vix_adaboost_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5be0a8e",
   "metadata": {},
   "source": [
    "# VIX Predictor using an adaBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fea938cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import appropriate modules\n",
    "\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "import datetime\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from datetime import datetime\n",
    "from pandas.tseries.offsets import DateOffset\n",
    "import hvplot\n",
    "import hvplot.pandas\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from vix_functions import garch_fit_and_predict, correlation_filter, retrieve_yahoo_close, retrieve_yahoo_volume\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a41e8d7",
   "metadata": {},
   "source": [
    "# CONTROL PANEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "269ef04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Key parameters of the model\n",
    "\n",
    "# Min return to set up a positive signal\n",
    "threshold= 0.00 \n",
    "\n",
    "# Split of data\n",
    "training_period_months = 120\n",
    "\n",
    "# Adaboost parameters\n",
    "adaboost_estimators = 22\n",
    "learning_rate_adaboost = 1\n",
    "\n",
    "\n",
    "# Inclusion of the first 4 components lag1\n",
    "#n: number of components to include\n",
    "number_of_pca_lag_component_to_include = 4\n",
    "num_pca_components = 40\n",
    "\n",
    "\n",
    "# Definition of demo mode or development mode\n",
    "demo_mode = True\n",
    "parameter_tuning_mode = False\n",
    "run_multiple_tuning_iterations = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e74104",
   "metadata": {},
   "source": [
    "# Generation of the Features Matrix X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daadf403",
   "metadata": {},
   "source": [
    "### X1: close prices\n",
    "#### 40 units: \n",
    "    * Close prices of international indexes of stocks and bonds, \n",
    "    * key stocks, \n",
    "    * currency exchange rates, \n",
    "    * commodities "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "007f1520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current number of tickers: 40\n"
     ]
    }
   ],
   "source": [
    "# Ticker List: VIX must be in first position\n",
    "ticker_list= [\"^VIX\", \"spy\", 'XLF', 'XLE',\n",
    "              'EURUSD=X', 'GBPUSD=X', 'AUDUSD=X', 'BRLUSD=X', \"DX-Y.NYB\",\"USDJPY=X\", \n",
    "              '^TNX', 'ZB=F', 'ZF=F', 'NQ=F','NKD=F',                                       \n",
    "              'LQD',\n",
    "              'AAPL', 'AMZN', 'GE','MU','MSFT', 'BMY', 'FDX', 'GS','PLD','NVDA',   \n",
    "              \"tlt\", \"ief\", \n",
    "              \"FXI\", \"EZU\", \"EEM\", \"EFA\", 'FEZ', \"^GDAXI\", '^FTSE','^HSI','^FCHI',              #'^GSPC',\n",
    "              \"gld\", \"slv\", \"CL=F\"]\n",
    "    \n",
    "# Some of the less familiar tickers are listed below\n",
    "# CAC 40 (^FCHI)\n",
    "# Yen Denominated TOPIX Futures,D (TPY=F)\n",
    "# FTSE 100 (^FTSE)\n",
    "# SPDR EURO STOXX 50 ETF (FEZ)\n",
    "# DAX PERFORMANCE-INDEX (^GDAXI)\n",
    "# S&P500 Index (^GSPC)\n",
    "# HANG SENG INDEX (^HSI)\n",
    "# Nikkei/USD Futures,Dec-2021 (NKD=F)\n",
    "# iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD)\n",
    "# Nasdaq 100 Dec 21 (NQ=F)\n",
    "# NVIDIA Corporation (NVDA)\n",
    "# Euro spot  'EURUSD'\n",
    "# Treasury Yield 10 Years (^TNX) -- 1985\n",
    "# American Funds U.S. Government Securities Fund Class C (UGSCX) - 2001\n",
    "# 13 Week Treasury Bill (^IRX) --eliminate\n",
    "# Micron Technology, Inc. (MU)\n",
    "# Microsoft Corporation (MSFT)\n",
    "# Bristol-Myers Squibb Company (BMY)\n",
    "# FEDEX CORP (FDX)\n",
    "# The Goldman Sachs Group, Inc. (GS)\n",
    "# Prologis, Inc. (PLD)\n",
    "# Energy Select Sector SPDR Fund (XLE)\n",
    "# Financial Select Sector SPDR Fund (XLF)\n",
    "# U.S. Treasury Bond Futures,Dec- (ZB=F) - 2000\n",
    "# Five-Year US Treasury Note Futu (ZF=F) - 2000\n",
    "print(f\"Current number of tickers: {len(ticker_list)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7ab40254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed retrieve of close prices\n"
     ]
    }
   ],
   "source": [
    "# X1: Upload of data using API\n",
    "def retrieve_close(close_prices_dict, ticker_list):\n",
    "    \"\"\"\n",
    "    This function retrieves close prices from Yahoo Finance\n",
    "    \n",
    "    Arg:\n",
    "    close_prices_dict: empty dictionary to be filled with the close prices\n",
    "    ticker_list: a list of tickers of the yahoo close prices to be retrieve\n",
    "    \n",
    "    Return:\n",
    "    A dictionary with close prices\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    for ticker in ticker_list:\n",
    "        close_price = retrieve_yahoo_close(ticker, start_date='2006-07-02', end_date='2021-10-02')\n",
    "        close_prices_dict[ticker] = close_price\n",
    "    return close_prices_dict\n",
    "\n",
    "\n",
    "if demo_mode == True:\n",
    "    close_prices_df = pd.read_csv(\"adaboost_close_prices.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "else:\n",
    "    close_prices_dict = {}\n",
    "    close_prices_dict = retrieve_close(close_prices_dict, ticker_list)\n",
    "    close_prices_df= pd.DataFrame(close_prices_dict)\n",
    "    close_prices_df.to_csv(\"adaboost_close_prices.csv\", index=True)\n",
    "print(\"Completed retrieve of close prices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b98e9214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of close prices\n"
     ]
    }
   ],
   "source": [
    "# X1 Fill of missing values\n",
    "close_prices_df=close_prices_df.ffill(axis='rows')\n",
    "close_prices_df=close_prices_df.bfill(axis='rows')\n",
    "\n",
    "# Apply correlation filter to keep just series with some minimum level of correlation of 0.2\n",
    "close_prices_component_df = correlation_filter(close_prices_df, min_corr=0.20, key_column='^VIX', eliminate_first_column=True)\n",
    "\n",
    "X1=close_prices_component_df\n",
    "vix=close_prices_df['^VIX']\n",
    "vix_ret=close_prices_df['^VIX'].pct_change()\n",
    "VIX=pd.DataFrame([vix, vix_ret]).T\n",
    "VIX.columns=['VIX','VIX_ret']\n",
    "\n",
    "X1_no_suffix=pd.concat([VIX,close_prices_component_df], axis=1)\n",
    "\n",
    "X1=X1_no_suffix.add_suffix(\"_close\")\n",
    "\n",
    "print(\"Completed inclusion of close prices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a5f899c9",
   "metadata": {},
   "outputs": [
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AAAzwxtcUIAAMA0X21xQgAAAGZWbnFCAADAy6hucUIAAIAx+25xQgAAQJdNb3FCAAAA/Z9vcUIAAEAul3BxQgAAAJTpcHFCAADA+TtxcUIAAIBfjnFxQgAAQMXgcXFCAACA9tdycUIAAEBcKnNxQgAAAMJ8c3FCAADAJ89zcUIAAICNIXRxQgAAwL4YdXFCAACAJGt1cUIAAECKvXVxQgAAAPAPdnFCAADAVWJ2cUIAAACHWXdxQgAAwOyrd3FCAACAUv53cUIAAEC4UHhxQgAAAB6jeHFCAABAT5p5cUIAAAC17HlxQgAAwBo/enFCAACAgJF6cUIAAEDm43pxQgAAgBfbe3FCAABAfS18cUIAAADjf3xxQgAAwEjSfHFCAACAriR9cUIAAMDfG35xQgAAgEVufnFCAABAq8B+cUIAAAARE39xQgAAwHZlf3FCAAAAqFyAcUIAAMANr4BxQgAAgHMBgXFCAABA2VOBcUIAAAA/poFxQgAAQHCdgnFCAAAA1u+CcUIAAMA7QoNxQgAAgKGUg3FCAABAB+eDcUIAAIA43oRxQgAAQJ4whXFCAAAABIOFcUIAAMBp1YVxQgAAgM8nhnFCAADAAB+HcUIAAIBmcYdxQgAAQMzDh3FCAAAAMhaIcUIAAMCXaIhxQgAAAMlfiXFCAADALrKJcUIAAICUBIpxQgAAQPpWinFCAAAAYKmKcUIAAECRoItxQgAAAPfyi3FCAADAXEWMcUIAAIDCl4xxQgAAQCjqjHFCAACAWeGNcUIAAEC/M45xQgAAACWGjnFCAADAitiOcUIAAIDwKo9xQgAAwCEikHFCAACAh3SQcUIAAEDtxpBxQgAAAFMZkXFCAADAuGuRcUIAAADqYpJxQgAAwE+1knFCAACAtQeTcUIAAEAbWpNxQgAAAIGsk3FCAABAsqOUcUIAAAAY9pRxQgAAwH1IlXFCAACA45qVcUIAAEBJ7ZVxQgAAgHrklnFCAABA4DaXcUIAAABGiZdxQgAAwKvbl3FCAACAES6YcUIAAMBCJZlxQgAAgKh3mXFCAABADsqZcUIAAAB0HJpxQgAAwNlumnFCAAAAC2abcUIAAMBwuJtxQgAAgNYKnHFCAABAPF2ccUIAAACir5xxQgAAQNOmnXFCAAAAOfmdcUIAAMCeS55xQgAAgASennFCAABAavCecUIAAICb559xQgAAQAE6oHFCAAAAZ4ygcUIAAMDM3qBxQgAAgDIxoXFCAADAYyiicUIAAIDJeqJxQgAAQC/NonFCAAAAlR+jcUIAAMD6caNxQgAAACxppHFCAADAkbukcUIAAID3DaVxQgAAQF1gpXFCAAAAw7KlcUIAAED0qaZxQgAAAFr8pnFCAADAv06ncUIAAIAloadxQgAAQIvzp3FCAACAvOqocUIAAEAiPalxQgAAAIiPqXFCAADA7eGpcUIAAIBTNKpxQgAAwIQrq3FCAACA6n2rcUIAAEBQ0KtxQgAAALYirHFCAADAG3WscUIAAABNbK1xQgAAwLK+rXFCAACAGBGucUIAAEB+Y65xQgAAAOS1rnFCAABAFa2vcUIAAAB7/69xQgAAwOBRsHFCAACARqSwcUIAAECs9rBxQgAAgN3tsXFCAABAQ0CycUIAAACpkrJxQgAAwA7lsnFCAACAdDezcUIAAMClLrRxQgAAgAuBtHFCAABAcdO0cUIAAADXJbVxQgAAwDx4tXFCAAAAbm+2cUIAAMDTwbZxQgAAgDkUt3FCAABAn2a3cUIAAAAFubdxQgAAQDawuHFCAAAAnAK5cUIAAMABVblxQgAAgGenuXFCAABAzfm5cUIAAID+8LpxQgAAQGRDu3FCAAAAypW7cUIAAMAv6LtxQgAAgJU6vHFCAADAxjG9cUIAAIAshL1xQgAAQJLWvXFCAAAA+Ci+cUIAAMBde75xQgAAAI9yv3FCAADA9MS/cUIAAIBaF8BxQgAAQMBpwHFCAAAAJrzAcUIAAEBXs8FxQgAAAL0FwnFCAADAIljCcUIAAICIqsJxQgAAQO78wnFCAACAH/TDcUIAAECFRsRxQgAAAOuYxHFCAADAUOvEcUIAAIC2PcVxQgAAwOc0xnFCAACATYfGcUIAAECz2cZxQgAAABksx3FCAADAfn7HcUIAAACwdchxQgAAwBXIyHFCAACAexrJcUIAAEDhbMlxQgAAAEe/yXFCAABAeLbKcUIAAADeCMtxQgAAwENby3FCAACAqa3LcUIAAEAPAMxxQgAAgED3zHFCAABApknNcUIAAAAMnM1xQgAAwHHuzXFCAACA10DOcUIAAMAIOM9xQgAAgG6Kz3FCAABA1NzPcUIAAAA6L9BxQgAAwJ+B0HFCAAAA0XjRcUIAAMA2y9FxQgAAgJwd0nFCAABAAnDScUIAAABowtJxQgAAQJm503FCAAAA/wvUcUIAAMBkXtRxQgAAgMqw1HFCAABAMAPVcUIAAIBh+tVxQgAAQMdM1nFCAAAALZ/WcUIAAMCS8dZxQgAAgPhD13FCAADAKTvYcUIAAICPjdhxQgAAQPXf2HFCAAAAWzLZcUIAAMDAhNlxQgAAAPJ72nFCAADAV87acUIAAIC9INtxQgAAQCNz23FCAAAAicXbcUIAAEC6vNxxQgAAACAP3XFCAADAhWHdcUIAAIDrs91xQgAAQFEG3nFCAACAgv3ecUIAAEDoT99xQgAAAE6i33FCAADAs/TfcUIAAIAZR+BxQgAAwEo+4XFCAACAsJDhcUIAAEAW4+FxQgAAAHw14nFCAADA4YficUIAAAATf+NxQgAAwHjR43FCAACA3iPkcUIAAEBEduRxQgAAAKrI5HFCAABA27/lcUIAAABBEuZxQgAAwKZk5nFCAACADLfmcUIAAEByCedxQgAAgKMA6HFCAABACVPocUIAAABvpehxQgAAwNT36HFCAACAOkrpcUIAAMBrQepxQgAAgNGT6nFCAABAN+bqcUIAAACdOOtxQgAAwAKL63FCAAAANILscUIAAMCZ1OxxQgAAgP8m7XFCAABAZXntcUIAAADLy+1xQgAAQPzC7nFCAAAAYhXvcUIAAMDHZ+9xQgAAgC2673FCAABAkwzwcUIAAIDEA/FxQgAAQCpW8XFCAAAAkKjxcUIAAMD1+vFxQgAAgFtN8nFCAADAjETzcUIAAIDylvNxQgAAQFjp83FCAAAAvjv0cUIAAMAjjvRxQgAAAFWF9XFCAADAutf1cUIAAIAgKvZxQgAAQIZ89nFCAAAA7M72cUIAAEAdxvdxQgAAAIMY+HFCAADA6Gr4cUIAAIBOvfhxQgAAQLQP+XFCAACA5Qb6cUIAAEBLWfpxQgAAALGr+nFCAADAFv76cUIAAIB8UPtxQgAAwK1H/HFCAACAE5r8cUIAAEB57PxxQgAAAN8+/XFCAADARJH9cUIAAAB2iP5xQgAAwNva/nFCAACAQS3/cUIAAECnf/9xQgAAAA3S/3FCAABAPskAckIAAACkGwFyQgAAwAluAXJCAACAb8ABckIAAEDVEgJyQgAAgAYKA3JCAABAbFwDckIAAADSrgNyQgAAwDcBBHJCAACAnVMEckIAAMDOSgVyQgAAgDSdBXJCAABAmu8FckIAAAAAQgZyQgAAwGWUBnJCAAAAl4sHckIAAMD83QdyQgAAgGIwCHJCAABAyIIIckIAAAAu1QhyQgAAQF/MCXJCAAAAxR4KckIAAMAqcQpyQgAAgJDDCnJCAABA9hULckIAAIAnDQxyQgAAQI1fDHJCAAAA87EMckIAAMBYBA1yQgAAgL5WDXJCAADA700OckIAAIBVoA5yQgAAQLvyDnJCAAAAIUUPckIAAMCGlw9yQgAAALiOEHJCAADAHeEQckIAAICDMxFyQgAAQOmFEXJCAAAAT9gRckIAAECAzxJyQgAAAOYhE3JCAADAS3QTckIAAICxxhNyQgAAQBcZFHJCAACASBAVckIAAECuYhVyQgAAABS1FXJCAADAeQcWckIAAIDfWRZyQgAAwBBRF3JCAACAdqMXckIAAEDc9RdyQgAAAEJIGHJCAADAp5oYckIAAADZkRlyQgAAwD7kGXJCAACApDYackIAAEAKiRpyQgAAAHDbGnJCAABAodIbckIAAAAHJRxyQgAAwGx3HHJCAACA0skcckIAAEA4HB1yQgAAgGkTHnJCAABAz2UeckIAAAA1uB5yQgAAwJoKH3JCAACAAF0fckIAAMAxVCByQgAAgJemIHJCAABA/fggckIAAABjSyFyQgAAwMidIXJCAAAA+pQickIAAMBf5yJyQgAAgMU5I3JCAABAK4wjckIAAACR3iNyQgAAQMLVJHJCAAAAKCglckIAAMCNeiVyQgAAgPPMJXJCAABAWR8mckIAAICKFidyQgAAQPBoJ3JCAAAAVrsnckIAAMC7DShyQgAAgCFgKHJCAADAUlcpckIAAIC4qSlyQgAAQB78KXJCAAAAhE4qckIAAMDpoCpyQgAAABuYK3JCAADAgOorckIAAIDmPCxyQgAAQEyPLHJCAAAAsuEsckIAAEDj2C1yQgAAAEkrLnJCAADArn0uckIAAIAU0C5yQgAAQHoiL3JCAACAqxkwckIAAEARbDByQgAAAHe+MHJCAADA3BAxckIAAIBCYzFyQgAAwHNaMnJCAACA2awyckIAAEA//zJyQgAAAKVRM3JCAADACqQzckIAAAA8mzRyQgAAwKHtNHJCAACAB0A1ckIAAEBtkjVyQgAAANPkNXJCAABABNw2ckIAAABqLjdyQgAAwM+AN3JCAACANdM3ckIAAECbJThyQgAAgMwcOXJCAABAMm85ckIAAACYwTlyQgAAwP0TOnJCAACAY2Y6ckIAAMCUXTtyQgAAgPqvO3JCAABAYAI8ckIAAADGVDxyQgAAwCunPHJCAAAAXZ49ckIAAMDC8D1yQgAAgChDPnJCAABAjpU+ckIAAAD05z5yQgAAQCXfP3JCAAAAizFAckIAAMDwg0ByQgAAgFbWQHJCAABAvChBckIAAIDtH0JyQgAAQFNyQnJCAAAAucRCckIAAMAeF0NyQgAAgIRpQ3JCAADAtWBEckIAAIAbs0RyQgAAQIEFRXJCAAAA51dFckIAAMBMqkVyQgAAAH6hRnJCAADA4/NGckIAAIBJRkdyQgAAQK+YR3JCAAAAFetHckIAAEBG4khyQgAAAKw0SXJCAADAEYdJckIAAIB32UlyQgAAQN0rSnJCAACADiNLckIAAEB0dUtyQgAAANrHS3JCAADAPxpMckIAAIClbExyQgAAwNZjTXJCAACAPLZNckIAAECiCE5yQgAAAAhbTnJCAADAba1OckIAAACfpE9yQgAAwAT3T3JCAACAaklQckIAAEDQm1ByQgAAADbuUHJCAABAZ+VRckIAAADNN1JyQgAAwDKKUnJCAACAmNxSckIAAED+LlNyQgAAgC8mVHJCAABAlXhUckIAAAD7ylRyQgAAwGAdVXJCAACAxm9VckIAAMD3ZlZyQgAAgF25VnJCAABAwwtXckIAAAApXldyQgAAwI6wV3JCAAAAwKdYckIAAMAl+lhyQgAAgItMWXJCAABA8Z5ZckIAAABX8VlyQgAAQIjoWnJCAAAA7jpbckIAAMBTjVtyQgAAgLnfW3JCAABAHzJcckIAAIBQKV1yQgAAQLZ7XXJCAAAAHM5dckIAAMCBIF5yQgAAgOdyXnJCAADAGGpfckIAAIB+vF9yQgAAQOQOYHJCAAAASmFgckIAAMCvs2ByQgAAAOGqYXJCAADARv1hckIAAICsT2JyQgAAQBKiYnJCAAAAePRickIAAECp62NyQgAAAA8+ZHJCAADAdJBkckIAAIDa4mRyQgAAQEA1ZXJCAACAcSxmckIAAEDXfmZyQgAAAD3RZnJCAADAoiNnckIAAIAIdmdyQgAAwDltaHJCAACAn79ockIAAEAFEmlyQgAAAGtkaXJCAADA0LZpckIAAAACrmpyQgAAwGcAa3JCAACAzVJrckIAAEAzpWtyQgAAAJn3a3JCAABAyu5sckIAAAAwQW1yQgAAwJWTbXJCAACA++VtckIAAEBhOG5yQgAAgJIvb3JCAABA+IFvckIAAABe1G9yQgAAwMMmcHJCAACAKXlwckIAAMBacHFyQgAAgMDCcXJCAABAJhVyckIAAACMZ3JyQgAAwPG5cnJCAAAAI7FzckIAAMCIA3RyQgAAgO5VdHJCAABAVKh0ckIAAAC6+nRyQgAAQOvxdXJCAAAAUUR2ckIAAMC2lnZyQgAAgBzpdnJCAABAgjt3ckIAAICzMnhyQgAAQBmFeHJCAAAAf9d4ckIAAMDkKXlyQgAAgEp8eXJCAADAe3N6ckIAAIDhxXpyQgAAQEcYe3JCAAAArWp7ckIAAMASvXtyQgAAAES0fHJCAADAqQZ9ckIAAIAPWX1yQgAAQHWrfXJCAAAA2/19ckIAAEAM9X5yQgAAAHJHf3JCAADA15l/ckIAAIA97H9yQgAAQKM+gHJCAACA1DWBckIAAEA6iIFyQgAAAKDagXJCAADABS2CckIAAIBrf4JyQgAAwJx2g3JCAACAAsmDckIAAEBoG4RyQgAAAM5thHJCAADAM8CEckIAAABlt4VyQgAAwMoJhnJCAACAMFyGckIAAECWroZyQgAAAPwAh3JCAABALfiHckIAAACTSohyQgAAwPiciHJCAACAXu+IckIAAEDEQYlyQgAAgPU4inJCAABAW4uKckIAAADB3YpyQgAAwCYwi3JCAACAjIKLckIAAMC9eYxyQgAAgCPMjHJCAABAiR6NckIAAADvcI1yQgAAwFTDjXJCAAAAhrqOckIAAMDrDI9yQgAAgFFfj3JCAABAt7GPckIAAAAdBJByQgAAQE77kHJCAAAAtE2RckIAAMAZoJFyQgAAgH/ykXJCAABA5USSckIAAIAWPJNyQgAAQHyOk3JCAAAA4uCTckIAAMBHM5RyQgAAgK2FlHJCAADA3nyVckIAAIBEz5VyQgAAQKohlnJCAAAAEHSWckIAAMB1xpZyQgAAAKe9l3JCAADADBCYckIAAIByYphyQgAAQNi0mHJCAAAAPgeZckIAAEBv/plyQgAAANVQmnJCAADAOqOackIAAICg9ZpyQgAAQAZIm3JCAACANz+cckIAAECdkZxyQgAAAAPknHJCAADAaDadckIAAIDOiJ1yQgAAwP9/nnJCAACAZdKeckIAAEDLJJ9yQgAAADF3n3JCAADAlsmfckIAAADIwKByQgAAwC0ToXJCAACAk2WhckIAAED5t6FyQgAAAF8KonJCAABAkAGjckIAAAD2U6NyQgAAwFumo3JCAACAwfijckIAAEAnS6RyQgAAgFhCpXJCAABAvpSlckIAAAAk56VyQgAAwIk5pnJCAACA74umckIAAMAgg6dyQgAAgIbVp3JCAABA7CeockIAAABSeqhyQgAAwLfMqHJCAAAA6cOpckIAAMBOFqpyQgAAgLRoqnJCAABAGruqckIAAACADatyQgAAQLEErHJCAAAAF1esckIAAMB8qaxyQgAAgOL7rHJCAABASE6tckIAAIB5Ra5yQgAAQN+XrnJCAAAARequckIAAMCqPK9yQgAAgBCPr3JCAADAQYawckIAAICn2LByQgAAQA0rsXJCAAAAc32xckIAAMDYz7FyQgAAAArHsnJCAADAbxmzckIAAIDVa7NyQgAAQDu+s3JCAAAAoRC0ckIAAEDSB7VyQgAAADhatXJCAADAnay1ckIAAIAD/7VyQgAAQGlRtnJCAACAmki3ckIAAEAAm7dyQgAAAGbtt3JCAADAyz+4ckIAAIAxkrhyQgAAwGKJuXJCAACAyNu5ckIAAEAuLrpyQgAAAJSAunJCAADA+dK6ckIAAAAryrtyQgAAwJAcvHJCAACA9m68ckIAAEBcwbxyQgAAAMITvXJCAABA8wq+ckIAAABZXb5yQgAAwL6vvnJCAACAJAK/ckIAAECKVL9yQgAAgLtLwHJCAABAIZ7AckIAAACH8MByQgAAwOxCwXJCAACAUpXBckIAAMCDjMJyQgAAgOnewnJCAABATzHDckIAAAC1g8NyQgAAwBrWw3JCAAAATM3EckIAAMCxH8VyQgAAgBdyxXJCAABAfcTFckIAAADjFsZyQgAAQBQOx3JCAAAAemDHckIAAMDfssdyQgAAgEUFyHJCAABAq1fIckIAAIDcTslyQgAAQEKhyXJCAAAAqPPJckIAAMANRspyQgAAgHOYynJCAADApI/LckIAAIAK4styQgAAQHA0zHJCAAAA1obMckIAAMA72cxyQgAAAG3QzXJCAADA0iLOckIAAIA4dc5yQgAAQJ7HznJCAAAABBrPckIAAEA1EdByQgAAAJtj0HJCAADAALbQckIAAIBmCNFyQgAAQMxa0XJCAACA/VHSckIAAEBjpNJyQgAAAMn20nJCAADALknTckIAAICUm9NyQgAAwMWS1HJCAACAK+XUckIAAECRN9VyQgAAAPeJ1XJCAADAXNzVckIAAACO09ZyQgAAwPMl13JCAACAWXjXckIAAEC/ytdyQgAAACUd2HJCAABAVhTZckIAAAC8ZtlyQgAAwCG52XJCAACAhwvackIAAEDtXdpyQgAAgB5V23JCAABAhKfbckIAAADq+dtyQgAAwE9M3HJCAACAtZ7cckIAAMDmld1yQgAAgEzo3XJCAABAsjreckIAAAAYjd5yQgAAwH3f3nJCAAAAr9bfckIAAMAUKeByQgAAgHp74HJCAABA4M3gckIAAABGIOFyQgAAQHcX4nJCAAAA3WnickIAAMBCvOJyQgAAgKgO43JCAABADmHjckIAAIA/WORyQgAAQKWq5HJCAAAAC/3kckIAAMBwT+VyQgAAgNah5XJCAADAB5nmckIAAIBt6+ZyQgAAQNM953JCAAAAOZDnckIAAMCe4udyQgAAANDZ6HJCAADANSzpckIAAICbfulyQgAAQAHR6XJCAAAAZyPqckIAAECYGutyQgAAAP5s63JCAADAY7/rckIAAIDJEexyQgAAQC9k7HJCAACAYFvtckIAAEDGre1yQgAAACwA7nJCAADAkVLuckIAAID3pO5yQgAAwCic73JCAACAju7vckIAAED0QPByQgAAAFqT8HJCAADAv+XwckIAAADx3PFyQgAAwFYv8nJCAACAvIHyckIAAEAi1PJyQgAAAIgm83JCAABAuR30ckIAAAAfcPRyQgAAwITC9HJCAACA6hT1ckIAAEBQZ/VyQgAAgIFe9nJCAABA57D2ckIAAABNA/dyQgAAwLJV93JCAACAGKj3ckIAAMBJn/hyQgAAgK/x+HJCAABAFUT5ckIAAAB7lvlyQgAAwODo+XJCAAAAEuD6ckIAAMB3MvtyQgAAgN2E+3JCAABAQ9f7ckIAAACpKfxyQgAAQNog/XJCAAAAQHP9ckIAAMClxf1yQgAAgAsY/nJCAABAcWr+ckIAAICiYf9yQgAAQAi0/3JCAAAAbgYAc0IAAMDTWABzQgAAgDmrAHNCAADAaqIBc0IAAIDQ9AFzQgAAQDZHAnNCAAAAnJkCc0IAAMAB7AJzQgAAADPjA3NCAADAmDUEc0IAAID+hwRzQgAAQGTaBHNCAAAAyiwFc0IAAED7IwZzQgAAAGF2BnNCAADAxsgGc0IAAIAsGwdzQgAAQJJtB3NCAACAw2QIc0IAAEAptwhzQgAAAI8JCXNCAADA9FsJc0IAAIBarglzQgAAwIulCnNCAACA8fcKc0IAAEBXSgtzQgAAAL2cC3NCAADAIu8Lc0IAAABU5gxzQgAAwLk4DXNCAACAH4sNc0IAAECF3Q1zQgAAAOsvDnNCAABAHCcPc0IAAACCeQ9zQgAAwOfLD3NCAACATR4Qc0IAAECzcBBzQgAAgORnEXNCAABASroRc0IAAACwDBJzQgAAwBVfEnNCAACAe7ESc0IAAMCsqBNzQgAAgBL7E3NCAABAeE0Uc0IAAADenxRzQgAAwEPyFHNCAAAAdekVc0IAAMDaOxZzQgAAgECOFnNCAABApuAWc0IAAAAMMxdzQgAAQD0qGHNCAAAAo3wYc0IAAMAIzxhzQgAAgG4hGXNCAABA1HMZc0IAAIAFaxpzQgAAQGu9GnNCAAAA0Q8bc0IAAMA2YhtzQgAAgJy0G3NCAADAzascc0IAAIAz/hxzQgAAQJlQHXNCAAAA/6Idc0IAAMBk9R1zQgAAAJbsHnNCAADA+z4fc0IAAIBhkR9zQgAAQMfjH3NCAAAALTYgc0IAAEBeLSFzQgAAAMR/IXNCAADAKdIhc0IAAICPJCJzQgAAQPV2InNCAACAJm4jc0IAAECMwCNzQgAAAPISJHNCAADAV2Ukc0IAAIC9tyRzQgAAwO6uJXNCAACAVAEmc0IAAEC6UyZzQgAAACCmJnNCAADAhfgmc0IAAAC37ydzQgAAwBxCKHNCAACAgpQoc0IAAEDo5ihzQgAAAE45KXNCAABAfzAqc0IAAADlgipzQgAAwErVKnNCAACAsCcrc0IAAEAWeitzQgAAgEdxLHNCAABArcMsc0IAAAATFi1zQgAAwHhoLXNCAACA3rotc0IAAMAPsi5zQgAAgHUEL3NCAABA21Yvc0IAAABBqS9zQgAAwKb7L3NCAAAA2PIwc0IAAMA9RTFzQgAAgKOXMXNCAABACeoxc0IAAABvPDJzQgAAQKAzM3NCAAAABoYzc0IAAMBr2DNzQgAAgNEqNHNCAABAN300c0IAAIBodDVzQgAAQM7GNXNCAAAANBk2c0IAAMCZazZzQgAAgP+9NnNCAADAMLU3c0IAAICWBzhzQgAAQPxZOHNCAAAAYqw4c0IAAMDH/jhzQgAAAPn1OXNCAADAXkg6c0IAAIDEmjpzQgAAQCrtOnNCAAAAkD87c0IAAEDBNjxzQgAAACeJPHNCAADAjNs8c0IAAIDyLT1zQgAAQFiAPXNCAACAiXc+c0IAAEDvyT5zQgAAAFUcP3NCAADAum4/c0IAAIAgwT9zQgAAwFG4QHNCAACAtwpBc0IAAEAdXUFzQgAAAIOvQXNCAADA6AFCc0IAAAAa+UJzQgAAwH9LQ3NCAACA5Z1Dc0IAAEBL8ENzQgAAALFCRHNCAABA4jlFc0IAAABIjEVzQgAAwK3eRXNCAACAEzFGc0IAAEB5g0ZzQgAAgKp6R3NCAABAEM1Hc0IAAAB2H0hzQgAAwNtxSHNCAACAQcRIc0IAAMByu0lzQgAAgNgNSnNCAABAPmBKc0IAAACkskpzQgAAwAkFS3NCAAAAO/xLc0IAAMCgTkxzQgAAgAahTHNCAABAbPNMc0IAAADSRU1zQgAAQAM9TnNCAAAAaY9Oc0IAAMDO4U5zQgAAgDQ0T3NCAABAmoZPc0IAAIDLfVBzQgAAQDHQUHNCAAAAlyJRc0IAAMD8dFFzQgAAgGLHUXNCAADAk75Sc0IAAID5EFNzQgAAQF9jU3NCAAAAxbVTc0IAAMAqCFRzQgAAAFz/VHNCAADAwVFVc0IAAIAnpFVzQgAAQI32VXNCAAAA80hWc0IAAEAkQFdzQgAAAIqSV3NCAADA7+RXc0IAAIBVN1hzQgAAQLuJWHNCAACA7IBZc0IAAEBS01lzQgAAALglWnNCAADAHXhac0IAAICDylpzQgAAwLTBW3NCAACAGhRcc0IAAECAZlxzQgAAAOa4XHNCAADASwtdc0IAAAB9Al5zQgAAwOJUXnNCAACASKdec0IAAECu+V5zQgAAABRMX3NCAABARUNgc0IAAACrlWBzQgAAwBDoYHNCAACAdjphc0IAAEDcjGFzQgAAgA2EYnNCAABAc9Zic0IAAADZKGNzQgAAwD57Y3NCAACApM1jc0IAAMDVxGRzQgAAgDsXZXNCAABAoWllc0IAAAAHvGVzQgAAwGwOZnNCAAAAngVnc0IAAMADWGdzQgAAgGmqZ3NCAABAz/xnc0IAAAA1T2hzQgAAQGZGaXNCAAAAzJhpc0IAAMAx62lzQgAAgJc9anNCAABA/Y9qc0IAAIAuh2tzQgAAQJTZa3NCAAAA+itsc0IAAMBffmxzQgAAgMXQbHNCAADA9sdtc0IAAIBcGm5zQgAAQMJsbnNCAAAAKL9uc0IAAMCNEW9zQgAAAL8IcHNCAADAJFtwc0IAAICKrXBzQgAAQPD/cHNCAAAAVlJxc0IAAECHSXJzQgAAAO2bcnNCAADAUu5yc0IAAIC4QHNzQgAAQB6Tc3NCAACAT4p0c0IAAEC13HRzQgAAABsvdXNCAADAgIF1c0IAAIDm03VzQgAAwBfLdnNCAACAfR13c0IAAEDjb3dzQgAAAEnCd3NCAADArhR4c0IAAADgC3lzQgAAwEVeeXNCAACAq7B5c0IAAEARA3pzQgAAAHdVenNCAABAqEx7c0IAAAAOn3tzQgAAwHPxe3NCAACA2UN8c0IAAEA/lnxzQgAAgHCNfXNCAABA1t99c0IAAAA8Mn5zQgAAwKGEfnNCAACAB9d+c0IAAMA4zn9zQgAAgJ4ggHNCAABABHOAc0IAAABqxYBzQgAAwM8XgXNCAAAAAQ+Cc0IAAMBmYYJzQgAAgMyzgnNCAABAMgaDc0IAAACYWINzQgAAQMlPhHNCAAAAL6KEc0IAAMCU9IRzQgAAgPpGhXNCAABAYJmFc0IAAICRkIZzQgAAQPfihnNCAAAAXTWHc0IAAMDCh4dzQgAAgCjah3NCAADAWdGIc0IAAIC/I4lzQgAAQCV2iXNCAAAAi8iJc0IAAMDwGopzQgAAACISi3NCAADAh2SLc0IAAIDttotzQgAAQFMJjHNCAAAAuVuMc0IAAEDqUo1zQgAAAFCljXNCAADAtfeNc0IAAIAbSo5zQgAAQIGcjnNCAACAspOPc0IAAEAY5o9zQgAAAH44kHNCAADA44qQc0IAAIBJ3ZBzQgAAwHrUkXNCAACA4CaSc0IAAEBGeZJzQgAAAKzLknNCAADAER6Tc0IAAABDFZRzQgAAwKhnlHNCAACADrqUc0IAAEB0DJVzQgAAANpelXNCAABAC1aWc0IAAABxqJZzQgAAwNb6lnNCAACAPE2Xc0IAAECin5dzQgAAgNOWmHNCAABAOemYc0IAAACfO5lzQgAAwASOmXNCAACAauCZc0IAAMCb15pzQgAAgAEqm3NCAABAZ3ybc0IAAADNzptzQgAAwDIhnHNCAAAAZBidc0IAAMDJap1zQgAAgC+9nXNCAABAlQ+ec0IAAAD7YZ5zQgAAQCxZn3NCAAAAkqufc0IAAMD3/Z9zQgAAgF1QoHNCAABAw6Kgc0IAAID0maFzQgAAQFrsoXNCAAAAwD6ic0IAAMAlkaJzQgAAgIvjonNCAADAvNqjc0IAAIAiLaRzQgAAQIh/pHNCAAAA7tGkc0IAAMBTJKVzQgAAAIUbpnNCAADA6m2mc0IAAIBQwKZzQgAAQLYSp3NCAAAAHGWnc0IAAEBNXKhzQgAAALOuqHNCAADAGAGpc0IAAIB+U6lzQgAAQOSlqXNCAACAFZ2qc0IAAEB776pzQgAAAOFBq3NCAADARpSrc0IAAICs5qtzQgAAwN3drHNCAACAQzCtc0IAAECpgq1zQgAAAA/VrXNCAADAdCeuc0IAAACmHq9zQgAAwAtxr3NCAACAccOvc0IAAEDXFbBzQgAAAD1osHNCAABAbl+xc0IAAADUsbFzQgAAwDkEsnNCAACAn1ayc0IAAEAFqbJzQgAAgDags3NCAABAnPKzc0IAAAACRbRzQgAAwGeXtHNCAACAzem0c0IAAMD+4LVzQgAAgGQztnNCAABAyoW2c0IAAAAw2LZzQgAAwJUqt3NCAAAAxyG4c0IAAMAsdLhzQgAAgJLGuHNCAABA+Bi5c0IAAABea7lzQgAAQI9iunNCAAAA9bS6c0IAAMBaB7tzQgAAgMBZu3NCAABAJqy7c0IAAIBXo7xzQgAAQL31vHNCAAAAI0i9c0IAAMCImr1zQgAAgO7svXNCAADAH+S+c0IAAICFNr9zQgAAQOuIv3NCAAAAUdu/c0IAAMC2LcBzQgAAAOgkwXNCAADATXfBc0IAAICzycFzQgAAQBkcwnNCAAAAf27Cc0IAAECwZcNzQgAAABa4w3NCAADAewrEc0IAAIDhXMRzQgAAQEevxHNCAACAeKbFc0IAAEDe+MVzQgAAAERLxnNCAADAqZ3Gc0IAAIAP8MZzQgAAwEDnx3NCAACApjnIc0IAAEAMjMhzQgAAAHLeyHNCAADA1zDJc0IAAAAJKMpzQgAAwG56ynNCAACA1MzKc0IAAEA6H8tzQgAAAKBxy3NCAABA0WjMc0IAAAA3u8xzQgAAwJwNzXNCAACAAmDNc0IAAEBoss1zQgAAgJmpznNCAABA//vOc0IAAABlTs9zQgAAwMqgz3NCAACAMPPPc0IAAMBh6tBzQgAAgMc80XNCAABALY/Rc0IAAACT4dFzQgAAwPgz0nNCAAAAKivTc0IAAMCPfdNzQgAAgPXP03NCAABAWyLUc0IAAADBdNRzQgAAQPJr1XNCAAAAWL7Vc0IAAMC9ENZzQgAAgCNj1nNCAABAibXWc0IAAIC6rNdzQgAAQCD/13NCAAAAhlHYc0IAAMDro9hzQgAAgFH22HNCAADAgu3Zc0IAAIDoP9pzQgAAQE6S2nNCAAAAtOTac0IAAMAZN9tzQgAAAEsu3HNCAADAsIDcc0IAAIAW09xzQgAAQHwl3XNCAAAA4nfdc0IAAEATb95zQgAAAHnB3nNCAADA3hPfc0IAAIBEZt9zQgAAQKq433NCAACA26/gc0IAAEBBAuFzQgAAAKdU4XNCAADADKfhc0IAAIBy+eFzQgAAwKPw4nNCAACACUPjc0IAAEBvleNzQgAAANXn43NCAADAOjrkc0IAAABsMeVzQgAAwNGD5XNCAACAN9blc0IAAECdKOZzQgAAAAN75nNCAABANHLnc0IAAACaxOdzQgAAwP8W6HNCAACAZWnoc0IAAEDLu+hzQgAAgPyy6XNCAABAYgXqc0IAAADIV+pzQgAAwC2q6nNCAACAk/zqc0IAAMDE8+tzQgAAgCpG7HNCAABAkJjsc0IAAAD26uxzQgAAwFs97XNCAAAAjTTuc0IAAMDyhu5zQgAAgFjZ7nNCAABAvivvc0IAAAAkfu9zQgAAQFV18HNCAAAAu8fwc0IAAMAgGvFzQgAAgIZs8XNCAABA7L7xc0IAAIAdtvJzQgAAQIMI83NCAAAA6Vrzc0IAAMBOrfNzQgAAgLT/83NCAADA5fb0c0IAAIBLSfVzQgAAQLGb9XNCAAAAF+71c0IAAMB8QPZzQgAAAK4393NCAADAE4r3c0IAAIB53PdzQgAAQN8u+HNCAAAARYH4c0IAAEB2ePlzQgAAANzK+XNCAADAQR36c0IAAICnb/pzQgAAQA3C+nNCAACAPrn7c0IAAECkC/xzQgAAAApe/HNCAADAb7D8c0IAAIDVAv1zQgAAwAb6/XNCAACAbEz+c0IAAEDSnv5zQgAAADjx/nNCAADAnUP/c0IAAADPOgB0QgAAwDSNAHRCAACAmt8AdEIAAEAAMgF0QgAAAGaEAXRCAABAl3sCdEIAAAD9zQJ0QgAAwGIgA3RCAACAyHIDdEIAAEAuxQN0QgAAgF+8BHRCAABAxQ4FdEIAAAArYQV0QgAAwJCzBXRCAACA9gUGdEIAAMAn/QZ0QgAAgI1PB3RCAABA86EHdEIAAABZ9Ad0QgAAwL5GCHRCAAAA8D0JdEIAAMBVkAl0QgAAgLviCXRCAABAITUKdEIAAACHhwp0QgAAQLh+C3RCAAAAHtELdEIAAMCDIwx0QgAAgOl1DHRCAABAT8gMdEIAAICAvw10QgAAQOYRDnRCAAAATGQOdEIAAMCxtg50QgAAgBcJD3RCAADASAAQdEIAAICuUhB0QgAAQBSlEHRCAAAAevcQdEIAAMDfSRF0QgAAABFBEnRCAADAdpMSdEIAAIDc5RJ0QgAAQEI4E3RCAAAAqIoTdEIAAEDZgRR0QgAAAD/UFHRCAADApCYVdEIAAIAKeRV0QgAAQHDLFXRCAACAocIWdEIAAEAHFRd0QgAAAG1nF3RCAADA0rkXdEIAAIA4DBh0QgAAwGkDGXRCAACAz1UZdEIAAEA1qBl0QgAAAJv6GXRCAADAAE0adEIAAAAyRBt0QgAAwJeWG3RCAACA/egbdEIAAEBjOxx0QgAAAMmNHHRCAABA+oQddEIAAABg1x10QgAAwMUpHnRCAACAK3wedEIAAECRzh50QgAAgMLFH3RCAABAKBggdEIAAACOaiB0QgAAwPO8IHRCAACAWQ8hdEIAAMCKBiJ0QgAAgPBYInRCAABAVqsidEIAAAC8/SJ0QgAAwCFQI3RCAAAAU0ckdEIAAMC4mSR0QgAAgB7sJHRCAABAhD4ldEIAAADqkCV0QgAAQBuIJnRCAAAAgdomdEIAAMDmLCd0QgAAgEx/J3RCAABAstEndEIAAIDjyCh0QgAAQEkbKXRCAAAAr20pdEIAAMAUwCl0QgAAgHoSKnRCAADAqwkrdEIAAIARXCt0QgAAQHeuK3RCAAAA3QAsdEIAAMBCUyx0QgAAAHRKLXRCAADA2ZwtdEIAAIA/7y10QgAAQKVBLnRCAAAAC5QudEIAAEA8iy90QgAAAKLdL3RCAADABzAwdEIAAIBtgjB0QgAAQNPUMHRCAACABMwxdEIAAEBqHjJ0QgAAANBwMnRCAADANcMydEIAAICbFTN0QgAAwMwMNHRCAACAMl80dEIAAECYsTR0QgAAAP4DNXRCAADAY1Y1dEIAAACVTTZ0QgAAwPqfNnRCAACAYPI2dEIAAEDGRDd0QgAAACyXN3RCAABAXY44dEIAAADD4Dh0QgAAwCgzOXRCAACAjoU5dEIAAED01zl0QgAAgCXPOnRCAABAiyE7dEIAAADxczt0QgAAwFbGO3RCAACAvBg8dEIAAMDtDz10QgAAgFNiPXRCAABAubQ9dEIAAAAfBz50QgAAwIRZPnRCAAAAtlA/dEIAAMAboz90QgAAgIH1P3RCAABA50dAdEIAAABNmkB0QgAAQH6RQXRCAAAA5ONBdEIAAMBJNkJ0QgAAgK+IQnRCAABAFdtCdEIAAIBG0kN0QgAAQKwkRHRCAAAAEndEdEIAAMB3yUR0QgAAgN0bRXRCAADADhNGdEIAAIB0ZUZ0QgAAQNq3RnRCAAAAQApHdEIAAMClXEd0QgAAANdTSHRCAADAPKZIdEIAAICi+Eh0QgAAQAhLSXRCAAAAbp1JdEIAAECflEp0QgAAAAXnSnRCAADAajlLdEIAAIDQi0t0QgAAQDbeS3RCAACAZ9VMdEIAAEDNJ010QgAAADN6TXRCAADAmMxNdEIAAID+Hk50QgAAwC8WT3RCAACAlWhPdEIAAED7uk90QgAAAGENUHRCAADAxl9QdEIAAAD4VlF0QgAAwF2pUXRCAACAw/tRdEIAAEApTlJ0QgAAAI+gUnRCAABAwJdTdEIAAAAm6lN0QgAAwIs8VHRCAACA8Y5UdEIAAEBX4VR0QgAAgIjYVXRCAABA7ipWdEIAAABUfVZ0QgAAwLnPVnRCAACAHyJXdEIAAMBQGVh0QgAAgLZrWHRCAABAHL5YdEIAAACCEFl0QgAAwOdiWXRCAAAAGVpadEIAAMB+rFp0QgAAgOT+WnRCAABASlFbdEIAAACwo1t0QgAAQOGaXHRCAAAAR+1cdEIAAMCsP110QgAAgBKSXXRCAABAeORddEIAAICp2150QgAAQA8uX3RCAAAAdYBfdEIAAMDa0l90QgAAgEAlYHRCAADAcRxhdEIAAIDXbmF0QgAAQD3BYXRCAAAAoxNidEIAAMAIZmJ0QgAAADpdY3RCAADAn69jdEIAAIAFAmR0QgAAQGtUZHRCAAAA0aZkdEIAAEACnmV0QgAAAGjwZXRCAADAzUJmdEIAAIAzlWZ0QgAAQJnnZnRCAACAyt5ndEIAAEAwMWh0QgAAAJaDaHRCAADA+9VodEIAAIBhKGl0QgAAwJIfanRCAACA+HFqdEIAAEBexGp0QgAAAMQWa3RCAADAKWlrdEIAAABbYGx0QgAAwMCybHRCAACAJgVtdEIAAECMV210QgAAAPKpbXRCAABAI6FudEIAAACJ8250QgAAwO5Fb3RCAACAVJhvdEIAAEC66m90QgAAgOvhcHRCAABAUTRxdEIAAAC3hnF0QgAAwBzZcXRCAACAgitydEIAAMCzInN0QgAAgBl1c3RCAABAf8dzdEIAAADlGXR0QgAAwEpsdHRCAAAAfGN1dEIAAMDhtXV0QgAAgEcIdnRCAABArVp2dEIAAAATrXZ0QgAAQESkd3RCAAAAqvZ3dEIAAMAPSXh0QgAAgHWbeHRCAABA2+14dEIAAIAM5Xl0QgAAQHI3enRCAAAA2Il6dEIAAMA93Hp0QgAAgKMue3RCAADA1CV8dEIAAIA6eHx0QgAAQKDKfHRCAAAABh19dEIAAMBrb310QgAAAJ1mfnRCAADAArl+dEIAAIBoC390QgAAQM5df3RCAAAANLB/dEIAAEBlp4B0QgAAAMv5gHRCAADAMEyBdEIAAICWnoF0QgAAQPzwgXRCAACALeiCdEIAAECTOoN0QgAAAPmMg3RCAADAXt+DdEIAAIDEMYR0QgAAwPUohXRCAACAW3uFdEIAAEDBzYV0QgAAACcghnRCAADAjHKGdEIAAAC+aYd0QgAAwCO8h3RCAACAiQ6IdEIAAEDvYIh0QgAAAFWziHRCAABAhqqJdEIAAADs/Il0QgAAwFFPinRCAACAt6GKdEIAAEAd9Ip0QgAAgE7ri3RCAABAtD2MdEIAAAAakIx0QgAAwH/ijHRCAACA5TSNdEIAAMAWLI50QgAAgHx+jnRCAABA4tCOdEIAAABII490QgAAwK11j3RCAAAA32yQdEIAAMBEv5B0QgAAgKoRkXRCAABAEGSRdEIAAAB2tpF0QgAAQKetknRCAAAADQCTdEIAAMByUpN0QgAAgNikk3RCAABAPveTdEIAAIBv7pR0QgAAQNVAlXRCAAAAO5OVdEIAAMCg5ZV0QgAAgAY4lnRCAADANy+XdEIAAICdgZd0QgAAQAPUl3RCAAAAaSaYdEIAAMDOeJh0QgAAAABwmXRCAADAZcKZdEIAAIDLFJp0QgAAQDFnmnRCAAAAl7madEIAAEDIsJt0QgAAAC4DnHRCAADAk1WcdEIAAID5p5x0QgAAQF/6nHRCAACAkPGddEIAAED2Q550QgAAAFyWnnRCAADAweiedEIAAIAnO590QgAAwFgyoHRCAACAvoSgdEIAAEAk16B0QgAAAIopoXRCAADA73uhdEIAAAAhc6J0QgAAwIbFonRCAACA7BejdEIAAEBSaqN0QgAAALi8o3RCAABA6bOkdEIAAABPBqV0QgAAwLRYpXRCAACAGquldEIAAECA/aV0QgAAgLH0pnRCAABAF0endEIAAAB9mad0QgAAwOLrp3RCAACASD6odEIAAMB5Nal0QgAAgN+HqXRCAABARdqpdEIAAACrLKp0QgAAwBB/qnRCAAAAQnardEIAAMCnyKt0QgAAgA0brHRCAABAc22sdEIAAADZv6x0QgAAQAq3rXRCAAAAcAmudEIAAMDVW650QgAAgDuurnRCAABAoQCvdEIAAIDS9690QgAAQDhKsHRCAAAAnpywdEIAAMAD77B0QgAAgGlBsXRCAADAmjiydEIAAIAAi7J0QgAAQGbdsnRCAAAAzC+zdEIAAMAxgrN0QgAAAGN5tHRCAADAyMu0dEIAAIAuHrV0QgAAQJRwtXRCAAAA+sK1dEIAAEArurZ0QgAAAJEMt3RCAADA9l63dEIAAIBcsbd0QgAAQMIDuHRCAACA8/q4dEIAAEBZTbl0QgAAAL+fuXRCAADAJPK5dEIAAICKRLp0QgAAwLs7u3RCAACAIY67dEIAAECH4Lt0QgAAAO0yvHRCAADAUoW8dEIAAACEfL10QgAAwOnOvXRCAACATyG+dEIAAEC1c750QgAAABvGvnRCAABATL2/dEIAAACyD8B0QgAAwBdiwHRCAACAfbTAdEIAAEDjBsF0QgAAgBT+wXRCAABAelDCdEIAAADgosJ0QgAAwEX1wnRCAACAq0fDdEIAAMDcPsR0QgAAgEKRxHRCAABAqOPEdEIAAAAONsV0QgAAwHOIxXRCAAAApX/GdEIAAMAK0sZ0QgAAgHAkx3RCAABA1nbHdEIAAAA8ycd0QgAAQG3AyHRCAAAA0xLJdEIAAMA4Zcl0QgAAgJ63yXRCAABABArKdEIAAIA1Act0QgAAQJtTy3RCAAAAAabLdEIAAMBm+Mt0QgAAgMxKzHRCAADA/UHNdEIAAIBjlM10QgAAQMnmzXRCAAAALznOdEIAAMCUi850QgAAAMaCz3RCAADAK9XPdEIAAICRJ9B0QgAAQPd50HRCAAAAXczQdEIAAECOw9F0QgAAAPQV0nRCAADAWWjSdEIAAIC/utJ0QgAAQCUN03RCAACAVgTUdEIAAEC8VtR0QgAAACKp1HRCAADAh/vUdEIAAIDtTdV0QgAAwB5F1nRCAACAhJfWdEIAAEDq6dZ0QgAAAFA813RCAADAtY7XdEIAAADnhdh0QgAAwEzY2HRCAACAsirZdEIAAEAYfdl0QgAAAH7P2XRCAABAr8badEIAAAAVGdt0QgAAwHpr23RCAACA4L3bdEIAAEBGENx0QgAAgHcH3XRCAABA3VnddEIAAABDrN10QgAAwKj+3XRCAACADlHedEIAAMA/SN90QgAAgKWa33RCAABAC+3fdEIAAABxP+B0QgAAwNaR4HRCAAAACInhdEIAAMBt2+F0QgAAgNMt4nRCAABAOYDidEIAAACf0uJ0QgAAQNDJ43RCAAAANhzkdEIAAMCbbuR0QgAAgAHB5HRCAABAZxPldEIAAICYCuZ0QgAAQP5c5nRCAAAAZK/mdEIAAMDJAed0QgAAgC9U53RCAADAYEvodEIAAIDGneh0QgAAQCzw6HRCAAAAkkLpdEIAAMD3lOl0QgAAACmM6nRCAADAjt7qdEIAAID0MOt0QgAAQFqD63RCAAAAwNXrdEIAAEDxzOx0QgAAAFcf7XRCAADAvHHtdEIAAIAixO10QgAAQIgW7nRCAACAuQ3vdEIAAEAfYO90QgAAAIWy73RCAADA6gTwdEIAAIBQV/B0QgAAwIFO8XRCAACA56DxdEIAAEBN8/F0QgAAALNF8nRCAADAGJjydEIAAABKj/N0QgAAwK/h83RCAACAFTT0dEIAAEB7hvR0QgAAAOHY9HRCAABAEtD1dEIAAAB4IvZ0QgAAwN109nRCAACAQ8f2dEIAAECpGfd0QgAAgNoQ+HRCAABAQGP4dEIAAACmtfh0QgAAwAsI+XRCAACAcVr5dEIAAMCiUfp0QgAAgAik+nRCAABAbvb6dEIAAADUSPt0QgAAwDmb+3RCAAAAa5L8dEIAAMDQ5Px0QgAAgDY3/XRCAABAnIn9dEIAAAAC3P10QgAAQDPT/nRCAAAAmSX/dEIAAMD+d/90QgAAgGTK/3RCAABAyhwAdUIAAID7EwF1QgAAQGFmAXVCAAAAx7gBdUIAAMAsCwJ1QgAAgJJdAnVCAADAw1QDdUIAAIAppwN1QgAAQI/5A3VCAAAA9UsEdUIAAMBangR1QgAAAIyVBXVCAADA8ecFdUIAAIBXOgZ1QgAAQL2MBnVCAAAAI98GdUIAAEBU1gd1QgAAALooCHVCAADAH3sIdUIAAICFzQh1QgAAQOsfCXVCAACAHBcKdUIAAECCaQp1QgAAAOi7CnVCAADATQ4LdUIAAICzYAt1QgAAwORXDHVCAACASqoMdUIAAECw/Ax1QgAAABZPDXVCAADAe6ENdUIAAACtmA51QgAAwBLrDnVCAACAeD0PdUIAAEDejw91QgAAAETiD3VCAABAddkQdUIAAADbKxF1QgAAwEB+EXVCAACAptARdUIAAEAMIxJ1QgAAgD0aE3VCAABAo2wTdUIAAAAJvxN1QgAAwG4RFHVCAACA1GMUdUIAAMAFWxV1QgAAgGutFXVCAABA0f8VdUIAAAA3UhZ1QgAAwJykFnVCAAAAzpsXdUIAAMAz7hd1QgAAgJlAGHVCAABA/5IYdUIAAABl5Rh1QgAAQJbcGXVCAAAA/C4adUIAAMBhgRp1QgAAgMfTGnVCAABALSYbdUIAAIBeHRx1QgAAQMRvHHVCAAAAKsIcdUIAAMCPFB11QgAAgPVmHXVCAADAJl4edUIAAICMsB51QgAAQPICH3VCAAAAWFUfdUIAAMC9px91QgAAAO+eIHVCAADAVPEgdUIAAIC6QyF1QgAAQCCWIXVCAAAAhughdUIAAEC33yJ1QgAAAB0yI3VCAADAgoQjdUIAAIDo1iN1QgAAQE4pJHVCAACAfyAldUIAAEDlciV1QgAAAEvFJXVCAADAsBcmdUIAAIAWaiZ1QgAAwEdhJ3VCAACArbMndUIAAEATBih1QgAAAHlYKHVCAADA3qoodUIAAAAQoil1QgAAwHX0KXVCAACA20YqdUIAAEBBmSp1QgAAAKfrKnVCAABA2OIrdUIAAAA+NSx1QgAAwKOHLHVCAACACdosdUIAAEBvLC11QgAAgKAjLnVCAABABnYudUIAAABsyC51QgAAwNEaL3VCAACAN20vdUIAAMBoZDB1QgAAgM62MHVCAABANAkxdUIAAACaWzF1QgAAwP+tMXVCAAAAMaUydUIAAMCW9zJ1QgAAgPxJM3VCAABAYpwzdUIAAADI7jN1QgAAQPnlNHVCAAAAXzg1dUIAAMDEijV1QgAAgCrdNXVCAABAkC82dUIAAIDBJjd1QgAAQCd5N3VCAAAAjcs3dUIAAMDyHTh1QgAAgFhwOHVCAADAiWc5dUIAAIDvuTl1QgAAQFUMOnVCAAAAu146dUIAAMAgsTp1QgAAAFKoO3VCAADAt/o7dUIAAIAdTTx1QgAAQIOfPHVCAAAA6fE8dUIAAEAa6T11QgAAAIA7PnVCAADA5Y0+dUIAAIBL4D51QgAAQLEyP3VCAACA4ilAdUIAAEBIfEB1QgAAAK7OQHVCAADAEyFBdUIAAIB5c0F1QgAAwKpqQnVCAACAEL1CdUIAAEB2D0N1QgAAANxhQ3VCAADAQbRDdUIAAABzq0R1QgAAwNj9RHVCAACAPlBFdUIAAECkokV1QgAAAAr1RXVCAABAO+xGdUIAAAChPkd1QgAAwAaRR3VCAACAbONHdUIAAEDSNUh1QgAAgAMtSXVCAABAaX9JdUIAAADP0Ul1QgAAwDQkSnVCAACAmnZKdUIAAMDLbUt1QgAAgDHAS3VCAABAlxJMdUIAAAD9ZEx1QgAAwGK3THVCAAAAlK5NdUIAAMD5AE51QgAAgF9TTnVCAABAxaVOdUIAAAAr+E51QgAAQFzvT3VCAAAAwkFQdUIAAMAnlFB1QgAAgI3mUHVCAABA8zhRdUIAAIAkMFJ1QgAAQIqCUnVCAAAA8NRSdUIAAMBVJ1N1QgAAgLt5U3VCAADA7HBUdUIAAIBSw1R1QgAAQLgVVXVCAAAAHmhVdUIAAMCDulV1QgAAALWxVnVCAADAGgRXdUIAAICAVld1QgAAQOaoV3VCAAAATPtXdUIAAEB98lh1QgAAAONEWXVCAADASJdZdUIAAICu6Vl1QgAAQBQ8WnVCAACARTNbdUIAAECrhVt1QgAAABHYW3VCAADAdipcdUIAAIDcfFx1QgAAwA10XXVCAACAc8ZddUIAAEDZGF51QgAAAD9rXnVCAADApL1edUIAAADWtF91QgAAwDsHYHVCAACAoVlgdUIAAEAHrGB1QgAAAG3+YHVCAABAnvVhdUIAAAAESGJ1QgAAwGmaYnVCAACAz+xidUIAAEA1P2N1QgAAgGY2ZHVCAABAzIhkdUIAAAAy22R1QgAAwJctZXVCAACA/X9ldUIAAMAud2Z1QgAAgJTJZnVCAABA+htndUIAAABgbmd1QgAAwMXAZ3VCAAAA97dodUIAAMBcCml1QgAAgMJcaXVCAABAKK9pdUIAAACOAWp1QgAAQL/4anVCAAAAJUtrdUIAAMCKnWt1QgAAgPDva3VCAABAVkJsdUIAAICHOW11QgAAQO2LbXVCAAAAU95tdUIAAMC4MG51QgAAgB6DbnVCAADAT3pvdUIAAIC1zG91QgAAQBsfcHVCAAAAgXFwdUIAAMDmw3B1QgAAABi7cXVCAADAfQ1ydUIAAIDjX3J1QgAAQEmycnVCAAAArwRzdUIAAEDg+3N1QgAAAEZOdHVCAADAq6B0dUIAAIAR83R1QgAAQHdFdXVCAACAqDx2dUIAAEAOj3Z1QgAAAHThdnVCAADA2TN3dUIAAIA/hnd1QgAAwHB9eHVCAACA1s94dUIAAEA8Inl1QgAAAKJ0eXVCAADAB8d5dUIAAAA5vnp1QgAAwJ4Qe3VCAACABGN7dUIAAEBqtXt1QgAAANAHfHVCAABAAf98dUIAAABnUX11QgAAwMyjfXVCAACAMvZ9dUIAAECYSH51QgAAgMk/f3VCAABAL5J/dUIAAACV5H91QgAAwPo2gHVCAACAYImAdUIAAMCRgIF1QgAAgPfSgXVCAABAXSWCdUIAAADDd4J1QgAAwCjKgnVCAAAAWsGDdUIAAMC/E4R1QgAAgCVmhHVCAABAi7iEdUIAAADxCoV1QgAAQCIChnVCAAAAiFSGdUIAAMDtpoZ1QgAAgFP5hnVCAABAuUuHdUIAAIDqQoh1QgAAQFCViHVCAAAAtueIdUIAAMAbOol1QgAAgIGMiXVCAADAsoOKdUIAAIAY1op1QgAAQH4oi3VCAAAA5HqLdUIAAMBJzYt1QgAAAHvEjHVCAADA4BaNdUIAAIBGaY11QgAAQKy7jXVCAAAAEg6OdUIAAEBDBY91QgAAAKlXj3VCAADADqqPdUIAAIB0/I91QgAAQNpOkHVCAACAC0aRdUIAAEBxmJF1QgAAANfqkXVCAADAPD2SdUIAAICij5J1QgAAwNOGk3VCAACAOdmTdUIAAECfK5R1QgAAAAV+lHVCAADAatCUdUIAAACcx5V1QgAAwAEalnVCAACAZ2yWdUIAAEDNvpZ1QgAAADMRl3VCAABAZAiYdUIAAADKWph1QgAAwC+tmHVCAACAlf+YdUIAAED7UZl1QgAAgCxJmnVCAABAkpuadUIAAAD47Zp1QgAAwF1Am3VCAACAw5KbdUIAAMD0iZx1QgAAgFrcnHVCAABAwC6ddUIAAAAmgZ11QgAAwIvTnXVCAAAAvcqedUIAAMAiHZ91QgAAgIhvn3VCAABA7sGfdUIAAABUFKB1QgAAQIULoXVCAAAA612hdUIAAMBQsKF1QgAAgLYConVCAABAHFWidUIAAIBNTKN1QgAAQLOeo3VCAAAAGfGjdUIAAMB+Q6R1QgAAgOSVpHVCAADAFY2ldUIAAIB736V1QgAAQOExpnVCAAAAR4SmdUIAAMCs1qZ1QgAAAN7Np3VCAADAQyCodUIAAICpcqh1QgAAQA/FqHVCAAAAdRepdUIAAECmDqp1QgAAAAxhqnVCAADAcbOqdUIAAIDXBat1QgAAQD1Yq3VCAACAbk+sdUIAAEDUoax1QgAAADr0rHVCAADAn0atdUIAAIAFma11QgAAwDaQrnVCAACAnOKudUIAAEACNa91QgAAAGiHr3VCAADAzdmvdUIAAAD/0LB1QgAAwGQjsXVCAACAynWxdUIAAEAwyLF1QgAAAJYasnVCAABAxxGzdUIAAAAtZLN1QgAAwJK2s3VCAACA+Ai0dUIAAEBeW7R1QgAAgI9StXVCAABA9aS1dUIAAABb97V1QgAAwMBJtnVCAACAJpy2dUIAAMBXk7d1QgAAgL3lt3VCAABAIzi4dUIAAACJirh1QgAAwO7cuHVCAAAAINS5dUIAAMCFJrp1QgAAgOt4unVCAABAUcu6dUIAAAC3Hbt1QgAAQOgUvHVCAAAATme8dUIAAMCzubx1QgAAgBkMvXVCAABAf169dUIAAICwVb51QgAAQBaovnVCAAAAfPq+dUIAAMDhTL91QgAAgEefv3VCAADAeJbAdUIAAIDe6MB1QgAAQEQ7wXVCAAAAqo3BdUIAAMAP4MF1QgAAAEHXwnVCAADApinDdUIAAIAMfMN1QgAAQHLOw3VCAAAA2CDEdUIAAEAJGMV1QgAAAG9qxXVCAADA1LzFdUIAAIA6D8Z1QgAAQKBhxnVCAACA0VjHdUIAAEA3q8d1QgAAAJ39x3VCAADAAlDIdUIAAIBoosh1QgAAwJmZyXVCAACA/+vJdUIAAEBlPsp1QgAAAMuQynVCAADAMOPKdUIAAABi2st1QgAAwMcszHVCAACALX/MdUIAAECT0cx1QgAAAPkjzXVCAABAKhvOdUIAAACQbc51QgAAwPW/znVCAACAWxLPdUIAAEDBZM91QgAAgPJb0HVCAABAWK7QdUIAAAC+ANF1QgAAwCNT0XVCAACAiaXRdUIAAMC6nNJ1QgAAgCDv0nVCAABAhkHTdUIAAADsk9N1QgAAwFHm03VCAAAAg93UdUIAAMDoL9V1QgAAgE6C1XVCAABAtNTVdUIAAAAaJ9Z1QgAAQEse13VCAAAAsXDXdUIAAMAWw9d1QgAAgHwV2HVCAABA4mfYdUIAAIATX9l1QgAAQHmx2XVCAAAA3wPadUIAAMBEVtp1QgAAgKqo2nVCAADA25/bdUIAAIBB8tt1QgAAQKdE3HVCAAAADZfcdUIAAMBy6dx1QgAAAKTg3XVCAADACTPedUIAAIBvhd51QgAAQNXX3nVCAAAAOyrfdUIAAEBsIeB1QgAAANJz4HVCAADAN8bgdUIAAICdGOF1QgAAQANr4XVCAACANGLidUIAAECatOJ1QgAAAAAH43VCAADAZVnjdUIAAIDLq+N1QgAAwPyi5HVCAACAYvXkdUIAAEDIR+V1QgAAAC6a5XVCAADAk+zldUIAAADF4+Z1QgAAwCo253VCAACAkIjndUIAAED22ud1QgAAAFwt6HVCAABAjSTpdUIAAADzdul1QgAAwFjJ6XVCAACAvhvqdUIAAEAkbup1QgAAgFVl63VCAABAu7frdUIAAAAhCux1QgAAwIZc7HVCAACA7K7sdUIAAMAdpu11QgAAgIP47XVCAABA6UrudUIAAABPne51QgAAwLTv7nVCAAAA5ubvdUIAAMBLOfB1QgAAgLGL8HVCAABAF97wdUIAAAB9MPF1QgAAQK4n8nVCAAAAFHrydUIAAMB5zPJ1QgAAgN8e83VCAABARXHzdUIAAIB2aPR1QgAAQNy69HVCAAAAQg31dUIAAMCnX/V1QgAAgA2y9XVCAADAPqn2dUIAAICk+/Z1QgAAQApO93VCAAAAcKD3dUIAAMDV8vd1QgAAAAfq+HVCAADAbDz5dUIAAIDSjvl1QgAAQDjh+XVCAAAAnjP6dUIAAEDPKvt1QgAAADV9+3VCAADAms/7dUIAAIAAIvx1QgAAQGZ0/HVCAACAl2v9dUIAAED9vf11QgAAAGMQ/nVCAADAyGL+dUIAAIAutf51QgAAwF+s/3VCAACAxf7/dUIAAEArUQB2QgAAAJGjAHZCAADA9vUAdkIAAAAo7QF2QgAAwI0/AnZCAACA85ECdkIAAEBZ5AJ2QgAAAL82A3ZCAABA8C0EdkIAAABWgAR2QgAAwLvSBHZCAACAISUFdkIAAECHdwV2QgAAgLhuBnZCAABAHsEGdkIAAACEEwd2QgAAwOllB3ZCAACAT7gHdkIAAMCArwh2QgAAgOYBCXZCAABATFQJdkIAAACypgl2QgAAwBf5CXZCAAAASfAKdkIAAMCuQgt2QgAAgBSVC3ZCAABAeucLdkIAAADgOQx2QgAAQBExDXZCAAAAd4MNdkIAAMDc1Q12QgAAgEIoDnZCAABAqHoOdkIAAIDZcQ92QgAAQD/ED3ZCAAAApRYQdkIAAMAKaRB2QgAAgHC7EHZCAADAobIRdkIAAIAHBRJ2QgAAQG1XEnZCAAAA06kSdkIAAMA4/BJ2QgAAAGrzE3ZCAADAz0UUdkIAAIA1mBR2QgAAQJvqFHZCAAAAAT0VdkIAAEAyNBZ2QgAAAJiGFnZCAADA/dgWdkIAAIBjKxd2QgAAQMl9F3ZCAACA+nQYdkIAAEBgxxh2QgAAAMYZGXZCAADAK2wZdkIAAICRvhl2QgAAwMK1GnZCAACAKAgbdkIAAECOWht2QgAAAPSsG3ZCAADAWf8bdkIAAACL9hx2QgAAwPBIHXZCAACAVpsddkIAAEC87R12QgAAACJAHnZCAABAUzcfdkIAAAC5iR92QgAAwB7cH3ZCAACAhC4gdkIAAEDqgCB2QgAAgBt4IXZCAABAgcohdkIAAADnHCJ2QgAAwExvInZCAACAssEidkIAAMDjuCN2QgAAgEkLJHZCAABAr10kdkIAAAAVsCR2QgAAwHoCJXZCAAAArPkldkIAAMARTCZ2QgAAgHeeJnZCAABA3fAmdkIAAABDQyd2QgAAQHQ6KHZCAAAA2owodkIAAMA/3yh2QgAAgKUxKXZCAABAC4QpdkIAAIA8eyp2QgAAQKLNKnZCAAAACCArdkIAAMBtcit2QgAAgNPEK3ZCAADABLwsdkIAAIBqDi12QgAAQNBgLXZCAAAANrMtdkIAAMCbBS52QgAAAM38LnZCAADAMk8vdkIAAICYoS92QgAAQP7zL3ZCAAAAZEYwdkIAAECVPTF2QgAAAPuPMXZCAADAYOIxdkIAAIDGNDJ2QgAAQCyHMnZCAACAXX4zdkIAAEDD0DN2QgAAACkjNHZCAADAjnU0dkIAAID0xzR2QgAAwCW/NXZCAACAixE2dkIAAEDxYzZ2QgAAAFe2NnZCAADAvAg3dkIAAADu/zd2QgAAwFNSOHZCAACAuaQ4dkIAAEAf9zh2QgAAAIVJOXZCAABAtkA6dkIAAAAckzp2QgAAwIHlOnZCAACA5zc7dkIAAEBNijt2QgAAgH6BPHZCAABA5NM8dkIAAABKJj12QgAAwK94PXZCAACAFcs9dkIAAMBGwj52QgAAgKwUP3ZCAABAEmc/dkIAAAB4uT92QgAAwN0LQHZCAAAADwNBdkIAAMB0VUF2QgAAgNqnQXZCAABAQPpBdkIAAACmTEJ2QgAAQNdDQ3ZCAAAAPZZDdkIAAMCi6EN2QgAAgAg7RHZCAABAbo1EdkIAAICfhEV2QgAAQAXXRXZCAAAAaylGdkIAAMDQe0Z2QgAAgDbORnZCAADAZ8VHdkIAAIDNF0h2QgAAQDNqSHZCAAAAmbxIdkIAAMD+Dkl2QgAAADAGSnZCAADAlVhKdkIAAID7qkp2QgAAQGH9SnZCAAAAx09LdkIAAED4Rkx2QgAAAF6ZTHZCAADAw+tMdkIAAIApPk12QgAAQI+QTXZCAACAwIdOdkIAAEAm2k52QgAAAIwsT3ZCAADA8X5PdkIAAIBX0U92QgAAwIjIUHZCAACA7hpRdkIAAEBUbVF2QgAAALq/UXZCAADAHxJSdkIAAABRCVN2QgAAwLZbU3ZCAACAHK5TdkIAAECCAFR2QgAAAOhSVHZCAABAGUpVdkIAAAB/nFV2QgAAwOTuVXZCAACASkFWdkIAAECwk1Z2QgAAgOGKV3ZCAABAR91XdkIAAACtL1h2QgAAwBKCWHZCAACAeNRYdkIAAMCpy1l2QgAAgA8eWnZCAABAdXBadkIAAADbwlp2QgAAwEAVW3ZCAAAAcgxcdkIAAMDXXlx2QgAAgD2xXHZCAABAowNddkIAAAAJVl12QgAAQDpNXnZCAAAAoJ9edkIAAMAF8l52QgAAgGtEX3ZCAABA0ZZfdkIAAIACjmB2QgAAQGjgYHZCAAAAzjJhdkIAAMAzhWF2QgAAgJnXYXZCAADAys5idkIAAIAwIWN2QgAAQJZzY3ZCAAAA/MVjdkIAAMBhGGR2QgAAAJMPZXZCAADA+GFldkIAAIBetGV2QgAAQMQGZnZCAAAAKllmdkIAAEBbUGd2QgAAAMGiZ3ZCAADAJvVndkIAAICMR2h2QgAAQPKZaHZCAACAI5FpdkIAAECJ42l2QgAAAO81anZCAADAVIhqdkIAAIC62mp2QgAAwOvRa3ZCAACAUSRsdkIAAEC3dmx2QgAAAB3JbHZCAADAghttdkIAAAC0Em52QgAAwBllbnZCAACAf7dudkIAAEDlCW92QgAAAEtcb3ZCAABAfFNwdkIAAADipXB2QgAAwEf4cHZCAACArUpxdkIAAEATnXF2QgAAgESUcnZCAABAquZydkIAAAAQOXN2QgAAwHWLc3ZCAACA291zdkIAAMAM1XR2QgAAgHIndXZCAABA2Hl1dkIAAAA+zHV2QgAAwKMednZCAAAA1RV3dkIAAMA6aHd2QgAAgKC6d3ZCAABABg14dkIAAABsX3h2QgAAQJ1WeXZCAAAAA6l5dkIAAMBo+3l2QgAAgM5NenZCAABANKB6dkIAAIBll3t2QgAAQMvpe3ZCAAAAMTx8dkIAAMCWjnx2QgAAgPzgfHZCAADALdh9dkIAAICTKn52QgAAQPl8fnZCAAAAX89+dkIAAMDEIX92QgAAAPYYgHZCAADAW2uAdkIAAIDBvYB2QgAAQCcQgXZCAAAAjWKBdkIAAEC+WYJ2QgAAACSsgnZCAADAif6CdkIAAIDvUIN2QgAAQFWjg3ZCAACAhpqEdkIAAEDs7IR2QgAAAFI/hXZCAADAt5GFdkIAAIAd5IV2QgAAwE7bhnZCAACAtC2HdkIAAEAagId2QgAAAIDSh3ZCAADA5SSIdkIAAAAXHIl2QgAAwHxuiXZCAACA4sCJdkIAAEBIE4p2QgAAAK5linZCAABA31yLdkIAAABFr4t2QgAAwKoBjHZCAACAEFSMdkIAAEB2pox2QgAAgKedjXZCAABADfCNdkIAAABzQo52QgAAwNiUjnZCAACAPueOdkIAAMBv3o92QgAAgNUwkHZCAABAO4OQdkIAAACh1ZB2QgAAwAYokXZCAAAAOB+SdkIAAMCdcZJ2QgAAgAPEknZCAABAaRaTdkIAAADPaJN2QgAAQABglHZCAAAAZrKUdkIAAMDLBJV2QgAAgDFXlXZCAABAl6mVdkIAAIDIoJZ2QgAAQC7zlnZCAAAAlEWXdkIAAMD5l5d2QgAAgF/ql3ZCAADAkOGYdkIAAID2M5l2QgAAQFyGmXZCAAAAwtiZdkIAAMAnK5p2QgAAAFkim3ZCAADAvnSbdkIAAIAkx5t2QgAAQIoZnHZCAAAA8GucdkIAAEAhY512QgAAAIe1nXZCAADA7AeedkIAAIBSWp52QgAAQLisnnZCAACA6aOfdkIAAEBP9p92QgAAALVIoHZCAADAGpugdkIAAICA7aB2QgAAwLHkoXZCAACAFzeidkIAAEB9iaJ2QgAAAOPbonZCAADASC6jdkIAAAB6JaR2QgAAwN93pHZCAACARcqkdkIAAECrHKV2QgAAABFvpXZCAABAQmamdkIAAACouKZ2QgAAwA0Lp3ZCAACAc12ndkIAAEDZr6d2QgAAgAqnqHZCAABAcPmodkIAAADWS6l2QgAAwDueqXZCAACAofCpdkIAAMDS56p2QgAAgDg6q3ZCAABAnoyrdkIAAAAE36t2QgAAwGkxrHZCAAAAmyitdkIAAMAAe612QgAAgGbNrXZCAABAzB+udkIAAAAycq52QgAAQGNpr3ZCAAAAybuvdkIAAMAuDrB2QgAAgJRgsHZCAABA+rKwdkIAAIArqrF2QgAAQJH8sXZCAAAA906ydkIAAMBcobJ2QgAAgMLzsnZCAADA8+qzdkIAAIBZPbR2QgAAQL+PtHZCAAAAJeK0dkIAAMCKNLV2QgAAALwrtnZCAADAIX62dkIAAICH0LZ2QgAAQO0it3ZCAAAAU3W3dkIAAECEbLh2QgAAAOq+uHZCAADATxG5dkIAAIC1Y7l2QgAAQBu2uXZCAACATK26dkIAAECy/7p2QgAAABhSu3ZCAADAfaS7dkIAAIDj9rt2QgAAwBTuvHZCAACAekC9dkIAAEDgkr12QgAAAEblvXZCAADAqze+dkIAAADdLr92QgAAwEKBv3ZCAACAqNO/dkIAAEAOJsB2QgAAAHR4wHZCAABApW/BdkIAAAALwsF2QgAAwHAUwnZCAACA1mbCdkIAAEA8ucJ2QgAAgG2ww3ZCAABA0wLEdkIAAAA5VcR2QgAAwJ6nxHZCAACABPrEdkIAAMA18cV2QgAAgJtDxnZCAABAAZbGdkIAAABn6MZ2QgAAwMw6x3ZCAAAA/jHIdkIAAMBjhMh2QgAAgMnWyHZCAABALynJdkIAAACVe8l2QgAAQMZyynZCAAAALMXKdkIAAMCRF8t2QgAAgPdpy3ZCAABAXbzLdkIAAICOs8x2QgAAQPQFzXZCAAAAWljNdkIAAMC/qs12QgAAgCX9zXZCAADAVvTOdkIAAIC8Rs92QgAAQCKZz3ZCAAAAiOvPdkIAAMDtPdB2QgAAAB810XZCAADAhIfRdkIAAIDq2dF2QgAAQFAs0nZCAAAAtn7SdkIAAEDnddN2QgAAAE3I03ZCAADAshrUdkIAAIAYbdR2QgAAQH6/1HZCAACAr7bVdkIAAEAVCdZ2QgAAAHtb1nZCAADA4K3WdkIAAIBGANd2QgAAwHf313ZCAACA3UnYdkIAAEBDnNh2QgAAAKnu2HZCAADADkHZdkIAAABAONp2QgAAwKWK2nZCAACAC93adkIAAEBxL9t2QgAAANeB23ZCAABACHncdkIAAABuy9x2QgAAwNMd3XZCAACAOXDddkIAAECfwt12QgAAgNC53nZCAABANgzfdkIAAACcXt92QgAAwAGx33ZCAACAZwPgdkIAAMCY+uB2QgAAgP5M4XZCAABAZJ/hdkIAAADK8eF2QgAAwC9E4nZCAAAAYTvjdkIAAMDGjeN2QgAAgCzg43ZCAABAkjLkdkIAAAD4hOR2QgAAQCl85XZCAAAAj87ldkIAAMD0IOZ2QgAAgFpz5nZCAABAwMXmdkIAAIDxvOd2QgAAQFcP6HZCAAAAvWHodkIAAMAitOh2QgAAgIgG6XZCAADAuf3pdkIAAIAfUOp2QgAAQIWi6nZCAAAA6/TqdkIAAMBQR+t2QgAAAII+7HZCAADA55DsdkIAAIBN4+x2QgAAQLM17XZCAAAAGYjtdkIAAEBKf+52QgAAALDR7nZCAADAFSTvdkIAAIB7du92QgAAQOHI73ZCAACAEsDwdkIAAEB4EvF2QgAAAN5k8XZCAADAQ7fxdkIAAICpCfJ2QgAAwNoA83ZCAACAQFPzdkIAAECmpfN2QgAAAAz483ZCAADAcUr0dkIAAACjQfV2QgAAwAiU9XZCAACAbub1dkIAAEDUOPZ2QgAAADqL9nZCAABAa4L3dkIAAADR1Pd2QgAAwDYn+HZCAACAnHn4dkIAAEACzPh2QgAAgDPD+XZCAABAmRX6dkIAAAD/Z/p2QgAAwGS6+nZCAACAygz7dkIAAMD7A/x2QgAAgGFW/HZCAABAx6j8dkIAAAAt+/x2QgAAwJJN/XZCAAAAxET+dkIAAMApl/52QgAAgI/p/nZCAABA9Tv/dkIAAABbjv92QgAAQIyFAHdCAAAA8tcAd0IAAMBXKgF3QgAAgL18AXdCAABAI88Bd0IAAIBUxgJ3QgAAQLoYA3dCAAAAIGsDd0IAAMCFvQN3QgAAgOsPBHdCAADAHAcFd0IAAICCWQV3QgAAQOirBXdCAAAATv4Fd0IAAMCzUAZ3QgAAAOVHB3dCAADASpoHd0IAAICw7Ad3QgAAQBY/CHdCAAAAfJEId0IAAECtiAl3QgAAABPbCXdCAADAeC0Kd0IAAIDefwp3QgAAQETSCndCAACAdckLd0IAAEDbGwx3QgAAAEFuDHdCAADApsAMd0IAAIAMEw13QgAAwD0KDndCAACAo1wOd0IAAEAJrw53QgAAAG8BD3dCAADA1FMPd0IAAAAGSxB3QgAAwGudEHdCAACA0e8Qd0IAAEA3QhF3QgAAAJ2UEXdCAABAzosSd0IAAAA03hJ3QgAAwJkwE3dCAACA/4ITd0IAAEBl1RN3QgAAgJbMFHdCAABA/B4Vd0IAAABicRV3QgAAwMfDFXdCAACALRYWd0IAAMBeDRd3QgAAgMRfF3dCAABAKrIXd0IAAACQBBh3QgAAwPVWGHdCAAAAJ04Zd0IAAMCMoBl3QgAAgPLyGXdCAABAWEUad0IAAAC+lxp3QgAAQO+OG3dCAAAAVeEbd0IAAMC6Mxx3QgAAgCCGHHdCAABAhtgcd0IAAIC3zx13QgAAQB0iHndCAAAAg3Qed0IAAMDoxh53QgAAgE4ZH3dCAADAfxAgd0IAAIDlYiB3QgAAQEu1IHdCAAAAsQchd0IAAMAWWiF3QgAAAEhRIndCAADAraMid0IAAIAT9iJ3QgAAQHlII3dCAAAA35ojd0IAAEAQkiR3QgAAAHbkJHdCAADA2zYld0IAAIBBiSV3QgAAQKfbJXdCAACA2NImd0IAAEA+JSd3QgAAAKR3J3dCAADACcond0IAAIBvHCh3QgAAwKATKXdCAACABmYpd0IAAEBsuCl3QgAAANIKKndCAADAN10qd0IAAABpVCt3QgAAwM6mK3dCAACANPkrd0IAAECaSyx3QgAAAACeLHdCAABAMZUtd0IAAACX5y13QgAAwPw5LndCAACAYowud0IAAEDI3i53QgAAgPnVL3dCAABAXygwd0IAAADFejB3QgAAwCrNMHdCAACAkB8xd0IAAMDBFjJ3QgAAgCdpMndCAABAjbsyd0IAAADzDTN3QgAAw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Application\",\"version\":\"2.3.2\"}};\n",
       "    var render_items = [{\"docid\":\"72bdeb06-3c5c-46fa-b220-d6738dc274cc\",\"root_ids\":[\"5459\"],\"roots\":{\"5459\":\"84da602a-ff99-473f-9f46-348aa4bbf3d4\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Curve   [Date]   (VIX_close)"
      ]
     },
     "execution_count": 105,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "5459"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Presentation graphs of the SPY and the VIX\n",
    "\n",
    "presentation_graph=pd.concat([X1['spy_close'], 2.5*X1['VIX_close']],axis=1).hvplot(\n",
    "        title='VIX and S&P (Scale adjusted)',\n",
    "        width=1000\n",
    ")\n",
    "presentation_graph\n",
    "\n",
    "graph1=X1['spy_close'].hvplot(\n",
    "                title= \"SPY: iShares S&P 500 ETF Close Price\",\n",
    "                ylabel= 'Close Price [$]'\n",
    ") \n",
    "\n",
    "graph2=X1['VIX_close'].hvplot(\n",
    "                color ='red',\n",
    "                title ='VIX: CBOE Volatility Index',\n",
    "                ylabel= '[%]'\n",
    "\n",
    ")\n",
    "display(graph1)\n",
    "graph2 \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e51f3e",
   "metadata": {},
   "source": [
    "### X2: returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5a336bd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of returns\n"
     ]
    }
   ],
   "source": [
    "# Inclusion of security returns X2\n",
    "# Include returns that are correlated more than 0.20 with the Vix return\n",
    "\n",
    "security_returns_df= close_prices_df.pct_change()\n",
    "security_returns_component_df = correlation_filter(\n",
    "                                        security_returns_df, \n",
    "                                        min_corr=0.20, \n",
    "                                        key_column='^VIX', \n",
    "                                        eliminate_first_column=True \n",
    ")\n",
    "\n",
    "X2_no_suffix=security_returns_component_df\n",
    "\n",
    "X2=X2_no_suffix.add_suffix(\"_returns\")\n",
    "\n",
    "print(\"Completed inclusion of returns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c8d137",
   "metadata": {},
   "source": [
    "### X3: volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9be0acfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed retrieve of volume\n"
     ]
    }
   ],
   "source": [
    "# inclusion of security volume X3\n",
    "volume_list = ticker_list[1:len(ticker_list)]\n",
    "\n",
    "def retrieve_volume(volume_dict, volume_list):\n",
    "    \"\"\"\n",
    "    This function retrieve volume trades from a list of tickers\n",
    "    \n",
    "    Args:\n",
    "    volume_dict: an initial dictionary to populate\n",
    "    volume_list: the list of tickers for which to retrieve the volume\n",
    "    \n",
    "    \n",
    "    Return:\n",
    "    The original dictionary filled with the volume of the list of tickers\n",
    "    \"\"\"\n",
    "    for ticker in volume_list:        \n",
    "        volume = retrieve_yahoo_volume(ticker)\n",
    "        volume_dict[ticker] = volume\n",
    "    return volume_dict\n",
    "\n",
    "if demo_mode == True:\n",
    "    volume_df = pd.read_csv(\"adaboost_volume.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "else:\n",
    "    volume_dict = {}\n",
    "    volume_dict = retrieve_volume(volume_dict, volume_list)\n",
    "    volume_df= pd.DataFrame(volume_dict)\n",
    "    volume_df.to_csv(\"adaboost_volume.csv\", index=True)\n",
    "print(\"Completed retrieve of volume\")\n",
    "\n",
    "volume_df_with_vix=pd.concat([vix, volume_df], axis=1)\n",
    "#print(volume_df_with_vix.corr())\n",
    "\n",
    "volume_component_df = correlation_filter(volume_df_with_vix, min_corr=0.20, key_column='^VIX', eliminate_first_column=True )\n",
    "X3_no_suffix=volume_component_df\n",
    "\n",
    "X3=X3_no_suffix.add_suffix(\"_volume\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1c1854",
   "metadata": {},
   "source": [
    "### X4: GARCH Models\n",
    "    * GJR-GARCH Model conditional volatility\n",
    "    * Model a response to shocks\n",
    "    * Allow an asymmetryc t-student distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "05502d14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GARCH Process fit and predictions completed\n"
     ]
    }
   ],
   "source": [
    "# Inclusion of GARCH series X4\n",
    "garch_series=pd.DataFrame()\n",
    "\n",
    "not_to_include=['^VIX']\n",
    "\n",
    "\n",
    "for ticker in ticker_list:\n",
    "    \n",
    "        if ticker in not_to_include:\n",
    "            continue\n",
    "    \n",
    "        if demo_mode == True:\n",
    "            print_series = False\n",
    "        else:\n",
    "            print_series = True\n",
    "        garch_series[ticker]=garch_fit_and_predict(security_returns_df[ticker], ticker, horizon=1, p=1, q=1, o=1, print_series_name=print_series)\n",
    "            \n",
    "        \n",
    "X4_no_suffix=garch_series\n",
    "X4=X4_no_suffix.add_suffix(\"_garch\")\n",
    "\n",
    "if demo_mode == False:\n",
    "    X4\n",
    "\n",
    "print('GARCH Process fit and predictions completed')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed07fa22",
   "metadata": {},
   "source": [
    "### X5: Return squared\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4ee9ad89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of return squared\n"
     ]
    }
   ],
   "source": [
    "# Inclusion of return squares in X5\n",
    "\n",
    "returns_squared_df_no_vix= security_returns_df.drop(columns='^VIX')**2\n",
    "returns_squared_and_vix_level_df=pd.concat([vix,returns_squared_df_no_vix], axis=1)\n",
    "returns_squared_component_df = correlation_filter(returns_squared_and_vix_level_df, min_corr=0.20, key_column='^VIX', eliminate_first_column=True)\n",
    "\n",
    "X5_no_suffix_df=returns_squared_component_df\n",
    "X5=X5_no_suffix_df.add_suffix(\"_return_squared\")\n",
    "\n",
    "if demo_mode == False:\n",
    "    X5\n",
    "print(\"Completed inclusion of return squared\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518aacb5",
   "metadata": {},
   "source": [
    "### X6: Google Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d41dab87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google Trends load completed\n"
     ]
    }
   ],
   "source": [
    "# Upload of Google Tremds -- X6\n",
    "keywords=['banking', \"consumer\", \"depression\", \"gdp\", \"inflation\",\n",
    "          'unemployment', 'liquidity','cci', 'vix_word','jobless_claims']\n",
    "google_trends_df=pd.DataFrame()\n",
    "\n",
    "for keyword in keywords:\n",
    "    file_path=f\"./Resources/Google_trends/{keyword}.csv\"\n",
    "    if demo_mode == False:\n",
    "        print(file_path)\n",
    "    trend=pd.read_csv(Path(file_path),\n",
    "                      index_col= 'Daily', \n",
    "                      parse_dates= True,\n",
    "                      infer_datetime_format=True\n",
    "                     )\n",
    "    #print(trend)\n",
    "    google_trends_df=pd.concat([google_trends_df, trend], axis=1)\n",
    "    #print(google_trends_df)\n",
    "\n",
    "if demo_mode == False:\n",
    "    google_trends_df\n",
    "print('Google Trends load completed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d5ea3f7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Working on preparing Google-trends data\n",
    "\n",
    "# Unifying google dates with VIX\n",
    "minimum_date=vix.index.min()\n",
    "maximum_date=vix.index.max()\n",
    "\n",
    "google_trends_df=google_trends_df.loc[minimum_date:maximum_date,:]\n",
    "#print(google_trends_df.iloc[0,:])\n",
    "\n",
    "vix_google_trends_df=pd.concat([vix, google_trends_df], axis=1)\n",
    "vix_google_trends_df.isna().sum()\n",
    "\n",
    "#print(vix_google_trends_df.head())\n",
    "\n",
    "#vix_google_trends_df=vix_google_trends_df.fillna(0)\n",
    "#vix_google_trends_df\n",
    "#vix_google_trends_df.loc[vix_google_trends_df['^VIX'].isna(),['^VIX','Banking: (United States)']]\n",
    "\n",
    "# We will drop Saturday Sunday, but we would like to average Fri-Sat-Sun and reset the value of Friday\n",
    "vix_google_trends_df=vix_google_trends_df.dropna()\n",
    "google_trends_df=vix_google_trends_df.iloc[:,1:]\n",
    "#google_trends_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6955efc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google Trends process inclusion completed\n"
     ]
    }
   ],
   "source": [
    "# Filtering by correlation X6\n",
    "\n",
    "google_trends_component_df = correlation_filter(\n",
    "                                vix_google_trends_df, \n",
    "                                min_corr=0.05, \n",
    "                                key_column='^VIX', \n",
    "                                eliminate_first_column=True)\n",
    "\n",
    "X6=google_trends_component_df\n",
    "\n",
    "# We will interpolate so we can fill the missing data only on Google Trends\n",
    "pro_interpolation_of_X6=pd.concat([vix, X6], axis=1)\n",
    "pro_interpolation_of_X6=pro_interpolation_of_X6.interpolate(method=\"polynomial\", order=2, axis=0)\n",
    "pro_interpolation_of_X6\n",
    "X6_no_suffix_df = pro_interpolation_of_X6.iloc[:,1:]\n",
    "X6 = X6_no_suffix_df.add_suffix(\"_google_trends\")\n",
    "\n",
    "if demo_mode == False:\n",
    "    X6.shape\n",
    "print('Google Trends process inclusion completed')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6d57d1",
   "metadata": {},
   "source": [
    "### X7: Economic and Financial Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f04fe0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Economic Series\n",
    "# Upload of csv files -- X7\n",
    "keywords=['JobClaimsWeeklySeries', 'vix_put_call_ratio']\n",
    "economic_series_df=pd.DataFrame()\n",
    "\n",
    "for keyword in keywords:\n",
    "    file_path=f\"./Resources/Economic_and_financial_Series/{keyword}.csv\"\n",
    "    if demo_mode == False:\n",
    "        print(file_path)\n",
    "    new_series=pd.read_csv(Path(file_path),\n",
    "                      index_col= 'DATE', \n",
    "                      parse_dates= True,\n",
    "                      infer_datetime_format=True\n",
    "                     )\n",
    "    new_series=new_series.iloc[:,0]\n",
    "    if keyword=='JobClaimsWeeklySeries':\n",
    "        new_series=new_series.shift(-1, freq='D')\n",
    "    if demo_mode == False:\n",
    "        print(new_series)\n",
    "    # Adjustment due to weekend data. We are going to assign data on the weekends to Friday, since are going to be \n",
    "    # consider for the the prediction of Monday\n",
    "    economic_series_df=pd.concat([economic_series_df, new_series], axis=1)\n",
    "    #print(economic_series_df.tail())\n",
    "\n",
    "economic_series_df\n",
    "economic_series_change_df = economic_series_df.pct_change().add_suffix('_change')\n",
    "\n",
    "if demo_mode == False:\n",
    "    economic_series_df.loc[:,:].tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "dab1f54b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of economic variables\n"
     ]
    }
   ],
   "source": [
    "# Preparation of economic variables\n",
    "\n",
    "# Changes of columns that are on a weekend - concat with vix to add week days\n",
    "vix_economic= pd.concat([vix,economic_series_df,economic_series_change_df ],axis=1)\n",
    "vix_economic['VIX Put/Call Ratio']= vix_economic['VIX Put/Call Ratio'].fillna(0)\n",
    "\n",
    "# Applying interpolation to appropiate columns. Levels: interpolation, changes: zeros\n",
    "vix_economic.loc[:,economic_series_df.columns]=vix_economic.loc[:,economic_series_df.columns].interpolate(method=\"polynomial\", order=2, axis=0)\n",
    "vix_economic.loc[:,economic_series_change_df.columns]=vix_economic.loc[:,economic_series_change_df.columns].fillna(0)\n",
    "\n",
    "#print(vix_economic)\n",
    "\n",
    "#Filtering for available dates\n",
    "economic_series_ready_df = vix_economic.loc[minimum_date:maximum_date,:]\n",
    "economic_series_ready_df = economic_series_ready_df.iloc[:,1:]\n",
    "\n",
    "X7_no_suffix_df=economic_series_ready_df\n",
    "X7=X7_no_suffix_df.add_suffix(\"_macroeconomics\")\n",
    "\n",
    "print(\"Completed inclusion of economic variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b34932c",
   "metadata": {},
   "source": [
    "### X8: volatility of the SPY in several rolling windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "31eec39c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Close spy\n",
      "Inclusion of rolling volatilities completed\n"
     ]
    }
   ],
   "source": [
    "# SPY volatility on varios tracks X8\n",
    "\n",
    "#if demo_mode == True:\n",
    "#    close_price_spy_df = pd.read_csv(\"adaboost_spy_data.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "    \n",
    "# Retrieve yahoo close prices for spy, so to be able to calculate rolling volatilities\n",
    "#else:\n",
    "close_price_spy_df = retrieve_yahoo_close('spy', start_date='2005-01-01', end_date='2021-10-02')\n",
    "close_price_spy_df.to_csv(\"adaboost_spy_data.csv\", index=True)\n",
    "    \n",
    "# Calculate returns\n",
    "spy_returns_df=close_price_spy_df.pct_change()\n",
    "\n",
    "#Initialize dataframe for volatility\n",
    "spy_volatility=pd.DataFrame()\n",
    "\n",
    "# Define rolling windows to create\n",
    "windows_for_lag=[10,20,30,60,90,120,180,200,260]\n",
    "\n",
    "# Loop to create the volatilities\n",
    "for window_size in windows_for_lag:\n",
    "    column_name=f\"{window_size}_spy_rolling_volatility\"\n",
    "    spy_volatility[column_name] = spy_returns_df.rolling(window=window_size).std()\n",
    "\n",
    "# Concatenate to vix to uniform index\n",
    "X8=pd.concat([vix, spy_volatility], axis=1)\n",
    "\n",
    "# Define min and max values for the window\n",
    "X8=X8.loc[minimum_date:maximum_date,:]\n",
    "    \n",
    "# Fill missing data\n",
    "X8=X8.ffill()\n",
    "X8=X8.iloc[:,1:]\n",
    "\n",
    "# Setting for demo\n",
    "if demo_mode == False:\n",
    "    X8.shape\n",
    "print(\"Inclusion of rolling volatilities completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bdd50c",
   "metadata": {},
   "source": [
    "### X9: Technical Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "74350774",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of technical indicators\n"
     ]
    }
   ],
   "source": [
    "# Inclusion of Technical Indicators\n",
    "technical_indicators = pd.read_csv(\"technical_indicators.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "technical_indicators = technical_indicators.drop(columns=[\"vix close\", \"vix return\", \"mean\"])\n",
    "X9 = pd.concat([security_returns_df['spy'], technical_indicators], axis=1)\n",
    "X9=X9.loc[minimum_date:maximum_date,:]\n",
    "if demo_mode == False:\n",
    "    display(X9.shape)\n",
    "X9=X9.ffill()\n",
    "X9=X9.iloc[:,1:]\n",
    "\n",
    "\n",
    "if demo_mode == False:\n",
    "    display(X9.shape)\n",
    "# display(X9.isna().sum())\n",
    "print(\"Completed inclusion of technical indicators\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede37326",
   "metadata": {},
   "source": [
    "### X10: Day of the Week effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b09ceb7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of day of the week effect\n"
     ]
    }
   ],
   "source": [
    "#Inclusion of day of week data\n",
    "day_of_week = pd.read_csv(\"prophet_output_day_of_week.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "day_of_week = day_of_week.drop(columns=[\"y\"])\n",
    "X10 = pd.concat([security_returns_df['spy'], day_of_week], axis=1)\n",
    "X10 = X10.loc[minimum_date:maximum_date,:]\n",
    "if demo_mode == False:\n",
    "    display(X10.shape)\n",
    "X10 = X10.ffill()\n",
    "X10 = X10.iloc[:,1:]\n",
    "if demo_mode == False:\n",
    "    display(X10.shape)\n",
    "#display(X10.isna().sum())\n",
    "print(\"Completed inclusion of day of the week effect\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bb99a9",
   "metadata": {},
   "source": [
    "### X11: Neural Netework prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e6af2bae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed inclusion of the Neural Network prediction\n"
     ]
    }
   ],
   "source": [
    "#Inclusion of neural network data\n",
    "predictions_train_test_df = pd.read_csv(\"neural_network_output.csv\", index_col=\"Date\", parse_dates=True, infer_datetime_format=True)\n",
    "X11 = pd.concat([security_returns_df['spy'], predictions_train_test_df], axis=1)\n",
    "X11 = X11.loc[minimum_date:maximum_date,:]\n",
    "if demo_mode == False:\n",
    "    display(X11.shape)\n",
    "X11 = X11.ffill()\n",
    "X11 = X11.fillna(0)\n",
    "X11 = X11.iloc[:,1:]\n",
    "if demo_mode == False:\n",
    "    display(X11.shape)\n",
    "print(\"Completed inclusion of the Neural Network prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c73eac0",
   "metadata": {},
   "source": [
    "# GENERATION OF THE FEATURE MATRIX **X**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "81d83e69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XY.shape: (3980, 201), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X1.shape: (3980, 21), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X2.shape: (3980, 28), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X3.shape: (3980, 21), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X4.shape: (3979, 39), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X5.shape: (3980, 33), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X6.shape: (3980, 4), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X7.shape: (3980, 4), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X8.shape: (3980, 9), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X9.shape: (3980, 36), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X10.shape: (3980, 5), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n",
      "X11.shape: (3980, 1), 2006-07-03 00:00:00, 2021-10-01 00:00:00 \n"
     ]
    }
   ],
   "source": [
    "# Concatenation of all sources of data\n",
    "XY=pd.concat([X1, X2, X3, X4, X5, X6, X7, X8, X9, X10, X11], axis=1)\n",
    "if parameter_tuning_mode == True:\n",
    "    print(XY.shape)\n",
    "\n",
    "XY.dropna(subset = ['VIX_close', 'VIX_ret_close'])\n",
    "if parameter_tuning_mode == True:\n",
    "    print(XY.shape)\n",
    "\n",
    "# Interpolation is not applied to numerical variables. We are just going to drop those.\n",
    "print(f\"XY.shape: {XY.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X1.shape: {X1.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X2.shape: {X2.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X3.shape: {X3.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X4.shape: {X4.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X5.shape: {X5.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X6.shape: {X6.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X7.shape: {X7.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X8.shape: {X8.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X9.shape: {X9.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X10.shape: {X10.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "print(f\"X11.shape: {X11.shape}, {XY.index.min()}, {XY.index.max()} \")\n",
    "\n",
    "#display(XY.isna().head(40))\n",
    "#display(XY.isna().sum().tail(40))\n",
    "#XY=XY.dropna()\n",
    "if parameter_tuning_mode == True:\n",
    "    XY.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72622893",
   "metadata": {},
   "source": [
    "# y:  Set the Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "bee6c7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007-07-13   -0.025097\n",
       "2007-07-16    0.029043\n",
       "2007-07-17    0.002566\n",
       "2007-07-18    0.023672\n",
       "2007-07-19   -0.048125\n",
       "                ...   \n",
       "2021-09-27    0.056901\n",
       "2021-09-28    0.239339\n",
       "2021-09-29   -0.029677\n",
       "2021-09-30    0.025709\n",
       "2021-10-01   -0.088159\n",
       "Freq: B, Name: VIX_ret_close, Length: 3711, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the Signal column\n",
    "vix_ret=vix.pct_change()\n",
    "XY[\"Signal\"] = 0.0\n",
    "\n",
    "XY.loc[(XY['VIX_ret_close'] >= threshold), 'Signal'] = 1\n",
    "\n",
    "# # Generate the trading signals 1 (entry) or -1 (exit)\n",
    "# # where 1 is when the ^VIX is greater than 3.6%.\n",
    "# # where 0 is when the ^VIX  is less than 3.6%.\n",
    "#for index, row in XY.iterrows():\n",
    "#    if row[\"VIX_ret\"] >= 0.036:\n",
    "#        XY.loc[index, \"Signal\"] = 1.0\n",
    "\n",
    "# Review the DataFrame\n",
    "if parameter_tuning_mode == True:\n",
    "    print(XY[\"Signal\"].head())\n",
    "    XY[\"Signal\"].value_counts()\n",
    "XY.loc[XY[\"Signal\"]==1, 'VIX_ret_close']\n",
    "#XY.shape  ## 3981\n",
    "XY.loc['2007-07-13':,'VIX_ret_close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e2808aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Validation on missing data on VIX\n",
    "vix_ret=vix.pct_change()\n",
    "vix_ret[vix_ret>=threshold].index\n",
    "vix_ret.shape\n",
    "\n",
    "\n",
    "# How many values of the vix we missed due to missing data on other series\n",
    "compare=pd.concat([XY.loc[XY[\"Signal\"]==1, 'VIX_ret_close'],vix_ret[vix_ret>=threshold] ], axis=1)\n",
    "missing_dates=compare.loc[compare[\"VIX_ret_close\"]!=compare[\"^VIX\"]]\n",
    "missing_dates=missing_dates.index\n",
    "missing_dates\n",
    "if parameter_tuning_mode == True:\n",
    "    vix[missing_dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "05ed9632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the target set y using the Signal column\n",
    "y = XY[\"Signal\"]\n",
    "# Display a sample of y\n",
    "if parameter_tuning_mode == True:\n",
    "    y\n",
    "#pd.concat([vix_ret,y], axis=1).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dcaa07bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outputs for the model tuning\n",
    "\n",
    "if parameter_tuning_mode == True:\n",
    "    display(y.isna().sum())\n",
    "    display(y.shape)\n",
    "    display(XY.shape)\n",
    "    display(XY.drop(columns=[\"Signal\"]).isna().sum())\n",
    "    display(XY.drop(columns=['Signal']).shift().isna().sum())\n",
    "    display(XY.drop(columns=['Signal']).shift().dropna().shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f9f0d7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up of X, y and outputs for the model tuning\n",
    "\n",
    "XY_modified = XY.shift().dropna()\n",
    "if parameter_tuning_mode == True:\n",
    "    display(XY_modified.shape)\n",
    "\n",
    "y = XY_modified[\"Signal\"].shift(-1)\n",
    "\n",
    "X = XY_modified\n",
    "\n",
    "if parameter_tuning_mode == True:\n",
    "    display(y.shape)\n",
    "    display(X.shape)\n",
    "    display(y.isna().sum())\n",
    "    display(X.isna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5dcb637c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review the features DataFrame\n",
    "if parameter_tuning_mode == True:\n",
    "    X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "99e71f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review of correlations\n",
    "if parameter_tuning_mode == True:\n",
    "    X.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65aa5b2",
   "metadata": {},
   "source": [
    "# Split of data in Train and Test (I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d28904cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing subsets\n",
    "\n",
    "def split_training_test_data(X, y):\n",
    "    \"\"\"\n",
    "    This function split the preprocessed data of a time series into two windows: a training window and a testing window,\n",
    "    Its give back the features and signals divded accordingly\n",
    "    \n",
    "    Args:\n",
    "    X: a pandas dataframe with the features in its columns, using a datetime index\n",
    "    y: a pandas dataframe with the signal, using a datetime index\n",
    "    \n",
    "    Return:\n",
    "    Two pandas dataframes and two series in the following order:\n",
    "    X_train: a pandas dataframe with the features of the train window\n",
    "    y_train: a pandas series with the signals of the train window \n",
    "    X_test: a pandas dataframe with the featires in the test window\n",
    "    y_test: a pandas series with the signals of the train window \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "\n",
    "    training_begin = X.index.min()\n",
    "    training_end = X.index.min() + DateOffset(months=training_period_months)\n",
    "\n",
    "    X_train = X.loc[training_begin:training_end]\n",
    "    y_train = y.loc[training_begin:training_end]\n",
    "\n",
    "    X_test = X.loc[training_end + DateOffset(days=1):]\n",
    "    y_test = y.loc[training_end + DateOffset(days=1):]\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        print(f\"Training dates: {training_begin} to {training_end}\")\n",
    "        display(y_train.value_counts())\n",
    "        display(y_test.shape)\n",
    "        display(X_test.shape)\n",
    "        display(X_train.shape)\n",
    "        display(y_train.shape)\n",
    "        display(X_train.tail(1))\n",
    "        display(X_test.head(1))\n",
    "    return X_train, y_train, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_test, y_test = split_training_test_data(X, y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ca3b8555",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling of the data\n",
    "\n",
    "def standard_scale(X_train, X_test):\n",
    "    \"\"\"\n",
    "    This function apply standard scaling to a divided set of features divided as train and test data\n",
    "    \n",
    "    Args:\n",
    "    The tow dataframes:\n",
    "    X_train: a pandas dataframe with features of the training window\n",
    "    X_test: a pandas dataframe with features of the test window\n",
    "    \n",
    "    Return:\n",
    "    Two arrays coming from the original dataframes after applying StandardScaler(), where the standarization is made using the X_train features\n",
    "    \"\"\"\n",
    "    # Create a StandardScaler instance\n",
    "    scaler =  StandardScaler() # MinMaxScaler() #\n",
    " \n",
    "    # Apply the scaler model to fit the X-train data\n",
    "    X_scaler = scaler.fit(X_train)\n",
    "\n",
    "    # Transform the X_train and X_test DataFrames using the X_scaler\n",
    "    X_train_scaled = X_scaler.transform(X_train)\n",
    "    X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(X_train_scaled.shape)\n",
    "        display(X_test_scaled.shape)\n",
    "    return X_train_scaled, X_test_scaled\n",
    "    \n",
    "X_train_scaled, X_test_scaled = standard_scale(X_train, X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f474f4a",
   "metadata": {},
   "source": [
    "### X lags: calculate principal components of Train data, and lagged them \n",
    "number of components tuned to 4, and considered lags: t=5 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1b70a9e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculation of Principal Components\n",
    "\n",
    "def adaboost_pca(X_train, X_test):\n",
    "    \n",
    "    \"\"\"\n",
    "    This function calculates the principal components of an X features matrix, already divided in a train and a test set.\n",
    "    \n",
    "    Args:\n",
    "    X_train: a pandas dataframe with the training set of features\n",
    "    X_test: a pandas dataframe with the test set of features\n",
    "    \n",
    "    \n",
    "    Returns:\n",
    "    An X pandas dataframe of features, with the train and test samples concatenated, which correspond\n",
    "    to the principal components of the original data, calculated using the transformation calculated from the train set of data.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Initiate and calculate principal components transformation based on the train data\n",
    "    pca = PCA(n_components = num_pca_components)\n",
    "    pca.fit(X_train)\n",
    "    \n",
    "    # Calculate train and test principal components using the trained model\n",
    "    principal_components_train = pca.transform(X_train)\n",
    "    principal_components_test  = pca.transform(X_test)\n",
    "    \n",
    "    # Name principal components columns properly\n",
    "    pca_column_list = []\n",
    "    for i in range(1, num_pca_components+1):\n",
    "        pca_column_list.append(f\"pca{i}\")\n",
    "\n",
    "    #Concatenate train and test principal components in one dataframe called principal_components_train_test_df\n",
    "    principal_components_train_test = np.concatenate((principal_components_train, principal_components_test), axis = 0)\n",
    "    principal_components_train_test_df = pd.DataFrame(data = principal_components_train_test, columns = pca_column_list, index = X.index)\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(sum(pca.explained_variance_ratio_))\n",
    "        display(principal_components_train_test_df.shape)\n",
    "        display(principal_components_train_test_df.head(5))\n",
    "    return principal_components_train_test_df\n",
    "principal_components_train_test_df = adaboost_pca(X_train_scaled, X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ea9646ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generation of lag principal components to include historical movements of the data in the model. We call this set LAG.\n",
    "\n",
    "def create_pca_lag(principal_components_train_test_df, shift_amount):\n",
    "    \"\"\"\n",
    "    This function creates a new dataframe by taking the first 3 first principal components (in the first three columns) and shifting them in a desire number\n",
    "    \n",
    "    Args:\n",
    "    principal_components_train_test_df: a pandas dataframe \n",
    "    shift_amount: the number of positions to shift the first three columnms\n",
    "    \n",
    "    Return:\n",
    "    A pandas dataframe containing the shifted 3 first columns of the dataframe,\n",
    "    with column names 'pca1_lag1','pca2_lag1','pca3_lag1'\n",
    "    \"\"\"\n",
    "    X_pc_lag = principal_components_train_test_df.iloc[:,0:(number_of_pca_lag_component_to_include-1)]\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(X_pc_lag.shape)\n",
    "\n",
    "    X_pc_lag.columns = ['pca1_lag1','pca2_lag1','pca3_lag1']\n",
    "    X_pc_lag = X_pc_lag.shift(shift_amount)\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        print(X_pc_lag)\n",
    "        X_pc_lag.shape\n",
    "    return X_pc_lag\n",
    "\n",
    "# Shift the LAG components by 1\n",
    "X_pca_lag1 = create_pca_lag(principal_components_train_test_df, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f10861fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift the LAG components by 2    \n",
    "X_pca_lag2 = create_pca_lag(principal_components_train_test_df, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "a588ac2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift the LAG components by 3\n",
    "X_pca_lag3 = create_pca_lag(principal_components_train_test_df, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1d4c1664",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift the LAG Components by 4\n",
    "X_pca_lag4 = create_pca_lag(principal_components_train_test_df, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "adce37ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift the LAG Components by 5\n",
    "X_pca_lag5 = create_pca_lag(principal_components_train_test_df, 5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4b3666e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def concatenate_lags(X_pc_lag1, X_pc_lag2, X_pc_lag3, X_pc_lag4, X_pc_lag5):\n",
    "    X_pc_lags=pd.concat([X_pc_lag1, \n",
    "                         X_pc_lag2, \n",
    "                         X_pc_lag3, \n",
    "                         X_pc_lag4, \n",
    "                         X_pc_lag5], \n",
    "                         axis=1\n",
    "    )\n",
    "    \n",
    "    if parameter_tuning_mode == True:\n",
    "        X_pc_lags.shape\n",
    "    return X_pc_lags\n",
    "\n",
    "X_pc_lags = concatenate_lags(X_pca_lag1, X_pca_lag2, X_pca_lag3, X_pca_lag4, X_pca_lag5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "7ea04430",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenation of all variables in X_pc, storing current variables plus lagged principal components\n",
    "\n",
    "def combine_train_test(X_train, X_test):\n",
    "    \"\"\"\n",
    "    This function concantenate the train and test arrays, and apply the proper index, to get back to an X dataframe\n",
    "    \n",
    "    Args:\n",
    "    X_train, X_test: arrays to concatenate, wich should have the same number of columns\n",
    "    \n",
    "    Return:\n",
    "    The X dataframe as a pandas dataframe, and the index as the last X dataframe of features\n",
    "    \"\"\"\n",
    "    \n",
    "    X_combined = np.concatenate([X_train, X_test], axis = 0)\n",
    "    X_combined = pd.DataFrame(data = X_combined, index=X.index)\n",
    "    return X_combined\n",
    "\n",
    "def concatenate_with_pca_lags(X_raw, X_pc_lags):\n",
    "    \"\"\"\n",
    "    This function concatenates all the sources of data: features and lags principal compoents.\n",
    "    It also eliminates NaNs due to lag\n",
    "    \n",
    "    Args:\n",
    "    X_raw: the combination of X_train and X_test features, excluding principal compoentns\n",
    "    X_pc_lags: the 3 main principal components of the set of features, lagged in 1, 2, 3, 4 and 5 days\n",
    "    \n",
    "    Returns:\n",
    "    A dataframe containing the concatenation of all features and principal components without missing values, with the proper datetime index\n",
    "    \"\"\"\n",
    "    \n",
    "    X_pc = pd.concat([X_raw, X_pc_lags], axis=1)\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        print(X_pc.shape)\n",
    "    return X_pc\n",
    "\n",
    "X_scaled_df = combine_train_test(X_train_scaled, X_test_scaled)\n",
    "X_pc = concatenate_with_pca_lags(X_scaled_df, X_pc_lags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d917f9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying outputs for tuning\n",
    "if parameter_tuning_mode == True:\n",
    "    print(f\"principal_components_train_test_df.shape: {principal_components_train_test_df.shape}, {principal_components_train_test_df.index.min()}, {principal_components_train_test_df.index.max()} \")\n",
    "    print(f\"X_pc_lags.shape: {X_pc_lags.shape}, {X_pc_lags.index.min()}, {X_pc_lags.index.max()} \")\n",
    "    print(f\"X_pc.shape: {X_pc.shape}, {X_pc.index.min()}, {X_pc.index.max()} \")\n",
    "    print(f\"y.shape: {y.shape}, {y.index.min()}, {y.index.max()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "fbc14f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Elimination of missing data in principal components\n",
    "\n",
    "def eliminate_nans_in_pca_data(X_pc, y):\n",
    "    X_pc = X_pc[5:-1]\n",
    "    y = y[5:-1]\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(X_pc.shape)\n",
    "        display(y.shape)\n",
    "    return X_pc, y\n",
    "\n",
    "X_pc, y = eliminate_nans_in_pca_data(X_pc, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "159cef92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redefinition of X and y with extended X, to apply convention to feature data\n",
    "X = X_pc\n",
    "\n",
    "if parameter_tuning_mode == True:\n",
    "    print(X.shape)\n",
    "    y.shape\n",
    "    \n",
    "column_names = [*X_train.columns, *X_pc_lags.columns]\n",
    "X.columns = column_names \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55baef4",
   "metadata": {},
   "source": [
    "# Split the data in train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4a7c9ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split of data in train and test, applying temporal window function that respect time series order, which is defined above in cell 177\n",
    "\n",
    "X_train, y_train, X_test, y_test = split_training_test_data(X, y)\n",
    "X_train_scaled, X_test_scaled = standard_scale(X_train, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "20f054a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting unique columns names to be able to apply random over sample model\n",
    "column_name_list = []\n",
    "for i in range(0, len(X.columns)):\n",
    "    column_name_list.append(f\"f_{i}\")\n",
    "X_train_unique_columns = X_train.copy()\n",
    "X_train_unique_columns.columns = column_name_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "f52a29cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random oversample was applied since depending on the Threshold (above which return we are predicting), the sample can get highly unbalanced\n",
    "# For the case of threshold = 0\n",
    "\n",
    "def random_over_sample(X_train, y_train):\n",
    "    # Use RandomOverSampler to resample the dataset using random_state=1\n",
    "    ros = RandomOverSampler(random_state = 1)\n",
    "    X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(y_train_resampled.value_counts())\n",
    "    return X_train_resampled, y_train_resampled\n",
    "\n",
    "X_train_resampled, y_train_resampled = random_over_sample(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f946b47c",
   "metadata": {},
   "source": [
    "# Adaboost Model Estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "4bc0bea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(learning_rate=1, n_estimators=22)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instance AdaBoost\n",
    "# Initiate the model instance\n",
    "adaboost_model=AdaBoostClassifier(n_estimators=adaboost_estimators, learning_rate= learning_rate_adaboost )\n",
    "adaboost_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "00b465d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tunning for the model\n",
    "if parameter_tuning_mode == True:\n",
    "    display(X_train_resampled.shape)\n",
    "    display(y_train_resampled.shape)\n",
    "    display(X_test_scaled.shape)\n",
    "    display(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "3c682ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model \n",
    "adaboost_model =adaboost_model.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "pred_adaboost=adaboost_model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a835bba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "if demo_mode == False and parameter_tuning_mode == True:\n",
    "    display(np.any(np.isnan(y_test)))\n",
    "    display(np.all(np.isfinite(y_test)))\n",
    "    display(np.any(np.isnan(pred_adaboost)))\n",
    "    display(np.all(np.isfinite(pred_adaboost)))\n",
    "    display(y_test.shape)\n",
    "    display(pred_adaboost.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "750fd042",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         AdaBoost Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.61      0.62      0.62       543\n",
      "         1.0       0.53      0.52      0.53       443\n",
      "\n",
      "    accuracy                           0.58       986\n",
      "   macro avg       0.57      0.57      0.57       986\n",
      "weighted avg       0.58      0.58      0.58       986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Use a classification report to evaluate the model using the predictions and testing data\n",
    "adaboost_report=classification_report(y_test, pred_adaboost)\n",
    "\n",
    "# Print the classification report\n",
    "print(\"         AdaBoost Classification Report\")\n",
    "print(adaboost_report)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed3fcf97",
   "metadata": {},
   "source": [
    "# Analysis of feature importance in the AdaBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "44f3d8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, VIX_close, Score: 0.0\n",
      "Feature: 1, VIX_ret_close, Score: 0.0\n",
      "Feature: 2, spy_close, Score: 0.0\n",
      "Feature: 3, XLF_close, Score: 0.0\n",
      "Feature: 4, XLE_close, Score: 0.0\n",
      "Feature: 5, USDJPY=X_close, Score: 0.0\n",
      "Feature: 6, NKD=F_close, Score: 0.0\n",
      "Feature: 7, LQD_close, Score: 0.0\n",
      "Feature: 8, GE_close, Score: 0.0\n",
      "Feature: 9, BMY_close, Score: 0.0\n",
      "Feature: 10, FDX_close, Score: 0.0\n",
      "Feature: 11, GS_close, Score: 0.045454545454545456\n",
      "Feature: 12, FXI_close, Score: 0.0\n",
      "Feature: 13, EZU_close, Score: 0.0\n",
      "Feature: 14, EEM_close, Score: 0.0\n",
      "Feature: 15, EFA_close, Score: 0.0\n",
      "Feature: 16, FEZ_close, Score: 0.0\n",
      "Feature: 17, ^GDAXI_close, Score: 0.0\n",
      "Feature: 18, ^FTSE_close, Score: 0.045454545454545456\n",
      "Feature: 19, ^HSI_close, Score: 0.0\n",
      "Feature: 20, ^FCHI_close, Score: 0.0\n",
      "Feature: 21, spy_returns, Score: 0.0\n",
      "Feature: 22, XLF_returns, Score: 0.0\n",
      "Feature: 23, XLE_returns, Score: 0.0\n",
      "Feature: 24, ^TNX_returns, Score: 0.0\n",
      "Feature: 25, ZB=F_returns, Score: 0.0\n",
      "Feature: 26, ZF=F_returns, Score: 0.0\n",
      "Feature: 27, NQ=F_returns, Score: 0.0\n",
      "Feature: 28, NKD=F_returns, Score: 0.0\n",
      "Feature: 29, AAPL_returns, Score: 0.0\n",
      "Feature: 30, AMZN_returns, Score: 0.0\n",
      "Feature: 31, GE_returns, Score: 0.0\n",
      "Feature: 32, MU_returns, Score: 0.0\n",
      "Feature: 33, MSFT_returns, Score: 0.0\n",
      "Feature: 34, BMY_returns, Score: 0.0\n",
      "Feature: 35, FDX_returns, Score: 0.0\n",
      "Feature: 36, GS_returns, Score: 0.0\n",
      "Feature: 37, PLD_returns, Score: 0.0\n",
      "Feature: 38, NVDA_returns, Score: 0.0\n",
      "Feature: 39, tlt_returns, Score: 0.0\n",
      "Feature: 40, ief_returns, Score: 0.0\n",
      "Feature: 41, FXI_returns, Score: 0.0\n",
      "Feature: 42, EZU_returns, Score: 0.0\n",
      "Feature: 43, EEM_returns, Score: 0.0\n",
      "Feature: 44, EFA_returns, Score: 0.0\n",
      "Feature: 45, FEZ_returns, Score: 0.045454545454545456\n",
      "Feature: 46, ^GDAXI_returns, Score: 0.0\n",
      "Feature: 47, ^FTSE_returns, Score: 0.0\n",
      "Feature: 48, ^FCHI_returns, Score: 0.0\n",
      "Feature: 49, spy_volume, Score: 0.0\n",
      "Feature: 50, XLF_volume, Score: 0.0\n",
      "Feature: 51, XLE_volume, Score: 0.0\n",
      "Feature: 52, NQ=F_volume, Score: 0.0\n",
      "Feature: 53, AAPL_volume, Score: 0.0\n",
      "Feature: 54, AMZN_volume, Score: 0.0\n",
      "Feature: 55, GE_volume, Score: 0.0\n",
      "Feature: 56, MSFT_volume, Score: 0.0\n",
      "Feature: 57, BMY_volume, Score: 0.0\n",
      "Feature: 58, FDX_volume, Score: 0.0\n",
      "Feature: 59, GS_volume, Score: 0.0\n",
      "Feature: 60, PLD_volume, Score: 0.045454545454545456\n",
      "Feature: 61, NVDA_volume, Score: 0.0\n",
      "Feature: 62, FXI_volume, Score: 0.0\n",
      "Feature: 63, EEM_volume, Score: 0.0\n",
      "Feature: 64, EFA_volume, Score: 0.0\n",
      "Feature: 65, ^GDAXI_volume, Score: 0.0\n",
      "Feature: 66, ^FTSE_volume, Score: 0.0\n",
      "Feature: 67, ^HSI_volume, Score: 0.0\n",
      "Feature: 68, ^FCHI_volume, Score: 0.0\n",
      "Feature: 69, gld_volume, Score: 0.045454545454545456\n",
      "Feature: 70, spy_garch, Score: 0.0\n",
      "Feature: 71, XLF_garch, Score: 0.0\n",
      "Feature: 72, XLE_garch, Score: 0.0\n",
      "Feature: 73, EURUSD=X_garch, Score: 0.0\n",
      "Feature: 74, GBPUSD=X_garch, Score: 0.0\n",
      "Feature: 75, AUDUSD=X_garch, Score: 0.0\n",
      "Feature: 76, BRLUSD=X_garch, Score: 0.0\n",
      "Feature: 77, DX-Y.NYB_garch, Score: 0.0\n",
      "Feature: 78, USDJPY=X_garch, Score: 0.0\n",
      "Feature: 79, ^TNX_garch, Score: 0.0\n",
      "Feature: 80, ZB=F_garch, Score: 0.0\n",
      "Feature: 81, ZF=F_garch, Score: 0.0\n",
      "Feature: 82, NQ=F_garch, Score: 0.045454545454545456\n",
      "Feature: 83, NKD=F_garch, Score: 0.045454545454545456\n",
      "Feature: 84, LQD_garch, Score: 0.045454545454545456\n",
      "Feature: 85, AAPL_garch, Score: 0.0\n",
      "Feature: 86, AMZN_garch, Score: 0.0\n",
      "Feature: 87, GE_garch, Score: 0.0\n",
      "Feature: 88, MU_garch, Score: 0.0\n",
      "Feature: 89, MSFT_garch, Score: 0.045454545454545456\n",
      "Feature: 90, BMY_garch, Score: 0.0\n",
      "Feature: 91, FDX_garch, Score: 0.0\n",
      "Feature: 92, GS_garch, Score: 0.0\n",
      "Feature: 93, PLD_garch, Score: 0.0\n",
      "Feature: 94, NVDA_garch, Score: 0.0\n",
      "Feature: 95, tlt_garch, Score: 0.0\n",
      "Feature: 96, ief_garch, Score: 0.0\n",
      "Feature: 97, FXI_garch, Score: 0.0\n",
      "Feature: 98, EZU_garch, Score: 0.0\n",
      "Feature: 99, EEM_garch, Score: 0.0\n",
      "Feature: 100, EFA_garch, Score: 0.0\n",
      "Feature: 101, FEZ_garch, Score: 0.0\n",
      "Feature: 102, ^GDAXI_garch, Score: 0.0\n",
      "Feature: 103, ^FTSE_garch, Score: 0.0\n",
      "Feature: 104, ^HSI_garch, Score: 0.0\n",
      "Feature: 105, ^FCHI_garch, Score: 0.0\n",
      "Feature: 106, gld_garch, Score: 0.0\n",
      "Feature: 107, slv_garch, Score: 0.0\n",
      "Feature: 108, CL=F_garch, Score: 0.0\n",
      "Feature: 109, spy_return_squared, Score: 0.045454545454545456\n",
      "Feature: 110, XLF_return_squared, Score: 0.0\n",
      "Feature: 111, XLE_return_squared, Score: 0.0\n",
      "Feature: 112, GBPUSD=X_return_squared, Score: 0.045454545454545456\n",
      "Feature: 113, BRLUSD=X_return_squared, Score: 0.0\n",
      "Feature: 114, DX-Y.NYB_return_squared, Score: 0.0\n",
      "Feature: 115, ^TNX_return_squared, Score: 0.0\n",
      "Feature: 116, ZF=F_return_squared, Score: 0.0\n",
      "Feature: 117, NQ=F_return_squared, Score: 0.0\n",
      "Feature: 118, NKD=F_return_squared, Score: 0.0\n",
      "Feature: 119, LQD_return_squared, Score: 0.0\n",
      "Feature: 120, AAPL_return_squared, Score: 0.045454545454545456\n",
      "Feature: 121, GE_return_squared, Score: 0.0\n",
      "Feature: 122, MU_return_squared, Score: 0.045454545454545456\n",
      "Feature: 123, MSFT_return_squared, Score: 0.0\n",
      "Feature: 124, BMY_return_squared, Score: 0.045454545454545456\n",
      "Feature: 125, FDX_return_squared, Score: 0.0\n",
      "Feature: 126, GS_return_squared, Score: 0.0\n",
      "Feature: 127, PLD_return_squared, Score: 0.0\n",
      "Feature: 128, NVDA_return_squared, Score: 0.0\n",
      "Feature: 129, tlt_return_squared, Score: 0.045454545454545456\n",
      "Feature: 130, ief_return_squared, Score: 0.0\n",
      "Feature: 131, FXI_return_squared, Score: 0.0\n",
      "Feature: 132, EZU_return_squared, Score: 0.0\n",
      "Feature: 133, EEM_return_squared, Score: 0.0\n",
      "Feature: 134, EFA_return_squared, Score: 0.0\n",
      "Feature: 135, FEZ_return_squared, Score: 0.0\n",
      "Feature: 136, ^GDAXI_return_squared, Score: 0.0\n",
      "Feature: 137, ^FTSE_return_squared, Score: 0.0\n",
      "Feature: 138, ^HSI_return_squared, Score: 0.0\n",
      "Feature: 139, ^FCHI_return_squared, Score: 0.0\n",
      "Feature: 140, gld_return_squared, Score: 0.045454545454545456\n",
      "Feature: 141, slv_return_squared, Score: 0.045454545454545456\n",
      "Feature: 142, Banking: (United States)_google_trends, Score: 0.0\n",
      "Feature: 143, Consumer: (United States)_google_trends, Score: 0.0\n",
      "Feature: 144, VIX:(United States)_google_trends, Score: 0.0\n",
      "Feature: 145, jobless claims: (United States)_google_trends, Score: 0.0\n",
      "Feature: 146, ICSA_macroeconomics, Score: 0.0\n",
      "Feature: 147, VIX Put/Call Ratio_macroeconomics, Score: 0.0\n",
      "Feature: 148, ICSA_change_macroeconomics, Score: 0.0\n",
      "Feature: 149, VIX Put/Call Ratio_change_macroeconomics, Score: 0.045454545454545456\n",
      "Feature: 150, 10_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 151, 20_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 152, 30_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 153, 60_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 154, 90_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 155, 120_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 156, 180_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 157, 200_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 158, 260_spy_rolling_volatility, Score: 0.0\n",
      "Feature: 159, sma 10, Score: 0.0\n",
      "Feature: 160, sma 15, Score: 0.0\n",
      "Feature: 161, sma 20, Score: 0.0\n",
      "Feature: 162, sma 50, Score: 0.0\n",
      "Feature: 163, sma 100, Score: 0.0\n",
      "Feature: 164, sma 150, Score: 0.0\n",
      "Feature: 165, sma 200, Score: 0.0\n",
      "Feature: 166, sma 250, Score: 0.0\n",
      "Feature: 167, a15, Score: 0.0\n",
      "Feature: 168, a50, Score: 0.0\n",
      "Feature: 169, a100, Score: 0.0\n",
      "Feature: 170, a150, Score: 0.0\n",
      "Feature: 171, a200, Score: 0.0\n",
      "Feature: 172, b15, Score: 0.0\n",
      "Feature: 173, b50, Score: 0.0\n",
      "Feature: 174, b100, Score: 0.0\n",
      "Feature: 175, b150, Score: 0.0\n",
      "Feature: 176, b200, Score: 0.0\n",
      "Feature: 177, vix_pos_return, Score: 0.0\n",
      "Feature: 178, vix_neg_return, Score: 0.0\n",
      "Feature: 179, c_1_15, Score: 0.0\n",
      "Feature: 180, c_1_50, Score: 0.0\n",
      "Feature: 181, c_1_100, Score: 0.0\n",
      "Feature: 182, c_1_150, Score: 0.0\n",
      "Feature: 183, c_1_200, Score: 0.0\n",
      "Feature: 184, c_pos_returns, Score: 0.0\n",
      "Feature: 185, c_neg_returns, Score: 0.0\n",
      "Feature: 186, c_3_15, Score: 0.0\n",
      "Feature: 187, c_3_50, Score: 0.0\n",
      "Feature: 188, c_3_100, Score: 0.0\n",
      "Feature: 189, c_3_150, Score: 0.0\n",
      "Feature: 190, c_3_200, Score: 0.0\n",
      "Feature: 191, c_3_pos_returns, Score: 0.0\n",
      "Feature: 192, c_3_neg_returns, Score: 0.0\n",
      "Feature: 193, >b_upper, Score: 0.0\n",
      "Feature: 194, <b_lower, Score: 0.0\n",
      "Feature: 195, Friday, Score: 0.045454545454545456\n",
      "Feature: 196, Monday, Score: 0.0\n",
      "Feature: 197, Thursday, Score: 0.0\n",
      "Feature: 198, Tuesday, Score: 0.0\n",
      "Feature: 199, Wednesday, Score: 0.0\n",
      "Feature: 200, neural network output, Score: 0.0\n",
      "Feature: 201, Signal, Score: 0.0\n",
      "Feature: 202, pca1_lag1, Score: 0.0\n",
      "Feature: 203, pca2_lag1, Score: 0.0\n",
      "Feature: 204, pca3_lag1, Score: 0.045454545454545456\n",
      "Feature: 205, pca1_lag1, Score: 0.0\n",
      "Feature: 206, pca2_lag1, Score: 0.0\n",
      "Feature: 207, pca3_lag1, Score: 0.045454545454545456\n",
      "Feature: 208, pca1_lag1, Score: 0.0\n",
      "Feature: 209, pca2_lag1, Score: 0.045454545454545456\n",
      "Feature: 210, pca3_lag1, Score: 0.0\n",
      "Feature: 211, pca1_lag1, Score: 0.0\n",
      "Feature: 212, pca2_lag1, Score: 0.0\n",
      "Feature: 213, pca3_lag1, Score: 0.0\n",
      "Feature: 214, pca1_lag1, Score: 0.0\n",
      "Feature: 215, pca2_lag1, Score: 0.0\n",
      "Feature: 216, pca3_lag1, Score: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Analysis of importance of the difference variables\n",
    "# get importance\n",
    "adaboost_importance_coeficients=adaboost_model.feature_importances_\n",
    "\n",
    "feature_importance_df=pd.Series(\n",
    "                                adaboost_importance_coeficients, \n",
    "                                index=X.columns )\n",
    "\n",
    "# Output of all levels\n",
    "for i,v in enumerate(adaboost_importance_coeficients):\n",
    "    #if v !=0:\n",
    "        print(f\"Feature: {i}, {X.columns[i]}, Score: {v}\" )\n",
    "        \n",
    "\n",
    "# Display of only features that impacted the model\n",
    "#n_important_features=feature_importance_df.loc[feature_importance_df>0].shape[0]\n",
    "#feature_importance_df.loc[feature_importance_df>0].hvplot(\n",
    "#                                            kind='barh', \n",
    "#                                            height=500,\n",
    "#                                            title= f\"{n_important_features} Features relevant for the VIX Prediction Model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b253ec",
   "metadata": {},
   "source": [
    "# Profitability Analysis\n",
    "The large size of VIX returns would make a huge compounding effect if it were possible to bet on it and track its performance,.\n",
    "As can be seen below, the average size of daily returns are around 5%, with returns that can get as large as 115% in one day. The median is \n",
    "\n",
    "In this section we will compare the results of betting on the model applying an imaginary strategy of betting at the open of the day, and close the bet at the end of the day. We will assume that the approximately the open level will be very similar than the close level of the previous day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4ceaa1e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3979.000000\n",
       "mean        5.325115\n",
       "std         6.011000\n",
       "min         0.000000\n",
       "25%         1.523817\n",
       "50%         3.750796\n",
       "75%         7.161985\n",
       "max       115.597925\n",
       "Name: ^VIX, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VIX size of returns (in absolute value and %)\n",
    "vix_return_statistics=abs(100 * security_returns_df['^VIX']).describe()\n",
    "display(vix_return_statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f001ae73",
   "metadata": {},
   "source": [
    "### In-sample analysis: Return on $1 invested on training data window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "23333fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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RxQgAAwH1IlXFCAACA45qVcUIAAEBJ7ZVxQgAAgHrklnFCAABA4DaXcUIAAABGiZdxQgAAwKvbl3FCAACAES6YcUIAAMBCJZlxQgAAgKh3mXFCAABADsqZcUIAAAB0HJpxQgAAAAtmm3FCAACA1gqccUIAAEA8XZxxQgAAAKKvnHFCAABA06adcUIAAMCeS55xQgAAgASennFCAABAavCecUIAAICb559xQgAAQAE6oHFCAAAAZ4ygcUIAAMDM3qBxQgAAgDIxoXFCAADAYyiicUIAAEAvzaJxQgAAAJUfo3FCAADA+nGjcUIAAAAsaaRxQgAAwJG7pHFCAACA9w2lcUIAAEBdYKVxQgAAAMOypXFCAABA9KmmcUIAAMC/TqdxQgAAgCWhp3FCAABAi/OncUIAAIC86qhxQgAAQCI9qXFCAAAAiI+pcUIAAMDt4alxQgAAgFM0qnFCAADAhCurcUIAAIDqfatxQgAAQFDQq3FCAAAAtiKscUIAAMAbdaxxQgAAAE1srXFCAADAsr6tcUIAAEB+Y65xQgAAAHv/r3FCAADA4FGwcUIAAIBGpLBxQgAAQKz2sHFCAACA3e2xcUIAAEBDQLJxQgAAAKmSsnFCAADADuWycUIAAIB0N7NxQgAAwKUutHFCAACAC4G0cUIAAEBx07RxQgAAANcltXFCAADAPHi1cUIAAABub7ZxQgAAwNPBtnFCAACAORS3cUIAAECfZrdxQgAAAAW5t3FCAABANrC4cUIAAACcArlxQgAAwAFVuXFCAABAzfm5cUIAAID+8LpxQgAAQGRDu3FCAAAAypW7cUIAAMAv6LtxQgAAgJU6vHFCAADAxjG9cUIAAIAshL1xQgAAQJLWvXFCAAAA+Ci+cUIAAMBde75xQgAAgFoXwHFCAABAwGnAcUIAAAAmvMBxQgAAQFezwXFCAADAIljCcUIAAICIqsJxQgAAQO78wnFCAACAH/TDcUIAAECFRsRxQgAAAOuYxHFCAADAUOvEcUIAAIC2PcVxQgAAwOc0xnFCAABAs9nGcUIAAAAZLMdxQgAAwH5+x3FCAAAAsHXIcUIAAMAVyMhxQgAAgHsayXFCAABA4WzJcUIAAABHv8lxQgAAQHi2ynFCAAAA3gjLcUIAAMBDW8txQgAAQA8AzHFCAACAQPfMcUIAAECmSc1xQgAAwHHuzXFCAACA10DOcUIAAMAIOM9xQgAAgG6Kz3FCAABA1NzPcUIAAAA6L9BxQgAAwJ+B0HFCAAAA0XjRcUIAAMA2y9FxQgAAgJwd0nFCAABAAnDScUIAAABowtJxQgAAQJm503FCAAAA/wvUcUIAAMBkXtRxQgAAgMqw1HFCAABAMAPVcUIAAIBh+tVxQgAAQMdM1nFCAAAALZ/WcUIAAMCS8dZxQgAAgPhD13FCAADAKTvYcUIAAICPjdhxQgAAQPXf2HFCAAAAWzLZcUIAAMDAhNlxQgAAAPJ72nFCAADAV87acUIAAIC9INtxQgAAQCNz23FCAAAAicXbcUIAAEC6vNxxQgAAACAP3XFCAADAhWHdcUIAAIDrs91xQgAAQOhP33FCAAAATqLfcUIAAMCz9N9xQgAAgBlH4HFCAADASj7hcUIAAICwkOFxQgAAQBbj4XFCAAAAfDXicUIAAMDhh+JxQgAAABN/43FCAADAeNHjcUIAAIDeI+RxQgAAQER25HFCAAAAqsjkcUIAAEDbv+VxQgAAAEES5nFCAADApmTmcUIAAEAJU+hxQgAAAG+l6HFCAADAa0HqcUIAAIDRk+pxQgAAQDfm6nFCAAAAnTjrcUIAAMACi+txQgAAADSC7HFCAADAmdTscUIAAID/Ju1xQgAAQGV57XFCAAAAy8vtcUIAAED8wu5xQgAAwMdn73FCAACALbrvcUIAAECTDPBxQgAAgMQD8XFCAACAW03ycUIAAMCMRPNxQgAAgPKW83FCAABAWOnzcUIAAAC+O/RxQgAAwCOO9HFCAAAAVYX1cUIAAMC61/VxQgAAgCAq9nFCAABAhnz2cUIAAADszvZxQgAAQB3G93FCAADA6Gr4cUIAAIBOvfhxQgAAQLQP+XFCAACA5Qb6cUIAAEBLWfpxQgAAALGr+nFCAADAFv76cUIAAIB8UPtxQgAAwK1H/HFCAACAE5r8cUIAAEB57PxxQgAAAN8+/XFCAADARJH9cUIAAAB2iP5xQgAAwNva/nFCAACAQS3/cUIAAECnf/9xQgAAAA3S/3FCAABAPskAckIAAACkGwFyQgAAwAluAXJCAACAb8ABckIAAEDVEgJyQgAAgAYKA3JCAABAbFwDckIAAADSrgNyQgAAwDcBBHJCAACAnVMEckIAAMDOSgVyQgAAgDSdBXJCAABAmu8FckIAAAAAQgZyQgAAwGWUBnJCAAAAl4sHckIAAMD83QdyQgAAgGIwCHJCAABAyIIIckIAAAAu1QhyQgAAwCpxCnJCAACAkMMKckIAAED2FQtyQgAAgCcNDHJCAABAjV8MckIAAADzsQxyQgAAwFgEDXJCAACAvlYNckIAAMDvTQ5yQgAAgFWgDnJCAABAu/IOckIAAAAhRQ9yQgAAwIaXD3JCAACAgzMRckIAAEDphRFyQgAAAE/YEXJCAABAgM8SckIAAADmIRNyQgAAwEt0E3JCAACAscYTckIAAEAXGRRyQgAAgEgQFXJCAABArmIVckIAAAAUtRVyQgAAwHkHFnJCAACA31kWckIAAMAQURdyQgAAQNz1F3JCAAAAQkgYckIAAADZkRlyQgAAwD7kGXJCAACApDYackIAAEAKiRpyQgAAAHDbGnJCAABAodIbckIAAAAHJRxyQgAAwGx3HHJCAACA0skcckIAAEA4HB1yQgAAgGkTHnJCAABAz2UeckIAAAA1uB5yQgAAwJoKH3JCAACAAF0fckIAAMAxVCByQgAAgJemIHJCAABA/fggckIAAABjSyFyQgAAwMidIXJCAAAA+pQickIAAMBf5yJyQgAAgMU5I3JCAAAAkd4jckIAAAAoKCVyQgAAwI16JXJCAACA88wlckIAAEBZHyZyQgAAgIoWJ3JCAABA8GgnckIAAABWuydyQgAAwLsNKHJCAACAIWAockIAAMBSVylyQgAAgLipKXJCAABAHvwpckIAAACETipyQgAAwOmgKnJCAAAAG5grckIAAMCA6ityQgAAgOY8LHJCAABATI8sckIAAACy4SxyQgAAQOPYLXJCAAAASSsuckIAAMCufS5yQgAAgBTQLnJCAABAeiIvckIAAICrGTByQgAAQBFsMHJCAAAAd74wckIAAMDcEDFyQgAAgEJjMXJCAADAc1oyckIAAIDZrDJyQgAAQD//MnJCAAAApVEzckIAAMAKpDNyQgAAADybNHJCAADAoe00ckIAAIAHQDVyQgAAQG2SNXJCAAAA0+Q1ckIAAEAE3DZyQgAAwM+AN3JCAACANdM3ckIAAECbJThyQgAAgMwcOXJCAAAAmME5ckIAAMD9EzpyQgAAgGNmOnJCAADAlF07ckIAAID6rztyQgAAQGACPHJCAAAAxlQ8ckIAAMArpzxyQgAAAF2ePXJCAADAwvA9ckIAAIAoQz5yQgAAQI6VPnJCAAAA9Oc+ckIAAEAl3z9yQgAAAIsxQHJCAADA8INAckIAAIBW1kByQgAAgO0fQnJCAABAU3JCckIAAAC5xEJyQgAAwB4XQ3JCAACAhGlDckIAAMC1YERyQgAAgBuzRHJCAABAgQVFckIAAADnV0VyQgAAwEyqRXJCAAAAfqFGckIAAMDj80ZyQgAAgElGR3JCAABAr5hHckIAAAAV60dyQgAAQEbiSHJCAADAEYdJckIAAIB32UlyQgAAQN0rSnJCAACADiNLckIAAEB0dUtyQgAAANrHS3JCAADAPxpMckIAAIClbExyQgAAwNZjTXJCAACAPLZNckIAAECiCE5yQgAAAAhbTnJCAADAba1OckIAAACfpE9yQgAAwAT3T3JCAACAaklQckIAAEDQm1ByQgAAADbuUHJCAABAZ+VRckIAAADNN1JyQgAAwDKKUnJCAACAmNxSckIAAECVeFRyQgAAAPvKVHJCAADAYB1VckIAAIDGb1VyQgAAwPdmVnJCAACAXblWckIAAEDDC1dyQgAAACleV3JCAADAjrBXckIAAADAp1hyQgAAwCX6WHJCAACAi0xZckIAAEDxnllyQgAAAFfxWXJCAABAiOhackIAAADuOltyQgAAwFONW3JCAACAud9bckIAAAAczl1yQgAAwIEgXnJCAACAfrxfckIAAEDkDmByQgAAAEphYHJCAADAr7NgckIAAADhqmFyQgAAwEb9YXJCAACArE9ickIAAEASomJyQgAAAHj0YnJCAABAqetjckIAAMB0kGRyQgAAgNriZHJCAABAQDVlckIAAIBxLGZyQgAAQNd+ZnJCAAAAPdFmckIAAMCiI2dyQgAAgAh2Z3JCAADAOW1ockIAAICfv2hyQgAAQAUSaXJCAAAAa2RpckIAAMDQtmlyQgAAAAKuanJCAADAZwBrckIAAIDNUmtyQgAAQDOla3JCAAAAmfdrckIAAEDK7mxyQgAAgPvlbXJCAABAYThuckIAAICSL29yQgAAQPiBb3JCAAAAXtRvckIAAMDDJnByQgAAgCl5cHJCAADAWnBxckIAAIDAwnFyQgAAQCYVcnJCAAAAjGdyckIAAMDxuXJyQgAAACOxc3JCAADAiAN0ckIAAIDuVXRyQgAAQFSodHJCAAAAuvp0ckIAAEDr8XVyQgAAAFFEdnJCAADAtpZ2ckIAAIAc6XZyQgAAQII7d3JCAACAszJ4ckIAAEAZhXhyQgAAAH/XeHJCAADA5Cl5ckIAAIBKfHlyQgAAwHtzenJCAACA4cV6ckIAAEBHGHtyQgAAAK1qe3JCAADAEr17ckIAAEB1q31yQgAAANv9fXJCAABADPV+ckIAAAByR39yQgAAwNeZf3JCAACAPex/ckIAAECjPoByQgAAgNQ1gXJCAABAOoiBckIAAACg2oFyQgAAwAUtgnJCAACAa3+CckIAAMCcdoNyQgAAgALJg3JCAABAaBuEckIAAADObYRyQgAAwDPAhHJCAAAAZbeFckIAAIAwXIZyQgAAQJauhnJCAAAA/ACHckIAAEAt+IdyQgAAAJNKiHJCAADA+JyIckIAAIBe74hyQgAAQMRBiXJCAACA9TiKckIAAEBbi4pyQgAAAMHdinJCAADAJjCLckIAAICMgotyQgAAgCPMjHJCAABAiR6NckIAAADvcI1yQgAAwFTDjXJCAAAAhrqOckIAAIBRX49yQgAAQLexj3JCAAAAHQSQckIAAEBO+5ByQgAAALRNkXJCAADAGaCRckIAAIB/8pFyQgAAQOVEknJCAACAFjyTckIAAEB8jpNyQgAAAOLgk3JCAACArYWUckIAAMDefJVyQgAAgETPlXJCAABAqiGWckIAAAAQdJZyQgAAwHXGlnJCAAAAp72XckIAAMAMEJhyQgAAgHJimHJCAABA2LSYckIAAEBv/plyQgAAwDqjmnJCAACAoPWackIAAEAGSJtyQgAAgDc/nHJCAABAnZGcckIAAAAD5JxyQgAAwGg2nXJCAACAzoidckIAAMD/f55yQgAAgGXSnnJCAABAyySfckIAAAAxd59yQgAAwJbJn3JCAAAAyMCgckIAAMAtE6FyQgAAgJNloXJCAABA+behckIAAABfCqJyQgAAQJABo3JCAAAA9lOjckIAAMBbpqNyQgAAgMH4o3JCAABAJ0ukckIAAIBYQqVyQgAAQL6UpXJCAAAAJOelckIAAMCJOaZyQgAAgO+LpnJCAADAIIOnckIAAICG1adyQgAAQOwnqHJCAAAAUnqockIAAMC3zKhyQgAAAOnDqXJCAADAThaqckIAAIC0aKpyQgAAQBq7qnJCAAAAgA2rckIAAECxBKxyQgAAwHyprHJCAACA4vusckIAAEBITq1yQgAAgHlFrnJCAAAARequckIAAMCqPK9yQgAAgBCPr3JCAADAQYawckIAAICn2LByQgAAQA0rsXJCAAAAc32xckIAAMDYz7FyQgAAAArHsnJCAADAbxmzckIAAIDVa7NyQgAAQDu+s3JCAABA0ge1ckIAAAA4WrVyQgAAwJ2stXJCAACAA/+1ckIAAEBpUbZyQgAAQACbt3JCAAAAZu23ckIAAMDLP7hyQgAAgDGSuHJCAADAYom5ckIAAIDI27lyQgAAQC4uunJCAAAAlIC6ckIAAMD50rpyQgAAACvKu3JCAADAkBy8ckIAAID2brxyQgAAQFzBvHJCAAAAwhO9ckIAAEDzCr5yQgAAAFldvnJCAADAvq++ckIAAIAkAr9yQgAAQIpUv3JCAACAu0vAckIAAEAhnsByQgAAAIfwwHJCAADA7ELBckIAAIBSlcFyQgAAwIOMwnJCAACA6d7CckIAAEBPMcNyQgAAALWDw3JCAADAGtbDckIAAABMzcRyQgAAwLEfxXJCAACAF3LFckIAAEB9xMVyQgAAAOMWxnJCAABAFA7HckIAAAB6YMdyQgAAwN+yx3JCAACARQXIckIAAEBCoclyQgAAAKjzyXJCAADADUbKckIAAIBzmMpyQgAAwKSPy3JCAACACuLLckIAAEBwNMxyQgAAANaGzHJCAADAO9nMckIAAABt0M1yQgAAwNIiznJCAACAOHXOckIAAECex85yQgAAAAQaz3JCAABANRHQckIAAACbY9ByQgAAwAC20HJCAACAZgjRckIAAEDMWtFyQgAAwC5J03JCAACAlJvTckIAAECRN9VyQgAAAPeJ1XJCAADAXNzVckIAAACO09ZyQgAAwPMl13JCAACAWXjXckIAAEC/ytdyQgAAACUd2HJCAABAVhTZckIAAMAhudlyQgAAgIcL2nJCAABA7V3ackIAAIAeVdtyQgAAQISn23JCAAAA6vnbckIAAMBPTNxyQgAAgLWe3HJCAADA5pXdckIAAIBM6N1yQgAAQLI63nJCAADAFCngckIAAIB6e+ByQgAAQODN4HJCAAAARiDhckIAAEB3F+JyQgAAAN1p4nJCAADAQrzickIAAICoDuNyQgAAQA5h43JCAACAP1jkckIAAAAL/eRyQgAAwHBP5XJCAACA1qHlckIAAMAHmeZyQgAAgG3r5nJCAABA0z3nckIAAAA5kOdyQgAAwJ7i53JCAAAA0NnockIAAMA1LOlyQgAAgJt+6XJCAABAAdHpckIAAABnI+pyQgAAQJga63JCAAAA/mzrckIAAMBjv+tyQgAAgMkR7HJCAABAL2TsckIAAIBgW+1yQgAAQMat7XJCAAAALADuckIAAMCRUu5yQgAAgPek7nJCAADAKJzvckIAAICO7u9yQgAAQPRA8HJCAAAAWpPwckIAAMC/5fByQgAAAPHc8XJCAADAVi/yckIAAEAi1PJyQgAAAIgm83JCAABAuR30ckIAAAAfcPRyQgAAwITC9HJCAACA6hT1ckIAAEBQZ/VyQgAAgIFe9nJCAABA57D2ckIAAABNA/dyQgAAwLJV93JCAACAGKj3ckIAAEAVRPlyQgAAAHuW+XJCAADA4Oj5ckIAAIDdhPtyQgAAQEPX+3JCAAAAqSn8ckIAAEDaIP1yQgAAAEBz/XJCAACACxj+ckIAAEBxav5yQgAAgKJh/3JCAABACLT/ckIAAABuBgBzQgAAwNNYAHNCAACAOasAc0IAAMBqogFzQgAAgND0AXNCAABANkcCc0IAAACcmQJzQgAAwAHsAnNCAAAAM+MDc0IAAID+hwRzQgAAQGTaBHNCAAAAyiwFc0IAAED7IwZzQgAAwMbIBnNCAACALBsHc0IAAECSbQdzQgAAgMNkCHNCAABAKbcIc0IAAACPCQlzQgAAwPRbCXNCAACAWq4Jc0IAAMCLpQpzQgAAgPH3CnNCAABAV0oLc0IAAAC9nAtzQgAAwCLvC3NCAAAAVOYMc0IAAMC5OA1zQgAAgB+LDXNCAABAhd0Nc0IAAADrLw5zQgAAwOfLD3NCAACATR4Qc0IAAECzcBBzQgAAgORnEXNCAABASroRc0IAAACwDBJzQgAAwBVfEnNCAACAe7ESc0IAAMCsqBNzQgAAgBL7E3NCAABAeE0Uc0IAAADenxRzQgAAwEPyFHNCAAAAdekVc0IAAMDaOxZzQgAAgECOFnNCAABApuAWc0IAAAAMMxdzQgAAQD0qGHNCAAAAo3wYc0IAAMAIzxhzQgAAgG4hGXNCAABA1HMZc0IAAIAFaxpzQgAAQGu9GnNCAAAA0Q8bc0IAAMA2YhtzQgAAgJy0G3NCAADAzascc0IAAIAz/hxzQgAAQJlQHXNCAAAA/6Idc0IAAMBk9R1zQgAAAJbsHnNCAADA+z4fc0IAAIBhkR9zQgAAQMfjH3NCAAAALTYgc0IAAEBeLSFzQgAAwCnSIXNCAACAjyQic0IAAED1diJzQgAAgCZuI3NCAAAA8hIkc0IAAMBXZSRzQgAAgL23JHNCAADA7q4lc0IAAIBUASZzQgAAACCmJnNCAADAhfgmc0IAAAC37ydzQgAAwBxCKHNCAACAgpQoc0IAAEDo5ihzQgAAAE45KXNCAABAfzAqc0IAAADlgipzQgAAwErVKnNCAACAsCcrc0IAAIBHcSxzQgAAABMWLXNCAACA3rotc0IAAMAPsi5zQgAAgHUEL3NCAABA21Yvc0IAAABBqS9zQgAAwKb7L3NCAAAA2PIwc0IAAMA9RTFzQgAAgKOXMXNCAABACeoxc0IAAABvPDJzQgAAQKAzM3NCAAAABoYzc0IAAMBr2DNzQgAAgNEqNHNCAABAN300c0IAAIBodDVzQgAAQM7GNXNCAAAANBk2c0IAAMCZazZzQgAAgP+9NnNCAADAMLU3c0IAAICWBzhzQgAAQPxZOHNCAAAAYqw4c0IAAMDH/jhzQgAAAPn1OXNCAADAXkg6c0IAAIDEmjpzQgAAQCrtOnNCAAAAkD87c0IAAEDBNjxzQgAAACeJPHNCAADAjNs8c0IAAIDyLT1zQgAAQO/JPnNCAAAAVRw/c0IAAMC6bj9zQgAAgCDBP3NCAADAUbhAc0IAAIC3CkFzQgAAQB1dQXNCAAAAg69Bc0IAAMDoAUJzQgAAABr5QnNCAADAf0tDc0IAAIDlnUNzQgAAQEvwQ3NCAAAAsUJEc0IAAEDiOUVzQgAAAEiMRXNCAADArd5Fc0IAAIATMUZzQgAAQHmDRnNCAACAqnpHc0IAAMDbcUhzQgAAgEHESHNCAADAcrtJc0IAAEA+YEpzQgAAAKSySnNCAADACQVLc0IAAAA7/EtzQgAAwKBOTHNCAACABqFMc0IAAEBs80xzQgAAANJFTXNCAABAAz1Oc0IAAMDO4U5zQgAAgDQ0T3NCAABAmoZPc0IAAIDLfVBzQgAAgGLHUXNCAADAk75Sc0IAAID5EFNzQgAAQF9jU3NCAAAAxbVTc0IAAMAqCFRzQgAAAFz/VHNCAADAwVFVc0IAAIAnpFVzQgAAQI32VXNCAAAA80hWc0IAAEAkQFdzQgAAAIqSV3NCAADA7+RXc0IAAIBVN1hzQgAAQLuJWHNCAACA7IBZc0IAAAC4JVpzQgAAwB14WnNCAACAg8pac0IAAMC0wVtzQgAAgBoUXHNCAABAgGZcc0IAAADmuFxzQgAAwEsLXXNCAAAAfQJec0IAAMDiVF5zQgAAgEinXnNCAABArvlec0IAAAAUTF9zQgAAQEVDYHNCAAAAq5Vgc0IAAMAQ6GBzQgAAgHY6YXNCAABA3Ixhc0IAAIANhGJzQgAAANkoY3NCAADAPntjc0IAAICkzWNzQgAAwNXEZHNCAACAOxdlc0IAAEChaWVzQgAAAAe8ZXNCAADAbA5mc0IAAACeBWdzQgAAwANYZ3NCAACAaapnc0IAAAA1T2hzQgAAwDHraXNCAACAlz1qc0IAAED9j2pzQgAAgC6Ha3NCAABAlNlrc0IAAAD6K2xzQgAAwF9+bHNCAACAxdBsc0IAAMD2x21zQgAAgFwabnNCAABAwmxuc0IAAAAov25zQgAAwI0Rb3NCAAAAvwhwc0IAAMAkW3BzQgAAQPD/cHNCAAAAVlJxc0IAAECHSXJzQgAAwFLucnNCAACAuEBzc0IAAEAek3NzQgAAgE+KdHNCAABAtdx0c0IAAAAbL3VzQgAAwICBdXNCAACA5tN1c0IAAMAXy3ZzQgAAgH0dd3NCAABA4293c0IAAABJwndzQgAAwK4UeHNCAAAA4At5c0IAAICrsHlzQgAAQBEDenNCAAAAd1V6c0IAAECoTHtzQgAAgNlDfHNCAABAP5Z8c0IAAIBwjX1zQgAAQNbffXNCAAAAPDJ+c0IAAMChhH5zQgAAgAfXfnNCAADAOM5/c0IAAICeIIBzQgAAQARzgHNCAAAAasWAc0IAAMDPF4FzQgAAAAEPgnNCAADAZmGCc0IAAIDMs4JzQgAAQDIGg3NCAAAAmFiDc0IAAEDJT4RzQgAAQGCZhXNCAACAkZCGc0IAAED34oZzQgAAAF01h3NCAADAwoeHc0IAAIAo2odzQgAAwFnRiHNCAACAvyOJc0IAAEAldolzQgAAAIvIiXNCAADA8BqKc0IAAAAiEotzQgAAwIdki3NCAACA7baLc0IAAEBTCYxzQgAAALlbjHNCAABA6lKNc0IAAABQpY1zQgAAwLX3jXNCAACAG0qOc0IAAECBnI5zQgAAgLKTj3NCAABAGOaPc0IAAAB+OJBzQgAAwOOKkHNCAACASd2Qc0IAAMB61JFzQgAAgOAmknNCAABARnmSc0IAAACsy5JzQgAAwBEek3NCAAAAQxWUc0IAAMCoZ5RzQgAAgA66lHNCAABAdAyVc0IAAADaXpVzQgAAQAtWlnNCAADA1vqWc0IAAIA8TZdzQgAAQKKfl3NCAACA05aYc0IAAACfO5lzQgAAwASOmXNCAACAauCZc0IAAMCb15pzQgAAgAEqm3NCAABAZ3ybc0IAAADNzptzQgAAwDIhnHNCAAAAZBidc0IAAMDJap1zQgAAgC+9nXNCAABAlQ+ec0IAAAD7YZ5zQgAAQCxZn3NCAAAAkqufc0IAAMD3/Z9zQgAAgF1QoHNCAABAw6Kgc0IAAID0maFzQgAAgIvjonNCAADAvNqjc0IAAIAiLaRzQgAAQIh/pHNCAAAA7tGkc0IAAMBTJKVzQgAAAIUbpnNCAADA6m2mc0IAAIBQwKZzQgAAQLYSp3NCAAAAHGWnc0IAAEBNXKhzQgAAALOuqHNCAACAflOpc0IAAEDkpalzQgAAgBWdqnNCAADARpSrc0IAAICs5qtzQgAAwN3drHNCAACAQzCtc0IAAECpgq1zQgAAAA/VrXNCAADAdCeuc0IAAACmHq9zQgAAwAtxr3NCAACAccOvc0IAAEDXFbBzQgAAAD1osHNCAABAbl+xc0IAAADUsbFzQgAAwDkEsnNCAACAn1ayc0IAAECc8rNzQgAAAAJFtHNCAADAZ5e0c0IAAIDN6bRzQgAAwP7gtXNCAACAZDO2c0IAAEDKhbZzQgAAADDYtnNCAADAlSq3c0IAAADHIbhzQgAAwCx0uHNCAACAksa4c0IAAED4GLlzQgAAAF5ruXNCAABAj2K6c0IAAAD1tLpzQgAAwFoHu3NCAACAwFm7c0IAAEAmrLtzQgAAgFejvHNCAACA7uy9c0IAAMAf5L5zQgAAAFHbv3NCAADAti3Ac0IAAADoJMFzQgAAwE13wXNCAACAs8nBc0IAAEAZHMJzQgAAAH9uwnNCAABAsGXDc0IAAAAWuMNzQgAAwHsKxHNCAACA4VzEc0IAAEBHr8RzQgAAgHimxXNCAAAAREvGc0IAAMCpncZzQgAAgA/wxnNCAADAQOfHc0IAAICmOchzQgAAQAyMyHNCAAAAct7Ic0IAAMDXMMlzQgAAAAkoynNCAADAbnrKc0IAAIDUzMpzQgAAQDofy3NCAAAAoHHLc0IAAEDRaMxzQgAAQGiyzXNCAACAmanOc0IAAABlTs9zQgAAwMqgz3NCAACAMPPPc0IAAMBh6tBzQgAAgMc80XNCAABALY/Rc0IAAACT4dFzQgAAwPgz0nNCAAAAKivTc0IAAMCPfdNzQgAAgPXP03NCAABAWyLUc0IAAADBdNRzQgAAQPJr1XNCAAAAWL7Vc0IAAMC9ENZzQgAAgCNj1nNCAABAibXWc0IAAIC6rNdzQgAAQCD/13NCAAAAhlHYc0IAAMDro9hzQgAAgFH22HNCAADAgu3Zc0IAAIDoP9pzQgAAQE6S2nNCAAAAtOTac0IAAMAZN9tzQgAAgBbT3HNCAABAfCXdc0IAAEATb95zQgAAAHnB3nNCAADA3hPfc0IAAIBEZt9zQgAAQKq433NCAACA26/gc0IAAEBBAuFzQgAAAKdU4XNCAADADKfhc0IAAIBy+eFzQgAAwKPw4nNCAACACUPjc0IAAEBvleNzQgAAANXn43NCAADAOjrkc0IAAABsMeVzQgAAwNGD5XNCAACAN9blc0IAAAADe+ZzQgAAQDRy53NCAADA/xboc0IAAIBlaehzQgAAQMu76HNCAACA/LLpc0IAAEBiBepzQgAAAMhX6nNCAADALarqc0IAAICT/OpzQgAAgCpG7HNCAABAkJjsc0IAAAD26uxzQgAAwFs97XNCAAAAjTTuc0IAAIBY2e5zQgAAQL4r73NCAAAAJH7vc0IAAEBVdfBzQgAAALvH8HNCAADAIBrxc0IAAICGbPFzQgAAQOy+8XNCAACAHbbyc0IAAECDCPNzQgAAAOla83NCAACAtP/zc0IAAMDl9vRzQgAAgEtJ9XNCAABAsZv1c0IAAAAX7vVzQgAAwHxA9nNCAAAArjf3c0IAAMATivdzQgAAgHnc93NCAABA3y74c0IAAABFgfhzQgAAQHZ4+XNCAADAQR36c0IAAIA+uftzQgAAQKQL/HNCAAAACl78c0IAAMBvsPxzQgAAgNUC/XNCAADABvr9c0IAAIBsTP5zQgAAQNKe/nNCAAAAOPH+c0IAAMCdQ/9zQgAAAM86AHRCAADANI0AdEIAAICa3wB0QgAAQAAyAXRCAAAAZoQBdEIAAECXewJ0QgAAAP3NAnRCAADAYiADdEIAAIDIcgN0QgAAQC7FA3RCAACAX7wEdEIAAEDFDgV0QgAAACthBXRCAADAkLMFdEIAAID2BQZ0QgAAwCf9BnRCAACAjU8HdEIAAEDzoQd0QgAAwL5GCHRCAAAA8D0JdEIAAMBVkAl0QgAAgLviCXRCAABAITUKdEIAAACHhwp0QgAAQLh+C3RCAADAgyMMdEIAAIDpdQx0QgAAQE/IDHRCAACAgL8NdEIAAABMZA50QgAAwLG2DnRCAACAFwkPdEIAAMBIABB0QgAAgK5SEHRCAABAFKUQdEIAAAB69xB0QgAAwN9JEXRCAAAAEUESdEIAAMB2kxJ0QgAAgNzlEnRCAABAQjgTdEIAAACoihN0QgAAAD/UFHRCAADApCYVdEIAAIAKeRV0QgAAQHDLFXRCAACAocIWdEIAAEAHFRd0QgAAwNK5F3RCAADAaQMZdEIAAIDPVRl0QgAAQDWoGXRCAAAAm/oZdEIAAMAATRp0QgAAADJEG3RCAACA/egbdEIAAEBjOxx0QgAAAMmNHHRCAABA+oQddEIAAABg1x10QgAAwMUpHnRCAACAK3wedEIAAECRzh50QgAAgMLFH3RCAABAKBggdEIAAACOaiB0QgAAwPO8IHRCAACAWQ8hdEIAAMCKBiJ0QgAAgPBYInRCAABAVqsidEIAAAC8/SJ0QgAAwCFQI3RCAAAAU0ckdEIAAMC4mSR0QgAAgB7sJHRCAABAhD4ldEIAAADqkCV0QgAAQBuIJnRCAAAAgdomdEIAAMDmLCd0QgAAgEx/J3RCAABAstEndEIAAIDjyCh0QgAAQEkbKXRCAAAAr20pdEIAAMAUwCl0QgAAgBFcK3RCAABAd64rdEIAAADdACx0QgAAwEJTLHRCAAAAdEotdEIAAMDZnC10QgAAgD/vLXRCAABApUEudEIAAAALlC50QgAAQDyLL3RCAAAAot0vdEIAAMAHMDB0QgAAgG2CMHRCAABA09QwdEIAAIAEzDF0QgAAQGoeMnRCAADAzAw0dEIAAIAyXzR0QgAAwGNWNXRCAAAAlU02dEIAAMD6nzZ0QgAAgGDyNnRCAABAxkQ3dEIAAAAslzd0QgAAQF2OOHRCAAAAw+A4dEIAAMAoMzl0QgAAgI6FOXRCAABA9Nc5dEIAAIAlzzp0QgAAAPFzO3RCAADAVsY7dEIAAIC8GDx0QgAAwO0PPXRCAACAU2I9dEIAAEC5tD10QgAAAB8HPnRCAADAhFk+dEIAAICB9T90QgAAQOdHQHRCAAAATZpAdEIAAEB+kUF0QgAAAOTjQXRCAADASTZCdEIAAICviEJ0QgAAQBXbQnRCAACARtJDdEIAAAASd0R0QgAAwHfJRHRCAACA3RtFdEIAAMAOE0Z0QgAAgHRlRnRCAABA2rdGdEIAAABACkd0QgAAwKVcR3RCAAAA11NIdEIAAMA8pkh0QgAAgKL4SHRCAABACEtJdEIAAABunUl0QgAAQJ+USnRCAAAABedKdEIAAMBqOUt0QgAAgNCLS3RCAABANt5LdEIAAIBn1Ux0QgAAQM0nTXRCAAAAM3pNdEIAAMCYzE10QgAAgP4eTnRCAADALxZPdEIAAICVaE90QgAAQPu6T3RCAAAAYQ1QdEIAAMDGX1B0QgAAAPhWUXRCAADAXalRdEIAAIDD+1F0QgAAQClOUnRCAAAAj6BSdEIAAEDAl1N0QgAAACbqU3RCAADAizxUdEIAAIDxjlR0QgAAQFfhVHRCAACAiNhVdEIAAEDuKlZ0QgAAAFR9VnRCAADAuc9WdEIAAIAfIld0QgAAQBy+WHRCAAAAghBZdEIAAMDnYll0QgAAABlaWnRCAADAfqxadEIAAIDk/lp0QgAAQEpRW3RCAABA4ZpcdEIAAIASkl10QgAAQHjkXXRCAACAqdtedEIAAEAPLl90QgAAAHWAX3RCAADA2tJfdEIAAIBAJWB0QgAAwHEcYXRCAACA125hdEIAAEA9wWF0QgAAAKMTYnRCAADACGZidEIAAAA6XWN0QgAAgAUCZHRCAABAa1RkdEIAAADRpmR0QgAAQAKeZXRCAADAzUJmdEIAAIAzlWZ0QgAAQJnnZnRCAACAyt5ndEIAAEAwMWh0QgAAAJaDaHRCAADA+9VodEIAAIBhKGl0QgAAwJIfanRCAACA+HFqdEIAAEBexGp0QgAAAMQWa3RCAADAKWlrdEIAAABbYGx0QgAAwMCybHRCAACAJgVtdEIAAECMV210QgAAAPKpbXRCAABAI6FudEIAAACJ8250QgAAgFSYb3RCAABAUTRxdEIAAAC3hnF0QgAAwBzZcXRCAACAgitydEIAAMCzInN0QgAAgBl1c3RCAABAf8dzdEIAAADlGXR0QgAAwEpsdHRCAAAAfGN1dEIAAMDhtXV0QgAAgEcIdnRCAABArVp2dEIAAAATrXZ0QgAAQESkd3RCAAAAqvZ3dEIAAMAPSXh0QgAAgHWbeHRCAABA2+14dEIAAIAM5Xl0QgAAQHI3enRCAAAA2Il6dEIAAMA93Hp0QgAAgKMue3RCAADA1CV8dEIAAIA6eHx0QgAAQKDKfHRCAAAABh19dEIAAMBrb310QgAAAJ1mfnRCAADAArl+dEIAAIBoC390QgAAQM5df3RCAAAANLB/dEIAAEBlp4B0QgAAwDBMgXRCAACAlp6BdEIAAED88IF0QgAAgC3ognRCAAAA+YyDdEIAAMBe34N0QgAAgMQxhHRCAADA9SiFdEIAAIBbe4V0QgAAACcghnRCAADAjHKGdEIAAAC+aYd0QgAAwCO8h3RCAACAiQ6IdEIAAEDvYIh0QgAAAFWziHRCAABAhqqJdEIAAADs/Il0QgAAwFFPinRCAACAt6GKdEIAAEAd9Ip0QgAAgE7ri3RCAABAtD2MdEIAAAAakIx0QgAAgHx+jnRCAABA4tCOdEIAAABII490QgAAwK11j3RCAAAA32yQdEIAAMBEv5B0QgAAgKoRkXRCAABAEGSRdEIAAAB2tpF0QgAAQKetknRCAAAADQCTdEIAAMByUpN0QgAAgNikk3RCAABAPveTdEIAAIBv7pR0QgAAQNVAlXRCAAAAO5OVdEIAAMCg5ZV0QgAAgAY4lnRCAADANy+XdEIAAICdgZd0QgAAQAPUl3RCAAAAaSaYdEIAAMDOeJh0QgAAAABwmXRCAADAZcKZdEIAAIDLFJp0QgAAQDFnmnRCAAAAl7madEIAAEDIsJt0QgAAAC4DnHRCAADAk1WcdEIAAID5p5x0QgAAQF/6nHRCAACAkPGddEIAAED2Q550QgAAAFyWnnRCAADAweiedEIAAIC+hKB0QgAAQCTXoHRCAAAAiimhdEIAAMDve6F0QgAAACFzonRCAADAhsWidEIAAIDsF6N0QgAAQFJqo3RCAAAAuLyjdEIAAEDps6R0QgAAAE8GpXRCAADAtFildEIAAIAaq6V0QgAAQID9pXRCAACAsfSmdEIAAEAXR6d0QgAAAH2Zp3RCAACA34epdEIAAEBF2ql0QgAAAEJ2q3RCAADAp8irdEIAAIANG6x0QgAAQHNtrHRCAAAA2b+sdEIAAEAKt610QgAAAHAJrnRCAADA1VuudEIAAIA7rq50QgAAQKEAr3RCAACA0vevdEIAAACenLB0QgAAwAPvsHRCAACAaUGxdEIAAMCaOLJ0QgAAgACLsnRCAABAZt2ydEIAAADML7N0QgAAwDGCs3RCAAAAY3m0dEIAAMDIy7R0QgAAgC4etXRCAABAlHC1dEIAAAD6wrV0QgAAQCu6tnRCAAAAkQy3dEIAAMD2Xrd0QgAAgFyxt3RCAABAwgO4dEIAAIDz+rh0QgAAAL+fuXRCAADAJPK5dEIAAIAhjrt0QgAAQIfgu3RCAAAA7TK8dEIAAMBShbx0QgAAAIR8vXRCAADA6c69dEIAAIBPIb50QgAAQLVzvnRCAAAAG8a+dEIAAEBMvb90QgAAALIPwHRCAADAF2LAdEIAAIB9tMB0QgAAQOMGwXRCAACAFP7BdEIAAEB6UMJ0QgAAAOCiwnRCAADARfXCdEIAAICrR8N0QgAAwNw+xHRCAACAQpHEdEIAAECo48R0QgAAAA42xXRCAADAc4jFdEIAAAClf8Z0QgAAwArSxnRCAACAcCTHdEIAAEDWdsd0QgAAADzJx3RCAACAnrfJdEIAAEAECsp0QgAAgDUBy3RCAABAm1PLdEIAAAABpst0QgAAwGb4y3RCAACAzErMdEIAAMD9Qc10QgAAgGOUzXRCAABAyebNdEIAAAAvOc50QgAAwJSLznRCAAAAxoLPdEIAAMAr1c90QgAAgJEn0HRCAABA93nQdEIAAABdzNB0QgAAwFlo0nRCAACAv7rSdEIAAEAlDdN0QgAAgFYE1HRCAABAvFbUdEIAAAAiqdR0QgAAwIf71HRCAACA7U3VdEIAAMAeRdZ0QgAAgISX1nRCAABA6unWdEIAAABQPNd0QgAAwLWO13RCAAAA54XYdEIAAICyKtl0QgAAQBh92XRCAAAAfs/ZdEIAAECvxtp0QgAAABUZ23RCAADAemvbdEIAAIDgvdt0QgAAQEYQ3HRCAACAdwfddEIAAEDdWd10QgAAAEOs3XRCAADAqP7ddEIAAIAOUd50QgAAwD9I33RCAACApZrfdEIAAEAL7d90QgAAAHE/4HRCAADA1pHgdEIAAAAIieF0QgAAwG3b4XRCAACA0y3idEIAAEA5gOJ0QgAAAJ/S4nRCAABA0MnjdEIAAAA2HOR0QgAAwJtu5HRCAABAZxPldEIAAED+XOZ0QgAAAGSv5nRCAADAyQHndEIAAIAvVOd0QgAAwGBL6HRCAACAxp3odEIAAEAs8Oh0QgAAAJJC6XRCAADA95TpdEIAAAApjOp0QgAAwI7e6nRCAACA9DDrdEIAAEBag+t0QgAAAMDV63RCAABA8czsdEIAAABXH+10QgAAwLxx7XRCAACAIsTtdEIAAECIFu50QgAAgLkN73RCAABAH2DvdEIAAACFsu90QgAAwOoE8HRCAACAUFfwdEIAAMCBTvF0QgAAgOeg8XRCAABATfPxdEIAAACzRfJ0QgAAwBiY8nRCAAAASo/zdEIAAMCv4fN0QgAAgBU09HRCAABAe4b0dEIAAADh2PR0QgAAQBLQ9XRCAAAAeCL2dEIAAMDddPZ0QgAAgEPH9nRCAABAqRn3dEIAAIDaEPh0QgAAAKa1+HRCAADACwj5dEIAAMCiUfp0QgAAQG72+nRCAAAA1Ej7dEIAAMA5m/t0QgAAAGuS/HRCAADA0OT8dEIAAIA2N/10QgAAQJyJ/XRCAAAAAtz9dEIAAEAz0/50QgAAAJkl/3RCAADA/nf/dEIAAIBkyv90QgAAQMocAHVCAACA+xMBdUIAAADHuAF1QgAAwCwLAnVCAADAw1QDdUIAAIAppwN1QgAAQI/5A3VCAAAA9UsEdUIAAMBangR1QgAAAIyVBXVCAADA8ecFdUIAAIBXOgZ1QgAAQL2MBnVCAAAAI98GdUIAAEBU1gd1QgAAALooCHVCAADAH3sIdUIAAEDrHwl1QgAAgBwXCnVCAABAgmkKdUIAAADouwp1QgAAwE0OC3VCAACAs2ALdUIAAMDkVwx1QgAAgEqqDHVCAABAsPwMdUIAAAAWTw11QgAAwHuhDXVCAAAArZgOdUIAAMAS6w51QgAAgHg9D3VCAABA3o8PdUIAAABE4g91QgAAQHXZEHVCAAAA2ysRdUIAAMBAfhF1QgAAgKbQEXVCAABADCMSdUIAAIA9GhN1QgAAQKNsE3VCAAAACb8TdUIAAMBuERR1QgAAwAVbFXVCAACAa60VdUIAAEDR/xV1QgAAADdSFnVCAADAnKQWdUIAAADOmxd1QgAAwDPuF3VCAACAmUAYdUIAAED/khh1QgAAAGXlGHVCAABAltwZdUIAAAD8Lhp1QgAAwGGBGnVCAACAx9MadUIAAEAtJht1QgAAgF4dHHVCAABAxG8cdUIAAAAqwhx1QgAAwI8UHXVCAABA8gIfdUIAAABYVR91QgAAwFTxIHVCAACAukMhdUIAAEAgliF1QgAAAIboIXVCAABAt98idUIAAAAdMiN1QgAAwIKEI3VCAACA6NYjdUIAAEBOKSR1QgAAgH8gJXVCAAAAS8UldUIAAMCwFyZ1QgAAgBZqJnVCAADAR2EndUIAAICtsyd1QgAAQBMGKHVCAAAAeVgodUIAAMDeqih1QgAAABCiKXVCAADAdfQpdUIAAIDbRip1QgAAQEGZKnVCAAAAp+sqdUIAAEDY4it1QgAAQG8sLXVCAACAoCMudUIAAABsyC51QgAAwNEaL3VCAACAN20vdUIAAMBoZDB1QgAAgM62MHVCAABANAkxdUIAAACaWzF1QgAAwP+tMXVCAAAAMaUydUIAAMCW9zJ1QgAAgPxJM3VCAABAYpwzdUIAAADI7jN1QgAAQPnlNHVCAAAAXzg1dUIAAMDEijV1QgAAgCrdNXVCAABAkC82dUIAAIDBJjd1QgAAQCd5N3VCAAAAjcs3dUIAAMDyHTh1QgAAgFhwOHVCAADAiWc5dUIAAIDvuTl1QgAAQFUMOnVCAAAAu146dUIAAMAgsTp1QgAAgB1NPHVCAABAg588dUIAAADp8Tx1QgAAQBrpPXVCAADA5Y0+dUIAAIBL4D51QgAAQLEyP3VCAACA4ilAdUIAAEBIfEB1QgAAAK7OQHVCAADAEyFBdUIAAIB5c0F1QgAAwKpqQnVCAACAEL1CdUIAAEB2D0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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[2343]}},\"selected\":{\"id\":\"5708\"},\"selection_policy\":{\"id\":\"5734\"}},\"id\":\"5707\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_color\":\"#fc4f30\",\"line_width\":2,\"x\":{\"field\":\"index\"},\"y\":{\"field\":\"value\"}},\"id\":\"5773\",\"type\":\"Line\"},{\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]},\"id\":\"5729\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"5740\"},\"glyph\":{\"id\":\"5743\"},\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"5745\"},\"nonselection_glyph\":{\"id\":\"5744\"},\"selection_glyph\":{\"id\":\"5773\"},\"view\":{\"id\":\"5747\"}},\"id\":\"5746\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"5708\",\"type\":\"Selection\"},{\"attributes\":{\"active_multi\":null,\"tools\":[{\"id\":\"5669\"},{\"id\":\"5688\"},{\"id\":\"5689\"},{\"id\":\"5690\"},{\"id\":\"5691\"},{\"id\":\"5692\"}]},\"id\":\"5694\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"5704\",\"type\":\"AllLabels\"},{\"attributes\":{\"months\":[0,2,4,6,8,10]},\"id\":\"5730\",\"type\":\"MonthsTicker\"},{\"attributes\":{\"axis_label\":\"\",\"formatter\":{\"id\":\"5703\"},\"major_label_policy\":{\"id\":\"5701\"},\"ticker\":{\"id\":\"5681\"}},\"id\":\"5680\",\"type\":\"DatetimeAxis\"},{\"attributes\":{\"children\":[{\"id\":\"5666\"},{\"id\":\"5671\"},{\"id\":\"5990\"}],\"margin\":[0,0,0,0],\"name\":\"Row07157\",\"tags\":[\"embedded\"]},\"id\":\"5665\",\"type\":\"Row\"},{\"attributes\":{},\"id\":\"5690\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"days\":[1,8,15,22]},\"id\":\"5727\",\"type\":\"DaysTicker\"},{\"attributes\":{},\"id\":\"5741\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"5701\",\"type\":\"AllLabels\"},{\"attributes\":{\"overlay\":{\"id\":\"5693\"}},\"id\":\"5691\",\"type\":\"BoxZoomTool\"}],\"root_ids\":[\"5665\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.2\"}};\n",
       "    var render_items = [{\"docid\":\"7f38c8bf-743c-4512-9103-aa9899a7c2c0\",\"root_ids\":[\"5665\"],\"roots\":{\"5665\":\"85b8f909-4092-4575-94c4-3d63d0180263\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [index]   (value)"
      ]
     },
     "execution_count": 150,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "5665"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results comnparison\n",
    "\n",
    "# Profitability on the train window\n",
    "fit_train= adaboost_model.predict(X_train_scaled)\n",
    "fit_train_df= pd.DataFrame(fit_train, index=X_train.index)\n",
    "\n",
    "fit_train_df.hvplot()\n",
    "\n",
    "y_train_df=pd.DataFrame(y_train, index=X_train.index)\n",
    "y_train_df\n",
    "\n",
    "vix_returns_train_df=vix_ret[y_train_df.index.min():y_train_df.index.max()]\n",
    "vix_returns_train_df = vix_returns_train_df[y_train_df.index]\n",
    "\n",
    "\n",
    "results_train_df=pd.concat([vix_returns_train_df, y_train_df, fit_train_df], axis=1)\n",
    "\n",
    "results_train_df.columns=['VIX Return', 'Correct Signal', 'Fit Signal']\n",
    "\n",
    "predicted_return=results_train_df['VIX Return']*results_train_df['Fit Signal']\n",
    "max_return=results_train_df['VIX Return']*results_train_df['Correct Signal']\n",
    "\n",
    "\n",
    "results_train_df=pd.concat([results_train_df, predicted_return, max_return], axis=1)\n",
    "results_train_df.columns=['VIX Return', 'Correct Signal', 'Predicted Signal', 'Fit Return', 'Max Return']\n",
    "\n",
    "\n",
    "return_of_one_dollar_in_train_window_df=(1+results_train_df[['VIX Return','Fit Return']]).cumprod()\n",
    "return_of_one_dollar_in_train_window_df.columns=['VROI', 'ROI Model (in sample)']\n",
    "\n",
    "profitability_train_plot=return_of_one_dollar_in_train_window_df.hvplot(\n",
    "                                            title=\"In-Sample Growth of $1 initial Investment in Daily Trade Strategy on VIX\",\n",
    "                                            ylabel=\"Dollars $\" ,\n",
    "                                            width=1000\n",
    ")\n",
    "\n",
    "profitability_train_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacc1e28",
   "metadata": {},
   "source": [
    "### Out-of--sample analysis: Return on $1 invested on training data window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "4b119e11",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of 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sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of sample)\",\" Model Cummulative Return (out of 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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[986]},\"value\":{\"__ndarray__\":\"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       "    var render_items = [{\"docid\":\"7b18ba77-b913-44b1-9125-2764f15c169b\",\"root_ids\":[\"6112\"],\"roots\":{\"6112\":\"fe6fcf96-463d-489c-bd2a-edc38ab3bce6\"}}];\n",
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       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [index]   (value)"
      ]
     },
     "execution_count": 151,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "6112"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results comnparison\n",
    "\n",
    "# Profitability on the test window\n",
    "\n",
    "# Out-of-sample Predictions \n",
    "prediction_test= adaboost_model.predict(X_test_scaled)\n",
    "prediction_test_df= pd.DataFrame(prediction_test, index=X_test.index)\n",
    "\n",
    "# Out-of-sample signals (1s or 0s) based on actual returns of the VIX\n",
    "y_test_df=pd.DataFrame(y_test, index=X_test.index)\n",
    "y_test_df\n",
    "\n",
    "# VIX returns in the test window\n",
    "vix_returns_df=vix_ret[y_test_df.index.min():y_test_df.index.max()]\n",
    "vix_returns_test_df = vix_returns_df[y_test_df.index]\n",
    "\n",
    "# Combination of VIX Returns, signals and predictions\n",
    "results_test_df=pd.concat([vix_returns_test_df, y_test_df, prediction_test_df], axis=1)\n",
    "results_test_df.columns=['VIX Return', 'Correct Signal', 'Predicted Signal']\n",
    "\n",
    "# Predicted returns\n",
    "predicted_return=results_test_df['VIX Return']*results_test_df['Predicted Signal']\n",
    "max_return=results_test_df['VIX Return']*results_test_df['Correct Signal']\n",
    "\n",
    "# DataFrame with out-of-sample results for comparison\n",
    "results_test_df=pd.concat([results_test_df, predicted_return, max_return], axis=1)\n",
    "results_test_df.columns=['VIX Return', 'Correct Signal', 'Predicted Signal', 'Predicted Return', 'Max Return']\n",
    "\n",
    "return_of_one_dollar_in_test_window_df=(1+results_test_df[['VIX Return', 'Predicted Return']]).cumprod()\n",
    "return_of_one_dollar_in_test_window_df.columns=['VIX Cummulative return', ' Model Cummulative Return (out of sample)']\n",
    "\n",
    "\n",
    "#Plot with out of sample return on a 1 dollar investment for the VIX, and the daily bet strategy\n",
    "profitability_test_plot=return_of_one_dollar_in_test_window_df.hvplot(\n",
    "                         title='Out-Of-Sample Growth of $1 initial Investment in Daily Trade Strategy on VIX ',\n",
    "                         ylabel= \"Dollars $\",\n",
    "                         width=1000\n",
    "                        )\n",
    "profitability_test_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7e8fad6b",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.holoviews_exec.v0+json": "",
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       "    var render_items = [{\"docid\":\"7132ab5b-e704-4041-ba91-2dbc7c5547c5\",\"root_ids\":[\"6559\"],\"roots\":{\"6559\":\"24d43961-b913-4d78-9c8f-9f1894e3e49f\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [index]   (value)"
      ]
     },
     "execution_count": 152,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "6559"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results in prediction of daily returns\n",
    "min_return=threshold\n",
    "results_test_for_plot_df=results_test_df[abs(results_test_df['VIX Return'])>min_return]*100\n",
    "\n",
    "results_test_for_plot_df.hvplot(\n",
    "                    y=['VIX Return', 'Predicted Return'],\n",
    "                    title= \"Out-of-sample predictions of VIX return\",\n",
    "                    width=1000,\n",
    "                    ylabel='Daily Return (%)'\n",
    "\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f9bd770c",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='7006'>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"5c9d44c9-f881-4ff9-b083-b4eb803ea198\" data-root-id=\"7006\"></div>\n",
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      ],
      "text/plain": [
       ":NdOverlay   [Element]\n",
       "   :Histogram   [Predicted Return]   (Predicted Return_count)"
      ]
     },
     "execution_count": 153,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "7006"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Histogram of returns out of sample\n",
    "results_test_for_plot_df.hvplot.hist(\n",
    "    ['VIX Return','Predicted Return'],\n",
    "     title= \"Out-of-sample VIX returns predictions\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea29545",
   "metadata": {},
   "source": [
    "### Analysis of good and bad predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "96ce4633",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis of the good and bad out-of-sample predictions\n",
    "good_predictions=results_test_df[results_test_df['Correct Signal']==results_test_df['Predicted Signal']]\n",
    "\n",
    "bad_predictions=results_test_df[results_test_df['Correct Signal']!=results_test_df['Predicted Signal']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "04e687e3",
   "metadata": {},
   "outputs": [
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VIX Returns on Bad Predictions\",\"text_color\":\"black\",\"text_font_size\":\"12pt\"},\"id\":\"7435\",\"type\":\"Title\"},{\"attributes\":{\"bottom\":{\"value\":0},\"fill_alpha\":{\"value\":1.0},\"fill_color\":{\"value\":\"#30a2da\"},\"hatch_alpha\":{\"value\":1.0},\"hatch_color\":{\"value\":\"black\"},\"hatch_scale\":{\"value\":12.0},\"hatch_weight\":{\"value\":1.0},\"left\":{\"field\":\"left\"},\"line_alpha\":{\"value\":1.0},\"line_cap\":{\"value\":\"butt\"},\"line_color\":{\"value\":\"black\"},\"line_dash\":{\"value\":[]},\"line_dash_offset\":{\"value\":0},\"line_join\":{\"value\":\"bevel\"},\"line_width\":{\"value\":1},\"right\":{\"field\":\"right\"},\"top\":{\"field\":\"top\"}},\"id\":\"7325\",\"type\":\"Quad\"},{\"attributes\":{},\"id\":\"7509\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"7262\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.2\"}};\n",
       "    var render_items = [{\"docid\":\"6314396e-112b-44fb-9609-5f14f2a0237c\",\"root_ids\":[\"7262\"],\"roots\":{\"7262\":\"f9633fc3-d16f-4ca7-bb1d-cf42dcc0f3c8\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
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       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "})(window);</script>"
      ],
      "text/plain": [
       ":Layout\n",
       "   .NdOverlay.I  :NdOverlay   [Element]\n",
       "      :Histogram   [Predicted Return]   (Predicted Return_count)\n",
       "   .NdOverlay.II :NdOverlay   [Element]\n",
       "      :Histogram   [Predicted Return]   (Predicted Return_count)"
      ]
     },
     "execution_count": 155,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "7262"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good=good_predictions.hvplot.hist(\n",
    "    ['VIX Return','Predicted Return'],\n",
    "     title= \"Out-of-sample VIX Returns of Good Predictions\"\n",
    ")\n",
    "\n",
    "bad=bad_predictions.hvplot.hist(\n",
    "    ['VIX Return','Predicted Return'],\n",
    "     title= \"Out-of-sample VIX Returns on Bad Predictions\"\n",
    ")\n",
    "good+bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a9c449b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX Return</th>\n",
       "      <th>Predicted Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>568.000000</td>\n",
       "      <td>568.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.006479</td>\n",
       "      <td>0.026031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.099241</td>\n",
       "      <td>0.076798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.233735</td>\n",
       "      <td>-0.086849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.058457</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.014041</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.024051</td>\n",
       "      <td>0.022339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.155979</td>\n",
       "      <td>1.155979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VIX Return  Predicted Return\n",
       "count  568.000000        568.000000\n",
       "mean    -0.006479          0.026031\n",
       "std      0.099241          0.076798\n",
       "min     -0.233735         -0.086849\n",
       "25%     -0.058457         -0.000000\n",
       "50%     -0.014041          0.000000\n",
       "75%      0.024051          0.022339\n",
       "max      1.155979          1.155979"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Good prediction statistics\n",
    "good_predictions[['VIX Return', 'Predicted Return']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "95a12381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>VIX Return</th>\n",
       "      <th>Predicted Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.014970</td>\n",
       "      <td>-0.018652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.087048</td>\n",
       "      <td>0.033174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.205029</td>\n",
       "      <td>-0.205029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.030892</td>\n",
       "      <td>-0.028258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000799</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.044268</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.616420</td>\n",
       "      <td>0.150261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       VIX Return  Predicted Return\n",
       "count  418.000000        418.000000\n",
       "mean     0.014970         -0.018652\n",
       "std      0.087048          0.033174\n",
       "min     -0.205029         -0.205029\n",
       "25%     -0.030892         -0.028258\n",
       "50%     -0.000799          0.000000\n",
       "75%      0.044268          0.000000\n",
       "max      0.616420          0.150261"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bad prediction statistics\n",
    "bad_predictions[['VIX Return', 'Predicted Return']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "2cb8a65c",
   "metadata": {},
   "outputs": [
    {
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       "<script type=\"application/javascript\">(function(root) {\n",
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(%)\",\"formatter\":{\"id\":\"8042\"},\"major_label_policy\":{\"id\":\"8043\"},\"ticker\":{\"id\":\"7984\"}},\"id\":\"7983\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"7966\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.2\"}};\n",
       "    var render_items = [{\"docid\":\"d339cbd2-ac6b-4f3f-8e47-ebf562c5734a\",\"root_ids\":[\"7966\"],\"roots\":{\"7966\":\"1721c3ee-4d33-4306-88b7-150b161551c9\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":BoxWhisker   [Variable]   (value)"
      ]
     },
     "execution_count": 158,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "7966"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Box Plot predictions for good and bad predictions\n",
    "good_pred=good_predictions['Predicted Return']\n",
    "bad_pred =bad_predictions['Predicted Return']\n",
    "\n",
    "predictions_comparison_df=pd.concat([good_predictions['Predicted Return'],bad_predictions['Predicted Return']], axis=1, ignore_index=True )*100\n",
    "predictions_comparison_df.hvplot(kind='box',\n",
    "                                height=800,\n",
    "                                ylabel='Return (%)',\n",
    "                                #clabel=['Good Predictions', 'Bad Predictions'],\n",
    "                                cmap=['blue','red'],\n",
    "                                title=\"Out-of-sample Good and Bad Returns Resulting from Model Predictions Box Plots\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#df = pd.DataFrame(np.random.randn(20), columns=['Value'])\n",
    "#df['Source'] = ['Preds'] *10 +['Real'] * 10\n",
    "#df['Item'] = ['item1'] *5 + ['item2']*5 + ['item1'] *5 + ['item2']*5\n",
    "#df.hvplot.box(y='Value', by=['Item', 'Source'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122ba76c",
   "metadata": {},
   "source": [
    "# OUTPUTS FOR TUNNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "c2e17da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_features(XY, pca_components):\n",
    "    XY_modified = XY.shift().dropna()\n",
    "    y = XY_modified[\"Signal\"].shift(-1)\n",
    "    X = XY_modified\n",
    "    pca = PCA(n_components = pca_components)\n",
    "    principal_components = pca.fit_transform(X)\n",
    "    \n",
    "    pca_column_list = []\n",
    "    for i in range(1, pca_components+1):\n",
    "        pca_column_list.append(f\"pca{i}\")\n",
    "\n",
    "    principal_components_train_test_df = pd.DataFrame(data = principal_components, columns = pca_column_list, index = XY_modified.index)\n",
    "    X_pca_lag1 = create_pca_lag1(principal_components_train_test_df)\n",
    "    X_pca_lag2 = create_pca_lag2(principal_components_train_test_df)\n",
    "    X_pca_lag3 = create_pca_lag3(principal_components_train_test_df)\n",
    "    X_pca_lag4 = create_pca_lag4(principal_components_train_test_df)\n",
    "    X_pca_lag5 = create_pca_lag5(principal_components_train_test_df)\n",
    "        \n",
    "    X_pc_lags = concatenate_lags(X_pca_lag1, X_pca_lag2, X_pca_lag3, X_pca_lag4, X_pca_lag5)\n",
    "    X_pc = concatenate_pca_with_lags(principal_components_train_test_df, X_pc_lags)\n",
    "    X, y = eliminate_nans_in_pca_data(X_pc, y)\n",
    "    \n",
    "    X_train, y_train, X_test, y_test = split_training_test_data(X, y)\n",
    "    X_train_resampled, y_train_resampled = random_over_sample(X_train, y_train)\n",
    "    X_train_scaled, X_test_scaled = standard_scale(X_train_resampled, X_test)\n",
    "    #principal_components_train_test\n",
    "    if parameter_tuning_mode == True:\n",
    "        display(sum(pca.explained_variance_ratio_))\n",
    "        display(principal_components_train_test_df.shape)\n",
    "    return X_train_scaled, X_test_scaled, y_train_resampled, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2cffd67c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_multiple_tuning_iterations == True: \n",
    "    for num_estimators in range (20,200, 2):\n",
    "        adaboost_model = AdaBoostClassifier(n_estimators=num_estimators)\n",
    "\n",
    "        # Fit the model \n",
    "        adaboost_model = adaboost_model.fit(X_train_resampled, y_train_resampled)\n",
    "        pred_adaboost = adaboost_model.predict(X_test)\n",
    "        # Use a classification report to evaluate the model using the predictions and testing data\n",
    "        adaboost_report=classification_report(y_test, pred_adaboost)\n",
    "\n",
    "        #if num_estimators % 10 == 0 and num_components == 88:\n",
    "        #    print(f\"components {num_components} esimators {num_estimators}\")\n",
    "        #    print(f\"f1 score 0 {f1_score(y_test, pred_adaboost, pos_label=0)} f1 score 1 {f1_score(y_test, pred_adaboost, pos_label=1)}\")\n",
    "        #    print(f\"accuracy {accuracy_score(y_test, pred_adaboost)}\")\n",
    "        #    print(adaboost_report)\n",
    "        f1_score_1 = f1_score(y_test, pred_adaboost, pos_label=1)\n",
    "        f1_score_0 = f1_score(y_test, pred_adaboost, pos_label=0)\n",
    "        recall_score_1 = recall_score(y_test, pred_adaboost, pos_label=1)\n",
    "        recall_score_0 = recall_score(y_test, pred_adaboost, pos_label=0)\n",
    "        accuracy_score_model = accuracy_score(y_test, pred_adaboost)\n",
    "        if  accuracy_score_model >= .55 and f1_score_1 >= .50 and f1_score_0 >= .50 and recall_score_1 >= .50 and recall_score_0 >= .50:\n",
    "            print(f\"estimators {num_estimators}\")\n",
    "            # print(f\"variance explained {sum(pca.explained_variance_ratio_)}\")\n",
    "            # Print the classification report\n",
    "            print(\"         AdaBoost Classification Report\")\n",
    "            print(adaboost_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "d0399d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of estimators? \n",
    "if run_multiple_tuning_iterations == True:\n",
    "    for n in range (50,200, 10):\n",
    "        # Instance AdaBoost\n",
    "        # Initiate the model instance\n",
    "        adaboost_model=AdaBoostClassifier(n_estimators=n)\n",
    "\n",
    "        # Fit the model \n",
    "        adaboost_model =adaboost_model.fit(X_train_scaled, y_train)\n",
    "        pred_adaboost=adaboost_model.predict(X_test_scaled)\n",
    "        print (n)\n",
    "        # Use a classification report to evaluate the model using the predictions and testing data\n",
    "        adaboost_report=classification_report(y_test, pred_adaboost)\n",
    "\n",
    "        # Print the classification report\n",
    "        print(\"         AdaBoost Classification Report\")\n",
    "        print(adaboost_report)\n",
    "#120 highest 1-recall\n",
    "#150 good overall accuracy, but lower 1-recall\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb178dfb",
   "metadata": {},
   "source": [
    "#### Future enhancements:\n",
    "\n",
    "* Clear variables that are not important\n",
    "* Try increasing the deph of the AdaBoost model\n",
    "* Include more features\n",
    "* X8: use a function to generate the rolling volatilities, and set the number on the column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59adef7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aca9e518",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b1b7142",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bef81dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f82a59a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d812aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40afb328",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9bc0e39",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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