4.2_ENet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.preprocessing\n",
    "import datetime\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "#Program that executes the rolling window train-val-test split \n",
    "%run TimeBasedCV.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>eom</th>\n",
       "      <th>prc</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>ret_exc_lead1m</th>\n",
       "      <th>ret_6_1</th>\n",
       "      <th>ret_12_1</th>\n",
       "      <th>tax_gr1a</th>\n",
       "      <th>...</th>\n",
       "      <th>cowc_gr1a</th>\n",
       "      <th>pi_nix</th>\n",
       "      <th>ret_6_0</th>\n",
       "      <th>ret_1_0</th>\n",
       "      <th>noa_at</th>\n",
       "      <th>cop_atl1</th>\n",
       "      <th>prc_highprc_252d</th>\n",
       "      <th>ocf_at</th>\n",
       "      <th>dbnetis_at</th>\n",
       "      <th>netdebt_me</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>11.486148</td>\n",
       "      <td>567.074646</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "      <td>-0.089504</td>\n",
       "      <td>-0.229560</td>\n",
       "      <td>-0.353251</td>\n",
       "      <td>-0.020556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041844</td>\n",
       "      <td>3.815513</td>\n",
       "      <td>-0.244158</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>0.692706</td>\n",
       "      <td>0.093015</td>\n",
       "      <td>0.398130</td>\n",
       "      <td>-0.007700</td>\n",
       "      <td>0.085202</td>\n",
       "      <td>0.135368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>35.853958</td>\n",
       "      <td>5221.931641</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.108428</td>\n",
       "      <td>0.129149</td>\n",
       "      <td>-0.035109</td>\n",
       "      <td>-0.033306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019596</td>\n",
       "      <td>1.284930</td>\n",
       "      <td>0.135715</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.751569</td>\n",
       "      <td>0.164124</td>\n",
       "      <td>0.918059</td>\n",
       "      <td>0.096164</td>\n",
       "      <td>0.063714</td>\n",
       "      <td>0.200036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comp_004367_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>36.068653</td>\n",
       "      <td>4139.599121</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "      <td>0.127227</td>\n",
       "      <td>-0.020286</td>\n",
       "      <td>0.087151</td>\n",
       "      <td>-0.037240</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026114</td>\n",
       "      <td>1.725004</td>\n",
       "      <td>-0.121981</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>0.961141</td>\n",
       "      <td>0.240563</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.070724</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.410184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>comp_004925_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>29.735168</td>\n",
       "      <td>15302.482422</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "      <td>0.064832</td>\n",
       "      <td>0.046064</td>\n",
       "      <td>0.023265</td>\n",
       "      <td>-0.007344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000724</td>\n",
       "      <td>1.961724</td>\n",
       "      <td>-0.027605</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>0.466852</td>\n",
       "      <td>0.221983</td>\n",
       "      <td>0.861333</td>\n",
       "      <td>0.084234</td>\n",
       "      <td>-0.008142</td>\n",
       "      <td>-0.075232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>39.181721</td>\n",
       "      <td>6355.691406</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.123795</td>\n",
       "      <td>0.012811</td>\n",
       "      <td>-0.012241</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014214</td>\n",
       "      <td>1.069862</td>\n",
       "      <td>0.027359</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>0.635742</td>\n",
       "      <td>0.108581</td>\n",
       "      <td>0.721256</td>\n",
       "      <td>0.069331</td>\n",
       "      <td>-0.038594</td>\n",
       "      <td>0.500641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id        eom        prc            me       ret   ret_exc  \\\n",
       "0  comp_001166_02W 2003-01-31  11.486148    567.074646 -0.018948 -0.019948   \n",
       "1  comp_001661_01W 2003-01-31  35.853958   5221.931641  0.005815  0.004815   \n",
       "2  comp_004367_02W 2003-01-31  36.068653   4139.599121 -0.103801 -0.104801   \n",
       "3  comp_004925_02W 2003-01-31  29.735168  15302.482422 -0.070425 -0.071425   \n",
       "4  comp_005959_01W 2003-01-31  39.181721   6355.691406 -0.085813 -0.086813   \n",
       "\n",
       "   ret_exc_lead1m   ret_6_1  ret_12_1  tax_gr1a  ...  cowc_gr1a    pi_nix  \\\n",
       "0       -0.089504 -0.229560 -0.353251 -0.020556  ...   0.041844  3.815513   \n",
       "1        0.108428  0.129149 -0.035109 -0.033306  ...  -0.019596  1.284930   \n",
       "2        0.127227 -0.020286  0.087151 -0.037240  ...   0.026114  1.725004   \n",
       "3        0.064832  0.046064  0.023265 -0.007344  ...  -0.000724  1.961724   \n",
       "4        0.001832  0.123795  0.012811 -0.012241  ...  -0.014214  1.069862   \n",
       "\n",
       "    ret_6_0   ret_1_0    noa_at  cop_atl1  prc_highprc_252d    ocf_at  \\\n",
       "0 -0.244158 -0.018948  0.692706  0.093015          0.398130 -0.007700   \n",
       "1  0.135715  0.005815  0.751569  0.164124          0.918059  0.096164   \n",
       "2 -0.121981 -0.103801  0.961141  0.240563          0.675468  0.070724   \n",
       "3 -0.027605 -0.070425  0.466852  0.221983          0.861333  0.084234   \n",
       "4  0.027359 -0.085813  0.635742  0.108581          0.721256  0.069331   \n",
       "\n",
       "   dbnetis_at  netdebt_me  \n",
       "0    0.085202    0.135368  \n",
       "1    0.063714    0.200036  \n",
       "2   -0.000607    0.410184  \n",
       "3   -0.008142   -0.075232  \n",
       "4   -0.038594    0.500641  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the cleansed and winsorised data\n",
    "df = pd.read_csv(r\"C:\\Users\\krist\\Documents\\Data\\ger_factor_data_from2003.csv\", dtype ={\"comp_tpci\": str}, parse_dates =[\"eom\"])\n",
    "#Convert to float 32 (format needed for the most ML models)\n",
    "df[df.columns[2:]] = df[df.columns[2:]].astype('float32')\n",
    "#Sort observations by date and stock id\n",
    "df = df.sort_values(by = ['eom', 'id'], ascending = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy date variable and stock id to use them as multindex\n",
    "df[\"id2\"] = df[\"id\"].copy()\n",
    "df[\"eom2\"] = df[\"eom\"].copy()\n",
    "df = df.set_index(['eom2','id2'])\n",
    "\n",
    "#Make a copy of  the \"me\" variable (market equity) before rank standartization to use afterwards for value weighting\n",
    "df[\"me2\"] = df[\"me\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate the potential heterogeneity in model predictability, a subgroup analysis for small (the bottom 30% stocks by market equity each month) and large (the top 30% stocks each month) stocks is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create two new dataframes containing only large and only small firms respectively.\n",
    "p=0.3 \n",
    "df_top = df.groupby('eom').apply(lambda x: x.nlargest(int(len(x)*p),\"me\")).reset_index(drop=True)  \n",
    "df_bottom = df.groupby('eom').apply(lambda x: x.nsmallest(int(len(x)*p),\"me\")).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank standardization  in interval [-1,1]\n",
    "\n",
    "A rank standardization procedure following Kelly, Pruitt, and Su (2019) and Freyberger, Neuhierl, and Weber (2020) is employed. The stock characteristics are ranked month-by-month cross-sectionally and the ranks are mapped into the [-1,1] interval, thereby transforming features into a uniform distribution and increasing the insensitivity to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardize all independent variables\n",
    "features = df.columns[~df.columns.isin([\"eom\",\"eom2\",\"me2\",'id',\"id2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "\n",
    "#Ranks in [0,1]interval\n",
    "df[features]=df.groupby(\"eom\")[features].rank(pct=True)\n",
    "\n",
    "#Multiply by 2 and substract 1 to get ranks in interval [-1,1] \n",
    "df[features] = 2*df[features] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardize the big firms dataset\n",
    "features = df_top.columns[~df_top.columns.isin([\"eom\",\"eom2\",\"me2\",'id',\"id2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "df_top[features]=df_top.groupby(\"eom\")[features].rank(pct=True)\n",
    "df_top[features] = 2*df_top[features] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#standardize the small firms dataset\n",
    "features = df_bottom.columns[~df_bottom.columns.isin([\"eom\",\"eom2\",\"me2\",'id',\"id2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "df_bottom[features]=df_bottom.groupby(\"eom\")[features].rank(pct=True)\n",
    "df_bottom[features] = 2*df_bottom[features] - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Elastic Net Model\n",
    "\n",
    "### Formula:\n",
    "\n",
    "$\\phi(\\theta ; \\lambda, \\rho)=\\lambda(1-\\rho) \\sum_{j=1}^{P}\\left|\\theta_{j}\\right|+\\frac{1}{2} \\lambda \\rho \\sum_{j=1}^{P} \\theta_{j}^{2}\n",
    "$\n",
    "\n",
    "### Hyperparameters:\n",
    "- overall strength of the penalty, $\\lambda \\in(0,1)$\n",
    "- $\\rho \\in(0,1)$, regulating the weight of the lasso and ridge penalization, where $p=0$ corresponds to the lasso and $p=1$ to the ridge method. Here $\\rho$ is set to $0.5 .$ \n",
    "\n",
    "- In sklearn the $\\lambda$ parameter is called  **alpha** and $\\rho $ is called **l1_ratio**\n",
    "## All firms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train period: 2003-01-31 - 2008-01-31 ,val period: 2008-01-31 - 2010-01-31 , Test period 2010-01-31 - 2011-01-31 # train records 37757 ,# val records 17356 , # test records 8974\n",
      "Train period: 2004-01-31 - 2009-01-31 ,val period: 2009-01-31 - 2011-01-31 , Test period 2011-01-31 - 2012-01-31 # train records 40925 ,# val records 17143 , # test records 9537\n",
      "Train period: 2005-01-31 - 2010-01-31 ,val period: 2010-01-31 - 2012-01-31 , Test period 2012-01-31 - 2013-01-31 # train records 42278 ,# val records 18511 , # test records 9107\n",
      "Train period: 2006-01-31 - 2011-01-31 ,val period: 2011-01-31 - 2013-01-31 , Test period 2013-01-31 - 2014-01-31 # train records 43971 ,# val records 18644 , # test records 9636\n",
      "Train period: 2007-01-31 - 2012-01-31 ,val period: 2012-01-31 - 2014-01-31 , Test period 2014-01-31 - 2015-01-31 # train records 45267 ,# val records 18743 , # test records 9919\n",
      "Train period: 2008-01-31 - 2013-01-31 ,val period: 2013-01-31 - 2015-01-31 , Test period 2015-01-31 - 2016-01-31 # train records 44974 ,# val records 19555 , # test records 9260\n",
      "Train period: 2009-01-31 - 2014-01-31 ,val period: 2014-01-31 - 2016-01-31 , Test period 2016-01-31 - 2017-01-31 # train records 45423 ,# val records 19179 , # test records 9109\n",
      "Train period: 2010-01-31 - 2015-01-31 ,val period: 2015-01-31 - 2017-01-31 , Test period 2017-01-31 - 2018-01-31 # train records 47173 ,# val records 18369 , # test records 9667\n",
      "Train period: 2011-01-31 - 2016-01-31 ,val period: 2016-01-31 - 2018-01-31 , Test period 2018-01-31 - 2019-01-31 # train records 47459 ,# val records 18776 , # test records 9569\n",
      "Train period: 2012-01-31 - 2017-01-31 ,val period: 2017-01-31 - 2019-01-31 , Test period 2019-01-31 - 2020-01-31 # train records 47031 ,# val records 19236 , # test records 8639\n",
      "R2OOS Elastic net:  0.008251968986335068\n"
     ]
    }
   ],
   "source": [
    "# Define the splitting scheme:\n",
    "# Rolling window  train-val-test split\n",
    "# The rolling window is set to 96 months (8 years)\n",
    "# 5 years for training, 2 years for validation, and 1 year for out-of-samlpe testing\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "#Define the idependent variables (X) and the dependent variable (y)\n",
    "features = df.columns[~df.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df[features]\n",
    "y = df[\"ret_exc_lead1m\"]\n",
    "\n",
    "\n",
    "#Empty containers to save results from each window\n",
    "predictions = []\n",
    "y_test_list =[]\n",
    "dates = []\n",
    "dic_r2_all = {}\n",
    "\n",
    "# Model’s complexity: dictionary to save the number of characteristics over time\n",
    "num_coef_time = {}\n",
    "\n",
    "# List of values to use for the alpha hyperparameter\n",
    "alphas = np.linspace(start=0.00001,stop=0.004,num=20)\n",
    "# Empty container to save the objective loss function (mean squared errors) for each alpha\n",
    "mse = np.full((len(alphas),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "    #Loop over the list containing potential alpha values, fit on the training sample and use \n",
    "    #validation set to generate predictions\n",
    "    for i in range(len(alphas)):\n",
    "        model_val = ElasticNet(alpha=alphas[i], l1_ratio=0.5)\n",
    "        model_val.fit(X_train,y_train)\n",
    "        Yval_predict = model_val.predict(X_val)\n",
    "        #calculate mean squared error for each potential value of the alpha hyperparameter\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val,Yval_predict))\n",
    " \n",
    "    #The optimal value of the alpha hyperparameter is the value that causes the lowest loss\n",
    "    optim_alpha = alphas[np.argmin(mse)]\n",
    "   \n",
    "    #Fit again using the train and validation set and the optimal alpha parameter\n",
    "    model = ElasticNet(alpha=optim_alpha, l1_ratio=0.5)\n",
    "    model.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    #Use test set to generate final predictions \n",
    "    preds = model.predict(X_test)\n",
    "\n",
    "    #Save predictions, dates and the true values of the dependent variable to list  \n",
    "    predictions.append(preds)\n",
    "    dates.append(y_test.index)\n",
    "    y_test_list.append(y_test)\n",
    "    \n",
    "    #Calculate OOS model performance the for current window\n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    #Save OOS model performance and the respective month to dictionary\n",
    "    dic_r2_all[\"r2.\" + str(y_test.index)] = r2\n",
    "    # Save the number of characteristics to inspect  model's complexity over time \n",
    "    num_coef = len(model.coef_[np.nonzero(model.coef_ != 0)])\n",
    "    num_coef_time[\"ncoef.\" + str(y_test.index)] = num_coef\n",
    "   \n",
    "        \n",
    "#Concatenate to get results over the whole OOS test period (Jan 2010-Dec 2019)\n",
    "predictions_all= np.concatenate(predictions, axis=0)\n",
    "y_test_list_all= np.concatenate(y_test_list, axis=0) \n",
    "dates_all= np.concatenate(dates, axis=0)\n",
    "\n",
    "#Calculate OOS model performance over the entire test period in line with Gu et al (2020)\n",
    "R2OOS_ENet = 1-sum(pow(y_test_list_all-predictions_all,2))/sum(pow(y_test_list_all,2))\n",
    "print(\"R2OOS Elastic net: \", R2OOS_ENet)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train period: 2003-01-31 - 2008-01-31 ,val period: 2008-01-31 - 2010-01-31 , Test period 2010-01-31 - 2011-01-31 # train records 11301 ,# val records 5199 , # test records 2687\n",
      "Train period: 2004-01-31 - 2009-01-31 ,val period: 2009-01-31 - 2011-01-31 , Test period 2011-01-31 - 2012-01-31 # train records 12252 ,# val records 5135 , # test records 2855\n",
      "Train period: 2005-01-31 - 2010-01-31 ,val period: 2010-01-31 - 2012-01-31 , Test period 2012-01-31 - 2013-01-31 # train records 12661 ,# val records 5542 , # test records 2728\n",
      "Train period: 2006-01-31 - 2011-01-31 ,val period: 2011-01-31 - 2013-01-31 , Test period 2013-01-31 - 2014-01-31 # train records 13170 ,# val records 5583 , # test records 2885\n",
      "Train period: 2007-01-31 - 2012-01-31 ,val period: 2012-01-31 - 2014-01-31 , Test period 2014-01-31 - 2015-01-31 # train records 13558 ,# val records 5613 , # test records 2969\n",
      "Train period: 2008-01-31 - 2013-01-31 ,val period: 2013-01-31 - 2015-01-31 , Test period 2015-01-31 - 2016-01-31 # train records 13469 ,# val records 5854 , # test records 2772\n",
      "Train period: 2009-01-31 - 2014-01-31 ,val period: 2014-01-31 - 2016-01-31 , Test period 2016-01-31 - 2017-01-31 # train records 13603 ,# val records 5741 , # test records 2729\n",
      "Train period: 2010-01-31 - 2015-01-31 ,val period: 2015-01-31 - 2017-01-31 , Test period 2017-01-31 - 2018-01-31 # train records 14124 ,# val records 5501 , # test records 2894\n",
      "Train period: 2011-01-31 - 2016-01-31 ,val period: 2016-01-31 - 2018-01-31 , Test period 2018-01-31 - 2019-01-31 # train records 14209 ,# val records 5623 , # test records 2864\n",
      "Train period: 2012-01-31 - 2017-01-31 ,val period: 2017-01-31 - 2019-01-31 , Test period 2019-01-31 - 2020-01-31 # train records 14083 ,# val records 5758 , # test records 2587\n",
      "R2OOS Elastic net Big only:  0.006368436070013028\n"
     ]
    }
   ],
   "source": [
    "# Same procedure for large firms only\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "features = df_top.columns[~df_top.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df_top[features]\n",
    "y = df_top[\"ret_exc_lead1m\"]\n",
    "\n",
    "predictions_top = []\n",
    "y_test_list_top =[]\n",
    "dates_top = []\n",
    "dic_r2_all_top = {}\n",
    "\n",
    "alphas = np.linspace(start=0.00001,stop=0.004,num=20)\n",
    "mse = np.full((len(alphas),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "\n",
    "    for i in range(len(alphas)):\n",
    "        model_val = ElasticNet(alpha=alphas[i], l1_ratio=0.5)\n",
    "        model_val.fit(X_train,y_train)\n",
    "        Yval_predict = model_val.predict(X_val)\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val,Yval_predict))\n",
    "\n",
    "    optim_alpha = alphas[np.argmin(mse)]\n",
    "   \n",
    "    model = ElasticNet(alpha=optim_alpha, l1_ratio=0.5)\n",
    "    model.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val))) \n",
    "    preds = model.predict(X_test)\n",
    "\n",
    "    predictions_top.append(preds)\n",
    "    dates_top.append(y_test.index)\n",
    "    y_test_list_top.append(y_test)\n",
    "    \n",
    "\n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    dic_r2_all_top[\"r2.\" + str(y_test.index)] = r2\n",
    "    \n",
    "predictions_all_top= np.concatenate(predictions_top, axis=0)\n",
    "y_test_list_all_top= np.concatenate(y_test_list_top, axis=0) \n",
    "dates_all_top= np.concatenate(dates_top, axis=0)\n",
    "\n",
    "R2OOS_ENet_top = 1-sum(pow(y_test_list_all_top-predictions_all_top,2))/sum(pow(y_test_list_all_top,2))\n",
    "print(\"R2OOS Elastic net Big only: \", R2OOS_ENet_top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train period: 2003-01-31 - 2008-01-31 ,val period: 2008-01-31 - 2010-01-31 , Test period 2010-01-31 - 2011-01-31 # train records 11301 ,# val records 5199 , # test records 2687\n",
      "Train period: 2004-01-31 - 2009-01-31 ,val period: 2009-01-31 - 2011-01-31 , Test period 2011-01-31 - 2012-01-31 # train records 12252 ,# val records 5135 , # test records 2855\n",
      "Train period: 2005-01-31 - 2010-01-31 ,val period: 2010-01-31 - 2012-01-31 , Test period 2012-01-31 - 2013-01-31 # train records 12661 ,# val records 5542 , # test records 2728\n",
      "Train period: 2006-01-31 - 2011-01-31 ,val period: 2011-01-31 - 2013-01-31 , Test period 2013-01-31 - 2014-01-31 # train records 13170 ,# val records 5583 , # test records 2885\n",
      "Train period: 2007-01-31 - 2012-01-31 ,val period: 2012-01-31 - 2014-01-31 , Test period 2014-01-31 - 2015-01-31 # train records 13558 ,# val records 5613 , # test records 2969\n",
      "Train period: 2008-01-31 - 2013-01-31 ,val period: 2013-01-31 - 2015-01-31 , Test period 2015-01-31 - 2016-01-31 # train records 13469 ,# val records 5854 , # test records 2772\n",
      "Train period: 2009-01-31 - 2014-01-31 ,val period: 2014-01-31 - 2016-01-31 , Test period 2016-01-31 - 2017-01-31 # train records 13603 ,# val records 5741 , # test records 2729\n",
      "Train period: 2010-01-31 - 2015-01-31 ,val period: 2015-01-31 - 2017-01-31 , Test period 2017-01-31 - 2018-01-31 # train records 14124 ,# val records 5501 , # test records 2894\n",
      "Train period: 2011-01-31 - 2016-01-31 ,val period: 2016-01-31 - 2018-01-31 , Test period 2018-01-31 - 2019-01-31 # train records 14209 ,# val records 5623 , # test records 2864\n",
      "Train period: 2012-01-31 - 2017-01-31 ,val period: 2017-01-31 - 2019-01-31 , Test period 2019-01-31 - 2020-01-31 # train records 14083 ,# val records 5758 , # test records 2587\n",
      "R2OOS Elastic net Small only:  0.011200704299881181\n"
     ]
    }
   ],
   "source": [
    "# Same procedure for small firms only\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "features = df_bottom.columns[~df_bottom.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df_bottom[features]\n",
    "y = df_bottom[\"ret_exc_lead1m\"]\n",
    "\n",
    "predictions_bottom = []\n",
    "y_test_list_bottom =[]\n",
    "dates_bottom = []\n",
    "dic_r2_all_bottom = {}\n",
    "\n",
    "alphas = np.linspace(start=0.00001,stop=0.004,num=20)\n",
    "mse = np.full((len(alphas),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "\n",
    "    for i in range(len(alphas)):\n",
    "        model_val = ElasticNet(alpha=alphas[i], l1_ratio=0.5)\n",
    "        model_val.fit(X_train,y_train)\n",
    "        Yval_predict = model_val.predict(X_val)\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val,Yval_predict))\n",
    "\n",
    "    optim_alpha = alphas[np.argmin(mse)]\n",
    "   \n",
    "    model = ElasticNet(alpha=optim_alpha, l1_ratio=0.5)\n",
    "    model.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val))) \n",
    "    preds = model.predict(X_test)\n",
    "\n",
    "    predictions_bottom.append(preds)\n",
    "    dates_bottom.append(y_test.index)\n",
    "    y_test_list_bottom.append(y_test)\n",
    "    \n",
    "\n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    dic_r2_all_bottom[\"r2.\" + str(y_test.index)] = r2\n",
    "    \n",
    "predictions_all_bottom= np.concatenate(predictions_bottom, axis=0)\n",
    "y_test_list_all_bottom= np.concatenate(y_test_list_bottom, axis=0) \n",
    "dates_all_bottom= np.concatenate(dates_bottom, axis=0)\n",
    "\n",
    "R2OOS_ENet_bottom = 1-sum(pow(y_test_list_all_bottom-predictions_all_bottom,2))/sum(pow(y_test_list_all_bottom,2))\n",
    "print(\"R2OOS Elastic net Small only: \", R2OOS_ENet_bottom)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full sample</th>\n",
       "      <td>0.008252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large firms</th>\n",
       "      <td>0.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small firms</th>\n",
       "      <td>0.011201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ENet\n",
       "Full sample  0.008252\n",
       "Large firms  0.006368\n",
       "Small firms  0.011201"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate table containing the R2oos measures for all three datasets\n",
    "chart = np.array([[R2OOS_ENet],\n",
    "                  [R2OOS_ENet_top],\n",
    "                  [R2OOS_ENet_bottom]])\n",
    "                     \n",
    "\n",
    "r2oos_enet = pd.DataFrame(chart, columns=['ENet'],\n",
    "                              index=[\"Full sample\", \"Large firms\", \"Small firms\"])\n",
    "r2oos_enet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's performance measures to compare with other models later.\n",
    "r2oos_enet.to_csv(r'C:\\Users\\krist\\Documents\\Data\\r2oos results\\r2oos_enet.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-varying model complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert dictionary containing the number of characterictics over time in dataframe\n",
    "pd.DataFrame(num_coef_time.items())\n",
    "num_coef =pd.DataFrame(num_coef_time.items(), columns=['Identifier', 'num_coef'])\n",
    "num_coef['Identifier'] = num_coef['Identifier'].astype(str)\n",
    "num_coef[\"time\"] = num_coef[\"Identifier\"].str[20:30]\n",
    "num_coef.drop([\"Identifier\"], axis = 1, inplace = True)\n",
    "\n",
    "#Plot time-varying model complexity\n",
    "num_coef.set_index('time').plot()\n",
    "plt.xticks(rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's complexity  to compare with other models later.\n",
    "num_coef.to_csv(r'C:\\Users\\krist\\Documents\\Data\\model_complexity\\comp_enet.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Importance\n",
    "\n",
    "Variable importance is defined following Gu et al. (2020), i.e., for each model, all values of a given predictor are set to zero and the reduction in predictive R2OOS is calculated. Then, the absolute reductions in predictive R2OOS are normalized to sum to 1, indicating the relative contribution of each variable to a model. In contrast to Gu et al. (2020), variable importance is calculated based on the last rolling window observation sample and does not represent an average over the whole sample.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.011214301328191678\n"
     ]
    }
   ],
   "source": [
    "# R2OOS based on the last rolling window when all variables are included\n",
    "features = df.columns[~df.columns.isin(['id',\"id2\",\"eom\",\"eom2\",\"me2\",\"prc\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\",\"year\"])].tolist()\n",
    "df[\"year\"] = df[\"eom\"].dt.year\n",
    "\n",
    "X_train = df[features].loc[(df[\"year\"]>=2012) & (df[\"year\"]<=2016)]\n",
    "y_train = df[\"ret_exc_lead1m\"].loc[(df[\"year\"]>=2012) & (df[\"year\"]<=2016)]\n",
    "\n",
    "X_val = df[features].loc[(df[\"year\"]>=2017) & (df[\"year\"]<=2018)]\n",
    "y_val = df[\"ret_exc_lead1m\"].loc[(df[\"year\"]>=2017) & (df[\"year\"]<=2018)]\n",
    "\n",
    "alphas = np.linspace(start=0.00001,stop=0.004,num=20)\n",
    "mse = np.full((len(alphas),1),np.nan, dtype = np.float32)\n",
    "\n",
    "for i in range(len(alphas)):\n",
    "    model_val = ElasticNet(alpha=alphas[i], l1_ratio=0.5)\n",
    "    model_val.fit(X_train,y_train)\n",
    "    Yval_predict = model_val.predict(X_val)\n",
    "    mse[i,0] = np.sqrt(mean_squared_error(y_val,Yval_predict))\n",
    "\n",
    "optim_alpha = alphas[np.argmin(mse)]\n",
    "model = ElasticNet(alpha=optim_alpha, l1_ratio=0.5)\n",
    "model.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "preds = model.predict(np.concatenate((X_train, X_val)))\n",
    "R2OOS_all = 1-sum(pow(np.concatenate((y_train, y_val))-preds,2))/sum(pow(np.concatenate((y_train, y_val)),2))\n",
    "print(R2OOS_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a separate dataframe for each independent variable, where all values of that variable are set to zero\n",
    "\n",
    "for j in features:\n",
    "    globals()['df_' + str(j)] =  df.copy()\n",
    "    globals()['df_' + str(j)][str(j)] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>eom</th>\n",
       "      <th>prc</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>ret_exc_lead1m</th>\n",
       "      <th>ret_6_1</th>\n",
       "      <th>ret_12_1</th>\n",
       "      <th>tax_gr1a</th>\n",
       "      <th>...</th>\n",
       "      <th>ret_6_0</th>\n",
       "      <th>ret_1_0</th>\n",
       "      <th>noa_at</th>\n",
       "      <th>cop_atl1</th>\n",
       "      <th>prc_highprc_252d</th>\n",
       "      <th>ocf_at</th>\n",
       "      <th>dbnetis_at</th>\n",
       "      <th>netdebt_me</th>\n",
       "      <th>me2</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eom2</th>\n",
       "      <th>id2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2003-01-31</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>-0.376652</td>\n",
       "      <td>0.162996</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "      <td>-0.089504</td>\n",
       "      <td>-0.665198</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.726872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.669604</td>\n",
       "      <td>-0.312775</td>\n",
       "      <td>0.585903</td>\n",
       "      <td>-0.735683</td>\n",
       "      <td>-0.859031</td>\n",
       "      <td>-0.797357</td>\n",
       "      <td>0.735683</td>\n",
       "      <td>-0.149780</td>\n",
       "      <td>567.074646</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.427313</td>\n",
       "      <td>0.718062</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.108428</td>\n",
       "      <td>0.669604</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.845815</td>\n",
       "      <td>...</td>\n",
       "      <td>0.577093</td>\n",
       "      <td>-0.110132</td>\n",
       "      <td>0.696035</td>\n",
       "      <td>-0.392070</td>\n",
       "      <td>0.607930</td>\n",
       "      <td>0.286344</td>\n",
       "      <td>0.647577</td>\n",
       "      <td>0.145374</td>\n",
       "      <td>5221.931641</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004367_02W</th>\n",
       "      <td>comp_004367_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.433921</td>\n",
       "      <td>0.674009</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "      <td>0.127227</td>\n",
       "      <td>0.114537</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.894273</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.356828</td>\n",
       "      <td>-0.766520</td>\n",
       "      <td>0.881057</td>\n",
       "      <td>0.458150</td>\n",
       "      <td>-0.365639</td>\n",
       "      <td>-0.110132</td>\n",
       "      <td>-0.207048</td>\n",
       "      <td>0.339207</td>\n",
       "      <td>4139.599121</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004925_02W</th>\n",
       "      <td>comp_004925_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.295154</td>\n",
       "      <td>0.881057</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "      <td>0.064832</td>\n",
       "      <td>0.392070</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.453744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096916</td>\n",
       "      <td>-0.612335</td>\n",
       "      <td>-0.264317</td>\n",
       "      <td>0.330396</td>\n",
       "      <td>0.422907</td>\n",
       "      <td>0.149780</td>\n",
       "      <td>-0.370044</td>\n",
       "      <td>-0.709251</td>\n",
       "      <td>15302.482422</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_005959_01W</th>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.477974</td>\n",
       "      <td>0.762115</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.643172</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.599119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259912</td>\n",
       "      <td>-0.691630</td>\n",
       "      <td>0.458150</td>\n",
       "      <td>-0.669604</td>\n",
       "      <td>-0.255507</td>\n",
       "      <td>-0.118943</td>\n",
       "      <td>-0.656388</td>\n",
       "      <td>0.436123</td>\n",
       "      <td>6355.691406</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-12-31</th>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>comp_333885_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>0.367156</td>\n",
       "      <td>0.484646</td>\n",
       "      <td>0.128645</td>\n",
       "      <td>0.128545</td>\n",
       "      <td>-0.037398</td>\n",
       "      <td>-0.957276</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.951936</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.853138</td>\n",
       "      <td>0.591455</td>\n",
       "      <td>0.457944</td>\n",
       "      <td>0.815754</td>\n",
       "      <td>-0.319092</td>\n",
       "      <td>0.877170</td>\n",
       "      <td>-0.930574</td>\n",
       "      <td>-0.316422</td>\n",
       "      <td>10829.783203</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334302_02W</th>\n",
       "      <td>comp_334302_02W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.842457</td>\n",
       "      <td>-0.487316</td>\n",
       "      <td>0.097961</td>\n",
       "      <td>0.097861</td>\n",
       "      <td>-0.033670</td>\n",
       "      <td>-0.420561</td>\n",
       "      <td>0</td>\n",
       "      <td>0.540721</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268358</td>\n",
       "      <td>0.417891</td>\n",
       "      <td>0.791722</td>\n",
       "      <td>0.345794</td>\n",
       "      <td>0.754339</td>\n",
       "      <td>0.054740</td>\n",
       "      <td>0.692924</td>\n",
       "      <td>-0.252336</td>\n",
       "      <td>297.482697</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_335706_02W</th>\n",
       "      <td>comp_335706_02W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.722296</td>\n",
       "      <td>-0.618158</td>\n",
       "      <td>0.114381</td>\n",
       "      <td>0.114281</td>\n",
       "      <td>0.089684</td>\n",
       "      <td>0.097463</td>\n",
       "      <td>0</td>\n",
       "      <td>0.311081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236315</td>\n",
       "      <td>0.511348</td>\n",
       "      <td>-0.241656</td>\n",
       "      <td>-0.935915</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>-0.917223</td>\n",
       "      <td>0.164219</td>\n",
       "      <td>-0.730307</td>\n",
       "      <td>196.243454</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>comp_340115_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.392523</td>\n",
       "      <td>-0.025367</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>0.155713</td>\n",
       "      <td>0.009796</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.871829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.687583</td>\n",
       "      <td>-0.415220</td>\n",
       "      <td>-0.436582</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.642190</td>\n",
       "      <td>0.562083</td>\n",
       "      <td>1799.958496</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>comp_340153_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>0.138852</td>\n",
       "      <td>0.711615</td>\n",
       "      <td>0.238817</td>\n",
       "      <td>0.238717</td>\n",
       "      <td>0.006943</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.879840</td>\n",
       "      <td>-0.815754</td>\n",
       "      <td>-0.636849</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>-0.129506</td>\n",
       "      <td>-0.110814</td>\n",
       "      <td>-0.578104</td>\n",
       "      <td>26788.144531</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156948 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         id        eom       prc        me  \\\n",
       "eom2       id2                                                               \n",
       "2003-01-31 comp_001166_02W  comp_001166_02W 2003-01-31 -0.376652  0.162996   \n",
       "           comp_001661_01W  comp_001661_01W 2003-01-31  0.427313  0.718062   \n",
       "           comp_004367_02W  comp_004367_02W 2003-01-31  0.433921  0.674009   \n",
       "           comp_004925_02W  comp_004925_02W 2003-01-31  0.295154  0.881057   \n",
       "           comp_005959_01W  comp_005959_01W 2003-01-31  0.477974  0.762115   \n",
       "...                                     ...        ...       ...       ...   \n",
       "2020-12-31 comp_333885_01W  comp_333885_01W 2020-12-31  0.367156  0.484646   \n",
       "           comp_334302_02W  comp_334302_02W 2020-12-31 -0.842457 -0.487316   \n",
       "           comp_335706_02W  comp_335706_02W 2020-12-31 -0.722296 -0.618158   \n",
       "           comp_340115_01W  comp_340115_01W 2020-12-31 -0.392523 -0.025367   \n",
       "           comp_340153_01W  comp_340153_01W 2020-12-31  0.138852  0.711615   \n",
       "\n",
       "                                 ret   ret_exc  ret_exc_lead1m   ret_6_1  \\\n",
       "eom2       id2                                                             \n",
       "2003-01-31 comp_001166_02W -0.018948 -0.019948       -0.089504 -0.665198   \n",
       "           comp_001661_01W  0.005815  0.004815        0.108428  0.669604   \n",
       "           comp_004367_02W -0.103801 -0.104801        0.127227  0.114537   \n",
       "           comp_004925_02W -0.070425 -0.071425        0.064832  0.392070   \n",
       "           comp_005959_01W -0.085813 -0.086813        0.001832  0.643172   \n",
       "...                              ...       ...             ...       ...   \n",
       "2020-12-31 comp_333885_01W  0.128645  0.128545       -0.037398 -0.957276   \n",
       "           comp_334302_02W  0.097961  0.097861       -0.033670 -0.420561   \n",
       "           comp_335706_02W  0.114381  0.114281        0.089684  0.097463   \n",
       "           comp_340115_01W  0.155813  0.155713        0.009796  0.001335   \n",
       "           comp_340153_01W  0.238817  0.238717        0.006943  0.001335   \n",
       "\n",
       "                            ret_12_1  tax_gr1a  ...   ret_6_0   ret_1_0  \\\n",
       "eom2       id2                                  ...                       \n",
       "2003-01-31 comp_001166_02W         0 -0.726872  ... -0.669604 -0.312775   \n",
       "           comp_001661_01W         0 -0.845815  ...  0.577093 -0.110132   \n",
       "           comp_004367_02W         0 -0.894273  ... -0.356828 -0.766520   \n",
       "           comp_004925_02W         0 -0.453744  ...  0.096916 -0.612335   \n",
       "           comp_005959_01W         0 -0.599119  ...  0.259912 -0.691630   \n",
       "...                              ...       ...  ...       ...       ...   \n",
       "2020-12-31 comp_333885_01W         0 -0.951936  ... -0.853138  0.591455   \n",
       "           comp_334302_02W         0  0.540721  ... -0.268358  0.417891   \n",
       "           comp_335706_02W         0  0.311081  ...  0.236315  0.511348   \n",
       "           comp_340115_01W         0  0.871829  ...  0.001335  0.687583   \n",
       "           comp_340153_01W         0  0.847797  ...  0.001335  0.879840   \n",
       "\n",
       "                              noa_at  cop_atl1  prc_highprc_252d    ocf_at  \\\n",
       "eom2       id2                                                               \n",
       "2003-01-31 comp_001166_02W  0.585903 -0.735683         -0.859031 -0.797357   \n",
       "           comp_001661_01W  0.696035 -0.392070          0.607930  0.286344   \n",
       "           comp_004367_02W  0.881057  0.458150         -0.365639 -0.110132   \n",
       "           comp_004925_02W -0.264317  0.330396          0.422907  0.149780   \n",
       "           comp_005959_01W  0.458150 -0.669604         -0.255507 -0.118943   \n",
       "...                              ...       ...               ...       ...   \n",
       "2020-12-31 comp_333885_01W  0.457944  0.815754         -0.319092  0.877170   \n",
       "           comp_334302_02W  0.791722  0.345794          0.754339  0.054740   \n",
       "           comp_335706_02W -0.241656 -0.935915         -0.313752 -0.917223   \n",
       "           comp_340115_01W -0.415220 -0.436582          0.001335  0.025367   \n",
       "           comp_340153_01W -0.815754 -0.636849          0.001335 -0.129506   \n",
       "\n",
       "                            dbnetis_at  netdebt_me           me2  year  \n",
       "eom2       id2                                                          \n",
       "2003-01-31 comp_001166_02W    0.735683   -0.149780    567.074646  2003  \n",
       "           comp_001661_01W    0.647577    0.145374   5221.931641  2003  \n",
       "           comp_004367_02W   -0.207048    0.339207   4139.599121  2003  \n",
       "           comp_004925_02W   -0.370044   -0.709251  15302.482422  2003  \n",
       "           comp_005959_01W   -0.656388    0.436123   6355.691406  2003  \n",
       "...                                ...         ...           ...   ...  \n",
       "2020-12-31 comp_333885_01W   -0.930574   -0.316422  10829.783203  2020  \n",
       "           comp_334302_02W    0.692924   -0.252336    297.482697  2020  \n",
       "           comp_335706_02W    0.164219   -0.730307    196.243454  2020  \n",
       "           comp_340115_01W    0.642190    0.562083   1799.958496  2020  \n",
       "           comp_340153_01W   -0.110814   -0.578104  26788.144531  2020  \n",
       "\n",
       "[156948 rows x 57 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#e.g., df_ret_12_1 is a dataframe where all values of the ret_12_1 variable are set to zero\n",
    "df_ret_12_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate R2OOS based on the last rolling window for each dataframe and save to dictionary\n",
    "\n",
    "dic = {}\n",
    "alphas = np.linspace(start=0.00001,stop=0.004,num=20)\n",
    "mse = np.full((len(alphas),1),np.nan, dtype = np.float32)\n",
    "    \n",
    "for j in features:\n",
    "    df_var = globals()['df_' + str(j)]\n",
    "    \n",
    "    X_train = df_var[features].loc[(df_var[\"year\"]>=2012) & (df_var[\"year\"]<=2016)]\n",
    "    y_train = df_var[\"ret_exc_lead1m\"].loc[(df_var[\"year\"]>=2012) & (df_var[\"year\"]<=2016)]\n",
    "\n",
    "    X_val = df_var[features].loc[(df_var[\"year\"]>=2017) & (df_var[\"year\"]<=2018)]\n",
    "    y_val = df_var[\"ret_exc_lead1m\"].loc[(df_var[\"year\"]>=2017) & (df_var[\"year\"]<=2018)]\n",
    "              \n",
    "    for i in range(len(alphas)):\n",
    "        model_val = ElasticNet(alpha=alphas[i], l1_ratio=0.5)\n",
    "        model_val.fit(X_train,y_train)\n",
    "        Yval_predict = model_val.predict(X_val)\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val,Yval_predict))\n",
    "\n",
    "    optim_alpha = alphas[np.argmin(mse)]\n",
    "    model = ElasticNet(alpha=optim_alpha, l1_ratio=0.5)\n",
    "    model.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds = model.predict(np.concatenate((X_train, X_val))) \n",
    "    \n",
    "    R2OOS_var = 1-sum(pow(np.concatenate((y_train, y_val))-preds,2))/sum(pow(np.concatenate((y_train, y_val)),2))\n",
    "    dic['R2OOS_' + str(j)] = R2OOS_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2OOS_me': 0.010357545525678136,\n",
       " 'R2OOS_ret_6_1': 0.011214302724197323,\n",
       " 'R2OOS_ret_12_1': 0.011216632718979547,\n",
       " 'R2OOS_tax_gr1a': 0.010713540801136956,\n",
       " 'R2OOS_be_me': 0.011214301502252,\n",
       " 'R2OOS_debt_me': 0.011214301328191678,\n",
       " 'R2OOS_ni_me': 0.01112470139700894,\n",
       " 'R2OOS_sale_gr3': 0.011214301328191678,\n",
       " 'R2OOS_sale_gr1': 0.011214301328191678,\n",
       " 'R2OOS_sale_me': 0.011214301328191678,\n",
       " 'R2OOS_lnoa_gr1a': 0.011214301359782408,\n",
       " 'R2OOS_inv_gr1a': 0.009917015665339313,\n",
       " 'R2OOS_oaccruals_at': 0.011214301328191678,\n",
       " 'R2OOS_taccruals_at': 0.011214301328191678,\n",
       " 'R2OOS_be_gr1a': 0.011214301328191678,\n",
       " 'R2OOS_taccruals_ni': 0.011214301328191678,\n",
       " 'R2OOS_ebit_sale': 0.011214301328191678,\n",
       " 'R2OOS_sale_bev': 0.011214301176105446,\n",
       " 'R2OOS_age': 0.010785867288713336,\n",
       " 'R2OOS_beta_60m': 0.011214301328191678,\n",
       " 'R2OOS_rmax1_21d': 0.011214299795824334,\n",
       " 'R2OOS_bidaskhl_21d': 0.011214301328191678,\n",
       " 'R2OOS_ret_9_1': 0.010899965767484199,\n",
       " 'R2OOS_ret_12_7': 0.010914990139931358,\n",
       " 'R2OOS_ni_be': 0.011209841403997611,\n",
       " 'R2OOS_ocf_debt': 0.011214301308388519,\n",
       " 'R2OOS_cash_gr1a': 0.011214301328191678,\n",
       " 'R2OOS_at_gr1': 0.011214301328191678,\n",
       " 'R2OOS_chcsho_12m': 0.011214301328191678,\n",
       " 'R2OOS_ca_cl': 0.011214301328191678,\n",
       " 'R2OOS_ivol_capm_252d': 0.010308124308950672,\n",
       " 'R2OOS_debt_gr1': 0.011174498264709287,\n",
       " 'R2OOS_debtlt_gr1a': 0.011214301328191678,\n",
       " 'R2OOS_ope_be': 0.011214301328191678,\n",
       " 'R2OOS_caliq_cl': 0.011214301332735488,\n",
       " 'R2OOS_dsale_drec': 0.01121430132579293,\n",
       " 'R2OOS_f_score': 0.011128629302234683,\n",
       " 'R2OOS_rvol_21d': 0.011202478950268824,\n",
       " 'R2OOS_gp_bev': 0.011216963887054177,\n",
       " 'R2OOS_cowc_gr1a': 0.011194881492483377,\n",
       " 'R2OOS_pi_nix': 0.011214301328191678,\n",
       " 'R2OOS_ret_6_0': 0.011214301230360935,\n",
       " 'R2OOS_ret_1_0': 0.011091900335224336,\n",
       " 'R2OOS_noa_at': 0.011059048602712673,\n",
       " 'R2OOS_cop_atl1': 0.011206394540061115,\n",
       " 'R2OOS_prc_highprc_252d': 0.011143751614238973,\n",
       " 'R2OOS_ocf_at': 0.011217074710189512,\n",
       " 'R2OOS_dbnetis_at': 0.011214301328191678,\n",
       " 'R2OOS_netdebt_me': 0.011214301328191678}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary, containing OOS predictive R2, when the all values of a given predictor are set to zero\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>R2OOS</th>\n",
       "      <th>red_R2OOS</th>\n",
       "      <th>var_imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>inv_gr1a</td>\n",
       "      <td>0.009917</td>\n",
       "      <td>1.297286e-03</td>\n",
       "      <td>2.493735e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ivol_capm_252d</td>\n",
       "      <td>0.010308</td>\n",
       "      <td>9.061770e-04</td>\n",
       "      <td>1.741918e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>me</td>\n",
       "      <td>0.010358</td>\n",
       "      <td>8.567558e-04</td>\n",
       "      <td>1.646917e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tax_gr1a</td>\n",
       "      <td>0.010714</td>\n",
       "      <td>5.007605e-04</td>\n",
       "      <td>9.625975e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>age</td>\n",
       "      <td>0.010786</td>\n",
       "      <td>4.284340e-04</td>\n",
       "      <td>8.235663e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ret_9_1</td>\n",
       "      <td>0.010900</td>\n",
       "      <td>3.143356e-04</td>\n",
       "      <td>6.042381e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ret_12_7</td>\n",
       "      <td>0.010915</td>\n",
       "      <td>2.993112e-04</td>\n",
       "      <td>5.753572e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>noa_at</td>\n",
       "      <td>0.011059</td>\n",
       "      <td>1.552527e-04</td>\n",
       "      <td>2.984378e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ret_1_0</td>\n",
       "      <td>0.011092</td>\n",
       "      <td>1.224010e-04</td>\n",
       "      <td>2.352879e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ni_me</td>\n",
       "      <td>0.011125</td>\n",
       "      <td>8.959993e-05</td>\n",
       "      <td>1.722354e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>f_score</td>\n",
       "      <td>0.011129</td>\n",
       "      <td>8.567203e-05</td>\n",
       "      <td>1.646849e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>prc_highprc_252d</td>\n",
       "      <td>0.011144</td>\n",
       "      <td>7.054971e-05</td>\n",
       "      <td>1.356157e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>debt_gr1</td>\n",
       "      <td>0.011174</td>\n",
       "      <td>3.980306e-05</td>\n",
       "      <td>7.651228e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>cowc_gr1a</td>\n",
       "      <td>0.011195</td>\n",
       "      <td>1.941984e-05</td>\n",
       "      <td>3.733019e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>rvol_21d</td>\n",
       "      <td>0.011202</td>\n",
       "      <td>1.182238e-05</td>\n",
       "      <td>2.272581e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>cop_atl1</td>\n",
       "      <td>0.011206</td>\n",
       "      <td>7.906788e-06</td>\n",
       "      <td>1.519899e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ni_be</td>\n",
       "      <td>0.011210</td>\n",
       "      <td>4.459924e-06</td>\n",
       "      <td>8.573183e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rmax1_21d</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>1.532367e-09</td>\n",
       "      <td>2.945625e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sale_bev</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>1.520862e-10</td>\n",
       "      <td>2.923510e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ret_6_0</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>9.783074e-11</td>\n",
       "      <td>1.880572e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ocf_debt</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>1.980316e-11</td>\n",
       "      <td>3.806704e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>dsale_drec</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>2.398748e-12</td>\n",
       "      <td>4.611044e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>oaccruals_at</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>dbnetis_at</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>debt_me</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sale_gr3</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>pi_nix</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sale_gr1</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sale_me</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ebit_sale</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>debtlt_gr1a</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ope_be</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>taccruals_at</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cash_gr1a</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>beta_60m</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>bidaskhl_21d</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>taccruals_ni</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>be_gr1a</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ca_cl</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>netdebt_me</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>at_gr1</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>chcsho_12m</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>caliq_cl</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>-4.543810e-12</td>\n",
       "      <td>-8.734434e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lnoa_gr1a</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>-3.159073e-11</td>\n",
       "      <td>-6.072595e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>be_me</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>-1.740603e-10</td>\n",
       "      <td>-3.345911e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ret_6_1</td>\n",
       "      <td>0.011214</td>\n",
       "      <td>-1.396006e-09</td>\n",
       "      <td>-2.683501e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ret_12_1</td>\n",
       "      <td>0.011217</td>\n",
       "      <td>-2.331391e-06</td>\n",
       "      <td>-4.481565e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>gp_bev</td>\n",
       "      <td>0.011217</td>\n",
       "      <td>-2.662559e-06</td>\n",
       "      <td>-5.118160e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ocf_at</td>\n",
       "      <td>0.011217</td>\n",
       "      <td>-2.773382e-06</td>\n",
       "      <td>-5.331192e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature     R2OOS     red_R2OOS       var_imp\n",
       "11          inv_gr1a  0.009917  1.297286e-03  2.493735e-01\n",
       "30    ivol_capm_252d  0.010308  9.061770e-04  1.741918e-01\n",
       "0                 me  0.010358  8.567558e-04  1.646917e-01\n",
       "3           tax_gr1a  0.010714  5.007605e-04  9.625975e-02\n",
       "18               age  0.010786  4.284340e-04  8.235663e-02\n",
       "22           ret_9_1  0.010900  3.143356e-04  6.042381e-02\n",
       "23          ret_12_7  0.010915  2.993112e-04  5.753572e-02\n",
       "43            noa_at  0.011059  1.552527e-04  2.984378e-02\n",
       "42           ret_1_0  0.011092  1.224010e-04  2.352879e-02\n",
       "6              ni_me  0.011125  8.959993e-05  1.722354e-02\n",
       "36           f_score  0.011129  8.567203e-05  1.646849e-02\n",
       "45  prc_highprc_252d  0.011144  7.054971e-05  1.356157e-02\n",
       "31          debt_gr1  0.011174  3.980306e-05  7.651228e-03\n",
       "39         cowc_gr1a  0.011195  1.941984e-05  3.733019e-03\n",
       "37          rvol_21d  0.011202  1.182238e-05  2.272581e-03\n",
       "44          cop_atl1  0.011206  7.906788e-06  1.519899e-03\n",
       "24             ni_be  0.011210  4.459924e-06  8.573183e-04\n",
       "20         rmax1_21d  0.011214  1.532367e-09  2.945625e-07\n",
       "17          sale_bev  0.011214  1.520862e-10  2.923510e-08\n",
       "41           ret_6_0  0.011214  9.783074e-11  1.880572e-08\n",
       "25          ocf_debt  0.011214  1.980316e-11  3.806704e-09\n",
       "35        dsale_drec  0.011214  2.398748e-12  4.611044e-10\n",
       "12      oaccruals_at  0.011214  0.000000e+00  0.000000e+00\n",
       "47        dbnetis_at  0.011214  0.000000e+00  0.000000e+00\n",
       "5            debt_me  0.011214  0.000000e+00  0.000000e+00\n",
       "7           sale_gr3  0.011214  0.000000e+00  0.000000e+00\n",
       "40            pi_nix  0.011214  0.000000e+00  0.000000e+00\n",
       "8           sale_gr1  0.011214  0.000000e+00  0.000000e+00\n",
       "9            sale_me  0.011214  0.000000e+00  0.000000e+00\n",
       "16         ebit_sale  0.011214  0.000000e+00  0.000000e+00\n",
       "32       debtlt_gr1a  0.011214  0.000000e+00  0.000000e+00\n",
       "33            ope_be  0.011214  0.000000e+00  0.000000e+00\n",
       "13      taccruals_at  0.011214  0.000000e+00  0.000000e+00\n",
       "26         cash_gr1a  0.011214  0.000000e+00  0.000000e+00\n",
       "19          beta_60m  0.011214  0.000000e+00  0.000000e+00\n",
       "21      bidaskhl_21d  0.011214  0.000000e+00  0.000000e+00\n",
       "15      taccruals_ni  0.011214  0.000000e+00  0.000000e+00\n",
       "14           be_gr1a  0.011214  0.000000e+00  0.000000e+00\n",
       "29             ca_cl  0.011214  0.000000e+00  0.000000e+00\n",
       "48        netdebt_me  0.011214  0.000000e+00  0.000000e+00\n",
       "27            at_gr1  0.011214  0.000000e+00  0.000000e+00\n",
       "28        chcsho_12m  0.011214  0.000000e+00  0.000000e+00\n",
       "34          caliq_cl  0.011214 -4.543810e-12 -8.734434e-10\n",
       "10         lnoa_gr1a  0.011214 -3.159073e-11 -6.072595e-09\n",
       "4              be_me  0.011214 -1.740603e-10 -3.345911e-08\n",
       "1            ret_6_1  0.011214 -1.396006e-09 -2.683501e-07\n",
       "2           ret_12_1  0.011217 -2.331391e-06 -4.481565e-04\n",
       "38            gp_bev  0.011217 -2.662559e-06 -5.118160e-04\n",
       "46            ocf_at  0.011217 -2.773382e-06 -5.331192e-04"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary to dataframe\n",
    "pd.DataFrame(dic.items())\n",
    "imp=pd.DataFrame(dic.items(), columns=['Feature', 'R2OOS'])\n",
    "# Feature: name of the variable whose values are set to zero\n",
    "imp[\"Feature\"] = imp[\"Feature\"].str[6:]\n",
    "\n",
    "# Calculate reduction in predictive R2OOS \n",
    "imp[\"red_R2OOS\"] = R2OOS_all -imp[\"R2OOS\"]\n",
    "imp[\"var_imp\"] = imp[\"red_R2OOS\"]/sum(imp[\"red_R2OOS\"])\n",
    "imp=imp.sort_values(by = ['var_imp'], ascending = False)\n",
    "imp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot variable importance measures for the top-20 most influential variables\n",
    "\n",
    "fea_imp_graph = imp.sort_values(['var_imp', 'Feature'], ascending=[True, False]).iloc[-20:]\n",
    "_ = fea_imp_graph.plot(kind='barh', x='Feature', y='var_imp', figsize=(20, 10))\n",
    "plt.title('ENet')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_imp_enet=imp[[\"Feature\", \"var_imp\"]]\n",
    "var_imp_enet.to_csv(r'C:\\Users\\krist\\Documents\\Data\\variable importance\\var_imp_enet.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning portfolios\n",
    "\n",
    "Explore whether predictability translates into portfolio gains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>yhat</th>\n",
       "      <th>y_true</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010-01</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>0.016103</td>\n",
       "      <td>0.035512</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>0.015240</td>\n",
       "      <td>-0.041371</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002410_04W</th>\n",
       "      <td>0.012910</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002597_02W</th>\n",
       "      <td>0.011465</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_003820_01W</th>\n",
       "      <td>0.004352</td>\n",
       "      <td>0.078967</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2019-12</th>\n",
       "      <th>comp_330716_01W</th>\n",
       "      <td>0.002540</td>\n",
       "      <td>-0.012904</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_331115_01W</th>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_332311_01W</th>\n",
       "      <td>0.011536</td>\n",
       "      <td>-0.051340</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334036_02W</th>\n",
       "      <td>-0.000597</td>\n",
       "      <td>-0.100876</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93417 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               yhat    y_true       date\n",
       "MonthYear id                                            \n",
       "2010-01   comp_001166_02W  0.016103  0.035512 2010-01-31\n",
       "          comp_001661_01W  0.015240 -0.041371 2010-01-31\n",
       "          comp_002410_04W  0.012910 -0.053836 2010-01-31\n",
       "          comp_002597_02W  0.011465  0.001862 2010-01-31\n",
       "          comp_003820_01W  0.004352  0.078967 2010-01-31\n",
       "...                             ...       ...        ...\n",
       "2019-12   comp_330716_01W  0.002540 -0.012904 2019-12-31\n",
       "          comp_331115_01W  0.002158  0.005400 2019-12-31\n",
       "          comp_332311_01W  0.011536 -0.051340 2019-12-31\n",
       "          comp_333885_01W  0.006443  0.003528 2019-12-31\n",
       "          comp_334036_02W -0.000597 -0.100876 2019-12-31\n",
       "\n",
       "[93417 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate a results dataframe containing the model predictions (yhat) and the true values (y_true) \n",
    "#of the dependent variable for each stock in each month\n",
    "\n",
    "yhat = predictions_all.tolist()\n",
    "y_true = y_test_list_all.tolist()\n",
    "i = dates_all.tolist()\n",
    "\n",
    "results = pd.DataFrame(\n",
    "    {'identifier': i,\n",
    "     'yhat': yhat,\n",
    "     'y_true': y_true\n",
    "    })\n",
    "\n",
    "results[\"identifier\"]= results[\"identifier\"].astype(\"str\")\n",
    "results[\"date\"] = results[\"identifier\"].str[12:22]\n",
    "results[\"id\"] = results[\"identifier\"].str[36:51]\n",
    "results.drop([\"identifier\"],axis = 1, inplace=True)\n",
    "results['date'] = pd.to_datetime(results['date'], format='%Y-%m-%d')\n",
    "results['MonthYear'] = results['date'].dt.to_period('M')\n",
    "results = results.sort_values(by = ['date', 'id'], ascending = True)\n",
    "results = results.set_index(['MonthYear','id'])\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to use for Diebold-Mariano test\n",
    "enet= results.reset_index()\n",
    "enet.to_csv(r'C:\\Users\\krist\\Documents\\Data\\Predictions\\enet.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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></th>\n",
       "      <th>me2</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2003-01</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>567.074646</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>5221.931641</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004367_02W</th>\n",
       "      <td>4139.599121</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004925_02W</th>\n",
       "      <td>15302.482422</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_005959_01W</th>\n",
       "      <td>6355.691406</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-12</th>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>10829.783203</td>\n",
       "      <td>0.128645</td>\n",
       "      <td>0.128545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334302_02W</th>\n",
       "      <td>297.482697</td>\n",
       "      <td>0.097961</td>\n",
       "      <td>0.097861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_335706_02W</th>\n",
       "      <td>196.243454</td>\n",
       "      <td>0.114381</td>\n",
       "      <td>0.114281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>1799.958496</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>0.155713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>26788.144531</td>\n",
       "      <td>0.238817</td>\n",
       "      <td>0.238717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156948 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    me2       ret   ret_exc\n",
       "MonthYear id                                               \n",
       "2003-01   comp_001166_02W    567.074646 -0.018948 -0.019948\n",
       "          comp_001661_01W   5221.931641  0.005815  0.004815\n",
       "          comp_004367_02W   4139.599121 -0.103801 -0.104801\n",
       "          comp_004925_02W  15302.482422 -0.070425 -0.071425\n",
       "          comp_005959_01W   6355.691406 -0.085813 -0.086813\n",
       "...                                 ...       ...       ...\n",
       "2020-12   comp_333885_01W  10829.783203  0.128645  0.128545\n",
       "          comp_334302_02W    297.482697  0.097961  0.097861\n",
       "          comp_335706_02W    196.243454  0.114381  0.114281\n",
       "          comp_340115_01W   1799.958496  0.155813  0.155713\n",
       "          comp_340153_01W  26788.144531  0.238817  0.238717\n",
       "\n",
       "[156948 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Import the original \"me\" avriable (before rank standartization) to use for the value weighting scheme\n",
    "\n",
    "unscaled_data = df[[\"id\", 'eom', \"me2\",\"ret\" ,\"ret_exc\"]].copy()\n",
    "unscaled_data['MonthYear'] = unscaled_data['eom'].dt.to_period('M')\n",
    "unscaled_data.drop('eom', axis=1, inplace=True)\n",
    "unscaled_data = unscaled_data.set_index(['MonthYear','id'])\n",
    "unscaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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></th>\n",
       "      <th>prc</th>\n",
       "      <th>prc_t+1</th>\n",
       "      <th>ret_t+1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>11.486148</td>\n",
       "      <td>10.468435</td>\n",
       "      <td>-0.088604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>10.468435</td>\n",
       "      <td>10.107208</td>\n",
       "      <td>-0.034506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-03</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>10.107208</td>\n",
       "      <td>13.610050</td>\n",
       "      <td>0.346569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-04</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>13.610050</td>\n",
       "      <td>14.518828</td>\n",
       "      <td>0.066773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-05</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>14.518828</td>\n",
       "      <td>15.212792</td>\n",
       "      <td>0.047798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>14.831518</td>\n",
       "      <td>17.142462</td>\n",
       "      <td>0.155813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>17.142462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10</th>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>21.909940</td>\n",
       "      <td>29.758645</td>\n",
       "      <td>0.358226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11</th>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>29.758645</td>\n",
       "      <td>36.865510</td>\n",
       "      <td>0.238817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12</th>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>36.865510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>274031 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 prc    prc_t+1   ret_t+1\n",
       "MonthYear id                                             \n",
       "2003-01   comp_001166_02W  11.486148  10.468435 -0.088604\n",
       "2003-02   comp_001166_02W  10.468435  10.107208 -0.034506\n",
       "2003-03   comp_001166_02W  10.107208  13.610050  0.346569\n",
       "2003-04   comp_001166_02W  13.610050  14.518828  0.066773\n",
       "2003-05   comp_001166_02W  14.518828  15.212792  0.047798\n",
       "...                              ...        ...       ...\n",
       "2020-11   comp_340115_01W  14.831518  17.142462  0.155813\n",
       "2020-12   comp_340115_01W  17.142462        NaN       NaN\n",
       "2020-10   comp_340153_01W  21.909940  29.758645  0.358226\n",
       "2020-11   comp_340153_01W  29.758645  36.865510  0.238817\n",
       "2020-12   comp_340153_01W  36.865510        NaN       NaN\n",
       "\n",
       "[274031 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import price data in t+1 and ret t+1 --> Import from the original dataframe\n",
    "# (otherwise there are missing values because shares with a price < $5 have been excluded)\n",
    "\n",
    "price_data= pd.read_csv(r\"C:\\Users\\krist\\Documents\\Data\\ger_factor_data.csv\", dtype ={\"comp_tpci\": str}, parse_dates =[\"eom\"])\n",
    "price_data[\"eom\"] = pd.to_datetime(price_data[\"eom\"], utc = False)\n",
    "\n",
    "# Only the data between 2003 and 2020 is needed\n",
    "price_data[\"year\"] = price_data[\"eom\"].dt.year\n",
    "price_data = price_data.loc[(price_data[\"year\"]>=2003) & (price_data[\"year\"]<=2020)]\n",
    "price_data = price_data[[\"id\", 'eom', 'prc', \"ret\"]]\n",
    "\n",
    "#Generate a MonthYear variable (monthly observations are given, the day is not needed)\n",
    "price_data['MonthYear'] = price_data['eom'].dt.to_period('M')\n",
    "price_data.drop('eom', axis=1, inplace=True)\n",
    "\n",
    "#The original dataframe is not winsorized --> winsorize  at the 1% and 99% level \n",
    "price = [\"prc\"]\n",
    "ret = [\"ret\"]\n",
    "price_data[\"prc\"] = price_data[price].apply(lambda x: x.clip(*x.quantile([0.01, 0.99])))\n",
    "price_data[\"ret\"] = price_data[ret].apply(lambda x: x.clip(*x.quantile([0.01, 0.99])))\n",
    "\n",
    "#Shift to get the price of each stock in month t+1\n",
    "price_data['prc_t+1'] = price_data.groupby(['id'])['prc'].shift(-1)\n",
    "# Shift to get return for each stock in month t+1\n",
    "price_data[\"ret_t+1\"]=price_data.groupby(['id'])['ret'].shift(-1)\n",
    "price_data.drop('ret', axis=1, inplace=True)\n",
    "price_data = price_data.set_index(['MonthYear','id'])\n",
    "price_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th>yhat</th>\n",
       "      <th>y_true</th>\n",
       "      <th>date</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>prc</th>\n",
       "      <th>prc_t+1</th>\n",
       "      <th>ret_t+1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>0.016103</td>\n",
       "      <td>0.035512</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>1233.673950</td>\n",
       "      <td>-0.099572</td>\n",
       "      <td>-0.099572</td>\n",
       "      <td>22.730003</td>\n",
       "      <td>23.537181</td>\n",
       "      <td>0.035512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>0.015240</td>\n",
       "      <td>-0.041371</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>6796.776367</td>\n",
       "      <td>0.036020</td>\n",
       "      <td>0.036020</td>\n",
       "      <td>22.730003</td>\n",
       "      <td>21.789641</td>\n",
       "      <td>-0.041371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_002410_04W</td>\n",
       "      <td>0.012910</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>132047.843750</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>-0.014941</td>\n",
       "      <td>9.453457</td>\n",
       "      <td>8.812790</td>\n",
       "      <td>-0.053836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_002597_02W</td>\n",
       "      <td>0.011465</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>18138.388672</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>13.191743</td>\n",
       "      <td>13.057401</td>\n",
       "      <td>0.001862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_003820_01W</td>\n",
       "      <td>0.004352</td>\n",
       "      <td>0.078967</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>225.897369</td>\n",
       "      <td>-0.125756</td>\n",
       "      <td>-0.125756</td>\n",
       "      <td>6.339377</td>\n",
       "      <td>6.839981</td>\n",
       "      <td>0.078967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93412</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>comp_330716_01W</td>\n",
       "      <td>0.002540</td>\n",
       "      <td>-0.012904</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>33.930836</td>\n",
       "      <td>0.066508</td>\n",
       "      <td>0.065108</td>\n",
       "      <td>6.169243</td>\n",
       "      <td>6.097655</td>\n",
       "      <td>-0.011604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93413</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>comp_331115_01W</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>36.342445</td>\n",
       "      <td>0.005615</td>\n",
       "      <td>0.004215</td>\n",
       "      <td>18.171224</td>\n",
       "      <td>18.292966</td>\n",
       "      <td>0.006700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93414</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>comp_332311_01W</td>\n",
       "      <td>0.011536</td>\n",
       "      <td>-0.051340</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>13412.494141</td>\n",
       "      <td>-0.020668</td>\n",
       "      <td>-0.022068</td>\n",
       "      <td>26.824988</td>\n",
       "      <td>25.482656</td>\n",
       "      <td>-0.050040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93415</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>comp_333885_01W</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>7151.834473</td>\n",
       "      <td>0.251631</td>\n",
       "      <td>0.250231</td>\n",
       "      <td>35.759173</td>\n",
       "      <td>35.931819</td>\n",
       "      <td>0.004828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93416</th>\n",
       "      <td>2019-12</td>\n",
       "      <td>comp_334036_02W</td>\n",
       "      <td>-0.000597</td>\n",
       "      <td>-0.100876</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>189.330505</td>\n",
       "      <td>0.377943</td>\n",
       "      <td>0.377359</td>\n",
       "      <td>21.424097</td>\n",
       "      <td>19.290764</td>\n",
       "      <td>-0.099576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93417 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MonthYear               id      yhat    y_true       date  \\\n",
       "0       2010-01  comp_001166_02W  0.016103  0.035512 2010-01-31   \n",
       "1       2010-01  comp_001661_01W  0.015240 -0.041371 2010-01-31   \n",
       "2       2010-01  comp_002410_04W  0.012910 -0.053836 2010-01-31   \n",
       "3       2010-01  comp_002597_02W  0.011465  0.001862 2010-01-31   \n",
       "4       2010-01  comp_003820_01W  0.004352  0.078967 2010-01-31   \n",
       "...         ...              ...       ...       ...        ...   \n",
       "93412   2019-12  comp_330716_01W  0.002540 -0.012904 2019-12-31   \n",
       "93413   2019-12  comp_331115_01W  0.002158  0.005400 2019-12-31   \n",
       "93414   2019-12  comp_332311_01W  0.011536 -0.051340 2019-12-31   \n",
       "93415   2019-12  comp_333885_01W  0.006443  0.003528 2019-12-31   \n",
       "93416   2019-12  comp_334036_02W -0.000597 -0.100876 2019-12-31   \n",
       "\n",
       "                  me       ret   ret_exc        prc    prc_t+1   ret_t+1  \n",
       "0        1233.673950 -0.099572 -0.099572  22.730003  23.537181  0.035512  \n",
       "1        6796.776367  0.036020  0.036020  22.730003  21.789641 -0.041371  \n",
       "2      132047.843750 -0.014941 -0.014941   9.453457   8.812790 -0.053836  \n",
       "3       18138.388672  0.035186  0.035186  13.191743  13.057401  0.001862  \n",
       "4         225.897369 -0.125756 -0.125756   6.339377   6.839981  0.078967  \n",
       "...              ...       ...       ...        ...        ...       ...  \n",
       "93412      33.930836  0.066508  0.065108   6.169243   6.097655 -0.011604  \n",
       "93413      36.342445  0.005615  0.004215  18.171224  18.292966  0.006700  \n",
       "93414   13412.494141 -0.020668 -0.022068  26.824988  25.482656 -0.050040  \n",
       "93415    7151.834473  0.251631  0.250231  35.759173  35.931819  0.004828  \n",
       "93416     189.330505  0.377943  0.377359  21.424097  19.290764 -0.099576  \n",
       "\n",
       "[93417 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge with the results dataframe\n",
    "bigdata = pd.merge(results, unscaled_data,left_index=True, right_index=True)\n",
    "bigdata = pd.merge(bigdata, price_data,left_index=True, right_index=True)\n",
    "bigdata = bigdata.reset_index()\n",
    "\n",
    "# Original \"me\" (before rank normalization) to use for the value weighting scheme\n",
    "bigdata.rename(columns={'me2': 'me'}, inplace=True)\n",
    "\n",
    "#Final df containing all information needed for the portfolio creation\n",
    "bigdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonthYear     0\n",
       "id            0\n",
       "yhat          0\n",
       "y_true        0\n",
       "date          0\n",
       "me            0\n",
       "ret           0\n",
       "ret_exc       0\n",
       "prc           0\n",
       "prc_t+1      12\n",
       "ret_t+1      12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing values (the original df has not yet been cleaned of missing values)\n",
    "bigdata.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93405"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the twelve observations where prc_t+1 and ret_t+1 are still missing\n",
    "bigdata = bigdata.dropna()\n",
    "len(bigdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the 1-month risk-free rate as the average monthly difference between returns and excess returns\n",
    "bigdata[\"risk_free_rate\"]=bigdata[\"ret_t+1\"]-bigdata[\"y_true\"]\n",
    "bigdata[\"risk_free_rate\"] =bigdata.groupby('MonthYear')[\"risk_free_rate\"].transform('mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
       "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
       "       118, 119, 120], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a variable \"NumMonth\", indicating the number of the respectie month: \n",
    "# January 2010 = 1,....,December 2019 = 120\n",
    "\n",
    "bigdata[\"MonthYear1\"]= bigdata[\"MonthYear\"].copy()\n",
    "bigdata[\"MonthYear\"]= bigdata[\"MonthYear\"].astype(\"int\")\n",
    "bigdata[\"NumMonth\"]= bigdata[\"MonthYear\"]-479\n",
    "bigdata[\"NumMonth\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a separate dataframe for each month,e.g.: \n",
    "# df_1=January 2010, df_2 = February 2010,....,df_120 = December 2019\n",
    "\n",
    "for i in bigdata[\"NumMonth\"].unique():\n",
    "    globals()['df_' + str(i)] =  bigdata[bigdata[\"NumMonth\"]==i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign decile ranks to the stocks within each month based on the predicted excess returns:\n",
    "\n",
    "# 1)Generate ranks starting from the lowest predicted excess return to the highest\n",
    "for i in bigdata[\"NumMonth\"].unique():\n",
    "    globals()['df_' + str(i)][\"rank\"]= globals()['df_' + str(i)]['yhat'].rank(method='first')\n",
    "    \n",
    "# 2)Split ranks into 10 deciles to get decile rank\n",
    "# DecileRank = 0: stoks with lowest predicted excess returns\n",
    "# DecileRank =9: stoks with highest predicted excess returns\n",
    "for i in bigdata[\"NumMonth\"].unique():\n",
    "    globals()['df_' + str(i)][\"DecileRank\"]=pd.qcut(globals()['df_' + str(i)]['rank'].values, 10, labels = False)\n",
    "\n",
    "#Drop normal rank, retain only decile ranks \n",
    "for i in bigdata[\"NumMonth\"].unique():\n",
    "     globals()['df_' + str(i)].drop('rank', axis=1, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a separate dataframe for each decile rank and each month,e.g.: \n",
    "# df_1_0: the bottom decile in January 2010,\n",
    "# d_1_9: the top decile in January 2010,....,\n",
    "# df_120_0: the bottom decile in December 2019,\n",
    "# df_120_9: the top decile in December 2019\n",
    "\n",
    "for i in bigdata[\"NumMonth\"].unique():\n",
    "    for j,g in globals()['df_' + str(i)].groupby('DecileRank'):\n",
    "        globals()['df_' + str(i)+ \"_\" + str(j)] =  g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All stocks are sorted into deciles based on their predicted returns for the next month. \n",
    "# Create 10 dataframes showing the portfolio composition of each decile portfolio over time\n",
    "\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)] = pd.concat([globals()['df_1_'+ str(j)], globals()['df_2_'+ str(j)]], axis=0)\n",
    "    \n",
    "# Generate 10 Dataframes for the 10 Decile portfolios 0-9: rank_9: top portfolio, rank_0: bottom portfolio\n",
    "for i in np.arange(2,120,1):\n",
    "    for j in np.arange(0,10,1):\n",
    "        globals()['rank_' + str(j)] = pd.concat([globals()['rank_' + str(j)], globals()['df_' + str(i+1)+ \"_\" + str(j)]], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th>yhat</th>\n",
       "      <th>y_true</th>\n",
       "      <th>date</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>prc</th>\n",
       "      <th>prc_t+1</th>\n",
       "      <th>ret_t+1</th>\n",
       "      <th>risk_free_rate</th>\n",
       "      <th>MonthYear1</th>\n",
       "      <th>NumMonth</th>\n",
       "      <th>DecileRank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_007152_01W</td>\n",
       "      <td>0.026002</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>5164.601074</td>\n",
       "      <td>-0.065609</td>\n",
       "      <td>-0.065609</td>\n",
       "      <td>22.438058</td>\n",
       "      <td>22.574668</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_010846_09W</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>-0.014764</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>52754.355469</td>\n",
       "      <td>-0.052386</td>\n",
       "      <td>-0.052386</td>\n",
       "      <td>30.765442</td>\n",
       "      <td>30.035846</td>\n",
       "      <td>-0.014764</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_014269_01W</td>\n",
       "      <td>0.021858</td>\n",
       "      <td>-0.034796</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>2671.458984</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>40.510844</td>\n",
       "      <td>39.101210</td>\n",
       "      <td>-0.034796</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_015321_02W</td>\n",
       "      <td>0.020980</td>\n",
       "      <td>-0.053604</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>32227.082031</td>\n",
       "      <td>-0.079324</td>\n",
       "      <td>-0.079324</td>\n",
       "      <td>30.438742</td>\n",
       "      <td>28.807106</td>\n",
       "      <td>-0.053604</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_017420_03W</td>\n",
       "      <td>0.019099</td>\n",
       "      <td>0.059737</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>11078.906250</td>\n",
       "      <td>-0.107508</td>\n",
       "      <td>-0.107508</td>\n",
       "      <td>13.198694</td>\n",
       "      <td>13.987147</td>\n",
       "      <td>0.059737</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93343</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_318573_01W</td>\n",
       "      <td>0.014097</td>\n",
       "      <td>0.070476</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>3576.373047</td>\n",
       "      <td>0.027055</td>\n",
       "      <td>0.025655</td>\n",
       "      <td>31.911809</td>\n",
       "      <td>34.202303</td>\n",
       "      <td>0.071776</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93345</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_318659_01W</td>\n",
       "      <td>0.018534</td>\n",
       "      <td>-0.148323</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>6149.301758</td>\n",
       "      <td>0.025512</td>\n",
       "      <td>0.024112</td>\n",
       "      <td>55.343714</td>\n",
       "      <td>47.206938</td>\n",
       "      <td>-0.147023</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93348</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_319508_01W</td>\n",
       "      <td>0.015665</td>\n",
       "      <td>-0.007458</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>600.979126</td>\n",
       "      <td>-0.006600</td>\n",
       "      <td>-0.008000</td>\n",
       "      <td>40.717001</td>\n",
       "      <td>40.466258</td>\n",
       "      <td>-0.006158</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93358</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_320978_03W</td>\n",
       "      <td>0.013318</td>\n",
       "      <td>0.051737</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>196.784821</td>\n",
       "      <td>0.223594</td>\n",
       "      <td>0.222194</td>\n",
       "      <td>11.216805</td>\n",
       "      <td>11.811713</td>\n",
       "      <td>0.053037</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93363</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_321632_01W</td>\n",
       "      <td>0.013791</td>\n",
       "      <td>-0.009555</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>12113.565430</td>\n",
       "      <td>0.025326</td>\n",
       "      <td>0.023926</td>\n",
       "      <td>33.100791</td>\n",
       "      <td>32.827559</td>\n",
       "      <td>-0.008255</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9386 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MonthYear               id      yhat    y_true       date  \\\n",
       "11           480  comp_007152_01W  0.026002  0.006088 2010-01-31   \n",
       "15           480  comp_010846_09W  0.023881 -0.014764 2010-01-31   \n",
       "21           480  comp_014269_01W  0.021858 -0.034796 2010-01-31   \n",
       "26           480  comp_015321_02W  0.020980 -0.053604 2010-01-31   \n",
       "41           480  comp_017420_03W  0.019099  0.059737 2010-01-31   \n",
       "...          ...              ...       ...       ...        ...   \n",
       "93343        599  comp_318573_01W  0.014097  0.070476 2019-12-31   \n",
       "93345        599  comp_318659_01W  0.018534 -0.148323 2019-12-31   \n",
       "93348        599  comp_319508_01W  0.015665 -0.007458 2019-12-31   \n",
       "93358        599  comp_320978_03W  0.013318  0.051737 2019-12-31   \n",
       "93363        599  comp_321632_01W  0.013791 -0.009555 2019-12-31   \n",
       "\n",
       "                 me       ret   ret_exc        prc    prc_t+1   ret_t+1  \\\n",
       "11      5164.601074 -0.065609 -0.065609  22.438058  22.574668  0.006088   \n",
       "15     52754.355469 -0.052386 -0.052386  30.765442  30.035846 -0.014764   \n",
       "21      2671.458984  0.006921  0.006921  40.510844  39.101210 -0.034796   \n",
       "26     32227.082031 -0.079324 -0.079324  30.438742  28.807106 -0.053604   \n",
       "41     11078.906250 -0.107508 -0.107508  13.198694  13.987147  0.059737   \n",
       "...             ...       ...       ...        ...        ...       ...   \n",
       "93343   3576.373047  0.027055  0.025655  31.911809  34.202303  0.071776   \n",
       "93345   6149.301758  0.025512  0.024112  55.343714  47.206938 -0.147023   \n",
       "93348    600.979126 -0.006600 -0.008000  40.717001  40.466258 -0.006158   \n",
       "93358    196.784821  0.223594  0.222194  11.216805  11.811713  0.053037   \n",
       "93363  12113.565430  0.025326  0.023926  33.100791  32.827559 -0.008255   \n",
       "\n",
       "       risk_free_rate MonthYear1  NumMonth  DecileRank  \n",
       "11           0.000353    2010-01         1           9  \n",
       "15           0.000353    2010-01         1           9  \n",
       "21           0.000353    2010-01         1           9  \n",
       "26           0.000353    2010-01         1           9  \n",
       "41           0.000353    2010-01         1           9  \n",
       "...               ...        ...       ...         ...  \n",
       "93343        0.001325    2019-12       120           9  \n",
       "93345        0.001325    2019-12       120           9  \n",
       "93348        0.001325    2019-12       120           9  \n",
       "93358        0.001325    2019-12       120           9  \n",
       "93363        0.001325    2019-12       120           9  \n",
       "\n",
       "[9386 rows x 15 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We get 10 separate dataframes (rank_0, rank_1,...., rank_9), showing the portfolio \n",
    "# composition of each decile portfolio over time\n",
    "#e.g.: compostion of the top decile portfolio over the entire OOS period (Jan 2010-Dec 2019):\n",
    "rank_9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio performance\n",
    "\n",
    "Next, the performance of the prediction-sorted portfolios is calculated over the 10-year out-of-sample testing for both equal and value weighting schemes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get equal und value weights per stock per month in each decile portfolio:\n",
    "# ew = Equally weighted\n",
    "# vw = Value weighted\n",
    "\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)][\"eq_weights\"] = 1/globals()['rank_' + str(j)].groupby('MonthYear')[\"id\"].transform('size')\n",
    "    globals()['rank_' + str(j)][\"me_weights\"] = globals()['rank_' + str(j)][\"me\"]/globals()['rank_' + str(j)].groupby('MonthYear')[\"me\"].transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The excess return of each decile portfolio in month t is modeled as the weighted sum of each stocks’s  excess return in month t:\n",
    "\n",
    "#### $$\n",
    "R_{P, t}=\\sum_{i=1}^{N} w_{i, t} * r_{i, t}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weighted excess return per stock in t+1\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['excess_return_stock_ew'] = globals()['rank_' + str(j)][\"y_true\"]*globals()['rank_' + str(j)][\"eq_weights\"]\n",
    "    globals()['rank_' + str(j)]['excess_return_stock_vw'] = globals()['rank_' + str(j)][\"y_true\"]*globals()['rank_' + str(j)][\"me_weights\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weighted return per stock in t+1 (to use for the sharpe ratio)\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['return_stock_ew'] = globals()['rank_' + str(j)][\"ret_t+1\"]*globals()['rank_' + str(j)][\"eq_weights\"]\n",
    "    globals()['rank_' + str(j)]['return_stock_vw'] = globals()['rank_' + str(j)][\"ret_t+1\"]*globals()['rank_' + str(j)][\"me_weights\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Portfolio excess return in t+1\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['excess_return_portfolio_ew'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"excess_return_stock_ew\"].transform('sum')\n",
    "    globals()['rank_' + str(j)]['excess_return_portfolio_vw'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"excess_return_stock_vw\"].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Portfolio return in t+1 (to use for the sharpe ratio) \n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['return_portfolio_ew'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"return_stock_ew\"].transform('sum')\n",
    "    globals()['rank_' + str(j)]['return_portfolio_vw'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"return_stock_vw\"].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weighted predicted excess return per stock in t+1\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['pred_excess_return_stock_ew'] = globals()['rank_' + str(j)][\"yhat\"]*globals()['rank_' + str(j)][\"eq_weights\"]\n",
    "    globals()['rank_' + str(j)]['pred_excess_return_stock_vw'] = globals()['rank_' + str(j)][\"yhat\"]*globals()['rank_' + str(j)][\"me_weights\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Portfolio predicted excess return in t+1\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['rank_' + str(j)]['pred_excess_return_portfolio_ew'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"pred_excess_return_stock_ew\"].transform('sum')\n",
    "    globals()['rank_' + str(j)]['pred_excess_return_portfolio_vw'] = globals()['rank_' + str(j)].groupby('MonthYear')[\"pred_excess_return_stock_vw\"].transform('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate dataframes, containing the portfolio returns on mohtly basis for each decile portfolio\n",
    "# e.g., montly_rank_0: dataframe, containing only the monthly portfolio excess returns (predicted and real) \n",
    "# for  the bottom rank\n",
    "\n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['montly_rank_' + str(j)] = globals()['rank_' + str(j)][[\"MonthYear1\", \"DecileRank\",\n",
    "                                                                      \"excess_return_portfolio_ew\",\n",
    "                                                                      \"excess_return_portfolio_vw\", \n",
    "                                                                      \"pred_excess_return_portfolio_ew\",\n",
    "                                                                      \"pred_excess_return_portfolio_vw\",\n",
    "                                                                      \"return_portfolio_ew\",\n",
    "                                                                      \"return_portfolio_vw\"]]\n",
    "    \n",
    "for j in np.arange(0,10,1):\n",
    "    globals()['montly_rank_' + str(j)]=globals()['montly_rank_' + str(j)].drop_duplicates()\n",
    "    globals()['montly_rank_' + str(j)]=globals()['montly_rank_' + str(j)].set_index(\"MonthYear1\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculate time-series averages of both predicted and realized excess returns for each decile portfolio\n",
    "- Calculate the standard deviations and the annualized sharpe ratios based on the realized excess returns\n",
    "- Get measures for both equal- and value-weighted  portfolios \n",
    "\n",
    "### $\\text { Sharpe Ratio }=\\frac{\\text { Portfolio return-Risk Free } \\text { Rate }}{\\text { Standard deviation of portfolio reurn }}=\\frac{R_{p}-R_{F}}{\\sigma_{P}}=\\frac{\\text { Portfolio excess return }}{\\text { Std(portfolio return) }}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in np.arange(0,10,1):\n",
    "    #Time-series average of realized excess returns\n",
    "    globals()[\"ew_mean_return_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"excess_return_portfolio_ew\"].mean()\n",
    "    globals()[\"vw_mean_return_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"excess_return_portfolio_vw\"].mean()\n",
    "    #Time-series average of predicted excess returns\n",
    "    globals()[\"ew_mean_pred_return_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"pred_excess_return_portfolio_ew\"].mean()\n",
    "    globals()[\"vw_mean_pred_return_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"pred_excess_return_portfolio_vw\"].mean()\n",
    "    #Standard deviation of realized excess returns\n",
    "    globals()[\"std_ew_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"excess_return_portfolio_ew\"].std()\n",
    "    globals()[\"std_vw_rank_\" +  str(j)]= globals()['montly_rank_' + str(j)][\"excess_return_portfolio_vw\"].std()\n",
    "    #Annualized sharpe ratio of realized excess returns\n",
    "    globals()[\"sharpe_ew_rank_\" +  str(j)]= (globals()['montly_rank_' + str(j)][\"excess_return_portfolio_ew\"].mean()/globals()['montly_rank_' + str(j)][\"return_portfolio_ew\"].std())* np.sqrt(12)\n",
    "    globals()[\"sharpe_vw_rank_\" +  str(j)]= (globals()['montly_rank_' + str(j)][\"excess_return_portfolio_vw\"].mean()/globals()['montly_rank_' + str(j)][\"return_portfolio_vw\"].std())* np.sqrt(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zero net investment long-short portfolio\n",
    "\n",
    "\n",
    "A zero-investment portfolio is a portfolio that has a net value of zero when the portfolio is assembled, and therefore requires an investor to take no equity stake in the portfolio. \n",
    " - e.g. short sell **USD 1,000** worth of the bottom portfolio, and use the proceeds to purchase **USD 1,000** worth of the top portfolio \n",
    " - this results in a net value of zero\n",
    " \n",
    " \n",
    "#### $\\sum_{i=1}^{N} w_{i, \\text { long }}=1$\n",
    "#### $\\sum_{i=1}^{N} w_{i, \\text { short }}=-1$\n",
    "#### $\\sum_{i=1}^{N} w_{i, \\text { long }} *\\text { prc }_{i, \\text { long }}=\\sum_{i=1}^{N} w_{i, \\text { short }} *\\text { prc }_{i, \\text { short }}$\n",
    "#### $\\text { Value Zero Net Portfolio in } t=\\sum_{i=1}^{N} w_{i, \\text { long }} * \\text { prc }_{i, \\text { long }}-\\sum_{i=1}^{N} w_{i, \\text { short }} * \\text { prc }_{i, \\text { short }}=0$\n",
    "\n",
    "\n",
    "#### $\\text { Return Zero Net Portfolio in } t=\\sum_{i=1}^{N} w_{i, \\text { long }} * \\text { ret }_{i, \\text { long }}-\\sum_{i=1}^{N} w_{i, \\text { short }} * \\text { ret }_{i, \\text { short }}$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# For the zero-net-investment long-short portfolio the top (long) and bottom(short) decile portfolios are needed\n",
    "\n",
    "long_monthly = rank_9[[\"NumMonth\",\"MonthYear1\", \"DecileRank\", \"excess_return_portfolio_ew\",\n",
    "                       \"excess_return_portfolio_vw\",\"pred_excess_return_portfolio_ew\",\n",
    "                       \"pred_excess_return_portfolio_vw\",\"return_portfolio_ew\",\n",
    "                        \"return_portfolio_vw\"]].drop_duplicates()\n",
    "\n",
    "short_monthly = rank_0[[\"NumMonth\",\"MonthYear1\", \"DecileRank\", \"excess_return_portfolio_ew\",\n",
    "                       \"excess_return_portfolio_vw\",\"pred_excess_return_portfolio_ew\",\n",
    "                       \"pred_excess_return_portfolio_vw\",\"return_portfolio_ew\",\n",
    "                        \"return_portfolio_vw\"]].drop_duplicates()\n",
    "\n",
    "# Create a column, indication the stategy \n",
    "long_monthly[\"Strategy\"]= \"long\"\n",
    "short_monthly[\"Strategy\"]= \"short\"\n",
    "\n",
    "# Merge to get the zero net investment portfolio\n",
    "zeronet_monthly= pd.concat([long_monthly, short_monthly])\n",
    "zeronet_monthly = zeronet_monthly.sort_values(by = ['NumMonth',\"Strategy\"])\n",
    "zeronet_monthly[\"return_portfolio_vw\"] = zeronet_monthly[\"return_portfolio_vw\"].astype('float64')\n",
    "\n",
    "#Create two new columns containing the exess return of the portfolio and initially set the values to zero.\n",
    "zeronet_monthly[\"excess_return_zeronet_ew\"] =0\n",
    "zeronet_monthly[\"excess_return_zeronet_vw\"] =0\n",
    "\n",
    "# excess return zeronet in t = (weigted excess return long in t) - (weigted excess return short in t)\n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i, 3]-zeronet_monthly.iloc[i+1, 3]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i-1, 3]-zeronet_monthly.iloc[i, 3]\n",
    "        \n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i, 4]-zeronet_monthly.iloc[i+1, 4]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i-1, 4]-zeronet_monthly.iloc[i, 4]\n",
    "\n",
    "#Create two new columns containing predicted the exess return of the portfolio and initially set the values to zero.\n",
    "zeronet_monthly[\"pred_excess_return_zeronet_ew\"] =0\n",
    "zeronet_monthly[\"pred_excess_return_zeronet_vw\"] =0\n",
    "\n",
    "# predicted excess return zeronet in t = (weigted predicted excess return long in t) - (weigted predicted excess return short in t)\n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i, 5]-zeronet_monthly.iloc[i+1, 5]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i-1, 5]-zeronet_monthly.iloc[i, 5]\n",
    "        \n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i, 6]-zeronet_monthly.iloc[i+1, 6]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i-1, 6]-zeronet_monthly.iloc[i, 6]\n",
    "\n",
    "#Create two new columns containing return of the portfolio and initially set the values to zero.       \n",
    "zeronet_monthly[\"return_zeronet_ew\"] =0\n",
    "zeronet_monthly[\"return_zeronet_vw\"] =0\n",
    "\n",
    "# return zeronet in t = (weigted return long in t) - (weigted return short in t)\n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i, 7]-zeronet_monthly.iloc[i+1, 7]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -2] = zeronet_monthly.iloc[i-1, 7]-zeronet_monthly.iloc[i, 7]\n",
    "        \n",
    "for i in range(0, len(zeronet_monthly)):\n",
    "    if zeronet_monthly.iloc[i,9] == \"long\":\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i, 8]-zeronet_monthly.iloc[i+1, 8]\n",
    "    else:\n",
    "        zeronet_monthly.iloc[i, -1] = zeronet_monthly.iloc[i-1, 8]-zeronet_monthly.iloc[i, 8]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>NumMonth</th>\n",
       "      <th>MonthYear1</th>\n",
       "      <th>excess_return_zeronet_ew</th>\n",
       "      <th>excess_return_zeronet_vw</th>\n",
       "      <th>pred_excess_return_zeronet_ew</th>\n",
       "      <th>pred_excess_return_zeronet_vw</th>\n",
       "      <th>return_zeronet_ew</th>\n",
       "      <th>return_zeronet_vw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>0.040038</td>\n",
       "      <td>0.037965</td>\n",
       "      <td>0.028807</td>\n",
       "      <td>0.025688</td>\n",
       "      <td>0.037194</td>\n",
       "      <td>0.038147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>0.063648</td>\n",
       "      <td>-0.020911</td>\n",
       "      <td>0.026961</td>\n",
       "      <td>0.024303</td>\n",
       "      <td>0.058772</td>\n",
       "      <td>-0.021637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>0.021012</td>\n",
       "      <td>0.006120</td>\n",
       "      <td>0.026824</td>\n",
       "      <td>0.025750</td>\n",
       "      <td>0.020061</td>\n",
       "      <td>0.006072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-04</td>\n",
       "      <td>0.010577</td>\n",
       "      <td>0.046644</td>\n",
       "      <td>0.028411</td>\n",
       "      <td>0.025116</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>0.047613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2984</th>\n",
       "      <td>5</td>\n",
       "      <td>2010-05</td>\n",
       "      <td>0.011104</td>\n",
       "      <td>-0.000962</td>\n",
       "      <td>0.027604</td>\n",
       "      <td>0.023640</td>\n",
       "      <td>0.010208</td>\n",
       "      <td>-0.000976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89867</th>\n",
       "      <td>116</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>-0.013286</td>\n",
       "      <td>-0.000581</td>\n",
       "      <td>0.018949</td>\n",
       "      <td>0.018325</td>\n",
       "      <td>-0.013979</td>\n",
       "      <td>-0.000601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90561</th>\n",
       "      <td>117</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.016870</td>\n",
       "      <td>-0.027894</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017104</td>\n",
       "      <td>0.016870</td>\n",
       "      <td>-0.027894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91269</th>\n",
       "      <td>118</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>0.014016</td>\n",
       "      <td>0.008461</td>\n",
       "      <td>0.018956</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>0.009538</td>\n",
       "      <td>0.008217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91977</th>\n",
       "      <td>119</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>0.012850</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.018639</td>\n",
       "      <td>0.018158</td>\n",
       "      <td>0.012850</td>\n",
       "      <td>0.015267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92694</th>\n",
       "      <td>120</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>0.007134</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>0.018626</td>\n",
       "      <td>0.017862</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.003643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NumMonth MonthYear1  excess_return_zeronet_ew  \\\n",
       "11            1    2010-01                  0.040038   \n",
       "735           2    2010-02                  0.063648   \n",
       "1458          3    2010-03                  0.021012   \n",
       "2219          4    2010-04                  0.010577   \n",
       "2984          5    2010-05                  0.011104   \n",
       "...         ...        ...                       ...   \n",
       "89867       116    2019-08                 -0.013286   \n",
       "90561       117    2019-09                  0.016870   \n",
       "91269       118    2019-10                  0.014016   \n",
       "91977       119    2019-11                  0.012850   \n",
       "92694       120    2019-12                  0.007134   \n",
       "\n",
       "       excess_return_zeronet_vw  pred_excess_return_zeronet_ew  \\\n",
       "11                     0.037965                       0.028807   \n",
       "735                   -0.020911                       0.026961   \n",
       "1458                   0.006120                       0.026824   \n",
       "2219                   0.046644                       0.028411   \n",
       "2984                  -0.000962                       0.027604   \n",
       "...                         ...                            ...   \n",
       "89867                 -0.000581                       0.018949   \n",
       "90561                 -0.027894                       0.018597   \n",
       "91269                  0.008461                       0.018956   \n",
       "91977                  0.015267                       0.018639   \n",
       "92694                  0.005747                       0.018626   \n",
       "\n",
       "       pred_excess_return_zeronet_vw  return_zeronet_ew  return_zeronet_vw  \n",
       "11                          0.025688           0.037194           0.038147  \n",
       "735                         0.024303           0.058772          -0.021637  \n",
       "1458                        0.025750           0.020061           0.006072  \n",
       "2219                        0.025116           0.015243           0.047613  \n",
       "2984                        0.023640           0.010208          -0.000976  \n",
       "...                              ...                ...                ...  \n",
       "89867                       0.018325          -0.013979          -0.000601  \n",
       "90561                       0.017104           0.016870          -0.027894  \n",
       "91269                       0.018571           0.009538           0.008217  \n",
       "91977                       0.018158           0.012850           0.015267  \n",
       "92694                       0.017862           0.005272           0.003643  \n",
       "\n",
       "[120 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Only the measures at portfolio level are needed\n",
    "zeronet_monthly = zeronet_monthly[['NumMonth', 'MonthYear1', 'excess_return_zeronet_ew',\n",
    "                                   'excess_return_zeronet_vw', 'pred_excess_return_zeronet_ew',\n",
    "                                   'pred_excess_return_zeronet_vw','return_zeronet_ew',\n",
    "                                   'return_zeronet_vw']].drop_duplicates()\n",
    "\n",
    "zeronet_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate zero-net portfolio performance measures                                            \n",
    "#Time-series average of realized excess returns                                             \n",
    "ew_mean_return_zeronet= zeronet_monthly[\"excess_return_zeronet_ew\"].mean()\n",
    "vw_mean_return_zeronet= zeronet_monthly[\"excess_return_zeronet_vw\"].mean()\n",
    "#Time-series average of predicted excess returns\n",
    "ew_mean_pred_return_zeronet = zeronet_monthly[\"pred_excess_return_zeronet_ew\"].mean()\n",
    "vw_mean_pred_return_zeronet = zeronet_monthly[\"pred_excess_return_zeronet_vw\"].mean()\n",
    "#Standard deviation of realized excess returns\n",
    "std_ew_zeronet = zeronet_monthly[\"excess_return_zeronet_ew\"].std()\n",
    "std_vw_zeronet = zeronet_monthly[\"excess_return_zeronet_vw\"].std()\n",
    "#Annualized sharpe ratio of realized excess returns\n",
    "sharpe_ew_zeronet = (zeronet_monthly[\"excess_return_zeronet_ew\"].mean()/zeronet_monthly[\"return_zeronet_ew\"].std())* np.sqrt(12)\n",
    "sharpe_vw_zeronet = (zeronet_monthly[\"excess_return_zeronet_vw\"].mean()/zeronet_monthly[\"return_zeronet_vw\"].std())* np.sqrt(12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate table containing the performance measures for each decile portfolio and for the long-short portfolio for each weignting sheme respectively\n",
    "\n",
    "#### Equally weighted table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred</th>\n",
       "      <th>Real</th>\n",
       "      <th>Std</th>\n",
       "      <th>Sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low (L)</th>\n",
       "      <td>-0.40%</td>\n",
       "      <td>-0.46%</td>\n",
       "      <td>4.61%</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.08%</td>\n",
       "      <td>4.64%</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.23%</td>\n",
       "      <td>0.29%</td>\n",
       "      <td>4.57%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.39%</td>\n",
       "      <td>0.56%</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.53%</td>\n",
       "      <td>0.57%</td>\n",
       "      <td>4.24%</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.66%</td>\n",
       "      <td>0.70%</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.79%</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>4.18%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.94%</td>\n",
       "      <td>1.14%</td>\n",
       "      <td>4.48%</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.10%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>4.32%</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.38%</td>\n",
       "      <td>1.46%</td>\n",
       "      <td>4.63%</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.78%</td>\n",
       "      <td>1.92%</td>\n",
       "      <td>2.45%</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.40%  -0.46%  4.61%  -0.34\n",
       "2          0.03%   0.08%  4.64%   0.06\n",
       "3          0.23%   0.29%  4.57%   0.22\n",
       "4          0.39%   0.56%  4.50%   0.43\n",
       "5          0.53%   0.57%  4.24%   0.46\n",
       "6          0.66%   0.70%  4.40%   0.55\n",
       "7          0.79%   0.61%  4.18%   0.50\n",
       "8          0.94%   1.14%  4.48%   0.87\n",
       "9          1.10%   1.28%  4.32%   1.02\n",
       "High (H)   1.38%   1.46%  4.63%   1.08\n",
       "H-L        1.78%   1.92%  2.45%   2.68"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_np = np.array([[ew_mean_pred_return_rank_0, ew_mean_return_rank_0, std_ew_rank_0, sharpe_ew_rank_0],\n",
    "                     [ew_mean_pred_return_rank_1, ew_mean_return_rank_1, std_ew_rank_1, sharpe_ew_rank_1],\n",
    "                     [ew_mean_pred_return_rank_2, ew_mean_return_rank_2, std_ew_rank_2, sharpe_ew_rank_2],\n",
    "                     [ew_mean_pred_return_rank_3, ew_mean_return_rank_3, std_ew_rank_3, sharpe_ew_rank_3],\n",
    "                     [ew_mean_pred_return_rank_4, ew_mean_return_rank_4, std_ew_rank_4, sharpe_ew_rank_4],\n",
    "                     [ew_mean_pred_return_rank_5, ew_mean_return_rank_5, std_ew_rank_5, sharpe_ew_rank_5],\n",
    "                     [ew_mean_pred_return_rank_6, ew_mean_return_rank_6, std_ew_rank_6, sharpe_ew_rank_6],\n",
    "                     [ew_mean_pred_return_rank_7, ew_mean_return_rank_7, std_ew_rank_7, sharpe_ew_rank_7],\n",
    "                     [ew_mean_pred_return_rank_8, ew_mean_return_rank_8, std_ew_rank_8, sharpe_ew_rank_8],\n",
    "                     [ew_mean_pred_return_rank_9, ew_mean_return_rank_9, std_ew_rank_9, sharpe_ew_rank_9],\n",
    "                     [ew_mean_pred_return_zeronet, ew_mean_return_zeronet, std_ew_zeronet, sharpe_ew_zeronet]])\n",
    "\n",
    "ew_df = pd.DataFrame(chart_np, columns=['Pred', 'Real', 'Std', 'Sharpe'],\n",
    "                              index=['Low (L)', '2', '3', '4', '5','6','7','8',\"9\",'High (H)', \"H-L\"])\n",
    "\n",
    "ew_df['Pred'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in ew_df['Pred']], index = ew_df.index)\n",
    "ew_df['Real'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in ew_df['Real']], index = ew_df.index)\n",
    "ew_df['Std'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in ew_df['Std']], index = ew_df.index)\n",
    "ew_df['Sharpe'] = pd.Series([(\"%.2f\" % round(val, 2)) for val in ew_df['Sharpe']], index = ew_df.index)\n",
    "ew_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Value weighted table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pred</th>\n",
       "      <th>Real</th>\n",
       "      <th>Std</th>\n",
       "      <th>Sharpe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low (L)</th>\n",
       "      <td>-0.29%</td>\n",
       "      <td>-0.04%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.04%</td>\n",
       "      <td>-0.13%</td>\n",
       "      <td>5.62%</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.24%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.39%</td>\n",
       "      <td>0.62%</td>\n",
       "      <td>5.26%</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.53%</td>\n",
       "      <td>0.65%</td>\n",
       "      <td>4.95%</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.66%</td>\n",
       "      <td>0.59%</td>\n",
       "      <td>4.59%</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.79%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>4.46%</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.94%</td>\n",
       "      <td>0.57%</td>\n",
       "      <td>4.80%</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.10%</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>4.86%</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.38%</td>\n",
       "      <td>0.64%</td>\n",
       "      <td>4.73%</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.67%</td>\n",
       "      <td>0.68%</td>\n",
       "      <td>4.02%</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.29%  -0.04%  5.46%  -0.02\n",
       "2          0.04%  -0.13%  5.62%  -0.08\n",
       "3          0.24%   0.27%  5.46%   0.17\n",
       "4          0.39%   0.62%  5.26%   0.41\n",
       "5          0.53%   0.65%  4.95%   0.45\n",
       "6          0.66%   0.59%  4.59%   0.44\n",
       "7          0.79%   0.35%  4.46%   0.27\n",
       "8          0.94%   0.57%  4.80%   0.41\n",
       "9          1.10%   0.88%  4.86%   0.62\n",
       "High (H)   1.38%   0.64%  4.73%   0.47\n",
       "H-L        1.67%   0.68%  4.02%   0.58"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart_np = np.array([[vw_mean_pred_return_rank_0, vw_mean_return_rank_0, std_vw_rank_0, sharpe_vw_rank_0],\n",
    "                     [vw_mean_pred_return_rank_1, vw_mean_return_rank_1, std_vw_rank_1, sharpe_vw_rank_1],\n",
    "                     [vw_mean_pred_return_rank_2, vw_mean_return_rank_2, std_vw_rank_2, sharpe_vw_rank_2],\n",
    "                     [vw_mean_pred_return_rank_3, vw_mean_return_rank_3, std_vw_rank_3, sharpe_vw_rank_3],\n",
    "                     [vw_mean_pred_return_rank_4, vw_mean_return_rank_4, std_vw_rank_4, sharpe_vw_rank_4],\n",
    "                     [vw_mean_pred_return_rank_5, vw_mean_return_rank_5, std_vw_rank_5, sharpe_vw_rank_5],\n",
    "                     [vw_mean_pred_return_rank_6, vw_mean_return_rank_6, std_vw_rank_6, sharpe_vw_rank_6],\n",
    "                     [vw_mean_pred_return_rank_7, vw_mean_return_rank_7, std_vw_rank_7, sharpe_vw_rank_7],\n",
    "                     [vw_mean_pred_return_rank_8, vw_mean_return_rank_8, std_vw_rank_8, sharpe_vw_rank_8],\n",
    "                     [vw_mean_pred_return_rank_9, vw_mean_return_rank_9, std_vw_rank_9, sharpe_vw_rank_9],\n",
    "                     [vw_mean_pred_return_zeronet, vw_mean_return_zeronet, std_vw_zeronet, sharpe_vw_zeronet]])\n",
    "\n",
    "vw_df = pd.DataFrame(chart_np, columns=['Pred', 'Real', 'Std', 'Sharpe'],\n",
    "                              index=['Low (L)', '2', '3', '4', '5','6','7','8',\"9\",'High (H)', \"H-L\"])\n",
    "\n",
    "vw_df['Pred'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in vw_df['Pred']], index = vw_df.index)\n",
    "vw_df['Real'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in vw_df['Real']], index = vw_df.index)\n",
    "vw_df['Std'] = pd.Series([\"{0:.2f}%\".format(val * 100) for val in vw_df['Std']], index = vw_df.index)\n",
    "vw_df['Sharpe'] = pd.Series([(\"%.2f\" % round(val, 2)) for val in vw_df['Sharpe']], index = vw_df.index)\n",
    "vw_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save to compare with the other ML models\n",
    "ew_df.to_csv(r'C:\\Users\\krist\\Documents\\Data\\decile_portfolios_results\\enet_ew.csv')\n",
    "vw_df.to_csv(r'C:\\Users\\krist\\Documents\\Data\\decile_portfolios_results\\enet_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional performance measures of zero net investment long-short  portfolio with monthly restructuring, 2010:01-2019:12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawdown, turnover, and risk-adjusted performance of the long-short portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trunover\n",
    "\n",
    "Portfolio turnover:\n",
    "\n",
    "### $$\n",
    "\\text { Turnover }=\\frac{1}{T} \\sum_{t=1}^{T}\\left(\\sum_{i}\\left|w_{i, t+1}-w_{i, t}\\right|\\right)\n",
    "$$\n",
    ", where $w_{i, t}$ denotes the weight of stock $i$ in the portfolio in month $t$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only stock weights are needed for the calculation of the portfolio turnover \n",
    "long = rank_9.copy()\n",
    "long=long[[\"MonthYear1\",'NumMonth','id', 'eq_weights', 'me_weights']]\n",
    "short = rank_0.copy()\n",
    "short=short[[\"MonthYear1\",'NumMonth','id', 'eq_weights','me_weights']]\n",
    "short[\"eq_weights\"]=short[\"eq_weights\"]*(-1)\n",
    "short[\"me_weights\"]=short[\"me_weights\"]*(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create lead weight variables $w_{i, t+1}$ for the equal- uand value-weighting scheme (eq_weights_lead1 and me_weights_lead1)\n",
    "\n",
    "* lead weight variable = 0, when the stock is not included in the portfolio in the subsequent month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equal weights in t+1 for the top decile portfolio\n",
    "def create_lead_eq_weights(long):\n",
    "    mn = long.NumMonth.min()\n",
    "    mx = long.NumMonth.max() + 1\n",
    "    d = long.set_index('NumMonth').reindex(pd.RangeIndex(mn, mx, name='NumMonth'))\n",
    "    return pd.concat([d, d['eq_weights'].shift(-1).rename('eq_weights_lead1')], axis=1)\n",
    "\n",
    "eq_weights_lead1_long=long.groupby('id')[['NumMonth', 'eq_weights']].apply(create_lead_eq_weights) \\\n",
    "    .sort_index(level=[1, 0]).dropna(subset=['eq_weights']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Value weights in t+1 for the top decile portfolio\n",
    "def create_lead_me_weights(long):\n",
    "    mn = long.NumMonth.min()\n",
    "    mx = long.NumMonth.max() + 1\n",
    "    d = long.set_index('NumMonth').reindex(pd.RangeIndex(mn, mx, name='NumMonth'))\n",
    "    return pd.concat([d, d['me_weights'].shift(-1).rename('me_weights_lead1')], axis=1)\n",
    "\n",
    "me_weights_lead1_long=long.groupby('id')[['NumMonth', 'me_weights']].apply(create_lead_me_weights) \\\n",
    "    .sort_index(level=[1, 0]).dropna(subset=['me_weights']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equal weights in t+1 for the bottom decile portfolio\n",
    "def create_lead_eq_weights(short):\n",
    "    mn = short.NumMonth.min()\n",
    "    mx = short.NumMonth.max() + 1\n",
    "    d = short.set_index('NumMonth').reindex(pd.RangeIndex(mn, mx, name='NumMonth'))\n",
    "    return pd.concat([d, d['eq_weights'].shift(-1).rename('eq_weights_lead1')], axis=1)\n",
    "\n",
    "eq_weights_lead1_short=short.groupby('id')[['NumMonth', 'eq_weights']].apply(create_lead_eq_weights) \\\n",
    "    .sort_index(level=[1, 0]).dropna(subset=['eq_weights']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Value weights in t+1 for the bottom decile portfolio\n",
    "def create_lead_me_weights(short):\n",
    "    mn = short.NumMonth.min()\n",
    "    mx = short.NumMonth.max() + 1\n",
    "    d = short.set_index('NumMonth').reindex(pd.RangeIndex(mn, mx, name='NumMonth'))\n",
    "    return pd.concat([d, d['me_weights'].shift(-1).rename('me_weights_lead1')], axis=1)\n",
    "\n",
    "me_weights_lead1_short=short.groupby('id')[['NumMonth', 'me_weights']].apply(create_lead_me_weights) \\\n",
    "    .sort_index(level=[1, 0]).dropna(subset=['me_weights']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge lead weight variables to the dataframe containing the top decile portfolio\n",
    "long=long.set_index(['NumMonth','id'])\n",
    "eq_weights_lead1_long=eq_weights_lead1_long.set_index(['NumMonth','id'])                    \n",
    "me_weights_lead1_long=me_weights_lead1_long.set_index(['NumMonth','id']) \n",
    "long = pd.merge(long, eq_weights_lead1_long[[\"eq_weights_lead1\"]],left_index=True, right_index=True)\n",
    "long = pd.merge(long, me_weights_lead1_long[[\"me_weights_lead1\"]],left_index=True, right_index=True)\n",
    "long=long.reset_index()\n",
    "\n",
    "# Merge lead weight variables to the dataframe containing the bottom decile portfolio\n",
    "short=short.set_index(['NumMonth','id'])\n",
    "eq_weights_lead1_short=eq_weights_lead1_short.set_index(['NumMonth','id'])                    \n",
    "me_weights_lead1_short=me_weights_lead1_short.set_index(['NumMonth','id']) \n",
    "short = pd.merge(short, eq_weights_lead1_short[[\"eq_weights_lead1\"]],left_index=True, right_index=True)\n",
    "short = pd.merge(short, me_weights_lead1_short[[\"me_weights_lead1\"]],left_index=True, right_index=True)\n",
    "short=short.reset_index()\n",
    "\n",
    "# Merge to get the zeronet portfolio\n",
    "long[\"Strategy\"]= \"long\"\n",
    "short[\"Strategy\"]= \"short\"\n",
    "zeronet= pd.concat([long, short])\n",
    "zeronet = zeronet.sort_values(by = ['NumMonth',\"id\",\"Strategy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Absolute difference between the weight of the stocks in the two following months $\\left|w_{i, t+1}-w_{i, t}\\right|$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeronet[\"abs_ew\"] = abs(zeronet[\"eq_weights_lead1\"] -zeronet[\"eq_weights\"])\n",
    "zeronet[\"abs_vw\"] = abs(zeronet[\"me_weights_lead1\"] -zeronet[\"me_weights\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sum of the absolute diffrernces over all stocks per month $\\sum_{i}\\left|w_{i, t+1}-w_{i, t}\\right|$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeronet[\"sum_abs_ew\"] = zeronet.groupby('NumMonth', sort=False)[\"abs_ew\"].transform('sum')\n",
    "zeronet[\"sum_abs_vw\"] = zeronet.groupby('NumMonth', sort=False)[\"abs_vw\"].transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-  $\\text { Turnover }=\\frac{1}{T} \\sum_{t=1}^{T}\\left(\\sum_{i}\\left|w_{i, t+1}-w_{i, t}\\right|\\right)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "turnover_ew = (zeronet[[\"NumMonth\",\"sum_abs_ew\"]].drop_duplicates().sum_abs_ew.sum())/zeronet[\"NumMonth\"].nunique()\n",
    "turnover_vw = (zeronet[[\"NumMonth\",\"sum_abs_vw\"]].drop_duplicates().sum_abs_vw.sum())/zeronet[\"NumMonth\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiply by 100 to get percent values\n",
    "turnover_ew_pct = turnover_ew*100\n",
    "turnover_vw_pct = turnover_vw*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  1.8885588836271763\n",
      "Trunover value weighted:  31.231638590494793\n"
     ]
    }
   ],
   "source": [
    "print(\"Trunover equally weighted: \", turnover_ew_pct)\n",
    "print(\"Trunover value weighted: \", turnover_vw_pct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Maximum Drawdown\n",
    "\n",
    "$$\n",
    "\\operatorname{MaxDD} \\max _{0 \\leq t_{1} \\leq t_{2} \\leq T}\\left(Y_{t_{1}}-Y_{t_{2}}\\right)\n",
    "$$\n",
    ", where $Y_{t}$ is the cumulative log excess return from date 0 through $t$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  6.505009850061338\n",
      "MaxDD value weighted:  31.71313376027811\n"
     ]
    }
   ],
   "source": [
    "def MDD(returns):\n",
    " \n",
    "    cum_rets = (1 + np.log(returns+1)).cumprod() - 1\n",
    "    nav = ((1 + cum_rets) * 100).fillna(100)\n",
    "    hwm = nav.cummax()\n",
    "    dd = nav / hwm - 1\n",
    "\n",
    "    return min(dd)\n",
    "\n",
    "maxDD_ew_pct = (MDD(zeronet_monthly[\"excess_return_zeronet_ew\"])*-100)\n",
    "maxDD_vw_pct = (MDD(zeronet_monthly[\"excess_return_zeronet_vw\"])*-100)    \n",
    "\n",
    "print(\"MaxDD equally weighted: \", maxDD_ew_pct)\n",
    "print(\"MaxDD value weighted: \", maxDD_vw_pct)\n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max 1M loss (%)\n",
    "\n",
    "- the most extreme negative monthly return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  6.297948112155102\n",
      "Max 1M loss value weighted:  10.078292499923844\n"
     ]
    }
   ],
   "source": [
    "max_1m_loss_ew= (zeronet_monthly[\"excess_return_zeronet_ew\"].min())*(-100)\n",
    "max_1m_loss_vw= (zeronet_monthly[\"excess_return_zeronet_vw\"].min())*(-100)\n",
    "\n",
    "print(\"Max 1M loss equally weighted: \", max_1m_loss_ew)\n",
    "print(\"Max 1M loss value weighted: \", max_1m_loss_vw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Risk-adjusted performance\n",
    "\n",
    "Mean active return and information ratio relative to DAX (benchmark)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get DAX price data\n",
    "dax = pd.read_csv(r\"C:\\Users\\krist\\Documents\\Data\\Dax monthly.csv\",parse_dates =[\"Date\"])\n",
    "dax= dax[dax['Date'] <= '2020-02-01']\n",
    "\n",
    "# Share prices are given on the first day of each month and in the data set at the end of the month. \n",
    "# Shift by one month to match the months in both datasets \n",
    "dax['MonthYear_t+1'] = dax['Date'].dt.to_period('M')\n",
    "dax['MonthYear'] = dax[\"MonthYear_t+1\"].shift(1)\n",
    "dax.drop([\"MonthYear_t+1\"], axis = 1, inplace = True)\n",
    "\n",
    "# We only need the prices from January 2010\n",
    "dax= dax[dax['MonthYear'] >= '2010-01']\n",
    "\n",
    "# Set MonthYear as index to  merge with the portfolio dataframe afterwards\n",
    "dax=dax.set_index([\"MonthYear\"])\n",
    "\n",
    "# Get monthly returns\n",
    "dax[\"monthly_return_dax\"] = dax[\"Close\"].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get Dax excess returns\n",
    "risk_free = bigdata[[\"MonthYear1\", \"risk_free_rate\"]].drop_duplicates()\n",
    "risk_free=risk_free.set_index(\"MonthYear1\")\n",
    "dax = pd.merge(dax[[\"monthly_return_dax\"]], risk_free ,left_index=True, right_index=True)\n",
    "dax[\"monthly_excess_return_dax\"] = dax[\"monthly_return_dax\"]- dax[\"risk_free_rate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge zeronet and DAX dataframes\n",
    "zeronet_monthly=zeronet_monthly.set_index(\"MonthYear1\")\n",
    "dax_zeronet = pd.merge(dax[[\"monthly_return_dax\",\"monthly_excess_return_dax\"]], zeronet_monthly[[\"excess_return_zeronet_ew\", \"excess_return_zeronet_vw\", \"return_zeronet_ew\", \"return_zeronet_vw\"]],left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate portfolio performance measures\n",
    "\n",
    "#Time-series average of portfolio excess_returns \n",
    "mean_ret_ew = dax_zeronet[\"excess_return_zeronet_ew\"].mean()\n",
    "mean_ret_vw = dax_zeronet[\"excess_return_zeronet_vw\"].mean()\n",
    "mean_ret_dax = dax_zeronet[\"monthly_excess_return_dax\"].mean()\n",
    "#Multiply by 100 to get percent values\n",
    "mean_ret_ew_pct = mean_ret_ew*100\n",
    "mean_ret_vw_pct= mean_ret_vw*100\n",
    "mean_ret_dax_pct = mean_ret_dax*100\n",
    "\n",
    "#Standard deviation ofportfolio excess_returns\n",
    "std_ew = dax_zeronet[\"excess_return_zeronet_ew\"].std()\n",
    "std_vw = dax_zeronet[\"excess_return_zeronet_vw\"].std()\n",
    "std_dax = dax_zeronet[\"monthly_excess_return_dax\"].std()\n",
    "#Multiply by 100 to get percent values\n",
    "std_ew_pct = std_ew*100\n",
    "std_vw_pct = std_vw*100\n",
    "std_dax_pct = std_dax*100\n",
    "\n",
    "#Annualized sharpe ratio of portfolio excess_returns\n",
    "sr_ew = ((dax_zeronet[\"excess_return_zeronet_ew\"]).mean() / (dax_zeronet[\"return_zeronet_ew\"]).std()) * np.sqrt(12)\n",
    "sr_vw = ((dax_zeronet[\"excess_return_zeronet_vw\"]).mean() / (dax_zeronet[\"return_zeronet_vw\"]).std()) * np.sqrt(12)\n",
    "sr_dax = ((dax_zeronet[\"monthly_excess_return_dax\"]).mean() / (dax_zeronet[\"monthly_return_dax\"]).std()) * np.sqrt(12)\n",
    "#Multiply by 100 to get percent values\n",
    "sr_ew_pct = sr_ew*100\n",
    "sr_vw_pct = sr_vw*100\n",
    "sr_dax_pcr= sr_dax*100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean active return:  difference between the benchmark return and the portfolio return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_act_ret_ew = (dax_zeronet[\"excess_return_zeronet_ew\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean()\n",
    "mean_act_ret_vw = (dax_zeronet[\"excess_return_zeronet_vw\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Information Ratio $=\\frac{R_{p}-R_{b}}{\\sigma_{\\left(R_{p}-R_{b}\\right)}}$\n",
    "- $R_{p}-$ Return of Portfolio\n",
    "- $R_{b}-$ Return of Benchmark (Dax)\n",
    "- $\\sigma_{\\left(R_{p}-R_{b}\\right)}-$ Tracking Error\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "ir_ew = ((dax_zeronet[\"excess_return_zeronet_ew\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_zeronet[\"excess_return_zeronet_ew\"]-dax_zeronet[\"monthly_excess_return_dax\"]).std())\n",
    "\n",
    "ir_vw = ((dax_zeronet[\"excess_return_zeronet_vw\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_zeronet[\"excess_return_zeronet_vw\"]-dax_zeronet[\"monthly_excess_return_dax\"]).std()) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This measures we only need to represent for the long-short portfolio \n",
    "turnover_dax=0\n",
    "ir_dax=0\n",
    "mean_act_ret_dax=0\n",
    "maxDD_dax=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAX</th>\n",
       "      <th>ENet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.924332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>2.446237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>2.676147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.505010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.888559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX      ENet\n",
       "Mean return [%]       0.752467  1.924332\n",
       "Std [%]               4.610969  2.446237\n",
       "Sharpe ratio          0.561110  2.676147\n",
       "Maximum drawdown [%]  0.000000  6.505010\n",
       "Turnover [%]          0.000000  1.888559\n",
       "Mean active return    0.000000  0.011544\n",
       "Information ratio     0.000000  0.231231"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate table representing the portfolio performance measures for the equal weighting scheme\n",
    "ew_chart = np.array([[mean_ret_dax_pct, mean_ret_ew_pct],\n",
    "                     [std_dax_pct,std_ew_pct],\n",
    "                     [sr_dax, sr_ew],\n",
    "                     [maxDD_dax, maxDD_ew_pct],\n",
    "                     [turnover_dax, turnover_ew_pct],\n",
    "                     [mean_act_ret_dax, mean_act_ret_ew],\n",
    "                     [ir_dax, ir_ew]])\n",
    "\n",
    "\n",
    "ew_chart = pd.DataFrame(ew_chart, columns=['DAX', 'ENet'],\n",
    "                        index=[\"Mean return [%]\",'Std [%]',\n",
    "                               \"Sharpe ratio\", \"Maximum drawdown [%]\",\"Turnover [%]\",\n",
    "                               \"Mean active return\",\"Information ratio\"])\n",
    "ew_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAX</th>\n",
       "      <th>ENet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.679581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.024785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.581069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.713134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.231639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX       ENet\n",
       "Mean return [%]       0.752467   0.679581\n",
       "Std [%]               4.610969   4.024785\n",
       "Sharpe ratio          0.561110   0.581069\n",
       "Maximum drawdown [%]  0.000000  31.713134\n",
       "Turnover [%]          0.000000  31.231639\n",
       "Mean active return    0.000000  -0.000991\n",
       "Information ratio     0.000000  -0.015442"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate table representing the portfolio performance measures for the value weighting scheme\n",
    "vw_chart = np.array([[mean_ret_dax_pct, mean_ret_vw_pct],\n",
    "                     [std_dax_pct,std_vw_pct],\n",
    "                     [sr_dax, sr_vw],\n",
    "                     [maxDD_dax, maxDD_vw_pct],\n",
    "                     [turnover_dax, turnover_vw_pct],\n",
    "                     [mean_act_ret_dax, mean_act_ret_vw],\n",
    "                     [ir_dax, ir_vw]])\n",
    "\n",
    "\n",
    "vw_chart = pd.DataFrame(vw_chart, columns=['DAX', 'ENet'],\n",
    "                        index=[\"Mean return [%]\",'Std [%]',\n",
    "                               \"Sharpe ratio\", \"Maximum drawdown [%]\",\"Turnover [%]\",\n",
    "                               \"Mean active return\",\"Information ratio\"])\n",
    "vw_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to compare with the other ML models\n",
    "ew_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_zeronet\\enet_zeronet_ew.csv')\n",
    "vw_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_zeronet\\enet_zeronet_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative performance of long-only investment portfolio with monthly restructuring, 2010:01-2019:12\n",
    "\n",
    "The H-L returns,  tend to be driven more by the long side (top decile) than the short side (bottom decile), so the long-only portfolio performace is also examined separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  0.9250527035647027\n",
      "Trunover value weighted:  13.848007520039877\n"
     ]
    }
   ],
   "source": [
    "long[\"abs_ew\"] = abs(long[\"eq_weights_lead1\"] -long[\"eq_weights\"])\n",
    "long[\"abs_vw\"] = abs(long[\"me_weights_lead1\"] -long[\"me_weights\"])\n",
    "long[\"sum_abs_ew\"] = long.groupby('NumMonth', sort=False)[\"abs_ew\"].transform('sum')\n",
    "long[\"sum_abs_vw\"] = long.groupby('NumMonth', sort=False)[\"abs_vw\"].transform('sum')\n",
    "turnover_ew_long = (long[[\"NumMonth\",\"sum_abs_ew\"]].drop_duplicates().sum_abs_ew.sum())/long[\"NumMonth\"].nunique()\n",
    "turnover_vw_long = (long[[\"NumMonth\",\"sum_abs_vw\"]].drop_duplicates().sum_abs_vw.sum())/long[\"NumMonth\"].nunique()\n",
    "turnover_ew_pct_long = turnover_ew_long*100\n",
    "turnover_vw_pct_long = turnover_vw_long*100\n",
    "print(\"Trunover equally weighted: \", turnover_ew_pct_long)\n",
    "print(\"Trunover value weighted: \", turnover_vw_pct_long)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum Drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  29.86089931333379\n",
      "MaxDD value weighted:  34.551554935545205\n"
     ]
    }
   ],
   "source": [
    "long_monthly = rank_9[['MonthYear1','NumMonth', 'return_portfolio_ew', 'return_portfolio_vw', \"excess_return_portfolio_ew\", \"excess_return_portfolio_vw\"]].drop_duplicates()\n",
    "\n",
    "maxDD_ew_pct_long = (MDD(long_monthly[\"excess_return_portfolio_ew\"])*-100)\n",
    "maxDD_vw_pct_long = (MDD(long_monthly[\"excess_return_portfolio_vw\"])*-100)    \n",
    "\n",
    "print(\"MaxDD equally weighted: \", maxDD_ew_pct_long)\n",
    "print(\"MaxDD value weighted: \", maxDD_vw_pct_long)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max 1M loss (%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  11.874481404931105\n",
      "Max 1M loss value weighted:  14.693904831826712\n"
     ]
    }
   ],
   "source": [
    "max_1m_loss_ew_long= (long_monthly[\"excess_return_portfolio_ew\"].min())*(-100)\n",
    "max_1m_loss_vw_long= (long_monthly[\"excess_return_portfolio_vw\"].min())*(-100)\n",
    "\n",
    "print(\"Max 1M loss equally weighted: \", max_1m_loss_ew_long)\n",
    "print(\"Max 1M loss value weighted: \", max_1m_loss_vw_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_monthly=long_monthly.set_index(\"MonthYear1\")\n",
    "dax_long = pd.merge(dax[[\"monthly_return_dax\",\"monthly_excess_return_dax\"]], long_monthly[[\"return_portfolio_ew\", \"return_portfolio_vw\", \"excess_return_portfolio_ew\", \"excess_return_portfolio_vw\"]],left_index=True, right_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Time-series average of portfolio excess returns \n",
    "mean_ret_ew_long = dax_long[\"excess_return_portfolio_ew\"].mean()\n",
    "mean_ret_vw_long = dax_long[\"excess_return_portfolio_vw\"].mean()\n",
    "mean_ret_dax = dax_long[\"monthly_excess_return_dax\"].mean()\n",
    "#Multiply by 100 to get percent values\n",
    "mean_ret_ew_long_pct = mean_ret_ew_long*100\n",
    "mean_ret_vw_long_pct= mean_ret_vw_long*100\n",
    "mean_ret_dax_pct = mean_ret_dax*100\n",
    "\n",
    "#Standard deviation ofportfolio excess returns\n",
    "std_ew_long = dax_long[\"excess_return_portfolio_ew\"].std()\n",
    "std_vw_long = dax_long[\"excess_return_portfolio_vw\"].std()\n",
    "std_dax = dax_long[\"monthly_excess_return_dax\"].std()\n",
    "#Multiply by 100 to get percent values\n",
    "std_ew_long_pct = std_ew_long*100\n",
    "std_vw_long_pct = std_vw_long*100\n",
    "std_dax_pct = std_dax*100\n",
    "\n",
    "#Annualized sharpe ratio of portfolio excess returns\n",
    "sr_ew_long = ((dax_long[\"excess_return_portfolio_ew\"]).mean() / (dax_long[\"return_portfolio_ew\"]).std()) * np.sqrt(12)\n",
    "sr_vw_long = ((dax_long[\"excess_return_portfolio_vw\"]).mean() / (dax_long[\"return_portfolio_vw\"]).std()) * np.sqrt(12)\n",
    "sr_dax = ((dax_long[\"monthly_excess_return_dax\"]).mean() / (dax_long[\"monthly_return_dax\"]).std()) * np.sqrt(12)\n",
    "#Multiply by 100 to get percent values\n",
    "sr_ew_long_pct = sr_ew_long*100\n",
    "sr_vw_long_pct = sr_vw_long*100\n",
    "sr_dax_pcr= sr_dax*100\n",
    "\n",
    "#Mean active excess_return:  difference between the benchmark excess return and the portfolio excess return\n",
    "mean_act_ret_ew_long = (dax_long[\"excess_return_portfolio_ew\"]-dax_long[\"monthly_excess_return_dax\"]).mean()\n",
    "mean_act_ret_vw_long = (dax_long[\"excess_return_portfolio_vw\"]-dax_long[\"monthly_excess_return_dax\"]).mean()\n",
    "\n",
    "# Calculate the information ratio\n",
    "ir_ew_long = ((dax_long[\"excess_return_portfolio_ew\"]-dax_long[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_long[\"excess_return_portfolio_ew\"]-dax_long[\"monthly_excess_return_dax\"]).std()) \n",
    "\n",
    "ir_vw_long = ((dax_long[\"excess_return_portfolio_vw\"]-dax_long[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_long[\"excess_return_portfolio_vw\"]-dax_long[\"monthly_excess_return_dax\"]).std()) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAX</th>\n",
       "      <th>ENet_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.460250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.629673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>1.081972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.860899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.925053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.198328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  ENet_long\n",
       "Mean return [%]       0.752467   1.460250\n",
       "Std [%]               4.610969   4.629673\n",
       "Sharpe ratio          0.561110   1.081972\n",
       "Maximum drawdown [%]  0.000000  29.860899\n",
       "Turnover [%]          0.000000   0.925053\n",
       "Mean active return    0.000000   0.007045\n",
       "Information ratio     0.000000   0.198328"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate table representing the portfolio performance measures for the equal weighting scheme\n",
    "ew_long_chart = np.array([[mean_ret_dax_pct, mean_ret_ew_long_pct],\n",
    "                     [std_dax_pct,std_ew_long_pct],\n",
    "                     [sr_dax, sr_ew_long],\n",
    "                     [maxDD_dax, maxDD_ew_pct_long],\n",
    "                     [turnover_dax, turnover_ew_pct_long],\n",
    "                     [mean_act_ret_dax, mean_act_ret_ew_long],\n",
    "                     [ir_dax, ir_ew_long]])\n",
    "\n",
    "\n",
    "ew_long_chart = pd.DataFrame(ew_long_chart, columns=['DAX', 'ENet_long'],\n",
    "                        index=[\"Mean return [%]\",'Std [%]',\n",
    "                               \"Sharpe ratio\", \"Maximum drawdown [%]\",\"Turnover [%]\",\n",
    "                               \"Mean active return\",\"Information ratio\"])\n",
    "\n",
    "ew_long_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAX</th>\n",
       "      <th>ENet_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.640130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.734662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.468595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.551555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.848008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.021710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  ENet_long\n",
       "Mean return [%]       0.752467   0.640130\n",
       "Std [%]               4.610969   4.734662\n",
       "Sharpe ratio          0.561110   0.468595\n",
       "Maximum drawdown [%]  0.000000  34.551555\n",
       "Turnover [%]          0.000000  13.848008\n",
       "Mean active return    0.000000  -0.000778\n",
       "Information ratio     0.000000  -0.021710"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate table representing the portfolio performance measures for the value weighting scheme\n",
    "vw_long_chart = np.array([[mean_ret_dax_pct, mean_ret_vw_long_pct],\n",
    "                     [std_dax_pct,std_vw_long_pct],\n",
    "                     [sr_dax, sr_vw_long],\n",
    "                     [maxDD_dax, maxDD_vw_pct_long],\n",
    "                     [turnover_dax, turnover_vw_pct_long],\n",
    "                     [mean_act_ret_dax, mean_act_ret_vw_long],\n",
    "                     [ir_dax, ir_vw_long]])\n",
    "\n",
    "\n",
    "vw_long_chart = pd.DataFrame(vw_long_chart, columns=['DAX', 'ENet_long'],\n",
    "                        index=[\"Mean return [%]\",'Std [%]',\n",
    "                               \"Sharpe ratio\", \"Maximum drawdown [%]\",\"Turnover [%]\",\n",
    "                               \"Mean active return\",\"Information ratio\"])\n",
    "\n",
    "vw_long_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to compare with the other ML models\n",
    "ew_long_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_long_only\\enet_long_ew.csv')\n",
    "vw_long_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_long_only\\enet_long_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative performance graph of expected return-sorted portfolios with monthly restructuring, 2010:01-2019:12\n",
    "\n",
    "\n",
    "Calculate the cumulative log returns of the long (top decile) and short (bottom decile) portfolios for both weighting schemes. Let's assume one invests 1 USD in January 2010 and buys a fraction of the top decile portfolio.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "long =rank_9.copy()\n",
    "long = long.set_index(\"MonthYear1\")\n",
    "long=long[[\"excess_return_portfolio_ew\", \"excess_return_portfolio_vw\"]].drop_duplicates()\n",
    "\n",
    "#Shift to get the returns in the current month\n",
    "long[\"curr_ret_port_ew\"]=long['excess_return_portfolio_ew'].shift(1)\n",
    "long[\"curr_ret_port_vw\"]=long['excess_return_portfolio_vw'].shift(1)\n",
    "\n",
    "#Calculate cumulative returns and cumulative log returns for the equal weighting scheme\n",
    "long[\"cumulative_return_ew\"]= (1 + long[\"curr_ret_port_ew\"]).cumprod()-1\n",
    "long[\"cumulative_log_return_ew\"]= (1 + np.log(long[\"curr_ret_port_ew\"]+1)).cumprod()-1\n",
    "# return in January 2010 = 0 (one invests in the end of the month)\n",
    "long.iloc[0, 2:] = 0\n",
    "#Plot cumulative log returns for the equal weighting scheme\n",
    "long[[\"cumulative_log_return_ew\"]].plot(grid=True,figsize=(15, 7))\n",
    "\n",
    "#Calculate cumulative returns and cumulative log returns for the value weighting scheme\n",
    "long[\"cumulative_return_vw\"]= (1 + long[\"curr_ret_port_vw\"]).cumprod()-1\n",
    "long[\"cumulative_log_return_vw\"]= (1 + np.log(long[\"curr_ret_port_vw\"]+1)).cumprod()-1\n",
    "# Return in January 2010 = 0(one invests in the end of the month)\n",
    "long.iloc[0, 2:] = 0\n",
    "# Plot cumulative log returns for the  value weighting scheme\n",
    "long[[\"cumulative_log_return_vw\"]].plot(grid=True,figsize=(15, 7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "short =rank_0.copy()\n",
    "short = short.set_index(\"MonthYear1\")\n",
    "short=short[[\"excess_return_portfolio_ew\", \"excess_return_portfolio_vw\"]].drop_duplicates()\n",
    "\n",
    "#Shift to get the returns in the current month\n",
    "short[\"curr_ret_port_ew\"]=short['excess_return_portfolio_ew'].shift(1)\n",
    "short[\"curr_ret_port_vw\"]=short['excess_return_portfolio_vw'].shift(1)\n",
    "\n",
    "#Calculate cumulative returns and cumulative log returns for the equal weighting scheme\n",
    "short[\"cumulative_return_ew\"]= (1 + short[\"curr_ret_port_ew\"]).cumprod()-1\n",
    "short[\"cumulative_log_return_ew\"]= (1 + np.log(short[\"curr_ret_port_ew\"]+1)).cumprod()-1\n",
    "# return in January 2010 = 0 (one invests in the end of the month)\n",
    "short.iloc[0, 2:] = 0\n",
    "#Plot cumulative log returns for the equal weighting scheme\n",
    "short[[\"cumulative_log_return_ew\"]].plot(grid=True,figsize=(15, 7))\n",
    "\n",
    "#Calculate cumulative returns and cumulative log returns for the value weighting scheme\n",
    "short[\"cumulative_return_vw\"]= (1 + short[\"curr_ret_port_vw\"]).cumprod()-1\n",
    "short[\"cumulative_log_return_vw\"]= (1 + np.log(short[\"curr_ret_port_vw\"]+1)).cumprod()-1\n",
    "# Return in January 2010 = 0(one invests in the end of the month)\n",
    "short.iloc[0, 2:] = 0\n",
    "# Plot cumulative log returns for the  value weighting scheme\n",
    "short[[\"cumulative_log_return_vw\"]].plot(grid=True,figsize=(15, 7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Merge results to compare cumulative returns of both portfolios for the equal weighting scheme\n",
    "long = long.rename(columns={'cumulative_log_return_ew': 'cumulative_log_return_long_ew'})\n",
    "short = short.rename(columns={'cumulative_log_return_ew': 'cumulative_log_return_short_ew'})\n",
    "long_short_ew = pd.merge(long[[\"cumulative_log_return_long_ew\"]],short[[\"cumulative_log_return_short_ew\"]],left_index=True, right_index=True)\n",
    "#Plot\n",
    "long_short_ew[[\"cumulative_log_return_long_ew\", \"cumulative_log_return_short_ew\"]].plot(grid=True,figsize=(17, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Merge results to compare cumulative returns of both portfolios for the value weighting scheme\n",
    "long = long.rename(columns={'cumulative_log_return_vw': 'cumulative_log_return_long_vw'})\n",
    "short = short.rename(columns={'cumulative_log_return_vw': 'cumulative_log_return_short_vw'})\n",
    "long_short_vw = pd.merge(long[[\"cumulative_log_return_long_vw\"]],short[[\"cumulative_log_return_short_vw\"]],left_index=True, right_index=True)\n",
    "#Plot\n",
    "long_short_vw[[\"cumulative_log_return_long_vw\", \"cumulative_log_return_short_vw\"]].plot(grid=True,figsize=(17, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DAX Returns\n",
    "\n",
    "Compare the cumulative returns of both portfolios with DAX cumulative returs over the same period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate cumulative returns\n",
    "dax[\"cumulative_return\"]= (1 + dax[\"monthly_excess_return_dax\"]).cumprod()-1\n",
    "dax[\"cumulative_log_return\"]= (1 + np.log(dax[\"monthly_excess_return_dax\"]+1)).cumprod()-1\n",
    "\n",
    "#replace NaN with 0: return and cumulative return on Jan 2010=0\n",
    "dax.iloc[0,4:]=0\n",
    "#plot\n",
    "dax[[\"cumulative_log_return\"]].plot(grid=True,figsize=(15, 7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge DAX cumulative log returns with the cumulative log returns of the equally weighted portfolios and plot\n",
    "dax = dax.rename(columns={'cumulative_log_return': 'cumulative_log_return_dax'})\n",
    "long_short_dax_ew = pd.merge(long_short_ew,dax[[\"cumulative_log_return_dax\"]],left_index=True, right_index=True)    \n",
    "long_short_dax_ew[[\"cumulative_log_return_long_ew\", \"cumulative_log_return_short_ew\",\"cumulative_log_return_dax\"]].plot(grid=True,figsize=(15, 9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge DAX cumulative log returns with the cumulative log returns of the value weighted portfolios and plot\n",
    "dax = dax.rename(columns={'cumulative_log_return': 'cumulative_log_return_dax'})\n",
    "long_short_dax_vw = pd.merge(long_short_vw,dax[[\"cumulative_log_return_dax\"]],left_index=True, right_index=True)   \n",
    "long_short_dax_vw[[\"cumulative_log_return_long_vw\", \"cumulative_log_return_short_vw\",\"cumulative_log_return_dax\"]].plot(grid=True,figsize=(15, 9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to compare with the other ML models\n",
    "long_short_dax_ew.to_csv(r'C:\\Users\\krist\\Documents\\Data\\cumulative_log_returns\\enet_ew.csv')\n",
    "long_short_dax_vw.to_csv(r'C:\\Users\\krist\\Documents\\Data\\cumulative_log_returns\\enet_vw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}