Portfolio_Rebalancing_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90c5cfb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "import statsmodels.api as sm\n",
    "\n",
    "from matplotlib import rc\n",
    "rc('font', family = 'AppleGothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "plt.style.use(['ggplot'])\n",
    "\n",
    "\n",
    "from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday\n",
    "import platform\n",
    "from datetime import datetime, timezone, timedelta\n",
    "import pytz\n",
    "from tqdm.notebook import tqdm as tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('retina')\n",
    "\n",
    "pd.options.display.float_format = '{:.4f}'.format\n",
    "pd.options.display.max_rows=1000\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "        \n",
    "# seed 설정\n",
    "np.random.seed(42)\n",
    "\n",
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.layers import Input, Dense, LSTM\n",
    "from tensorflow.keras.layers import Activation, BatchNormalization\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras import regularizers\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8abf7bf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8184 entries, 0 to 8183\n",
      "Data columns (total 22 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Date            8184 non-null   object \n",
      " 1   VIX Index       8183 non-null   float64\n",
      " 2   MOVE Index      8184 non-null   float64\n",
      " 3   MXWD Index      8184 non-null   float64\n",
      " 4   SBWGU Index     8184 non-null   float64\n",
      " 5   SPGSCI Index    8184 non-null   float64\n",
      " 6   .TED G Index    8184 non-null   float64\n",
      " 7   KOSPI2 Index    8182 non-null   float64\n",
      " 8   SPX Index       8184 non-null   float64\n",
      " 9   SX5E Index      8184 non-null   float64\n",
      " 10  NKY Index       8184 non-null   float64\n",
      " 11  UKX Index       8184 non-null   float64\n",
      " 12  HSI Index       8184 non-null   float64\n",
      " 13  MXWO000V Index  8184 non-null   float64\n",
      " 14  MXWOMOM Index   8184 non-null   float64\n",
      " 15  MXUS000V Index  8184 non-null   float64\n",
      " 16  MXUSMMT Index   7033 non-null   float64\n",
      " 17  MXUSQU Index    8184 non-null   float64\n",
      " 18  MXUSMVOL Index  8184 non-null   float64\n",
      " 19  DXY Curncy      8184 non-null   float64\n",
      " 20  GOLDLNPM Index  8184 non-null   float64\n",
      " 21  USYC2Y10 Index  8184 non-null   float64\n",
      "dtypes: float64(21), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('Toy_data.csv')\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbbfecf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10793 entries, 0 to 10792\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Date    10793 non-null  object \n",
      " 1   TLT     8185 non-null   float64\n",
      " 2   EMB     7380 non-null   float64\n",
      " 3   AGG     10793 non-null  float64\n",
      " 4   Cash    8964 non-null   float64\n",
      " 5   SPY     10793 non-null  float64\n",
      " 6   EFA     10793 non-null  float64\n",
      " 7   VWO     8706 non-null   float64\n",
      " 8   GLD     10793 non-null  float64\n",
      " 9   DBC     8465 non-null   float64\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 843.3+ KB\n"
     ]
    }
   ],
   "source": [
    "target = pd.read_csv('Toy_data_target.csv')\n",
    "target.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0514619",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.set_index('Date')\n",
    "target = target.set_index('Date')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "efd12fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test['1995-01-01': '2021-04-30']\n",
    "target = target['1995-01-01':'2021-04-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05a1e5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Max_SharpeRatio import MaximizeSharpeRatioOptmzn, StockReturnsComputing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "331cfd7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asset labels of Portfolio: \n",
      " ['TLT', 'EMB', 'AGG', 'Cash', 'SPY', 'EFA', 'VWO', 'GLD', 'DBC']\n",
      "\n",
      "Mean Returns:\n",
      " [0.031 0.037 0.021 0.018 0.047 0.028 0.03  0.027 0.023]\n",
      "\n",
      "Variance-Covariance Matrix of Returns:\n",
      " [[ 0.624  0.024  0.159  0.    -0.287 -0.314 -0.354  0.089 -0.168]\n",
      " [ 0.024  0.481  0.042  0.     0.25   0.245  0.37   0.072  0.122]\n",
      " [ 0.159  0.042  0.082  0.    -0.019 -0.014 -0.021  0.039 -0.005]\n",
      " [ 0.     0.     0.     0.001  0.     0.    -0.001 -0.    -0.001]\n",
      " [-0.287  0.25  -0.019  0.     1.435  1.124  1.255  0.018  0.378]\n",
      " [-0.314  0.245 -0.014  0.     1.124  1.568  1.441  0.15   0.495]\n",
      " [-0.354  0.37  -0.021 -0.001  1.255  1.441  2.286  0.222  0.648]\n",
      " [ 0.089  0.072  0.039 -0.     0.018  0.15   0.222  1.033  0.339]\n",
      " [-0.168  0.122 -0.005 -0.001  0.378  0.495  0.648  0.339  1.273]]\n",
      "\n",
      "Risk free rate (daily %): 0.000\n",
      "Maximal Sharpe Ratio:  [[11.713]] \n",
      "Annualized Risk (%):   [[0.378]] \n",
      "Annualized Expected Portfolio Return(%):   [4.455]\n",
      "\n",
      "Optimal weights (%):\n",
      " [[ 0.366]\n",
      " [ 0.   ]\n",
      " [ 0.   ]\n",
      " [99.49 ]\n",
      " [ 0.   ]\n",
      " [ 0.   ]\n",
      " [ 0.   ]\n",
      " [ 0.144]\n",
      " [ 0.   ]]\n"
     ]
    }
   ],
   "source": [
    "Rows = 7380\n",
    "Columns = 9\n",
    "assets = target.columns[:Columns].tolist()\n",
    "print('Asset labels of Portfolio: \\n', assets)\n",
    "\n",
    "target_prices = np.asarray(target)\n",
    "[Rows, Cols] = target_prices.shape\n",
    "target_returns = StockReturnsComputing(target_prices, Rows, Cols)\n",
    "\n",
    "#set precision for printing results\n",
    "np.set_printoptions(precision=3, suppress = True)\n",
    "\n",
    "#compute mean returns and variance covariance matrix of returns\n",
    "meanReturns = np.mean(target_returns, axis = 0)\n",
    "covReturns = np.cov(target_returns, rowvar=False)\n",
    "print('\\nMean Returns:\\n', meanReturns)\n",
    "print('\\nVariance-Covariance Matrix of Returns:\\n', covReturns)\n",
    "#obtain maximal Sharpe Ratio for k-portfolio 1 of Dow stocks\n",
    "\n",
    "#set portfolio size\n",
    "portfolioSize = Columns\n",
    "\n",
    "#set risk free asset rate of return\n",
    "Rf=0.03  # April 2019 average risk  free rate of return in USA approx 0.03%\n",
    "annRiskFreeRate = Rf/100\n",
    "\n",
    "#compute daily risk free rate in percentage\n",
    "r0 = (np.power((1 + annRiskFreeRate),  (1.0 / 360.0)) - 1.0) * 100 \n",
    "print('\\nRisk free rate (daily %): ', end=\"\")\n",
    "print (\"{0:.3f}\".format(r0)) \n",
    "\n",
    "#initialization\n",
    "xOptimal =[]\n",
    "minRiskPoint = []\n",
    "expPortfolioReturnPoint =[]\n",
    "maxSharpeRatio = 0\n",
    "\n",
    "#compute maximal Sharpe Ratio and optimal weights\n",
    "result = MaximizeSharpeRatioOptmzn(meanReturns, covReturns, r0, portfolioSize)\n",
    "xOptimal.append(result.x)\n",
    "\n",
    "    \n",
    "#compute risk returns and max Sharpe Ratio of the optimal portfolio   \n",
    "xOptimalArray = np.array(xOptimal)\n",
    "Risk = np.matmul((np.matmul(xOptimalArray,covReturns)), np.transpose(xOptimalArray))\n",
    "expReturn = np.matmul(np.array(meanReturns),xOptimalArray.T)\n",
    "annRisk =   np.sqrt(Risk*251) \n",
    "annRet = 251*np.array(expReturn) \n",
    "maxSharpeRatio = (annRet-Rf)/annRisk \n",
    "\n",
    "#set precision for printing results\n",
    "np.set_printoptions(precision=3, suppress = True)\n",
    "\n",
    "\n",
    "#display results\n",
    "print('Maximal Sharpe Ratio: ', maxSharpeRatio, '\\nAnnualized Risk (%):  ', \\\n",
    "      annRisk, '\\nAnnualized Expected Portfolio Return(%):  ', annRet)\n",
    "print('\\nOptimal weights (%):\\n',  xOptimalArray.T*100 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79362872",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimize the portfolio\n",
    "from pypfopt.efficient_frontier import EfficientFrontier\n",
    "from pypfopt import risk_models\n",
    "from pypfopt import expected_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "28754ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the expected annualized returns and \n",
    "# the annualized sample covariance of the daily asset returns\n",
    "\n",
    "mu = expected_returns.mean_historical_return(target)\n",
    "S = risk_models.sample_cov(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46df6b26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'TLT': 0.00134, 'EMB': 0.00219, 'AGG': 0.00302, 'Cash': 0.99102, 'SPY': 0.00112, 'EFA': 0.0, 'VWO': 0.0, 'GLD': 0.00072, 'DBC': 0.00059}\n",
      "Expected annual return: 4.5%\n",
      "Annual volatility: 0.4%\n",
      "Sharpe Ratio: 6.60\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.04485147169244269, 0.003768061385886521, 6.595293745883554)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Optimize for the maximal Sharpe ratio\n",
    "\n",
    "ef = EfficientFrontier(mu, S) \n",
    "weights = ef.max_sharpe()\n",
    "\n",
    "cleaned_weights = ef.clean_weights()\n",
    "print(cleaned_weights)\n",
    "ef.portfolio_performance(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6544b27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight = Clusetered_Bond -> 6, Clustered_Stock -> 4\n",
    "# TLT, AGG, GLD, CASH = 0.6\n",
    "# EMB, SPY, EFA, VWD, DBC = 0.4\n",
    "\n",
    "\n",
    "weights = np.array([0.15, 0.15, 0.15, 0.15, 0.06, 0.01, \n",
    "                    0.14, 0.08, 0.11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54f5b266",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to compute asset returns \n",
    "def TargetReturnsComputing(TargetPrice, Rows, Columns):\n",
    "    \n",
    "    import numpy as np\n",
    "    \n",
    "    TargetReturn = np.zeros([Rows-1, Columns])\n",
    "    for j in range(Columns):        # j: Assets\n",
    "        for i in range(Rows-1):     # i: Daily Prices\n",
    "            TargetReturn[i,j]=((TargetPrice[i+1, j]-TargetPrice[i,j])/TargetPrice[i,j])* 100\n",
    "\n",
    "    return TargetReturn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "857448b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily returns of target [[-0.399  0.    -0.136 ... -1.809 -0.613 -1.12 ]\n",
      " [ 0.888  0.     0.401 ... -1.883 -1.391  0.063]\n",
      " [-0.628  0.    -0.29  ...  0.866 -0.08   0.883]\n",
      " ...\n",
      " [ 0.065  0.335  0.052 ...  0.804  0.294  0.168]\n",
      " [-0.295  0.117 -0.052 ... -0.223 -0.413  0.669]\n",
      " [ 0.231  0.289  0.105 ... -1.469 -0.337 -0.831]]\n"
     ]
    }
   ],
   "source": [
    "# compute daily returns in percentage of the Dow stocks\n",
    "\n",
    "targetPriceArray = np.asarray(target)\n",
    "[Rows, Cols]=targetPriceArray.shape\n",
    "targetReturns = TargetReturnsComputing(targetPriceArray, Rows, Cols)\n",
    "print('Daily returns of target', targetReturns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5eff7821",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean returns of target:\n",
      " [0.031 0.037 0.021 0.018 0.047 0.028 0.03  0.027 0.023]\n",
      "Variance-covariance matrix of returns of target:\n",
      "\n",
      "[[ 0.624  0.024  0.159  0.    -0.287 -0.314 -0.354  0.089 -0.168]\n",
      " [ 0.024  0.481  0.042  0.     0.25   0.245  0.37   0.072  0.122]\n",
      " [ 0.159  0.042  0.082  0.    -0.019 -0.014 -0.021  0.039 -0.005]\n",
      " [ 0.     0.     0.     0.001  0.     0.    -0.001 -0.    -0.001]\n",
      " [-0.287  0.25  -0.019  0.     1.435  1.124  1.255  0.018  0.378]\n",
      " [-0.314  0.245 -0.014  0.     1.124  1.568  1.441  0.15   0.495]\n",
      " [-0.354  0.37  -0.021 -0.001  1.255  1.441  2.286  0.222  0.648]\n",
      " [ 0.089  0.072  0.039 -0.     0.018  0.15   0.222  1.033  0.339]\n",
      " [-0.168  0.122 -0.005 -0.001  0.378  0.495  0.648  0.339  1.273]]\n"
     ]
    }
   ],
   "source": [
    "# compute mean returns and variance covariance matrix of returns\n",
    "meanReturns = np.mean(targetReturns, axis = 0)\n",
    "print('Mean returns of target:\\n',  meanReturns)\n",
    "covReturns = np.cov(targetReturns, rowvar=False)\n",
    "print('Variance-covariance matrix of returns of target:\\n')\n",
    "print(covReturns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f7e0bd3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute portfolio risk\n",
    "\n",
    "portfolioRisk = np.matmul((np.matmul(weights, covReturns)),\n",
    "                          np.transpose(weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45b437ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute annualized portfolio risk for trading days = 251\n",
    "\n",
    "annualizedRisk  =   np.sqrt(portfolioRisk*251) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04c2b9b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute expected portfolio return\n",
    "\n",
    "portfolioReturn = np.matmul(np.array(meanReturns),weights.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3f9fb8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute annualized expected portfolio return\n",
    "\n",
    "annualizedReturn = 251 * np.array(portfolioReturn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff5c4dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute Sharpe Ratio\n",
    "\n",
    "SharpeRatio = (annualizedReturn - 0) / annualizedRisk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84219a82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Daily Portfolio Risk: 0.17 %\n",
      "\n",
      " Annualized Portfolio Risk: 6.62 %\n",
      "\n",
      " Daily Portfolio Return: 0.03 %\n",
      "\n",
      " Annualized Expected Portfolio Return: 6.97 %\n",
      "\n",
      " PortfolioSharpe Ratio: 1.05 %\n"
     ]
    }
   ],
   "source": [
    "#display results\n",
    "\n",
    "print(\"\\n Daily Portfolio Risk: %4.2f\" % portfolioRisk,\"%\")\n",
    "print(\"\\n Annualized Portfolio Risk: %4.2f\" % annualizedRisk,\"%\")\n",
    "print(\"\\n Daily Portfolio Return: %4.2f\" % portfolioReturn,\"%\")\n",
    "print(\"\\n Annualized Expected Portfolio Return: %4.2f\" % annualizedReturn,\"%\")\n",
    "print(\"\\n PortfolioSharpe Ratio: %4.2f\" % SharpeRatio,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cad8b207",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Rebalance_code import GenerateBoundedPopln, DetermineBounds\n",
    "from Rebalance_code import PortfolioRebalancingWeightRepair, AdjustPosNegWgtsLowbounds\n",
    "from Rebalance_code import AdjustPosNegWgtsUpbounds, AdjustPosNegWgtsUpbounds\n",
    "from Rebalance_code import ComputeConstraintViolation, ComputeFitness\n",
    "from Rebalance_code import RandomVariablePointArithmeticCrossover, RealNumberUniformMutation\n",
    "from Rebalance_code import WeightNormalization, ConstructNewGeneration, ComputePMeasure\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c92e2c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['TLT', 'EMB', 'AGG', 'Cash', 'SPY', 'EFA', 'VWO', 'GLD', 'DBC']\n"
     ]
    }
   ],
   "source": [
    "AssetLabels = target.columns.tolist()\n",
    "print(AssetLabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4667ed5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain optimal weights, maximal diversification ratio, risk and return of the original portfolio invested in\n",
    "\n",
    "PortfolioSize = 9\n",
    "OriginalPortfolioOptimalWeights = np.reshape(np.array([0.12, 0.14, 0.18, 0.24, 0.05, 0.01, \n",
    "                                                       0.11, 0.06, 0.09]),(1, PortfolioSize))\n",
    "OriginalPortfolioSharpeRatio = 1.19\n",
    "OriginalPortfolioDailyRisk = 0.12\n",
    "OriginalPortfolioDailyReturn = 0.03\n",
    "OriginalPortfolioAnnReturn= 6.58\n",
    "OriginalPortfolioAnnRisk = 5.53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "407e00d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "PortfolioParams = np.array(target.iloc[0:, 0:])\n",
    "MeanData = np.array(PortfolioParams[0, :])\n",
    "CovData = np.array(PortfolioParams[1:PortfolioSize+1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86fe7f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Transaction cost\n",
    "\n",
    "p = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50e67505",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Risk-free rate\n",
    "\n",
    "RiskFreeRate = 0.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b499852e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set lower and upper bounds of buy and sell weights as defined by equations (2-3)\n",
    "\n",
    "BuyLowBounds = np.zeros((1,PortfolioSize), dtype = float)  \n",
    "BuyUpBounds = np.ones((1,PortfolioSize), dtype = float)*0.025  \n",
    "SellLowBounds = np.zeros((1,PortfolioSize), dtype = float)  \n",
    "SellUpBounds = OriginalPortfolioOptimalWeights  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c3d1260a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set control  parameters of Evolution Strategy with Hall of Fame \n",
    "PoplnSize = 300  \n",
    "ChromosomeLength = PortfolioSize    \n",
    "TotalGenerations = 316 \n",
    "\n",
    "# The penalized objective function is an outcome of the application of Joines and Houck's (1994)  dynamic penalty function strategy \n",
    "# Set parameters (C, alpha, beta) described in equation (8)\n",
    "C = 0.5   \n",
    "BetaParam=2  \n",
    "AlphaParam=2  \n",
    "EPSILON = 0.0009  \n",
    "\n",
    "# initialize Hall of Fame\n",
    "HOFFitness = -9999.99  \n",
    "\n",
    "# generation counter\n",
    "GenerationCount = 0  \n",
    "\n",
    "# initialize index for tracing HOF fitness\n",
    "i1=0   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "93d65cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate random initial population of buy / sell weights (xi+, xi-) subject to their respective bounds                \n",
    "SourcePopln = GenerateBoundedPopln(PoplnSize, PortfolioSize, SellLowBounds, SellUpBounds, BuyLowBounds,  BuyUpBounds)  \n",
    "\n",
    "# determine the respective lower and upper bounds of the genes in the population of buy/sell weights and \n",
    "# store it in a Python dictionary for further use\n",
    "LowUpBoundsInit ={}\n",
    "LowUpBoundsInit = DetermineBounds(SourcePopln, BuyLowBounds,  BuyUpBounds, SellLowBounds, SellUpBounds)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5a744d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# repair buy/sell weights (xi+, xi-)so that constraints described by equations (4-6) are satisfied and \n",
    "# a feasible solution set is obtained\n",
    "StandardizedSourcePopln = PortfolioRebalancingWeightRepair(SourcePopln, LowUpBoundsInit,p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b8e3aa4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the population of rebalanced weights,  which is the initial (parent) population\n",
    "InitialPopln = np.ones((PoplnSize,1)) * OriginalPortfolioOptimalWeights + StandardizedSourcePopln \n",
    "\n",
    "# compute constraint violation function values of the parent population\n",
    "[ParentG, ParentPsi]  = ComputeConstraintViolation(InitialPopln, CovData, OriginalPortfolioDailyRisk, C, BetaParam, AlphaParam, GenerationCount)   #compute constraint violation functions using Joines and Houck's dynamic penalty functions              \n",
    "\n",
    "# compute fitness function values of the parent population\n",
    "ParentFitness = np.zeros(PoplnSize)\n",
    "ParentFitness= ComputeFitness(InitialPopln, MeanData, CovData, ParentPsi, RiskFreeRate ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "45d615e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parent population parameters for the generation cycle\n",
    "# (the buy/sell weights and the respective rebalanced portfolio weights)\n",
    "FeasParentPoplnX = StandardizedSourcePopln  \n",
    "FeasParentPoplnW = InitialPopln  \n",
    "FeasParentPoplnWFitness = ParentFitness  \n",
    "FeasParentPoplnWPsi = ParentPsi  \n",
    "\n",
    "#set counter variable for P measure\n",
    "PMeasureCount =0   \n",
    "\n",
    "HOFGenarray = np.zeros(TotalGenerations)\n",
    "HOFFitarray = np.zeros(TotalGenerations)\n",
    "PerformanceAnalysisMeasure = np.zeros((TotalGenerations, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "10a08fe3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation: 0\n",
      "HOF Fitness 919.2757548820165\n",
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      "Generation: 315\n",
      "Optimal Rebalanced Portfolio Weights [0.037 0.113 0.205 0.265 0.075 0.033 0.133 0.024 0.115]\n",
      "Rebalanced portfolio annualized return  87171276.65960883\n",
      "Rebalanced portfolio annualized risk  92991.70099711063\n",
      "Rebalanced portfolio Sharpe Ratio 937.4092063582893\n",
      "Original portfolio annualized return  6.58\n",
      "Original portfolio annualized risk  5.53\n",
      "Original portfolio Sharpe Ratio 1.19\n",
      "Successful Execution!\n"
     ]
    }
   ],
   "source": [
    "# ES HOF generation cycles begin\n",
    "while (GenerationCount <= TotalGenerations-1): \n",
    "    \n",
    "        print(\"Generation:\", GenerationCount )   \n",
    "    \n",
    "        # perform crossover operation on the parent population \n",
    "        OffspringPoplnXSource= RandomVariablePointArithmeticCrossover(FeasParentPoplnX, PoplnSize, ChromosomeLength)  \n",
    "        \n",
    "        # perform mutation operation to yield offspring population\n",
    "        OffspringPoplnXMutated = RealNumberUniformMutation(OffspringPoplnXSource, PoplnSize, ChromosomeLength)  \n",
    "        \n",
    "        # normalize offspring population so that the buy/sell weights lie within their respective bounds\n",
    "        OffspringPoplnXNormalized= WeightNormalization(OffspringPoplnXMutated, SellLowBounds , SellUpBounds, BuyLowBounds,  BuyUpBounds)  \n",
    "        \n",
    "        # determine lower and upper bounds of the population of  buy /sell weights and store it in Python dictionary for future use\n",
    "        LowUpBounds ={}\n",
    "        LowUpBounds = DetermineBounds(OffspringPoplnXNormalized, BuyLowBounds,  BuyUpBounds, SellLowBounds , SellUpBounds)  \n",
    "        \n",
    "        # repair weights of the offspring population\n",
    "        OffspringPoplnX = PortfolioRebalancingWeightRepair(OffspringPoplnXNormalized, LowUpBounds, p )  \n",
    "        \n",
    "        # obtain the population of rebalanced weights\n",
    "        OffspringPoplnW = np.add(np.matmul(np.ones((PoplnSize,1)),(OriginalPortfolioOptimalWeights)) , OffspringPoplnX )  # Xi\n",
    "        \n",
    "                # compute constraint violation function values\n",
    "        [OffspringWG, OffspringWPsi]  = ComputeConstraintViolation(OffspringPoplnW, CovData, OriginalPortfolioDailyRisk, C, BetaParam, AlphaParam, GenerationCount)   #compute constraint violation functions using Joines and Houck's dynamic penalty functions              \n",
    "        \n",
    "        # compute fitness function values\n",
    "        OffspringWFitness = ComputeFitness(OffspringPoplnW, MeanData, CovData,   OffspringWPsi, RiskFreeRate)  \n",
    "       \n",
    "        # construct the new generation of chromosomes,  selecting the best from among the parent and offspring populations\n",
    "        [NextGenPoolX, NextGenPoolW, NextGenPoolFitness, NextGenPoolPsi] = ConstructNewGeneration(FeasParentPoplnX, FeasParentPoplnW, FeasParentPoplnWFitness, FeasParentPoplnWPsi ,  OffspringPoplnX, OffspringPoplnW, OffspringWFitness, OffspringWPsi)  \n",
    "        \n",
    "        # induct the best fit  individual whose constraint violation function value is 0, from the new generation into \n",
    "        # the  Hall of Fame, \n",
    "        for i in range(PoplnSize):\n",
    "            if (NextGenPoolPsi[i] == 0):\n",
    "                                    \n",
    "                    if (NextGenPoolFitness[i] > HOFFitness):\n",
    "                         \n",
    "                        HOFFitness = NextGenPoolFitness[i]\n",
    "                        HOFIndividualW = NextGenPoolW[i,:]  \n",
    "                        HOFIndividualX = NextGenPoolX[i,:] \n",
    "                        print('HOF Fitness', HOFFitness)\n",
    "               \n",
    "                        HOFGenarray[i1] = GenerationCount  \n",
    "                        HOFFitarray[i1] = HOFFitness  \n",
    "                        i1=i1+1  \n",
    "                        \n",
    "            else:\n",
    "                continue  \n",
    "            \n",
    "        \n",
    "        # compute Population measure to study the convergence of the evolutionary algorithm            \n",
    "        GenPMeasure = ComputePMeasure(NextGenPoolW)  \n",
    "        PerformanceAnalysisMeasure[PMeasureCount, :]= [GenerationCount, GenPMeasure]  \n",
    "        PMeasureCount=PMeasureCount+1  \n",
    "        \n",
    "        # set parent population and its parameters  for the next generation cycle    \n",
    "        FeasParentPoplnX = NextGenPoolX  \n",
    "        FeasParentPoplnW = NextGenPoolW   \n",
    "        FeasParentPoplnWFitness = NextGenPoolFitness  \n",
    "        FeasParentPoplnWPsi = NextGenPoolPsi  \n",
    "    \n",
    "        GenerationCount = GenerationCount + 1  \n",
    "        \n",
    "# extract the optimal solution (rebalanced weights)  \n",
    "OptimalRebalWeights = HOFIndividualW \n",
    "print('Optimal Rebalanced Portfolio Weights', OptimalRebalWeights)\n",
    "\n",
    "# compute  risk, return and Sharpe Ratio of the optimal rebalanced portfolio\n",
    "DailyRebalancedPortfolioReturn = np.sum(np.multiply(MeanData, OptimalRebalWeights))  \n",
    "\n",
    "# number of trading days = 261\n",
    "AnnualRebalancedPortfolioReturn =  261 * DailyRebalancedPortfolioReturn*100\n",
    "print('Rebalanced portfolio annualized return ', AnnualRebalancedPortfolioReturn)\n",
    "\n",
    "DailyRebalancedPortfolioRisk= np.sqrt(np.matmul( np.matmul(OptimalRebalWeights, CovData), OptimalRebalWeights.T)) \n",
    " \n",
    "\n",
    "AnnualRebalancedPortfolioRisk = np.sqrt(261)* DailyRebalancedPortfolioRisk*100\n",
    "print('Rebalanced portfolio annualized risk ', AnnualRebalancedPortfolioRisk)\n",
    "\n",
    "SharpeRatio  = (AnnualRebalancedPortfolioReturn-RiskFreeRate)/AnnualRebalancedPortfolioRisk\n",
    "print('Rebalanced portfolio Sharpe Ratio', SharpeRatio)\n",
    "\n",
    "# compare results with risk, return and Sharpe Ratio of the original portfolio\n",
    "print('Original portfolio annualized return ', OriginalPortfolioAnnReturn)\n",
    "print('Original portfolio annualized risk ', OriginalPortfolioAnnRisk)\n",
    "print('Original portfolio Sharpe Ratio', OriginalPortfolioSharpeRatio)\n",
    "print('Successful Execution!')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf19dba",
   "metadata": {},
   "source": [
    "### Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a384b7a9",
   "metadata": {},
   "source": [
    "[PAI 2018] Vijayalakshmi Pai G. A., \"Metaheuristics for Portfolio Optimization- An Introduction using MATLAB\", Wiley-ISTE, 2018. https://www.mathworks.com/academia/books/metaheuristics-for-portfolio-optimization-pai.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c159925e",
   "metadata": {},
   "source": [
    "## Deep-learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6c0ceaff",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>1470.4600</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4667.0999</td>\n",
       "      <td>2728.9588</td>\n",
       "      <td>5261.8863</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4937.2000</td>\n",
       "      <td>748.5352</td>\n",
       "      <td>1222.3530</td>\n",
       "      <td>13.2000</td>\n",
       "      <td>107.8000</td>\n",
       "      <td>155.6000</td>\n",
       "      <td>320.2706</td>\n",
       "      <td>180.7578</td>\n",
       "      <td>82.5331</td>\n",
       "      <td>113.5100</td>\n",
       "      <td>459.2700</td>\n",
       "      <td>1315.7500</td>\n",
       "      <td>19723.0600</td>\n",
       "      <td>3065.5000</td>\n",
       "      <td>8191.0400</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>251.4800</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>158.0600</td>\n",
       "      <td>232.8800</td>\n",
       "      <td>430.7400</td>\n",
       "      <td>88.7200</td>\n",
       "      <td>383.2500</td>\n",
       "      <td>25.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>1464.5900</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4660.7636</td>\n",
       "      <td>2733.4166</td>\n",
       "      <td>5287.1543</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4847.9000</td>\n",
       "      <td>743.9453</td>\n",
       "      <td>1208.6620</td>\n",
       "      <td>14.2500</td>\n",
       "      <td>108.3800</td>\n",
       "      <td>154.9200</td>\n",
       "      <td>319.3149</td>\n",
       "      <td>178.6776</td>\n",
       "      <td>85.7914</td>\n",
       "      <td>111.5600</td>\n",
       "      <td>459.1100</td>\n",
       "      <td>1317.3400</td>\n",
       "      <td>19723.0600</td>\n",
       "      <td>3065.7000</td>\n",
       "      <td>7844.1400</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>250.5200</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.2500</td>\n",
       "      <td>231.8700</td>\n",
       "      <td>430.8300</td>\n",
       "      <td>89.1100</td>\n",
       "      <td>380.9000</td>\n",
       "      <td>16.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>1477.6000</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4679.4476</td>\n",
       "      <td>2734.2471</td>\n",
       "      <td>5312.4222</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4756.6000</td>\n",
       "      <td>733.5938</td>\n",
       "      <td>1209.4180</td>\n",
       "      <td>13.5300</td>\n",
       "      <td>114.2400</td>\n",
       "      <td>154.3300</td>\n",
       "      <td>318.5380</td>\n",
       "      <td>177.8712</td>\n",
       "      <td>66.2011</td>\n",
       "      <td>109.3000</td>\n",
       "      <td>460.7100</td>\n",
       "      <td>1318.6400</td>\n",
       "      <td>19684.0400</td>\n",
       "      <td>3051.6000</td>\n",
       "      <td>7887.0100</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>249.3000</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.8000</td>\n",
       "      <td>232.3100</td>\n",
       "      <td>431.3300</td>\n",
       "      <td>89.3100</td>\n",
       "      <td>375.6000</td>\n",
       "      <td>23.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>1468.3200</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4665.8814</td>\n",
       "      <td>2735.0194</td>\n",
       "      <td>5312.4222</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4797.8000</td>\n",
       "      <td>733.0078</td>\n",
       "      <td>1220.0990</td>\n",
       "      <td>13.5000</td>\n",
       "      <td>112.2700</td>\n",
       "      <td>154.1800</td>\n",
       "      <td>318.5789</td>\n",
       "      <td>178.3762</td>\n",
       "      <td>55.7157</td>\n",
       "      <td>106.9900</td>\n",
       "      <td>460.3400</td>\n",
       "      <td>1308.5300</td>\n",
       "      <td>19616.1100</td>\n",
       "      <td>3032.3000</td>\n",
       "      <td>7918.3800</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>248.6700</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.3600</td>\n",
       "      <td>231.9300</td>\n",
       "      <td>431.1100</td>\n",
       "      <td>88.9800</td>\n",
       "      <td>375.3000</td>\n",
       "      <td>19.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>1474.4900</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4671.4866</td>\n",
       "      <td>2735.6631</td>\n",
       "      <td>5317.8313</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4717.7000</td>\n",
       "      <td>732.8125</td>\n",
       "      <td>1214.2890</td>\n",
       "      <td>13.1300</td>\n",
       "      <td>109.8800</td>\n",
       "      <td>153.5400</td>\n",
       "      <td>318.2854</td>\n",
       "      <td>176.9645</td>\n",
       "      <td>53.8886</td>\n",
       "      <td>107.1700</td>\n",
       "      <td>460.6800</td>\n",
       "      <td>1310.1500</td>\n",
       "      <td>19519.4600</td>\n",
       "      <td>3065.0000</td>\n",
       "      <td>7683.2500</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>248.1700</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>158.2800</td>\n",
       "      <td>232.4200</td>\n",
       "      <td>431.6700</td>\n",
       "      <td>89.6100</td>\n",
       "      <td>375.2000</td>\n",
       "      <td>24.2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TLT       EMB       AGG      Cash       SPY       EFA  \\\n",
       "Date                                                                     \n",
       "1995-01-02 1470.4600 1182.3655 4667.0999 2728.9588 5261.8863 7863.8483   \n",
       "1995-01-03 1464.5900 1182.3655 4660.7636 2733.4166 5287.1543 7863.8483   \n",
       "1995-01-04 1477.6000 1182.3655 4679.4476 2734.2471 5312.4222 7863.8483   \n",
       "1995-01-05 1468.3200 1182.3655 4665.8814 2735.0194 5312.4222 7863.8483   \n",
       "1995-01-06 1474.4900 1182.3655 4671.4866 2735.6631 5317.8313 7863.8483   \n",
       "\n",
       "                 VWO      GLD       DBC  VIX Index  MOVE Index  MXWD Index  \\\n",
       "Date                                                                         \n",
       "1995-01-02 4937.2000 748.5352 1222.3530    13.2000    107.8000    155.6000   \n",
       "1995-01-03 4847.9000 743.9453 1208.6620    14.2500    108.3800    154.9200   \n",
       "1995-01-04 4756.6000 733.5938 1209.4180    13.5300    114.2400    154.3300   \n",
       "1995-01-05 4797.8000 733.0078 1220.0990    13.5000    112.2700    154.1800   \n",
       "1995-01-06 4717.7000 732.8125 1214.2890    13.1300    109.8800    153.5400   \n",
       "\n",
       "            SBWGU Index  SPGSCI Index  .TED G Index  KOSPI2 Index  SPX Index  \\\n",
       "Date                                                                           \n",
       "1995-01-02     320.2706      180.7578       82.5331      113.5100   459.2700   \n",
       "1995-01-03     319.3149      178.6776       85.7914      111.5600   459.1100   \n",
       "1995-01-04     318.5380      177.8712       66.2011      109.3000   460.7100   \n",
       "1995-01-05     318.5789      178.3762       55.7157      106.9900   460.3400   \n",
       "1995-01-06     318.2854      176.9645       53.8886      107.1700   460.6800   \n",
       "\n",
       "            SX5E Index  NKY Index  UKX Index  HSI Index  MXWO000V Index  \\\n",
       "Date                                                                      \n",
       "1995-01-02   1315.7500 19723.0600  3065.5000  8191.0400        962.9000   \n",
       "1995-01-03   1317.3400 19723.0600  3065.7000  7844.1400        962.9000   \n",
       "1995-01-04   1318.6400 19684.0400  3051.6000  7887.0100        962.9000   \n",
       "1995-01-05   1308.5300 19616.1100  3032.3000  7918.3800        962.9000   \n",
       "1995-01-06   1310.1500 19519.4600  3065.0000  7683.2500        962.9000   \n",
       "\n",
       "            MXWOMOM Index  MXUS000V Index  MXUSMMT Index  MXUSQU Index  \\\n",
       "Date                                                                     \n",
       "1995-01-02       251.4800        597.3400       158.0600      232.8800   \n",
       "1995-01-03       250.5200        597.3400       157.2500      231.8700   \n",
       "1995-01-04       249.3000        597.3400       157.8000      232.3100   \n",
       "1995-01-05       248.6700        597.3400       157.3600      231.9300   \n",
       "1995-01-06       248.1700        597.3400       158.2800      232.4200   \n",
       "\n",
       "            MXUSMVOL Index  DXY Curncy  GOLDLNPM Index  USYC2Y10 Index  \n",
       "Date                                                                    \n",
       "1995-01-02        430.7400     88.7200        383.2500         25.4500  \n",
       "1995-01-03        430.8300     89.1100        380.9000         16.8500  \n",
       "1995-01-04        431.3300     89.3100        375.6000         23.3500  \n",
       "1995-01-05        431.1100     88.9800        375.3000         19.3500  \n",
       "1995-01-06        431.6700     89.6100        375.2000         24.2500  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat in one dataframe\n",
    "\n",
    "df = pd.concat([target, test], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e064ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e793f6c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "04646e5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TLT</th>\n",
       "      <th>EMB</th>\n",
       "      <th>AGG</th>\n",
       "      <th>Cash</th>\n",
       "      <th>SPY</th>\n",
       "      <th>EFA</th>\n",
       "      <th>VWO</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "      <th>VIX Index</th>\n",
       "      <th>MOVE Index</th>\n",
       "      <th>MXWD Index</th>\n",
       "      <th>SBWGU Index</th>\n",
       "      <th>SPGSCI Index</th>\n",
       "      <th>.TED G Index</th>\n",
       "      <th>KOSPI2 Index</th>\n",
       "      <th>SPX Index</th>\n",
       "      <th>SX5E Index</th>\n",
       "      <th>NKY Index</th>\n",
       "      <th>UKX Index</th>\n",
       "      <th>HSI Index</th>\n",
       "      <th>MXWO000V Index</th>\n",
       "      <th>MXWOMOM Index</th>\n",
       "      <th>MXUS000V Index</th>\n",
       "      <th>MXUSMMT Index</th>\n",
       "      <th>MXUSQU Index</th>\n",
       "      <th>MXUSMVOL Index</th>\n",
       "      <th>DXY Curncy</th>\n",
       "      <th>GOLDLNPM Index</th>\n",
       "      <th>USYC2Y10 Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>-0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0181</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0044</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>-0.0115</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0424</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.3379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0188</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0505</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>-0.2283</td>\n",
       "      <td>-0.0203</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0046</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.3858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.1584</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0077</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.1713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>-0.0328</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.2532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TLT    EMB     AGG   Cash    SPY    EFA     VWO     GLD  \\\n",
       "Date                                                                     \n",
       "1995-01-02     nan    nan     nan    nan    nan    nan     nan     nan   \n",
       "1995-01-03 -0.0040 0.0000 -0.0014 0.0016 0.0048 0.0000 -0.0181 -0.0061   \n",
       "1995-01-04  0.0089 0.0000  0.0040 0.0003 0.0048 0.0000 -0.0188 -0.0139   \n",
       "1995-01-05 -0.0063 0.0000 -0.0029 0.0003 0.0000 0.0000  0.0087 -0.0008   \n",
       "1995-01-06  0.0042 0.0000  0.0012 0.0002 0.0010 0.0000 -0.0167 -0.0003   \n",
       "\n",
       "               DBC  VIX Index  MOVE Index  MXWD Index  SBWGU Index  \\\n",
       "Date                                                                 \n",
       "1995-01-02     nan        nan         nan         nan          nan   \n",
       "1995-01-03 -0.0112     0.0795      0.0054     -0.0044      -0.0030   \n",
       "1995-01-04  0.0006    -0.0505      0.0541     -0.0038      -0.0024   \n",
       "1995-01-05  0.0088    -0.0022     -0.0172     -0.0010       0.0001   \n",
       "1995-01-06 -0.0048    -0.0274     -0.0213     -0.0042      -0.0009   \n",
       "\n",
       "            SPGSCI Index  .TED G Index  KOSPI2 Index  SPX Index  SX5E Index  \\\n",
       "Date                                                                          \n",
       "1995-01-02           nan           nan           nan        nan         nan   \n",
       "1995-01-03       -0.0115        0.0395       -0.0172    -0.0003      0.0012   \n",
       "1995-01-04       -0.0045       -0.2283       -0.0203     0.0035      0.0010   \n",
       "1995-01-05        0.0028       -0.1584       -0.0211    -0.0008     -0.0077   \n",
       "1995-01-06       -0.0079       -0.0328        0.0017     0.0007      0.0012   \n",
       "\n",
       "            NKY Index  UKX Index  HSI Index  MXWO000V Index  MXWOMOM Index  \\\n",
       "Date                                                                         \n",
       "1995-01-02        nan        nan        nan             nan            nan   \n",
       "1995-01-03     0.0000     0.0001    -0.0424          0.0000        -0.0038   \n",
       "1995-01-04    -0.0020    -0.0046     0.0055          0.0000        -0.0049   \n",
       "1995-01-05    -0.0035    -0.0063     0.0040          0.0000        -0.0025   \n",
       "1995-01-06    -0.0049     0.0108    -0.0297          0.0000        -0.0020   \n",
       "\n",
       "            MXUS000V Index  MXUSMMT Index  MXUSQU Index  MXUSMVOL Index  \\\n",
       "Date                                                                      \n",
       "1995-01-02             nan            nan           nan             nan   \n",
       "1995-01-03          0.0000        -0.0051       -0.0043          0.0002   \n",
       "1995-01-04          0.0000         0.0035        0.0019          0.0012   \n",
       "1995-01-05          0.0000        -0.0028       -0.0016         -0.0005   \n",
       "1995-01-06          0.0000         0.0058        0.0021          0.0013   \n",
       "\n",
       "            DXY Curncy  GOLDLNPM Index  USYC2Y10 Index  \n",
       "Date                                                    \n",
       "1995-01-02         nan             nan             nan  \n",
       "1995-01-03      0.0044         -0.0061         -0.3379  \n",
       "1995-01-04      0.0022         -0.0139          0.3858  \n",
       "1995-01-05     -0.0037         -0.0008         -0.1713  \n",
       "1995-01-06      0.0071         -0.0003          0.2532  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연습\n",
    "df_returns = df.pct_change()\n",
    "df_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e9fd4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 연습\n",
    "df_returns = df_returns.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad42e42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TLT</th>\n",
       "      <th>EMB</th>\n",
       "      <th>AGG</th>\n",
       "      <th>Cash</th>\n",
       "      <th>SPY</th>\n",
       "      <th>EFA</th>\n",
       "      <th>VWO</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "      <th>VIX Index</th>\n",
       "      <th>MOVE Index</th>\n",
       "      <th>MXWD Index</th>\n",
       "      <th>SBWGU Index</th>\n",
       "      <th>SPGSCI Index</th>\n",
       "      <th>.TED G Index</th>\n",
       "      <th>KOSPI2 Index</th>\n",
       "      <th>SPX Index</th>\n",
       "      <th>SX5E Index</th>\n",
       "      <th>NKY Index</th>\n",
       "      <th>UKX Index</th>\n",
       "      <th>HSI Index</th>\n",
       "      <th>MXWO000V Index</th>\n",
       "      <th>MXWOMOM Index</th>\n",
       "      <th>MXUS000V Index</th>\n",
       "      <th>MXUSMMT Index</th>\n",
       "      <th>MXUSQU Index</th>\n",
       "      <th>MXUSMVOL Index</th>\n",
       "      <th>DXY Curncy</th>\n",
       "      <th>GOLDLNPM Index</th>\n",
       "      <th>USYC2Y10 Index</th>\n",
       "      <th>PF_returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>-0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0181</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0044</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>-0.0115</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0424</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.3379</td>\n",
       "      <td>-0.2865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0188</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0505</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>-0.2283</td>\n",
       "      <td>-0.0203</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0046</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.3858</td>\n",
       "      <td>-0.1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.1584</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0077</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.1713</td>\n",
       "      <td>0.0693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>-0.0328</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.2532</td>\n",
       "      <td>-0.1895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-09</th>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0142</td>\n",
       "      <td>-0.0075</td>\n",
       "      <td>-0.0113</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0092</td>\n",
       "      <td>-0.1158</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>-0.0201</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>-0.0134</td>\n",
       "      <td>-0.0075</td>\n",
       "      <td>0.1278</td>\n",
       "      <td>-0.2547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TLT    EMB     AGG   Cash    SPY    EFA     VWO     GLD  \\\n",
       "Date                                                                     \n",
       "1995-01-03 -0.0040 0.0000 -0.0014 0.0016 0.0048 0.0000 -0.0181 -0.0061   \n",
       "1995-01-04  0.0089 0.0000  0.0040 0.0003 0.0048 0.0000 -0.0188 -0.0139   \n",
       "1995-01-05 -0.0063 0.0000 -0.0029 0.0003 0.0000 0.0000  0.0087 -0.0008   \n",
       "1995-01-06  0.0042 0.0000  0.0012 0.0002 0.0010 0.0000 -0.0167 -0.0003   \n",
       "1995-01-09 -0.0021 0.0000  0.0000 0.0010 0.0010 0.0000 -0.0142 -0.0075   \n",
       "\n",
       "               DBC  VIX Index  MOVE Index  MXWD Index  SBWGU Index  \\\n",
       "Date                                                                 \n",
       "1995-01-03 -0.0112     0.0795      0.0054     -0.0044      -0.0030   \n",
       "1995-01-04  0.0006    -0.0505      0.0541     -0.0038      -0.0024   \n",
       "1995-01-05  0.0088    -0.0022     -0.0172     -0.0010       0.0001   \n",
       "1995-01-06 -0.0048    -0.0274     -0.0213     -0.0042      -0.0009   \n",
       "1995-01-09 -0.0113     0.0152     -0.0100     -0.0029       0.0004   \n",
       "\n",
       "            SPGSCI Index  .TED G Index  KOSPI2 Index  SPX Index  SX5E Index  \\\n",
       "Date                                                                          \n",
       "1995-01-03       -0.0115        0.0395       -0.0172    -0.0003      0.0012   \n",
       "1995-01-04       -0.0045       -0.2283       -0.0203     0.0035      0.0010   \n",
       "1995-01-05        0.0028       -0.1584       -0.0211    -0.0008     -0.0077   \n",
       "1995-01-06       -0.0079       -0.0328        0.0017     0.0007      0.0012   \n",
       "1995-01-09       -0.0092       -0.1158        0.0255     0.0003     -0.0043   \n",
       "\n",
       "            NKY Index  UKX Index  HSI Index  MXWO000V Index  MXWOMOM Index  \\\n",
       "Date                                                                         \n",
       "1995-01-03     0.0000     0.0001    -0.0424          0.0000        -0.0038   \n",
       "1995-01-04    -0.0020    -0.0046     0.0055          0.0000        -0.0049   \n",
       "1995-01-05    -0.0035    -0.0063     0.0040          0.0000        -0.0025   \n",
       "1995-01-06    -0.0049     0.0108    -0.0297          0.0000        -0.0020   \n",
       "1995-01-09    -0.0038    -0.0030    -0.0201          0.0000        -0.0026   \n",
       "\n",
       "            MXUS000V Index  MXUSMMT Index  MXUSQU Index  MXUSMVOL Index  \\\n",
       "Date                                                                      \n",
       "1995-01-03          0.0000        -0.0051       -0.0043          0.0002   \n",
       "1995-01-04          0.0000         0.0035        0.0019          0.0012   \n",
       "1995-01-05          0.0000        -0.0028       -0.0016         -0.0005   \n",
       "1995-01-06          0.0000         0.0058        0.0021          0.0013   \n",
       "1995-01-09          0.0000        -0.0004       -0.0007         -0.0015   \n",
       "\n",
       "            DXY Curncy  GOLDLNPM Index  USYC2Y10 Index  PF_returns  \n",
       "Date                                                                \n",
       "1995-01-03      0.0044         -0.0061         -0.3379     -0.2865  \n",
       "1995-01-04      0.0022         -0.0139          0.3858     -0.1760  \n",
       "1995-01-05     -0.0037         -0.0008         -0.1713      0.0693  \n",
       "1995-01-06      0.0071         -0.0003          0.2532     -0.1895  \n",
       "1995-01-09     -0.0134         -0.0075          0.1278     -0.2547  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연습\n",
    "target = ['TLT', 'EMB', 'AGG', 'Cash', 'SPY', 'EFA', 'VWO', 'GLD', 'DBC']\n",
    "weight = np.array([0.043, 0.159, 0.193, 0.265, 0.075, 0.034, 0.135, 0.06,  0.037])\n",
    "\n",
    "#bm_weight = np.array([0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.12])\n",
    "tr = targetReturns.dot(weight)\n",
    "#tr = np.append(tr, tr[0])\n",
    "df_returns['PF_returns'] = tr.tolist()\n",
    "df_returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "490eefc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, x_test, y_train, y_test = train_test_split(df_returns.drop('PF_returns', 1),\n",
    "                                                    df_returns['PF_returns'], test_size=0.2,\n",
    "                                                    random_state=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e898af08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5495, 30), (5495,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5e46f141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1374, 30), (1374,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0d1f0104",
   "metadata": {},
   "outputs": [],
   "source": [
    "WINDOW_SIZE = 20\n",
    "BATCH_SIZE = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dde85b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_x = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "ds_x = ds_x.window(WINDOW_SIZE, stride = 1, shift = 1, drop_remainder = True)\n",
    "ds_x = ds_x.flat_map(lambda x: x.batch(WINDOW_SIZE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "153e3802",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_y = tf.data.Dataset.from_tensor_slices(y_train[WINDOW_SIZE:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "947f2736",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = tf.data.Dataset.zip((ds_x, ds_y)).batch(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0845c042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[[-0.004  0.    -0.001 ...  0.004 -0.006 -0.338]\n",
      "  [ 0.009  0.     0.004 ...  0.002 -0.014  0.386]\n",
      "  [-0.006  0.    -0.003 ... -0.004 -0.001 -0.171]\n",
      "  ...\n",
      "  [ 0.004  0.     0.002 ... -0.    -0.    -0.234]\n",
      "  [ 0.011  0.     0.006 ...  0.    -0.008  0.047]\n",
      "  [-0.001  0.     0.    ... -0.006 -0.007 -0.436]]\n",
      "\n",
      " [[ 0.009  0.     0.004 ...  0.002 -0.014  0.386]\n",
      "  [-0.006  0.    -0.003 ... -0.004 -0.001 -0.171]\n",
      "  [ 0.004  0.     0.001 ...  0.007 -0.     0.253]\n",
      "  ...\n",
      "  [ 0.011  0.     0.006 ...  0.    -0.008  0.047]\n",
      "  [-0.001  0.     0.    ... -0.006 -0.007 -0.436]\n",
      "  [ 0.005 -0.04   0.002 ...  0.01  -0.003  0.299]]\n",
      "\n",
      " [[-0.006  0.    -0.003 ... -0.004 -0.001 -0.171]\n",
      "  [ 0.004  0.     0.001 ...  0.007 -0.     0.253]\n",
      "  [-0.002  0.     0.    ... -0.013 -0.007  0.128]\n",
      "  ...\n",
      "  [-0.001  0.     0.    ... -0.006 -0.007 -0.436]\n",
      "  [ 0.005 -0.04   0.002 ...  0.01  -0.003  0.299]\n",
      "  [-0.004  0.    -0.002 ... -0.002  0.004  0.758]]], shape=(3, 20, 30), dtype=float64)\n",
      "\n",
      "tf.Tensor([-0.259  0.135  0.064], shape=(3,), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "for x,y in train_data.take(1):\n",
    "    print(x[:3])\n",
    "    print()\n",
    "    print(y[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c726bd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TLT</th>\n",
       "      <th>EMB</th>\n",
       "      <th>AGG</th>\n",
       "      <th>Cash</th>\n",
       "      <th>SPY</th>\n",
       "      <th>EFA</th>\n",
       "      <th>VWO</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "      <th>VIX Index</th>\n",
       "      <th>MOVE Index</th>\n",
       "      <th>MXWD Index</th>\n",
       "      <th>SBWGU Index</th>\n",
       "      <th>SPGSCI Index</th>\n",
       "      <th>.TED G Index</th>\n",
       "      <th>KOSPI2 Index</th>\n",
       "      <th>SPX Index</th>\n",
       "      <th>SX5E Index</th>\n",
       "      <th>NKY Index</th>\n",
       "      <th>UKX Index</th>\n",
       "      <th>HSI Index</th>\n",
       "      <th>MXWO000V Index</th>\n",
       "      <th>MXWOMOM Index</th>\n",
       "      <th>MXUS000V Index</th>\n",
       "      <th>MXUSMMT Index</th>\n",
       "      <th>MXUSQU Index</th>\n",
       "      <th>MXUSMVOL Index</th>\n",
       "      <th>DXY Curncy</th>\n",
       "      <th>GOLDLNPM Index</th>\n",
       "      <th>USYC2Y10 Index</th>\n",
       "      <th>PF_returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>-0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0181</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.0112</td>\n",
       "      <td>0.0795</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0044</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>-0.0115</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0424</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>-0.0061</td>\n",
       "      <td>-0.3379</td>\n",
       "      <td>-0.2865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0188</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>-0.0505</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>-0.2283</td>\n",
       "      <td>-0.0203</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0046</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>0.3858</td>\n",
       "      <td>-0.1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>-0.0172</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.1584</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0077</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.1713</td>\n",
       "      <td>0.0693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0167</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>-0.0328</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.2532</td>\n",
       "      <td>-0.1895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-09</th>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0142</td>\n",
       "      <td>-0.0075</td>\n",
       "      <td>-0.0113</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0029</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>-0.0092</td>\n",
       "      <td>-0.1158</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>-0.0030</td>\n",
       "      <td>-0.0201</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>-0.0134</td>\n",
       "      <td>-0.0075</td>\n",
       "      <td>0.1278</td>\n",
       "      <td>-0.2547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-10</th>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0476</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>-0.0034</td>\n",
       "      <td>-0.0608</td>\n",
       "      <td>-0.0176</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0991</td>\n",
       "      <td>-0.0058</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>-0.1499</td>\n",
       "      <td>-0.5609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-11</th>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>-0.0296</td>\n",
       "      <td>-0.0198</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.1485</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>-0.0036</td>\n",
       "      <td>-0.0198</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.9505</td>\n",
       "      <td>0.2662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-12</th>\n",
       "      <td>-0.0037</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0560</td>\n",
       "      <td>-0.0333</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.1435</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0071</td>\n",
       "      <td>-0.0053</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>-0.0036</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>-0.2822</td>\n",
       "      <td>0.2412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-13</th>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>-0.0077</td>\n",
       "      <td>-0.1348</td>\n",
       "      <td>-0.0166</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>-0.0097</td>\n",
       "      <td>0.1096</td>\n",
       "      <td>-0.0269</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0022</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>-0.0223</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>-0.1966</td>\n",
       "      <td>0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-16</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>-0.1110</td>\n",
       "      <td>-0.0217</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0082</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.0028</td>\n",
       "      <td>-0.1338</td>\n",
       "      <td>0.1090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TLT    EMB     AGG   Cash    SPY    EFA     VWO     GLD  \\\n",
       "Date                                                                     \n",
       "1995-01-03 -0.0040 0.0000 -0.0014 0.0016 0.0048 0.0000 -0.0181 -0.0061   \n",
       "1995-01-04  0.0089 0.0000  0.0040 0.0003 0.0048 0.0000 -0.0188 -0.0139   \n",
       "1995-01-05 -0.0063 0.0000 -0.0029 0.0003 0.0000 0.0000  0.0087 -0.0008   \n",
       "1995-01-06  0.0042 0.0000  0.0012 0.0002 0.0010 0.0000 -0.0167 -0.0003   \n",
       "1995-01-09 -0.0021 0.0000  0.0000 0.0010 0.0010 0.0000 -0.0142 -0.0075   \n",
       "1995-01-10  0.0034 0.0000  0.0018 0.0003 0.0010 0.0000 -0.0476  0.0046   \n",
       "1995-01-11  0.0020 0.0000  0.0020 0.0004 0.0007 0.0000  0.0112  0.0040   \n",
       "1995-01-12 -0.0037 0.0000 -0.0009 0.0004 0.0003 0.0000  0.0127  0.0130   \n",
       "1995-01-13  0.0088 0.0000  0.0064 0.0004 0.0118 0.0000  0.0108 -0.0050   \n",
       "1995-01-16  0.0000 0.0000  0.0000 0.0015 0.0060 0.0000  0.0014 -0.0028   \n",
       "\n",
       "               DBC  VIX Index  MOVE Index  MXWD Index  SBWGU Index  \\\n",
       "Date                                                                 \n",
       "1995-01-03 -0.0112     0.0795      0.0054     -0.0044      -0.0030   \n",
       "1995-01-04  0.0006    -0.0505      0.0541     -0.0038      -0.0024   \n",
       "1995-01-05  0.0088    -0.0022     -0.0172     -0.0010       0.0001   \n",
       "1995-01-06 -0.0048    -0.0274     -0.0213     -0.0042      -0.0009   \n",
       "1995-01-09 -0.0113     0.0152     -0.0100     -0.0029       0.0004   \n",
       "1995-01-10 -0.0034    -0.0608     -0.0176      0.0000       0.0058   \n",
       "1995-01-11  0.0078    -0.0296     -0.0198      0.0026       0.0021   \n",
       "1995-01-12  0.0031     0.0560     -0.0333      0.0005       0.0035   \n",
       "1995-01-13 -0.0077    -0.1348     -0.0166      0.0031       0.0041   \n",
       "1995-01-16  0.0058     0.0036      0.0000      0.0083       0.0036   \n",
       "\n",
       "            SPGSCI Index  .TED G Index  KOSPI2 Index  SPX Index  SX5E Index  \\\n",
       "Date                                                                          \n",
       "1995-01-03       -0.0115        0.0395       -0.0172    -0.0003      0.0012   \n",
       "1995-01-04       -0.0045       -0.2283       -0.0203     0.0035      0.0010   \n",
       "1995-01-05        0.0028       -0.1584       -0.0211    -0.0008     -0.0077   \n",
       "1995-01-06       -0.0079       -0.0328        0.0017     0.0007      0.0012   \n",
       "1995-01-09       -0.0092       -0.1158        0.0255     0.0003     -0.0043   \n",
       "1995-01-10       -0.0015        0.0991       -0.0058     0.0018     -0.0021   \n",
       "1995-01-11        0.0052        0.1485        0.0065    -0.0000     -0.0024   \n",
       "1995-01-12       -0.0019       -0.1435        0.0010    -0.0000      0.0012   \n",
       "1995-01-13       -0.0097        0.1096       -0.0269     0.0094      0.0022   \n",
       "1995-01-16        0.0106       -0.1110       -0.0217     0.0073      0.0149   \n",
       "\n",
       "            NKY Index  UKX Index  HSI Index  MXWO000V Index  MXWOMOM Index  \\\n",
       "Date                                                                         \n",
       "1995-01-03     0.0000     0.0001    -0.0424          0.0000        -0.0038   \n",
       "1995-01-04    -0.0020    -0.0046     0.0055          0.0000        -0.0049   \n",
       "1995-01-05    -0.0035    -0.0063     0.0040          0.0000        -0.0025   \n",
       "1995-01-06    -0.0049     0.0108    -0.0297          0.0000        -0.0020   \n",
       "1995-01-09    -0.0038    -0.0030    -0.0201          0.0000        -0.0026   \n",
       "1995-01-10     0.0029     0.0015     0.0017          0.0000         0.0055   \n",
       "1995-01-11     0.0024    -0.0036    -0.0198          0.0000         0.0024   \n",
       "1995-01-12    -0.0071    -0.0053     0.0034          0.0000        -0.0016   \n",
       "1995-01-13    -0.0041     0.0050    -0.0223          0.0000         0.0025   \n",
       "1995-01-16     0.0000     0.0093     0.0347          0.0000         0.0085   \n",
       "\n",
       "            MXUS000V Index  MXUSMMT Index  MXUSQU Index  MXUSMVOL Index  \\\n",
       "Date                                                                      \n",
       "1995-01-03          0.0000        -0.0051       -0.0043          0.0002   \n",
       "1995-01-04          0.0000         0.0035        0.0019          0.0012   \n",
       "1995-01-05          0.0000        -0.0028       -0.0016         -0.0005   \n",
       "1995-01-06          0.0000         0.0058        0.0021          0.0013   \n",
       "1995-01-09          0.0000        -0.0004       -0.0007         -0.0015   \n",
       "1995-01-10          0.0000         0.0068        0.0057          0.0009   \n",
       "1995-01-11          0.0000        -0.0019        0.0007          0.0009   \n",
       "1995-01-12          0.0000        -0.0012        0.0011          0.0020   \n",
       "1995-01-13          0.0000         0.0098        0.0072          0.0048   \n",
       "1995-01-16          0.0000         0.0082        0.0064          0.0053   \n",
       "\n",
       "            DXY Curncy  GOLDLNPM Index  USYC2Y10 Index  PF_returns  \n",
       "Date                                                                \n",
       "1995-01-03      0.0044         -0.0061         -0.3379     -0.2865  \n",
       "1995-01-04      0.0022         -0.0139          0.3858     -0.1760  \n",
       "1995-01-05     -0.0037         -0.0008         -0.1713      0.0693  \n",
       "1995-01-06      0.0071         -0.0003          0.2532     -0.1895  \n",
       "1995-01-09     -0.0134         -0.0075          0.1278     -0.2547  \n",
       "1995-01-10      0.0002          0.0046         -0.1499     -0.5609  \n",
       "1995-01-11     -0.0001          0.0040          0.9505      0.2662  \n",
       "1995-01-12     -0.0036          0.0130         -0.2822      0.2412  \n",
       "1995-01-13      0.0005         -0.0050         -0.1966      0.3484  \n",
       "1995-01-16     -0.0014         -0.0028         -0.1338      0.1090  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([X_train, y_train], axis=1).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "684a25e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def windowed_dataset(x, y, window_size, batch_size, shuffle):\n",
    "    ds_x = tf.data.Dataset.from_tensor_slices(x)\n",
    "    ds_x = ds_x.window(WINDOW_SIZE, shift=1, stride=1, drop_remainder=True)\n",
    "    ds_x = ds_x.flat_map(lambda x: x.batch(WINDOW_SIZE))\n",
    "    \n",
    "    ds_y = tf.data.Dataset.from_tensor_slices(y[WINDOW_SIZE:])\n",
    "    ds = tf.data.Dataset.zip((ds_x, ds_y))\n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(1000)\n",
    "    return ds.batch(batch_size).prefetch(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "036efae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = windowed_dataset(X_train, y_train, WINDOW_SIZE, BATCH_SIZE, True)\n",
    "test_data = windowed_dataset(x_test, y_test, WINDOW_SIZE, BATCH_SIZE, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "58a518ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "데이터셋(X) 구성(batch_size, window_size, feature갯수): (32, 20, 30)\n",
      "데이터셋(Y) 구성(batch_size, window_size, feature갯수): (32,)\n"
     ]
    }
   ],
   "source": [
    "for data in train_data.take(1):\n",
    "    print(f'데이터셋(X) 구성(batch_size, window_size, feature갯수): {data[0].shape}')\n",
    "    print(f'데이터셋(Y) 구성(batch_size, window_size, feature갯수): {data[1].shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0534c77d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Conv1D, Lambda\n",
    "from tensorflow.keras.losses import Huber\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "\n",
    "model = Sequential([\n",
    "    # 1차원 feature map 생성\n",
    "    Conv1D(filters=32, kernel_size=5,\n",
    "           padding=\"causal\",\n",
    "           activation=\"relu\",\n",
    "           input_shape=[WINDOW_SIZE, 30]),\n",
    "    # LSTM\n",
    "    LSTM(16, activation='tanh'),\n",
    "    Dense(16, activation=\"relu\"),\n",
    "    Dense(1),\n",
    "])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "74976993",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sequence 학습에 비교적 좋은 퍼포먼스를 내는 Huber()를 사용합니다.\n",
    "loss = Huber()\n",
    "optimizer = Adam(0.0005)\n",
    "model.compile(loss=Huber(), optimizer=optimizer, metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ac52b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# earlystopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "# filename = os.path.join('tmp', 'ckeckpointer.ckpt')\n",
    "# checkpoint = ModelCheckpoint(filename, \n",
    "#                              save_weights_only=True, \n",
    "#                              save_best_only=True, \n",
    "#                              monitor='val_loss', \n",
    "#                              verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37a9fe1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "172/172 [==============================] - 1s 4ms/step - loss: 0.0556 - mse: 0.1203 - val_loss: 0.0659 - val_mse: 0.1638\n",
      "Epoch 2/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0560 - mse: 0.1226 - val_loss: 0.0659 - val_mse: 0.1636\n",
      "Epoch 3/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0580 - mse: 0.1273 - val_loss: 0.0663 - val_mse: 0.1640\n",
      "Epoch 4/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0567 - mse: 0.1248 - val_loss: 0.0662 - val_mse: 0.1635\n",
      "Epoch 5/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0564 - mse: 0.1236 - val_loss: 0.0660 - val_mse: 0.1631\n",
      "Epoch 6/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0554 - mse: 0.1212 - val_loss: 0.0672 - val_mse: 0.1641\n",
      "Epoch 7/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0575 - mse: 0.1275 - val_loss: 0.0663 - val_mse: 0.1627\n",
      "Epoch 8/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0567 - mse: 0.1258 - val_loss: 0.0665 - val_mse: 0.1632\n",
      "Epoch 9/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0562 - mse: 0.1245 - val_loss: 0.0672 - val_mse: 0.1645\n",
      "Epoch 10/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0538 - mse: 0.1176 - val_loss: 0.0672 - val_mse: 0.1645\n",
      "Epoch 11/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0546 - mse: 0.1204 - val_loss: 0.0676 - val_mse: 0.1652\n",
      "Epoch 12/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0540 - mse: 0.1198 - val_loss: 0.0677 - val_mse: 0.1656\n",
      "Epoch 13/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0531 - mse: 0.1151 - val_loss: 0.0678 - val_mse: 0.1665\n",
      "Epoch 14/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0513 - mse: 0.1129 - val_loss: 0.0687 - val_mse: 0.1683\n",
      "Epoch 15/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0529 - mse: 0.1162 - val_loss: 0.0704 - val_mse: 0.1712\n",
      "Epoch 16/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0538 - mse: 0.1189 - val_loss: 0.0694 - val_mse: 0.1702\n",
      "Epoch 17/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0502 - mse: 0.1114 - val_loss: 0.0692 - val_mse: 0.1709\n",
      "Epoch 18/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0517 - mse: 0.1101 - val_loss: 0.0685 - val_mse: 0.1694\n",
      "Epoch 19/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0522 - mse: 0.1150 - val_loss: 0.0699 - val_mse: 0.1719\n",
      "Epoch 20/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0506 - mse: 0.1088 - val_loss: 0.0706 - val_mse: 0.1742\n",
      "Epoch 21/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0516 - mse: 0.1119 - val_loss: 0.0695 - val_mse: 0.1729\n",
      "Epoch 22/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0536 - mse: 0.1184 - val_loss: 0.0726 - val_mse: 0.1792\n",
      "Epoch 23/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0512 - mse: 0.1134 - val_loss: 0.0713 - val_mse: 0.1770\n",
      "Epoch 24/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0500 - mse: 0.1085 - val_loss: 0.0739 - val_mse: 0.1842\n",
      "Epoch 25/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0511 - mse: 0.1134 - val_loss: 0.0722 - val_mse: 0.1787\n",
      "Epoch 26/50\n",
      "172/172 [==============================] - 0s 3ms/step - loss: 0.0510 - mse: 0.1119 - val_loss: 0.0719 - val_mse: 0.1784\n",
      "Epoch 27/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0514 - mse: 0.1106 - val_loss: 0.0730 - val_mse: 0.1834\n",
      "Epoch 28/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0480 - mse: 0.1059 - val_loss: 0.0726 - val_mse: 0.1812\n",
      "Epoch 29/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0496 - mse: 0.1084 - val_loss: 0.0741 - val_mse: 0.1858\n",
      "Epoch 30/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0503 - mse: 0.1114 - val_loss: 0.0730 - val_mse: 0.1822\n",
      "Epoch 31/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0512 - mse: 0.1138 - val_loss: 0.0754 - val_mse: 0.1919\n",
      "Epoch 32/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0482 - mse: 0.1060 - val_loss: 0.0737 - val_mse: 0.1840\n",
      "Epoch 33/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0476 - mse: 0.1016 - val_loss: 0.0730 - val_mse: 0.1831\n",
      "Epoch 34/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0478 - mse: 0.1045 - val_loss: 0.0734 - val_mse: 0.1850\n",
      "Epoch 35/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0469 - mse: 0.1019 - val_loss: 0.0757 - val_mse: 0.1920\n",
      "Epoch 36/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0459 - mse: 0.0983 - val_loss: 0.0759 - val_mse: 0.1876\n",
      "Epoch 37/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0469 - mse: 0.1015 - val_loss: 0.0784 - val_mse: 0.2046\n",
      "Epoch 38/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0454 - mse: 0.0981 - val_loss: 0.0748 - val_mse: 0.1933\n",
      "Epoch 39/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0448 - mse: 0.0963 - val_loss: 0.0761 - val_mse: 0.1947\n",
      "Epoch 40/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0446 - mse: 0.0950 - val_loss: 0.0750 - val_mse: 0.1902\n",
      "Epoch 41/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0432 - mse: 0.0930 - val_loss: 0.0760 - val_mse: 0.1964\n",
      "Epoch 42/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0429 - mse: 0.0909 - val_loss: 0.0747 - val_mse: 0.1900\n",
      "Epoch 43/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0432 - mse: 0.0921 - val_loss: 0.0813 - val_mse: 0.2150\n",
      "Epoch 44/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0439 - mse: 0.0927 - val_loss: 0.0749 - val_mse: 0.1884\n",
      "Epoch 45/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0432 - mse: 0.0917 - val_loss: 0.0773 - val_mse: 0.1987\n",
      "Epoch 46/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0436 - mse: 0.0934 - val_loss: 0.0765 - val_mse: 0.1947\n",
      "Epoch 47/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0419 - mse: 0.0892 - val_loss: 0.0754 - val_mse: 0.1924\n",
      "Epoch 48/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0435 - mse: 0.0933 - val_loss: 0.0762 - val_mse: 0.1952\n",
      "Epoch 49/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0423 - mse: 0.0902 - val_loss: 0.0795 - val_mse: 0.2100\n",
      "Epoch 50/50\n",
      "172/172 [==============================] - 0s 2ms/step - loss: 0.0413 - mse: 0.0877 - val_loss: 0.0746 - val_mse: 0.1886\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_data, \n",
    "                    validation_data=(test_data), \n",
    "                    epochs=50, \n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "24619c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "536017c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1354, 1)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "db616805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 520,
       "width": 707
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 9))\n",
    "plt.plot(np.asarray(y_test)[20:], label='actual')\n",
    "plt.plot(pred, label='prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0ab29465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TLT</th>\n",
       "      <th>EMB</th>\n",
       "      <th>AGG</th>\n",
       "      <th>Cash</th>\n",
       "      <th>SPY</th>\n",
       "      <th>EFA</th>\n",
       "      <th>VWO</th>\n",
       "      <th>GLD</th>\n",
       "      <th>DBC</th>\n",
       "      <th>VIX Index</th>\n",
       "      <th>MOVE Index</th>\n",
       "      <th>MXWD Index</th>\n",
       "      <th>SBWGU Index</th>\n",
       "      <th>SPGSCI Index</th>\n",
       "      <th>.TED G Index</th>\n",
       "      <th>KOSPI2 Index</th>\n",
       "      <th>SPX Index</th>\n",
       "      <th>SX5E Index</th>\n",
       "      <th>NKY Index</th>\n",
       "      <th>UKX Index</th>\n",
       "      <th>HSI Index</th>\n",
       "      <th>MXWO000V Index</th>\n",
       "      <th>MXWOMOM Index</th>\n",
       "      <th>MXUS000V Index</th>\n",
       "      <th>MXUSMMT Index</th>\n",
       "      <th>MXUSQU Index</th>\n",
       "      <th>MXUSMVOL Index</th>\n",
       "      <th>DXY Curncy</th>\n",
       "      <th>GOLDLNPM Index</th>\n",
       "      <th>USYC2Y10 Index</th>\n",
       "      <th>PF_returns</th>\n",
       "      <th>BM_returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-02</th>\n",
       "      <td>1470.4600</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4667.0999</td>\n",
       "      <td>2728.9588</td>\n",
       "      <td>5261.8863</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4937.2000</td>\n",
       "      <td>748.5352</td>\n",
       "      <td>1222.3530</td>\n",
       "      <td>13.2000</td>\n",
       "      <td>107.8000</td>\n",
       "      <td>155.6000</td>\n",
       "      <td>320.2706</td>\n",
       "      <td>180.7578</td>\n",
       "      <td>82.5331</td>\n",
       "      <td>113.5100</td>\n",
       "      <td>459.2700</td>\n",
       "      <td>1315.7500</td>\n",
       "      <td>19723.0600</td>\n",
       "      <td>3065.5000</td>\n",
       "      <td>8191.0400</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>251.4800</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>158.0600</td>\n",
       "      <td>232.8800</td>\n",
       "      <td>430.7400</td>\n",
       "      <td>88.7200</td>\n",
       "      <td>383.2500</td>\n",
       "      <td>25.4500</td>\n",
       "      <td>-0.2865</td>\n",
       "      <td>-0.3889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-03</th>\n",
       "      <td>1464.5900</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4660.7636</td>\n",
       "      <td>2733.4166</td>\n",
       "      <td>5287.1543</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4847.9000</td>\n",
       "      <td>743.9453</td>\n",
       "      <td>1208.6620</td>\n",
       "      <td>14.2500</td>\n",
       "      <td>108.3800</td>\n",
       "      <td>154.9200</td>\n",
       "      <td>319.3149</td>\n",
       "      <td>178.6776</td>\n",
       "      <td>85.7914</td>\n",
       "      <td>111.5600</td>\n",
       "      <td>459.1100</td>\n",
       "      <td>1317.3400</td>\n",
       "      <td>19723.0600</td>\n",
       "      <td>3065.7000</td>\n",
       "      <td>7844.1400</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>250.5200</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.2500</td>\n",
       "      <td>231.8700</td>\n",
       "      <td>430.8300</td>\n",
       "      <td>89.1100</td>\n",
       "      <td>380.9000</td>\n",
       "      <td>16.8500</td>\n",
       "      <td>-0.1760</td>\n",
       "      <td>-0.1550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>1477.6000</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4679.4476</td>\n",
       "      <td>2734.2471</td>\n",
       "      <td>5312.4222</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4756.6000</td>\n",
       "      <td>733.5938</td>\n",
       "      <td>1209.4180</td>\n",
       "      <td>13.5300</td>\n",
       "      <td>114.2400</td>\n",
       "      <td>154.3300</td>\n",
       "      <td>318.5380</td>\n",
       "      <td>177.8712</td>\n",
       "      <td>66.2011</td>\n",
       "      <td>109.3000</td>\n",
       "      <td>460.7100</td>\n",
       "      <td>1318.6400</td>\n",
       "      <td>19684.0400</td>\n",
       "      <td>3051.6000</td>\n",
       "      <td>7887.0100</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>249.3000</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.8000</td>\n",
       "      <td>232.3100</td>\n",
       "      <td>431.3300</td>\n",
       "      <td>89.3100</td>\n",
       "      <td>375.6000</td>\n",
       "      <td>23.3500</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.0946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>1468.3200</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4665.8814</td>\n",
       "      <td>2735.0194</td>\n",
       "      <td>5312.4222</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4797.8000</td>\n",
       "      <td>733.0078</td>\n",
       "      <td>1220.0990</td>\n",
       "      <td>13.5000</td>\n",
       "      <td>112.2700</td>\n",
       "      <td>154.1800</td>\n",
       "      <td>318.5789</td>\n",
       "      <td>178.3762</td>\n",
       "      <td>55.7157</td>\n",
       "      <td>106.9900</td>\n",
       "      <td>460.3400</td>\n",
       "      <td>1308.5300</td>\n",
       "      <td>19616.1100</td>\n",
       "      <td>3032.3000</td>\n",
       "      <td>7918.3800</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>248.6700</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>157.3600</td>\n",
       "      <td>231.9300</td>\n",
       "      <td>431.1100</td>\n",
       "      <td>88.9800</td>\n",
       "      <td>375.3000</td>\n",
       "      <td>19.3500</td>\n",
       "      <td>-0.1895</td>\n",
       "      <td>-0.1705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>1474.4900</td>\n",
       "      <td>1182.3655</td>\n",
       "      <td>4671.4866</td>\n",
       "      <td>2735.6631</td>\n",
       "      <td>5317.8313</td>\n",
       "      <td>7863.8483</td>\n",
       "      <td>4717.7000</td>\n",
       "      <td>732.8125</td>\n",
       "      <td>1214.2890</td>\n",
       "      <td>13.1300</td>\n",
       "      <td>109.8800</td>\n",
       "      <td>153.5400</td>\n",
       "      <td>318.2854</td>\n",
       "      <td>176.9645</td>\n",
       "      <td>53.8886</td>\n",
       "      <td>107.1700</td>\n",
       "      <td>460.6800</td>\n",
       "      <td>1310.1500</td>\n",
       "      <td>19519.4600</td>\n",
       "      <td>3065.0000</td>\n",
       "      <td>7683.2500</td>\n",
       "      <td>962.9000</td>\n",
       "      <td>248.1700</td>\n",
       "      <td>597.3400</td>\n",
       "      <td>158.2800</td>\n",
       "      <td>232.4200</td>\n",
       "      <td>431.6700</td>\n",
       "      <td>89.6100</td>\n",
       "      <td>375.2000</td>\n",
       "      <td>24.2500</td>\n",
       "      <td>-0.2547</td>\n",
       "      <td>-0.3760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TLT       EMB       AGG      Cash       SPY       EFA  \\\n",
       "Date                                                                     \n",
       "1995-01-02 1470.4600 1182.3655 4667.0999 2728.9588 5261.8863 7863.8483   \n",
       "1995-01-03 1464.5900 1182.3655 4660.7636 2733.4166 5287.1543 7863.8483   \n",
       "1995-01-04 1477.6000 1182.3655 4679.4476 2734.2471 5312.4222 7863.8483   \n",
       "1995-01-05 1468.3200 1182.3655 4665.8814 2735.0194 5312.4222 7863.8483   \n",
       "1995-01-06 1474.4900 1182.3655 4671.4866 2735.6631 5317.8313 7863.8483   \n",
       "\n",
       "                 VWO      GLD       DBC  VIX Index  MOVE Index  MXWD Index  \\\n",
       "Date                                                                         \n",
       "1995-01-02 4937.2000 748.5352 1222.3530    13.2000    107.8000    155.6000   \n",
       "1995-01-03 4847.9000 743.9453 1208.6620    14.2500    108.3800    154.9200   \n",
       "1995-01-04 4756.6000 733.5938 1209.4180    13.5300    114.2400    154.3300   \n",
       "1995-01-05 4797.8000 733.0078 1220.0990    13.5000    112.2700    154.1800   \n",
       "1995-01-06 4717.7000 732.8125 1214.2890    13.1300    109.8800    153.5400   \n",
       "\n",
       "            SBWGU Index  SPGSCI Index  .TED G Index  KOSPI2 Index  SPX Index  \\\n",
       "Date                                                                           \n",
       "1995-01-02     320.2706      180.7578       82.5331      113.5100   459.2700   \n",
       "1995-01-03     319.3149      178.6776       85.7914      111.5600   459.1100   \n",
       "1995-01-04     318.5380      177.8712       66.2011      109.3000   460.7100   \n",
       "1995-01-05     318.5789      178.3762       55.7157      106.9900   460.3400   \n",
       "1995-01-06     318.2854      176.9645       53.8886      107.1700   460.6800   \n",
       "\n",
       "            SX5E Index  NKY Index  UKX Index  HSI Index  MXWO000V Index  \\\n",
       "Date                                                                      \n",
       "1995-01-02   1315.7500 19723.0600  3065.5000  8191.0400        962.9000   \n",
       "1995-01-03   1317.3400 19723.0600  3065.7000  7844.1400        962.9000   \n",
       "1995-01-04   1318.6400 19684.0400  3051.6000  7887.0100        962.9000   \n",
       "1995-01-05   1308.5300 19616.1100  3032.3000  7918.3800        962.9000   \n",
       "1995-01-06   1310.1500 19519.4600  3065.0000  7683.2500        962.9000   \n",
       "\n",
       "            MXWOMOM Index  MXUS000V Index  MXUSMMT Index  MXUSQU Index  \\\n",
       "Date                                                                     \n",
       "1995-01-02       251.4800        597.3400       158.0600      232.8800   \n",
       "1995-01-03       250.5200        597.3400       157.2500      231.8700   \n",
       "1995-01-04       249.3000        597.3400       157.8000      232.3100   \n",
       "1995-01-05       248.6700        597.3400       157.3600      231.9300   \n",
       "1995-01-06       248.1700        597.3400       158.2800      232.4200   \n",
       "\n",
       "            MXUSMVOL Index  DXY Curncy  GOLDLNPM Index  USYC2Y10 Index  \\\n",
       "Date                                                                     \n",
       "1995-01-02        430.7400     88.7200        383.2500         25.4500   \n",
       "1995-01-03        430.8300     89.1100        380.9000         16.8500   \n",
       "1995-01-04        431.3300     89.3100        375.6000         23.3500   \n",
       "1995-01-05        431.1100     88.9800        375.3000         19.3500   \n",
       "1995-01-06        431.6700     89.6100        375.2000         24.2500   \n",
       "\n",
       "            PF_returns  BM_returns  \n",
       "Date                                \n",
       "1995-01-02     -0.2865     -0.3889  \n",
       "1995-01-03     -0.1760     -0.1550  \n",
       "1995-01-04      0.0693      0.0946  \n",
       "1995-01-05     -0.1895     -0.1705  \n",
       "1995-01-06     -0.2547     -0.3760  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add portfolio returns column\n",
    "\n",
    "target = ['TLT', 'EMB', 'AGG', 'Cash', 'SPY', 'EFA', 'VWO', 'GLD', 'DBC']\n",
    "weight = np.array([0.043, 0.159, 0.193, 0.265, 0.075, 0.034, 0.135, 0.06,  0.037])\n",
    "\n",
    "bm_weight = np.array([0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.11, 0.12])\n",
    "tr = targetReturns.dot(weight)\n",
    "tr = np.append(tr, tr[0])\n",
    "df['PF_returns'] = tr.tolist()\n",
    "\n",
    "bm_tr = targetReturns.dot(bm_weight)\n",
    "bm_tr = np.append(bm_tr, bm_tr[0])\n",
    "df['BM_returns'] = bm_tr.tolist()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aa457822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VIX Index',\n",
       " 'MOVE Index',\n",
       " 'MXWD Index',\n",
       " 'SBWGU Index',\n",
       " 'SPGSCI Index',\n",
       " '.TED G Index',\n",
       " 'KOSPI2 Index',\n",
       " 'SPX Index',\n",
       " 'SX5E Index',\n",
       " 'NKY Index',\n",
       " 'UKX Index',\n",
       " 'HSI Index',\n",
       " 'MXWO000V Index',\n",
       " 'MXWOMOM Index',\n",
       " 'MXUS000V Index',\n",
       " 'MXUSMMT Index',\n",
       " 'MXUSQU Index',\n",
       " 'MXUSMVOL Index',\n",
       " 'DXY Curncy',\n",
       " 'GOLDLNPM Index',\n",
       " 'USYC2Y10 Index']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testLabel = test.columns.tolist()\n",
    "testLabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "21455416",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature1_list = ['TLT', 'EMB', 'AGG', 'Cash', 'SPY', 'EFA', 'VWO', 'GLD', 'DBC']\n",
    "\n",
    "feature2_list = ['BM_returns']\n",
    "\n",
    "feature3_list = ['VIX Index','MOVE Index','MXWD Index','SBWGU Index',\n",
    "                 'SPGSCI Index','.TED G Index','KOSPI2 Index','SPX Index',\n",
    "                 'SX5E Index','NKY Index','UKX Index','HSI Index',\n",
    "                 'MXWO000V Index','MXWOMOM Index','MXUS000V Index','MXUSMMT Index',\n",
    "                 'MXUSQU Index','MXUSMVOL Index','DXY Curncy','GOLDLNPM Index','USYC2Y10 Index']\n",
    "\n",
    "feature4_list = ['PF_returns']\n",
    "\n",
    "all_features = feature1_list + feature2_list + feature3_list + feature4_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "57fecb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_flag = '3'\n",
    "\n",
    "if phase_flag == '1':\n",
    "    train_from = '1995-01-02'\n",
    "    train_to = '2003-01-01'\n",
    "    \n",
    "    val_from = '2003-01-01'\n",
    "    val_to = '2003-06-01'\n",
    "    \n",
    "    test_from = '2003-06-01'\n",
    "    test_to - '2004-01-01'\n",
    "    \n",
    "elif phase_flag == '2':\n",
    "    train_from = '2004-01-01'\n",
    "    train_to = '2012-01-01'\n",
    "    \n",
    "    val_from = '2012-01-01'\n",
    "    val_to = '2012-06-01'\n",
    "    \n",
    "    test_from = '2012-06-01'\n",
    "    test_to = '2013-01-01'\n",
    "    \n",
    "else:\n",
    "    train_from = '2003-01-01'\n",
    "    train_to = '2015-01-01'\n",
    "    \n",
    "    val_from = '2015-01-01'\n",
    "    val_to = '2018-01-01'\n",
    "    \n",
    "    test_from = '2018-01-01'\n",
    "    test_to = '2021-04-30'\n",
    "    \n",
    "train_df = df.loc[train_from:train_to,all_features].copy()\n",
    "val_df = df.loc[val_from:val_to,all_features].copy()\n",
    "test_df = df.loc[test_from:test_to,all_features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8300863f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def min_max_normal(tmp_df):\n",
    "    eng_list = []\n",
    "    sample_df = tmp_df.copy()\n",
    "    for x in all_features:\n",
    "#        if x in feature2_list :   \n",
    "#            continue\n",
    "#        if x in feature4_list :\n",
    "#            continue\n",
    "        series = sample_df[x].copy()\n",
    "        values = series.values\n",
    "        values = values.reshape((len(values), 1))\n",
    "        # train the normalization\n",
    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "        scaler = scaler.fit(values)\n",
    "        print('columns : %s , Min: %f, Max: %f' % (x, scaler.data_min_, scaler.data_max_))\n",
    "        # normalize the dataset and print\n",
    "        normalized = scaler.transform(values)\n",
    "        new_feature = '{}_normal'.format(x)\n",
    "        eng_list.append(new_feature)\n",
    "        sample_df[new_feature] = normalized\n",
    "    return sample_df, eng_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff055037",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "columns : TLT , Min: 3217.710000, Max: 7891.753911\n",
      "columns : EMB , Min: 3166.306190, Max: 9224.163533\n",
      "columns : AGG , Min: 8960.194963, Max: 15172.581920\n",
      "columns : Cash , Min: 5627.195511, Max: 8470.965490\n",
      "columns : SPY , Min: 9935.796600, Max: 34481.519520\n",
      "columns : EFA , Min: 7203.238746, Max: 23288.449270\n",
      "columns : VWO , Min: 2697.100000, Max: 13850.108240\n",
      "columns : GLD , Min: 624.804688, Max: 3590.650877\n",
      "columns : DBC , Min: 1892.784000, Max: 9301.915407\n",
      "columns : BM_returns , Min: -4.122592, Max: 6.519579\n",
      "columns : VIX Index , Min: 9.890000, Max: 80.860000\n",
      "columns : MOVE Index , Min: 48.869500, Max: 264.600000\n",
      "columns : MXWD Index , Min: 171.390000, Max: 433.790000\n",
      "columns : SBWGU Index , Min: 518.593300, Max: 962.090400\n",
      "columns : SPGSCI Index , Min: 212.586600, Max: 890.294000\n",
      "columns : .TED G Index , Min: 9.312600, Max: 463.617100\n",
      "columns : KOSPI2 Index , Min: 65.640000, Max: 295.350000\n",
      "columns : SPX Index , Min: 676.530000, Max: 2090.570000\n",
      "columns : SX5E Index , Min: 1809.980000, Max: 4557.570000\n",
      "columns : NKY Index , Min: 7054.980000, Max: 18261.980000\n",
      "columns : UKX Index , Min: 3287.000000, Max: 6878.490000\n",
      "columns : HSI Index , Min: 8409.010000, Max: 31638.220000\n",
      "columns : MXWO000V Index , Min: 1032.700000, Max: 2789.500000\n",
      "columns : MXWOMOM Index , Min: 443.960000, Max: 1460.120000\n",
      "columns : MXUS000V Index , Min: 755.550000, Max: 2196.240000\n",
      "columns : MXUSMMT Index , Min: 461.270000, Max: 1503.520000\n",
      "columns : MXUSQU Index , Min: 513.740000, Max: 1507.590000\n",
      "columns : MXUSMVOL Index , Min: 676.050000, Max: 1946.500000\n",
      "columns : DXY Curncy , Min: 71.329000, Max: 102.980000\n",
      "columns : GOLDLNPM Index , Min: 319.900000, Max: 1895.000000\n",
      "columns : USYC2Y10 Index , Min: -19.026000, Max: 291.027000\n",
      "columns : PF_returns , Min: -3.330521, Max: 5.404431\n",
      "columns : TLT , Min: 7215.924109, Max: 9236.376734\n",
      "columns : EMB , Min: 8752.802050, Max: 10820.105370\n",
      "columns : AGG , Min: 15021.887850, Max: 16234.262800\n",
      "columns : Cash , Min: 8470.965490, Max: 8833.501591\n",
      "columns : SPY , Min: 30838.119800, Max: 47099.107850\n",
      "columns : EFA , Min: 17903.638070, Max: 25947.638560\n",
      "columns : VWO , Min: 8488.276570, Max: 14347.775500\n",
      "columns : GLD , Min: 1954.929374, Max: 2538.879422\n",
      "columns : DBC , Min: 2418.182621, Max: 3757.536863\n",
      "columns : BM_returns , Min: -1.911222, Max: 1.633202\n",
      "columns : VIX Index , Min: 9.140000, Max: 40.740000\n",
      "columns : MOVE Index , Min: 43.965600, Max: 98.469200\n",
      "columns : MXWD Index , Min: 353.350000, Max: 513.510000\n",
      "columns : SBWGU Index , Min: 856.065300, Max: 976.965200\n",
      "columns : SPGSCI Index , Min: 271.806000, Max: 451.838100\n",
      "columns : .TED G Index , Min: 14.470600, Max: 68.935900\n",
      "columns : KOSPI2 Index , Min: 221.530000, Max: 338.830000\n",
      "columns : SPX Index , Min: 1829.080000, Max: 2690.160000\n",
      "columns : SX5E Index , Min: 2680.350000, Max: 3828.780000\n",
      "columns : NKY Index , Min: 14952.020000, Max: 22939.180000\n",
      "columns : UKX Index , Min: 5536.970000, Max: 7687.770000\n",
      "columns : HSI Index , Min: 18319.580000, Max: 30003.490000\n",
      "columns : MXWO000V Index , Min: 2039.300000, Max: 2878.900000\n",
      "columns : MXWOMOM Index , Min: 1324.500000, Max: 1987.390000\n",
      "columns : MXUS000V Index , Min: 1859.190000, Max: 2631.530000\n",
      "columns : MXUSMMT Index , Min: 1411.290000, Max: 2258.700000\n",
      "columns : MXUSQU Index , Min: 1388.860000, Max: 2065.350000\n",
      "columns : MXUSMVOL Index , Min: 1817.700000, Max: 2505.010000\n",
      "columns : DXY Curncy , Min: 90.276000, Max: 103.300000\n",
      "columns : GOLDLNPM Index , Min: 1049.400000, Max: 1366.250000\n",
      "columns : USYC2Y10 Index , Min: 51.303000, Max: 177.258000\n",
      "columns : PF_returns , Min: -1.461504, Max: 1.168040\n",
      "columns : TLT , Min: 7643.408570, Max: 12375.416020\n",
      "columns : EMB , Min: 9000.154700, Max: 12721.238310\n",
      "columns : AGG , Min: 15663.281670, Max: 19242.807160\n",
      "columns : Cash , Min: 8833.501591, Max: 9194.888375\n",
      "columns : SPY , Min: 41689.427830, Max: 79294.138670\n",
      "columns : EFA , Min: 18725.455240, Max: 32320.288890\n",
      "columns : VWO , Min: 10290.699420, Max: 18547.800720\n",
      "columns : GLD , Min: 2161.120930, Max: 3919.836579\n",
      "columns : DBC , Min: 2225.425529, Max: 3773.820921\n",
      "columns : BM_returns , Min: -5.200095, Max: 3.645665\n",
      "columns : VIX Index , Min: 9.150000, Max: 82.690000\n",
      "columns : MOVE Index , Min: 36.620000, Max: 163.700000\n",
      "columns : MXWD Index , Min: 384.040000, Max: 708.070000\n",
      "columns : SBWGU Index , Min: 911.924500, Max: 1100.000000\n",
      "columns : SPGSCI Index , Min: 228.243900, Max: 508.653700\n",
      "columns : .TED G Index , Min: 6.602800, Max: 151.183400\n",
      "columns : KOSPI2 Index , Min: 199.280000, Max: 437.300000\n",
      "columns : SPX Index , Min: 2237.400000, Max: 4211.470000\n",
      "columns : SX5E Index , Min: 2385.820000, Max: 4032.990000\n",
      "columns : NKY Index , Min: 16552.830000, Max: 30467.750000\n",
      "columns : UKX Index , Min: 4993.890000, Max: 7877.450000\n",
      "columns : HSI Index , Min: 21696.130000, Max: 33154.120000\n",
      "columns : MXWO000V Index , Min: 1837.700000, Max: 3220.100000\n",
      "columns : MXWOMOM Index , Min: 1754.630000, Max: 3301.640000\n",
      "columns : MXUS000V Index , Min: 1821.330000, Max: 3228.800000\n",
      "columns : MXUSMMT Index , Min: 1970.100000, Max: 3874.350000\n",
      "columns : MXUSQU Index , Min: 1832.130000, Max: 3540.370000\n",
      "columns : MXUSMVOL Index , Min: 2178.590000, Max: 3406.300000\n",
      "columns : DXY Curncy , Min: 88.593000, Max: 102.817000\n",
      "columns : GOLDLNPM Index , Min: 1178.400000, Max: 2067.150000\n",
      "columns : USYC2Y10 Index , Min: -5.331000, Max: 157.624000\n",
      "columns : PF_returns , Min: -4.363727, Max: 2.766246\n"
     ]
    }
   ],
   "source": [
    "train_sample_df, eng_list =  min_max_normal(train_df)\n",
    "val_sample_df, eng_list =  min_max_normal(val_df)\n",
    "test_sample_df, eng_list = min_max_normal(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4bff14af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dateset_binary(data, feature_list, step, n):\n",
    "\n",
    "    train_xdata = np.array(data[feature_list[0:n]])\n",
    "    \n",
    "    m = np.arange(len(train_xdata) - step)\n",
    "\n",
    "    x, y = [], []\n",
    "    for i in m:\n",
    "        a = train_xdata[i:(i+step)]\n",
    "        x.append(a)\n",
    "    x_batch = np.reshape(np.array(x), (len(m), step, n))\n",
    "    \n",
    "    train_ydata = np.array(data[[feature_list[n]]])\n",
    "\n",
    "    for i in m + step :\n",
    "        next_rtn = train_ydata[i][0]\n",
    "        if next_rtn > 0 :\n",
    "            label = 1\n",
    "        else :\n",
    "            label = 0\n",
    "        y.append(label)\n",
    "    y_batch = np.reshape(np.array(y), (-1,1))\n",
    "    return x_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f01403e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_step = 12\n",
    "num_unit = 100\n",
    "eng_list = eng_list + feature4_list\n",
    "n_feature = len(eng_list)-1\n",
    "\n",
    "x_train, y_train = create_dateset_binary(train_sample_df[eng_list], eng_list, num_step, n_feature)\n",
    "x_val, y_val = create_dateset_binary(val_sample_df[eng_list], eng_list, num_step, n_feature)\n",
    "x_test, y_test = create_dateset_binary(test_sample_df[eng_list], eng_list, num_step, n_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bed1eb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "y_train = to_categorical(y_train, 2)\n",
    "y_val = to_categorical(y_val, 2)\n",
    "y_test = to_categorical(y_test, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1e015995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   1330.0000\n",
      "1   1790.0000\n",
      "dtype: float32\n",
      "0   349.0000\n",
      "1   422.0000\n",
      "dtype: float32\n",
      "0   363.0000\n",
      "1   495.0000\n",
      "dtype: float32\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(y_train).sum())\n",
    "print(pd.DataFrame(y_val).sum())\n",
    "print(pd.DataFrame(y_test).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8220d618",
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTM model\n",
    "\n",
    "K.clear_session()\n",
    "input_layer = Input(batch_shape=(None, x_train.shape[1], x_train.shape[2]))\n",
    "layer_lstm_1 = LSTM(num_unit, return_sequences = True, recurrent_regularizer = regularizers.l2(0.01))(input_layer)\n",
    "layer_lstm_1 = BatchNormalization()(layer_lstm_1)\n",
    "layer_lstm_2 = LSTM(num_unit, return_sequences = True, recurrent_regularizer = regularizers.l2(0.01))(layer_lstm_1)\n",
    "layer_lstm_2 = Dropout(0.25)(layer_lstm_2)\n",
    "layer_lstm_3 = LSTM(num_unit, return_sequences = True, recurrent_regularizer = regularizers.l2(0.01))(layer_lstm_2)\n",
    "layer_lstm_3 = BatchNormalization()(layer_lstm_3)\n",
    "layer_lstm_4 = LSTM(num_unit, return_sequences = True, recurrent_regularizer = regularizers.l2(0.01))(layer_lstm_3)\n",
    "layer_lstm_4 = Dropout(0.25)(layer_lstm_4)\n",
    "layer_lstm_5 = LSTM(num_unit , recurrent_regularizer = regularizers.l2(0.01))(layer_lstm_4)\n",
    "layer_lstm_5 = BatchNormalization()(layer_lstm_5)\n",
    "output_layer = Dense(2, activation='sigmoid')(layer_lstm_5)\n",
    "\n",
    "model = Model(input_layer, output_layer)\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fe802065",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 12, 32)]          0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 12, 100)           53200     \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 12, 100)           400       \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 12, 100)           80400     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 12, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 12, 100)           80400     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 12, 100)           400       \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 12, 100)           80400     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 12, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 202       \n",
      "=================================================================\n",
      "Total params: 376,202\n",
      "Trainable params: 375,602\n",
      "Non-trainable params: 600\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d09097ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "156/156 [==============================] - 2s 11ms/step - loss: 5.8562 - accuracy: 0.5181 - val_loss: 5.8124 - val_accuracy: 0.5175\n",
      "Epoch 2/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 5.8307 - accuracy: 0.5521 - val_loss: 5.8607 - val_accuracy: 0.5473\n",
      "Epoch 3/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 5.8695 - accuracy: 0.5717 - val_loss: 5.8984 - val_accuracy: 0.5759\n",
      "Epoch 4/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 5.9202 - accuracy: 0.5422 - val_loss: 5.9376 - val_accuracy: 0.5590\n",
      "Epoch 5/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 5.9541 - accuracy: 0.5464 - val_loss: 6.0259 - val_accuracy: 0.5149\n",
      "Epoch 6/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.0774 - accuracy: 0.5512 - val_loss: 6.1865 - val_accuracy: 0.5473\n",
      "Epoch 7/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.1955 - accuracy: 0.5514 - val_loss: 6.2581 - val_accuracy: 0.5473\n",
      "Epoch 8/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 6.3172 - accuracy: 0.5483 - val_loss: 6.5649 - val_accuracy: 0.5473\n",
      "Epoch 9/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.5894 - accuracy: 0.5668 - val_loss: 6.6603 - val_accuracy: 0.5473\n",
      "Epoch 10/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.6520 - accuracy: 0.5712 - val_loss: 6.6597 - val_accuracy: 0.5486\n",
      "Epoch 11/50\n",
      "156/156 [==============================] - 2s 11ms/step - loss: 6.7439 - accuracy: 0.5412 - val_loss: 6.8858 - val_accuracy: 0.5370\n",
      "Epoch 12/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.8995 - accuracy: 0.5593 - val_loss: 6.9398 - val_accuracy: 0.4682\n",
      "Epoch 13/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.9337 - accuracy: 0.5647 - val_loss: 6.9743 - val_accuracy: 0.5006\n",
      "Epoch 14/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 6.9752 - accuracy: 0.5550 - val_loss: 7.0721 - val_accuracy: 0.5344\n",
      "Epoch 15/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 7.1342 - accuracy: 0.5689 - val_loss: 7.3395 - val_accuracy: 0.5590\n",
      "Epoch 16/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 7.3981 - accuracy: 0.5465 - val_loss: 7.6515 - val_accuracy: 0.5473\n",
      "Epoch 17/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 7.7178 - accuracy: 0.5693 - val_loss: 7.9240 - val_accuracy: 0.5473\n",
      "Epoch 18/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 7.8815 - accuracy: 0.5663 - val_loss: 8.4395 - val_accuracy: 0.5473\n",
      "Epoch 19/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 8.6672 - accuracy: 0.5607 - val_loss: 9.0249 - val_accuracy: 0.5473\n",
      "Epoch 20/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 9.0347 - accuracy: 0.5612 - val_loss: 9.3048 - val_accuracy: 0.5473\n",
      "Epoch 21/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 9.3007 - accuracy: 0.5597 - val_loss: 9.4937 - val_accuracy: 0.4838\n",
      "Epoch 22/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 9.5247 - accuracy: 0.5507 - val_loss: 9.6181 - val_accuracy: 0.5110\n",
      "Epoch 23/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 9.5940 - accuracy: 0.5637 - val_loss: 9.6541 - val_accuracy: 0.5473\n",
      "Epoch 24/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 9.6247 - accuracy: 0.5635 - val_loss: 9.7623 - val_accuracy: 0.5473\n",
      "Epoch 25/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 9.7835 - accuracy: 0.5736 - val_loss: 10.3524 - val_accuracy: 0.5279\n",
      "Epoch 26/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.3072 - accuracy: 0.5644 - val_loss: 11.2398 - val_accuracy: 0.5473\n",
      "Epoch 27/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 10.3931 - accuracy: 0.5454 - val_loss: 12.6688 - val_accuracy: 0.5473\n",
      "Epoch 28/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.4473 - accuracy: 0.5658 - val_loss: 14.6757 - val_accuracy: 0.5473\n",
      "Epoch 29/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.4644 - accuracy: 0.5625 - val_loss: 15.2953 - val_accuracy: 0.5473\n",
      "Epoch 30/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 10.4778 - accuracy: 0.5652 - val_loss: 15.3532 - val_accuracy: 0.5473\n",
      "Epoch 31/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.5134 - accuracy: 0.5854 - val_loss: 15.5990 - val_accuracy: 0.5473\n",
      "Epoch 32/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.5313 - accuracy: 0.5682 - val_loss: 15.8508 - val_accuracy: 0.5473\n",
      "Epoch 33/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.5507 - accuracy: 0.5867 - val_loss: 15.6948 - val_accuracy: 0.5486\n",
      "Epoch 34/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.5734 - accuracy: 0.5727 - val_loss: 15.6979 - val_accuracy: 0.5473\n",
      "Epoch 35/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 10.6010 - accuracy: 0.5831 - val_loss: 15.0838 - val_accuracy: 0.5447\n",
      "Epoch 36/50\n",
      "156/156 [==============================] - 1s 10ms/step - loss: 10.6001 - accuracy: 0.5783 - val_loss: 14.0425 - val_accuracy: 0.5370\n",
      "Epoch 37/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.6397 - accuracy: 0.5665 - val_loss: 13.3352 - val_accuracy: 0.5305\n",
      "Epoch 38/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.7186 - accuracy: 0.5657 - val_loss: 13.7632 - val_accuracy: 0.5305\n",
      "Epoch 39/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.7764 - accuracy: 0.5922 - val_loss: 14.9601 - val_accuracy: 0.5318\n",
      "Epoch 40/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.8414 - accuracy: 0.5678 - val_loss: 15.3231 - val_accuracy: 0.5344\n",
      "Epoch 41/50\n",
      "156/156 [==============================] - 2s 11ms/step - loss: 10.9028 - accuracy: 0.5711 - val_loss: 15.3809 - val_accuracy: 0.5305\n",
      "Epoch 42/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 10.9600 - accuracy: 0.5862 - val_loss: 15.0783 - val_accuracy: 0.5357\n",
      "Epoch 43/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 11.1004 - accuracy: 0.5653 - val_loss: 13.8691 - val_accuracy: 0.4994\n",
      "Epoch 44/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 11.1813 - accuracy: 0.5768 - val_loss: 13.9858 - val_accuracy: 0.5396\n",
      "Epoch 45/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 11.2053 - accuracy: 0.5589 - val_loss: 13.9248 - val_accuracy: 0.5422\n",
      "Epoch 46/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 11.2814 - accuracy: 0.5808 - val_loss: 17.1263 - val_accuracy: 0.5473\n",
      "Epoch 47/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 11.9726 - accuracy: 0.5841 - val_loss: 18.1466 - val_accuracy: 0.5473\n",
      "Epoch 48/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 12.0395 - accuracy: 0.5772 - val_loss: 18.3593 - val_accuracy: 0.5473\n",
      "Epoch 49/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 12.0659 - accuracy: 0.5579 - val_loss: 18.3422 - val_accuracy: 0.5473\n",
      "Epoch 50/50\n",
      "156/156 [==============================] - 2s 10ms/step - loss: 12.0979 - accuracy: 0.5738 - val_loss: 17.8388 - val_accuracy: 0.5473\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=50, batch_size=20,\n",
    "                   validation_data = (x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1a775674",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    ax = plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history[\"loss\"])\n",
    "    plt.title(\"Train loss\")\n",
    "    ax = plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history[\"val_loss\"])\n",
    "    plt.title(\"Test loss\")\n",
    "    plt.savefig('sample.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "37e3a6e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nf/lGM5LlG4GG2rJli9LS0o4qngXKyMhQly5dlJ+f34rJAAAAgBayc5u0b5fTjouXGTXe3Tz1MKd9R3btCt89r/epx+SZ/v9k2of3Fl6u7IEGAAAAtDW2slJ23Ve+vmH/M6DBEhMTVVlZKa/XW+cxxcXFys3NVe/evVsxGQAAANAy7JKA2WfHTZCJi3cxTf2MMfLc8HOpfUdn4HCuvM/9zbeVV7iigAYAAAC0hi3rpZIip53aSerRx908QBiZOHGiDh8+rFdffbXWm/DS0lI9/vjj8ng8mjJligsJAQAAgOCxXq/sks99fTMh9Ff0Mx1S5Lnxl/6BlYtlP//AvUBB4MoeaAAAAEBbY9cs9bXNiLEyxriYBggvY8eO1YUXXqg33nhDixcv1qRJk5Senq7y8nLt3r1b8+bNk8fj0e9//3ulp6e7HRcAAABons1fS3k5Trtde2noca7GaSgzYqyznOOn70qS7Luvyp54hkxMjMvJmoYCGgAAANAK7OqA/c9GjHMxCRCerr76ao0aNUoffvihPvzwQx0+fFjR0dHq3r27pkyZonPPPVft27d3OyYAAADQbHbJfF/bjJ0oEx0+pRxzyQ2yG9ZIHVPkufHWsC2eSRTQAAAAgBZnD+yT9u50OjGx0pCR7gYCQtDMmTOPeUxmZqYyMzNbIQ0AAADgDltZIbtsga9vxof+8o2BTGycPL++R2rXUcYT3ruIUUADAAAAWphd4599psEjZOLi3AsDAAAAAAhdX6+Uigqcdmq61H+Iq3GawnRIcTtCUIR3+Q8AAAAIA4EFNDNyrItJAAAAAAChzC4OWL5x3KSwn8UVzvibBwAAAFqQLSuV1q/x9c0ICmgAAAAAgKPZslLZlYt8fTMhvJZvjDQU0AAAAICWtH61VFnhtLv3lunUxd08AAAAAICQZFculsrLnE63XlLPDFfztHUU0AAAAIAWZFcHLN/I7DMAAAAAQB3skoDlGydMljHGxTSggAYAAAC0EGst+58BAAAAAI7JFuZL61b4+mb8KS6mgUQBDQAAAGg5u7OkvBynnZgk9R/qahwAAAAAQGiyyxdKVVVOp99gmfSu7gYCBTQAAACgpdRYvnH4aJmoKBfTAAAAAABClV0yz9c24ye7mATVKKABAAAALSRw+Uax/xkAAAAAoBY294C0cZ3TMR6ZcSe7GwiSKKABAAAALcIW5ktbNjgdY2QyR7sbCAAAAAAQkuzSz/2doaNkOqS4FwY+FNAAAACAFmDXfSVZr9PpO0imfUd3AwEAAAAAQpJdHLB844RTXEyCQBTQAAAAgJYQuP8ZyzcCAAAAAGph9+6Udm5zOtExMsef6G4g+FBAAwAAAILMVlXJrl3u65uRFNAAAAAAAEezS+b7O6PGySQkuhcGNVBAAwAAAIJt6wapuNBpJ6dKvfq5mwcAAAAAEHKstTWWb/SMn+xiGnwbBTQAAAAgyOyapb62GTFWxhgX0wAAAAAAQlLWJunAPqedkCiNGONuHtRAAQ0AAAAIMsv+ZwAAAACAYwicfWZGnygTE+tiGnwbBTQAAAAgiOzBA9Lu7U4nOloaOsrdQAAAAACAkGO9VbJLP/f1Dcs3hhwKaAAAAEAQ2TX+2WcaNEImPsG9MAAAAACA0LR+jZR/yGl3SJaGjHAzDWpBAQ0AAAAIosACmhnJ8o0AAAAAgKPZJQHLN46bJOOJcjENakMBDQAAAAgSW14mrV/l67P/GQAAAADg22xFueyKL319M4HlG0MRBTQAAAAgWDaslcrLnXbXHjKdu7mbBwAAAAAQetYsk0qKnXZ6VyljoLt5UCsKaAAAAECQ2DVLfW1mnwEAAAAAauNdPN/XNhMmyxjjYhrUhQIaAAAAEATWWtnVAfufUUADAAAAAHyLLS6SVgc8fDn+FBfToD4U0AAAAIBg2LNTOrjfaccnSAOHuZsHAAAAABBy7FeLpMoKp9O7n0y3Xu4GQp0ooAEAAABBELh8o4YdLxMd414YAAAAAEBIsksClm8cP9nFJDgWCmgAAABAENg1Acs3jmT5RgAAAABALbZt8DXNmJNcDIJjoYAGAAAANJMtKpQ2f+Prm8wxLqYBAAAAAIQiW1IslRQ7nZhYKa2zu4FQLwpoAAAAQDPZr7+SvF6n02eATMcUdwMBAAAAAEJPXo6/nZImY4x7WXBMFNAAAACA5lrN8o0AAAAAgGPIDSygdXIvBxqEAhoAAADQDNZbJbt2ua9vRoxzMQ0AAAAAIFTZgBloJpUCWqijgAYAAAA0x7ZNUmG+0+6QLPXp72ocAAAAAECIymMGWjihgAYAAAA0g10TsHzjiDEyHn7EBgAAAADUgiUcwwp39wAAAEAT2bIy2eULfX2WbwQAAAAA1IUlHMNLtNsBAAAAgHBkS4rl/dsMad8uZyA6Whp2nKuZAAAAAAAhLO+gv80MtJBHAQ0AAABoJFuYL+9jf5KyNvnGzEXXyiQkupgKAAAAABCqrLU1l3BkBlrIo4AGAAAANII9nCfvI3+Udm/3jZlLb5Tn7KkupgIAAAAAhLSSIqmsxGnHxkmJ7dzNg2OigAYAAAA0kD14QN6H75L273EGjJG5+ifyTD7H3WAAAAAAgND2reUbjTHuZUGDUEADAAAAGsDu3yPvQ3dJuQecAY9H5sZfynPCae4GAwAAAACEPpZvDDsU0AAAAIBjsLt3yPvIXdLhPGcgKlqeH/5WZvSJ7gYDAAAAAIQFm3fA1zYpFNDCAQU0AAAAoB52+2Z5H50uFRY4A7Gx8vzkDzKZo90NBgAAAAAIHzWWcExzLwcajAIaAAAAUAe7+Wt5/98MqaTYGYhPkOfnd8kMynQ3GAAAAAAgvLCEY9ihgAYAAADUwn69Ut6Z90rlZc5AYjt5bv2TTN+B7gYDAAAAAIQdm+cvoJmUdBeToKEooAEAAADfYlculnfW/VJlpTPQIVmeX82Q6Znhai4AAAAAQJgKKKCxhGN4oIAGAAAABPAumS/774clr9cZSO0kz6/ukenaw91gAAAAAICwZK1lCccwRAENAAAAOML7+Yey/5kpWesMpHeV59d/lknr7G4wAAAAAED4Ki70bw8QlyAlJLmbBw1CAQ0AAABtlu8pwG0bZNevlp33vv/F7r2dZRuTU90LCAAAAAAIf99avtEY414WNBgFNAAAALQZtrRYytosu22j7NaN0rYN0uG8ow/s3V+eW/8k075D64cEAAAAAEQWlm8MSxTQAAAAEJGst0ras1N220Zp6wbn656dkvXWf+KAYfL8/C6ZRJbUAAAAAAA0nw0ooJkUCmjhggIaAAAAIoL1Vjmzy9Yul924TsraLJWVHPvEuASp70CZvoNk+g+VRoyW8US1fGAAAAAAQNuQd9DfpoAWNiigAQAAIGzZ/EOyX38lrVkh+/UKqbCg/hOMR+rRW6bvIKnvIJl+g6VuPSmYAQAAAABaTt4Bf5slHMMGBTQAAACEDeutkrZtcmaZrVku7dgiWVv3CcmpTqGs72CnWNanv0x8QusFBgAAAAC0eSzhGJ4ooAEAACCk2fxDsuu+ktYud74W1TPLrGOKzPDRUuZomf5DZXiyDwAAAADgNpZwDEsU0AAAABByrLdK9pN3ZBfNk7ZvrvtAj0fqN0Qmc7TMiLFSr74yxrReUAAAAAAA6mGtlfL8M9BYwjF8UEADAABASLFFBfL+80Hp65W1H9AxVSbzeJnMMdKw42QS27VqPgAAAAAAGqywQKood9oJiTIJie7mQYNRQAMAAEDIsLuy5H3i/6QD+/yDHo/Uf4hM5hinaMYsMwAAAABAuAicfZac5l4ONBoFNAAAAIQEu+wLeZ9+TCov842Z8y6VmTKVWWYAAAAAgPDE8o1hiwIaAAAAXGW9VbL/fV72f6/7B+MS5LnplzKjT3IvGAAAAAAAzWRz/QU0k0IBLZxQQAMAAIBrbFGhvP/6q7R2hX+wczd5fnqHTI/e7gUDAAAAACAYAmegUUALK56WeuMZM2Zo5syZDT4+JydHl112mdatW9dSkQAAABBC7O4d8t57W83iWeYYee54iOIZAAAAACAysIRj2GqRGWgVFRXKyspSWlrDN8TbvHlzS0QBAABACLIrFsr71KNSWalvzJx3qcyFV8l4otwLBgAAAABAELGEY/gKWgGtsrJSJSUlys7O1uzZs1VYWHjMc0pLS1VcXKz169fr6aefDlYUAAAAhCjr9crOeVH2vVf9g3Hx8tz4S5kxJ7sXDAAAAACAlsAMtLAVtALavHnzNGvWrEadc++992rDhg3BigAAAIAQZosL5f3Xw9KaZf7B9K7y3HKHTI8+7gUDAAAAAKAFWGulvIP+gZSGr9oH9wWtgDZ69GhNnz7d13/00UePec5NN92k4uJiSVJWVpaeffbZYMUBAABACLF7d8r7+L3S/j3+wWHHy/PD38gktXcvGICwU1xcrLfffluLFy9Wdna2YmJi1LdvX02ePFkTJ05UdHSL7FQAAAAANF7BYamywmknJMnEJ7qbB40StDuLlJQUpaSk+PqxsbHHPKdv375B+exp06bVOn7//fdLkjp1cndaZPUNnNs5ED64ZtAUXDdoLK4ZNFZTrhlrrcoWzVP+3/4slRT7xhOnXqN2V/9IJor9ziIZ/86gMWbMmKG0tDTdcsstdR6za9cu3XvvvcrPz9dZZ52lgQMHKj8/XwsXLtQTTzyhjIwMZWRktF5oAAAAoD6Bs89YvjHs8GgeAAAAgq5y326Vzv9QpfM+UNWeHf4X4uLV8Wd/UPzEM90LByDkVFRUKCsrS2lpdS9pU1BQoHvuuUfGGP3lL39R7969fa+de+65WrBgQYMe5AQAAABaTd4BfzuFAlq4iYgCWvVMs7rk5OTU+3pLq37i1u0cCB9cM2gKrhs0FtcMGutY14wtKpBdtkB20afS5m9qeYMu8vz0Dyrs1VeFXHdtQkv8O9O9e/egvRfcVVlZqZKSEmVnZ2v27NkqLCys9/jnn39e+fn5mjFjRo3iWbWTTz65paICAAAATWJz/fdChv3Pwk5EFNAAAADgDltRIa1ZKu+iz6Q1y6TKyqMPik+QGX+KzNRrZdp1aPWMAELTvHnzNGvWrAYde/jwYX3++ec68cQTNXDgwBZOBgAAAAQJSziGNQpoAAAAaBTr9Upb1ssu+lR22RdScdHRB3k80vDRMieeJjNqvExsXOsHBRDSRo8erenTp/v6jz76aJ3HrlixQpWVlZo4cWIrJAMAAACCJGAGmlLS3cuBJqGABgAAgAap3L1dpfM+kPeT96SD+2s/KGOgzAmnyYybKNMhuVXzAQgvKSkpSklJ8fXr279s8+bNkqQhQ4bUGC8vL1dMTIyMMS0TEgAAAGgGG7AHGks4hh8KaAAAAKiTLSmWXfaF7IKPdHDL+toPSussc8Kpzp+uPVs3IIA2IS8vTx07dlRiYqJ27Niht956SytXrlR+fr5iY2OVmZmpyy67TP369WvQ+02bNq3W8er9tav373NLdHR0SORA+OCaQVNw3aCxuGbQWFwzUk7+IVUdaaf0H6joNvx30RChds1QQAMAAEAN1lpp0zrZLz6SXb5AKi87+qDEJJmxk2ROOFUaMJTZHwBaVFlZmZKSkvT+++/rueee03HHHacrrrhCqamp2rlzp95++23deeeduv322zVy5Ei34wIAAACyXq+qAlZviUrr7GIaNAUFNAAAAEiSbG6O7JefyC78WNq/9+gDoqIUN/ZkVYw+SRoxTiYmpvVDAmiTEhISlJ2drTfffFN//OMfayzlOHr0aE2aNEnTpk3TrFmz9Pjjjx+zqF8906wuOTk59b7e0qqfuHU7B8IH1wyagusGjcU1g8Zq69eMPZwnVVY6ncR2OlhYJBXWsoc4fFrimunevXuTz6WABgAA0IbZigpp1WJ5F3wsrftKst6jD+reW2biWep07lR5klPb7M0PAPekp6fL6/XqrrvuUs+eRy8Vm5qaqnPOOUevvPKKdu/eXesxAAAAQKvKC7h3Tg2NJQnROC1WQJs5c2ajjh8+fLheffXVFkoDAACAQHbnNtkFH8ku+kwqKjj6gIREmfGnyJx8lpQxQMYYeZJTWz0nAEjS0KFD9e6776q4uLjOY7p27SpJ9R4DAAAAtJrcgAJaCgW0cMQMNAAAgDbC7t8ju/xL2WWfSzu21n7Q0FEyJ50hc/yJMnFxrRsQAOpw/PHHKzk5We+8845uu+22Wo/Zvn27jDG+QhoAAHCfXblY3lf/LXmiZCadLTPxLJmkdm7HAlqFDZiBZiighSUKaAAAABHM7t0lu2Kh7PIF0s5ttR+Umi5z8hkyJ54uk84vngGEnpiYGF1zzTV6/PHH9b///U/nnntujde3bdumDz74QBMmTFCHDh1cSgkAAKrZ0hLZV/8t+/mH/rHXnpZ960WZk06XOf18mW4suYwIxxKOYY8CGgAAQASx1kp7dsguXyC7fKG0Z0ftB0bHyIw+SebkM6QhI2U8ntYNCgCNdMopp2jfvn16+umn9dVXX2nChAmKjo7W5s2b9fHHH6tnz5764Q9/6HZMAADaPLt1g7z/fljav/foF8vLZD/7n+xn/5MyR8tzxgXS8ONljGn9oEBLYwnHsEcBDQAAIMxZa6WdW2WXL3SKZtm7az8wOloaPtopnB03XiaRpVMAhJfLLrtMgwcP1ttvv63nnntOlZWV6tatmy677DKdd955io2NdTsiAABtlq2qkn1vtuw7L0ter2/cjDlZGnac7CfvSLu3+09Yu0LetSukrj1lzjhf5sTTZOLiXUgOtIyaSzimuZgETUUBDQAAIAxZr1favtkpmq1YKB3YV/uBsbFS5hinaDZynExCYusGBYAGmjlzZoOOGzVqlEaNGtXCaQAAQGPY/XudWWdbN/gH4xNkrvqxzAmnyhgjO+lsacMaeT96S1q9VLLWOW7fLtkX/i775nPOPmmnfVcmLd2dbwQIpryD/nYq13Q4ooAGAAAQJmxZqfTNKtnVS2VXL5MO59Z+YFy8Uywbc5JTPOMpTgAAAAAtwForu+Aj2ZeflMpK/S8MGCbPzb+S6dTFN2SMkYaMVNSQkbL798p++q7sF3Ol0hLngOIi2Q/elJ07Rzr+BGd5xwFDWd4RYcl6q6RDAQU0ZqCFJQpoAAAAIczmHvAXzL5ZJVVW1H5gQqLMqPEyo09y9hCIjWvdoAAAAADaFFuQL+9zj0srF/kHo6JkLrxaZspUGU9Uneeazt1kLv++7AVXyS78WPbjt/2rani90vKF8i5fKKV2kqKindlqXq/z1XoD+l7Ja2v2rZXad5Dn5ttkBmW28N8CUIf8Q1JVldNu15579DBFAQ0AACCEWK9XytrkFM1WLZV2bav74HbtZUaMkxl7sjT0OJmYmNYLCgAAAKDNsmuXy/vM/5MO5/kHu/aQ5/u/lukzoMHvYxISZc44X/a086Q1y+X9+G3nwcFquTl1n1yf3Bx5n3pUnhkzKVzAHYHLN6Z0ci8HmoUCGgAAgMtsaYn09UrZ1UucmWYFh+s+uHtvmVHjZEaOk/oNrvepTgAAAAAIJltWKu+Ls2Q/fbfGuDntPJmLb5SJa1qxyniipFHjFTVqvOyuLNlP3pFd9JlUUd70sAf3y374psx3r2j6ewBNFVj8Zf+zsEUBDQAAwCX2cJ7sWy/JLvxIqqys/aCoaGnwCGdPs5FjZdK7tm5IAAAAAJBUsWWDDj/6J9ldWf7BDsny3PALmRFjg/Y5pmeGzHU/k73sZmcPKY9HMh7JGOer50jb863+kWPsl5/KvvgPSZL932uyJ54hk0YBA63L5h3wtQ37n4UtCmgAAACtzJaWyH74X9kP36y50Xa19h1lRoyVGTVOGnacTHxi64cEAAAAAEm2qkr2/deV+87LNR/8O26CPNf9TKZ9xxb5XBOfIHXt2fgTJ0+R/fwDaec2qbxc9vVnZH742+AHBOrDEo4RgQIaAABAK7FVVbILPpJ968WaewVIUo8+MqPGO0sz9h0k4/G4ExIAAAAAjrDZe+R96hFp6wb/YFy8zOXfl5l4lowx7oWrg/FEyXPFD+V98PeSJLv0c9nJ58oMznQ5GdqUGks4UkALVxTQAAAAWpi1Vlq9TN7Xn5H27qz5Yo8+8lxygzR8dEjefAIAAABoe6zXK/vZe7KvPyOV+/chixk0XFXX/VymS3f3wjWAGTRcZtwk2aWfS5K8Lz8pz10Ps4c0Wo3N8xfQDDPQwhYFNAAAgBZkt2+Wd/bT0oY1NV9ITpW56BqZE0/jJg4AAABAyLC5B+R95v9J36zyD0ZFq90VNytx6tU6mHfItWyNYS65QXbVEqm8TNq1TXb+hzKnnut2LLQVAQU0lnAMXxTQAAAAWoDNyZZ983nZJfNqvhCfIHPOxTJnXigTF+dOOAAAAAD4Fmut7KLPZF/6p1RS5H+hRx95bvqVkkaPdy9cE5jUdJlzL5Gd84Ikyf73edlxE2WS2rucDJHOequkQ7n+gZQ098KgWSigAQAABJEtKpR9b7bsJ2/X3GDb45E55RyZ86+Q6ZDsWj4AAAAA+DZbcFje55+QVnzpHzRG5uypMhdeLRMT4164ZjBTpsou+EjKyZaKCmTnvChz1Y/cjoVIdyhP8nqddvuOMjGx7uZBk1FAAwAACAJbWiz7+VzZd16Rigtrvnj8CfJ87zqZrj3dCQcAAAAAdbArF8n73Eyp4LB/ML2rPDfeKjNwmHvBgsDExMpz6U3y/v0vkiT72f9kT5ki0zPD3WCIbCzfGDEooAEAADSSLS2RdmyV3bFZytosu32LlL1bsrbmgf0Gy3PJjWF/0wkAAAAg8tjiItlX/iW78OMa42byOTKX3CgTn+BSsiA7/gRp6ChnTzfrlfflJ+X59Z9ljHE7GSJVYAEtlQJaOKOABgAAUA9bVirt3CqbtVnavkV2+2Zp366ji2WB0rvK873rpDEnc1MGAAAAIOTY9avlffoxKfeAfzA5VZ7rfy6TOca9YC3AGCPPFT+Q90+/cJbV27BGWrFQGnOy29EikrVWOpwr7d2l4oJD8rTvIDt4lIzH43a0VmNz/QU0w/5nYY0CGgAAiCjWWqmwwJkRln9IkpWsjny1R44J6FcXwnwFMSsVFUrbj8ws27tLst5jf7DHI3XvLTPxLOeJzejw3CMAAAAAQGSyxUXS3p2yS+bLfvJOjdfM+FNkrvqRTFJ7l9K1LNO9t8xp35H9+G1Jknf20/KMGCsTG+dysvBlvV7p4H7nmtq788jXXc49dEmRJKmg+uDjTpDn+7fJxMW7lrdV5R30t1PS3cuBZqOABgAAwpItK5X275Xdt9splmXvkT3y9ag9yILNeKTuvWT6DJD69He+9urLzRcAAAAAV1lrnb3Mqosae3bK7tsl7d0pHco9+oSk9jJX/0SecRNbP2wrM+dfKbt4nlSYLx3cL/vBmzLnX+F2rLBgc3OkbRsDCmU7nfvw8vKGvcHKRfI+cLs8P7urTczIsnkBMztZwjGsUUADAAAhy1ZWOk+0Ze+Wzd4T8HVPzTXFW5LxSN16yvTpL/UZ6Hzt1bftPDkHAAAAICTZvIPS7qwjs36qZwHtkooKjn2yJI0YK891P5NJTm3ZoCHCJLWTmXqN7H+ekCTZ91+TPekMmTRmCNXH+/E7srP/LVVVNfykhCSpW0/FJqeqfMWXztiOrfL+36/l+dmdzkOokYwlHCMGBTQAAOAqa62z1GL27iOzyapnku2WDuxr3A/p1WLjpC7dpdR0pwBmJBkjyThtSUbGGaveo8y3V5mRYmKknhkyGQOkXv0olgEAAAAIKd7Xn5V9//XGnRQdLXXpIdO1p3T8Cc6yjW1sz2Yz8SzZee9LO7ZK5eWyrz0t86PfuR0rZHk/fkf25X/WfUD7js5WBt16Sl17yXTvJXXrKXVMlTFGKZ06qfjDOSr451+de/tDufI+8Ht5bv6VzOiTWu8baW01lnBkBlo4o4AGAABanLVWKi2RDux1ZpAdWXbR7tst7d8jlRQ3/k2joqROXaUu3WW6dHduBI98VXJqm7sRBAAAANA22KxNsh+8UfcBcfFS154y3Xo5S88fKW4ovatMVFTrBQ1BxhMlzxU/lPeB2yVJdtkXsqeeKzN4hMvJQo/30/dqFs+695YZdryzQkv3XlK3Xg3aMy/x7AtVlNBO3n/cJxUXSeVl8v79PpnvXSdzzsURd+9uq6qkw3n+AWaghTUKaAAAoFlsWamzlv6hXNlDB6XDuc4Pi4dyZY+M63CuVFbatA9I6SR17ibTtUfNIllaZ5lofpQBAAAA0HZYa+V9+UnJWmegczeZISOdokZXp6ih1E4RV5QIJjNwmMz4ybJL5kmSvC8/Kc+dj7T54mIg77z3ZV/8h3+g/xB5br1bJj6xSe9nho6S5/cPyvu3e6T9eyVJ9o3nnIdrr/2pTHRMMGKHhkO5kvU67Q7JkfW9tUH81gkAAPhYr9eZDVaULxUVSoUFskUFzhr6RQVSofPV5h/yF8aaMnvs2xIS/cUxX6Gsh3MzGJ/Q/PcHAAAAgAhgl8yXtqx3OlHR8vxyukzn7u6GCkPm4utlVy6SysukXVmy8z+QOe08t2OFBO/8D2Sff8I/0HeQPL+Y3uTiWTXTtadTRPv7fdLGtZIku/Bj2Zx98vz49zLtOzTr/UNG4H7tLN8Y9iigAQDQhtiKCufmIGujDmfvlvfQQVXl5TqFseICqajI/6RUsMXGOnuSVRfHugYUzNon84QkAAAAANTDlpXKvvaMr2/OvIDiWROZ1E4y510q+9/nJUl2zguy4ybKtIuQIk4Teb+YK/ufmf6BPgOcmWeJSUF5f9Ougzy/+pPs83+XXfCRM7hxnbx/+Y08P/+js9xomLOBBbRUCmjhjgIaAAARynq90v69slkbpW2bZLdtlHZulSorJUlNXFDxaNHRUsdUKdn5Y5LTfH3TMcU3roQkimQAAAAA0ET2/delQwedTodkme9c5m6gMGfOvkj2i7lSTraz0sqcF2Wu/rHbsVzjXfix7HOP+wd695fnVzNkEtsF9XNMdIx0/c+lbj1lX3/WWY70wD55//JbeX48TWbYcUH9vFaX6y+gGWaghT0KaAAARAh7OE/atlF22yanaJa1ydmgt7ESEqXEdlK7DlJSO2dT4HbtpST/H9MhWaoujiW1pzAGAAAAAC3I5mTLfvCmr2++d51MQvOW1GvrTEysPJfdLO8T/ydJsvPel508RaZnX5eTtT7vok9ln/l//r31evWV57YZMknBLZ5VM8bITPmebOfu8v7rIWcpzZIieR+7W+aqH8sz+ZwW+dxWwQy0iEIBDQCAEGW9VU4BrLhIKi488qdItrjQWWqxup9/SNqxucZTTvVK7yrTd5CShh+nqK49VOC1/uJYYjuZaH48AAAAAIBQYl97Rqoodzp9BsiceLqreSLGcROkYcdJX6+UrFfel/8lz6//3KYeEvUunif71GP+4lnPvvLcdo/zMG0LM8efIM/v7pP38Xucfda9Xtnnn5B33y6ZS2+U8US1eIZgq7GEY3Kae0EQFPyGDACAVmQrK6X8PCnvoHQo1/nB6tBBKS9X9nCuVFQolRwpjpUUN/8D23WQ+g6S6TtIpu9Aqc9A38a8SZ2cJ6EKcxpYeAMAAAAAtDq7Ya3s8gW+vueKH8h4PC4mihzGGHku/768f/qF5PVKG9ZIyxdIYye6Ha1VeJd+LvvvR/x7offo4xTPWnEvONOnvzx/eEjex/8s7dgiSbIfvSWbvUeeH02TiYtrtSxBEbiEY2q6i0EQDBTQAAAIIltcKG3f4hTGahTJcp1CWf4h/1NdwRYbK/Ue4BTK+g6SyRgoderSpp6cAwAAAIBIYr1V8r78pK9vxk+WGTDUxUSRx3TvLXP6d2U/ekuS5H33VUW1gQKaXfaF7L8e8hfPuvd2Zt+1b73iWTWTkibP7/4i71OPSCu+dAbXLJN971WZqde2ep5myTvob7OEY9ijgAYAQDNYa6XdWbJrlsuuWSZtWe88tRYsCUlSYvWfds6eZIntnHb1WGKSTPfeUvfeMlHht7wBAAAAAKB29ou50q5tTic2Tubi690NFKHMd6+Qnfe+s0zmrizZPTuc++wIZVcslPfJv/p/f9Gtlzy/vkemfUfXMpm4eHl+NE329WdlP3T2+7NLP5e96JqweTDYVlY4qw5JkjFSx1R3A6HZKKABANBItrREWr9KdvUy2TXLnZllDWWM1L6jsw52SppMcqq/3TFVat/BKZoltZMSEsNyvW8AAAAAQPPZ4kLZN5/39c25l8gwo6VFmKR20six0vKFkpzCjbnwapdTtQy7cpG8/3zQXzzr2sOZedYhxd1gkrM06UXXyM77n1RWKh3YJ2Xvlrr2dDtawxzK9a861CGFPeYjAP8FAQA4BmutlL3bP8ts4zqpqrL2g42ReveX6dL9SGEsVSY57Ui7k9SRH6AAAAAAAMdm335ZKsx3OmmdZc6+yNU8kc4z7hR5qwtoSz6XveCqsJn51FB21RJ5//GAVFXlDHTu7hTPOrpfPKtmYmKkYcdJXy2SJNnVS2XCpYDG8o0Rh9/gAQBQC1tRLq1fI7tmmeza5c5TT3VJTJIZPlrKHCOTOVqmQ3Kr5QQAAAAARB67d6fsp+/6+p5Lb5SJjXMxURswYowUlyCVlUj790g7tkp9+rudKmjshrXy/uM+/wPBnbvJ85t7nYd+Q4wZOU7WV0BbJp091eVEDWNzD/g7KaH394rGo4AGAEAAu3eX7Pz3ZRd+IhUX1n1gz74yI8bIjBgr9RvM3mMAAAAAgKCw1sr7yr/8s4QGZUqjT3I3VBtgYuNkjp8gu+gzSZJdOl8mggpo3v8+L1UeKZ6ld5Xn1/fKhGiRx4wYK1vd2bROtrjQ2Q8+1OXl+JomhRlokYACGgCgzbOVFbJfLXI2DN6wpvaD4hKkYaNkRoyVGT6adecBAAAAAC1jzTJp3VdO23jkufz7EbeUYKgy408JKKB9Ifu96519ucKcLciXtqx3OsbI86sZIf17DdMxRcoYKGVtkrxe2XVfyYyb5HasY2MJx4hDAQ0A0GbZA/tk538gu+AjqeDw0QekdZYZfaIzy2zgMJnomNYPCQAAAABoM2xlhbyv/NvXN5POlundz8VEbczQUVJSe6moQMo9IG3dIA0Y6naqZrNrl0vW63T6DZZJ7+puoAYwI8bKZm1yOquXSmFQQLO5/hloYgZaRKCABgBoU2xVlbR6qbzz/ud/oi+QxyONGi/PKedIw46LiCfNAAAAAADhwX7yjrP/liQlJMlcdLW7gdoYEx0jM/pE2c8/lCTZpZ/LREABTauW+Jpm1HgXgzScGTVO9u2XJEl2zXJZb5WMJ8S3z2AJx4hDAQ0A0CbY3AOyn8+V/eJD6VDu0QekdHKe7Jt4VsiuAQ4AAAAAiFw2P0/2nVd8fXPBFTLtO7qYqG0y4yb5C2jLvpC9/ObQL9zUw1ZWyK5b4eubkeNcTNMIvfpJHVOlw7nOjMCtG6QBw9xOVb+AAhpLOEYGCmgAgIhmt6yX93+vSauX+ZcrqGaMlDlGnsnnSJljZKLC9wdiAAAAAEB4s/99QSopdjpde8qc+h1X87RZgzOlDslS/iHnz4a1ztKO4WrT11JpidNO6yx17+1ungYyHo/MyLH+YubqpTIhXECzFRXO9SJJxuMU/xD2WJcKABCx7NYN8v71D85SBYHFsw7JMuddKs///VNRv/ijzKjxFM8AAAAAAK6x27fIfjHX1/dcfrNMNHMf3GA8UTJjJ/r6dunnLqZpPvut5RuNMS6maRwzcqyvbVcvczFJAxw66G93TOH3TBGCf4UBABHL+9/npcpK/8DQUc5ss1HjZaJj3AsGAAAAAMAR1lp5X35SstYZGDFWJnOMu6HaODNukrMfnSS7fKHsVT8Ky98jWGtlVy/19cNm+cZqQ0ZJ0TFSZYW0e7vswf0yaZ3dTlU7lm+MSMxAAwBEJLv5G+mbVU7H45HnzocVdds9MmNODssfegEAAAAAkcku/Vza/LXTiYqS57Kb3Q0Eqd9gKTXdaRcXSl+vdDVOk+3bJR3Y57TjEqRBme7maSQTnyANGeHrh/IsNJsbUEBLSXMvCIKKAhoAICJ533nZ1zYTJsv0GeBiGgAAAAAAjmbLymRfe8bXN2ecL9O1h3uBIOnI/lvjJvn64bqMY+DyjRp+vExM+D1QHDhrLnA2XcjJ8y/haFLSXQyCYKKABgCIOHbrBmndV07HeGTOu8zdQAAAAAAA1MJ+8Lp/6bf2HWW+c7m7geBjxgcU0L5aLFte5mKaprGrwnj5xiPMCP8+aFq/Wras1L0w9ck74G+zhGPEoIAGAIg43rcDZp+Nn8TTewAAAACAkGOrqmTnzvH1zdRrZRKTXEyEGnr1k7oc+X1CWYm0Zrm7eRrJFuZLW9Y7HWNkRoTnvnqmUxepe2+nU1nh364jxAQu4WhYwjFiUEADAEQUu22jtPbID7XG8PQeAAAAACA0Ze+WSkucdsdUmZPPcDcPajDG1FjG0bt0votpGs+uXS5Zr9PpN1imQ7KreZojLJZxDFjCUSnMQIsUFNAAABHF+84rvrYZN0mmW08X0wAAAAAAUDu7K8vf6d1PxhPlWhbUzoyb6O+sXiZbWuxemMYKXL4xcBnEMFSjgLZmmay1LqapQ55/BppS2QMtUlBAAwBEDLt9s1T9JJIxMt9h7zMAAAAAQIgKKKCZnn3cy4E6me69pZ4ZTqeiXHblElfzNJStrJRdt8LXN6PGu5gmCPoNlpLaO+1DudLOre7m+RZbUS4VHHY6Ho/UMdnVPAgeCmgAgIhRY++zMSc7P+gCAAAAABCC7O7t/k6PDNdyoH6ByzjaJWGyjOOmdVLJkdlyaZ2lHuFdoDVRUTKZo339kFvGMXD5xuRUZpNGEApoAICIYHdskVb5nwQz32XvMwAAAABACKsxA62vezlQr8ACmr7+SraowL0wDRRYYDIjx8oY42KaIKmxD9oyF4PUInD5RvY/iygU0AAAEcH7tn/vM405SSbMn64CAAAAAEQuW1wo5R5wOlHRUpfu7gZCnUx6V6nvIKdTVSW74kt3Ax2DtVY28AHjkWG+fOMRZvhoZ3lESdq2UTY/z91AAWyuv4BmKKBFFApoAICwZ3duk1Yu8vU9373CxTQAAAAAABzD7h3+drdeMtHR7mXBMZnxAcs4Lv3cxSQNsG+3dGCf045LkAaPcDdPkJikdtKAob6+XbPcxTTfEjgDLZUCWiShgAYACHvedwJmn40+UaZ6g18AAAAAAEKQrbF8Y4ZrOdAwZsxEqXoZxPVrZA+Hzuynb7Or/bPPNPw4mZgY98IEmamxjGMI7YPGEo4RiwIaACCs2V1Z0oqFvr7nO+x9BgAAAAAIcQEFNPVkC4JQZ1LSpIHDnY71yi5f4G6getTc/2xcPUeGnxrfz7qVshUV7oUJwBKOkYsCGgAgrNnA2WfHTZDp3c+9MAAAAAAANIDdneVrmx4ZruVAw5lxob+Moy0qkDZ/43SMkRkx1t1Awda1p5Te1WmXlUib1rqbpxpLOEYsCmgAgLBld++QDZx9xt5nAAAAAIAQZ71eafd2/wBLOIYFM+YkyXPk1+mbv5E9eMDdQLWwa5ZLXq/T6TtIpkOyq3mCzRjzrWUcl7mYJgBLOEYsCmgAgLBl331FstbpjBov06e/u4EAAAAAADiWg/ul0hKn3a6D1DHF3TxoENO+ozR0lK9vl33hYpo6RPDyjdXMSP+sOrt6qWz174VcYsvKpMICpxMVJXXo6GoeBBcFNABAWLJ7d9b4YdXzXfY+AwAAAACEgYDlG9Wjj4wxrkVB45jxp/jadsl8F5MczVZWyq5d4eubUZFZQNPATCkuwWkf2Cft2+1unkMH/e3kNBlPlHtZEHTRbn54VVWV3n33XX322WfKzs5W+/btNWbMGF1++eXq0KGDm9EAACHOvvOqf/bZiLEyGQPdDQQAAAAAQAPYXVm+tmH5xrBijjtBNnqmVFkp7dgiu2+3TNcebsdybP5aKily2qnpUoTurWdiYqThx0krvpTkzEIz3Xq6Fyg3YClPlm+MOC02A23GjBmaOXNmna9XVlbqvvvu0+uvv64xY8boJz/5ic455xytWLFCt99+u/Ly8loqGgAgzNl9u2ps2Os5n73PAAAAAABhYhf7n4Urk5gkZQYsIbjs83qObl32W8s3RvLMRjOi5jKObrIB+5+ZlDQXk6AltEgBraKiQllZWfUe89prr+nrr7/W3XffrauvvloTJ07URRddpPvuu0/GGD3xxBMtEQ0AEAHsu69K9simuJmjZfoOcjcQAAAAAAANZAOWcDQROksokpnxk3xtu+Rz1/fgqmZXBRTQInX5xiMCC2ja/LVsUaF7YfIClnBMZQZapAlaAa2yslIFBQXavHmz/vrXv6qwsO6LNj8/X2+//bbOP/989e3bt8ZrHTt21A033KBVq1bp66+/DlY8AECEsPt2yy72rzPu+S6zzwAAAAAA4cGWl0nZe52OMVL33u4GQqOZkeOk2Dins3entHt7/Se0Artvl7R/j9OJi5cGj3A3UAszHVOk6q08vF7ZdSvqP6El5fpnoCkl3b0caBFB2wNt3rx5mjVrVoOOXbRokSoqKnTqqafW+vqYMWPUrl07ffHFFxo2bFiwIgIAIoB9b7Z/9tmw42X6D3E3EAAAAAAADbV3p/+etnN3mbg4d/Og0UxcvMyo8b6tJezSz13fy67GMoZDj5OJiXUvTCsxI8fJZm1yOquXSuNPcSUHSzhGtqAV0EaPHq3p06f7+o8++midx65fv17Jycnq2rVrra97PB4NHDhQGzZsCFY8AEAEsPv3yi7+zNf3nH+5e2EAAAAAAGgkuyvL3+nZx7UcaB4zflKNApq96BpX9xxrS8s3VjMjx8m+9aIkya5dIVtVJRMV1fpBAgpoLOEYeYJWQEtJSVFKSoqvHxtbd5V779696tKlS73v16VLF61ZsyZY8QAAEcC+96rkPfKk3tBRMgOYpQwAAAAACCMBBTT2Pwtjw8dICUlSSZF0YJ+UtVnqO9CVKLaoUNrs3wrJjBxbz9ERpHc/KTlVOpQrFRVIWzdIA134PVEuBbRIFrQCWmMUFxfXOfusWkJCgiorK1VeXl5vMU6Spk2bVuv4/fffL0nq1MndCzc6OjokciB8cM2gKSL9uqnct1sHv/zM10+55keKjdDvtbVE+jWD4OOaQWNxzaAxZsyYobS0NN1yyy0NPmfu3Ll68skndckll+iyyy5rwXQAAARH4Aw0t5f9Q9OZmBiZ0SfILvhYkmSXzpdxq4C2drn/YeO+g2Q6pNR/QoQwxsiMGCv7+YeSJLtmqUwrF9BsWalUXOh0oqKldh1b9fPR8jxuffCxprRaa1spCQAgHBS9/pzkrZIkxWSOVuyw49wNBAAAgqaiokJZWVmNOuebb77RM8880yJ5AABoCdbaGjPQRAEtrJlx/j237NIvZKuLWK0tYP8zM7JtLN9YLfD7tauXtX6AwOUbU9JkPK6VW9BCXJmBlpCQoJKSknqPKSkpUXR09DFnn0n+mWZ1ycnJqff1llb9xK3bORA+uGbQFJF83dicbHk/fc/Xrzrnkoj8PltbJF8zaBlcM2islrhmunfvHrT3grsqKytVUlKi7OxszZ49W4WFhQ0+NycnRw8//LAuv/xyPf/88y2YEgCAIMo/JBXmO+24eCmts6tx0ExDRkrtOjj/TQ8ddJZRHJTZqhFsZaUzA+0IM2p8q36+64aOkqJjpMoKafd22ZxsmU71bx0VVCzfGPFcKYl27dpV2dnZ9R6zf/9+bo4BAJIk+/bLUpUz+0yDhssMbt0fSAEAQPDNmzdPN998s/7whz/oq6++avB55eXlevDBBzV8+HBdcMEFLZgQAIAgC5x91qMPs1XCnImKkhl7sq9vl37e+iG2rJeKi5x2aqc2N6vRxMVLQ0b4+nZN685CswEz0EwyBbRI5Mq/0gMHDlRubq4OHDhQ6+ter1ebNm3SoEGDWjkZACDU2N07ZL/81Nf3XHC1i2kAAECwjB49WtOnT/f96dixYXtG/OMf/5C1Vj/5yU9aOCEAAMFld2f52ux/FhnMuEm+tl0yX7a6mNVK7Ool/iwjxx1z26RIVHMZx6X1HNkC8piBFulcWcJxwoQJ+s9//qN58+bpkksuOer1ZcuWqbCwUJMmTarlbABAW+L9738ke2Qd8czRzD4DACBCpKSkKCXFv8l9Q5bvnzNnjlatWqX77rtPcXFxTf7sadOm1TpevT1A9fKjbomOjg6JHAgfXDNoCq6b1nf4wD6VHmm3G5ypxDD7u+eaOZpNPUUHu/VU1d5dUnGREr78WO0uv6nVPj9n7QodWa9HHSeeqbgQ+2/TGtdM1eSzlfPiLKezfo1SkxLlSUhs8PnekmKVr1ikyt3bFdW5q6J79VN0zwyZBvysmV9cqOqNqtr1ygi7/02HolD7d8aVAlqnTp10xhlnaM6cOZowYYJ69erley0vL0/PPvusjj/+eA0dOtSNeACAEGG3rJdWLvb1PVOvczENAABw08qVK/XKK6/oD3/4g9LT092OAwBAo1Vu3+xrR/fp52ISBIvxeJR02Y3Kf+weSVLxWy8r8TuXyNOuQ4t/duXuHaras8PpxMUrdsToFv/MUBTVuZuie/dT5Y6tUmWFylcvU/yEU+o9x1tUqLKlX6hs0Wcq+2qRVF5e8wCPR1Fduiu6dz9F9+qr6N79FNW7n6K795aJifEdVpWz339KGj+fRiJXCmiSdN111ykrK0t//OMfde6556pHjx7au3ev5s6dq7i4ON1yyy1uRQMAhABrrbxvPOvrm/GnyPTmBgMAgLZo7969euyxx3TNNdcoM7P5s9GrZ5rVJScnp97XW1r1E7du50D44JpBU3DdtC5bWSnvzm2+/uF2KTJh9nfPNVM7O3S01KWHlL1btrhQOa88Lc+FLb/9hHfeh/7O0FE6mF8gqaDFP7cxWuua8Q4/XtqxVZKUv+ATFfYfdtQxtjBfduVi2RVfSl+vlKoq63lDr6r27lLV3l0qWzzfPx4VJXXuLtO9t9S9t2xAUbwgOk6F/G+j2VrimunevXuTz3WtgBYXF6e7775bc+bM0eeff66cnBy1b99eEyZM0OWXX66kpCS3ogEAQsHaFdLGdU47KkrmwqvczQMAAFxRUlKiBx54QMOHD9fkyZNVVHT03iIVFRW+8YSEBHk8rmz3DQBA3fbvkSqP/MI+tZNMUjt38yBoTFSUzPlXyP7rIUmS/egt2TMvkElq36KfG7jfV+A+YG2RGTlO9n+vS5Ls6mWy1soYI5ufJ/vVYtnlC6QNaySvt/Y36NFHZvAI2bwcafcO6cBeydqjj6uqkvbulN27U1q+oOZr7IEWkVqsgDZz5sxjHhMTE6NLLrmk1n3QAABtl/V65X3jOV/fTJoi07npT4sAAIDwtXXrVu3evVu7d+/WkiVLaj1mzpw5mjNnjiTp8ccfV+fOnVszIgAAx2R3Zfk7PTLcioEWYsZNlH33VWnvTqm0RPbD/8pMvbbFPs8WFUqb1vk/f8TYFvussNBvsJTUXioqkA7nyr7xnLxbNzh/R7UVwiSpd3+ZMSfJjD5JpmuPGi/Z8jJp3y7ZPTuk3TuOfN0uHdxf+3slJEmtsGwnWp9rM9AAAKiLXfq5tOvI0haxcTLfuczdQAAAwDUZGRmaPn16na//6U9/0uTJk3XqqadKkpKTk1snGAAAjRFQQDM9+7iXAy3CeKJkzr9S9p8PSJLsx2/LnnmhTPuWKarYdSv8s6kyBsokp7bI54QL44mSGTFGdtFnkiT7/uu1H9hvsFM0O/5EmfSudb9fbJxTYOvdv8a4LS1xZqAdKajZPTuk/EMyZ14gY0ywvh2EEApoAICQYisrZOe84OubMy9o8z8IAgDQliUlJWn48OH1HpOenn7MYwAAcBMz0CKfGXOSbI8+zkylslLZD96QueSGlvmwVQHLN45q28s3+owYKx0poPkYIw0cJjP6ZJnjT5Bp5jKLJj5B6jtIpu+gZr0PwgcFNABASLGfz5UO7HM6Se1lpnzP3UAAAAAAADTX7u2+punZ18UgaCnG45Hn/Cvl/cd9kiT76buyZ18k0yE5qJ9jq6pk1y73f24b3/+smjn+BNl+g6WsTdLgETJjTpY5foJMhxS3oyGMUUADAIQMW1Yq+87Lvr459xKZxCQXEwEAAAAA0Dy2uFDKPeB0oqKlLuzxHbGOP0Hq2dfZlqK8zJmFdulNwf2MLd9IxYVOO6WT1KtfcN8/TJmYWHluf0CyVsbjcTsOIgRXEgAgZNiP3pLyDzmdlE4yp53nah4AAAAAAJptl3/2mbr1kolmTkOkMh6PPBde6evbT9+TPZQb1M+wgcs3jhzL3lsBjDEUzxBU/GsNAAgJtjBf9oM3fH1z/hXOpq0AAKBNmDlzZpPOe/XVV4OcBACA4LI1lm/McC8IWseoCVLv/tKOLVJFuez7r8tc8YOgvLXds0P28w98fZZvBFoW5VgAQEiw/3tdKil2Ol17yJx0hruBAAAAAAAIhl1Z/nbPPq7FQOswxshzwVW+vp33vuyhg81+X1twWN6/3eP/3UlaZ2nocc1+XwB1o4AGAHCdzT0g+8k7vr7nomtloqJcTAQAAAAAQHDY3Vm+tumR4VoOtKKRY6WMgU67skL2vdea9Xa2okLeJ/4i5WQ7A3Hx8txyh0xMTDODAqgPBTQAgOvs2y9LlRVOJ2OgNPpEdwMBAAAAABAE1uutuQcaSzi2CUfNQvv8A9ncA016L2ut7AtPSJu/rn5zeb7/a5lefYMRFUA9KKABAFxl9+6UXfCxr+/53nVsgAsAAAAAiAwH90tlJU67XQepY4q7edB6MkdL/QY77cpK2fdmN+lt7Idv1vi9ibn4epnjJgQjIYBjoIAGAHCV97/PS9brdIYdJzN0lLuBAAAAAAAIloDlG9WjDw+MtiHGGHkuDJiF9sVHsgf3N+o97MrFsq8/63/Pk86QOXtq0DICqB8FNACAa+y2jdKKL319z/euczENAAAAAADBZXdl+dqG5RvbnqHHSQOGOe2qStl3X23wqXbnNnn/9ZBkrTMwcJjMNT+lCAu0IgpoAABXWGvlDXyKauxEmT4DXEwEAAAAAEBwBRbQ2P+s7XH2QrvS17cLP5Y9sO+Y59nDefI+fo9UVuoMdOoiz09+LxMT01JRAdSCAhoAwB1fr5Q2rHHaHo/MhVe7GgcAAAAAgKDbvd3XZAZaGzVkpDQo02lXVcm++0q9h9uKcnmf+D8pN8cZiE+Q52d3ybTv2MJBAXwbBTQAQKuzXq+8bzzn65uJZ8t07eFiIgAAAAAAgsuWlUnZe52OMVK33u4GgiucWWgBe6F9+ans/j21HmutlX3mb9LWDUdO9sjzw9/J9ODaAdxAAQ0A0Ors8gXSji1OJyZW5vzL3Q0EAAAAAECw7d0hWa/T7txdJi7O3TxwjRmc6cxEkySvV/ad2meh2fdmyy6Z5z/vsptkRoxpjYgAakEBDQDQqmxlpex/n/f1zRnnyySnuZgIAAAAAIDgswHLN6pnH/eCICTUmIW2aJ7svl01XrfLF9b8fckpU2TOOL/V8gE4GgU0AECrsgs+kvYfWcIiMUnmnIvdDQQAAAAAQEvYleVrmh4ZrsVAaDADh0nDjnc61iv7tn8Wmt2+Wd6nHvYfPHiEzJU/kjGmlVMCCEQBDQDQKmxlheyOrbJvv+wbM+dcIpPUzsVUAAAAAAC0DBtYQOuZ4VoOhA7PBVf62nbpfNm9O2UPHZT38T9L5eXOC527yfOT22Wio11KCaAa/ysEAASdLS2Rdm2T3bFV2rFVdudWac8OqbLSf1Byqszp33UvJAAAAAAALcRaW2MGmiigQZLpP0TKHCOtXS5ZK+8bz0l5B6VDuc4BiUny/PwumaT27gYFIIkCGgCgmWz+IWnnkWLZzq3O1/17JGvrPc+cfyUbKAMAAAAAIlP+Iakw32nHJUhpnV2Ng9DhueAqedcudzorFwe84JHnR9NkuvZ0JxiAo1BAAwA0mC0vk7I2yW7+RnbrBmn7Zv9TUg2R1lnq1U9m+PEyk85uuaAAAAAAALgpcPZZj94yHnbSgcP0HSiNGi+tWlJz/Mofygw7zp1QAGpFAQ0AUCebnydt/sYpmG3+RtqxVaqqPPaJHo/UrZdMr75Owax3P+cr+50BAAAAANoA9j9DfTwXXClvQAHNnPYdeU49z8VEAGpDAQ0AIEmyXq+0b5fs5q99RTMd2HfsE2NjpR4ZTpGsdz+ZXv2dp+tiWZ4RAAAAANBG7c7ytymg4VtM7/4yU6bKfvhfmXGnyFz+fbcjAagFBTQAaMNsWansvP/Jrl8jbVkvFRce+6SuPWUGDJUGDJXpO0jq2kPGE9XyYQEAAAAACBM1ZqD16ONeEIQszyU3yl50rUw0v6IHQhX/6wSANsqWlcn71zukrE11HxQdI2UMlBkw1Cma9R8i065D64UEAAAAACDM2MpKae9O/0CPDNeyILRRPANCG/8LBYA2yHq98j71yNHFs3YdnJllA4bKDBgm9e4vExPjTkgAAAAAAMJR9h6p8sj+4amd2A8cAMIUBTQAaIPsnBekFQt9ffPdy2UmnCp16S5jjHvBAAAAAAAIczZw/zNmnwFA2KKABgBtjHfhx7Lvzfb1zRnny3Ph1S4mAgAAAAAgggTuf9aT/c8AIFx53A4AAGg9duM62edm+gcyx8hcepN7gQAAAAAAiDA2oIDGDDQACF8U0ACgjbD798r79/+Tqo6sw96jjzw//K1MVJS7wQAAAAAAiCQBSziann3dywEAaBYKaADQBtjiQnn/do9UWOAMtO8oz8/vkklIdDcYAAAAAAARxBYXSrk5TicqWurS3d1AAIAmo4AGABHOVlbK+4/7pX27nIHoGHluuUMmrbO7wQAAAAAAiDS7tvvb3XrJREe7lwUA0CwU0AAggllrZV/6p/TNKt+YufGXMv2HuJgKAAAAAIDIZGss35jhWg4AQPNRQAOACGY/fkt2/vu+vrngKnnGn+JiIgAAAAAAIljgDDQKaAAQ1iigAUCEsquWyr76lK9vxk+W+e7lLiYCAAAAACCy1ZiB1qOPe0EAAM1GAQ0AIpDduU3eJx+UrHUG+g+RueHnMsa4GwwAAAAAgAhlvV5moAFABKGABgARxh7Klffxe6SyUmcgrbM8P/2DTEysu8EAAAAAAIhkB/dLZSVOu10HqWOKu3kAAM1CAQ0AIogtK5N35r1Sbo4zEJ8gz8//KNMh2dVcAAAAAABEvIDlG9WjD6vAAECYo4AGABHCer06/P/ukbI2OQPGI8+PfifTo7e7wQAAAAAAaAPsrixf27B8IwCEPQpoABAhil7+l8oWfuLrmyt/IJM5xsVEAAAAAAC0HYEFNPY/A4DwRwENACKAd9FnKpr9jK9vTvuOPKd9x71AAAAAAAC0Nbu3+5rMQAOA8EcBDQDCnM3eI/ufmf6BzNEyl3/fvUAAAAAAALQxtqxMyt7rdIyRurGdAgCEu2i3AwAAms5WVcn71CNSeZkkKapHH9kf/FYmKsrlZAAAAAAAtCF7d0jW67Q7d5eJi3M3DwCg2ZiBBgBhzL7/urR1g9OJilLH2+6WSUxyNRMAAAAAAG1Nzf3P+riWAwAQPBTQACBM2e1bZN9+yddPuvxmxfQb7GIiAAAAAADaqMD9z3pkuJcDABA0FNAAIAzZinJ5//2wVFXlDPQbrKTvXeNuKAAAAAAA2qjAGWimZ4ZrOQAAwUMBDQDCkH3jP9LenU4nNk6em38lE8W2lgAAAAAAtDZrrbRrm3+AAhoARAQKaAAQZuz61bIfzfH1zaU3yXTu7mIiAAAAAADasEO5UmGB045LkNI6u5sHABAUFNAAIIzY4iJ5n37MP5A5RmbyOe4FAgAAAACgjbOb1vk7ffrJePiVKwBEAv41B4AwYl9+Uso94HSS2stz/c9ljHE3FAAAAAAAbdmGNb6mGTzCxSAAgGCigAYAYcKu+FL2y098fc81P5FJTnUxEQAAAAAAsBvW+toU0AAgclBAA4AwYA/nyfufmb6+mTBZZuxEFxMBAAAAAAB76KCUvdvpRMdI/Qa7GwgAEDQU0AAgxFlr5X3ucakw3xlI6SRz1Y/cDQUAAAAAAGrMPlP/ITIxse6FAQAEFQU0AAhx9ou50uqlvr7nxl/KJLZzMREAAAAAAJAkbQxYvnFQpotBAADBRgENAEKYPbBP9pV/+/rmjPNlho5yMREAAAAAAKhm16/xtdn/DAAiCwU0AAhR1lsl71OPSGUlzkDXnjLfu87dUAAAAAAAQJJk8w5K+/c4negYqd8gdwMBAIKKAhoAhCj7wX+lzd84nagoeW7+lUxsnKuZAAAAAACAw25k/zMAiGQU0AAgBNmd22TnvODrm+9cLpMx0MVEAAAAAACghg0s3wgAkYwCGgCEGFtRIe+/H5aqKp2BvoNkzrvU3VAAAAAAAKAGu8E/A80MznQxCQCgJVBAA4AQY+c8L+3e7nRiY+W56VcyUVHuhgIAAAAAAD419j+LiZX6sv8ZAEQaCmgAEELsxrWyH/7X1zeX3CjTtYd7gQAAAAAAwFFswPKN6jeY/c8AIAJFux1AkhYsWKD3339fWVlZMsaod+/eOvPMMzV58mQZY9yOBwCtwpaVyvv0Y5K1zsCw42Qmn+tuKAAAAAAAcLSNAcs3DmH/MwCIRK4X0J588knNnTtXEyZM0Pe//31Za7Vo0SI98cQTWrZsmW699VZFR7seEwBanH3rJSkn2+kkJslzwy9lPEwUBgAAAAAg1ATOQDODKKABQCRytTL1ySefaO7cubrqqqt00UUX+cZPPfVUvffee3rmmWf04osv6rrrrnMvJAC0Art9i+zcOb6+uez7MilpLiYCAAAAAAC1sbk50v69Tof9zwAgYrk6teGdd95Rjx49dMEFFxz12nnnnaeTTjpJ7733nrKzs11IBwCtw1ZVyfufmZL1OgODR8icdLq7oQAAAAAAQK3sxoD9z/oPkYmJcS8MAKDFuFZAKykp0a5duzRq1Ch56lii7KKLLpLX69Wnn37ayukAoPXYT96Rtm92OtEx8lx7C/s/AgAAAAAQqjYE7H82ONPFIACAluRqAU2SOnToUOcxGRkZSklJ0fr161srFgC0KpuTLfvf5319c/4VMl26u5gIAAAAAADUp8b+Z4NHupgEANCSXNsDrX379oqKitKuXbvqPGbPnj0qLS3VwYMHWzEZALQOa628L/xDKi9zBnr0kTl7qruhAAAAQtjGjRs1f/58rV27VgcOHFB0dLT69eunKVOm6IQTTnA7HgCgDbC5B6QD+5xObKyUMdDdQACAFuNaAS0mJkYjR47U4sWLtX//fnXu3LnG6/v379f999+v0tJSRUfXH3PatGm1jt9///2SpE6dOgUndBNV53c7B8IH10zbUPr5XB1eu9zpGKOUn9+h2K5dm/x+XDdoLK4ZNBbXDBqLawaNMWPGDKWlpemWW26p9fVnnnlG7733njIyMjR58mR16dJFBQUF+vLLL/Xwww/rnHPO0U033dTKqQEAbY3d6F++Uf2Hsv8ZAEQw1wpoknT11Vfrjjvu0L333qvrrrtO/fv3V0FBgVasWKG33npLQ4cOVa9evbR9+3Y3YwJA0HkL8lXw70d9/YRzL1Ys66YDAIA2qqKiQllZWUpLS6vzmKioKE2bNk1jxoypMT5lyhT985//1Pvvv6+TTjpJQ4YMaem4AIC2bH3A8o2DuI8HgEjmagGtd+/e+tOf/qQnn3zSN1tMkrp27aqrrrpKp59+uu655x6lpqbW+z6B59YmJycnKHmbqvqJW7dzIHxwzUQ+77N/kz2c53SS01R2ziXN/u/NdYPG4ppBY3HNoLFa4prp3p29QiNFZWWlSkpKlJ2drdmzZ6uwsLDe46+99to6X7vooov00UcfafXq1RTQAAAtKnAGmhkywsUkAICW5moBTZL69eunv/zlLzpw4IDy8vKUmpqqtLQ0GWNUWVmprVu36swzz3Q7JgAEjd2wRvaLub6+5+ofyyQkupgIAACg9c2bN0+zZs0KynslJjo/S1lrg/J+AADUxh5k/zMAaEtcL6BVS09PV3p6eo2xxYsXq7i4mM2gAUQMW1Eu73+e8A+MPknmuAnuBQIAAHDJ6NGjNX36dF//0UcfbfJ7zZs3T5I0ePDg5sYCAKBOR+1/Fs3+ZwAQyUKmgPZtxcXFeumllzR8+HANGDDA7TgAEBT23Vel7N1OJyFRnit/4G4gAAAAl6SkpCglJcXXj42NbdL7bNiwQS+99JIGDRqkkSNHNuicadOm1TpevT1A9fKjbomOjg6JHAgfXDNoCq6bxju8fZNKj7STjp+gdm3s745rBo3FNYPGCrVrxuPmh1dWVuqbb745arywsFAPPfSQCgoK9OMf/9iFZAAQfHb3dtn3X/f1zfeul0lOczERAABAeFuwYIHuueceJScn69Zbb5XH4+otLgAgwlWsXeFrx2aOdjEJAKA1uDoDrbKyUtOnT9eoUaM0ZswYdejQQbt27dLcuXNVVVWlO+64Q126dHEzIgAEhfV65f3PTKmqyhkYMFTmlCnuhgIAAAhTlZWVevbZZ/XBBx9oxIgR+vnPf67k5OQGn18906wuOTk5zUzYPNVP3LqdA+GDawZNwXXTOPbgfnmz9zid2DgdTkmXaWN/d1wzaCyuGTRWS1wz3bt3b/K5rhbQ4uPjdeedd+qzzz7Tm2++qYKCAqWmpurkk0/WRRdd1KgbIAAIZXbe+9KW9U4nKlqea2+R4QlpAACARtu/f78eeeQRZWVl6corr9SFF17IzDMAQIuzGwL2PxvA/mcA0Ba4vgfayJEjG7xOPQCEI5t3UPaNZ319c+4lMt17u5gIAAAgPH399dd68MEHlZSUpBkzZmjgwIFuRwIAtBUb1/iaZlCmi0EAAK3F9QIaAEQ670uzpNISp9O1h8x5l7gbCAAAIAzt379fDz74oLp27arf//736tChg9uRAABtSOAMNDN4hItJAACthXUuAKAF2RVfSl8t8vU9194iExPrYiIAAIDw9NxzzykuLo7iGQCg1dmD+6WcbKcTGydlMAMaANoCCmgA0EJsSbEz++wIM+lslnkAAABogsOHD2vp0qW69NJLKZ4BAFqd3eBfvlEDhslEs6gXALQF/GsPAC3EvvGcdCjX6XRIlrn4BlfzAAAAhKstW7bIWitJWrJkSZ3HxcXFadSoUa0VCwDQVtRYvpEHYwGgraCABgAtwG7+Rnbe/3x9c8UPZZLauZgIAAAgfBUUFEiSZs2aVe9x6enpmjlzZmtEAgC0IYEz0Nj/DADaDgpoABBk1uuV9/knpCNPSWvEWJmxJ7sbCgAAIMTVV/iaPHmyJk+e3IppAABw2Jxs6eB+pxMXL/UZ4G4gAECrYQ80AAi2HVuk3duddly8PFf/RMYYdzMBAAAAAIBGswHLN2rAUPY/A4A2hAIaAASZzdrka5uR42TS0l1MAwAAAAAAmozlGwGgzaKABgDBlrXZ385gaQcAAAAAAMKV3eifgWYGZbqYBADQ2iigAUCQ1ZiBljHQxSQAAAAAAKCp2P8MANo2CmgAEES2rEzas9PpGCP17uduIAAAAAAA0CQ19j8bOIz9zwCgjaGABgDBtHOrZL1Ou2tPmfhEd/MAAAAAAICm2bDa1zSD2P8MANoaCmgAEEQ1l29kaQcAAAAAAMKRtbbGDDQzmP3PAKCtoYAGAMG0fbO/3Yf9zwAAAAAACEs52VLuAacdlyD17u9uHgBAq6OABgBBZLP8BTRmoAEAAAAAEJ7sxsD9z4ay/xkAtEEU0AAgSGxJsZS92+l4PFKvvu4GAgAAAAAATbN+ja9pBrP/GQC0RRTQACBYdmyRrHXa3fvIxMa5mwcAAAAAADSatVZ2IwU0AGjrKKABQJCwfCMAAAAAABEgJ1vKzXHa7H8GAG0WBTQACJasTf52xkD3cgAAAAAAgCazG/yzzzRwmExUlHthAACuoYAGAEFitzMDDQAAAACAsLdhra9phrB8IwC0VRTQACAIbFGBdGCf04mOlnr0cTcQAAAAAABoNGttjRloZhAFNABoqyigAUAwBOx/pp59ZaJj3MsCAAAAAACa5sA+Ke/I/mfxCVLvfu7mAQC4hgIaAASBDdj/jOUbAQAAAAAITzX3PxvO/mcA0IZRQAOAIAjc/0x9KKABAAAAABCWNgbsfzY408UgAAC3UUADgGAIWMLRZAx0MQgAAAAAAGgKZ/+zwAIa+58BQFtGAQ0AmskezvOvjx4bK3Xr5W4gAAAAAADQeNl7/Pf3CYlSL/Y/A4C2jAIaADRX4PKNvfqxPjoAAAAAAGHIfvmJvzMok/t7AGjjKKABQDPZrE2+Nss3AgAAAAAQfmxlpewXc319z8lnupgGABAKKKABQDPZgP3PlDHAvSAAAAAAAKBpVi2W8g857eRUaeQ4V+MAANxHAQ0AmsFaW2MJR9OHGWgAAAAAAIQb77z3fW0z8SyWbwQAUEADgGbJy/E/oRafIHXp7mocAAAAAADQOHb/HumbVU7HeGQmnu1uIABASKCABgDNEbh8Y58BMh7+WQUAAAAAIJzY+R/4OyPGyKSluxcGABAy+E0vADSDrbF8I/ufAQAAAAAQTmxFheyCj319z+RzXEwDAAglFNAAoBls1iZ/J4P9zwAAAAAACCd2xUKpMN/ppKZLmaPdDQQACBkU0ACgiay1NZZwNBnMQAMAAAAAIJwELt9oJp0l44lyMQ0AIJRQQAOApsrJlooLnXZSe6lTF3fzAAAAAACABrN7d0kb1zodj0dm4lnuBgIAhBQKaADQRDWWb+wzQMYY98IAAAAAAIBGsfPf93dGjZdJTnMvDAAg5FBAA4CmYvlGAAAAAADCki0vk134ia/vmXyui2kAAKGIAhoANJHdHlhAG+hiEgAAAAAA0Bh22QL/tgzpXaWho9wNBAAIORTQAKAJrNcrBRTQ1IcZaAAAAAAAhIvA5RvNpLNlPPyaFABQE//PAABNkb1HKi1x2h2SpRTWSQcAAAAAIBzYXVnSlvVOJypa5uQzXM0DAAhNFNAAoAns9k3+TsZAGWPcCwMAAAAAABqsxuyz40+Q6ZDiYhoAQKiigAYATZEVsP8ZyzcCAAAAABAWbFmp7KLPfH0z+Rz3wgAAQhoFNABoApvln4FmMiigAQAAAAAQDuzSz6WSYqfTpYc0eIS7gQAAIYsCGgA0kq2qknZu9Q9QQAMAAAAAICzYeQHLN55yNlsyAADqRAENABpr7w6pvNxpp3ZirXQAAAAAAMKA3b5Fql5RJjpG5sQz3A0EAAhpFNAAoJFswP5nYv8zAAAAAADCgp0fMPtszEky7Tu4mAYAEOoooAFAY233F9BMxkAXgwAAAAAAgIawpcWyi+f7+mbyuS6mAQCEAwpoANBIdtsmX9uw/xkAAAAAACHPLp4vlZU4nW69pAFD3Q0EAAh5FNAAoBFsRYW0K8s/wBKOAAAAAACENGut7Lz/+fpm8jkyxriYCAAQDiigAUBj7NkuVVU67fSuMknt3c0DAAAAAADql7VJ2rnNacfGypxwmrt5AABhgQIaADRCzeUb2f8MAAAAAIBQZ+e972ubsZNkktq5mAYAEC4ooAFAY2zf7G+zfCMAAAAAACHNFhfKLp3v65vJ57iYBgAQTiigAUAj2Cx/AY0ZaAAAAAAAhDa76DOpvNzp9Owr9R3kah4AQPiggAYADWTLypw90CTJGKl3P3cDAQAAAACAOllray7fOHmKjDEuJgIAhBMKaADQULu2SV6v0+7SQyYh0d08AAAAAACgblu+kfbscNpx8TITTnU1DgAgvFBAA4AGYvlGAAAAAADCh533ga9txp/Cg7AAgEahgAYADZW1yd/OGOBeDgAAAAAAUC9bmC+77Atf30w+x8U0AIBwRAENABrIbg+YgdaHAhoAAAAAAKHKfvmpVFnhdPoM4D4eANBoFNAAoAFsabG0b5fT8XikXv3cDQQAAAAAAGplrZWd/76vz+wzAEBTUEADgIbYvlWy1ml37y0TF+duHgAAAAAAULu1K6R9u512QqLMuEnu5gEAhCUKaADQAHa7f/8zln0AAAAAACA02YoKeV9+0tc3J5wmE5/gYiIAQLiKdjuAJG3cuFFvvfWWNmzYoKKiIqWlpWngwIG68MIL1adPH7fjAYCU5d//TBkD3csBAAAAAADqZD94Q9q/x+kkJMl89zJ3AwEAwpbrBbRPP/1U//jHP9SvXz9NnTpVHTp00P79+/Xxxx9r2rRp+tnPfqaJEye6HRNAG2ezAmagZTADDQAAAACAUGMP7JN9b7avb6ZeI9MhxcVEAIBw5moBLT8/X//+9781fvx43XbbbTLG+F4777zzdNddd/lej42NdTEpgLbMFhVKB/Y5nahoqUeGq3kAAAAAAEBN1lp5X/qnVFHuDPTuLzP5HHdDAQDCmqt7oK1fv17l5eU6++yzaxTPJCk+Pl6TJ09WUVGRcnJyXEoIAJK2Byzf2DNDJibGvSwAAAAAAOBoqxZLa5Y5bWPkueYnMp4odzMBAMKaqwW0qqoqSdKhQ4dqfT07O1sej0epqamtmAoAamL5RgAAAAAAQpctK5X35X/5+mbSFJm+g1xMBACIBK4u4Th06FDFxMTo+eefV69evZSRkSFJ8nq9+uCDDzR37lydfvrpio+PdzMmgDbOBs5A60MBDQAAwC1VVVV699139dlnnyk7O1vt27fXmDFjdPnll6tDhw5uxwMAuMS++6p0cL/TaddB5nvXuhsIABARXC2gJScn68Ybb9STTz6padOmacSIERo0aJAWLVqk3bt367TTTtNNN910zPeZNm1areP333+/JKlTp05Bzd1Y0dHRIZED4YNrJrQc2LFV3iPtlOPHKyZE/7tw3aCxuGbQWFwzaCyuGTTGjBkzlJaWpltuuaXW1ysr/397dx5eVXW2f/xeOxMJmUMSZB6DBBQQBJxABS2KiiBCLYhIrVrhV30dSh2qra2v+lprtVJtnaeCdUTqgKiAQ0RFmRTCDDIGAiEQEpKT7PX748BJIkkgcJKdnHw/1+VF1rP3OdzoMrB4zl6rVA8++KBWrVql888/X+3bt1dubq5mz56tRYsW6b777lNSUlI9pwYAeM1u2yz74duBsRk9UaZ5nHeBAAAhw9MGmiQNHTpU7dq107Rp07R06VItXbpUkv/ptHPPPTew6AYAL5Tt2S03N8c/iIxSeNsOnuYBAAAIRT6fTxs2bFBKSkq197z++utavny5/vznP6tjx46B+jnnnKM77rhD//jHP3TnnXfWR1wAQANhrZX77yelslJ/ofOJMqed620oAEDI8Lw7tWDBAr322mvatm2bUlNTlZmZqZUrV2rFihW66667dPrpp+uGG25QZGRkte9x6Emz6uTm5gY7dq0c+sSt1znQeDBnGg576ABiSWrbUbvy9niW5UiYN6gt5gxqizmD2qqLOdOqVaugvRe8VVpaqqKiIuXk5Oi1115TQUFBtffu3btXs2bN0sUXX1ypeSZJCQkJmjhxoh566CEtX75cmZmZdR0dANBA2G8+k7L9H8aX48gZ/2sZx/E2FAAgZHjaQJs+fbreeusttW3bVrfeeqv69esn5+BvcqtWrdL06dOVlZWlZs2a6frrr/cyKoAmyq5fHfjadOjqYRIAAIDQMn/+fP3zn/88qnsXLFggn8+ns88+u8rrffv2VWxsrD7//HMaaADQRNiiQtn/PBsYm3MvkmnTsYZXAABQO5410LZv3663335bmZmZuuOOOw57wiwjI0N33XWX7r77bn366aeaNGlSjU+hAUBdsBvXlA/ad/EuCAAAQIg55ZRTdM899wTGf/vb36q9Nzs7W4mJiWrZsmWV1x3HUdeuXbVy5cpgxwQANFD2nX9L+bv9g4RkmUt+4W0gAEDI8ayBtm7dOllrddlll1XbGAsLC1O/fv20evVqFRYW0kADUK+stVKFBhpPoAEAAARPUlKSkpKSAuOa1nvbtm1Tenp6je+Xnp6uZcuWBS0fAKDhspvWy37838DYjJkkEx3jYSIAQCjyrIEWE+P/Tc3n89V436ZNm5SQkKD4+Pj6iAUA5Tatk/Lz/F9HRUvpnLkCAADghcLCwmqfPjskOjpapaWlKikpOeKHL6dOnVpl/dD52ofO7/NKeHh4g8iBxoM5g2PRWOeNdV3lPXynXOtKkiJP7qfEC0bKGONxstDXWOcMvMOcQW01tDnj2amamZmZSk5O1vTp07V3794q75k3b56++OILjRgxInA2GgDUB/er+XL/7/byQocuHEQMAADgoSP9xai1tp6SAAC8dOCT9+TLPvjEcXi44q69heYZAKBOePYEWmRkpG6++WY98MADmjJligYNGqQOHTqoWbNm2rlzp7755hutXbtWI0aM0EUXXeRVTABNjPWVyP7nGdl575cXIyLljBjnXSgAAIAmLjo6WkVFRTXeU1RUpPDw8KPa+v/Qk2bVyc3NrVW+YDv0iVuvc6DxYM7gWDTGeWML9sp94e+BsTl/pPZENZca0a+hMWuMcwbeYs6gtupizrRqdey7innWQJOkjIwMPfzww3r33Xe1aNEizZ8/X6WlpUpKSlJmZqYmTZqkLl26eBkRQBNid26X++SD0o9ry4tpreT8eqpMm47eBQMAAGjiWrZsqezs7Brv2bFjx3EtjgEADZ996yWpYJ9/kJImc+EYbwMBAEKapw00yX9w9Pjx4zV+/HivowBowuyiBXKfe1Qq2h+omb5nyFz1/ziIGAAAwGNdu3ZVVlaWdu7cqdTU1MOuu66r1atXa+DAgR6kAwDUB7tupexnHwbGzs9/JRMV5WEiAECo40AfAE2aLS2V+59n5P7jf8ubZ2HhMldcK3Pdb2meAQAANAADBgyQ4ziaP39+ldcXLlyogoICnXXWWfWcDABQH6xbJveVJ6RD512efKpM7wHehgIAhDzPn0ADAK/Y3Tvl/ushaW2F7YBS0uRcN1WmY1fvggEAAKCSFi1aaMiQIZo5c6YGDBigtm3bBq7l5eXphRdeUJ8+fdS9e3cPUwIA6oqd97704zr/ICJSzs9/5W0gAECTQAMNQJNkv/9O7jMPl++dLkknnypn0k0yzeO8CwYAAIAqTZgwQRs2bNDdd9+tCy64QK1bt9a2bds0Z84cRUVFafLkyV5HBADUAZufJ/v2K4GxufBymdSWHiYCADQVNNAANCnWLZN9Z7rse6+Vb/3gODKjJsicd6mMw862AAAADVFUVJT+8Ic/aObMmfrss8+Um5uruLg4DRgwQGPHjlXz5s29jggAqAP29efKj1xIayXzs1HeBgIANBk00AA0GTY/T+5Tf5FWLisvJiTLufY2mYwe3gUDAACApk2bdsR7IiIiNHr0aI0ePboeEgEAvGCtlTatk13yjeySr6WNawLXnHHXyUREeJgOANCU0EAD0CTYld/LfeohKT+vvNi9l5xrbpGJT/QsFwAAAAAATZ31lUjZS2WXfC27dKGUl3vYPabfmTKZfTxIBwBoqmigAQh5dt1KuY/8Xior8xeMkblorP8fJ8zbcAAAAAAANEF2b57s0oWyS76RViyWig9UfWNYmNR7gMz4G+o1HwAANNAAhDRrrdwZT5U3z2Lj/U+d9eBTawAAAAAA1BdrrbRl48GnzL6R1q8qP5v8p2JiZXr2lXqdKtPzFJmY2PoNCwCAaKABCHF24ef+P5RLUni4nN/9n0x6K29DAQAAAAAQQqzrSgX50p7dUn6e7J7d/q/37JbNP/j17p3Svvzq3yStlUzv/jIn95e6dJcJY8cYAIC3aKABCFnW55N944XA2Ay5mOYZAAAAAADHyG7fLLvkayk3x98ky8/zN8f25pXv/HK0jCN17S5zcn+ZXqfKtGxTN6EBADhGNNAAhCw797/Srh3+QfM4mQsv9zYQAAAAAACNjN21U3bhZ7Jffyr9uO743iw6RqbHKQe3ZuwrExsfnJAAANQBGmgAQpIt2Cv77n8CY3Pxz9kzHQAAAACAo2D37pH99gvZrz+T1iw/uhc1j5MSk6WEZJmEJP/XickyB2v+cQpbMwIAGg0aaABCkv3vq1Lhfv8grZXM4GHeBgIAAAAAoAGzhftlFy3wP2mWvURy3cNvCo+QTuork9FTJimlvDGWkCQTEVn/oQEAqEM00ACEHJuzVXbee4Gxc9lVMuERHiYCAAAAAKDhscUH5H7zuew3n0rLvpVKfYff5DhS914ypw6S6TNQJqZ5/QcFAMADNNAAhBz3zRfKDy/umin1GehtIAAAAAAAGhC7ca3yX56m4q8+kz1QWPVNXTJl+g+S6Xu6THxiveYDAKAhoIEGIKTY1cul774MjJ3LJ8kY42EiAAAAAAAaBrt9s+zbr8h++4UOVHVDu07+plm/s2RSUus7HgAADQoNNAAhw7qu3NeeDYxN/0EyHTM8TAQAAAAAgPfs7p2ys2bIZn18+Nlm6a1l+p/lX0O3bONNQAAAGiAaaABChl34ubR+lX8QHiEz8kpvAwEAAAAA4CG7L1/2vdf954T/5HyzqAGD1fzyq7QnPoWdWwAAqAINNAAhwfpKZN98MTA2Qy6WaZHuYSIAAAAAALxhiwpl57wt++FMqbio8sXuveSMvFKJp54uSTK5uR4kBACg4aOBBiAk2E/+K+3a4R/ExslcONrbQAAAAAAA1DPrK5Gd+57s+69JBfsqX+yYIWfklTLde3kTDgCARoYGGoBGz+7bK/vua4GxufgKmZhYDxMBAAAAAFB/bFmZbNbHsrNmSHk/eaLshLZyLh0v9RnIVo0AANQCDTQAjZ797wypaL9/kNZKZtAwbwMBAAAAAFAPrOvKfpslO/MVKWdL5YspaTKX/EJm4GAZJ8ybgAAANGI00AA0anb7Ftn57wfGzuiJMuF8awMAAAAAhDa7L1/uU3+RViypfCE+UWb4GJmzfiYTEeFNOAAAQgB/ywygUXPfeEEqK/MPMnpIvQd4GwgAAAAAgDpm16+S+8QDlbdrjG4u87ORMkMvkYlq5l04AABCBA00AI2WXfW9tHhBYOxcPon93AEAAAAAIctaKzv/A9lXn5JKS/1FY/xNs+FjZJrHeRsQAIAQQgMNQKNkXVfua88FxmbAYJkOXT1MBAAAAABA3bElxbIvPyH75SflxZjmcq65Reakft4FAwAgRNFAA9Ao2W8+kzas9g/CI2RGXultIAAAAAAA6ojduV3uE/dLm9aXF9t2lPPr22VSW3oXDACAEEYDDUCjY30lsm++GBiboZfIpKR5mAgAAAAAgLphly2U+/TDUuH+QM2cdq7M+F/LREZ5mAwAgNBGAw1Ao2M/miXt3ukfxMbLXDDa20AAAAAAAASZdctkZ70q+98Z5cXwcJmfXysz6GecAQ4AQB2jgQagUbH78mXffy0wNpdcIRPT3MNEAAAAAAAEl92/z//U2ffflReTW8i5/ncyHTO8CwYAQBNCAw1Ao2JnzZCKCv2Dlq1lzvqZt4EAAAAAAAgiu3Gt/7yzXTvKi917yfnVrTJxCd4FAwCgiaGBBqDRsNs2y376QWDsXDZRJpxvYwAAAACA0OB+8ZHsy09Ipb5AzVwwWubScTJOmIfJAABoevibZwCNgi31yX3mr1JZmb+Q0VPq1d/bUAAAAAAABIH1lchO/5fsZx+WF6Nj5Ey6Sab3QO+CAQDQhNFAA9Ao2Hf+LW1c4x+EhcsZew0HJgMAAAAAGj3rlsl97F4pe2l5sXV7Ob++XSa9lXfBAABo4migAWjw7Iolsh+8GRibUVfKtOvkYSIAAAAAAILDzp9dqXlm+g+WmTBZJqqZh6kAAAANNAANmi3YK/fZRyRr/YXM3jJDR3gbCgAAAACAILD5ebJvvRQYm5+NlLlsIjuuAADQADheBwCA6lhr5b7wuLRnt78QGy/n6ptkHL51AQAAAAAaP/v6c1LRfv8g7QSZEeNongEA0EDwt9AAGiz76Wxp8YLA2Jl4o0xisoeJAAAAAAAIDpu9VHbBvMDYGXe9TESkd4EAAEAlNNAANEh264+y/3k6MDbnDJfpdaqHiQAAAAAACA5b6pP7ypOBsTn1LJnMPh4mAgAAP0UDDUCDY30lcp/6i1RS4i+0bi8zeqKnmQAAAAAACBb74dvS9s3+QbNomTGTPM0DAAAORwMNQINj33hB2rzBPwiPkPOrW2UiozzNBAAAAABAMNid22XffTUwNiPGySSmeJgIAABUhQYagAbFLlso+/GswNiMmSTTur2HiQAAAAAACA5rrdwZT5XvuNK2o8w5w70NBQAAqkQDDUCDYffmyX3u0fJCr/4yZ1/oWR4AAAAAAIJq8VfS0m/8XxsjZ/wNMmFh3mYCAABVooEGoEGwrutvnu3L9xcSkuVc9RsZY7wNBgAAAABAENjiA3Jn/CswNmedL9Opm4eJAABATWigAWgQ7CezpO+/C4ydSTfJxMV7mAgAAAAAgOCxs2ZIu3P9g7gEmVETvA0EAABqRAMNgOfsj+tk33ghMDY/GymT2du7QAAAAAAABJHdslH2o5mBsRk9UaZ5nIeJAADAkdBAA+ApW3xA7lN/kUpL/YV2nWUuHe9tKAAAAAAAgsRaK/eVJ6SyMn+ha6bMaed6GwoAABwRDTQAnrL/eUbavtk/iIyS86tbZcIjvA0FAAAAAECQ2C8/kVYv9w/CwuSMu4HzvgEAaARooAHwjP0uS/bT2YGxueJamZatPUwEAAAAAEDw2P37ZF97LjA2Q0fItG7nYSIAAHC0aKAB8ITdnSv3hccDY9P3DJkzhnqYCAAAAACA4LJvvigV7PUPklvIXPxzbwMBAICjRgMNQL2zbpncZx+RCgv8heQWMldOZgsLAAAAAEDIsGuzZT/7MDB2fn6tTFQzDxMBAIDaoIEGoN7Z/74qrVzmHxhHzi9vkWke620oAAAAAACCxJaVyX3lCclaf+HkU6XeA7wNBQAAaoUGGoB6Zb/Lkp01IzA2w8fIZPTwMBEAAAAAAMFl574rbVrvH0RGyrniWnZdAQCgkaGBBqDe2M3r5T7zSHmhey+Zi8Z6FwgAAAAAgCCze3bJznwlMDbDx8q0SPcwEQAAOBY00ADUC7svX+7j90klxf5Caks5194mExbmbTAAAAAAAILI/udZ6UCRf9Cyjcz5l3qaBwAAHBsaaADqnC0tlfvkg9KuHf5CVLScyXfJxMZ7GwwAAAAAgCCyyxfJfvNZYOyMu14mPMLDRAAA4FjRQANQ5+yrT0mrvvcPjJHzq1tkWrfzNhQAAAAAAEFkS4rlvvLPwNgMPFvmxJM9TAQAAI4HDTQAdcqd957svPcDYzNinEyv/h4mAgAAAAAg+OybL0o7tvoH0c1lLr/a20AAAOC40EADUGfsyu9lZzwVGJtTz5K58HIPEwEAAAAAEHx2+SLZj2cFxubyq2XikzxMBAAAjhcNNAB1wu7cLvfJ+6WyMn+hXWeZq34jY4y3wQAAAAAACCK7f5/c5x4tL/TqL3Pmed4FAgAAQUEDDUDQ2QNFcqfdJxXs8xfiE+VMvkMmKsrbYAAAAAAABJl95Ulpz27/IC5BzoTJfHgUAIAQEO7VT+y6roqKio54nzFGMTEx9ZAIQDBY15X77CPSlo3+Qli4nF/fLpOc6m0wAAAAAACCzP1qvuw3nwXGzoTJbN0IAECI8KyBlpubqylTphzxvtTUVE2bNq0eEgEIBvvfGdKiBYGxGf9rmS7dPUwEAAAAAEDw2d07Zf/9ZGBszjxPpvdADxMBAIBg8qyBlpiYqHvuuafa667r6uGHH1bHjh3rMRWA42G/zZKdNSMwNkMulsO+7wAAAACAEGNdV+7zj0mF+/2FFukyY3/pbSgAABBUnjXQIiMj1aNHj2qvf/HFFyosLNSIESPqMRWAY2U3rfdv3XhI914yl0/yLhAAAAAAAHXEfvJfacUS/8A4cn75PzLNOIIEAIBQ4ngdoDrvvPOOMjMz1bVrV6+jADgCuy9f7rT7pJJifyG1pZzrfisTFuZtMAAAAAAAgsxu/VH2jRcCYzNslEyXTA8TAQCAutAgG2hLly7V+vXrefoMaARsqU/ukw9Iu3b4C82i5Uy5S6Z5nLfBAAAAAAAIMlvqk/vMX6VSn7/QtqPMJVd4GwoAANQJz7ZwrMnMmTPVrl079enTx+soAI7AznhKWvWDf2CMnGtukWnVzttQAAAACGmrVq3SO++8o5UrV2r//v1KSUlR165dNWLECLVv397reABCmJ01Q/pxnX8QHiHnl7fIhEd4GwoAANSJBtdAW7dunZYtW6YpU6Yc9WumTp1aZf3BBx+UJLVo0SIo2Y5VeHh4g8iBxqMhzxlbUixf9jKVLF2o4qULZVcvD1yLHXedmg+50MN0TVtDnjdomJgzqC3mDGqLOYO6MHfuXD355JPq1KmTRo4cqfj4eO3YsUMff/yxpk6dqilTpujMM8/0OiaAEGTXLJd9/43A2Fw2QaY1HyAFACBUNbgG2jvvvKMWLVrojDPO8DoKAEm2rFSla1eqZOlC/z/ZyyRfyWH3RZ05VDGjrvQgIQAAAJqKvXv36plnnlH//v118803yxgTuHbhhRfq97//feB6ZGSkh0kBhBp7oFDus3+TrOsvnHiyzLkXe5oJAADUrQbVQNuxY4cWLFigCRMmKCws7Khfd+hJs+rk5uYeb7TjcugTt17nQOPh5Zyx1kpbf5RdsUQ2e6m06nupqLD6Fxgjc8rp8v38Ou3atav+guIwfK9BbTFnUFvMGdRWXcyZVq1aBe290PhkZ2erpKRE559/fqXmmSQ1a9ZMgwcP1osvvqjc3FzmCoCgsv95Vtq53T+Ibi7n6htlHMfbUAAAoE41qAbaO++8o5iYGJ177rleRwGaDFvqk7Zukt24Rspe6m+a7d1T84vSW8t0P1nmxF5St54ysfH1khUAAABNW1lZmSRpz549VV7PycmR4zhKTk6ux1QAQp1d/JXsZx8Gxmbc9TLJqR4mAgAA9aHBNND27t2refPm6ZJLLlGzZs28jgOEJLtvr7R5veym9dKm9bKb10vbNkkH/yKiWokpMt1Plk7sJXPiyTLJnGMCAACA+te9e3dFRETo5ZdfVtu2bdWhQwdJkuu6mj17tubMmaNzzz2XNSWAoLF798h98fHA2Jx6lkz/QR4mAgAA9aXBNNDef/99SdKwYcM8TgI0ftZ1pR3bAs2yQw0z7TnKLRabx0ndTip/yiy91WFb5AAAAAD1LTExUVdffbWeeuopTZ06VSeddJIyMjK0YMECbdmyReecc44mTZrkdUwAIcJa62+e7cv3FxKT/U+fsT4GAKBJaBANtAMHDujDDz/UOeeco/h4toIDjoW1VjbrE9nPZkubN0jFB47+xaktpTYdZDp3lznxZKltR/ZyBwAAQIM0dOhQtWvXTtOmTdPSpUu1dOlSSf6n084991yFhx95mTt16tQq64fO1z50fp9XDv0avM6BxoM5UzeKPpqlvUu+DowTb7xbUe07epgouJg3qC3mDGqLOYPaamhzpkE00D755BPt379fF198sddRgEbJ5myV+9I0aeWymm+MiJRatZNp18nfMGt78MfomPoJCgAAABynBQsW6LXXXtO2bduUmpqqzMxMrVy5UitWrNBdd92l008/XTfccIMiIyO9jgqgESvdtln7nvlbYBw9fLSievf3LhAAAKh3njfQysrK9O6772rgwIFKS0vzOg7QqNhSn+zst2T/+6pU6qt8MSGpcpOsXScprZVMWJg3YQEAAIDjNH36dL311ltq27atbr31VvXr10/OwZ0TVq1apenTpysrK0vNmjXT9ddfX+37HHrSrDq5ublBzV1bhz5x63UONB7MmeCyxQfkPnK3dKDIX2jZRsUXjA25f7/MG9QWcwa1xZxBbdXFnGnVqtUxv9bzBlpYWJimTZvmdQyg0bHrVvr3Yt+ysbzoODLnjZA571KZhCTvwgEAAABBtn37dr399tvKzMzUHXfccdgTZhkZGbrrrrt0991369NPP9WkSZN4Cg1Ardltm+Q+8YC0bZO/EBYm55f/IxMV5W0wAABQ7zxvoAGoHXugUPatl2XnvitZW36hfRc5EybLtOvsXTgAAACgjqxbt07WWl122WXVNsbCwsLUr18/rV69WoWFhTTQANSK+9V82ZemVTpT3IwYL9Ohq4epAACAV2igAY2IXfKN3H8/Ie2u8AhrZJTMiHEyQy5me0YAAACErJgY/7m9Pp+vxvs2bdqkhIQExcfH10csACHA+kpkZzwt++kH5cXISJlfXC/njKHeBQMAAJ6igQY0AjY/T3b6v2S//aLyhZ6nyBn3a5kW6d4EAwAAAOpJZmamkpOTNX36dHXp0qXKBtm8efP0xRdf6MorrwycjQYANbE7t8t98kHpx7XlxfTWcq6fKtOmg2e5AACA92igAQ2YtVb28zmyrz8nFe4vvxCXIDP2Gpn+g2SM8S4gAAAAUE8iIyN1880364EHHtCUKVM0aNAgdejQQc2aNdPOnTv1zTffaO3atRoxYoQuuugir+MCaATsd1/Kff4xqah8vW1OPUtmwmSZZjEeJgMAAA0BDTSggSrdslHuY/dJq76vVDennSszZpJMLFvSAAAAoGnJyMjQww8/rHfffVeLFi3S/PnzVVpaqqSkJGVmZmrSpEnq0qWL1zEBNHC2tFT2zRdk58wsL4aFy4z9pczZF/JBVQAAIIkGGtDgWNfV/jdfUsGMZyRfSfmF1JZyxt8gk9nbs2wAAACA15KSkjR+/HiNHz/e6ygAGiG7e6fcfz0krc0uL6akybluqkzHrt4FAwAADQ4NNKABsfvy5T7zVxX8sKi86Dgy54+UuejnMlFR3oUDAAAAAKARs99/K/eZv0oF+8qLvfrLufommeax3gUDAAANEg00oIGwq5f7PwW3Z1d5sX0XOROmyLTr5F0wAAAAAAAaMeuWyb4zXfa91yRr/UXHkRk1wf+BVbZsBAAAVaCBBnjMuq7s7Ldk335Jct1APWbUlTpw/iiZsDAP0wEAAAAA0HjZ/Dy5T/1FWrmsvJiYLOdXt8lk9PAuGAAAaPBooAEesgV75T77N2nZwvJi8zgl/s89iup7uopzcz3LBgAAAABAY2Y3rJb7+H1S/u7yYvdecq65RSY+0bNcAACgcaCBBnjErlnh37Ixr0KTrPOJcq69TVEZ3b0LBgAAAABAI2cXf+V/8qyk2F8wxn+2+EVjZBx2egEAAEdGAw2oZ9Za2Q/fln3rRamsLFA3Pxspc+mVMuH8bwkAAAAAwLFyP/mv7IynJXvwmISYWDnX3SaT2cfbYAAAoFHhb+qBemT375P73KPSkq/LizGxcibdJNOrv3fBAAAAAABo5Kzryr7+nOycmeXFFulybrxHpmUb74IBAIBGiQYaUE/s2mz/lo27d5YXO2bIue63Milp3gUDAAAAAKCRsyXFcp95RPouq7zYMUPOlLs47wwAABwTGmhAHbPWyn70juwbz1fesvG8ETKjJsiER3gXDgAAAACARs7uy5f7+J+ldSvLi70HyrnmFpmoKO+CAQCARo0GGlCH7P4Cuc8/Ji1eUF6MaS7n6htleg/0LhgAAAAAACHAbt8i97E/Sju3B2pmyMUyYybJOGEeJgMAAI0dDTQgyOz+fbJLF8ou/kr6YZFUXFR+sUNXOdfeJpPa0ruAAAAAAACEALtmudzH75P27/MXjJEZ80s5Qy/xNhgAAAgJNNCAILA7t8su+Up28dfS6h8k1z3sHjPkYpnRE9myEQAAAACA4+R+87nss49IpT5/ITJSzjW3yvRhtxcAABAcNNCAY2BdV9q4VnbxV7JLvpK2bKz+5rRWci67SuaU0+ovIAAAAAAAIchaKzv7Tdk3XigvxiXImXKXTKdu3gUDAAAhhwYacJSszydlL/U/abbka2nP7upv7tRNpld/md4DpBPayhhTf0EBAAAAAAhBtqxMdvo/Zed/UF5s2VrOb+7hqAQAABB0NNCAI7ClPtnXn5f9/KPK55lVFB4hde8l03uAv3GWkFS/IQEAAAAACGH2QJHcfz0kLVtYXszoIeeGO2Sax3kXDAAAhCwaaMAR2P88Izv3vcMvxMbJnHSq/ymzzN4yzaLrPxwAAAAAACHO7top9x//K/24NlAz/QfJTLxRJoJzxgEAQN2ggQbUwF0wr3LzLLWl/ymz3gOkzt1lwsK8CwcAAAAAQIizS76W++zfpMKCQM1cMFrm0vEyjuNdMAAAEPJooAHVsFs2yr40rbzQ93Q5103lPDMAAAAAAOqYLfXJvvmi7JyZ5UXHkRl3vZxBw7wLBgAAmgwaaEAVbOF+uf+4Xyop9hdatpYz8Tc0zwAAAAAAqGM2N8d/3tn6VeXFpBZyfnWrTNdM74IBAIAmhQYa8BPWWrnPPyrt2OovRDWT8+vbZZrFeBsMAAAAAIAQZxct8K/JC/eXF0/qJ+fqm2Ti4r0LBgAAmhwaaMBP2NlvSosWBMZmwhSZVu08TAQAAAAAQGizPp/sG8/LfjyrvBgWJjNygsx5IzjvDAAA1DsaaEAFduUy2TdfCozNkIvl9B/kYSIAAAAAAEKb3bld7j//T9q4pryYnCrn2ttkOp/oXTAAANCk0UADDrJ5u/x/YLeuv9D5RJnREz3NBAAAAABAKLPffiH3hb9LRYXlxV795Vx9o0zzOO+CAQCAJo8GGiDJlvrk/vNBaV++vxCXIOe6qTLhEd4GAwAAAAAgBFlfiex/npWd9155MSxcZvRVMkMukTHGu3AAAACigQZIkuzrz0trs/0Dx5Fz3W9lklI8zQQAAAAAQCiyOVvl/uv/pB/XlRdT0vwfZO3Y1btgAAAAFdBAQ5PnfjW/0iHFZtQEmW4neZgIAAAAAIDQY11X9pvPZF/+h3SgqPzCKafJuer/ycTEehcOAADgJ2igoUmzW36UffHx8kKfgTLnj/QuEAAAAAAAIcTu2SW7fLG0fLH/x0NHJ0hSeLjMmF/KnH0hWzYCAIAGhwYamixbVCj3ifulkmJ/Ia2VnIk38od2AAAAAACOkS0ullb/ILt8kb9htmVj1TemtvRv2di+c73mAwAAOFo00NAkWWvlPv+YlLPFX4iMknPD7TIxzb0NBgAAAABAI2JdV9q8obxhtnq5VOqr/gWx8TL9zvQfnxAdU285AQAAaosGGpokO+dt6buswNhMmCLTur13gQAAAAAAaMCstdL+fVL+Hil/t+yuHdLKZYdvy/hT4eFSl0yZzD4yPXpLbTrKOE49pQYAADh2NNDQ5NhV38u+8UJgbM4ZLmfAYA8TAQAAAADgDVtaKu3dI+Xn+Rtje/OkPXlSfp7/64N15e+RykqP7k1btZPJ7C2T2UfK6CET1awufwkAAAB1ggYamhS7Z5fcf/6f5Lr+QqduMmMmeRsKAAAAAIA6ZF1X2rNL2r5FNmerlLNFNmeLtH2LtGunZN3j+wli42Uye0uZffyNs6SUoOQGAADwEg00NBm21Cf3nw/5P1knSXEJ/gOLwyM8zQUAAAAAQDDYwv2Vm2M5W2W3b5F2bJVKio//J4iOkRKSpPgkmYQkqW1H/1NmbdmWEQAAhB4aaGgSbHGx3CcfkNYs9xeMI+dXt8okt/A2GAAAAAAAx8EWF2vfy0/qwCfvyc3Lrf0bGCPFJfgbYwnJMgmJUkKylHCwSXawrvgkmaiooOcHAABoqGigIeTZwv1yH/+TtHp5oGZGjpfp3svDVAAAAAAAHB/7/XdyX3lChbk5R745Nk5Kby2T3lpqefDH9NZSWkuZiMi6DwsAANDI0EBDSLN798h99A/Sj+sCNTN8jMywy7wLBQAAAADAcbB798i++ozs1/MrXwiPkNJbSemtftIoayUTG+9NWAAAgEaKBhpClt21U+4jd0s5WwI1c/kkOedf6l0oAAAAAACOkbVW9ouPZF97TiosCNRNbJzirvp/Kjj5VBknzMOEAAAAoYMGGkKS3b7Z3zzbfXD/d+PITJgs58zzvA0GAAAAAMAxsNs3y335CWnlskp1M2CwWlx/m5zEZO3PPYYz0AAAAFAlGmgIOfbHtXL/9gdpX76/EB4u55pbZfqe7mkuAAAAAABqy5b6ZD94Q/bd/0ilpeUXWqTLGfdrmZ6nyElM9i4gAABAiKKBhpBiV/0g9/E/SUWF/kJklJzJd8hk9vE2GAAAAAAAtWRXL5f70jRp26byouPInHepzMVXyERFeRcOAAAgxNFAQ8iwyxbKfeIByVfiL8Q0l/Obe2Q6n+htMAAAAAAAasEWFsi+8YLsp7MrX+jQVc6EKTJtO3oTDAAAoAmhgYaQ4H79qeyzj0hlZf5CQpKcm/4o06aDp7kAAAAAAE2TtTbwtTHmqF9jF34h++pTUn5e+YWoaJmR42XOuVDGCQt2VAAAAFSBBhoaPXf+B7KvPCEdWpykpMm5+V6ZtFbeBgMAAAAANBl2/z4pe5nsisWyK5ZIO7Yd+UWVGmsHv7Zu5Xt69Zfzi+tkklODlhUAAABHRgMNjZr7/uuyb75YXjihrZz/uVcmKcW7UAAAAACAkGd9JdKaFf6G2fIl0o9ryz/YedRvUvH+n7w2MVnOFddKfU476ifYAAAAEDw00NAoWWv9+8HPfrO82KGrnBvvkYmN9y4YAAAAACAkWdeVNq0vf8Js9fLyM7iDKTJK5owhMpdeKRPTPPjvDwAAgKNCAw2NjvWVyM54qvJhyt1OkjPlTplmMd4FAwAAAACEFJufJ7vkK2n5EtmVS6WCfdXfbBypQxeZ7r1lMntJnU6UiYg4/D0PPXVW7dNnRsZxgpIfAAAAx44GGhoNW3xA9tPZsrPfkvJ3l1/oPUDOtbfJRER6Fw4AAAAAEFLst1lyn31EKimu/qaWrWW695Lp3lvq1lMmJvaI7xvYjpFtGQEAABo0Gmho8GxRoey892TnzJT25Ve6ZgaeIzPxNzJhYR6lAwAAAACEEmut7Ow3Zd944fCLcQn+ZllmL5kTe8mkpNZ7PgAAANQPGmhosOz+AtmPZ8l+PEsqLKh8MSFZ5oLRMudcyNYWAAAAAICgsKWlsv9+UvazD8uLqS1lzhku072X1Lp9+RNkAAAACGk00NDg2H35snNmys59VzpQVPlicqrMBZfJnDGULRsBAAAAAEFjCwvkPvmgtGJJeTGjh5xf3y4TG+9dMAAAAHiCBhoaDLtnt+yHb8nO/+DwPeZTW8pceLnMwLNlwg8/hBkAAAAAgGNld26X+/c/Sds2BWpm4DkyE6bIRLAGBQAAaIpooMFzdvdO2Q/ekP1sjlTqq3zxhLb+xtmpZ3HOGQAAAAAg6OzabLnT7qt05rYZ8QuZ4WPZrhEAAKAJo4EGz9iCvbJvviib9YlUVlr5YpuOci4aI/U5jTPOAAAAAAB1wi78XO6zf5N8Jf5CeLjMxBvlDBjsaS4AAAB4jwYaPGFztsp97I/Sjm2VL3ToKueisdLJp/JJPwAAAABAnbDWyr7/uuxbL5UXY+PkTL5Tpkumd8EAAADQYNBAQ72za5b7t8co2Fde7JLpb5xl9qZxBgAAAACoM7bUJ/vyE7JffFReTG8t5ze/l0lr5V0wAAAANCg00FCv3G8+l332kfKzziIj5Vx9k0y/M70NBgAAAAAIeXZ/gdwn7pdWLisvdjtJzq9/J9M8zrtgAAAAaHAaTAPtyy+/1EcffaT169eruLhYaWlp6tevn4YNG6aUlBSv4+E4WWtlZ78p+8YL5cW4BDlT7pLp1M27YAAAAACAJsHu3C73sXul7ZsDNXPauTITJsuER3iYDAAAAA2R5w20srIy/f3vf1dWVpZ69uypcePGKTw8XCtWrND7778vn8+niRMneh0Tx8GWlclO/6fs/A/Kiy1by/nNPTKpLb0LBgAAAABoEuzq5XL/8b9Swd5AzVw6XubCyzlGAAAAAFXyvIH2zDPPKCsrS1dddZWGDx8eqA8ePFijRo1Sdna2h+lwvOyBIrn/ekhatrC8mNFDzg13sD0GAAAAACDo7J7d0sa1shvXyP64Vtq4Rtqzu/yG8AiZSTfJOfUs70ICAACgwfO0gbZixQp99NFHGjZsWKXm2SFpaWlKS0vzIBmCwe7ZJffvf5J+XBeomf6DZSb+RiaC7TEAAAAAAMfniM2yn4qNlzP5Tpku3esvJAAAABolTxtos2bNUlRUlMaMGeNlDNQBu3mDf2/5vNxAzQwfIzNiHNtjAAAAAABqzbqulL1Uds2Ko2uWVRQZJXU7Sc4V13KUAAAAAI6KZw00n8+nxYsXq1+/foqNjfUqBuqAXb5I7hMPSAeK/AXHkRl/g5yzzvc2GAAAAACg0bHFxbJffiw75x1px9YjvyAySmrXSaZ9F6ldZ/+PJ7SWccLqPiwAAABChmcNtA0bNqi0tFTdu1feNqGsrEzWWoWHe348G46B+/kc2Zf/IZWV+QvNouVc/zuZHn28DQYAAAAgpHz55Zf66KOPtH79ehUXFystLU39+vXTsGHDlJKS4nU8BIHNz5P95F3Z+e9L+/dVfdNPm2UdukgtaZYBAADg+HnWpcrLy5MknXDCCSoqKtLs2bM1b9485eTkqKysTG3atNGwYcN03nnnHXHLv6lTp1ZZf/DBByVJLVq0CG74WjrUDPQ6R12y1mr/9Ke0/7XnAzUnJU2Jd/1FER26eBeskWoKcwbBx7xBbTFnUFvMGdQWcwZ1oaysTH//+9+VlZWlnj17aty4cQoPD9eKFSv0/vvvy+fzaeLEiV7HxHGwmzfIfjRT9qv5Umlp5YsxzWX6D5Y6dZNp35lmGQAAAOqMZw204uJiSdLu3bv129/+VpI0ZMgQtWnTRgUFBZo3b56efvpprVmzRjfccINXMXEUynbtVMFL/9CB+bMDtfCOXZV4518UlpLqYTIAAAAAoeaZZ55RVlaWrrrqKg0fPjxQHzx4sEaNGqXs7GwP0+FYWWul5Yvlfvi2tHzR4Te0SJcZOkLmjCEyzaLrPR8AAACaHs8aaNHR/j/wPvXUU7rssss0cuRIhYWVf2rs7LPP1uOPP6558+Zp0KBB6tmzZ7XvdehJs+rk5uYGJ/QxOvSJW69zBIMtKZZ+XCu7bqXsupXSulVS3k9+XT37yr3uNuVZI4XAr9kLoTRnUH+YN6gt5gxqizmD2qqLOdOqVaugvRcanxUrVuijjz7SsGHDKjXPDklLS1NaWpoHyXCsrM8n+/V82TkzpS0bD7+h84lyzrtU6jOAJ80AAABQrzxroKWm+p9MGj58uEaPHl3lPVdccYU+/fRTLV68uMYGGuqGtVbKzTnYKDvYMNu0XiorrfY1ZtAwmV9cJxPGwgYAAABAcM2aNUtRUVEaM2aM11FwnGzBXtn5H8jOfVfKz6t80TjSKQPlnHepTOcTvQkIAACAJs+zBlrbtm3VvHlzFRYWVntPSkqKIiIiarwHwWMPFErrV/ufLlu/Slq3UtqXf+QXRkZJHTNkTjtH5vQhRzyzDgAAAABqy+fzafHixerXr59iY2O9joNasiXF/g9mrvpedtUP0tpsqdRX+aaoZjJnnicz5GKZ1JbeBAUAAAAO8qyB5jiOzj77bM2dO1ejR49WcnLyYfds3bpVPp+PbVrqgHVdKWdL5afLtvwoWffIL27ZRqZTN/+hzZ26Sa3a8cQZAAAAgDq1YcMGlZaWqnv37pXqZWVlstYqPNyz5S2qYIsPSGtXyK78QXbV99KGVVJpNbuZJKbIDLlIZtDPZGJojgIAAKBh8HSFcemllyorK0uPPvqobr/9djVr1ixwraSkRM8995yio6N15plnepgyNNj9+6T1q/xPl61dKa1fJRXtP/ILY5r7ny471CzrmCHTPK7uAwMAAABABXl5/m3+TjjhBBUVFWn27NmaN2+ecnJyVFZWpjZt2mjYsGE677zzjrgrxtSpU6usHzpf+9D5fV451Az0OkdtuIX75VuxVCU/LJLvh0Xyrc2WyspqfE14p26KuXismp0xRCYiop6ShqbGOGfgPeYNaos5g9pizqC2Gtqc8bSBlpCQoNtuu03333+/brnlFg0dOlTp6enKycnRJ598ory8PN18881KTEz0MmajY90yacuP/qfK1mbLrl8pbd9y5BcaR2rdTqbTieVPl6W3knGcug8NAAAAADUoLi6WJO3evVu//e1vJUlDhgxRmzZtVFBQoHnz5unpp5/WmjVrdMMNN3gZNWRZ15W7Z7fcXTtUtmun/8ecrSpZsUSl61ZJbs07moS16aDIHn0U2aO3Inr0Vlhyaj0lBwAAAGrP8z0uunTpooceekhvvvmmPvzwQ+3Zs0fx8fE66aSTNGrUKLZvPAq2uFjasEp29XLZNcv9e8kfKDryC+MSAo0y06mb1KGLTLOYug8MAAAAALUUHR0tSXrqqad02WWXaeTIkQqrsJX82Wefrccff1zz5s3ToEGD1LNnz2rf69CTZtXJzc0NTuhjdOgTt/WZw5aVSfl5Ul6utGeXbF6utDtXyjv4dd4uKX/3EZ8qq6R1e5mMnjLdekpde0jxiSqRVCJJriSP/z2HEi/mDBo/5g1qizmD2mLOoLbqYs4cT4/J8waaJCUnJ+uaa67RNddc43WURsHu2yutXe5vmK1eLv249siLmLAwqW2nymeXtUg/4tYmAAAAANAQpKb6n1YaPny4Ro8eXeU9V1xxhT799FMtXry4xgZaU2OLCqU9u/zNsIM/as/uSl9r756jOxO7OsZIbTr4G2YZ/oaZiYsP2q8BAAAAqG8NooGG6llrpZ3b/U+WrVnhb5ht33zkFyYkS51PlOl8sFnWrrNMZFTdBwYAAACAOtC2bVs1b95chYWF1d6TkpKiiIiIGu8JNbasTNq9079uzN0u5eZIeQebY3t2SXm7peKj2KHkaMXGSYktpKQUmaQWUlKyTNtOUpdMmeaxwft5AAAAAI/RQPOIdV2paL+0N1/at0faly9b1dc7tvm30TiSE9rKdM30L1q6dOfpMgAAAAAhxXEcnX322Zo7d65Gjx6t5OTkw+7ZunWrfD5fyB0FYIsKpZ3bpdztsju3+5tlB3/U7p2121axJnEJUlLF5liKlNyi/OvEFD6YCQAAgCaDBlods5vWa987r8jNz1NZ7o6DDbN8qSD/2Bc5YeH+88q6ZPqbZp1PlIllawwAAAAAoe3SSy9VVlaWHn30Ud1+++1q1qxZ4FpJSYmee+45RUdH68wzz/Qw5fGxOVtV8OGbKtu+RWWbN/qbZAV7j+9NIyKlxGR/YyzR3whTUrKUkCKTlOwfJyTLREQE5xcBAAAAhAAaaHVt1w4Vznr1+N4jOkbq3F2mS3d/w6xDVz71BwAAAKDJSUhI0G233ab7779ft9xyi4YOHar09HTl5OTok08+UV5enm6++WYlJiZ6HfXY5e/W/teer91rEpKl1JYyqelSi5b+p8YSDzbGklKkmFh2KAEAAABqiQZaXYtLqP5adIz/elyCFJcoE3/w6/jE8nF8kpR+gowTVm+RAQAAAKCh6tKlix566CG9+eab+vDDD7Vnzx7Fx8frpJNO0qhRoxr/9o2pJxxeC4+QWqQfbJK1lFLTZVJP8DfLWqTLRPEBSwAAACDYaKDVtbRWip1wg5yEJBWYcOlQkywuQSYi0ut0AAAAANDoJCcn65prrtE111zjdZTgS0hS88snKiy9lQqaxUqpLaXEZBnH8ToZAAAA0KTQQKtjJi5ezUeOlyTtz831OA0AAAAAoCEzjqPYX1wriTUkAAAA4CU+wgYAAAAAAAAAAABUQAMNAAAAAAAAAAAAqIAGGgAAAAAAAAAAAFABDTQAAAAAAAAAAACgAhpoAAAAAAAAAAAAQAU00AAAAAAAAAAAAIAKaKABAAAAAAAAAAAAFdBAAwAAAAAAAAAAACqggQYAAAAAAAAAAABUQAMNAAAAAAAAAAAAqIAGGgAAAAAAAAAAAFABDTQAAAAAAAAAAACgAhpoAAAAAAAAAAA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      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 320,
       "width": 872
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history) # 3단계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d2266cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = model.predict(x_test)\n",
    "y_pred = np.argmax(predicted, axis=1)\n",
    "Y_test = np.argmax(y_test, axis=1)\n",
    "cm = confusion_matrix(Y_test, y_pred)\n",
    "report = classification_report(Y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "98da619a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, names, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(names))\n",
    "    plt.xticks(tick_marks, names, rotation=45)\n",
    "    plt.yticks(tick_marks, names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "\n",
    "# Plot an ROC. pred - the predictions, y - the expected output.\n",
    "def plot_roc(pred,y):\n",
    "    fpr, tpr, _ = metrics.roc_curve(y, pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8157a9bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 397
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC 값 : 0.5000\n"
     ]
    }
   ],
   "source": [
    "# 3단계\n",
    "plot_roc(y_pred,Y_test)\n",
    "from sklearn.metrics import roc_auc_score\n",
    "roc_score = metrics.roc_auc_score(Y_test ,y_pred)\n",
    "print('ROC AUC 값 : {0:.4f}'.format(roc_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c84eec3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tn = cm[0][0]\n",
    "fn = cm[1][0]\n",
    "tp = cm[1][1]\n",
    "fp = cm[0][1]\n",
    "if tp == 0:\n",
    "    tp = 1\n",
    "if tn == 0:\n",
    "    tn = 1\n",
    "if fp == 0:\n",
    "    fp = 1\n",
    "if fn == 0:\n",
    "    fn = 1\n",
    "TPR = float(tp)/(float(tp)+float(fn))\n",
    "FPR = float(fp)/(float(fp)+float(tn))\n",
    "accuracy = round((float(tp) + float(tn))/(float(tp) +\n",
    "                                          float(fp) + float(fn) + float(tn)), 3)\n",
    "specitivity = round(float(tn)/(float(tn) + float(fp)), 3)\n",
    "sensitivity = round(float(tp)/(float(tp) + float(fn)), 3)\n",
    "mcc = round((float(tp)*float(tn) - float(fp)*float(fn))/math.sqrt(\n",
    "    (float(tp)+float(fp))\n",
    "    * (float(tp)+float(fn))\n",
    "    * (float(tn)+float(fp))\n",
    "    * (float(tn)+float(fn))\n",
    "), 3)\n",
    "\n",
    "f_output = open('binary_lstm_open_close_phase3_dropout_batch_Normal_3단계 test.txt', 'a')\n",
    "f_output.write('=======\\n')\n",
    "f_output.write('{}epochs_{}batch\\n'.format(\n",
    "    25, 10))\n",
    "f_output.write('TN: {}\\n'.format(tn))\n",
    "f_output.write('FN: {}\\n'.format(fn))\n",
    "f_output.write('TP: {}\\n'.format(tp))\n",
    "f_output.write('FP: {}\\n'.format(fp))\n",
    "f_output.write('TPR: {}\\n'.format(TPR))\n",
    "f_output.write('FPR: {}\\n'.format(FPR))\n",
    "f_output.write('accuracy: {}\\n'.format(accuracy))\n",
    "f_output.write('specitivity: {}\\n'.format(specitivity))\n",
    "f_output.write(\"sensitivity : {}\\n\".format(sensitivity))\n",
    "f_output.write(\"mcc : {}\\n\".format(mcc))\n",
    "f_output.write(\"{}\".format(report))\n",
    "f_output.write('=======\\n')\n",
    "f_output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b6ad3880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "858\n",
      "858\n"
     ]
    }
   ],
   "source": [
    "print(len(y_test))\n",
    "print(len(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c145bf02",
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_book_df = test_sample_df[['PF_returns','BM_returns']].copy()\n",
    "\n",
    "t1 = pd.DataFrame(data = y_pred,columns=['position'],index = lstm_book_df.index[12:])\n",
    "lstm_book_df = lstm_book_df.join(t1,how='left')\n",
    "lstm_book_df.fillna(0,inplace=True)\n",
    "lstm_book_df['ret'] = lstm_book_df['PF_returns'].pct_change()\n",
    "lstm_book_df['lstm_ret'] = lstm_book_df['BM_returns'] * lstm_book_df['position'].shift(1)\n",
    "lstm_book_df['lstm_cumret'] = (lstm_book_df['lstm_ret'] + 1).cumprod()\n",
    "lstm_book_df['bm_cumret'] = (lstm_book_df['ret'] + 1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "842661ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 372
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "book_df = train_sample_df[['PF_returns', 'BM_returns']]\n",
    "book_df['PF_returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7e8d1e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 372
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "book_df['BM_returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c53e0665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 367
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_book_df['BM_returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d22e9084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 367
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lstm_book_df['PF_returns'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ec95f10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 367
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "historical_max = lstm_book_df['PF_returns'].cummax()\n",
    "daily_drawdown = lstm_book_df['PF_returns'] / historical_max - 1.0\n",
    "historical_dd = daily_drawdown.cummin()\n",
    "historical_dd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c33b25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}