4.1_OLS-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import packages\n",
    "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 HuberRegressor\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": {
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       "        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": 4,
   "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 inreval [-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": 5,
   "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": 6,
   "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": 7,
   "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": [
    "# Simple OLS (size, book-to-market and momentum) with Huber loss function\n",
    "\n",
    "\n",
    "## All firms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 Huber Regression:  0.004757098459068798\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",
    "X = df[[\"eom\", \"me\", \"be_me\",\"ret_12_1\"]]\n",
    "y = df[\"ret_exc_lead1m\"]\n",
    "\n",
    "#Empty containers to save results from each window\n",
    "predictions = []\n",
    "y_test_list =[]\n",
    "dates = []\n",
    "dic_r2_all = {}\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",
    "    #OLS regression with huber loss function\n",
    "    reg_huber = HuberRegressor(max_iter=1000)\n",
    "    #No hyperparameters in OLS --> use train and validation set for training\n",
    "    reg_huber.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    #Use test set to generate predictions\n",
    "    preds = reg_huber.predict(X_test) \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",
    "   \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_LR = 1-sum(pow(y_test_list_all-predictions_all,2))/sum(pow(y_test_list_all,2))\n",
    "print(\"R2OOS Huber Regression: \", R2OOS_LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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 Huber Regression Big:  0.004792118948121926\n"
     ]
    }
   ],
   "source": [
    "# Same procedure only for large firms\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "X = df_top[[\"eom\", \"me\", \"be_me\",\"ret_12_1\"]]\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",
    "\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",
    "\n",
    "    reg_huber = HuberRegressor(max_iter=1000)\n",
    "    reg_huber.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "\n",
    "\n",
    "    preds = reg_huber.predict(X_test)    \n",
    "    predictions_top.append(preds)\n",
    "    dates_top.append(y_test.index)\n",
    "    y_test_list_top.append(y_test)\n",
    "    \n",
    "    r2_top = 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",
    "    \n",
    "    \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_LR_top = 1-sum(pow(y_test_list_all_top-predictions_all_top,2))/sum(pow(y_test_list_all_top,2))\n",
    "print(\"R2OOS Huber Regression Big: \", R2OOS_LR_top)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 Huber Regression Small:  0.003650688361125809\n"
     ]
    }
   ],
   "source": [
    "# Same procedure only for large firms\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "import numpy as np\n",
    "X = df_bottom[[\"eom\", \"me\", \"be_me\",\"ret_12_1\"]]\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",
    "\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",
    "\n",
    "    reg_huber = HuberRegressor(max_iter=1000)\n",
    "    reg_huber.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "\n",
    "\n",
    "    preds = reg_huber.predict(X_test)    \n",
    "    predictions_bottom.append(preds)\n",
    "    dates_bottom.append(y_test.index)\n",
    "    y_test_list_bottom.append(y_test)\n",
    "    \n",
    "    r2_bottom = 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",
    "    \n",
    "    \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_LR_bottom = 1-sum(pow(y_test_list_all_bottom-predictions_all_bottom,2))/sum(pow(y_test_list_all_bottom,2))\n",
    "print(\"R2OOS Huber Regression Small: \", R2OOS_LR_bottom)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>OLS-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full sample</th>\n",
       "      <td>0.004757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large firms</th>\n",
       "      <td>0.004792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small firms</th>\n",
       "      <td>0.003651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                OLS-3\n",
       "Full sample  0.004757\n",
       "Large firms  0.004792\n",
       "Small firms  0.003651"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate table containing the R2oos measures for all three datasets\n",
    "chart = np.array([[R2OOS_LR],\n",
    "                  [R2OOS_LR_top],\n",
    "                  [R2OOS_LR_bottom]])\n",
    "                     \n",
    "\n",
    "r2oos_ols = pd.DataFrame(chart, columns=['OLS-3'],\n",
    "                              index=[\"Full sample\", \"Large firms\", \"Small firms\"])\n",
    "r2oos_ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's performance measures to compare with other models later.\n",
    "r2oos_ols.to_csv(r'C:\\Users\\krist\\Documents\\Data\\r2oos results\\r2oos_ols_simple.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning portfolios\n",
    "\n",
    "Explore whether predictability translates into portfolio gains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>0.011093</td>\n",
       "      <td>0.035512</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>0.015908</td>\n",
       "      <td>-0.041371</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002410_04W</th>\n",
       "      <td>0.012309</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002597_02W</th>\n",
       "      <td>0.010735</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_003820_01W</th>\n",
       "      <td>0.003406</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>0.000889</td>\n",
       "      <td>-0.012904</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_331115_01W</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_332311_01W</th>\n",
       "      <td>0.006984</td>\n",
       "      <td>-0.051340</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>0.005756</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334036_02W</th>\n",
       "      <td>0.001975</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  0.011093  0.035512 2010-01-31\n",
       "          comp_001661_01W  0.015908 -0.041371 2010-01-31\n",
       "          comp_002410_04W  0.012309 -0.053836 2010-01-31\n",
       "          comp_002597_02W  0.010735  0.001862 2010-01-31\n",
       "          comp_003820_01W  0.003406  0.078967 2010-01-31\n",
       "...                             ...       ...        ...\n",
       "2019-12   comp_330716_01W  0.000889 -0.012904 2019-12-31\n",
       "          comp_331115_01W  0.001201  0.005400 2019-12-31\n",
       "          comp_332311_01W  0.006984 -0.051340 2019-12-31\n",
       "          comp_333885_01W  0.005756  0.003528 2019-12-31\n",
       "          comp_334036_02W  0.001975 -0.100876 2019-12-31\n",
       "\n",
       "[93417 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to use for Diebold-Mariano test\n",
    "ols= results.reset_index()\n",
    "ols.to_csv(r'C:\\Users\\krist\\Documents\\Data\\Predictions\\ols_simple.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
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       "    }\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": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>0.011093</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>0.015908</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>0.012309</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>0.010735</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>0.003406</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>0.000889</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>0.001201</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>0.006984</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>0.005756</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>0.001975</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  0.011093  0.035512 2010-01-31   \n",
       "1       2010-01  comp_001661_01W  0.015908 -0.041371 2010-01-31   \n",
       "2       2010-01  comp_002410_04W  0.012309 -0.053836 2010-01-31   \n",
       "3       2010-01  comp_002597_02W  0.010735  0.001862 2010-01-31   \n",
       "4       2010-01  comp_003820_01W  0.003406  0.078967 2010-01-31   \n",
       "...         ...              ...       ...       ...        ...   \n",
       "93412   2019-12  comp_330716_01W  0.000889 -0.012904 2019-12-31   \n",
       "93413   2019-12  comp_331115_01W  0.001201  0.005400 2019-12-31   \n",
       "93414   2019-12  comp_332311_01W  0.006984 -0.051340 2019-12-31   \n",
       "93415   2019-12  comp_333885_01W  0.005756  0.003528 2019-12-31   \n",
       "93416   2019-12  comp_334036_02W  0.001975 -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')"
   ]
  },
  {
   "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>1</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>0.015908</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",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>-0.025090</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>10540.222656</td>\n",
       "      <td>-0.086605</td>\n",
       "      <td>-0.086605</td>\n",
       "      <td>32.489308</td>\n",
       "      <td>31.674164</td>\n",
       "      <td>-0.025090</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>13</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_009818_02W</td>\n",
       "      <td>0.015042</td>\n",
       "      <td>-0.002665</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>33656.117188</td>\n",
       "      <td>0.146633</td>\n",
       "      <td>0.146633</td>\n",
       "      <td>33.504164</td>\n",
       "      <td>33.414878</td>\n",
       "      <td>-0.002665</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>14</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_010787_02W</td>\n",
       "      <td>0.014315</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>13389.733398</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>35.554730</td>\n",
       "      <td>35.565172</td>\n",
       "      <td>0.000294</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>18</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_012383_03W</td>\n",
       "      <td>0.017130</td>\n",
       "      <td>-0.055717</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>9021.623047</td>\n",
       "      <td>-0.130033</td>\n",
       "      <td>-0.130033</td>\n",
       "      <td>7.229114</td>\n",
       "      <td>6.826329</td>\n",
       "      <td>-0.055717</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>93199</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_273099_01W</td>\n",
       "      <td>0.010393</td>\n",
       "      <td>0.063963</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>14850.152344</td>\n",
       "      <td>0.054048</td>\n",
       "      <td>0.052648</td>\n",
       "      <td>285.579848</td>\n",
       "      <td>304.217565</td>\n",
       "      <td>0.065263</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>93226</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_278556_01W</td>\n",
       "      <td>0.010237</td>\n",
       "      <td>0.049259</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>1287.912476</td>\n",
       "      <td>0.067723</td>\n",
       "      <td>0.066323</td>\n",
       "      <td>17.834719</td>\n",
       "      <td>18.736432</td>\n",
       "      <td>0.050559</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>93233</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_279327_02W</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.052700</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>30837.185547</td>\n",
       "      <td>0.032008</td>\n",
       "      <td>0.030608</td>\n",
       "      <td>33.953268</td>\n",
       "      <td>35.632480</td>\n",
       "      <td>0.054000</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>93341</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_318434_01W</td>\n",
       "      <td>0.010988</td>\n",
       "      <td>-0.051188</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>12813.749023</td>\n",
       "      <td>0.176332</td>\n",
       "      <td>0.174932</td>\n",
       "      <td>50.677524</td>\n",
       "      <td>48.149303</td>\n",
       "      <td>-0.049888</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>93381</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_325735_01W</td>\n",
       "      <td>0.010362</td>\n",
       "      <td>0.137527</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>3445.624023</td>\n",
       "      <td>0.051852</td>\n",
       "      <td>0.050452</td>\n",
       "      <td>20.930558</td>\n",
       "      <td>23.836289</td>\n",
       "      <td>0.138827</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",
       "1            480  comp_001661_01W  0.015908 -0.041371 2010-01-31   \n",
       "9            480  comp_005959_01W  0.016813 -0.025090 2010-01-31   \n",
       "13           480  comp_009818_02W  0.015042 -0.002665 2010-01-31   \n",
       "14           480  comp_010787_02W  0.014315  0.000294 2010-01-31   \n",
       "18           480  comp_012383_03W  0.017130 -0.055717 2010-01-31   \n",
       "...          ...              ...       ...       ...        ...   \n",
       "93199        599  comp_273099_01W  0.010393  0.063963 2019-12-31   \n",
       "93226        599  comp_278556_01W  0.010237  0.049259 2019-12-31   \n",
       "93233        599  comp_279327_02W  0.010416  0.052700 2019-12-31   \n",
       "93341        599  comp_318434_01W  0.010988 -0.051188 2019-12-31   \n",
       "93381        599  comp_325735_01W  0.010362  0.137527 2019-12-31   \n",
       "\n",
       "                 me       ret   ret_exc         prc     prc_t+1   ret_t+1  \\\n",
       "1       6796.776367  0.036020  0.036020   22.730003   21.789641 -0.041371   \n",
       "9      10540.222656 -0.086605 -0.086605   32.489308   31.674164 -0.025090   \n",
       "13     33656.117188  0.146633  0.146633   33.504164   33.414878 -0.002665   \n",
       "14     13389.733398  0.001993  0.001993   35.554730   35.565172  0.000294   \n",
       "18      9021.623047 -0.130033 -0.130033    7.229114    6.826329 -0.055717   \n",
       "...             ...       ...       ...         ...         ...       ...   \n",
       "93199  14850.152344  0.054048  0.052648  285.579848  304.217565  0.065263   \n",
       "93226   1287.912476  0.067723  0.066323   17.834719   18.736432  0.050559   \n",
       "93233  30837.185547  0.032008  0.030608   33.953268   35.632480  0.054000   \n",
       "93341  12813.749023  0.176332  0.174932   50.677524   48.149303 -0.049888   \n",
       "93381   3445.624023  0.051852  0.050452   20.930558   23.836289  0.138827   \n",
       "\n",
       "       risk_free_rate MonthYear1  NumMonth  DecileRank  \n",
       "1            0.000353    2010-01         1           9  \n",
       "9            0.000353    2010-01         1           9  \n",
       "13           0.000353    2010-01         1           9  \n",
       "14           0.000353    2010-01         1           9  \n",
       "18           0.000353    2010-01         1           9  \n",
       "...               ...        ...       ...         ...  \n",
       "93199        0.001325    2019-12       120           9  \n",
       "93226        0.001325    2019-12       120           9  \n",
       "93233        0.001325    2019-12       120           9  \n",
       "93341        0.001325    2019-12       120           9  \n",
       "93381        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",
       "    .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>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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>-0.019149</td>\n",
       "      <td>-0.061159</td>\n",
       "      <td>0.018907</td>\n",
       "      <td>0.018909</td>\n",
       "      <td>-0.022294</td>\n",
       "      <td>-0.062391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>0.106204</td>\n",
       "      <td>0.097371</td>\n",
       "      <td>0.018762</td>\n",
       "      <td>0.018849</td>\n",
       "      <td>0.103686</td>\n",
       "      <td>0.096628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-0.000497</td>\n",
       "      <td>0.018203</td>\n",
       "      <td>0.017806</td>\n",
       "      <td>-0.003246</td>\n",
       "      <td>-0.000846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-04</td>\n",
       "      <td>-0.046062</td>\n",
       "      <td>-0.031210</td>\n",
       "      <td>0.017551</td>\n",
       "      <td>0.016748</td>\n",
       "      <td>-0.043544</td>\n",
       "      <td>-0.026975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2980</th>\n",
       "      <td>5</td>\n",
       "      <td>2010-05</td>\n",
       "      <td>-0.035301</td>\n",
       "      <td>-0.015593</td>\n",
       "      <td>0.017708</td>\n",
       "      <td>0.017298</td>\n",
       "      <td>-0.035301</td>\n",
       "      <td>-0.015593</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>89853</th>\n",
       "      <td>116</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>0.004033</td>\n",
       "      <td>-0.029226</td>\n",
       "      <td>0.013736</td>\n",
       "      <td>0.013545</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>-0.030473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90557</th>\n",
       "      <td>117</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>-0.027689</td>\n",
       "      <td>0.013662</td>\n",
       "      <td>0.013418</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>-0.027689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91265</th>\n",
       "      <td>118</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>-0.007732</td>\n",
       "      <td>-0.009260</td>\n",
       "      <td>0.013523</td>\n",
       "      <td>0.013061</td>\n",
       "      <td>-0.012210</td>\n",
       "      <td>-0.010951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91977</th>\n",
       "      <td>119</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>0.035826</td>\n",
       "      <td>0.031903</td>\n",
       "      <td>0.013519</td>\n",
       "      <td>0.013056</td>\n",
       "      <td>0.035826</td>\n",
       "      <td>0.031903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92694</th>\n",
       "      <td>120</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>-0.019472</td>\n",
       "      <td>0.026674</td>\n",
       "      <td>0.013511</td>\n",
       "      <td>0.013147</td>\n",
       "      <td>-0.020830</td>\n",
       "      <td>0.026486</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",
       "1             1    2010-01                 -0.019149   \n",
       "727           2    2010-02                  0.106204   \n",
       "1458          3    2010-03                 -0.002440   \n",
       "2215          4    2010-04                 -0.046062   \n",
       "2980          5    2010-05                 -0.035301   \n",
       "...         ...        ...                       ...   \n",
       "89853       116    2019-08                  0.004033   \n",
       "90557       117    2019-09                  0.011367   \n",
       "91265       118    2019-10                 -0.007732   \n",
       "91977       119    2019-11                  0.035826   \n",
       "92694       120    2019-12                 -0.019472   \n",
       "\n",
       "       excess_return_zeronet_vw  pred_excess_return_zeronet_ew  \\\n",
       "1                     -0.061159                       0.018907   \n",
       "727                    0.097371                       0.018762   \n",
       "1458                  -0.000497                       0.018203   \n",
       "2215                  -0.031210                       0.017551   \n",
       "2980                  -0.015593                       0.017708   \n",
       "...                         ...                            ...   \n",
       "89853                 -0.029226                       0.013736   \n",
       "90557                 -0.027689                       0.013662   \n",
       "91265                 -0.009260                       0.013523   \n",
       "91977                  0.031903                       0.013519   \n",
       "92694                  0.026674                       0.013511   \n",
       "\n",
       "       pred_excess_return_zeronet_vw  return_zeronet_ew  return_zeronet_vw  \n",
       "1                           0.018909          -0.022294          -0.062391  \n",
       "727                         0.018849           0.103686           0.096628  \n",
       "1458                        0.017806          -0.003246          -0.000846  \n",
       "2215                        0.016748          -0.043544          -0.026975  \n",
       "2980                        0.017298          -0.035301          -0.015593  \n",
       "...                              ...                ...                ...  \n",
       "89853                       0.013545           0.001054          -0.030473  \n",
       "90557                       0.013418           0.011367          -0.027689  \n",
       "91265                       0.013061          -0.012210          -0.010951  \n",
       "91977                       0.013056           0.035826           0.031903  \n",
       "92694                       0.013147          -0.020830           0.026486  \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>-0.45%</td>\n",
       "      <td>-0.36%</td>\n",
       "      <td>4.48%</td>\n",
       "      <td>-0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.15%</td>\n",
       "      <td>0.29%</td>\n",
       "      <td>4.39%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03%</td>\n",
       "      <td>0.49%</td>\n",
       "      <td>4.41%</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.19%</td>\n",
       "      <td>0.45%</td>\n",
       "      <td>4.24%</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.34%</td>\n",
       "      <td>0.61%</td>\n",
       "      <td>4.29%</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.50%</td>\n",
       "      <td>0.63%</td>\n",
       "      <td>4.38%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.65%</td>\n",
       "      <td>0.98%</td>\n",
       "      <td>4.33%</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.80%</td>\n",
       "      <td>1.12%</td>\n",
       "      <td>4.59%</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.95%</td>\n",
       "      <td>1.11%</td>\n",
       "      <td>4.81%</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.20%</td>\n",
       "      <td>0.90%</td>\n",
       "      <td>4.92%</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.64%</td>\n",
       "      <td>1.26%</td>\n",
       "      <td>3.10%</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.45%  -0.36%  4.48%  -0.27\n",
       "2         -0.15%   0.29%  4.39%   0.22\n",
       "3          0.03%   0.49%  4.41%   0.38\n",
       "4          0.19%   0.45%  4.24%   0.36\n",
       "5          0.34%   0.61%  4.29%   0.49\n",
       "6          0.50%   0.63%  4.38%   0.50\n",
       "7          0.65%   0.98%  4.33%   0.77\n",
       "8          0.80%   1.12%  4.59%   0.84\n",
       "9          0.95%   1.11%  4.81%   0.79\n",
       "High (H)   1.20%   0.90%  4.92%   0.63\n",
       "H-L        1.64%   1.26%  3.10%   1.39"
      ]
     },
     "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>-0.38%</td>\n",
       "      <td>-0.14%</td>\n",
       "      <td>5.64%</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.14%</td>\n",
       "      <td>0.34%</td>\n",
       "      <td>5.59%</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04%</td>\n",
       "      <td>0.13%</td>\n",
       "      <td>5.48%</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.20%</td>\n",
       "      <td>0.06%</td>\n",
       "      <td>5.01%</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.35%</td>\n",
       "      <td>0.44%</td>\n",
       "      <td>5.27%</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.50%</td>\n",
       "      <td>0.38%</td>\n",
       "      <td>5.05%</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.65%</td>\n",
       "      <td>0.36%</td>\n",
       "      <td>4.74%</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.80%</td>\n",
       "      <td>0.47%</td>\n",
       "      <td>4.92%</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.96%</td>\n",
       "      <td>0.80%</td>\n",
       "      <td>4.46%</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.23%</td>\n",
       "      <td>0.65%</td>\n",
       "      <td>4.96%</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.61%</td>\n",
       "      <td>0.79%</td>\n",
       "      <td>4.03%</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.38%  -0.14%  5.64%  -0.08\n",
       "2         -0.14%   0.34%  5.59%   0.20\n",
       "3          0.04%   0.13%  5.48%   0.08\n",
       "4          0.20%   0.06%  5.01%   0.04\n",
       "5          0.35%   0.44%  5.27%   0.29\n",
       "6          0.50%   0.38%  5.05%   0.26\n",
       "7          0.65%   0.36%  4.74%   0.26\n",
       "8          0.80%   0.47%  4.92%   0.33\n",
       "9          0.96%   0.80%  4.46%   0.62\n",
       "High (H)   1.23%   0.65%  4.96%   0.46\n",
       "H-L        1.61%   0.79%  4.03%   0.67"
      ]
     },
     "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\\ols_ew_simple.csv')\n",
    "vw_df.to_csv(r'C:\\Users\\krist\\Documents\\Data\\decile_portfolios_results\\ols_vw_simple.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:  2.1482311434517904\n",
      "Trunover value weighted:  22.803901036580402\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:  13.374396770894204\n",
      "MaxDD value weighted:  21.303176806878167\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)"
   ]
  },
  {
   "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:  6.6773025292455195\n",
      "Max 1M loss value weighted:  11.460675569371707\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"
   ]
  },
  {
   "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",
       "    }\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>OLS-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.261584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>3.101429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>1.389447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.374397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.148231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.117258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX      OLS-3\n",
       "Mean return [%]       0.752467   1.261584\n",
       "Std [%]               4.610969   3.101429\n",
       "Sharpe ratio          0.561110   1.389447\n",
       "Maximum drawdown [%]  0.000000  13.374397\n",
       "Turnover [%]          0.000000   2.148231\n",
       "Mean active return    0.000000   0.005358\n",
       "Information ratio     0.000000   0.117258"
      ]
     },
     "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', 'OLS-3'],\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>OLS-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.790654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.026373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.668037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.303177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.803901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX      OLS-3\n",
       "Mean return [%]       0.752467   0.790654\n",
       "Std [%]               4.610969   4.026373\n",
       "Sharpe ratio          0.561110   0.668037\n",
       "Maximum drawdown [%]  0.000000  21.303177\n",
       "Turnover [%]          0.000000  22.803901\n",
       "Mean active return    0.000000   0.000962\n",
       "Information ratio     0.000000   0.015920"
      ]
     },
     "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', 'OLS-3'],\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\\ols_zeronet_ew_simple.csv')\n",
    "vw_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_zeronet\\ols_zeronet_vw_simple.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:  1.097186381687451\n",
      "Trunover value weighted:  8.944690227508545\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:  34.721898718708445\n",
      "MaxDD value weighted:  35.32498525289011\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:  15.46144894194565\n",
      "Max 1M loss value weighted:  15.843380709419485\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>OLS-3_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.903416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.924387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.632068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.721899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.097186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  OLS-3_long\n",
       "Mean return [%]       0.752467    0.903416\n",
       "Std [%]               4.610969    4.924387\n",
       "Sharpe ratio          0.561110    0.632068\n",
       "Maximum drawdown [%]  0.000000   34.721899\n",
       "Turnover [%]          0.000000    1.097186\n",
       "Mean active return    0.000000    0.001792\n",
       "Information ratio     0.000000    0.052747"
      ]
     },
     "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', 'OLS-3_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>OLS-3_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.653190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.958899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.455830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.324985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.944690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.016900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  OLS-3_long\n",
       "Mean return [%]       0.752467    0.653190\n",
       "Std [%]               4.610969    4.958899\n",
       "Sharpe ratio          0.561110    0.455830\n",
       "Maximum drawdown [%]  0.000000   35.324985\n",
       "Turnover [%]          0.000000    8.944690\n",
       "Mean active return    0.000000   -0.000582\n",
       "Information ratio     0.000000   -0.016900"
      ]
     },
     "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', 'OLS-3_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\\ols_long_ew_simple.csv')\n",
    "vw_long_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_long_only\\ols_long_vw_simple.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\\ols_ew_simple.csv')\n",
    "long_short_dax_vw.to_csv(r'C:\\Users\\krist\\Documents\\Data\\cumulative_log_returns\\ols_vw_simple.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
}