07e6f3895"
      },
      "source": [
        "nfactors = len(factors)\n",
        "\n",
        "fig, axs = plt.subplots(ncols=nfactors, figsize = (20,2))\n",
        "\n",
        "for i, factor in enumerate(factors):\n",
        "  plot_pacf(ff_factors[factor], ax = axs[i], lags = 10, zero = False, title = factor);"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PWIcyRiQq2nGt2WEBAMLQ/PnzvZb79+/v+f9zzz3nc5uVK1e2aUwAAJiJoWcAgm7djiM6WuFUvUs6WuHUuh1HzA4JAAAAAKAQ71FErwXAmo5VOv0uAwAAILwtW7ZMe/bskc1mU05OjgYPHuxZV1NTo8cee0wHDhxQQUFBi7YB0HIh3aOIXguANfWMdfhdBgAAQPjauXOnDh8+rPz8fC1dulRLly71Wr98+XINGDDgqrYB0HIh3VBErwXAmjJGJOpM+QHV19epV1xDbz8AAABAkoqKipSWliZJSkpKUnV1tZzOy9dyc+fO9axv6TYAWi6kG4rotQBYU7TjWn22+Vntzp+jqWl9GRIKAAAAj8rKSnXt2tWzHBMTo4qKCs+yw9H4uq65bQC0XEjXKMoYkagVeW+rc+xNSkzoQq8FAJZGXTUAAIDGXC5Xm20TFxd11fu+kt1uM2Q/Ru3DKMTiW7jEEtINRe5eC5K0sHCbydEAgH/uumrS5bpqU9P6mhwVAABAcMXHx6uystKzXF5erri4OMO3kaSKijOBByqpvt5lyH7i4qJavQ+jEItvoRhLU41NAQ89W7ZsmTIzM5WVlaW9e/d6rdu+fbsmTZqkzMxMvfDCC5Kk4uJijRgxQtnZ2crOztbjjz8e6EsD8IPcbL+oqwYAACClpqZqw4YNkqR9+/YpPj7e53Cz1m4DwLeAehRdWVG+pKREOTk5ys/P96xfsmSJ8vLylJCQoGnTpmncuHGSpJSUFD333HPGRI6AMbwldJGb7VvPWIenR5F7GQAAINwMGzZMAwcOVFZWlmw2mxYvXqyCggJFRUUpPT1ds2fP1vHjx3Xo0CFlZ2drypQpuvvuuxttAyAwATUUNVVR3uFwqLS0VF26dFGPHj0kSaNGjVJRUZH69etnXNRoFYa3hC5ys32jrhoAAECD+fPney3379/f8/+mbnB+dRsAgQlo6Jm/ivIVFRWKiYnxue7gwYOaMWOG7r//fm3bRs0gszC8JXSRm+0bs8EBAAAAMJshxaxbUlH+xhtv1KxZszRhwgSVlpZq+vTp2rhxoyIjI5vd1l817+PVNX63dVehj47u1OzrtIRR+zFCoLEkdr9e//jitNdya48rFM5LW/AXSzAq5pOb5mhNLJyX4DA7NwEAAACrCqihyF9F+a+uKysrU3x8vBISEpSRkSFJSkxMVGxsrMrKytS7d+9mX89fNe+qqi/9buuuQt/c81oiOrqTIfsxQmtiGXtLL6/hLWNv6dWq4wqV82K05mJpSZX6q71gDbfc9NTbOnFWPbt1tkS9rdZ+Bvmb1fbMyE0AAACgvQho6Jm/ivK9evWS0+nU0aNHdfHiRW3ZskWpqakqLCxUXl6epIYhMCdOnFBCQoJBh4GrwfCW0BVuuemut1Vf7/LU2wIA4Gr4my20pqZGCxYs0L333tvibQAAaO8C6lHUXBX63NxczZs3T5KUkZGhPn36KC4uTvPnz9fmzZtVW1ur3NzcFg1tAdBy4Zab1NsCALRGc7OFLl++XAMGDNCBAwdavA0AAO1dwDWK/FWhHz58eKMvTIfDoZUrVwb6cgBaKJxyk+nkAQCt4W+2UEmaO3euqqqqVFhY2OJtAABo7wIaegYAVpAxIlFnyg/IVV+nXnEOppMHAFwVf7OFSvLZ+NPcNgAAtHeGzHoGAGZw19uy221asOZ9s8MBALRzLZktNNBtWlsE3z0rphHF9K1UkJ9YfCMWAGaioQgAAABhyd9soUZuI7VsRkV/3LNitnY/cXFRrd6HUYjFt1CMpT03Nn16+JTf9bUX61v0vOYcr66xzAyxxOJbe4ul/w1d/a73h6FnABBiqpw1enXTAS16cbte3XRAVc4as0MCAEvyN1uokdsAAGCEYP3Op0cRAISYdTuOeIp8H61wat2OI5qa1tfkqADAepqbLXT27Nk6fvy4Dh06pOzsbE2ZMkV33313o20AAAiGYP3Op6EIAELMsUqn32UAwGX+Zgt97rnnWrQNAMBYVc4ardtxRMdOnFXPbp2VMSJR0Y5rzQ6rVTzHVOlUz1hHQMcUrN/5DD2Dqdxd557844cMkQEM0jPW4XcZ1rJs2TJlZmYqKytLe/fu9VpXU1OjBQsW6N577zUpOgAAgOBz95ypr3d5es60d55jcingYwrW73waimAqI5IFgLeMEYk6U35Arvo69YpruFsBa9q5c6cOHz6s/Px8LV26VEuXLvVav3z5cg0YMMCk6AAAAMwRij3kjTimYP3OZ+gZTBWKfwBCsZsk2pdox7X6bPOzstttWrDmfbPDgR9FRUVKS0uTJCUlJam6ulpOp9NTGHfu3LmqqqpSYWGhmWECAACLM2JYk5X0jHV4avG4l9s7I44pWL/z6VEEU4XiEJlQ7CYJoG1UVlaqa9fLU5fGxMSooqLCs8xMSgDQPpRXndPP/7BbP1i+RT//w26VV50zOySEmVAbqRGKPeTdx1TfDo6JHkUwVcaIRK3Ie1udY29SYkIXSydLS4ViLykAweFyuVq9j7i4qFZtb7fbDNmPUfswCrH4RiyAMV5e+4n2l1ZJkvaXVunltZ9o4XeGmRxV+7Zs2TLt2bNHNptNOTk5Gjx4sGfd9u3b9dRTTykiIkJ33nmnZs6cqeLiYs2ZM0d9+zbMANWvXz89+uijZoUfdKF2DRKKPeTdxyRJCwu3mRyNfzQUwVTtKVlaKhS7SQKhwIrDQuPj41VZWelZLi8vV1xcXKv2WVFxplXb19e7DNlPXFxUq/dhFGLxLRRjobEJZin5vNrvMq7OlTX8SkpKlJOTo/z8fM/6JUuWKC8vTwkJCZo2bZrGjRsnSUpJSWlytsJQxzUIjERDUZCE2phRNM3dS8oRe5N6h0gvKSAUuLtkS5e7ZE9N62tqTKmpqXr++eeVlZWlffv2KT4+nuFmQJj69PApv+trL9a36HnNOV5do6qqL1u1D6NYIRajbiL06NbZ6yK9R7fOAb9XVjgvbi2Jpf8NXf2uD4S/Gn6lpaXq0qWLevToIUkaNWqUioqK1K9fP8PjaE9CcaQGzEONoiAJtTGjaJq7l9SHb/xEU9P60iCIsFblrNGrmw5o0Yvb9eqmA6py1pgWixW7ZA8bNkwDBw5UVlaWlixZosWLF6ugoEB//vOfJUmzZ8/WT3/6Ux06dEjZ2dn605/+ZHLEABBajKot2Z5qj7QH/mr4VVRUKCYmxue6gwcPasaMGbr//vu1bVtojFZoKfc1yO78OVyDoNXoURQkVrxAAYC2ZqVePFbtkj1//nyv5f79+3v+H67d5wEgWIz6jR6K5RSspCU1/G688UbNmjVLEyZMUGlpqaZPn66NGzcqMjLS73bNDVk9Xu3/Jpe7tl90dKdmY2xOa/dhpViMYOTxGMUK71FL99Ga4dg0FAWJVS9QAKAtWamRnGGhAICv4je6Nfmr4ffVdWVlZYqPj1dCQoIyMjIkSYmJiYqNjVVZWZl69+7t97Waq4/W3NA7d22/1g4XjI7u1Op9WCkWI9TXu2S32ywRi2Sd96il56Ultf+aakxi6FmQ0B0VQDj66g9uM3+AMywUAPBVoTgFdyhITU3Vhg0bJKlRDb9evXrJ6XTq6NGjunjxorZs2aLU1FQVFhYqLy9PUsPwtBMnTighIcG0YwDaM3oUBQndUQGEI3rxAACsLBSn4A4FV9bws9lsnhp+UVFRSk9PV25urubNmydJysjIUJ8+fRQXF6f58+dr8+bNqq2tVW5ubrPDzgD4RkMRAKARo2Zq5Ac4AAAIhL8afsOHD1d+fr7XeofDoZUrVwYlNiDU0VAEAGjESkWoYSym4DYXsfhm1hTckrRs2TLt2bNHNptNOTk5Gjx4sGfd9u3b9dRTTykiIkJ33nmnZs6cqeLiYs2ZM0d9+zb8TezXr58effTRNokNAAAz0FAEAGjESkWoAaCt7Ny5U4cPH1Z+fr5KSkqUk5Pj1UthyZIlysvLU0JCgqZNm6Zx48ZJklJSUpiVEAAQsihmDQBoxEpFqAGgrRQVFSktLU2SlJSUpOrqajmdDQ3jpaWl6tKli3r06CG73a5Ro0apqKjIzHABAAgKGooAAI0wUyMCVeWs0aubDmjRi9v16qYDqnLWmB0S0KTKykp17Xp5SFtMTIwqKiokNcyaFBMT43PdwYMHNWPGDN1///3ato1JShB6+FsOhDeGngEAGmGmRgSK+lZoz1wuV7PPufHGGzVr1ixNmDBBpaWlmj59ujZu3Njs7EpxcVF+1x+v9n8hbrfbJEnR0Z2ajbE5RuyjNU6ePq/VfzmgI2VnlJgQpUmj+yrm+o6mxWPUuQ2l9+j1rSVef8s37jqqh789yOdzm/tswz/PBCInzqpnt84BTyACGImGIgAAYBjqW6E9iY+PV2VlpWe5vLxccXFxPteVlZUpPj5eCQkJysjIkCQlJiYqNjZWZWVl6t27t9/Xqqg443d9c8W86+tdLXpec6KjO5lexPyPmw54GiH+8cVp/XHjZ6Y2KNfXu2S321p9XkLpPTpy/HSj5aZiau6zLdGY5A83WGBFAQ89W7ZsmTIzM5WVlaW9e/d6rdu+fbsmTZqkzMxMvfDCCy3aBoAxyE0AZqK+FdqT1NRUbdiwQZK0b98+xcfHy+Fo+Mz26tVLTqdTR48e1cWLF7VlyxalpqaqsLBQeXl5khqGp504cUIJCQmmHUN7RIOy9fG3PHjIB1hRQD2KApkh4uTJk363QcvQNRH+kJsAzJYxIlEr8t6WI/Ym9U7oQn0rWNqwYcM0cOBAZWVlyWazafHixSooKFBUVJTS09OVm5urefPmSZIyMjLUp08fxcXFaf78+dq8ebNqa2uVm5vb7LAzeOsZ6/D0oHAvw1r4Wx485AOsKKCGoqZmiHA4HF4zREjyzBBx8uTJJrfx56ElG1VX1/R48dq6er/bD7r7Z5KklYX7Wnx8TbHbbZ4upYEwIpaz52pVdymGoxVO5a39RJ2vu8aUWNyscF6MisUog+7+mWTzf0zXRDTfoe+VxeOu6nXDMTdbcq6DySr5YMR5sVIsRjErN8OJu76V3W7TgjXvmx0O0Kz58+d7Lffv39/z/+HDhze6eeJwOLRy5cqgxBaqaISwPv6WB08o5oOnc0OlUz1jHXRuaIcCaiiqrKzUwIEDPcvuWSAcDofPGSJKS0t16tSpJrdpTkSErcl1da6m10lSQo+eze6/JcqOH2vYX/fA92dELHVfuQCtuzSm2oxYJOucF6NiMWIfUsuOyd/nOlDhmJtG7Meo991K+WDEfqwUS3vPTQDAZTRCAJeFYj5Qd6n9M6SYdUtmiAh0m7xFY/0WSPv08Kmrfu1ATLnnB7LbbXrU5OR99Yrif5LUK85hatJZ5bwYFcuUe34gSXrUgFmemitE2P+Grk2uM0o45KbU+qKPRr3vVsoHyRrFMN2s8h61JJZg5CYAAECoou5S+xdQMetAZojwtw1aLmNEonrFOWS329QrzhESXRNhHHITAAAAgJkoht7+BdRQFMgMEf62QctFO67V1LS+WvLDkZqa1pexnvBCbgIAAAAwU8aIRJ0pP6D6+jo6N7RTAQ09C2SGiD59+jTaBoCxyE0ACG0UCAUAWJ277pIkLTSgbACCL+AaRVc7Q4SvbQAYj9w0R5WzRslj5sgRe5Ne3XSAizcAbYICoQAAoK0FNPQMAOBt3Y4jiorvK5s9wnPxBgBGo0AoAABoazQUAYABuHgDEAwUCAUAAG2NhiIAMAAXbwCCgQKhAMLFsmXLlJmZqaysLO3du9dr3fbt2zVp0iRlZmbqhRdeaNE2AFou4BpFAIDLMkYkNhSYPXFWPbt15uINQJugQCiAcLBz504dPnxY+fn5KikpUU5OjledzSVLligvL08JCQmaNm2axo0bp5MnT/rdBkDL0VAEAAaIdlyrqWl9FR3dSVVVX5odDtDuMbsXAISvoqIipaWlSZKSkpJUXV0tp9Mph8Oh0tJSdenSRT169JAkjRo1SkVFRTp58mST2zTloSUbVVfn8htLbV293/WD7v6ZJGll4b4WH5/Pfdhatw+jYpEku92m+nr/5yUYsRh1XozS3s7LNRHNDyB7ZfE4n4/TUAQAACyH2b0AIHxVVlZq4MCBnuWYmA8OrN4AABGaSURBVBhVVFTI4XCooqJCMTExXutKS0t16tSpJrfxJyLC5nd9ncv/+oQePf2ubwkj9mHUfsqOH2vYV/fW7ctK58WIY2qP56W5z7Y/NBQBAADLCbUC8Z4eUlcMT6WHFAC0jMt19b04WrJN3qKxqqg44/c5nx4+ddWvHQir9Eqfcs8PZLfb9Oia980ORZIx52XKPT+QJD3aiiHb7fG89L+ha8D7p6EIAABYTs9Yh6dHkXv5almpcYYeUr5Z4T1atmyZ9uzZI5vNppycHA0ePNizbvv27XrqqacUERGhO++8UzNnzmx2GwCtFx8fr8rKSs9yeXm54uLifK4rKytTfHy8rrnmmia3AXB1mPUMAABYjhGze7kbZ+rrXZ7GGbOEWg8po5j9Hl1ZMHfp0qVaunSp1/olS5bo+eef12uvvaZt27bp4MGDzW4DoPVSU1O1YcMGSdK+ffsUHx/vGULWq1cvOZ1OHT16VBcvXtSWLVuUmprqdxuErypnjZLHzNGwzGf16qYDqnLWmB1Su0CPIgAAYDlGzO5lpcYZI3pIhSKz36NgFcwFcHWGDRumgQMHKisrSzabTYsXL1ZBQYGioqKUnp6u3NxczZs3T5KUkZGhPn36qE+fPo22AdbtOKKo+IYevPTobTkaigAAlua+E9Q59ia9uukAtV3QYlZqnMkYkagVeW/LEXuTeid0CaiHlFGsMNzLzez3KFgFc60ys5JkzKw9RrDSbEahOOOUEYIxs5I/8+fP91ru37+/5//Dhw9Xfn5+s9sAZt+QaK9oKAIu4WIUsKZQvxMUSH0UtIyVGmfcPaTsdpsWmFwI00r1kqz0HkltVzBXssbMSkbN2mPEfqw0m1EozjgVzPPSmpmVgLZm9g2J9oqGIuCSUL8YBdqrUL4TdGWtk5KSEuXk5HjdIV2yZIny8vKUkJCgadOmady4cfqnf/onEyNuX6zUOGMlVsops9+jYBXMtcLMSlXOGq3I+0iO2Jvk6HhNq26IGTGDkGSd2YyMisUIRs2sFMzz0pqZlYC2ljEisaEXbaVTPWMDq3kYjihmDVxipR/OAC776p2fULoT1FR9FEle9VHsdrunPgrQWqGcU1crnArmum+I2ewRphd3B4BgiXZcq6lpfTU/a6impvVlxEgL0aMIuIRuiYA1hfKdoEDqo/hjlTooVqr3QR2UxurrXaqvq5XN3kEdIuyqctaYen7MrIMSTgVzuSEGAGgpGoqAS0L5YhRoz9x3gsJBIPVRvsoKdVCsVO+DOiiN2e02xUQbczMkFOqghEvBXG6IWZ+7XqaDepkATEZDEXBJOF2MArCGQOqj+GOFOihuVqn3IVEHpSnt7bxQB6V1PDfErpjtDtZCvUwAVkGNIoQE9x2YoZOf0aubDqjKWWN2SADQrEDqowBAINw3xJb8cCR1OiyK4YEArIIeRQgJ3IEB0B4FUh8FsAr3TZrODJMBDMHwQABWQUMRQgJ3YAC0V4HUR0Fw0SDiGzdpAGMxPBCAVdBQhJDAHRgAQFuhQcQ3btIAxnIPD7RSLTMA4YkaRQgJGSMS1SvOIbvdpl5xzFgGADAODSK+ffWmDDdpAAAIDfQoQkjgDgwAoK3Qa9U3zzCZSqd6xnKTBgCAUEFDEQAAgB80iPjmvkkDAABCS0ANRbW1tVq4cKGOHTumiIgI/ed//qd69+7t9ZzCwkL99re/ld1u15QpUzR58mQVFBTo2WefVWJiww+skSNH6pFHHmn9UQCQRG4CQFugQQQAAISTgBqK3nnnHV1//fVasWKF3n//fa1YsULPPPOMZ/2XX36pF154QatXr9Y111yjSZMmKT09XVLD9L4LFiwwJnoAXshNAAAQLpiREADaRkDFrIuKijwXlyNHjtTu3bu91u/Zs0eDBg1SVFSUOnbsqGHDhjV6Tnvi/hIaOvkZvbrpgKqcNWaHBPgUbrkJAADCl3tGQrs9wjMjIQCg9QJqKKqsrFRMTEzDDux22Ww2Xbhwwed6SYqJiVFFRYUkaefOnXrooYf03e9+Vx9//HFrYg8a95eQjS8hLzSgWU+45SYAAAhfzEgYmmprazVv3jzdf//9mjZtmkpLSxs9p7CwUPfdd58mT56sN954Q5JUUFCgUaNGKTs7W9nZ2fr1r38d7NCBkNHs0LM33njDk3xue/bs8Vp2uVx+9+Fef/PNNysmJkbf+MY39OGHH2rBggX605/+1GyQcXFRTa47Xt32jRPHTpxttBwd3anNX7c5Zsfw+tYSRcU31Gw4WuHUxl1H9fC3B5kak2T+ebmSv1j8fa5bgtz01pr33W63tXofRsViNGLxrS1zEwAQHMxIGJoopQCrcXeQcITRMNdmG4omT56syZMnez22cOFCVVRUqH///qqtrZXL5VJkZKRnfXx8vCorKz3L5eXlGjJkiJKSkpSUlCRJGjp0qE6ePKm6ujpFRET4jaGi4kyT64IxFXrPbp29v4S6dTZ9CnYrTAN/5PjpRstmx2SF8+LWXCz+Ptdu/i5Yyc3LWvu+19c3NJgZEXN7+gwGU3uKpbW5CaB9YAKIwFipLhAzEoamoqIiffvb35bUkF85OTle668spSCJUgpoc+4RRpI8I4xCfZKLgIaepaamav369ZKkLVu26NZbb/Vaf/PNN+tvf/ubTp8+rbNnz2r37t265ZZb9NJLL+mdd96RJO3fv18xMTHNXohaQcaIRPWKc8hut6lXHF9Cbl+9a8NdHPOFW24CABAod6+F1157TTNmzNCKFSu81rt7LbzyyitatWqVfvvb36qqqkpSQ6+FVatWadWqVWHVSCRZqy6Qe0bC+VlDNTWtb8jf4Q8XlFKA1YTjMNeAZj3LyMjQ9u3bdf/99ysyMlI///nPJUn/9V//peHDh2vo0KGaN2+eHnroIdlsNs2cOVNRUVG6++679a//+q/64x//qIsXL2rp0qWGHkxbcX8JWemOuBV47uKcOKue3TrTgGYB4ZabAAAEil4LgQnHCya0HauXUpCCW07BCsP0jS7JYASzY0nsfr3+8cVpr2WzY5KaPy+t6QEfUEORu3vuVz388MOe/48fP17jx4/3Wt+9e3etWrUqkJeEBdGAZj3kJgAALdNUrwX3kO2mei1cc801nl4LFy9e1IIFC/TP//zPphyDGagLBCNZvZSCFLxyCla5pqqvd8lut1kiFska52XsLb28OkiMvaWX6TG15Ly0ppxCQA1FAAAAQHtBrwVvrbkTnjU2Wav/ckBHys4oMSFKk0b3VfT1HU2JxWihEsvJ0+c9hXdf31qiSaP7KqYN3yOj6/a5SynccccdTZZSWLRokU6fPq2IiAjt3r1bOTk5eumll9SjRw9985vfpJQCDBWOHSRoKAIAAEBIo9fCZa290LFLmvKNpMsP1NcHvD8rXXSFUix/3HTAU3j3H1+c1h83fhZw4d227rXgC6UUAPPRUAQAAICwQ68FhKr2XkeKUgqA+WgoAgAAQNih1wJCFXWkALQWDUUAAAAIO/RaQKjyzExc6VTPWAczEwO4ajQUAQAAAECIcBfeBYBA2c0OAAAAAAAAANZAjyIAYa3KWaPkMXPUOfYmvbrpgDJGJCraca3ZYQEAAACAKehRBCCsrdtxRFHxfWW3R+hohVPrdhwxOyQAAAAAMA09igCEtfY+hSwAAABgBHdPewc97cMeDUUAwhpTyAIAAKAp/W/oGpTXiYuLUkXFmaC8VlN+/ofdiopvKIR+tMKprX89poXfGWZqTFY4L27hFEu7bygKVuJK4fXBuBrE4puVYjFDe8nNWfcN0strP1HJ59VK+loXfW/iAMVHX2dKLEYjFt+sFIsZwukHrxux+EYs1kJumotYfLNSLGh7JZ9X+11G+Gj3DUUA0Brx0deZfqcEAAAAMFvS17pof2mV1zLCE8WsAQAAAAAIc9+bOED9ekcrwm5Tv97R+t7EAWaHBJPQowgAAAAAgDDn7mnPkEPQowgAAAAAAACSJJvL5XKZHQQAAAAAAADMR48iAAAAAAAASKKhCAAAAAAAAJfQUAQAAAAAAABJNBQBAAAAAADgEhqKAAAAAAAAIImGIgAAAAAAAFxi2YaiZcuWKTMzU1lZWdq7d6+psSxfvlyZmZm67777tHHjRlNjkaTz588rLS1NBQUFpsZRWFioe+65R/fee6+2bt1qWhxnz57VrFmzlJ2draysLL333numxLF//36lpaXp97//vSTpiy++UHZ2tqZOnao5c+bowoULpsRlNHKzaeSmN3IzuMjNppGb3sjN4CI3m0ZuerNCboZLXkrkpj/kprewzE2XBRUXF7sefvhhl8vlch08eNA1ZcoU02IpKipyff/733e5XC7XyZMnXaNGjTItFrennnrKde+997refPNN02I4efKka+zYsa4zZ864ysrKXIsWLTItllWrVrmefPJJl8vlch0/ftw1bty4oMdw9uxZ17Rp01yLFi1yrVq1yuVyuVwLFy50rVu3zuVyuVwrVqxw/eEPfwh6XEYjN/0jN72Rm8FDbvpHbnojN4OH3PSP3PRmdm6GS166XORmc8hNb+GYm5bsUVRUVKS0tDRJUlJSkqqrq+V0Ok2JZfjw4Xr22WclSddff73OnTunuro6U2KRpJKSEh08eFDf+MY3TItBaniPbrvtNjkcDsXHx+vxxx83LZauXbuqqqpKknT69Gl17do16DFERkbqpZdeUnx8vOex4uJijRkzRpJ01113qaioKOhxGY3cbBq52Ri5GTzkZtPIzcbIzeAhN5tGbjZmdm6GS15K5KY/5GZj4Ziblmwoqqys9Dr5MTExqqioMCWWiIgIderUSZK0evVq3XnnnYqIiDAlFkl64okntHDhQtNe3+3o0aM6f/68ZsyYoalTp5r6pTFx4kQdO3ZM6enpmjZtmhYsWBD0GDp06KCOHTt6PXbu3DlFRkZKkrp162baZ9hI5GbTyM3GyM3gITebRm42Rm4GD7nZNHKzMbNzM1zyUiI3/SE3GwvH3Oxg6N7aiMvlMjsEbdq0SatXr9bLL79sWgxr1qzRkCFD1Lt3b9NiuFJVVZV++ctf6tixY5o+fbq2bNkim80W9Djefvtt9ezZU3l5efr000+Vk5Nj+njar7LCZ7gtWOG4yM3GyM2Ws8JnuC1Y4bjIzcbIzZazwme4LVjhuMjNxsjNlrHC57etWOHYyM3GyM2WaYvPryUbiuLj41VZWelZLi8vV1xcnGnxvPfee1q5cqX++7//W1FRUabFsXXrVpWWlmrr1q06fvy4IiMj1b17d40cOTLosXTr1k1Dhw5Vhw4dlJiYqM6dO+vkyZPq1q1b0GPZvXu3br/9dklS//79VV5errq6OlNb4iWpU6dOOn/+vDp27KiysjKvroLtFbnpG7npG7kZPOSmb+Smb+Rm8JCbvpGbvlkxN0MxLyVysynkpm/hmJuWHHqWmpqqDRs2SJL27dun+Ph4ORwOU2I5c+aMli9frhdffFHR0dGmxOD2zDPP6M0339Trr7+uyZMn60c/+pEpSStJt99+u3bs2KH6+nqdOnVKX375pSk1DiTphhtu0J49eyRJn3/+uTp37mz6j11JGjlypOdzvHHjRt1xxx0mR9R65KZv5KZv5GbwkJu+kZu+kZvBQ276Rm76ZsXcDMW8lMjNppCbvoVjblqyR9GwYcM0cOBAZWVlyWazafHixabFsm7dOp06dUo/+clPPI898cQT6tmzp2kxWUFCQoLGjRunKVOmSJIWLVoku92cdsfMzEzl5ORo2rRpunjxonJzc4Mew0cffaQnnnhCn3/+uTp06KANGzboySef1MKFC5Wfn6+ePXvq29/+dtDjMhq5aX3kpjdyM/jITd/ITW/kZvCRm76Rm5eFS15K5GZ7QG5eZkZu2lxWGJAJAAAAAAAA01ly6BkAAAAAAACCj4YiAAAAAAAASKKhCAAAAAAAAJfQUAQAAAAAAABJNBQBAAAAAADgEhqKAAAAAAAAIImGIgAAAAAAAFxCQxEAAAAAAAAkSf8PNPZ23P0N1wkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x144 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATmDqJotOiRf"
      },
      "source": [
        "### **Chapter 4.** Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oL2TIJKOkTl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "4b024c79-58e2-4004-f74d-24fe44373641"
      },
      "source": [
        "cols_to_keep = features_short+['R1M_Usd','date']\n",
        "corr_data = data[cols_to_keep]\n",
        "corr_data = corr_data.groupby('date').corr()[['R1M_Usd']].reset_index()\n",
        "corr_data = corr_data.loc[corr_data['level_1']!='R1M_Usd']\n",
        "\n",
        "sns.boxplot(x='R1M_Usd',y='level_1', data = corr_data);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "4-HCbBddVbnV",
        "outputId": "4eb8d246-a067-42e6-9b6e-7bf2655589ec"
      },
      "source": [
        "x = pd.melt(data[['R1M_Usd','Mkt_Cap_12M_Usd','Vol1Y_Usd']], id_vars='R1M_Usd')\n",
        "\n",
        "sns.lineplot(data = x, y='R1M_Usd', x='value', hue='variable');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "fkdhCgP4Qlph",
        "outputId": "f5538ba5-9484-4693-b2bb-1a50e8e47727"
      },
      "source": [
        "cols_to_keep = ['stock_id'] + features\n",
        "\n",
        "pd.melt(data[cols_to_keep], id_vars='stock_id').groupby(['stock_id','variable']).apply(lambda x: x['value'].autocorr(lag=1)).hist(bins=50,range=[-0.1,1]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9pc6jjmhjlP"
      },
      "source": [
        "The below is the \"impact of rescaling\" exercise done in the book. Note that it's purely numerical - it does not use data at all."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "rLigrQIzcH3R",
        "outputId": "dc2fbf79-5d19-41bc-eb15-fedde778623d"
      },
      "source": [
        "length = 100\n",
        "x = np.exp(np.sin(np.linspace(1,length,length)))\n",
        "df = pd.DataFrame(data=x,columns=['data'])\n",
        "\n",
        "df.plot(kind='bar', figsize=(15,5));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "ZGCx2s2Zhnyq",
        "outputId": "610fbc46-2f3c-40a1-a1c9-34f66b4829be"
      },
      "source": [
        "def norm_0to1(x):\n",
        "  return ((x-np.min(x))/(np.max(x)-np.min(x)))\n",
        "\n",
        "df = pd.DataFrame.from_dict(dict(\n",
        "    index = np.linspace(1,length,length),\n",
        "    standard = (x - np.mean(x))/np.std(x),\n",
        "    norm0to1 = norm_0to1(x),\n",
        "    unif = ECDF(x)(x)\n",
        "))\n",
        "\n",
        "fig, axs = plt.subplots(3, sharex=True, sharey=True)\n",
        "for i, col in enumerate(df.columns[1:]):\n",
        "  axs[i].plot(df['index'],df[col])\n",
        "  axs[i].set_ylabel(col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6J_aX8idmEzg",
        "outputId": "7ca998d8-0ed0-47a0-ee5d-41e40c7345ca"
      },
      "source": [
        "df.drop('index',axis=1).hist(bins=25,layout=(1,3),figsize=(15,3));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AL6Zb1TOxZqI"
      },
      "source": [
        "### **Chapter 5.** Penalized regressions and sparse hedging for minimum variance portfolios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PkitjR6CxTgc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "0f700203-da4d-42e1-fe8f-8ce535a27301"
      },
      "source": [
        "y_penalized = data['R1M_Usd'].values\n",
        "X_penalized = data[features].values\n",
        "\n",
        "alphas = np.arange(1e-3,2.5e-3,1e-4) # sklearn alpha = glmnet lambda\n",
        "\n",
        "coef_path = {}\n",
        "\n",
        "for alpha in alphas:\n",
        "  lasso = Lasso(alpha=alpha)\n",
        "  lasso.fit(X_penalized,y_penalized)\n",
        "\n",
        "  coef_path[alpha] = lasso.coef_\n",
        "\n",
        "coef_path_df = pd.DataFrame.from_dict(coef_path).T\n",
        "coef_path_df.columns = features\n",
        "relevant_predictors = (coef_path_df.abs().sum() != 0.0)\n",
        "coef_path_df.loc[:,relevant_predictors].plot(xlabel='Lambda',ylabel='beta');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "T2bOtD3_8Cya",
        "outputId": "f8f6748a-a8be-4cc5-ab1c-0846cb952df5"
      },
      "source": [
        "alphas = np.logspace(-3,3,50)\n",
        "\n",
        "coef_path = {}\n",
        "\n",
        "for alpha in alphas:\n",
        "  ridge = Ridge(alpha=alpha)\n",
        "  ridge.fit(X_penalized,y_penalized)\n",
        "\n",
        "  coef_path[alpha] = ridge.coef_\n",
        "\n",
        "coef_path_df = pd.DataFrame.from_dict(coef_path).T\n",
        "coef_path_df.columns = features\n",
        "relevant_predictors = (coef_path_df.abs().sum() != 0.0)\n",
        "coef_path_df.loc[:,relevant_predictors].plot(xlabel='Lambda',ylabel='beta',figsize=(15,10),legend=False, logx=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ec-6_1_O9Du2"
      },
      "source": [
        "t_oos = returns.index[returns.index > separation_date].values\n",
        "\n",
        "Tt = len(t_oos)\n",
        "nb_port = 3\n",
        "\n",
        "def weights_sparsehedge(returns, alpha = 0.1, l1_ratio = 0.1):\n",
        "\n",
        "  weights = []\n",
        "\n",
        "  lr = ElasticNet(alpha=alpha,l1_ratio=l1_ratio)\n",
        "\n",
        "  for col in returns.columns:\n",
        "    y = returns[col].values\n",
        "    X = returns.drop(col, axis=1).values\n",
        "\n",
        "    lr.fit(X,y)\n",
        "    err = y - lr.predict(X)\n",
        "    w = (1-np.sum(lr.coef_))/np.var(err)\n",
        "\n",
        "    weights.append(w)\n",
        "\n",
        "  return weights / np.sum(weights)\n",
        "\n",
        "def weights_multi(returns, j, alpha = 0.1, l1_ratio = 0.1):\n",
        "\n",
        "  N = returns.shape[1]\n",
        "\n",
        "  if j == 0:\n",
        "    return np.repeat(1/N,N)\n",
        "  elif j == 1:\n",
        "    sigma = np.cov(returns.T) + 1e-2 * np.identity(N) # hardcoded penalty....\n",
        "    w = np.matmul(np.linalg.inv(sigma),np.repeat(1,N))\n",
        "    return w / np.sum(w)\n",
        "  elif j == 2:\n",
        "    return weights_sparsehedge(returns, alpha, l1_ratio)\n",
        "\n",
        "port_weights = {}\n",
        "port_returns = {}\n",
        "\n",
        "for t, month in np.ndenumerate(t_oos):\n",
        "\n",
        "  temp_data = returns.loc[returns.index < month]\n",
        "  realised_returns = returns.loc[returns.index == month].values\n",
        "\n",
        "  inner_dict_weights = {}\n",
        "  inner_dict_returns = {}\n",
        "\n",
        "  for j in range(nb_port):\n",
        "\n",
        "    wgts = weights_multi(temp_data, j, 0.1, 0.1)\n",
        "    rets = np.sum(wgts * realised_returns)\n",
        "\n",
        "    inner_dict_weights[j] = wgts\n",
        "    inner_dict_returns[j] = rets\n",
        "\n",
        "  port_weights[month] = inner_dict_weights\n",
        "  port_returns[month] = inner_dict_returns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-wwZQsZ6dwA",
        "outputId": "833678f1-f42f-4279-ff68-7c008cb4ebd3"
      },
      "source": [
        "port_returns_df = pd.concat({\n",
        "        k: pd.DataFrame.from_dict(v, 'index') for k, v in port_returns.items()\n",
        "    }, \n",
        "    axis=0).reset_index()\n",
        "\n",
        "colnames = ['date','strategy','return']\n",
        "port_returns_df.columns = colnames\n",
        "\n",
        "stratmap = {0:'EW',1:'MV',2:'Sparse'}\n",
        "port_returns_df['strategy'] = port_returns_df['strategy'].replace(stratmap)\n",
        "port_returns_df.groupby('strategy')['return'].std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "strategy\n",
              "EW        0.044998\n",
              "MV        0.044598\n",
              "Sparse    0.041618\n",
              "Name: return, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0vQvhtnOziTB",
        "outputId": "28411406-1ba2-44f5-9019-a40b8c4807d9"
      },
      "source": [
        "y_penalized_train = training_sample['R1M_Usd'].values\n",
        "X_penalized_train = training_sample[features].values\n",
        "\n",
        "model = ElasticNet(alpha=0.1, l1_ratio=0.1)\n",
        "model.fit(X_penalized_train,y_penalized_train)\n",
        "\n",
        "y_penalized_test = testing_sample['R1M_Usd'].values\n",
        "X_penalized_test = testing_sample[features].values\n",
        "\n",
        "mse = np.mean((model.predict(X_penalized_test) - y_penalized_test)**2)\n",
        "hitratio = np.mean(model.predict(X_penalized_test) * y_penalized_test > 0)\n",
        "print(f'MSE: {mse} \\nHit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 0.03949451472449276 \n",
            "Hit Ratio: 0.5494447675223616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmBQnl2CFDnC"
      },
      "source": [
        "### **Chapter 6.** Tree-based methods"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jc3Ny1DPFJK_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "dccd023d-2c3e-4fcf-c755-8f01705851e2"
      },
      "source": [
        "y = data['R1M_Usd'].values\n",
        "X = data[features].values\n",
        "\n",
        "clf = tree.DecisionTreeRegressor(max_depth=3, ccp_alpha=1e-4)\n",
        "clf = clf.fit(X,y)\n",
        "\n",
        "dot_data = tree.export_graphviz(clf,out_file=None,\n",
        "                                feature_names=features,\n",
        "                                filled = True)\n",
        "\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.files.Source at 0x7f01e28e6588>"
            ],
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text-anchor=\"middle\" x=\"195.5\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.032</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 283380</text>\n<text text-anchor=\"middle\" x=\"195.5\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.013</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<polygon fill=\"#ffffff\" stroke=\"#000000\" points=\"183,-261 76,-261 76,-193 183,-193 183,-261\"/>\n<text text-anchor=\"middle\" x=\"129.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Dps &lt;= 0.985</text>\n<text text-anchor=\"middle\" x=\"129.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.416</text>\n<text text-anchor=\"middle\" x=\"129.5\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5680</text>\n<text text-anchor=\"middle\" x=\"129.5\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 0.061</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M173.8891,-296.9465C168.3622,-288.2373 162.3494,-278.7626 156.5854,-269.6801\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"159.4729,-267.6979 151.1594,-261.13 153.5626,-271.4487 159.4729,-267.6979\"/>\n<text text-anchor=\"middle\" x=\"145.7058\" y=\"-281.8279\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<polygon fill=\"#ffffff\" stroke=\"#000000\" points=\"323.5,-253.5 201.5,-253.5 201.5,-200.5 323.5,-200.5 323.5,-253.5\"/>\n<text text-anchor=\"middle\" x=\"262.5\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 0.024</text>\n<text text-anchor=\"middle\" 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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "kB1zMg2dDIkc",
        "outputId": "09fbf872-29de-4477-d78d-77b190c67d4e"
      },
      "source": [
        "cols_that_matter = []\n",
        "\n",
        "for val in np.where(clf.feature_importances_ > 0)[0]:\n",
        "  cols_that_matter.append(features[val])\n",
        "\n",
        "cols_that_matter\n",
        "\n",
        "temp = pd.melt(data[['R1M_Usd']+cols_that_matter], id_vars='R1M_Usd')\n",
        "\n",
        "sns.lineplot(data = temp, y='R1M_Usd', x='value', hue='variable');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGdBss5FFDIg",
        "outputId": "37a5d3ec-0cb4-4871-8422-67cd52e2a8ae"
      },
      "source": [
        "y_train = training_sample['R1M_Usd'].values\n",
        "X_train = training_sample[features].values\n",
        "\n",
        "dt = tree.DecisionTreeRegressor(max_depth=3, ccp_alpha=1e-4)\n",
        "dt.fit(X_train,y_train)\n",
        "\n",
        "y_test = testing_sample['R1M_Usd'].values\n",
        "X_test = testing_sample[features].values\n",
        "\n",
        "mse = np.mean((dt.predict(X_test) - y_test)**2)\n",
        "hitratio = np.mean(dt.predict(X_test) * y_test > 0)\n",
        "print(f'MSE: {mse} \\nHit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 0.03949451472449276 \n",
            "Hit Ratio: 0.5494447675223616\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Bzzryp3FboQ",
        "outputId": "0d15ca98-79a4-43f1-a3e0-670873ddfc04"
      },
      "source": [
        "rf = RandomForestRegressor(n_estimators=40,\n",
        "                           max_features = 30,\n",
        "                           min_samples_split=10000,\n",
        "                           bootstrap=False)\n",
        "rf.fit(X_train,y_train)\n",
        "\n",
        "mse = np.mean((rf.predict(X_test) - y_test)**2)\n",
        "hitratio = np.mean(rf.predict(X_test) * y_test > 0)\n",
        "print(f'MSE: {mse} \\nHit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MSE: 0.039443868412626454 \n",
            "Hit Ratio: 0.5462047575531002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSpdc2eVIjfn",
        "outputId": "171eb370-42c7-4429-ab44-5d1ec4e01eaf"
      },
      "source": [
        "y_train_clf = training_sample['R1M_Usd_C']\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=40,\n",
        "                              max_features = 30,\n",
        "                              min_samples_split=10000,\n",
        "                              bootstrap=False)\n",
        "clf.fit(X_train,y_train_clf)\n",
        "\n",
        "y_test_clf = testing_sample['R1M_Usd_C']\n",
        "\n",
        "hitratio = np.mean(clf.predict(X_test) == y_test_clf)\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.4978815319431752\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3vhE1N7NR2P",
        "outputId": "ac752bff-930c-4936-9032-d22642d90381"
      },
      "source": [
        "ada = AdaBoostClassifier()\r\n",
        "\r\n",
        "ada.fit(X_train, y_train_clf)\r\n",
        "\r\n",
        "hitratio = np.mean(ada.predict(X_test) == y_test_clf)\r\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.5003323087147961\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwNKw5MZdnMj",
        "outputId": "e63ad517-dd51-4153-f6bb-8314a44ca8f4"
      },
      "source": [
        "xgb = XGBClassifier()\r\n",
        "\r\n",
        "xgb.fit(X_train, y_train_clf)\r\n",
        "\r\n",
        "hitratio = np.mean(xgb.predict(X_test) == y_test_clf)\r\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.49918307440945975\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RrwQ79pewIM"
      },
      "source": [
        "### **Chapter 7.** Neural networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPuRhbcyerft"
      },
      "source": [
        "NN_train_features = training_sample[features].values\r\n",
        "NN_train_labels = training_sample['R1M_Usd'].values\r\n",
        "\r\n",
        "NN_test_features = testing_sample[features].values\r\n",
        "NN_test_labels = testing_sample['R1M_Usd'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYKE9bbdeuFb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a7d3bbd-bba4-45be-db61-ca2a1b820955"
      },
      "source": [
        "model = keras.Sequential(\r\n",
        "    [\r\n",
        "        keras.layers.Dense(16, activation=\"relu\"),\r\n",
        "        keras.layers.Dense(8, activation=\"tanh\"),\r\n",
        "        keras.layers.Dense(1)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "loss_fn = keras.losses.MeanSquaredError() #regression yay\r\n",
        "optimizer = keras.optimizers.RMSprop()\r\n",
        "\r\n",
        "model.compile(optimizer,loss=loss_fn,metrics=['MeanAbsoluteError'])\r\n",
        "\r\n",
        "fitted_model = model.fit(\r\n",
        "            NN_train_features,\r\n",
        "            NN_train_labels,\r\n",
        "            batch_size=512,\r\n",
        "            epochs = 10,\r\n",
        "            validation_data=(NN_test_features,NN_test_labels),\r\n",
        "            verbose = True\r\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "413/413 [==============================] - 2s 3ms/step - loss: 0.0388 - mean_absolute_error: 0.1026 - val_loss: 0.0395 - val_mean_absolute_error: 0.0679\n",
            "Epoch 2/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0365 - mean_absolute_error: 0.0856 - val_loss: 0.0396 - val_mean_absolute_error: 0.0681\n",
            "Epoch 3/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0291 - mean_absolute_error: 0.0852 - val_loss: 0.0395 - val_mean_absolute_error: 0.0674\n",
            "Epoch 4/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0285 - mean_absolute_error: 0.0852 - val_loss: 0.0398 - val_mean_absolute_error: 0.0691\n",
            "Epoch 5/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0270 - mean_absolute_error: 0.0851 - val_loss: 0.0396 - val_mean_absolute_error: 0.0683\n",
            "Epoch 6/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0266 - mean_absolute_error: 0.0850 - val_loss: 0.0394 - val_mean_absolute_error: 0.0672\n",
            "Epoch 7/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0313 - mean_absolute_error: 0.0854 - val_loss: 0.0396 - val_mean_absolute_error: 0.0683\n",
            "Epoch 8/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0255 - mean_absolute_error: 0.0850 - val_loss: 0.0395 - val_mean_absolute_error: 0.0673\n",
            "Epoch 9/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0292 - mean_absolute_error: 0.0853 - val_loss: 0.0395 - val_mean_absolute_error: 0.0674\n",
            "Epoch 10/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: 0.0278 - mean_absolute_error: 0.0849 - val_loss: 0.0397 - val_mean_absolute_error: 0.0689\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HweeWwuC5W4L",
        "outputId": "3717615f-d43a-4c2e-8b45-01b3431d1afa"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                1504      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 1,649\n",
            "Trainable params: 1,649\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "2q0V3NVg6Koz",
        "outputId": "bd8cbc57-1b59-4105-dfe6-d77e2f79765e"
      },
      "source": [
        "def plot_model_performance(mdl, loss, metric):\r\n",
        "  x = pd.DataFrame(mdl.history).reset_index()\r\n",
        "  x = pd.melt(x, id_vars='index')\r\n",
        "\r\n",
        "  x['validation'] = (x['variable'].str[:4] == 'val_').replace({True:'validation',False:'training'})\r\n",
        "  x['loss'] = (x['variable'].str[-4:] == 'loss').replace({True:loss,False:metric})\r\n",
        "\r\n",
        "  g = sns.FacetGrid(x, col='loss', hue='validation',sharey=False)\r\n",
        "  g.map(sns.lineplot, 'index','value')\r\n",
        "  g.add_legend()\r\n",
        "\r\n",
        "  return g\r\n",
        "\r\n",
        "plot_model_performance(fitted_model,'mean_sq_error','mean_abs_error');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 510.125x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PF-wmERq-3lo",
        "outputId": "7f2ba212-3536-4c23-a04b-31349b0835e1"
      },
      "source": [
        "NN_preds = model.predict(NN_test_features, use_multiprocessing=True)\r\n",
        "NN_preds = NN_preds.reshape(NN_preds.shape[0])\r\n",
        "\r\n",
        "hitratio = np.mean(np.multiply(NN_preds,NN_test_labels) > 0)\r\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.5492924593614135\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmdjUQWSC6Bg"
      },
      "source": [
        "NN_train_labels_C = training_sample['R1M_Usd_C'].values\r\n",
        "NN_train_labels_C = keras.utils.to_categorical(NN_train_labels_C)\r\n",
        "\r\n",
        "NN_test_labels_C = testing_sample['R1M_Usd_C'].values\r\n",
        "NN_test_labels_C = keras.utils.to_categorical(NN_test_labels_C)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6qlqOz0DTNm"
      },
      "source": [
        "model_C = keras.Sequential(\r\n",
        "    [\r\n",
        "        keras.layers.Dense(16, \r\n",
        "                           activation=\"tanh\",\r\n",
        "                           kernel_initializer = 'random_normal',\r\n",
        "                           kernel_constraint = keras.constraints.NonNeg()\r\n",
        "                          ),\r\n",
        "        keras.layers.Dropout(0.25),\r\n",
        "        keras.layers.Dense(8,\r\n",
        "                           activation=\"elu\",\r\n",
        "                           bias_initializer = keras.initializers.Constant(value=0.2),\r\n",
        "                           kernel_regularizer = keras.regularizers.l2(l2=0.01)\r\n",
        "                           ),\r\n",
        "        keras.layers.Dense(2,\r\n",
        "                           activation = 'softmax'\r\n",
        "                           )\r\n",
        "    ]\r\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JQgIqa1dbGU",
        "outputId": "7cc2e774-1d57-46d9-d38c-dfd611c35079"
      },
      "source": [
        "loss_fn = keras.losses.BinaryCrossentropy()\r\n",
        "optimizer = keras.optimizers.Adam(\r\n",
        "    learning_rate=0.005,\r\n",
        "    beta_1=0.9,\r\n",
        "    beta_2=0.95\r\n",
        ")\r\n",
        "\r\n",
        "model_C.compile(optimizer=optimizer,\r\n",
        "                loss=loss_fn,\r\n",
        "                metrics=['categorical_accuracy']\r\n",
        ")\r\n",
        "\r\n",
        "fitted_model_C = model_C.fit(\r\n",
        "            NN_train_features,\r\n",
        "            NN_train_labels_C,\r\n",
        "            batch_size=512,\r\n",
        "            epochs = 20,\r\n",
        "            validation_data=(NN_test_features,NN_test_labels_C),\r\n",
        "            verbose = True,\r\n",
        "            callbacks = [keras.callbacks.EarlyStopping(\r\n",
        "                            monitor='val_loss',\r\n",
        "                            min_delta=0.001,\r\n",
        "                            patience=3,\r\n",
        "                            verbose=False\r\n",
        "            )]\r\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "413/413 [==============================] - 2s 3ms/step - loss: 0.7319 - categorical_accuracy: 0.5022 - val_loss: 0.6934 - val_categorical_accuracy: 0.4951\n",
            "Epoch 2/20\n",
            "413/413 [==============================] - 1s 3ms/step - loss: 0.6933 - categorical_accuracy: 0.5035 - val_loss: 0.6934 - val_categorical_accuracy: 0.4971\n",
            "Epoch 3/20\n",
            "413/413 [==============================] - 1s 3ms/step - loss: 0.6933 - categorical_accuracy: 0.5017 - val_loss: 0.6935 - val_categorical_accuracy: 0.4963\n",
            "Epoch 4/20\n",
            "413/413 [==============================] - 1s 3ms/step - loss: 0.6933 - categorical_accuracy: 0.5022 - val_loss: 0.6932 - val_categorical_accuracy: 0.4926\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZqk5zXmjowg"
      },
      "source": [
        "model_C.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "6LGiYZ-sgEUM",
        "outputId": "2f067956-ea68-4fc5-de61-4fb770313dd4"
      },
      "source": [
        "plot_model_performance(fitted_model_C,'bin_cross_entropy','categorical_accuracy');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 510.125x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrFGAxyuhisV",
        "outputId": "3e9e6990-526f-43e9-871c-fdea3a5b503a"
      },
      "source": [
        "model_custom = keras.Sequential(\r\n",
        "    [\r\n",
        "        keras.layers.Dense(16, activation=\"relu\"),\r\n",
        "        keras.layers.Dense(8, activation=\"sigmoid\"),\r\n",
        "        keras.layers.Dense(1)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "\r\n",
        "def custom_loss(y_true, y_pred):\r\n",
        "  gamma = 5\r\n",
        "\r\n",
        "  term1 = tf.square(y_pred - tf.reduce_mean(y_pred))\r\n",
        "  term2 = (y_true - tf.reduce_mean(y_true))*(y_pred - tf.reduce_mean(y_pred))\r\n",
        "  \r\n",
        "  return tf.reduce_mean(term1) - gamma * tf.reduce_mean(term2)\r\n",
        "\r\n",
        "optimizer = keras.optimizers.RMSprop()\r\n",
        "\r\n",
        "model_custom.compile(optimizer,loss=custom_loss,metrics=['MeanAbsoluteError'])\r\n",
        "\r\n",
        "fitted_model_custom = model_custom.fit(\r\n",
        "            NN_train_features,\r\n",
        "            NN_train_labels,\r\n",
        "            batch_size=512,\r\n",
        "            epochs = 10,\r\n",
        "            validation_data=(NN_test_features,NN_test_labels),\r\n",
        "            verbose = True\r\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "413/413 [==============================] - 2s 3ms/step - loss: 8.4461e-05 - mean_absolute_error: 0.3941 - val_loss: 1.5944e-04 - val_mean_absolute_error: 0.3962\n",
            "Epoch 2/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -7.5746e-04 - mean_absolute_error: 0.4116 - val_loss: 1.9209e-04 - val_mean_absolute_error: 0.4307\n",
            "Epoch 3/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0012 - mean_absolute_error: 0.4492 - val_loss: 2.6640e-04 - val_mean_absolute_error: 0.4315\n",
            "Epoch 4/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0010 - mean_absolute_error: 0.4379 - val_loss: 1.4040e-04 - val_mean_absolute_error: 0.4447\n",
            "Epoch 5/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0013 - mean_absolute_error: 0.4468 - val_loss: 9.8087e-05 - val_mean_absolute_error: 0.4656\n",
            "Epoch 6/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0013 - mean_absolute_error: 0.4658 - val_loss: 3.1756e-04 - val_mean_absolute_error: 0.4630\n",
            "Epoch 7/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0014 - mean_absolute_error: 0.4718 - val_loss: 1.9119e-04 - val_mean_absolute_error: 0.4624\n",
            "Epoch 8/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0012 - mean_absolute_error: 0.4582 - val_loss: 0.0011 - val_mean_absolute_error: 0.4551\n",
            "Epoch 9/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0019 - mean_absolute_error: 0.4711 - val_loss: 2.0665e-04 - val_mean_absolute_error: 0.4648\n",
            "Epoch 10/10\n",
            "413/413 [==============================] - 1s 2ms/step - loss: -0.0016 - mean_absolute_error: 0.4695 - val_loss: -3.6818e-05 - val_mean_absolute_error: 0.4649\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "MfJAgQtAoMMc",
        "outputId": "b03c0bc4-ee07-49da-d1f5-64a0e0a53695"
      },
      "source": [
        "plot_model_performance(fitted_model_custom, 'custom_loss', 'mean_abs_error');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 510.125x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evdUn-3Dr6V1",
        "outputId": "39452b36-519b-4066-a512-dd2ffe9d6514"
      },
      "source": [
        "NN_preds = model_custom.predict(NN_test_features, use_multiprocessing=True)\r\n",
        "NN_preds = NN_preds.reshape(NN_preds.shape[0])\r\n",
        "\r\n",
        "hitratio = np.mean(np.multiply(NN_preds,NN_test_labels) > 0)\r\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.4435629032704716\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "729CvWCmA1fx"
      },
      "source": [
        "data_rnn = data[data['stock_id'].isin(stock_ids_short)]\r\n",
        "training_sample_rnn = data_rnn[data_rnn['date'] < separation_date]\r\n",
        "testing_sample_rnn = data_rnn[data_rnn['date'] > separation_date]\r\n",
        "\r\n",
        "nb_stocks = len(stock_ids_short)\r\n",
        "nb_feats = len(features)\r\n",
        "nb_dates_train = training_sample_rnn.shape[0] // nb_stocks\r\n",
        "nb_dates_test = testing_sample_rnn.shape[0] // nb_stocks"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxD-RogrBViR"
      },
      "source": [
        "nn_train_features = training_sample_rnn[features].values\r\n",
        "train_features_rnn = np.reshape(nn_train_features, (nb_stocks, nb_dates_train, nb_feats))\r\n",
        "\r\n",
        "nn_test_features = testing_sample_rnn[features].values\r\n",
        "test_features_rnn = np.reshape(nn_test_features, (nb_stocks, nb_dates_test, nb_feats))\r\n",
        "\r\n",
        "nn_train_labels = training_sample_rnn['R1M_Usd'].values\r\n",
        "train_labels_rnn = np.reshape(nn_train_labels, (nb_stocks, nb_dates_train, 1))\r\n",
        "\r\n",
        "nn_test_labels = testing_sample_rnn['R1M_Usd'].values\r\n",
        "test_labels_rnn = np.reshape(nn_test_labels, (nb_stocks, nb_dates_test, 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aj3rVjxkFXVv",
        "outputId": "d1bab4eb-3f36-40e1-ee01-ef77214a2f56"
      },
      "source": [
        "model_rnn = keras.Sequential(\r\n",
        "    [\r\n",
        "        keras.layers.GRU(16, batch_input_shape = (nb_stocks, nb_dates_train, nb_feats), activation='tanh'),\r\n",
        "        keras.layers.Dense(1)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "optimizer = keras.optimizers.RMSprop()\r\n",
        "loss_fn = keras.losses.MeanSquaredError()\r\n",
        "\r\n",
        "model_rnn.compile(loss = loss_fn, optimizer = optimizer, metrics=['MeanAbsoluteError'])\r\n",
        "\r\n",
        "fitted_model_rnn = model_rnn.fit(train_features_rnn,train_labels_rnn,\r\n",
        "                                 epochs = 10, batch_size = nb_stocks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1831 - mean_absolute_error: 0.3560\n",
            "Epoch 2/10\n",
            "1/1 [==============================] - 0s 130ms/step - loss: 0.1501 - mean_absolute_error: 0.3091\n",
            "Epoch 3/10\n",
            "1/1 [==============================] - 0s 132ms/step - loss: 0.0835 - mean_absolute_error: 0.2074\n",
            "Epoch 4/10\n",
            "1/1 [==============================] - 0s 126ms/step - loss: 0.0686 - mean_absolute_error: 0.1818\n",
            "Epoch 5/10\n",
            "1/1 [==============================] - 0s 137ms/step - loss: 0.0648 - mean_absolute_error: 0.1737\n",
            "Epoch 6/10\n",
            "1/1 [==============================] - 0s 139ms/step - loss: 0.0626 - mean_absolute_error: 0.1701\n",
            "Epoch 7/10\n",
            "1/1 [==============================] - 0s 131ms/step - loss: 0.0609 - mean_absolute_error: 0.1665\n",
            "Epoch 8/10\n",
            "1/1 [==============================] - 0s 131ms/step - loss: 0.0594 - mean_absolute_error: 0.1636\n",
            "Epoch 9/10\n",
            "1/1 [==============================] - 0s 127ms/step - loss: 0.0580 - mean_absolute_error: 0.1606\n",
            "Epoch 10/10\n",
            "1/1 [==============================] - 0s 127ms/step - loss: 0.0566 - mean_absolute_error: 0.1579\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "sHuowoMOFs3t",
        "outputId": "f7ce4eab-28a6-45c7-81d8-e19a191b57a7"
      },
      "source": [
        "plot_model_performance(fitted_model_rnn,'mean_sq_error','mean_abs_error');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 500.625x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJcpWlJTHaLX"
      },
      "source": [
        "new_model_rnn = keras.Sequential(\r\n",
        "    [\r\n",
        "        keras.layers.GRU(16, batch_input_shape = (nb_stocks, nb_dates_test, nb_feats), activation='tanh', return_sequences = True),\r\n",
        "        keras.layers.Dense(1)\r\n",
        "    ]\r\n",
        ")\r\n",
        "\r\n",
        "new_model_rnn.set_weights(model_rnn.get_weights())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxuD2YfKMQkG",
        "outputId": "93b7ae59-3523-4026-dff9-af3fa2c67545"
      },
      "source": [
        "NN_preds = new_model_rnn.predict(test_features_rnn, batch_size = nb_stocks)\r\n",
        "\r\n",
        "hitratio = np.mean(np.multiply(NN_preds,test_labels_rnn) > 0)\r\n",
        "print(f'Hit Ratio: {hitratio}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hit Ratio: 0.5076378221539511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXvERXEnSCle"
      },
      "source": [
        "### **Chapter 8.** Support vector machines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ar3SCszRSJlv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}