model_GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from src.data import data_layer\n",
    "from src.model.model_utils import calculateStatistics\n",
    "from src.model.model_utils import getFactorWithLargeCap\n",
    "from src.model.model_GAN import FeedForwardModelWithNA_GAN_Ensembled\n",
    "from src.model.model_RtnFcst import FeedForwardModelWithNA_Return_Ensembled\n",
    "from src.utils import deco_print\n",
    "from src.utils import load_dataframe\n",
    "from src.utils import sharpe\n",
    "from src.utils import max_1_month_loss\n",
    "from src.utils import max_drawdown\n",
    "from src.utils import eval_RtnFcst\n",
    "from src.utils import construct_decile_portfolios\n",
    "from src.utils import plot_decile_portfolios\n",
    "from src.utils import plot_SMV\n",
    "from src.utils import plot_SDF\n",
    "from src.utils import plotReturnDecile\n",
    "from src.utils import OLS\n",
    "from src.utils import GRS\n",
    "from src.utils import calculateStatisticsDecile\n",
    "from src.utils import calculateStatisticsDoubleSorted5\n",
    "from src.utils import calculateTurnover_with_dl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdirs = ['model_char_shift/model_GAN/%d' %k for k in range(1,10)]\n",
    "with open(os.path.join('model_char_shift/model_GAN/config.json'), 'r') as file:\n",
    "    config = json.load(file)\n",
    "logdirs_RF = ['model_char_shift/model_GAN_RF_normalized/%d' %k for k in range(1,10)]\n",
    "with open(os.path.join('model_char_shift/model_GAN_RF_normalized/config.json'), 'r') as file:\n",
    "    config_RF = json.load(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl = data_layer.DataInRamInputLayer(config['individual_feature_file'],\n",
    "                                    pathMacroFeature=config['macro_feature_file'])\n",
    "meanMacroFeature, stdMacroFeature = dl.getMacroFeatureMeanStd()\n",
    "dl_valid = data_layer.DataInRamInputLayer(config['individual_feature_file_valid'],\n",
    "                                          pathMacroFeature=config['macro_feature_file_valid'],\n",
    "                                          meanMacroFeature=meanMacroFeature,\n",
    "                                          stdMacroFeature=stdMacroFeature)\n",
    "dl_test = data_layer.DataInRamInputLayer(config['individual_feature_file_test'],\n",
    "                                         pathMacroFeature=config['macro_feature_file_test'],\n",
    "                                         meanMacroFeature=meanMacroFeature,\n",
    "                                         stdMacroFeature=stdMacroFeature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = np.load('datasets/char_all_na_rm_all_50/TSplit/CharAll_na_rm_all_50_train.npz')\n",
    "value = tmp['data'][:,:,19]\n",
    "tmp_valid = np.load('datasets/char_all_na_rm_all_50/TSplit/CharAll_na_rm_all_50_valid.npz')\n",
    "value_valid = tmp_valid['data'][:,:,19]\n",
    "tmp_test = np.load('datasets/char_all_na_rm_all_50/TSplit/CharAll_na_rm_all_50_test.npz')\n",
    "value_test = tmp_test['data'][:,:,19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap Stocks\n",
    "tmp = np.load('datasets/char_all_na_rm_all_50/TSplit/mask_LME.npz')\n",
    "mask_train = tmp['train']\n",
    "mask_valid = tmp['valid']\n",
    "mask_test = tmp['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap Stocks 0.001%\n",
    "tmp = np.load('datasets/char_all_na_rm_all_50/TSplit/mask_LME_0001.npz')\n",
    "mask_train_0001 = tmp['train']\n",
    "mask_valid_0001 = tmp['valid']\n",
    "mask_test_0001 = tmp['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Risk-free rate\n",
    "df_tmp = load_dataframe('datasets/F-F_Research_Data_5_Factors_2x3.CSV', skiprows=2, nrows=659)\n",
    "df_train = df_tmp.loc['196701':'198612']\n",
    "df_valid = df_tmp.loc['198701':'199112']\n",
    "df_test = df_tmp.loc['199201':'201612']\n",
    "rf_train = df_train.loc[:,'RF'].values / 100\n",
    "rf_valid = df_valid.loc[:,'RF'].values / 100\n",
    "rf_test = df_test.loc[:,'RF'].values / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "global_step = tf.train.get_or_create_global_step()\n",
    "model = FeedForwardModelWithNA_GAN_Ensembled(logdirs, config, 'test', config['tSize'], global_step=global_step)\n",
    "model_valid = FeedForwardModelWithNA_GAN_Ensembled(logdirs, config, 'test', config['tSize_valid'], force_var_reuse=True, global_step=global_step)\n",
    "model_test = FeedForwardModelWithNA_GAN_Ensembled(logdirs, config, 'test', config['tSize_test'], force_var_reuse=True, global_step=global_step)\n",
    "model_plot = FeedForwardModelWithNA_GAN_Ensembled(logdirs, config, 'plot', 1, force_var_reuse=True, global_step=global_step)\n",
    "gpu_options = tf.GPUOptions(allow_growth=True)\n",
    "sess_config = tf.ConfigProto(gpu_options=gpu_options)\n",
    "sess = tf.Session(config=sess_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "INITIAL_train = model.getZeroInitialState()\n",
    "INITIAL_valid = model.getNextInitialState(sess, dl, initial_state=INITIAL_train)\n",
    "INITIAL_test = model_valid.getNextInitialState(sess, dl_valid, initial_state=INITIAL_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Variable Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121caba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_test.plotIndividualFeatureImportance(sess, dl_test, plotPath='model_char_shift/model_GAN/plot', initial_state=INITIAL_test, top=46, figsize=(12,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bce10dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_test.plotMacroFeatureImportance(sess, dl_test, plotPath='model_char_shift/model_GAN/plot', initial_state=INITIAL_test, top=46, figsize=(12,9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Macro State Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be957518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SMV = np.load('model_char_shift/model_GAN/4/SMV.npy')\n",
    "plot_SMV(SMV, plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "w = model.getWeightWithData(sess, dl, initial_state=INITIAL_train, normalized=True)\n",
    "w_valid = model_valid.getWeightWithData(sess, dl_valid, initial_state=INITIAL_valid, normalized=True)\n",
    "w_test = model_test.getWeightWithData(sess, dl_test, initial_state=INITIAL_test, normalized=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "Ftrain = model.getNormalizedSDFFactor(sess, dl, initial_state=INITIAL_train)\n",
    "Fvalid = model_valid.getNormalizedSDFFactor(sess, dl_valid, initial_state=INITIAL_valid)\n",
    "Ftest = model_test.getNormalizedSDFFactor(sess, dl_test, initial_state=INITIAL_test)\n",
    "# np.savez('model_char_shift/model_GAN/SDF_normalized.npz', train=Ftrain, valid=Fvalid, test=Ftest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> SDF Portfolio Sharpe Ratio: Train 2.68\tValid 1.43\tTest 0.75\n",
      ">==================> Max 1 Month Loss: Train 0.38\tValid -0.28\tTest -5.76\n",
      ">==================> Max Drawdown: Train 0\tValid 1\tTest 5\n"
     ]
    }
   ],
   "source": [
    "SR_train = sharpe(Ftrain)\n",
    "SR_valid = sharpe(Fvalid)\n",
    "SR_test = sharpe(Ftest)\n",
    "deco_print('SDF Portfolio Sharpe Ratio: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(SR_train, SR_valid, SR_test))\n",
    "loss_train = max_1_month_loss(Ftrain / Ftrain.std())\n",
    "loss_valid = max_1_month_loss(Fvalid / Fvalid.std())\n",
    "loss_test = max_1_month_loss(Ftest / Ftest.std())\n",
    "deco_print('Max 1 Month Loss: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(loss_train, loss_valid, loss_test))\n",
    "drawdown_train = max_drawdown(Ftrain)\n",
    "drawdown_valid = max_drawdown(Fvalid)\n",
    "drawdown_test = max_drawdown(Ftest)\n",
    "deco_print('Max Drawdown: Train %d\\tValid %d\\tTest %d' %(drawdown_train, drawdown_valid, drawdown_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "Ftrain = model.getSDFFactor(sess, dl, initial_state=INITIAL_train)\n",
    "Fvalid = model_valid.getSDFFactor(sess, dl_valid, initial_state=INITIAL_valid)\n",
    "Ftest = model_test.getSDFFactor(sess, dl_test, initial_state=INITIAL_test)\n",
    "# np.savez('model_char_shift/model_GAN/SDF.npz', train=Ftrain, valid=Fvalid, test=Ftest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> SDF Portfolio Sharpe Ratio: Train 3.21\tValid 1.45\tTest 0.72\n",
      ">==================> Max 1 Month Loss: Train 0.18\tValid -0.27\tTest -5.95\n",
      ">==================> Max Drawdown: Train 0\tValid 1\tTest 5\n"
     ]
    }
   ],
   "source": [
    "SR_train = sharpe(Ftrain)\n",
    "SR_valid = sharpe(Fvalid)\n",
    "SR_test = sharpe(Ftest)\n",
    "deco_print('SDF Portfolio Sharpe Ratio: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(SR_train, SR_valid, SR_test))\n",
    "loss_train = max_1_month_loss(Ftrain / Ftrain.std())\n",
    "loss_valid = max_1_month_loss(Fvalid / Fvalid.std())\n",
    "loss_test = max_1_month_loss(Ftest / Ftest.std())\n",
    "deco_print('Max 1 Month Loss: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(loss_train, loss_valid, loss_test))\n",
    "drawdown_train = max_drawdown(Ftrain)\n",
    "drawdown_valid = max_drawdown(Fvalid)\n",
    "drawdown_test = max_drawdown(Ftest)\n",
    "deco_print('Max Drawdown: Train %d\\tValid %d\\tTest %d' %(drawdown_train, drawdown_valid, drawdown_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap\n",
    "Ftrain = getFactorWithLargeCap(-w, dl, mask_train, normalized=True, norm='l1')\n",
    "Fvalid = getFactorWithLargeCap(-w_valid, dl_valid, mask_valid, normalized=True, norm='l1')\n",
    "Ftest = getFactorWithLargeCap(-w_test, dl_test, mask_test, normalized=True, norm='l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> SDF Portfolio Sharpe Ratio: Train 0.97\tValid 0.54\tTest 0.26\n",
      ">==================> Max 1 Month Loss: Train -6.91\tValid -1.36\tTest -5.01\n",
      ">==================> Max Drawdown: Train 2\tValid 2\tTest 7\n"
     ]
    }
   ],
   "source": [
    "SR_train = sharpe(Ftrain)\n",
    "SR_valid = sharpe(Fvalid)\n",
    "SR_test = sharpe(Ftest)\n",
    "deco_print('SDF Portfolio Sharpe Ratio: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(SR_train, SR_valid, SR_test))\n",
    "loss_train = max_1_month_loss(Ftrain / Ftrain.std())\n",
    "loss_valid = max_1_month_loss(Fvalid / Fvalid.std())\n",
    "loss_test = max_1_month_loss(Ftest / Ftest.std())\n",
    "deco_print('Max 1 Month Loss: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(loss_train, loss_valid, loss_test))\n",
    "drawdown_train = max_drawdown(Ftrain)\n",
    "drawdown_valid = max_drawdown(Fvalid)\n",
    "drawdown_test = max_drawdown(Ftest)\n",
    "deco_print('Max Drawdown: Train %d\\tValid %d\\tTest %d' %(drawdown_train, drawdown_valid, drawdown_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap 0.001%\n",
    "Ftrain = getFactorWithLargeCap(-w, dl, mask_train_0001, normalized=True, norm='l1')\n",
    "Fvalid = getFactorWithLargeCap(-w_valid, dl_valid, mask_valid_0001, normalized=True, norm='l1')\n",
    "Ftest = getFactorWithLargeCap(-w_test, dl_test, mask_test_0001, normalized=True, norm='l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> SDF Portfolio Sharpe Ratio: Train 2.32\tValid 1.09\tTest 0.41\n",
      ">==================> Max 1 Month Loss: Train -1.17\tValid -1.14\tTest -4.84\n",
      ">==================> Max Drawdown: Train 1\tValid 1\tTest 5\n"
     ]
    }
   ],
   "source": [
    "SR_train = sharpe(Ftrain)\n",
    "SR_valid = sharpe(Fvalid)\n",
    "SR_test = sharpe(Ftest)\n",
    "deco_print('SDF Portfolio Sharpe Ratio: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(SR_train, SR_valid, SR_test))\n",
    "loss_train = max_1_month_loss(Ftrain / Ftrain.std())\n",
    "loss_valid = max_1_month_loss(Fvalid / Fvalid.std())\n",
    "loss_test = max_1_month_loss(Ftest / Ftest.std())\n",
    "deco_print('Max 1 Month Loss: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(loss_train, loss_valid, loss_test))\n",
    "drawdown_train = max_drawdown(Ftrain)\n",
    "drawdown_valid = max_drawdown(Fvalid)\n",
    "drawdown_test = max_drawdown(Ftest)\n",
    "deco_print('Max Drawdown: Train %d\\tValid %d\\tTest %d' %(drawdown_train, drawdown_valid, drawdown_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EV, XS-R2\n",
    "EV_train, XSR2_train, WXSR2_train = calculateStatistics(beta_train, dl)\n",
    "EV_valid, XSR2_valid, WXSR2_valid = calculateStatistics(beta_valid, dl_valid)\n",
    "EV_test, XSR2_test, WXSR2_test = calculateStatistics(beta_test, dl_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Explained Variation: Train 0.20\tValid 0.09\tTest 0.08\n",
      ">==================> XS-R2: Train -0.04\tValid 0.04\tTest 0.04\n",
      ">==================> (Weighted) XS-R2: Train 0.12\tValid 0.01\tTest 0.23\n"
     ]
    }
   ],
   "source": [
    "deco_print('Explained Variation: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(EV_train, EV_valid, EV_test))\n",
    "deco_print('XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(XSR2_train, XSR2_valid, XSR2_test))\n",
    "deco_print('(Weighted) XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(WXSR2_train, WXSR2_valid, WXSR2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap\n",
    "EV_train, XSR2_train, WXSR2_train = calculateStatistics(beta_train, dl, mask_train)\n",
    "EV_valid, XSR2_valid, WXSR2_valid = calculateStatistics(beta_valid, dl_valid, mask_valid)\n",
    "EV_test, XSR2_test, WXSR2_test = calculateStatistics(beta_test, dl_test, mask_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Explained Variation: Train 0.28\tValid 0.34\tTest 0.18\n",
      ">==================> XS-R2: Train 0.07\tValid 0.13\tTest 0.06\n",
      ">==================> (Weighted) XS-R2: Train 0.27\tValid 0.23\tTest 0.32\n"
     ]
    }
   ],
   "source": [
    "deco_print('Explained Variation: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(EV_train, EV_valid, EV_test))\n",
    "deco_print('XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(XSR2_train, XSR2_valid, XSR2_test))\n",
    "deco_print('(Weighted) XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(WXSR2_train, WXSR2_valid, WXSR2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Large Cap 0.001%\n",
    "EV_train, XSR2_train, WXSR2_train = calculateStatistics(beta_train, dl, mask_train_0001)\n",
    "EV_valid, XSR2_valid, WXSR2_valid = calculateStatistics(beta_valid, dl_valid, mask_valid_0001)\n",
    "EV_test, XSR2_test, WXSR2_test = calculateStatistics(beta_test, dl_test, mask_test_0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Explained Variation: Train 0.23\tValid 0.22\tTest 0.14\n",
      ">==================> XS-R2: Train 0.04\tValid 0.12\tTest -0.00\n",
      ">==================> (Weighted) XS-R2: Train 0.20\tValid 0.13\tTest 0.26\n"
     ]
    }
   ],
   "source": [
    "deco_print('Explained Variation: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(EV_train, EV_valid, EV_test))\n",
    "deco_print('XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(XSR2_train, XSR2_valid, XSR2_test))\n",
    "deco_print('(Weighted) XS-R2: Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(WXSR2_train, WXSR2_valid, WXSR2_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Turnover\n",
    "_, _, T_train_plus_mean, T_train_minus_mean = calculateTurnover_with_dl(dl, -w, rf_train)\n",
    "_, _, T_valid_plus_mean, T_valid_minus_mean = calculateTurnover_with_dl(dl_valid, -w_valid, rf_valid)\n",
    "_, _, T_test_plus_mean, T_test_minus_mean = calculateTurnover_with_dl(dl_test, -w_test, rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Turnover (Long): Train 0.47\tValid 0.40\tTest 0.40\n",
      ">==================> Turnover (Short): Train 1.05\tValid 1.04\tTest 1.02\n"
     ]
    }
   ],
   "source": [
    "deco_print('Turnover (Long): Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(T_train_plus_mean, T_valid_plus_mean, T_test_plus_mean))\n",
    "deco_print('Turnover (Short): Train %0.2f\\tValid %0.2f\\tTest %0.2f' %(T_train_minus_mean, T_valid_minus_mean, T_test_minus_mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Performance of Extreme Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "deciles = [0.01, 0.05, 0.1, 0.25, 0.5]\n",
    "SR = pd.DataFrame(np.zeros((len(deciles), 3)), index=deciles, columns=['train','valid','test'])\n",
    "SR_value = pd.DataFrame(np.zeros((len(deciles), 3)), index=deciles, columns=['train','valid','test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
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      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
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      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
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      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "for decile in deciles:\n",
    "    SR.loc[decile, 'train'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_train, quantile=decile)[1]\n",
    "    SR.loc[decile, 'valid'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_valid, quantile=decile)[1]\n",
    "    SR.loc[decile, 'test'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_test, quantile=decile)[1]\n",
    "for decile in deciles:\n",
    "    SR_value.loc[decile, 'train'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_train, value=value, quantile=decile)[1]\n",
    "    SR_value.loc[decile, 'valid'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_valid, value=value_valid, quantile=decile)[1]\n",
    "    SR_value.loc[decile, 'test'] = eval_RtnFcst(model_plot_RF, sess, dl_RF_test, value=value_test, quantile=decile)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>1.135911</td>\n",
       "      <td>0.591520</td>\n",
       "      <td>0.524199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>1.130473</td>\n",
       "      <td>1.201378</td>\n",
       "      <td>0.684209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>1.020629</td>\n",
       "      <td>1.229314</td>\n",
       "      <td>0.613818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.971881</td>\n",
       "      <td>1.209865</td>\n",
       "      <td>0.580315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.943000</td>\n",
       "      <td>1.146694</td>\n",
       "      <td>0.546381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train     valid      test\n",
       "0.01  1.135911  0.591520  0.524199\n",
       "0.05  1.130473  1.201378  0.684209\n",
       "0.10  1.020629  1.229314  0.613818\n",
       "0.25  0.971881  1.209865  0.580315\n",
       "0.50  0.943000  1.146694  0.546381"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train</th>\n",
       "      <th>valid</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>0.845565</td>\n",
       "      <td>0.439122</td>\n",
       "      <td>0.328434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>0.675191</td>\n",
       "      <td>0.473635</td>\n",
       "      <td>0.424064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>0.538629</td>\n",
       "      <td>0.439842</td>\n",
       "      <td>0.355067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.499210</td>\n",
       "      <td>0.103518</td>\n",
       "      <td>0.261861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>0.358931</td>\n",
       "      <td>0.246714</td>\n",
       "      <td>0.176904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train     valid      test\n",
       "0.01  0.845565  0.439122  0.328434\n",
       "0.05  0.675191  0.473635  0.424064\n",
       "0.10  0.538629  0.439842  0.355067\n",
       "0.25  0.499210  0.103518  0.261861\n",
       "0.50  0.358931  0.246714  0.176904"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SR_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = ['GAN (hidden state)', 'UNC', 'GAN (no macro)', 'FFN (no macro)', 'EN (no macro)', 'LS (no macro)', 'GAN (all macro)', 'FFN (all macro)', 'EN (all macro)', 'LS (all macro)']\n",
    "SR = [[2.68, 1.43, 0.75],\n",
    "     [1.93, 1.33, 0.53],\n",
    "     [1.90, 1.35, 0.69],\n",
    "     [0.45, 0.42, 0.44],\n",
    "     [1.37, 1.15, 0.50],\n",
    "     [1.80, 0.58, 0.42],\n",
    "     [1.07, 0.05, 0.05],\n",
    "     [1.46, 0.46, 0.21],\n",
    "     [1.95, 0.54, 0.19],\n",
    "     [2.63, 0.34, 0.10]]\n",
    "df_SR = pd.DataFrame(SR, columns=['Training', 'Validation', 'Test'], index=models)\n",
    "color_models = ['brown'] + ['red'] + ['orange'] * 4 + ['yellow'] * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4a049b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pos = np.arange(len(models))\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "\n",
    "ax1 = plt.subplot(131)\n",
    "ax1.barh(y_pos, df_SR.loc[:,'Test'].values, align='center', color=color_models)\n",
    "ax1.set_yticks(y_pos)\n",
    "ax1.set_yticklabels(models)\n",
    "ax1.invert_yaxis()\n",
    "ax1.set_title('Test')\n",
    "\n",
    "ax2 = plt.subplot(132)\n",
    "ax2.barh(y_pos, df_SR.loc[:,'Validation'].values, align='center', color=color_models)\n",
    "ax2.invert_yaxis()\n",
    "ax2.set_yticks([])\n",
    "ax2.set_title('Validation')\n",
    "\n",
    "ax3 = plt.subplot(133)\n",
    "ax3.barh(y_pos, df_SR.loc[:,'Training'].values, align='center', color=color_models)\n",
    "ax3.invert_yaxis()\n",
    "ax3.set_yticks([])\n",
    "ax3.set_title('Training')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Predictive Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "global_step = tf.train.get_or_create_global_step()\n",
    "model_plot_RF = FeedForwardModelWithNA_Return_Ensembled(logdirs_RF, config_RF, 'plot', global_step=global_step)\n",
    "gpu_options = tf.GPUOptions(allow_growth=True)\n",
    "sess_config = tf.ConfigProto(gpu_options=gpu_options)\n",
    "sess = tf.Session(config=sess_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl_RF_train = data_layer.DataInRamInputLayer(config['individual_feature_file'])\n",
    "dl_RF_valid = data_layer.DataInRamInputLayer(config['individual_feature_file_valid'])\n",
    "dl_RF_test = data_layer.DataInRamInputLayer(config['individual_feature_file_test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    }
   ],
   "source": [
    "beta_train = model_plot_RF.getPrediction(sess, dl_RF_train)\n",
    "beta_valid = model_plot_RF.getPrediction(sess, dl_RF_valid)\n",
    "beta_test = model_plot_RF.getPrediction(sess, dl_RF_test)\n",
    "# np.savez('model_char_shift/model_GAN_RF_normalized/forecasts.npz', train=beta_train, valid=beta_valid, test=beta_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "decile = 10\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_train = construct_decile_portfolios(beta_train, R, mask, decile=decile)\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl_valid.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_valid = construct_decile_portfolios(beta_valid, R, mask, decile=decile)\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl_test.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_test = construct_decile_portfolios(beta_test, R, mask, decile=decile)\n",
    "df = pd.DataFrame(np.concatenate([portfolios_train, portfolios_valid, portfolios_test]), columns=['decile %d' %(k+1) for k in range(decile)])\n",
    "# np.save('model_char_shift/model_GAN/decile_portfolios.npy', df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4231cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decile_portfolios(df, plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "decile = 10\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_train = construct_decile_portfolios(beta_train, R, mask, value=value, decile=decile)\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl_valid.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_valid = construct_decile_portfolios(beta_valid, R, mask, value=value_valid, decile=decile)\n",
    "for _, (I_macro, I, R, mask) in enumerate(dl_test.iterateOneEpoch(subEpoch=False)):\n",
    "    portfolios_test = construct_decile_portfolios(beta_test, R, mask, value=value_test, decile=decile)\n",
    "df = pd.DataFrame(np.concatenate([portfolios_train, portfolios_valid, portfolios_test]), columns=['decile %d' %(k+1) for k in range(decile)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0af7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decile_portfolios(df, plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Plotting Variable: A2ME\tElapse / Estimate: 1.40s / 64.33s\n",
      ">==================> Plotting Variable: AC\tElapse / Estimate: 3.43s / 78.82s\n",
      ">==================> Plotting Variable: AT\tElapse / Estimate: 5.77s / 88.51s\n",
      ">==================> Plotting Variable: ATO\tElapse / Estimate: 7.14s / 82.15s\n",
      ">==================> Plotting Variable: BEME\tElapse / Estimate: 8.61s / 79.25s\n",
      ">==================> Plotting Variable: Beta\tElapse / Estimate: 10.03s / 76.90s\n",
      ">==================> Plotting Variable: C\tElapse / Estimate: 11.51s / 75.62s\n",
      ">==================> Plotting Variable: CF\tElapse / Estimate: 12.91s / 74.21s\n",
      ">==================> Plotting Variable: CF2P\tElapse / Estimate: 15.39s / 78.64s\n",
      ">==================> Plotting Variable: CTO\tElapse / Estimate: 18.10s / 83.28s\n",
      ">==================> Plotting Variable: D2A\tElapse / Estimate: 20.12s / 84.13s\n",
      ">==================> Plotting Variable: D2P\tElapse / Estimate: 22.82s / 87.47s\n",
      ">==================> Plotting Variable: DPI2A\tElapse / Estimate: 25.54s / 90.38s\n",
      ">==================> Plotting Variable: E2P\tElapse / Estimate: 27.90s / 91.67s\n",
      ">==================> Plotting Variable: FC2Y\tElapse / Estimate: 30.45s / 93.38s\n",
      ">==================> Plotting Variable: IdioVol\tElapse / Estimate: 31.82s / 91.48s\n",
      ">==================> Plotting Variable: Investment\tElapse / Estimate: 33.40s / 90.39s\n",
      ">==================> Plotting Variable: Lev\tElapse / Estimate: 34.80s / 88.94s\n",
      ">==================> Plotting Variable: LME\tElapse / Estimate: 36.16s / 87.55s\n",
      ">==================> Plotting Variable: LT_Rev\tElapse / Estimate: 37.47s / 86.19s\n",
      ">==================> Plotting Variable: LTurnover\tElapse / Estimate: 38.80s / 84.99s\n",
      ">==================> Plotting Variable: MktBeta\tElapse / Estimate: 40.10s / 83.84s\n",
      ">==================> Plotting Variable: NI\tElapse / Estimate: 41.42s / 82.84s\n",
      ">==================> Plotting Variable: NOA\tElapse / Estimate: 42.72s / 81.89s\n",
      ">==================> Plotting Variable: OA\tElapse / Estimate: 44.05s / 81.05s\n",
      ">==================> Plotting Variable: OL\tElapse / Estimate: 45.34s / 80.22s\n",
      ">==================> Plotting Variable: OP\tElapse / Estimate: 46.64s / 79.46s\n",
      ">==================> Plotting Variable: PCM\tElapse / Estimate: 47.95s / 78.78s\n",
      ">==================> Plotting Variable: PM\tElapse / Estimate: 49.39s / 78.34s\n",
      ">==================> Plotting Variable: PROF\tElapse / Estimate: 50.82s / 77.93s\n",
      ">==================> Plotting Variable: Q\tElapse / Estimate: 52.14s / 77.36s\n",
      ">==================> Plotting Variable: r2_1\tElapse / Estimate: 53.57s / 77.01s\n",
      ">==================> Plotting Variable: r12_2\tElapse / Estimate: 54.99s / 76.65s\n",
      ">==================> Plotting Variable: r12_7\tElapse / Estimate: 56.31s / 76.18s\n",
      ">==================> Plotting Variable: r36_13\tElapse / Estimate: 57.67s / 75.79s\n",
      ">==================> Plotting Variable: Rel2High\tElapse / Estimate: 58.93s / 75.30s\n",
      ">==================> Plotting Variable: Resid_Var\tElapse / Estimate: 60.25s / 74.90s\n",
      ">==================> Plotting Variable: RNA\tElapse / Estimate: 61.58s / 74.55s\n",
      ">==================> Plotting Variable: ROA\tElapse / Estimate: 62.89s / 74.18s\n",
      ">==================> Plotting Variable: ROE\tElapse / Estimate: 64.21s / 73.85s\n",
      ">==================> Plotting Variable: S2P\tElapse / Estimate: 65.60s / 73.60s\n",
      ">==================> Plotting Variable: SGA2S\tElapse / Estimate: 66.99s / 73.37s\n",
      ">==================> Plotting Variable: Spread\tElapse / Estimate: 68.29s / 73.05s\n",
      ">==================> Plotting Variable: ST_REV\tElapse / Estimate: 69.63s / 72.79s\n",
      ">==================> Plotting Variable: SUV\tElapse / Estimate: 70.94s / 72.52s\n",
      ">==================> Plotting Variable: Variance\tElapse / Estimate: 72.22s / 72.22s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc6d2f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotReturnDecile(dl_test, beta_test, plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Plotting Variable: A2ME\tElapse / Estimate: 1.51s / 69.67s\n",
      ">==================> Plotting Variable: AC\tElapse / Estimate: 2.94s / 67.54s\n",
      ">==================> Plotting Variable: AT\tElapse / Estimate: 4.38s / 67.14s\n",
      ">==================> Plotting Variable: ATO\tElapse / Estimate: 5.95s / 68.42s\n",
      ">==================> Plotting Variable: BEME\tElapse / Estimate: 7.45s / 68.58s\n",
      ">==================> Plotting Variable: Beta\tElapse / Estimate: 8.97s / 68.75s\n",
      ">==================> Plotting Variable: C\tElapse / Estimate: 10.43s / 68.52s\n",
      ">==================> Plotting Variable: CF\tElapse / Estimate: 11.86s / 68.17s\n",
      ">==================> Plotting Variable: CF2P\tElapse / Estimate: 13.36s / 68.30s\n",
      ">==================> Plotting Variable: CTO\tElapse / Estimate: 14.80s / 68.09s\n",
      ">==================> Plotting Variable: D2A\tElapse / Estimate: 16.25s / 67.95s\n",
      ">==================> Plotting Variable: D2P\tElapse / Estimate: 17.62s / 67.54s\n",
      ">==================> Plotting Variable: DPI2A\tElapse / Estimate: 19.07s / 67.46s\n",
      ">==================> Plotting Variable: E2P\tElapse / Estimate: 20.66s / 67.89s\n",
      ">==================> Plotting Variable: FC2Y\tElapse / Estimate: 22.17s / 67.97s\n",
      ">==================> Plotting Variable: IdioVol\tElapse / Estimate: 23.79s / 68.41s\n",
      ">==================> Plotting Variable: Investment\tElapse / Estimate: 25.36s / 68.62s\n",
      ">==================> Plotting Variable: Lev\tElapse / Estimate: 26.92s / 68.80s\n",
      ">==================> Plotting Variable: LME\tElapse / Estimate: 28.44s / 68.85s\n",
      ">==================> Plotting Variable: LT_Rev\tElapse / Estimate: 30.00s / 69.00s\n",
      ">==================> Plotting Variable: LTurnover\tElapse / Estimate: 31.51s / 69.02s\n",
      ">==================> Plotting Variable: MktBeta\tElapse / Estimate: 33.01s / 69.01s\n",
      ">==================> Plotting Variable: NI\tElapse / Estimate: 34.49s / 68.99s\n",
      ">==================> Plotting Variable: NOA\tElapse / Estimate: 36.05s / 69.10s\n",
      ">==================> Plotting Variable: OA\tElapse / Estimate: 37.65s / 69.27s\n",
      ">==================> Plotting Variable: OL\tElapse / Estimate: 39.50s / 69.89s\n",
      ">==================> Plotting Variable: OP\tElapse / Estimate: 41.02s / 69.89s\n",
      ">==================> Plotting Variable: PCM\tElapse / Estimate: 42.52s / 69.86s\n",
      ">==================> Plotting Variable: PM\tElapse / Estimate: 44.04s / 69.85s\n",
      ">==================> Plotting Variable: PROF\tElapse / Estimate: 45.54s / 69.83s\n",
      ">==================> Plotting Variable: Q\tElapse / Estimate: 47.15s / 69.97s\n",
      ">==================> Plotting Variable: r2_1\tElapse / Estimate: 48.70s / 70.01s\n",
      ">==================> Plotting Variable: r12_2\tElapse / Estimate: 50.32s / 70.14s\n",
      ">==================> Plotting Variable: r12_7\tElapse / Estimate: 52.00s / 70.36s\n",
      ">==================> Plotting Variable: r36_13\tElapse / Estimate: 53.81s / 70.72s\n",
      ">==================> Plotting Variable: Rel2High\tElapse / Estimate: 55.41s / 70.80s\n",
      ">==================> Plotting Variable: Resid_Var\tElapse / Estimate: 56.99s / 70.85s\n",
      ">==================> Plotting Variable: RNA\tElapse / Estimate: 58.55s / 70.87s\n",
      ">==================> Plotting Variable: ROA\tElapse / Estimate: 60.09s / 70.88s\n",
      ">==================> Plotting Variable: ROE\tElapse / Estimate: 61.60s / 70.84s\n",
      ">==================> Plotting Variable: S2P\tElapse / Estimate: 63.09s / 70.79s\n",
      ">==================> Plotting Variable: SGA2S\tElapse / Estimate: 64.58s / 70.73s\n",
      ">==================> Plotting Variable: Spread\tElapse / Estimate: 66.11s / 70.72s\n",
      ">==================> Plotting Variable: ST_REV\tElapse / Estimate: 67.67s / 70.74s\n",
      ">==================> Plotting Variable: SUV\tElapse / Estimate: 69.17s / 70.71s\n",
      ">==================> Plotting Variable: Variance\tElapse / Estimate: 70.65s / 70.65s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd854ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotReturnDecile(dl_test, beta_test, value=value_test, plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Time Series Pricing Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = load_dataframe('datasets/F-F_Research_Data_5_Factors_2x3.CSV', skiprows=2, nrows=659)\n",
    "df_FF = df5.loc[196701:201612].copy()\n",
    "df_FF.drop(['RF'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vList = ['Mkt-RF']\n",
    "# vList = ['Mkt-RF', 'SMB', 'HML']\n",
    "vList = ['Mkt-RF', 'SMB', 'HML','RMW','CMA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.20 & -11.99 & -0.12 & -4.35 \n",
      "-0.09 & -8.29 & -0.05 & -2.68 \n",
      "-0.04 & -4.87 & -0.01 & -1.05 \n",
      "-0.02 & -2.86 & -0.01 & -0.54 \n",
      "0.01 & 1.36 & 0.03 & 2.17 \n",
      "0.01 & 1.51 & 0.02 & 2.20 \n",
      "0.04 & 5.16 & 0.04 & 3.41 \n",
      "0.07 & 8.05 & 0.07 & 4.86 \n",
      "0.11 & 8.58 & 0.11 & 5.39 \n",
      "0.25 & 10.43 & 0.27 & 6.59 \n",
      "0.46 & 18.13 & 0.39 & 9.96 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "s = ''\n",
    "for i in range(1,11):\n",
    "    alpha, t, _ = OLS(df_FF.loc[:,vList].values, df.loc[:,'decile %d'%i].values*12, alpha=True)\n",
    "    alpha_test, t_test, _ = OLS(df_FF.loc[:,vList].values[300:], df.loc[:,'decile %d'%i].values[300:]*12, alpha=True)\n",
    "    s += '%0.2f & %0.2f & %0.2f & %0.2f \\n' %(alpha, t, alpha_test, t_test)\n",
    "alpha, t, _ = OLS(df_FF.loc[:,vList].values, df.loc[:,'decile 10'].values*12 - df.loc[:,'decile 1'].values*12, alpha=True)\n",
    "alpha_test, t_test, _ = OLS(df_FF.loc[:,vList].values[300:], df.loc[:,'decile 10'].values[300:]*12 - df.loc[:,'decile 1'].values[300:]*12, alpha=True)\n",
    "s += '%0.2f & %0.2f & %0.2f & %0.2f \\n' %(alpha, t, alpha_test, t_test)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.64 & 0.00 & 10.75 & 0.00 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "s, p = GRS(df_FF.loc[:,vList].values, df.values)\n",
    "s_test, p_test = GRS(df_FF.loc[:,vList].values[300:], df.values[300:])\n",
    "print('%0.2f & %0.2f & %0.2f & %0.2f \\n' %(s, p, s_test, p_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Performance on Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = np.load('model_char_shift/model_FFN/forecasts.npz')\n",
    "beta_FFN_train = tmp['train']\n",
    "beta_FFN_valid = tmp['valid']\n",
    "beta_FFN_test = tmp['test']\n",
    "\n",
    "tmp = np.load('model_char_shift/model_GAN_RF_normalized/forecasts.npz')\n",
    "beta_GAN_train = tmp['train']\n",
    "beta_GAN_valid = tmp['valid']\n",
    "beta_GAN_test = tmp['test']\n",
    "\n",
    "tmp = np.load('model_char_shift/model_Linear_LS/ElasticNet/forecasts.npz')\n",
    "beta_EN_train = tmp['train']\n",
    "beta_EN_valid = tmp['valid']\n",
    "beta_EN_test = tmp['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decile Sorted Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printTable(EV_EN, EV_FFN, EV_GAN, R2_EN, R2_FFN, R2_GAN):\n",
    "    s = ''\n",
    "    for i in range(10):\n",
    "        s += '%d & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\\\n' %(i+1, EV_EN[i], EV_FFN[i], EV_GAN[i], R2_EN[i], R2_FFN[i], R2_GAN[i])\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EV & XS-R2\n",
      "1 & 0.92 & 0.93 & 0.92 & 0.95 & 0.99 & 0.98 \\\\\n",
      "2 & 0.97 & 0.96 & 0.96 & 0.93 & 0.98 & 0.96 \\\\\n",
      "3 & 0.96 & 0.94 & 0.96 & 0.97 & 1.00 & 0.98 \\\\\n",
      "4 & 0.92 & 0.90 & 0.94 & 0.99 & 1.00 & 0.96 \\\\\n",
      "5 & 0.86 & 0.84 & 0.92 & 0.98 & 0.97 & 0.98 \\\\\n",
      "6 & 0.72 & 0.74 & 0.92 & 0.82 & 0.87 & 1.00 \\\\\n",
      "7 & 0.46 & 0.58 & 0.89 & 0.53 & 0.70 & 1.00 \\\\\n",
      "8 & 0.12 & 0.33 & 0.82 & 0.07 & 0.39 & 0.99 \\\\\n",
      "9 & -0.26 & -0.06 & 0.66 & -0.66 & -0.35 & 0.93 \\\\\n",
      "10 & -0.38 & -0.53 & 0.33 & -1.40 & -2.35 & 0.79 \\\\\n",
      "All & 0.57 & 0.59 & 0.82 & 0.75 & 0.78 & 0.97 \\\\\n",
      "\n",
      "EV & Alpha\n",
      "1 & 0.92 & 0.93 & 0.92 & 0.15 & 0.07 & 0.09 \\\\\n",
      "2 & 0.97 & 0.96 & 0.96 & -0.09 & -0.05 & -0.07 \\\\\n",
      "3 & 0.96 & 0.94 & 0.96 & -0.05 & -0.01 & -0.05 \\\\\n",
      "4 & 0.92 & 0.90 & 0.94 & -0.02 & 0.01 & -0.05 \\\\\n",
      "5 & 0.86 & 0.84 & 0.92 & 0.04 & 0.05 & -0.03 \\\\\n",
      "6 & 0.72 & 0.74 & 0.92 & 0.10 & 0.09 & -0.01 \\\\\n",
      "7 & 0.46 & 0.58 & 0.89 & 0.15 & 0.12 & -0.00 \\\\\n",
      "8 & 0.12 & 0.33 & 0.82 & 0.20 & 0.16 & 0.02 \\\\\n",
      "9 & -0.26 & -0.06 & 0.66 & 0.26 & 0.23 & 0.05 \\\\\n",
      "10 & -0.38 & -0.53 & 0.33 & 0.28 & 0.33 & 0.08 \\\\\n",
      "All & 0.57 & 0.59 & 0.82 & 0.50 & 0.47 & 0.17 \\\\\n"
     ]
    }
   ],
   "source": [
    "### Equally Weighted\n",
    "charName = 'ST_REV'\n",
    "UV_EN_all, Alpha_EN_all, R2_EN_all, UV_EN, Alpha_EN, R2_EN = calculateStatisticsDecile(dl_test, beta_EN_test, charName=charName)\n",
    "UV_FFN_all, Alpha_FFN_all, R2_FFN_all, UV_FFN, Alpha_FFN, R2_FFN = calculateStatisticsDecile(dl_test, beta_FFN_test, charName=charName)\n",
    "UV_GAN_all, Alpha_GAN_all, R2_GAN_all, UV_GAN, Alpha_GAN, R2_GAN = calculateStatisticsDecile(dl_test, beta_GAN_test, charName=charName)\n",
    "print('EV & XS-R2\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, R2_EN, R2_FFN, R2_GAN)+'All & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all))\n",
    "print('\\nEV & Alpha\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, Alpha_EN, Alpha_FFN, Alpha_GAN)+'All & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, Alpha_EN_all, Alpha_FFN_all, Alpha_GAN_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "charRank = model_test._variable_rank\n",
    "s = ''\n",
    "for charName in charRank:\n",
    "    deco_print('Processing %s          ' %charName, '\\r')\n",
    "    UV_EN_all, _, R2_EN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_EN_test, charName=charName)\n",
    "    UV_FFN_all, _, R2_FFN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_FFN_test, charName=charName)\n",
    "    UV_GAN_all, _, R2_GAN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_GAN_test, charName=charName)\n",
    "    s += '%s & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\\\n' %(charName, 1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EV & XS-R2\n",
      "1 & 0.84 & 0.74 & 0.77 & 0.61 & 0.48 & 0.81 \\\\\n",
      "2 & 0.86 & 0.81 & 0.82 & 1.00 & 0.98 & 1.00 \\\\\n",
      "3 & 0.80 & 0.82 & 0.84 & 0.91 & 0.99 & 0.98 \\\\\n",
      "4 & 0.69 & 0.80 & 0.82 & 0.83 & 1.00 & 0.99 \\\\\n",
      "5 & 0.58 & 0.68 & 0.71 & 0.79 & 0.99 & 0.97 \\\\\n",
      "6 & 0.43 & 0.66 & 0.75 & 0.47 & 0.98 & 1.00 \\\\\n",
      "7 & 0.23 & 0.64 & 0.77 & 0.38 & 0.99 & 1.00 \\\\\n",
      "8 & -0.07 & 0.49 & 0.67 & -0.46 & 0.97 & 0.94 \\\\\n",
      "9 & -0.25 & 0.29 & 0.58 & -1.15 & 0.80 & 1.00 \\\\\n",
      "10 & -0.24 & -0.04 & 0.35 & -0.79 & -0.18 & 0.74 \\\\\n",
      "All & 0.43 & 0.58 & 0.70 & 0.45 & 0.79 & 0.94 \\\\\n",
      "\n",
      "EV & Alpha\n",
      "1 & 0.84 & 0.74 & 0.77 & -0.18 & -0.21 & -0.13 \\\\\n",
      "2 & 0.86 & 0.81 & 0.82 & 0.00 & -0.05 & 0.00 \\\\\n",
      "3 & 0.80 & 0.82 & 0.84 & 0.13 & 0.04 & 0.06 \\\\\n",
      "4 & 0.69 & 0.80 & 0.82 & 0.16 & 0.03 & 0.03 \\\\\n",
      "5 & 0.58 & 0.68 & 0.71 & 0.13 & -0.03 & -0.04 \\\\\n",
      "6 & 0.43 & 0.66 & 0.75 & 0.22 & 0.05 & 0.01 \\\\\n",
      "7 & 0.23 & 0.64 & 0.77 & 0.20 & 0.03 & -0.02 \\\\\n",
      "8 & -0.07 & 0.49 & 0.67 & 0.23 & 0.03 & -0.05 \\\\\n",
      "9 & -0.25 & 0.29 & 0.58 & 0.30 & 0.09 & -0.01 \\\\\n",
      "10 & -0.24 & -0.04 & 0.35 & 0.47 & 0.38 & 0.18 \\\\\n",
      "All & 0.43 & 0.58 & 0.70 & 0.74 & 0.46 & 0.24 \\\\\n"
     ]
    }
   ],
   "source": [
    "### Value Weighted\n",
    "charName = 'ST_REV'\n",
    "UV_EN_all, Alpha_EN_all, R2_EN_all, UV_EN, Alpha_EN, R2_EN = calculateStatisticsDecile(dl_test, beta_EN_test, charName=charName, value=value_test)\n",
    "UV_FFN_all, Alpha_FFN_all, R2_FFN_all, UV_FFN, Alpha_FFN, R2_FFN = calculateStatisticsDecile(dl_test, beta_FFN_test, charName=charName, value=value_test)\n",
    "UV_GAN_all, Alpha_GAN_all, R2_GAN_all, UV_GAN, Alpha_GAN, R2_GAN = calculateStatisticsDecile(dl_test, beta_GAN_test, charName=charName, value=value_test)\n",
    "print('EV & XS-R2\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, R2_EN, R2_FFN, R2_GAN)+'All & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all))\n",
    "print('\\nEV & Alpha\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, Alpha_EN, Alpha_FFN, Alpha_GAN)+'All & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, Alpha_EN_all, Alpha_FFN_all, Alpha_GAN_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ST_REV & 0.43 & 0.58 & 0.70 & 0.45 & 0.79 & 0.94 \\\\ \n",
      "SUV & 0.42 & 0.75 & 0.83 & 0.64 & 0.97 & 0.99 \\\\\n",
      "r12_2 & 0.26 & 0.27 & 0.54 & 0.66 & 0.71 & 0.93 \\\\\n",
      "NOA & 0.58 & 0.69 & 0.78 & 0.94 & 0.96 & 0.95 \\\\\n",
      "SGA2S & 0.52 & 0.63 & 0.73 & 0.93 & 0.95 & 0.96 \\\\\n",
      "LME & 0.83 & 0.78 & 0.86 & 0.96 & 0.95 & 0.97 \\\\\n",
      "RNA & 0.50 & 0.48 & 0.69 & 0.93 & 0.87 & 0.96 \\\\\n",
      "LTurnover & 0.52 & 0.57 & 0.68 & 0.88 & 0.89 & 0.96 \\\\\n",
      "Lev & 0.52 & 0.63 & 0.73 & 0.90 & 0.92 & 0.95 \\\\\n",
      "Resid_Var & 0.52 & 0.27 & 0.65 & 0.84 & 0.73 & 0.97 \\\\\n",
      "ROA & 0.51 & 0.44 & 0.70 & 0.92 & 0.93 & 0.98 \\\\\n",
      "E2P & 0.48 & 0.44 & 0.67 & 0.86 & 0.80 & 0.95 \\\\\n",
      "D2P & 0.47 & 0.51 & 0.72 & 0.82 & 0.85 & 0.94 \\\\\n",
      "Spread & 0.49 & 0.32 & 0.60 & 0.76 & 0.71 & 0.92 \\\\\n",
      "CF2P & 0.46 & 0.47 & 0.66 & 0.90 & 0.89 & 0.99 \\\\\n",
      "BEME & 0.70 & 0.75 & 0.82 & 0.97 & 0.94 & 0.98 \\\\\n",
      "Variance & 0.48 & 0.27 & 0.61 & 0.74 & 0.72 & 0.89 \\\\\n",
      "D2A & 0.57 & 0.71 & 0.78 & 0.96 & 0.96 & 0.97 \\\\\n",
      "PCM & 0.66 & 0.79 & 0.82 & 0.97 & 0.98 & 0.99 \\\\\n",
      "A2ME & 0.72 & 0.79 & 0.83 & 0.97 & 0.96 & 0.98 \\\\\n",
      "AT & 0.77 & 0.70 & 0.83 & 0.77 & 0.89 & 0.92 \\\\\n",
      "Rel2High & 0.46 & 0.33 & 0.60 & 0.90 & 0.83 & 0.97 \\\\\n",
      "CF & 0.61 & 0.64 & 0.78 & 0.89 & 0.85 & 0.96 \\\\\n",
      "Q & 0.68 & 0.70 & 0.78 & 0.97 & 0.92 & 0.96 \\\\\n",
      "Investment & 0.54 & 0.65 & 0.75 & 0.91 & 0.94 & 0.98 \\\\\n",
      "PM & 0.52 & 0.42 & 0.68 & 0.90 & 0.86 & 0.93 \\\\\n",
      "DPI2A & 0.57 & 0.70 & 0.78 & 0.90 & 0.95 & 0.97 \\\\\n",
      "ROE & 0.59 & 0.56 & 0.76 & 0.91 & 0.86 & 0.97 \\\\\n",
      "S2P & 0.69 & 0.79 & 0.82 & 0.98 & 0.98 & 0.97 \\\\\n",
      "FC2Y & 0.56 & 0.71 & 0.76 & 0.91 & 0.94 & 0.95 \\\\\n",
      "AC & 0.63 & 0.79 & 0.82 & 0.96 & 0.98 & 0.98 \\\\\n",
      "CTO & 0.59 & 0.73 & 0.79 & 0.92 & 0.96 & 0.97 \\\\\n",
      "LT_Rev & 0.60 & 0.59 & 0.72 & 0.93 & 0.85 & 0.94 \\\\\n",
      "OP & 0.56 & 0.48 & 0.74 & 0.97 & 0.88 & 0.98 \\\\\n",
      "PROF & 0.58 & 0.62 & 0.76 & 0.91 & 0.98 & 0.95 \\\\\n",
      "IdioVol & 0.43 & 0.27 & 0.66 & 0.79 & 0.72 & 0.97 \\\\\n",
      "r12_7 & 0.37 & 0.42 & 0.66 & 0.84 & 0.86 & 0.93 \\\\\n",
      "Beta & 0.45 & 0.46 & 0.62 & 0.83 & 0.87 & 0.97 \\\\\n",
      "OA & 0.65 & 0.78 & 0.83 & 0.88 & 0.92 & 0.93 \\\\\n",
      "ATO & 0.58 & 0.70 & 0.77 & 0.96 & 0.98 & 0.99 \\\\\n",
      "MktBeta & 0.44 & 0.44 & 0.64 & 0.81 & 0.85 & 0.97 \\\\\n",
      "OL & 0.60 & 0.73 & 0.78 & 0.95 & 0.97 & 0.97 \\\\\n",
      "C & 0.51 & 0.65 & 0.73 & 0.90 & 0.93 & 0.95 \\\\\n",
      "r36_13 & 0.54 & 0.53 & 0.69 & 0.92 & 0.82 & 0.93 \\\\\n",
      "NI & 0.51 & 0.60 & 0.75 & 0.88 & 0.96 & 0.99 \\\\\n",
      "r2_1 & 0.51 & 0.52 & 0.69 & 0.87 & 0.90 & 0.95 \\\\\n",
      "\n"
     ]
    }
   ],
   "source": [
    "charRank = model_test._variable_rank\n",
    "s = ''\n",
    "for charName in charRank:\n",
    "    deco_print('Processing %s          ' %charName, '\\r')\n",
    "    UV_EN_all, _, R2_EN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_EN_test, charName=charName, value=value_test)\n",
    "    UV_FFN_all, _, R2_FFN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_FFN_test, charName=charName, value=value_test)\n",
    "    UV_GAN_all, _, R2_GAN_all, _, _, _ = calculateStatisticsDecile(dl_test, beta_GAN_test, charName=charName, value=value_test)\n",
    "    s += '%s & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\\\n' %(charName, 1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Double Sorted Portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printTable(EV_EN, EV_FFN, EV_GAN, R2_EN, R2_FFN, R2_GAN):\n",
    "    s = ''\n",
    "    for j in range(5):\n",
    "        for k in range(5):\n",
    "            i = j * 5 + k\n",
    "            s += '%d & %d & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\\\n' %(j+1, k+1, EV_EN[i], EV_FFN[i], EV_GAN[i], R2_EN[i], R2_FFN[i], R2_GAN[i])\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EV & XS-R2\n",
      "1 & 1 & 0.58 & 0.73 & 0.82 & 0.75 & 0.95 & 0.96 \\\\\n",
      "1 & 2 & 0.84 & 0.85 & 0.87 & 0.99 & 1.00 & 0.99 \\\\\n",
      "1 & 3 & 0.93 & 0.92 & 0.87 & 0.96 & 0.96 & 0.96 \\\\\n",
      "1 & 4 & 0.89 & 0.90 & 0.86 & 0.96 & 0.99 & 0.99 \\\\\n",
      "1 & 5 & 0.85 & 0.85 & 0.83 & 0.95 & 1.00 & 1.00 \\\\\n",
      "2 & 1 & 0.44 & 0.46 & 0.73 & 0.81 & 0.87 & 1.00 \\\\\n",
      "2 & 2 & 0.74 & 0.71 & 0.88 & 0.99 & 1.00 & 0.97 \\\\\n",
      "2 & 3 & 0.88 & 0.85 & 0.85 & 0.99 & 1.00 & 0.96 \\\\\n",
      "2 & 4 & 0.87 & 0.90 & 0.80 & 0.97 & 1.00 & 0.99 \\\\\n",
      "2 & 5 & 0.82 & 0.81 & 0.81 & 0.96 & 1.00 & 0.99 \\\\\n",
      "3 & 1 & 0.16 & 0.06 & 0.60 & 0.29 & 0.15 & 0.98 \\\\\n",
      "3 & 2 & 0.48 & 0.51 & 0.83 & 0.78 & 0.92 & 0.95 \\\\\n",
      "3 & 3 & 0.72 & 0.75 & 0.84 & 0.92 & 0.97 & 0.97 \\\\\n",
      "3 & 4 & 0.83 & 0.83 & 0.80 & 0.97 & 0.99 & 0.99 \\\\\n",
      "3 & 5 & 0.75 & 0.72 & 0.75 & 0.99 & 0.97 & 1.00 \\\\\n",
      "4 & 1 & -0.29 & -0.56 & 0.33 & -1.24 & -2.54 & 0.84 \\\\\n",
      "4 & 2 & -0.09 & 0.10 & 0.73 & -0.37 & 0.29 & 0.91 \\\\\n",
      "4 & 3 & 0.28 & 0.53 & 0.82 & 0.39 & 0.85 & 0.98 \\\\\n",
      "4 & 4 & 0.51 & 0.68 & 0.81 & 0.66 & 0.90 & 1.00 \\\\\n",
      "4 & 5 & 0.48 & 0.54 & 0.72 & 0.71 & 0.84 & 0.99 \\\\\n",
      "5 & 1 & -0.75 & -1.41 & -0.05 & -16.56 & -65.57 & -0.20 \\\\\n",
      "5 & 2 & -0.56 & -0.50 & 0.41 & -2.31 & -2.27 & 0.94 \\\\\n",
      "5 & 3 & -0.21 & 0.09 & 0.63 & -0.41 & 0.09 & 0.92 \\\\\n",
      "5 & 4 & 0.07 & 0.27 & 0.58 & 0.20 & 0.61 & 0.98 \\\\\n",
      "5 & 5 & 0.17 & 0.30 & 0.57 & 0.34 & 0.58 & 0.89 \\\\\n",
      "All & & 0.39 & 0.39 & 0.69 & 0.73 & 0.78 & 0.97 \\\\\n",
      "\n",
      "EV & Alpha\n",
      "1 & 1 & 0.58 & 0.73 & 0.82 & 0.21 & 0.10 & 0.08 \\\\\n",
      "1 & 2 & 0.84 & 0.85 & 0.87 & 0.03 & -0.02 & -0.02 \\\\\n",
      "1 & 3 & 0.93 & 0.92 & 0.87 & -0.05 & -0.05 & -0.05 \\\\\n",
      "1 & 4 & 0.89 & 0.90 & 0.86 & -0.05 & -0.02 & -0.02 \\\\\n",
      "1 & 5 & 0.85 & 0.85 & 0.83 & -0.06 & -0.01 & -0.01 \\\\\n",
      "2 & 1 & 0.44 & 0.46 & 0.73 & 0.09 & 0.08 & 0.01 \\\\\n",
      "2 & 2 & 0.74 & 0.71 & 0.88 & 0.02 & 0.01 & -0.03 \\\\\n",
      "2 & 3 & 0.88 & 0.85 & 0.85 & -0.02 & -0.01 & -0.04 \\\\\n",
      "2 & 4 & 0.87 & 0.90 & 0.80 & -0.04 & -0.00 & -0.02 \\\\\n",
      "2 & 5 & 0.82 & 0.81 & 0.81 & -0.04 & 0.01 & -0.02 \\\\\n",
      "3 & 1 & 0.16 & 0.06 & 0.60 & 0.13 & 0.14 & 0.02 \\\\\n",
      "3 & 2 & 0.48 & 0.51 & 0.83 & 0.06 & 0.04 & -0.03 \\\\\n",
      "3 & 3 & 0.72 & 0.75 & 0.84 & 0.04 & 0.02 & -0.02 \\\\\n",
      "3 & 4 & 0.83 & 0.83 & 0.80 & 0.03 & 0.02 & -0.02 \\\\\n",
      "3 & 5 & 0.75 & 0.72 & 0.75 & 0.02 & 0.03 & -0.01 \\\\\n",
      "4 & 1 & -0.29 & -0.56 & 0.33 & 0.17 & 0.21 & 0.04 \\\\\n",
      "4 & 2 & -0.09 & 0.10 & 0.73 & 0.10 & 0.07 & -0.03 \\\\\n",
      "4 & 3 & 0.28 & 0.53 & 0.82 & 0.09 & 0.05 & -0.02 \\\\\n",
      "4 & 4 & 0.51 & 0.68 & 0.81 & 0.09 & 0.05 & -0.01 \\\\\n",
      "4 & 5 & 0.48 & 0.54 & 0.72 & 0.11 & 0.08 & 0.02 \\\\\n",
      "5 & 1 & -0.75 & -1.41 & -0.05 & 0.10 & 0.20 & -0.03 \\\\\n",
      "5 & 2 & -0.56 & -0.50 & 0.41 & 0.15 & 0.15 & 0.02 \\\\\n",
      "5 & 3 & -0.21 & 0.09 & 0.63 & 0.17 & 0.14 & 0.04 \\\\\n",
      "5 & 4 & 0.07 & 0.27 & 0.58 & 0.13 & 0.09 & 0.02 \\\\\n",
      "5 & 5 & 0.17 & 0.30 & 0.57 & 0.19 & 0.15 & 0.08 \\\\\n",
      "All & & 0.39 & 0.39 & 0.69 & 0.52 & 0.46 & 0.17 \\\\\n"
     ]
    }
   ],
   "source": [
    "### Equally Weighted\n",
    "charName1 = 'ST_REV'\n",
    "charName2 = 'r12_2'\n",
    "UV_EN_all, Alpha_EN_all, R2_EN_all, UV_EN, Alpha_EN, R2_EN = calculateStatisticsDoubleSorted5(dl_test, beta_EN_test, charName1, charName2)\n",
    "UV_FFN_all, Alpha_FFN_all, R2_FFN_all, UV_FFN, Alpha_FFN, R2_FFN = calculateStatisticsDoubleSorted5(dl_test, beta_FFN_test, charName1, charName2)\n",
    "UV_GAN_all, Alpha_GAN_all, R2_GAN_all, UV_GAN, Alpha_GAN, R2_GAN = calculateStatisticsDoubleSorted5(dl_test, beta_GAN_test, charName1, charName2)\n",
    "print('EV & XS-R2\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, R2_EN, R2_FFN, R2_GAN)+'All & & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all))\n",
    "print('\\nEV & Alpha\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, Alpha_EN, Alpha_FFN, Alpha_GAN)+'All & & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, Alpha_EN_all, Alpha_FFN_all, Alpha_GAN_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EV & XS-R2\n",
      "1 & 1 & 0.35 & 0.32 & 0.62 & 0.77 & 0.85 & 0.94 \\\\\n",
      "1 & 2 & 0.55 & 0.48 & 0.72 & 0.99 & 0.95 & 0.92 \\\\\n",
      "1 & 3 & 0.66 & 0.61 & 0.74 & 0.92 & 0.87 & 0.94 \\\\\n",
      "1 & 4 & 0.74 & 0.62 & 0.67 & 0.93 & 0.96 & 0.99 \\\\\n",
      "1 & 5 & 0.69 & 0.58 & 0.58 & 0.81 & 0.94 & 0.98 \\\\\n",
      "2 & 1 & 0.17 & 0.16 & 0.53 & 0.43 & 0.60 & 0.87 \\\\\n",
      "2 & 2 & 0.32 & 0.39 & 0.67 & 0.60 & 0.85 & 0.92 \\\\\n",
      "2 & 3 & 0.59 & 0.61 & 0.71 & 0.88 & 0.99 & 1.00 \\\\\n",
      "2 & 4 & 0.72 & 0.74 & 0.59 & 1.00 & 0.98 & 0.99 \\\\\n",
      "2 & 5 & 0.56 & 0.61 & 0.54 & 0.93 & 0.97 & 0.97 \\\\\n",
      "3 & 1 & -0.02 & -0.01 & 0.48 & -1.08 & -0.69 & 0.99 \\\\\n",
      "3 & 2 & 0.13 & 0.33 & 0.65 & 0.23 & 0.97 & 0.95 \\\\\n",
      "3 & 3 & 0.41 & 0.62 & 0.66 & 0.53 & 0.98 & 1.00 \\\\\n",
      "3 & 4 & 0.46 & 0.60 & 0.48 & 0.95 & 0.81 & 0.74 \\\\\n",
      "3 & 5 & 0.39 & 0.53 & 0.42 & 0.83 & 1.00 & 0.99 \\\\\n",
      "4 & 1 & -0.24 & -0.27 & 0.31 & -2.03 & -1.53 & 0.85 \\\\\n",
      "4 & 2 & -0.24 & 0.15 & 0.58 & -1.95 & 0.65 & 0.74 \\\\\n",
      "4 & 3 & 0.02 & 0.51 & 0.68 & -0.31 & 0.96 & 0.60 \\\\\n",
      "4 & 4 & 0.19 & 0.53 & 0.51 & 0.39 & 0.99 & 0.92 \\\\\n",
      "4 & 5 & 0.17 & 0.47 & 0.51 & 0.47 & 0.99 & 0.99 \\\\\n",
      "5 & 1 & -0.58 & -0.88 & 0.08 & -6.81 & -12.45 & -1.86 \\\\\n",
      "5 & 2 & -0.41 & -0.12 & 0.42 & -45.62 & -7.04 & -7.84 \\\\\n",
      "5 & 3 & -0.28 & 0.23 & 0.53 & -1.74 & 0.89 & 0.90 \\\\\n",
      "5 & 4 & -0.06 & 0.31 & 0.44 & -0.14 & 1.00 & 0.84 \\\\\n",
      "5 & 5 & -0.01 & 0.27 & 0.36 & -0.08 & 0.66 & 0.84 \\\\\n",
      "All & & 0.20 & 0.26 & 0.53 & 0.50 & 0.77 & 0.92 \\\\\n",
      "\n",
      "EV & Alpha\n",
      "1 & 1 & 0.35 & 0.32 & 0.62 & 0.16 & 0.13 & 0.08 \\\\\n",
      "1 & 2 & 0.55 & 0.48 & 0.72 & -0.02 & -0.04 & -0.05 \\\\\n",
      "1 & 3 & 0.66 & 0.61 & 0.74 & -0.06 & -0.07 & -0.05 \\\\\n",
      "1 & 4 & 0.74 & 0.62 & 0.67 & -0.06 & -0.05 & -0.02 \\\\\n",
      "1 & 5 & 0.69 & 0.58 & 0.58 & -0.10 & -0.06 & -0.03 \\\\\n",
      "2 & 1 & 0.17 & 0.16 & 0.53 & 0.22 & 0.19 & 0.11 \\\\\n",
      "2 & 2 & 0.32 & 0.39 & 0.67 & 0.18 & 0.11 & 0.08 \\\\\n",
      "2 & 3 & 0.59 & 0.61 & 0.71 & 0.08 & 0.03 & 0.01 \\\\\n",
      "2 & 4 & 0.72 & 0.74 & 0.59 & 0.00 & -0.03 & -0.02 \\\\\n",
      "2 & 5 & 0.56 & 0.61 & 0.54 & 0.08 & 0.05 & 0.06 \\\\\n",
      "3 & 1 & -0.02 & -0.01 & 0.48 & 0.18 & 0.16 & 0.01 \\\\\n",
      "3 & 2 & 0.13 & 0.33 & 0.65 & 0.12 & 0.02 & -0.03 \\\\\n",
      "3 & 3 & 0.41 & 0.62 & 0.66 & 0.13 & 0.02 & -0.00 \\\\\n",
      "3 & 4 & 0.46 & 0.60 & 0.48 & 0.03 & -0.06 & -0.07 \\\\\n",
      "3 & 5 & 0.39 & 0.53 & 0.42 & 0.08 & -0.01 & -0.02 \\\\\n",
      "4 & 1 & -0.24 & -0.27 & 0.31 & 0.26 & 0.24 & 0.06 \\\\\n",
      "4 & 2 & -0.24 & 0.15 & 0.58 & 0.14 & 0.05 & -0.04 \\\\\n",
      "4 & 3 & 0.02 & 0.51 & 0.68 & 0.11 & -0.02 & -0.06 \\\\\n",
      "4 & 4 & 0.19 & 0.53 & 0.51 & 0.11 & -0.01 & -0.04 \\\\\n",
      "4 & 5 & 0.17 & 0.47 & 0.51 & 0.14 & 0.02 & -0.01 \\\\\n",
      "5 & 1 & -0.58 & -0.88 & 0.08 & 0.13 & 0.17 & -0.08 \\\\\n",
      "5 & 2 & -0.41 & -0.12 & 0.42 & 0.14 & 0.06 & -0.06 \\\\\n",
      "5 & 3 & -0.28 & 0.23 & 0.53 & 0.16 & 0.03 & -0.03 \\\\\n",
      "5 & 4 & -0.06 & 0.31 & 0.44 & 0.12 & -0.00 & -0.05 \\\\\n",
      "5 & 5 & -0.01 & 0.27 & 0.36 & 0.29 & 0.16 & 0.11 \\\\\n",
      "All & & 0.20 & 0.26 & 0.53 & 0.71 & 0.48 & 0.28 \\\\\n"
     ]
    }
   ],
   "source": [
    "### Value Weighted\n",
    "charName1 = 'ST_REV'\n",
    "charName2 = 'r12_2'\n",
    "UV_EN_all, Alpha_EN_all, R2_EN_all, UV_EN, Alpha_EN, R2_EN = calculateStatisticsDoubleSorted5(dl_test, beta_EN_test, charName1, charName2, value=value_test)\n",
    "UV_FFN_all, Alpha_FFN_all, R2_FFN_all, UV_FFN, Alpha_FFN, R2_FFN = calculateStatisticsDoubleSorted5(dl_test, beta_FFN_test, charName1, charName2, value=value_test)\n",
    "UV_GAN_all, Alpha_GAN_all, R2_GAN_all, UV_GAN, Alpha_GAN, R2_GAN = calculateStatisticsDoubleSorted5(dl_test, beta_GAN_test, charName1, charName2, value=value_test)\n",
    "print('EV & XS-R2\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, R2_EN, R2_FFN, R2_GAN)+'All & & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, R2_EN_all, R2_FFN_all, R2_GAN_all))\n",
    "print('\\nEV & Alpha\\n'+printTable(1-UV_EN, 1-UV_FFN, 1-UV_GAN, Alpha_EN, Alpha_FFN, Alpha_GAN)+'All & & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f & %0.2f \\\\\\\\' %(1-UV_EN_all, 1-UV_FFN_all, 1-UV_GAN_all, Alpha_EN_all, Alpha_FFN_all, Alpha_GAN_all))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Relationship with Fama-French Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>Rsdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mkt-RF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200945</td>\n",
       "      <td>-0.150677</td>\n",
       "      <td>-0.478305</td>\n",
       "      <td>-0.342449</td>\n",
       "      <td>-0.095775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>0.200945</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131863</td>\n",
       "      <td>-0.481567</td>\n",
       "      <td>-0.026220</td>\n",
       "      <td>-0.087971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HML</th>\n",
       "      <td>-0.150677</td>\n",
       "      <td>-0.131863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409205</td>\n",
       "      <td>0.647233</td>\n",
       "      <td>0.011501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMW</th>\n",
       "      <td>-0.478305</td>\n",
       "      <td>-0.481567</td>\n",
       "      <td>0.409205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264014</td>\n",
       "      <td>0.172972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMA</th>\n",
       "      <td>-0.342449</td>\n",
       "      <td>-0.026220</td>\n",
       "      <td>0.647233</td>\n",
       "      <td>0.264014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rsdf</th>\n",
       "      <td>-0.095775</td>\n",
       "      <td>-0.087971</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.172972</td>\n",
       "      <td>0.045875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mkt-RF       SMB       HML       RMW       CMA      Rsdf\n",
       "Mkt-RF  1.000000  0.200945 -0.150677 -0.478305 -0.342449 -0.095775\n",
       "SMB     0.200945  1.000000 -0.131863 -0.481567 -0.026220 -0.087971\n",
       "HML    -0.150677 -0.131863  1.000000  0.409205  0.647233  0.011501\n",
       "RMW    -0.478305 -0.481567  0.409205  1.000000  0.264014  0.172972\n",
       "CMA    -0.342449 -0.026220  0.647233  0.264014  1.000000  0.045875\n",
       "Rsdf   -0.095775 -0.087971  0.011501  0.172972  0.045875  1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = load_dataframe('datasets/F-F_Research_Data_5_Factors_2x3.CSV', skiprows=2, nrows=659)\n",
    "df_test = df5.loc[199201:201612].copy()\n",
    "df_test.loc[:,'Rsdf'] = Ftest[:,0] * 100\n",
    "df_test.drop(['RF'], inplace=True, axis=1)\n",
    "df_test.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_test.loc[:,'Rsdf'].values\n",
    "X = df_test.loc[:,['Mkt-RF', 'SMB', 'HML', 'RMW', 'CMA']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.037\n",
      "Model:                            OLS   Adj. R-squared:                  0.020\n",
      "Method:                 Least Squares   F-statistic:                     2.246\n",
      "Date:                Mon, 22 Apr 2019   Prob (F-statistic):             0.0499\n",
      "Time:                        12:02:50   Log-Likelihood:                -432.18\n",
      "No. Observations:                 300   AIC:                             876.4\n",
      "Df Residuals:                     294   BIC:                             898.6\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             0.0024      0.017      0.138      0.891      -0.032       0.036\n",
      "x2            -0.0017      0.022     -0.077      0.938      -0.045       0.041\n",
      "x3            -0.0395      0.028     -1.420      0.157      -0.094       0.015\n",
      "x4             0.0757      0.029      2.567      0.011       0.018       0.134\n",
      "x5             0.0354      0.040      0.877      0.381      -0.044       0.115\n",
      "const          0.7585      0.063     11.992      0.000       0.634       0.883\n",
      "==============================================================================\n",
      "Omnibus:                       66.220   Durbin-Watson:                   1.695\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              556.452\n",
      "Skew:                          -0.603   Prob(JB):                    1.47e-121\n",
      "Kurtosis:                       9.562   Cond. No.                         5.34\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "OLS(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Correlation between SDF Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = np.load('model_char_shift/model_Linear_LS/LS/SDF_normalized.npz')\n",
    "SDF_LS_train = tmp['train'] / tmp['train'].std()\n",
    "SDF_LS_valid = tmp['valid'] / tmp['valid'].std()\n",
    "SDF_LS_test = tmp['test'] / tmp['test'].std()\n",
    "\n",
    "tmp = np.load('model_char_shift/model_Linear_LS/ElasticNet/SDF_normalized.npz')\n",
    "SDF_Elastic_train = tmp['train'] / tmp['train'].std()\n",
    "SDF_Elastic_valid = tmp['valid'] / tmp['valid'].std()\n",
    "SDF_Elastic_test = tmp['test'] / tmp['test'].std()\n",
    "\n",
    "tmp = np.load('model_char_shift/model_FFN/SDF_normalized.npz')\n",
    "SDF_FFN_train = tmp['train'] / tmp['train'].std()\n",
    "SDF_FFN_valid = tmp['valid'] / tmp['valid'].std()\n",
    "SDF_FFN_test = tmp['test'] / tmp['test'].std()\n",
    "\n",
    "tmp = np.load('model_char_shift/model_GAN/SDF_normalized.npz')\n",
    "SDF_GAN_train = tmp['train'] / tmp['train'].std()\n",
    "SDF_GAN_valid = tmp['valid'] / tmp['valid'].std()\n",
    "SDF_GAN_test = tmp['test'] / tmp['test'].std()\n",
    "\n",
    "df = pd.DataFrame({'LS':np.concatenate([SDF_LS_train, SDF_LS_valid, SDF_LS_test]),\n",
    "                    'EN':np.concatenate([SDF_Elastic_train, SDF_Elastic_valid, SDF_Elastic_test]),\n",
    "                    'FFN':np.concatenate([SDF_FFN_train, SDF_FFN_valid, SDF_FFN_test]),\n",
    "                    'GAN':np.concatenate([SDF_GAN_train, SDF_GAN_valid, SDF_GAN_test])[:,0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c7e7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_SDF(df, columns=['GAN','FFN','EN','LS'], plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corr_heat_map(df, plotPath=None):\n",
    "    corr = df.corr()\n",
    "    mask = np.ones_like(corr)\n",
    "    mask[np.triu_indices_from(mask)] = False\n",
    "    with sns.axes_style(\"white\"):\n",
    "        x = sns.heatmap(corr, mask=mask.T, square=True, \n",
    "            xticklabels=corr.columns, yticklabels=corr.columns, \n",
    "            annot=True, fmt=\"0.2f\", linewidths=.5, cmap=\"YlGnBu\")\n",
    "    if plotPath is not None:\n",
    "        plt.savefig(os.path.join(plotPath, 'corr.pdf'))\n",
    "        plt.savefig(os.path.join(plotPath, 'corr.png'))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = np.load('model_char_shift/model_Linear_LS/LS/SDF_normalized.npz')\n",
    "SDF_LS = np.concatenate([tmp['train'], tmp['valid'], tmp['test']])\n",
    "tmp = np.load('model_char_shift/model_Linear_LS/ElasticNet/SDF_normalized.npz')\n",
    "SDF_Elastic = np.concatenate([tmp['train'], tmp['valid'], tmp['test']])\n",
    "tmp = np.load('model_char_shift/model_FFN/SDF_normalized.npz')\n",
    "SDF_FFN = np.concatenate([tmp['train'], tmp['valid'], tmp['test']])\n",
    "tmp = np.load('model_char_shift/model_GAN/SDF_normalized.npz')\n",
    "SDF_GAN = np.concatenate([tmp['train'], tmp['valid'], tmp['test']])\n",
    "\n",
    "df5 = load_dataframe('datasets/F-F_Research_Data_5_Factors_2x3.CSV', skiprows=2, nrows=659)\n",
    "df = df5.loc[196701:201612].copy()\n",
    "SDF_MKT = df.loc[:,'Mkt-RF'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17aa0f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heat_map(df_SDF.loc[300:,['GAN','FFN','EN','LS','Mkt']], plotPath='model_char_shift/model_GAN/plot') ### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17aa0f128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_SDF = pd.DataFrame({'LS':SDF_LS, 'EN':SDF_Elastic, 'FFN':SDF_FFN, 'GAN':SDF_GAN[:,0], 'Mkt': SDF_MKT})\n",
    "corr_heat_map(df_SDF.loc[:,['GAN','FFN','EN','LS','Mkt']], plotPath='model_char_shift/model_GAN/plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot Weight As Function of Firm Characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      ">==================> Plotting Variable: ST_REV\tElapse / Estimate: 6.00s / 6.00s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122868e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot.plotWeight1DWithChar(sess, dl_test, 'ST_REV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Please enter the idx for the plot you want! \n",
      "Please select from 1.1D, 2.2D Contour 3.2D Contour Slice 4. 1D Curves: 4\n",
      "Enter variable idx for x (0 - 45, 46 - 223): 43\n",
      "Enter variable idx for y (0 - 45, 46 - 223): 32\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba8922b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot.plotWeight(sess, dl_test, plotPath='model_char_shift/model_GAN/plot/2D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Please enter the idx for the plot you want! \n",
      "Please select from 1.1D, 2.2D Contour 3.2D Contour Slice 4.1D Curves (2 Variables) 5.1D Curves (3 Variables): 5\n",
      "Enter variable idx for x (0 - 45, 46 - 223): 43\n",
      "Enter variable idx for y (0 - 45, 46 - 223): 18\n",
      "Enter variable idx for z (0 - 45, 46 - 223): 4\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c8b99f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot.plotWeight(sess, dl_test, plotPath='model_char_shift/model_GAN/plot/3D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_plot.plotWeight1DWithChar(sess, dl_test, 'All', plotPath='model_char_shift/model_GAN/plot/1D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Please enter the idx for the plot you want! \n",
      "Please select from 1.1D, 2.2D Contour 3.2D Contour Slice 4.1D Curves (2 Variables) 5.1D Curves (3 Variables): 2\n",
      "Enter variable idx for x (0 - 45, 46 - 223): 18\n",
      "Enter variable idx for y (0 - 45, 46 - 223): 11\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177c2c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot.plotWeight(sess, dl_test, plotPath='model_char_shift/model_GAN/plot/2D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Please enter the idx for the plot you want! \n",
      "Please select from 1.1D, 2.2D Contour 3.2D Contour Slice 4.1D Curves (2 Variables) 5.1D Curves (3 Variables): 3\n",
      "Enter variable idx for x (0 - 45, 46 - 223): 18\n",
      "Enter variable idx for y (0 - 45, 46 - 223): 11\n",
      "Enter variable idx for z (0 - 45, 46 - 223): 43\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177c14588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot.plotWeight(sess, dl_test, plotPath='model_char_shift/model_GAN/plot/3D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot Beta As Function of Firm Characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">==================> Please enter the idx for the plot you want! \n",
      "Please select from 1.1D, 2.2D Contour 3.2D Contour Slice 4.1D Curves (2 Variables) 5.1D Curves (3 Variables): 3\n",
      "Enter variable idx for x (0 - 45, 46 - 45): 18\n",
      "Enter variable idx for y (0 - 45, 46 - 45): 11\n",
      "Enter variable idx for z (0 - 45, 46 - 45): 43\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/1/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/2/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/3/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/4/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/5/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/6/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/7/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/8/model-best\n",
      ">==================> Restored checkpoint\n",
      "INFO:tensorflow:Restoring parameters from model_char_shift/model_GAN_RF_normalized/9/model-best\n",
      ">==================> Restored checkpoint\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x175a1bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_plot_RF.plotConditionalReturn(sess, dl_test, plotPath='model_char_shift/model_GAN/plot/beta', label='beta')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}