Capstone Project_stock_Apple_alpha.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock market direction prediction for apple using Alpha 101 & Classification algorithm\n",
    "\n",
    "We prepare have imported dataset from yahoo finance and features data from alpha 101 and few calculations using artificial intelligence techniques to predict stock direction. Here the direction of stock and our prediction signal i.e. alpha should match to give good prediction.. This paper can help in predicting the stock market share like google direction to help in building trading strategy. minimizing pending cases. With the help of this algorithm user will be able to perform sell and buy correctly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from pandas.plotting import scatter_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import *\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import *\n",
    "from sklearn.ensemble import *\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "from sklearn.svm import SVC\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from time import time\n",
    "from scipy import stats\n",
    "import pandas_techinal_indicators as ta\n",
    "import math\n",
    "from numpy import *\n",
    "from scipy.stats import rankdata\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>71.801430</td>\n",
       "      <td>72.532860</td>\n",
       "      <td>71.328575</td>\n",
       "      <td>71.647141</td>\n",
       "      <td>60.977806</td>\n",
       "      <td>100366000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>72.264282</td>\n",
       "      <td>72.779999</td>\n",
       "      <td>71.822861</td>\n",
       "      <td>72.684288</td>\n",
       "      <td>61.860519</td>\n",
       "      <td>94170300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-02-05</td>\n",
       "      <td>72.365715</td>\n",
       "      <td>73.611427</td>\n",
       "      <td>72.321426</td>\n",
       "      <td>73.227142</td>\n",
       "      <td>62.322529</td>\n",
       "      <td>82086200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-02-06</td>\n",
       "      <td>72.865715</td>\n",
       "      <td>73.357140</td>\n",
       "      <td>72.544289</td>\n",
       "      <td>73.215714</td>\n",
       "      <td>65.021019</td>\n",
       "      <td>64441300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-02-07</td>\n",
       "      <td>74.482857</td>\n",
       "      <td>74.704285</td>\n",
       "      <td>73.911430</td>\n",
       "      <td>74.239998</td>\n",
       "      <td>65.930649</td>\n",
       "      <td>92570100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Open       High        Low      Close  Adj Close     Volume\n",
       "0 2014-02-03  71.801430  72.532860  71.328575  71.647141  60.977806  100366000\n",
       "1 2014-02-04  72.264282  72.779999  71.822861  72.684288  61.860519   94170300\n",
       "2 2014-02-05  72.365715  73.611427  72.321426  73.227142  62.322529   82086200\n",
       "3 2014-02-06  72.865715  73.357140  72.544289  73.215714  65.021019   64441300\n",
       "4 2014-02-07  74.482857  74.704285  73.911430  74.239998  65.930649   92570100"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"AAPL.xlsx\" )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.drop(\"Adj Close\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dffa1762e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x = \"Date\", y = ['Open','High','Low','Close'],figsize=(12,8), title= \"Stock Trend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1.259000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>132.610080</td>\n",
       "      <td>133.760306</td>\n",
       "      <td>131.445262</td>\n",
       "      <td>132.634135</td>\n",
       "      <td>4.183949e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.787727</td>\n",
       "      <td>37.141989</td>\n",
       "      <td>36.424603</td>\n",
       "      <td>36.775360</td>\n",
       "      <td>2.150498e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>72.244215</td>\n",
       "      <td>72.975282</td>\n",
       "      <td>71.848299</td>\n",
       "      <td>72.425074</td>\n",
       "      <td>1.147590e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>105.824841</td>\n",
       "      <td>106.976422</td>\n",
       "      <td>105.160657</td>\n",
       "      <td>106.001135</td>\n",
       "      <td>2.651850e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>121.233428</td>\n",
       "      <td>122.242913</td>\n",
       "      <td>120.325448</td>\n",
       "      <td>121.520893</td>\n",
       "      <td>3.637910e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>158.881338</td>\n",
       "      <td>160.040362</td>\n",
       "      <td>157.322679</td>\n",
       "      <td>158.789626</td>\n",
       "      <td>5.114135e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>229.012454</td>\n",
       "      <td>231.515750</td>\n",
       "      <td>226.606253</td>\n",
       "      <td>229.101775</td>\n",
       "      <td>1.899779e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close        Volume\n",
       "count  1259.000000  1259.000000  1259.000000  1259.000000  1.259000e+03\n",
       "mean    132.610080   133.760306   131.445262   132.634135  4.183949e+07\n",
       "std      36.787727    37.141989    36.424603    36.775360  2.150498e+07\n",
       "min      72.244215    72.975282    71.848299    72.425074  1.147590e+07\n",
       "25%     105.824841   106.976422   105.160657   106.001135  2.651850e+07\n",
       "50%     121.233428   122.242913   120.325448   121.520893  3.637910e+07\n",
       "75%     158.881338   160.040362   157.322679   158.789626  5.114135e+07\n",
       "max     229.012454   231.515750   226.606253   229.101775  1.899779e+08"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df.sort_values(by='Date', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>166.960007</td>\n",
       "      <td>168.979996</td>\n",
       "      <td>165.929993</td>\n",
       "      <td>166.520004</td>\n",
       "      <td>32647100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>166.393336</td>\n",
       "      <td>168.993332</td>\n",
       "      <td>165.016663</td>\n",
       "      <td>166.466669</td>\n",
       "      <td>40739600</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>164.597144</td>\n",
       "      <td>167.368567</td>\n",
       "      <td>162.281425</td>\n",
       "      <td>165.771430</td>\n",
       "      <td>61109800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>160.145334</td>\n",
       "      <td>162.441334</td>\n",
       "      <td>157.923332</td>\n",
       "      <td>159.855997</td>\n",
       "      <td>41587200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>157.897416</td>\n",
       "      <td>159.287098</td>\n",
       "      <td>155.722905</td>\n",
       "      <td>158.020645</td>\n",
       "      <td>26192100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close    Volume\n",
       "1258 2019-02-01  166.960007  168.979996  165.929993  166.520004  32647100\n",
       "1257 2019-01-31  166.393336  168.993332  165.016663  166.466669  40739600\n",
       "1256 2019-01-30  164.597144  167.368567  162.281425  165.771430  61109800\n",
       "1255 2019-01-29  160.145334  162.441334  157.923332  159.855997  41587200\n",
       "1254 2019-01-28  157.897416  159.287098  155.722905  158.020645  26192100"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Exponential Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_exp_preprocessing(df, alpha=.5):\n",
    "    edata = df.ewm(alpha=alpha).mean()    \n",
    "    return edata\n",
    "\n",
    "df['Open'] = get_exp_preprocessing(df['Open'], .5)\n",
    "df['Close'] = get_exp_preprocessing(df['Close'], .5)\n",
    "df['High'] = get_exp_preprocessing(df['High'], .5)\n",
    "df['Low'] = get_exp_preprocessing(df['Low'], .5)\n",
    "df = df.dropna(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>166.960007</td>\n",
       "      <td>168.979996</td>\n",
       "      <td>165.929993</td>\n",
       "      <td>166.520004</td>\n",
       "      <td>32647100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>166.582227</td>\n",
       "      <td>168.988887</td>\n",
       "      <td>165.321106</td>\n",
       "      <td>166.484448</td>\n",
       "      <td>40739600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>165.447894</td>\n",
       "      <td>168.062990</td>\n",
       "      <td>163.584145</td>\n",
       "      <td>166.077009</td>\n",
       "      <td>61109800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>162.619862</td>\n",
       "      <td>165.064774</td>\n",
       "      <td>160.565045</td>\n",
       "      <td>162.759136</td>\n",
       "      <td>41587200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>160.182470</td>\n",
       "      <td>162.082748</td>\n",
       "      <td>158.065876</td>\n",
       "      <td>160.313463</td>\n",
       "      <td>26192100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close    Volume\n",
       "1258 2019-02-01  166.960007  168.979996  165.929993  166.520004  32647100\n",
       "1257 2019-01-31  166.582227  168.988887  165.321106  166.484448  40739600\n",
       "1256 2019-01-30  165.447894  168.062990  163.584145  166.077009  61109800\n",
       "1255 2019-01-29  162.619862  165.064774  160.565045  162.759136  41587200\n",
       "1254 2019-01-28  160.182470  162.082748  158.065876  160.313463  26192100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Calulate the Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#returns = daily close-to-close returns\n",
    "def returns (row):\n",
    "    result = ( row[\"Close\"] - row[\"Open\"])/row[\"Close\"]\n",
    "    return result\n",
    "df[\"returns\"] = df.apply(returns, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Calculate Target Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SANJAY\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in sign\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 1., -1., nan])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']= np.sign( df[\"Close\"] - df[\"Close\"].shift(-1))\n",
    "df.drop(df[df.target == 0].index, inplace=True)\n",
    "df['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>returns</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>166.960007</td>\n",
       "      <td>168.979996</td>\n",
       "      <td>165.929993</td>\n",
       "      <td>166.520004</td>\n",
       "      <td>32647100</td>\n",
       "      <td>-0.002642</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>166.582227</td>\n",
       "      <td>168.988887</td>\n",
       "      <td>165.321106</td>\n",
       "      <td>166.484448</td>\n",
       "      <td>40739600</td>\n",
       "      <td>-0.000587</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>165.447894</td>\n",
       "      <td>168.062990</td>\n",
       "      <td>163.584145</td>\n",
       "      <td>166.077009</td>\n",
       "      <td>61109800</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>162.619862</td>\n",
       "      <td>165.064774</td>\n",
       "      <td>160.565045</td>\n",
       "      <td>162.759136</td>\n",
       "      <td>41587200</td>\n",
       "      <td>0.000856</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>160.182470</td>\n",
       "      <td>162.082748</td>\n",
       "      <td>158.065876</td>\n",
       "      <td>160.313463</td>\n",
       "      <td>26192100</td>\n",
       "      <td>0.000817</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close    Volume  \\\n",
       "1258 2019-02-01  166.960007  168.979996  165.929993  166.520004  32647100   \n",
       "1257 2019-01-31  166.582227  168.988887  165.321106  166.484448  40739600   \n",
       "1256 2019-01-30  165.447894  168.062990  163.584145  166.077009  61109800   \n",
       "1255 2019-01-29  162.619862  165.064774  160.565045  162.759136  41587200   \n",
       "1254 2019-01-28  160.182470  162.082748  158.065876  160.313463  26192100   \n",
       "\n",
       "       returns  target  \n",
       "1258 -0.002642     1.0  \n",
       "1257 -0.000587     1.0  \n",
       "1256  0.003788     1.0  \n",
       "1255  0.000856     1.0  \n",
       "1254  0.000817     1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Alpha Library\n",
    "##### alpha 101 library creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def a101 (row):\n",
    "    return ( row[\"Close\"] - row[\"Open\"] ) / ( ( row[\"High\"] - row[\"Low\"] ) + 0.001) \n",
    "df[\"A101\"] = df.apply(a101, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Calculate simple moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AvgP5'] = ta.sma(df['Close'], 5)\n",
    "df['AvgP10'] = ta.sma(df['Close'], 10)\n",
    "df['AvgP15'] = ta.sma(df['Close'], 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>target</th>\n",
       "      <th>A101</th>\n",
       "      <th>AvgP5</th>\n",
       "      <th>AvgP10</th>\n",
       "      <th>AvgP15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>152.398557</td>\n",
       "      <td>153.980746</td>\n",
       "      <td>151.271632</td>\n",
       "      <td>152.804721</td>\n",
       "      <td>27023200</td>\n",
       "      <td>0.002658</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.149870</td>\n",
       "      <td>154.339298</td>\n",
       "      <td>155.386814</td>\n",
       "      <td>158.401480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>152.377879</td>\n",
       "      <td>153.837745</td>\n",
       "      <td>151.098121</td>\n",
       "      <td>152.899565</td>\n",
       "      <td>35780700</td>\n",
       "      <td>0.003412</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.190353</td>\n",
       "      <td>153.842334</td>\n",
       "      <td>154.768609</td>\n",
       "      <td>157.493451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>152.100734</td>\n",
       "      <td>153.975062</td>\n",
       "      <td>150.687710</td>\n",
       "      <td>153.025885</td>\n",
       "      <td>45099100</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.281342</td>\n",
       "      <td>153.380970</td>\n",
       "      <td>154.354932</td>\n",
       "      <td>156.596213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>151.396260</td>\n",
       "      <td>153.470623</td>\n",
       "      <td>150.043178</td>\n",
       "      <td>152.488489</td>\n",
       "      <td>41025300</td>\n",
       "      <td>0.007163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.318579</td>\n",
       "      <td>152.927871</td>\n",
       "      <td>154.016442</td>\n",
       "      <td>155.690312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>150.546074</td>\n",
       "      <td>152.184354</td>\n",
       "      <td>148.846246</td>\n",
       "      <td>151.214513</td>\n",
       "      <td>54777800</td>\n",
       "      <td>0.004420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.200185</td>\n",
       "      <td>152.486635</td>\n",
       "      <td>153.647120</td>\n",
       "      <td>154.920670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close    Volume  \\\n",
       "1244 2019-01-11  152.398557  153.980746  151.271632  152.804721  27023200   \n",
       "1243 2019-01-10  152.377879  153.837745  151.098121  152.899565  35780700   \n",
       "1242 2019-01-09  152.100734  153.975062  150.687710  153.025885  45099100   \n",
       "1241 2019-01-08  151.396260  153.470623  150.043178  152.488489  41025300   \n",
       "1240 2019-01-07  150.546074  152.184354  148.846246  151.214513  54777800   \n",
       "\n",
       "       returns  target      A101       AvgP5      AvgP10      AvgP15  \n",
       "1244  0.002658    -1.0  0.149870  154.339298  155.386814  158.401480  \n",
       "1243  0.003412    -1.0  0.190353  153.842334  154.768609  157.493451  \n",
       "1242  0.006046     1.0  0.281342  153.380970  154.354932  156.596213  \n",
       "1241  0.007163     1.0  0.318579  152.927871  154.016442  155.690312  \n",
       "1240  0.004420     1.0  0.200185  152.486635  153.647120  154.920670  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' Correlation between various paramters')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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54fMrNVesOTExkaFjJjBh9GDmz/iSBUtXcfDwsRQxo7/4H080b8LP08bTs3N7xk6aBsCqdZvYs+8gc77+nO8mjWHqrLnEXYzPiTJyB5st6x53mCzt7IiIAHOBX4wxFYF7gWLAsKx8ntykZkgVShT3uH6gC9Ga84a8UPMTrZox89s5AGzYuJUSniXw8/NNE1e0aBH++59XGDFinLNTvCUhobX4c+5KAA5t208RjyKU8PFMEVPCx5NCHkU4tHUfAH/OXUm10FrJ29sO7MycETMwmBT7Nencgq0LNxAbGZO9Rdyk4NCabJpr/+b26LYDFPYoQvFUNRf38aSQR2GObN0PwKa5q6kSWtO+f9OabJxj33/jnNVUaWpfX+OJh9n++0aiTkYCEBcZ65R6bkRIaC3Wz10FJLVz0QzauXByO6+fu4qQ0NrJ2+3tPDNFO1+IjOXIjoMkXrvmhCqyR174/ErNFWveuXcf5cr4c1eAH/nz56dFkwYsX7s+RczBI8epU6MqALWrP8gKa/vBI8eoFRKMu7sbRQoX4r6gQNZu2OL0GlTOy+qRnUeBS8aYqQDGmETgDaCLiPSyRnx+F5G/RWRQ0k4i0kFENopIuIhMEhE3a32ciAwTke0isl5ESmdxvkqpPCwgwI8Tx08mL/9z4hRlAvzSxA3+4G0+HTuJ+PgEZ6Z3yzxLe3PeOjkHiIo4j6efd8oYP2+iTjnEnDqPZ2l7TNXHahJ9+jwn9h5NdVwvqjWrzcpvF2dj9rfGs7QX0Q41x0Scp4SfV4qYEn5eRJ86n7wcfeo8nqXtMR4+JYg9Gw1A7NloPEoVB8Cngj9FShTl1e/fp8+vw6nV5pHsLuWGlSztlaqdI/FMVbOnn1eqdo6kpFVz1cdqEpVOOyuVW5w5G4mfr0/ycmmfUpw5F5ki5r6gQJas+gOApav/5GJ8AtExsdwXFMia9VtIuHSJqOgYNm3dQcSZlCPVeYkxtix73GmyurNTGUjRbTbGxALHsN/5rTbwAhACPCsiNUXkfqAt8LAxJgRItGIAigLrjTFVgdVA+mP0Sil1C+yD0SkZk3Iko2rVytwTdDfz5v3urLRuWzplQaq6MoopUKgAj7/6NPPGzE6zud37L/HTyJmY3DiNIZ16UrdlejWnjkktn5sbd1WpwOSXPmJCxxE0e60NPoH+t5Np1kn3/Zs6JP33eIFCBQh7tQ3z02lnpXKL9P53Sqr/7H16d2Fz+C6e6fI6m8N3UtrHGzc3Nx6uXZ1HHqpJh5596Tt4FFWDK+Hm5uacxHOjPDyNLatvPS1k9N60r19ijIkEEJG5QH3gGlAD2GR9KBcGzlj7XQF+s37eAjRN90lFXgFeAfjyk6F069g+K2pRSrmgnj060bWr/fuUzZvDKXtXQPK2MmX9OXnqdIr4unVqUL1aFfbvW4+7uzu+vt4sXfIjjzV91ql5X0/jF5vzSPsmABzZfhCvgH9Hckr6eRF9+nyK+KhTkZT0d4jx9yL6zHl8yvtRqqwvgxaOtvb1ZuBvHzOs9buUf7ACr3z+BgDFSnpQpVF1EhMTCV+8KbvLS1f9F0N5qP2jABzbfhBPh5pL+HkRezoqRXz0qfN4+v878uHp70XMGXvMhbMxFPfxJPZsNMV9PLlwzj5dLSYikr1RF7iScJkrCZc5uPEvytxfjrOHT2V3eelq9GIzGrR/DIDD2w+kamdvYq7bzt5En4lKbuf3F45K3nfAbx8zvPW7ySNcSuW00j7eKUZjTp89h0+plKOXvqW8GTfsPQDi4xNYumodHsXsNxjp3rEt3Tu2BeDtwaMoXzYAlfdkdWdnN/C04woRKQ7chX3EJnVHyGDvCE0zxrybzvGumn+/dkskg3yNMZOByQBXzx3K/Gs6pVSeNmHiNCZMtF/A2qJFE3r17Mzs2fOoU7s6sTGxREScSRE/afJ0Jk2eDkD58mX55Zdpua6jA7Bixu+smGEffarSuDqPdmrBxvl/UKFaRRIuxBOT6gQ25mw0l+ISqFCtIoe27eehNo1Y/s0C/vn7GG/W7JocN3Ltlwxt1Y+4qAu8+0jv5PUvje7NjmVbcqyjA7B2xmLWzrBPqXugcTUe6dSMrfPXUb5aEJcuxKc5aY89G83luEuUrxbE0W0HqNWmAWu+sb9mu5ZuofYzDVg6YT61n2nAriWbAdi5eDPPDOlCPrd8uOV3p3xIECv/93/OLdTByhmLWDljEWBv58adml+3nS87tHPdNg1Z/s1C/vn7GG/V/PcmDiPWfsGwVu8QF3XBqfUolZngSvdy7MRJTpyMoLSPNwuXrebjQX1TxERFx1CiuAf58uVjyswfeSrM/r14YmIiF+Iu4lmiOH8fOMy+g4epV+vNnCgjd7gDp59llazu7CwDRopIR2PMdOvam0+Ab4B4oKmIeAEJQGugi7V+noh8aow5Y233MMbcEZOI+w4ayaZtO4iOjqVJ6w706voiT7dqltNpZSutWWt2FQsXLqNF80f5a+8fJCQk0K3bv78IN29aTM1aoTmY3a3buWIrVRpXZ/iq8VxJuMzUvl8mb3t/wSiGhNlPFmYOmEKX0b3JX6gAu1ZuY+fKbTmV8m3bs2IbDzQOYeCqcVxJuMx3fScmb+u7YCSjwt4B4IcB/+OF0T3JX6gAe1aGs2dlOABLJ8zjpS/+S93nGhN1MpKpvT4F4PTBk+xdFU6/3z/G2Ax/zl7OqX0nnF9gOuztXI1hqz7nSsIVvun7RfK21O38UnI7h7PrOu1c3MeTAfNHUqhYYYwxPNblcd5v+gaX4u6Ma9Ygb3x+peaKNbu7u9H/jR50f+t9Em02nnq8KUGB5Rn/1UwqV6pI4/p12LRtJ2MnT0MQalQNZsCbPQG4di2Rjr37AVCsaBFGDuyDu3tensaWd/+oqFxvvvJNH1DkLuBLoBL2a4IWAH2A9kAY9utwgoDvjDGDrX3aAu9a8VeB3saY9SISZ4wpZsU8A7Q0xnTO7Pl1ZEcp11AkIPdcCO4snQIeyukUnK5oln/nlvvFk/dOOr7c/FFOp6CyWx49mc7vWzG9KyBznct/rcqy8+OClRreETUnyfLfMsaY40Cav0JmXY9zxhjzajr7zAbSXCWZ1NGxfp4DzMnSZJVSSimllHJ1Oo1NKaWUUkop5ZLuwLuoZRWndXaMMd9gv3ZHKaWUUkoppbKdjuwopZRSSinlynQam1JKKaWUUsol5eFpbPlyOgGllFJKKaWUyg46sqOUUkoppZQLMyZv3hoctLOjlFJKKaWUa8vD1+zoNDallFJKKaWUS9KRHaWUUkoppVxZHr5BgXZ2lFJKKaWUcmV5eBqbdnaUUkoppZRyZTa9QYFSSuUqIpLTKTidkPdqzov0YlmllHIe7ewopZRSSinlynQam1JKKaWUUsol5eEbFOhoulJKKaWUUsol6ciOUkoppZRSrkynsSmllFJKKaVckk5jU0oppZRSSinXoiM7SimllFJKubI8PLKjnR2llFJKKaVcmDF594+K6jQ2pZRSSimllEvSkR2llFJKKaVcmU5jU0oppZRSSrmkPHzraZ3GppRSSimllHJJOrKjlFJKKaWUK9NpbOpWDRg+htV/bMSrpCe/zJyY0+k4hdasNd/pxowZQvPmj5IQn0DXbm8QHr4rTcyvv87E388Xd3c31v6xkddffw+bzcaDVe5n/PiRFCtWlKNHj9Ox02tcuBCXA1Vkrt2gl6jSuDpXEi4ztc8XHNt9OE1MueAKvDS6NwUKFWDniq18P3gqAE++2ZaQprUwxhB7Loapfb4g5kwUhT2K0PXT1/AqUwo3NzcWTZnPuh9XOrmylNoM6sQDjatxNeEy3/aZwIndR9LElA0O5IXRPclfqAB7Vmxj7uBpABQpUZTO4/+DV1kfzp84y9Te40iIvUhw0xo8/uZz2IzBdi2Rn4dM59Dmvwl66AGeGtgx+bil7wlg2mufsXPxZmeVm0bbQS8RbLXzN32+4HgG7dx5dG/yFyrArhVbmW218xNvtqWq1c4XzsXwjdXOAPfWfYDn3n8JN3c34qIu8EnbQU6t63a58udXRly15rUbtjBy3GQSbTaebhlKtw7Ppth+MuIMA0eM5Xx0LCWKF2PkwD74+ZYCYMyEqaz+cxMA3Tu1o0WTBk7PP9fQaWzZQ0TiUi13FpHx1s89RKRj+numjc+tWoc1ZeKYoTmdhlNpzXmDq9bcvPmjBAUF8sAD9enZqx/jPx+Rbtzzz/egZq1QQqo1waeUN8883RKAiRNH8d6AEVSv8Ri/zPudt97s4cz0b0hwo2r4BvrzXqPXmNF/Ei8MeznduA5DX2ZG/0m81+g1fAP9CW4UAsCiyfMZ3KIPQ8L6smP5Flr95xkAGr/YjFMHTjCkRV9GtfuA597rhFv+nPvO7IFGIfgE+jO00X/5vv8Unh3WLd2454Z2ZXb/KQxt9F98Av2536rzsZ5Psm/dLoY2foN963bxWK8nAdj3xy4+atGPUWHvMOvtSbT76BUADvy5h1Fh7zAq7B2+aP8hVxKu8NfqHc4pNh1J7Tyw0WvMzKSdnx/6MjP7T2Kg1c6VrfoXT57Phy36MNRq58etdi5cvAjtP3yZL7p9xODQN5nc6xOn1ZRVXPXzKzOuWHNiYiJDx0xgwujBzJ/xJQuWruLg4WMpYkZ/8T+eaN6En6eNp2fn9oydZP8yY9W6TezZd5A5X3/Od5PGMHXWXOIuxudEGSqH5dg1O8aYicaY6Tn1/FmlZkgVShT3yOk0nEprzhtcteZWrUL5duYcADZu3IqnZ3H8/HzTxCWN1ri7u1OgQH6MMQDce+89rFmzHoBly1bz1FNhTsr8xoWE1mL93FUAHNq2nyIeRSnh45kipoSPJ4U8CnNo6z4A1s9dRUhobQAuxSUkxxUsUhCrdAyGgkULA1CoSCEuRsdhu5Zzf7shOLQmm+auBuDotgMU9ihC8VR1FrfqPLJ1PwCb5q6mSmhN+/5Na7Jxjn3/jXNWU6Wpff2V+MvJ+xdwqN9R1bC67F0ZztVLV7K8rhtV1aGdD2/bT2GPounWX/gG2xmrztpP1Cf89w1EnTwHwIXI2OwuJcu56udXZlyx5p1791GujD93BfiRP39+WjRpwPK161PEHDxynDo1qgJQu/qDrLC2HzxyjFohwbi7u1GkcCHuCwpk7YYtTq8h17DZsu5xh8mxzo6IfCAifayfa4nIDhH5U0RGiYjjnJIAEfldRPaLyMc5lK5SykUEBPhx/MTJ5OUT/5wiIMAv3djffpvJPyfCuXDhIj/N/T8Adu/+m1atQgF4+umWlC0bkP1J36SSpb04fzIyeTkqIhJPP68UMZ5+XkSdcog5FUnJ0v/GtO7Tno/WTaDOk48wb8xsAJZP+x3/oDKM2jiZQYs+4fvBU5M7gTnBs7QX0Q51xkScp0SqOkv4eRF96nzycvSp83hadXr4lCD2bDQAsWej8ShVPDnuwWa16L/sE175uh+z3k47Jah6q4fYOn9dltZzszxTtXN0RCQlU9VfMp129nRo5yf7tGfEugnUfvIR5lvtXLpCAEVKFOXN7z+g/68fUbdNHp76o3LUmbOR+Pn6JC+X9inFmXORKWLuCwpkyao/AFi6+k8uxicQHRPLfUGBrFm/hYRLl4iKjmHT1h1EnDnr1PxzFWPLuscdJrs7O4VFJDzpAQzJIG4q0MMY8xCQ+mvCEKAtUAVoKyJ3pd5ZRF4Rkc0isvmr6bOyMn+llIsRkTTrMjphb9myA+XK16BgwQI0bvwwAK90f4sePTqx/s8FeBQrxpUrV7M131uSbo2pQzJ/HX4ZPYt+9XqyYd4aHu3UHIDKDUI4vucIfWu/wpCwvjw/pCuFihXO2txvRtoS0rRlOmXeUAdtx6JNDG/yFv97ZTRhbz6XYltxH08C7ivH3tXbbyrdrJZ+G6YJSrujQ9C80bN4t15PNs5bQ2OrnfO5uVGuSgXGvzSCcR2HEvbaM/gG+mdl6krdkPT+p0qq//h9endhc/gununyOpvDd1Laxxs3Nzcerl2dRx6qSYeefek7eBRVgyvh5ubmnMRVrpLdk60TjDEhSQsi0hmo6RggIp6AhzEm6Suy74CWDiHLjDExVuweoDxw3PEYxpjJwGSAq+cO5dzXjErLdhB/AAAgAElEQVSpXKlHj0507fI8AJs3b+cuh9GYsmX8OXXqdIb7Xr58md9+W0yrVs1YtmwNf/99kMcffwGAihUDadGiSfYmf4MavdiMBu0fA+Dw9gN4BXgnbyvp503M6fMp4qNORVLS3yHG35to6+J0RxvmreX1r99l/qc/8PCzjfl9ws8AnD0awbnjZ/C7pwxHth/IjpLSVf/FUB5q/ygAx7YfxNOhzhJ+XsSeTllD9KnzePr/O5Lh6e+VfBH+hbMxFPfxJPZsNMV9PLlwLu10rYMb/6JU+dIULenBxagLAFRr+RA7Fm3KkSl8jV5sRn2rnY9Y7XzQ2ubp5030LbbzxnlrefXrd/n10x+IiogkLiqWKwmXuZJwmf0b91L2/vKcOXwq2+pSKj2lfbxTjMacPnsOn1IpRy99S3kzbth7AMTHJ7B01To8ihUFoHvHtnTv2BaAtwePonwuHIl3mjtw+llWyQ1/Zyedr51SuOzwcyJ6Bzml1E2aOHEatWo3o1btZsz/9Xde6GC/ELt27erExFwgIuJMiviiRYskX8fj5uZG8+aP8vff9hN6Hx/7iaOI8O47/2HylBlOrCRjK2csYkhYX4aE9SV88SbqtmkIQIVqFUm4EE+MNV0rSczZaC7HJVChWkUA6rZpSPhi+12LfO/+d1pfyGM1iThon/Z3/uQ5Kj1cBQCPUiUoXSGAc8cy7ihmh7UzFiffJGDn4s3UsqZYla8WxKUL8cnT0pLEno3mctwlylcLAqBWmwbssu6etmvpFmo/Y9+/9jMN2LXEvr5U+dLJ+5etfDdu+d2TOzoA1Z+ox5Zf/8i+IjOxcsYihob1ZWiqdg602jm9+i/FJRDo0M7b02nnqg7tvH3xJirWup98bvnIX6gAgSFBRBz4xxnlKZVCcKV7OXbiJCdORnD16lUWLltN4/p1UsRERcdgs07kp8z8kafCmgL2mxtEx9i/wPj7wGH2HTxMvVrVnVtAbpKHr9nJ8Y6DMSZKRC6ISF1jzHqgXU7ndDP6DhrJpm07iI6OpUnrDvTq+iJPt2qW02llK61Za76TLVy4nObNH2Xv3rUkxF+i28tvJm/btHERtWo3o2jRIsz96WsKFiyIm1s+Vqxcx+TJ9k5N27at6dmjEwC//LKQadNm50gdmdm5YitVGldj2KrPuZJwhW/6fpG87f0FoxgS1heAmQOm8FLSLYlXhrNr5TYA2vR7Ab8KARibIfKfs8x8bwoAv302h5dG92bQ758gAj+NnEmcQyfA2fas2MYDjUMYuGocVxIu813ff6+t6btgJKPC3gHghwH/+/fW0yvD2bMyHIClE+bx0hf/pe5zjYk6GcnUXp8CULVFHWq1eYTEa4lcvXSFaa+OSz6uV1kfPP29Obh+rxMrTd8uq52HWu08zaGdBywYxVCrnb8bMIVO1i3GHdv5qX4vUNpq5/P/nOVbq50jDv7D7lXhDPz9E4zNxh+zl3Fy3/G0CeRirvr5lRlXrNnd3Y3+b/Sg+1vvk2iz8dTjTQkKLM/4r2ZSuVJFGtevw6ZtOxk7eRqCUKNqMAPe7AnAtWuJdOzdD4BiRYswcmAf3N11GlteJNl5camIxBljijksdwZqGmNeFZEPgDhjzGgRqQNMAS4CK4EGxpiHHeOt/X8DRhtjVmb0nDqNTSnXULRM3rsoupP/QzmdgtMVIe+dfFxKc2mq6xu/+aOcTkFlN1vee18D5PeteL0ZSrlCwm9jsuz8uHDLN++ImpNk68iOY0fHWv4G+Mb6+QOHTbuNMQ8CiMg7wObU8day47U8SimllFJKqeu5A6efZZUcn8ZmeVxE3sWez1Ggc86mo5RSSimllLrT5YrOjjFmNpD7Jr4rpZRSSil1p7sD/z5OVskVnR2llFJKKaVUNsnD09hyw62nlVJKKaWUUirL6ciOUkoppZRSrkynsSmllFJKKaVckk5jU0oppZRSSinXoiM7SimllFJKuTInjuyISHNgHOAGfGWMGZlq+6dAY2uxCOBrjPG0tiUCO61tx4wxT9xuPtrZUUoppZRSypUZ45SnERE34AugKXAC2CQi840xe/5NxbzhEP8aUM3hEAnGmJCszEmnsSmllFJKKaWyQm3ggDHmkDHmCvA98GQm8e2BWdmZkHZ2lFJKKaWUcmU2W5Y9ROQVEdns8HjF4ZnKAMcdlk9Y69IQkfJAILDcYXUh65jrRaR1VpSu09iUUrmSzUlD7rlJXvz2yS2nE8gB+ZCcTkEplddk4TU7xpjJwOQMNqf3AZfRL/R2wBxjTKLDunLGmJMiUgFYLiI7jTEHbyPdPPm7VSmllFJKKZX1TgB3OSyXBU5mENuOVFPYjDEnrX8PAStJeT3PLdHOjlJKKaWUUq7M2LLukblNQEURCRSRAtg7NPNTB4nIfUBJ4E+HdSVFpKD1cyngYWBP6n1vlk5jU0oppZRSypU56dbTxphrIvIqsAj7TOWvjTG7RWQIsNkYk9TxaQ98b0yKOev3A5NExIZ9QGak413cbpV2dpRSSimllFJZwhizAFiQat37qZY/SGe/dUCVrM5HOztKKaWUUkq5sjx4058k2tlRSimllFLKlTlpGltupDcoUEoppZRSSrkkHdlRSimllFLKleXhkR3t7CillFJKKeXKrn/LaJel09iUUkoppZRSLklHdpRSSimllHJhxqZ3Y1NKKaWUUkq5ojx8zY5OY1NKKaWUUkq5JB3ZuU0Dho9h9R8b8SrpyS8zJ+Z0Ok6hNWvNruTTMUNo0fxR4hMS6Nr1DbaF78ow9ue5UwkMLEdItSZOzPDWtB30EsGNq3Ml4TLf9PmC47sPp4kpF1yBzqN7k79QAXat2MrswVMBeOLNtlRtWgtjDBfOxfBNny+IORNF6XsC6DyqN3dVDmTe6FksmfKrs8tKoVLDqrR+vxP53PKxfvZylk+Yn2K7WwF3nh/Tm7uCA7kYHcf0V8cRdeIsAE16PUmd5xpjS7Tx8+Bv+Hv1DtwL5ufV2YNwL5iffG752L5wA4s+nZN8vBZ92lI1rC7GZmPdzCWs+eZ3p9abnucGvUTlxtW4knCZ6X2+zKCdA+lotfPuFdv4wWrnNu92oMpjNbh25Rrnjp1met8vSYiNxy2/G88Pf4XyVe7BGBs/DP6G/ev3OLu025JXPr8cuWrNazdsYeS4ySTabDzdMpRuHZ5Nsf1kxBkGjhjL+ehYShQvxsiBffDzLQXAmAlTWf3nJgC6d2pHiyYNnJ5/rqE3KMh+IhLnrOdyptZhTZk4ZmhOp+FUWnPekBdqbtH8USoGBVLpgfr07NmPL8aPyDC2desWxMVddGJ2ty64UTV8A/0Z2Og1ZvafxAvDXk437vmhLzOz/yQGNnoN30B/KjcKAWDx5Pl82KIPQ8P6smP5Fh7/zzMAxEfH8f0HX+d4JwdA8glthnRhcueRfNT0Lao/8TClg8qkiKnzXGMSYuIY3ui/rPrf/9HynecBKB1Uhmqt6vFRaB8mdxrB0x92RfIJ1y5f5cvnP2R0i36MDnuHSg1DKF8tCIBazzbE09+bj5q8yUePvcW2X9c5vebUKjeqhm+gH4Mavc53/SfTfli3dOPaD32Zb/tPYlCj1/EN9Etu571rd/Bh6FsMa9GX04dP0azXUwDUb/cYAEOb9+GzDkN55r2OiIhzisoieeHzKzVXrDkxMZGhYyYwYfRg5s/4kgVLV3Hw8LEUMaO/+B9PNG/Cz9PG07Nze8ZOmgbAqnWb2LPvIHO+/pzvJo1h6qy5xF2Mz4kycgebybrHHUansd2mmiFVKFHcI6fTcCqtOW/ICzW3atWMGd/av7nfsHErJTxL4OfnmyauaNEivPGfVxg+YpyzU7wlVUNrsX7uKgAOb9tPYY+iFPfxTBFT3MeTwh6FObR1HwDr564iJLQ2AJfiEpLjChYpCNbvtguRsRzdcZDEa9ecUEXmyoUEce5oBOePnyHxaiLbfl1HcGjNFDHBoTXZ9NNqAHYs2EDFepWT12/7dR2JV65x/sRZzh2NoFyIvVNzJf4yAG7ubri5u2Gs2h9+oSlLPvsJY62Ii4x1RpmZqhpak/Vz7fUd3rafIhm0cyGPwhzeuh+A9XNXUzW0FgB71+zAlmiz9t9HST8vAPwrluXvP+wjnBciY4mPvUi5Bys4paaskhc+v1JzxZp37t1HuTL+3BXgR/78+WnRpAHL165PEXPwyHHq1KgKQO3qD7LC2n7wyDFqhQTj7u5GkcKFuC8okLUbtji9BpXzcrSzIyLlRWSZiOyw/i0nIm4ickjsPEXEJiINrPg1IhKUkzkrpVxHmQA/Thw/mbz8z4lTlAnwSxM35IO3GTN2EvHxCWm25Uaepb04fzIyeTk6IjL5RDZJST8vok79GxN1KhLP0v/GPNmnPSPWTaD2k48wf8zs7E/6JpUo7UW0Y42nzlOitFeGMbZEG5cuJFC0pEeafWMc9pV8wlsLRjJky2T2rd3JsfADAHiXL01Iy4d4Y/4wXv7mHUrdnfZ94myepb2IOnkueTkqIhLPVO3s6edF9CnH1yllOyep9+yj7F4ZDsCJvUd4sGlN8rnlw7usD+WqVMDLv1Q2VaFUxs6cjcTP1yd5ubRPKc6ci0wRc19QIEtW/QHA0tV/cjE+geiYWO4LCmTN+i0kXLpEVHQMm7buIOLMWafmn6vYbFn3uMPk9MjOeGC6MeZB4FvgM2NMIrAPeACoD2wBHhGRgkBZY8yB1AcRkVdEZLOIbP5q+iwnpq+UupOlNzUn6Zv7JFWrVuaeoLuZNy/nr8+4UenXlSYo7Y4OQfNGz+Ldej3ZOG8NjTs1z+IMb1/66Zsbi0lvRpa1r7EZPgl7h8EP9aJc1Xvwu7csAO4F8nP18lU+feI91s9aRruPe9xuCbfvOm1oD7n+e6F576ewJSay8Zc1AKz7YQXREed559eRPDuoM4e2/E1iYmKWpa3UjUpvwpSk+g/cp3cXNofv4pkur7M5fCelfbxxc3Pj4drVeeShmnTo2Ze+g0dRNbgSbm5uzkk8N8rDnZ2cvkHBQ0Ab6+cZwMfWz2uABkAgMAJ4GVgFbErvIMaYycBkgKvnDt15kwmVUk7Ts0cnunZ9AYDNm8Mpe1dA8rYyZf05eep0ivi6dWpQvVoVDuxbj7u7O76+3ixb8iNNmqa8SDanNXqxGfXb26+1OLL9AF4B3hy0tnn6eRN9+nyK+KhTkZT0905eLunvTfSZqDTH3ThvLa9+/S6/fvpDtuV+K6IjzuMZ8G/+nv5exKbKPykmJuI8+dzyUcijMPHRccSk2reEvxcxqfa9FBvPgfV7qNQwhIh9J4iOiGTH7xsB2LloE+1G9czG6jLW8MVmPNzefoOMo9sPUjKgFPA3ACX9vIk+nbKOqFORePo7vk7exJz5971Q9+mGVGlSg7HPD0leZ0u0MefDacnLfX76kDOHT2VHOUplqrSPd4rRmNNnz+FTKuXIpG8pb8YNew+A+PgElq5ah0exogB079iW7h3bAvD24FGULxtAnpXmG6+8I6dHdlJLaok1wCNAbWAB4Ak0AlbnTFpKKVcxYeI0atYKpWatUObPX8SLL9gvvq9TuzqxMbFERJxJET9p8nTK3V2DoHvr0rBxa/btP5TrOjoAK2csYmhYX4aG9SV88SbqtmkIQGC1iiRciCf2bHSK+Niz0VyKSyCwWkUA6rZpyPbF9u+TfB2maFV9rCYRB0+S2xzffhCfu/3wKuuDW343qrWqx64lKefj716yhVpP2+++9GBYHQ6s2w3AriVbqNaqHm4F3PEq64PP3X4cCz9AUS8PChUvAkD+gvm59+EqnLFq37V4MxUfsl/zc0/dBzibQyf/q2YsYnjY2wwPe5vtizdSt429vhtv5wZsX7wZgAcaViW0x5NM6PYRVy9dSd4nf6ECFChcEIBK9atgu5ZIxIF/nFGeUikEV7qXYydOcuJkBFevXmXhstU0rl8nRUxUdAw2a7RhyswfeSqsKWC/uUF0jP3aur8PHGbfwcPUq1XduQWoXCGnR3bWAe2wj+q8AKy11m8ApgOHjDGXRCQc6A60zJEsM9F30Eg2bdtBdHQsTVp3oFfXF3m6VbOcTitbac1as6tYsHAZzZs/yt97/yA+IYFu3d5M3rZ502Jq1grNwexu3a4VW6nSuBpDV33OlYQrTOv7RfK2AQtGMTSsLwDfDZhCp9G9KVCoALtWhrNr5TYAnur3AqUrBGBshvP/nOXb96YA9ovd+88fSaFihTHG0KTL43zQ9I0UNzRwFluijbnvT+WV6f3J55aPjT+s4PT+EzR/41mO7zzE7qVb2PDDCp4f05v+K8cSHx3H9Nc+A+D0/hOE//Yn/ZZ8gu1aIj+9PxVjMxT3LUn7T3qSL18+JF8+tv/fn+xZvhWAZRPm0WHsqzTsGsbl+Ev88M4kp9ec2q4V2whuXJ0hqz7jSsIVpvf9Mnlb/wUfMzzsbQBmDfiKTqN72W89vTKc3VY7tx3cFfcC7rw+cyBgv8nBrPem4FGqBK9Pew+bsRETcZ5v3hzv/OJuU174/ErNFWt2d3ej/xs96P7W+yTabDz1eFOCAssz/quZVK5Ukcb167Bp207GTp6GINSoGsyAN+2jrteuJdKxdz8AihUtwsiBfXB3z+PT2PIoST3HOdueSMQGOH49OAaYC3wNlALOAi8ZY45Z8WuANcaY/iLyPPAl4GVM5jcK12lsSrmGwgGP5HQKTtc1oF5Op+B0Rcl7Jx8J5L2Tjs82j8zpFFR2s+XN67ry+1a8I+7LHj+6W5adHxfp89UdUXMSp43sGGMymjL3aAbxjzj8/B3wXXbkpZRSSimllHJNOT2NTSmllFJKKZWdMp8Y5dK0s6OUUkoppZQrs+Xdqzxy293YlFJKKaWUUipL6MiOUkoppZRSLszk4buxaWdHKaWUUkopV6bT2JRSSimllFLKtejIjlJKKaWUUq5M78amlFJKKaWUckk6jU0ppZRSSimlXIuO7CillFJKKeXK9G5sSimllFJKKZeUh6exaWdHKZUrSU4nkAPy5cmq8x5tZaWUch7t7CillFJKKeXK9G5sSimllFJKKZeUh6ex6d3YlFJKKaWUUi5JR3aUUkoppZRyYUbvxqaUUkoppZRySTqNTSmllFJKKaVci47sKKWUUkop5cry8MiOdnaUUkoppZRyZXn41tM6jU0ppZRSSinlknRkRymllFJKKVem09iUUkoppZRSrsjk4c6OTmNTSimllFJKuSQd2blNA4aPYfUfG/Eq6ckvMyfmdDpOoTVrza7k0zFDaN78URISEuja9Q22he/KMHbu3KkEBpajWrUmTszw1jw36CUqN67GlYTLTO/zJcd3H04TUy44kI6je5O/UAF2r9jGD4OnAtDm3Q5UeawG165c49yx00zv+yUJsfHUerI+Tbs/kbx/mUrlGNGyHyf2HHVaXY4qNaxK6/c7kc8tH+tnL2f5hPkptrsVcOf5Mb25KziQi9FxTH91HFEnzgLQpNeT1HmuMbZEGz8P/oa/V+8AYMDaz7kcl4DNZsN2LZFPn3gPgOZvPkdw0xoYY4g7F8usPhOIPRPl3ILT8azVzlczaee7UrXzj1Y7P2W1c+KVa5w9dpoZVjt7lfXh/aWfcvrQSQCObNvPrPemOLWu25VXPr8cuWrNazdsYeS4ySTabDzdMpRuHZ5Nsf1kxBkGjhjL+ehYShQvxsiBffDzLQXAmAlTWf3nJgC6d2pHiyYNnJ5/rqEjO1lDRPxE5HsROSgie0RkgYjcKyIZnz3c4VqHNWXimKE5nYZTac15Q16ouXnzRwkKCuT+B+rTs2c/xo8fkWFs69YtiIu76MTsbl3lRtXwDfRjUKPX+a7/ZNoP65ZuXPuhL/Nt/0kMavQ6voF+VG4UAsDetTv4MPQthrXoy+nDp2jW6ykANs1by/Cwtxke9jbfvPE550+czbGOjuQT2gzpwuTOI/mo6VtUf+JhSgeVSRFT57nGJMTEMbzRf1n1v/+j5TvPA1A6qAzVWtXjo9A+TO40gqc/7Irkk+T9vmz/IZ+EvZPc0QFYMflXRrfoxydh77Bn+VZC/9PGOYVmIqmdP2j0Ot/2n0y7TNr5u/6T+MBq5wesdv5r7Q6GWu18xqGdAc4djWBE2NuMCHv7juvoQN74/ErNFWtOTExk6JgJTBg9mPkzvmTB0lUcPHwsRczoL/7HE82b8PO08fTs3J6xk6YBsGrdJvbsO8icrz/nu0ljmDprLnEX43OijNzBZsu6xx0myzo7IiLAz8BKY8w9xpgHgP5A6ax6jtyoZkgVShT3yOk0nEprzhvyQs1PtGrGzG/nALBh41ZKeJbAz883TVzRokX4739eYcSIcc5O8ZZUDa3J+rmrATi8bT9FPIpS3MczRUxxH08KeRTm8Nb9AKyfu5qqobUA2LtmB7ZEm7X/Pkr6eaV5jlpP1GfT/D+ys4xMlQsJ4tzRCM4fP0Pi1US2/bqO4NCaKWKCQ2uy6Sf767BjwQYq1qucvH7br+tIvHKN8yfOcu5oBOVCgjJ9vstxCck/FyhSEJMLviR9MLQmG6x2PnKD7bwhk3b2TKed71R54fMrNVeseefefZQr489dAX7kz5+fFk0asHzt+hQxB48cp06NqgDUrv4gK6ztB48co1ZIMO7ubhQpXIj7ggJZu2GL02tQOS8rR3YaA1eNMcljp8aYcOB40rKIFBKRqSKyU0S2iUhja31lEdkoIuEiskNEKlrrOzisnyQiblmYr1IqjwsI8OPE8ZPJy/+cOEWZAL80cYM/eJtPx04iPj4hzbbcyLO0F1EnzyUvR0VEpjmR9fTzIvpUZPJy9KlIPEunPdmt9+yj7F4ZnmZ9jZYPsTkHOzslSnsRfdIx//OUSJW/Y4wt0calCwkULemRZt8Yh32NMXSf0Z83fh1O3fYppyu26NOWgeu+oPqT9fl9zA/ZVdoNu5V2jsqknfc4tLP3Xb68+38f8cbsD7inVqVsyF6p6ztzNhI/X5/k5dI+pThzLjJFzH1BgSxZZf8sWrr6Ty7GJxAdE8t9QYGsWb+FhEuXiIqOYdPWHUScOevU/HMVm8m6xx0mKzs7wcD1usy9AYwxVYD2wDQRKQT0AMYZY0KAmsAJEbkfaAs8bK1PBF5I76Ai8oqIbBaRzV9Nn5U11SilXJ59QDolk+or+6pVK3NP0N3Mm/e7s9K6fenUlXooIv3aUy437/0UtsRENv6yJsX6u0OCuJJwhZP7jpNT0i8xdY0ZxKSzPqn4z58exJiW7zKl80jqdwylQu1/T/QXjp7Nh/V6s3XeWup3anY76WeJG3n/phdDOu2c6NDOsWeiGFCvFyMe78ecD6fRZdzrFCpWOMvyVupGpXdaLan+A/fp3YXN4bt4psvrbA7fSWkfb9zc3Hi4dnUeeagmHXr2pe/gUVQNroSbWx7+zjwPd3acfYOC+sDnAMaYv0TkKHAv8CfwnoiUBeYaY/aLSBOgBrDJ+rAuDJxJ76DGmMnAZICr5w7dea2glHKanj060bWr/XuTzZvDKXtXQPK2MmX9OXnqdIr4unVqUL1aFfbvW4+7uzu+vt4sXfIjjzVNeZFsTmv4YjMetkYijm4/SMmAUsDfAJT08yb6dMqL6aNOReLp75287OnvTcyZ88nLdZ9uSJUmNRj7/JA0z1Wz1cM5OqoDEB1xHs8Ax/y90twwICkmJuI8+dzyUcijMPHRccSk2reEvxcx1r5Jx4iLjGXnok2UqxrEoY1/pTju1nl/0O3rfiz6dE52lZehBtdp55jrtHNJf2+iHdq5ztMNCW5Sg3EO7XztyjWuXYkD4Piuw5w9dhrfQH+O7TyUXWUpla7SPt4pRmNOnz2HT6mUI5O+pbwZN8x+fV18fAJLV63Do1hRALp3bEv3jm0BeHvwKMqXDUDlPVk5srMbe+ckM+l9n4Yx5jvgCSABWCQij1qx04wxIdbjPmPMB1mYr1IqD5owcRo1a4VSs1Yo8+YvosMLzwBQp3Z1YmNiiYhI+Z3KpMnTKX93DSreW5dGjVuzb/+hXNfRAVg1Y1HyzQO2L95I3Tb2uw4FVqtIwoV4Ys9Gp4iPPRvNpbgEAqtVBKBumwZsX7wZgAcaViW0x5NM6PYRVy9dSbGfiFA9rC6bf83Zzs7x7QfxudsPr7I+uOV3o1qreuxaknJywe4lW6j1tP11eDCsDgfW7QZg15ItVGtVD7cC7niV9cHnbj+OhR+gQOGCFCxaCIAChQty7yMPEmGNXpW6+9/pjZUfq8GZgyfJCatnLEq+ccCOxRupY7Xz3Zm08+W4BO622rlOmwbsSNXOE1O1czEvj+QbNnjf5Yvv3f6cO5bySwClnCG40r0cO3GSEycjuHr1KguXraZx/TopYqKiY7BZF81PmfkjT4U1Bew3N4iOiQXg7wOH2XfwMPVqVXduAbmIMSbLHnearBzZWQ4MF5GXjTFTAESkFlDEIWY19qloy0XkXqAc8LeIVAAOGWM+s35+EFgMzBORT40xZ0TEC/AwxuTMrX8y0HfQSDZt20F0dCxNWnegV9cXebpVzk9vyE5as9bsKhYuXEaL5o/y194/SEhIoFu3N5O3bd60mJq1QnMwu1u3a8U2ghtXZ8iqz7iScIXpfb9M3tZ/wccMD3sbgFkDvqLT6F72WxKvDGf3ym0AtB3cFfcC7rw+cyBgv8lB0h25gurcT3REJOeOpzvQ7jS2RBtz35/KK9P7k88tHxt/WMHp/Sdo/sazHN95iN1Lt7DhhxU8P6Y3/VeOJT46jumvfQbA6f0nCP/tT/ot+QTbtUR+en8qxmYoVqoEXSa/BUA+t3xsnfcHf63aDkDLfu3xqRCAsdmI+n/27jw+puv/4/jrTBaxRDaRBLXzU2KJfSuxNEhRpdVqKaWbrYvS2mqroijdF76ttVWlWqrWtoLWTuzUvgshu+wz5/fHjJhsBMmEzOf5eMwjuawZriYAACAASURBVHPPnTnv3Jl758w59+TiNZaO+l++Zb/p4IZQarSqy3jLfl5gtZ9HrJrKZKv9/GIW+7n7+H44OTsy2LKfb04xXblhdToO6Y7JaMRkNLFo1Gziox+OmQhvsofjV0YFMbOjowMj336d194Zg9Fk4qknHqdyhXJ88b+F1KhWhVbNG7Ez9ACfzJqHQlGvtj+jh/QHIDXVyIsD3wOgWNEiTHl/KI6Odj6MzU6p3GyhKaVKAZ9g7uFJBM4AbwG/aq39LdfnfGNZnwoM0VpvUEqNAHoCKUAY8LzWOkIp9SwwAnMPVAowUGu9jduQYWxCFAxFSj2W31WwuZdLNcvvKthcYTv839aJPHxTt96vT3dNye8qiLxmMuZ3DfKFU8kqWY5aetDEvBKUa5+Pi89e91BkvilXr9nRWl8Cumexyt+yPhHok8V2k4FM/+BCa70YWJybdRRCCCGEEMKu2HHPjq0nKBBCCCGEEELYkLbjxo79jR8QQgghhBBC2AXp2RFCCCGEEKIgs+OeHWnsCCGEEEIIUZDZ37woaWQYmxBCCCGEEKJAkp4dIYQQQgghCjCZoEAIIYQQQghRMJl07t3uQCnVXin1n1LqhFJqeBbr+yilwpVSey23l63W9VZKHbfceudGdOnZEUIIIYQQQtw3pZQD8CXwOHAB2KmUWqG1Ppyh6GKt9aAM23oCY4H6gAZ2W7aNvJ86Sc+OEEIIIYQQBZkpF2+31xA4obU+pbVOBn4CnsxhLdsB67XWEZYGznqgfQ63zZY0doQQQgghhCjAtEnn2k0p9apSapfV7VWrpyoNnLdavmC5L6NuSqn9SqmlSqlH7nLbuyLD2IQQQgghhBA5orWeBczKZrXKapMMy78Di7TWSUqp14F5QOscbnvXClxjp0ipx/K7CjalVFavi4LNpO1vRhH728sQf2lzflfB5t6sn+k6zgIvhtT8roLN2eO/u7C3c7P9naXsV2ryxfyuQs7Y7sBzAXjEarkMcMm6gNb6utXibOAjq20DM2wbcr8VkmFsQgghhBBCFGC5OYztDnYCVZRSFZRSzsBzwArrAkopP6vFzsARy+9rgSCllIdSygMIstx3Xwpcz44QQgghhBDC9rTWqUqpQZgbKQ7A91rrQ0qpCcAurfUK4A2lVGcgFYgA+li2jVBKfYC5wQQwQWsdcb91ksaOEEIIIYQQBZkNx89qrVcBqzLcN8bq9xHAiGy2/R74PjfrI40dIYQQQgghCjBtjxcLWkhjRwghhBBCiILMjhs7MkGBEEIIIYQQokCSnh0hhBBCCCEKMBnGJoQQQgghhCiY7LixI8PYhBBCCCGEEAWS9OwIIYQQQghRgMkwNiGEEEIIIUSBZM+NHRnGJoQQQgghhCiQpGdHCCGEEEKIAkx6dsRdmTljAkcO/8Oe3esJqON/27LLls0hNPQvG9Usd82YMYHDh/9h96711Mkm5++/L2TXznXsDf2LL76YjMFgfknVqvkomzYuZ8/uP/l12RxcXYvZsuq5YuaMCRzN4X7+ddkc9j6k+9mavby2szN60gxaPPEcXXq+nt9VuW/PjH2JcSGfMWr1NB6pUSHLMo/4V2DUmumMC/mMZ8a+lHb/UyN6MuavmYxaPY1Xvx1K4eJFAPAs480nRxcyYtVURqyaSo8PX7FJlpzqMbYvk0I+Z9zqjymbTeZy/hUZt+ZjJoV8To+xfTOtD3qlM/87s5RiHq4A/F/jGny2fx5jVk1jzKppdHzj6TzNcLeeH9uXySGfM/4OmSes+ZjJIZ/zfBaZ273Sme+tMhd2LcIb/xvO+NXT+WDdTJo/0ypPM+QWezx+2et5yt4y5wqtcu/2kLltY0cpFaKUapfhvreUUl9lU768UupgblbwQdO+fWsqV67Ao9Wb07//e3zxxeRsy3bp0oG4uBs2rF3uuZmzevXm9B/wHl98nnXO559/nfoNgqgT0AbvEl483a0jAN98M41RoydTt15bflu+hneGPFwfHju0b02VyhWoZtnPXxbQ/WzNXl7bt9Ml+HG+mTExv6tx32oEBlCygi/jAt/gh5GzeO7Dl7Ms12PiK/w48lvGBb5ByQq+VA+sA8DRf/YzMegdPuwwjKunL9NuwFNp21w7G8bk4HeZHPwui0bNtkmenKgZGEDJCn6MDBzM/JHf0PPDV7Ms13PiK8wf+S0jAwdTsoIf/oEBaes8/Lyo/lgtrl8IT7fN8Z1HmRA8jAnBw1j52dI8zXE3agYG4FPBjxGBg5k38htezCZzr4mvMG/kt4wIHIxPBT9qZshc47FaXLPK3LpXey6duMDYDkOZ+txYuo96EQenB3sgiD0ev+zxPGWPmcX9u1PPziLguQz3PWe53y517tSOhT+YT3bbd+zBzd0NX9+SmcoVLVqEt958lcmTP7V1FXNFp05B/LDQnHPHjj24uxfPMmdsbBwAjo6OODs7obUGoGrVSmzevA2Av/7axFNPBduo5rmjU6d2LMjhfn77zVeZ9JDuZ2v28tq+nfp1auJW3DW/q3HfagXVZ/uyTQCcCT1OEdeiFPd2T1emuLc7Lq6FOb3nOADbl22idlADAI5s3o/JaB7zcDr0GO6+njas/b2pE9SArctCADgVepwirkVwy5DZzdsdF9cinNpzDICty0IIsGQGePb9PiydvACNtlm970dAUAO25CBzYdcinLRk3pIhc4/3+7Bk8gKwyqzRuBR1AaBQERduRMVhSjXmbZj7ZI/HL3s8T9lj5tyiTbl3e9jcqbGzFOiolCoE5p4boBTwj1JqmlLqoFLqgFLq2YwbKqX6KKW+sFpeqZQKtPwep5T6SCm1Wyn1p1KqoaUX6ZRSqrOljIPlOXYqpfYrpV7LlcT3qVQpXy6cv5S2fPHCZUqX8s1Ubvy4d5n5ybfExyfYsnq5plQpX85fuJXzwsXLlMoiJ8DKlQu5eGEvsbE3+GXZHwAcOvQfnToFAdCtW0fKlCmV95XORaVzuJ8njHuXGQ/xfrZmL69te+Du40nkpWtpy5Fh1zM1WNx9PYm6fP1WmcvXcffJ3Khp+kxrDofsTVv2eqQkI/74iLcXj6NSg2p5UPt74+7jRcQlqzxhEbj7eqUv4+tFZLrMEbj7mMvUblufqCsRXDhyNtNjV6pblbGrp/Pm3FGUqlImjxLcPY8MmSPCIvDIkNkjQ+aIyxF4WDLXaVufyCsRnM+Q+e95q/GrXIYZO2YzYe3HLBo/J+2LrAeVPR6/7PE8ZY+Zc4s2qVy7PWxu29jRWl8HdgDtLXc9BywGugJ1gNpAW2CaUsrvLp63KBCita4HxAITgceBp4AJljL9gGitdQOgAfCKUirLAclKqVeVUruUUrtMprztslQq807OeBKoXbsGlSqXZ/nyNXlal7yUk5w3dezYk7Ll6lGokDOtWjUD4NXX3uH113uzbesqXIsVIzk5JU/rm9vsZT9bs8fMBVVO9mVWZTJ2aLQf+BRGo5Edv20GIOZqJKObDmDyE++x9IN59P30DVyKFc61et+PrOKQKXPWZZxdnHliUDeWz1icafXZg6d4r1l/xncYyt9zVzFw1nu5U+HckNUuzHiczqaMs4szHQd147csMtdoUYfzh88wpOErjAsexgsT+j0w+zk79nj8ksxmBT2zuH85GYR7cyjbcsvPvkAvYJHW2ghcUUptxNwg2Z/D500Gbr4KDwBJWusUpdQBoLzl/iCgllLq5tWgbkAV4HTGB9NazwJmATg5l871r5/6v96bfv1eAGDXrr2UeeRWL0XpMn5cunwlXfnGjepRN6Amx49tw9HRkZIlvfhz/RLaPv5MblctV73+em/69X0egF279vGIVW9MmdJ+XM6Q01pSUhIrV66jU6d2/PXXZv777yRPPGH+m1WpUoEOHdrkbeVzwb3u5xNW+/mv9Uto84DvZ2v28tq2By16taNZD/P77Oy+k3iUKgH8B5i/3Y++EpmufOTl67j73eoF8PDzIupqRNpyo24t8W9Tj0+fn5B2X2pyKqnJ5qGr5w+eJvzcFUpW8OPcgVN5Feu2WvVqz2OWzGf2ncSzlFUeX0+irkSkKx95+Toe6TJ7EnU1Au9yvpQoU5Kxq6dbtvXi/ZVT+bDLCGLCo9LKHwgJ5YWJDhTzcCUuMjYvo2Wrda/2tLBkPp0hs2cOMntmyDzeKvPYlVP5oMsImj/TilVf/wbA1bNhXDt/Fb9KpTm970Rex7sr9nj8kvOUfWTOCw/j8LPckpPZ2H4D2iil6gKFtdZ7yPK7okxSMzy+i9XvKfpWU9wEJAForU3caoApYLDWuo7lVkFrvS4Hz5vrvv5mHvUbBFG/QRDLV6yl5wvm9lejhnWJiY4hLOxquvLfzppPufL1qFK1MYGtunDs+KmH4mD6zTfzaNCwHQ0atmPF72t4oac5Z8OGdYmOjs2Us2jRImljZR0cHGjfvjX//Wc+GXp7m0+uSilGDH+TWbMX2DDJvbHezytWrKVXDvZz2fL1qFy1MS0t+/lhO5jay2vbHmxasDZt4oD963bQqGsLAMoHVCEhNj7dh3aAmPAokuISKB9QBYBGXVuwf90uAKq3rE3Q60/yzcsfkZKYnLZNMU9XlMF8+Pd6pCQly/tx7Vz2X4LktQ0L1qRNHBC6bgdNugYCUNGSOTpD5ujwKBLjEqhoydykayB71+3k4n/nGFK/H8ObD2B48wFEhl3ng47vEhMele5apwq1K6OUyreGDsDfC9YwLngY4yyZm1pljs9B5qZdAwm1ZH6rfj/ebT6Ady2Zx1syR1y6RvVmNQEoXsIN34qlCM/H/Zwdezx+2ft5yl4y5wWtVa7dHjZ37NnRWscppUKA77k1McEm4DWl1DzAE2gBDCN9g+YMMEApZQBKAw3vsm5rgf5Kqb8tvT5VgYta63ydWmP16r/o0L41R4/8S0JCAi+/PCRt3a6d66jfICgfa5d7Vq/+m/btW3PkyD8kxCfy8iu3cu7csZYGDdtRtGgRlv3yPYUKFcLBwcCGkC3MmmVu1Dz7bBf6v94bgN9+W828eZmHSjzIVq3+y9x4O/Iv8QV4P1uzl9f27QwbO4WdofuJioqhTZeeDOjXi26d2t15wwfMwQ2h1GhVl/EbPyM5IZkFw25NoDli1VQmB78LwKLR/+PF6QNwcnHmUMheDoWEAtB9fD+cnB0ZvPB9wDzJwaJRs6ncsDodh3THZDRiMppYNGo28dEPxmxHBzbsoWarukza+AXJCUnMsco8ZtU0JgQPA2Dh6Nn0nT4QJxdnDoaEcsCSOTv1OjQmsGc7TEYjyYnJzBr8SZ7muBv7N+yhVqu6TLFk/t4q87hV0xhnybzAktnZxZkDOcj8+2dL6Tt9EBPWfAxKsWTKwnxt4OWEPR6/7PE8ZY+Zxf1TObnoUCn1FLAMeFRrfVSZB01OBTpgHuU9UWu92DKBwUqttb+lzELM1/YcBHyAcVrrEKVUnNa6mOWxxwFxWuvpluU4rXUxSyNpItAJcy9PONBFax19u7rmxTC2B1mW4+4LONMDfqFsXrC/vQzxlzbndxVs7s36w/O7CjaXjP2NrbC/xLDg0tb8roJN2d9Zyn6lJl98KE7RFxq1zrWXZZntfz8UmW/K0cT5Wutfsfq8ZRmCNsxysy53BvC3KvNCNo9XzOr3cVmtswxpG2m5CSGEEEIIIe7BwziLWm7JyTU7QgghhBBCCPHQebD/JbIQQgghhBDivtjhFQBppLEjhBBCCCFEASbD2IQQQgghhBCigJGeHSGEEEIIIQowe+7ZkcaOEEIIIYQQBZg9X7Mjw9iEEEIIIYQQBZL07AghhBBCCFGAyTA2IYQQQgghRIGktf02dmQYmxBCCCGEEKJAkp4dIYQQQgghCjBtyu8a5J8C19jpXapJflfBphT21y1pj92RBjvcz2/WH57fVbC5T3dNye8q2FzK3En5XQWbc3jq5fyugs25tvkkv6tgU/Z4nrK/s9TDxSTD2IQQQgghhBCiYClwPTtCCCGEEEKIW+x5ggJp7AghhBBCCFGA2fPU0zKMTQghhBBCCFEgSc+OEEIIIYQQBZjW+V2D/CONHSGEEEIIIQowGcYmhBBCCCGEEAWM9OwIIYQQQghRgNnz/9mRxo4QQgghhBAFmD1PPS3D2IQQQgghhBAFkvTsCCGEEEIIUYDJbGxCCCGEEEKIAkmu2RFZ6jG2LzVbBZCckMz3Q7/g3KHTmcqU86/IS9MH4uzizIENoSwa/3269UGvdKb7qBd5K+Al4iJj0+4vX6sSI3+dxLeDZrJ79bY8z5JTz419iZqt6pKckMScoV9mmblsusx7+Gn8HACeHPIsdR5vgNaamGvRzBn6JdFXIynsWoR+MwfjWboEDg4OrJ29gi1LQmycLHvPjn0Jf0vmuUO/5Hw2mftMH4iTizMHN+xhsSVz5yHPUtuSOfZaNHMtmX0qlaLPtIE8UqMCy6cvYv3s320d67a6j32JGq0CSE5IYv7Qr7LJXIEXLZkPbQjlZ0vmriN6UrNtPVKTU7l27grzh31FQkw8DZ5szuOvdU7bvnS1skzu+B4XDp+1Wa7sPGPJm3KbvI9kyLvEkvcpS15jcirh566wwJLXs4w3Y/6cyZVTlwA4E3qcRaNm2zRXbhg9aQab/t2Bp4c7vy38Jr+rkysM5Wvg3LoHKAOpBzaTumN1pjIO/1cfp6adQWtM4RdI/sO875SrJ87teqNcPQFN0i+fomOu2zjB3ftn134++vYHTCYTXdu1pF/3junWX7pyjTGffEdkdAxursWYNOw1fEt4AlCnYx+qlH8EAF9vTz4f+7bN638nXcf2prrlPfzD0K+5cOhMpjJl/CvwwvT+OLk4c3hDKMvGzwOgiFtR+nzxJp5lvIm4EM6cgZ+SEHOD1q92pF6X5gA4ODjgU7k0o+q+Qnz0DQCUQTH090lEh0Uyq99Um2UFqNayNk+N6Y1yMLB98d/89fWKdOsdnB15YcZAyvhXID4qjnmDPiXyQjgAbQY8SaPurdBGE8vGz+W/TfsBeG7qa1RvXZe46zFMbTcs7bE6jXiBGm3rYrQc0xcN+4bEmHjbhbWo1rI2Xcb0xuBgYNviv/k7i8zPzxjII/4VuBEVx/wsMpuMJn61ygzm/fi2ZT9+Z9mPlZvUoPOonjg4OXLh4CkWv/stJqPJdmGFTeX6NTtKqbeUUkVy+3FtrWZgACUr+DEycDDzR35Dzw9fzbJcz4mvMH/kt4wMHEzJCn74BwakrfPw86L6Y7W4bnkz3qQMBroN78mhTfvyNMPd8rdkHhU4mAUjv+WFD1/JslzPia+wYOS3jErLXAeAtbNWML7DUCYED2P/37vp9ObTALTq1Y7LJy4wocMwpj03ju6jeuPg9GC0s29mfj9wMAtvk/n5ia+wcOS3vG/JXMOSed2sFXzQYSgTLZmfsGSOj4rjp3HfP3CNHIAagQGUrODL2MA3+HHkLHp8+HKW5XpMfIUfRn7L2MA3KFnBNy3zkX/280HQO3zYYRhXTl+m3YCnANi5/B8mBb/LpOB3mfv250RcCH8gGjo3844LfIMfRs7iudvk/XHkt4yz5K1uyXv0n/1MtOS9apUX4NrZMCYHv8vk4HcfyoYOQJfgx/lmxsT8rkbuUQrnti+Q9MsnJM55H8dqDVFefumLuJfEqWEwiT9OIXHuWJI3/JS2zjm4Hyk715I4530SF36Ijo/N+AwPHKPRxKSv5vP1hHf47ZvJrN64jZPnLqYr8/F3P9GpTTN++epDXuvxJJ/NWZK2rpCzM0u++IAlX3zwQDZ0qgfWwbuCHxMD3+KnkbN5Jpv3cPeJ/Vg8cjYTA9/Cu4Ifj1rew237P8mxLQeZ2Optjm05SNsBTwLw96yVTAsezrTg4fw+dREnth9Oa+gAtHypA1dOXMr7gBkog6LbhL7M6jOFjx5/h4DOzfCpXDpdmcbdW5EQHcekwLfY+N0fdBr+PAA+lUsT0KkpHwUN5dvek3n6g34og/kb/R1LNzKr9+RMz3fsnwNMDRrGtA7vEX46jLYDuuR9yAyUQdHVKnPdLDI3ypC5YxaZZ/WeTDerzAAtXurAVav9qJSix8cDWDD4M6a1G0bkhWs06NbSNkHzkdYq124Pm3tq7Ciz7LZ9C7irxo5S6sH45GulTlADti4LAeBU6HGKuBbBzds9XRk3b3dcXItwas8xALYuCyEgqEHa+mff78PSyQvQpB8o2aZPB/as3k7M9ei8DXGX6gQ1YNuyjcDNzEWzyVw4LfO2ZRupE9QQgMS4hLRyhYoUShsfqtEUKloYAJciLtyIisOUaszrODlS2yrz6dDjFHYtSvEMmYt7u1M4h5lv7urY6zGc3X8SY2qqDVLcndpB9dm2bBNgzlwkm8wuroU5vec4ANuWbaK25bV9ZPP+tG/ATocew8PXM9NzNOjcnJ0r/s3LGDlWK6g+2y15z+Qw7/bb5HXPIu/DrH6dmrgVd83vauQag28FdORVdPQ1MBlJPboDh0p10pVxrNWClL0bIMny7bWlQaO8/EAZMJ09bL4/JQlSk21Z/Xty8NgpypbyoYxfSZycHGnfohEbtu5JV+bUuYs0qlMdgIa1H2XDtj1ZPdQDyT+oPjst7+GzoSco7Fok2/fwGct7eOeyTdQMqm/e/vH67Fhq3n7H0k3UfLx+pueo17kZe1ZsSVt28/WkRuu6bP3p7zzJdDtl61Tm2tkwrp+/ijHFSOjvW/APSl9n/6D67PjFnGnfqu1UaVoj7f7Q37dgTE4l4kI4186GUbZOZQBO7TjKDavG3E3/WR3jzoYez5dj3M3MEXfIvNOSeX8OM7v5evJo67pss9qPRTyKYUxOIfz0ZQD+++cAtTo0tEXMfKV17t0eNjlu7CilyiuljiilvgL2AL2UUluVUnuUUkuUUsWUUm8ApYANSqkNlu3irB7jaaXUXMvvc5VSMyzlPlJKjVNKfa+UClFKnbI8FkqpokqpP5RS+5RSB5VSz+Ze/Oy5+3gRcenW0IXIsAjcfb3Sl/H1IvKyVZnLEbj7mMvUblufqCsRXDiS/pttdx9PAto1JOSHdXlY+3vj4eOZIfP1TAc9d1/PDJmv4+Fzq0yXoT34aMvXNHryMZbPWAzA3/PW4Fe5NNN2zGLs2o/5afwc9APybnHPkDkq7HqmD+8eWWR2t8r85NAeTN7yNQ2ffIwVlswPMncfTyIvXUtbzm4/R1lljsqQ+aamz7TmUMjeTPfX69iEXQ9IY+de8mbcxzc1faY1h63yej1SkhF/fMTbi8dRqUG1PKi9uFvK1QMdG5m2rOMiUa4e6ct4+GDw8KFQj+EUen4EhvLmD00GDx9Iise58wBceo3BqeXToB78bzGvXI/Ep8St16tPCU+uXo9MV6ZqhbL8+c8uAP7aspsbCYlExZhPz8nJKTz3xlheeHsCf2/ZbbuK55C7jydRVsfp6LAI3DK8h918PYm6HJG2HHU5Iu097OrtRkx4FAAx4VG4liieblsnF2eqtazNvtXb0+7rOqY3yyf/kC/nqkx5L0fgluF45GZVxmQ0kRibQFEP13T3Q/q/Q040eiaQI1kc0/NaVvW+18zWf68uY3qzMsN+vBERi8HRgTI1KwJQO7gR7n7pP9+JguVue3b+D5gPPA70A9pqresCu4AhWuvPgEtAK611qxw8XlXLY7xjWa4GtAMaAmOVUk5Ae+CS1rq21tofWHOXdb4nWZ7fMhz0sivj7OLME4O6pX3Yt/bcmJf4ZcpCtOkBHBuaRaCMx3mVZZlbhX6bvoj3mvZn+/LNtO7dHoAaLepw/vAZhjV8lQnBw3h+Qj9cihXO3brfo6zzZCqUeUOrQsunL2JE0/7sWL6ZVpbMD7Q75DEXufPfpf3ApzAZjez4bXO6+8vXqUxyQjKXjp2/76rmhju9ZrMrQxZ5jVZ5Y65GMrrpACY/8R5LP5hH30/feGBe1yKDjPvbYEB5lCRp8TSS/5iNc7veUKgwGBwwlKlCysafSVw4EeXmjUONZvlU6buQxQfyjK/pd15+jt0Hj9J90PvsOnCUkl4eODiYPwKsnTeDnz4bz0fvvs7UWT9y/vIVm1Q7x7I8ZN35fJzThop/23qc3vVf2hC2Gq3rEnc9mgsHM1/bZxP3+PlDa31ff4e2A7tgNBrZ/ds/OSqfm3JS72zLZPP3qn6b/bjgjc/o8v6LvPXbRJLiEjAaH4zRJnnJpFWu3R42dzt87KzWeptSqiNQHfjXckB1Brbew/Mv0Vpbv8L+0FonAUlKqauAD3AAmK6U+ghYqbXenPFBlFKvAq8CNPMMoJprxXuoCrTq1Z7HerQB4My+k3iWutXS9/D1JOpKRLrykZev42H1bYCHnydRVyPwLudLiTIlGbt6umVbL95fOZUPu4ygXK2KvPq5eUx0MQ9XagbWxWg0snfdznuq8/0K7NWOFj3aAnB634kMmb2IvmNmL6Kupv8GEWD78n944/sRrJj5M82eacWar38FIPxsGNfOX8W3UmnO7DuRF5HuKLBXO5pbMp+xZD5pWefu65WD/Zx15h3L/2HQ9yP4febPeVb3e9WyVzuaWV7bZ/edxKNUCeA/wLyfo66kzxN5+Xq6b7rc/byIvnrr79K4W0tqtqnHJ89PyPRc9Ts1y/denRZ3yBt9h7zmfXwrb6NuLfFvU49PrfKmJqeSmmz+Zvz8wdOEn7tCyQp+nDtwKq9iiRzQsel7clQxD3RcVLoypthITJdPgcmIjr6GjriCwcMHHRuJ6ep58xA4wHgiFINfRYwHbRrhrvmU8OTKtVuv1yvXIvD2TD/Mq6SXBzNHvwFAfEIif/67C9eiRdLWAZTxK0n9WtU4cvIcj/j52Kj2WWveK4gmPVoDcG7fSdytzk1uvp7EZHgPR12OwN3vVk+Au58n0ZbjdGx4NMW93YkJj6K4tzux12LSbVu3U5N0Q9gq1K+Kf9t6PNoqAKdCTrgUK0yvmQNZZwIVngAAIABJREFU8PaXuZ4zK1FhEenzWmXJWCY6LAKDgwEX18LER8Vl2tY9i22z0qBbC2q0qctXz+fP9XtZ1Tsmh5mjs/l7+betRw3LfnS07McXZg7kh7e/5Oye43zRfRwAVR+rhXeF9Nf1FUQP47U2ueVue3ZuDvZUwHqtdR3LrbrWul8221g3zV2yebybkqx+NwKOWutjQD3MjZ7JSqkxmZ5A61la6/pa6/r32tAB2LBgDROChzEheBih63bQpGsgABUDqpAQG090ePoTZnR4FIlxCVQMqAJAk66B7F23k4v/nWNI/X4Mbz6A4c0HEBl2nQ86vktMeBQjHhuYdv/u1dv44f3Z+dbQAQhZsDYt8951O2nc1XyR3u0yJ1llbty1ZVr9S5b3TStXp219wk6aLwiMuHSNas1qAuBawg2fiqW4di7/vjkMWbCWicHDmJghcwVL5pgMmWMs+7mCVeZ9WWSubZX5QbNxwdq0yQP2rdtB464tgLvJ3IJ968xDYKq3rE3Q60/y9csfkZKY/noGpRR1gxuz6/f8bexsWrA2beKA/et20MiSt/xt8ibFJVDekrdR1xbsz5D3mwx5i3m6pl0E6/VISUqW98vX17UwM4WdQXn4oNxKgMEBx2oNMZ5MPxmM8UQoDmUtww4LF0N5+GCKCscUdhpVqAgULgaAQ9lH0dcv2zrCXatRtQJnL13hQlg4KSmprNm0ncDGAenKREbHYrKMKPjfzyt5Ksj8noiJvUFySkpamb2Hj1OpbCnbBsjCPwvWpU0ecGDdLhpY3sPlAiqTmO17OJFyAeZrNRp0bcFBy3v44J+7afi0efuGT7fg4Ppdadu5uBamUqPqHLC6b+XUnxjbZCATmg9m3uDPOL7lkM0aOgDn953Eu7wvnmW8cXByIKBTUw6tTz+88OD63TTsZs5UO7gRJ7YcAuDQ+t0EdGqKg7MjnmW88S7vy7m9t/9isVrL2rR+vTP/e3lapmO6rWSV+WCGzIfW76aBJXMtq8wHs8n8x9SfmNBkIBObD2aBZT/+YNmPxbzMQxkdnB1p/Xpntvzwpw3TClu714kBtgFfKqUqa61PWGZfK2NpmMQCrsDNQfJXlFKPYv5a9SnL+hxTSpUCIrTWCy3X//S5xzrflQMb9lCzVV0mbfzCPA3zsK/S1o1ZNY0JweZpGxeOnk3fm1MSh4RyICTUFtXLE+bMAXy48XOSE5KZO+zWwT1j5pfSMu/loCVz1/dewLdiKbRJc/1iOAsts1Ot/GwpL00fyNg1H6MU/DJlYbppuPPTQUvmiZbM86wyj141jYmWzD+Onk1vy3Tb1pmfeu8FfCyZIy6G84Mlc3Fvd0aumIJLscJorWnT9wnGPf52ugkN8svBDaH4t6rLhI2fkZyQzHyr1/bIVVOZFPwuAItG/4/e0weYp2IO2cshS+Znx/fD0dmRNxa+D5gnObg5E1nlRo8SFXada+ev2jhV9g5uCKVGq7qMt+RdYJV3xKqpTLbK+2IWebuP74eTsyODLXlvTjFduWF1Og7pjsloxGQ0sWjU7HQzOT0sho2dws7Q/URFxdCmS08G9OtFt07t8rta906bSP7rRwp1ewsMBlIP/Iu+fgmnZk9iCjuD8eQ+TGcOocvXwOWlCWAykbJxCSSa913yxiW4dB8KgOnKWVL3b8rPNDni6ODAyP696D96GkaTiS5BLahcrgxfLlhG9SrladW4LjsPHOWzuUtQQF3//2PUwBcBOHX+EhM+n4vBoDCZNH2feYJKZUvf/glt7PCGUKq3qsP7Gz8lOSGJH4fdmiJ92KopTAseDsDPo7+7NfV0yN606+v+/Ho5L335Fo27tyLy0nXmDJiZtn2tdg35b/N+khOSeFCYjCZ+GTOH1+aPxOBgYPvPGwg7foH2bz/D+QOnOPTnbrb/vIEXZgxkZMgnxEfFsWDwZwCEHb/A3pVbGb7+Y0ypRpaOmYM2mb9z7vXZYCo3rk5RD1fGbv2SNTOXsv3nDXQd/xKOzk70XzgKME9SsGTUdzbPvGzMHF61ZN7x8wauZJH5eavM8y2Zr1gyv2fJ/ItV5uy0erUT1dvURSnFlh/Wc2LrIVvEzFcP4/Cz3KJyOpZTKVUe8zAyf8tya+AjoJClyGit9Qql1GBgIHBZa91KKfW0pdx54CBQTGvdxzJRwUqt9VLL440D4rTW0y3LB4GOmK8TmgaYgBSgv9b61lcwGbxc/ukH48p3G1FZDlYt2HJ9vvSHgMEO97P9JYZPd03J7yrYXMrcSfldBZtzeCrrqZMLsnfbfJLfVbApezxP2eMxG2DGmZ8eiujbSnXNtc/HjS8teygy35Tjnh2t9RnA32r5b6BBFuU+Bz63Wl4KLM2iXJ8My+MyLN98rjPA2pzWUwghhBBCCHGLPffs2OOXD0IIIYQQQgg78MD9M08hhBBCCCFE7rHn2diksSOEEEIIIUQB9gD+d0ebkWFsQgghhBBCiAJJenaEEEIIIYQowLTdzpcnPTtCCCGEEEIUaCade7c7UUq1V0r9p5Q6oZQansX6IUqpw0qp/Uqpv5RS5azWGZVSey23FbmRXXp2hBBCCCGEEPdNKeUAfAk8DlwAdiqlVmitD1sVCwXqa63jlVL9ganAs5Z1CVrrOrlZJ+nZEUIIIYQQogAzoXLtdgcNgRNa61Na62TgJ+BJ6wJa6w1a63jL4jagTK4HtiKNHSGEEEIIIQowjcq1m1LqVaXULqvbq1ZPVRo4b7V8wXJfdvoBq62WXSyPuU0p1SU3ssswNiGEEEIIIUSOaK1nAbOyWZ1V10+WV/oopXoC9YGWVneX1VpfUkpVBP5WSh3QWp+8n/pKY0cIIYQQQogCzIb/Z+cC8IjVchngUsZCSqm2wCigpdY66eb9WutLlp+nlFIhQAAgjR1rRQteJJGBQ35XQNhEDKn5XQWbS5k7Kb+rYHNOfUbmdxVsLmXJzPyugs0VsbNR8/Z4njLY8dTGDwMbTj29E6iilKoAXASeA563LqCUCgC+Bdprra9a3e8BxGutk5RSJYBmmCcvuC/SMhBCCCGEEELcN611qlJqELAWc7v/e631IaXUBGCX1noFMA0oBixRSgGc01p3Bh4FvlVKmTDPKzAlwyxu90QaO0IIIYQQQhRgNhzGhtZ6FbAqw31jrH5vm812W4CauV0faewIIYQQQghRgNmysfOgsa9BtEIIIYQQQgi7IT07QgghhBBCFGA2nKDggSONHSGEEEIIIQowk/22dWQYmxBCCCGEEKJgkp4dIYQQQgghCjCTDGMTQgghhBBCFEQ6vyuQj2QYmxBCCCGEEKJAkp4dIYQQQgghCjB7/j870tgRQgghhBCiADMp+71mR4axCSGEEEIIIQok6dm5ja5je1O9VQApCUn8MPRrLhw6k6lMGf8KvDC9P04uzhzeEMqy8fMAKOJWlD5fvIlnGW8iLoQzZ+CnJMTcAKBy4+o8NeZFHBwduBEZy+fPTrBlrEzyIqf/4/V4Ykh3TFpjSjXy64T5nNr1H5WbVOep919Me1yfSqWYN/gzDqzbZau4VGtZmy5jemNwMLBt8d/8/fWKdOsdnB15fsZAHvGvwI2oOOYP+pTIC+EAtBnwJI26t8JkNPHr+Ln8t2k/joWcGLR4LI6FnDA4GNi3ejtrZy5Ne7wOQ5+ldnBjtMnEloXr2Tx3jc2y5kVegNH/fE5SXAImkwlTqpGZnUcB0H5Id/wfr4fWmrhrMSwa+jUxVyNtmjcrPcb2pWarAJITkvl+6BecO3Q6U5ly/hV5afpAnF2cObAhlEXjv0+3PuiVznQf9SJvBbxEXGQs/9e4BgNnvcu1C1cB2LNmOys/W5rpcfOboXwNnFv3AGUg9cBmUneszlTG4f/q49S0M2iNKfwCyX/MBkC5euLcrjfK1RPQJP3yKTrmuo0T5L7Rk2aw6d8deHq489vCb/K7Orni3zPXmLbxKCaTpot/Gfo2qJCpzLpjYXyz7SQKqOrtyuQOtQD4dPMxNp8xv+dfaViJdv/na8uq35WqLWvRccyLGBwM7Fy8gY1f/55uvYOzI91n9Ke0fwXio+L4cdBnRF24RuXm/rR/rwcOTg4YU4ysmvQDp7YexrmoC68tGZO2vZuvF3t/+4eVExbYOlq2qrSsRbAl8+7FG9iUReanZ/SnlCXzYkvm0rUr0WVyP3Mhpfj7k184stZ8rm3arwP1nm0FWnPlv/MsG/YtqUkpto6WrcotaxE8phfKwcCexSFsziJz1xn9KeVfnoSoOH4e9DlRF66lrXcr5cWg9VMJ+eQX/p29CsdCTvRd/D6OhRwxODhwaPUONsz8xdax8pU9T1BwX40dpZQ78LzW+qtcqk92zxMIJGutt+Tl81irHlgH7wp+TAx8i3IBlXnmw5eZ2WV0pnLdJ/Zj8cjZnNlznNfmDufRwDocCdlL2/5PcmzLQf78egVt+3em7YAn+X3KjxQuXoRnPujLN70nE3npOsW8itsqUpbyKuexfw9ycP1uAEpVK0ufL99kUpt3OLH1MNOChwPmhtLojZ9y1PIB2haUQdF1Ql++6fkh0WHXeXvFJA6t382VExfTyjTq3oqE6DgmBb5FnU5N6Dj8eRYM+hSfyqUJ6NSUj4KG4lbSg9d/GM3kVm+RmpTCV89/QHJ8EgZHBwYvHc/RkL2cDT1Bg2da4u7nxUdthqC1tvn+zou82mQ+ZH7V4wNuRMame74Ns35nzYyfAXisT3uC3uzK0lHf2S5wFmoGBlCygh8jAwdTMaAKPT98lUldRmQq13PiK8wf+S2n9hzjzbmj8A8M4GBIKAAefl5Uf6wW1y2NwJuO7zzK5/0m2yTHPVEK57YvkLRkBjo2EpeeozGe3Iu+fvlWEfeSODUMJvHHKZAUD0Vc09Y5B/cjZdsfmM4eBqdCoAvG6bJL8OM8360zIz+Ynt9VyRVGk2bKhiN83bUePsVceGHRNlpW9KaSV7G0Mmcjb/D9ztPM7d6Q4i5ORMQnAbD5dDhHwmP46YUmpBhN9Fuyi2blS1Cs0IP3XagyKDpPeInvek4mJuw6A1dM5Mj6PVy1Op416B5IQvQNpgcOoVanJnQY3oNFgz7nRmQs8/pNI/ZqFD5Vy/DS/OFMaTyI5BuJfB48Mm37Qb9/yME1O/MjXpaUQdFpwkvMsWR+3ZI53CpzPUvmmYFDqNmpCe2G92DxoM+5+t95vu40GpPRRDFvdwatnsx/f+6haAk3mvRpx6dth5GalMKzX7xBzU5NCF26KR+T3qIMio4T+jCv52RiwiJ4bcUHHM2QuW73QBKjb/Bp4Dv4d2rM48N7sGTQ52nr27/fk+Mh+9KWU5NSmPv8h2nn6ZeXjuF4yD4uhJ6wabb8ZM/X7NzvMDZ3YEBOCyuze3nOQKDpPWx3z/yD6rNzmfmNfzb0BIVdi1Dc2z1dmeLe7ri4FubMnuMA7Fy2iZpB9c3bP16fHZYDx46lm6j5uPn+ep2bsW/NDiIvmb8djbseY5M82cmrnMmWEymAc5FCWX5Gqh3cmCMhe0lJTM71XNkpW6cy186GEXH+KsYUI6G/b8HfkuUm/6D67PzFnGn/qu1UaVoj7f7Q37dgTE4l4kI4186GUbZOZeBWXgdHBxwcHdLyNnvhcdZ/9gvacoet93de5c1OUlxC2u/Z7XdbqxPUgK3LQgA4FXqcIq5FcMvwGnfzdsfFtQin9hwDYOuyEAKCGqStf/b9PiydvAD9kH03ZvCtgI68io6+BiYjqUd34FCpTroyjrVakLJ3g7mhAxBvbsAqLz9QBnNDByAlCVJt917NS/Xr1MStuOudCz4kDoZF84hbEcq4FcHJwUC7qr6EnLyarsyvBy/SvfYjFHdxAsCzSCEATl2Po15pTxwNBgo7OVLV25UtZ69leo4HwSN1KnP97BUiLcezfb9v5dGgeunKPBpUnz2/bAbg4KrtVGrqD8DlQ2eJvRoFwJVjF3Aq5ISDc/oGnVd5X4p6FefMjqM2SJMzZTJkPpBN5lBL5kOrtlPRkjklMRmT0fwR16mQU7qv9g0ODji5OGNwMOBU2JnYK/nfA39TmTqViDh7hcjz4ZbM26iWKXM99lrOW4dX7aCi5bwFUC2oHpHnrhJ+/EK6bazP0wZHhwLz5Y24s/v96mYKUEkptRfYANQCPAAnYLTWerlSqjyw2rK+CdBFKdUWeA+4BBwHkrTWg5RS3sA3QFnL478FXAReB4xKqZ7AYK315vus9x25+3gSdenWcI3osAjcfD2JCY9Ku8/N15OoyxFpy1GXI3D38QTA1dstrWxMeBSuJczf6HtX9MPB0YFBP43BpagLG+esZueyPI+TrbzKCVCrXQM6vvscxbzcmNX3o0zPXbdTE0L+tyrXM92OW4a8UZcjKJfhA7x1GZPRRGJsAkU9XHHz8eRs6PG0ctGXI3Cz/B2UQTFk5WRKlPPl3wXrOLfX/G2RVzkf6nRsQs12DYiLiOXXcXO5diYsr2NmmQVyL6/WmtcWjERrzdYf/2Lbor/SynUY+iz1u7YgMTaer3rk7xBNAHcfLyKs/gaRYRG4+3oRbfUad/f1IvKyVZnLEbj7eAFQu219oq5EcOHI2UyPXaluVcaunk7UlUiWfDiPSxlOrvlNuXqgY299iNFxkRj8KqYv4+GDAXDsMRyUImXLCkxnDmHw8IGkeJw7D8DgVgLjucOkbPpFPiA8gK7eSMTH1SVt2cfVhYNh0enKnI00D6Pus3gHJq15rXElmpUvQVVvV77ddoqedcuRmGpk1/kIKnoWtWn9c6q4jwfRVu/lmMsRPJLheFbcxyPD8SyeIh6uxFv1Qvt3aMilQ2cxJqem27Z25ybsX7k1DxPcvawyl8kic7RV5iSrzGXqVOKpqa/hXroES4d8hcloIvZKJP/M/oOhWz4nNTGZE5sPcGLzAZvmuh1XH88sMlfKUMaD6EvmzyW3MhcjJTGFx17vxLyek2n26hPptlEGxesrP8SznA87Fqznwt6TeR/mAWKy3/kJ7rtnZzhwUmtdBxgGPKW1rgu0Aj5WKm3qh/8D5mutA4AU4H2gMfA4UM3q8T4FZmqtGwDdgP9prc9gbgDN1FrXyaqho5R6VSm1Sym162BsLr14s3hR6Awn+awmtshYJiODgwOP1KzIrJc+4usXJ9NucFe8K/jdT03vTx7lBNi/dieT2rzDd69OJ3hI93Trinu7U+r/ynJk075sts4bOcmSbZmsDhSWbbVJ83HwcMY3GUDZ2pXwrVoGAEdnJ1KSUpjZeRTbFv3Fc1Nfv98IdyWv8n7ebSwzOo5gdp8pNH8xiIoNb72NV09fzAdNB7Jn+T80793ufqqfK7KcgCYHfwO0xtnFmScGdWP5jMWZVp89eIr3mvVnfIeh/D13FQNnvZc7Fc5rGbMbDCiPkiQtnkbyH7NxbtcbChUGgwOGMlVI2fgziQsnoty8cajRLJ8qLW4rB+1Po9aci4pn9tP1mdyhJhP+PERsYgpNypWgeYUS9Fm8gxGr91PLzw1HwwP6qSiLN2rm41mWB7S0X0tWKU374T34deT/MhWr1akJ+1Y8WI2dnGS+XZkLe0/yedC7fNN5NC37P4ljISdcihfl0cfr8fFjb/JRo4E4FSlE7S4Pzns7Z+etrDJD67e7seW71elGl6StN2m+Dh7Jx00GU6Z2JUpaztP2woTKtdvDJjdnY1PAJKXUfuBPoDTgY1l3Vmu9zfJ7Q2Cj1jpCa50CLLF6jLbAF5aeohVAcaXUHccaaK1naa3ra63r+7tWulPxbDXvFcSwVVMYtmoK0VcicS/llbbOzdeTmAzdvFGXI3D380xbdvfzJNpyMXZseHTacLDi3u7EXjMPX4oOu86RjftITkjiRmQsJ3ccpfSjZbElW+S0dnLHUUqU86Gox61dGdCxCfvX7sSUaszVbHcSFRaRLq+7n2emC+ityxgcDLi4FiY+Ko7oDNu6Wf0dbkqMiefEtsNUa1nH8ljX2b9mBwAH1u7Er5pt93Ve5b35GHHXYziwdidla2ce3rZn+b/Uat8o1zPlRKte7RmzahpjVk0j6koknlY5PHw9iboSka585OXrePhZlfHzJOpqBN7lfClRpiRjV09nyj9f4eHrxfsrp1Lc253EuASS4hMBOBASioOTA8U8HqyhUTo2EuXqkbasinmg46LSlTHFRmI8sRdMRnT0NXTEFQwePujYSExXz5uHwGkTxhOhGHxs+/oVOVOymAtXYhPTlq/EJuJdtFCmMoEVvXFyMFDarQjlPYpyLso8dPHlhhVZ3LMJ33StjwYecS9iy+rnWExYBG5W7+XiWRzPojMdz4oQHxVnLu/rSa9vh7BkyNdEnEs/zM/30bI4ODhw6WDmyUvyU1aZYzNkti5jcDBQyLUICZbMN4WfvERyQiIlq5ahUnN/Is9fJT4iFlOqkcNrdlK2XtW8D5NDWWeOyqKM+XOJdeYydSoRNKIHb//zCY37tuexgU/S8MXH022bGBPP6W1HqNKyVt6HEQ+E3GzsvAB4A/UsPT1XgJv96jesyt2uSWgAmlh6cOporUtrrWNvUz5X/bNgHdOChzMteDgH1u2iQdcWAJQLqExibHy6oV1gHraVFJdIuQDzB70GXVtw0DKr2ME/d9PwafP2DZ9uwcH15vsPrNtFpQbVzONkXZwpV6dyuovFbcEWOUuU80nbvkyN8jg4Oaa7mL1u56bs/v3fvAuZjfP7TuJd3hfPMt44ODkQ0Klp2kQKNx1av5sG3cyZagU34sSWQwAcXL+bgE5NcXB2xLOMN97lfTm39wRFPV1xKW7+cOBUyImqzWpy9eQl8zbrdlGliXkscaXG1Qk/fRlbyou8zoULUaio+a3tXLgQVR+rRdix8wCUKH9rFqcabeul/R1sbcOCNUwIHsaE4GGErttBk66BAFQMqEJCbHy6IWwA0eFRJMYlUDGgCgBNugayd91OLv53jiH1+zG8+QCGNx9AZNh1Puj4LjHhUemubatQuzJKKeIibXa4yhFT2BmUhw/KrQQYHHCs1hDjyfS9qcYToTiUtfTMFS6G8vDBFBWOKew0qlARKGy+yN2h7KPpJjYQD44avsU5FxXPxeh4Uowm1h4LI7BSyXRlWlUqyc4L5kZ+ZEIyZyNvUNqtMEaTJirBfC3WsfBYjl+LpUk5r0zP8SC4sO8kJcr74mE5ntXu1IQjGY5nR9bvpm63xwDwD27EScvxzKV4EfrMGcaaqT9xdvexTI9du3NT9v1uszmQcuzivpN4WWWu2akJRzNkPrp+NwGWzDWCG3HKktmjjDcGB/PHPPfSJShRsRRRF64RfekaZQKq4OTiDEClZjXSXfyf3y7uO4VneV/c0zI3ziLzHupYzlvVgxty2pL5u+4fMLP5W8xs/hbbvl/D5i+Xs2P+eopYnacdCzmZM5+0r+OZzsXbw+Z+r9mJBW5+lekGXNVapyilWgHlstlmBzBTKeVh2b4bcHOw6DpgEDANQClVR2u911LOptNYHd4QSvVWdXh/46ckJyTx47Bb05MOWzUlbUaxn0d/d2tK5pC9HA7ZC8CfXy/npS/fonH3VkReus6cATMBuHLyEkc27uW9NVPRJs3WxX9z+Vj+jfPPq5y1OzSiQdfHMKYaSUlMZt6gT9Me17OMN+5+XpzcdsSGSc1MRhPLxszh1fkjMTgY2PHzBq4cv0D7t5/h/IFTHPpzN9t/3sDzMwYyMuQT4qPimD/4MwCuHL/A3pVbeW/9x5hSjfwyZg7apCle0oMeH/fHYDCgDAb2/bGVw3/vAeCvr5fT85NBtOwXTFJ8Ij8P//ahz1ushBt9Z70DmL9R27P8X45uNH+A7vheD7wrlkKbTERevMbSUZmHitjagQ17qNmqLpM2fkFyQhJzht2aPHLMqmlMCB4GwMLRs+k7fSBOLs4cDAnlgGUmtuzU69CYwJ7tMBmNJCcmM2vwJ3ma455oE8l//Uihbm+BwUDqgX/R1y/h1OxJTGFnMJ7ch+nMIXT5Gri8NAFMJlI2LoFE8/dTyRuX4NJ9KACmK2dJ3f9gzNZ0v4aNncLO0P1ERcXQpktPBvTrRbdO+T/k8l45Ggy816oaA37dg0lrnqxRmkpexfhq6wmqlyxOYKWSNC3nxdaz1+k6/18clOKtx6riXtiZpFQjfZeYZx8r5uzIh+1q4mh4MP8Fn8loYsWYufSdPxzlYGDXzyFcPX6Rtm8/zcUDpzjy5x52/RxC9xkDGBoyg/ioGywabJ6hq8mLQXiV86H1G0/R+o2nAPi+1xRuWCaNqfVEY+a+NDXfsmXHZDSxcsxces8fbp562pK5jSXz0T/3sPvnEJ6eMYC3Q2aQEHWDxZbM5Rr8H4/174wpNRVt0vz+/hziI2OJj4zl0OrtDPhjEqZUI5cPnWHnor/zOektJqOJP8bM5cX575nPMT9vJPz4RVq/3Y2LB07z35972PNzCF1n9OfNkI9JiLrBksGf3/YxXUu60/Xj183Ddg2KQ39s59jftz/GFzT2fM2Oysm1F7d9AKV+xDwxwU7M1984AXuBZkAHS7GVWmt/q21eBYZinqDgCBChtR6llCoBfAk8irkhtklr/bpSqiqwFPPMebedoODN8s89jI1OcRcc8rsCwiZiSL1zoQLms0Hudy5UwDj1GXnnQgVMypKZ+V0Fm/vgo4f//zPdDXs8Txkewms5csOEMz88FMHnl+6Za5+PX7y48KHIfNN9T6SvtX4+B8X8Myz/qLWepZRyBH7F3KOD1voa8GwWz3EMc4NKCCGEEEIIcRfk/+zY3jjLJAQHgdPAb/lUDyGEEEIIIQo0uWbHxrTWQ/PjeYUQQgghhBD2I18aO0IIIYQQQgjbsOcJCqSxI4QQQgghRAEm1+wIIYQQQgghRAEjPTtCCCGEEEIUYPbcsyONHSGEEEIIIQowbcfX7MgwNiGEEEIIIUSBJD07QgghhBBCFGAyjE0IIYQQQgh/h8jtAAAgAElEQVRRINlzY0eGsQkhhBBCCCEKpALXsxOPMb+rYFP22Fo1YH9X2dlfYvv8FsrhqZfzuwo2l7JkZn5Xweacnnk7v6tgc/EfjczvKtiUQ35XIB/Y47n5YaLzuwL5qMA1doQQQgghhBC3mOy4LWqPHQNCCCGEEEIIOyA9O0IIIYQQQhRg9jg0/CZp7AghhBBCCFGA2XNjR4axCSGEEEIIIQok6dkRQgghhBCiAJPZ2IQQQgghhBAFkj3PxiaNHSGEEEKI/2fvzuNjuv4/jr/OTBIRsoskpEiFrxIkxE4FFeRXS1GlraWlWpRvtaKqSi21lKK6WLpZ2m/RVltaFK2lat+XtvYtiMieSGwz5/fHjEgiIUgmJJ+nxzxk7j135rxzMpOce849I0QhJtfsCCGEEEIIIUQhIyM7QgghhBBCFGJyzY4QQgghhBCiUDIX4e6OTGMTQgghhBBCFEoysiOEEEIIIUQhVpQXKJDOzm10HfUC1ZvV4mraFb4a8gmnD564pUy5wEd5YcoAHBwd2L92FwtHf5Vpf9hLbXn67R4MDn6RlPhkfCqWodfkAZSr5s9PU75l1WfLbBUnV54Z9QKB1sxzh3zCmRwy95oyAHtHBw6s3cUia+Z2rz9DzZZ10FqTHJPI3CGfkBgdD0Dl+lXpMvIFjHZGUuKT+eCZUTbNdTtdRr1AtWbBXE27wvwhn+aQ2Z8e1swH1+5msTVzx7eep/oTtbl+9Toxpy8wP+JT0pJSMdobeXZ8X8pXr4jWZhaPnsuRLX/bOlqOnrZmvnabzI9kyfydNfNT1symq9e5ePoCC6yZPfy8GLlmGheOnwPg5O4jfPv2ZzbNdTvPjnqR6s2CuZp2lS+GfJzt67l84KP0tmbev3Y3/xv9Zab9rV5qxzNv92BQ8AukxCdT3NmJl6YNwrNsKQxGI799tpSN3621VaRc2bhjH5Nmf4PZbKZjq6b07vJkpv3nLsQwcvoXxCcm4epckvERL+NTygOAoCd7UanCIwD4eHnw0ajBNq//vfjrZAyT1/+L2azpEOjHi3X8bymz6nAUs7YcQwGVvZyZ0KYGAB/+eZg/T14E4KW6FWn1Hx9bVj3fjBg/lQ1/bcPD3Y2fvp5V0NW5ax1H9aSq9T3rmyEziTx48pYyfoH+PDelH/aODvy9djdLRs8DwMm1BL0+/i8efl7ERV7kqwEfkpZ0iYD6VekzZwixkdEA7Fu5jd9mLAFg5MaPuJKShtlsxnzdxAft3rZZVoAqTWvSYWRPDEYDWxb9wR8zl2bab3Sw49mpA3gk0J9LCSnMf/VD4iMtP7ct+renXpdmmE1mfhw9l0Mb9mFXzJ5XF43Crpg9BqOBvSu28tu07wEIaFCNdm8/j9HejsgDx1k0dDZmk+3/NP5P05q0H9kDg9HA1kVrWZtN5m5T++MX6E9qQgoLXv2Q+MgYAJr3b0/dLqGYTWZ+Gj2Pwxv2AdCkdxvqPdMctOb8oTMsipjF9SvXeHpSXx6p8SigiDlxnoVDZnI19YqtI9tU0Z3Elk/T2JRSTymltFKqSoZtK5VSCUqpX7KU9VdKbVVKHVFKLVJKOVi3P66U2qWUuq6U6pwf9bydwNBgSvv78nboQBYMn81z772Ubbnnx73EguGzeTt0IKX9fQkMDUrf5+7rSdUmNYi1vgEBXEpIYeG7Xz5wnRy4mfmd0IF8fZvMz457ia+Hz+Yda+Zq1syr5ixlbJshjAuPYN8fO/m//1qarbiLE93GvsQnfSYxOux15vT/wGaZ7qRaaDCl/X0YFTqI/w2fQ7f3+mRbrtu4l/hm+GxGhQ6itL9PeuZ/Nu5jbNgbvNcmggsnztOq/1MANO76BADjWg9hxvPj6Px2D5R6MBa5v5H53dBBfDN8Dl1vk/l/w2fzrjVzVWvmfzfuY5w1c3SGzAAxp6KYED6UCeFDH6iOTvXQYLz9fXkrdCDzhs+ix3t9sy3XfdxLzBs+m7dCB+Lt70v10OD0fe6+nlRrUoOYDK/n5t1bc+5oJKPaDOH9rqPo8nYPjPYPzjkkk8nM+E/nM3PMG/w0awIr1m/h2Omzmcp88MVC2rZoxA+fvsfL3doz46vv0vcVc3Dgu4/H8t3HYx+ajo7JrJm49h8+7lCLH3o0YuWh8xyLTclU5lT8Jb7cfoK5XeryQ49GRDT9DwB/nrjIPxeTWPhcAxZ0rce8nSdJuXK9IGLkuQ7hLZk1dVxBV+OeVA0Nwsvfl3Ghr7Fw+Gc8ncN7VpdxvVk0/DPGhb6Gl78vj1nfs57o157Dmw4wrtlgDm86wBP926cfc3z7v0wOH8bk8GHpHZ0bPu42lsnhw2ze0VEGRccxLzKn10QmtXyDWu0a4R1QNlOZel2akZaYwvjQ11j/xa88OexZALwDyhLctiGTwoYwp+cEOo3tjTIorl+5xqfPjmVKmzeZEj6MKk2DKB8cgFKKbh/0Z8HAGUxuFUF8ZAx1OjW1ad4bmZ8a8wKf95rE5JZDCG7XMIfMl5gYOpgNXyzn/zJkDmrbgMlhEXzecyIdx76IMihcvN1p0qs109sOZ0qroRgMBoLaNgBg6dgFTG0zjKlt3iT+XAyNerayeWZhO/l1zU43YCPQNcO2yUD3bMpOAqZprSsB8UBv6/bTQC/gf/lUx9sKCqvDliXrATi++whOziVw9XLLVMbVyw1H5+Ic33UYgC1L1hMUVjd9/zPv9OL7CV+jM/Snk2OTOLnvGKbrD94v0JoZMp/YfYTiziVwyZLZxcuN4jlkvpySll6umFOx9NMIdds1Zs/KrcSfs5yBSY5Nyu8ouVYzLIQtSzYAlsxOOWR2dC7OiV1HANiyZAM1w+oA8M+f+9LPgJ3YfRh3H8sZcd9Kfhz66wBgyZuadIlyNR61SaY7qREWwlZr5pO5zLz1NpndrJkfZMFhddi0ZB1w4/XslO3rubizE8esP9ublqwj2JoZoNs7vfhuwgIynh/TaBxLOAJQzMmRSwkpmK+b8jfMXThw+Djlynjj51sae3s7Wj9ej7Wbd2Uqc/z0WeoFVQWgbs3HWLtlV3YP9dA4EJXII65O+Lk6YW800KqyD+uORWcq8+OBs3Sp+QgujvYAeDgVA+B4bAq1y3pgZzBQ3N6Oyl7ObDoVY/MM+SEkqDquLs4FXY17EhgWwnbre9ap3Ucp7uyU43vWSet71vYlG6geFmI5vmUI2763HL/t+w1Ubxliw9rfvXJBAcSciiLuTDSmayZ2L9tEYFjmOgeGhbD9B0umfcu3UqlhtfTtu5dtwnT1OnGRF4k5FUW5oACA9JELo50Ro50RrcHJvSSmq9e4eOI8AIc27qdGm7rYWrmgAGIzZN6zbDPVsmSuFlabHZkyB1q3h7Bn2eb0zLEZMhuMRuwdHTAYDdgXdyDpgmW2yZUMf6/YOzqALvzjHuY8vD1s8ryzo5QqCTTC0mlJ7+xorX8HkrOUVUBz4HvrpnlAB2v5k1rrfRTQ99Xd24O4c7Hp9+OjYm/5o87Nx4P48xnKnI/F3dtSpuYTIcRfiCPyn1O2qXAecMuSOSEqNv2P9xvcs8ns5n2zTPsh3ZiwaSZ12zdh6dRFAHg/WgYn1xK8vvBdhi+bRP2Oj+dzktxz8/ZI74RBzu2ckCFzQpbMNzR8ujkH1+0BIPKfk9RoGYLBaMDTz4ty1R/Fw7dUPqW4O/eSOWs739Dw6eb8bc0M4PlIad76dRKDF71LxTpVbilfUNy9PTP9bMdFxeHu45m5jI9npp/tuPNxuHtbygRZX89nsrye/5i3At8AP6Zu+4wxv33At6O/Qj9AvzQvxMbjXepmu3mX8iA6Nj5Tmcr+5VizcQcAv2/ayaW0yyQkWUZCrl69RtdBo3hu8Bj+2LTTdhW/D9GXLuPt7Jh+39vZkYuXMk9PORV/idPxqfRatI0eC7fy10nL66GylzN/nYwh7ZqJ+LSr7DgTR1TyZZvWX9zKzduDhAyv38SoOFyzvGe5+niQcD4u/X7C+bj09yxnL1eSLiYAkHQxAedSLunlKtSqxNAVk3h57jB8KvndfECt6bdgOEOWjadBtxb5EStHrlnyJpyPwzXL+2/GMmaTmcvJaZRwd77l2MQMxyqD4o3lExmzcw6HN+7n9J6jXIpLxmBnxK+65WRczfB6uPlmfm+0BVdv9yyZY3H1ds9SJnPmtORUnNydszk2Dldvd5IuxLPus18YseljRm6byeXkVA7/uT+93DOTX2bU9lmUrliGjXN/y+eEBc+s8u72sMmP+RYdgJVa68NKqTilVC2tdU6nCj2BBK31jWGOSKBsDmVzpJTqC/QFaOxRiyrOeXAGPZspR1n/hsluWpLWGgdHB8Jf7cj07g/XlIHs89xS6NYDMxT6ecq3/DzlW1r370Cznq1ZNm0xBqORctUfZdqzY7B3dODNJe9xfPcRoq1nkgrUHfJYitz5+9J6wFOYTSa2/fQnAJsWr8UnwI9hyyYSd/Yix3cewmR6MM745/Rze6cyWSf8th7wFKYMmZOi4xnRsD+XElJ4JNCfV+ZEMDbsjUwjfgUm22bO+oLOvoyDowNPvtqJD7qPvWV/tceDOPP3SSZ3e5fS5X144+t3ONzmnwcjM2R7tjJr277RpysTZi5g6Zo/qRX4H0p7umM0Ws6D/TZvKqU93Yk8H02ftyZRyd+PR3y9bVL1e5aLvqZJa04npPJZ5xCiUy7z4nfb+f75hjQoX4qDF5LotWgb7k721PB1xc7wEP5mL2xy8frN/q389j8MZw6c4N1Gr3I19QpVQ4PoM+cNxjWzTNec3mkUSdHxlPR0of/XbxN97CzHtv17zxHuRm6y5Fgmux9X67HarPkgfBiOLk68OPsNfCr7EXU4kgWDZtDhnR7YOdhx6M99BfO76n7+/sjh2OIuJQhsGcL4JoNIS0qlx6f/pVaHxuz6aSMAiyJmW6bPjX6BoLYN2P7d+rxIIh5A+dHZ6QZMt3690Ho/p85Oti/Lu31CrfUcYA7ASxWevufTqqHdW/F4N8u1Fif2HsWjzM2zG+4+niReiMtUPv58LO4ZzoC4+3qSEB2PV3kfSvmVZuSKyenHjvjlfcZ3eCv97NKDIrR7KxpbM5+0Zj5m3efm40lCLjNnte3njbz65Vssm7aY+KhYUuKTuJp2hatpVziy7R/8HitfYJ2dpt1b0ch6pu7U3mO4lykFHAIsbZVwIXOe+POxmc50ufl6khh98/tSv1NTqreozfRnx6RvM5vMfD92Xvr9IT+MLdDO3eN3yJx4h8yWdr6ZuV6npgS2qM2HGTJfv3qd61ctIwJnDpzg4ukLlPb35fT+4/kV67aad2/N49bMJ/Yey/R69vDxuOPPtoevBwnRcemv59ErpgCW79eoX95nbIe3aPx0M5bP/AmA6FNRxJyJxrdiWU7sPZrf8XLFu5QHF2Ju5rwQE4eXR+bpP6U93Zk2YhAAqWmXWfPXDpxLOKXvA/DzLU1IjSr8c+z0A9/ZKV3SkQsZRmMuJF/Gq0SxW8rU8HHF3migrKsTFdxLcDohlWo+rvSp+yh96lpOmL21Yh+PuDnZtP7ConH3MBp0aw7A6b3HcMvw+nX18UifjnRDwvk43Hxvjn64+XqkL5CTfDERFy83ki4m4OLlRnKMZSp1xqlMf6/bQ+dxvSnh7syl+GSSrMemxCax77ftlKsZYLPOTkJUXKa8br4e6fXJWiYxKg6D0YCjc3FSE1JIzHKsa4bvww2Xk1I5uuVvqjQNIupwJKd2HeHjLu8CULlJDbz8ffMvXA6y1tvN1/OWzIlRsZkyF3d2yjbzje9XpcaBxJ6J5lKcZVLR/pXbqVC7cnpnBywdwD2/bCa075OFvrMjn7OTR5RSnlimpX2ulDoJRADPqJyvzI4B3JRSNzpdfsC5vKzT3Vi34DfGhEcwJjyCPau2U7+j5SK9R4MrkZacSmKWjkrixQSupKTxaHAlAOp3bMqeVds5e+g0b4T04a3GA3ir8QDio2IZ9+TQB66jA5bM48IjGJcls781c9Y6J11M4HJKGv4ZMu9dtR2A0hVurlpU84kQoo5ZmnLvqu1UqvOYZc6sowP+QQFEHc18kbQtrV/wG+PDhzI+fCh7V21Ln1aX+8yPs3eVZdpP1aY1CXulPTP7TOLa5avpx9g7OuBQ3PIHVpXG1TFfNxVo5g0LfktfOGDfqm3Us2aucJvMV1LSqGDNXK/j4+zLknlWlswlPZxR1rPgno+UpnQFX2JOX7BFvGz9sWAl74ZH8G54BLtXbaNhx1DA8npOzeH1fDnD67lhx1B2W1/Pr4X0Zmjj/gxt3J/4qFhGW1/PcediqNqoOgAupVzxebQMFwswc1bVKvtz6twFIqMucu3adVZu2Epo/eBMZeITkzGbLbOFP1/8C0+FWX42kpIvcfXatfQye/4+QsVyZWwb4B5U83HhdEIqZxNTuWYy89vhKEIrls5UplnF0myPtHQC49Oucir+EmVdi2MyaxLSLD/Thy8mcyQmmQblbT+lR8DGBavSFw7Yv2oHdazvWeWDA7ic43vWZcoHW67VqNPxcQ5Y37MOrNlJ3c6W4+t2fpwDqy3bnb1c048vV7MiBqW4FJ+MQ/FiFLNei+dQvBhVmtTg/OEz+Rs4gzN7j+FVwQcPPy+M9kaC2zbkwOrM00gPrt5JnU6WTDXC63F000EADqzeSXDbhhgd7PDw88Krgg+n9xylhIczji6Wjrt9MXsqN6pOtPV3dElPy7Q+o4MdzV9px6Zv1tgqaroze49RKkPmoLYNOJhN5pBsMh9cvZOgtg3SM5eyZk44F0P54EqWa3KASo0CuWD9PexZ/uZJm6otaqV/LwoznYe3h01ej+x0BuZrrV++sUEptR5oDPyZtbDWWiul1lqPWwj0BH7O4zrdk/1rd1G9WTDvrf+Iq2lXmRvxSfq+kcsnMyY8AoCvR3zGCzeWYV63hwPrdt/2cV283BixdCKOJYujteaJF/+PkS0HPxDTXg5YM4+zZp6XIfOI5ZMZZ838vxGf0dO63HbGzE+9+Rzej5ZBmzVxZy/yjXU1rqhjZzm4fg/vrPwAbTbz16LfOWfDXxy3c2DtbgKb1WLM+hlcTbvK/IhP0/cNX/4+48OHAvDtiM/pOaW/ZRnmdXs4aM38zOje2DnYMejrdwDLIgffvv0ZzqVcGTTvbczaTGJUHHNf/9j24XJwYO1uqjWrxWhr5gUZMr+1/H0mZMjcI5vMXUb3xt7BjoHWzDeWmA6oW5UnX++C2WTCbDLz7dufkZp4yfYBs7Fv7S5qNKvFxPUfczXtCl9myPzu8sm8a/3ZXjDiM168sZT8ut3sv8PredmM73lxyquMWfkBKMV3E78mJT75tsfYkp3RyPB+3ek3YjIms5kOYY8TUN6PTxYsoWqlCjSrX4vt+/9lxtzvUECtwP/w9oAeABw/c44xH83FYFCYzZoXn/4/Kpa761nGNmdnMPBmsyr0/3EXZq1pX60sFT1L8unmo1Qt7UJoxdI0LO/J5lOxdJz/F0aleK1JZdyKO3DluokXv7OcvCnpYMd7rapjZygcn70dMWoi23fvIyEhiRYdnqd/7+50avtwrED199rdVG0WxDvrP+Rq2hX+F3Fz6eyI5ROZHD4MgMUjvri59PS6PenXE66Z+TMvfPIa9bs0I/5cLF/1nwZAUJv6NHr+CcwmM9cuX2XuwBkAOJdypfecNwAwGA3s/Pkv/l2/12Z5zSYzS0Z+Rd/5wzEYDWxbvJYLRyJpPfhpzuw/zsE1O9m6eC3PTh3A8HXTSU1IYb617heORLLnl828ufoDzNdN/DDyK7RZ41LanW4f9MNgMKAMBvb+upm//7BMumnWty1VW9RCKcWmb1ZzdPNBm2XNmPnHkXN5af5bKKOB7YvXceFIJK0Gd+bM/hP8vWYn2xavo9vU/gxbN43UhBS+HvhReua9v2whYvUUzNdN/GjNfHrPMfat2MrgX8djvm7m7MGTbPn2d5RSdP2gH44li6OU4tw/p/hhxJd3qKF4mKm8vJhWKbUOmKi1Xplh2yDgMSAQqAKUBGKB3lrr35RSj2Lp6HgAu4HntdZXlFJ1gB8Bd+AyEKW1rnanOtzPNLaHUeH4NXx3DNnOfizcil5iuPxQrvlyf2b+PqSgq2BzplXfFHQVbM7+6YdjGe+8NCRkeEFXwaaMBV2BAlAUfzcDTDn57UMR/K0Kz+bZ38cTTv7voch8Q56O7GitQ7PZNuMOxxwHblnnUGu9Hcu0NiGEEEIIIcQ9kmt2hBBCCCGEEKKQeXA+6lsIIYQQQgiR54ruuI6M7AghhBBCCFGomfPwdidKqdZKqUNKqaNKqWHZ7C+mlFpk3b9VKVUhw763rNsPKaXyZBUV6ewIIYQQQggh7ptSygh8ArQBqgLdlFJVsxTrDcRrrQOAacAk67FVga5ANaA18Kn18e6LdHaEEEIIIYQoxMzoPLvdQV3gqNb6uNb6KpYVl9tnKdMeuPHJ698DLayfydkeWKi1vqK1PgEcJZtFzO6WdHaEEEIIIYQoxPLyQ0WVUn2VUjsy3PpmeKqyQMYPU4y0biO7Mlrr60Ai4JnLY++aLFAghBBCCCGEyBWt9RxgTg67s/sMnqzDQTmVyc2xd006O0IIIYQQQhRiNvyY7kjgkQz3/YBzOZSJVErZAa5AXC6PvWsyjU0IIYQQQohCTOfhvzvYDlRSSvkrpRywLDiwNEuZpUBP69edgT+01tq6vat1tTZ/oBKw7X6zy8iOEEIIIYQQ4r5pra8rpV4FfgOMwJda64NKqTHADq31UuALYIFS6iiWEZ2u1mMPKqUWA38D14EBWmvT/dZJOjtCCCGEEEIUYjacxobWejmwPMu2kRm+vgw8ncOx7wHv5WV9Cl1n59Mdkwq6CkKIPOBUpklBV8HmnFtML+gq2JxTEZxNnTppeEFXweam7Bhf0FUQokjLxZLRhVbR+y0jhBBCCCGEKBIK3ciOEEIIIYQQ4qaiO64jnR0hhBBCCCEKNZnGJoQQQgghhBCFjIzsCCGEEEIIUYjZcjW2B410doQQQgghhCjEcvFhoIWWTGMTQgghhBBCFEoysiOEEEIIIUQhJtPYhBBCCCGEEIWSTGMTQgghhBBCiEJGRnaEEEIIIYQoxGQamxBCCCGEEKJQMuuiO41NOjv3acT4qWz4axse7m789PWsgq6OTUhmyVyYTJs6htatm5OWlkbv3oPZvedAjmWXLPkKf/9yBAe3sGENc6/jqJ5UbRbMtbQrfDNkJpEHT95Sxi/Qn+em9MPe0YG/1+5myeh5ADi5lqDXx//Fw8+LuMiLfDXgQ9KSLtG875PU7tAYAKPRiHdAWd6u9RKpiZcAUAbFkGXjSYyKZ07v922WNavKTWvw5MgeGIwGti9ay/qZyzLtNzrY0WVqP8oG+pOakML/Xp1BQmQMAY0Daf1mN4z2RkzXTCwf/w3HN/+NQwlHXv5uZPrxrj6e7PlpI7+MWWDraLfIj3YOqF+VPnOGEBsZDcC+ldv4bcYSAEZu/IgrKWmYzWbM10180O5tm2W9V0Xl/SsjyVw0Mou7d9/X7CilnlJKaaVUlft4jJNKqf1Kqb1KqVVKKR/r9nVKqUNKqT3WW+n7rW9e6xDekllTxxV0NWxKMhcNRSFz69bNCQjw57GqjenX700+/nhCjmU7dGhDSsolG9bu7lQNDcLL35dxoa+xcPhnPP1en2zLdRnXm0XDP2Nc6Gt4+fvyWGgQAE/0a8/hTQcY12wwhzcd4In+7QH4Y84vTA4fxuTwYSx7/1uObv07vaMD0PSFNlw4ei7/A96GMijajXmBr3q9z7SWEdRs15DSAWUzlanTJZS0xEtMCX2djV+soM2wbgBcik9mXu/JfNh6GN+9MZMu0/oDcPXSZT4KH55+Szgbw4GV222eLav8ameA49v/TW/rGx2dGz7uNpbJ4cMeio4OFI33r6wks7gdnYe3h01eLFDQDdgIdL3Px2mmta4J7ACGZ9j+nNY6yHqLvs/nyHMhQdVxdXEu6GrYlGQuGopC5nZtW/H1N98DsHXbLlzdXPHxufWcSokSTrz2375MmPChrauYa4FhIWxfsgGAU7uPUtzZCRcvt0xlXLzccHQuzsldRwDYvmQD1cNCLMe3DGHb95bjt32/geotQ255jtrtGrFr6ab0+64+HlRrXovNC//Il0y59UhQALGnLhB/JhrTNRN7l23msbDamco8FhbCrh/+BODA8q1UbBgIwPmDp0iOTgDgwuFI7IvZY3TIPOnBs4IPJTxdOLntXxukuT1btHNhUBTev7KSzOJ2zOg8uz1s7quzo5QqCTQCemPt7CilFimlwjOUmauU6qSUclJKLVZK7bOW2aqUyu5ddgMQcD/1EkKI3ChTxofIMzdHJc5GnqdsGZ9byo1+dyjTps8mNTXNltW7K27eHiSci02/nxgVh6uPR6Yyrj4eJJyPS7+fcD4ON29LGWcvV5IuWv7oT7qYgHMpl0zH2js6UKVpTfau2Jq+rePInvw84Rt0Ac8Fd/F2JzFD9qTzcbh6e9xS5sb3x2wyczk5FSf3zH8kBbapy7mDpzBdvZ5pe812Ddj3y+Z8qv3dyc92rlCrEkNXTOLlucPwqeR38wG1pt+C4QxZNp4G3R7MKZxCCJGT+x3Z6QCs1FofBuKUUrWAhcAzAEopB6AFsBzoD8RrrWsAY4Ha2T8kTwL7M9z/yjqF7R2llMruAKVUX6XUDqXUjs/nf3ufkYQQRUV2bylZ/3CvWbMaFQMq8PPPK21VrXuTzbtj1izZvYPmtqMS+ERtTuw4lD6FrVrzWqTEJhJ54MRdVzXP5aIds/31kaFM6UplaT2sGz8O//yWYjXaNmDv0gejs5Nf7XzmwAnebfQq77d5kz/nrqTPnDfS903vNIopT77FrF4TadIjjIp173nWuhCigOg8/Pewud8FCroB061fL7TefweYoZQqBrQGNv3b6E8AACAASURBVGit05RSjYEPAbTWB5RS+7I81lqllAnYB4ywbntOa31WKeUM/AB0B+ZnrYTWeg4wB+BazPGHrxWEEDbT75We9O79HAA7duzB75Ey6fvK+vly7vyFTOXr16tNreDqHDm8BTs7O0qX9mTN6u94ouXTNq13dhp3D6NBt+YAnN57DLcynun7XH08SLoQn6l8wvk43HxvjgK4+XqQGG0pk3wxERcvN5IuJuDi5UZyTFKmY2u1bZBpCpt/SGUCn6jNY82CsS9mj2PJ4nSfNoAFgz/J85x3khQVh2uG7C6+HiRFZ86eGBWHWxlPkqLiMBgNODo7kZqQYinv40H32a/z3esziTudeba0z2PlMBqNnCvATp0t2vlKys1Ry7/X7aHzuN6UcHfmUnxy+vcyJTaJfb9tp1zNAI49AFP6hBC5V5SXnr7nkR2llCfQHPhcKXUSiMAyonMFWAe0st5feOOQOzxkM+t1OT201gkAWuuz1v+Tgf8Bde+1vkIIATBz1jxC6oQRUieMn5f+xvPPdQagXt1aJCUmERWV+Y/d2XPmU75CbSpVrk9osw4cPnL8gejoAGxcsCr9gvL9q3ZQp+PjAJQPDuBycmr6dKUbki4mcCXlMuWDLTOF63R8nAOrdgBwYM1O6na2HF+38+McWL0j/ThH5+JUrFeV/Rm2/fL+QkY1GMCYxgOZN3AGRzYdLJCODkDk3mOUquCDu58XRnsjNds24J/VOzOV+Wf1Tmp1agJAYHg9jm06CICjixO9vopg5fsLObXz8C2PXbNdQ/Yu23TLdluyRTs7e7mmH1+uZkUMSnEpPhmH4sUoVsIRAIfixajSpAbnD5/J38BCCJGH7mdkpzMwX2v98o0NSqn1QGMsHZw+QAjQy7p7I9AFywhOVaD67R5cKWUHuGmtY5RS9limt625j/rmi4hRE9m+ex8JCUm06PA8/Xt3p1PbVgVdrXwlmSVzYbFixe+0ad2cf//5i7S0NPr0eT19347tqwipE1aAtbs7f6/dTdVmQbyz/kOupl3hfxE3l2GNWD6RyeHDAFg84oubSxKv28Pf6/YAsGbmz7zwyWvU79KM+HOxfNV/WvrxNVrV5dCf+7iadsW2oXLJbDKzdORcXpw/DGU0sGPxOqKPnOWJwZ05u/84/6zZxY7F6+gytT9D1k0lNeES3w78CIAGPcLwLO9N80FP0XzQUwB82X0il2ItIx41/q8+c18ouCW1s8qvdg5qU59Gzz+B2WTm2uWrzB04AwDnUq70tk5pMxgN7Pz5L/5dv9eWke9JUXj/ykoyF43M9+phXFggr6h7vbBUKbUOmKi1Xplh2yDgMWAQEAUs1Vq/YN1XApgHVAZ2A4FAV631EevIUIjWOibDY5XAsliBPWDE0tF5XWttul29ZBqbEIWDU5kmBV0Fm+tfBDM75cmioA+X1CI4oWTKjvEFXQUh8oV9qUfvNHPpgdC5fLs8+/v4+1NLH4rMN9zzyI7WOjSbbTMy3PXMsvsy8LzW+rJSqiLwO3DKelyFbB7rEjkvYiCEEEIIIYQQt3W/CxTcDScsU9jssVy/009rfdWGzy+EEEIIIUSRU/TGk2+yWWfHushA4fz0MiGEEEIIIR5QBf15aAWp6E2WFkIIIYQQQhQJtpzGJoQQQgghhLCxorwam3R2hBBCCCGEKMTkmh0hhBBCCCFEoaSL8MiOXLMjhBBCCCGEKJRkZEcIIYQQQohCTK7ZEUIIIYQQQhRKsvS0EEIIIYQQQhQyMrIjhBBCCCFEISarsQkhxAOmKA64F8WhdmNBV6AAFMXMQoiCJauxCSGEEEIIIUQhIyM7QgghhBBCFGKyGpsQQgghhBCiUJLV2IQQQgghhBCikJGRHSGEEEIIIQoxmcYmhBBCCCGEKJRkNTYhhBBCCCGEKGRkZEcIIYQQQohCzFyEFyiQzo4QQgghhBCFWNHt6sg0NiGEEEIIIUQhJSM7QgghhBBCFGKyGpsQQgghhBCiUCrKnR2ZxiaEEEIIIYQolGRk5z6NGD+VDX9tw8PdjZ++nlXQ1bEJySyZC5NpU8fQpnVzUtPS6N17MLv3HMix7I9LvsLfvxxBwS1sWMPcqdK0Jk+N7IkyGti66A9+n7k0036jgx3PTR2AX6A/qQkpzHv1Q+IjLwLQon976nVphjaZWTJ6Loc27AOg6/svU7V5LVJik3i/VUT6Y7V96zmqPVEL09XrxJy+wLcRs7iclGq7sNmo1LQG4SN7YDAa2LloLRtmLsu03+hgR+ep/Shjzb/o1RkkRMZQtmZFOkzobSmkFH9M/4F/ftsBQMPebaj9TDPQmguHzrAkYjbXr1yzdbRMqjStSYeRPTEYDWxZ9Ad/ZNPOz04dwCOB/lxKSGF+Nu1sNpn50drOdsXseXXRKOyK2WMwGti7Yiu/TfsegIAG1Wj39vMY7e2IPHCcRUNnYzaZbZ75XhSV96+MJHPRyHyvdBFejS1XIztKqaeUUlopVeVen0gpdVIptV8ptVcptUop5WPd/p5S6oxSKiVL+WJKqUVKqaNKqa1KqQr3+tz5qUN4S2ZNHVfQ1bApyVw0FIXMbVo3p1KAP1WqNqZfvzf55OMJOZbt0KENKSmXbFi73FMGRacxLzKn10QmtXyD4HaN8A4om6lM/S7NSEtMYXzoa6z/4lfaDnsWAO+AsgS3bciksCHM7jmBzmN7owwKgG3fr2dOz1u/J4c37uf9sAgmt3mTiyeieKJ/h/wPeRvKoGg75gXm93qfGS0jqN6uIV5Z8tfuEkpa4iWmhb7Opi9W0GpYNwCiD51hZtsRfBI+nHk9JtH+vd4YjAacvd1p0KsVM9u+zUet3kQZDFRv26Ag4qVTBkXHDO1cK5t2rpelnZ/Mpp3n9JxAJ2s7X79yjU+fHcuUNm8yJXwYVZoGUT44AKUU3T7oz4KBM5jcKoL4yBjqdGpaELHvSVF4/8pKMovbMaPz7Pawye00tm7ARqDrfT5fM611TWAHMNy6bRlQN5uyvYF4rXUAMA2YdJ/PnS9Cgqrj6uJc0NWwKclcNBSFzG3btmLBN5az2Fu37cLVzRUfn9K3lCtRwonB/+3L+Akf2rqKuVIuKICYU1HEnonGdM3E7mWbCAwLyVQmMCyEbT9sAGDv8q1UalgtffvuZZswXb1OXORFYk5FUS4oAIDj2/7lUuKtHbxDf+5LP8N/avcR3Hw88jPeHfkFBRB76gLx1vz7l23msbDamco8FhbC7h/+BODg8q082jAQgGuXr6ZnsS9mn2l9VoPRiL2jAwajAfviDiRfiLdNoBzcaOe4O7Tzdms778tlO19NvQKA0c6I0c6I1uDkXhLT1WtcPHEegEMb91OjTXa/qh9MReH9KyvJLET27tjZUUqVBBph6Xx0tW5bpJQKz1BmrlKqk1LKSSm1WCm1z1pmq1IqJJuH3QAEAGitt2itz2dTpj0wz/r190ALpZS6q3RCCHEbZcv4EHnmXPr9s5HnKVvG55ZyY94dytTps0lNTbNl9XLNzduDhHOx6fcTz8fh6p25A+KaoYzZZOZychol3J0zbQdIOB+Hm3fuOy/1ng7ln3V77jPB/XHxdicxQ4ak83G4ZMmQsYzZZOZKcipO7pY/kvyCKjJw1fu8+tskfh7xBWaTmeQL8Wz87FeGbPqIN7d9ypXkNI7+ud92obKRXVvdaztn/BlRBsUbyycyZuccDm/cz+k9R7kUl4zBzohf9UcBqBleDzdfz/yOKITIJzoP/z1scjOy0wFYqbU+DMQppWoBC4FnAJRSDkALYDnQH8toTA1gLFA7+4fkSeBOvzXKAmcAtNbXgUQg23dapVRfpdQOpdSOz+d/m4tIQggB2Z0/yTqvuWbNalQMqMDPP6+0VbXuXnangbLkyO5UkdY6x+258cSADphMJnb+tDFX5fNNLtrxdmUi9xzjo7ChzGo3gqb92mNXzB5HlxI81rI2HzT5L5PqDcDeqRg1OzTKl+rnVm7aKscyt/kZ0WbNB+HDGN2gP+VqVsSnsh8ACwbNoMM7PXjtp3FcSUnDZDLdbwQhRAHRWufZ7WGTmwUKugHTrV8vtN5/B5ihlCoGtAY2aK3TlFKNgQ8BtNYHlFL7sjzWWqWUCdgHjLjD82b71pxdQa31HGAOwLWY4w9fKwghbKbfKz3p3fs5AHbs2IPfI2XS95X18+Xc+QuZytevV5tawdU5engLdnZ2lC7tye+rv6NFy6dtWu/bSYiKw63MzXNBrr4eJEbHZ1smMSoOg9GAo3NxUhNSbjnWLZtjs1On0+NUa1GLT58t+PnySVFxuGbI4OLrQXKWDDfKJFnzF3N2Ii0h06WiXDx2jqtplyld2Q/3R0oTfyaa1LhkAP5euZ1ytSuz96e/8j9QDrJrq6RctnNiLn5GLielcnTL31RpGkTU4UhO7TrCx13eBaBykxp4+fvmXzghhMgntx3ZUUp5As2Bz5VSJ4EILCM6V4B1QCvr/YU3DrnD8zXTWgdprXtorRPuUDYSeMRaDzvAFYi7wzFCCHFbM2fNI6ROGCF1wli69De6P9cZgHp1a5GUmERUVHSm8rPnzKdchdoEVK5P02YdOHzk+APV0QE4s/cYXhV88PDzwmhvJLhtQw6u3pmpzIHVO6nb6XHAMiXp6KaDABxcvZPgtg0xOtjh4eeFVwUfTu85etvnq9K0Js1facfnfSZz7fLV/Al1F87uPYZnBR/crfmrt23Av1ny/7t6J8GdmgBQLbwex6353f28MBgtvwrdypai1KNlSIiMIfFcDH7BlbB3dACgYqNqXDx61oapbpVdOx/IkvPg6p3UsbZzjQztfCCHdi7h4YyjixNguWapcqPqRB+zTO0s6ekCWFZ4a/5KOzZ9s8ZWUYUQeawoL1CgbjccpZR6GailtX45w7b1WEZlXIA+QAhQUWt9VSkVATyqte6nlKoK7AUaaK13WDtLIVrrmByeK0VrXTLD/QFAda31K0qprkBHrXWXOwWy9chOxKiJbN+9j4SEJDw93Ojfuzud2rayZRVsTjJLZlsoXqaJTZ5nxofv0SoslNS0NPr0eZ2duywD0ju2ryKkTlimsuXL+/HzT/PybenpgfeR+bHQoPQlibcuXsuaT36i9eCnObP/OAfX7MSumD3PTR1A2WoVSE1IYcHAGcSesXTsnhjQwbIk8XUTP46dz7/Wa3C6zxhIQP2qlHB3JjkmkZXTvmfr4rUMXzcdOwd7UhMsox6ndh/hu7e/uKd6l8ijj3urHBpE+MjulqWnF69j/Sc/02JwZ87uP86/a3ZhV8yezlP741utPGkJl1g08CPiz0QT9FRjmvRrh/n6dbRZs3bGj/yzyrL0dPPBnaj+ZAPM102cP3iSH4d9hunq9fuuayr3vnzzY6FBtLe287Yc2vnZqQPws7bz/IEziMvQznWt7fyTtZ19q5Sj2wf9MBgMKIOBvb9uZtWMJYBlifGqLWqhlGLTN6vZ8OWKe673pB3j7/nYe1HQ718FQTIXTGb7Uo8+FNeTB/s0yrO/j3dH/fVQZL7hTp2ddcBErfXKDNsGAY8Bg4AoYKnW+gXrvhJYFhWoDOwGAoGuWusjOXV2lFLvA88CZYBzwOda63eVUo7AAiAYy4hOV6318TsFkmlsQhQOtursPEjup7PzsMqrzs7D5H46Ow8rW3d2hLAV6ew8+G57zY7WOjSbbTMy3M26YMBl4Hmt9WWlVEXgd+CU9bgKOTzHUGBoNtsvAw/WXBEhhBBCCCEeMg/j9LO8kpsFCu6GE5ZFCOyxXL/TT2td8BO6hRBCCCGEKKIexiWj80qezh/QWidrrUO01jW11jW01vc+wVcIIYQQQghRaCilPJRSq5VSR6z/u2dTJkgptVkpddD62Z3PZNg3Vyl1Qim1x3oLutNzFr3J0kIIIYQQQhQhZq3z7HafhgG/a60rYbncZVg2ZVKBHlrralg+4ma6Usotw/4I6+rOQVrrO36qtXR2hBBCCCGEKMR0Hv67T+2xLGaG9f8Ot9RV68Na6yPWr88B0YDXvT6hdHaEEEIIIYQQuaKU6quU2pHh1vcuDvfWWp8HsP5f+g7PVRdwAI5l2PyedXrbNKVUsTs9YV4vUCCEEEIIIYR4gOTB9LN0Wus5wJyc9iul1gA+2ex6+26eRynli+VjaHpqrW+s2f8Wlo++cbDW4U1gzO0eRzo7QgghhBBCFGK2XI1Na/1ETvuUUheUUr5a6/PWzkx0DuVcgF+BEVrrLRke+7z1yytKqa+AIXeqj0xjE0IIIYQQQtjCUqCn9euewM9ZCyilHIAfgfla6++y7PO1/q+wXO9z4E5PKCM7QgghhBBCFGJ5OY3tPk0EFiulegOngacBlFIhwCta6z5AF+BxwFMp1ct6XC/rymvfKKW8sHye5x7glTs9oXR2hBBCCCGEKMQelA8V1VrHAi2y2b4D6GP9+mvg6xyOb363zynT2IQQQgghhBCFUuEb2TGbCroGQghxT1RBV6AAGIpg6qKYWQhRsB6gaWw2V/g6O0IIIYQQQoh0D8o0toIg09iEEEIIIYQQhZKM7AghhBBCCFGI3fxMzqJHOjtCCCGEEEIUYmaZxiaEEEIIIYQQhYuM7AghhBBCCFGIaVmNTQghhBBCCFEYyTQ2IYQQQgghhChkZGRHCCGEEEKIQkymsQkhhBBCCCEKJXMR7uzINDYhhBBCCCFEoSQjO0IIIYQQQhRiuggvUCCdnVzYuHUnEz+cg8lsptOTYfR5/ulM+89FRfPOhOnEJSTh6lKSie8Mwad0KQCmzvyKDZu3A/Byz660afG4zet/LySzZIbCmTmraVPH0KZ1c1LT0ujdezC79xzIseyPS77C378cQcEtbFjD3KnStCYdRvbEYDSwZdEf/DFzaab9Rgc7np06gEcC/bmUkML8Vz8kPvIiAC36t6del2aYTWZ+HD2XQxv2pR+nDIrBy8aTGBXPF73fByCgQTXavf08Rns7Ig8cZ9HQ2ZhNBfvp3AFNaxA+sjvKaGDXonX8OXNZpv1GBzs6Tu1HmcAKpCWksPjVj0iIjEnf71rGk1dXv8+66T/w12fLsStmz4uL3sGumB0Go5GDK7axdtoPto51i/80rUn7kT0wGA1sXbSWtdm0c7ep/fEL9Cc1IYUFr35IvDVn8/7tqdslFLPJzE+j53HY2s5Nereh3jPNQWvOHzrDoohZXL9yjacn9eWRGo8CipgT51k4ZCZXU6/YOvI9GTF+Khv+2oaHuxs/fT2roKtjE5K5aGS+V0X5mp1cTWNTSj2llNJKqSr3+kRKqZNKqf1Kqb1KqVVKKR/r9veUUmeUUilZyvdSSl1USu2x3vrc63PfD5PJxLipM5k5ZTRLF3zK8jXrOXbidKYyUz75gnatW/DjvI/p16sb02fPA2D9pu38ffgY33/5Ef+bPZWvvl1CyqXUgohxVySzZL6hsGXOqk3r5lQK8KdK1cb06/cmn3w8IceyHTq0ISXlkg1rl3vKoOg45kXm9JrIpJZvUKtdI7wDymYqU69LM9ISUxgf+hrrv/iVJ4c9C4B3QFmC2zZkUtgQ5vScQKexvVEGlX7c4y+0IfrouZvPpRTdPujPgoEzmNwqgvjIGOp0amqboDlQBsWTY3qxoNf7fNxyKNXbNcArS/5aXUK5nHiJD0PfYNMXK2g5rFum/a3feZ4j6/am379+5Rpzn32PT9sM59Pw4VRqWgO/4ACb5MmJMiieGvMCn/eaxOSWQwhu1zCHdr7ExNDBbPhiOf+XoZ2D2jZgclgEn/ecSMexL6IMChdvd5r0as30tsOZ0mooBoOBoLYNAFg6dgFT2wxjaps3iT8XQ6OerWye+V51CG/JrKnjCroaNiWZxe2Y0Xl2e9jk9pqdbsBGoOt9Pl8zrXVNYAcw3LptGVA3h/KLtNZB1tvn9/nc92T/P4cpV9aXR8r4YG9vT5sWj/PHxi2Zyhw7eYZ6tWsCULdWDdZa9x87eZo6QYHY2RlxKu7IfwL82bh1p80z3C3JLJlvKGyZs2rbthULvvkegK3bduHq5oqPT+lbypUo4cTg//Zl/IQPbV3FXCkXFEDMqSjizkRjumZi97JNBIaFZCoTGBbC9h82ALBv+VYqNayWvn33sk2Yrl4nLvIiMaeiKBdk+aPe1ceDx5rXYsvCP9Ifx8m9JKar17h44jwAhzbup0abnN7CbcMvqCJxpy4Qf+Yipmsm9i/bQpWw2pnKPBZWmz3W/H8v38aj1vwAVcJqE386motHIjMdc2MUw2hnxGBnhAI+M1ouKIDYDO28Z9lmqmVp52phtdmRqZ0DrdtD2LNsc3o7x2ZoZ4PRiL2jAwajAfviDiRdiAfgSkpa+uPaOzoUeP67ERJUHVcX54Kuhk1JZiGyd8fOjlKqJNAI6I21s6OUWqSUCs9QZq5SqpNSykkptVgptc9aZqtSKiSbh90ABABorbdorc/nSZp8EH0xFp/SXun3vb1KER0Tm6nMfwL8Wb3+LwDWbNjMpdQ0EhKT+E+AP39u2Una5cvEJySyfdc+oqIv2rT+90IyS+YbClvmrMqW8SHyzM1Ri7OR5ylbxueWcmPeHcrU6bNJTU27Zd+DwNXbg4RzN9su4Xwcrt4eOZYxm8xcTk6jhLvzLccmZji2w8ie/DLhm0zTHy7FJWOwM+JX/VEAaobXw83XM9+y5YaztweJGTIknY/Dxds9Sxl3Es/FAZb8V5JTcXIviX3xYjR5pS3rPlxyy+Mqg6Lf8vEM3TmTYxsPELnnWP4GuQNXb/cs7RyLa5acWds5LTkVJ3fnbI6Nw9XbnaQL8az77BdGbPqYkdtmcjk5lcN/7k8v98zklxm1fRalK5Zh49zf8jmhECK/aK3z7Pawyc01Ox2AlVrrw0qpOKVULWAh8AywXCnlALQA+gEDgHitdQ2lVCCwJ4fHfBLYn8O+jDoppR4HDgODtdZncnFMnsquSRUq0/0hA17kvWmz+HnF79SuWQ1vL0+MRiON6tbiwL9HeL5fBO5urtQMrILRaLRNxe+DZLaQzIUvc1ZKqVu2ZX0jr1mzGhUDKvBGxLuUL+9nq6rdlWxi3JIjxzLZbEdrqjavRUpsIpEHTlCxftVMuxcMmkGHd3pg52DHoT/3YTKZ7qP29y93+bNra2g+uBObvliR7bUo2qyZGT4cRxcnus0eTOnKfkQfjrylnM3kkOFOZdA6x2OLu5QgsGUI45sMIi0plR6f/pdaHRqz66eNACyKmG2ZPjf6BYLaNmD7d+vzIokQwsaK8tLTuensdAOmW79eaL3/DjBDKVUMaA1s0FqnKaUaAx8CaK0PKKX2ZXmstUopE7APGHGH510GfKu1vqKUegWYBzTPrqBSqi/QF+DTyWPo0+N+Z9vd5O3lmemM9YWLMXiVynzGtHQpTz58720AUlPTWLN+E84lSwDwco9neLnHMwAMHT2Z8n5l8qxu+UUyS+YbCltmgH6v9KR37+cA2LFjD36P3Kx3WT9fzp2/kKl8/Xq1qRVcnaOHt2BnZ0fp0p78vvo7WrTMvJhDQUqIisOtzM3RFTdfD5Ki47MtkxgVh8FowNG5OKkJKSRmOdbV14PE6HgCn6hNtSdq81izYOyK2eNYsjjPTRvAN4M/4dSuI3zc5V0AKjepgZe/r01y5iQpKg7XDBlcfD1Ijk7IpowHSdb8xZydSEtIwS+oIlXD6xL2VjccXZzQZs21K9fYNn91+rGXk1I5seUfKjWtUaCdnaxt5ebreUs7J0bFZmrn4s5O2bbzjZ+RSo0DiT0TzaW4ZAD2r9xOhdqV0zs7YOn07fllM6F9n5TOjhDioXPbaWxKKU8sHYzPlVIngQgsIzpXgHVAK+v9hTcOucPzNbNef9NDa51wu4Ja61it9Y1TbZ8BtW9Tdo7WOkRrHZKXHR2AwCqVOR15jshzUVy7do0Vv2+gWeN6mcrEJyRiNltWIvrs6+94KrwlYLkAPCExCYBDR09w+NgJGtaplaf1yw+SWTLfUNgyA8ycNY+QOmGE1Alj6dLf6P5cZwDq1a1FUmISUVHRmcrPnjOfchVqE1C5Pk2bdeDwkeMPVEcH4MzeY3hV8MHDzwujvZHgtg05sDrzNVQHV++kTifLink1wutxdNNBAA6s3klw24YYHezw8PPCq4IPp/cc5df3FzKmwQDGNR7IgoEzOLLpIN8M/gSAkp4ugGXlr+avtGPTN2tsmPZWZ/cex6OCD27W/NXb1uffLPn/Xb2LIGv+quF1OWHN/0WXsUxr/BrTGr/Gli9X8ucnP7Nt/mqcPJxxdHECwK6YPRUbVePisYKdcX1m7zFKZWjnoLYNOJhNO4dk084HV+8kqG2D9HYuZW3nhHMxlA+uZLkmB6jUKJALR88C4FneO/1xq7aoRfSxcwghHk4yjS1nnYH5WuuXb2xQSq0HGmPp4PQBQoBe1t0bgS5YRnCqAtXvtWJKKd8M1/K0A/6518e6H3Z2RoYPfoWX3xiJyWzmqf9rSYB/eT7+/GuqValEs8b12L57P9PnzEOhqF0zkBGv9wPg+nUTPQa8CUDJEk5MfGcIdnYP/lQfySyZC2vmrJav+J3WrZtz6J+/SE1Lo0+f19P37di+ipA6YQVYu9wzm8wsGfkVfecPx2A0sG3xWi4ciaT14Kc5s/84B9fsZOvitTw7dQDD100nNSGF+QNnAHDhSCR7ftnMm6s/wHzdxA8jv0Kbb//LrFnftlRtUQulFJu+Wc3RzQdtETNHZpOZX0fOpcf8NzEYDexavJ6LR87SfHAnzu4/waE1u9i1eB0dp/bjv+s+IC3hEt8N/Oi2j+lc2o2OH7yCMhhQBsXBX7dy+I/dNkqUPbPJzI8j5/LS/LdQRgPbF6/jwpFIWg3uzJn9J/h7zU62LV5Ht6n9GbZuGqkJKXxtzXnhSCR7f9lCxOopmK+b+NHazqf3HGPfiq0M/nU85utmzh48yZZvf0cpmjM2kwAADdNJREFURdcP+uFYsjhKKc79c4ofRnxZoPnvRsSoiWzfvY+EhCRadHie/r2706ntw7Oa3L2QzEUj8716GFdRyyvqdj00pdQ6YKLWemWGbYOAx4BBQBSwVGv9gnVfCSzTzSoDu4FAoKvW/9/e3UfLVdVnHP8+hhQIEVKRViPIlUhEBLk1iUskoYmE2qpLjWADIhqbqmBtVFqD2FqzbJECVRYsFgsQeavyUqAookJIBBKQECANCSASoKEGWhHBNBEDgfz6x96TO5nOzH2ZlzN35vmsddedOffMmf07Z59z9uu5sS73DE2NiGcqvuMM4CPAROAp4KKIWCTpNFIl5yXgWeDEiHh4sIC2Pr2ud4+mWRfZde+ZRSeh7RZMnFF0EtpuPKOvktyo5yn2fxIV4bR7Ty06CWYtMfbV+w02qqkj7DF+UtPKxxs3PzYqYi6p27MTETOrLDun7G3lI3i2AB+NiC2SJgFLgSfy5/pqfMdCYGGV5acAp9RLn5mZmZmZ1Tcah581y1AeUDAc40hD2MaS5u+cGBEvNvk7zMzMzMxsiPw0tiaJiE2kOTxmZmZmZmaFanbPjpmZmZmZdZDo4QcUuLJjZmZmZtbFenkYW93/s2NmZmZmZjZauWfHzMzMzKyL+WlsZmZmZmbWlXp5zo6HsZmZmZmZWVdyz46ZmZmZWRfzMDYzMzMzM+tKvVzZ8TA2MzMzMzPrSu7ZMTMzMzPrYr3brwPq5W6tZpP0qYi4sOh0tEuvxQuOuVc45t7gmLtfr8ULjtmskoexNdenik5Am/VavOCYe4Vj7g2Oufv1WrzgmM124MqOmZmZmZl1JVd2zMzMzMysK7my01y9Nl601+IFx9wrHHNvcMzdr9fiBcdstgM/oMDMzMzMzLqSe3bMzMzMzKwrubJjZmZmZmZdyZWdOiTtLen7ktZJekzS2ZJ+r+h0tZqkzRXv50k6N78+QdLHBvn89vVHk8q4u5Wk10i6KufphyT9SNJkSQ8UnbZGSLpN0rsrln1e0nk11u8b7TEPV94f44pORzNJmiDpM234npmS3tnq7xkJSXMkhaQDypbdJOk3km6sWPcNku7O97WrS/c0SYdLWiXpJUlHtzuGoagW5wi2sV7SWkn3S1os6TV5+W2Sfi5pdf75g+alfNhpbGWcp0r6RZX7/M45Pzya80dfY1EMK61FxDtP0q/KjvdfNhqHdS5XdmqQJODfge9FxP7AZGA8cGqhCStYRJwfEZcXnQ4bmZyvrwdui4hJEXEg8GXgD4tNWVNcCRxTseyYvLxnKKl1bf88MKzKjqSdGk9VS00AhlzZGWT/1DMT6MjKDnAscAc75v8zgeOrrHs6cFa+rz0HzM/L/wuYB1zRumQ2rFqcIzErIg4B7iVd/0qOi4j+/PN0g9/RiFbG+QPg7VXWnQ88FxFvBM4i5ZN2KSJegKvLjvdFDX63dTBXdmp7F7AlIi4BiIiXgS8AfyHpM7nH56bcEvTV0ockfVTSytxScIGkMXn55tzCcL+kFZJGZeFS0iJJf5tfT5O0RtJdks6saCWfmPfPOklnFJTchknaV9LSHOdSSa+XNEbS47nQNEHSNkmH5/WXS3pj0emuYxawNSLOLy2IiNXAL0rvJe0i6ZLcSvYfkmbl5W8py9trJO2fl1fN8wW4FnifpJ1zuvqAicAdpfyZY5pb+UFV9EZKulHSzPx6s6TTJd0naYmkt+dW4MclvT+vMyZ/xz1533y69eHukP4+ST9T6sVaBRyfz8tVkq6RNF7SAtL+uFXSraXYyrZxtKRL8+tLJX0zr3d6Pu8vLot7QV5vN0k/zNe1B6rt2zb4Z2BSzn9n5fN0VT7WH8jprNw/+0iaL+mRHNO3NNB7vZek6/KxvEfSYTkvnQB8IX/PjALirErSeOAwUmF1e2ExIpYCmyrWFenedm1edBnwwbz++ohYA2xrQ7KHrVqcSj0R7ylb51JJR0kaJ+nf8rl4tVJPxdQqm10GdNT1utVxRsSKiPjvKut8gJQfIOWPI3J+aakC47Ue4spObW8B7itfEBH/S2r92onUUnAc0A98WNJUSW8G5gKHRUQ/8HJeB2A3YEVudVgGfLItUYzMrhro2l0NfK3GepcAJ0TEoaRYy/WT9sXBwFxJ+7QuuS11LnB5RLwV+C5wTq74PgIcCEwn5ZMZuZC9d0Q8WlhqB3cQFfm6ir8CiIiDSS1ul0nahVTYOzvn7anAhkHyfFtFxK+BlcCf5kXHAFcDHyLlx0OA2cCZkl47jE3vRuoJm0IqPP4TcCQwh4FzYz6wMSKmAdOAT0p6Q2MRDdubgMtz2uYDsyPibaRWzpMi4hzgKVLr56whbG9y3sbf5PcHAO8mXfu+KmksaV8/FRGHRMRBwE1NjWhovgQ8lvPfF4E5Oe5ZwDfKCmxvIp3LfwRsBb4CvIO0v8qHz5xN6vmYBhwFXBQR64Hz8/L+iFjehriG6oPATRHxCPCspLfVWXdP4DcR8VJ+vwF4XasT2CTV4ryKdP1BaTjeEcCPSD19z+Xr9j8CU2ps833A2rL3l+T73lfaUdCvoR1xVvM6cqNXzh8bSfml1YqKF+CoXHG6dhSXUWwIXNmpTUC153KXlt8SEb+OiN+RhrtNJ52QU4B7ciXhCGC//LkXgdLY6fuAvtYlvWG/K+va7Qf+oXIFSROAV0bET/OiyqEPSyNiY0RsAR4C9m1tklvmUAZi+1fScQZYDhyef07Ly6cB97Q7gS0wnRQrEfEw8ASp4HsX8GVJJwP75rxfL88XoXwoW2kI23Tgyoh4OSJ+CdxOOlZD9SIDhfi1wO0RsTW/7svL/wT4WN4Hd5MKCfs3EMdIPBERK0gF+AOBO3N6Ps7Izr9rcsW+5IcR8UJEPAM8TRr6uBaYrdTzNSMiNjYYQ6MEfF3SGmAJqQBX6kUv7R9IFbbbI+LZfCyvKdvGbODcvO9uAHaX9Mr2JH9EjiUVDsm/j62zbrUC/Gj5/xPV4vwx8K7c0PRnwLJ8XZpeWjciHgDWVGzr1nx8dyddvyENYTsYmJF/qg0BbIdWx1lLUXmjqHh/APTlitMSBnq1rAt1+ljsIj1IatXbTtLuwD6k1uvKi0CQLhaXRcQpVba3NQb+qdHLjP59P1ir1wtlr7sh3pLSMVxO6umYSKoMfpE0pn9ZMckasgeBwSYfVz22EXGFpLuB9wI3K03orJfni/A94Ju5dXDXiFilQR6okb3Ejo0/u5S9Lj93t5HzdkRs08B8FgF/HRE3N5b8hvy2LC23RES9Qm9J+XVsl4q//bbi/f87pyPiEUlTgPcAp0laHBG1eoLb4ThgL2BKRGyVtJ6BuMrjqXf9egVwaC5cDXygsIb+2iTtSRqWdpCkAMYAIWlhWZ4t9wwwQdJOufV+b1JvX0erFSewELiN1OM4l4H5eYMdrFm50r5dRDyZf2+SdAWpQtzW+antiLOODaTyzYZ8XdsDeHZYAQxTkfHmkQAl36K9c5SszdyzU9tSYFypoKQ0D+EbwKXA88CRkl4laVdSN+yd+TNHKz/FJf99tPZo1BURzwGbJL0jL2p0YmGn+ikDsR1HmkQJqfX+ncC23Hu1Gvg0qRLUyX4C7Cxp+zBKSdPYseV/GXkomqTJwOuBn0vaD3g8D4e6AXgrHZbnI2Iz6SZ5MQM3yGWkoZRjJO1F6o1bWfHR9UC/pFfk4Qy1JrTWcjNwYh7ahdLT7XYbWRQNWwEcpjx3LI9zn5z/tgko76X4paQ3K03YnzPcL5I0EXg+Ir4D/AtQbwhVq5THtAfwdK7ozKJ2j9ZK4I8l/X4u2JU3bC0GPlt6I6m/yvd0iqNJQ/P2jYi+iNgH+E8GeqB3kCtAtzLQ4PFx4PttSWlj6sV5FfAJUm9MqbHhDuDPASQdSBpOXZOknSS9Or8eSxoGVcSTGlsa5yBuIOWHUjp+UqPC3EyFxVsxlPn9wM9Gui3rfK7s1JBP8jmk+TjrSHM0tjDwhI87SEN9VgPXRcS9EfEQ8PfA4jyM4hZgOHMDRpv5wIWS7iK1uBQ9hKVR4yRtKPs5CVgAfCIfz+OBzwFExAuk8c2lYTHLSQWhoYwTLkxZvj5S6dHTDwKL2LF19zxgjKS1pDkv83K8c4EH8jCBA0g3qU7M81eS5ueUhkZcTxrucD+psrcwIv6n4jN3km6ya0mF9lXD/M6LSMM1Vyk9qOMCCurNjIhfkZ6qdWU+JisYmJNyIfBj5QcUkOa73EjaLyOZxHswsDLnib8jzWdqq9xCe2fe7/3AVEn3kirsD9f4zJPA10mNFktIx650/VqQt7FG0kOkHlxIw17mqLMeUHAsKX+Xuw74iKTlpOF5R+TrWemx7CcDJ0l6lDTc8tuw/YEzG4APAxfka0OnqBknqXJ6OLAkIl7MfzsP2Cvn/5NJ53+9+9POpN7qNaR7+pOk1v52a3WcSDojH+fS/W5R/tO3gT1zvjiJdG1otSLjXSDpQUn3k875eU2IxzqUWl9x7z6S5gFTI+Kzg63bzSSNzy3pSPoS8NqI+FzByTIzG1Tp+pV7dq4HLo6IyoKXjUJ5JMbYiNgiaRKpB3pyWaG5K/RKnCW9Fq81T7fMo7BivFfSKaR89ARuGTGz0WORpNmkOT2LSfO9rDuMI01WH0sadXBilxaIeyXOkl6L15rEPTtmZmZmZtaVPGfHzMzMzMy6kis7ZmZmZmbWlVzZMTMzMzOzruTKjpmZmZmZdSVXdszMzMzMrCv9HzEWfyea2aboAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.heatmap(df.corr(), annot = True)\n",
    "plt.title(\" Correlation between various paramters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Stacking classifier for A101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[ \"Open\", \"High\", \"Low\", \"Close\",\"AvgP5\",\"AvgP10\", \"AvgP15\",\"returns\",\"A101\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "X = stats.zscore(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3-fold cross validation:\n",
      "\n",
      "Accuracy: 0.82 (+/- 0.02) [KNN]\n",
      "Accuracy: 0.83 (+/- 0.02) [Random Forest]\n",
      "Accuracy: 0.84 (+/- 0.02) [Support vector]\n",
      "Accuracy: 0.83 (+/- 0.02) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "y = df[\"target\"]\n",
    "clf1 = KNeighborsClassifier(n_neighbors= 100,leaf_size=20, weights = 'uniform', metric='minkowski')\n",
    "clf2 = RandomForestClassifier(n_estimators = 50, max_depth = 3, criterion='gini', bootstrap= False)\n",
    "clf3 = SVC(kernel='rbf',gamma= 0.001, C= 1000)\n",
    "lr = LogisticRegression()\n",
    "sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],meta_classifier=lr)\n",
    "\n",
    "print('3-fold cross validation:\\n')\n",
    "for clf, label in zip([clf1, clf2, clf3, sclf], ['KNN', 'Random Forest','Support vector','StackingClassifier']):\n",
    "    scores = model_selection.cross_val_score(clf, X, y,cv=5, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Apply PCA on all the feature for dimension reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(X)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.540940</td>\n",
       "      <td>0.702983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.514366</td>\n",
       "      <td>0.928661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.482390</td>\n",
       "      <td>1.579323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.436398</td>\n",
       "      <td>1.851298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.383268</td>\n",
       "      <td>1.106645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0               1.540940               0.702983\n",
       "1               1.514366               0.928661\n",
       "2               1.482390               1.579323\n",
       "3               1.436398               1.851298\n",
       "4               1.383268               1.106645"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3-fold cross validation:\n",
      "\n",
      "Accuracy: 0.82 (+/- 0.02) [KNN]\n",
      "Accuracy: 0.82 (+/- 0.03) [Random Forest]\n",
      "Accuracy: 0.82 (+/- 0.02) [Support vector]\n",
      "Accuracy: 0.82 (+/- 0.03) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "y = df[\"target\"]\n",
    "clf1 = KNeighborsClassifier(n_neighbors= 100,leaf_size=20, weights = 'uniform', metric='minkowski')\n",
    "clf2 = RandomForestClassifier(n_estimators = 50, max_depth = 3, criterion='gini', bootstrap= False)\n",
    "clf3 = SVC(kernel='rbf',gamma= 0.001, C= 1000)\n",
    "lr = LogisticRegression()\n",
    "sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],meta_classifier=lr)\n",
    "\n",
    "print('3-fold cross validation:\\n')\n",
    "for clf, label in zip([clf1, clf2, clf3, sclf], ['KNN', 'Random Forest','Support vector','StackingClassifier']):\n",
    "    scores = model_selection.cross_val_score(clf, principalDf, y,cv=5, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. Model Comparision using K-fold vaidation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: 0.808679 (0.042406)\n",
      "BC: 0.827926 (0.053446)\n",
      "ABC: 0.808630 (0.077859)\n",
      "GB: 0.818200 (0.073212)\n",
      "SVM: 0.835988 (0.066471)\n",
      "RDF: 0.829601 (0.050077)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('BC', BaggingClassifier(n_estimators=40, max_samples=.5)))\n",
    "models.append(('ABC', AdaBoostClassifier( n_estimators= 50)))\n",
    "models.append(('GB', GradientBoostingClassifier(n_estimators = 50, learning_rate = 0.05)))\n",
    "models.append(('SVM', SVC(kernel='rbf',gamma= 0.0001, C= 1000)))\n",
    "models.append(('RDF', RandomForestClassifier(n_estimators = 100,oob_score=True)))\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "scoring = 'accuracy'\n",
    "for name, model in models:\n",
    "\tkfold = model_selection.KFold(n_splits=15, random_state=19)\n",
    "\tcv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "\tprint(msg)\n",
    "# boxplot algorithm comparison\n",
    "fig = plt.figure(figsize= (9,5), )\n",
    "fig.suptitle('Algorithm Comparison',fontsize=16)\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10. Apply Machine learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X,y,train_size=0.7, test_size=0.3, random_state= 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. Apply Grid Serach Algorithm to find the best best param for\n",
    "#### 1. Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyper Parameters:\n",
      " {'criterion': 'entropy', 'max_depth': 20, 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 10, 'n_jobs': -1, 'random_state': 123}\n"
     ]
    }
   ],
   "source": [
    "#making the instance\n",
    "model=RandomForestClassifier()\n",
    "#hyper parameters set\n",
    "params = {'criterion':['gini','entropy'],'n_estimators':[10,15,20,25,30],'max_depth': [10,15,20,25,50],'min_samples_leaf':[1,2,3],\n",
    "          'min_samples_split':[3,4,5,6,7], 'random_state':[123],'n_jobs':[-1]}\n",
    "#Making models with hyper parameters sets\n",
    "model1 = GridSearchCV(model, param_grid=params, n_jobs=-1)\n",
    "#learning\n",
    "model1.fit(X_train,Y_train)\n",
    "#The best hyper parameters set\n",
    "print(\"Best Hyper Parameters:\\n\",model1.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Accuracy Score: 83.9572192513369%\n",
      "RF Confusion Matrix: \n",
      "[[125  28]\n",
      " [ 32 189]]\n",
      "RF Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.80      0.82      0.81       153\n",
      "        1.0       0.87      0.86      0.86       221\n",
      "\n",
      "avg / total       0.84      0.84      0.84       374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Prediction\n",
    "Y_pred=model1.predict(X_test)\n",
    "print('RF Accuracy Score: ',accuracy_score(Y_test,Y_pred)*100,'%',sep='')\n",
    "print('RF Confusion Matrix: ',confusion_matrix(Y_test,Y_pred), sep = '\\n')\n",
    "print('RF Classification Report: ',classification_report(Y_test,Y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. K- Nearest Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyper Parameters:\n",
      " {'leaf_size': 3, 'n_jobs': -1, 'n_neighbors': 60, 'weights': 'distance'}\n"
     ]
    }
   ],
   "source": [
    "#making the instance\n",
    "model = KNeighborsClassifier(n_jobs=-1)\n",
    "#Hyper Parameters Set\n",
    "params = {'n_neighbors':[20, 40, 60],'leaf_size':[3,5],'weights':['uniform', 'distance'],'n_jobs':[-1]}\n",
    "#Making models with hyper parameters sets\n",
    "model2 = GridSearchCV(model, param_grid=params, n_jobs=1)\n",
    "#Learning\n",
    "model2.fit(X_train,Y_train)\n",
    "#The best hyper parameters set\n",
    "print(\"Best Hyper Parameters:\\n\",model2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Accuracy Score: 85.8288770053476%\n",
      "KNN Confusion Matrix: \n",
      "[[124  29]\n",
      " [ 24 197]]\n",
      "KNN Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.84      0.81      0.82       153\n",
      "        1.0       0.87      0.89      0.88       221\n",
      "\n",
      "avg / total       0.86      0.86      0.86       374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y_pred=model2.predict(X_test)\n",
    "print('KNN Accuracy Score: ',accuracy_score(Y_test,Y_pred)*100,'%',sep='')\n",
    "print('KNN Confusion Matrix: ',confusion_matrix(Y_test,Y_pred), sep = '\\n')\n",
    "print('KNN Classification Report: ',classification_report(Y_test,Y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Support vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyper Parameters:\n",
      " {'C': 500, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "model=SVC()\n",
    "#Hyper Parameters Set\n",
    "params = {'C': [500, 1000], 'kernel': ['linear','rbf']}\n",
    "#Making models with hyper parameters sets\n",
    "model3 = GridSearchCV(model, param_grid=params, n_jobs=-1)\n",
    "#Learning\n",
    "model3.fit(X_train,Y_train)\n",
    "#The best hyper parameters set\n",
    "print(\"Best Hyper Parameters:\\n\",model3.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy Score: 88.23529411764706%\n",
      "SVM Confusion Matrix: \n",
      "[[131  22]\n",
      " [ 22 199]]\n",
      "SVM Classification Report: \n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       -1.0       0.86      0.86      0.86       153\n",
      "        1.0       0.90      0.90      0.90       221\n",
      "\n",
      "avg / total       0.88      0.88      0.88       374\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Prediction\n",
    "Y_pred=model3.predict(X_test)\n",
    "#importing the metrics module\n",
    "\n",
    "print('SVM Accuracy Score: ',accuracy_score(Y_test,Y_pred)*100,'%',sep='')\n",
    "print('SVM Confusion Matrix: ',confusion_matrix(Y_test,Y_pred), sep = '\\n')\n",
    "print('SVM Classification Report: ',classification_report(Y_test,Y_pred), sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Trend Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filter data for november month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>returns</th>\n",
       "      <th>target</th>\n",
       "      <th>A101</th>\n",
       "      <th>AvgP5</th>\n",
       "      <th>AvgP10</th>\n",
       "      <th>AvgP15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>152.214399</td>\n",
       "      <td>153.419486</td>\n",
       "      <td>150.989231</td>\n",
       "      <td>152.188785</td>\n",
       "      <td>27023200</td>\n",
       "      <td>-0.000168</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.010535</td>\n",
       "      <td>153.871018</td>\n",
       "      <td>154.644139</td>\n",
       "      <td>157.538409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>152.357202</td>\n",
       "      <td>153.694748</td>\n",
       "      <td>150.924615</td>\n",
       "      <td>152.994406</td>\n",
       "      <td>35780700</td>\n",
       "      <td>0.004165</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.229944</td>\n",
       "      <td>153.345443</td>\n",
       "      <td>154.154754</td>\n",
       "      <td>156.636702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>151.823593</td>\n",
       "      <td>154.112377</td>\n",
       "      <td>150.277305</td>\n",
       "      <td>153.152203</td>\n",
       "      <td>45099100</td>\n",
       "      <td>0.008675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346347</td>\n",
       "      <td>152.919673</td>\n",
       "      <td>153.942605</td>\n",
       "      <td>155.749071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>150.691791</td>\n",
       "      <td>152.966187</td>\n",
       "      <td>149.398651</td>\n",
       "      <td>151.951097</td>\n",
       "      <td>41025300</td>\n",
       "      <td>0.008288</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.352891</td>\n",
       "      <td>152.474813</td>\n",
       "      <td>153.678295</td>\n",
       "      <td>154.827716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>149.695892</td>\n",
       "      <td>150.898091</td>\n",
       "      <td>147.649319</td>\n",
       "      <td>149.940541</td>\n",
       "      <td>54777800</td>\n",
       "      <td>0.001632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075282</td>\n",
       "      <td>152.045407</td>\n",
       "      <td>153.277766</td>\n",
       "      <td>154.166685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close    Volume  \\\n",
       "1244 2019-01-11  152.214399  153.419486  150.989231  152.188785  27023200   \n",
       "1243 2019-01-10  152.357202  153.694748  150.924615  152.994406  35780700   \n",
       "1242 2019-01-09  151.823593  154.112377  150.277305  153.152203  45099100   \n",
       "1241 2019-01-08  150.691791  152.966187  149.398651  151.951097  41025300   \n",
       "1240 2019-01-07  149.695892  150.898091  147.649319  149.940541  54777800   \n",
       "\n",
       "       returns  target      A101       AvgP5      AvgP10      AvgP15  \n",
       "1244 -0.000168    -1.0 -0.010535  153.871018  154.644139  157.538409  \n",
       "1243  0.004165    -1.0  0.229944  153.345443  154.154754  156.636702  \n",
       "1242  0.008675     1.0  0.346347  152.919673  153.942605  155.749071  \n",
       "1241  0.008288     1.0  0.352891  152.474813  153.678295  154.827716  \n",
       "1240  0.001632     1.0  0.075282  152.045407  153.277766  154.166685  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample = df[(df[\"Date\"]  >= \"2018-09-1\") &  (df[\"Date\"]  <= \"2018-11-30\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot price return with Alpha 101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e6e24f60>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sample.plot( x = \"Date\", y =  [\"returns\",\"A101\"],secondary_y=\"A101\", figsize=(12,8) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBS: no of A101 trend is in negative and return is in negative is hit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13. Deploy the model which can be used for further stocks \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('knn.pkl', 'wb') as model_pkl:\n",
    "    pickle.dump(model3, model_pkl, protocol=2)"
   ]
  }
 ],
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