garch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb85d67b",
   "metadata": {},
   "source": [
    "# Times Series with GARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0c64b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Installations \n",
    "from arch import arch_model\n",
    "from statsmodels.tsa.stattools import adfuller,q_stat,acf\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import glob\n",
    "import cufflinks as cf\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import scipy.optimize as sp_optimize\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from statsmodels.tsa.statespace.varmax import VARMAX, VARMAXResults\n",
    "from statsmodels.tsa.statespace.kalman_filter import FilterResults\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f43e6d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
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    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imports\n",
    "plt.style.use(\"seaborn\")\n",
    "cf.set_config_file(offline = True)\n",
    "%matplotlib inline\n",
    "cf.set_config_file(offline = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e8d6ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3025, 6)\n",
      "Data loaded\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-10-06</th>\n",
       "      <td>1159.81</td>\n",
       "      <td>1162.33</td>\n",
       "      <td>1154.85</td>\n",
       "      <td>1159.97</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.000672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-07</th>\n",
       "      <td>1161.57</td>\n",
       "      <td>1163.87</td>\n",
       "      <td>1151.41</td>\n",
       "      <td>1158.06</td>\n",
       "      <td>391055008</td>\n",
       "      <td>-0.001647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-08</th>\n",
       "      <td>1158.36</td>\n",
       "      <td>1167.73</td>\n",
       "      <td>1155.58</td>\n",
       "      <td>1165.15</td>\n",
       "      <td>387142016</td>\n",
       "      <td>0.006122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close     Volume    Return\n",
       "Date                                                               \n",
       "2010-10-06  1159.81  1162.33  1154.85  1159.97          0 -0.000672\n",
       "2010-10-07  1161.57  1163.87  1151.41  1158.06  391055008 -0.001647\n",
       "2010-10-08  1158.36  1167.73  1155.58  1165.15  387142016  0.006122"
      ]
     },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>3651.94</td>\n",
       "      <td>3736.74</td>\n",
       "      <td>3640.61</td>\n",
       "      <td>3719.04</td>\n",
       "      <td>2637914627</td>\n",
       "      <td>0.019672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>3687.01</td>\n",
       "      <td>3687.01</td>\n",
       "      <td>3610.40</td>\n",
       "      <td>3640.47</td>\n",
       "      <td>2601213630</td>\n",
       "      <td>-0.021126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>3633.48</td>\n",
       "      <td>3671.44</td>\n",
       "      <td>3584.13</td>\n",
       "      <td>3585.62</td>\n",
       "      <td>3146085245</td>\n",
       "      <td>-0.015067</td>\n",
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      "text/plain": [
       "               Open     High      Low    Close      Volume    Return\n",
       "Date                                                                \n",
       "2022-09-28  3651.94  3736.74  3640.61  3719.04  2637914627  0.019672\n",
       "2022-09-29  3687.01  3687.01  3610.40  3640.47  2601213630 -0.021126\n",
       "2022-09-30  3633.48  3671.44  3584.13  3585.62  3146085245 -0.015067"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Load the index and clean the data\n",
    "index_path = './DATA/INDICES/SP_OHLCV.csv'\n",
    "def load_index(path):\n",
    "    \n",
    "    df = pd.read_csv(path, parse_dates=True, infer_datetime_format=True, index_col=\"Date\" )\n",
    "    df['Return'] = df.Close.pct_change().dropna()\n",
    "\n",
    "    # dropna's and display data     \n",
    "    df.dropna(inplace=True)\n",
    "    print(df.shape)\n",
    "    print('Data loaded')\n",
    "    display(df.head(3))\n",
    "    display(df.tail(3))\n",
    "    \n",
    "    return df\n",
    "\n",
    "df_SP = load_index(index_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b03fbb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open      0\n",
       "High      0\n",
       "Low       0\n",
       "Close     0\n",
       "Volume    0\n",
       "Return    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing value\n",
    "df_SP.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6aad57e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_Open</th>\n",
       "      <th>log_High</th>\n",
       "      <th>log_Low</th>\n",
       "      <th>log_Close</th>\n",
       "      <th>log_volume</th>\n",
       "      <th>log_Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-10-11</th>\n",
       "      <td>0.005991</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.005557</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>-0.434973</td>\n",
       "      <td>-3.736746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-12</th>\n",
       "      <td>-0.000893</td>\n",
       "      <td>0.003332</td>\n",
       "      <td>-0.005445</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.486510</td>\n",
       "      <td>3.264715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-13</th>\n",
       "      <td>0.006028</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.013416</td>\n",
       "      <td>0.007096</td>\n",
       "      <td>0.198143</td>\n",
       "      <td>0.623148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-18</th>\n",
       "      <td>-0.000544</td>\n",
       "      <td>0.003659</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.007218</td>\n",
       "      <td>-0.251912</td>\n",
       "      <td>1.273290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-21</th>\n",
       "      <td>0.011148</td>\n",
       "      <td>0.005471</td>\n",
       "      <td>0.003790</td>\n",
       "      <td>0.001772</td>\n",
       "      <td>-0.083420</td>\n",
       "      <td>-1.780462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            log_Open  log_High   log_Low  log_Close  log_volume  log_Return\n",
       "Date                                                                       \n",
       "2010-10-11  0.005991  0.000813  0.005557   0.000146   -0.434973   -3.736746\n",
       "2010-10-12 -0.000893  0.003332 -0.005445   0.003811    0.486510    3.264715\n",
       "2010-10-13  0.006028  0.010013  0.013416   0.007096    0.198143    0.623148\n",
       "2010-10-18 -0.000544  0.003659  0.006346   0.007218   -0.251912    1.273290\n",
       "2010-10-21  0.011148  0.005471  0.003790   0.001772   -0.083420   -1.780462"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check stationarity\n",
    "# Fix stationarity by applying log and diff\n",
    "df_transformed = pd.DataFrame({'Open': np.log(df_SP.Open).diff(),\n",
    "                              'High': np.log(df_SP.High).diff(),\n",
    "                               'Low': np.log(df_SP.Low).diff(),\n",
    "                               'Close': np.log(df_SP.Close).diff(),\n",
    "                               'Volume':np.log(df_SP.Volume).diff(),\n",
    "                               'Return':np.log(df_SP.Return).diff()\n",
    "                               \n",
    "                              }).rename(columns={\n",
    "                                'Open':\"log_Open\",\n",
    "                                'High': 'log_High',\n",
    "                                'Low': \"log_Low\",\n",
    "                                'Close':'log_Close',\n",
    "                                'Volume': 'log_volume',\n",
    "                                'Return': 'log_Return'}).dropna()                                                                                       \n",
    "                                 \n",
    "display(df_transformed.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10d8b5e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmneted Dickey_fuller Statistic: -3.625497\n",
      "p-value: 0.005292\n",
      "critical values at different levels:\n",
      "\t1%: -3.431\n",
      "\t5%: -2.862\n",
      "\t10%: -2.567\n"
     ]
    }
   ],
   "source": [
    "# Reset date column\n",
    "df_SP.reset_index(\"Date\")\n",
    "\n",
    "# Save a dataframe to new csv called Dickey_Fuller \n",
    "df_SP.to_csv(\"Dickey-Fuller-Test.csv\")\n",
    "\n",
    "# Set header and index_col = 0\n",
    "df_SP_Test = pd.read_csv(\"Dickey-Fuller-Test.csv\", header=0, index_col=0)\n",
    "\n",
    "# Get values of our dataframe then reshape it to 1 dimentional array to be fitted into adfuller model \n",
    "values = df_SP_Test.values.reshape(-1)\n",
    "\n",
    "# Results \n",
    "res = adfuller(values)\n",
    "\n",
    "# Print results \n",
    "print('Augmneted Dickey_fuller Statistic: %f' % res[0])\n",
    "print('p-value: %f' % res[1])\n",
    "print('critical values at different levels:')\n",
    "for k, v in res[4].items():\n",
    "    print('\\t%s: %.3f' % (k, v))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dc84a8",
   "metadata": {},
   "source": [
    "Our results show that Dickey_fuller Statistic is greater than critical values of 1%, 5%, 10% and p-value is less than 0.05.\n",
    "So, we proved the stationarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a690dc5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample daily data\n",
    "df_transformed['log_Return'] = df_transformed['log_Close'] - df_transformed['log_Close'].shift(1)\n",
    "df_transformed['squared_log_Return'] = np.power(df_transformed['log_Return'], 2)\n",
    "df_transformed.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59ccf13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily volatility: 0.01099%\n",
      "\n",
      "Monthly volatility: 0.05038%\n",
      "\n",
      "Annual volatility: 0.17453%\n"
     ]
    }
   ],
   "source": [
    "# Calculate daily std of returns\n",
    "std_daily = df_SP['Return'].std()\n",
    "print(f'Daily volatility: {round(std_daily,5)}%')\n",
    "\n",
    "# Convert daily volatility to monthly volatility\n",
    "std_monthly = np.sqrt(21) * std_daily\n",
    "print(f'\\nMonthly volatility: {round(std_monthly,5)}%')\n",
    "\n",
    "# Convert daily volatility to annaul volatility\n",
    "std_annual = np.sqrt(252) * std_daily\n",
    "print(f'\\nAnnual volatility: {round(std_annual,5)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3eb899d",
   "metadata": {},
   "source": [
    "### Apply ACF & PACF plot to find p,q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "081eb512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5542594686192984"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check autocorrelation log_close \n",
    "df_transformed.log_Return.autocorr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0276721b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JKSyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlMKCWZIkSUphwSxJkiSlsGCWJEmSUlgwS5IkSSk6alkohNAGfAY4FRgE3h5j3Fw2/f3A24De0qh3AT9KW0aSNHvM25JUu1pbmC8EFsYYzwQ+AHyiYvoa4H/GGM8u/YsZlpEkzZ4LMW9LUk1qLZjPAm4HiDFuAF5cMf104IMhhHtCCB/MuIwkafaYtyWpRjV1yQCWAjvLhkdCCB0xxuHS8BeBTwO7gJtCCK/JsMykli9fREdHe9WAenq6phN/0zDufBl3/lo19laNO0Uueduc3ZxaNW5o3diNO1+zHXetBfMuoDyytrEEGkIoAH8dY9xZGr4V+Pm0ZdL09e2tGkxPTxe9vQPZo28Sxp0v485fq8Zer7ib7MCTS942ZzefVo0bWjd2485XHjm71i4Z9wLnA4QQ1gIPlU1bCvwghLCklIT/B/BAlWUkSbPLvC1JNaq1hfkm4FUhhPuAAvDWEMLFwJIY49+FEP4QuIvkquqvxxj/rXSF9oRl6hC/JCkb87Yk1aimgjnGOAq8u2L0w2XTrwOuy7DMrNvav49rbt3Eo0/t5HlHLWP9utWs6u7MOwxJaqhWydvmbEnNaM4/uOSaWzfxyBP9jIwWeeSJfq65dVOjQ5IkTcGcLakZzfmC+dGndqYOS5KahzlbUjOa8wXz845aljosSWoe5mxJzWjOF8zr162mo70AwElHd7N+3eoGRyRJmoo5W1IzqvUuGS1jVXcnyxYvAOADb1rT4GgkSWnM2ZKa0ZxvYZYkSZJmwoJZkiRJSmHBLEmSJKWwYJYkSZJSWDBLkiRJKSyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlMKCWZIkSUphwSxJkiSlsGCWJEmSUlgwS5IkSSksmCVJkqQUFsySJElSCgtmSZIkKYUFsyRJkpTCglmSJElKYcEsSZIkpbBgliRJklJYMEuSJEkpLJglSZKkFBbMkiRJUgoLZkmSJCmFBbMkSZKUwoJZkiRJSmHBLEmSJKWwYJYkSZJSdNSyUAihDfgMcCowCLw9xri5bPobgd8BRoDvA++JMY6GEL4H7CzNtiXG+NYZxC5Jysi8LUm1q6lgBi4EFsYYzwwhrAU+AVwAEELoBD4CnBJj3BtC+ALwmhDC1wBijGfPOGpJ0nRdiHlbkmpSa5eMs4DbAWKMG4AXl00bBF4aY9xbGu4A9pO0aiwKIXwthPCNUsKWJOXDvC1JNaq1hXkpB0/RAYyEEDpijMMxxlHgGYAQwm8BS4B/B14IfBz4HPB84LYQQogxDqdtaPnyRXR0tFcNqKena8pp7e2FqvM0SjPGlIVx56tV44bWjb1V406RS942ZzenVo0bWjd2487XbMdda8G8CyiPrK08gZb6yl0OnAT8aoyxGEJ4BNgcYywCj4QQtgPPAZ5I21Bf3960yUCyk3p7B6acPjJSBEidpxGqxd2sjDtfrRo3tG7s9Yq7yQ48ueRtc3bzadW4oXVjN+585ZGza+2ScS9wPkDpFN1DFdP/FlgIXFh2im89SZ85QghHkrR2/LTG7UuSpse8LUk1qrWF+SbgVSGE+4AC8NYQwsUkp/G+C7wNuBv4RggB4JPA1cDnQwj3AEVgfbXuGJKkujFvS1KNaiqYS/3d3l0x+uGyv6dqub64lu1JkmbGvC1JtfPBJZIkSVIKC2ZJkiQphQWzJEmSlMKCWZIkSUphwSxJkiSlsGCWJEmSUtR6H2ZJkg4LW/v3cc2tm3j0qZ0876hlrF+3mlXdnY0OS1KObGGWJCnFNbdu4pEn+hkZLfLIE/1cc+umRockKWcWzJIkpXj0qZ2pw5LmPgtmSZJSPO+oZanDkuY+C+YmtbV/Hx+7YSPvuPwuPnbDRrb272t0SJJ0WFq/bjUd7QUATjq6m/XrVjc4Ikl586K/JjXWZw4Y7zP3gTetaWxQOdu2bzvXbbqRx3Y+zgnLjuWS1RexsnNFo8OSdJhZ1d3JssULaG8vHHZ5WFLCFuYGGGs9vvD3bpmy9dg+c3DdphvZ3L+F0eIom/u3cN2mGxsdkiRJOgxZMDdAliuu7TMHj+18PHVYkiQpDxbMDZCl9ThLn7m53s/5hGXHpg5LkiTlwYK5AbK0Ho/1mVuxdCEfeNOaSW+SP9fvDXrJ6oto27sCigVO7D6eS1Zf1OiQJEnSYciL/hpg/brVfOizGxgeKc7oiuus/ZzjM0/xtxv/kf3ztrHwwEreteZiwrOOqmmbeVrZuYKFP3k5AJe956UNjkaSJB2ubGFugLHW41XLO6dsPc4iaz/nv934jwwu6KXQVmRwQS9/u/Efa9qeJLWSud5tTVJ+LJhbWNZ7g+6fty11WJLmornebU1SfuySQeve73espRpIvTfowgMrGVzQO2FYkua6Zrw959b+fVxz6yYefWonzztqGevXra75LKOk/NjCzNy/3++71lzMyK7lFEcLLBjs4V1rLm50SHPatn3buXLjVfzWXR/gyo1XsW3f9kaHJB2WmvH2nLZ6S63Jgpm5f7/f8KyjGI5nMPTAuVxx3u+1xAV/rWyu/wCTWkUzPtK6GVu9JVVnwYz3+1V9zfUfYFKryHJ7zrw1Y6u3pOrsw0xyv98/+/rnGO3cwYnLjzts7vdbLBaT/1M8ZNyUy1BkeGSY4dHh+sZyaHBlW4ShkSGK4/EVy5YplmYtew0V8Y5ZMFhg99CeSbe/fX8fX37kZh4feIJju47m9SddwIqFyw9Zx+SxFyeEfEzXc/nxrp+MTz+m67n0D05sRaq2n8u17R2mb//uzPNPFV8jFPYcYMcMYq809W6r72ss7hli+76DcS9bsJR5baZLzVy9bisqKV+H9RFgaOQAgyODFCgw7/GXUiwWWXfxMezYN8COvbvG5ytO/M+0TbbUgdEDjNDGpt5Hk3mKB+ceKwkPjB4A4KFnNkOR8YJxvNAtFhkaSebZ+FQcH3ew1ExWXCwrmb71k/9mtLLqKJbFmKGQ6+pbyMCu/amvL9U0FhgcTl7fdx5/ZMar7epfwMDA4KTT7vzpHfQOPgPAll2P8/cPfYlznnNu9kDLnNJ1Oj/+6W5Y1EfPwlWc0nU6P3jqiZoj7xpYyMDA/tR5mlWrxr5kYCG7dx+M+4XPXkD3osM6XapOsl6sLam5HCZHgKRk3Lp7B3sO7Gfv0CD7DgwyNDzCyMgofbuG2bXnAKOjcM1XfsLaNYtZsrh9ViMaGSkyWhzlqe0DqfMAPNM3dQvd6Ggyz46BbPcXHdg7NI0oJ9e2b5i9g/VtYZ7KWP2+b2jm2+sY7GD/FOvZNrj1kOGp5q26HTppe2wtAGed2w1Fal4XQMfgCPuHRmpevpFaNfaOoYlxj4w2rpVektR4c7Jg3rlvD8/s6mfvgaQw3j98AIrw/Sd/Mun89313gNHR5O+t24fZsHEP57x8aY4Rq9GOmN/D9qGtE4Y1d+wZ3s0D/RvYMdTLEfN7OL17LYs7ljQ6LEmzzNv4qV7mZMG8bfcAP+k7eN/haufqe3cMpw4fDnbvGWHDxj307him54iOmlvZ67WesXXtGxxldBTuvHvXrLb8n969lq89dg8s7mPFgqSg0tzxQP+G8R9E24e28kD/Bl6x8pwGR6Vydz784Pjf+w8MHTKuVvVa1/4DQxRGCk0VU1ZLnpzYxaiVzDT2O+/exdbtyTH9kSf6ueLL38mlQaxV9/lcifuck0+r+zbmZME8XT1HdIx/ocaGG23P8G5Gj98Ai/v45rbZbxHbsHHP+D6YqpX9YDG8Y8piOMt6phNTXi3/izuW0LYlKZJfcW73lPPV8wdBvdh6Wt2Ood7UYUlzkw1iqhdvKwesXbN4/O9VKzomDE/X7j0j3Hn3Lr5w8w7uvHsXu/fU1n/zgf4NsGQHFIrjLWKzKUtSGSuGi8WDBWwt66lnTHnLsg/yNtZ6WiSfz0orquxiY5cbHQ7GjkefveHpGR2Ppru9mR7/6qmyAawZGsRa1Z7h3Xxz2538y9Nf4Jvb7mTPcP3ugNQKLJiBJYvbKRSgUIBzXr50Ri2GWQqqsdbjkRf825QfurxbxLIklSwFbD2TU5Z15Z2gs+yDsa4ke/aNziimrAe7Vm09zTP5nt69FnYfAcUCK+avssuNmlq98lreP/CbsUFh7ZrFtJUqnZk2iGWR94+UPB3ujTMWzHWWpaDK0nqcd4tYlqSSpYDNmpyyHBCytPznnaCz7IPJupLUIutra9XW0zyT71iXm7YfnMcrVp5jlxU1tXrltbzP0jXjWcEli9vpXNDG4s62GTWIbR3YyS2P3s4/P/kFbnn0drYOTP6ExjyPSXk3GLVq40y9eG5iGrL0X83SHzrLh+707rXcvvluCkv6Wblw9i9CG0sqwJT9hF90WpG7n/4WhcX9dAwu50WrzjxknsKCfSw4+duwuI/5C3ooLFgLHFqcZOnrPNbynxZTvRJ01gsM165ZzL9+fSejo1MX8fWKKet6Tl7wEu7eft/4+3Jy10tq2l49+2eP96t+eup+1fVKvvbh1lxTrxyS9/U5K1YNsmv5g7Qt6Wd0dzdL+06b1e3lacPW+xnp3EEBGOncwYat9/O6rlcfMl+ePxrqec1QFs14N6ksx5p6qenbE0JoAz4DnAoMAm+PMW4um/5a4I+BYeCaGONnqy3TCrJ8OLMUVFk+dIs7ljAUzwDgFRccUc+XUbOHB79DW1cfkCSMhwe/w6quiXcaGG89J/1uBHkeELJerJjlAsMsPyymF9PMfnwBfP/BAoPbzzg4vKLAOS+fdNZU9Uy+We5KUa/k6x0wsjlc83Yrqlehm+V4VE/zTvgB7SPJMaJ9aR/zlv8AeE5N68qSt/P8sTy8oI9CxfBk8vyRUs/iPMv+bsa7SeWZ/2vtknEhsDDGeCbwAeATYxNCCPOAK4FfBn4ReGcI4dlpy7SKLB/OLKd/Tu9ey+iu5RRHW6s/ZZYWwaythvXq65ylC0jeFyvWqytJ1u4tWftVVzt1t23PLuaf/C0WvvgO5p/8Lbbt2XXIPFn7HWc9i1KPfsWH+2nCabiQwzBv56Wep8fr1e927Hi0ZHH7jK/PyWLnyLbUYcieQ7LkyDy7dXUMLk8dHpNnn+l6XjOUZX/n3bUty2clz/xfKGZ4FHKlEMIVwLdjjF8sDT8VYzyq9PeLgMtjjK8uDV8J3AecOdUyaS74XzdXD7BQmPBI58oFxiYVCkxpOvNUbnom68pjnnqta374Fm1LD/6qHt21fLwVfDrzJBub+D4Vxv9T/7izvG9Z39s8Y5ruttLWlWWeeeFbtJe9dyO7lnOgxvc303wZPwPVZI0p63dlMjUsAsDNH7+g1kXrLq+8XUvOhomfhZm8V5Xqta56faezxpRX3MlM+X0Xp/t9LVf5GhacfgeFtrJj/2iBwQfOnXJdU+2DwoK9zDvuIQpL+inu7ubAj0+hOLho4jzz99Jx/EPj3U2Gt5xCcWjRpOury3uX5T2p0/sGwPy9zCt7fQe2nAKTvL7cXhsz+zzNRs6utYV5KVDe430khNAxxbQBYFmVZaY2dvuKtH8V89Wyo8pXNZW2hXsntMC1Ldxbw5ayby/LPPXcXjUHfnzKeMv46K7lHPjxKTXNkwQ0Mbap3rR6xF1YMPF9Kyw49H2r3MaMt1nH9y7LttKGIds+aFvSnzoMUKgYVzk85sCPT2Gk9DkYmeJzcMgP20nXlCTotN/1WbY1HVNuL0sumiw3NY988nYNObvWfVXtszHd9dRjXXmqV9z1/C5Wy/9Zc0gWo7u7U4ezmnfcQ7Qt7aPQVqRtaR/zjnvokHmKQ4s4EM9g8IFzORDPmLJYzqrae5flPSmU1SQLTv4WhZSapNp7N+/4h2gv7YP2pX3MO/7QfVC5vmrbmslrg2yflaEtE/P/0JbSZ66WfF0lD9Xafr8L6CobbosxDk8xrQvor7LMlD73+6+sGkxPTxe9vQPjw4/2/owt2382PnzzHf0AXJDyQIosvrltI9uHDvbPWvXihyftK5N1e0uWzPyJOvV6bdnWdQTw3INxnzz1POMmnSfr9rKptp67nnmA/rJ+dSvWbOKVz3rVrG0vi60DO7n76YMX6q1ddSarupZNuq1CW4HXverQadNxy6PfZqTz4D6Yf9IPeN3zJl6w8s1tE/sUr1zYc0j/+VseXc5I547x4Y7B5fzKJH3s77y7g60PH2wJWLWig3NePbE/9Bdu3jEhoRYK8MaKde3eM8It/57Ua1P1q8uyrd17Rsb7cqZd0DjV9n7+uSeyYknLX0iYS96uJWfDxKfeZf2OfeHm5LNY+bkpV21d5U+Cg+R9n6zvfrXvYuV6Vq2YfD1ZYsozbqjfdzFL/s+SZ8a2V9n/unJ7X7z9FDiurNX3x6dM+lmoti//5en+CUVb+9L+1M9Umj3Duw/p51vZdSHLe5flPSmvSQpL+zhyippkbH0w9Xelch90LO3n1yrmzRJ3vV4bwFce7qZQ1npc3N19yHxfuBmGyvL/2Lpm40l/tbYw3wucDxBCWAuU/xTZBDw/hHBECGE+8Arg/irLtAT7SramLP3q8jZ2AWWhrTh+AeVsGu3sSx2GpE/xivmrKDB1n+K9P3rhhF/ze3/0wkm3V697dpf3o5tJ//Ost/rLsr0Wdljm7Wrqdf1C3vf7red1F/X6LmaR9dqFLP2vVy5eytDDZ7D/u+cy9PAZrFxc20XK9bw1Z5bbxtYrP9azJsmyD7LEXc/nNXT1nTbheNM1yV1X8nwwTa0F803A/hDCfSQXilwWQrg4hPDOGOMB4P3AHSQJ95oY41OTLTPz8PPVqve7Pdw14/uW94+vLPtgcccSXrHyHC488o1TXtCR9QCV9Z7dq1Z0UCjM7BZ99XroznTma1GHZd6upl4H3Hrd7zerehYKWYr9en03ioOdDD78C+z7zrkMbfoFioOdNa0HsuWQLOr5YKMsuT3Le/ei04osWJ10t1hyyrd50WmHdlyo57EtS4NJlrjr+byGl75oFd29L2PwgXPp7n0ZL33RqknXVY/PQBY1fcNijKPAuytGP1w2/avAVzMs01JO7157yC1s1Pya8X3L+36W9doHa9csPuR2eLXON9ZylCbLLZqybCvrrZ7yvm9tng7XvF1N3rdey2LsabAs7uOb2yY/rV/PuOt1u8wsst7CM4ssOSSLsSJ+dBSGVnRQXNNZc4fVLLk9yzMNstzGtZ7HtrEGk7SuollybZbPZZbP29h81d7f8TMQdejiWs3cORrkYOwDlSZLolO+siSCvGVJdPX8LGX57GaR9QBVrySWNflWiylroZ/lQKa5JevBO09Z7mef9SFR9VKvAr0Zz+LUs4jPktuzFMNZWqqz5vXde0bG+wynPZirmiy5Nu/PZZ4smOss64M7dHjLkuj8LNWvmMla6Gc5kEmzLUuxVM/8kOXHeb2+i814FqeeRXyWBpos7289z0Le9/2tzD85eQJj/+5u7vv+afzymbU9UKaauXzcqrUPs6bghYGqFz9LBw/koy+8LfUhB/XiPlczyNI3tZ6f1SwXqtVL1j6nY9/9kRf826x/9/O8cAyyvb9Z+hRnNbD8wQm3jBtY/mDN66pmLufQOVkwz2vvoL2tMS+tGS8wU2vys5TvgRzc52oOWYqlLJ/VrEVnnkXO2NmeN15wROrFkc1YxNdLlvc3y0XYWbV19acO19N0Ppd5NYTUS+PPhcyCY45YydHLV7B/eJA9Q/u5tf0hihRZvriT/QcOMDw6yshIkdFZuEt9M15g1ozs612dn6X8Wyvc541Tft/UO75x3yHjJvPFm79Rdb4s68o6T3t7YcbryTrfBZyVuo7T9h3NdZtu5LGdj3PCsmO5ZPVFrOxcMWGeKzdeNeH0+GNDD3HZCy89ZF0P7b2fzf1bxoef133cpLFlfX31cPNdX5ww3HdgW83bzRL3hafXtOopTXav8XLV3t962rjrXh7f/fj48LFdx0y5L6rFXU09P5fTMdO4s5iTBTNAoVCgc95COuctpKMt+QV7+tGB0eIogyNDDI4MsndokL1D+9l7YD9DwyOMjCS9/tPK6Gol9mKWc17XeZliXNI5j+LwSLLOUvFeLP2nbGi8s36RYvJ3+WMlSw+maW9ro1g2ofyR51l+FuRdwM7lfk710owXK+Yt77uJuM9Vq237trP/mLsZ7dzBlRv/a9JCoZ5Wdq7gsjXpRcZjOx9PHR5zyeqLDilyGu2EZcdOKOJPWHZsTevJ+31pRutPeUNu7289P5fNZs4WzFNpK7TR2bGQzo6FdC84OP7A6DAHRoYmFJfF8eqzYrhOVqxYzPbt07/5+1gcO/b3UTzhNoqdO3hg19H82kmvY/mC5RPmHJt/QtE9WpywntEifP6/b5hQwD68byNvXn1x+WrGHXHEIrbvmNkDHW7+6cSHh/Qd2MYLjjzykNc4PjzhB0BxfFzf4E6KJ2yguKiP7+w6klcf+yq6FywtW+7gOrq7O+nvn/7jzCvf9Y72XQAc/6wjKrZxaIyHLD/ZD5kqH6sk7n0pEaVr5NN+u5d10r9wfs3Lr+s6hzufvIuf7XmGZy9+Fuc895UsW9B1yHzFOn9Hu7sX0b/g4KnihfNn9566an3XbbqR0UXbAdjcv4XrNt1YtXCYbVmLzixFTt7GivgtOx/n+BkUec34vuSt2d7fev0YytthVzBPZV5bB/Pa8t0dyxZ2MTQ//dnlaa7+wfUUS4lgy66fcNPmW2v+Ujyx+6kJw0/ufppnL1k56bw93V3MPzCzUx+TfWGOXHroTcmr+crGmykuTgr9J3c/xX889Z9T7oOeni56CzOLe9u+7Qwfdz+jnTu4ZctxubRW9PR00dsxu6eaZktPTxe982YW+xlHnVafYKahHnHr8NKMrWb1KjobYazIm+mp9mZ8Xw53zXhGIwsL5hZWz0SQ9y++en1h8k6GtlZImkwztprVq+hsZc34vhzumq3FO6s5eZeMw0XlF38mieCS1RdxYvfxtBXaOLH7+Fn/xTf2hfnUKz/GZWsurbmVtp77IAtbKyRNJu8cqmx8X1QvtjC3sHqe1mjVX3x5n9qxtULSZFo1h851vi+qFwvmFmYiyH8ftGrfK0mSVDsLZmka/JEiSdLhxz7MkiRJUgoLZkmSJCmFBbMkSZKUwoJZkiRJSmHBLEmSJKWwYJYkKSfb9m1n/zF3szfczJUbr2Lbvu2NDimTVo1bqhcLZkmScnLdphsZXbQdCkU292/huk03NjqkTFo1bqleLJglSUox1ro6cOK/zLh19bGdj6cON6tWjVuqFwtmSZJS1LN19YRlx6YON6tWjVuqFwtmSZJS1LN19ZLVF3Fi9/G0Fdo4sft4Lll90UzDy0Wrxi3Vi4/GliQpxQnLjmVz/5YJw7Va2bmCy9ZcWo+wctWqcUv1YguzJGlOynJnhyzzjLWuttu6Kh22LJglSXNSlr7HWeYZa139wkWf5rI1l7Kyc0Ue4UtqIhbMkqSWs7V/H6NFGC3Cx27YyNb+fYfMk6XvsXd/kJSFBbMkqeVcc+um8b8feaJ/wvCYLHd28O4PkrKwYJYktZxHn9qZOgzZ7uzg3R8kZeFdMiRJLed5Ry3jkSf6JwxXynJnB+/+ICkLW5glSS1n/brVdLQXADjp6G7Wr1vd4IgkzWW2MEuSWs6q7k6WLV4AwAfetKbB0Uia62xhliRJklLU1MIcQugErgdWAQPAm2OMvRXzXAa8oTT4bzHGPwshFIAngR+Vxt8fY/xgTZFLkjIxZ0vSzNTaJeNS4KEY45+GEN4AfAh439jEEMIJwJuAM4AicHcI4SZgL7AxxvjamYUtSZoGc7YkzUCtBfNZwOWlv28DPlwx/Qng1THGEYAQwjxgP3A6cFQI4S5gH3BZjDGmbWj58kV0dLRXDainp2vKae2lC0PS5mmUZowpC+POV6vGDa0be6vGPYU5mbMbkdtb9XPRqnFD68Zu3Pma7birFswhhLcBl1WMfgYYu+nlADDhfj4xxgPAttLpvL8CvhdjfCSE8GzgozHGL4cQziI5RfiStO339e2t+iJ6erro7R2YcvrISBEgdZ5GqBZ3szLufLVq3NC6sdcr7kYceA6nnJ13bj/cP8+N0KqxG3e+8sjZVQvmGOPVwNXl40II/wyMrbUL6K9cLoSwELiGJDm/pzT6u8Bwab33hBCOCiEUYozFqq9CklSVOVuS6q/WLhn3AucD3wbOA+4un1hqpbgZ+EaM8S/LJv0JsB24PIRwKvATE68kzTpztiTNQK0F81XAtSGEe4Ah4GKAEML7gc1AO/CLwIIQwnmlZT4IfAy4PoSwjqTV4i21hy5JysicLUkzUFPBHGPcC7x+kvFXlA0unGLxdbVsU5JUG3O2JM2MDy6RJEmSUlgwS5IkSSksmCVJkqQUFsySpJazbd929h9zN3vDzVy58Sq27dve6JAkzWEWzJKklnPdphsZXbQdCkU292/huk03NjokSXOYBbMkqeU8tvPx1GFJqicLZklSyzlh2bGpw5JUTxbMkqSWc8nqizix+3jaCm2c2H08l6y+qNEhSZrDan3SnyRJDbOycwWXrbm00WFIOkzYwixJkiSlsGCWJEmSUlgwS5IkSSksmCVJkqQUFsySJElSCgtmSZIkKYUFsyRJkpTCglmSJElKYcEsSZIkpbBgliRJklJYMEuSJEkpLJglSZKkFBbMkiRJUgoLZkmSJCmFBbMkSZKUwoJZkiRJSmHBLEmSJKWwYJYkSZJSWDBLkiRJKSyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlKKjloVCCJ3A9cAqYAB4c4yxt2KevwFeVpoOcAEwVG05SVJ9mbMlaWZqbWG+FHgoxvhy4B+AD00yzxrg3Bjj2aV/OzMuJ0mqL3O2JM1ArQXzWcDtpb9vA84pnxhCaAOeD/xdCOHeEML6LMtJkmaFOVuSZqBql4wQwtuAyypGPwPsLP09ACyrmL4Y+BRwBdAO3BVC+C6wtMpyh1i+fBEdHe3VZqOnp2vKae3tharzNEozxpSFceerVeOG1o29VeM2Z8+uZowpi1aNG1o3duPO12zHXbVgjjFeDVxdPi6E8M/AWGRdQH/FYnuBT8YY95bm/wZwKrCrynKH6OvbW20Wenq66O0dmHL6yEgRIHWeRqgWd7My7ny1atzQurHXK+5GHHjM2bPncP88N0Krxm7c+cojZ9faJeNe4PzS3+cBd1dMPwm4J4TQHkKYR3Jab2OG5SRJ9WfOlqQZqOkuGcBVwLUhhHtIrqK+GCCE8H5gc4zxlhDCDcAG4ADwDzHGH4YQtky2nCRpVpmzJWkGaiqYS6ftXj/J+CvK/r4cuDzLcpKk2WPOlqSZ8cElkiRJUgoLZkmSJCnFnC+Yt+3bzv5j7mZvuJkrN17Ftn3bGx2SJGkK5mxJzWjOF8zXbbqR0UXboVBkc/8Wrtt0Y6NDkiRNwZwtqRnN+YL5sZ2Ppw5LkpqHOVtSM5rzBfMJy45NHZYkNQ9ztqRmNOcL5ktWX8SJ3cfTVmjjxO7juWT1RY0OSZI0BXO2pGZU64NLWsbKzhVctubSRochScrAnC2pGc35FmZJkiRpJiyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlMKCWZIkSUphwSxJkiSlsGCWJEmSUlgwS5IkSSksmCVJkqQUFsySJElSCgtmSZIkKYUFsyRJkpTCglmSJElKYcEsSZIkpbBgliRJklJYMEuSJEkpLJglSZKkFBbMkiRJUgoLZkmSJCmFBbMkSZKUwoJZkiRJSmHBLEmSJKWwYJYkSZJSWDBLkiRJKTpqWSiE0AlcD6wCBoA3xxh7y6afBvx12SJrgQuBO4AngR+Vxt8fY/xgLTFIkrIxZ0vSzNRUMAOXAg/FGP80hPAG4EPA+8YmxhgfBM4GCCG8Hng6xnh7COFEYGOM8bUzilqSNB3mbEmagVoL5rOAy0t/3wZ8eLKZQgiLgT8DXlEadTpwVAjhLmAfcFmMMdYYgyQpG3O2JM1A1YI5hPA24LKK0c8AO0t/DwDLplj8bcCXY4zbSsM/BT4aY/xyCOEsklOEL0nb/vLli+joaK8WJj09XVXnaUbGnS/jzl+rxt6qcZuzZ5dx569VYzfufM123FUL5hjj1cDV5eNCCP8MjEXWBfRPsfibgF8rG/4uMFxa7z0hhKNCCIUYY3Gq7ff17a0WIj09XfT2DlSdr9kYd76MO3+tGnu94m7EgcecPXuMO3+tGrtx5yuPnF3rXTLuBc4v/X0ecHflDCGEZcCCGOMTZaP/BPid0vRTgZ+kJV5JUl2YsyVpBmrtw3wVcG0I4R5gCLgYIITwfmBzjPEW4CTgxxXLfQy4PoSwjqTV4i01bl+SlJ05W5JmoFAsNndjQW/vQNUAD/dTCHkz7ny1atzQurHX8fReoQ7htBRzdvNp1bihdWM37nzlkbN9cIkkSZKUwoJZkiRJSmHBLEmSJKWwYJYkSZJSWDBLkiRJKSyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlMKCWZIkSUphwSxJkiSlsGCWJEmSUlgwS5IkSSksmCVJkqQUFsySJElSCgtmSZIkKYUFsyRJkpTCglmSJElKYcEsSZIkpbBgliRJklJYMEuSJEkpLJglSZKkFBbMkiRJUgoLZkmSJCmFBbMkSZKUwoJZkiRJSmHBLEmSJKWwYJYkSZJSWDBLkiRJKSyYJUmSpBQWzJIkSVIKC2ZJkiQphQWzJEmSlKJjJguHEP4/4PUxxosnmfYO4F3AMPCRGOO/hhA6geuBVcAA8OYYY+9MYpAkZWPOlqTa1NzCHEL4JPDRydYRQng28NvAy4BzgY+GEBYAlwIPxRhfDvwD8KFaty9Jys6cLUm1m0mXjPtIkulkfgG4N8Y4GGPcCWwGXgScBdxemuc24JwZbF+SlJ05W5JqVLVLRgjhbcBlFaPfGmP8Ugjh7CkWWwrsLBseAJZVjB8bl6qnp6tQbZ7SfFlmazrGnS/jzl+rxt6qcZuzZ5dx569VYzfufM123FUL5hjj1cDV01zvLqA88i6gv2L82DhJUp2YsyWp/mZ00V+KbwN/EUJYCCwAVgM/AO4Fzi9NPw+4e5a2L0nKzpwtSSnqWjCHEN4PbI4x3hJC+BuS5NoG/FGMcX8I4Srg2hDCPcAQcMiV2pKkfJizJSmbQrFYbHQMkiRJUtPywSWSJElSCgtmSZIkKcVsXfQ360IIbcBngFOBQeDtMcbNjY0quxDC9zh4u6YtMca3NjKeakIIZwB/GWM8O4RwIvB5oEhyYdBvxhhHGxnfVCriXgN8FfhRafJVMcYvNS66Q4UQ5gHXAMeRXHz1EeC/afL9PUXcT9L8+7sd+CwQgBHgrUCBJt/fraqV87Y5Ox/m7Hy0as6GxuXtli2YgQuBhTHGM0MIa4FPABc0NqRsSleiE2M8u8GhZBJC+H3gEmBPadQVwIdijP8RQvh/JPv9pkbFN5VJ4l4DXBFj/ETjoqrqN4DtMcZLQggrgO8BD9L8+3uyuP+c5t/frwWIMb6sdI/iK0gSb7Pv71Z1IS2Yt83Z+TBn56pVczY0KG+3cpeM8SdQxRg3AC9ubDjTciqwKITwtRDCN0oHjmb2KPArZcOnA/9Z+ruZn/41WdzrQgjfDCFcHUJoxruzfxn4cNnwMK2xv6eKu6n3d4zxX4B3lgaPBZ6hNfZ3q2rVvG3Ozoc5Oz8tmbOhcXm7lQvmyidTjYQQWqXFfC/wceBc4N3ADc0ce4zxK8CBslGFGOPY7VUyPf2rESaJ+9vA78UYXwE8BvxJQwJLEWPcHWMcKCWqfwI+RAvs7ynibvr9DRBjHA4hXAt8iiT2pt/fLaxV87Y5Owfm7Py0cs6GxuTtVi6YK59M1RZjHG5UMNP0CHB9jLEYY3wE2A48p8ExTUd5v6BWevrXTTHGB8b+Bn6+kcFMJYRwNHAXcF2M8R9pkf09Sdwtsb8BYoxvBk4i6RfXWTapafd3i2rVvG3OboyWyCHm7MbIO2+3csE89gQqSqfHHmpsONOynqTvHiGEI0laXX7a0Iim53ulfkPQWk//uiOE8Aulv38JeCBt5kYIITwL+BrwBzHGa0qjm35/TxF3K+zvS0IIHywN7iU50H232fd3C2vVvG3OboxWyCHm7Jw1Km837SmlDG4CXhVCuI+ks3dTX7Fc4Wrg86WnZxWB9S3SyjLmd4HPhhDmA5tIToe0gkuB/xtCGAJ+xsE+UM3kD4HlwIdDCGP9y94H/E2T7+/J4n4/8NdNvr//Gfj7EMI3gXnA75Ds41b8fLeCVs3b5uzGMGfPnlbN2dCgvO2T/iRJkqQUrdwlQ5IkSZp1FsySJElSCgtmSZIkKYUFsyRJkpTCglmSJElKYcEsSZIkpbBgliRJklJYMEuSJEkp/n816zXt1DK/2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplot()\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "plot_acf(df_transformed['squared_log_Return'][1:],zero=False,alpha=0.1, title='Squared Daily Log Return ACF',ax=axes[0])\n",
    "plot_acf(df_transformed['log_Return'][1:],zero=False,alpha=0.1, title='Daily Squared Log Return ACF',ax=axes[0])\n",
    "plot_pacf(df_transformed['squared_log_Return'][1:],zero=False,alpha=0.1, title='Squared Daily Log Return PACF',ax=axes[1])\n",
    "plot_pacf(df_transformed['log_Return'][1:],zero=False,alpha=0.1, title='Daily Squared Log Return PACF',ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82f73fe",
   "metadata": {},
   "source": [
    "### Resample data by weekly return to get futher forcasting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c82d0e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>log_Open</th>\n",
       "      <th>log_High</th>\n",
       "      <th>log_Low</th>\n",
       "      <th>log_Close</th>\n",
       "      <th>log_volume</th>\n",
       "      <th>log_Return</th>\n",
       "      <th>squared_log_Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-10-24</th>\n",
       "      <td>0.003267</td>\n",
       "      <td>0.003568</td>\n",
       "      <td>0.004952</td>\n",
       "      <td>0.004457</td>\n",
       "      <td>-0.005208</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-31</th>\n",
       "      <td>0.003568</td>\n",
       "      <td>0.003195</td>\n",
       "      <td>0.005078</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>-0.065955</td>\n",
       "      <td>-0.000875</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            log_Open  log_High   log_Low  log_Close  log_volume  log_Return  \\\n",
       "Date                                                                          \n",
       "2010-10-24  0.003267  0.003568  0.004952   0.004457   -0.005208    0.000448   \n",
       "2010-10-31  0.003568  0.003195  0.005078   0.003415   -0.065955   -0.000875   \n",
       "\n",
       "            squared_log_Return  \n",
       "Date                            \n",
       "2010-10-24            0.000011  \n",
       "2010-10-31            0.000008  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>log_Open</th>\n",
       "      <th>log_High</th>\n",
       "      <th>log_Low</th>\n",
       "      <th>log_Close</th>\n",
       "      <th>log_volume</th>\n",
       "      <th>log_Return</th>\n",
       "      <th>squared_log_Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-11</th>\n",
       "      <td>0.010019</td>\n",
       "      <td>0.008448</td>\n",
       "      <td>0.010140</td>\n",
       "      <td>0.008210</td>\n",
       "      <td>0.031322</td>\n",
       "      <td>-0.000442</td>\n",
       "      <td>0.00007</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-18</th>\n",
       "      <td>0.010997</td>\n",
       "      <td>0.009677</td>\n",
       "      <td>0.011209</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.021186</td>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.00006</td>\n",
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      "text/plain": [
       "            log_Open  log_High   log_Low  log_Close  log_volume  log_Return  \\\n",
       "Date                                                                          \n",
       "2022-09-11  0.010019  0.008448  0.010140   0.008210    0.031322   -0.000442   \n",
       "2022-09-18  0.010997  0.009677  0.011209   0.009628    0.021186    0.001313   \n",
       "\n",
       "            squared_log_Return  \n",
       "Date                            \n",
       "2022-09-11             0.00007  \n",
       "2022-09-18             0.00006  "
      ]
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create GARCH dataframe of 5 day window by apply mean()\n",
    "df_Garch = df_transformed.rolling(window=5).mean()\n",
    "\n",
    "# Dropnas\n",
    "df_Garch.dropna(inplace=True)\n",
    "\n",
    "# Resample by weekly \n",
    "df_Garch = df_Garch.resample('W').mean()\n",
    "\n",
    "# Check if there are nas\n",
    "df_Garch.fillna(method='ffill',inplace=True) \n",
    "\n",
    "# Data frame\n",
    "# print(df_Garch.shape)\n",
    "display(df_Garch.head(2))\n",
    "display(df_Garch.tail(2))\n",
    "df_Garch.loc[:,['log_Close','log_Return','squared_log_Return']].plot(figsize=(12,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a49283",
   "metadata": {},
   "source": [
    "### Implement a basic GARCH model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b8b6c98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply train test split \n",
    "split = int(len(df_Garch) * 0.70)\n",
    "df_train, df_test = df_Garch.iloc[:split], df_Garch.iloc[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acd43f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Order: p = 0, q = 1\n",
      "Testing Order: p = 0, q = 2\n",
      "Testing Order: p = 1, q = 0\n",
      "Testing Order: p = 1, q = 1\n",
      "Testing Order: p = 1, q = 2\n",
      "Testing Order: p = 2, q = 0\n",
      "Testing Order: p = 2, q = 1\n",
      "Testing Order: p = 2, q = 2\n"
     ]
    }
   ],
   "source": [
    "# Use for loop to make a guess finding right p,q\n",
    "for p in range(3):\n",
    "    for q in range(3):\n",
    "        if p == 0 and q == 0:\n",
    "            continue\n",
    "            \n",
    "        print(f'Testing Order: p = {p}, q = {q}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "244387a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inequality constraints incompatible    (Exit mode 4)\n",
      "            Current function value: -3355.966087179481\n",
      "            Iterations: 1\n",
      "            Function evaluations: 7\n",
      "            Gradient evaluations: 1\n",
      "                     Constant Mean - GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:             log_Return   R-squared:                       0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:                3355.97\n",
      "Distribution:                  Normal   AIC:                          -6699.93\n",
      "Method:            Maximum Likelihood   BIC:                          -6673.33\n",
      "                                        No. Observations:                  622\n",
      "Date:                Wed, Oct 05 2022   Df Residuals:                      621\n",
      "Time:                        18:31:22   Df Model:                            1\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu         3.7325e-05  4.085e-08    913.660      0.000 [3.724e-05,3.741e-05]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      4.4063e-07  2.472e-11  1.783e+04      0.000 [4.406e-07,4.407e-07]\n",
      "alpha[1]       0.1000  3.667e-02      2.727  6.397e-03   [2.812e-02,  0.172]\n",
      "alpha[2]       0.1000  9.459e-02      1.057      0.290  [-8.539e-02,  0.285]\n",
      "beta[1]        0.2500      1.510      0.166      0.869     [ -2.710,  3.210]\n",
      "beta[2]        0.2500      1.456      0.172      0.864     [ -2.604,  3.104]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n",
      "WARNING: The optimizer did not indicate successful convergence. The message was Inequality constraints incompatible.\n",
      "See convergence_flag.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/arch/univariate/base.py:753: ConvergenceWarning:\n",
      "\n",
      "The optimizer returned code 4. The message is:\n",
      "Inequality constraints incompatible\n",
      "See scipy.optimize.fmin_slsqp for code meaning.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GARCH model with constant mean\n",
    "model = arch_model(df_Garch.log_Return, p = p, q = q,\n",
    "                      mean = 'constant', vol = 'GARCH', dist = 'normal')\n",
    "\n",
    "# Fit the model freq to avoid display unnecessary value\n",
    "result = model.fit(update_freq = 5)\n",
    " \n",
    "# Display model fitting summary\n",
    "print(result.summary())\n",
    "\n",
    "# Plot fitted results\n",
    "result.plot(annualize='D')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4836d0ce",
   "metadata": {},
   "source": [
    "### Forecast with GARCH model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "596d8fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-09-18 00:00:00', freq='W-SUN')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Garch.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ba96aa12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 h.1       h.2       h.3       h.4       h.5\n",
      "Date                                                        \n",
      "2022-09-18  0.000001  0.000001  0.000001  0.000001  0.000001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# horizon = how many observations we want our model to predict for each day \n",
    "# For each date, we'll get the predicted value for the next date\n",
    "pred_grach = result.forecast(start = \"2022-09-18\",horizon= 5)\n",
    "\n",
    "# Prediction value for next week\n",
    "print(pred_grach.variance[-1:])\n",
    "\n",
    "# Plot prediction\n",
    "pred_grach.variance[-1:].T.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9880f7",
   "metadata": {},
   "source": [
    "### Plot distribution of standardized residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "33a5f71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals and volatility\n",
    "model_resid = result.resid\n",
    "model_std = result.conditional_volatility\n",
    "\n",
    "# Calculate the standardized residuals\n",
    "result_std = model_resid /model_std\n",
    "\n",
    "# Plot the histogram of the standardized residuals\n",
    "plt.figure(figsize=(7,4))\n",
    "sns.distplot(result_std, norm_hist=True, fit=stats.norm, bins=50, color='r')\n",
    "plt.legend(('normal', 'standardized residuals'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cac8a09",
   "metadata": {},
   "source": [
    "### Fit GARCH with skewed t-distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2c91d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/arch/univariate/base.py:753: ConvergenceWarning:\n",
      "\n",
      "The optimizer returned code 4. The message is:\n",
      "Inequality constraints incompatible\n",
      "See scipy.optimize.fmin_slsqp for code meaning.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Garch model skew t\n",
    "model_garch_skewt = arch_model(df_Garch.log_Return, p = p, q = q,\n",
    "                       mean = 'constant', vol = 'GARCH', dist = 'skewt')\n",
    "\n",
    "# Fit the model\n",
    "model_garch_skewt_result = model_garch_skewt.fit(disp='off')\n",
    "\n",
    "# Get estimated volatility \n",
    "vol_Grch_model = model_garch_skewt_result.conditional_volatility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da195ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot model fitting results\n",
    "plt.plot(vol_Grch_model, color = 'red', label = 'Skewed-t Volatility')\n",
    "plt.plot(df_Garch.log_Return, color = 'grey', \n",
    "         label = 'Weekly Returns', alpha = 0.5)\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4382d879",
   "metadata": {},
   "source": [
    "### Goodness of fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f353a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GARCH BIC : -6673.3345338025265\n",
      "GARCH AIC : -6699.932174358962\n",
      "Log-likelihood of GARCH : 3355.966087179481\n",
      "Log-likelihood of skewt GARCH : 3367.709939519275\n"
     ]
    }
   ],
   "source": [
    "# Print the Criteria (BIC) (AIC) of GARCH\n",
    "print(f'GARCH BIC : { result.bic}')\n",
    "print(f'GARCH AIC : { result.aic}')\n",
    "\n",
    "# Print the log-likelihodd of GARCH\n",
    "print('Log-likelihood of GARCH :', result.loglikelihood)\n",
    "\n",
    "# Print the log-likelihodd of skewt GARCH\n",
    "print('Log-likelihood of skewt GARCH :', model_garch_skewt_result.loglikelihood)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7420f7b8",
   "metadata": {},
   "source": [
    "### Backtesting with MAE, MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5d69faf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_pred): \n",
    "    # Calculate MAE\n",
    "    mae = mean_absolute_error(y_true, y_pred)\n",
    "    print(f'Mean Absolute Error (MAE): {round(mae,3)}')\n",
    "    # Calculate MSE\n",
    "    mse = mean_squared_error(y_true, y_pred)\n",
    "    print(f'Mean Squared Error (MSE): {round(mse,3)}')\n",
    "    return mae, mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2fdd4708",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weekly resample, apply std then calculate y_true, y_pred\n",
    "Garch_std = df_transformed.log_Return.rolling(window=5).std().dropna()\n",
    "Garch_std = Garch_std.resample('W').std().fillna(0)\n",
    "\n",
    "# Output \n",
    "y_true = Garch_std\n",
    "y_pred = model_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9850e520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0010623459146684884"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print MAE\n",
    "mean_absolute_error(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c5d6ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.719089343808273e-06"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print MSE\n",
    "mean_squared_error(y_true,y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}