4.3_PLS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.preprocessing\n",
    "import datetime\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=Warning)\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "#Program that executes the rolling window train-val-test split \n",
    "%run TimeBasedCV.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>eom</th>\n",
       "      <th>prc</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>ret_exc_lead1m</th>\n",
       "      <th>ret_6_1</th>\n",
       "      <th>ret_12_1</th>\n",
       "      <th>tax_gr1a</th>\n",
       "      <th>...</th>\n",
       "      <th>cowc_gr1a</th>\n",
       "      <th>pi_nix</th>\n",
       "      <th>ret_6_0</th>\n",
       "      <th>ret_1_0</th>\n",
       "      <th>noa_at</th>\n",
       "      <th>cop_atl1</th>\n",
       "      <th>prc_highprc_252d</th>\n",
       "      <th>ocf_at</th>\n",
       "      <th>dbnetis_at</th>\n",
       "      <th>netdebt_me</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>11.486148</td>\n",
       "      <td>567.074646</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "      <td>-0.089504</td>\n",
       "      <td>-0.229560</td>\n",
       "      <td>-0.353251</td>\n",
       "      <td>-0.020556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041844</td>\n",
       "      <td>3.815513</td>\n",
       "      <td>-0.244158</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>0.692706</td>\n",
       "      <td>0.093015</td>\n",
       "      <td>0.398130</td>\n",
       "      <td>-0.007700</td>\n",
       "      <td>0.085202</td>\n",
       "      <td>0.135368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>35.853958</td>\n",
       "      <td>5221.931641</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.108428</td>\n",
       "      <td>0.129149</td>\n",
       "      <td>-0.035109</td>\n",
       "      <td>-0.033306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019596</td>\n",
       "      <td>1.284930</td>\n",
       "      <td>0.135715</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.751569</td>\n",
       "      <td>0.164124</td>\n",
       "      <td>0.918059</td>\n",
       "      <td>0.096164</td>\n",
       "      <td>0.063714</td>\n",
       "      <td>0.200036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comp_004367_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>36.068653</td>\n",
       "      <td>4139.599121</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "      <td>0.127227</td>\n",
       "      <td>-0.020286</td>\n",
       "      <td>0.087151</td>\n",
       "      <td>-0.037240</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026114</td>\n",
       "      <td>1.725004</td>\n",
       "      <td>-0.121981</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>0.961141</td>\n",
       "      <td>0.240563</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.070724</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.410184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>comp_004925_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>29.735168</td>\n",
       "      <td>15302.482422</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "      <td>0.064832</td>\n",
       "      <td>0.046064</td>\n",
       "      <td>0.023265</td>\n",
       "      <td>-0.007344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000724</td>\n",
       "      <td>1.961724</td>\n",
       "      <td>-0.027605</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>0.466852</td>\n",
       "      <td>0.221983</td>\n",
       "      <td>0.861333</td>\n",
       "      <td>0.084234</td>\n",
       "      <td>-0.008142</td>\n",
       "      <td>-0.075232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>39.181721</td>\n",
       "      <td>6355.691406</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.123795</td>\n",
       "      <td>0.012811</td>\n",
       "      <td>-0.012241</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014214</td>\n",
       "      <td>1.069862</td>\n",
       "      <td>0.027359</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>0.635742</td>\n",
       "      <td>0.108581</td>\n",
       "      <td>0.721256</td>\n",
       "      <td>0.069331</td>\n",
       "      <td>-0.038594</td>\n",
       "      <td>0.500641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id        eom        prc            me       ret   ret_exc  \\\n",
       "0  comp_001166_02W 2003-01-31  11.486148    567.074646 -0.018948 -0.019948   \n",
       "1  comp_001661_01W 2003-01-31  35.853958   5221.931641  0.005815  0.004815   \n",
       "2  comp_004367_02W 2003-01-31  36.068653   4139.599121 -0.103801 -0.104801   \n",
       "3  comp_004925_02W 2003-01-31  29.735168  15302.482422 -0.070425 -0.071425   \n",
       "4  comp_005959_01W 2003-01-31  39.181721   6355.691406 -0.085813 -0.086813   \n",
       "\n",
       "   ret_exc_lead1m   ret_6_1  ret_12_1  tax_gr1a  ...  cowc_gr1a    pi_nix  \\\n",
       "0       -0.089504 -0.229560 -0.353251 -0.020556  ...   0.041844  3.815513   \n",
       "1        0.108428  0.129149 -0.035109 -0.033306  ...  -0.019596  1.284930   \n",
       "2        0.127227 -0.020286  0.087151 -0.037240  ...   0.026114  1.725004   \n",
       "3        0.064832  0.046064  0.023265 -0.007344  ...  -0.000724  1.961724   \n",
       "4        0.001832  0.123795  0.012811 -0.012241  ...  -0.014214  1.069862   \n",
       "\n",
       "    ret_6_0   ret_1_0    noa_at  cop_atl1  prc_highprc_252d    ocf_at  \\\n",
       "0 -0.244158 -0.018948  0.692706  0.093015          0.398130 -0.007700   \n",
       "1  0.135715  0.005815  0.751569  0.164124          0.918059  0.096164   \n",
       "2 -0.121981 -0.103801  0.961141  0.240563          0.675468  0.070724   \n",
       "3 -0.027605 -0.070425  0.466852  0.221983          0.861333  0.084234   \n",
       "4  0.027359 -0.085813  0.635742  0.108581          0.721256  0.069331   \n",
       "\n",
       "   dbnetis_at  netdebt_me  \n",
       "0    0.085202    0.135368  \n",
       "1    0.063714    0.200036  \n",
       "2   -0.000607    0.410184  \n",
       "3   -0.008142   -0.075232  \n",
       "4   -0.038594    0.500641  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the cleansed and winsorised data\n",
    "df = pd.read_csv(r\"C:\\Users\\krist\\Documents\\Data\\ger_factor_data_from2003.csv\", dtype ={\"comp_tpci\": str}, parse_dates =[\"eom\"])\n",
    "#Convert to float 32 (format needed for the most ML models)\n",
    "df[df.columns[2:]] = df[df.columns[2:]].astype('float32')\n",
    "#Sort observations by date and stock id\n",
    "df = df.sort_values(by = ['eom', 'id'], ascending = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy date variable and stock id to use them as multindex\n",
    "df[\"id2\"] = df[\"id\"].copy()\n",
    "df[\"eom2\"] = df[\"eom\"].copy()\n",
    "df = df.set_index(['eom2','id2'])\n",
    "\n",
    "#Make a copy of  the \"me\" variable (market equity) before rank standartization to use afterwards for value weighting\n",
    "df[\"me2\"] = df[\"me\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate the potential heterogeneity in model predictability, a subgroup analysis for small (the bottom 30% stocks by market equity each month) and large (the top 30% stocks each month) stocks is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": [
    "# Partial Least Squares\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 partial least squares:  0.008098545089842979\n"
     ]
    }
   ],
   "source": [
    "# Define the splitting scheme:\n",
    "# Rolling window  train-val-test split\n",
    "# The rolling window is set to 96 months (8 years)\n",
    "# 5 years for training, 2 years for validation, and 1 year for out-of-samlpe testing\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "#Define the idependent variables (X) and the dependent variable (y)\n",
    "features = df.columns[~df.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df[features]\n",
    "y = df[\"ret_exc_lead1m\"]\n",
    "\n",
    "\n",
    "#Empty containers to save results from each window\n",
    "predictions = []\n",
    "y_test_list =[]\n",
    "dates = []\n",
    "dic_r2_all = {}\n",
    "\n",
    "# Model’s complexity: dictionary to save the number of components over time\n",
    "numpc_time = {}\n",
    "\n",
    "# List of prespecified values to use to determine the optimal number of components \n",
    "numpc =np.arange(1, 20, 1).tolist()\n",
    "# Empty container to save the objective loss function (mean squared errors) for each number of components\n",
    "mse = np.full((len(numpc),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "    \n",
    "    #Loop over the list containing potential number of components, fit on the training sample and use \n",
    "    #validation set to generate predictions\n",
    "    for i in range(len(numpc)):\n",
    "        pls_val = PLSRegression(n_components = numpc[i], scale = False)\n",
    "        pls_val.fit(X_train, y_train)\n",
    "        Yval_predict=pls_val.predict(X_val)\n",
    "        #predictions are transformed into 1D array \n",
    "        Yval_predict = Yval_predict.ravel()\n",
    "        #calculate mean squared error for each potential value of the numpc hyperparameter\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val, Yval_predict))\n",
    "       \n",
    "       \n",
    "      \n",
    "    #The optimal value of the numpc hyperparameter is the value that causes the lowest loss\n",
    "    optim_numpc = numpc[np.argmin(mse)]\n",
    "    \n",
    "    #Fit again using the train and validation set and the optimal numpc parameter\n",
    "    pls = PLSRegression(n_components=optim_numpc, scale = False)\n",
    "    pls.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    #Use test set to generate final predictions\n",
    "    preds = pls.predict(X_test)\n",
    "    #predictions are transformed into 1D array \n",
    "    preds = preds.ravel()\n",
    "\n",
    "    #Save predictions, dates and the true values of the dependent variable to list  \n",
    "    predictions.append(preds)\n",
    "    dates.append(y_test.index)\n",
    "    y_test_list.append(y_test)\n",
    "    \n",
    "    #Calculate OOS model performance the for current window\n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    #Save OOS model performance and the respective month to dictionary\n",
    "    dic_r2_all[\"r2.\" + str(y_test.index)] = r2\n",
    "    # Save the number of components to inspect  model's complexity over time \n",
    "    numpc_time[\"numpc.\" + str(y_test.index)] = optim_numpc\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_PLS = 1-sum(pow(y_test_list_all-predictions_all,2))/sum(pow(y_test_list_all,2))\n",
    "print(\"R2OOS partial least squares: \", R2OOS_PLS)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 partial least squares Big only:  0.004246617526554752\n"
     ]
    }
   ],
   "source": [
    "# Same procedure for large firms only\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "features = df_top.columns[~df_top.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df_top[features]\n",
    "y = df_top[\"ret_exc_lead1m\"]\n",
    "\n",
    "predictions_top = []\n",
    "y_test_list_top =[]\n",
    "dates_top = []\n",
    "dic_r2_all_top = {}\n",
    "\n",
    "numpc =np.arange(1, 20, 1).tolist()\n",
    "mse = np.full((len(numpc),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "    for i in range(len(numpc)):\n",
    "        pls_val = PLSRegression(n_components = numpc[i], scale = False)\n",
    "        pls_val.fit(X_train, y_train)\n",
    "        Yval_predict=pls_val.predict(X_val)\n",
    "        Yval_predict = Yval_predict.ravel()\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val, Yval_predict))\n",
    "\n",
    "    optim_numpc = numpc[np.argmin(mse)]\n",
    "    \n",
    "    pls = PLSRegression(n_components=optim_numpc, scale = False)\n",
    "    pls.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds = pls.predict(X_test)\n",
    "    preds = preds.ravel()\n",
    "\n",
    "    predictions_top.append(preds)\n",
    "    dates_top.append(y_test.index)\n",
    "    y_test_list_top.append(y_test)\n",
    "    \n",
    "  \n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    dic_r2_all_top[\"r2.\" + str(y_test.index)] = r2\n",
    "\n",
    "\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",
    "\n",
    "R2OOS_PLS_top = 1-sum(pow(y_test_list_all_top-predictions_all_top,2))/sum(pow(y_test_list_all_top,2))\n",
    "print(\"R2OOS partial least squares Big only: \", R2OOS_PLS_top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 partial least squares Small only:  0.011422886686476508\n"
     ]
    }
   ],
   "source": [
    "# Same procedure for small firms only\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\n",
    "features = df_bottom.columns[~df_bottom.columns.isin(['id',\"id2\",\"me2\",\"prc\",\"eom2\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\"])].tolist()\n",
    "X = df_bottom[features]\n",
    "y = df_bottom[\"ret_exc_lead1m\"]\n",
    "\n",
    "predictions_bottom = []\n",
    "y_test_list_bottom =[]\n",
    "dates_bottom = []\n",
    "dic_r2_all_bottom = {}\n",
    "\n",
    "numpc =np.arange(1, 20, 1).tolist()\n",
    "mse = np.full((len(numpc),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "    for i in range(len(numpc)):\n",
    "        pls_val = PLSRegression(n_components = numpc[i], scale = False)\n",
    "        pls_val.fit(X_train, y_train)\n",
    "        Yval_predict=pls_val.predict(X_val)\n",
    "        Yval_predict = Yval_predict.ravel()\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val, Yval_predict))\n",
    "\n",
    "    optim_numpc = numpc[np.argmin(mse)]\n",
    "    \n",
    "    pls = PLSRegression(n_components=optim_numpc, scale = False)\n",
    "    pls.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds = pls.predict(X_test)\n",
    "    preds = preds.ravel()\n",
    "\n",
    "    predictions_bottom.append(preds)\n",
    "    dates_bottom.append(y_test.index)\n",
    "    y_test_list_bottom.append(y_test)\n",
    "    \n",
    "  \n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    dic_r2_all_bottom[\"r2.\" + str(y_test.index)] = r2\n",
    "\n",
    "\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",
    "\n",
    "R2OOS_PLS_bottom = 1-sum(pow(y_test_list_all_bottom-predictions_all_bottom,2))/sum(pow(y_test_list_all_bottom,2))\n",
    "print(\"R2OOS partial least squares Small only: \", R2OOS_PLS_bottom)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>PLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full sample</th>\n",
       "      <td>0.008099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large firms</th>\n",
       "      <td>0.004247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small firms</th>\n",
       "      <td>0.011423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  PLS\n",
       "Full sample  0.008099\n",
       "Large firms  0.004247\n",
       "Small firms  0.011423"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate table containing the R2oos measures for all three datasets\n",
    "chart = np.array([[R2OOS_PLS],\n",
    "                  [R2OOS_PLS_top],\n",
    "                  [R2OOS_PLS_bottom]])\n",
    "                     \n",
    "\n",
    "r2oos_pls = pd.DataFrame(chart, columns=['PLS'],\n",
    "                              index=[\"Full sample\", \"Large firms\", \"Small firms\"])\n",
    "r2oos_pls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's performance measures to compare with other models later.\n",
    "r2oos_pls.to_csv(r'C:\\Users\\krist\\Documents\\Data\\r2oos results\\r2oos_pls.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-varying model complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert dictionary containing the number of components over time in dataframe\n",
    "pd.DataFrame(numpc_time.items())\n",
    "numpc_table =pd.DataFrame(numpc_time.items(), columns=['Identifier', 'num_comp'])\n",
    "numpc_table['Identifier'] = numpc_table['Identifier'].astype(str)\n",
    "numpc_table[\"time\"] = numpc_table[\"Identifier\"].str[20:30]\n",
    "numpc_table[\"time\"] = pd.to_datetime(numpc_table[\"time\"], utc = False)\n",
    "numpc_table[\"time\"] = numpc_table[\"time\"].dt.year\n",
    "numpc_table.drop([\"Identifier\"], axis = 1, inplace = True)\n",
    "\n",
    "#Plot time-varying model complexity\n",
    "numpc_table.set_index('time').plot();\n",
    "#plt.xticks(rotation=45);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's complexity  to compare with other models later.\n",
    "numpc_table.to_csv(r'C:\\Users\\krist\\Documents\\Data\\model_complexity\\comp_pls.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Importance\n",
    "\n",
    "Variable importance is defined following Gu et al. (2020), i.e., for each model, all values of a given predictor are set to zero and the reduction in predictive R2OOS is calculated. Then, the absolute reductions in predictive R2OOS are normalized to sum to 1, indicating the relative contribution of each variable to a model. In contrast to Gu et al. (2020), variable importance is calculated based on the last rolling window observation sample and does not represent an average over the whole sample.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01132706929691063\n"
     ]
    }
   ],
   "source": [
    "# R2OOS based on the last rolling window when all variables are included\n",
    "features = df.columns[~df.columns.isin(['id',\"id2\",\"eom\",\"eom2\",\"me2\",\"prc\",\"ret\",\"ret_exc\",\"ret_exc_lead1m\",\"year\"])].tolist()\n",
    "df[\"year\"] = df[\"eom\"].dt.year\n",
    "\n",
    "X_train = df[features].loc[(df[\"year\"]>=2012) & (df[\"year\"]<=2016)]\n",
    "y_train = df[\"ret_exc_lead1m\"].loc[(df[\"year\"]>=2012) & (df[\"year\"]<=2016)]\n",
    "\n",
    "X_val = df[features].loc[(df[\"year\"]>=2017) & (df[\"year\"]<=2018)]\n",
    "y_val = df[\"ret_exc_lead1m\"].loc[(df[\"year\"]>=2017) & (df[\"year\"]<=2018)]\n",
    "\n",
    "numpc =np.arange(1, 20, 1).tolist()\n",
    "mse = np.full((len(numpc),1),np.nan, dtype = np.float32)\n",
    "\n",
    "for i in range(len(numpc)):\n",
    "    pls_val = PLSRegression(n_components = numpc[i], scale = False)\n",
    "    pls_val.fit(X_train, y_train)\n",
    "    Yval_predict=pls_val.predict(X_val)\n",
    "    Yval_predict = Yval_predict.ravel()\n",
    "    mse[i,0] = np.sqrt(mean_squared_error(y_val, Yval_predict))\n",
    "       \n",
    "optim_numpc = numpc[np.argmin(mse)]\n",
    "pls = PLSRegression(n_components=optim_numpc, scale = False)\n",
    "pls.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "preds = pls.predict(np.concatenate((X_train, X_val))) \n",
    "preds = preds.ravel()\n",
    "R2OOS_all = 1-sum(pow(np.concatenate((y_train, y_val))-preds,2))/sum(pow(np.concatenate((y_train, y_val)),2))\n",
    "print(R2OOS_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate a separate dataframe for each independent variable, where all values of that variable are set to zero\n",
    "\n",
    "for j in features:\n",
    "    globals()['df_' + str(j)] =  df.copy()\n",
    "    globals()['df_' + str(j)][str(j)] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>eom</th>\n",
       "      <th>prc</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>ret_exc_lead1m</th>\n",
       "      <th>ret_6_1</th>\n",
       "      <th>ret_12_1</th>\n",
       "      <th>tax_gr1a</th>\n",
       "      <th>...</th>\n",
       "      <th>ret_6_0</th>\n",
       "      <th>ret_1_0</th>\n",
       "      <th>noa_at</th>\n",
       "      <th>cop_atl1</th>\n",
       "      <th>prc_highprc_252d</th>\n",
       "      <th>ocf_at</th>\n",
       "      <th>dbnetis_at</th>\n",
       "      <th>netdebt_me</th>\n",
       "      <th>me2</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eom2</th>\n",
       "      <th>id2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2003-01-31</th>\n",
       "      <th>comp_001166_02W</th>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>-0.376652</td>\n",
       "      <td>0.162996</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "      <td>-0.089504</td>\n",
       "      <td>-0.665198</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.726872</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.669604</td>\n",
       "      <td>-0.312775</td>\n",
       "      <td>0.585903</td>\n",
       "      <td>-0.735683</td>\n",
       "      <td>-0.859031</td>\n",
       "      <td>-0.797357</td>\n",
       "      <td>0.735683</td>\n",
       "      <td>-0.149780</td>\n",
       "      <td>567.074646</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.427313</td>\n",
       "      <td>0.718062</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.108428</td>\n",
       "      <td>0.669604</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.845815</td>\n",
       "      <td>...</td>\n",
       "      <td>0.577093</td>\n",
       "      <td>-0.110132</td>\n",
       "      <td>0.696035</td>\n",
       "      <td>-0.392070</td>\n",
       "      <td>0.607930</td>\n",
       "      <td>0.286344</td>\n",
       "      <td>0.647577</td>\n",
       "      <td>0.145374</td>\n",
       "      <td>5221.931641</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004367_02W</th>\n",
       "      <td>comp_004367_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.433921</td>\n",
       "      <td>0.674009</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "      <td>0.127227</td>\n",
       "      <td>0.114537</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.894273</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.356828</td>\n",
       "      <td>-0.766520</td>\n",
       "      <td>0.881057</td>\n",
       "      <td>0.458150</td>\n",
       "      <td>-0.365639</td>\n",
       "      <td>-0.110132</td>\n",
       "      <td>-0.207048</td>\n",
       "      <td>0.339207</td>\n",
       "      <td>4139.599121</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_004925_02W</th>\n",
       "      <td>comp_004925_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.295154</td>\n",
       "      <td>0.881057</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "      <td>0.064832</td>\n",
       "      <td>0.392070</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.453744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096916</td>\n",
       "      <td>-0.612335</td>\n",
       "      <td>-0.264317</td>\n",
       "      <td>0.330396</td>\n",
       "      <td>0.422907</td>\n",
       "      <td>0.149780</td>\n",
       "      <td>-0.370044</td>\n",
       "      <td>-0.709251</td>\n",
       "      <td>15302.482422</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_005959_01W</th>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>0.477974</td>\n",
       "      <td>0.762115</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.643172</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.599119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259912</td>\n",
       "      <td>-0.691630</td>\n",
       "      <td>0.458150</td>\n",
       "      <td>-0.669604</td>\n",
       "      <td>-0.255507</td>\n",
       "      <td>-0.118943</td>\n",
       "      <td>-0.656388</td>\n",
       "      <td>0.436123</td>\n",
       "      <td>6355.691406</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-12-31</th>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>comp_333885_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>0.367156</td>\n",
       "      <td>0.484646</td>\n",
       "      <td>0.128645</td>\n",
       "      <td>0.128545</td>\n",
       "      <td>-0.037398</td>\n",
       "      <td>-0.957276</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.951936</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.853138</td>\n",
       "      <td>0.591455</td>\n",
       "      <td>0.457944</td>\n",
       "      <td>0.815754</td>\n",
       "      <td>-0.319092</td>\n",
       "      <td>0.877170</td>\n",
       "      <td>-0.930574</td>\n",
       "      <td>-0.316422</td>\n",
       "      <td>10829.783203</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334302_02W</th>\n",
       "      <td>comp_334302_02W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.842457</td>\n",
       "      <td>-0.487316</td>\n",
       "      <td>0.097961</td>\n",
       "      <td>0.097861</td>\n",
       "      <td>-0.033670</td>\n",
       "      <td>-0.420561</td>\n",
       "      <td>0</td>\n",
       "      <td>0.540721</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268358</td>\n",
       "      <td>0.417891</td>\n",
       "      <td>0.791722</td>\n",
       "      <td>0.345794</td>\n",
       "      <td>0.754339</td>\n",
       "      <td>0.054740</td>\n",
       "      <td>0.692924</td>\n",
       "      <td>-0.252336</td>\n",
       "      <td>297.482697</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_335706_02W</th>\n",
       "      <td>comp_335706_02W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.722296</td>\n",
       "      <td>-0.618158</td>\n",
       "      <td>0.114381</td>\n",
       "      <td>0.114281</td>\n",
       "      <td>0.089684</td>\n",
       "      <td>0.097463</td>\n",
       "      <td>0</td>\n",
       "      <td>0.311081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236315</td>\n",
       "      <td>0.511348</td>\n",
       "      <td>-0.241656</td>\n",
       "      <td>-0.935915</td>\n",
       "      <td>-0.313752</td>\n",
       "      <td>-0.917223</td>\n",
       "      <td>0.164219</td>\n",
       "      <td>-0.730307</td>\n",
       "      <td>196.243454</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>comp_340115_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>-0.392523</td>\n",
       "      <td>-0.025367</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>0.155713</td>\n",
       "      <td>0.009796</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.871829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.687583</td>\n",
       "      <td>-0.415220</td>\n",
       "      <td>-0.436582</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.642190</td>\n",
       "      <td>0.562083</td>\n",
       "      <td>1799.958496</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>comp_340153_01W</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>0.138852</td>\n",
       "      <td>0.711615</td>\n",
       "      <td>0.238817</td>\n",
       "      <td>0.238717</td>\n",
       "      <td>0.006943</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>0.879840</td>\n",
       "      <td>-0.815754</td>\n",
       "      <td>-0.636849</td>\n",
       "      <td>0.001335</td>\n",
       "      <td>-0.129506</td>\n",
       "      <td>-0.110814</td>\n",
       "      <td>-0.578104</td>\n",
       "      <td>26788.144531</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156948 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         id        eom       prc        me  \\\n",
       "eom2       id2                                                               \n",
       "2003-01-31 comp_001166_02W  comp_001166_02W 2003-01-31 -0.376652  0.162996   \n",
       "           comp_001661_01W  comp_001661_01W 2003-01-31  0.427313  0.718062   \n",
       "           comp_004367_02W  comp_004367_02W 2003-01-31  0.433921  0.674009   \n",
       "           comp_004925_02W  comp_004925_02W 2003-01-31  0.295154  0.881057   \n",
       "           comp_005959_01W  comp_005959_01W 2003-01-31  0.477974  0.762115   \n",
       "...                                     ...        ...       ...       ...   \n",
       "2020-12-31 comp_333885_01W  comp_333885_01W 2020-12-31  0.367156  0.484646   \n",
       "           comp_334302_02W  comp_334302_02W 2020-12-31 -0.842457 -0.487316   \n",
       "           comp_335706_02W  comp_335706_02W 2020-12-31 -0.722296 -0.618158   \n",
       "           comp_340115_01W  comp_340115_01W 2020-12-31 -0.392523 -0.025367   \n",
       "           comp_340153_01W  comp_340153_01W 2020-12-31  0.138852  0.711615   \n",
       "\n",
       "                                 ret   ret_exc  ret_exc_lead1m   ret_6_1  \\\n",
       "eom2       id2                                                             \n",
       "2003-01-31 comp_001166_02W -0.018948 -0.019948       -0.089504 -0.665198   \n",
       "           comp_001661_01W  0.005815  0.004815        0.108428  0.669604   \n",
       "           comp_004367_02W -0.103801 -0.104801        0.127227  0.114537   \n",
       "           comp_004925_02W -0.070425 -0.071425        0.064832  0.392070   \n",
       "           comp_005959_01W -0.085813 -0.086813        0.001832  0.643172   \n",
       "...                              ...       ...             ...       ...   \n",
       "2020-12-31 comp_333885_01W  0.128645  0.128545       -0.037398 -0.957276   \n",
       "           comp_334302_02W  0.097961  0.097861       -0.033670 -0.420561   \n",
       "           comp_335706_02W  0.114381  0.114281        0.089684  0.097463   \n",
       "           comp_340115_01W  0.155813  0.155713        0.009796  0.001335   \n",
       "           comp_340153_01W  0.238817  0.238717        0.006943  0.001335   \n",
       "\n",
       "                            ret_12_1  tax_gr1a  ...   ret_6_0   ret_1_0  \\\n",
       "eom2       id2                                  ...                       \n",
       "2003-01-31 comp_001166_02W         0 -0.726872  ... -0.669604 -0.312775   \n",
       "           comp_001661_01W         0 -0.845815  ...  0.577093 -0.110132   \n",
       "           comp_004367_02W         0 -0.894273  ... -0.356828 -0.766520   \n",
       "           comp_004925_02W         0 -0.453744  ...  0.096916 -0.612335   \n",
       "           comp_005959_01W         0 -0.599119  ...  0.259912 -0.691630   \n",
       "...                              ...       ...  ...       ...       ...   \n",
       "2020-12-31 comp_333885_01W         0 -0.951936  ... -0.853138  0.591455   \n",
       "           comp_334302_02W         0  0.540721  ... -0.268358  0.417891   \n",
       "           comp_335706_02W         0  0.311081  ...  0.236315  0.511348   \n",
       "           comp_340115_01W         0  0.871829  ...  0.001335  0.687583   \n",
       "           comp_340153_01W         0  0.847797  ...  0.001335  0.879840   \n",
       "\n",
       "                              noa_at  cop_atl1  prc_highprc_252d    ocf_at  \\\n",
       "eom2       id2                                                               \n",
       "2003-01-31 comp_001166_02W  0.585903 -0.735683         -0.859031 -0.797357   \n",
       "           comp_001661_01W  0.696035 -0.392070          0.607930  0.286344   \n",
       "           comp_004367_02W  0.881057  0.458150         -0.365639 -0.110132   \n",
       "           comp_004925_02W -0.264317  0.330396          0.422907  0.149780   \n",
       "           comp_005959_01W  0.458150 -0.669604         -0.255507 -0.118943   \n",
       "...                              ...       ...               ...       ...   \n",
       "2020-12-31 comp_333885_01W  0.457944  0.815754         -0.319092  0.877170   \n",
       "           comp_334302_02W  0.791722  0.345794          0.754339  0.054740   \n",
       "           comp_335706_02W -0.241656 -0.935915         -0.313752 -0.917223   \n",
       "           comp_340115_01W -0.415220 -0.436582          0.001335  0.025367   \n",
       "           comp_340153_01W -0.815754 -0.636849          0.001335 -0.129506   \n",
       "\n",
       "                            dbnetis_at  netdebt_me           me2  year  \n",
       "eom2       id2                                                          \n",
       "2003-01-31 comp_001166_02W    0.735683   -0.149780    567.074646  2003  \n",
       "           comp_001661_01W    0.647577    0.145374   5221.931641  2003  \n",
       "           comp_004367_02W   -0.207048    0.339207   4139.599121  2003  \n",
       "           comp_004925_02W   -0.370044   -0.709251  15302.482422  2003  \n",
       "           comp_005959_01W   -0.656388    0.436123   6355.691406  2003  \n",
       "...                                ...         ...           ...   ...  \n",
       "2020-12-31 comp_333885_01W   -0.930574   -0.316422  10829.783203  2020  \n",
       "           comp_334302_02W    0.692924   -0.252336    297.482697  2020  \n",
       "           comp_335706_02W    0.164219   -0.730307    196.243454  2020  \n",
       "           comp_340115_01W    0.642190    0.562083   1799.958496  2020  \n",
       "           comp_340153_01W   -0.110814   -0.578104  26788.144531  2020  \n",
       "\n",
       "[156948 rows x 57 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#e.g., df_ret_12_1 is a dataframe where all values of the ret_12_1 variable are set to zero\n",
    "df_ret_12_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate R2OOS based on the last rolling window for each dataframe and save to dictionary\n",
    "\n",
    "dic = {}    \n",
    "numpc =np.arange(1, 20, 1).tolist()\n",
    "mse = np.full((len(numpc),1),np.nan, dtype = np.float32)\n",
    "    \n",
    "for j in features:\n",
    "    df_var = globals()['df_' + str(j)]\n",
    "    \n",
    "    X_train = df_var[features].loc[(df_var[\"year\"]>=2012) & (df_var[\"year\"]<=2016)]\n",
    "    y_train = df_var[\"ret_exc_lead1m\"].loc[(df_var[\"year\"]>=2012) & (df_var[\"year\"]<=2016)]\n",
    "\n",
    "    X_val = df_var[features].loc[(df_var[\"year\"]>=2017) & (df_var[\"year\"]<=2018)]\n",
    "    y_val = df_var[\"ret_exc_lead1m\"].loc[(df_var[\"year\"]>=2017) & (df_var[\"year\"]<=2018)]\n",
    "\n",
    "    for i in range(len(numpc)):\n",
    "        pls_val = PLSRegression(n_components = numpc[i], scale = False)\n",
    "        pls_val.fit(X_train, y_train)\n",
    "        Yval_predict=pls_val.predict(X_val)\n",
    "        Yval_predict = Yval_predict.ravel()\n",
    "        mse[i,0] = np.sqrt(mean_squared_error(y_val, Yval_predict))\n",
    "       \n",
    "    optim_numpc = numpc[np.argmin(mse)]\n",
    "    pls = PLSRegression(n_components=optim_numpc, scale = False)\n",
    "    pls.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds = pls.predict(np.concatenate((X_train, X_val))) \n",
    "    preds = preds.ravel()\n",
    "    R2OOS_var = 1-sum(pow(np.concatenate((y_train, y_val))-preds,2))/sum(pow(np.concatenate((y_train, y_val)),2))\n",
    "    dic['R2OOS_' + str(j)] = R2OOS_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2OOS_me': 0.011169748571873583,\n",
       " 'R2OOS_ret_6_1': 0.011367493897590397,\n",
       " 'R2OOS_ret_12_1': 0.011357415684201122,\n",
       " 'R2OOS_tax_gr1a': 0.011239570615093064,\n",
       " 'R2OOS_be_me': 0.011343151499101145,\n",
       " 'R2OOS_debt_me': 0.012256507314767351,\n",
       " 'R2OOS_ni_me': 0.012208409447506563,\n",
       " 'R2OOS_sale_gr3': 0.011345607008761416,\n",
       " 'R2OOS_sale_gr1': 0.011345825548169763,\n",
       " 'R2OOS_sale_me': 0.011334668196326558,\n",
       " 'R2OOS_lnoa_gr1a': 0.011328283634585712,\n",
       " 'R2OOS_inv_gr1a': 0.01120060869331807,\n",
       " 'R2OOS_oaccruals_at': 0.01132775436802902,\n",
       " 'R2OOS_taccruals_at': 0.011318328101004527,\n",
       " 'R2OOS_be_gr1a': 0.011355096514273577,\n",
       " 'R2OOS_taccruals_ni': 0.011316408823588309,\n",
       " 'R2OOS_ebit_sale': 0.011339767345678986,\n",
       " 'R2OOS_sale_bev': 0.011349875726056169,\n",
       " 'R2OOS_age': 0.011273788886919434,\n",
       " 'R2OOS_beta_60m': 0.012260173727965396,\n",
       " 'R2OOS_rmax1_21d': 0.011352338475986157,\n",
       " 'R2OOS_bidaskhl_21d': 0.011321128679089631,\n",
       " 'R2OOS_ret_9_1': 0.011154833021512656,\n",
       " 'R2OOS_ret_12_7': 0.011004829559165707,\n",
       " 'R2OOS_ni_be': 0.01134808052883518,\n",
       " 'R2OOS_ocf_debt': 0.011372984666987773,\n",
       " 'R2OOS_cash_gr1a': 0.011343535236772562,\n",
       " 'R2OOS_at_gr1': 0.011358152192013082,\n",
       " 'R2OOS_chcsho_12m': 0.011308109266858657,\n",
       " 'R2OOS_ca_cl': 0.011323036992544,\n",
       " 'R2OOS_ivol_capm_252d': 0.011231431365686495,\n",
       " 'R2OOS_debt_gr1': 0.01129942480033752,\n",
       " 'R2OOS_debtlt_gr1a': 0.011317673189808475,\n",
       " 'R2OOS_ope_be': 0.011351852374465232,\n",
       " 'R2OOS_caliq_cl': 0.011302383365809687,\n",
       " 'R2OOS_dsale_drec': 0.011318325907197724,\n",
       " 'R2OOS_f_score': 0.011242513048340563,\n",
       " 'R2OOS_rvol_21d': 0.01133094068596563,\n",
       " 'R2OOS_gp_bev': 0.011331188293295824,\n",
       " 'R2OOS_cowc_gr1a': 0.011279924266784036,\n",
       " 'R2OOS_pi_nix': 0.011321476832567567,\n",
       " 'R2OOS_ret_6_0': 0.011322604379834234,\n",
       " 'R2OOS_ret_1_0': 0.011158757335834935,\n",
       " 'R2OOS_noa_at': 0.011201089105517315,\n",
       " 'R2OOS_cop_atl1': 0.01135869045998461,\n",
       " 'R2OOS_prc_highprc_252d': 0.011255559012750771,\n",
       " 'R2OOS_ocf_at': 0.011376237574570625,\n",
       " 'R2OOS_dbnetis_at': 0.011327551142114034,\n",
       " 'R2OOS_netdebt_me': 0.011364351524459027}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dictionary, containing OOS predictive R2, when the all values of a given predictor are set to zero\n",
    "dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>R2OOS</th>\n",
       "      <th>red_R2OOS</th>\n",
       "      <th>var_imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>beta_60m</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>-9.331044e-04</td>\n",
       "      <td>0.586456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>debt_me</td>\n",
       "      <td>0.012257</td>\n",
       "      <td>-9.294380e-04</td>\n",
       "      <td>0.584151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ni_me</td>\n",
       "      <td>0.012208</td>\n",
       "      <td>-8.813402e-04</td>\n",
       "      <td>0.553922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>ocf_at</td>\n",
       "      <td>0.011376</td>\n",
       "      <td>-4.916828e-05</td>\n",
       "      <td>0.030902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ocf_debt</td>\n",
       "      <td>0.011373</td>\n",
       "      <td>-4.591537e-05</td>\n",
       "      <td>0.028858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ret_6_1</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>-4.042460e-05</td>\n",
       "      <td>0.025407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>netdebt_me</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>-3.728223e-05</td>\n",
       "      <td>0.023432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>cop_atl1</td>\n",
       "      <td>0.011359</td>\n",
       "      <td>-3.162116e-05</td>\n",
       "      <td>0.019874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>at_gr1</td>\n",
       "      <td>0.011358</td>\n",
       "      <td>-3.108290e-05</td>\n",
       "      <td>0.019536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ret_12_1</td>\n",
       "      <td>0.011357</td>\n",
       "      <td>-3.034639e-05</td>\n",
       "      <td>0.019073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>be_gr1a</td>\n",
       "      <td>0.011355</td>\n",
       "      <td>-2.802722e-05</td>\n",
       "      <td>0.017615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rmax1_21d</td>\n",
       "      <td>0.011352</td>\n",
       "      <td>-2.526918e-05</td>\n",
       "      <td>0.015882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ope_be</td>\n",
       "      <td>0.011352</td>\n",
       "      <td>-2.478308e-05</td>\n",
       "      <td>0.015576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>sale_bev</td>\n",
       "      <td>0.011350</td>\n",
       "      <td>-2.280643e-05</td>\n",
       "      <td>0.014334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ni_be</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>-2.101123e-05</td>\n",
       "      <td>0.013206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sale_gr1</td>\n",
       "      <td>0.011346</td>\n",
       "      <td>-1.875625e-05</td>\n",
       "      <td>0.011788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sale_gr3</td>\n",
       "      <td>0.011346</td>\n",
       "      <td>-1.853771e-05</td>\n",
       "      <td>0.011651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cash_gr1a</td>\n",
       "      <td>0.011344</td>\n",
       "      <td>-1.646594e-05</td>\n",
       "      <td>0.010349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>be_me</td>\n",
       "      <td>0.011343</td>\n",
       "      <td>-1.608220e-05</td>\n",
       "      <td>0.010108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ebit_sale</td>\n",
       "      <td>0.011340</td>\n",
       "      <td>-1.269805e-05</td>\n",
       "      <td>0.007981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sale_me</td>\n",
       "      <td>0.011335</td>\n",
       "      <td>-7.598899e-06</td>\n",
       "      <td>0.004776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>gp_bev</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>-4.118996e-06</td>\n",
       "      <td>0.002589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>rvol_21d</td>\n",
       "      <td>0.011331</td>\n",
       "      <td>-3.871389e-06</td>\n",
       "      <td>0.002433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lnoa_gr1a</td>\n",
       "      <td>0.011328</td>\n",
       "      <td>-1.214338e-06</td>\n",
       "      <td>0.000763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>oaccruals_at</td>\n",
       "      <td>0.011328</td>\n",
       "      <td>-6.850711e-07</td>\n",
       "      <td>0.000431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>dbnetis_at</td>\n",
       "      <td>0.011328</td>\n",
       "      <td>-4.818452e-07</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ca_cl</td>\n",
       "      <td>0.011323</td>\n",
       "      <td>4.032304e-06</td>\n",
       "      <td>-0.002534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ret_6_0</td>\n",
       "      <td>0.011323</td>\n",
       "      <td>4.464917e-06</td>\n",
       "      <td>-0.002806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>pi_nix</td>\n",
       "      <td>0.011321</td>\n",
       "      <td>5.592464e-06</td>\n",
       "      <td>-0.003515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>bidaskhl_21d</td>\n",
       "      <td>0.011321</td>\n",
       "      <td>5.940618e-06</td>\n",
       "      <td>-0.003734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>taccruals_at</td>\n",
       "      <td>0.011318</td>\n",
       "      <td>8.741196e-06</td>\n",
       "      <td>-0.005494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>dsale_drec</td>\n",
       "      <td>0.011318</td>\n",
       "      <td>8.743390e-06</td>\n",
       "      <td>-0.005495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>debtlt_gr1a</td>\n",
       "      <td>0.011318</td>\n",
       "      <td>9.396107e-06</td>\n",
       "      <td>-0.005905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>taccruals_ni</td>\n",
       "      <td>0.011316</td>\n",
       "      <td>1.066047e-05</td>\n",
       "      <td>-0.006700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>chcsho_12m</td>\n",
       "      <td>0.011308</td>\n",
       "      <td>1.896003e-05</td>\n",
       "      <td>-0.011916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>caliq_cl</td>\n",
       "      <td>0.011302</td>\n",
       "      <td>2.468593e-05</td>\n",
       "      <td>-0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>debt_gr1</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>2.764450e-05</td>\n",
       "      <td>-0.017375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>cowc_gr1a</td>\n",
       "      <td>0.011280</td>\n",
       "      <td>4.714503e-05</td>\n",
       "      <td>-0.029631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>age</td>\n",
       "      <td>0.011274</td>\n",
       "      <td>5.328041e-05</td>\n",
       "      <td>-0.033487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>prc_highprc_252d</td>\n",
       "      <td>0.011256</td>\n",
       "      <td>7.151028e-05</td>\n",
       "      <td>-0.044944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>f_score</td>\n",
       "      <td>0.011243</td>\n",
       "      <td>8.455625e-05</td>\n",
       "      <td>-0.053144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tax_gr1a</td>\n",
       "      <td>0.011240</td>\n",
       "      <td>8.749868e-05</td>\n",
       "      <td>-0.054993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ivol_capm_252d</td>\n",
       "      <td>0.011231</td>\n",
       "      <td>9.563793e-05</td>\n",
       "      <td>-0.060108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>noa_at</td>\n",
       "      <td>0.011201</td>\n",
       "      <td>1.259802e-04</td>\n",
       "      <td>-0.079178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>inv_gr1a</td>\n",
       "      <td>0.011201</td>\n",
       "      <td>1.264606e-04</td>\n",
       "      <td>-0.079480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>me</td>\n",
       "      <td>0.011170</td>\n",
       "      <td>1.573207e-04</td>\n",
       "      <td>-0.098876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ret_1_0</td>\n",
       "      <td>0.011159</td>\n",
       "      <td>1.683120e-04</td>\n",
       "      <td>-0.105784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ret_9_1</td>\n",
       "      <td>0.011155</td>\n",
       "      <td>1.722363e-04</td>\n",
       "      <td>-0.108250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ret_12_7</td>\n",
       "      <td>0.011005</td>\n",
       "      <td>3.222397e-04</td>\n",
       "      <td>-0.202527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature     R2OOS     red_R2OOS   var_imp\n",
       "19          beta_60m  0.012260 -9.331044e-04  0.586456\n",
       "5            debt_me  0.012257 -9.294380e-04  0.584151\n",
       "6              ni_me  0.012208 -8.813402e-04  0.553922\n",
       "46            ocf_at  0.011376 -4.916828e-05  0.030902\n",
       "25          ocf_debt  0.011373 -4.591537e-05  0.028858\n",
       "1            ret_6_1  0.011367 -4.042460e-05  0.025407\n",
       "48        netdebt_me  0.011364 -3.728223e-05  0.023432\n",
       "44          cop_atl1  0.011359 -3.162116e-05  0.019874\n",
       "27            at_gr1  0.011358 -3.108290e-05  0.019536\n",
       "2           ret_12_1  0.011357 -3.034639e-05  0.019073\n",
       "14           be_gr1a  0.011355 -2.802722e-05  0.017615\n",
       "20         rmax1_21d  0.011352 -2.526918e-05  0.015882\n",
       "33            ope_be  0.011352 -2.478308e-05  0.015576\n",
       "17          sale_bev  0.011350 -2.280643e-05  0.014334\n",
       "24             ni_be  0.011348 -2.101123e-05  0.013206\n",
       "8           sale_gr1  0.011346 -1.875625e-05  0.011788\n",
       "7           sale_gr3  0.011346 -1.853771e-05  0.011651\n",
       "26         cash_gr1a  0.011344 -1.646594e-05  0.010349\n",
       "4              be_me  0.011343 -1.608220e-05  0.010108\n",
       "16         ebit_sale  0.011340 -1.269805e-05  0.007981\n",
       "9            sale_me  0.011335 -7.598899e-06  0.004776\n",
       "38            gp_bev  0.011331 -4.118996e-06  0.002589\n",
       "37          rvol_21d  0.011331 -3.871389e-06  0.002433\n",
       "10         lnoa_gr1a  0.011328 -1.214338e-06  0.000763\n",
       "12      oaccruals_at  0.011328 -6.850711e-07  0.000431\n",
       "47        dbnetis_at  0.011328 -4.818452e-07  0.000303\n",
       "29             ca_cl  0.011323  4.032304e-06 -0.002534\n",
       "41           ret_6_0  0.011323  4.464917e-06 -0.002806\n",
       "40            pi_nix  0.011321  5.592464e-06 -0.003515\n",
       "21      bidaskhl_21d  0.011321  5.940618e-06 -0.003734\n",
       "13      taccruals_at  0.011318  8.741196e-06 -0.005494\n",
       "35        dsale_drec  0.011318  8.743390e-06 -0.005495\n",
       "32       debtlt_gr1a  0.011318  9.396107e-06 -0.005905\n",
       "15      taccruals_ni  0.011316  1.066047e-05 -0.006700\n",
       "28        chcsho_12m  0.011308  1.896003e-05 -0.011916\n",
       "34          caliq_cl  0.011302  2.468593e-05 -0.015515\n",
       "31          debt_gr1  0.011299  2.764450e-05 -0.017375\n",
       "39         cowc_gr1a  0.011280  4.714503e-05 -0.029631\n",
       "18               age  0.011274  5.328041e-05 -0.033487\n",
       "45  prc_highprc_252d  0.011256  7.151028e-05 -0.044944\n",
       "36           f_score  0.011243  8.455625e-05 -0.053144\n",
       "3           tax_gr1a  0.011240  8.749868e-05 -0.054993\n",
       "30    ivol_capm_252d  0.011231  9.563793e-05 -0.060108\n",
       "43            noa_at  0.011201  1.259802e-04 -0.079178\n",
       "11          inv_gr1a  0.011201  1.264606e-04 -0.079480\n",
       "0                 me  0.011170  1.573207e-04 -0.098876\n",
       "42           ret_1_0  0.011159  1.683120e-04 -0.105784\n",
       "22           ret_9_1  0.011155  1.722363e-04 -0.108250\n",
       "23          ret_12_7  0.011005  3.222397e-04 -0.202527"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary to dataframe\n",
    "pd.DataFrame(dic.items())\n",
    "imp=pd.DataFrame(dic.items(), columns=['Feature', 'R2OOS'])\n",
    "# Feature: name of the variable whose values are set to zero\n",
    "imp[\"Feature\"] = imp[\"Feature\"].str[6:]\n",
    "\n",
    "# Calculate reduction in predictive R2OOS \n",
    "imp[\"red_R2OOS\"] = R2OOS_all -imp[\"R2OOS\"]\n",
    "# imp[\"abs_red_R2OOS\"] = abs(imp[\"red_R2OOS\"])\n",
    "imp[\"var_imp\"] = imp[\"red_R2OOS\"]/sum(imp[\"red_R2OOS\"])\n",
    "imp=imp.sort_values(by = ['var_imp'], ascending = False)\n",
    "imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot variable importance measures for the top-20 most influential variables\n",
    "\n",
    "fea_imp_graph = imp.sort_values(['var_imp', 'Feature'], ascending=[True, False]).iloc[-20:]\n",
    "_ = fea_imp_graph.plot(kind='barh', x='Feature', y='var_imp', figsize=(20, 10))\n",
    "plt.title('PLS')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_imp_pls=imp[[\"Feature\", \"var_imp\"]]\n",
    "var_imp_pls.to_csv(r'C:\\Users\\krist\\Documents\\Data\\variable importance\\var_imp_pls.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning portfolios\n",
    "\n",
    "Explore whether predictability translates into portfolio gains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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>0.015823</td>\n",
       "      <td>0.035512</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>0.013639</td>\n",
       "      <td>-0.041371</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002410_04W</th>\n",
       "      <td>0.011019</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002597_02W</th>\n",
       "      <td>0.010179</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_003820_01W</th>\n",
       "      <td>0.006027</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>-0.003497</td>\n",
       "      <td>-0.012904</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_331115_01W</th>\n",
       "      <td>-0.001282</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_332311_01W</th>\n",
       "      <td>0.013978</td>\n",
       "      <td>-0.051340</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>0.005321</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334036_02W</th>\n",
       "      <td>-0.007156</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  0.015823  0.035512 2010-01-31\n",
       "          comp_001661_01W  0.013639 -0.041371 2010-01-31\n",
       "          comp_002410_04W  0.011019 -0.053836 2010-01-31\n",
       "          comp_002597_02W  0.010179  0.001862 2010-01-31\n",
       "          comp_003820_01W  0.006027  0.078967 2010-01-31\n",
       "...                             ...       ...        ...\n",
       "2019-12   comp_330716_01W -0.003497 -0.012904 2019-12-31\n",
       "          comp_331115_01W -0.001282  0.005400 2019-12-31\n",
       "          comp_332311_01W  0.013978 -0.051340 2019-12-31\n",
       "          comp_333885_01W  0.005321  0.003528 2019-12-31\n",
       "          comp_334036_02W -0.007156 -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",
    "pls= results.reset_index()\n",
    "pls.to_csv(r'C:\\Users\\krist\\Documents\\Data\\Predictions\\pls.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 5,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01</td>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>0.015823</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>0.013639</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>0.011019</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>0.010179</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>0.006027</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>-0.003497</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>-0.001282</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>0.013978</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>0.005321</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>-0.007156</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  0.015823  0.035512 2010-01-31   \n",
       "1       2010-01  comp_001661_01W  0.013639 -0.041371 2010-01-31   \n",
       "2       2010-01  comp_002410_04W  0.011019 -0.053836 2010-01-31   \n",
       "3       2010-01  comp_002597_02W  0.010179  0.001862 2010-01-31   \n",
       "4       2010-01  comp_003820_01W  0.006027  0.078967 2010-01-31   \n",
       "...         ...              ...       ...       ...        ...   \n",
       "93412   2019-12  comp_330716_01W -0.003497 -0.012904 2019-12-31   \n",
       "93413   2019-12  comp_331115_01W -0.001282  0.005400 2019-12-31   \n",
       "93414   2019-12  comp_332311_01W  0.013978 -0.051340 2019-12-31   \n",
       "93415   2019-12  comp_333885_01W  0.005321  0.003528 2019-12-31   \n",
       "93416   2019-12  comp_334036_02W -0.007156 -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": 8,
     "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": 9,
   "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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93405"
      ]
     },
     "execution_count": 10,
     "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the 1-month risk-free rate as the average monthly difference between returns and excess returns\n",
    "bigdata[\"risk_free_rate\"]=bigdata[\"ret_t+1\"]-bigdata[\"y_true\"]\n",
    "bigdata[\"risk_free_rate\"] =bigdata.groupby('MonthYear')[\"risk_free_rate\"].transform('mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonthYear</th>\n",
       "      <th>id</th>\n",
       "      <th>yhat</th>\n",
       "      <th>y_true</th>\n",
       "      <th>date</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>prc</th>\n",
       "      <th>prc_t+1</th>\n",
       "      <th>ret_t+1</th>\n",
       "      <th>risk_free_rate</th>\n",
       "      <th>MonthYear1</th>\n",
       "      <th>NumMonth</th>\n",
       "      <th>DecileRank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_007152_01W</td>\n",
       "      <td>0.026640</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>5164.601074</td>\n",
       "      <td>-0.065609</td>\n",
       "      <td>-0.065609</td>\n",
       "      <td>22.438058</td>\n",
       "      <td>22.574668</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_010846_09W</td>\n",
       "      <td>0.023576</td>\n",
       "      <td>-0.014764</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>52754.355469</td>\n",
       "      <td>-0.052386</td>\n",
       "      <td>-0.052386</td>\n",
       "      <td>30.765442</td>\n",
       "      <td>30.035846</td>\n",
       "      <td>-0.014764</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_014269_01W</td>\n",
       "      <td>0.022033</td>\n",
       "      <td>-0.034796</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>2671.458984</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>40.510844</td>\n",
       "      <td>39.101210</td>\n",
       "      <td>-0.034796</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_015321_02W</td>\n",
       "      <td>0.022066</td>\n",
       "      <td>-0.053604</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>32227.082031</td>\n",
       "      <td>-0.079324</td>\n",
       "      <td>-0.079324</td>\n",
       "      <td>30.438742</td>\n",
       "      <td>28.807106</td>\n",
       "      <td>-0.053604</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_017420_03W</td>\n",
       "      <td>0.021982</td>\n",
       "      <td>0.059737</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>11078.906250</td>\n",
       "      <td>-0.107508</td>\n",
       "      <td>-0.107508</td>\n",
       "      <td>13.198694</td>\n",
       "      <td>13.987147</td>\n",
       "      <td>0.059737</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93291</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_289259_03W</td>\n",
       "      <td>0.015476</td>\n",
       "      <td>0.067988</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>9538.038086</td>\n",
       "      <td>0.035048</td>\n",
       "      <td>0.033648</td>\n",
       "      <td>15.350197</td>\n",
       "      <td>16.413779</td>\n",
       "      <td>0.069288</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>93340</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_317978_02W</td>\n",
       "      <td>0.018051</td>\n",
       "      <td>-0.015342</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2772.189453</td>\n",
       "      <td>0.007870</td>\n",
       "      <td>0.006470</td>\n",
       "      <td>14.303055</td>\n",
       "      <td>14.102214</td>\n",
       "      <td>-0.014042</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>93343</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_318573_01W</td>\n",
       "      <td>0.015148</td>\n",
       "      <td>0.070476</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>3576.373047</td>\n",
       "      <td>0.027055</td>\n",
       "      <td>0.025655</td>\n",
       "      <td>31.911809</td>\n",
       "      <td>34.202303</td>\n",
       "      <td>0.071776</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93345</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_318659_01W</td>\n",
       "      <td>0.022307</td>\n",
       "      <td>-0.148323</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>6149.301758</td>\n",
       "      <td>0.025512</td>\n",
       "      <td>0.024112</td>\n",
       "      <td>55.343714</td>\n",
       "      <td>47.206938</td>\n",
       "      <td>-0.147023</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93363</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_321632_01W</td>\n",
       "      <td>0.018599</td>\n",
       "      <td>-0.009555</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>12113.565430</td>\n",
       "      <td>0.025326</td>\n",
       "      <td>0.023926</td>\n",
       "      <td>33.100791</td>\n",
       "      <td>32.827559</td>\n",
       "      <td>-0.008255</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9386 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MonthYear               id      yhat    y_true       date  \\\n",
       "11           480  comp_007152_01W  0.026640  0.006088 2010-01-31   \n",
       "15           480  comp_010846_09W  0.023576 -0.014764 2010-01-31   \n",
       "21           480  comp_014269_01W  0.022033 -0.034796 2010-01-31   \n",
       "26           480  comp_015321_02W  0.022066 -0.053604 2010-01-31   \n",
       "41           480  comp_017420_03W  0.021982  0.059737 2010-01-31   \n",
       "...          ...              ...       ...       ...        ...   \n",
       "93291        599  comp_289259_03W  0.015476  0.067988 2019-12-31   \n",
       "93340        599  comp_317978_02W  0.018051 -0.015342 2019-12-31   \n",
       "93343        599  comp_318573_01W  0.015148  0.070476 2019-12-31   \n",
       "93345        599  comp_318659_01W  0.022307 -0.148323 2019-12-31   \n",
       "93363        599  comp_321632_01W  0.018599 -0.009555 2019-12-31   \n",
       "\n",
       "                 me       ret   ret_exc        prc    prc_t+1   ret_t+1  \\\n",
       "11      5164.601074 -0.065609 -0.065609  22.438058  22.574668  0.006088   \n",
       "15     52754.355469 -0.052386 -0.052386  30.765442  30.035846 -0.014764   \n",
       "21      2671.458984  0.006921  0.006921  40.510844  39.101210 -0.034796   \n",
       "26     32227.082031 -0.079324 -0.079324  30.438742  28.807106 -0.053604   \n",
       "41     11078.906250 -0.107508 -0.107508  13.198694  13.987147  0.059737   \n",
       "...             ...       ...       ...        ...        ...       ...   \n",
       "93291   9538.038086  0.035048  0.033648  15.350197  16.413779  0.069288   \n",
       "93340   2772.189453  0.007870  0.006470  14.303055  14.102214 -0.014042   \n",
       "93343   3576.373047  0.027055  0.025655  31.911809  34.202303  0.071776   \n",
       "93345   6149.301758  0.025512  0.024112  55.343714  47.206938 -0.147023   \n",
       "93363  12113.565430  0.025326  0.023926  33.100791  32.827559 -0.008255   \n",
       "\n",
       "       risk_free_rate MonthYear1  NumMonth  DecileRank  \n",
       "11           0.000353    2010-01         1           9  \n",
       "15           0.000353    2010-01         1           9  \n",
       "21           0.000353    2010-01         1           9  \n",
       "26           0.000353    2010-01         1           9  \n",
       "41           0.000353    2010-01         1           9  \n",
       "...               ...        ...       ...         ...  \n",
       "93291        0.001325    2019-12       120           9  \n",
       "93340        0.001325    2019-12       120           9  \n",
       "93343        0.001325    2019-12       120           9  \n",
       "93345        0.001325    2019-12       120           9  \n",
       "93363        0.001325    2019-12       120           9  \n",
       "\n",
       "[9386 rows x 15 columns]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "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>11</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>0.033933</td>\n",
       "      <td>0.026314</td>\n",
       "      <td>0.031209</td>\n",
       "      <td>0.028369</td>\n",
       "      <td>0.031089</td>\n",
       "      <td>0.026471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>0.065736</td>\n",
       "      <td>-0.027284</td>\n",
       "      <td>0.029358</td>\n",
       "      <td>0.026888</td>\n",
       "      <td>0.060859</td>\n",
       "      <td>-0.027966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1471</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>0.023870</td>\n",
       "      <td>0.003381</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>0.026999</td>\n",
       "      <td>0.023724</td>\n",
       "      <td>0.003352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-04</td>\n",
       "      <td>0.008382</td>\n",
       "      <td>0.048335</td>\n",
       "      <td>0.030776</td>\n",
       "      <td>0.026955</td>\n",
       "      <td>0.011169</td>\n",
       "      <td>0.048709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>5</td>\n",
       "      <td>2010-05</td>\n",
       "      <td>0.007305</td>\n",
       "      <td>-0.009707</td>\n",
       "      <td>0.030549</td>\n",
       "      <td>0.025582</td>\n",
       "      <td>0.006408</td>\n",
       "      <td>-0.009717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89867</th>\n",
       "      <td>116</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>-0.011540</td>\n",
       "      <td>0.035398</td>\n",
       "      <td>0.024443</td>\n",
       "      <td>0.025307</td>\n",
       "      <td>-0.012233</td>\n",
       "      <td>0.035356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90583</th>\n",
       "      <td>117</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.012902</td>\n",
       "      <td>-0.037330</td>\n",
       "      <td>0.024280</td>\n",
       "      <td>0.022286</td>\n",
       "      <td>0.012902</td>\n",
       "      <td>-0.037330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91269</th>\n",
       "      <td>118</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>-0.016430</td>\n",
       "      <td>-0.012361</td>\n",
       "      <td>0.024308</td>\n",
       "      <td>0.023746</td>\n",
       "      <td>-0.022165</td>\n",
       "      <td>-0.013842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91983</th>\n",
       "      <td>119</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>0.008716</td>\n",
       "      <td>0.029418</td>\n",
       "      <td>0.024132</td>\n",
       "      <td>0.023176</td>\n",
       "      <td>0.006693</td>\n",
       "      <td>0.029279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92694</th>\n",
       "      <td>120</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>-0.012263</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.023999</td>\n",
       "      <td>0.024044</td>\n",
       "      <td>-0.016234</td>\n",
       "      <td>-0.003387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NumMonth MonthYear1  excess_return_zeronet_ew  \\\n",
       "11            1    2010-01                  0.033933   \n",
       "735           2    2010-02                  0.065736   \n",
       "1471          3    2010-03                  0.023870   \n",
       "2227          4    2010-04                  0.008382   \n",
       "2992          5    2010-05                  0.007305   \n",
       "...         ...        ...                       ...   \n",
       "89867       116    2019-08                 -0.011540   \n",
       "90583       117    2019-09                  0.012902   \n",
       "91269       118    2019-10                 -0.016430   \n",
       "91983       119    2019-11                  0.008716   \n",
       "92694       120    2019-12                 -0.012263   \n",
       "\n",
       "       excess_return_zeronet_vw  pred_excess_return_zeronet_ew  \\\n",
       "11                     0.026314                       0.031209   \n",
       "735                   -0.027284                       0.029358   \n",
       "1471                   0.003381                       0.028663   \n",
       "2227                   0.048335                       0.030776   \n",
       "2992                  -0.009707                       0.030549   \n",
       "...                         ...                            ...   \n",
       "89867                  0.035398                       0.024443   \n",
       "90583                 -0.037330                       0.024280   \n",
       "91269                 -0.012361                       0.024308   \n",
       "91983                  0.029418                       0.024132   \n",
       "92694                  0.001090                       0.023999   \n",
       "\n",
       "       pred_excess_return_zeronet_vw  return_zeronet_ew  return_zeronet_vw  \n",
       "11                          0.028369           0.031089           0.026471  \n",
       "735                         0.026888           0.060859          -0.027966  \n",
       "1471                        0.026999           0.023724           0.003352  \n",
       "2227                        0.026955           0.011169           0.048709  \n",
       "2992                        0.025582           0.006408          -0.009717  \n",
       "...                              ...                ...                ...  \n",
       "89867                       0.025307          -0.012233           0.035356  \n",
       "90583                       0.022286           0.012902          -0.037330  \n",
       "91269                       0.023746          -0.022165          -0.013842  \n",
       "91983                       0.023176           0.006693           0.029279  \n",
       "92694                       0.024044          -0.016234          -0.003387  \n",
       "\n",
       "[120 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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": 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>-0.92%</td>\n",
       "      <td>-0.61%</td>\n",
       "      <td>4.45%</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.26%</td>\n",
       "      <td>0.07%</td>\n",
       "      <td>4.47%</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06%</td>\n",
       "      <td>0.37%</td>\n",
       "      <td>4.43%</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31%</td>\n",
       "      <td>0.38%</td>\n",
       "      <td>4.57%</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.52%</td>\n",
       "      <td>0.68%</td>\n",
       "      <td>4.51%</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.72%</td>\n",
       "      <td>0.73%</td>\n",
       "      <td>4.38%</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.91%</td>\n",
       "      <td>0.79%</td>\n",
       "      <td>4.38%</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.12%</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>4.41%</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.38%</td>\n",
       "      <td>1.33%</td>\n",
       "      <td>4.44%</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.83%</td>\n",
       "      <td>1.52%</td>\n",
       "      <td>4.46%</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>2.75%</td>\n",
       "      <td>2.13%</td>\n",
       "      <td>2.28%</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.92%  -0.61%  4.45%  -0.46\n",
       "2         -0.26%   0.07%  4.47%   0.05\n",
       "3          0.06%   0.37%  4.43%   0.29\n",
       "4          0.31%   0.38%  4.57%   0.28\n",
       "5          0.52%   0.68%  4.51%   0.52\n",
       "6          0.72%   0.73%  4.38%   0.57\n",
       "7          0.91%   0.79%  4.38%   0.62\n",
       "8          1.12%   0.96%  4.41%   0.75\n",
       "9          1.38%   1.33%  4.44%   1.03\n",
       "High (H)   1.83%   1.52%  4.46%   1.17\n",
       "H-L        2.75%   2.13%  2.28%   3.13"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "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>-0.76%</td>\n",
       "      <td>-0.51%</td>\n",
       "      <td>5.50%</td>\n",
       "      <td>-0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.24%</td>\n",
       "      <td>0.03%</td>\n",
       "      <td>5.44%</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.07%</td>\n",
       "      <td>0.41%</td>\n",
       "      <td>5.15%</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>5.20%</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.52%</td>\n",
       "      <td>0.76%</td>\n",
       "      <td>4.89%</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.72%</td>\n",
       "      <td>0.46%</td>\n",
       "      <td>4.82%</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.91%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>4.92%</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.12%</td>\n",
       "      <td>0.62%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.38%</td>\n",
       "      <td>0.88%</td>\n",
       "      <td>4.49%</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.81%</td>\n",
       "      <td>0.85%</td>\n",
       "      <td>4.81%</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>2.58%</td>\n",
       "      <td>1.36%</td>\n",
       "      <td>3.75%</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.76%  -0.51%  5.50%  -0.32\n",
       "2         -0.24%   0.03%  5.44%   0.02\n",
       "3          0.07%   0.41%  5.15%   0.27\n",
       "4          0.31%   0.35%  5.20%   0.23\n",
       "5          0.52%   0.76%  4.89%   0.53\n",
       "6          0.72%   0.46%  4.82%   0.33\n",
       "7          0.91%   0.54%  4.92%   0.38\n",
       "8          1.12%   0.62%  4.71%   0.46\n",
       "9          1.38%   0.88%  4.49%   0.68\n",
       "High (H)   1.81%   0.85%  4.81%   0.61\n",
       "H-L        2.58%   1.36%  3.75%   1.24"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "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\\pls_ew.csv')\n",
    "vw_df.to_csv(r'C:\\Users\\krist\\Documents\\Data\\decile_portfolios_results\\pls_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional performance measures of zero net investment long-short  portfolio with monthly restructuring, 2010:01-2019:12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawdown, turnover, and risk-adjusted performance of the long-short portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trunover\n",
    "\n",
    "Portfolio turnover:\n",
    "\n",
    "### $$\n",
    "\\text { Turnover }=\\frac{1}{T} \\sum_{t=1}^{T}\\left(\\sum_{i}\\left|w_{i, t+1}-w_{i, t}\\right|\\right)\n",
    "$$\n",
    ", where $w_{i, t}$ denotes the weight of stock $i$ in the portfolio in month $t$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "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": 35,
   "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": 36,
   "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": 37,
   "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": 38,
   "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": 39,
   "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": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  1.79640197399322\n",
      "Trunover value weighted:  31.205368041992188\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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  3.2923315324707825\n",
      "MaxDD value weighted:  10.328595347037073\n"
     ]
    }
   ],
   "source": [
    "def MDD(returns):\n",
    " \n",
    "    cum_rets = (1 + np.log(returns+1)).cumprod() - 1\n",
    "    nav = ((1 + cum_rets) * 100).fillna(100)\n",
    "    hwm = nav.cummax()\n",
    "    dd = nav / hwm - 1\n",
    "\n",
    "    return min(dd)\n",
    "\n",
    "maxDD_ew_pct = (MDD(zeronet_monthly[\"excess_return_zeronet_ew\"])*-100)\n",
    "maxDD_vw_pct = (MDD(zeronet_monthly[\"excess_return_zeronet_vw\"])*-100)    \n",
    "\n",
    "print(\"MaxDD equally weighted: \", maxDD_ew_pct)\n",
    "print(\"MaxDD value weighted: \", maxDD_vw_pct)         \n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Max 1M loss (%)\n",
    "\n",
    "- the most extreme negative monthly return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  3.2387242185796277\n",
      "Max 1M loss value weighted:  7.781402697597637\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": 46,
   "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": 47,
   "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": 48,
   "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": 49,
   "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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean active return:  difference between the benchmark return and the portfolio return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "ir_ew = ((dax_zeronet[\"excess_return_zeronet_ew\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_zeronet[\"excess_return_zeronet_ew\"]-dax_zeronet[\"monthly_excess_return_dax\"]).std())\n",
    "\n",
    "ir_vw = ((dax_zeronet[\"excess_return_zeronet_vw\"]-dax_zeronet[\"monthly_excess_return_dax\"]).mean() / \n",
    "         (dax_zeronet[\"excess_return_zeronet_vw\"]-dax_zeronet[\"monthly_excess_return_dax\"]).std()) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAX</th>\n",
       "      <th>PLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>2.130826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>2.283563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>3.129342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.292332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.796402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.277292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX       PLS\n",
       "Mean return [%]       0.752467  2.130826\n",
       "Std [%]               4.610969  2.283563\n",
       "Sharpe ratio          0.561110  3.129342\n",
       "Maximum drawdown [%]  0.000000  3.292332\n",
       "Turnover [%]          0.000000  1.796402\n",
       "Mean active return    0.000000  0.013677\n",
       "Information ratio     0.000000  0.277292"
      ]
     },
     "execution_count": 53,
     "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', 'PLS'],\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": 54,
   "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>PLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.355973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>3.749198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>1.243735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.328595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.205368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX        PLS\n",
       "Mean return [%]       0.752467   1.355973\n",
       "Std [%]               4.610969   3.749198\n",
       "Sharpe ratio          0.561110   1.243735\n",
       "Maximum drawdown [%]  0.000000  10.328595\n",
       "Turnover [%]          0.000000  31.205368\n",
       "Mean active return    0.000000   0.005928\n",
       "Information ratio     0.000000   0.097467"
      ]
     },
     "execution_count": 54,
     "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', 'PLS'],\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": 56,
   "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\\pls_zeronet_ew.csv')\n",
    "vw_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_zeronet\\pls_zeronet_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative performance of long-only investment portfolio with monthly restructuring, 2010:01-2019:12\n",
    "\n",
    "The H-L returns,  tend to be driven more by the long side (top decile) than the short side (bottom decile), so the long-only portfolio performace is also examined separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  0.8678896032046154\n",
      "Trunover value weighted:  13.544044494628906\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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  23.38361639481513\n",
      "MaxDD value weighted:  33.11412450197692\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": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  10.809162855619867\n",
      "Max 1M loss value weighted:  15.889321823166034\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": 60,
   "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": 61,
   "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": 62,
   "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>PLS_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.522817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.459234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>1.170073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.383616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.867890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX   PLS_long\n",
       "Mean return [%]       0.752467   1.522817\n",
       "Std [%]               4.610969   4.459234\n",
       "Sharpe ratio          0.561110   1.170073\n",
       "Maximum drawdown [%]  0.000000  23.383616\n",
       "Turnover [%]          0.000000   0.867890\n",
       "Mean active return    0.000000   0.007735\n",
       "Information ratio     0.000000   0.217476"
      ]
     },
     "execution_count": 62,
     "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', 'PLS_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": 63,
   "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>PLS_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.846354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.806958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.608482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.114125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.544044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX   PLS_long\n",
       "Mean return [%]       0.752467   0.846354\n",
       "Std [%]               4.610969   4.806958\n",
       "Sharpe ratio          0.561110   0.608482\n",
       "Maximum drawdown [%]  0.000000  33.114125\n",
       "Turnover [%]          0.000000  13.544044\n",
       "Mean active return    0.000000   0.001335\n",
       "Information ratio     0.000000   0.038295"
      ]
     },
     "execution_count": 63,
     "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', 'PLS_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": 64,
   "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\\pls_long_ew.csv')\n",
    "vw_long_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_long_only\\pls_long_vw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cumulative performance graph of expected return-sorted portfolios with monthly restructuring, 2010:01-2019:12\n",
    "\n",
    "\n",
    "Calculate the cumulative log returns of the long (top decile) and short (bottom decile) portfolios for both weighting schemes. Let's assume one invests 1 USD in January 2010 and buys a fraction of the top decile portfolio.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IAwBrKxnsrGUor2keoMosz+aDGVDayjEv2tfYpRARERERWRSzC3IKKylmRXr36VgXlTVH5AykpLIBPx3Ngq21DLMivSGXmdXgLhERERGRWbOon76dVdYoZ5AziN2xeRAB1DdpcTaj3NjlEBERERFZFIsKci4OHJEzhLpGDQ6dLcCkke5Q2cpx7FyRsUsiIiIiIrIoZje1sj9cVNZobNahoUkLW2uLunWDOpCYjxaNHgsn+kNpI8f+hHw0NGlgay03dmlERERERBbBokbknFUKAFy5sj80Wj1+jcvDyAAn+LopMXmkB7Q6EXGppcYujYiIiIjIYlhUkHNRWQMAn5PrhxPJxaiub8H8CX4AgAAPe3g42+JYEqdXEhERERENFosKcs6XghxH5PpGFEXsis2Bt9oOIwOdAbRu1D45wgOpuVUor2a/EhERERENBosKcg5KK0glAsoY5PrkXFYF8krrce14XwiC0Pb6pBHuAIDjyRyVIyIiIiIaDBYV5CSCACd7BSq4KXif/HIyFw52Vph01WbsakcbhPg44Ni5YoiiaKTqiIiIiIgsh0UFOQBwdbDMveRiU0pwMa+qz8fnldThXGYF5ozz6XDz78kjPVBQVo+c4rp+VElERERERD1hcUHOWWV5e8ml5VbhvS1J+Nv/jvX53n+JzYGVXIJZUd4dvh8d5gapROCeckREREREg8Aig1xlbTN0er2xSxkUWp0eX/ySCid7BTRaHd7feg5aXe/uvaquGcfPFWPaKE8obTreK05pI8foYS44kVwMvZ7TK4mIiIiIBpLFBTkXlQKiCFTVthi7lEGx83g2CsrqcduCUDx4fSTS86vxw4GLvTrHnlN50OtFzBvv22W7ySM9UF3fgvPZlf0pmYiIiIiIumGBQc7095KrqGmCRtv/EcOiigZsP5qN8WFuGD3MFTOifDB7rDd+OZmLhLSebeDd3KLD/oR8RA1Xw93Jtsu2Y4JdYKOQcXolEREREdEAs7ggZ+p7yVXUNOGZ/x1HzInsfp1HFEV8EZMCuUyCtXND2l6/6ZoQ+HvY46Ofz6OkqrHb8xw+W4j6Ji3mT+h6NA4A5DIpxoepcSqtFM0tun7VT0REREREnbO4IGfqI3K/nMyFRqtHWm5Vv85z5GwRUnKqcP2sYXBQKtpel8sk+OOKCAgA3tucBI2248DV1KLFlkMZ+G5/OoZ5qRDs7dCj604e6YHmFh0S0ns24kdERERERL1ncUFOYSWF0kaOchPcS66moQUHEvMhAMgorIW+j3uy1TS04Nu9FxDs44AZkV7t3lc72uDOJeHILq7FN3vSr3hPp9djf2I+nvngOLYdycLoIBfctzziig3AuxLi6whnlQLHzxX3qXYiIiIiIuqezNgFGIOzSmGSUyt/jWsdjZs/0Q8xJ3JQXNEATxe7Xp/n2z3paGrR4bb5oZB0EsCiQtRYMMEPMSdzEOLjgIkj3HH6Yjm+25eOwvIGBHs74IFVo3o8EneZRBAwaYQHYk7koKa+BSo7q17XT0REREREXbO4ETmgdXqlqU2tbGjSYs+pfIwNVWNqhAcAILOwptfnOZdVgWPnirBwkh+81cou266aGYRgHwd8HpOKf3ydgLe+PwO9XsQDK0fhmZvH9jrEXTZ5pDv0ooiT5zkqR0REREQ0ECwyyJnipuD7EvLQ2KzFkskB8HSxg8JKioyC3gW5Fo0OG2JS4eZkgyWTA7ptL5NKcN+ykbCSS1BQVo9184bjpbsmYlyousdTKTvirVbC390ee07l9XrPOiIiIiIi6p5FBjkXlTUam3VoaNIauxQAQLNGh12xuYgIcoa/hz0kEgGBHva9HpHbfjQLJVWNuHV+KKzk0h4d46yyxst3T8I/75+COeN8IJMa5iOxckYQiisbsSs21yDnIyIiIiKi31hkkHNWta7iaCqjcgdPF6C2QXPFKFqglwo5xXU93k9Oo9Vhd2wuJo5wx4gA515dX2kjh8KqZ8Gvp0YPc0FUiCu2H8kymX4mIiIiIhoqLDLIuTiYzhYEWp0eMSdyMNzHAcN9HdteD/JUQacXkVNS26PzXMirRotWj4kj3Aeo0t5bMycEelHEt3vTu29MREREREQ9ZplBzoT2kjuaVITK2mYsmRJwxeuBnioAQGYPn5M7l1UBqURA6O/CoLG5Otpg8SR/xKaUIDmrwtjlEBERERENGRYZ5FR2VpBKBKMHOZ1ejx3Hs+HvYY+RgVdOh3RWWcNRadXj5+SSsyoxzEsFG4Vp7SixcJIf1I7W+Gp3Ghc+ISIiIiIyEIsMchJBuLSXnHE3BY9LKUVJZSOWTPbvcJXIQE9Vj1aurGvUIKeottfPxg0GuUyKNXOHo7C8Ab/G5Rm7HCIiIiKiIcEigxxg/L3k9KKIn49lwdPFFlHD1R22CfJSobiyEXWNmi7PdT67EiKAEYGmF+QAIDLYFWOGuWDrkUxU1ho3PBMRERERDQUWG+SMvZfcmfRy5JXWY/Fkf0g62bMt6NJzclndTK88l1kBG4UUgZ72Bq/TUNbMGw6dTsSmfVz4hIiIiIiovyw6yFXWNkOnH/zntsRLo3GuDtaYEN75KpP+HioIADK6CXLJWRUI83OCVGK6/zvdHG2waJIfTiQXIyW70tjlEBERERGZNdP9yX+AuTpYQxSBqtqWQb92YXkDLhbUYF60b5cbcNtay+DhYtvlypUllQ0oq24yyefjrrZokj9cHbjwCRERERFRf1lskLu8KbgxnpNLTC8DAIwL7fjZuN8L8lIho7AGoih2+P65rNbRrREBToYrcIBYyaVYMzcE+WX12HuKC58QEREREfWVxQY5Y+4ll3ihDH7uSjhfqqErQZ4q1DZoUF7dcZ3JWRVwslfAw9nW0GUOiMhgV4T7O2F3XF6n4ZSIiIiIiLpmsUHO2b41RPV3wZP4tFI0Nmt73L6moQUX86sRGezao/aBXq0LnnT0nJxeLyIluxIjA5w73L7AFAmCgPFhbiivaUJheYOxyyEiIiIiMksWG+QUVlIobeQo78dechcLqvH2j2ex43h2j485k14OEUBUSPfTKgHAR62ETCrpcD+57OJa1DdpMSLQ9KdV/t6oIBcAwJmL5UauhIiIiIjIPFlskANwaVPwvo/IHT9XDAA4dq4I+h5OE0xML4OTvQJ+7soetZdJJfD3UCKzgxG5c5kVAIAR/qa/0MnvuThYw9vVDmczGOSIiIiIiPqiz0GuoKAA69atw4IFC3D//fejvr6+07ZHjhzBbbfd1tdLDZj+bAqu0+sRe74Y9rZyVNQ092hJfY1Wh6TMckQGu/ZqKmSgpwrZRbXtVnpMzqqAr5sSKjurXtdvbKOGuSAtt6pX01KJiIiIiKhVn4Pc+vXrsXbtWsTExCAiIgLvvvtuuzZ6vR6ffPIJ/vznP0NvhP3auuPSj03Bk7MqUdOgwdq5w2GjkOJoUlG3x5zPrkKLRo/IkJ49H3dZkJcKLVo9Csp+C8vNGh3S86vNYrXKjowOcoFOL+I895QjIiIiIuq1PgU5jUaD2NhYzJ8/HwCwatUqxMTEtGt38eJFXLx4ES+99FL/qhwgziprNDbr0NDU+1Gh4+eKYKuQYexwNcaHueFUaimaWro+T2J6GRRyKcL8HHt1rSDP9gueXMitglYnYqQZ7B/XkWAfB1hbSTm9koiIiIioD/oU5CorK6FUKiGTyQAAarUaxcXF7dqFhITg5ZdfhoODQ/+qHCAuDn1bubK5RYf4tDJEh7lBLpNgSoQnmjU6nEot7fQYURSReKEUEYHOkMukvbqe2tEGShv5FQuenMuqgEwqIMTXsVfnMhUyqQQjA5xx5mI5tyEgIiIiIuolWXcNdu7ciVdeeeWK1/z9/ds94zUQy9+7uPRsQZC+GtagAQBoBAFqtX2Pj9sfn4dmjQ4LpgZCrbaHq6sSHjEpiE0txYprhnd4THpuFarqWjB9rE+vrnVZqL8Tckvq2o5Ny6vGiEAX+Hg59uo8fbn2QJkS6Y1TaaVo0AEBnqZTV2+YUn8OFexTw2J/Ghb70/DYp4bF/jQ89qlhsT8Np9sgt3DhQixcuPCK1zQaDSZOnAidTgepVIrS0lK4ubkZvLjy8jro9QM3WiO59NxeZm4lAtV2PT5u9/EsOKsUcLO3QmlpLQBgYrg7th3OREp6adtI3+/ti82GIAABbnZtx/SGt4st4lNKkJNXiRatHpkFNVg9M6hX51Kr7ft07YEScKnPD8TlwG6Sv5Gr6T1T68+hgH1qWOxPw2J/Gh771LDYn4bHPjUs9mfvSCRClwNbfZpaKZfLER0djR07dgAAtmzZghkzZvStQiNS2VlBKhF6tXJlTUMLkjIqMDHcHZLfjUJOifCAiNatCDqSmF6GYG8HqGz7tsJkkJcKIoDsolqcz7q07YCZPh93mZO9Ar5uSpzlfnJERERERL3S51Urn3/+eWzatAmLFi1CXFwcHnnkEQDAxo0b8eabbxqqvgElEYRLe8n1fFPw2PMl0IsiJo30uOJ1taMNhvs64khSUbtnvipqmpBTXNfr1Sp/L/DSgieZhTVIzqqEnbUM/u7mPzQ9epgLLuRV92nBGSIiIiIiS9Xt1MrOeHt7Y8OGDe1eX7NmTbvXJk6ciIkTJ/b1UgOqt3vJHU8ugrfaDr5u7Yc5p0R44LOdKcgoqMEw798WeElMLwMARAb3PcjZ21pB7WiNjIIaZBTWIMzfCRKJ4Z9LHGyjglzw87FsJGdVIDrM8NNziYiIiIiGoj6PyA0VvdlLrqSqERfzazBphHuH74+/tIrl1XvKJV4og7uTDTycbftVa6CnCmczylFZ22y22w5cbZi3CrYKGc5weiURERERUY9ZfJBzVlmjsrYZuh5sWH7i0vNvk0Z4dPi+zaV95U6eL4ZG23q+xmYtUnIqERni2u+VPYO8HNBy6bzmuhH41aQSCUYGOuNsBrchICIiIiLqKYsPci4O1hBFoKq2pct2oijieHIxhvs6drgq5WVTIzxQ36TF6UvTKc9lVkCrE/s1rfKyyxuDuzpYw82pf6N7pmT0MBdU17cgp7jO2KUQEREREZkFiw9yzioFAHT7nFxOcR0KyxswaWTH0yovGxHgDAelVdv0ysT0MthZyxDs0/9N0f3clZBJJYgIHBrTKi+LCHIBAJzJ4PRKIiIiIqKesPgg56JqHV3rLsgdO1cEqURAdGjXC3JIJAImj/TA2YxyVNU148zFcowe5gqppP9dbSWX4ul1Y7Fq5rB+n8uUONhZwd/DntsQEBERERH1kMUHOWf71iDX1YIner2IE+eLMXqYC5Q28m7POTXCAzq9iK92p6GuUYOofmw7cLUgL1WPajA3o4NccLGgGnWNGmOXQkRERERk8iw+yCmspFDayFHexV5yKTmVqK5rabd3XGe81Ur4e9jjVGoppBIBI4fYVMiBMHqYC0Sx9ZlCIiIiIiLqmsUHOaD7LQiOnyuGtZUUY4a59PicUyJaQ1+YvxNsFH3ers9iBHq2jjRyGwIiIiIiou4xyKF1wZPOnpHLLanDqbQSjAtVw0ou7fE5J45wh521rNM95+hKEomAiEBnJGWWQ89tCIiIiIiIusShIrSOyKXkVF7xWmlVI7YcysDxc8WwUcgwd5xvr86psrXCfx6eBkk/946zJKOGueB4cjGyi2oReGmrBSIiIiIiao9BDq2bgjc269DQpIVWp8f2o1nYn5APiUTAgkl+WDTJH3bWvV9gxBArVVqSiEBnCADOXCxnkCMiIiIi6gKDHNC2wfc3ey8gNqUEGo0e08d4YtnUQDjZK4xcneWwt7VCoJcK5zIrsHxaoLHLISIiIiIyWQxy+G1T8MNnChEdqsbKGUHwdLEzclWWyd/DHifOFUMURQiclkpERERE1CEGOQCBHipcN2sYwv2dOKXPyDycbNHQrEVtgwYqOytjl0NEREREZJIY5NC6YuKiSf7GLoMAuDvbAgCKKhoY5IiIiIiIOsHVOMikeDjbAACKKxqMXAkRERERkelikCOT4upgA6lEQFElgxwRERERUWcY5MikSCQC3JxsUFTOIEdERERE1BkGOTI5Hs62KK5sNHYZREREREQmi0GOTI67sy1KKhug14vGLoWIiIiIyCQxyJHJ8XC2hVYnorymydilEBERERGZJAY5Mjkev9uCgIiIiIiI2mOQI5PjziBHRERERNQlBjkyOSpbOWwUMu4lR0RERETUCQY5MjmCIMDD2YZBjoiIiIioEwxyZJLcnW05tZKIiIiIqBMMcmSSPJxsUV7TjBaNztilEBERERGZHAY5MkkeLq0LnpRwY3AiIiIionYY5MgkuTtx5UoiIiIios4wyJFJcne2AcAgR0RERETUEQY5MknWVjI4Kq24ciURERERUQcY5MhkeTjboqiSQY6IiIiI6GoMcmSyPJxtUVzBxU6IiIiIiK7GIEcmy93ZFnWNGtQ1aoxdChERERGRSWGQI5Pl7syVK4mIiIiIOsIgRybL81KQ44InRERERERXYpAjk+XiYA2pROCIHBERERHRVRjkyGTJpBK4OtowyBERERERXYVBjkyah5MNp1YSEREREV2FQY5MmoeLLYorG6EXRWOXQkRERERkMhjkyKS5O9tCo9WjsqbZ2KUQEREREZkMBjkyaR5O3IKAiIiIiOhqDHJk0jxcGOSIiIiIiK7W5yBXUFCAdevWYcGCBbj//vtRX1/frk1JSQnuvPNOLF++HCtXrsSxY8f6VSxZHgc7KyispFzwhIiIiIjod/oc5NavX4+1a9ciJiYGERERePfdd9u1ee2113DNNddg69ateOONN/D4449Dp9P1q2CyLIIgwMPJFkWVDHJERERERJf1KchpNBrExsZi/vz5AIBVq1YhJiamXbt58+ZhyZIlAAB/f380NzejoYE/kFPvuDvboKicnxsiIiIiostkfTmosrISSqUSMlnr4Wq1GsXFxe3aXQ56APDxxx8jPDwc9vb2Pb6Oi4uyL+VRF9Tqnve/qRjm64TYlBI4OtlCLpMau5wrmGN/mjr2qWGxPw2L/Wl47FPDYn8aHvvUsNifhtNtkNu5cydeeeWVK17z9/eHIAhXvHb117/32Wef4dtvv8WXX37Zq+LKy+ug13P/MENRq+1RWlpr7DJ6zV4hhSgC5y6UwtvVztjltDHX/jRl7FPDYn8aFvvT8NinhsX+NDz2qWGxP3tHIhG6HNjqNsgtXLgQCxcuvOI1jUaDiRMnQqfTQSqVorS0FG5ubh0e/9prr+HAgQP46quv4OHh0cvyiVr3kgOA4ooGkwpyRERERETG0qdn5ORyOaKjo7Fjxw4AwJYtWzBjxox27T777DOcOHECGzduZIijPvNw5hYERERERES/16dn5ADg+eefx9NPP4333nsPnp6e+Ne//gUA2LhxI0pKSvDwww/jnXfegVKpxC233NJ23P/+9z+4u7v3v3KyGDYKGRzsrBjkiIiIiIgu6XOQ8/b2xoYNG9q9vmbNmrY/x8bG9vX0RFdwd7blXnJERERERJf0eR85osHk4WzDIEdEREREdAmDHJkFd2db1DRo0NCkMXYpRERERERGxyBHZuG3BU8ajVwJEREREZHxMciRWfD43RYERERERESWjkGOzILa0QaCwC0IiIiIiIgABjkyEzKpBGoHGwY5IiIiIiIwyJEZ8XCxRWE5gxwREREREYMcmQ0ftRKF5fXQaHXGLoWIiIiIyKgY5MhsBHjYQ6cXkVdab+xSiIiIiIiMikGOzEaApz0AIKuwxsiVEBEREREZF4McmQ0XlTWUNnJkFtUauxQiIiIiIqNikCOzIQgCAjztkc0gR0REREQWjkGOzEqAhwr5pfVo0XDBEyIiIiKyXAxyZFYCPeyhF0XkltQZuxQiIiIiIqNhkCOzEuCpAgBkcXolEREREVkwBjkyK45KK6jsrLhyJRERERFZNAY5MiuCICDAw54jckRERERk0RjkyOwEeNijoLweTS1aY5dCRERERGQUDHJkdgI8VRBFIKeYC54QERERkWVikCOzE+BhD4ALnhARERGR5WKQI7PjqFTAyV6B7CIueEJERERElolBjswSFzwhIiIiIkvGIEdmyd/DHkXlDWhs5oInRERERGR5GOTILAV4qCACyCnmqBwRERERWR4GOTJLlxc8ySzsPsiduViG7UcyB7okIiIiIqJBwyBHZkllZwUXlQJZ3Sx4oteL+Gp3GrYdyYJGqxuk6oiIiIiIBhaDHJmtAA9VtwuenMkoR2lVE3R6EQVlDYNUGRERERHRwGKQI7MV4GmPkspGNDRpOm2zJy4XCispAD5PR0RERERDB4Mcma0ADxUAILuTUbmCsnqcy6rEwgl+UMilyCmpG8zyiIiIiIgGDIMcmS3/SwuedDa9ck98HmRSAbOivOHrpkQuR+SIiIiIaIiQGbsAor5S2sjh6mCNzA6CXEOTFkfPFmFCuDtUdlbwc1fiaFIR9KIIiSAMaF1puVX4bl86NDo99HoRehHQ6UWIehE6vQiVnRWeXBPVNuWTiIiIiKi3OCJHZi3AU4WswvYrVx4+W4hmjQ5zo30AAH7u9mhq0aGsqnHAazp4ugB5pfVwtreG2tEGXi628HdXIshbhUAvFTILa3A8uWjA6yAiIiKioYsjcmTWAj3sEZdSgrpGDZQ2cgCAXhSx91Qegr0d2p6j83VTAgByiuvg5mQ7YPWIoojkrAqMHuaC+1dEdPj+8+UN2JeQjxljvCAM8OggEREREQ1NHJEjsxbQ9pzcb6NyZy+Wo6SqEXPG+bS95u1qB4kgDPiCJ0UVDaiqa8GIAKcO3xcEAbPHeiOnuA4ZHYwkEhERERH1BIMcmbW2BU8Kf3tObs+pPDgqrTAuVN32mpVcCk8X2wFf8CQ5qxIAMCLAudM2k0a4Q2Elxb74/AGthYiIiIiGLgY5Mmu21nK4O9m0rVxZWF6PpMwKzIryhkx65cfb11054CNyyVkVUDu2PhvXGRuFDFMiPHDyfOuUUCIiIiKi3mKQI7MX4KlC9qWplXtP5UMmFTAz0rtdOz83e1TWNqO2oWVA6tDp9UjJqUS4f+ejcZfNjvKGVqfH4TOFA1ILEREREQ1tDHJk9vzd7VFe04ziygYcTirE+DB3ONhZtWvn69664EnuAI3KZRXWorFZ1+nzcb/no1YixMcB+xPyoRfFAamHiIiIiIYuBjkye4Gerc/JfbUrDc0tv205cLXfr1w5EJKzW5+PC/fvPsgBwOyx3iipakRyVsWA1ENEREREQxeDHJk9P3d7CACSMiswzEuFQE9Vh+1UtlZwslcgt2RgFjw5n1UBP3cl7G3bjwZ2ZNxwN9jbyrnoCRERERH1GoMcmT0bhQweLq17w83pZDTuMl+3gVnwpLlFh/T86i5Xq7yaXCbB9NFeSEwvQ0VNk8FrIiIiIqKhi0GOhoThvo5wVikQHerWZTs/d3sUljVAo9UZ9PoX8qqg1YkY0cNplZfNivQCROBAYoFB6yEiIiKioa3PQa6goADr1q3DggULcP/996O+vr5dm5KSEtx+++1YtmwZrr/+epw/f75fxRJ1Zs2cELzwhwntthy4mp+bEnpRRF5p+89rfyRnV0ImFRDi49ir41wdbTBqmAsOni6AVqc3aE1ERERENHT1OcitX78ea9euRUxMDCIiIvDuu++2a/Pvf/8b8+fPx7Zt2/DQQw9h/fr1/SqWqDNWcimUNvJu2/kN0MqVyVkVCPZ2gMJK2utjZ0d5o7q+BQkXygxaExERERENXX0KchqNBrGxsZg/fz4AYNWqVYiJiWnX7uWXX8aNN94IAMjLy4NK1fEiFESDxdXRBtZWUuQUG27Bk9qGFuQU1yG8F8/H/d6oIBe4qKyxLz7PYDURERER0dAm68tBlZWVUCqVkMlaD1er1SguLm7XTiJpzYkLFixAfn5+h6N2RINJIggGX/Dk/KVtB3qyf1yHNUkEzIrywg8HMlBQVg8vVzuD1UZEREREQ1O3QW7nzp145ZVXrnjN398fgiBc8drVX/9eTEwMzp8/jzvuuAM7d+6Eo6Njj4pzcVH2qB31nFptb+wSjC7U3xl74nLg4qKERNL557Yn1Gp7ZO6/CFtrGcZHeEHazTN6nVkxezi2Hs7EidRS3BPu0a+azB0/o4bF/jQs9qfhsU8Ni/1peOxTw2J/Gk63QW7hwoVYuHDhFa9pNBpMnDgROp0OUqkUpaWlcHNrv1rg/v37MX78eNjZ2SE8PBxeXl7Izc3tcZArL6+DXi/27E6oW2q1PUpLB2YPNXPiqlKgsVmH5PQSuDvZ9vk8l/szPqUYob6OqKjo3wIq40Ld8OvJHCye4Asree+ftRsK+Bk1LPanYbE/DY99aljsT8NjnxoW+7N3JBKhy4GtPg0fyOVyREdHY8eOHQCALVu2YMaMGe3abd68GZs2bQIApKeno6ysDEFBQX25JJHBtC14Utz/6ZUlVY0orWrq1f5xnYkOdUNjs9bgK2oSERER0dDT51Urn3/+eWzatAmLFi1CXFwcHnnkEQDAxo0b8eabbwIA/vKXv+Dw4cNYtmwZnnnmGbzxxhuws+PzP2Rc3q52kAgCckr6/xuh81kVAPr+fNzv+bi1fm/klxp+w3IiIiIiGlr6tNgJAHh7e2PDhg3tXl+zZk3bn93d3fHxxx/39RJEA0Iuk8LT1RY5BhiRS86qhJO9Ah7OfZ+ieZnawQZWMgnyyzgiR0RERERd6/OIHJE583NT9nsvOb1exPnsSoT7O3W52E9PSSQCPF3tkMcROSIiIiLqBoMcWSQ/d3tU1jajpqGlz+fIKqxBXaPGINMqL/NR2yGfz8gRERERUTcY5Mgi+bn1f8GTxLRSAEC4f/8XOrnM21WJ6voW1PYjYBIRERHR0McgRxbJ1711D5P+LHhy+kIpvFzt4GSvMFRZ8FG3LnhSwOfkaABpdXpotHpjl0FERET9wCBHFklpI4ezStHnETmNVo+kjHKM8DfctEoA8Fa3jhRyCwIaSB/9lIwn3z+KwnJ+zoiIiMwVgxxZLD83e+T0ccGTjIJqtGh0CDfg83EA4Ki0gq1CxpUracAUltcj9nwJaupa8I+vEzj6S0REZKYY5Mhi+bopUVhejxaNrtfHJlwog1QiINTXsEFOEAT4qLlyJQ2cnSdyIJdJ8NS6sQCA1zYm8BcHREREZohBjiyWn7sSoohe/xDb1KLFoTOFmDLaC7bWfd6KsVPeaiXyS+shiqLBz02WrbK2GceSijBttCeG+zriqbVREAD8k2GOiIjI7DDIkcVqW/CkuHcLnhw5W4TGZi2WTQ8aiLLgrbZDY7MWlbXNA3J+sly743KhF0VcO8EPAODpYocnL4e5r+MHLMw1NmtRXNEwIOcmIiKyVAxyZLFcHaxho5D26jk5vSji11N5CPRUIdTAC51c5u3aunIlR0jIkBqaNNifkI/xYW5wc7Rpe70tzAlCa5gz8LTe/LJ6rP80Fs99fBLV9dxWg4iIyFAY5MhiSQQBvmplr1auTMqoQHFFA+ZF+0AQhAGp6/LKldwYnAxpX0I+mlp0WDjRv917bWFOIuC1jQnI6+MiQFdLTC/Dy1/EobFFC61Oj0OnCwxyXiIiImKQIwsX4KlCVlENSqoae9R+d1wuHJRWiA5zG7CalDZyOCitDD4yQpZLo9Xh17g8jAx0hr+HfYdtPF3s8OSaKEgkAv72yUk899EJbNiVipPni1Fd17tpvqIo4udjWfjv92fg7myL528fj3B/JxxIzIdez2c/iYiIDMHwKzUQmZFrx/viwOkCfPlLKh69YUyXo2wFZfU4l1mBldMDIZMO7O9AfNRK7iVHBnM0qQjV9S24e6Jfl+08Xezw3K3ROHauCKk5VTiaVIR98fkAAHdnW4T6OiDUzwkj/J3goFR0eI4WjQ6f7kzBieRiTBzhjj8sDIOVXIrZUd54d0sSzlwsR2SIq8HvkYiIyNIwyJFFc1ZZY9WMIGz89QJiU0owIdy907a/nsqDTCrBzCjvAa/L29UO+xJaRy8kkoGZwknm7/LoVlefEb1eRMyJHPh72CO8B891OqussXhyABZPBnR6PXKK65CaU4W03CrEpZTi4OlCAK2L8owMcMaIACcM93WEtZUMFTVN+O+PZ5FTVIvVM4OwaJJ/2y9HIkNc4aC0wr6EfAY5IiIiA2CQI4s3Z6wPjiYV4etfLyAi0Bm21vJ2beqbNDiaVIhJI9yhsrUa8Jq81XbQaPUorWqEu7PtgF+PzE9jsxZvfJuI2oYW3LNsJIZ5OXTYLj6tFMWVjbh/RUSvn+uUSiQI9FQh0FOFBRP9oNeLyC2pw7msCiRnVWBvfD52xeZCKhEwzNsBxRUNaNbo8NB1oxEZfGVYk0klmDnGC9uPZKGkqvGKBVeIiIio9/iMHFk8iUTA7QvCUNvQgu8PZHTY5uDpArRo9Jgb7TMoNflcWvCE0yupIy0aHd76/gyyi2qh1Yl49ct4/HwsC/qr9h4URRE7T2TDzdEG44ar+31diUSAv4c9Fk3yx+M3ReHtR6bjsZsice0EXzRrdHC0V+Cvt4xrF+IumxnpDUEQcCAhv9+1EBERWTqOyBEB8Pewx7xoX+yOzcWUCA8Ee/82uqHT67H3VB7C/Bzh597xQhGG5uVyeQuCOowL7f8P4DR0aHV6vL/1HNJyq3D3shEYHeSCz2NS8cOBDCRnVeKuJSPgZN/6/FrSxXJkFtbilvmhAzJF10ouxcgAZ4wMcO5Reyd7BaJCXHHoTCFWTA+EXCY1eE1ERESWgiNyRJesmB4IR3sFvohJgVanb3s9Ia0M5TXNmBvtO2i1KKykUDtacwsCuoJeFPHpjvNITC/DzdcOx6QRHrC1luO+5SNx+8IwXCyoxvOfnERiehkA4Pt9F6CylWNqhIeRK//NrLHeqGvUIC6l1NilEBERmTUGOaJLrK1kuHnecOSV1mN3bG7b67vjcuHqYN3pdLGB0rpyJbcgsAQHEvPx87Es1DZ0vmG2KIrY+OsFHDtXjJUzgjB77G/TfAVBwIwxXnj+9vFwslfgre/P4INt5xCfUoK50b6wkpvOyFe4vxPcnW2xj9MriYiI+oVBjuh3ooarERXiiq2HM1Fa1YjsolpcyKvGnHE+g756pLfaDsUVjdBo9d03JrMVm1LSNjXy8XeP4vOYFBSUtR+J3Xo4E3tO5WH+BF8smdx+U2+gdfuAZ28dh7nRPjiRXAwbhRSzxw78Kqu9IREEzI70Qnp+NXKKa41dDhERkdlikCO6yrp5wyFIBHy1Ow2743KhkEsxfbTnoNfh7aqEXhRRVNEw6NcebBqtHlsPZ6KgzLJGIHNL6vDxz8kY5q3CC38Yj8kjPXA0qQjPfnQC/9qUiHOZFRBFEbtic7HtSBamjfbEDbODu1x9Ui6TYu3c4Xjipkj85fYJsOtgFVZjmzraE1YyCfZzVI6IiKjPuNgJ0VWcVdZYNT0IG/dcgABg9ljvDrckGGje6ksLnpTWwddNOejXHyyiKOKLmBQcSSrC6YvleGZdlEUsglHXqMF/fzgDW4UMD6wcBUelArcvDMOqmUHYn5CPvfH5eOPbRLg72aC4shHjQtW4fUFYj7cQCA9whlptj9JS0xv1srOWY0K4O46dK8b1s4Nho+A/RURERL3FETmiDswZ5wN/d3uIwKAucvJ7Hs62kEoE5HcwzW4o+eVkLo4kFSEqxBVZhTX4oZMtIIYSnV6P97YkoaquGQ+sag1xl6lsrbBsaiD+ef8U3Lk4HNYKGSKDXXHP0pFDanP42WO90azR4WhSkbFLISIiMkv8NShRByQSAQ+sjEB2cS08jLQht0wqgYeL7ZBeufLMxXJ8tz8d0aFq3LciAj8eysSOo1kYFeSCkYE9W9LeHH237yLOZ1fiD4vCOt3IWy6TYOooT0wdNfjTegdDoKcKAR722JeQj2vGevd6s3IiIiJLxxE5ok64OtpgXKibUWvwdrUz2sqVer3YfaN+KCirxwfbkuCrVuLOxSMgEQT8YelIeLrY4uOfk1HXqBnQ6xvL0aRC7IrNxZxxPpg+2svY5RjV7LHeKCirR1pulbFLISIiMjsMckQmzFutRFl1ExqbtYN63ZTsStz3xgG88U0CTqWWQqc37MqZdY0avPXDGcilEjy0ejQUVq3PxFlbyXDvspGobdDg850pEMWBDZODLbOwBp/tTEWYnyNuvCbY2OUY3YRwd9gqZNyKgIiIqA8Y5IhMmI9r64InBeWDN72yWaPDpzvPw95WjoLyBryz+SyefO8Yth3ORGVtc7/Pr9Pr8f7WJJRXN+HBVaPh4mB9xft+7vZYNTMIp9JKcfhMYb+vZyqq61vw9o9n4WBnhftWREAm5V+/CrkU00Z7IjalBHtO5Rm7HCIiIrPCZ+SITNhvK1fWd/oslaFtPpiB0qomPLU2CsE+DjiTXo59CfnYcjgT245kIWq4K66J8kaYv1Ofnmv6Zk86krNanw8L9un4nuZP8MPZi+X4+tcLGO7nCHcn4zynaCiiKOJ/286hvlGDZ24eB5WtlbFLMhkrpwehtKoRX+1OQ0F5PdbODYFUwpBLRETUHf5rSWTCXB1tYCWXDNqCJxcLqrE7LhezorwR6ucEqUSCqOFq/PnGSLx67yRcO8EXqTlV+Oc3ifh69wXoezn18UBiPvacysO14327fD5MIgi4a8kISCUCPtyeDK3OvDdFj08rw/nsStxwTTD8PeyNXY5JUVhJ8cCqUVg40Q/74vPxn02n0dA0NJ+PJCIiMiQGOSITJhEEeLvaIX8QNsrWaPX4bEcKHJUKXD9rWLv33ZxsccPsYLzxwBTMi/bFnvg8fPrz+R49PyeKIvbG5+HLXWmICHTG9bPbn/9qzipr3LogFBkFNfjpaFZfbskkaHV6fLcvHV6udpgZadmLm3RGIgi4fnYw/rAoDCk5VXh5wymUVDYYuywiIiKTxiBHZOK8XZWDMiL387Es5JfV49b5oV1u0CyXSXHTnGCsmBaII0lFeH/LOWi0nYe5Fo0OH/98Hl/uSsPIQGfctzyix1PnJoS7Y2qEB7YfzUJSZnmv78kU7D2Vh5KqRtx4TTCnDHZj+mgvPH5TJGrqW/D3L04hNafS2CURERGZLP5UQWTivNV2qK5vQW1Dy4BdI6+kDj8fy8akke4YE+zabXtBELBsWiDWzAnBqbRSvPXDGTS36Nq1K61qxP99eQpHk4qwfFogHr5uNGyte/do7tp5w+Hlaoc3vzuDg6cLenWssdU1arDtSBYiAp0xKsjF2OWYhVA/Jzx3WzTsbeV4/ZvEIbXgDRERkSExyBGZuN8veDIQdHo9Pt15HrbWMqyZE9KrY+eN98UfFoYhOasCb2xKvOLZpqSMcrz4WSzKqprwp+tGY/m0QEj6sDiKjUKGZ9aNQ5i/Ez7bmYLv9qX3+tk8Y9l2OBONLVrcwK0GesXNyRZ/vWUcQv0c8emO8yjmNEsiIqJ2GOSITJy3qxIAkF82MEFud2weMgtrsW7ecNj3YTXF6WO8cP/yCGQW1OC1rxNQXd+C7Uez8O9Np+Fkb42/3R7do1G+rthay/DI9aMxO8obO0/k4L3NSWjWtB8BNCWF5fXYl5CPmWO84KNWGrscs2NrLcddS0ZAIhFwIMG8RmKJiIgGA4MckYlzVFrBzlqG/FLDL3hSXNmAzYcyEBnsivFhbn0+T3SYGx6+bjSKKhrw9PvHsPlgBiaOcMdfbx0HNwNtHSCVSHDztcOxZk4I4tNK8Y+v4lFV1/997QbKd/suQi6TYPn0IGOXYrYclQpEhbji8NlCaLSmHdyJiIgGG4MckYkTBAHeaiXyrhqR04siCsvrceRsIX4+loWaXj5DpxdFfL4zBTKpgFvmh/ZpT7jfGxXkgj/fGAknewXWzA3B3UtHQCGX9uucVxMEAfPG++Kh1aNRWN6Alz6PQ05xrUGvYQjnsyqQmF6GxZP94WDHPeP6Y1aUN+oaNYhLLTV2KURERCaFG4ITmQFvtR2OnyvC6fQyZBTUIKOwBhkFNWhs1ra1+eVkLm6aE4zJIz26DWXZRbX4cncqLubX4PaFYXCyVxikzuG+jvi/eyYZ5FxdiQxxxTM3j8Wb35/BK1/F4+HVoxHu7zTg121s1uKVL+NhJZdgxbRAjAx0btfXer2Ib/emw0VljWvH+w54TUNdmL8T3J1ssD8hH5NHehi7HCIiIpPBIEdkBnzUSjQ26/Dm92cgCK1fTwh3Q5CXCkFeDhBFEZ/HpOCjn87jWFIRbl0QBrWjTbvz1DVqsPlgBvYn5MPeVo47FoVj6ijz/OHYz90ez94ajX99m4i3fzyLZ28dB08XuwG7niiK+GxnCgrK6uGgtMK/Np1GsI8DVk4PuiJEHkkqRE5JHe5dNhJymWFHJC2RRBAwM9Ibm/alI6+kDj5ufN6QiIgIYJAjMguTRrhDAODpYgt/D3tYW7X/1n3m5nHYn5CP7/dfxHMfn8DK6UGYG+0DqUQCvV7EoTMF+OFABhqatJgT7YMV0wJhay0f/JsxICd7Bf50/Wj8/fM4/Oe703j21ugeLdiiF0UcSCxAkKcK/h72PbrWvoR8xKaUYPXMIFw73g+HzhTgp6NZ+OfGBIT5OWLljCD4uinx48EMDPNSYUJ43585pCtNG+2JHw9mYH9iPm6+NtTY5RAREZkEBjkiM2CjkGFWlHeXbSSCgGvG+iAy2BVf7krDt3vTcTy5GAsn+iHmRA6yimox3NcR6+YNh+8QGtVwdbDBQ6tH4x9fJ+CdH8/isZuiIJd1/vivRqvDhz+dR1xKCRRWUjx6/RgM93Xs8hpZRTX4Zs8FjB7mgoWT/Nv6evpoT+xPKMDPx7PxypfxUDtao7quBQ+sGNXvZw7pN0obOcaHueFoUhGumzWsw19kEBERWRoudkI0xDirrPHQ6lG4f0UEKmua8P7Wc6isa8Y9S0fgqbVRQyrEXTbM2wF3Lg5HWl41vohJgdjJPnN1jRq8/k0i4lJKsGxqAJyUCvxrUyJSsis7PXdDkxbvbUmCva0V7lwcfsVeeHKZFPPG++If907G9bOHobFZhykRHgj2cTD4PVq62VHeaGrR4URysbFLISIiMgn8tSbRECQIAsaHuSHc3wmn08swdrgaNoqh/e0+cYQ7iioasPVwJjxcbLF4csAV75dWNeLfm06jrLoR9y0fiQnh7pgd5Y1/fpOI/3x3Gg+tHo2Rgc5XHCOKIj7dcR4VNc14au3YTqdtKqykWDjRH/Mn+A3U7Vm8Yd4q+KjtsC8hHzPGeHHEk4iILB5H5IiGMKWNHFNHeQ75EHfZsqkBmDjCHT8cyMCp1JK21zMLa/DyF3GobWjB4zdFYUK4OwDAQanAk2uj4OZkize/P4MzF8uvON+vp/JwKq0Uq2cO69Eom0QQrhixI8MRBAGzoryRU1yHrCLT23KCiIhosPU5yBUUFGDdunVYsGAB7r//ftTX13fatq6uDnPnzsWJEyf6ejkiom4JgoA7FoVhmJcKH25PRmZhDRLTy/CPr+Mhl0nxzM3j2j0Pp7K1wpNro+Dtaoe3fzyDhAut+5VlFtZg0950RAa7Yv4EbiNgCiaP9IBCLsW+hHxjl0JERGR0fQ5y69evx9q1axETE4OIiAi8++67nbZ96aWXUFNT09dLERH1mFwmxYOrR8Pe1gr/+jYR//3hDDyd7fDsrePg5drx9gRKGzmeWBMJXzd7vLs5CYdOF+C9LUlwVCpwx+JwTuMzETYKGSaNdMfJ5GLUN2mMXQ4REZFR9SnIaTQaxMbGYv78+QCAVatWISYmpsO2O3bsgJ2dHUJDuWQ0EQ0OBzsr/On60RBFYFSQC55aFwUHZdebnttay/HYjZEI9FTh050pqKxtxn0rRkJpY95bNAw1syK90aLV42hSkbFLISIiMqo+PThTWVkJpVIJmaz1cLVajeLi9iuJFRQU4PPPP8fnn3+Ou+++u9fXcXEZeqvrGZta3bM9s6hn2J+GZ6g+Vavt8fkL86GQS3s1ovbyH6fivR/OIHK4GpPG+BikFmMaap9Rtdoew/0ccfhsIdYsGPzR0qHWn6aAfWpY7E/DY58aFvvTcLoNcjt37sQrr7xyxWv+/v7t/vG8+mu9Xo+//vWveO6552Btbd2n4srL66DXd7yMOPWeWm2P0lIuEmAo7E/DG4g+7cvZbr12OACY/f/fofoZnRrhgU93pOBIfC5C/ZwG7bpDtT+NiX1qWOxPw2OfGhb7s3ckEqHLga1ug9zChQuxcOHCK17TaDSYOHEidDodpFIpSktL4ebmdkWbjIwMZGRk4K9//SsAICcnB88++yxeeuklTJo0qS/3QkREhAnh7vh2Tzr2JeQPapAjIiIyJX2aWimXyxEdHY0dO3Zg6dKl2LJlC2bMmHFFm+DgYBw4cKDt61tuuQUPPvggJk6c2L+KiYjIoinkUkwZ5YF98fmorm+Bg13H+/sRERENZX1etfL555/Hpk2bsGjRIsTFxeGRRx4BAGzcuBFvvvmmoeojIiJqZ1akN3R6EYfPFBi7FCIiIqPo8y7B3t7e2LBhQ7vX16xZ02H7jtoSERH1hZerHcL8HLE/oQALJ/pDIuEWEUREZFn6PCJHRERkTLPH+qC8pglnM8qNXQoREdGgY5AjIiKzFBXiCgc7K+xLyDd2KURERIOOQY6IiMySTCrB9DFeOHuxHKVVjcYuh4iIaFAxyBERkdmaFekFCMCBRC56QkSGlZJdicZmrbHLIOoUgxwREZktZ5U1IoNdcehMATRavbHLIaIhIqe4Fq9tTMD/Np81dilEnWKQIyIiszY7yhu1DRqcSisxdilENERcfvb219gcZBTUGLkaoo4xyBERkVkbEegMN0cb7IvnoidE1H+NzVocP1eM6DA3ONkr8NXuNOhF0dhlEbXDIEdERGZNIgiYFeWNC3nVyCupM3Y5RGTmjiYVoVmjw8KJfrh9yUhkFtbgWFKRscsiaodBjoiIzN600Z6QSSXYl8hROSLqO1EUsT8hH/4e9gj0VGHWWB8M81Lhu/0XufAJmRwGOSIiMntKGznGh7nhWFIRf9gioj67kFeN/LJ6zI7yBgBIJALWzhuO2voWbD+SZdziiK7CIEdEREPC7LHeaGrR4XhysbFLISIztT8hHzYKGSaGu7e9FuipwrTRntgdl4vC8nojVkd0JQY5IiIaEoZ5qeDnpsS++HyIXJiAiHqppqEFcaklmBrhAYWV9Ir3Vs8cBiu5BBv3XODfL2QyGOSIiGhIEAQBs8Z6I6+0Dhfz2y8X3qzR4UJeFRLSSvmDGBG1c/hMIbQ6ETMvTav8PZWdFZZPDURSRgVOXyw3QnVE7cmMXQAREZGhTBrhjk1707EnPg9ymQSZRTXIKqxBRkEtCsrq25YQf/i60YgMdjVytURkKvSXFjkJ9XWEt6tdh22uGeeDA6cL8M2vFzAywBlyGcdDyLj4CSQioiHD2kqGKREeOJFcjPWfxeKLmFScSi2Fo9IKiyb746HVo+CsUiDmeLaxSyUiE5KUUYGy6ibMHtt+NO4ymVSCNXNDUFLViF2xOYNYHVHHOCJHRERDytIpAVDayOHpYodALxXUDtYQBKHt/dKqJnyz5wLS86sR7O1gxEqJyFTsT8iHys4KY4eru2wXEeiCqBBX/HQ0G1MiPOFkrxikCona44gcERENKQ5KBVZMD8LEEe5wc7S5IsQBwIwxnrCzliHmBH+jTkRAeXUTTl8sw/RL+1F258Y5IdCLIj7Zcb5tujaRMTDIERGRRbG2kmH2WB8kpJVyKXEiwoHT+YAIzIz06lF7N0cbrJkbgnOZFfj5aNbAFkfUBQY5IiKyOHPH+UAmk+CXkxyVI7JkWp0eB08XYvQwF7g62PT4uJljvDBppDu2HM7E+ezKAayQqHMMckREZHFUdlaYNsoTR5OKUFXXbOxyiMhI4tNKUVPf0uUiJx0RBAG3zg+Fh7MtPth2DtX8e4SMgEGOiIgs0vwJvtDpReyOyzV2KURkJPsT8uHqYI2IQJdeH2ttJcP9KyLQ1KzFB9vOQa/n83I0uBjkiIjIIrk52SI61A37E/LR2Kw1djlENIgam7X4IiYFKTlVmBXlDYlE6P6gDviolbj52lCk5FRh6+FMA1dJ1DUGOSIislgLJvqhsVmH/Yn5xi6FiLpQ09CCz3amIPFCGcR+rhR5PqsCf/v4JA4kFmDBBD9cO963X+ebNtoTU0d54KejWUjKLO/XuYh6g0GOiIgsVqCnCuH+TtgdmwuNVm/scoioA3pRxEc/JePg6QK89cMZvPh5HBLTex/omlt0+HJXKv75TSJkUgHP3DwON1wT3KMtB7pz87Wh8FLb4X/bklFZy+flaHBwQ3AiIrJoCyf54V/fnsbxc0WYPqZny48T0eCJOZGDpIwKrJs3HFZyCX46moW3vj8Dfw97LJ8WiDHDXNrtF3m1tNwqfPxzMkqrmjA32gerZw6DQi41WI0KuRR/XBGBFz+Lw/tbk3D30hGoa9SgtkGDmvoW1DZoUNvQgsZmLWZEeiHAQ2Wwa5PlYpAjIiKLNjLAGb5uSsSczMHU0Z6QdPMDIRENnvS8avx4IAPjw9xwzVhvCIKAySM9cOxcEbYf+S3QLZkcACd7BZo1OjRrdGjR6NDc0vrn/LJ6HEwsgKujNZ5aG4VQP6cBqdXTxQ63LgjFh9uT8eR7x9q9L5NKIBGAhPQyvHjHBNjbWg1IHWQ5GOSIiMiiCYKAhRP98L/tyTidXoaoELWxSyIiAHWNGry/LQkuDgrctiCsbdRNJpVg+miv1kCXVITtR7PwzuaznZ5HEIBZY71x/axhsLYa2B99J4/0gI1Chpr6FtjbymFvawXVpf9aW0mRW1KHv38Rh093pOCh1aO6HUkk6gqDHBERWbzx4W744UAGdh7P6XWQq2loweaDGVg5Iwgq/oadyCBEUcQnP59HdV0L/nLLONhat/+RVSaVYPoYL0yO8MD57Ero9SIUcikUVlJYyaVQyCVQyKWwtpJCLjPcNMruRAa7dvqen7s9rpsVjG/2XMD+hHzMHuszaHXR0MMgR0REFk8qkWD+BF98/esFnM+uRLh/z6debTuciQOJBZBJJFh37fABrJLIcvwal4fE9DKsmROCQM+unyeTSSUYFdT7feCMZW60D5IyyvHN3nQM93OCt6udsUsiM8VVK4mIiADMGOMFJ3sFvt+f3uPV8MqqG3EgsQAKKyn2J+ajrKqxx9fTaPXYcyoPNQ0tfS2ZaEjKLKzBpn3piApxxdzooTdiJREE3Lk4HNZWUnyw9Rw0Wp2xSyIzxSBHREQEwEouxYrpgcgsrMWp1NIeHbPtSBYEAXj8xkgIgoBtR7J6fL2fj2Xhq91p+Ne3idyQnOiShiYt3tuSBEelFf6wKHzIPkPmoFTgjkXhyCutw/f7M4xdDpkpBjkiIqJLpkZ4wtvVDj8czIBW1/W+ckUVDTh6tgizorwxzNsB14z1xpGkQhSW13d7ncLyeuw4no0gLxXyS+vx3x/O8LfyZPFEUcRnMSmoqGnGvcsjoLSRG7ukATUm2BVzxvpgd1wuzmZwI3HqPQY5IiKiSyQSAatmBqG4ogGHzxR22Xbr4UzIZAIWTw4AACya7A8ruRRbDmV2eZwoivhyVxrkMikeWjUKdywKR0pOFf63LRl6fe82OCYaKi7mV+OVL+MRl1KC1TODEOztYOySBsX1s4fBW22Hj38+j5p6TrOm3mGQIyIi+p3IYFcE+zhg6+FMNLd0PEqWV1KHk8nFmDvOFw52rStVqmytMC/aF7EpJcgpru30/MfPFeN8diWumzUMDkoFJkd44KY5ITiVVooNu1J7/Hwe0VBQVtWI97cm4eUNp1Ba1YjbF4ZhwUQ/Y5c1aKzkUty7dCQamrT4ZMd5fv9TrzDIERER/Y4gCLh+1jBU17dgV1xuh202H8qAtULa7gfOBRN8YauQ4ceDHT/zUteowTd7LyDIS4WZkV5tr1873heLJ/vjQGIBNh/i8zJkHvrzbGdDkxbf7U/HXz48gcQLZVg6JQCv3DsJM8Z4Ddnn4jrj46bEDbOH4czFchw4XWDscsiMMMgRERFdJcTHEVEhrog5kY3aq1aVzCysQcKFMsyf4NfuGR5bazkWTvLDmYvlSM+rbnfe7/dfRH2jFrfOD4Xkqh9WV80IwowxnvjpaDZ2x3YcIIlMRVxKCR7898Fef1b1ehH7EvLxzP+OYefxHEwId8P/3TMJK2cEDfhm3aZszjgfBPs44OejWd0+n0t0GYMcERFRB1bNHIamFh1+PpZ9xes/HsyA0kaOedG+HR43d5wvVHZW+PHgxSumSaXnVePg6QLMG+8DP3f7dscJgoBb5odi7HA1Nu65gGPnigx7Q0QGtC8hHwCwcc8FbDmU0aMpgQ1NGrz1wxls+CUVXi52+Nvt0bhryQg4q6wHulyTJwgCFk30R3lNM+JSSoxdDpkJBjkiIqIOeLvaYeooT+yNz0NZdev+cKk5lTiXWYFFk/xho+h49EBhJcXiyf5IyalCcnYlAECr0+PzX1LgrFJg+bTATq8plUhw77IRCPV1xCc/n0dabpXB74uov8qqG3E+uxJLpwZg2ihPbDuSha93X4C+izCXX1aPlz6Pw7nMCtxy7XA8uTYKAR5db/RtaUYHu8DTxRYxJ3L4rBz1CIMcERFRJ1ZMC4QgCNhyKBOiKGLzwQw4KK0we6x3l8fNivSGs0qBHw+0jlTsjs1Ffmk91s0d3u30MblMiodWj4azSoFPd5xHi4bbEpBpOZbUOlo8bZQn/rAoDPMn+GJPfB4++im5w2mBp1JL8fcv4tDYosOTa6Mwe6yPxT0H1xMSQcD8CX7IKalr+yUQUVcY5IiIiDrhrLLG3HE+OJZUhM37LyItrxpLJgdAIZd2eZxcJsGyqYHILKzBr6fysPVwJqJCXBE1XN2j69pay3DrgjAUVzZi+9EsA9wJkWGIoogjZ4sQ5ucIV0cbCIKAG2YHY9WMIBw/V4x3fjzb9ssH/aVffryz+Sy8XOzw/O3jEeLjaNwbMHGTR3rAwc4KMSdyjF0KmQEGOSIioi4smtw6jfLTn87BRWWNGWO8uj8IwNRRHnB3ssHGXy9AEASsnTu8V9cdGeCMaaM8sfN4TpfbGRANpgt51SipasTUUZ5trwmCgCVTAnDLtcNx5mI5/rXpNMqrm/Df789g+9EsTBvtiafXRcHJXmHEys2DXCbB3GgfnMus4Pc9dYtBjoiIqAt21nIsnuwPAFg2NQByWc/+6ZRKJFg5IwgAsHxaIFwcer+gww3XBENpK8enO1Kg03MlOzK+I2cLobCSIjrUrd17s8f64J5lI3ExvxpPvX8MSZkVuPna4fjDwjDIZV2PYtNvZkV5Q2ElxS8nOSpHXevzOq8FBQV44oknUF5ejsDAQLz++uuws7O7ok1+fj6WLFkCP7/WfXZcXV3x8ccf969iIiKiQXbtBF+MCFbD18WmV8dNCHeHj1oJTxfbPl1XaSPHunnD8d6WJOyKzcXCif59Og+RITS36BCbUoLxoW5QWHUczCaOcIettQw/Hc3C6pnDMNzXcXCLHALsrOWYOcYLe07lYdWMYX36JRBZhj6PyK1fvx5r165FTEwMIiIi8O6777Zrk5SUhKVLl2Lr1q3YunUrQxwREZklqUSC6HD3dnu/9YSXq12/FnaIDlUjKsQVWw5loriyoc/nIeqv+LRSNLXoMHWUR5ftRgW54JmbxzHE9cO8aF+IIrA7jntKUuf6FOQ0Gg1iY2Mxf/58AMCqVasQExPTrt3Zs2eRlpaG5cuX49Zbb0Vqamr/qiUiIrIwgiDg5mtDIZMK+CImlcuSk9EcPlsIVwdrhDCgDTgXB2tMGOGGA6cL0NCkMXY5ZKL6FOQqKyuhVCohk7XOzFSr1SguLm7XTqFQYNmyZdi8eTPuvPNOPPDAA2hpaelfxURERBbGyV6B62cF43x2JQ6dKTR2OWSByqobkZJdiamjPPs0Mk29t2CCH5pbdG2br3ekrlGDHw5cxJZDGSitahzE6sgUdPuM3M6dO/HKK69c8Zq/v3+7aSIdTRt56KGH2v48c+ZMvPHGG8jIyEBYWFiPinNxUfaoHfWcWm1v7BKGFPan4bFPDYv9aVjG7M/Vc0Nx6kIZvtuXjtkT/OGsGhrPzfAzalgD1Z97EwsgAlgyYxjULnbdth9KjPUZVavtETlcjb3x+Vi3aMQVC8bo9SL2xuXg05+SUdfQAhHAtiNZGB3sirkT/DB5lGe3e1YaC7/nDafb/8MLFy7EwoULr3hNo9Fg4sSJ0Ol0kEqlKC0thZtb+9WLNmzYgCVLlsDJyQlA694jl0fxeqK8vA56PaeQGIpabY/SUi5layjsT8NjnxoW+9OwTKE/180Nwd8+Pom3vonHAytHGbUWQzCFPh1KBqo/RVHErhPZCPNzhFSvt6j/Z8b+jM6J8sYbaaXYvj8d0y9tfZJTXIsvd6UhPb8awd4O+PMNY2BnLcfRpEIcPluIf30dDxuFFBPC3TFttCeCPFUmswG7sfvT3EgkQpcDW32K6nK5HNHR0dixYweWLl2KLVu2YMaMGe3axcbGoqmpCXfffTdOnjwJvV6PoKCgvlySiIjI4nk422L5tAD8cCADO49nY8FEP5P5AY2GrvT8apRUNmLplABjl2JxRgQ4wc9NiZiTORgbqsaWQ5nYG58HpY0cdywKx5RRHm1TXZdODcTiKQG4kFuFQ2cKcSypCAcSC3DteF/cNCfEyHdCA6HPY67PP/88nn76abz33nvw9PTEv/71LwDAxo0bUVJSgj/96U/461//iqeffhpbt26FQqHAG2+8AYmEW9cRERH11fwJfsgqqsV3+y8iu7gWf1gY3ulS8ESGcORsIRRyKcaFqo1disURBAHzJ/rhw+3JeOLdo2hu0WHWWG+smhEEO2t5u/YSQUConxNC/Zywbt5wfB6Tgr3x+VgyJQBKm/btybz1Och5e3tjw4YN7V5fs2ZN25/d3d3x6aef9vUSREREdBWZVII/rojAjuPZ+PFABgrKGvDgqgi4OfVtrzqirjRrdDh5vgTRoWqTfeZqqBsf5oYdx7KhsJLi5muHI8BD1aPjbBQyLJ0SgJPnS3DwdAEWTeI+lEMNh8eIiIjMjCAIWDw5AI/eMAaVtU148bM4nM0oN3ZZNAT9tnecp7FLsVgyqQQv3jkBz94a3eMQd5m3WokwP0fsi8/juhNDEIMcERGRmYoIcsFzt4+Hi4M1/rPpNH46msV95sigjlzaO264n6OxS7Fo/XkWds44X5TXNON0epkBKyJTwCBHRERkxtwcbfCXW8Zh4gh3/HgwA+9sTkJTi9bYZdEQUFHThPNZlZgS4cG948xYZIgLnFUK7InPM3YpZGAMckRERGZOIZfi7qUjsGZOCBIvlOHLXWnGLonMnF4v4otfUiEIAqdVmjmpRILZUd5IzqpEQVm9scshA2KQIyIiGgIEQcC88b5YNNkfR5OKcOYip1FR332//yLOXCzHunkhUDvaGLsc6qfpY7wgkwrYF59v7FLIgBjkiIiIhpClUwLg7WqHz2NS0dDEKZbUe4fPFCLmZA6uGeuN2WN9jF0OGYDK1goTwt1xOKkQjc38e2GoYJAjIiIaQuQyCe5YHI6qumZs2pdu7HLIzFzIq8LnMSkI93fiJtJDzJxxPmhu0eFoUpGxSyEDYZAjIiIaYgI9VVgwwQ8HTxfgXFaFscshM1FW1Yi3fzwLVwdr/HFlBGRS/pg4lAR6qhDoqcKeU3lc3XaI4HcoERHRELR8WiDcnW3x2Y4UrmJJ3Wps1uKtH85AqxPx8HWjYWctN3ZJNADmjvNBUUUDkrMrjV0KGQCDHBER0RBkJZfijkVhqKhpwvf7Lxq7HDJhelHEh9uTUVDWgPtXjISni52xS6IBEh3mBntbOfae4lYEQwGDHBER0RAV4uOIOdE+2Bufj9Scof8b+PT8amQV1Ri7DLPz44EMJKaX4aY5wYgIdDF2OTSA5DIJZkZ6ITG9DGVVjcYuh/qJQY6IiGgIWz1jGNSO1vh0RwqaNTpjlzNgiisa8Po3CfjH1wnI515ZPXYgMR87jmdjVqQX5ozjCpWWYFakNwQI2JfIrQjMHYMcERHREKawkuL2heEoqWrE5oMZxi5nQOj0enz0UzLkUgkUMgne/vEst17ogcNnCvFFTCoigpyxdt5wCIJg7JJoEDirrBE13BUHEwvQMoR/uWMJGOSIiIiGuHB/J8yK8sbu2FxczK82djkGt+N4Di4W1ODma0Nx/4oIlFY24uOfk6HnynydOpZUhE93nMeIACc8uHIUV6i0MHPG+qC+SYsT54uNXQr1A79riYiILMD1s4bB0V6Br39NG1IBJ7uoFtsOZ2JCuBsmjnBHqJ8TbrwmGAkXyvDz0Sxjl2eSTiQX46OfkxHq54gHV4+GlVxq7JJokIX6OcLTxRZHz3JPOXPGIEdERGQBbBQyrJoRhMzCWpxIHhq/hddodfjwp2TY28px87Whba/PjfbBpJHu2HIoE2culhuxQtMTm1KCD7cnI8THEX+6bgwUDHEWSRAERIWokZ5fjcZmTkM2VwxyREREFmJyhAf8Pezx/f6LQ2Lhkx8OZKCgrB53LAqH0ua3fc8EQcBtC8Lg66bE/7adQ3FlgxGrNB2nUkvxv23nEOStwiPXj4bCiiHOko0KcoZOL+I895QzWwxyREREFkIiCLjpmmBU1jZj18mcHh2jF0X8fCzL5Ea2UrIrsTs2F7PHeiMiqP2S+Qq5FA+sGgVBAN758SyaW8w/uPZH4oUyvL81CQEe9nj0+jGwtpIZuyQysmHeDrC2kiIps8LYpVAfMcgRERFZkFA/J4wbrsaO4zmorG3utv3mgxn44UAG/vPdaXy4/RzqGjWDUGXXGpu1+PjnZLg52eCGWcGdtlM72uDeZSORX1qPT3eehziEng3sqYqaJmw9nIl3t5yFn7sSj94QCRsFQxwBMqkE4f5OSMoot8jvjaGAQY6IiMjCXD97GLQ6PTYf6no7gmPnivDzsWxMH+2JZVMDcPJ8CZ798DjiUkq6PK5Fo8PRpEK88W0ijicbfjGFr39NQ0VtM+5aMqLb6YERQS5YNTMIJ8+XYHdcnsFr6YwoikZbVEan1yMhrRT/+e40nnjvKLYezsSIAGf8+cZI2FozxNFvIoJcUFbdhKIKTj82R/xuJiIisjBuTraYG+2DXSdzMWesD/w97Nu1Sc+vxqc7UhDm54hb5odCJpVg7HA1Pt2Rgne3JGFcqBo3XxsKBzurtmMKyuqxPzEfx5KKUN+khQCgrkGDSSM8DFZ7fFopjpwtwpIp/hjm7dCjYxZN8seFvGpsOZSBKREeVzxPZ2jVdc04fLYQB08XQCaV4IU/jIdcNjjPohWV12PLgYs4fLYQ1XUtcFBaYfFkf0wf7QW1o82g1EDmJSLQGQCQlFEBTxc7I1dDvcUgR0REZIGWTgnAkbNF+HbvBTyxJuqKzaDLqhvx9g9n4GyvwB9/t8eYn7s9nr1tHGJO5GDr4SykZB/HTXNCIJEIOJCQj7S8akglAsaFqjEz0htZRTX4bt9FlFc3wcXBut816/UiNv6aBj83JZZNDezxcYIgYPXMYXj+k5P4NS4XK6YH9buWK+oSRSRnVeBAYgESL5RBpxcR4GGPrKJa/BqXh4WT/A16vY7si8/Dhl1pEARgVJALZl7rhdHBLpBKOPmKOqd2tIGHsy2SMiswb7yvscuhXmKQIyIiskC21nIsnxaIr3anIfFCGaKGqwEATS1avPX9WWh0ejx53eh2o1dSiQSLJwdg7HA1PtlxHh//fB4A4OZog+tnDcPUUZ5QXRqlc7ZX4Lt9F5FwoRRzo/v/Q+KZi+Uor2nGjdeE9HoDa183JaJCXPFrXB6uHe9nkCmGNfUtOHSmAAcSC1BW3QSljRxzo30wY4wXPF3s8OZ3p7H9aBamjPK8YuTS0EoqG/Dt3nREhqhx87wQOKv6H5rJckQEOuPg6QK0aHTcU9DMMMgRERFZqJmRXtgbn4dN+9IxapgLJBIBH25PRn5ZHR65fgy8XDufauXpYodn1o3DqbRS2FrLEO7vBMnvRvUAwN3ZFl6udki4UGaQILc/MR8OSitEhrj26fhlUwOx/kIs9sTnYemUgD7XUVHThJgTOThwugAarR5hfo5YNTMI44a7QS77LWDecE0w/vbxSWw5lIHbFoT1+XpdEUURn+1MgVQq4E83RUHUcE8w6p2IIBf8eioPaXlViAhsvwIsmS4GOSIiIgslk0pw4zXB+M93Z7A3Ph819S1IuFCGNXNDMKqDJf2vJpEIGB/m1mWbqBBX7Dyeg7pGTb+eTSutasTZi+VYMiWg16Nxl/l72GP0MBfsOpmDueN8er16Y2lVI3Yez8bhs4XQ64HJEe5YONG/08Dr6WKH2VHe2BOfhzljfeDjpuxT3V05dKYQKTlVuHVBKFwdbVBaWmvwa9DQFurnCJlUgqSMCgY5M8OJ00RERBZsVJALRgY44YcDF7HjeDZmRXph7jgfg51/7HA19KKIMxfL+nWeA4kFgNA6itgfS6cGoL5Ji/0J+T0+prC8Hh//lIxnPjiOw2cLMW20F165dxLuXDyiy1FLAFg2LRC2Chm+2XvB4Eu8V9Y249u9FxDm54gZY/rXL2S5FHIpQv0cuZ+cGWKQIyIismCCIODGOSHQ6USE+Tli7bzhVyx80l/+HvZwslcgIa3vQU6r0+PQmQKMGeba7+e/hnk5YGSgM345mYNmTdebhIuiiE170/HshycQm1KCOeN88I/7puDW+aE9XgVSaSPHsmmBSM6qxGkDbqouiiI2/JIKnU7EbQvD2k1rJeqNiEBnFJTVo7y6ydilUC8wyBEREVk4H7USL901AY9cP6bP0xY7IxEERIa44mxmOVq6CU6dOZVaitoGDWaP9TZITUunBKCmQdM6yteFLYcyEXMyB9NGe+K1+6dgzdwQONkren292VHe8HC2xbd706HV6fta9hViU0qQmF6GFdOD4O5ka5BzkuWKuDSVOinTcL9soIHHIEdERETwdLEbsBXrokJc0aLRIzmrsk/H70/Ih6uDNUZe2vOqv4b7OiLMzxE7T2RDo+04XO6OzcX2o1mYNtoTty8Ma1uJsy9kUgluuCYYxRUN2Bff8ymdnaltaMFXu9MQ6GmPeeMNNw2WLJeXiy2cVQpOrzQzDHJEREQ0oML8nGCjkCL+Qmmvj80vq0dqbhVmRXkbdPrg0qmBqK5rwcHThe3eO5pUiI17LmDscDVuWxBqkKmmY4a5YESAE7YdyURdo6Zf59q45wIamrT4w6Jw7hNHBiEIAiICnZGcVWGwUWMaePzuJyIiogElk0owepgrTqeXQa/v3YIfBxLyIZMKmDba06A1hfk5ItjHATuOZ0Oj/e0H18QLZfjk5xSE+zvh3mUjDBaUBEHATdeEoKFZi62HM/t8ntPpZTh+rhiLJ/vDR234VTDJckUEuqCxWYeMghpjl0I9xCBHREREAy4qxBW1DRqk51f3+JjmFh2OJBUhOtQNKlvDbqgtCAKWTQ1AZW0zjiS1jsql5lTiva1J8HNX4sFVoyCXGXaqqY+bEjPHeGFffD4Ky+t7fXxjsxZf/JIKb1c7LOnHPnhEHRkR0LoXJJ+TMx8MckRERDTgRgW5QCYVkNCL6ZUnzhejsVmLWVGGWeTkaiMDnBHoqcKOY9nIKKjBWz+cgauDNR69YUyv95jrqRXTg6CwkuDrXy/0anRSL4r4dMd5VNU14w+Lwg2+KA2RrbUcw7xVSMrgc3Lmgn8LEBER0YCzUcgQ7u+MhLSyHu+ntj8hH96udgjxcRiQmi6PypVVN+GVL0/BRiHDYzdGwt7Ao3+/p7KzwqoZw3AuswJf7k7rcV9s2puOuNRSXD8rGEFeqgGrjyxbRKAzsopqUVPfYuxSqAcY5IiIiGhQRA13RUlVI/LLup9WmFlYg6yiWsyK8jbovnZXGz3MBYGe9rC1bg1x/d2nrifmjPPBwkl+2J+Qj+/3X+w2zO06mYNdsbmYO84H8yf4Dnh9ZLkub0NwLoujcuZgYOYNEBEREV0lMtgVXyAVCWml3S7UsT8hH1ZyCSaP9BjQmgRBwGM3RkIvtm7ePViumzkMTc067DyRAxuFrNNn3k6eL8Y3e9MxLlSNm+aEDGioJfL3sIfSRo6kjIoB/96j/uOIHBEREQ0KR6UCw7xUiL9Q1mW7hiYNTiQXY9IID9haD/zvnG2t5YMa4oDWALnu2uGYNNIdPx7MwJ5Tee3apOZU4qOfkhHs44C7l4yARMIQRwNLcmkbgnOZ5dD3cNovGQ+DHBEREQ2aqOFqZBfVoqKmqdM2R5OK0KLVY/YALXJiKiSCgDsWhSMqxBVf7U7DkbO/7WmXX1qH//5wFmpHGzy8evSAbdZOdLWIIGfUNGiQW1xn7FKoGwxyRERENGiiQlwBAAkdjMppdXrsOZWHrYczEeipgr+H/WCXN+hkUgnuWz4S4f5O+GTHeZxKLUVlbTP+/d1pyGUSPHr9mEEfLSTLNjKw9Tm5sxnchsDUMcgRERHRoPF0sYOniy3i037bhkAvijieXIS/fngcX+1Og6+bEncuDjdilYNLLpPiodWjEOSlwgfbkvDa1/Gob9LikevHwNXRxtjlkYVxsLOCv4c9jp0rgk6vN3Y51AUGOSIiIhpUUSFqpOZUoa6hBUkZ5Xjxs1j8b1syrK1kePSGMXhiTRS8XO2MXeagsraS4ZHrx8DTxQ5l1U14YEWERYxIkmlaMjkAheUNOJhYYOxSqAtctZKIiIgGVdRwV+w4no0//+cgCsvr4epgjbuXjsDEEe6QWPCqjHbWcjy9biyq6prh6WJZQZZMy9jhrgjzc8TmQ5mYMMIddtac3muKOCJHREREgyrQUwVXB2vUN2mwZm4IXr57EiaP9LDoEHeZjULGEEdGJwgCbpoTgvpGDbYfyTJ2OdSJPo/IFRQU4IknnkB5eTkCAwPx+uuvw87uyr94Wlpa8NprryEuLg4ajQbPPPMMpk2b1u+iiYiIyHxJBAF/u308PD1UqK1uNHY5RNQBP3d7TB/jhT2n8jAryhsezrbGLomu0ucRufXr12Pt2rWIiYlBREQE3n333XZtPvroI1RWVmLz5s34z3/+g2eeeQYi96QgIiKyeEobOayt+IQHkSlbOSMIcpkE3+65YOxSqAN9CnIajQaxsbGYP38+AGDVqlWIiYlp127nzp24++67IQgCQkJC8OmnnzLIERERERGZAQc7KyydGoDTF8uRlMntCExNn34VVllZCaVSCZms9XC1Wo3i4uJ27bKzsxEbG4sXX3wROp0Ojz76KIKDg3t8HRcXZV/Koy6o1VwBy5DYn4bHPjUs9qdhsT8Nj31qWOxPw7P0Pl2zIByHzhTi+wMZmDHOD1Jp/5bYsPT+NKRug9zOnTvxyiuvXPGav78/hKseSL76awDQ6XQoKirCV199hdTUVNx1113YuXMn7O179j+wvLwOej1H8AxFrbZHaWmtscsYMtifhsc+NSz2p2GxPw2PfWpY7E/DY5+2Wj1jGN7ZfBbf/5qKa8b69Pk87M/ekUiELge2ug1yCxcuxMKFC694TaPRYOLEidDpdJBKpSgtLYWbm1u7Y11dXbF48WIIgoCwsDB4eHggMzMTo0eP7sOtEBERERHRYLu8HcGWQ5mYyO0ITEafxkblcjmio6OxY8cOAMCWLVswY8aMdu1mz57d1iY3NxeFhYUIDAzsR7lERERERDSYfr8dwbbDWcYuhy7p8yTX559/Hps2bcKiRYsQFxeHRx55BACwceNGvPnmmwCAxx9/HCUlJVi8eDHuu+8+/P3vf+/xtEoiIiIiIjINl7cj2Bufh8LyemOXQ+jHPnLe3t7YsGFDu9fXrFnT9melUonXXnutr5cgIiIiIiITsWpGEE6eL8aPBzPwwMpRxi7H4vVv2RkiIiIiIrIIKjsrzBjjhcQLZWho0hi7HIvHIEdERERERD0yIdwdOr2IhAtlxi7F4jHIERERERFRjwR62sNFZY3YlBJjl2LxGOSIiIiIiKhHBEHA+HA3nMusQD2nVxoVgxwREREREfXY+DA36PQi4lNLe3zMlkMZePqdw0jJrjRYHQcS8/HxT8kQRdFg5zQnfV61koiIiIiILE+Ahz3Ujq3TK6eP8eq2fW1DC3aeyIFOL+JcRjkig11x3axh8HK163MN57Mq8MUvqRBFYHKEB0YEOPf5XOaKI3JERERERNRjgiBgfJg7krMqUdfY/fTK/Qn50Gj1eONPM7B6ZhBScyvxt49P4otfUlFd39Lr61fUNOH9befg4WwLe1s5fo3L68ttmD0GOSIiIiIi6pXxYW7QiyLi07qeXqnR6rEnPh8RQc4I9nHE4skBeOXeyZgd5Y1Dpwvw9AfHsO1IJppbdD26rkarx7tbktCi1ePBVaMwK9Ibp9PLUFzZYIjbMil6fddTRhnkiIiIiIioV/zclXBzskHs+eIu2508X4ya+hZcO9637TWVrRXWXTscL901EREBzthyKBPPfnQCF/Oru73uN3svIKOgBncuCoenix1mj/WGRCJgzxAbldOLIjbtS++yDYMcERERERH1Suv0Sjecz65CTUPH0yNFUcSu2Fx4u9phZAfPsHk42+KBVaPw1NooCALw6lfxiDmRA30ni5ccSyrCvvh8LJjgh+gwNwCAo1KBCeFuOHy2EI3NWsPdoBGJooivdqd1O9rJIEdERERERL3W3fTKlOxK5JbUYd54XwiC0Ol5Qv2c8MIfxiMy2BWb9qXjre/PoPaqcJhbUofPY1IQ6uuI1bOCrnhvbrQvmlp0OHymsP83ZQJ+PJiBffH5mDnGu8t2DHJERERERNRrvm5KuDvbIvZ8x5uD74rNhb2tHJNHund7LltrOf64MgI3XzscyVkVeOHTWKTmtG5V0NCkwTs/noWNtQz3LR8JqeTKCBPoqUKwtwN+PZXb7XNlpm7H8Wz8fCwbMyO9sGiyX5dtGeSIiIiIiKjXBEHAhDA3pORUtlt9srC8HqcvlmN2lDfkMmmPz3fNWB/89ZZoWMkkeG1jArYfycRHP51HeU0T/rgiAg5KRYfHzo32QWlVE05fLOv2OnWNmraQaEr2JeTj+/0XMSHcDbdcG9rlKCbAIEdERERERH00PtwNogjEp145KvdrXB5kUgGzx/r0+pz+Hvb42+3jMTHcHZsPZSIxvQw3XBOMEB/HTo8ZF6qGs0rR7VYE9U0a/OPrePzj6wR8v/9ip8/jDbbj54rw5S+pGDPMBXctGQGJpOsQBzDIERERERFRH3m72sHTxRaxKb8FubpGDY6cLcSkkR5wsLPq03ltFDLcvXQE7loSjlUzgjB3XNeBUCqR4JqxPjifXYm8kroO2zRrdHjz+zMormhAVIgrdhzPxkfbk6HR6vtUo6EkXCjFRz+dR6ifI+5fEQGZtGcRjUGOiIiIiIj65PLqlak5VaiuawYAHEjMR4tWj2ujfbs5uvtzT4nwxJIpAd1OMwSAGWO8YCWTYHdcbrv3tDo93t+ShIt51bhn6Ug8uGoUVs8MwvHkYvx7UyIamrrf2NzQ9KKI/Yn5eG/LOfh7KPHQ6tGwkvdsGirAIEdERERERP0wPswNIoC41FJodXrsOZWHkQFO8HFTDmodShs5pkR44Hhy8RWrXoqiiM93puD0xXLcPD8U0WFuEAQBiycH4O4lI3AhrxqvfBmP8uqmQas1r6QOr34Zjy9iUhHsrcKjN0TCRiHr1TkY5IiIiIiIqM+81Up4u9ohNqUEsSklqKprwbzxXa+4OFDmRPtCo9XjQGJB22vf7b+II0lFWDEtELOjrlzSf3KEB/58wxhU1Dbh5Q1xyCmuHdD6mjU6fLc/Hes/i0VRRQPuXByOJ9ZEQWkj7/W5GOSIiIiIiKhfxoe54UJuFbYdzoSniy0igtpvAD4YWjcfd8Le+DxodXrEnMhBzIkcXDPWG0unBnR4THiAM55ZNw6CIODVr+JxLrNiQGo7c7Ecz310AjuP52ByhAf+755JmDrKs0fTRjvCIEdERERERP0yPrx1emVxZSPmjfeFpI/hxBDmRvuiqq4F/9uejE370jE+zA1r5w7vMjD5uCnx11vGwdXBBm9+fxqF5fUGq6euUYP3tiThP9+dhlwmwVNro3DHovA+jcL9HoMcERERERH1i6eLHXzUytbn1EZ6GLWWUcNc4O5kg7iUEowIcOrxcv7OKms8dlMkrGRSfLkrDaIBtiaoqW/Ba1/HI+FCKVZOD8QLf5iAUD+nfp8XAHr3RB0REREREVEH7lk6As1aXa9WXhwIEkHAjXNCcPxcEW5bEAa5rOdjVw52Vlg9MwgbdqXheHIxJvcjlFbVNeP1bxJRVtWIP10/BiMDDDvdlEGOiIiIiIj6bbBXqexKZLArIoNd+3TszEhvHD5bhG/3XMCYYS6wte79FMjK2ma8tjEBVbXNePSGMQYbhfs9Tq0kIiIiIiK6RCIRcOv8UNQ2avDDwYxeH19e3YR/fBWP6rpm/PnGgQlxAIMcERERERHRFfw97DFnrA/2x+cjo6Cmx8eVVjXiH1/Ho7ZRg8dujESIj+OA1cggR0REREREdJWVM4KgUlphwy+p0Ou7X/ikuLIBr34Vj8ZmLR6/KRLDvB0GtD4GOSIiIiIioqvYKGRYMycE2cW12Buf12XbtNwqvPpVPDRaPZ5YE4VAT9WA18fFToiIiIiIiDowPswNh84U4seDGRgX6gYne8UV7xeW1+P7/ReRcKEMTvYKPLY2Ej7qwVn0hSNyREREREREHRAEATdfOxxanYhv915oe726rhlfxKTguY9O4nx2JVbOCML/3T1p0EIcwBE5IiIiIiKiTrk72WLJZH9sOZyJ8WElyC2pwy8nc6HV6TF7rDeWTg2AytZq0OtikCMiIiIiIurCwkn+OHauCO9sTgIARIe5YfXMILg72RqtJgY5IiIiIiKiLshlEty1ZAR2x+Vi3nhfDPMa2BUpe4JBjoiIiIiIqBvDvB0GfEuB3uBiJ0RERERERGaGQY6IiIiIiMjMMMgRERERERGZGQY5IiIiIiIiM8MgR0REREREZGYY5IiIiIiIiMwMgxwREREREZGZYZAjIiIiIiIyMwxyREREREREZkbW1wMLCgrwxBNPoLy8HIGBgXj99ddhZ2d3RZv77rsPhYWFAAC9Xo+0tDR8//33GDVqVP+qJiIiIiIismB9HpFbv3491q5di5iYGERERODdd99t1+b999/H1q1bsXXrVsydOxc33HADQxwREREREVE/9SnIaTQaxMbGYv78+QCAVatWISYmptP2GRkZ2LJlC5566qm+VUlERERERERt+hTkKisroVQqIZO1zsxUq9UoLi7utP27776LO++8E0qlsm9VEhERERERURtBFEWxqwY7d+7EK6+8csVr/v7+yMnJwYEDBwAAWq0WUVFROHv2bLvjq6ursWDBAuzfvx8KhcKApRMREREREVmmbhc7WbhwIRYuXHjFaxqNBhMnToROp4NUKkVpaSnc3Nw6PP7AgQOYMWNGn0JcZWU99Poucyb1gouLEuXldcYuY8hgfxoe+9Sw2J+Gxf40PPapYbE/DY99aljsz96RSAQ4Odl1+n6fVq2Uy+WIjo7Gjh07sHTpUmzZsgUzZszosG1iYiKio6P7cpkuC6e+cXHh9FZDYn8aHvvUsNifhsX+NDz2qWGxPw2PfWpY7E/D6fOqlc8//zw2bdqERYsWIS4uDo888ggAYOPGjXjzzTfb2uXm5sLd3b3fhRIREREREVGrbp+RIyIiIiIiItPS5xE5IiIiIiIiMg4GOSIiIiIiIjPDIEdERERERGRmGOSIiIiIiIjMDIMcERERERGRmWGQIyIiIiIiMjMMckRERERERGZm0IPc22+/jcWLF2Px4sV47bXXAABHjx7F0qVLce211+Lf//53u2OefPJJ/Pjjj21fFxQUYN26dViwYAHuv/9+1NfXD1r9psYQ/QkA3333HZ5++ulBqdmUGaI/T506heuuuw7Lly/Hbbfdhvz8/EGr3xQZok/j4uKwatUqLF26FPfddx+qq6sHrX5TY6jveQBITk5GRETEgNds6gzRp5s3b8a0adOwfPlyLF++vMNjLIUh+rOkpAT33HMPVqxYgZtuugl5eXmDVr+p6W9/lpeXt30uly9fjmuuuQZRUVGDeg+mxhCf0by8PKxbtw7Lly/HLbfcYtH/1huiP8+cOYPVq1dj6dKluPfee1FaWjpo9Zs1cRAdOXJEvPHGG8Xm5maxpaVFvPXWW8Xt27eLM2fOFHNyckSNRiPecccd4v79+0VRFMWioiLx3nvvFUePHi3+8MMPbee55557xJ9++kkURVF8++23xddee20wb8NkGKI/m5qaxH/+859iZGSk+NRTTxnzdozOUJ/P2bNni+fPnxdFURS/++478b777jPK/ZgCQ/Xp3LlzxQsXLoiiKIr//Oc/xTfeeMMo92NshupPURTFhoYG8aabbhKHDx9ujFsxGYbq0xdffFHcvn27sW7DZBiqP2+77Tbx66+/FkVRFL/++mvxT3/6kzFux+gM+T0viqKo0+nEm2++Wdy2bdtg34rJMFSfPv744+JXX30liqIofvHFF+Jjjz1mlPsxNkP0p16vF2fOnCkeO3ZMFEVR/Pnnn8V7773XaPdkTgZ1RE6tVuPpp5+GlZUV5HI5hg0bhqysLPj7+8PX1xcymQxLly5FTEwMAGD79u2YM2cOFi5c2HYOjUaD2NhYzJ8/HwCwatWqtvaWxhD9GRsbC71ejyeeeMJYt2EyDNGfLS0t+NOf/oSwsDAAQGhoKAoLC41yP6bAEH0KADt27EBwcDA0Gg2Ki4uhUqmMcTtGZ6j+BIBXX30Vt91222DfgskxVJ+ePXsWmzdvxtKlS/H4449b7KixIfqzoqICKSkpuOmmmwAAq1evxiOPPGKM2zE6Q37PA8APP/wAGxsbLF26dDBvw6QYqk/1ej3q6uoAAI2NjbC2th70ezEFhujPyspKNDU1YdKkSQCA2bNn4/Dhw2hpaTHKPZmTQQ1yISEhiIyMBABkZWVh586dEAQBarW6rY2bmxuKi4sBAHfddReuv/76K85RWVkJpVIJmUwGoPUDdLm9pTFEf06bNg1PPvmkxf4F9HuG6E8rKyssX74cQOtf8m+//Tbmzp07ODdgggzRpwAgl8uRmpqKmTNn4sSJE1i8ePGg1G9qDNWfe/bsQVNTExYsWDAodZsyQ/WpWq3GH//4R2zbtg2enp548cUXB6V+U2OI/szNzYWXlxdeffVVrF69Gg8//DDkcvmg3YMpMdTnEwB0Oh3ef/99PPbYYwNetykzVJ/+6U9/wmeffYbp06fjk08+wd133z0o9ZsaQ/Snk5MTbG1tcfjwYQDAzz//DI1Gg8rKysG5CTNmlMVOLly4gDvuuANPPvkkfH19IQhC23uiKF7x9dU6er+r9pagP/1J7RmiP1taWvD4449Dq9Xi3nvvHchyzYIh+jQ0NBRHjx7FH//4Rzz66KMDWa7J609/lpaW4r333sNzzz03GKWajf5+Rt955x2MGzcOgiDgrrvuwqFDhwa6ZJPWn/7UarVITk7GpEmT8MMPP2DOnDkW/wy3If4OPXToEAICAhAaGjqQpZqN/vbpU089hRdffBGHDh3C+vXr8eCDD0IUxYEu22T1pz8FQcBbb72FDz74ACtWrEBtbS0cHR0t9hc4vTHoQe7UqVO4/fbb8dhjj2HlypXw8PC44oHG0tJSuLm5dXq8s7MzamtrodPpetR+qOtvf9KVDNGf9fX1uOuuu6DVavHee+9Z/F9E/e3T5uZm/Prrr21fL1u2DKmpqQNasynrb3/u378fVVVVbQ/pA8Dy5cvbpghZov72aW1tLT777LO2r0VRhFQqHciSTVp/+1OtVsPOzg6zZ88GACxZsgRnzpwZ8LpNlaH+nf/111+xaNGigSzVbPS3TysqKpCRkdE242b+/PkoLS212BEkQ3xGZTIZNmzYgC1btmDZsmXQ6/VwdHQc4MrN36AGucLCQjzwwAN4/fXX26ZGjRkzBpmZmcjOzoZOp8NPP/2EGTNmdHoOuVyO6Oho7NixAwCwZcuWLtsPZYboT/qNofrziSeegL+/P/7zn//AyspqMEo3WYboU5lMhvXr1yMpKQkAsHPnTowdO3ZQ6jc1hujP66+/Hr/++iu2bt2KrVu3AgC2bt0KpVI5KPdgagzRp7a2tvjoo49w+vRpAMCXX36JefPmDUr9psYQ/enn5wcPDw8cOHAAALBv3z6MHDlyUOo3NYb8dz4xMRHR0dEDXbLJM0SfOjk5QaFQIC4uDkBrkLGzs4Ozs/Og3IMpMdRn9C9/+UvbL2w+/fRTLFiwABIJd0nrjmwwL/bxxx+jubkZr776attrN910E1599VU89NBDaG5uxsyZM7t9buP555/H008/jffeew+enp7417/+NdClmyRD9Se1MkR/JicnY8+ePQgODsbKlSsBtM4N//DDDwe8flNkiD6VSqX497//jb/97W/Q6XRwd3fHyy+/PBjlmxx+zxueoT6j//nPf/DCCy+gqakJAQEBbUtwWxpDfUb/+9//4vnnn8c///lPKJXKK85nSQz5PZ+bmwsPD4+BLNcsGKJPBUHA22+/jZdeeglNTU2ws7PDf//738Eo3+QY6jP6wgsv4Pnnn0djYyNCQ0Mt9t/53hJES57QS0REREREZIY4ZklERERERGRmGOSIiIiIiIjMDIMcERERERGRmWGQIyIiIiIiMjMMckRERERERGaGQY6IiExaXl4eQkNDcfPNN7d77+mnn0ZoaCgqKir6dO633367bcP5p59+Gh9//HG7NjExMRg/fjyKioqueP21117DzTffDJ1O16drdyQlJQXTpk0z2PmIiGjoYpAjIiKTp1AokJmZifz8/LbXGhoaEB8f36/znjhxAlqttss2CxYswLx58/DMM8/g8o49cXFx2Lp1K9544w1IpdJ+1QAAWq0Wn332Ge68807U19f3+3xERDT0DeqG4ERERH0hlUqxcOFCbN++Hffddx8AYNeuXZgzZw4++eQTAMC3336LDRs2QCKRwNXVFc899xwCAwPx9NNPQ6lUIjU1FUVFRQgNDcU//vEPbNmyBUlJSXjttdfawlhCQgJuuukmlJWVISQkBG+88QZsbW3x7LPPYuXKlfj666+xYsUKPP3003jllVfg7u6O+Ph4vP7662hsbIREIsGDDz6I2bNno6GhAS+88AKys7NRVVUFOzs7vP766wgKCsItt9wCBwcHZGRkYM2aNRgzZgxSU1Px9ttv44477jBaPxMRkfngiBwREZmFFStWYOvWrW1fb9myBStXrgQAHD9+HB999BG++OILbNu2DUuWLMEDDzzQNoKWlJT0/+3cTyhsYRzG8WfuSCjT6UhKJmkyGydRymosRP6EibIxiWzt1WxkI3UWZzELZSELUf6lLE4zkgVWbCyU2E2hTlazsJCYu9A9xb3XLel2T/f7Wb79Ou/bu3vO731fLS8vy3Vd3d7eKpvNKpVKybIszczMqLu7W5LkeZ5WVlaUy+XkeZ729/clSRUVFXIcR5lMRul0Wn19fero6FChUFA6nZZt29rd3dXi4qLm5uZ0d3eno6MjRSIRbWxsKJfLybIsra2t+euPRCJyXVfj4+Nqbm7WwsKCqqur/9Z2AgACjo4cACAQLMtSOBzWxcWFqqqq9PDwoHg8Lkk6Pj5Wf3+/TNOUJI2MjGh+fl43NzeSpEQiodLSUklSPB5XoVD45RxdXV0qLy+XJDU2Nr65e9fU1KSxsTEdHBzIcRxJ0vn5ue7v7zU9Pe3XhUIhXV1dqbe3V9FoVKurq8rn8zo9PVVra6tf19bW9lVbAwD4DxHkAACBMTQ0pL29PZmmqWQy6Y+HQqGfaovFon//rays7E3tj07deyUlJR/WRaNR1dbW+nXPz8+KxWLa2tryazzPk2maWl9f1+bmplKplAYHB2UYhh8spdcuHwAAn8XRSgBAYCSTSWWzWbmuq4GBAX88kUjIdV2/g7azsyPDMFRfX//h98Lh8B8fO/lIS0uL8vm8zs7OJEmXl5fq6emR53k6OTnR8PCwRkdH1dDQoMPDwy994RIA8H+jIwcACIyamhrFYjFVVlbKMAx/vL29XZOTk5qYmNDLy4tM09TS0pK+ffv4f2VnZ6ccx9HT09On1mOapjKZjGzb1uPjo4rFomzbVl1dnaampjQ7O6vt7W1Jr6Hv+vr6U/MAAPBeqPi78yUAAAAAgH8SRysBAAAAIGAIcgAAAAAQMAQ5AAAAAAgYghwAAAAABAxBDgAAAAAChiAHAAAAAAFDkAMAAACAgCHIAQAAAEDAfAcOk/ErYQpxZQAAAABJRU5ErkJggg==\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": 67,
   "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": 68,
   "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": 69,
   "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": 70,
   "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": 71,
   "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": 72,
   "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\\pls_ew.csv')\n",
    "long_short_dax_vw.to_csv(r'C:\\Users\\krist\\Documents\\Data\\cumulative_log_returns\\pls_vw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}