4.5_GBRT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.preprocessing\n",
    "import datetime\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.metrics import mean_squared_error\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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>eom</th>\n",
       "      <th>prc</th>\n",
       "      <th>me</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_exc</th>\n",
       "      <th>ret_exc_lead1m</th>\n",
       "      <th>ret_6_1</th>\n",
       "      <th>ret_12_1</th>\n",
       "      <th>tax_gr1a</th>\n",
       "      <th>...</th>\n",
       "      <th>cowc_gr1a</th>\n",
       "      <th>pi_nix</th>\n",
       "      <th>ret_6_0</th>\n",
       "      <th>ret_1_0</th>\n",
       "      <th>noa_at</th>\n",
       "      <th>cop_atl1</th>\n",
       "      <th>prc_highprc_252d</th>\n",
       "      <th>ocf_at</th>\n",
       "      <th>dbnetis_at</th>\n",
       "      <th>netdebt_me</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>comp_001166_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>11.486148</td>\n",
       "      <td>567.074646</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>-0.019948</td>\n",
       "      <td>-0.089504</td>\n",
       "      <td>-0.229560</td>\n",
       "      <td>-0.353251</td>\n",
       "      <td>-0.020556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041844</td>\n",
       "      <td>3.815513</td>\n",
       "      <td>-0.244158</td>\n",
       "      <td>-0.018948</td>\n",
       "      <td>0.692706</td>\n",
       "      <td>0.093015</td>\n",
       "      <td>0.398130</td>\n",
       "      <td>-0.007700</td>\n",
       "      <td>0.085202</td>\n",
       "      <td>0.135368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comp_001661_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>35.853958</td>\n",
       "      <td>5221.931641</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.004815</td>\n",
       "      <td>0.108428</td>\n",
       "      <td>0.129149</td>\n",
       "      <td>-0.035109</td>\n",
       "      <td>-0.033306</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.019596</td>\n",
       "      <td>1.284930</td>\n",
       "      <td>0.135715</td>\n",
       "      <td>0.005815</td>\n",
       "      <td>0.751569</td>\n",
       "      <td>0.164124</td>\n",
       "      <td>0.918059</td>\n",
       "      <td>0.096164</td>\n",
       "      <td>0.063714</td>\n",
       "      <td>0.200036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>comp_004367_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>36.068653</td>\n",
       "      <td>4139.599121</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>-0.104801</td>\n",
       "      <td>0.127227</td>\n",
       "      <td>-0.020286</td>\n",
       "      <td>0.087151</td>\n",
       "      <td>-0.037240</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026114</td>\n",
       "      <td>1.725004</td>\n",
       "      <td>-0.121981</td>\n",
       "      <td>-0.103801</td>\n",
       "      <td>0.961141</td>\n",
       "      <td>0.240563</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.070724</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.410184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>comp_004925_02W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>29.735168</td>\n",
       "      <td>15302.482422</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>-0.071425</td>\n",
       "      <td>0.064832</td>\n",
       "      <td>0.046064</td>\n",
       "      <td>0.023265</td>\n",
       "      <td>-0.007344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000724</td>\n",
       "      <td>1.961724</td>\n",
       "      <td>-0.027605</td>\n",
       "      <td>-0.070425</td>\n",
       "      <td>0.466852</td>\n",
       "      <td>0.221983</td>\n",
       "      <td>0.861333</td>\n",
       "      <td>0.084234</td>\n",
       "      <td>-0.008142</td>\n",
       "      <td>-0.075232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>comp_005959_01W</td>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>39.181721</td>\n",
       "      <td>6355.691406</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>-0.086813</td>\n",
       "      <td>0.001832</td>\n",
       "      <td>0.123795</td>\n",
       "      <td>0.012811</td>\n",
       "      <td>-0.012241</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014214</td>\n",
       "      <td>1.069862</td>\n",
       "      <td>0.027359</td>\n",
       "      <td>-0.085813</td>\n",
       "      <td>0.635742</td>\n",
       "      <td>0.108581</td>\n",
       "      <td>0.721256</td>\n",
       "      <td>0.069331</td>\n",
       "      <td>-0.038594</td>\n",
       "      <td>0.500641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id        eom        prc            me       ret   ret_exc  \\\n",
       "0  comp_001166_02W 2003-01-31  11.486148    567.074646 -0.018948 -0.019948   \n",
       "1  comp_001661_01W 2003-01-31  35.853958   5221.931641  0.005815  0.004815   \n",
       "2  comp_004367_02W 2003-01-31  36.068653   4139.599121 -0.103801 -0.104801   \n",
       "3  comp_004925_02W 2003-01-31  29.735168  15302.482422 -0.070425 -0.071425   \n",
       "4  comp_005959_01W 2003-01-31  39.181721   6355.691406 -0.085813 -0.086813   \n",
       "\n",
       "   ret_exc_lead1m   ret_6_1  ret_12_1  tax_gr1a  ...  cowc_gr1a    pi_nix  \\\n",
       "0       -0.089504 -0.229560 -0.353251 -0.020556  ...   0.041844  3.815513   \n",
       "1        0.108428  0.129149 -0.035109 -0.033306  ...  -0.019596  1.284930   \n",
       "2        0.127227 -0.020286  0.087151 -0.037240  ...   0.026114  1.725004   \n",
       "3        0.064832  0.046064  0.023265 -0.007344  ...  -0.000724  1.961724   \n",
       "4        0.001832  0.123795  0.012811 -0.012241  ...  -0.014214  1.069862   \n",
       "\n",
       "    ret_6_0   ret_1_0    noa_at  cop_atl1  prc_highprc_252d    ocf_at  \\\n",
       "0 -0.244158 -0.018948  0.692706  0.093015          0.398130 -0.007700   \n",
       "1  0.135715  0.005815  0.751569  0.164124          0.918059  0.096164   \n",
       "2 -0.121981 -0.103801  0.961141  0.240563          0.675468  0.070724   \n",
       "3 -0.027605 -0.070425  0.466852  0.221983          0.861333  0.084234   \n",
       "4  0.027359 -0.085813  0.635742  0.108581          0.721256  0.069331   \n",
       "\n",
       "   dbnetis_at  netdebt_me  \n",
       "0    0.085202    0.135368  \n",
       "1    0.063714    0.200036  \n",
       "2   -0.000607    0.410184  \n",
       "3   -0.008142   -0.075232  \n",
       "4   -0.038594    0.500641  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the cleansed and winsorised data\n",
    "df = pd.read_csv(r\"C:\\Users\\krist\\Documents\\Data\\ger_factor_data_from2003.csv\", dtype ={\"comp_tpci\": str}, parse_dates =[\"eom\"])\n",
    "#Convert to float 32 (format needed for the most ML models)\n",
    "df[df.columns[2:]] = df[df.columns[2:]].astype('float32')\n",
    "#Sort observations by date and stock id\n",
    "df = df.sort_values(by = ['eom', 'id'], ascending = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy date variable and stock id to use them as multindex\n",
    "df[\"id2\"] = df[\"id\"].copy()\n",
    "df[\"eom2\"] = df[\"eom\"].copy()\n",
    "df = df.set_index(['eom2','id2'])\n",
    "\n",
    "#Make a copy of  the \"me\" variable (market equity) before rank standartization to use afterwards for value weighting\n",
    "df[\"me2\"] = df[\"me\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate the potential heterogeneity in model predictability, a subgroup analysis for small (the bottom 30% stocks by market equity each month) and large (the top 30% stocks each month) stocks is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "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": [
    "# Gradient boosted regression trees\n",
    "\n",
    "### Tuning parameters:\n",
    "\n",
    "- shrinkage/learning weight **𝜈**: **learning_rate**\n",
    "- debth of the simple trees **𝐿**: **max_depth**\n",
    "- the number of trees **𝐵** added to the ensemble model: **n_estimators** \n",
    "\n",
    "## All firms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define huber loss function to minimize in the validation step\n",
    "def huber_loss_error(y_true, y_pred, delta=1.35):\n",
    "    res = []\n",
    "    for i in zip(y_true, y_pred):\n",
    "        if abs(i[0]-i[1])<=delta:\n",
    "            res.append(0.5*((i[0]-i[1])**2))\n",
    "        else:\n",
    "            res.append(delta*((abs(i[0]-i[1]) )-0.5*(delta**2)))\n",
    "    return np.sum(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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 gradient boosted regression tree:  0.010896097303274899\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",
    "# Model’s complexity: dictionary to save the number of predictors randomly considered as potential split variables over time\n",
    "dic_max_depth_all = {}\n",
    "\n",
    "# Grid of prespecified values for each hyperparameter  \n",
    "param_grid = {'max_depth': [1,2,3,4,5,6,7,8], \n",
    "              'n_estimators': [100], \n",
    "              \"learning_rate\": [0.01,0.1], \n",
    "              \"max_features\": [\"sqrt\"],\n",
    "               \"min_samples_split\" : [5000,8000,10000],\n",
    "               \"min_samples_leaf\": [50,100,200]}\n",
    "#convert grid to list containing all posible hyperparameter combinations to iterate over\n",
    "grid = list(ParameterGrid(param_grid))\n",
    "# Empty container to save the objective loss function (huber loss error) for each hyperparameter combination\n",
    "huber_loss = np.full((len(grid),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "    #Loop over the list containing all pyperparameter combinations, fit on the training sample and use \n",
    "    #validation set to generate predictions\n",
    "    for i in range(len(grid)):\n",
    "        GBR_val = GradientBoostingRegressor(loss='huber',\n",
    "                                            max_depth=grid[i][\"max_depth\"], \n",
    "                                            learning_rate=grid[i][\"learning_rate\"],\n",
    "                                            n_estimators = grid[i][\"n_estimators\"],\n",
    "                                            max_features= grid[i][\"max_features\"],\n",
    "                                            min_samples_split= grid[i][\"min_samples_split\"],\n",
    "                                            min_samples_leaf= grid[i][\"min_samples_leaf\"])\n",
    "    \n",
    "        GBR_val.fit(X_train, y_train)\n",
    "        Yval_predict=GBR_val.predict(X_val)\n",
    "        #calculate huber loss for each hyperparameter combination\n",
    "        #set delta to 1.35 following Huber (2004)\n",
    "        huber_loss[i,0]= huber_loss_error(y_val,Yval_predict, delta=1.35)\n",
    "\n",
    "\n",
    "    #The optimal combination of hyperparameters is the one that causes the lowest loss \n",
    "    optim_param = grid[np.argmin(huber_loss)]\n",
    "\n",
    "    \n",
    "    #Fit again using the train and validation set and the optimal value for each hyperparameter \n",
    "    GBR = GradientBoostingRegressor(loss='huber',\n",
    "                                    max_depth=optim_param[\"max_depth\"], \n",
    "                                    learning_rate=optim_param[\"learning_rate\"],\n",
    "                                    n_estimators = optim_param[\"n_estimators\"],\n",
    "                                    max_features= optim_param[\"max_features\"],\n",
    "                                    min_samples_split= optim_param[\"min_samples_split\"],\n",
    "                                    min_samples_leaf= optim_param[\"min_samples_leaf\"])\n",
    "\n",
    "    GBR.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds=GBR.predict(X_test)\n",
    "    \n",
    "    #Save predictions, dates and the true values of the dependent variable to list\n",
    "    predictions.append(preds)\n",
    "    dates.append(y_test.index)\n",
    "    y_test_list.append(y_test)\n",
    "\n",
    "    #Calculate OOS model performance the for current window\n",
    "    r2 = 1-sum(pow(y_test-preds,2))/sum(pow(y_test,2))\n",
    "    #Save OOS model performance and the respective month to dictionary\n",
    "    dic_r2_all[\"r2.\" + str(y_test.index)] = r2\n",
    "    # Save the number of predictors randomly considered as potential split variables\n",
    "    dic_max_depth_all[\"feat.\" + str(y_test.index)]= optim_param[\"max_depth\"]\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_GBR = 1-sum(pow(y_test_list_all-predictions_all,2))/sum(pow(y_test_list_all,2))\n",
    "print(\"R2OOS gradient boosted regression tree: \", R2OOS_GBR)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define huber loss function to minimize in the validation step\n",
    "def huber_loss_error(y_true, y_pred, delta=1.35):\n",
    "    res = []\n",
    "    for i in zip(y_true, y_pred):\n",
    "        if abs(i[0]-i[1])<=delta:\n",
    "            res.append(0.5*((i[0]-i[1])**2))\n",
    "        else:\n",
    "            res.append(delta*((abs(i[0]-i[1]) )-0.5*(delta**2)))\n",
    "    return np.sum(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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 gradient boosted regression tree Big only:  0.005249387813597628\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\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",
    "\n",
    "predictions_top = []\n",
    "y_test_list_top =[]\n",
    "dates_top = []\n",
    "dic_r2_all_top = {}\n",
    "\n",
    "param_grid = {'max_depth': [1,2,3,4,5,6,7,8], \n",
    "              'n_estimators': [100], \n",
    "              \"learning_rate\": [0.01,0.1], \n",
    "              \"max_features\": [\"sqrt\"],\n",
    "               \"min_samples_split\" : [5000,8000,10000],\n",
    "               \"min_samples_leaf\": [50,100,200]}\n",
    "\n",
    "grid = list(ParameterGrid(param_grid))\n",
    "huber_loss = np.full((len(grid),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "\n",
    "    for i in range(len(grid)):\n",
    "        GBR_val = GradientBoostingRegressor(loss='huber',\n",
    "                                            max_depth=grid[i][\"max_depth\"], \n",
    "                                            learning_rate=grid[i][\"learning_rate\"],\n",
    "                                            n_estimators = grid[i][\"n_estimators\"],\n",
    "                                            max_features= grid[i][\"max_features\"],\n",
    "                                            min_samples_split= grid[i][\"min_samples_split\"],\n",
    "                                            min_samples_leaf= grid[i][\"min_samples_leaf\"])\n",
    "    \n",
    "        GBR_val.fit(X_train, y_train)\n",
    "        Yval_predict=GBR_val.predict(X_val)\n",
    "        huber_loss[i,0]= huber_loss_error(y_val,Yval_predict, delta=1.35)\n",
    "\n",
    "\n",
    "    optim_param = grid[np.argmin(huber_loss)]\n",
    "\n",
    "\n",
    "    GBR = GradientBoostingRegressor(loss='huber',\n",
    "                                    max_depth=optim_param[\"max_depth\"], \n",
    "                                    learning_rate=optim_param[\"learning_rate\"],\n",
    "                                    n_estimators = optim_param[\"n_estimators\"],\n",
    "                                    max_features= optim_param[\"max_features\"],\n",
    "                                    min_samples_split= optim_param[\"min_samples_split\"],\n",
    "                                    min_samples_leaf= optim_param[\"min_samples_leaf\"])\n",
    "\n",
    "    GBR.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds=GBR.predict(X_test)\n",
    "    \n",
    "\n",
    "    predictions_top.append(preds)\n",
    "    dates_top.append(y_test.index)\n",
    "    y_test_list_top.append(y_test)\n",
    "\n",
    "\n",
    "\n",
    "predictions_all_top= np.concatenate(predictions_top, axis=0)\n",
    "y_test_list_all_top= np.concatenate(y_test_list_top, axis=0) \n",
    "dates_all_top= np.concatenate(dates_top, axis=0)\n",
    "\n",
    "#Calculate OOS model performance over the entire test period in line with Gu et al (2020)\n",
    "R2OOS_GBR_top = 1-sum(pow(y_test_list_all_top-predictions_all_top,2))/sum(pow(y_test_list_all_top,2))\n",
    "print(\"R2OOS gradient boosted regression tree Big only: \", R2OOS_GBR_top)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 gradient boosted regression tree Small only:  0.005550206799517898\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tscv = TimeBasedCV(train_period=60,\n",
    "                   val_period=24,\n",
    "                   test_period=12,\n",
    "                   freq='months')\n",
    "\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",
    "\n",
    "predictions_bottom = []\n",
    "y_test_list_bottom =[]\n",
    "dates_bottom = []\n",
    "dic_r2_all_bottom = {}\n",
    "\n",
    "param_grid = {'max_depth': [1,2,3,4,5,6,7,8], \n",
    "              'n_estimators': [100], \n",
    "              \"learning_rate\": [0.01, 0.1], \n",
    "              \"max_features\": [\"sqrt\"],\n",
    "               \"min_samples_split\" : [5000,8000,10000],\n",
    "               \"min_samples_leaf\": [50,100,200]}\n",
    "\n",
    "grid = list(ParameterGrid(param_grid))\n",
    "huber_loss = np.full((len(grid),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for train_index, val_index, test_index in tscv.split(X, first_split_date= datetime.date(2008,1,31), second_split_date= datetime.date(2010,1,31)):\n",
    "\n",
    "    X_train   = X.loc[train_index].drop('eom', axis=1)\n",
    "    y_train = y.loc[train_index]\n",
    "    \n",
    "    X_val   = X.loc[val_index].drop('eom', axis=1)\n",
    "    y_val = y.loc[val_index]\n",
    "\n",
    "    X_test    = X.loc[test_index].drop('eom', axis=1)\n",
    "    y_test  = y.loc[test_index]\n",
    "    \n",
    "\n",
    "    for i in range(len(grid)):\n",
    "        GBR_val = GradientBoostingRegressor(loss='huber',\n",
    "                                            max_depth=grid[i][\"max_depth\"], \n",
    "                                            learning_rate=grid[i][\"learning_rate\"],\n",
    "                                            n_estimators = grid[i][\"n_estimators\"],\n",
    "                                            max_features= grid[i][\"max_features\"],\n",
    "                                            min_samples_split= grid[i][\"min_samples_split\"],\n",
    "                                            min_samples_leaf= grid[i][\"min_samples_leaf\"])\n",
    "    \n",
    "        GBR_val.fit(X_train, y_train)\n",
    "        Yval_predict=GBR_val.predict(X_val)\n",
    "        huber_loss[i,0]= huber_loss_error(y_val,Yval_predict, delta=1.35)\n",
    "\n",
    "\n",
    "    optim_param = grid[np.argmin(huber_loss)]\n",
    "\n",
    "\n",
    "    GBR = GradientBoostingRegressor(loss='huber',\n",
    "                                    max_depth=optim_param[\"max_depth\"], \n",
    "                                    learning_rate=optim_param[\"learning_rate\"],\n",
    "                                    n_estimators = optim_param[\"n_estimators\"],\n",
    "                                    max_features= optim_param[\"max_features\"],\n",
    "                                    min_samples_split= optim_param[\"min_samples_split\"],\n",
    "                                    min_samples_leaf= optim_param[\"min_samples_leaf\"])\n",
    "\n",
    "    GBR.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds=GBR.predict(X_test)\n",
    "    \n",
    "\n",
    "    predictions_bottom.append(preds)\n",
    "    dates_bottom.append(y_test.index)\n",
    "    y_test_list_bottom.append(y_test)\n",
    "\n",
    "\n",
    "\n",
    "predictions_all_bottom= np.concatenate(predictions_bottom, axis=0)\n",
    "y_test_list_all_bottom= np.concatenate(y_test_list_bottom, axis=0) \n",
    "dates_all_bottom= np.concatenate(dates_bottom, axis=0)\n",
    "\n",
    "#Calculate OOS model performance over the entire test period in line with Gu et al (2020)\n",
    "R2OOS_GBR_bottom = 1-sum(pow(y_test_list_all_bottom-predictions_all_bottom,2))/sum(pow(y_test_list_all_bottom,2))\n",
    "print(\"R2OOS gradient boosted regression tree Small only: \", R2OOS_GBR_bottom)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>GBRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Full sample</th>\n",
       "      <td>0.010896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large firms</th>\n",
       "      <td>0.005249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small firms</th>\n",
       "      <td>0.005550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GBRT\n",
       "Full sample  0.010896\n",
       "Large firms  0.005249\n",
       "Small firms  0.005550"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Generate table containing the R2oos measures for all three datasets\n",
    "chart = np.array([[R2OOS_GBR],\n",
    "                  [R2OOS_GBR_top],\n",
    "                  [R2OOS_GBR_bottom]])\n",
    "                     \n",
    "\n",
    "r2oos_gbrt = pd.DataFrame(chart, columns=['GBRT'],\n",
    "                              index=[\"Full sample\", \"Large firms\", \"Small firms\"])\n",
    "r2oos_gbrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's performance measures to compare with other models later.\n",
    "r2oos_gbrt.to_csv(r'C:\\Users\\krist\\Documents\\Data\\r2oos results\\r2oos_gbrt.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-varying model complexity"
   ]
  },
  {
   "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>max_depth</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>2011-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>2012-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>2013-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2014-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>2017-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth        time\n",
       "0          7  2010-01-31\n",
       "1          8  2011-01-31\n",
       "2          7  2012-01-31\n",
       "3          7  2013-01-31\n",
       "4          6  2014-01-31\n",
       "5          3  2015-01-31\n",
       "6          1  2016-01-31\n",
       "7          2  2017-01-31\n",
       "8          1  2018-01-31\n",
       "9          2  2019-01-31"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 predictors randomly considered as potential split variables over time in dataframe\n",
    "pd.DataFrame(dic_max_depth_all.items())\n",
    "max_depth =pd.DataFrame(dic_max_depth_all.items(), columns=['Identifier', 'max_depth'])\n",
    "max_depth['Identifier'] = max_depth['Identifier'].astype(str)\n",
    "max_depth[\"time\"] = max_depth[\"Identifier\"].str[19:29]\n",
    "max_depth.drop([\"Identifier\"], axis = 1, inplace = True)\n",
    "\n",
    "# #Plot time-varying model complexity\n",
    "max_depth.set_index('time').plot();\n",
    "plt.xticks(rotation=45);\n",
    "max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model's complexity  to compare with other models later.\n",
    "n_feat.to_csv(r'C:\\Users\\krist\\Documents\\Data\\model_complexity\\comp_grbt.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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.018856414496517537\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",
    "\n",
    "\n",
    "param_grid = {'max_depth': [1,2,3,4,5,6,7,8], \n",
    "              'n_estimators': [100], \n",
    "              \"learning_rate\": [0.01, 0.1], \n",
    "              \"max_features\": [\"sqrt\"],\n",
    "               \"min_samples_split\" : [5000,8000,10000],\n",
    "               \"min_samples_leaf\": [50,100,200]}\n",
    "grid = list(ParameterGrid(param_grid))\n",
    "huber_loss = np.full((len(grid),1),np.nan, dtype = np.float32)\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(grid)):\n",
    "    GBR_val = GradientBoostingRegressor(loss='huber',\n",
    "                                            max_depth=grid[i][\"max_depth\"], \n",
    "                                            learning_rate=grid[i][\"learning_rate\"],\n",
    "                                            n_estimators = grid[i][\"n_estimators\"],\n",
    "                                            max_features= grid[i][\"max_features\"],\n",
    "                                            min_samples_split= grid[i][\"min_samples_split\"],\n",
    "                                            min_samples_leaf= grid[i][\"min_samples_leaf\"])\n",
    "    \n",
    "    \n",
    "    GBR_val.fit(X_train, y_train)\n",
    "    Ypred_val=GBR_val.predict(X_val)\n",
    "    huber_loss[i,0]= huber_loss_error(y_val,Ypred_val, delta=1.35)\n",
    "\n",
    "optim_param = grid[np.argmin(huber_loss)]\n",
    "GBR = GradientBoostingRegressor(loss='huber',\n",
    "                                    max_depth=optim_param[\"max_depth\"], \n",
    "                                    learning_rate=optim_param[\"learning_rate\"],\n",
    "                                    n_estimators = optim_param[\"n_estimators\"],\n",
    "                                    max_features= optim_param[\"max_features\"],\n",
    "                                    min_samples_split= optim_param[\"min_samples_split\"],\n",
    "                                    min_samples_leaf= optim_param[\"min_samples_leaf\"])\n",
    "\n",
    "GBR.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "preds = GBR.predict(np.concatenate((X_train, X_val)))\n",
    "R2OOS_all = 1-sum(pow(np.concatenate((y_train, y_val))-preds,2))/sum(pow(np.concatenate((y_train, y_val)),2))\n",
    "print(R2OOS_all)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 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></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": 11,
     "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate R2OOS based on the last rolling window for each dataframe and save to dictionary\n",
    "\n",
    "dic = {}    \n",
    "param_grid = {'max_depth': [1,2,3,4,5,6,7,8], \n",
    "              'n_estimators': [100], \n",
    "              \"learning_rate\": [0.01, 0.1], \n",
    "              \"max_features\": [\"sqrt\"],\n",
    "               \"min_samples_split\" : [5000,8000,10000],\n",
    "               \"min_samples_leaf\": [50,100,200]}\n",
    "grid = list(ParameterGrid(param_grid))\n",
    "huber_loss = np.full((len(grid),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",
    "\n",
    " \n",
    "    for i in range(len(grid)):\n",
    "        GBR_val = GradientBoostingRegressor(loss='huber',\n",
    "                                            max_depth=grid[i][\"max_depth\"], \n",
    "                                            learning_rate=grid[i][\"learning_rate\"],\n",
    "                                            n_estimators = grid[i][\"n_estimators\"],\n",
    "                                            max_features= grid[i][\"max_features\"],\n",
    "                                            min_samples_split= grid[i][\"min_samples_split\"],\n",
    "                                            min_samples_leaf= grid[i][\"min_samples_leaf\"])\n",
    "\n",
    "\n",
    "        GBR_val.fit(X_train, y_train)\n",
    "        Ypred_val=GBR_val.predict(X_val)\n",
    "        huber_loss[i,0]= huber_loss_error(y_val,Ypred_val, delta=1.35)\n",
    "\n",
    "    optim_param = grid[np.argmin(huber_loss)]\n",
    "    GBR = GradientBoostingRegressor(loss='huber',\n",
    "                                    max_depth=optim_param[\"max_depth\"], \n",
    "                                    learning_rate=optim_param[\"learning_rate\"],\n",
    "                                    n_estimators = optim_param[\"n_estimators\"],\n",
    "                                    max_features= optim_param[\"max_features\"],\n",
    "                                    min_samples_split= optim_param[\"min_samples_split\"],\n",
    "                                    min_samples_leaf= optim_param[\"min_samples_leaf\"])\n",
    "\n",
    "    GBR.fit(np.concatenate((X_train, X_val)), np.concatenate((y_train, y_val)))\n",
    "    preds = GBR.predict(np.concatenate((X_train, X_val)))\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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R2OOS_me': 0.02914905162297754,\n",
       " 'R2OOS_ret_6_1': 0.01992845682874389,\n",
       " 'R2OOS_ret_12_1': 0.018517392024735124,\n",
       " 'R2OOS_tax_gr1a': 0.02286265344414562,\n",
       " 'R2OOS_be_me': 0.020106233220387182,\n",
       " 'R2OOS_debt_me': 0.01820418995805373,\n",
       " 'R2OOS_ni_me': 0.014480537616093136,\n",
       " 'R2OOS_sale_gr3': 0.028082949853317163,\n",
       " 'R2OOS_sale_gr1': 0.01811709898133751,\n",
       " 'R2OOS_sale_me': 0.03213821291133323,\n",
       " 'R2OOS_lnoa_gr1a': 0.031814591403341974,\n",
       " 'R2OOS_inv_gr1a': 0.023736069171471308,\n",
       " 'R2OOS_oaccruals_at': 0.014513566262698063,\n",
       " 'R2OOS_taccruals_at': 0.013456393536848354,\n",
       " 'R2OOS_be_gr1a': 0.0196942601129263,\n",
       " 'R2OOS_taccruals_ni': 0.02247514013100327,\n",
       " 'R2OOS_ebit_sale': 0.03101920679102299,\n",
       " 'R2OOS_sale_bev': 0.018392822546836163,\n",
       " 'R2OOS_age': 0.007763086953788689,\n",
       " 'R2OOS_beta_60m': 0.025757531661522393,\n",
       " 'R2OOS_rmax1_21d': 0.024605408031850518,\n",
       " 'R2OOS_bidaskhl_21d': 0.023151063585874043,\n",
       " 'R2OOS_ret_9_1': 0.02295764440247805,\n",
       " 'R2OOS_ret_12_7': 0.024354910411117814,\n",
       " 'R2OOS_ni_be': 0.027015566782757627,\n",
       " 'R2OOS_ocf_debt': 0.024565184487451708,\n",
       " 'R2OOS_cash_gr1a': 0.01806978973387352,\n",
       " 'R2OOS_at_gr1': 0.013920264598195065,\n",
       " 'R2OOS_chcsho_12m': 0.01601382184952327,\n",
       " 'R2OOS_ca_cl': 0.032326693043860155,\n",
       " 'R2OOS_ivol_capm_252d': 0.02203456427173789,\n",
       " 'R2OOS_debt_gr1': 0.01780515552179318,\n",
       " 'R2OOS_debtlt_gr1a': 0.01884139980190369,\n",
       " 'R2OOS_ope_be': 0.03321583611715562,\n",
       " 'R2OOS_caliq_cl': 0.014296950603614578,\n",
       " 'R2OOS_dsale_drec': 0.026555938807683255,\n",
       " 'R2OOS_f_score': 0.019330520097567194,\n",
       " 'R2OOS_rvol_21d': 0.023094562311901767,\n",
       " 'R2OOS_gp_bev': 0.022902266854012532,\n",
       " 'R2OOS_cowc_gr1a': 0.018455813464936077,\n",
       " 'R2OOS_pi_nix': 0.023384094800962485,\n",
       " 'R2OOS_ret_6_0': 0.03033787110425834,\n",
       " 'R2OOS_ret_1_0': 0.025444699861671083,\n",
       " 'R2OOS_noa_at': 0.018866715189941763,\n",
       " 'R2OOS_cop_atl1': 0.01887314478176405,\n",
       " 'R2OOS_prc_highprc_252d': 0.02610033819574098,\n",
       " 'R2OOS_ocf_at': 0.0139749974139467,\n",
       " 'R2OOS_dbnetis_at': 0.019151060212256765,\n",
       " 'R2OOS_netdebt_me': 0.022881242811158997}"
      ]
     },
     "execution_count": 13,
     "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": 15,
   "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>33</th>\n",
       "      <td>ope_be</td>\n",
       "      <td>0.033216</td>\n",
       "      <td>-0.014359</td>\n",
       "      <td>0.096519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ca_cl</td>\n",
       "      <td>0.032327</td>\n",
       "      <td>-0.013470</td>\n",
       "      <td>0.090543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sale_me</td>\n",
       "      <td>0.032138</td>\n",
       "      <td>-0.013282</td>\n",
       "      <td>0.089276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lnoa_gr1a</td>\n",
       "      <td>0.031815</td>\n",
       "      <td>-0.012958</td>\n",
       "      <td>0.087101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ebit_sale</td>\n",
       "      <td>0.031019</td>\n",
       "      <td>-0.012163</td>\n",
       "      <td>0.081754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature     R2OOS  red_R2OOS   var_imp\n",
       "33     ope_be  0.033216  -0.014359  0.096519\n",
       "29      ca_cl  0.032327  -0.013470  0.090543\n",
       "9     sale_me  0.032138  -0.013282  0.089276\n",
       "10  lnoa_gr1a  0.031815  -0.012958  0.087101\n",
       "16  ebit_sale  0.031019  -0.012163  0.081754"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary to dataframe\n",
    "pd.DataFrame(dic.items())\n",
    "imp=pd.DataFrame(dic.items(), columns=['Feature', 'R2OOS'])\n",
    "# Feature: name of the variable whose values are set to zero\n",
    "imp[\"Feature\"] = imp[\"Feature\"].str[6:]\n",
    "\n",
    "# Calculate reduction in predictive R2OOS \n",
    "imp[\"red_R2OOS\"] = R2OOS_all -imp[\"R2OOS\"]\n",
    "imp[\"var_imp\"] = imp[\"red_R2OOS\"]/sum(imp[\"red_R2OOS\"])\n",
    "imp=imp.sort_values(by = ['var_imp'], ascending = False)\n",
    "imp.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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('GBRT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_imp_gbrt=imp[[\"Feature\", \"var_imp\"]]\n",
    "var_imp_gbrt.to_csv(r'C:\\Users\\krist\\Documents\\Data\\variable importance\\var_imp_gbrt.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": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>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>0.000585</td>\n",
       "      <td>0.035512</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_001661_01W</th>\n",
       "      <td>0.000725</td>\n",
       "      <td>-0.041371</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002410_04W</th>\n",
       "      <td>-0.000684</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_002597_02W</th>\n",
       "      <td>-0.001740</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>2010-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_003820_01W</th>\n",
       "      <td>-0.005298</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>0.003477</td>\n",
       "      <td>-0.012904</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_331115_01W</th>\n",
       "      <td>0.005012</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_332311_01W</th>\n",
       "      <td>-0.001808</td>\n",
       "      <td>-0.051340</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>0.007719</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>2019-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334036_02W</th>\n",
       "      <td>0.002562</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  0.000585  0.035512 2010-01-31\n",
       "          comp_001661_01W  0.000725 -0.041371 2010-01-31\n",
       "          comp_002410_04W -0.000684 -0.053836 2010-01-31\n",
       "          comp_002597_02W -0.001740  0.001862 2010-01-31\n",
       "          comp_003820_01W -0.005298  0.078967 2010-01-31\n",
       "...                             ...       ...        ...\n",
       "2019-12   comp_330716_01W  0.003477 -0.012904 2019-12-31\n",
       "          comp_331115_01W  0.005012  0.005400 2019-12-31\n",
       "          comp_332311_01W -0.001808 -0.051340 2019-12-31\n",
       "          comp_333885_01W  0.007719  0.003528 2019-12-31\n",
       "          comp_334036_02W  0.002562 -0.100876 2019-12-31\n",
       "\n",
       "[93417 rows x 3 columns]"
      ]
     },
     "execution_count": 143,
     "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": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save results to use for Diebold-Mariano test\n",
    "gbrt= results.reset_index()\n",
    "gbrt.to_csv(r'C:\\Users\\krist\\Documents\\Data\\Predictions\\gbrt.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-12</th>\n",
       "      <th>comp_333885_01W</th>\n",
       "      <td>10829.783203</td>\n",
       "      <td>0.128645</td>\n",
       "      <td>0.128545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_334302_02W</th>\n",
       "      <td>297.482697</td>\n",
       "      <td>0.097961</td>\n",
       "      <td>0.097861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_335706_02W</th>\n",
       "      <td>196.243454</td>\n",
       "      <td>0.114381</td>\n",
       "      <td>0.114281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340115_01W</th>\n",
       "      <td>1799.958496</td>\n",
       "      <td>0.155813</td>\n",
       "      <td>0.155713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comp_340153_01W</th>\n",
       "      <td>26788.144531</td>\n",
       "      <td>0.238817</td>\n",
       "      <td>0.238717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156948 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    me2       ret   ret_exc\n",
       "MonthYear id                                               \n",
       "2003-01   comp_001166_02W    567.074646 -0.018948 -0.019948\n",
       "          comp_001661_01W   5221.931641  0.005815  0.004815\n",
       "          comp_004367_02W   4139.599121 -0.103801 -0.104801\n",
       "          comp_004925_02W  15302.482422 -0.070425 -0.071425\n",
       "          comp_005959_01W   6355.691406 -0.085813 -0.086813\n",
       "...                                 ...       ...       ...\n",
       "2020-12   comp_333885_01W  10829.783203  0.128645  0.128545\n",
       "          comp_334302_02W    297.482697  0.097961  0.097861\n",
       "          comp_335706_02W    196.243454  0.114381  0.114281\n",
       "          comp_340115_01W   1799.958496  0.155813  0.155713\n",
       "          comp_340153_01W  26788.144531  0.238817  0.238717\n",
       "\n",
       "[156948 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the original \"me\" avriable (before rank standartization) to use for the value weighting scheme\n",
    "\n",
    "unscaled_data = df[[\"id\", 'eom', \"me2\",\"ret\" ,\"ret_exc\"]].copy()\n",
    "unscaled_data['MonthYear'] = unscaled_data['eom'].dt.to_period('M')\n",
    "unscaled_data.drop('eom', axis=1, inplace=True)\n",
    "unscaled_data = unscaled_data.set_index(['MonthYear','id'])\n",
    "unscaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>yhat</th>\n",
       "      <th>y_true</th>\n",
       "      <th>date</th>\n",
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       "      <th>ret_exc</th>\n",
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       "      <th>ret_t+1</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2010-01</td>\n",
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       "      <td>2010-01-31</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01</td>\n",
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       "      <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>-0.000684</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>-0.001740</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>-0.005298</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>0.003477</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>0.005012</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>-0.001808</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>0.007719</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>0.002562</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  0.000585  0.035512 2010-01-31   \n",
       "1       2010-01  comp_001661_01W  0.000725 -0.041371 2010-01-31   \n",
       "2       2010-01  comp_002410_04W -0.000684 -0.053836 2010-01-31   \n",
       "3       2010-01  comp_002597_02W -0.001740  0.001862 2010-01-31   \n",
       "4       2010-01  comp_003820_01W -0.005298  0.078967 2010-01-31   \n",
       "...         ...              ...       ...       ...        ...   \n",
       "93412   2019-12  comp_330716_01W  0.003477 -0.012904 2019-12-31   \n",
       "93413   2019-12  comp_331115_01W  0.005012  0.005400 2019-12-31   \n",
       "93414   2019-12  comp_332311_01W -0.001808 -0.051340 2019-12-31   \n",
       "93415   2019-12  comp_333885_01W  0.007719  0.003528 2019-12-31   \n",
       "93416   2019-12  comp_334036_02W  0.002562 -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": 9,
     "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": 10,
   "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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93405"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the 1-month risk-free rate as the average monthly difference between returns and excess returns\n",
    "bigdata[\"risk_free_rate\"]=bigdata[\"ret_t+1\"]-bigdata[\"y_true\"]\n",
    "bigdata[\"risk_free_rate\"] =bigdata.groupby('MonthYear')[\"risk_free_rate\"].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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>26</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_015321_02W</td>\n",
       "      <td>0.019927</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>0.020434</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>54</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_028272_03W</td>\n",
       "      <td>0.020349</td>\n",
       "      <td>-0.023354</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>67574.304688</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>46.572178</td>\n",
       "      <td>43.838682</td>\n",
       "      <td>-0.023354</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>55</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_028338_02W</td>\n",
       "      <td>0.020431</td>\n",
       "      <td>-0.054058</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>26708.611328</td>\n",
       "      <td>0.005135</td>\n",
       "      <td>0.005135</td>\n",
       "      <td>83.551878</td>\n",
       "      <td>79.035232</td>\n",
       "      <td>-0.054058</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>57</th>\n",
       "      <td>480</td>\n",
       "      <td>comp_029789_02W</td>\n",
       "      <td>0.023151</td>\n",
       "      <td>-0.015783</td>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>1495.001343</td>\n",
       "      <td>-0.018092</td>\n",
       "      <td>-0.018092</td>\n",
       "      <td>22.090505</td>\n",
       "      <td>21.741856</td>\n",
       "      <td>-0.015783</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>93411</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_330213_01W</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.150887</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>703.357910</td>\n",
       "      <td>0.029799</td>\n",
       "      <td>0.028399</td>\n",
       "      <td>9.814704</td>\n",
       "      <td>11.308379</td>\n",
       "      <td>0.152187</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>93412</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_330716_01W</td>\n",
       "      <td>0.003477</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",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93413</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_331115_01W</td>\n",
       "      <td>0.005012</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",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93415</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_333885_01W</td>\n",
       "      <td>0.007719</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",
       "      <td>0.001325</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93416</th>\n",
       "      <td>599</td>\n",
       "      <td>comp_334036_02W</td>\n",
       "      <td>0.002562</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",
       "      <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",
       "26           480  comp_015321_02W  0.019927 -0.053604 2010-01-31   \n",
       "41           480  comp_017420_03W  0.020434  0.059737 2010-01-31   \n",
       "54           480  comp_028272_03W  0.020349 -0.023354 2010-01-31   \n",
       "55           480  comp_028338_02W  0.020431 -0.054058 2010-01-31   \n",
       "57           480  comp_029789_02W  0.023151 -0.015783 2010-01-31   \n",
       "...          ...              ...       ...       ...        ...   \n",
       "93411        599  comp_330213_01W  0.006987  0.150887 2019-12-31   \n",
       "93412        599  comp_330716_01W  0.003477 -0.012904 2019-12-31   \n",
       "93413        599  comp_331115_01W  0.005012  0.005400 2019-12-31   \n",
       "93415        599  comp_333885_01W  0.007719  0.003528 2019-12-31   \n",
       "93416        599  comp_334036_02W  0.002562 -0.100876 2019-12-31   \n",
       "\n",
       "                 me       ret   ret_exc        prc    prc_t+1   ret_t+1  \\\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",
       "54     67574.304688  0.002238  0.002238  46.572178  43.838682 -0.023354   \n",
       "55     26708.611328  0.005135  0.005135  83.551878  79.035232 -0.054058   \n",
       "57      1495.001343 -0.018092 -0.018092  22.090505  21.741856 -0.015783   \n",
       "...             ...       ...       ...        ...        ...       ...   \n",
       "93411    703.357910  0.029799  0.028399   9.814704  11.308379  0.152187   \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",
       "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",
       "       risk_free_rate MonthYear1  NumMonth  DecileRank  \n",
       "26           0.000353    2010-01         1           9  \n",
       "41           0.000353    2010-01         1           9  \n",
       "54           0.000353    2010-01         1           9  \n",
       "55           0.000353    2010-01         1           9  \n",
       "57           0.000353    2010-01         1           9  \n",
       "...               ...        ...       ...         ...  \n",
       "93411        0.001325    2019-12       120           9  \n",
       "93412        0.001325    2019-12       120           9  \n",
       "93413        0.001325    2019-12       120           9  \n",
       "93415        0.001325    2019-12       120           9  \n",
       "93416        0.001325    2019-12       120           9  \n",
       "\n",
       "[9386 rows x 15 columns]"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "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": 31,
   "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": 32,
   "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": 33,
   "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>26</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01</td>\n",
       "      <td>0.021630</td>\n",
       "      <td>0.006976</td>\n",
       "      <td>0.019913</td>\n",
       "      <td>0.021491</td>\n",
       "      <td>0.018787</td>\n",
       "      <td>0.007002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-02</td>\n",
       "      <td>0.026231</td>\n",
       "      <td>-0.014065</td>\n",
       "      <td>0.019889</td>\n",
       "      <td>0.022052</td>\n",
       "      <td>0.023717</td>\n",
       "      <td>-0.014076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-03</td>\n",
       "      <td>0.018594</td>\n",
       "      <td>-0.014832</td>\n",
       "      <td>0.015325</td>\n",
       "      <td>0.015841</td>\n",
       "      <td>0.017788</td>\n",
       "      <td>-0.014844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-04</td>\n",
       "      <td>0.008998</td>\n",
       "      <td>-0.000811</td>\n",
       "      <td>0.020331</td>\n",
       "      <td>0.020520</td>\n",
       "      <td>0.014295</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>5</td>\n",
       "      <td>2010-05</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>0.034082</td>\n",
       "      <td>0.017424</td>\n",
       "      <td>0.018560</td>\n",
       "      <td>0.022068</td>\n",
       "      <td>0.034079</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>89871</th>\n",
       "      <td>116</td>\n",
       "      <td>2019-08</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.003299</td>\n",
       "      <td>0.009111</td>\n",
       "      <td>0.009493</td>\n",
       "      <td>0.006073</td>\n",
       "      <td>0.003290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90585</th>\n",
       "      <td>117</td>\n",
       "      <td>2019-09</td>\n",
       "      <td>0.015373</td>\n",
       "      <td>0.009064</td>\n",
       "      <td>0.008839</td>\n",
       "      <td>0.009653</td>\n",
       "      <td>0.015373</td>\n",
       "      <td>0.009064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91278</th>\n",
       "      <td>118</td>\n",
       "      <td>2019-10</td>\n",
       "      <td>0.013842</td>\n",
       "      <td>-0.004540</td>\n",
       "      <td>0.009083</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.010670</td>\n",
       "      <td>-0.004562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91986</th>\n",
       "      <td>119</td>\n",
       "      <td>2019-11</td>\n",
       "      <td>0.050351</td>\n",
       "      <td>0.051110</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>0.008405</td>\n",
       "      <td>0.054774</td>\n",
       "      <td>0.052976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92703</th>\n",
       "      <td>120</td>\n",
       "      <td>2019-12</td>\n",
       "      <td>0.033305</td>\n",
       "      <td>0.007154</td>\n",
       "      <td>0.009860</td>\n",
       "      <td>0.008564</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.007492</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",
       "26            1    2010-01                  0.021630   \n",
       "764           2    2010-02                  0.026231   \n",
       "1477          3    2010-03                  0.018594   \n",
       "2227          4    2010-04                  0.008998   \n",
       "3009          5    2010-05                  0.025121   \n",
       "...         ...        ...                       ...   \n",
       "89871       116    2019-08                  0.006755   \n",
       "90585       117    2019-09                  0.015373   \n",
       "91278       118    2019-10                  0.013842   \n",
       "91986       119    2019-11                  0.050351   \n",
       "92703       120    2019-12                  0.033305   \n",
       "\n",
       "       excess_return_zeronet_vw  pred_excess_return_zeronet_ew  \\\n",
       "26                     0.006976                       0.019913   \n",
       "764                   -0.014065                       0.019889   \n",
       "1477                  -0.014832                       0.015325   \n",
       "2227                  -0.000811                       0.020331   \n",
       "3009                   0.034082                       0.017424   \n",
       "...                         ...                            ...   \n",
       "89871                  0.003299                       0.009111   \n",
       "90585                  0.009064                       0.008839   \n",
       "91278                 -0.004540                       0.009083   \n",
       "91986                  0.051110                       0.009964   \n",
       "92703                  0.007154                       0.009860   \n",
       "\n",
       "       pred_excess_return_zeronet_vw  return_zeronet_ew  return_zeronet_vw  \n",
       "26                          0.021491           0.018787           0.007002  \n",
       "764                         0.022052           0.023717          -0.014076  \n",
       "1477                        0.015841           0.017788          -0.014844  \n",
       "2227                        0.020520           0.014295           0.000262  \n",
       "3009                        0.018560           0.022068           0.034079  \n",
       "...                              ...                ...                ...  \n",
       "89871                       0.009493           0.006073           0.003290  \n",
       "90585                       0.009653           0.015373           0.009064  \n",
       "91278                       0.009380           0.010670          -0.004562  \n",
       "91986                       0.008405           0.054774           0.052976  \n",
       "92703                       0.008564           0.036242           0.007492  \n",
       "\n",
       "[120 rows x 8 columns]"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "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": 35,
   "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>-0.26%</td>\n",
       "      <td>-0.27%</td>\n",
       "      <td>4.70%</td>\n",
       "      <td>-0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11%</td>\n",
       "      <td>0.05%</td>\n",
       "      <td>4.44%</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29%</td>\n",
       "      <td>0.44%</td>\n",
       "      <td>4.15%</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.42%</td>\n",
       "      <td>0.44%</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.53%</td>\n",
       "      <td>0.64%</td>\n",
       "      <td>4.34%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.63%</td>\n",
       "      <td>0.73%</td>\n",
       "      <td>4.28%</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.72%</td>\n",
       "      <td>0.83%</td>\n",
       "      <td>4.34%</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.82%</td>\n",
       "      <td>0.92%</td>\n",
       "      <td>4.41%</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.93%</td>\n",
       "      <td>1.18%</td>\n",
       "      <td>4.60%</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.12%</td>\n",
       "      <td>1.25%</td>\n",
       "      <td>4.88%</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.39%</td>\n",
       "      <td>1.52%</td>\n",
       "      <td>2.56%</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred    Real    Std Sharpe\n",
       "Low (L)   -0.26%  -0.27%  4.70%  -0.19\n",
       "2          0.11%   0.05%  4.44%   0.04\n",
       "3          0.29%   0.44%  4.15%   0.36\n",
       "4          0.42%   0.44%  4.40%   0.34\n",
       "5          0.53%   0.64%  4.34%   0.50\n",
       "6          0.63%   0.73%  4.28%   0.59\n",
       "7          0.72%   0.83%  4.34%   0.66\n",
       "8          0.82%   0.92%  4.41%   0.72\n",
       "9          0.93%   1.18%  4.60%   0.88\n",
       "High (H)   1.12%   1.25%  4.88%   0.88\n",
       "H-L        1.39%   1.52%  2.56%   1.98"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "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>-0.28%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>4.70%</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11%</td>\n",
       "      <td>0.27%</td>\n",
       "      <td>5.23%</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29%</td>\n",
       "      <td>0.35%</td>\n",
       "      <td>5.39%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.42%</td>\n",
       "      <td>0.55%</td>\n",
       "      <td>5.14%</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.53%</td>\n",
       "      <td>0.54%</td>\n",
       "      <td>4.79%</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.63%</td>\n",
       "      <td>0.39%</td>\n",
       "      <td>4.75%</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.72%</td>\n",
       "      <td>0.78%</td>\n",
       "      <td>4.66%</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.82%</td>\n",
       "      <td>0.62%</td>\n",
       "      <td>4.87%</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.93%</td>\n",
       "      <td>0.75%</td>\n",
       "      <td>4.83%</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High (H)</th>\n",
       "      <td>1.10%</td>\n",
       "      <td>0.65%</td>\n",
       "      <td>4.85%</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H-L</th>\n",
       "      <td>1.38%</td>\n",
       "      <td>0.30%</td>\n",
       "      <td>3.07%</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pred   Real    Std Sharpe\n",
       "Low (L)   -0.28%  0.35%  4.70%   0.26\n",
       "2          0.11%  0.27%  5.23%   0.18\n",
       "3          0.29%  0.35%  5.39%   0.22\n",
       "4          0.42%  0.55%  5.14%   0.37\n",
       "5          0.53%  0.54%  4.79%   0.39\n",
       "6          0.63%  0.39%  4.75%   0.28\n",
       "7          0.72%  0.78%  4.66%   0.58\n",
       "8          0.82%  0.62%  4.87%   0.44\n",
       "9          0.93%  0.75%  4.83%   0.54\n",
       "High (H)   1.10%  0.65%  4.85%   0.47\n",
       "H-L        1.38%  0.30%  3.07%   0.34"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "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\\gbrt_ew.csv')\n",
    "vw_df.to_csv(r'C:\\Users\\krist\\Documents\\Data\\decile_portfolios_results\\gbrt_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": 38,
   "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": 39,
   "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": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "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": 44,
   "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": 45,
   "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": 46,
   "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": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  1.6220694438160532\n",
      "Trunover value weighted:  25.36196072896322\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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  9.865260638813433\n",
      "MaxDD value weighted:  19.711845211281474\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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  5.618274064625722\n",
      "Max 1M loss value weighted:  9.823907620819224\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": 55,
   "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": 56,
   "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": 57,
   "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": 58,
   "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": 59,
   "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": 60,
   "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()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 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>GBRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.518064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>2.564569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>1.984347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.865261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.622069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX      GBRT\n",
       "Mean return [%]       0.752467  1.518064\n",
       "Std [%]               4.610969  2.564569\n",
       "Sharpe ratio          0.561110  1.984347\n",
       "Maximum drawdown [%]  0.000000  9.865261\n",
       "Turnover [%]          0.000000  1.622069\n",
       "Mean active return    0.000000  0.007602\n",
       "Information ratio     0.000000  0.152916"
      ]
     },
     "execution_count": 62,
     "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', 'GBRT'],\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": 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>GBRT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.300293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>3.074068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.336677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.711845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.361961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.084460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX       GBRT\n",
       "Mean return [%]       0.752467   0.300293\n",
       "Std [%]               4.610969   3.074068\n",
       "Sharpe ratio          0.561110   0.336677\n",
       "Maximum drawdown [%]  0.000000  19.711845\n",
       "Turnover [%]          0.000000  25.361961\n",
       "Mean active return    0.000000  -0.004555\n",
       "Information ratio     0.000000  -0.084460"
      ]
     },
     "execution_count": 63,
     "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', 'GBRT'],\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": 64,
   "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\\gbrt_zeronet_ew.csv')\n",
    "vw_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_zeronet\\gbrt_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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trunover equally weighted:  0.7540146562472829\n",
      "Trunover value weighted:  12.23594347635905\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": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaxDD equally weighted:  30.248626758426422\n",
      "MaxDD value weighted:  32.66153641038487\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": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max 1M loss equally weighted:  12.211740091207279\n",
      "Max 1M loss value weighted:  17.46867114630833\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": 68,
   "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": 69,
   "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": 70,
   "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>GBRT_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>1.250926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.876647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.881337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.248627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.754015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  GBRT_long\n",
       "Mean return [%]       0.752467   1.250926\n",
       "Std [%]               4.610969   4.876647\n",
       "Sharpe ratio          0.561110   0.881337\n",
       "Maximum drawdown [%]  0.000000  30.248627\n",
       "Turnover [%]          0.000000   0.754015\n",
       "Mean active return    0.000000   0.005118\n",
       "Information ratio     0.000000   0.144442"
      ]
     },
     "execution_count": 70,
     "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', 'GBRT_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": 71,
   "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>GBRT_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean return [%]</th>\n",
       "      <td>0.752467</td>\n",
       "      <td>0.651898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Std [%]</th>\n",
       "      <td>4.610969</td>\n",
       "      <td>4.846547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.561110</td>\n",
       "      <td>0.465611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maximum drawdown [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.661536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover [%]</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.235943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean active return</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information ratio</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.024213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DAX  GBRT_long\n",
       "Mean return [%]       0.752467   0.651898\n",
       "Std [%]               4.610969   4.846547\n",
       "Sharpe ratio          0.561110   0.465611\n",
       "Maximum drawdown [%]  0.000000  32.661536\n",
       "Turnover [%]          0.000000  12.235943\n",
       "Mean active return    0.000000  -0.000854\n",
       "Information ratio     0.000000  -0.024213"
      ]
     },
     "execution_count": 71,
     "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', 'GBRT_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": 72,
   "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\\gbrt_long_ew.csv')\n",
    "vw_long_chart.to_csv(r'C:\\Users\\krist\\Documents\\Data\\performance_long_only\\gbrt_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": 73,
   "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": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UkZjI0axO1fUgKVaLWKN6Xq9bkR2DNXlmvLqvAd19w36KjoiIiIgo8jCRoxmNjLpQ2WSbdezAdG69IheCIGDn29U+joyIiIiIKHIxkaMZlTdY4faIWD6P/XHnitarcN2FGThe3Y0T1d0+jo6IiIiIKDIxkaMZnajphkohRW6KYcHnuHJtKpJitXjm7So4nG4fRkdEREREFJmYyNEkoiiivNGKX+w8jg9PtWNVbixk0oV/q8ikEtxxVR66+0bw+oEG3wVKRERERBShZIEOgIKHKIo4VduD1w40oLbVDoNWgZsvzcGlq5IXfe78NBM2FiVg98dN2FiUgMQYrQ8iJiIiIiKKTEzkCB5RxLFKC1470ICmzgHE6JW446o8XLgicU4DwOfq5stycKKmG0+9WYXvfHYlBEHw2bmJiIiIiCIJE7kIV91iw5NvVKHFMoB4kxp3by/AhqL4RZVSTsegVeBTF2fhqTercKi8C+sL431+DSIiIiKiSMBELkL1D43iH+/V4qNT7YjRK/Hl6wpxwbJ4SCT+XSW7ZGUyPjrVjmferkJBhgl6jcKv1yMiIiIiCkdsdhJhPKKID0624d//8DEOnOnAtvVp+M8vbsCGwgS/J3EAIJEIuPuaAgw7XHhyTyVEUfT7NYmIiIiIwg1X5CJIfVsf/u/Z46hp7UNeigGf25qPFLNuyeNIMetw40VZ+Md7tfi4tBMbixOWPAYiIiIiolDGRC5CvH6gAS9+WA+NUoa7txdg8/KEgDYb2XpBGo7XdOOpt6qQn2ZEtF4VsFiIiIiIiEINSysjwLDDhZc+rMfq/Dj815c34MIViQHvGCmRCLjnmgK4PR78ZXcFSyyJiIiIiOaBiVwEqGyywe0RceMl2dCp5YEOZ1y8SYNbLs1BaX0v3jveGuhwiIiIiIhCBhO5CFBa3wuFXIKCjOhAhzLJJauSUZQZjb/vrUGndSjQ4RARERERhQQmchGgtKEXealGnw739hVBEPCFbcsglUjw+Ovl8HhYYklERERENBsmcmGup28EHb1DKA7C1bgx0XoVPndlHmpa+vDGoaZAh0NEREREFPSYyIW50oZeAEBhZvAmcgCwoSgea/LMePHDOrR0DQQ6HCIiIiKioMZELsyVNfTCoFMgOVYb6FBmJAgC7rg6HxqlDM++Wx3ocIiIiIiIghoTuTDmEUWUNVhRlBEd8HEDc6HXKLAiOxbtPWx6QkREREQ0EyZyYaypsx8Dw04UBXlZ5bmMUQr0DYyy6QkRERER0QyYyIWx0vqz++OCuNHJ+Uw6JTyiCPvQaKBDISIiIiIKWrJAB+BL9qFRPLGrAi6PB3KpBHKZZPy/MpkEGqUMWy9Ig1oZVl/2tErre5Fi1sGgVQQ6lDkz6pQAAGu/Y/z/iYiIiIhoorDKaA6c6cCJmm5kJETB5RbhdHvgcnngdHsw6nRjZNSNFLMOa5fFBTpUv3OMulHT2ocr1qQGOpR5MUZ5kzdbvwNIDHAwRERERERBKqwSuUPlnUiPj8JPPr9u0nN9g6O479cfoW8wMkr2KpttcLlFFGaaAh3KvJjGErkBR4AjISIiIiIKXovaI/fqq69i+/btuOqqq/D0009Per68vBw33XQTtm7dih/+8IdwuVyLudyMumzDqG/vxwWFU6+2RanlEADYIySRK2vohUwqQV6KMdChzIteo4BEEGBlIkdERERENK0FJ3KdnZ341a9+hWeeeQYvvfQS/v73v6OmpmbCMf/2b/+Gn/zkJ3jjjTcgiiKee+65RQc8ncPlnQCAddOUTUokAnQaecQ00Sht6EVeqgEKuTTQocyLRCLAoFPA2s9EjoiIiIhoOgtO5Pbv348NGzbAaDRCo9Fg69at2LNnz/jzra2tGBkZwcqVKwEAN91004Tn52I+LegPl3chO0mPWIN62mP0WkVErMhZ+x1otQyiKIS6VZ7LqFPANhD+f09ERERERAu14D1yXV1dMJvN43+Oi4vDqVOnpn3ebDajs7NzXteobLXjwpXJsx7X0tWPpq4BfPH6YpjNUdMeF2tUY3jUPeMx4eB0oxUAcOHq1Elfayh87fExWrR1D4ZErKEQY6jhPfUt3k/f4v30Pd5T3+L99D3eU9/i/fSdBSdyHo8HgiCM/1kUxQl/nu35udi9vx65SVGQzPK6N/bVQwBQkGKAxdI/7XFquRRtloEZjwkHB061IUojh1YuTPhazeaokPjaNQopemzDQR9rqNzPUMJ76lu8n77F++l7vKe+xfvpe7ynvsX7OT8SiYCYGN30zy/0xAkJCbBYLON/tlgsiIuLm/b57u7uCc/PRUfvEE7X9sx63KGKLuSmGsc7Hk7HW1rpnFcMoUYURZQ1WFGUET1rAhysjDolBkdcGHW6Ax0KEREREVFQWnAit2nTJhw4cAC9vb0YHh7Gm2++iS1btow/n5ycDKVSiaNHjwIAXn755QnPz4VBp8Seg00zHtNiGUBb9yAuKJg9SdRrFXA43XCMhm+C0GIZhH1wFIUhuj8O+GQEATtXEhERERFNbcGJXHx8PO677z7ceeeduOGGG7Bjxw6sWLECX/rSl3D69GkAwC9/+Us89NBDuPrqqzE0NIQ777xzXte4cHkiKpttqG+3T3vMofIuCAKwJn8OiZxGAQDoC+POlaX1vQCAoszQTeSMunOGghMRERER0SSLGgh+7bXX4tprr53w2B//+Mfx/1+2bBmef/75BZ9/fWE8nn2nGrsPNuHrNxRPel4URRwq78SyNBMMWsWs59Nr5QC8s+TijNN3twxlpQ29SIrVzlpmGsyMXJEjIiIiIprRogaC+5tKIcUlq5JwtLILXdahSc83dQ6gyzqM9YXxczqf/myy1x+mIwicLjeqmm0ozDAFOpRFMY2vyIXn3xMRERER0WIFdSIHAFesSYVEEPDG4eZJzx0q74RUImB1nnmKV04W7qWVVS19cLo8KA7hskoAUCulUMglsHFFjoiIiIhoSkGfyJmilNhYlIB9p9rRf04C5i2r7EJhRjR0avmczjW2IheuQ8HL6nshlQjITw3tFTlBEGDSKWHlHjkiIiIioikFfSIHAFvXp2HU5cG7x1rHH6trs6PHPjKnbpVjZFIJtCpZ2CZypfW9yE0xQKmQBjqURTPqlFyRIyIiIiKaRkgkcsmxWpRkx+Cdoy1wnJ0tdqi8CzKpgFW5cyurHOOdJRd+idzQiBPNXQNYlh7aq3FjTFFckSMiIiIimk5IJHIAcPX6NAwMO7HvdDs8oogjlV1YnhUDjWp+jTf1mvBM5Gpa7RAB5CYbAh2KTxijlLANjEIUxUCHQkREREQUdEImkctLNSIzUY83DzWjqskGa78D6+ZRVjlGr1Wgb8jphwgDq6bVBokgICspTBI5nRIutweDI65Ah0JEREREFHRCJpETBAHb1qehyzaMv+6pgEImwcqc2HmfJxRW5Orb7RiaZwJT09KH1HhdWOyPAzA+B4/llUREREREk4VMIgcAq/PMiDOq0WkdxorsGKgU859nrtfKMexwweny+CHCxRscceK/njyKV/bVz/k1LrcHdW32sCmrBM6ZJceGJ0REREREk4RUIieRCNh6QSoA4IKCuQ0BP9/4UPAgnSVX1WSD2yOitKF3zq9p7hrAqMuDnJTwSeSMOu/fE1fkiIiIiIgmm/+SVoBdvDIZ0XoVlmfHLOj1Y4lc3+AoovUqX4bmExVNNgBAq2UQtgEHjGdXpmZS09IHAMhNMfoxsqVl4IocEREREdG0QmpFDvCuypXkxEIiCAt6fbAPBa9oso6vRpXNcVWuurUPMXrV+L6ycCCXSaBTy2HjihwRERER0SQhl8gtlkETvIncwLB3FtwlK5OhU8tR1mCd9TWiKKKmxYbcMCqrHMNZckREREREUwu50srFihpbkQvCPXKVZ8sql6Wb0NYziNKGXoiiCGGG1ceevhHYBkbDan/cGNPZWXJERERERDRRxK3IKeVSKBVS9AXhilxFkxUKuQRZSXoUZkSjb2AUbd2DM76mutW7Py4njDpWjjHqFLByjxwRERER0SQRl8gB3vLKYCytrGiyIjfZAJlUgsIMEwDMWl5Z09IHlUKKFLNuKUJcUkadEv2Do3C5g3NUBBERERFRoERkIqfXKtA/5Ax0GBPYh0bRahlEfpo3gYs1qBFvUs86hqC6pQ/ZSXpIJAtr/hLMjFFKiAjO/YxERERERIEUsYlcsCUHVefsjxtTmBmNyibbtCtSQyMutFoGkBNGYwfONTYUnA1PiIiIiIgmithELtj2yJU3WaGUS5GREDX+WGF6NBxON+ra7FO+pq6tDyIQlo1OAIyPU+AsOSIiIiKiiSIzkdPIMTjshNsTPHuvKpu8IwRk0k/+SgrSjRAEoLR+6vLK6pY+CAKQlahfqjCXlJErckREREREU4rIRM6gVUAEgmafXN+gtzvluWWVAKBRyZGZqJ92MHhNax9S43RQK8NzioROI4dUInAEARERERHReSIykdNrg2soeGWTtzPlsjTTpOcKM6JR127H0IhrwuNujwd1bXbkJhuXIsSAkAiCdwQBV+SIiIiIiCaIyEQuShNciVxFkw0qhRTpCZNHCBRlmCCK3tEE52ruGoDD6Q7b/XFjjFFK7pEjIiIiIjpPRCZyhrMrcsHS8KSi0Yq8VCOkksl/HdnJBijl0kljCKpbvIPAc8M8kTPpmMgREREREZ0vIhO5sdLKYNgjZ+13oKN3aMqySgCQSSXITzNOGgxe09KHaL0S0XrVUoQZMEadkqWVRERERETnichETqWQQi6TBEVpZWWzN0HLTzNOe0xhRjQ6e4fQ0zcy/lhNax9yksN7NQ7wjiAYGXVj2OGa/WAiIiIioggRkYmcIAjQa4JjllxFow1qpQzp8VHTHlOY4V2tG+te2dM3Amu/A7lhOgj8XGMjCFheSURERET0iYhM5ABveaV9KAgSuSYr8lONkEiEaY9JjtXCoFOM75OrbrUBQESsyBnHhoKzvJKIiIiIaFzEJnIGrSLgpZW99hF0WYdnLKsEvCuIhenRKGuwwiOKqGnpg1IhRUqcdmkCDSDTWCLHWXJEREREROMiNpGL0sgDnshVNtkATD0/7nyFGSYMDDvR0jWAmpY+ZCfpp+xyGW6MOm9jGitLK4mIiIiIxoV/JjANvVaB/iEnPKIYsBjKm6zQqmRIjZ88P+58hRnRAICjlRY0WwYioqwSAFQKGdRKKUsriYiIiIjOEdGJnEcUMTAcuBEElU3e+XESYfr9cWNMUUokx2rxztEWiCLCfhD4uYw6JVfkiIiIiIjOEbGJ3NhQ8P4AlVf29I3AYhuZU1nlmIIME4YcLggCkJ0UWYkcV+SIiIiIiD4RsYmcXuNN5AK1T66iyTs/bln63BO5orPllSlmHdRKmV/iCkamKCXHDxARERERnSNyE7mzK3J9ARpBUNFkhU4tR7J57p0n89OMkEklyEs1+i+wIGTUKWEbGA3ofkYiIiIiomASOcs65xlL5OyDgdkjV9Fo886Pm8P+uDEqhQw/vGMNYgwqP0YWfExRSrg9IgaGnON/b0REREREkSxiV+S0KhmkEiEgpZV9Aw702EeQu4CVtfSEKOjUct8HFcSMOu8sOSv3yRERERERAYjgRE4QhIDNkmuxDAIAUudRVhnJjFGcJUdEREREdK6ITeQAb3mlPQB75FosAwCA5LjZ58cRYDq7IseGJ0REREREXhGfyPUFZEVuAHqtYrxzJs1Mr1VAADiCgIiIiIjorIhO5AwaBfoDsCLXahlECssq50wmlUCvVXBFjoiIiIjorIhO5PRaBeyDoxAX2Nbe4XTjFzuPo7atb86v8XhEtHUPIjmWZZXzYYxSwtofmFERRERERETBJuITOZdbxLDDtaDX17XZUd5oxdEKy5xfY7ENY9Tl4YrcPJl0SnatJCIiIiI6K+ITOQAL3idXd3YlrqHDPufXjDU6SWGjk3kxRilZWklEREREdBYTOWDBIwjq2rwJXENHPzxzLM9ssQxCAJAUyxW5+TDqFBgYdsLp8gQ6FCIiIiKigIvsRO5s10j7kHPerxVFEXVtdijlUoyMutHZOzSn17VYBmA2qaGUS+d9zUjGEQRERERERJ+I7ERuEStyvXYH+gZHsbEoHgDQ0N4/p9d5O1ayrHK+TFFM5IiIiIiIxkR0IhellkMQFrZHrq7dW1a5eXkiFHIJ6uewT27U6UandQjJLKucN+PZFTk2PCEiIiIiivBETiIREKWWL2iWXF1bH2RSCdITopAWH4WGjtlX5Np7hiCKbHSyEMbxFTmOICAiIiIiiuhEDvhkltx81bbZkR6vg0wqQWaCHk2d/XB7Zm7EMd6xkqMH5k2rkkEmlcDGFTkiIiIiIiZyC0nkXG4PGjv6kZVkAABkJEZh1OlBe8/MDU9aLAOQSSWIM6kXHG+kEgQBpigFrNwjR0RERES08ESura0Nt99+O66++mp87Wtfw+Dg4LTH7tu3D3fddddCL+VXeq1i3nvkWi2DcLo8yErSAwAyEqIAzN7wpMUyiKRYDaSSiM+fF8SkU3JFjoiIiIgIi0jkHnjgAdx2223Ys2cPiouL8dhjj006xuPx4M9//jO+/e1vwzNL2WGg6DUK2Oe5R25sEPhYIhcfrYFKIZ214UmrZQDJsdwft1AmvQo99pFAh0FEREREFHALSuScTicOHz6MrVu3AgBuuukm7NmzZ9JxtbW1qK2txYMPPri4KP1Ir1Vg1OnByKhrzq+pa7MjSiNHrEEFAJAIAjISomZckRsYdsI2MIqUOO6PWyizUY0e+whc7uD8UICIiIiIaKksKJGzWq3Q6XSQyWQAALPZjM7OzknH5ebm4mc/+xkMBsPiovSj8aHg8yivrG2zIytRD0EQxh/LSNCjuWtg2iSjdbzRCVfkFirepIYoAhbbcKBDISIiIiIKKNlsB+zevRsPPfTQhMfS09MnJDEAJv3ZF2Ji/J/0pCZ5G5RIFXKYzVGzHj8wNIqO3iFcsT5twvEr8uKw51AThlwishMmn+dgpQUAULIsHjGGwDU7mcvXGKyWZTkBACOe4Pk6giWOcMJ76lu8n77F++l7vKe+xfvpe7ynvsX76TuzJnLbtm3Dtm3bJjzmdDqxfv16uN1uSKVSWCwWxMXF+Ty4np4BeDyiz887gcsNAGhstSFGK5/18DP1PQCAeIMKFssnpZTRWu+tPF7eAb1SOul1FfU90KpkcDucsFjmXsbpS2Zz1ISYQ41C8H4vVNf3IDMIRjiE+v0MRrynvsX76Vu8n77He+pbvJ++x3vqW7yf8yORCDMubC2otFIul2Pt2rXYtWsXAOCll17Cli1bFhZhgOm18yutrGuzQwCQmaCf8LjZqIZWJZt2MHirZRDJZp1fVi4jhU4th0YpQ6eVpZVEREREFNkW3LXy/vvvx3PPPYft27fjyJEjuPfeewEAO3fuxKOPPuqr+PwuSuNdhZtPIpcQo4FGNXExU5ih4YkoimjtHkByEKwihTJBEBAfrUandeZ5fURERERE4W7W0srpJCcn48knn5z0+K233jrpsfXr12P9+vULvZRfyaQSaFUy9M1hBIEoiqhrs2NlTuyUz2ck6rHnYBOcLjfksk/KK3vsIxh2uNnoxAfiTRpUt/QFOgwiIiIiooDiZGp4yyvnsiJnsQ1jYNg5Pj/ufBkJUXB7RDR3TRyO3mLx/jmFK3KLFmdSo9c+AufZvY1ERERERJGIiRzODgWfQyJX1+Yd+D19Iud9vOG8weBjowc4DHzx4qM1EAF02TgYnIiIiIgiFxM5zH1Frq7NDoVcMu1et2i9ElEa+aR9ci2WQcTolZP21dH8xZs0AICuXu6TIyIiIqLIxUQOZxO5Ieesx9W125ERHwWpZOrb5m14op9yRS6Z++N8Ij7aO4OPnSuJiIiIKJIxkYM3kRt2uGbcd+V0edDU2Y+sZMOM58pIiEJr9yAcTu+5XG4P2nuG2LHSR7QqOXRqOTtXEhEREVFEYyIHwDA+S276Vbmmrn643CKyEqfeHzcmM1EPUQSaO7374jp6h+D2iOxY6UPxJjW6uCJHRERERBGMiRy8zU4AwD7DCILZGp2MSU+IAgDUt3uPbznb6ISJnO/EmTRckSMiIiKiiMZEDkCU1jsUvKVrYNpj6tvsMOoUiNarZjyXKUoJo04xvk+u1TIIqURAYozGdwFHuPhoNXrtDow6OYKAiIiIiCITEzkAaXE6pJi1ePLNShyttEx5TF2bHVlJM++PG+NteOLtXNnSNYCEaA1kUt5qXxnvXGljeSURERERRSZmFwDkMim+e9tqpMdH4bGXTuOjU+0Tnu8fGkWXbRjZs5RVjslIjEJHzxCGHS60dg+y0YmPjXeu7GUiR0RERESRiYncWTq1HN/57CoUppvw513lePNw8/hzc90fNyYjQQ8RQGWzDd19Ixw94GNxxrMrctwnR0REREQRioncOZQKKb716RKsyTfj2Xeq8eIHdRBFEXVtdgjCJ41MZpNx9rh9p70reylckfMpjUqGKM3cRxB8eKoNj79W5ueoiIiIiIiWDhO588hlEnz1+iJcuCIRr+5vwDNvVaO2rQ/JsTqoFLI5nUOvVSBGr8SJ6m4A7FjpD/EmzZxLKz842YYDpZ1wezx+joqIiIiIaGkwkZuCVCLBF7Ytw1XrUvHOsRaUNVjnXFY5JiNRD7dHhFIhRYxh5k6XNH/xJvWcVuQcTjca2vvhEUX02h1LEBkRnW/Y4ULzDF2BiYiIaP6YyE1DEATcclkObtySBQAoSDfN6/Vj5ZXJsVpIBMHn8UW6uGgNbAOjcIzOPIKgrs0Ot0cEAFjY5ZIoIB5/vRwP/vUIhh2uQIdCREQUNuZWKxihBEHAtZsysKkoAdF65bxem5HoXcHj/jj/iDed7VxpHUJa/PR7F6uabeP/z0SOaOmVNfTiWJV3rEtFoxWr8swBjoiIiCg8cEVuDmIMKgjzXFXLTIiCWilFborRP0FFuPFZctaZk7OqZhtSzFpIJQLnzhEtMbfHg51vVyPWoIJSIcXp+t5Ah0RERBQ2mMj5iUYlxyPf2IxNxQmBDiUsxZ2zIjcdl9uD2rY+5KeZEGtUw2IbWarwpo3nxQ/q8N9PH4PDOXNJKFE4eO94G1q7B3HLZbkoSDPhTF0PRFEMdFhERERhgaWVfjTXLpc0f2qlDAatYsbOlY2d/Rh1epCfakSndSigpZUtlgH86bUyNHV6Gz6U1feyxIzC2sCwEy99WIeCdBNW58XCPujAiZpudFqHkRCtCXR4REREIY8rchSyZutcObY/LjfVCLNRje4AJHIej4jdBxvxH08chq3fga/fUAy1UooTNd1LHgvRmPaeQZys6fbr6thLH9ZhyOHCrVfkQhAEFGXFAABO1/X47ZpERESRhEtGFLLiojU4VTv9m8KqJhviozUwaBUwG9QYHHFhcMQJrUq+JPF1WYfw+OvlqG7pw5o8M+64Oh96jQJHKrtwsrYHHlFkR1MKiGferkZpfS+WpRlxx9Z8JMb4tilTS9cA9h5vxaWrksfnaMYZ1Yg3qVFa34sr16b69HpERESRiCtyFLLiTWrYB0enbGnuEUVUt/QhL8UAADAbvXvqupdgn5woith7vBX3//kwWiyD+NKOQnz9xmLoNQoAQElOLOyDo6hvt/s9FqLzuT0e1LT2IT0hCk2dA/jJ44fwz/drfbZvUxRFPPN2FTRKGW64KGvCc8VZMahotMLp4h5RIiKixWIiRyFrps6VrZZBDDlcyEs1AgDMRu9Q9qXoXPnByTY8+UYlcpL1ePCeC7CxOGFC19PlWTGQCAJOVLO8kpZeS9cgHKNuXH1BGn725Q24oCAerx9oxI//dBAnfVDye6zKgoomG264KAs69cTV7+VZ0Rh1eVDV3Lfo6xAREUU6JnIUsuLPNkyYap/c2P64/PFEzrsitxQNT87U9SLWoMK3b1mJaL1q0vM6tRy5KQafvGkmmq+qFhsAIDfFAINWgS9dW4jv3roKcpkEjz5/Cv/vhdPo6VvYyrXT5cbf361BslmLS1YlTXo+P9UEmVTAmXrukyMiIlosJnIUsj4ZQTA5OatqtsEUpUSMwZtIqZUyRGnkfk/kRFFETWsfclMMM84eXJkbixbLYEAasFBkq27pQ4xeNeFDhmXpJjxw9wX41MVZOFPXg+///gB++9IZVDRa59UQ5Y1DzejuG8Gtl+dCKpn860WpkCIv1YgzdZwnR0REtFhM5ChkKeVSmKKU6OqduCIniiKqmm3ITzVOSKbMRrXfE7nuvhH0DY4iJ9kw43Erc2IBgN0raUmJoojqFhtyUyd/f8qkElyzMQP/+aX1uHxNCsoaevHwzuP40Z8O4q3DzRgacU57Xo8oorV7EK8faMTqPDMKM6KnPbY4Mwat3YPotQd2riMREVGoY9dKCmneEQQTk7Mu2zD6BkeRe7ascozZqEZdm3/35tS0es+fPUsiFx+tQWKMBidqunEFO/jRErH0jaBvYBS5KcZpj4k1qPHZy3Nx05YsHCrvwt7jrdj5TjX++X4tLiiMR2GGCVa7A5a+EXTbhmGxDaPHPgKXW4RMKsHNl+XMGENxVjSe2wucqe/FlpLJ5ZdEREQ0N0zkKKTFmdQ4fl7TkKomGwCMNzoZYzaqcLi8C26PZ8qyL1+oae2DSiEdb7k+k5KcWLx1uBnDDhfUSv5TJP+rHputmDLzBw0AoJBLceGKRFy4IhGNHf3Ye7wVH5d14KNT7QAArUqGWKMaqfFRWJ1nRqxRjbxUI+LO7kedTnKsFqYoJc7U9TCRIyIiWgS+e6SQFm/SoH/IiaERFzQq77dzVYsNOrUcSTGaCceaDWp4RBE9dsesbzYXqqalD9lJekgks8+HW5kTiz0Hm3CmvhfrlsX5JR6ic1W32KBRypAUO7+5cekJUfj8tmW4+dIc9NpHEGNQLfjDB0EQUJwZjSOVFr9+qEJERBTu+BuUQlqcaXLnyqpm25TNRvzduXLY4UKLZWDWssoxOckG6NRynKi2+CUeovNVt/QhJ8Ww4EH0GpUMKXG6Ra8gF2fFYNjhQn1b/6LOQ0REFMmYyFFIi48e61zpTeSs/Q5YbCPjYwfONdbl0l+JXF27HaII5MyhbA0AJBIBK7JjcKq2B26Pxy8xEY2xD42ivWdoTmWV/laYYYIgAKfrOIaAiIhooZjIUUgbK5Hs6vUmZ2Pz485vdAIARp0SMqngt0SutqUPAoCsxLm/UV6ZE4vBERdqWjggmfyr9uz32EyNTpaKViVHdpKB8+SIiIgWgYkchTSFXIpovXJ8Ra6q2QalQoq0+MnNRiQSATEGNSw2/7Q9r2ntQ7JZO75Xby6KMqMhlQg4WcM3tORf1S19kEkFZCZGBToUAEBxZjQa2vvRPzQa6FCIiIhCEhM5CnnxJs34CIKqFhtykg3TNlAwG1V+WZHzeETUtvUhZ56rHWqlDMvSTTjOeXLkZ9UtNmQk6iGXSQMdCgDvPjkRQGkDh4MTEREtBBM5CnnxJjU6e4cwMOxEq2Vw0tiBc5mNalisvk/k2roHMexwIydZP+/XrsyJRWfvENp7Bn0eFxEAOJxuNHT0Iy8IyirHZCREQaeW40wdEzkiIqKFYCJHIS/OpMHgiAsnzs6Tm6rRyRizQY0hhwuDI06fxjA2CDxnjh0rz1WSEwMALK8kv2lot8PtEYOi0ckYiURAYYYJZ+p74RHFQIdDREQUcpjIUcgb61z54am2WfcA+WsEQU1rH/Qa+fj55yPWoEaKWYcTLK8kP6k62+hkrh1Vl8ryrBjYB0fR0jUQ6FCIiIhCDhM5CnnxZ2fJVbf0IWuWPUCfjCDwbcOTmtY+ZCdPnl03VytzY1HdYsPAsG9XCokA7/64ZLMWWpU80KFMUJQZDYBjCIiIiBaCiRyFPLNRjbH8aaqxA+eKNagA+HZFrm9wFF3W4UWtdqzMiYUoAqdr+YaWfMvjEVHb2hcUYwfOZ9QpkRqnQ2k998kRERHNFxM5CnlymQQxem+CNtP+OMDbJTJKI/dpIld7dn9cbvLM155JRmIUDFoFyyvJ51osAxh2uINqf9y5VmTHoLLJhuferYHD6Q50OERERCFj7gOviIJYvEmNHvsIsufQbMRsVKPLh50ra1q987nSEybPrpsriSCgJCcGh8q74HJ7IJPyMxbyjerxQeDBmchdszEdA8NO7DnUhGNVFty1bRkK0k2BDouIiCjo8d0ihYX1hQm4bHUK1MrZP5swG9U+XZGrae1DekLUoudzLUszYWTUjc7eIR9FRuTdH2eKUo6vWgcblUKGu65ehu/eugoQgF/sPI4ndldgyMedZYmIiMINEzkKCxeuSMTtV+bN6VizUYVeuwMut2fR13W6PGho71/Q2IHzxZ1t2tLlh4HlFJ4co24MO1zTPi+KIqpb+pCbsvBGPEtlWboJD9x9Aa5en4YPT7XhR386iONVlkCHRUREFLSYyFHEMRvV8Igievsdiz5XY2c/XG6PTxI5s3GsEYtvO2pSeHK6PPjZk0fx73/4GK2Wqdv3W6zDsPY7grLRyVSUciluvjQHP7pzLXRqBX79wmn88dUyiJwzR0RENAkTOYo4cT6cJVfTsvBB4OfTqeVQKaQ+n3FH4emlD+vQYhmAy+3Bz585jsaO/knHlNV7u6AG6/646WQm6vGTz6/FFWtScKC0A82cM0dERDQJEzmKOL4cCl7b2gezUQWDTrnocwmC4PP9exSeqppt2HOwCRevTMKP7loLpVyCh3ceR83ZDqpjyup7oVZKkWJeeCOeQJFJJdi2IR0AUNZgDXA0REREwYeJHEUco04JmVSAZZGdK0VRRE1rn09W48YwkaPZjIy68PjrZYgxqHDzpTmIN2nw/dvXQK+R45FnT6C88ZOkp6y+B9nJBkgkwb0/bjqmKCUSYzQoa+CcOSIiovMxkaOII5EIiDEsPmHq7B1C3+CoTxO5OKMa3X0j8HBPUESpbLJOSMBm8ty7Nei2jeCLOwrHu7TGGFT4/u2rEWtU4X//cRKnarsxOOJEY0d/yOyPm05hRjSqmm1wujhjjoiI6FxM5CgimY2qRTcVKT+7SjCX2XVzZTaq4HR50Dcw6rNzUnBzuT34zYtn8Iudx/HiB3UzJvGnanvw3ok2bL0gDXmpxgnPGXRKfO+21UiK1eLX/zyN59+rBQDkhdj+uPMVZURj1OUZ349KREREXkzkKCL5ooSxvKEXKoVv9x/5cv8ehYaTNd0YGHYiN8WAV/c34P+eP4XBKWaoDQw78Zfd5UiO1eLGLZlTnkunluPfPrsKmUl6vH+iDTKpgIxEvb+/BL/KTzNCKhFQyn1yREREEzCRo4gUZ1RjyOGa8g3zXFU09CI7Se/T/UdM5CLPvtMdMOoU+O5tq3DHVXkore/Fg08cQct5nRqffqsKA0NOfHFH4YzD5zUqGf715pVYlRuLTcuToJQvblB9oKmVMmQl6VHKfXJEREQTMJGjiLTYhGnY4UJju92nZZWAd6+TsIi4KLT0DY7iVG0PNhYnQCqR4NLVKfjebavhcLnxn08ewaHyTgDAofJOHCzrxHWbM5CeEDXreZUKKb75qRX4tzvW+vtLWBJFGdFo6ujHwPDCP3ghIiIKNwtO5Nra2nD77bfj6quvxte+9jUMDg5OOqarqwv33HMPrr/+etx44404cODAooIl8pWxRK5rgZ0r69rt8IhAjo/3H8mkEkTrlUzkIsSBMx3wiCIuXJ44/lhOigH3f34d0uKj8LuXS/HUm5V48o1KZCbqsX1jegCjDZzCzGiIwJwbwhAREUWCBSdyDzzwAG677Tbs2bMHxcXFeOyxxyYd8/DDD+Oyyy7Dyy+/jEceeQTf+c534Haz8xgFXqxBBWDhK18VjVZIBCAr0feNJLz79xbXiIWCnyiK2HemHdlJeiTGaCc8Z9Qp8d1bV+Gy1cl491grnC4PvrijAFJJZBZRZCZGQa2UorSe5ZVERERjFvSuwOl04vDhw9i6dSsA4KabbsKePXsmHXfllVdix44dAID09HQ4HA4MDQ0tIlwi31ArZYjSyBeUMHlEER+XdmBlXhw0KpnPYzMb1ejiilzYa+zsR6tlEJvPWY07l0wqweeuyse/3LQc//Kp5ZOSvUgilUiwLM2EsoZeiBzNQURLxNrvgMfDnzkUvBaUyFmtVuh0Oshk3jexZrMZnZ2dk47bunUrDAbvisXjjz+OgoICREXNvr+DaCkstHNlZZMNPXYHLl2b6oeovHHZB0fhGOXqdTj76FQ7ZFIJLiiIm/G41XlmFGfGLFFUwaswIxrdfSP8kIOIlkTf4Ci+97sDeOXD2kCHQjStWZcTdu/ejYceemjCY+np6RCEiZ36zv/zuZ544gn8/e9/x1NPPTWv4GJifNfWnbzMZibSY1Lj9aho7J33PXn6nWqolTJsKE6ASuH7FbnsNBMAwC2RROTfVyR8zU6XG4fKu7BpeSLSU6P9eq1wuZ8XrUnF029Voal7CMV58QGLI1zuZzDhPfUt3k/fKG1qhsvtwa79Dbh+S/aM73Npfvg96juzvgvdtm0btm3bNuExp9OJ9evXw+12QyqVwmKxIC5u6k+VH374Ybz//vt4+umnkZCQMK/genoGuKTtQ2ZzFCyW/kCHETSi1DJYrMNo7+iDTDq3xWnHqBsfnWzDBcvioFLI/HI/lWfHGVTWd0Mji6xfHJHyPXqkogsDw06szY/169cbTvdTLoqI0Stx8HQ71uXGBiSGcLqfwYL31Ld4P33n41NtAID27kF8dLQZy9JNAY4oPPB7dH4kEmHGha0FlVbK5XKsXbsWu3btAgC89NJL2LJly6TjnnjiCRw8eBA7d+6cdxJH5G9mowoeUUSvfe775I5WdcEx6samYv99P5uNY41Y2PAkXH10uh2mKCUK0/27GhdOBEFAQUY0yhut/ICPiPxKFEWUNVqxMicWWpUMH5xsC3RIRFNacAu0+++/H8899xy2b9+OI0eO4N577wUA7Ny5E48++ihEUcRvfvMb9Pb24o477sD111+P66+/fsq9dESBEDc+S27uCdO+0x2INaiQm2r0U1SATi2HWimNuBEEp+t6MOxwBToMv7MNOHC6rgebihN8Okw+EhRlRGPY4UJ9hz3QoRBRGGvvGYK134GSnBhcsiYVRyotnGNJQWnBG3ySk5Px5JNPTnr81ltvHf//w4cPL/T0RH4336HgvfYRVDRace3mDEj8WCsvCALMhoU1YglVHb1D+NVzJ1HWZMMtl2QHOhy/OlDaAVHEtN0qaXoFGd7SprL6XmQn+X70BxERAJQ1eEedFGVEQ6VV4vV99ThQ2oEr/dTkjGihInMoERG8s7pkUmHOCdOB0g6IADYtwRvwhXbUDFUVTd5Bz28dbESLZSDA0fiPKIrYd7oDOckGJERrAh1OyNFrFEiL16G0gYPBich/yhqsiDOqEWtUIzPJgMzEKHxwso3jTyjoMJGjiCWRCIid48rX2Bvw3BTDeEmmP40NBfdEyC+NyiYbojRyqFVy/GNv+LZ6rm/vR1v3IDYv557hhSrKiEZtax9GRsO/DJeIlp7L7UFFkxWFGZ80N7moJAmtlkHUtbGsm4ILEzmKaGMJ02zq2/vR0Tu0ZOVwZqMKLrcHfQOjS3K9QBJFEZVNVhSkm3DLFXk4XdeD0vreQIflF/tOt0Muk2DdssC1zw91hZnRcHtEVDbZAh0KEYWhhvZ+jIy6UZjxSTOq9QXxUMqleJ9NTyjIMJGjiBYfrUZr9yCaOmduhbvvjPcN+Nr8mYc3+8p89++Fsi7rMGwDo1iWZsKOCzMRa1Dh7+/WhF1nQqfLjYNlnViTZ4ZG5fv5g5EiN9kAmVSCMpZXEpEflDb0QgAmjBtQK2W4oCAOh8o7I6IpF4UOJnIU0batT0eURo7//cfJaccQOF0eHCrrxOolfANuNkVOIlfZbAMA5KcZIZdJ8elLstFiGcD+Mx2BDczHjld3Y8jhwuYVbHKyGAq5FHmphvFmBEREvlTW0IuMxCjo1PIJj28pScKo04OD5ey+TsGDiRxFNFOUEvd9pgQOpxu/+sdJDI1M/qTtZE03Bkdcfp0dd74YvQqC4F2tCneVTVbotYrx5h/rlsUhK0mPFz+sg8PpDnB0vvPR6XZE65UoSONQ2cUqyohGa/cgrP2OQIdCRGFk2OFCXZt9QlnlmKwkPZLNWnxwguWVFDyYyFHES4nT4es3LkdHzxB+8+JpuNyeCc/vP9MBg06Boil+sPuLTCpBdJQKlr7wTuREUURFkw35qUYIZ0c6CIKAmy/NgbXfgTcPNwc4Qt+wD42irN6KDYWcHecLY2+yuCpHRL5U2WyD2yNOmcgJgoAtJUlo6OifdTsG0VJhIkcE7yf8n9+2DOWNVjyxu2K8xbB9aBSn63qwsWjp34CbjaqwL6209I3A2u9AfppxwuN5qUaszjNj18eN6BsM/YYvxyot8IgiLihYmj2W4S41XgedWs5Ejoh8qqy+FwqZBDnJ+imf31iUAJlUgg/Y9ISCBBM5orM2L0/EDRdmYv+ZDrz8UT0A4GBpJ9wecUnLKsfMtaNmKKs8Oz8uP9U46blPX5INl8sz/ncRyg6VdyI+WoPUOF2gQwkLEkFAYYYJZQ1WznUiIp8pa7QiN9W7X3sqOrUca/PNOFDaGVal/xS6mMgRnePazRm4cEUiXtnXgA9PtmH/mQ6kx0chxbz0b8DNRjXsg6NwjIbvL4vKJht0ajmSYrWTnkuI1uCSVcn44EQbWrsHAxCdb/QNOFDZbMMFy+LGy0dp8YoyotE3OIrmrvAdIE9ES8fa70Bb9+Cs2yguKknCsMOFo5VdSxQZ0fSYyBGdQxAE3Lk1H0WZ0XhiTwUaO/uxKUDDm+PGOleG8T65yiYb8tOM0yY4123OgFIhwfN7a3x+7YFh55KMODhSaYEogmWVPrYiOwYAcLK2J8CREFE4GCvVPncQ+FSWpRkRZ1Kz6QkFBSZyROeRSSX4+g3FSDHrIJMKWF8YmOHN47PkAtC50jHqxisf1U/ZxdNXum3D6LGPTFlWOSZKo8COjRk4WduDFz6oRad1yCfXbusexL/9dj92vl3tk/PN5FB5J5JjtUgOwKpuODPolMhMjMKpmu5Ah0JEYaCswYoojRwps5TAjzU9qWrpQ3tP6FaLUHhgIkc0BbVShu/dtho/+fw66DWKgMQQyKHge4+34qWP6v26oXtsftyyWdrxX7E2BcVZ0XhtfyN+8PuP8R9PHMaeg03Tzv2bzajTjd++fAaOUTc+PNWGgWHngs4zF732EVS39GEdV+P8oiQ7FnVtdtjDoCEOEQWOKIooa+xFQboJkjmUwG8u9jY9+eOrZRgc8d/vEKLZMJEjmoZGJQvI3rgxWpUMaqV0yRueeDwi3j3WAgDYd6bdb80kKpts0KpkSDJP3h93LrlMim/fvBK//Pom3HxpDgDgub01+M5j+/HQU0fxztEWjIzOfeXw2Xeq0WoZxM2X5mDU5fFrsnqkwruH4oKCwKzqhruSnFiIAE7XsbySiBaurXsQfQOjcx4zZNAp8fUbitFiGcAvdh736weCRDNhIkcUpARBgNmgXvI9cseru9HdN4KizGi0WgbR1OmfZhKVzVbkpRrn9OknAETrVbh6fRp+8vl1eOgrG3DjliwMjbjw9FtV+Pkzx9E/NPuqzOGKLrx3og3b1qfh6vVpKEg34d1jLXB7PLO+diEOVXQhLV43PuycfCstXgejToGTLK8kokUobfB2UJ5qftx0VubG4pufWoG27iE8/MwxVgZQQDCRIwpiZpN6yUsr3z7SjBi9Cl+6thAyqYB9p9t9fo1e+wgstpFZyyqnE2/S4NpNGXjwi+vxzU8tR1v3IP776WOw9jumfU2XbRhP7C5HdpIeN27JAuAt2+y1O3CsyveJQLdtGHVtdq7G+ZEgCFiRHYsz9b1wuf2TjBNR+Ctr6EW8SY0Yg2per1ueFYP/7zMr0GUdxsM7j6NvYPrfQUT+wESOKIiNzZLzLNGsrKbOflQ223D5mhToNQqszInFx2WdPn+TXNlkA4BJg8AXYlWuGfd9pgS9/Q489NRRdE3REMXl9uD3L5+BAAFfua4IMqn3R19JdizMRhXeOty86DjOd/hsWeW6Zdwf508lOTEYGXWj6uyeSyKi+XC5PahssqEwc+6rcecqyojGvZ8pQU/fCH7+zPEZP1AURRG2AYffqkAo8jCRIwpiZqMaLrcHfQNLU7Lx1pFmKOQSXFSSCADYtDwRA8NOnPZxi/fKZis0St/tQVyWbsJ3b12FYYcLDz11DC2WieWg/3y/FvXt/fjC9mWIPdtEBgAkEgGXr0lFTWsf6tvtc7rWXEcWHCrvQmaifrxpDflHYUY05DIJTtZwnxwRzV9dmx0OpxuF6QtL5ADv76D7bi6BdcCBnz9zbLwZlyiK6Owdwgcn2/DHV0vxb7/dj2//v314+aMGH0VPkY6JHFEQMxu9ZR5TrTL5mn1wFAfLOrG5OBFalRwAUJwZDb1Gjn1nOnx6rYomm3d/nMR3A7IzE/X4/u2rAQH4+dPHUNfmTcxO1nTjjUPNuGx1MtbkT14du3B5IpQKKd4+Mvuq3MCwEz/600E89tKZGRO6TusQGjv7OTtuCSjlUhSkm3CypttvjXmIKHyV1vdCEICCdOOizpOXasR3blmJ/qFR/PfTx/C7l8/g27/Zhx/84WM8sbsCpfW9yErUI8WsxaGyTv68Ip9gIkcUxD4ZQeD/zpXvnWiFyy3iirUp44/JpBJsKErAyZpun3XlsvY70GUd9klZ5fmSzTr84HNroFHJ8Itnj+NAaQcef70caXE63HJZzpSv0ahkuHB5Ig6Vd8E2w/4Gj0fEH14tRZd1GEcquvD0W1XT/iI+VM6yyqVUkh2DLtswOnr9/4EHEYWXssZeZCbqoTn7AeZiZCcb8J3ProLT7UFVsw3L0ky4c2s+fval9fjVNy/E129cjsvXpKDLNozmLv80EqPIwkSOKIjF6FUQBP/PknO5Pdh7rBXFWdFIjJk4DmBTcQLcHhEHyzp9cq3KJm93MH8kcgAQZ1Tj+7evQYxehT++Wgany4Ov3lAMuUw67WuuWJMCj0fEe8dbpz3mlX31OFPXi89dlYdt69Ow93grdn3cOOWxh8s7kZNiQLR+fhvnaWFWZMcCAMsriWhemjr7Ud/WP69ulbPJTNTjkW9sxiPf2IyvXFeES1YlIzFGC+Fsh+ZVuWYIAI5VWXx2TYpcTOSIgphMKkGMXuX3EQSHy7vQNziKK9emTnouLT4KqXE6n3WvrGy2Qa2UIi0uyifnm4opSonv374a65bF4UvXFs7a/j8+WoMV2TF473grnK7Jm9BP1fbg1X0N2FycgItXJuFTl2RjQ1E8/vl+3aT70tY9iBbLIC7gatySiTGokGLWcQwBEc1ZaX0vHnr6GIxRCmw5uy/cVySCMJ64nU+vVSA31YijTOTIB5jIEQU5b+dK/yVyoijirSPNSIjWoGiarl2bixPQ0NGP1u7BRV+vssmG3BTf7o+bik4tx9duKMbqPPOcjr9iXSrsQ04cKp+48mixDeOPr5YiJU6Hz23NhyAIkAgC7t5egIJ0E57YXYEz9Z+sBB0q74QAYC0TuSVVkhOD6pY+DI5wMC8RzWz/mXb87z9OwmxQ44d3rEWsYWmbUq3JN6PVMshycFo0JnJEQc5sVPl1j1xtqx0NHf24Ym3KtMO51xclQCII2L/IVTnbgAMdvUN+K6tcjMJ0E5JjtXjrSPP43jeny43HXjwDjwh848ZiKOWflGfKpBJ848blSIzR4jcvnkFjRz9EUcSh8i7kpxlh1CkD9aVEpJKcWHhEEWfqegMdChEFKVEU8fqBBvzptXLkpRrx/dtXwxS19D+r15z9gPFoZdeSX5vCCxM5oiBnNqphHxzFyKjLL+d/60gzNEoZNhUnTHuMQavA8qxoHCjtmHP7/amMzfpa6CBwfxIEAZevTUFT5wCqW/oAAE+9WYXGzn58aUch4kyTyzM1Khnuu7kEWpUM//uPkzhR3Y2O3iGs4xDwJZeVqIdOLcfJ2pnLKz1n24ETUWTxeEQ89WYV/vl+HTYUxuO+m0ugUckCEku0XoXMxCjuk6NFYyJHFOTGOld2+2FVrtc+gqOVFmwpSYJKMfMvtM3LE2EbGEVZw8JXPCqbbFAppEiL9838OF/bWJQArUqGtw4344OTbfjwVDuu2ZiOlbmx077GFKXEfTevhNPlwf978TQkgoA1+XMr5yTfkUgErMiOwenanmmH7XpEEX95vRw/+MPHTOaIQszAsBOPvXQGh8rn37rf4XTjNy+ext7jrdi2IQ1fvLYQMmlg3wKvyY9DfXs/evr835WawhcTOaIg98kIAt/vk3vnWAtEiLhsTfKsx5bkxEKrki1qplxFkxU5KQZIJcH5o0cpl2LLyiQcq7bgqTerUJhhwo0XZc36uuRYLb716RWQSiQozDBBr1EsQbR0vpKcWAyOuFDbOnm4uyiK2PlW9fj3b01r31KHR0SLcKSyC0cquvC7l0vx8DPH0dTZP+trRFFERaMVDz9zHCequ3H7lXn4zCU5024jWEpj+7e5KkeLEZzvpoho3GyJ3NCIC4crutDU2T/tSsT5nC4Palv78MGJNqzONc9po7dcJsEFBfE4VmXB0Mj8yzzbugfR3jOE/FTjvF+7lC5fnQIBAvRaOb58XdGcm7LkpRrxwN3r8MVrC/0cIU2nKCMaUokwZXnlCx/U4Z1jLbhqXSqUcikaOmZ/E0hEweNkdTdiDSrceXU+WrsH8cATh/G3PRXoHxqddOzIqAt7j7fiJ48fwsM7j6PLOoSv31iMy9ekTHHmwEiI1iDZrGX3SlqUwBQHE9GcaVUyqJWySQ1PRFHE0UoLnn67Cn0D3l9kCrkEGQl6ZCXpkZXo/a8pSglL3wjq2vpQ12pHXbsdTZ39cLlFyKQCrl6fNudYNi1PwN7jrThS2YUtJUlzfp3T5cEfXimFTi3H5uW+bfPsa9F6Fe67uQRmo2reK2vnz+CjpaVRyZCXasSpmh585pJPBsC/fqABrx9oxCUrk3DLZTloaLejoX3yqh0RBSeH042yRiu2lCThkpXJWLcsDi9/WI93j7XiUHkXrr8oE5euSkZ33wjePdaCfafbMexwIy1ehy9sX4b1BfFQyKefJRooa/LMeHVfA/oGR2HQspKD5o+JHFGQEwTB27nynFlyPX0jePqtKpyo6UZavA5fvKYQ/cOjqGuzo67NjrePNMPl9u4hUMglGHV6V+oUMgkyEvW4Ym0qshL1yEkxzKu7YlaiHgnRGuw73T6vRO6f79eiqWsA3/rUipDo5jjdGAYKfiU5sXj2nWpYbMMwG9V452iLt7lBUfz4+IiMRD32Hm+Fy+0J+D4ZIppdeaMVTpcHK3O8+5W1KjluuzIPF69Mws53qrHz7Wrs+rgRfQOjkEoErF0Wh8vXpCA7ST/tPLdgsCY/Dq/sa8DxagsuWTn7Fgei8zGRIwoBZqMarZZBeDwi3j7aghc/qIMIEbdcloMr1qaM7znbUOjtPOl0edDcNYC6tj509g4jyaxFdpIeyWbtovanCYKAzcsT8M/369DSNYCUuNmblpyq7cGbh5tx+eqUGZuGEPlCSU4Mnn2nGqdqe6BSSPH0W1VYlRuLu7cXjO+LyUiMgvOwB23dg0iL999geiLyjZM13VAqpMg7rzQ/2azDv96yEseru7H3WAsuXZmMLSuTQuIDQwBIMWsRZ1TjWCUTOVoYJnJEIcBsVONkTTce/NsRNHb0Y0V2DD53ZR5ijVPvbZPLJN7yyiS9z2PZvDwRbxxqxi+fPY77bl6J9ITp3wj3DTjw+OtlSDFrcfNl2T6Pheh88SYNEqI12HOwCb39IyjMMOGr1xdNWHnLTPT+u2jo6GciRxTkRFHEyZpuFGdGQy6b/EGkIAhYnWcebx4SSoSzXY7fPNyMoREnNCp5oEOiEMOaEqIQEGdUw+UWYe134KvXF+H/+/SKaZM4fzPqlPjB51ZDLpPgv585htJpxhF4RBF/eq0MjlE3vnJ9MeSy4NufQOGpJCcGPfYRZCcZ8M2bVkz63oszqqFRylDPfXJEQa+pcwC2gdHxsspwszrfDLdHxImamWdgEk2FK3JEIWB9YTzcHhEbi+KD4hO7xBgt/v2Otfif507gf587iS/uKMT6wolDsN881IzSBivuvDofybFsAkJL54o1qRBF4LrNGVAqJn+A4N0nF4WGdnauJAp2J2u6IQBYnhUT6FD8IjPR25TsaKUFm4oX1gyssaMfBp0iZEpKyXe4IkcUAtRKGS5fkxIUSdwYU5QSP7h9NbKT9Pj9K6V460jz+HP17Xb88/1arMk34+J5NEUh8oUYgwqfvTx3xn8vGQl6tFgG4HS5lzAyIpqvEzXdyErWQx+mXR0lZ0tDz9T3wjE6/59HrZYB/OzJI/jf507CM89B6RT6mMgR0YJpVHJ8+5aVWJUbi51vV+P592ox7HDh96+UwqBT4PPblgV1xzCKXJmJUXB7RDR3DQY6FCKahrXfgYaOfpRkh2dZ5Zg1eWY4XR6cruuZ1+tcbg/++FoZAKCpawAfl3b4IzwKYkzkiGhRFHIpvnHjcly8Mgm7Pm7Ejx8/CIttGF++tgjaIFpBJDpXRoK34Qn3yREFr7HEJlz3x43JTTVAp5bPezj4q/sa0NQ5gK9cV4T0hCi88EEdqwwiDBM5Ilo0iUTAnVvzcd3mDPTaHbhuc+akNtFEwSRar4ReI0dDBxM5omB1orobMXoVks3hvc9aKpFgdV4sTtZ0w+nyzOk1dW12vH6gEZuKE7AmPw43X5qDXrsDbx9t8XO0FEyYyBGRTwiCgBsuysIvvrYJ123OCHQ4RDMaGwzOhidEwWnU6UZZYy9KcmIiokR/dV4cRkbdKJumE/S5HE43/vRaGYxRCtx2RS4AoCDdhBXZMXhtfyMGhp3+DpeCBBM5IvKpGIMqIn7pUujLSIhCW88gRkZdgQ6FiM5T0WTFqNMT9mWVYwozTNCp5XhiTwXKG60zHvvP92rR0TuEu7cXTGjq9OlLsjEy6sJr+xv8HC0FCyZyREQUkTIS9RBF75wqIgouJ2p6oJRLkZ9mDHQoS0ImleA7n10JtUKGX+48jhc/qIPbM7nMsryhF28fbcHla1JQmBE94bkUsw4XLk/EO0dbYLENL1XoFEBM5IiIKCJlJkQBABrY8IQoqIiiiJM13SjKjIZcNnkWZLhKi4/C/Z9fh83LE/Hq/gY8/Mxx9PSNjD8/NOLC47vKER+twacvyZ7yHDdclAWpRMALH9QtVdgUQEzkiIgoIhl0SpiilKjv4D45omDS3DUAa78DJTnhOQR8JkqFFHdfU4AvX1uI5q4B/PQvh3C00tvNcufbVbD2O/DFHQVQyqdOcE1RSlx1QSoOlnWyK28EYCJHREQRKzNRzxU5oiBzsqYbAoAVYT4/biYbihLw0y+sg9moxm9ePI3//cdJ7DvTgWs2ZiA7yTDja7etT0eURo7n3q2ByCHhYY2JHBERRayMhCh0WocxNMIub0TB4kRNDzKT9DBoFYEOJaDiTBr8+x1rsPWCVJyq7UFavG5OXaHVShmu25yJymYbTtXOb8g4hRYmckREFLEyE72DwRsCUF5pHxzFQ08dxana7iW/NlGw6hscRX27HSXZkVdWORWZVIJbLsvFTz6/Ft++ZSVk0rm9db94ZRLiTWr8473aKZumUHhgIkdERBErfazhSQASuRM13ahu6cNjL55BdYttya9PFIxO1Xg/2CiJkLEDc5WRoIdeM/cVSplUgk9dnI227kF8dKrdj5FRIDGRIyKiiKVTyxFnVAekKUBpfS/0WgVMehUe/ccptHRxDALRiZpuROuVSI3TBTqUkLcm34y8FAOe21vDcQRhiokcERFFtIzEKDS0L+2KnEcUUd5oRXFmNP71lhIoFVI88twJvtmisGcbcOB3L5/BH18txdNvVuGFD+qw52ATPjjZhsMVXShrsKIkOxaCIAQ61JAnCALu2VEIAPjDK6VwuVliGW6YyBERUUTLSNCjxz4C+9Dokl2zqbMfA8NOFGVEI9agxrdvLoHL5cEjz55A3+DSxUG01F7Z14CjlRZUt/Th47IOvH6gAc/trcETuyvw25fOwOF0Y3WeOdBhhg2zUY27rl6G2jY7XtlXH+hwyMdkgQ6AiIgokDITxwaD92PFEjVYKK3vBQAUZpgAAMlmHe79TAl+8exx/OrvJ/Dd21ZDo+KvaAov3X3D+PBkG7aUJOGOrfkAvMO/R0bdGHa4MDTigkcUWVbpYxcUxONMfS9e39+IgvRoFKSbAh0S+QhX5IiIKKKlxUdBABY1T87hdM/r+LIGK1LMWhh0yvHHspMN+MaNy9HaPYhf//MUnK75nZMo2L1+oBGCAFyzMX38MUEQoFbKEK1XISVO5/33yLJKn7v9ijzER2vwx1dL0b+E1QfkXwtO5Nra2nD77bfj6quvxte+9jUMDg5OOqarqwuf//zncd111+Ezn/kMysvLFxUsERGRr6mVMiTEaBbcubKhw45/+dUHqGnpm9Pxo043qlv6UJgRPem55VkxuGdHAaqabfjdy6VsG05ho9s2jI9OteOikiRE61WBDifiKBVSfOW6IgwMO/GXXRUcFB4mFpzIPfDAA7jtttuwZ88eFBcX47HHHpt0zK9+9Sts3boVr7zyCr75zW/igQceWFSwRERE/pCZqEd9u31Bb25O1/bA7RHxwcm2OR1f1WKDy+1BUebkRA4ANhQm4LYr83C8uhvvHZ/bOYmC3Wtjq3Eb0mc/mPwiPSEKn7kkBydquvHusdZAh0M+sKBEzul04vDhw9i6dSsA4KabbsKePXsmHfezn/0Mt9xyCwCgpaUFer1+EaESERH5R0ZCFPoGR2EbmH/JUVWzDQBwpLJrTiWWZfVWyKQC8lKM0x5z+ZoUZCXp8daRZnj4yTmFOIttGPtOt2MLV+MC7oq1KViRHYO/v1uDZo48CXkL2klttVqh0+kgk3lfbjab0dnZOek4icSbJ1599dVobW2dctVuJjEx3Ozqa2ZzVKBDCCu8n77He+pbvJ9zs6owAc+8XY2eQSfysqYfRHz+/XS7PahtsyMjUY+GdjvqOgewZVXKjNeqbLGhICMGKcnGGY/79GV5ePipI2i0DOGCooQ5fy2hht+jvhWM93PnuzWQSATcuaMIMQZ1oMOZt2C8p4vx3TvX4Zu/3Is/vV6G/7n3YqgUS9tYKdzuZyDN+je3e/duPPTQQxMeS09Pn7QRdaaNqXv27EF5eTnuvvtu7N69G0ajcU7B9fQMwOPhJ5G+YjZHwWJZ2llJ4Yz30/d4T32L93PuouQSSAQBp6o6kZMw9YeIU93P+nY7Rkbd2LouFc/trcEbBxpQkGKY9jp9g6Oob7Pjpi1Zs/7d5CTqYIpS4vl3qpAZp53/FxUC+D3qW8F4P7tsw3j3SDMuWZUMz6gr6OKbTTDeU1+455oCPPLsCfzpxVO45bLcJbtuuN5Pf5FIhBkXtmZN5LZt24Zt27ZNeMzpdGL9+vVwu92QSqWwWCyIi4ub9Nr33nsP69atg1arRUFBAZKSktDc3DznRI6IiGgpKORSJJu1qJ/nYPDKJhsAID/NiA1F8XjjYDP6Bkdh0CqmPL68wTt2YLr9ceeSSSW4fE0Knn+vFk2d/UiL56fYFHpe298AQRCwnXvjgkphRjSKsqLHR6FQaFrQHjm5XI61a9di165dAICXXnoJW7ZsmXTciy++iOeeew4AUFNTg+7ubmRlZS0iXCIiIv/ITIxCwzwbnlQ12xBvUsOoU2JTUQI8oohDZZO3GowpbeiFViVD+hyTsi0lSVDIJXj7SMucYyIKFl3WIew/3YFLVibBFKWc/QW0pNLiotDeMwSXm91xQ9WCu1bef//9eO6557B9+3YcOXIE9957LwBg586dePTRRwEA//7v/46PPvoI1113HX7wgx/gkUcegVYbnuUhREQU2jIS9BgccaHLNjyn4z2iiOoWG3JTjQC8Q73T4nXYX9ox5fGiKKKswYqCdBMkkrnNydKp5dhcnIiPyzpgH+TsJwotr+1vhFQqYBtX44JSapwObo+I9p6hQIdCC7Tg3Y3Jycl48sknJz1+6623jv9/fHw8Hn/88YVegoiIaMkUZJgAACdrenDVOs2sx7d1D2JwxIX8s4kcAGwqSsCz79agrXsQSbETP7hs7xmCtd+BwjmUVZ7rirUp2Hu8FXuPt+L6CzPn9VqiQOm0DmH/mQ5ctiaZq3FBKiXOu/eqpWsAqXFsMBiKFrwiR0REFE7iTRqkmLU4VmWZ0/FjYwfyzknk1hfGQxCAA1OsypWO7Y+bYhD4TBJjtFiRHYO9x1rgdLEEioKDKIrotY/APjQ6ZWnea/saIJVyb1wwS4hWQyYV0GzhGIJQtbT9RomIiILY6jwzXt3XMGPDkjFVzTaYopSINXwyF8ugU6IoMxofl3bgxi1ZkJzT0bm8wYo4oxpm4/zbr1+5NhWP/P0EDpZ14sIVifN+PZGvnajuxq9fOD3+Z6VcCo1KBo1SBrVKhrpWOy5fkwKjjqtxwUoqkSApVosWzpMLWVyRIyIiOmtNfhxEACeqZ16VE0URVc025KUaJ43f2VSUgB67A1VnO1oCgMvtQUWTdd5llWMKM0xIjtXirSPN82rGQuQvx2u6oVbKcNsVubjhokxcvDIJRRnRiI/WQCYRkJdqwPaNXI0LdqlmHVfkQhhX5IiIiM5KMWsRZ1TjaJUFF69MnvY4i20YtoHRCWWVY1blmaFUSLG/tAPL0r377uravPPmCs/+eb4EQcCV61LxxO4KVDbZxs9LFAiiKKK0vheFGSZcsTY10OHQIqTE6bDvTAfsQ6PQa2auQqDgwxU5IiKiswRBwOp8M8obrBgacU57XOUU++PGKOVSrM0z42hlF0adbgBAWUMvBOGThioLsaEwHjq1HG8ebl7wOYh8oaPX27hnvvs9KfiMNTxpZXllSGIiR0REdI41eWa4PSJO1vZMe0xVsw06tRxJMVN3t9xYnIBhhxsnaroBeBudZCTooVXJFxyXQi7FJauScbKmG51WtgunwBkbIr3QUmEKHqlmbyLXbBkMcCS0EEzkiIiIzpGZpIdRp8Cxyun3yVU39yE3xTBpf9yYZWkmmKKU2H+mA0MjLtS39aMoc/HlkJetToZEInBAOAVUWYMVZqMKcQto3EPBRa9VQK9VsOFJiGIiR0REdA6JIGB1nhmn63rgOFsaeS5rvwNdtuEJ8+MmnUMiYENRPM7U9eJwRSc8ouiTMjSjTokLCuLx0an2GUs/ifzF5fagvMmKosyYQIdCPpJq1rLhSYhiIkdERHSeNXlmjLo8OFPXO+m5sflxuTMkcoC3e6VHFPH8e7VQyqXITjb4JLar1qXC4XRjz6Emn5yPaD7q2uxwjLpRtIj9nhRcUuJ0aOsehNvDOZWhhokcERHRefLSjNCqZDhW1TXpuapmG5QKKdLidTOeI9msQ1q8DoMjLuSnGSGT+uZXbnpCFDYWxWP3x01o7ea+FlpapfVnG/ewc2rYSDHr4HR50GUdDnQoNE9M5IiIiM4jlUiwKteMkzU9cLknfkpd1WJDbrIBUsnsv0I3FSUAAAp93N3vlstzoVJI8dc9FfBwrhwtobKGXmQl6qFZROMeCi6pZztXNnOfXMhhIkdERDSF1flmDDlcqGiyjj82MOxEq2VwyrEDU9m8IhGblydgfWG8T2PTaxS4+dIc1LT04cOTbT49N9F0BkecqGu3+/yDCQqsxBgtJIKAFu6TCzlM5IiIiKZQlGGCUiGd0L2yeob5cVPRquS455pCGLS+H7R74YpE5Kca8Y+9tegbcPj8/ETnq2i0QhSBIo4dCCtymQSJMRq0dLFUO9QwkSMiIpqCXCbFiqwYHKvuhsfjLV+sarFBJpUgMzEqwNF5h5ffeXU+Rl1u7HynOtDhUAQobbBCqZAiK0kf6FDIx1LidCytDEFM5IiIiKaxJt8M++Aoalr7AHgbnWQl6SGXSQMcmVdijBbXbMzAofIunK6bfoA5kS+U1feiIM3ks8Y9FDxSzFr02EcwNOIKdCg0D/yXSERENI3lWTGQSQUcq7JgaMSJxo4B5KX6ZoyAr2zfkI7EGA2efKMSjtHJc++IfKHLNowu2zAKOXYgLI01POE+udDCRI6IiGgaaqUMRRnROFppQUWDFR5RnPP+uKUil0lw59Z8dPeN4OV99YEOh8JUWb13piL3x4WnFDMTuVDERI6IiGgGq/PN6LGP4NWP6iARBGQnBdeKHADkp5lw0YpEvHmoGU2d/YEOh8JQaUMvTFFKJERrAh0K+YEpSgmtSoYW7pMLKUzkiIiIZrAyJxYSQcCR8k6kJ+igVsoCHdKUPnNpDnRqGf66p3K8OQuRL3g8IsobrCjKjIYgCIEOh/xAEASkmNnwJNQwkSMiIppBlEaB/DQjACA3xRjQWGaiU8vx2ctzUd9ux6HyzkCHQ2GkvsOOIYcLRZwfF9ZS43RosQzCI/KDoFDBRI6IiGgWq/PMAOY+Py5Q1hfGQ6uSobzROvvBRHM0tj+ugI1OwlpKnA4OpxvdtuFAh0JzFJz1IUREREHkwuWJUKnlWJEdE+hQZiQIArKSDKhrtwc6FAojpQ1WpMXroNf4frA9BY+xzpXNXYOIM3EvZCjgihwREdEslAopbrg4JyTmZ2Un6dFmGcSwg/OgaPGGHS7UtvaxW2UESIrVQgA7V4aS4P+NRERERHOWlayHCKCeq3LkA5XNNrg9IvfHRQClXIq4aA07V4YQJnJERERhJCtRDwCobWMiR4tXVt8LuUyC3JTgG7tBvpdq1qKZK3Ihg4kcERFRGNGo5EiM0aCutS/QoVAYKG3oRX6qEXKZNNCh0BJIidPBYh3GyChLs0MBEzkiIqIwk5WkR22bHSLbiNMi9NpH0N4zhEKWVUaMVLMOIoDW7sFAh0JzwESOiIgozGQnGTAw7ISlbyTQoVAI23emAwBQzEYnESPlbOdK7pMLDUzkiIiIwkxWknefHMsraaGauwbwykf1WJNvRrJZG+hwaInEGFRQKaRo6eKKXChgIkdERBRmks1aKOVSNjyhBXG6PPjjq2XQquW4c2s+BEEIdEi0RCSCgBSzjg1PQgQTOSIiojAjlUiQmRiFujauyNH8vfRRHVosA/j8tmWI4hDwiJMSp0NL1wD32IYAJnJERERhKDNJj6bOAThd7kCHQiGkqtmGPR83YUtJIlbmxAY6HAqAVLMWQw4XrP2OQIdCs2AiR0REFIaykwxwe0Q0drJEiuZmZNSFx18vQ4xBhVsuyw10OBQgYw1PmtnwJOgxkSMiIgpDbHhC8/XcuzXoto3gizsKoVbKAh0OBUhy7NnOldwnF/SYyBEREYUho06JGL2KDU9oTk7VduO9E23Yuj4NeanGQIdDAaRRyRBrUHE1PwQwkSMiIgpT2cl6NjyhWQ0MO/GXXRVINmtx40WZgQ6HgkBRZjROVHejb4D75IIZEzkiIqIwlZVkQI/dwaYFNC1RFPG3NyoxMOzEl3YUQi6TBjokCgJXr0+D2+PBG4ebAx0KzYCJHBERUZga3yfH8kqaxnvHW3Gkogs3XJSJtPioQIdDQSLepMEFBfHYe7wVA8POQIdD02AiR0REFKbS43WQSgTUtbO8kib76FQ7nnyzCiuyY3D1+rRAh0NB5poN6XCMuvHO0ZZAh0LTYCJHREQUpuQyKdLio1DXyhU5mujj0g78ZVc5ijJM+MaNxZBK+JaQJkqJ02FlTizePtKMYYcr0OHQFPivloiIKIxlJ+lR32GH2+MJdCgUJI5UdOFPr5UjP82If/nUCu6Lo2ldsykdgyMuvH+iLdCh0BSYyBEREYWxrGQ9Rp0etFoGAx0KBYHj1Rb8/pVSZCXr8a1Pr4BSziSOppedZEBBuglvHGqC0+UOdDh0HiZyREREYSw7yQAAETFP7vevlOIvu8oDHUbQOl3Xg9++dAZp8VG47zMlUCk49Jtmt2NTBvoGR/HRqfZAh0LnYSJHREQUxmINKkRp5GE/T27Y4cKRii7sO93BcQtTKGvoxa//eRpJsVp8+5YSqJVM4mhulqUZkZ2kx66Pm+Bys0Q7mDCRIyIiCmOCICA7yRD2IwgqGq1we0R4RBEfnOR+HgBwezyoae3DSx/W4f+eP4WEaDW+89lV0KrkgQ6NQoggCLhmUwZ67CM4WNYZ6HAm2Hu8Ff/z9xMQRTHQoQQEP44hIiIKc1lJepyo6cbgiDNs38SfruuBUiFFVqIeH5xsw45N6RHZibG7bxil9b04U9+L8gYrhhwuCADy04z46vXF0KnD8++f/KskOwYpZh12fdyIjcUJkAhCoEOCRxSx++NGdPeNoLlrICLnIDKRIyIiCnPZZweD17fZUZwVE+BofE8URZyu60Vhugmblyfi/71wGqdqerAqzxzo0JZMZZMVP378EFotAwAAU5QSa/LNKMqMRmFGNBM4WhRBELBjUzp+93IpjlVasHZZXKBDQk1LH7r7RgAARystTOSIiIgo/GQk6iHA2/AkHBO5jt4h9NhHcM3GdJTkxMCoU2DvidaISeRGRl3442tlkMuk+OzluSjOjEZijAZCEKyaUPhYmx+HeFMdXjvQgDX55oB/f+0/0wGlXIqkWA2OVVlw45asgMYTCJFXc0BERBRh1EoZksxa1IZpw5PTtT0AgOKsaEglEmwpSUJpXS+6bMMBjmxpvPRhPXrtDvzrbWtw1bpUJMVqA/4mm8KPRCJg+4Z0NHUO4Ex9b0BjcbrcOFzRhTX5ZmwoSkBr9yDaeyJvxAoTOSIiogiQnaRHfZs9LJsCnK7vRWKMBrEGNQDg4pXJEAQBH0TAEOPGjn68daQZl6xKRkFmdKDDoTC3sTgB0Xol3jrSHNA4TtT0YNjhwsbiBKw5u/J+rMoS0JgCgYkcERFRBMhKMmBwxIVOa3itUjmcblQ22bD8nJJRU5QSJTkx+PBUW1i3S/d4RDyxpwJRGgU+fXHklZXR0pNJJSjOjEFDe39APxTaf7odpiglCtJMiNarkJkYxURuPtra2nD77bfj6quvxte+9jUMDk6/nDkwMIArrrgCBw8eXOjliIiIaBHGGp7UtoZXeWVlkxUut2dCIgcAl65KRv+QM6zf3L1zrAWNHf247YpcaMK0GykFn9Q4HQaGnbANjAbk+vbBUZyp78WGwnhIJN4S4tV5ZtS396PnbPOTSLHgRO6BBx7Abbfdhj179qC4uBiPPfbYtMc++OCDsNvDe34NERFRMEuM1UKtlIbdPLnTtb1QyCXISzVMeLwwMxqxBhX2HmsNUGT+1WsfwQsf1KE4KxrrgqCDIEWOFLMWANBytkPqUjtY3gm3R8TG4oTxx9bke/8NHKsO3w9uprKgRM7pdOLw4cPYunUrAOCmm27Cnj17pjx2165d0Gq1yM/PX3iUREREtCgSQUBmoj7sGp6cru/BsjQT5DLphMclgoBLViWjstmGtu7wa4Lw9FtVED0i7rgqn41NaEmlxukAAM1dgUnkDpzpQFq8Dilm3fhjCdEaJJu1OFY5t0TOI4oYdrj8FeKSWVAiZ7VaodPpIJN5pxeYzWZ0dk6e9N7W1oa//vWv+O53v7u4KImIiGjRspIMaOkahGPUHehQfKLTOoQu6/CkssoxFy5PhFQi4L0T4bUqd6zKguPV3bj+wkyYjepAh0MRRqOSI0avREsAErm27kE0dPRjU3HipOfW5JlR1WKDfXD2ks/n99biu7/dD4cztH8WzjpHbvfu3XjooYcmPJaenj7p05/z/+zxePDDH/4QP/7xj6FSqRYUXEyMbvaDaF7M5sgbluhPvJ++x3vqW7yfvhXq93N1QTxe298A24gLxcnGQIcDYHH39ODZT98vXpsGc6x2inMDm1ck4UBpJ77yqRKoFKE/PndoxIln36lGRqIet20vhEw68TP5UP8eDUa8p5NlpRjR3ju0oHuzmPu5+3CzdwzCRVkwRU3ML67YkIFX9jWgpmMAWzekT3uO5k5vp1e3R0S7bQTrChOmPTbYzfoTbdu2bdi2bduEx5xOJ9avXw+32w2pVAqLxYK4uIn12XV1dairq8MPf/hDAEBTUxN+9KMf4cEHH8SGDRvmFFxPzwA8nvBrkxwoZnMULJb+QIcRNng/fY/31Ld4P30rHO5njNbbEONYWQfi9coAR7P4e3rgVBviTGrIRM+059lYGIcPTrRi94d1uHDF5E/xQ80zb1ehp28EX7muCNbeiSWj4fA9Gmx4T6cWZ1DhWEUX2tr7IJfNvcBvMffTI4p453ATijOj4RpxwjLinPC8VibAbFTh/aPNWJ099SgOURTxm3+chEIuhccj4oNjLcgwT/4QKFhIJMKMC1sLKq2Uy+VYu3Ytdu3aBQB46aWXsGXLlgnH5OTk4P3338fLL7+Ml19+GcXFxfjP//zPOSdxRERE5FtRGgXiTGrUhkHDE6fLjYpGK5ZnTl1WOSYv1YjEGE1YlFc2dNjxztEWXLI6GdnJhtlfQOQnqXE674rWEg7hrmyyodfuwMaiqVfQBEHAmrw4lDX0Yui8JG/MydoelNb34voLM1GYYcKp2u6Qnq254K6V999/P5577jls374dR44cwb333gsA2LlzJx599FFfxUdEREQ+lJ2kR21rX0i/eQGAymYbRl0eLJ/mk/cxwtmmJ3VtdjR2hPbKyssf1kOrkuNTW7IDHQpFOF81PHG5PThebcFjL53B718pRd+AY9pj959ph1opxarc2GmPWZ1vhtsj4mRtz5TXevadaiTGaHDZ6mSU5MSi1+5AiyV0myEtuFg8OTkZTz755KTHb7311imPn+pYIiIiWlrZyQYcKO1Ej30EsYa5Nco4UtGF9ISooGqscaauFzKpBPlpplmP3VScgH++V4v3T7TizquXLUF0vtfRO4STtT24bnMGNKrQ3+tHoS3OpIZcJlnQCAJRFFHXbseBMx04VN6FgWEnojRyOEbdKG/oxRd3FKL4vAZGDqcbRyotWLcsDgq5dJozA1lJehh0ChyrtExauXv7SAu6rMO47+YSyKQSrMj2XuNUbfd4Yhpq+JOAiIgogmQneUvy6trsc0rk+gYceOylMzAbVfjxXeugUwfH4OnTdT3ITzNCOcObujFalRyr88w4UmnB7VflQSpZcEFSwLx1pBkyqYBLV6cEOhQiSCUSJMVq59W5cmDYiXfeqsTbh5rQ2TsEuUyCVbmx2FiUgKLMaHRah/G7l8/gf547iavXp+GmLVnjzXyOV1ngGHVjc/HMjUkkgoDVeWbsO9UOh9M9/vOhb3AUr+yrx4rsmPEut0adEukJUThZ04NrNmYs7EYEWOj9JCMiIqIFSzZroZBJUNs6t31yp+q8JUo9fQ789qUzcLk9/gxvTrr7htHeM4TlmTOXVZ5rVZ4ZA8NO1LQszRy91u5B1Lf7Zi/iwLAT+063Y0NhAgxahU/OSbRYqWYdmudRlvjE7go8vacCJp0CX9i2DL/6lwvx1euLUZITC5lUguRYLX5851pcsioZew424aGnjqHLNgwA2H+mAzF6JXJTjbNeZ02eGaMuD87U9Y4/9sL7tXC6PPjs5bkTji3JjkFtWx/6h2YfWRCMmMgRERFFEJlUgoyEKNTNcTD4qZoemKKU+ML2ZShvtOLv79T4OcLZjb1BO7/8aibFmdGQSQWcqOn2V1gAvHuGfvPiafz4Twfx0FPH0N03vOhzfnCyDaNOD65al+qDCIl8IzVOB/vgKPrmMLfN5fagtKEX2zZm4Lu3rcZFJUlTlggr5FLcuTUfX7+hGB29Q3jgL4fwztEWlDb0YmNxAiTnjTubSl6qEVqVDMequgB4mwR9dKodV6xNQUK0ZsKxJTmxEEVMSPpCCRM5IiKiCJOVbEBjZz+crplX15wuD8409KIkOwablydi6wWpeOdYS8A7QJ6u60GMXoXEGM3sB5+lVsqwLN2E49X+6VLX1NmP37xwGvf/+RDKGnpx9QVpEATg+fdqF3Vel9uDd462oCDdhJQQ3cdD4Wns+3Eu5ZUNHf1wjLpRkmue07nXLovDA19Yh6RYLZ5+qwqiiGm7VZ5PJpVgZW4sTtT0wOny4Jm3qxGlkePaTZmTjk1PiIJeq8DJWv9+wOMvTOSIiIgiTHaSHi63iKaumbs4VrXY4Bh1Y0WOt0vcZy7JQXFWNJ5+swqVTdalCHUSl9uDskYrlmdFQ5jDp/PnWpVrRpd1GG09Qz6Lp6mzH//vhdP46V8Oo6yxF9dtzsDDX9uEmy/LwdYL0nCovAs1rQsv5zxS0QVrv4OrcRR0Us7OX5tL58ryRu/Pi+U503ecPF+sUY3v3bYa123OwBVrUpAYM/d5b2vy4jDscOFvb1SgpqUPN12cPeUKoEQQsCIrBmfqeuH2BL5sfL6YyBEREUWYrLGGJ7PskztZ0w25TIKCdG9nSIlEwFevK4LZqMZvXjyDbtviywbnq7qlD45R93jDgvlYefZN5Ilqy6LjcLk9+NsblfjpXw6j/JwE7oaLsqBVeRvCbN+QBoNOgWffqYZnAauAoijizcPNSIjWYHn2/L9eIn+K0ihg1Cnm1LmyvKEXaXE66Oe5x1MmleCGi7Jw25V583pdUaYJSrkU+053ID0+ChcuT5z22JKcGAw5XEu2f9aXmMgRERFFGFOUEtF6JWpn2CcniiJO1fSgIN00oTOkRiXHtz69Ah6PiP/752mMjLqWIuRxZ+p6IJUIWJY++9iB85milMhIiMLx6sWVUQ07XPi/50/hveOtuGpd6qQEboxKIcOntmSjrs2Og2Wd875OdUsfGjr6ceXalDntDSJaailxullLK0edbtS02hf0b3ah5DLp+HiBW6/IhUQy/b+fwoxoSCXClLPngh0TOSIiogiUlWRAXdv0K3IdvUPosg2jZIqVoIRoDb56QxFauwfwp9fKF7TatFClDb3ISTZArVzYBKVVubGoa7PDNsPg4ZlY+x34+dPHUNZgxee3LcNnL8+dlMCda9PyBKTHR+H592rhcLrnda23DjdDq5JhU/H0qwlEgZQap0Nbz+CM3WxrW/vgcntQmLF0iRwAfOriLHz1+iLkzdLpUq2UIT/NiFNM5IiIiCgUZCfp0d03gr5pEpqTNd43NSuyp97TUpwZg1suy8WxKgteP9DotzjPNep0o6VrEDkphgWfY9XZZgsL6V7ZahnAfz15BJ3WYXzr0yuwpSRp1tdIBAGfvTwH1n4H3jjUNOdrWWzDOFZtwSWrkqFUzD4rjygQUs06uNwiOnqn33da1miFRBCQm2JcusAAxJk0uKAgfk7HrsiORVv3ICwBKBdfDCZyREREEejcweBTOVXbjRSzFjEG1bTnuHJtCi4oiMMrH9XPqeHBYjV1DcAjishM1C/4HMlmLWINKpyYZ3llRaMV//XUMbjcIr5/++rxsq25yE8zYU2eGbs+boS1f24rgW8faYFEEHAZB4BTEJtL58qKRisyk6IWvIq+FEpyvP+eQ21VjokcERFRBEqL10EqEVA7RSI3NOJEdUsfSmbpMCcIAm6/Mg9alQx/fr3c78PCG84O2M5IiFrwOQRBwKpcM8oarHPe3/dxaQf+57kTMOoU+OGda5C+gOt/5tJseDwiXvhg9nEEww4XPjzVhnUFcTBFKed9LaKlkhCtgUwqoHmahifDDhfq2/vHGyYFq3iTBvHRGpz085xJX2MiR0REFIEUcinS4nVTDgY/U98Lt0dEyTRlleeK0ijwuavy0djZj90H5146uBD17f0waBWLTm5W5cZ6BxTXzz4E+N1jLfjDq2XITjLg3+9Yg1iDekHXjDNpcMXaVOw/3YGGjpm7hX54sg0jo26OHKCgJ5NKkBSjRUvX4JTPVzbb4BFFFKRHL3Fk81eSHYOKprl/wBMMmMgRERFFqOwkA+ra7ZPmJ52q7YFOLUdW0txKGNcui8O6Zd4Sy7m0Il+ohg47MhKi5j0/7ny5qQZoVbJZu1e2dQ/i2XeqsSI7Bt++ZeWMTU3mYsfGDOg0cjz7dvW0Q8k9HhFvH21BXooBGQkLLyElWiopcTo0TzOTsqLRCplUgpzk4P9eLsmOgcstorwhMDMyF4KJHBERUYTKStZj1OlBq+WTT9M9HhGnanuwPCt6xpbd57v9qjyold4SS38M1h12uNDRM7So/XFjpBIJVmTH4GRN97Sxejwi/rK7HEq5FF/YXgC5bPFvmTQqGW64KAtVLX04VN6FXvsIGjv6cbquB/vPtGPPwSb8ZXc5uvtGcOW6tEVfj2gppJh1sA2Mon9odNJz5Y1W5KYYIJcFf8Oe3FQj1EppSI0hCN5dh0RERORXYw1PatvsSIv37vuqa7djYNg5bbfK6eg1Cnzuqjz87uVS7DnYhGs2Zvg01qbOfogAMhIXvj/uXKtyzThQ2omalj7kp03ev/POsRbUttrxxR0FMMxziPFMtpQk4t2jLfj9K6VTPi+TCijOisaq3Pndf6JASR1reGIZREH6J/9W7EOjaO4awE1bsgIV2rzIpBIUZUTjVG03RFFc9Mq/L8y275iJHBERUYSKNaig18hR19qHS1clA/B2q5QI3mRivtYti8Phii68/FE9VuaakRyr9Vms9e3e0i1flRsWZUZDJhVwvLp7UiLXbRvGP9+vxfKsGGwsSvDJ9cZIJRJ89foiHK/uRpRGjiiNAnqNYvz/1UppULyBJJqrcztXntvUpLLJBgBB3+jkXCU5sThSaUFT58CCmhr5UmfvEP7+bg0e/NrmaY9haSUREVGEEgQBWUmGCZ0rT9b0IDfFsKD9YIIg4HNX5UOl8H2JZUOHHTF6JfQ+Wh1TK2UoSI/G8WrLhP1qoijiiT0VEAQBd27N90tSlWzWYcemDFy8Mhmr88zISTEgPloDjUrGJI5CjkGrgF6rmDSCpLzRCpVC6rNV9KWwPCsGAoCTtYHtXnmovBMPPHEYtmnmfI5hIkdERBTBspP16OgdwsCwE732ETR3DWBFztxnpJ3PoFXg9ivzUN9ux5uHm30WZ327HRk+2B93rlW5sbDYRtDa/ckewY9Ot6OswYrPXJI94ww9IvpEqlk7aQRBeaMVealGSCWhk27otQpkJukDNk/O6XLjyTcq8buXS5Fs1uLem0tmPD507iwRERH5XNbZfXL17fbxNy9zGTswkwsK4rAqNxYvflCP9p6p25LPx8CwExbbyKLmx01lbE7e2HBw24ADf3+nBnkpBlxyttSUiGaXEqdDW/fg+Cp8r30Enb1DKAyhssoxJdkxqG+zw9o/82qYr3Vah/CzJ49i7/FWXL0+Dd+7bTWMuplHrTCRIyIiimDedv5AbWsfTtZ0w2xUITFGs6hzjpUlymUCdn+8+NlyjR3e/XG+6Fh5LlOUEpmJehyv9jY3eOrNKoy6PPj89gJIWOJINGcpZh2cLg+6rMMAvKtxALAsBBO5NflxEAEcq7Is2TUPV3Thgb8cRk/fCL71qRW4+dIcyKSzp2lM5IiIiCKYWilDcqwOFU02lDdasSI71if7tAw6JfJTTahusS36XPXt3j18vl6RA7zllfXtdrx9tAXHqiy44aJMJEQvLpElijRjnSvH9slVNFqhU8vHG6GEkqRYLZJjtThc0eX3aw2NuPDn18vx25fOIDlWi59+4QKsnEfHWiZyREREES47WY+qZhtGXR6ULGJ/3PlyUwzotA7DPjh5vtR8NHT0I96khmaRA7mnMtbmf+fb1UiPj8LWC1J9fg2icJcYo4VUIqC5awCiKKKs0YplacaQXdlek29GdbMNfbM0G1mM0vpe/OTPB7HvTDuu2ZiO792+et77cpnIERERRbisJG/JolIuRX6q70qhclK8++9qWvsWdR5/NDoZkxSrhdmoglQi4Avbl4VUYwaiYCGXSZAQo0FL1wC6rMOw9jtQkDH/ESbBYt0yb3nlUT+UV46MuvC3NyrxyN9PQCGT4t/vWINPXZw9p1LK83GOHBERUYQbGwxemGGCXOa7RCYjIQoyqYCalj6szjMv6Bx9Aw5Y+x3I9NNMp7GRCcMO1/hQdCKav1SzDtUttvH9caE0P+58yWYdEmM0OFLRhctWp/jsvJVNVjz+ejl6+kZw1bpU3LQlCwq5dMHnYyJHREQU4RJiNNhQGI+LViT69LxymRQZCXpUt9oWfI76s41O/LUiB3hnRxHR4qTE6fBxWSeOVnbBFKVEvEkd6JAWZd2yOLy6vwF9g6MwLHJ+pdPlwfPv1eLtI82INarwvdtXIy/VuOgYWT9AREQU4SSCgC9fV+SXUqicFAMaO/rhdLkX9PqGdjsEAUiLD72mCUSRZKzhSWmDFQXpppAfbr92WRxEEThWufimJ8++U423jjTjktXJeODuC3ySxAFM5IiIiMiPcpMNcLlFNJxdWZuvho5+JMVooVKwiIgomKWYP/mwJZTLKsckx2qRGKNZdPfKE9Xd2Hu8FVsvSMUdV+X79GcZEzkiIiLym+yxhict8294IooiGtrtfhk7QES+ZdQpoFN7O8suSwv9RE4QBKzNj0Nls23BnXf7Bkfxl93lSI3T4aYt2T6OkIkcERER+ZFeo0B8tAbVC0jkeu0O2Iecft0fR0S+IQgC0uN1iI/WzLuNfrAaK69cSPdKURTxl13lGBl148vXFvq0kdQY1ikQERGRX+Uk63GypgeiKM5r30xDx9lB4IlckSMKBXddvQxOtyfQYfhMilmL+Ghv98pLVyXP67XvHmvFqdoe3H5lHpLN/tnjyxU5IiIi8qvcFCMGhp3o6B2a1+saOvohlQhIi2OjE6JQEGtUIzFGG+gwfEYQBKxbZkZFkxX2obmXV7Z2D+K5vTVYnhWDy1bPLwGcDyZyRERE5Fc5yQvbJ1ffbkeyWQu5bOFzloiIFmNt/tnulXMsr3S6PPjjK6VQyqW4e/syv3bvZCJHREREfpUQo4FWJUN169wTOW+jk35kcn8cEQVQapwO8SY1jsyxe+WLH9ahqWsAd28vgEGn9GtsTOSIiIjIrySCgJxkw7xW5LpswxhyuNixkogCShAErF0Wh4pGG/pnKa8sb7TijYNNuGRlElbmxvo9NiZyRERE5Hc5KQZ09A7N+kZoTEO7d+4cV+SIKNDWLYuDRxRnLK+0D47iT6+VIT5ag1suy12SuJjIERERkd/lphgBALWt9jkdX99uh1wmQVJs+DROIKLQlBqnQ5xx6vJKjyjiw1Nt+NGfDqJ/aBRfvq4QSsXS7Ovl+AEiIiLyu4yEKEglAqpbbXMqOWro6EdanA4yKT9zJqLAGiuv3HOwCf1Do4jSKAAAzV0DePLNStS09CEnxYA7r8pHyhJ22WUiR0RERH6nkEuRkRA1p31yHo+Ixo5+XLg8cQkiIyKa3bplcdj1cSOOV3dj3bI4vPxRPd4+0gKNSoa7txdg0/IESPzYoXIqTOSIiIhoSeSkGPDO0VY4XR7IZdOvtLX3DsHhdHMQOBEFjbR4HcxGFd483IyXPqyDbWAUF69MwqcuzoZOLQ9ITKxXICIioiWRk2yAy+1BY2f/jMc1tHv30WWw0QkRBQnvcPB4tHUPQq9R4Id3rMFdVy8LWBIHcEWOiIiIlkjO2YYnNS1940PCp9LQ3g+lQorEaM0SRUZENLsdm9KRnazHiuwYSCWBXw8LfAREREQUEQxaBeKMalS32KY9xu3xoKLZivT4KEgkS7vfhIhoJiqFDKtyzUGRxAFM5IiIiGgJ5aQYUNvaB1EUJz0niiKeerMKrZZBbClhoxMiopkwkSMiIqIlk5NigH3IiS7b8KTndh9swvsn2rB9Qzo2FTORIyKaCRM5IiIiWjK5Z/fGnT+G4GBZJ55/rxYXFMThpouzAhEaEVFIYSJHRERESyYxVguNUobqcxK5qmYbHn+9DHkpBtxzTeGSz2IiIgpFTOSIiIhoyUgEATkpBtS0ehO59p5B/PqfpxBrUONfPrVixvlyRET0Cf60JCIioiWVk2xAW/cgWrr68avnTkIiEXDvzSUBncdERBRqmMgRERHRkspN8e6T+/5vPoJ9cBTf+vQKxBnVAY6KiCi0MJEjIiKiJZWRqIdUIsA+OIovX1eE7KTph4MTEdHUZIEOgIiIiCKLUi7F9g3pyEo1oSTTFOhwiIhCEhM5IiIiWnI3bsmC2RwFi6U/0KEQEYWkBSdybW1t+Ld/+zf09PQgMzMTv/zlL6HVaicc09raih07diAtLQ0AEBsbi8cff3xxERMREREREUW4Be+Re+CBB3Dbbbdhz549KC4uxmOPPTbpmDNnzuDaa6/Fyy+/jJdffplJHBERERERkQ8sKJFzOp04fPgwtm7dCgC46aabsGfPnknHnT59GlVVVbj++utx5513orKycnHREhERERER0cISOavVCp1OB5nMW5lpNpvR2dk56TilUonrrrsOL774Iu655x584xvfwOjo6OIiJiIiIiIiinCCKIriTAfs3r0bDz300ITH0tPT0dTUhPfffx8A4HK5sGrVKpw+fXrGi1133XV4+OGHsWzZskWGTUREREREFLlmbXaybds2bNu2bcJjTqcT69evh9vthlQqhcViQVxc3KTXPvnkk9ixYwdMJm9rYVEUx1fx5qKnZwAez4x5Js0Du4P5Fu+n7/Ge+hbvp2/xfvoe76lv8X76Hu+pb/F+zo9EIiAmRjf98ws5qVwux9q1a7Fr1y4AwEsvvYQtW7ZMOu7w4cN4/vnnAQCHDh2Cx+NBVlbWQi5JREREREREZy24a+X999+P5557Dtu3b8eRI0dw7733AgB27tyJRx99FADwwx/+EPv378eOHTvw85//HI888ggkkgVfkoiIiIiIiLCIOXLJycl48sn/v727DYqq7MMAfi1vKhDhFoJOhKawzcgoFk1+EBkUEtRlAWPCwHDIxMSiBsW1SUUaR1pedHTLnHwbSWbMiDdFdLAszDLUzBiUnFFJUIkSGQN5W+7ng9M+j2nWI4ez57jX7xN79ubs/77m7O789z5nt/Cu7XPnzrX+7e3tjR07djzoQxAREREREdE9cHmMiIiIiIhIZdjIERERERERqQwbOSIiIiIiIpVhI0dERERERKQybOSIiIiIiIhU5oG/tVIODg4aW5fw0GGm0mKe0mOm0mKe0mKe0mOm0mKe0mOm0mKe/94/ZaURQgiZaiEiIiIiIiIJ8NRKIiIiIiIilWEjR0REREREpDJs5IiIiIiIiFSGjRwREREREZHKsJEjIiIiIiJSGTZyREREREREKsNGjoiIiIiISGXYyBEREREREakMGzkiIiIiIiKVkb2RM5vNmDVrFmbNmgWTyQQAOHbsGPR6PV544QWsX7/+rv/JzMzE559/br195coVJCYmIjIyEq+//jo6Ojpkq19ppMgTAPbu3Quj0ShLzUomRZ4nT57Eiy++CIPBgOTkZDQ3N8tWvxJJkemJEycQFxcHvV6PRYsWob29Xbb6lUaq5zwA1NfXIzAwcNBrVjopMi0pKcGUKVNgMBhgMBju+T/2Qoo8f/31VyxcuBAxMTFISEhAU1OTbPUrzUDz/P33363HpcFgwLRp0zBp0iRZ56A0UhyjTU1NSExMhMFgwLx58+z6vV6KPM+cOYM5c+ZAr9cjNTUVra2tstWvakJG33zzjXjppZdEd3e36OnpEa+88oqoqKgQoaGh4pdffhG9vb0iJSVFHDlyRAghxLVr10RqaqqYMGGCKC4utu5n4cKFYt++fUIIIcxmszCZTHJOQzGkyLOrq0vk5uaKoKAgsXz5cltOx+akOj7DwsLE2bNnhRBC7N27VyxatMgm81ECqTINDw8X58+fF0IIkZubK/Lz820yH1uTKk8hhOjs7BQJCQkiICDAFlNRDKkyzc7OFhUVFbaahmJIlWdycrIoKioSQghRVFQk0tPTbTEdm5PyOS+EEBaLRSQlJYny8nK5p6IYUmW6dOlSsXv3biGEELt27RIZGRk2mY+tSZFnf3+/CA0NFd9++60QQoj9+/eL1NRUm81JTWRdkfPy8oLRaISLiwucnZ0xduxYXLp0CX5+fvD19YWTkxP0ej2qqqoAABUVFZg+fTqioqKs++jt7UVtbS1mzJgBAIiLi7OOtzdS5FlbW4v+/n4sW7bMVtNQDCny7OnpQXp6Op5++mkAgE6nw9WrV20yHyWQIlMAqKysxLhx49Db24uWlhZ4eHjYYjo2J1WeAJCTk4Pk5GS5p6A4UmX6008/oaSkBHq9HkuXLrXbVWMp8rx+/TrOnTuHhIQEAMCcOXPw1ltv2WI6Niflcx4AiouLMWzYMOj1ejmnoShSZdrf348//vgDAHDr1i0MHTpU9rkogRR5trW1oaurC5MnTwYAhIWF4ejRo+jp6bHJnNRE1kbO398fQUFBAIBLly7hwIED0Gg08PLyso4ZMWIEWlpaAAALFixAfHz8Hftoa2uDu7s7nJycANw+gP4cb2+kyHPKlCnIzMy02xeg/yVFni4uLjAYDABuv8ibzWaEh4fLMwEFkiJTAHB2dkZDQwNCQ0Nx/PhxzJo1S5b6lUaqPA8fPoyuri5ERkbKUreSSZWpl5cXFi9ejPLycowcORLZ2dmy1K80UuR5+fJljBo1Cjk5OZgzZw7efPNNODs7yzYHJZHq+AQAi8WCjz76CBkZGYNet5JJlWl6ejp27tyJkJAQbN++Ha+99pos9SuNFHkOHz4crq6uOHr0KABg//796O3tRVtbmzyTUDGbfNnJ+fPnkZKSgszMTPj6+kKj0VjvE0Lccfuv7nX//cbbg4HkSXeTIs+enh4sXboUfX19SE1NHcxyVUGKTHU6HY4dO4bFixfj7bffHsxyFW8geba2tmLz5s1YuXKlHKWqxkCP0Q8++ADPPvssNBoNFixYgJqamsEuWdEGkmdfXx/q6+sxefJkFBcXY/r06XZ/DbcUr6E1NTUYPXo0dDrdYJaqGgPNdPny5cjOzkZNTQ3WrFmDJUuWQAgx2GUr1kDy1Gg02LhxI7Zs2YKYmBjcvHkTnp6edvsBzv9D9kbu5MmTmD9/PjIyMhAbGwsfH587LmhsbW3FiBEj/vb/tVotbt68CYvF8q/GP+wGmifdSYo8Ozo6sGDBAvT19WHz5s12/0I00Ey7u7tRXV1tvR0dHY2GhoZBrVnJBprnkSNHcOPGDetF+gBgMBispwjZo4FmevPmTezcudN6WwgBR0fHwSxZ0Qaap5eXF9zc3BAWFgYAmD17Ns6cOTPodSuVVO/z1dXVmDlz5mCWqhoDzfT69eu4cOGC9YybGTNmoLW11W5XkKQ4Rp2cnFBYWIjS0lJER0ejv78fnp6eg1y5+snayF29ehVpaWnIy8uznho1ceJEXLx4EY2NjbBYLNi3bx+mTp36t/twdnZGcHAwKisrAQClpaX3Hf8wkyJP+i+p8ly2bBn8/PywYcMGuLi4yFG6YkmRqZOTE9asWYO6ujoAwIEDB/DMM8/IUr/SSJFnfHw8qqurUVZWhrKyMgBAWVkZ3N3dZZmD0kiRqaurK7Zu3Yoff/wRAPDJJ58gIiJClvqVRoo8n3zySfj4+OCrr74CAHz55ZcYP368LPUrjZTv86dPn0ZwcPBgl6x4UmQ6fPhwDBkyBCdOnABwu5Fxc3ODVquVZQ5KItUx+s4771g/sNmxYwciIyPh4MBfSfsnTnI+2LZt29Dd3Y2cnBzrtoSEBOTk5OCNN95Ad3c3QkND//G6jdWrV8NoNGLz5s0YOXIkCgoKBrt0RZIqT7pNijzr6+tx+PBhjBs3DrGxsQBunxv+8ccfD3r9SiRFpo6Ojli/fj1WrVoFi8UCb29vrF27Vo7yFYfPeelJdYxu2LABWVlZ6OrqwujRo61fwW1vpDpGN23ahNWrVyM3Nxfu7u537M+eSPmcv3z5Mnx8fAazXFWQIlONRgOz2Yz33nsPXV1dcHNzw6ZNm+QoX3GkOkazsrKwevVq3Lp1Czqdzm7f5/9fGmHPJ/QSERERERGpENcsiYiIiIiIVIaNHBERERERkcqwkSMiIiIiIlIZNnJEREREREQqw0aOiIiIiIhIZdjIERGRojU1NUGn0yEpKemu+4xGI3Q6Ha5fv/5A+zabzdYfnDcajdi2bdtdY6qqqvDcc8/h2rVrd2w3mUxISkqCxWJ5oMe+l3PnzmHKlCmS7Y+IiB5ebOSIiEjxhgwZgosXL6K5udm6rbOzE6dOnRrQfo8fP46+vr77jomMjERERARWrFiBP3+x58SJEygrK0N+fj4cHR0HVAMA9PX1YefOnXj11VfR0dEx4P0REdHDT9YfBCciInoQjo6OiIqKQkVFBRYtWgQAOHToEKZPn47t27cDAPbs2YPCwkI4ODjg8ccfx8qVKzFmzBgYjUa4u7ujoaEB165dg06nw/vvv4/S0lLU1dXBZDJZm7EffvgBCQkJ+O233+Dv74/8/Hy4urri3XffRWxsLIqKihATEwOj0Yh169bB29sbp06dQl5eHm7dugUHBwcsWbIEYWFh6OzsRFZWFhobG3Hjxg24ubkhLy8PTz31FObNm4dHH30UFy5cwNy5czFx4kQ0NDTAbDYjJSXFZjkTEZF6cEWOiIhUISYmBmVlZdbbpaWliI2NBQB899132Lp1K3bt2oXy8nLMnj0baWlp1hW0uro6bNu2DZWVlWhubkZVVRUSExMRGBiIzMxMREREAABaWlqwY8cOHDx4EC0tLTh06BAAwNXVFQUFBdi4cSNWrFiBqKgoTJ06Fe3t7VixYgVMJhNKSkrw4YcfIisrC1euXMHXX38NDw8P7NmzBwcPHkRgYCB2795trd/DwwOVlZWYN28eJkyYgHXr1sHLy0uuOImISOW4IkdERKoQGBgIR0dH1NXV4bHHHkNHRwcCAgIAADU1NZg5cya0Wi0AIC4uDmvXrkVTUxMAICQkBC4uLgCAgIAAtLe33/MxwsPDMWzYMACAv7//HdfejR8/Hi+//DKqq6tRUFAAADh9+jRaW1uRlpZmHafRaNDQ0IDIyEj4+vqisLAQjY2N+P777zFp0iTruODgYKmiISIiO8RGjoiIVCM6Ohrl5eXQarUwGAzW7RqN5q6xQgjr9W9Dhw69Y+yfK3V/5eTkdN9xvr6+GDVqlHWcxWLB2LFjsXfvXuuYlpYWaLVaFBUV4dNPP0ViYiL0ej08PT2tjSVwe5WPiIjoQfHUSiIiUg2DwYCqqipUVlZi9uzZ1u0hISGorKy0rqAVFxfD09MTfn5+992fo6PjP37Zyf0EBQWhsbERtbW1AICzZ89ixowZaGlpwdGjRxEbG4v4+HiMGTMGX3zxhaTfcElERPaNK3JERKQa3t7eGDt2LB555BF4enpatz///POYP38+kpOT0d/fD61Wiy1btsDB4f6fV06bNg0FBQXo7e19oHq0Wi02btwIk8mE7u5uCCFgMpnwxBNPICUlBatWrcJnn30G4HbT9/PPPz/Q4xAREf2VRvzd+SVERERERESkSDy1koiIiIiISGXYyBEREREREakMGzkiIiIiIiKVYSNHRERERESkMmzkiIiIiIiIVIaNHBERERERkcqwkSMiIiIiIlIZNnJEREREREQq8x/qGxNeVAsPXQAAAABJRU5ErkJggg==\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": 75,
   "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": 76,
   "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": 77,
   "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": 78,
   "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": 79,
   "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": 80,
   "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\\gbrt_ew.csv')\n",
    "long_short_dax_vw.to_csv(r'C:\\Users\\krist\\Documents\\Data\\cumulative_log_returns\\gbrt_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
}