_main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import bs4 as bs\n",
    "import pickle\n",
    "import requests\n",
    "from dateutil.relativedelta import relativedelta as rd\n",
    "from scipy.optimize import minimize\n",
    "from sklearn.neighbors import DistanceMetric as DM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_results (rs, pos, plot=True, savefig=True):\n",
    "    \n",
    "    \"\"\" \n",
    "    Compute the backtest results using the original returns as a bench mark and after positioning.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    benchmark = 100*rs.cumsum().apply(np.exp).mean(1)\n",
    "    myperform = 100*(pos*rs.apply(np.exp)).sum(1).replace(0,1).apply(np.log).cumsum().apply(np.exp)\n",
    "    \n",
    "    b_sharpe = benchmark.pct_change().mean()/benchmark.pct_change().std()\n",
    "    m_sharpe = myperform.pct_change().mean()/myperform.pct_change().std()\n",
    "    \n",
    "    b_ar = (1+benchmark.pct_change().mean())**252 - 1\n",
    "    m_ar = (1+myperform.pct_change().mean())**252 - 1\n",
    "    \n",
    "    if plot:\n",
    "        \n",
    "        plt.figure(figsize=(15,5))\n",
    "        benchmark.plot(linewidth=2, color='black')\n",
    "        myperform.plot(linewidth=2, color='red')\n",
    "        plt.title('Performance')\n",
    "        plt.legend(['benchmark', 'portfolio performance'], fontsize=15)\n",
    "        plt.grid()\n",
    "        \n",
    "        print('Sharpe: \\n benchmark: {} \\n portfolio: {}'.format(round(b_sharpe,5), round(m_sharpe,5)))\n",
    "        print('Annual return: \\n benchmark: {} \\n portfolio: {}'.format(round(100*b_ar, 3), round(100*m_ar, 3)))\n",
    "        \n",
    "        if savefig: plt.savefig('fig.png')\n",
    "    \n",
    "    return myperform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_sp500_tickers():\n",
    "    resp = requests.get('http://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
    "    soup = bs.BeautifulSoup(resp.text, 'lxml')\n",
    "    table = soup.find('table', {'class': 'wikitable sortable'})\n",
    "    tickers = []\n",
    "    for row in table.findAll('tr')[1:]:\n",
    "        ticker = row.findAll('td')[0].text\n",
    "        if not '.' in ticker:\n",
    "            tickers.append(ticker.replace('\\n',''))\n",
    "        \n",
    "    with open(\"sp500tickers.pickle\",\"wb\") as f:\n",
    "        pickle.dump(tickers,f)\n",
    "        \n",
    "    return tickers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = save_sp500_tickers()\n",
    "\n",
    "prices = pd.read_csv('SP500 Adj Close.csv', index_col=0, header=0)\n",
    "\n",
    "andreas_tickers = pd.read_csv('tickers.csv')['Tick'].to_list()\n",
    "dates = pd.read_csv('Dates.csv')['Date'].to_list()\n",
    "preds = pd.read_csv('XGBpredictions.csv', index_col=0, header=0)\n",
    "preds.columns = andreas_tickers\n",
    "preds.index = pd.to_datetime(dates[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = set(andreas_tickers).intersection(set(prices.columns))\n",
    "preds = preds[tickers]\n",
    "prices = prices[tickers]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtest 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-09-04</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>0.006944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.007246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>0.006757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.022727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-08-01  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-09-04  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-10-01  0.000000  0.006944  0.006944  0.006944  0.006944  0.006944   \n",
       "2007-11-01  0.006757  0.006757  0.006757  0.006757  0.006757  0.006757   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2018-01-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2018-02-06  0.007246  0.007246  0.007246  0.007246  0.007246  0.007246   \n",
       "2018-03-01  0.006757  0.006757  0.006757  0.006757  0.006757  0.006757   \n",
       "2018-04-03  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2018-05-01  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-08-01  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-09-04  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-10-01  0.000000  0.006944  0.006944  0.006944  ...  0.006944  0.006944   \n",
       "2007-11-01  0.006757  0.006757  0.006757  0.006757  ...  0.006757  0.006757   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2018-01-02  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2018-02-06  0.007246  0.007246  0.007246  0.007246  ...  0.007246  0.007246   \n",
       "2018-03-01  0.006757  0.006757  0.006757  0.006757  ...  0.006757  0.006757   \n",
       "2018-04-03  0.022727  0.022727  0.000000  0.000000  ...  0.000000  0.022727   \n",
       "2018-05-01  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-08-01  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-09-04  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-10-01  0.006944  0.006944  0.006944  0.006944  0.006944  0.006944   \n",
       "2007-11-01  0.006757  0.006757  0.006757  0.006757  0.006757  0.006757   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2018-01-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2018-02-06  0.007246  0.007246  0.007246  0.007246  0.007246  0.007246   \n",
       "2018-03-01  0.006757  0.006757  0.006757  0.006757  0.006757  0.006757   \n",
       "2018-04-03  0.022727  0.022727  0.000000  0.000000  0.000000  0.000000   \n",
       "2018-05-01  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "\n",
       "                XRAY       XRX  \n",
       "2007-07-02  0.006711  0.006711  \n",
       "2007-08-01  0.006711  0.006711  \n",
       "2007-09-04  0.006711  0.006711  \n",
       "2007-10-01  0.006944  0.006944  \n",
       "2007-11-01  0.006757  0.006757  \n",
       "...              ...       ...  \n",
       "2018-01-02  0.006711  0.006711  \n",
       "2018-02-06  0.007246  0.007246  \n",
       "2018-03-01  0.006757  0.006757  \n",
       "2018-04-03  0.022727  0.022727  \n",
       "2018-05-01  0.007143  0.007143  \n",
       "\n",
       "[131 rows x 149 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = preds.apply(np.sign)\n",
    "pos = pos.replace(-1,0)\n",
    "pos = pos.dropna()\n",
    "pos = (pos.T/pos.sum(1)).T\n",
    "\n",
    "pos = pos.T.sort_index().T\n",
    "pos = pos.sort_index()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
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       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
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       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1970-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1970-01-05</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.002066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1970-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2019-10-14</td>\n",
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       "      <td>-0.019785</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>0.010355</td>\n",
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       "      <td>-0.005236</td>\n",
       "      <td>-0.005945</td>\n",
       "      <td>-0.011687</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.008939</td>\n",
       "      <td>-0.000790</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.011524</td>\n",
       "      <td>-0.005113</td>\n",
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       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.010166</td>\n",
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       "      <td>0.004578</td>\n",
       "      <td>0.007891</td>\n",
       "      <td>0.008686</td>\n",
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       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
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       "      <td>-0.002012</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.006796</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.023148</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>-0.001320</td>\n",
       "      <td>0.019108</td>\n",
       "      <td>0.004066</td>\n",
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       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.004965</td>\n",
       "      <td>-0.013913</td>\n",
       "      <td>-0.005284</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>0.012184</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>-0.001809</td>\n",
       "      <td>-0.004135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>-0.011507</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>-0.005858</td>\n",
       "      <td>0.005326</td>\n",
       "      <td>-0.007809</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>0.007748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12563 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "1970-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN       NaN  0.012334   \n",
       "1970-01-06       NaN       NaN       NaN       NaN       NaN -0.008097   \n",
       "1970-01-07       NaN       NaN       NaN       NaN       NaN  0.004219   \n",
       "1970-01-08       NaN       NaN       NaN       NaN       NaN -0.012382   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.001440  0.002383 -0.001834 -0.006447 -0.000369 -0.003363   \n",
       "2019-10-15 -0.002335  0.026701  0.005779  0.003229  0.010166 -0.003156   \n",
       "2019-10-16 -0.004045 -0.002443 -0.024236 -0.015744  0.000244  0.009331   \n",
       "2019-10-17  0.003875  0.007310 -0.012769  0.003771 -0.002012  0.004310   \n",
       "2019-10-18  0.004791  0.004965 -0.013913 -0.005284 -0.001772  0.012184   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "1970-01-02       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-06       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-07       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-08       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14 -0.001250 -0.019785 -0.001696  0.010355  ... -0.005236 -0.005945   \n",
       "2019-10-15  0.001874  0.004578  0.007891  0.008686  ...  0.002179  0.015622   \n",
       "2019-10-16  0.006843 -0.004282 -0.005856 -0.007110  ...  0.001496  0.002656   \n",
       "2019-10-17  0.006796  0.009426 -0.000339  0.001721  ...  0.003526  0.023148   \n",
       "2019-10-18  0.008584 -0.016107 -0.001809 -0.004135  ... -0.011506 -0.011507   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "Date                                                                     \n",
       "1970-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN       NaN  0.016000   \n",
       "1970-01-06       NaN       NaN       NaN       NaN       NaN -0.002066   \n",
       "1970-01-07       NaN       NaN       NaN       NaN       NaN -0.005903   \n",
       "1970-01-08       NaN       NaN       NaN       NaN       NaN  0.001918   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.011687  0.001579  0.001298 -0.008939 -0.000790  0.002895   \n",
       "2019-10-15 -0.005950  0.000946  0.001297  0.003016 -0.002056  0.003463   \n",
       "2019-10-16  0.006381  0.005783 -0.014796 -0.000921  0.005370 -0.017291   \n",
       "2019-10-17  0.001508  0.003941 -0.000439  0.003511  0.002831 -0.001320   \n",
       "2019-10-18  0.000646  0.005541  0.004813 -0.005858  0.005326 -0.007809   \n",
       "\n",
       "                XRAY       XRX  \n",
       "Date                            \n",
       "1970-01-02       NaN       NaN  \n",
       "1970-01-05       NaN       NaN  \n",
       "1970-01-06       NaN       NaN  \n",
       "1970-01-07       NaN       NaN  \n",
       "1970-01-08       NaN       NaN  \n",
       "...              ...       ...  \n",
       "2019-10-14 -0.011524 -0.005113  \n",
       "2019-10-15  0.004663  0.019628  \n",
       "2019-10-16  0.003158 -0.013156  \n",
       "2019-10-17  0.019108  0.004066  \n",
       "2019-10-18 -0.000910  0.007748  \n",
       "\n",
       "[12563 rows x 149 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = prices.astype(float).apply(np.log).diff(1)\n",
    "rs.index = pd.to_datetime(rs.index)\n",
    "rs = rs.T.sort_index().T\n",
    "\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-06</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-09</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "      <td>0.006711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.007143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3098 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-03  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-05  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-06  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-09  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-15  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-16  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-17  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-18  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-07-03  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-07-05  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-07-06  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "2007-07-09  0.006711  0.006711  0.006711  0.006711  ...  0.006711  0.006711   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "2019-10-15  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "2019-10-16  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "2019-10-17  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "2019-10-18  0.007143  0.007143  0.007143  0.007143  ...  0.007143  0.007143   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "Date                                                                     \n",
       "2007-07-02  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-03  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-05  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-06  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "2007-07-09  0.006711  0.006711  0.006711  0.006711  0.006711  0.006711   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-15  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-16  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-17  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "2019-10-18  0.007143  0.007143  0.007143  0.007143  0.007143  0.007143   \n",
       "\n",
       "                XRAY       XRX  \n",
       "Date                            \n",
       "2007-07-02  0.006711  0.006711  \n",
       "2007-07-03  0.006711  0.006711  \n",
       "2007-07-05  0.006711  0.006711  \n",
       "2007-07-06  0.006711  0.006711  \n",
       "2007-07-09  0.006711  0.006711  \n",
       "...              ...       ...  \n",
       "2019-10-14  0.007143  0.007143  \n",
       "2019-10-15  0.007143  0.007143  \n",
       "2019-10-16  0.007143  0.007143  \n",
       "2019-10-17  0.007143  0.007143  \n",
       "2019-10-18  0.007143  0.007143  \n",
       "\n",
       "[3098 rows x 149 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = pos.reindex(rs.index)\n",
    "pos = pos.ffill()\n",
    "pos = pos.dropna()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = rs.fillna(0)\n",
    "rs = rs.reindex(pos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: \n",
      " benchmark: 0.03881 \n",
      " portfolio: 0.04286\n",
      "Annual return: \n",
      " benchmark: 11.915 \n",
      " portfolio: 13.962\n"
     ]
    },
    {
     "data": {
      "image/png": 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t8fHxbs9PbKrrZ599xowZM2jdujXbtm2jsPUXQQghhBBCZA3//gtTp5rt9evdj2DdK3/9BStW2Pf//BNq1jTbPXvC1avwxhum/lpCW7bA2LFm+8knYdQoyJXL+ZwiRZz3L1yAnTvBz89eo83x79ljx6BGDbOdP3+SXfc8z6S4ayEhIezcudO2f/78eX7//XcaNmxIy5YtiYuL4+bNm9SvX9/lx3HkKzmtWrXin3/+ybDi1DExMcTFxZHL4UEODw9n9erVKbpP/vz5Wb9+PUop2rRpQ1hYWFp3VQghhBBCpKe33rJvd+oEbdqApSbvPXXpkpnKOGGC2Z882R6wWVlHu776yh5kAezeDQ7LhXjpJfcp/xPOHitXzozeOQaqjn/TV69uvhMAh4EOdyRou4eKFCnCc889x9KlS1m5ciVPP/00xYoVY8CAAVSrVo2XX36ZXr16MW3aNDZv3szatWv56KOPePHFF1P0Pq1bt6ZNmzb06dOHTz75hC1btvDNN98wePDgdPpkzvz9/WnQoAETJ07kxx9/ZOXKlTzxxBOJTv1MSuHChdm4cSM3btygffv2REVFpUOPhRBCCCFEmjt7FhYtcm7bsAFmzrz3ffnzT+f9xx5zPcexrUEDWLLETHGcO9f5vGbN3L9Hnjwp79f335u+JTMCKUHbPVS+fHk+/vhjxo8fT69evWwjSb6Wub2ff/45Y8aMYcmSJbRr144BAwawdu1amiX2YCRCKcXKlSsZNGgQM2fOpG3btrz//vsUSThkm46WLl1KxYoV6devH2+88QZdu3Z1yV7pqZIlS7J582aCgoLo0qULt2/fTuPeCiGEEEJkIUePwj+ZuAzy5s1m6t+OHe6Pjxlj1n2ll+vXTR00pcy0w9BQGDrU+ZyE69EAKlWyb58/D/37m6yQ58+bNn9/872nYNmSE0s2dic5c5oRv2Qy46vMUEKtfv36ev/+/YkeP3bsGDWs8z3TUXonIhFZX3Z/Ru7V71p2FRgYSPPmzTO6GyKTkudDJEWeD5EUp+fj5EmoVg0qVDAjWZnN0qXw7LPObZMmQUAADBwI1sR1c+cmOyUw1QoXTj6ojY93HyiNGgXTptn3x4yBb76BM2dMVshq1ZK+b2LBV506yQaqSqkDWuv67o7JSJsQQgghhBBZxd695jUoyBSDzkw+/9w1YANo3Nis7XJcy+aullla2Lgx+YCtV6/Egytr2n6rmBiTUASgbNnU9yuxrJEekqBNCCGEEEKIrMIaQEDi0w8zQnw8vPaa+2PW+r7e3vDBB2Y7MjJ9+vHbb/b3rFjR+ViePCb5h7UP7iSchfjVVxAbazJDJgzo3KlQwX17ErMKPSFBmxBCCCGEEFlBXBzMn2/fv3gx4/ri6MoVSFiLt0cPE+jcvOk8qmWt03brlvP5d+6Y4Mjq1i3o29f1vsn5+Wfz2r49nD7tfCwszKwHdFy7ltCLL5q1a9YALbn6aglt2mSmVCb077+eXZ8ICdqEEEIIIYTI7LQ20w+DguxtmSEZyZYtUL48dOlibzt40GRFvHbNtf6Yn595dQzaoqNNINWqlX2k68MPzVoya0p8T1lHtBKuWfPyMj/JJPygdGnT74T1lT0ZZQOoXBkmTnRt37TJs+sTIUGbEEIIIYQQmV2/fqbws6Nz5zKmL1a//WYCLcfM3lOmmKQbibEGbdaEJGA+x4ULsH27qacGJhhMKcdAcMAA52MpyaLu42OmUjoGjNZ+e8qa2G32bAgOdq7zlgoStAkhhBBCCJHZff21fduaOj41gU1a2bDBfa0zH5+kr7NOj1y8GN5912w7Th38+2/zevhwyvoTFQWDBpntYsVMBkmAH34wo2Q//JCy+wE0aWLf9nSkzWrtWjh0CF59FUqUSPl7JyBBmxBCCCGEEJlY/iNHnBtGjjSvCafw3Svx8dC7d+qudRyx+vBD8+oYtLVsaYpNh4e7np+UWbPsga2/v729WzeT9CSFdY8BKFfOfb89UbEi1K6d8vdMhARtQgghhBBCZFa3bvGwY2Hoc+fcrwu7Fw4dMmvCvL0TX0/Xv3/S97COtFnFx7ve68EH7dvWEbOkaG2mZVqltvh1Qo61a0uVSpt7ppIEbUIIIYQQQmRW+/Y575crZw98oqNN0JMeNmwwI0WOiU/cJQVp2NDUXLt0yWRnTC7LYsIRq4iIpBOqJEzB786+fc414EaMSP4aT9Ss6X47A0jQlo1s2LCBmTNnuj02ceJESpcujZeXFwMSLsxMglKK2bNn2/abN29Ot27d7rar98yqVauoUaMGOXPmpEJidTOEEEIIITKr33+3b+/caV69vCBXLrPtmNAjrfz1F7RpY9aVVaxo0vZr7Zr4JG9e2LoVChUyI1H58iV/74QjbVeugONIYkKefL4zZ8xrrlwmpX+HDslf4wkvh1ApYc23eyxHhr67SFMbNmxg+fLlDBs2zKl9//79jBs3jilTptC8eXOK3cWQ8Zw5c/BJboFpJhEXF0e/fv1o27YtCxYsIE+ePBndJSGEEEIIz92+DV98YbbnzoXGje3H/PwgJsZMkUxpkozkfPed836BAq7FoYcONevaUvre3t7O+7Nm2beffx7++1/n49evm+QkVaokfs+rV83riy86T2lMC88+a0oYWAuEZxAJ2rKB2NhYvLwSHzQ9fvw4AK+++ir5E9bKSKEHHnjgrq6/F+Li4oiLiyMkJISwsDD69OlD06ZN7+qeyX3HQgghhBBp6sYNePRRsPwdxxNPOB/Pl8+cExZm1n2dPQtvvgljx8LDD6f8/WJiTMKOQoXcZ258803z2rs3LF2a8vtbJZzuuGCB8/aRI65TQg8fdh+0Xb1qgklr0Fa8eOr7lZivvzZ9Tq6+WzqTv0LvkQEDBlC/fn1++uknqlevjq+vL02bNuXo0aNO50VFRTF06FBKlCiBr68vDRo0YMOGDU7nWKcozp8/n8qVK+Pr68ugQYOYPn06586dQymFUooBAwYwYMAAnnvuOQD8/f1RShEYGAjA2bNn6dSpE/nz5ydfvnw888wznDp1KsnP4W565JYtW2jUqBG+vr4UL16cIUOGEOE4r9iN8ePHU6RIEXbs2MHDDz+Mr68vderU4bfffnM5d+HChdSsWZNcuXJRvnx5Pvroo0S/25o1a+Lr68u0adMoW7YsAB07dkQpxfjx4+/qO758+TJTpkyhSJEi7Nmzh/r16+Pn50fTpk05e/YsISEhdOrUibx581KjRg22JEjDu2TJEpo2bUqhQoUoWLAgLVq0YH+Cf7WyfpaNGzfy0EMPkSdPHpo2bcpff/3ldF5cXBwffvghVatWJVeuXJQpU8Zl2uuqVauoX78+vr6+lChRghEjRhAbG5vkfxchhBBCZBJNm8KxY/b9gADn44UKmdcTJ0wx50qVYNUqeOUVM40xpWvdHnrIBH9168Lq1aZt+3b7cet2rVopu29C9erBCy/Ae++ZfevfJs88Y6YjnjxpP7dMGfMaGen+Xl27Qvv29iyUaZWAJKEMDthARtruqXPnzvHWW28xadIk/Pz8GDduHG3atOHvv//G1zK/96WXXmL16tVMmTKFgIAAFixYwNNPP83WrVudRot27NjB6dOnmTZtGrlz56ZWrVpER0ezZcsWVq5cCUBRy0LQsmXLMnnyZLZs2YKfnx8PPPAAMTExtGrVCh8fHxYsWECOHDkYN24cjz/+OEeOHKGQ9X8IknH06FGeeuopWrduzY8//siFCxcYNWoUZ86cYd26dUleGxUVRd++fRk9ejQlS5Zk+vTptG3blr///psSlnoWH3/8Me+++y4jRoygefPmHDhwgDFjxpA7d25ee+01272CgoIYMWIEY8eOpXjx4lSvXp1atWrRpUsXPvnkE5o0aUIZyy9+ar9jf0v62KioKAYNGsSIESPIkycPQ4cO5bnnniNXrly0bduWIUOG8NFHH9G9e3cuXLhAbsu0gaCgIPr160flypW5ffs2S5cupVmzZvz5559UqlTJ9r7nz59n+PDhvPfee/j5+fHOO+/Qo0cP/vzzT5TlfzQGDx7MkiVLGDFiBI8//jj//PMPy5cvt91j2bJl9O7dm8GDBzNlyhROnz7N6NGjiY+P55NPPvHov60QQgghMsiCBWZdGcDTT7Orf38aJzynYEHzOnUq/PqrvX3vXqhQAZ58Etat8yzgGDjQHiwdOmS//yOPwMsvw7x59nPvNmhTChYuNNtz5tjT/VuLX9+8aT/38cfhm2+ck4w42rHDeT89RtoyC611hv/Uq1dPJ+Xo0aOujWagMuN+Uqh///4a0Dt27LC1BQUFaW9vbz137lzb51RK6UWLFtnOiYuL0zVr1tRPPvmkre3xxx/Xvr6+Ojg42Ok93n77bV2+fHmX9/7qq680oMPDw21tc+fO1d7e3vr06dO2tgsXLmgfHx89ZcoUh68Z/dlnnzm9d9euXW37PXv21AEBAfrOnTu2tu+//14DeufOnYl+H+PGjdOA/uabb2xt4eHhumDBgnrkyJFaa61v3ryp8+TJo8ePH+907ZgxY3Tx4sVt72n9bg8ePOh03tmzZzWg//e//9na7vY7HjVqlAZ0YGCgre3zzz/XgJ4wYYKt7a+//tKA/vnnn91+/ri4OB0bG6urVavmdF3//v21t7e3PnnypK1t5cqVGtDHjh3TWmt97NgxDehZs2a5vXd8fLwuV66cHjBggFP7l19+qX19fXVoaKjb66zfj0i9rVu3ZnQXRCYmz4dIijwfwiYuTmsfH/P3ZsuWWutEno8uXcw5uXMn/vfqmjWu192+rXVMjNbx8eZn5Ur31zr8/acnTzZtDRpofetW2n3WSpXs7zd5smkbMcLsV6um9Ztv2o9v3ux6fcI+b9uWdn3LAMB+nUi8JNMj76FixYrx6KOP2vbLly9PvXr12Lt3LwD79u1Da0337t1t53h5edG9e3eXaYP16tWzjUalxt69e3n44YedRnjKlClDkyZN3E5RTOo+nTt3xtthUWnXrl3JkSOHR/fp3LmzbTtv3ry0bt3a9n3s2rWLyMhIunfvzp07d2w/LVu25OrVq1y8eNF2benSpalTp06y75cW33HOnDl57LHHbPsBlukKLR0WqFrbLl26ZGs7duwYnTt3pnjx4nh7e+Pj48OJEyc46TgNAKhQoQJVHOZtW9cRWj/v1q1bARLNAnry5EnOnz9Pjx49XL636Oho/vzzz0S+HSGEEEJkmG3b7DXQrFMG16xJ/HzrSFtUVOLntG/vvB8ba6Yn5s1rClCPGAEJlp3YPP64ffu990xYtHeva/bHuxEaat8uXdq8TptmErAcP+5cPsBd9vKE5QUaNEi7vmUyyQZtSilfpdRepdQfSqm/lFITLO2LlFJnlVKHLD91LO1KKfUfpdQppdRhpVQqVkJ6IB3Gz8LDwjw/PxXcZW0sVqwYwcHBAAQHB5M3b17bdDqr4sWLExUVRUxMjFPb3QgODnZ7j+LFi/NPUrUyPLiPt7c3hQsXTvY+efPmxS9BrQ7H7yPU8otcs2ZNfHx8bD8tWrQA4MKFC0799rS/d/sd58uXzykpSc6cOQEoUKCAS1u0JU1teHg4Tz75JBcuXGDGjBls376dffv2Ubt2bds5Vo73cXev69evkydPnkSTyli/t3bt2jl9bxUtqWodvzchhBBCZBKOQZJVwppmjv7+27P7Wv9//8wZUz7gyBETvIWHwyefwB9/mON9+9qnZMK9qUsWFmbfdvzbzJqp/MUX7W3u6r9Zg9tnnzWBXloGlJmMJ2vaYoCWWusIpZQP8JtS6hfLseFa6+UJzm8LVLH8NALmWl7veyEhIW7balp+KUqWLElERARRUVFOQcXVq1fJnTs3uaz1OMC2tim1SpYs6ZLcwvpenq5ns94n4eeKi4vj+vXryd4nIiKCW7duOQVuISEhlCxZEsB2/Zo1a9wGUNWqVbNte/p93Mvv2NGuXbu4ePEiGzdupHr16rb2m47ztj1UuHBhIiMjCQsLcxu4Wb+3+fPnU7duXZfjFTO4zogQQgghPGBZS5+opAI6R+XKwU8/uS+MDfaRusWLTSKQO3dc0/LfC+5KBxQtCsuWQY8e9qQkYIK1xx4z2TMBFi2CHNk7VUeyI+K1GKIAACAASURBVG2WKZbW1X8+lp+khpo6Akss1+0GCiilSt59V7O+kJAQdlqLImISTvz+++80bNgQgAYNGqCUckooobVm+fLlHqWsz5kzp8uoTWIaNWrEgQMHOHv2rK3t0qVL7Ny5M0Xp8Rs1asTKlSuJi4uzta1YsYI7d+54dB9r0hQwQdzGjRtt30fjxo3x8/Pj8uXL1K9f3+UnnycFHBO42+84tW7dugXgFBTu3LmToKCgFN/LOg1zyZIlbo9Xq1aN0qVLExQU5PZ7K1y4cMo/gBBCCCHST8IpjuPHmxGxpCQsRfTKK877PXvat//zn+T7YL1fRgRskHi9twoVzKs1YQmYEcM9e+z72TxgAw+zRyqlvIEDQADwudZ6j1LqFeADpdRYYDMwSmsdA5QGHOdfXbS0Badpz7OgIkWK8Nxzz9myR44dO5ZixYrZ1ibVqFGD3r1789prrxEWFmbLbHj8+HHmzp2b7P2rV6/O1atXWbRoEbVq1aJIkSJUsD7oCQwYMIBp06bRtm1bJk6ciLe3ty0N/+DBgz3+TO+//z5169alU6dOvPLKK1y8eJGRI0fSpk0bGjd2yXPkxM/Pj/fee4+IiAhKlSrFJ598wu3bt3njjTcAM01w/PjxvPHGG5w7d45mzZoRHx/PyZMn2bp1q1PA56m7/Y5T65FHHiFv3ry89NJLjBgxgosXLzJ+/HhKW+dvp0C1atUYNGgQb7/9NiEhITRr1owbN26wfPlyvvvuO7y8vJg+fTrPPfccYWFhtG3blpw5c3LmzBl++uknli9f7jI9VAghhBAZ6MoV81qoEPz2m2cFoocOhV9+se/PmWMKcIMJ+gYOhO+/N/sJyhC5GDs2xV1OE//3f2ApTYWlVJMLywwsLl+2t507Z99Ox390z0w8Ctq01nFAHaVUAWClUqoWMBq4AuQE5gMjgYmAuzllLiNzSqlBwCAwa4estcPc8ff3Jzw83JOu3pW4uLh0e5/Y2FjKli3L22+/zdixY7lw4QJ169Zl4cKFxMbG2upnzZgxg7x58zJx4kRu3rxJzZo1WbZsGbVr17b1LS4ujjt37rj0tW3btjz77LMMHz6c0NBQ+vTpw7x585zWVWmH9Xg//fQT7777Li+88AJaa5o2bcqSJUvw8fFxund0dHSi712uXDl+/PFHJkyYQJcuXciXLx9du3Zl0qRJSX6XMTEx+Pn5MW/ePIYPH86JEyeoWrUqy5cvJ2/evLZrX3nlFQoWLMicOXOYPn06vr6+VK5cma5du9rOiY2NJT4+3uX9rLXioqKinI7dzXdszeDj2B5l+dexyMhIl/Ot313u3LlZvHgx77//Ph07dqRy5crMmDGDWbNmOb2Pu8/i7nNMmzaNEiVKsGTJEqZOnUrRokVp0aKF7Xi7du349ttvmT59Ov/973/x9vamQoUKtGnThpiYGKeR0YT9Tep3USQtIiJCvj+RKHk+RFLk+bi/5T9yhIeBsOLF+f3qVXuxaAu3z4evL80tm9cee4y/AgNt+0e8vfnn+HHcrJID4J/69bn65JPUmDIFgMAWLSAjnr8yZcj7xRd43b5N2KVL4JDAzUrFxdHMywtCQti2cSPax4cyv/5KABBVtiz7332X+PvhdyextJKJ/QDjgHcStDUH1li2vwB6Oxw7AZRM6p6pSvmfDsLCwtLt3v3799fJfc77ybhx43ThwoUzuhsplp7PSGYgKf/vjqTsFkmR50MkRZ6P+1hMjNZ165pUd506uT0l0efDmiJv1Ciz//HHWvftq3VsrEnn7y6dnmN5qGXLtN63L00/TrooVcr0PSjI7A8fbvYdylRlB9xNyn+lVFHLCBtKKT/gCeC4dZ2aMtkaOgHWPOKrgX6WLJKPADe11vf91EghhBBCCHEf+/NPKFYMnngC4uPNVMZu3aByZTh40JyT2oyN1sRk77xjphzmyJF4Ue3Nm+3b3btD/fqpe897yZqExDoSZ50qWapUxvQnA3gyPbIksNiyrs0LWKa1XqOU2qKUKoqZDnkIeNly/s9AO+AUEAUMTPtuCyGEEEIIkYUsXgzXrpmgyV2yj5EjYcyY1N3b00ySO3eaIDGrseYAsNboPX3auf0+kGzQprU+DLjkDddat3RzOpahvVfvvmvZy6JFizK6C5nK+PHjGT9+fEZ3QwghhBAifW3aBA8+6Jw0xJ0pU1wzQiZn0CD4+mvo08ez81NQ1ilTcRxpi4qCAwfMd5UVRgnTSAqfDCGEEEIIIYRH1q+H1q2hRAnnwtUJDRqU8oAN4IsvTK2yYsU8O79EiZS/R2bgONK2f7+p01a7NhQokLH9uockaBNCCCGEECI9rF/vvD91qgk8Zs6E6GjYsQP69oUPPkj9e/j4JH5s6VLo1Qv+/tvUfUuuYHdmZR1pu3gRrl832+XKZVx/MkCWqUSntUYltqBSCHHXtHapzCGEEEKI1IqJcQ7aFi+Gfv3MtqUmLY8+an7SS+/e5iers44QhoTA4cNmO1eujOtPBsgSQZuPjw+3bt2SgsBCpKNbt27hk9S/1gkhhBDCcx9/DEePQtWqZkStSJGM7lHWZc2OeeiQvZ7cnj0Z1p2MkCWmRxYrVoxLly4RFRUlowFCpDGtNVFRUVy6dIlins6JF0IIIYR7hw5BtWr2TJAffigB292yBm03btjb3BTizs6yxEhbfst/qMuXLxMbG5tu7xMdHY2vr2+63V9kfdn1GfHx8aF48eK23zUhhBBCpMLJk/D44xAWZvb79YPOnTO2T9mBu79P7ty59/3IQFkiaAMTuKX3H5SBgYHUretS3UAIG3lGhBBCiPtQWJiZlvfhh/Djj4kXdf78c3vAVqwYzJiReJFr4bl8+TK6BxkuS0yPFEIIIYQQIl1dvQpt20LOnPDNN/b22FiTdbFjR9i926SfVwq2b3e+Pi4Ovv3WbO/ZYzIdFi587/qfneXJ4xr89u2bMX25Czdu3GDixIl89NFH3Lx5kx07dnDmzBkApk6dmuS1WWakTQghhBBCiDR3/LgJtn74AY4dM219+8LDD5tCzl984f66IUNMGn0wQdojj5jtypWhQQMZYUtLSpnRNuso5o8/wlNPZWyfUuG9995jzpw5AIwcORKAatWqMX36dEaPHp3ktRK0CSGEEEKI+9ebb8K6da7tDzzg2tahg1mz9vbb8OefMHCgCeqmTLGf8+qrErClB19fe9DWpUvG9iWV/vjjD5e2EydO0L59+2SvlaBNCCGEEEJkf2FhZrQmLs785MoFu3a5D9jciY+3B2OzZsH587BokZkGaf1jPFcueOWVdOn+fS+Tl/6KiYlh165dlClThoCAAJfjJ0+eZMeOHam+v6xpE0IIIYQQWVNsrJnCmJyvvjLr0ho2BB8fM2rz3HNm1MzRjBnuR3E6d3YePXOsa7ppE1y7ZtbC3bxp7i3SXiYN2iIjIzl37hy+vr60aNGCKlWq8Pfff7ucN3nyZAD8/PyIiYlhwoQJKXofCdqEEEIIIUTW1Lo1lCgBV65Anz6werX78z7/3Lzu329v+/prE/QVLWqmOB4/DkOHmvVSp05B9+7mPC8v+/VWH3zg+h4DB5qRNpE+/PwyugdOjh8/zty5c6lSpQoVKlRwOrZ7926X860JR+bPn0/OnDkZO3as0/EGDRok+X4yPVIIIYQQQmRNv/5qXmvUMIWXv/3WTF28csUkBvn8czOiFhyc+D1OnXKtA1a5MixbBpGRZgTNcWQNoGdPM/LWs6fZf/JJmDcv7T6XcJXJRtpef/11Nm3a5PbY5cuXnfbj4+M5e/YsAI8++qjba3In8/kkaBNCCCGEEFnL+fNQvrx9/8YN+/Ybbzifu2GDfTtfPnj2WecAK6k6wHnyJH6sSxeYNAkKFYJBgzzrt0i9TDDSFh0dTZs2bahdu3aiARvA9u3b6dOnD97e3syYMYMFCxYQZkmiUqZMGbfXSNAmhBBCCCGyj9BQ54DNU7Vrw6FDZjs+HubPh3r1Ut+PHDng/fdTf71Imb59TQCecB3iPbR//362bdvGtm3bnNo7dOiAj48PBQsWZOHChaxdu5Zy5cq5vUfOnDlt22PHjmXixIkADBw4kF9++SXR95Y1bUIIIYQQIusYPty+XaqUWcvmTr9+zvuTJtm3R42CkSPhf/9L+/6J9NG3rylonoH/zc6fP++0/+yzzxIcHMyqVatYvnw5/RI+cwk8//zzTvsTJkwgMjKSffv20a1btySvlaBNCCGEEEJkDXv3mjT7AJMnw6VLsHAhfP89hITAnTtmHdvKlbB4Mfz0k5kOOWcOONbCqlgRpk6FkiUz5GOIVFAKmjY1U1zTyN9//83atWsZM2YMSimUUrRo0QKttdvzg4KCnPZHjRpFiRIlbPulS5dO9L1atGjBl19+6dKeO3du6tevj0qmtp9MjxRCCCGEEJnXtWtmdG3ECJPZEWDIEHjvPbPt5wc9etjPHzLEvt2xo/kRwkFMTAyTJk3iAzdZQAMDA1m/fj1PPfWUU/uGDRt4z/rMAY0bN+aBBAXYkwraQkND76rPMtImhBBCCCEyjtZw9KgpeO3OiBFm1KxmTdiyxbS1anXv+ieynVWrVrkN2KwOHDjgtL9w4ULatGlj21+3bh3bt2/Hy8s5lMqVRMkHxxG51JCgTQghhBBCZJyBA01AZik+DMDatXDhgkndb50OCfY6a7Vr39Muirszffp0BgwYwO3btzO6K2it6du3b5LnrFq1yml/3bp1tu0ePXrQunVrvL29U/S+y5cvT9H5CUnQJoQQQgghMsaJE2YUDWD8ePjwQ3j5ZbP+rFw592vOcuc2a9JEljB//nzeeecdFi9eTNmyZXnssce4efNmhvVn3759xMbG2vZPnDiB1hqtNfPnz7edM2zYMFauXAnY17LNnj2b77//3mWELSk1atRg48aN5E+qtIQHJGgTQgghhBAZ44cfnPfffRe++CLx8zt1gqtXIQV/NIuMs3//fgYPHmzbDwkJ4bfffmNeBhUi//TTT50yPP77779UrVrVtt+iRQvb9qxZs+jSpQu//PKLLWjr0qVLsu/x2muvAVC2bFk6d+7MH3/8wRNPPHHXfU/2iVdK+Sql9iql/lBK/aWUmmBpr6iU2qOU+lsp9b1SKqelPZdl/5TleIW77qUQQgghhMh+zpxJ+nitWmZN24oVZu3bypWQN++96ZtItVu3bhEbG8vatWvdHh81apTLurH0dvjwYd566y1OnDgBwPr16ylQoIDTOQEBATRp0sSpbfPmzVy/fh2lFMWLF0/2fWbOnMnJkyc5d+4cK1aswMfHJ03678k/U8QALbXWtYE6wFNKqUeAacCnWusqwL/AC5bzXwD+1VoHAJ9azhNCCCGEEMLZv/+aV3c108LC4MgRmDYNOne+930TqRISEkLu3LnJmTMn48ePB2DOnDmcOnWK//znP7bztm/fnm59iI+PZ9iwYSilCAgI4ObNm7z77rtO5zz44INur23btq3T/vTp0wHw9/f3aFqkt7c3VapUSTaFf0ol+87aiLDs+lh+NNASsK6oWwx0smx3tOxjOd5KpXWvhRBCCCFE1vXpp/Dkk7Bjh9lv186sY7OMgvDhh2lajys7+eeff6hYsSJvvfVWRnfFxZUrV9yORg0ePJjKlSvz+uuv08NSnuHatWvp0of4+Hg6dOjArFmzADh9+jQFChRwGfVLLJtjzZo13bYXLFgwbTuaQh7VaVNKeQMHgADgc+A0cENrfcdyykXAWpigNHABQGt9Ryl1EygMhCa45yBgEEDx4sUJDAy8qw+SFiIiIjJFP0TmJc+ISIo8HyIp8nyIpNxPz4d3RASPOQQcMYULszsmBm39/Fu3mtf75PvwhOPzERgYSFBQEJ9++imtWrUiT548Gds5B88//7xL29dff822bdts+0WKFAFMAhDHZ/7kyZPkz5//rlPj//zzz4lOy7R6+eWX+fXXX90e8/f3Z/jw4TzwwAN88MEHnDp1CoAcOXJk6O+oR0Gb1joOqKOUKgCsBGq4O83y6m5UzaWsuNZ6PjAfoH79+rp58+aedCVdBQYGkhn6ITIveUZEUuT5EEmR50Mk5b55Pq5ehaZNnZpyDRnC461bZ1CHsgbH52Pfvn229tjY2Ezz3Ozfv5+zZ88CULVqVdvasYSs7T4+PjRr1gwvLy9u3LhBmzZtuH37NpGRkeTOnRuA//3vfzz00EOUL1/e4360bNnStr1t2zaaNWtm21+xYgW3b9+mR48eSU5ftCYkOXPmDJMmTQKgXLlyGfpdpyj1jtb6BhAIPAIUUEpZg74ywGXL9kWgLIDluD/wT1p0VgghhBBCZJAVK0Ap8zN7dsqvj4yEEiXAMnLBwIHw0kvwzjtp289s7OzZs0yZMsW2f+7cuQzsjXH27Flq1apFU0swXqlSpSRHpKwjg9999x3PPPMMAJcvX7bVcLMGpQsXLqRDhw507NjR477cuXMHrc1Y0ZgxY3jsscecjnfu3JmePXt6vN7M39/ftm0dIcwonmSPLGoZYUMp5Qc8ARwDtgLdLKf1B6xV6FZb9rEc36Kt354QQgghhMh6PvoIuna177/+OixZAnfuJH5NQm+84bz/+ecwfz7cZf2q+8X27dupVKkSN27csLWdPHkyw/qjtWb37t1UqlSJv/76i5iYGABeeeUVSrqrr2fhOJ3z559/Bsw6PauhQ4dy8+ZNPvjgAwD++OMPj/vkmNzkHcs/BlgTjjim8/fUK6+8YttOmFXyXvNkpK0ksFUpdRjYB2zUWq8BRgJvKaVOYdasfWk5/0ugsKX9LWBU2ndbCCGEEEKki+3bISDAPqqmlMnumFD//ib9vmPWR60hPt79fb/80r69YQP4+aVtv7M5xxGnvn37ArBu3bqM6g6zZ8+mcePGLu3e3t5JXmed+mgVGRnplJTk8OHD9OjRw1YbLbn7Wa1bt842NbJGjRq2YtYrVqzgpZde4rvvvvPoPgn72r9/fx566CEGDhyY4uvTUrJr2rTWh4G6btrPAA3dtEcD3dOkd0IIIYQQ4t6Jjobu3c3aM3c6d4agIDh40OzHxJgRt3/+gQED7OedPAlVqjhfW7QoXLsG+/ZB/frp0ftsa926dfxrLY+AqQX29ddfExoamsRV6ScwMJChQ4e6PZZccJMwcUpISIhL0eoNGzbYtpMatbPSWjul6n/kkUds2wEBAcyfPz/ZeyRm0aJFqb42LUk5eSGEEEIIYRw4kHjAduyYWde2Zg04JoaIjHQO2AAmT7Zv37wJs2aZgC1PHqhXL827nZ1dvnyZadPsZY/Xr19vW2sVERFBeq1C0lrbpjxaRUREUKpUKZephkOGDOHYsWNcuXLFpWB1Qrly5XLa79WrV7L9SM4Oa+kI4NFHH+Wzzz5L9pqsRoI2IYQQQghhfPyxee3dG0JDYf9+s//AA1C9utkuVQrOnIFvvjH7v/ziep8lS0zh7O3boUABGDbMtNeoYaZbCo/t2rXLtt2rVy+efPJJcuTIga+vL/Hx8URHRwNw+/ZtFi9e7LQ+LLWuXr1Ko0aN8PX1ZbJDAN6zZ0+Cg4Odzv3000+ZPXs21atXd1ujLaE7CdZB7t2717btWBrAKjQ0lKioqCTvefr0aQB8fX3ZsWNHpiqDkFYkaBNCCCGEEIY1kcNbb0HhwmZU7OBBSPjHtJcXVKiQ9L0KFQKHdOuACdqER4KCgujatSvdupm8f++99x6LFy+2Hc9nKT4eFhbG9evX6du3LwMGDGD06NH8+uuvyQY6SZkzZ44ti+OYMWNYu3YtUVFRtsQhAF27duXChQsMGzbM42yMAPXr16dZs2aMGDHCKcjr1asXjz32GPUTTJ2NiYlhq7V2XwJ79uxh2LBh/P333wC8/vrrHvcjq/GoTpsQQgghhMhmYmIgVy749Vf46ivw9jZr0wBq17afV6eO++vdJRLZuhVatUo8GYkEbR4JDAx0mYL41ltvkTNnTtt+0aJFuXbtGp999pkt0yLA/PnzmT9/PuXKlePUqVP4+Ph49J7WaYhKKZcRr/bt27Nq1Srb/rx58xg4cKBTfzzl4+NjK2z9ww8/2NqtWR4ds2NaJTZ62Lp1a8LDw237pUuXTnF/sgoZaRNCCCGEuN+sXGnWl332GTRvDosXw3//az/uyR/6ORL82/+DD5p7XbiQ+DW1aqWmt/eNyMhIDh486BSw1a5dm4ULF1KoUCGnc4sVKwbAsmXL3N7r/PnzPPvssy7tI0eO5KWXXnJZK/bEE0/g5eWFUspWZ+0b6xRY7NkrhwwZwuDBg1MVsCVkLcYN2ApoX3VYU2mt49avXz++/fZbl+sdAzYwBbCzKwnahBBCCCHuN9OmQVwcJJIB0CO1akFdhwTj1qmVpUqZ6ZTVqsGzz5pMkt9+a96zffu763c21qJFC/LmzcvDDz9sa4uKiuLQoUNUrlzZ5Xxr0GadGujODz/8QEREBGBG0s6fP89HH33EwoULuWAJrrXWDBkyhC1btjhd27lzZ/r06WMLpqxatWqVug+YDGuKfmug2K5dO6pVq2Y7PmnSJJdrEgayCadWZicStAkhhBBCZDfx8fDdd3Dpkuux6GjYsyfxa1eu9Ow9lILff4fffoOLF8GS0RCAxx6D48fh669N6v9evWDECElCkgTr6JYjvyRq2cXFxXl033r16nHs2DFKlChhKzgNZpQrPj6ecePGMXfuXJfrRowYYTsvb968gCk23aFDB4/e1xMBAQG2besavWeeeQatNWvXrqWZw5rI/AmKsIeHh9vKIAwYMIATJ05QtmzZNOtbZiNBmxBCCCFEdrJjh1mT1rs3NGhgCl47mj078WvffBM6dUrZ+zVpAtl4LdG9kHCaX9WqVVm/fn2S14SFhSV5vF+/fgCcOnWKoUOHEhIS4rSGrHnz5gwbNoz/+7//s7WNHj3atl2zZk3ArHELDw8nPj6eOXPmkCPhtNi7sHXrVh588EEefvhh6rkpBfHMM8+4TUISGhpK/vz5bVM8v/zyS6pWrZpm/cqMJBGJEEIIIUR28vjjZuojQHAwNG1qimK/8475mT7dHCtfHs6dc762SJF721cBwJEjRwAoVaoUCxYsoF27dsleYx2ZAsiRIwdRUVG2dWYff/wxAwcOZMmSJcTHx7Np0ya393CsZ7Z582ZatmxJo0aNCAgIcLo/kKIMkZ4qU6YMhw8fTvIc6/TMCw5rJQ9ai7tbeHll/3Go7P8JhRBCCCHuF5s22QM2q507YfhwMzXRGrCVKwd//AE//gh9+tjPlaDtnrt9+zYLFiwAoEGDBh4FbABTp061bbdu3RofHx/berju3bvbCnB7onbt2rRs2RIwCUeso2yZQdmyZfHx8eHy5cu2Mga///677bjj6GF2JkGbEEIIIURWd+UKRESYEbXkVK4MQUFmDVqXLvDQQ/ZjErTdc2+//TaLFi0CoGLFih5fV6VKFdt2HUtZho0bN3Lu3DnKly+f6DTGzz77jEGDBjm1ffnllyns9b2TI0cO2/dy5swZwEz5BFNPzlrHLruToE0IIYQQIis7dw4CAqBSJRO4Wdtu3wZfX9fza9Z0TgjiuBZIgrZ0pbUmJiaGuLg4Zs6ciVKK2ZY1ho0bN2bs2LGpuq81SUehQoWSTHtfs2ZN+vTpwxdffMG1a9coV64cvXr1crueLDOxZs/cv38/ABcvXgSgZMmSGdane02CNiGEEEKIrGjBAmjWDJ56CiIj4do1016unPnx8TGFsy21rmwqVHDer17dvi1BW7pZt24dXl5e+Pr6kiNHDt58803bMX9/f7Zt20bBggVTde8yZcp4dN6KFStsafKLFCnCmTNn3NY/y2waNGgAwN69e4mMjLQV/3Ysj5DdSSISIYQQQmQ/0dHw4Ycme2J2rQ02caJJtZ+Q4x/wDRvC6tWwcSM8+aRpSzA1Doe06ySoeyXSTlJrr4YMGZKqrIw//vgjGzZsoFevXm6P58+fn7CwMMqVK0eFChWoVKmS03Fvb+8Uv2dGsK6xu3r1KlOmTCEqKoqGDRtm62LaCUnQJoQQQoisa+NGWLcOpkyBXLlMW2SkKfpsLTp89CjUqGG/JjwcvL0hd+5739+0EhPjPmADeOst17bWrU3dtOLFoUAB52M+PjBhgsk0Wbx42vf1Pnb9+nW6d+9OaGioLUOk1csvv8xLL73E6tWrGT58eKru36VLF7p06ZLo8SNHjvDXX3/Rtm3bVN0/syhueS5/+eUXVqxYAUB8fHxGdumek6BNCCGEEJnPzp2mQHN8PHz2Gbz2mus5339vijYDzJiR+L0eeAAOHoQ6dcyUwkGDTHBy+jTkyZM+/U9vlrU9NrVqmZG07t3hkUfcX1OtWuL3S+VaKpG0r7/+2qnOWOvWrfnll1/w8vKypdBPzyl+5cqVyxajUSVKlADg1q1btrYDBw5kVHcyhKxpE0IIIUTmsmOHKdhs/Zf011+HQ4dMhsSZM2HzZpg3zx6wJSZvXvt23brQs6d9auDVqyYNPkC/fmbkLSbG+fo7d+DECdfi1BktOtrUXrM6dgyOHDHp/BML2MQ9d+vWLZYsWWLbnzVrFuvXr8fb2ztdap5lZ8XdjADrzPZ7mc5kpE0IIYQQmUdoqHNAYvXqq2b0LSU++sis+7pyxewvW+Z8fO5cM1r3zz9mf9AgWLzYbO/bZ4LCM2fMeS+/nLL3Tk/r1tm3W7d2TiQiMtyJEyfYuHEjK1eu5Pfff6dMmTIcOnSIwoULZ3TXsix/f39y5cpFTMJ/WLmPyEibEEIIIe6dTZtg/HjXUS0wRaF//NG+//XXZnQM7IFXQsOHw8iRZqqjozFjTBAWHAyff554f6wBG8CSJWYkb9Ikk8DDUhOKzZuT/VhO7twxo3cffODcrrVJw383rl1zrsVmDTJFpvDFF19QvXp1Xn/9dbZs2QLAvHnzJGC7S0opl9G29tk1wVAiJGgT2sFM4gAAIABJREFUQgghxL3x7bdmZGjCBFi1ygRpYAK1o0ehfn37iNZDD8Gzz5rgB+wBlKPhw81o2tSppkbZjh0mgPvPf8x7WDPj9ejhfN3QoYn38c03Xdd3Xb+ess+5ZQv83//B+++boFBrs5bOywvKljXTG1PLMaj19YX7qE5VetFac+HCBWJjY5k/fz7Vq1dn1apVTudYa6oppXjzzTf56quv+Pfff53OuXPnDhMmTHBqmzdvHk8//XS6f4b7gb+/v2174cKFTlNP7wcyPVIIIYQQaSsoyARP586ZwGzuXChWzHmKYc+eULo0zJ4N3bq53uPBB81rYinoH37YZIx09Oij5iehIkXM1MrvvjPXdeoEISFm3+qpp5ynHTpK8Md5oqKiYMQI55G9NWvM2jzrWrqQELMGrW5dz+6Z0F9/2bffeSd19xBOxo0bx6RJk5zaOnXqxMSJE2nVqhW7d+/m7bffth2bOXMmYEbVlixZQpUqVZg7dy6vvvoqAMWKFWPbtm2EhobSpEmTe/dBsrlc1uywwAsvvJCBPckYErQJIYQQIm0NGGCKOls1aGDWpIWFOZ936ZKZiuiOdWqhpaiuzfPPmwDwjTcgJXWtGjc2P1ZLlpgRuTFjTPDYsqVr0Pbf/5r3c5xC6U50tElssmuX61TM/v1dz58zx4y8xcfD7t2waRPK0wQix4+b12eeMX0XdyUiIoIPEk5jtRg7dixjk8iquWfPHqpVq0aDBg0IDg62tU+bNo1q1apRLalsnSLFHIO2+5FMjxRCCCFE2omJgT17XNutwcxXXzm3//6767k//ADly5ttb2/o08f5Ph9+CJYU4Knm4wNFi5oslGPGmGmLVs8/b6Y0WutfJRe0vfQSVKgAvXt79t4LF5qEKz17miyZ48ZRNmGSlMRYg4NJkyBnTs+uEWit2bdvH6dOnWLYsGFMnTqViIgIGjVq5FTvK2/evBQpUsTtPa5fv05wcDBaa4oWLWpr37dvHxctNfO+++47BgwYkK6f5X4lQVsylFJllVJblVLHlFJ/KaXesLSPV0pdUkodsvy0c7hmtFLqlFLqhFKqTXp+ACGEEEJkEj/9ZNZZRUeboKpYMddzateGdu1c263KlXOdLvnRR2ba48KF5v7poWxZ+1TMhx4yr/nzm6AxIiLpBCKO0yytVqwAx+QT9es7Hy9aFJYvt+36O057TEx4uH16pBTBdhEZGUmcdZ1kAlOnTqVhw4ZUqVKFWbNmMXr0aPLly8fRo0cBeP3119mwYQMhISGcOHGCxo6jsph0/YUKFbLVC+vlptzEyy+/TE9r4hyR5iRoS94d4G2tdQ3gEeBVpdQDlmOfaq3rWH5+BrAc6wXUBJ4C5iilvNOh70IIIYTILL7/3jmr4ejRJsD48EPn8/LnN9MRE9OwoWtb6dImyUh6rmNRytRk++wzePFFe1vBgmbbcV3bli3wyy9mOyrKZIt0NGeO+S5GjbK3hYWZKY2J8aTmVNu29u1ERoPuNwMHDqRu3brs2bOHvHnzkiNHDq5evUpkZKTTeXPnzk30HgcOHOA///kPrVu3xs/Pj0KFCrFz50601mzdupVLly4xNEHympkzZ7Jz505KWhLBfPrpp0m+h7h7L730EgAdO3bM4J5kjGQng2utg4Fgy3a4UuoYkMT/2tIR+E5rHQOcVUqdAhoCu9Kgv0IIIYS4V27dMtMIk1s7dvWqc6HrMWNgyBBzXf78zufmz2+mEjrq1MmMZm3aBJ98kiZdT5UiReC115zbChUyUxlHjYJZs0wGyFatzLHLl2HDBufzV6yADh3Mdp489vbNm81+3bomQUsChffsMeUGhg1z37c9e0zgapWS9XzZ1JEjR1i0aBEAjzisCSxRogTe3t7ExcVRqFAh6tSpw4ULF2zH27dvz5o1a2z7Dz/8cKLv0bx5c7ftXl5eNG7cmMuXL6O1lmLZ90CnTp04fPgwVatWzeiuZIgU/cYrpSoAdYE9QBPgNaVUP2A/ZjTuX0xAt9vhsou4CfKUUoOAQWCqnAcGBqa892ksIiIiU/RDZF7yjIikyPMhkpKlno/4eIr89hs1x48nqlw5jr7/PvlOneJq69Zob9fJMzUmTcI6We/AvHmEV6sGv/0GQM6iRXHM5/jrwYN416pFvRIlCK9WjdgCBTjfpw8xRYvCu+/C2bPmJ5OomyMH/gD/z959x+d0/QEc/9xEbDFTK3aovWvv0VIzVlulVFvaWrVaVVU1i9p+Ru1Sq3YRrREqVs3ahKaIkiBEkP2c3x83z8ozkkhift+v1/N67j333HvP87gkX+ec71myhLADBwgtUwbPuGPH166l9NixGAdsHlyzhsjs2WHfPgA8bt2iTNyxPZcvA5Bu4kRqxhtCF+HhQfrbt3n844/8VbGi3XbUb9wYY1hwpVcvrr8oz1IqUEqxePFili1b5rCOcZhkSEiIab20ChUqmDI/Zs+enWXLltGwYcMX4u/lC/XvRyo7ePAV7QdSSiXqBWQGjgHt4vZzA67oQyzHAoviyv8HdLE4byHQ3tm1q1Spop4Hvr6+z7oJ4jknz4hwRp4P4cwzfz4MBqU+/lip+fOd1/P1VUofrGf7GjbM/jlp05rrPH5sezxnTvNxy/a8CN5+2/H3AUq98Yb+Pn267bmxsUqNHq3UgQPmssePba+xfr3+Xq+e/Tb88491/VfczJkzFWB65c+f32rf0WvPnj2ma9y7d0+tWLFCRUREPMNPknjP/N8P8VQAR5WDeClR2SM1TXMD1gG/KKXWxwV7QUqpWKWUAZiPPgQS9J41ixRMeAL/JSmSFEIIIUTK2LoVsmbVh/UtWKBnOtQ0/eXlBYMGmedT3bsHDRs6vta4cdap/EFfc8yYpMPfHzJksD3PMvuj0YsynMzROnFG/v76e8eOtsdcXPQFti2TWmTIoP85GNWrpydfAeslEQwGfY7d7dv6ouRGW7cmrf0vmcuXLzNkyBBA7y07efIk169f56+//iI8PNy0UHZwcDBRUVEMHTqU4sWLs2TJEurXr2+6TrZs2Xjvvfde+eQW4sWRmOyRGnpv2Xml1BSL8rwW1byBM3Hbm4F3NU1Lp2laEaA48FfKNVkIIYQQTimlr4tWvbqe/CL++mhGV67AlCn6XC3Q1xkz+vVX+xkTGzSwvp7lOmpeXvbv89VX+nu8jHwvhISCtvv39Xl6SVmC4KOPYOFCPd3/ypXmLJOWSwu0bAklS+oZOC3WALNKRvKKUEpx7tw5DAYD8+fPJyIigvr163P79m0qVKiApmm88cYbpI/LLOrp6YmHhwdubm6MHz+eS5cu0c3eenlCvEAS09NWG+gKNIqX3n+ipmmnNU07BTQEBgAopc4Ca4BzwHagt1LKfv5VIYQQQqS8s2f1DIZ//WWdlbBWLahTx7b+8uV6PWP2x8GD9bT7bm4QG2sb9GXNqq8x5u8Pxix9cZnd7MqfH27dgt9/T97nehaM2SMtffwx5LX4v2sPj6T3HPbooc/7y5fPHBjevWs+bsxOCWDMSjhu3IvTQ5mCNmzYQJkyZejUqRMTJ04E4KuvvsLVzvxKIV5WCQZtSik/pZSmlCqvLNL7K6W6KqXKxZW3VnqWSeM5Y5VSxZRSryulfJxdXwghhBApKDTUNjV+gQJ65sH9+/UkGSEhMGAAnD6tHz95EpYtM/esdeliPtfFBbJksV0we80asEyaMWCA83blzq1f50UTP2irVQvmz4cWLcxl2bIl7x5ZsmBwddUD4MhI2+PGtceqVEnefZ5T48ePR9M0NmzYYCpbu3YtFy5cYPXq1bRv3x6AdevWmY7XqlXL5jpCvMwSNadNCCGEEM8xgwGCg/Xtn37Se9iM0qXTgzXLX3KzZ9eHRZYtq+/HxoLl8LEKFWzvUa2a3iNn6fFj83apUsn7DM8ry7T9P/5oyorJ55+byyMikncPTSPGuDRCSIjtum+gz4WrVy9590mGe/fuMXToUDRNI2fOnKZU+8n1119/MWzYMADatWtnun7Hjh0pVaqU3UWsp0+fTtasWVPk/kK8KGSRDyGEEOJFoJTt0LibN/XyUaNg3jw9qciMGfqxjz+GsWP1gMxyKF9Czp51fKxNG0if3hykZM+uJy/p2zdpn+VFkjateXvQIPN20aLm7RQIpmIyZiTtvXv6cMlx42wrZMumf/dPQXh4OHv27GHRokVkz56datWqmRY2Bj2N/ocffsipU6do164dtWvXfuJ1ylatWmVTFmI5t89C37596dy5s9WabEK8KiRoE0IIIZ53kZF6T5m7O/j6woUL8MEHcOSIdb3Jk/X3ypX15CIZMyZ87WrVzD1zDRtC6dKO62bOrC+CbVzY+d49/b1Tp6R9nheJo+yClj09Hh7Jvs2jokXJeOOGvhPX82TFMhlJKjIYDLRs2dK0thnA/Pnz7dadOnUqU6dOZenSpbz99tv4+/tTM5HJZvz8/Dhy5AhXLRYaz5AhA+Hh4ab99u3bM2jQIGrUqCGLV4tXngyPFEIIIZ53K1fC8eOwZw/kyqXPJYsfsFnasydxARvoCS+Mc9Peey/h+q6u0LateT9nTj1L5cvK2Tyyli317yNuzlVyXPjyS/sHnlLvmtGIESOsAjZLbdu2ZeHChQwePNiqfOvWrbRt25ZatWqhaRp9+/Y1rtVr1927d2nZsiUDBw5k/fr1AOzZs4fHjx+zN25JiS+++IK1a9dSs2ZNCdiEQHrahBBCiOfLhQv6/LAuXfSgIGNGWLzYfNwyw6CLiz6fDfRhdf/9B82aJS3hR44cegB44ID1vDdnypWDjRv17UaN9CyTLysvLzh61P4Q07Vr9ZT/uXMn+zaxmTPbFnbrpi+p0KeP/SGTqcDYqzZhwgQqV65M2rRp6dq1Kx06dGCysScXeO+99/j88885fPgwa9assbrGrFmzOHPmDDt27CBNGutfNR8+fEiuXLmsygoWLEjt2rUBqFevHgaDQQI1IeKRoE0IIYR4XgQEmBN6LF9um/jD0pYt0Lix3tMTG6v3yPz7r76uV1KlSZO0eVlVq5q3E9uj9yJz1NuWLl2KBGwO9e2rD3Vt1UrPAJrClFJomkZYWBhnz55l5cqVBMcltBkwYABuccG45RBGo8qVKzNhwgQaNGhg99p79uxhxIgRjLMINv38/Khbt65pv0KFCoSFhbF582ar4E4CNiFsyfBIIYQQ4nlhmZEwIaVK6YGam5t5CF3hwk8niLIM2kqUSP37vSoss3EWKKAHi5oGBQum+Pps586dI2fOnIwePZratWtTs2ZNZhiT2IApYHMmk2VmzTgVLZaBMKbyT58+PSEhIXgb1wEEXFxcOHHiBFeuXKFMmTLJ/DRCvPwkaBNCCCGeB598Atu3Oz6+bJl1gFSkSOq3yZF8+eDLL/XhlI7mYomky5ABvv1W345bRPpJXLt2jV27drFu3TpuGJObxNO5c2fu3bvHiBEjOG1cry9OC8s16Jw2N4PV/qRJkzhx4oTVemoAkZGRlC5dmjt37gAwcOBAYmJipEdNiCSQoE0IIYRIbYGBuERFwR9/wMGD1j0qoM9dW7BA3+7VC4KC4LffYMwYiI7W0/p36QKzZkGhQrBrV4r3vCTZhAn6+m9pZKZFivr+e7h2DeysT+bMo0ePiI6OJiIigho1atCkSRM6dOiAp6cngwYN4tNPP0UpxYMHDxgyZAh///233eu89dZbbNq0KVH3LF26ND179gRg4sSJpgQl3t7efPjhh6SzyLwZFBRkOmfy5MkSsAmRRPIvrRBCCJFaYmPh009hwQJsZowNHQrjx8Pt27B1q7l89mw9wUjLlvrLUtOm+rw18fLStCTPX7t58yb58uXD09OTx48f26xzNmXKFADmzZtHzZo1OXjwoMNr1a9fH1dX10Q2VWPevHnMmzfPpnzRokUsWrSI27dv85rFPMvKlSsn9mMJISxIT5sQQgiRWubMMfegxWfMuLdjh/6eLh38/bcesAmRCLt372bQoEF88MEHAAQGBjpcmNrIGLC1bt2aiIgIli1bRrdu3UzH8+fPn6Jt9PDw4KbFGnOWc96EEIknPxmEEEKI1BAQAPHWs7Ji/EX2p5/09759oXz51G/XSyY0NJTz58/blK9btw5N0+jevbvNsbCwMCIiIp5C61LXoEGDmDJlCjt37rQ5dvXqVbZs2WL3vEaNGrFp0ybSpUtHly5dqFGjhumYp6dnirczT5481K5dG1dXV5o1a5bi1xfiVSBBmxBCCJEcs2ZBmzbQvbsepPn66r1r5cpBZKSerCMkhMuffQZLl4Kfn56mPzxcHwoXt5hwklLuvwIuXLhAw4YN0TSN999/H6UU4eHh+Pr6cvfuXZRS9OjRg2zZslG6dGk0TSNv3rz4+PjQo0cPOnToAMDSpUspVaoUUVFRAPz888+4u7uTIUMGU6/UnDlzePPNN4mJibFqQ3BwMN999x2BgYFP98MnQmRkJCdPnrR7rGfPnhQsWJAWLVoQERHBrVu3yJkzp+l4KeOyEnEs0/CndE+b0a5du7h165ZkihTiCcmcNiGEEOJJ3bmj95BZsliAGNDT+GfPTmCnTngZ17SKjbW9VtOmqdLEF5Gvry+NGjUy7a9YsYIGDRrQv39/wsPDHZ5369Yt3n77bZvyCxcukC5dOj766CMWLlxoKq9Tpw7ly5dn9erVABQvXpzLly/z+PFjFixYwMCBAwG4ffs2s2fPTqmPl2yxsbFkzZrVtN+qVSs6derE+++/T2BgIHktFgJPly4duXPnJiAggK5du3Lo0CH6xntmLYO4AqmwHpyxHZaJSYQQSSNBmxBCCJFUp0/DgQN6kpGEWAQfJhUq6PPXjAYONK+19oq7deuWVcBmNGvWLKcBmz1ffvklEy1S51sGbADnz5+3Glr577//Wi3ybHTo0KEk3TcmJoZPP/2UYsWK8fXXX5vu9c033+Dl5cWECROSlT1x1apVREZGAnqvmmUiEEdBV5YsWdi4caPdYy4uLty4cYPIyEgyvgqLpQvxApLhkUIIIURS+Prqc8/iB2y9e4OPj219Dw/bsr17oX59834qDUl73jx+/JhPP/2UChUq2F0/7Pr169SqVcu0v3r1ahbEJXI5deqUTf0MGTJw5coVhgwZwpkzZ6yOtW7dmuHDhxMeHs7keL2flkMFE8NgMCSqnlKKU6dOMWbMGBYuXMiwYcM4d+4cly5dokqVKmzYsIFJkyaxefNm0zlXrlxh1qxZxNrrfbXjzz//pEuXLqZ9Y2bI5MqXLx9FnuXaf0IIpyRoE0IIIRJLKfs9Z4cP63PbmjXTs0G2aAHVqunJSOytY5Y1Kyxfbt5//fXUa/MzZDAYTJkDlVJ88MEHzJs3j1OnTtGzZ0+GDRvGgwcPyJs3L2nTpqVgwYIEBAQA0KRJEzp16kSlSpXsXrt8+fIEBwdTtGhRJk6cSJkyZVi2bBlt27Zl5syZbNy4kSxZspA+fXr69u1r1Xs3a9Ys6tSpY3W9X375xeYeb731FqAPj0yMP//8kwoVKvD999+bylq1asXrr79u1UvYtm1bJk6cSI0aNfDy8qJv3742C1I7YtnObt26kSlTpkSdJ4R4scnwSCGEECKxVq60LfviCz1AM2rSRH8lJH9+6NABbt0CO/OwXmRHjhyhmsV38r///Y8MGTJYBSbbtm1j27ZtBAQEcOvWLZtrfPzxxwA2QdvFixc5evQobdu2tRnK16VLF6teKCM3Nzf++OMP/P39KVGiBC4uLlSvXp1z586Zergse/h69uzJtGnTiI6OJmvWrDx48MDp5/3tt98YPHgwly5dsjn2zz//2D3nq6++stp3lFTEUlRUFGvXrgWgc+fONuujCSFeXhK0CSGEEPH5+cGWLTBiBBgDg/BweP99cx2DAS5fhqJFn+wemga//pr8tj6HPo03dLR37964ubkB2CQDWbVqlc35jRo1ol27doC+UPOaNWvo1KkTZcuWpUSJEpQoUSLJbXJ1daVkyZKm/SJFitgMBxw+fDhjxoyhdevWZMiQgbRp0wLw6NEjDAYDLnbW0FNK0bp1a5vyX3/9lY4dO1qVeXp6OsxEefDgQcLCwsiSJYvDz9C6dWtTxsslS5aYvlMhxMtPhkcKIYQQ8b3/PkyYACNHmsuMi2AbtzUNihfX0/cLAEJCQpg7dy7Hjx+3ORYdHQ1Anz59rNYFiy9Pnjzs2rXLKiDp2LEjx48fZ9++fSnfaAujR4/m7t27tGjRAtADvQwZMqCUMgVLsbGx1KxZk/LlyxMREWG1cLSRn58fHTp0YMyYMaayVq1a4ePjQ7Zs2Rzev1evXg6PBQQE8Pvvv5v2JWAT4tUiQZsQQohXS0AADBoE+/c7rnPtmv4+aRLcuKHvt2mjl737buKGP75i9u3bR+HChfnss88A6NSpE4cPHzb1mBllz56dihUrOrxO165d7ZZXqlTJacCTUnLkyGG1nzlzZgA8PDyYMGECHTt25NChQ5w+fZo1a9bY9LLdunWL2rVrA9YJT8aOHUvZsmW5fv26VX1N03j33XcBrBKUANy/fx+lFAaDgaIWPbr2smsKIV5uErQJIYR4tXz7LUyZAnXq6L1le/ZYH4/LVmji6QmFCpn33d1TvYkvmoCAAOrVq0dYWBigr/s1ceJEqlWrRufOna3q5siRg+bNm1uVrV+/nj179jBkyBBGjRr11NqdGLlz5zZtDx06lA0bNpj2u3XrxrFjx0z7wcHBVvUtF6ouV64coAeBM2bMMJVPnz6dwYMHA+Dl5WUqHzFiBNmzZ6d///5s27bNVJ43b162b9+eEh9NCPECSTBo0zStgKZpvpqmndc07aymaf3jynNomrZD0zT/uPfsceWapmkzNE27rGnaKU3TKqf2hxBCCCESdPMmrF4NFkPMABg+3LwdGgqffKJvO1pk2HJe20tKKYWfnx9eXl60b9+emzdvsnPnToep7wcNGmTaPn78OGfPnqVQXKDr7e1tVTdz5sy0bt2aFStWcOrUKYKCgmjbti3169dn4sSJpH/O1qvLn8ByDK5xw2OvXr2KR7zlHVq2bMn06dM5ceKEVXnfvn25desWu3btom/fvqbFsC2HWo4ePRqAmTNn4mOxlMQ///wjQyOFeAUlpqctBhiklCoF1AB6a5pWGhgK7FJKFQd2xe0DNAeKx716AnNSvNVCCCFEUty/DwUL6kMb79yxPvbff7BpE0RHw86d5vK//9bLLc2YAfXqpX57U5DBYGD//v2EhIRQoEABPv74Y0JDQx3WP3DgAC4uLtStW5crV66wfv168uXLR9OmTenevTtKKav60dHRpt6n7NmzU65cOauFo11cXPjyyy9N+8Zj7733HuXKleO1115L1kLTqS2hoC02NpYCBQpQsGBBm2OaptGvXz+7w0Fz585tGubo4eGBpmkEBweb5s5ZWrFiBcBzGdQKIZ6OBIM2pdRNpdTxuO0w4DyQH2gDLI2rthRoG7fdBvhZ6Q4B2TRNy5viLRdCCPFq8/eHiRMh3qLKNo4dg+zZISbGXDZokN7zBvoct7Zt9R62SZP0sokT9XNat4a//jKfF29tr+fZ+vXrGTZsGJ9//jl16tQhZ86cBAYGsnDhQrJly4amabi5udGqVSvTsMaQkBAaN27s8JrLli2jU6dOVmXGoX2g9xSlsbMu3YgRI6hYsaIpjf+LxF7QZjAY+MTYIwu89tprybqHm5sb6dKlA/R5cJbXBn1uG2A3S6UQ4tWQpJT/mqYVBioBh4HcSqmboAd2mqYZ/8XKD1jOsg2MK7NNrySEEEI8idBQMKZ9//FHvVcsr4P/H5w927z92muwfTtUqqQvlJ0hg57KH2Bp3P9DurlB9+7mc6pWhVatIDIS4uYlPe8CAgJo3759gvViYmLYsmULw4cPZ/r06fz5559EREQAsHv3burWrUvFihUJDg42LTC9du1asmXLxqxZs2jcuLFpflaWLFlMgUd8mTJlshki+KKwTCYC4O/vj6ZpfP3118yfPx/AbqCaVOnTpzd99wviz6uM8/pLugi7ECJhif5XRtO0zMA64Aul1AMnQxnsHVA2lTStJ/rwSXLnzs2e+BPBn4GHDx8+F+0Qzy95RoQz8nw8JQYDZUaOxDR76PZtyJePv5Yu5XHBguRfv56M167h378/aBoVjx0jG/CgZEn+njyZ2NBQU/KRjHPmUM0yQAMCW7bk8tmz1vccOFB/9/N74mY/refjzp079OnTx+6xH374AdATalj69ddfefPNN03zz7p3746mafj5+TFr1iwAPvvsMy5cuABAaGgoXbt2JVeuXKZrjBgx4qV8/m/cuGHa3rZtG4GBgQQGBlrN7ytRokSyP/vw4cOtei3teRm/X5E48vNFoJRK8AW4Ab8DAy3KLgJ547bzAhfjtucB79mr5+hVpUoV9Tzw9fV91k0Qzzl5RoQz8nw8JcOGKaX3k9m+unc3b//9t1LR0UqlT6/vX77s+Jpt2+p13npLqdu3U6XZqfl8HDt2TA0cOFAFBQWpdu3aKfT/LFWAypIli9q/f79V/aioKLVq1Sp1//59pWmaVX1ABQUF2dzj0aNHqlatWjZ1ja+XlY+Pj8PPWK9ePQUoHx+fZN/H19fX5jvdsWOH6tChgwJUgwYNkn0P8eKSny+vBuCochAvJdjTpuldaguB80qpKRaHNgPdgB/i3jdZlPfRNG0VUB0IVXHDKIUQQohkuXQJxo0z7x84ALVqmfeXLDFv374NK1dCRISeCbJYMcfX3bBBD/We04QYUVFRfPbZZ3h6evL9998TFhbGggULWL16NVeuXOFOXHKVKVPMP6YXLlxIy5YtiY2NNWUnNHJzc+Odd94BsEksMmzYMLtztDJmzMi2bdvw8vIy3S937twEBQUxderUFP28z5M333yTnj172l0QfNOmTVy5coXKlVMmUfYvv/zC+3HZSQsVKkSTJk2dgIutAAAgAElEQVSoVKkSVatWpXu8HmEhxKslMcMjawNdgdOapp2MKxuGHqyt0TTtI+Aa0DHu2DbgbeAy8Bj4MEVbLIQQ4tUREwP//GOev2aZbv/6dX0NtXPnoHRp23MtF8AeMCDhez0nAVt0dDQLFy6kRIkSVK1aFRcXF7Zu3cqiRYsAfRHr8+fPc+vWLYfXmDVrFj169EjU/UqWLGka9pguXTrGjBnjsG7WrFm5ffu2KdtjUFAQAG2MC4+/hFxcXJg3b57dY9myZaNKlSopdq/OnTtTtGhRvv32W9N8uZw5c/LVV1+l2D2EEC+mBIM2pZQf9uepAdikmIrr2uudzHYJIYQQMH06DB4Mc+boi2Ib0/V/840esAGUKgVvvgl//GH/GpkyQd++T6e9yRQWFoZ7Aot3+/r6Oj3u7u5O796J/zG8Y8cOmjRpwsWLF1m6dGmi0u9//fXXjB8/HoAGDRpQpEiRRN9POFejRg127NjxrJshhHjOJD/dkRBCCJGSlIIRIyBHDj1gA/jsM+s68YeqLV4MDRpAoULWa62BvjRACmT3exqMiUISY/369dStWxc3NzcePXpEvnz52Lx5s901wZzx9PTk/Pnz+Pv7U7x48USdU8di6YPcuXMn6X5CCCGS7sX4KSaEEOLl98knsHy5PgfNma5doVkz67J8+fT5bgAjR8L33+vbU6Y4XgrgORITE0OnTp1Mi1TbU7ZsWc5YrElXr149Uzr6rFmzAk++jpemaZQwDkFNhFoW8wg7duzopKYQQoiUkODi2kIIIUSq69MHFixIOGDz8YGff3bec/b11+btpk1Tpn3J8Ouvv1KxYkXGjRuHpmlkzZqVvn37EhAQAOiJQOrWrWsK2Lp06cKVK1fYuXMnU6dOJSQkhJ9++glfX1/TkERvb2+b9cOepmzZsrFv3z4WLVqUqPXghBBCJI/0tAkhhHi2jh+H//0vcXXj97DZky4d3LsH165B2bLJa1syrVixwpQN0OjBgwfMmjWLixcv8scff3DhwgUOHTpkOv7zzz+jaRpFixalcWN96vgnn3wCQL9+/ahQoQJNn4NgtE6dOlbDJIUQQqQe6WkTQgjx9F24AJ0763PX5s7Vyzp1guBgmDEDbt40r77m76/PV7MIbBKULRuUL58qTY/v/v373Llzh7CwMAIDA03lPj4+NgGbpWPHjgGwYMECAFq2bInBYHCaCCRjxow0b96cNC/IHD0hhBApQ/7VF0II8fRERenp+a9csT32+efg4WGb6dHLCxLImPisREVFUbVqVa7E+zx58uQxpeSvU6cOS5cuZejQoWTLlo3XX3+dIUOGEBISQtmyZTl79iwArVq1SlTmRiGEEK8eCdqEEEKkvsePoVEjOHzY/vHvvoP69Z9um5Lhjz/+4PDhw4wePZro6Gib45ZrqE2ZMoWiRYvy+eef06BBAwAGx2XFNAZsAB06dEjdRgshhHhhyfBIIYQQqSs6GgoXtg7YsmWzrvMCzY2aNm0ab731FiNGjLAK2KpXr85c41BPC2XKlLEpa9WqldX+l19+SY4cOVK+sUIIIV4KErQJIYRIPUqBtzfcvm1dfu0a7Ntn3i9X7um26wmFhIQwYMAAm3I/Pz8OHTpEz549WbduHV5eXgDMnz+fjBkz2tTfsGEDX331lWlfFqcWQgjhjARtQgghUs/587B1q769YQPExoLBAFmy6L1re/fCli3wnC3QHB0dzY0bN6zKlFJUqVLFtN+zZ09cXV2ZO3cutWvXBvT1ztq1a4e/vz9KKT7++GO713d1dWXEiBGm/ebNm6fCpxBCCPGykKBNCCFEyjtyBPLkAePQwDZtoG1bcHEBy2Qb9epBixbPpo1O9OrVC09PT7Zv324q279/P//++y8A3bt3Z968eTx69IhevXo90T0yZszIkSNHOHbsGIUKFUqJZgshhHhJSdAmhBAiZSkF1atDUJC5zM6QwmfF39+fQYMGsWPHDod1Fi9eDOg9YMOHD+fcuXPUrVsXAE9PT9PctXTp0iWrLVWrVqVy5crJuoYQQoiXnwRtQgghUsbUqXov2oEDeuBmdPv2c5UZcvjw4UyZMoU333wTTdNYt26d1fH4c9bGjh1rlUykXbt2yQ7WhBBCiKSQoE0IIUTyxcbCwIH6tjETZJUqEB4OuXI9u3ZZuHLlCrNmzWLNmjVW5ePGjSMyMhKAq1evMm3aNABTev74jOn6hRBCiKdFgjYhhBD2KQXbt8Ply46P79wJGzdCGjvLfpYvD+nTp2LzFMHBwXbXSbMUGxvL/Pnz8fLyoq/Fwt05c+YE4Pjx46RPn56pU6cy0Bh4oq/F9vjxY4oWLWoq27dvHwUKFEjhTyKEEEI4J4trCyHEq+76dX1oY9myULMmREXB4sV6D9m338Ibb8CcOVC1ql7//HkoXhwGD4a4XikTDw9zev+mTVOtyVFRUaYhinXq1GH79u1kypTJbt3JkydbpdcHPalIhQoVyJw5s6nMMmA7fPgwbm5uuLm5cenSJQoXLkx4eDjlXpClCYQQQrxcJGgTQohX3dSp+suRI0fMARvAyJF6ohHLgC1XLujTB77+GipWhP/+g7ffTpXmxsTEmFLsg75GWubMmfn3338pVKgQBw4c4MaNG3Ts2BEAHx8fU91Ro0bRo0cP8ufPD8D06dPp37+/1fVr1KhBtWrVTPuurq6cOnUKTdPImjVrqnwmIYQQwhkJ2oQQ4lV2+bLzgM2etGlhwgTz/vjx0LcvGHu6Dh2C6GhIhQBHKcVbb73F0aNHbY4VLlyYhg0b4uvrC+hz2HLnzs3+/fsBOHnyJBUqVLA6p1+/frRp04avv/6alStX0qFDB5YuXWpz7ezZs6f4ZxFCCCESS+a0CSHEq+TePdi9Wx8CCda9YcOHm7cLFrQ9191df1+2TE/nX7gw3LgBQ4eaAzZjvbj5Yilt2rRp7N6927R/4sQJq+PGgA3g7NmzVKtWjejoaEqXLm0TsBkVKlSIFStWoJTi119/JWPGjKnSdiGEEOJJSdAmhBCvilOnIG9eaNwY0qWD5s3B318/Nm0ajB4NO3bAzZuwf7+epv/4cejaFQYNgpMnra83cCDky5eqTd62bRuvv/46e/fupU6dOlbzziIjI6lYsSK3jXPo4mndujXnzp0DYOHChanaTiGEECI1yfBIIYR4lgwGfW2zBw9gzx6oVMm6l0spvVcsueuC+flB3OLQJtu3m7eN87qaNDGX7dmjv//8s7ns1Ck9KyRAixbJa5MDSilmz55Njhw5GDlyJJcuXbJJv79mzRrSpk0LQK5cudi0aRNt2rSxe70vv/ySGjVqpEpbhRBCiKdBetqEEOJZ0TRwdQUXF8iWDdq2hU6dzMdXrdKPpU+vB0tP4o8/oEwZ64CtUiXrOr16Jf565crBggUwaxZYpMJPrpiYGHr06EHu3LlxcXGhT58+dO7cmUuXLtnU9fb2pnXr1lZlrVq1YsuWLVy7do13333XVD58+HAmWM6/E0IIIV5A0tMmhBBPg1Jw6xbs26cPQeza1X69w4f1XrFNm2DyZHP5xo3mHi5n/vlHDwbz5oWWLWHXLuvjly7p6fpBT0KSJw9YpL1PlI8+Slr9BISHh1OxYkW7AVp8N27cIJ+dIZmaptEirudv3LhxrFq1CoC2bdumaFuFEEKIZyHBoE3TtEVASyBYKVU2rmwk8AlgnEgwTCm1Le7Y18BHQCzQTyn1eyq0WwghXhxXrkCdOnrQZrRggeP68YcxAnz3nZ7444MPbI8dOQLbtkGjRlCvnh60jR1rG7BlzWoO2AC8vJL0MVJSREQEixcvxtPTk0OHDtkN2GrVqsW1a9eoVasWa9asYcCAAXYDtviKFClCcHAwFy9epEqVKqnRfCGEEOKpSkxP2xJgFvBzvPKpSqkfLQs0TSsNvAuUAfIBOzVNK6GUik2BtgohxIslIgK6dYM1axzX2bRJD7Jq1NDfPTwc1+3WTU8i8vff+mLXCxbo6feNa4qNHKm/KwXDhtmev2TJk36SFBMZGUm1atU4ZWe458CBA5k0aRIPHz7E3ZipMs7q1auTdB8PDw88nH2XQgghxAskwaBNKfWnpmmFE3m9NsAqpVQkEKBp2mWgGnDwiVsohBAvmt27oX17uH/furxQIb3X7ZtvzOucxZubZeX112HiRChd2txD1rUrGNPae3vDwUT+8xoU5DwgTGWBgYEUKFDA4fFWrVoxadIkXFxcbAI2IYQQ4lWnKaUSrqQHbVviDY/sDjwAjgKDlFL3NE2bBRxSSi2Pq7cQ8FFKrbVzzZ5AT4DcuXNXMc4/eJYePnxI5qTO7RCvFHlGhDMPHz4ke2wstePNo4rJmJHTY8cSWqECaBpp796lUr9+3GjThkDLxCNA4SVLeG3nTk7MmEF0jhym8oxXr1Kte/cE26A0jeBGjci9axdhxYvzoFQpIvLm5bpFco6n7fjx44wcOZKwsDBTWYkSJayGRE6fPp3yiZmz9wKTfz+EM/J8CGfk+Xg1NGzY8JhSqqq9Y08atOUG7gAKGA3kVUr10DTtf8DBeEHbNqXUOmfXr1q1qjp69GjiP1Eq2bNnj01aaSEsyTMinPHbtIk6I0farmc2ezZ89lnyLq6UnkkyMaKj9WQm1arBM14o+v79+2TPnt2q7M0332Tr1q1cvXoVLy8vPD09uXTpEhkyZHhGrXw65N8P4Yw8H8IZeT5eDZqmOQzanijlv1IqSCkVq5QyAPPRh0ACBAKW4188gf+e5B5CCPGiKTZ3rjlgy5oV+vaFHj3gk0+Sf3FNM6/V1qiR9bFFi+CLL/TtFSsgTRpo0OCZBmzbtm0jS5YspoCtevXqREVFoZTi999/J02aNBQrVozHjx9z8eLFlz5gE0IIIZLjiVL+a5qWVyl1M27XGzgTt70ZWKFp2hT0RCTFgb+S3UohhHgBpLfMDtmkCcyYkbI32LEDVq6EoUP1+XGg9+B9+CHExEDv3s80I2RMTAzh4eFkzJiRDz74gIcPH5qODRw4EDc3N5tzJFgTQgghEpaYlP8rgQZALk3TAoHvgAaaplVEHx75L9ALQCl1VtO0NcA5IAboLZkjhRCvhDt3yG7sZXNzg88/T/l71K1rXg5g+XLYvh2++krfT5PmmQZsAGXKlOHSpUt4e3tz9+5dAAoWLMiBAwfInz//M22bEEII8SJLTPbI9+wUL3RSfywwNjmNEkK8wiIj9WGAjx9Dhgz6sEBnLlyAmTPh++8hV66n00Z7BgzQ39OkgTt3ILUzIL7/vv5KASEhISxYsAAvLy+qV6/OvXv3WL58OW+//Tbjxo2jdevWfPbZZ4SHh/PgwQPy5MkD6D1r48ePJ2fOnMyfP9+UWGTDhg0AtG7dmk2bNqVIG4UQQohX2RMNjxRCiFSxaJE+/8tgMJd9+y18+qm+xliPHhAXMAD68MP+/fXtq1ehSxcICdHrJzZpR0qIiQHjOmJTp6Z+wJbChgwZwqJFi2zKJ8QtS/D7779jMBjo27cvAPv27SNr1qwJZnts2rRpyjdWCCGEeAVJ0CaEeD4cPAgffWRbPnq0/gIIDTWvbwbw88/m7a1b9RfAuXMwa1bqtdWSwQAjRkB0NI89PcnYu/fTue8Tio6O5tatW3h6eqJpGn379rUbsMVnDNgAZs2aRUBAgE2dwoUL4+PjQ6lSpXBxceHdZ7jMgBBCCPEyeYr/FS2EEBbWroVSpaBkSdi4EWrVSvicEyfM2waDPjTSnuXL9RT5qSE2Vk/+8fnneg9b48YwfjwAIW+8kfBwzmfo2rVrNGnShIIFC+Li4kKBAgWYFRfcFilSBF/jot3Ae+/ZGxmvW716NX/9peeY6tWrF1evXiU2NpaAgABKlixJcHAwV69eJdezHK4qhBBCvEQkaBNCpI4tW6ByZdi7Vw/Kfv9dD3hAn7fWsaMedF28CN7e5vNWroRp0/TyHTvMae7BHKRt2QKurvDoERQoAJMnW987NBQePHjytgcHw48/wpUrei/ftWvmYyNHwty5MGcOvP027NljOhTUpMkT3zIsLIyNGzdy9erVeE0J5ubNmw7OSpzbt28zceJEihQpwp9//mkqDwwMNG0fPnyYBg0a8PjxYwwGA8uWLWP9+vUEBQXxzjvv0KtXL3bv3m113VGjRjF37lxTEGjk4eGBp6dnstoshBBCCDMZHimESFmPHkGRInD7tr4ffzHQhw8hc2bzvru7OcB6802wHFJXooR+nZs39QDw+nXbnqz69WHgQHjjDVizBlat0hOB7NkDbdo82Wfo0kUPGIcM0fdHjNDXXOvTB8aMMdfbsUN/L1IEBg0irHTpJN2mcuXKnLDsPUTPtvjvv/+ilOL7779n1KhRAPj5+VG7du0kXf/w4cMsWbKEuXPnmso8PDwoXrw4Bw4cMJWNHTsWDw8PwJyC39XVFe+4YHrVqlUAKKX48ccfGTx4MACdO3dOUnuEEEII8WQkaBNCpJzQUChaVE8G4ohlwDZ6NHz8MYwapc9DmzbNtn6WLPrr889h0iTb423b6u/GdPj588PXX0PPntCsmXVPXWIZgzFLM2dChQr268+erd/LotfNnrCwMLZs2UKfPn348MMPbQI20IcwuthJovLZZ59x4sQJXF1dbY49fvyYW7duUbRoUWbOnElgYCBdu3alRo0aNnUDAwNJmzat03Y6omkagwYNwtPTk4iICIoVK/ZE1xFCCCFE0sjwSCFE8gQF6RkT27WDbNnMAdtHH4GTeVG0a6cHV3ny6EHPnj36HDdHxo2z3s+fXx8m2b69dfmgQXrvXXAw3LiR9M9jMXzQhnHx7P79re+bwNytY8eO4eLigru7O507dyYkJITJ8Yd0Au3jfxYLp0+fJn/+/BgMBs6fP8/3339PeHg4SinatGlD8eLFadKkCf369WPixImUK1fO5hqLFi164oDN0jvvvEO3bt2SfR0hhBBCJI70tAkhnkxEBAQEgL0hgYMH671iMTHQtCkULqz3UuXMaa6zdm3SknakSaMHYrNm6UlGRozQy+JzczMPt9ywQQ/ikuKDDxwfM/b0ubvrKf6nToVTp6BSJbvVQ0NDadq0KUeOHHF4SX9/fzJlysRrr72Gq6srWrzv5IMPPuC///5j586dBAUF0aNHD5YuXQrA+fPnWW1cagDYtWuXw/v4+fnZ7XkTQgghxPNPetqEEIkXGqovIv322/rC1/YCtqpV4Ycf9O00aeDDD6FhQ8iRQ59/BnrikCfJsujhoS+iPWqU/YAtPsvhh9HR+vBLf3/7dZXSg814iUBo2ND8OUND9Xd3dz0RyuDB+rIDdoYsrl69mpIlS1oFbM2aNSM2NtY0n6xGjRp4eXmRN29e07DHnj17AnDkyBGuX7/OokWL2LFjh2nNNGPAZryHPcbMj9WqVcPb25vBgwdTu3Ztu0MrhRBCCPH8k6BNCJE4mzfrwx+nTQMfH+tjW7fCpUtw/Djs3283iAH03rV16/TAzw6lFMePHycsLIyAgABiYmKcNkkpxYYNG2jYsCGXLl0iNjaWqKgofdgl6JklT5yAsDC9Z27AAGjZEi5fhmrVYPFi88V8fa2zUK5bp2eJ3LULjh6F7NnNx6pUcdim8PBwGjduzLvvvsutuOGUHTt2JCQkBB8fH1xcXKhZsybHjh3j999/tzl/9uzZPHjwgKpVq+Lp6WkKtIYYk6IkwvLlyzlx4gR+fn6sX7+eSfbmAgohhBDihSFBmxDCuUOHoFEj60yMLVtC5856Gv979/Set+LF9WGCzuZM5cqlz2Vz0Mv23XffUaVKFdzd3SlatChubm4MHjyY06dPM2DAAP7991+r+kOHDqVdu3bs2bOHjh07kiZNGgoUKIB/WJheYfZsPetkr1762m2gB5fFi8ORI9Cjhz68EfQlCCy1a6efp2l6r6Jl0OZgTbkDBw6QMWNGq9T4bm5uTJ48meyW56NnjnR3d7e5hqurK1myZLEp1zSNhg0bApiyNxr5+vqydu1aypYty/Hjx3FxcaFixYq4ubnZbacQQgghXiwyp00I4VhMjB6Q3btnLjt9GsqWTfallVJcuHCB9OnTU6RIEb755hvGxU82AkyePNmUtCNt2rSmYYIAv/32m2n7VFzwFRwczNqVK/kazHPbVq503JDx4/V5cpYZL0+ftq1Xty788w80b26TkVIpxZYtW/jmm29MZR07dmTRokW4ubmR7kkyWNqxfPly9u/fj7e3N4cPH+bgwYOsWbOGBnHLKjhLZCKEEEKIF5f0tAkhdGFheq+UpkGrVvpC1gMHmgO2efPAYEh0wGYwGDh9+jRKKbvHGzduTOnSpSlatCiFCxe2G7DFd/z4cUAfgrhx40YuXLiAq6srWbNmtaq3+e7dRLUR0Nd1M2Z/dHEBpbiTJw8zZswgODiYn376iaCgIJgxA1auJHb+fDZu3Mj58+eJjY2lVatWuLi40Lp1a9Ml169fz+rVq8mcOXOKBWwA+fLlM/Uo/vnnn0RGRprWUhNCCCHEy0uCNiGEztfXnLhjyxY9/f7Mmfp+xYr6umeJTB5y8OBBXF1dKV++PO+//z6apqFpmqlnbObMmfj6+prqX7VI/nHjxg0OHz7Mo0ePuHLlCs2aNTMd27lzJ0OHDqV58+Z4e3ujlKJ69ercvHmTHDlykCYuOcnhJ/0O4oZ2fvTRR/Tv35/cuXPTq1cv8uTJw2dffcW/NWqQxtMTb29vSpcuzY8//siWLVtMp7u7u3P9+nW8vb1tskCmBnvruQkhhBDi5SM/8YV4VUVFmQMxV1frOWvxGbM+JsKqVauoZTHna6XF0MTWrVvTrl07+vXrB0CpUqVM2wBjx44lX758VKtWjYwZM1K0aFF8fHxQSlEpLq3+hAkT2Lt3r+mcunXrkiFDBu7evUt0dDTjxo3Dft8eeor+336DGjWgQwfb44sXExMTw+bNm20OzZ07lx3xFt0eOnSoadvNzY3169fj6enp8LsRQgghhHgSErQJ8aysXasnt9A0fSHqqKind++9e/V5WfPn6/sGg/mYZWbI+vX14YMjRzq8VFhYGCtWrGDq1KlommZKN+/Ihg0bTNsbN25k2rRp3L59Gz8/P7788kuH5/3yyy82ZQ0aNLDJqujsGnTvridROXgQuna1PV6nDsOHD3d4+gnLJQQs+Pj4EBUVJSn1hRBCCJEqJBGJEE9bRAT06QMLF5rLFi3SE120bw92MgemGINBz5oYl7jCSp8+enmzZnDsmF63alWHlwoODubUqVM0bdrU5li5cuU4efIk169fp0uXLhgMBpo0acKoUaNMdQIDA8mfPz8AuXLlIpdxXpkDpUqV4vDhw7z11lvcv3+f//77j7x589rUcxg4lSnD4QsXKFW6tJ61sX59KF/enD0S2HDokFWik/jmzJkDwKhRoxgxYoSpPE+ePE7bLoQQQgiRHNLTJsTTEh2tLzSdIYN1wGb04YdQoYK+yHNquHJFHwZZo4a57I8/YMcOfUHpmTP1oBH0hCR2ArYtW7ZQvnx5cufOTe7cue0GbAD79u3DxcWFQoUKsW/fPvbv38/3339PsWLFAH3NMWPAlhTVqlXj3r17KKXsBmxGlSpV4g3gYbp0BKxcSfiRI/zcrx81atbkiy++wN/fnwr16jGmY0dUXCAG0M4i7b+bmxsVKlTg/PnzrF+/3ur6Xl5e7Nmzh9dff50vvviCChUqJPmzCCGEEEIklvS0CZGaNm2Ctm0dHy9dGs6dM+8HBOg9P0kNAgwGPcujvz+88Ybt4tZnztguCF22LDgIuiw9evSIffv20bx5c4d1fHx80DSN//77j2bNmtlkczTasWMH69ato3///gneNzm6dOnCoBMnqFywIP7vvUeXLl1M89EWL17M4rhFtU+dOkWlX3+lRdu2fP3HH/D4sekaURbDVUuWLEnRokX5559/AGjWrBnZs2fnwoULqfo5hBBCCCFAgjYhUo+/v/2ArUIFPZjz9NSDq127oEkT8/GaNeHRI3OmxpMnYfFi8/BJy6yEUVHQrZs+78zo66/BmD4/KAgOHNDnclnOmQsPt1lrzB6lFHXr1rWay1WvXj2GDRtG1qxZKVeuHOnTp0/0XK4iRYrYLAydGoyLSvv7+wP6+maOtOzYkc2bN/PDxo2mMntz16pUqcI///xD9erVbRbKFkIIIYRITRK0CZEatm+HLl3M+x4eetDVoAH07Wtdt0AB6/3wcNi2DV57TV837Ntv9eQgM2bA6tXQqZO57vTp1gEbwI8/QsmSeqKNd97Rk46AnvRk2DBo1AjSp7dpssFgYOjQoSxZsoRhw4bRuHFjZs+ebQpgSpYsibe3N2PGjHnuU82//vrrSapvXGMtc+bMhIWF2a0zffp0atWqZbUemxBCCCHE0yBB29OkFEFbtmAwGMjrLL26eP7duAGZMkG2bLbHoqPh7bf1uWkeHnqPm4PhggBYzu0qVkyfe9aypf26vr560HbsGHzwgfXQSsv7d+umB4MWqfE5eRIKFnTYjF27djFp0iQABgwYYHWsfv367Nmzx/FneM44mmuXkGrVqjk8ljdvXr744osnbZIQQgghxBN7bv67/N4//3Bs4kSOTZ6Mskw/nkiPLl/mwFtvcWbePFPZgwcPiImJsakbExPDjq5d2VG5MjGRkYSGhhIaGpqs9ifG8d69yd26NXnbtuXMoEEcrVEDPy8v7ly5kur3Fikj/a1b+vBET09o2FAPkHbvhthYfS5a3rz6As3GZCJnzjgM2Pbv30+DBg2oWLs2weXKod54w7S4s0Nz58K8eXqSEMuAbdAguHULMmc2lzVqZN6+cMEmYAsKCqJ69eqmha/ffPNNh7etn4R12p4Hjha2vnDhAkFBQfzyyy8sWrTI5vhM42LiQgghhBDPE6WU0xewCAgGzliU5QB2AP5x79njyjVgBnAZOAVUTuj6SikqlC6tlP5rrv81b1UAAB5vSURBVFKg/nV1VYbYWJWQoL17Vcjx42p327ZW568vVEit79NHKVAHQW0ePdrqvFVdu5rqrsuQQR0C9RDU7LFjE7xnohkMKuLMGRUbHKxuvvWWVfviv3597TV1/u+/lcFgSLn7i5Rz8qRSP/2k1I8/2v75DRyov3/yiVJVq1of+9//HF5y48aNKkOGDAowvfz27VOqfHn7z4mPj9NnSMXE6Bc+dkypIkWsj73xhs39J02aZHVvy5ePj49pe8iQIerEiRMqMjIytb7dVBP/c2mapmIt/l0JCQlRXl5eVnUiIiKSdU9fX99ktlq8zOT5EM7I8yGckefj1QAcVY5iMkcHTBWgHlA5XtA2ERgatz0UmBC3/TbgExe81QAOJ3R9pRRV7PwSemLmTKcf6s7p085/iY33unz6tFJKqQ3t2zusEwzqXnBwinzpJ999N0ntU6Dmpk+fIvcWKeTBA6WWLVMqQ4Yk/1kqB8/vsWPHHAZLgOrbtKlSBQooVbCg9fUCA+0HbgUKKHX8uO2NKlY01xkwwFS8ZMkSu/ctX768GjNmjNq3b59SSqlr166pa9eupcrX+rR06dJFAapZs2bqf//7nzp79qzdegsXLjR9D8klP1SFM/J8CGfk+RDOyPPxakhW0KafT+F4QdtFIG/cdl7gYtz2POA9e/WcvYxB2wOLX0b3FSmi9laooP4sWVIZYmNVTHCwCj93Tp2sW1cpUEfq1bP5Bfbs6tVOf5EeUaJEgr9sb3rnHeffZni4uubtre4tXWpzyHDihArOl8/p9Y8vXKj8ihRR+/LmVXtr1rQ53iFtWsf3Nhj0Hp8PPlBq716lhg9X6ocfEn4CRMJCQ5UKD1fqv/+U2r1bqZEjlXJ3t/nzuV2zplIPHyo1bpz9P+MVK2wu/eDBAzVixAhVo0YNm2CpQIECNmWXL1/WT7x/X6kmTfTePKWUevRIqbRpzffauVN/JuwJC1Nq0CClfvlFqYcPlcFgUL1797a514MHD1RERISKjo5OpS/22YmOjjZ/l05ERUWpvn37qpUrVyb7nvJDVTgjz4dwRp4P4Yw8H68GZ0Gbph93TtO0wsAWpVTZuP37SqlsFsfvKaWya5q2BfhBKeUXV74L+EopddTONXsCPQHyurtXmV6rFulbtiR2yRLaHrWu/luDBrRykAThDrCzTRty9+uH5uLCvblz8V69mr/d3QkdOJB6I0faPe/3du0gJIS39uwhyM0Nv/r1ab9zJwBewIilSylYsCDuU6aQ6epV7n30EWmPHiX3li3kv3cPAN9Nm9Dc3QFIe/cur330EV7x5satdXcnoGhRCn32GTmLFbNJje42eza1f/3VqmzKt99S2XI+UhyvKVPw/O03m/LQ0qXxHzCARwULohKaEyX0kMdizpP76dNU7tfP6SmHVq4k/a1bBBYtSmZ3d7SoKLIfP07+TZvIeeiQqd4eX1+L2yhWrVrFTz/9ZHO9L7/8kjJlylCgQAGOHz9ulQY/c+bM/Gbx53zw4EFWrVpFkSJF+NHNDa+1a4nw8ODoggVEZ8mCj48Phw4dYt++fWTJkoXvvvuOKnFrssXGxvLgwQNGjx5tlcY+e/bs9O/f/4Wbq/a8e/jwIZkt5xUKYUGeD+GMPB/CGXk+Xg0NGzY8ppSqavego2jO8oVtT9v9eMfvxb1vBepYlO8CqiR0/SpVqpgizLNLliRu+Fnca3+8+WpKKRUdFGTaDjtzxuacQGNvlsGgos+eVcpgUMG//25VZ66npzJERCR4/8i7d1XounUqPE0am2OnnfWaWXrwQIWuW2c6byOofXv3KkNsrLr2wQcqyrJ3JaFX585KvYDzj54ayx6yGzf0Hktn32e3bkpZDKsz/k9XSEiIevz4sV4YGqrU1q36e5w5c+aobNmy2fRs1ahRw6ZX6/Llyzb1vv32W/Xee++pfv36qTRp0pjKZ82cqdSVK0rFXePnn3+2OTd//vxqx44dSiml2rdvbypPly6dKlmypFq+fHnqfb+vOPmfUOGMPB/CGXk+hDPyfLwaeO6HR1oEbTGRkeqQp6fTX6QPZM6szri5qevHjiXqCzDcu6ce/PGHHmS5uCj/OXNsK8XE2NzncqtWSQogja81vXqplZUrq7O7diXhj0mpC/ESsvzn4Pp9QF0DdcJRG+SXcn3Y4OTJSs2erdSdO3rZxYvW31Pz5krVr2/7/VWqpNSMGXpAZyEyMlI1b97cFARVq1bN5rbh4eHqv//+U+nTpzfVq1+/vjp27JgKdjBfMjIy0lS3cuXKNkFY/IAsJiZGzZ8/32k9QO3du9dq/9ChQyn9LYt45IeqcEaeD+GMPB/CGXk+Xg2pEbRNwjoRycS47RZYJyL5KzHXtwzajB4FB6ugv/9WQZpm+mV6W86catv776fS16TUXh8fZShTxmEwtgnU4Dp11KohQ+wePwlqcePGT3x/QyKSq0SCOn78uBo/fryaN2+eGuLhYb9uMrPgvdBiY5X69lvr72POHOffrY+PUlFRNpcyGAxq0qRJqn79+nYDo4CAALVr1y4VHR1tEyQVL15cPXz4MFFN/umnn9S0adNU5syZ7d6ndu3apu0qVarYHO/evbs6e/ascnd3t3u+n59fSn/Lwg75oSqckedDOCPPh3BGno9XQ7KCNmAlcBOIBgKBj4CccUMf/ePec8TV1YD/AVeA00DVhK6vHARtRocnTlSXXV3VnxYZ8FKL8S/E+SxZrH6hH+HqqpaDunP7tqnu/uHDbQOqlBiWeO6cCi1e3Oq60XHvf4Ia9+67VtXnzZun6toJQgwTJya/LS+a3bv1oYwjRjgOztzc9LT4lmVNm6pLly4pf39/dfr0aXXx4kX1999/q86dOyfYm2V8tWjRwqYsyGKYbmJ98803dq9/4sQJp71wxuUizp07Z9OWhg0bpvQ3LRyQH6rCGXk+hDPyfAhn5Pl4NSS7py21X86CtqfJ+BfiTKdOpl/o/xg2zO76abExMeqPwoVVBKjd+fKpgD//TNnGHD2qQitVUiFDh6rQ+/fV0f371ZkzZ+xm+fv111/VO/GCk3v586dse55XUVFKnTunVMaMtgHaO+/YBnCLFqn9+/ere40bm8pmdeyYYFBWokQJVaxYMTVlyhSllFIDBgxwWLdTp04qICDgiT5ORESE2rlzp9q2bZtavXq1VVAWGBho07s2efJku/9Z8MMPP5jqrV69OjnfsEgC+aEqnJHnQzgjz4dwRp6PV4MEbYlk+gthMKibq1apUz/+6LS+wWCwWqz3WVqzcqX6DtRsywBl//5n3azUExqqVKZMjnvUevc2p8P/8ku99zFPHlWrenUFKBdQl4cPV8vGjnUarKVNm1atWbPGdFvjM3L79m3VsmVLm/pHjx5N0Y958+ZNU8ITg8FgGiapaVqC6ewfP34sC7Y/ZfJDVTgjz4dwRp4P4Yw8H68GZ0FbGoQtTSPPO++QJ8FqGppF6vhnqX2nTly6coXL+/eDjw8AIatXk6NWrWfcshSmFPz8M3TvbnusRw9YuxYyZSJw4EBaVKzInTt3WDl3LnXTpGHopUscWLsWAAPgNWaM1ellypQhc+bMFC1alEqVKlG4cGE6duxotxm5cuXit99+Y8OGDaxevZrVq1fzxRdfmFLtp5Q8ecxPoaZpbN68mZUrV1KrVi2KFSvm9NwMGTKkaFuEEEIIIcSzIUHbS8LFxYVvvvmGcydOmIK2E6dP0/gZtyvFLV4MH31k/9iAATBuHA8fPeKT3r05deoUAPVbt7aqljdvXm7evGnaz5UrF7du3bJZQy8xvL298fb2ZtWqVUk+90nkyJGD3r17P5V7CSGEEEKI54PLs26ASFlFS5Vid9x2pqioZ9qWJLtyBe7fd3xcKZgzR9/OkwcOHtTLRozgXp8+bLt2jYv379N/7Fi2b99u9xLvvPMO165dsyrr16/fEwVsQgghhBBCPA0StL1k0qdPz66SJQHIES84ea4dPgxeXpA9O2ia3qMW3+efw9GjkCkT+Ptzt3hx/vzzT0a5upJj1ixatGhByZIlWbRoEQALFy5k69atVpeYN28eadKk4fr16wCMHz+eb775JtU/nhBCCCGEEE9Khke+hBp99x2G996jUGAgPH4MGTM+6yZZUwoqV4boaDhwANzdYckS6zo9ekCOHNChA/9v796DrKjPNI5/HxgEMiBgEBiZyC0QFEVUBNQ1YMW7Zk0kRAu8JFhSa1YIKl5iSBg2KqQ0oJBoRHfVMt4xRhdTi5YZg8ZlAwSISAoNiBJA8EIQBBGHd//onnG4zBlkLufMOc+nippz+nT3ebvq5XS//bs0ffvC00/Dr38NwIczZ1J2003MnDmzxq+YOnUqo0ePBmD16tVMmDCB8ePH065dOwBKS0uTmXjMzMzMzHKci7Y8dNL557NY4vgINj33HB1qmEwja268EZYsSV737w9HHlk1Dm833/pW8nfZMujdG4B1xxxD17QY29Odd97J1KlTKS4u5tprr61a3q1bN5588sl6PQQzMzMzs8bi7pF5qHXr1mzq3BmA1S+/nLRs5Ypdu+A3v/n8/dtv716w7dgBf/xjjZvf/Y9/VL0eNGgQ06dPp3nz5nTv3p0rrriClStX8tprr1FU5PsRZmZmZpYfXLTlqejQAYBjZ86kYsaMdGEk3QzXrdu/nWzYALfcAlu21D2gbdtgwQLo0SP5/oMP3muVyYBatuTwiy/m/8aNSxYeeSSUlgLwYceOTP3gAwBmzZrFq6++yvjx49m8eTPLli2jdevWtG7dmlatWtU9XjMzMzOzHOGiLU9tP+ywqtfNx4/nk29+k08fegguuIAdxx5b+w42bUpmaJw4EeravfL992HkSBg0CNLJUX5bUsL6ysLtq1/l9gsvpCxdfc2aNQyZMYOHy8ooXr6cL33wAVs/+oiSzZv5DLjhhhu44oorqmZ8LC4upri4uG4xmpmZmZnlKPchy1PtBg2CF1+set9qzhyYMweAlhs31r6D9CHUAMyd+/nr7duhVSvYuhXatq19P2+/nYxH27lzt8U/WrGCN4AhQ4bQs2dPHnnkkb02vbisrOo7Bw8ZwqfpPqZMmVL795qZmZmZ5Qm3tOWptl27fvGN3n0XrrsOVqxgY1rgVZk3D55/PpmJsk2bpHtjtck+9rJ9e7J+9+57FWxtgTfS1/Pnz9+tYKuoqGDZsmV77W758uUAjB07Fklf/NjMzMzMzJooF215qnWXLhk/f/uss4iKCpg/n7j3Xhg1CkpK4PbboW9fOj37LABVT3obOhTOPDN5vW1b8nfatJonOTnjjM/XTy0GLgS2kszouKf777+fZs2a0a9fP9asWcOAAQMYM2ZM1edFRUVMnDgx84GbmZmZmeUZF215qu2RR2b8vNvcuSy94AI48UQ0Zgzso3siwPdq+6J9PStt3jx45ZXdFo0DjgOeSN9PnjyZ2267DYAJEyawdu1avve9z7+ttLSUxYsXc88997Bu3TrKyspYsGABnTp1qi0iMzMzM7O84jFteapr377cAvx4j+X/BNqnrwekrWk1+QVw1JVXsvnuu2lXwzof33orxVddBc3S+j8iaZWr5nqgemlXXl7O0KFDkcSECRNqPZaSkhImTZpU63pmZmZmZvnILW15ShIjV61ixp13cm27z0uul2p4MDXAx8C/AQOBLsDiUaO45ec/pz0wDbgfOB+YBZQCq4HiDRvY+fjjn3eT3OMh2X8D7gAGDx7M7NmzefPNNxk2bJjHpZmZmZmZ7Se3tOWxHj16MG7cOJb16gXnnQdA6eGH17j+iPbtOffmmzmvWzd69erFEUccAcDSpUsZPXo0H330EVOmTKG8vJw/Xn01TwwaxPUffkiLkSPZ9e67NLv6aj79xS84KN1fL2AdcONPfsKkSZOqpug3MzMzM7P956KtAPQ75xwWA8cCbU8+mQVlZZxQOZ1+6lHg4VWr6JA+lLu6/v37s3Dhwqr3w4cPT/Z7++2Qttw1u+YaOO44DvrDHwB4BOhy0kl8/+yzPXmImZmZmVkduGgrAJLo9MYbvLxoEaecdhqcdhovPPwwp7/5JgDlv/89wwYM2GfBlsmZl1zCK6NH8y/p+82TJ9OOpNvk5cDWefPcumZmZmZmVkce01YguvbuzSkXXVT1vmVFRdXrU88+m5KSki+8z6KiIt657z7eS9+3Ky8HoAx4a/16F2xmZmZmZvXARVuB+vIhh9TLfkZefjnPn3PObsvOKiujSy3PiTMzMzMzs/3joq1A9flx8jCAjwcMqPO+jtljfNxJI0bUeZ9mZmZmZpaoU9EmabWk1yQtkbQwXXaIpBckvZn+/WIDpaxRtDj/fFiwgOKXX67zvo464QTKhg9nHvDcQQfRtU+fugdoZmZmZmZA/bS0nRoRAyJiYPr+RuDFiOgNvJi+t1wjwcCB0KZNveyubPZsSleupO/y5TQv8vw2ZmZmZmb1pSGurs8HhqWvHwReAm5ogO+xHNOzZ89sh2BmZmZmlnfq2tIWwPOSFkkaky7rHBHrAdK/ner4HWZmZmZmZgVLEXHgG0uHRcQ6SZ2AF4CxwLMR0b7aOpsiYq9xbWmRNwagc+fOxz/22GMHHEd92bp1K23qqbug5SfniGXi/LBMnB+WifPDMnF+FIZTTz11UbUhZ7upU/fIiFiX/t0o6WlgELBBUklErJdUAmysYdtZwCyAgQMHxrBhw+oSSr146aWXyIU4LHc5RywT54dl4vywTJwflonzww64e6SkYkltK18DZwDLgGeBy9LVLgOeqWuQZmZmZmZmhaouLW2dgaclVe7nkYj4H0kLgCckXQ68A/ihXWZmZmZmZgfogIu2iFgFHLOP5R8A36hLUGZmZmZmZpaoj+e0mZmZmZmZWQNx0WZmZmZmZpbD6jTlf70FIb0HvJ3tOICOwPvZDsJymnPEMnF+WCbOD8vE+WGZOD8KQ7eIOHRfH+RE0ZYrJC2s6dkIZuAcscycH5aJ88MycX5YJs4Pc/dIMzMzMzOzHOaizczMzMzMLIe5aNvdrGwHYDnPOWKZOD8sE+eHZeL8sEycHwXOY9rMzMzMzMxymFvazMzMzMzMcpiLNjMzMzMzsxxWcEWbpBbZjsHMzMzMrDpJynYMlrsKpmiTNETSY8Btko7KdjyWmyQ1T//6h9P24ryw2khql/4tmPOr7T9J/SS1ynYclrNaZzsAy10FcVKRNAK4G5gDtAKuSZf7AswAkHSypAeBiZIOCc/QY9VIGizpXuAGSYdmOx7LLZKaSTpY0hxgBkBE7MpyWJZDJPWX9ApwM/DlbMdjuSVtWHgK+JWkMypvIJtVVxBFG9Ab+O+I+A0wHZJukr4wNwBJPYG7gHKgG/AzSedmNyrLBZKaS5pCMtXyn4DjgEmSOmc3MsslaYG2BWgBdJV0Ibi1zXYzEZgdEd+OiLXgG8eWkDSM5Brkt8AK4GKgQzZjstyUlycUSd+VdI2kE9NFK4ALJF0P/C9wGMndjBOyFqTlkuOBv0XEA8C1wBLgPElfyWpUlguaAe8AI9L8GA8MwV1YbG99gfeBO4BRktpGxC5fmBe2tBW2F7A1Iu5Il50uqT3g7vgGcDSwICIeBh4iufmzNbshWS7Kq6ItvSv+U+CGdNG9kv6V5O7FD4GvA5dGxFnAe8BwSV2yE61lS9oNoU+1RQuAUklfiYhNJC0q/wS+nZUALav2yI9dwKMR8YaklhGxDvgH0DF7EVq2Vc+Rahfcfwc+Bd5K/10m6XD36Cg81fMjbYXdCJwi6VxJvwMmkHSjvS5dxzlSQPZxDfIyMCK9fv0LUALclQ7tMauSV0VbRFQAXwOujYhpwCTgaqBPRLwIfELS6gbwDNAf+DgbsVrjk9Re0nPAC8B3JbVJP/oEeAX4bvp+BbAc+LIHjBeOfeVHRFRExD8BImKHpLZAD2BdNmO17NhHjhRXu+AeCHwUEa8Dr5Ocf+6W1MLdJAvDvvIDICK2APcDPwP+KyLOBO4DhkgakrWArVHVdA0SEUuAs4DuwA8iYhjJzeOzJB2RpXAtBzX5E4mkSyUNTbsaAGwAOkgqioinSE6eF6UtaiuB76TrHUtysW6FoxiYC4xNX389Xf4eMB84WtKgtPhfC5wcEc6RwrFnfpyyj3UGA69HxDpJbST1bswALetq+g2BpBttW0mPA9cDi4A3ImKnJyUpGJnyYw7JRXnlWKWFJNcrOxoxPsuuGs8xEfFn4FBgdbroD0Bb3LBg1TTJok2JEknlwGXAKJIxam1IxhQcDVS2ovySpJtbBfA8cIKk+cAI4Kb0DpjlqWpF/cHp4O9ZwBMkBfsgSV3TIm0+sBiYnuZRP+AdSV/KWvDW4GrJj8GSDkvXK0o3aQ+skfR9km61A7IRtzWe/c0RkovxQ4F3SW4KXgl8zXfK89t+5EdXgIj4K0l3yKskdSSZbOIo4IMshW6N4AucY1oCrwL/nm76DZJZRn3j2KqoqXWlltQ8IirS/sA/jYiL0wuqGUBLkokkHgduARZGxDZJTwLlEXFXekHeIyJey9pBWINKx5h0AR4hGZO0kuSu1g8j4v10nZNJukMujIiHqm07DSglmUXy0ohYgeWVL5gfC9JZZyu3fYjkJtGDwPT0QszyzIH+hkjqWO3zNsBBEfFhFg7BGlAdzzHXAD1JZrW+OiKWN3L41sDq8PvRj6RbdRdgJ3BVRPyt8Y/AclVR7avkhrQw+w+guaTfAweTtJ4REZ9JuorkDuc0kv8oF5EM5nycZHD4onTdrYALtjxVrahvC6ytVtRPJ7nDdQFARPxJ0iCSO+HtgF1pq+t1wJfcApufDiA/+ko6mCQ/tgLPAc9ExOxsHYM1rDr8hnwWEe8reb5SpPlieaau55iImKbkkUM7s3cU1lAOMD/aAzsi4nVJlwElEbEqawdhOatJdI+UNJSk6OpAMkPXz0juQpyaJn3lDE2Tgdsi4kGSrpCXSlpMUpy6UMtjkook3QrcmubL16hW1APjgBPTzyrdS9KN9gXg75IOSyeecMGWZ+qYHy8CKyWVRMRjLtjyUz38hqyq9hviMWx5pr7OMen6LtjyTD3kx+p0uMZ2F2xWkyZRtJE0L98eEVdGxL3AMpIZ3H4K3A1VDzF9CtimZOr23wGXA8Mj4sKI2Jal2K2B7WdRHyQttWXVNj0X+AGwFDg6nc7d8kw95McSkvxY34hhWyPyb4hl4vywTOrxHLO2EcO2JqipdI9cBPy5stmZZCrUoyLiR5LGSxobETMllQI7I2INQES8m82grdFUFvWV/cKPZfei/vi0qH+a5Ee0e0SsJhnge1pEzMtO2NZInB9WG+eIZeL8sEycH9YomkRLW0Rsi4gdacEGcDrJNO0A3weOkDQHeJTkwYRWWBYBT6RjSSAp6g+PiAdIxkCOTbsrlQIV6Y8lEfGMfywLgvPDauMcsUycH5aJ88MaRZMo2ipJap7eregMPJsu3gLcBEwFhkXEbdmKz7LjQIr6dHYnKwDOD6uNc8QycX5YJs4PayxNpXtkpV3AQSTPYusv6Q6SZ5yMjYhXshqZZV3lrG3su6g/Cnirss942r/cCojzw2rjHLFMnB+WifPDGlqTKtoiItK+wqNI+gvfHxH/meWwLHe4qLdMnB9WG+eIZeL8sEycH9agmuLDtUuBS4BpEbEj2/FYbpE0BHg1/eei3nbj/LDaOEcsE+eHZeL8sIbU5Io2s0xc1Fsmzg+rjXPEMnF+WCbOD2tILtrMzMzMzMxyWJOaPdLMzMzMzKzQuGgzMzMzMzPLYS7azMzMzMzMcpiLNjMzMzMzsxzmos3MzPKWpApJSyS9LmmppGskZTz3SeouaWRjxWhmZlYbF21mZpbPtkfEgIjoB5wOnANMqmWb7oCLNjMzyxme8t/MzPKWpK0R0aba+57AAqAj0A14CChOP74qIl6VNB84AngLeBCYAUwFhgEtgV9FxD2NdhBmZlbwXLSZmVne2rNoS5dtAvoCW4BdEfGJpN7AoxExUNIwYEJEnJeuPwboFBE3S2oJ/AkYERFvNerBmJlZwSrKdgBmZmaNTOnfFsAvJQ0AKoA+Nax/BtBf0nfS9+2A3iQtcWZmZg3ORZuZmRWMtHtkBbCRZGzbBuAYkjHen9S0GTA2IuY2SpBmZmZ78EQkZmZWECQdCvwa+GUkYwPaAesjYhdwCdA8XXUL0LbapnOBKyW1SPfTR1IxZmZmjcQtbWZmls9aS1pC0hXyM5KJR6aln90FPCVpBFAOfJwu/yvwmaSlwAPAnSQzSv5FkoD3gG811gGYmZl5IhIzMzMzM7Mc5u6RZmZmZmZmOcxFm5mZmZmZWQ5z0WZmZmZmZpbDXLSZmZmZmZnlMBdtZmZmZmZmOcxFm5mZmZmZWQ5z0WZmZmZmZpbDXLSZmZmZmZnlsP8HRzZT79OIsBQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_rs = compute_results (rs, pos, plot=True, savefig=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supply chain clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yaoleixu/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (1,2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "splyc = pd.read_csv('GSPLY.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "splyc = splyc.loc[(splyc['supplier_ticker'].isin(tickers)) & (splyc['customer_ticker'].isin(tickers))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read and format supply chain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>supplier_ticker</th>\n",
       "      <th>customer_ticker</th>\n",
       "      <th>revenue_dependency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reporting_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>CAG</td>\n",
       "      <td>WMT</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>CAG</td>\n",
       "      <td>WMT</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>CAG</td>\n",
       "      <td>WMT</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>CAG</td>\n",
       "      <td>WMT</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019.0</td>\n",
       "      <td>CAG</td>\n",
       "      <td>WMT</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               supplier_ticker customer_ticker  revenue_dependency\n",
       "reporting_year                                                    \n",
       "2019.0                     CAG             WMT                0.25\n",
       "2019.0                     CAG             WMT                0.25\n",
       "2019.0                     CAG             WMT                0.25\n",
       "2019.0                     CAG             WMT                0.25\n",
       "2019.0                     CAG             WMT                0.25"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = splyc[['reporting_year', 'supplier_ticker', 'customer_ticker', 'revenue_dependency']].dropna()\n",
    "df = df.set_index('reporting_year').sort_index()\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize structural relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tab = df.loc[2018]\n",
    "companies = list(set(tab['supplier_ticker']).union(set(tab['customer_ticker'])))\n",
    "w_matrix = pd.DataFrame(index=companies, columns=companies)    \n",
    "\n",
    "for i in range(tab.shape[0]):\n",
    "    row = tab.iloc[i]\n",
    "    w_matrix[row['supplier_ticker']][row['customer_ticker']] = row['revenue_dependency']\n",
    "\n",
    "w_matrix = w_matrix.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yaoleixu/opt/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform it in a links data frame (3 columns only):\n",
    "links = w_matrix.stack().reset_index()\n",
    "links.columns = ['var1', 'var2','value']\n",
    "\n",
    "# Keep only correlation over a threshold and remove self correlation (cor(A,A)=1)\n",
    "links_filtered=links.loc[ abs(links['value'] > 0.0) & (links['var1'] != links['var2']) ]\n",
    " \n",
    "# Build your graph\n",
    "G=nx.from_pandas_edgelist(links_filtered, 'var1', 'var2')\n",
    "\n",
    "# Plot the network:\n",
    "fig, ax = plt.subplots()\n",
    "nx.draw_kamada_kawai(G, with_labels=True, node_color='orange', node_size=500, edgelinewidths=1, font_size=10, ax=ax)\n",
    "fig.set_size_inches((12,8))\n",
    "fig.tight_layout()\n",
    "plt.savefig('fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the network:\n",
    "fig, ax = plt.subplots()\n",
    "nx.draw_spring(G, with_labels=True, node_color='orange', node_size=500, edge_color='black', linewidths=1, font_size=10, ax=ax)\n",
    "fig.set_size_inches((12,8))\n",
    "fig.tight_layout()\n",
    "plt.savefig('fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CPB', 'CLX', 'HRL', 'GIS', 'PEP', 'CL', 'NWL', 'K', 'PG', 'HAS', 'TSN',\n",
       "       'CHD', 'CAG', 'KMB'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_matrix.loc['WMT'][w_matrix.loc['WMT']!=0].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter preds by structural clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007-07-02 00:00:00\n",
      "2007-08-01 00:00:00\n",
      "2007-09-04 00:00:00\n",
      "2007-10-01 00:00:00\n",
      "2007-11-01 00:00:00\n",
      "2007-12-03 00:00:00\n",
      "2008-01-02 00:00:00\n",
      "2008-02-01 00:00:00\n",
      "2008-03-03 00:00:00\n",
      "2008-04-01 00:00:00\n",
      "2008-05-01 00:00:00\n",
      "2008-06-02 00:00:00\n",
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      "2008-08-01 00:00:00\n",
      "2008-09-02 00:00:00\n",
      "2008-10-01 00:00:00\n",
      "2008-11-03 00:00:00\n",
      "2008-12-01 00:00:00\n",
      "2009-01-02 00:00:00\n",
      "2009-02-02 00:00:00\n",
      "2009-03-02 00:00:00\n",
      "2009-04-01 00:00:00\n",
      "2009-05-01 00:00:00\n",
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      "2010-04-01 00:00:00\n",
      "2010-05-03 00:00:00\n",
      "2010-06-01 00:00:00\n",
      "2010-07-01 00:00:00\n",
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      "2011-01-03 00:00:00\n",
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      "2012-01-03 00:00:00\n",
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      "2013-01-02 00:00:00\n",
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      "2013-09-03 00:00:00\n",
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      "2013-11-01 00:00:00\n",
      "2013-12-02 00:00:00\n",
      "2014-01-02 00:00:00\n",
      "2014-02-03 00:00:00\n",
      "2014-03-03 00:00:00\n",
      "2014-04-01 00:00:00\n",
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      "2014-06-02 00:00:00\n",
      "2014-07-01 00:00:00\n",
      "2014-08-01 00:00:00\n",
      "2014-09-02 00:00:00\n",
      "2014-10-01 00:00:00\n",
      "2014-11-03 00:00:00\n",
      "2014-12-01 00:00:00\n",
      "2015-01-02 00:00:00\n",
      "2015-02-02 00:00:00\n",
      "2015-03-02 00:00:00\n",
      "2015-04-01 00:00:00\n",
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      "2015-12-01 00:00:00\n",
      "2016-01-04 00:00:00\n",
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      "2016-04-01 00:00:00\n",
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      "2017-01-03 00:00:00\n",
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      "2017-05-01 00:00:00\n",
      "2017-06-01 00:00:00\n",
      "2017-07-03 00:00:00\n",
      "2017-08-01 00:00:00\n",
      "2017-09-01 00:00:00\n",
      "2017-10-02 00:00:00\n",
      "2017-11-01 00:00:00\n",
      "2017-12-01 00:00:00\n",
      "2018-01-02 00:00:00\n",
      "2018-02-06 00:00:00\n",
      "2018-03-01 00:00:00\n",
      "2018-04-03 00:00:00\n",
      "2018-05-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "preds = preds.dropna()\n",
    "for t in preds.index:\n",
    "    \n",
    "    print(t)\n",
    "    \n",
    "    preds_row = preds.loc[t]\n",
    "    preds_row_ordered = preds_row.sort_values(ascending=False)\n",
    "    \n",
    "    # Generate weight matrix ------------------------------\n",
    "    tab = df.loc[t.year-5:t.year] # get past 5 years\n",
    "    companies = list(set(tab['supplier_ticker']).union(set(tab['customer_ticker'])))\n",
    "    w_matrix = pd.DataFrame(index=companies, columns=companies)    \n",
    "\n",
    "    for i in range(tab.shape[0]):\n",
    "        row = tab.iloc[i]\n",
    "        w_matrix[row['supplier_ticker']][row['customer_ticker']] = row['revenue_dependency']\n",
    "\n",
    "    w_matrix = w_matrix.fillna(0)\n",
    "    \n",
    "    # Filter out same cluster stocks & update preds matrix ----------------------------- \n",
    "    # Get rid of customer\n",
    "    for tmp_ticker in preds_row_ordered.index:\n",
    "        if preds.loc[t][tmp_ticker] != 0:\n",
    "            if tmp_ticker in w_matrix.columns:\n",
    "                ticker_cluster = list(w_matrix[tmp_ticker][w_matrix[tmp_ticker]!=0].index)\n",
    "                for delete_tick in ticker_cluster:\n",
    "                    preds.loc[t][delete_tick] = 0    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
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       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
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       "    <tr>\n",
       "      <td>2007-09-04</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
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       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.007752</td>\n",
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       "    <tr>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
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       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
       "      <td>0.007519</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2018-01-02</td>\n",
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       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
       "      <td>0.008696</td>\n",
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       "      <td>0.008696</td>\n",
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       "      <td>2018-03-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007874</td>\n",
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       "      <td>0.007874</td>\n",
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       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "      <td>0.007874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.026316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.026316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 149 columns</p>\n",
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      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "2007-07-02  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-08-01  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-09-04  0.007519  0.007519  0.007519  0.007519  0.007519  0.007519   \n",
       "2007-10-01  0.000000  0.007752  0.007752  0.007752  0.007752  0.007752   \n",
       "2007-11-01  0.007519  0.007519  0.007519  0.007519  0.007519  0.007519   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2018-01-02  0.000000  0.007874  0.007874  0.000000  0.000000  0.007874   \n",
       "2018-02-06  0.000000  0.008696  0.008696  0.000000  0.000000  0.008696   \n",
       "2018-03-01  0.000000  0.007874  0.007874  0.000000  0.000000  0.007874   \n",
       "2018-04-03  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2018-05-01  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "2007-07-02  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-08-01  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-09-04  0.007519  0.007519  0.007519  0.007519  ...  0.007519  0.007519   \n",
       "2007-10-01  0.000000  0.007752  0.007752  0.007752  ...  0.007752  0.007752   \n",
       "2007-11-01  0.007519  0.007519  0.007519  0.007519  ...  0.007519  0.007519   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2018-01-02  0.007874  0.007874  0.007874  0.007874  ...  0.007874  0.007874   \n",
       "2018-02-06  0.008696  0.008696  0.008696  0.008696  ...  0.008696  0.008696   \n",
       "2018-03-01  0.007874  0.007874  0.007874  0.007874  ...  0.007874  0.007874   \n",
       "2018-04-03  0.026316  0.026316  0.000000  0.000000  ...  0.000000  0.026316   \n",
       "2018-05-01  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "\n",
       "                 WEC       WHR       WMB  WMT       XEL       XOM      XRAY  \\\n",
       "2007-07-02  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-08-01  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-09-04  0.007519  0.007519  0.007519  0.0  0.007519  0.000000  0.007519   \n",
       "2007-10-01  0.007752  0.007752  0.007752  0.0  0.007752  0.000000  0.007752   \n",
       "2007-11-01  0.007519  0.007519  0.007519  0.0  0.007519  0.000000  0.007519   \n",
       "...              ...       ...       ...  ...       ...       ...       ...   \n",
       "2018-01-02  0.007874  0.007874  0.007874  0.0  0.007874  0.007874  0.007874   \n",
       "2018-02-06  0.008696  0.008696  0.008696  0.0  0.008696  0.008696  0.008696   \n",
       "2018-03-01  0.007874  0.007874  0.007874  0.0  0.007874  0.007874  0.007874   \n",
       "2018-04-03  0.026316  0.026316  0.000000  0.0  0.000000  0.000000  0.026316   \n",
       "2018-05-01  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "\n",
       "                 XRX  \n",
       "2007-07-02  0.007463  \n",
       "2007-08-01  0.007463  \n",
       "2007-09-04  0.007519  \n",
       "2007-10-01  0.007752  \n",
       "2007-11-01  0.007519  \n",
       "...              ...  \n",
       "2018-01-02  0.007874  \n",
       "2018-02-06  0.008696  \n",
       "2018-03-01  0.007874  \n",
       "2018-04-03  0.026316  \n",
       "2018-05-01  0.008403  \n",
       "\n",
       "[131 rows x 149 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = preds.apply(np.sign)\n",
    "pos = pos.replace(-1,0)\n",
    "pos = pos.dropna()\n",
    "pos = (pos.T/pos.sum(1)).T\n",
    "\n",
    "pos = pos.T.sort_index().T\n",
    "pos = pos.sort_index()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
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       "      <th>VMC</th>\n",
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       "      <th>WHR</th>\n",
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       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
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       "      <th>XRX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1970-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.012334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.002066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012382</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>0.002383</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.006447</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>-0.003363</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>-0.019785</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>0.010355</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005236</td>\n",
       "      <td>-0.005945</td>\n",
       "      <td>-0.011687</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.008939</td>\n",
       "      <td>-0.000790</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.011524</td>\n",
       "      <td>-0.005113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>-0.003156</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>0.007891</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>-0.005950</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>0.004663</td>\n",
       "      <td>0.019628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>-0.004045</td>\n",
       "      <td>-0.002443</td>\n",
       "      <td>-0.024236</td>\n",
       "      <td>-0.015744</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>0.006843</td>\n",
       "      <td>-0.004282</td>\n",
       "      <td>-0.005856</td>\n",
       "      <td>-0.007110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.002656</td>\n",
       "      <td>0.006381</td>\n",
       "      <td>0.005783</td>\n",
       "      <td>-0.014796</td>\n",
       "      <td>-0.000921</td>\n",
       "      <td>0.005370</td>\n",
       "      <td>-0.017291</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>-0.013156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>-0.012769</td>\n",
       "      <td>0.003771</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.006796</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.023148</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>-0.001320</td>\n",
       "      <td>0.019108</td>\n",
       "      <td>0.004066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.004965</td>\n",
       "      <td>-0.013913</td>\n",
       "      <td>-0.005284</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>0.012184</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>-0.001809</td>\n",
       "      <td>-0.004135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>-0.011507</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>-0.005858</td>\n",
       "      <td>0.005326</td>\n",
       "      <td>-0.007809</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>0.007748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12563 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "1970-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN       NaN  0.012334   \n",
       "1970-01-06       NaN       NaN       NaN       NaN       NaN -0.008097   \n",
       "1970-01-07       NaN       NaN       NaN       NaN       NaN  0.004219   \n",
       "1970-01-08       NaN       NaN       NaN       NaN       NaN -0.012382   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.001440  0.002383 -0.001834 -0.006447 -0.000369 -0.003363   \n",
       "2019-10-15 -0.002335  0.026701  0.005779  0.003229  0.010166 -0.003156   \n",
       "2019-10-16 -0.004045 -0.002443 -0.024236 -0.015744  0.000244  0.009331   \n",
       "2019-10-17  0.003875  0.007310 -0.012769  0.003771 -0.002012  0.004310   \n",
       "2019-10-18  0.004791  0.004965 -0.013913 -0.005284 -0.001772  0.012184   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "1970-01-02       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-06       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-07       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "1970-01-08       NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14 -0.001250 -0.019785 -0.001696  0.010355  ... -0.005236 -0.005945   \n",
       "2019-10-15  0.001874  0.004578  0.007891  0.008686  ...  0.002179  0.015622   \n",
       "2019-10-16  0.006843 -0.004282 -0.005856 -0.007110  ...  0.001496  0.002656   \n",
       "2019-10-17  0.006796  0.009426 -0.000339  0.001721  ...  0.003526  0.023148   \n",
       "2019-10-18  0.008584 -0.016107 -0.001809 -0.004135  ... -0.011506 -0.011507   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "Date                                                                     \n",
       "1970-01-02       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1970-01-05       NaN       NaN       NaN       NaN       NaN  0.016000   \n",
       "1970-01-06       NaN       NaN       NaN       NaN       NaN -0.002066   \n",
       "1970-01-07       NaN       NaN       NaN       NaN       NaN -0.005903   \n",
       "1970-01-08       NaN       NaN       NaN       NaN       NaN  0.001918   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.011687  0.001579  0.001298 -0.008939 -0.000790  0.002895   \n",
       "2019-10-15 -0.005950  0.000946  0.001297  0.003016 -0.002056  0.003463   \n",
       "2019-10-16  0.006381  0.005783 -0.014796 -0.000921  0.005370 -0.017291   \n",
       "2019-10-17  0.001508  0.003941 -0.000439  0.003511  0.002831 -0.001320   \n",
       "2019-10-18  0.000646  0.005541  0.004813 -0.005858  0.005326 -0.007809   \n",
       "\n",
       "                XRAY       XRX  \n",
       "Date                            \n",
       "1970-01-02       NaN       NaN  \n",
       "1970-01-05       NaN       NaN  \n",
       "1970-01-06       NaN       NaN  \n",
       "1970-01-07       NaN       NaN  \n",
       "1970-01-08       NaN       NaN  \n",
       "...              ...       ...  \n",
       "2019-10-14 -0.011524 -0.005113  \n",
       "2019-10-15  0.004663  0.019628  \n",
       "2019-10-16  0.003158 -0.013156  \n",
       "2019-10-17  0.019108  0.004066  \n",
       "2019-10-18 -0.000910  0.007748  \n",
       "\n",
       "[12563 rows x 149 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = prices.astype(float).apply(np.log).diff(1)\n",
    "rs.index = pd.to_datetime(rs.index)\n",
    "rs = rs.T.sort_index().T\n",
    "\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
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       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
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       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-06</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-09</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.007463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3098 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "2007-07-02  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-07-03  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-07-05  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-07-06  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "2007-07-09  0.007463  0.007463  0.007463  0.007463  0.007463  0.007463   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "2019-10-15  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "2019-10-16  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "2019-10-17  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "2019-10-18  0.000000  0.008403  0.008403  0.000000  0.000000  0.008403   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "2007-07-02  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-07-03  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-07-05  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-07-06  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "2007-07-09  0.007463  0.007463  0.007463  0.007463  ...  0.007463  0.007463   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "2019-10-15  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "2019-10-16  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "2019-10-17  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "2019-10-18  0.008403  0.008403  0.008403  0.008403  ...  0.008403  0.008403   \n",
       "\n",
       "                 WEC       WHR       WMB  WMT       XEL       XOM      XRAY  \\\n",
       "Date                                                                          \n",
       "2007-07-02  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-07-03  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-07-05  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-07-06  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "2007-07-09  0.007463  0.007463  0.007463  0.0  0.007463  0.000000  0.007463   \n",
       "...              ...       ...       ...  ...       ...       ...       ...   \n",
       "2019-10-14  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "2019-10-15  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "2019-10-16  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "2019-10-17  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "2019-10-18  0.008403  0.008403  0.008403  0.0  0.008403  0.008403  0.008403   \n",
       "\n",
       "                 XRX  \n",
       "Date                  \n",
       "2007-07-02  0.007463  \n",
       "2007-07-03  0.007463  \n",
       "2007-07-05  0.007463  \n",
       "2007-07-06  0.007463  \n",
       "2007-07-09  0.007463  \n",
       "...              ...  \n",
       "2019-10-14  0.008403  \n",
       "2019-10-15  0.008403  \n",
       "2019-10-16  0.008403  \n",
       "2019-10-17  0.008403  \n",
       "2019-10-18  0.008403  \n",
       "\n",
       "[3098 rows x 149 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = pos.reindex(rs.index)\n",
    "pos = pos.ffill()\n",
    "pos = pos.dropna()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = rs.fillna(0)\n",
    "rs = rs.reindex(pos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: \n",
      " benchmark: 0.03881 \n",
      " portfolio: 0.04368\n",
      "Annual return: \n",
      " benchmark: 11.915 \n",
      " portfolio: 14.293\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_filtered_EW = compute_results (rs, pos, plot=True, savefig=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equal Vol Weighting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-09-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  ABT  ADBE  ADM  ADP  AEP  AES  ALB  AME  AMGN  ...  VMC  \\\n",
       "2007-07-02   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-08-01   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-09-04   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-10-01   0.0  1.0   1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-11-01   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "...          ...  ...   ...  ...  ...  ...  ...  ...  ...   ...  ...  ...   \n",
       "2018-01-02   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-02-06   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-03-01   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-04-03   0.0  0.0   0.0  0.0  0.0  0.0  1.0  1.0  0.0   0.0  ...  0.0   \n",
       "2018-05-01   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "\n",
       "            WBA  WEC  WHR  WMB  WMT  XEL  XOM  XRAY  XRX  \n",
       "2007-07-02  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-08-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-09-04  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-10-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-11-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "...         ...  ...  ...  ...  ...  ...  ...   ...  ...  \n",
       "2018-01-02  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-02-06  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-03-01  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-04-03  1.0  1.0  1.0  0.0  0.0  0.0  0.0   1.0  1.0  \n",
       "2018-05-01  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "\n",
       "[131 rows x 149 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos[pos!=0] = 1\n",
    "pos = pos.reindex(preds.index)\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
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       "      <th>XOM</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <td>1970-01-02</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002066</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.012382</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>0.002383</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.006447</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>-0.003363</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>-0.019785</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>0.010355</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005236</td>\n",
       "      <td>-0.005945</td>\n",
       "      <td>-0.011687</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.008939</td>\n",
       "      <td>-0.000790</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.011524</td>\n",
       "      <td>-0.005113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>-0.003156</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>0.007891</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>-0.005950</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>0.004663</td>\n",
       "      <td>0.019628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>-0.004045</td>\n",
       "      <td>-0.002443</td>\n",
       "      <td>-0.024236</td>\n",
       "      <td>-0.015744</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>0.006843</td>\n",
       "      <td>-0.004282</td>\n",
       "      <td>-0.005856</td>\n",
       "      <td>-0.007110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.002656</td>\n",
       "      <td>0.006381</td>\n",
       "      <td>0.005783</td>\n",
       "      <td>-0.014796</td>\n",
       "      <td>-0.000921</td>\n",
       "      <td>0.005370</td>\n",
       "      <td>-0.017291</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>-0.013156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>-0.012769</td>\n",
       "      <td>0.003771</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.006796</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.023148</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>-0.001320</td>\n",
       "      <td>0.019108</td>\n",
       "      <td>0.004066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.004965</td>\n",
       "      <td>-0.013913</td>\n",
       "      <td>-0.005284</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>0.012184</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>-0.001809</td>\n",
       "      <td>-0.004135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>-0.011507</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>-0.005858</td>\n",
       "      <td>0.005326</td>\n",
       "      <td>-0.007809</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>0.007748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12563 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  0.000000  0.012334   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  0.000000 -0.008097   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  0.000000  0.004219   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  0.000000 -0.012382   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.001440  0.002383 -0.001834 -0.006447 -0.000369 -0.003363   \n",
       "2019-10-15 -0.002335  0.026701  0.005779  0.003229  0.010166 -0.003156   \n",
       "2019-10-16 -0.004045 -0.002443 -0.024236 -0.015744  0.000244  0.009331   \n",
       "2019-10-17  0.003875  0.007310 -0.012769  0.003771 -0.002012  0.004310   \n",
       "2019-10-18  0.004791  0.004965 -0.013913 -0.005284 -0.001772  0.012184   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14 -0.001250 -0.019785 -0.001696  0.010355  ... -0.005236 -0.005945   \n",
       "2019-10-15  0.001874  0.004578  0.007891  0.008686  ...  0.002179  0.015622   \n",
       "2019-10-16  0.006843 -0.004282 -0.005856 -0.007110  ...  0.001496  0.002656   \n",
       "2019-10-17  0.006796  0.009426 -0.000339  0.001721  ...  0.003526  0.023148   \n",
       "2019-10-18  0.008584 -0.016107 -0.001809 -0.004135  ... -0.011506 -0.011507   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "Date                                                                     \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  0.000000  0.016000   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  0.000000 -0.002066   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  0.000000 -0.005903   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  0.000000  0.001918   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.011687  0.001579  0.001298 -0.008939 -0.000790  0.002895   \n",
       "2019-10-15 -0.005950  0.000946  0.001297  0.003016 -0.002056  0.003463   \n",
       "2019-10-16  0.006381  0.005783 -0.014796 -0.000921  0.005370 -0.017291   \n",
       "2019-10-17  0.001508  0.003941 -0.000439  0.003511  0.002831 -0.001320   \n",
       "2019-10-18  0.000646  0.005541  0.004813 -0.005858  0.005326 -0.007809   \n",
       "\n",
       "                XRAY       XRX  \n",
       "Date                            \n",
       "1970-01-02  0.000000  0.000000  \n",
       "1970-01-05  0.000000  0.000000  \n",
       "1970-01-06  0.000000  0.000000  \n",
       "1970-01-07  0.000000  0.000000  \n",
       "1970-01-08  0.000000  0.000000  \n",
       "...              ...       ...  \n",
       "2019-10-14 -0.011524 -0.005113  \n",
       "2019-10-15  0.004663  0.019628  \n",
       "2019-10-16  0.003158 -0.013156  \n",
       "2019-10-17  0.019108  0.004066  \n",
       "2019-10-18 -0.000910  0.007748  \n",
       "\n",
       "[12563 rows x 149 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = prices.astype(float).apply(np.log).diff(1)\n",
    "rs.index = pd.to_datetime(rs.index)\n",
    "rs = rs.T.sort_index().T\n",
    "rs = rs.fillna(0)\n",
    "\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in preds.index:\n",
    "        \n",
    "    past_rs = rs.loc[t+rd(years=-1):t][pos.loc[t][pos.loc[t]!=0].index]\n",
    "    vols = past_rs.std()/past_rs.std().sum()\n",
    "    pos.loc[t][pos.loc[t][pos.loc[t]!=0].index] = pos.loc[t][pos.loc[t][pos.loc[t]!=0].index]*vols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.006543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.006543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.006543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-06</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.006543</td>\n",
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       "    <tr>\n",
       "      <td>2007-07-09</td>\n",
       "      <td>0.011193</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.010829</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.008970</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009688</td>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.008400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.006543</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005096</td>\n",
       "      <td>0.008623</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.009639</td>\n",
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       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.012111</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.008623</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005096</td>\n",
       "      <td>0.008623</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.010379</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005096</td>\n",
       "      <td>0.008623</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.006361</td>\n",
       "      <td>0.007906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009998</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.005117</td>\n",
       "      <td>0.009501</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.009639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3098 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE     ADM       ADP       AEP  \\\n",
       "Date                                                                   \n",
       "2007-07-02  0.011193  0.005791  0.010829  0.0105  0.005981  0.005651   \n",
       "2007-07-03  0.011193  0.005791  0.010829  0.0105  0.005981  0.005651   \n",
       "2007-07-05  0.011193  0.005791  0.010829  0.0105  0.005981  0.005651   \n",
       "2007-07-06  0.011193  0.005791  0.010829  0.0105  0.005981  0.005651   \n",
       "2007-07-09  0.011193  0.005791  0.010829  0.0105  0.005981  0.005651   \n",
       "...              ...       ...       ...     ...       ...       ...   \n",
       "2019-10-14  0.000000  0.007065  0.010379  0.0000  0.000000  0.005096   \n",
       "2019-10-15  0.000000  0.007065  0.010379  0.0000  0.000000  0.005096   \n",
       "2019-10-16  0.000000  0.007065  0.010379  0.0000  0.000000  0.005096   \n",
       "2019-10-17  0.000000  0.007065  0.010379  0.0000  0.000000  0.005096   \n",
       "2019-10-18  0.000000  0.007065  0.010379  0.0000  0.000000  0.005096   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "2007-07-02  0.008970  0.009459  0.007258  0.007667  ...  0.009688  0.007011   \n",
       "2007-07-03  0.008970  0.009459  0.007258  0.007667  ...  0.009688  0.007011   \n",
       "2007-07-05  0.008970  0.009459  0.007258  0.007667  ...  0.009688  0.007011   \n",
       "2007-07-06  0.008970  0.009459  0.007258  0.007667  ...  0.009688  0.007011   \n",
       "2007-07-09  0.008970  0.009459  0.007258  0.007667  ...  0.009688  0.007011   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14  0.008623  0.012111  0.006361  0.007906  ...  0.009998  0.009232   \n",
       "2019-10-15  0.008623  0.012111  0.006361  0.007906  ...  0.009998  0.009232   \n",
       "2019-10-16  0.008623  0.012111  0.006361  0.007906  ...  0.009998  0.009232   \n",
       "2019-10-17  0.008623  0.012111  0.006361  0.007906  ...  0.009998  0.009232   \n",
       "2019-10-18  0.008623  0.012111  0.006361  0.007906  ...  0.009998  0.009232   \n",
       "\n",
       "                 WEC       WHR       WMB  WMT       XEL       XOM      XRAY  \\\n",
       "Date                                                                          \n",
       "2007-07-02  0.004474  0.009249  0.008400  0.0  0.005229  0.000000  0.006769   \n",
       "2007-07-03  0.004474  0.009249  0.008400  0.0  0.005229  0.000000  0.006769   \n",
       "2007-07-05  0.004474  0.009249  0.008400  0.0  0.005229  0.000000  0.006769   \n",
       "2007-07-06  0.004474  0.009249  0.008400  0.0  0.005229  0.000000  0.006769   \n",
       "2007-07-09  0.004474  0.009249  0.008400  0.0  0.005229  0.000000  0.006769   \n",
       "...              ...       ...       ...  ...       ...       ...       ...   \n",
       "2019-10-14  0.005117  0.009501  0.008254  0.0  0.005134  0.006252  0.008871   \n",
       "2019-10-15  0.005117  0.009501  0.008254  0.0  0.005134  0.006252  0.008871   \n",
       "2019-10-16  0.005117  0.009501  0.008254  0.0  0.005134  0.006252  0.008871   \n",
       "2019-10-17  0.005117  0.009501  0.008254  0.0  0.005134  0.006252  0.008871   \n",
       "2019-10-18  0.005117  0.009501  0.008254  0.0  0.005134  0.006252  0.008871   \n",
       "\n",
       "                 XRX  \n",
       "Date                  \n",
       "2007-07-02  0.006543  \n",
       "2007-07-03  0.006543  \n",
       "2007-07-05  0.006543  \n",
       "2007-07-06  0.006543  \n",
       "2007-07-09  0.006543  \n",
       "...              ...  \n",
       "2019-10-14  0.009639  \n",
       "2019-10-15  0.009639  \n",
       "2019-10-16  0.009639  \n",
       "2019-10-17  0.009639  \n",
       "2019-10-18  0.009639  \n",
       "\n",
       "[3098 rows x 149 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = pos.reindex(rs.index)\n",
    "pos = pos.ffill()\n",
    "pos = pos.dropna()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = rs.fillna(0)\n",
    "rs = rs.reindex(pos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: \n",
      " benchmark: 0.03881 \n",
      " portfolio: 0.04254\n",
      "Annual return: \n",
      " benchmark: 11.915 \n",
      " portfolio: 15.521\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_filtered_EV = compute_results (rs, pos, plot=True, savefig=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minimum Variance Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Objective function to minimize using the minimizer from scipy\n",
    "def portfolio_variance (w, cov):   \n",
    "    var = 100*float(w.T.dot(cov).dot(w)) # note the variance is multiplied for computational reasons\n",
    "    return var\n",
    "\n",
    "# Function that computes the optimal weights to minimize the variance\n",
    "def compute_w_opt (cov, past_rs):\n",
    "    \n",
    "    # initialize weights\n",
    "    w = np.random.random((past_rs.shape[1],1))-0.5\n",
    "\n",
    "    # Constraints of the optimization\n",
    "    cons = ({'type': 'eq', 'fun': lambda w: np.sum(w)-1})\n",
    "    bnds = [(0,0.3) for i in range(w.shape[0])]\n",
    "\n",
    "    # Minimize\n",
    "    w_opt = minimize(portfolio_variance, w, args=(cov), constraints=cons, bounds=bnds).x\n",
    "    \n",
    "    return w_opt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-08-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-09-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  ABT  ADBE  ADM  ADP  AEP  AES  ALB  AME  AMGN  ...  VMC  \\\n",
       "2007-07-02   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-08-01   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-09-04   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-10-01   0.0  1.0   1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  ...  1.0   \n",
       "2007-11-01   1.0  1.0   1.0  1.0  1.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "...          ...  ...   ...  ...  ...  ...  ...  ...  ...   ...  ...  ...   \n",
       "2018-01-02   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-02-06   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-03-01   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "2018-04-03   0.0  0.0   0.0  0.0  0.0  0.0  1.0  1.0  0.0   0.0  ...  0.0   \n",
       "2018-05-01   0.0  1.0   1.0  0.0  0.0  1.0  1.0  1.0  1.0   1.0  ...  1.0   \n",
       "\n",
       "            WBA  WEC  WHR  WMB  WMT  XEL  XOM  XRAY  XRX  \n",
       "2007-07-02  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-08-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-09-04  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-10-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "2007-11-01  1.0  1.0  1.0  1.0  0.0  1.0  0.0   1.0  1.0  \n",
       "...         ...  ...  ...  ...  ...  ...  ...   ...  ...  \n",
       "2018-01-02  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-02-06  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-03-01  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "2018-04-03  1.0  1.0  1.0  0.0  0.0  0.0  0.0   1.0  1.0  \n",
       "2018-05-01  1.0  1.0  1.0  1.0  0.0  1.0  1.0   1.0  1.0  \n",
       "\n",
       "[131 rows x 149 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos[pos!=0] = 1\n",
    "pos = pos.reindex(preds.index)\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
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       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
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       "      <th>WHR</th>\n",
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       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>1970-01-02</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1970-01-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.008097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002066</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1970-01-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.012382</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>0.002383</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.006447</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>-0.003363</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>-0.019785</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>0.010355</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005236</td>\n",
       "      <td>-0.005945</td>\n",
       "      <td>-0.011687</td>\n",
       "      <td>0.001579</td>\n",
       "      <td>0.001298</td>\n",
       "      <td>-0.008939</td>\n",
       "      <td>-0.000790</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.011524</td>\n",
       "      <td>-0.005113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>-0.002335</td>\n",
       "      <td>0.026701</td>\n",
       "      <td>0.005779</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.010166</td>\n",
       "      <td>-0.003156</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>0.007891</td>\n",
       "      <td>0.008686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>-0.005950</td>\n",
       "      <td>0.000946</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>0.004663</td>\n",
       "      <td>0.019628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>-0.004045</td>\n",
       "      <td>-0.002443</td>\n",
       "      <td>-0.024236</td>\n",
       "      <td>-0.015744</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.009331</td>\n",
       "      <td>0.006843</td>\n",
       "      <td>-0.004282</td>\n",
       "      <td>-0.005856</td>\n",
       "      <td>-0.007110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.002656</td>\n",
       "      <td>0.006381</td>\n",
       "      <td>0.005783</td>\n",
       "      <td>-0.014796</td>\n",
       "      <td>-0.000921</td>\n",
       "      <td>0.005370</td>\n",
       "      <td>-0.017291</td>\n",
       "      <td>0.003158</td>\n",
       "      <td>-0.013156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.003875</td>\n",
       "      <td>0.007310</td>\n",
       "      <td>-0.012769</td>\n",
       "      <td>0.003771</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.006796</td>\n",
       "      <td>0.009426</td>\n",
       "      <td>-0.000339</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.023148</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.000439</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.002831</td>\n",
       "      <td>-0.001320</td>\n",
       "      <td>0.019108</td>\n",
       "      <td>0.004066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.004965</td>\n",
       "      <td>-0.013913</td>\n",
       "      <td>-0.005284</td>\n",
       "      <td>-0.001772</td>\n",
       "      <td>0.012184</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>-0.016107</td>\n",
       "      <td>-0.001809</td>\n",
       "      <td>-0.004135</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>-0.011507</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>-0.005858</td>\n",
       "      <td>0.005326</td>\n",
       "      <td>-0.007809</td>\n",
       "      <td>-0.000910</td>\n",
       "      <td>0.007748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12563 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABT      ADBE       ADM       ADP       AEP  \\\n",
       "Date                                                                     \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  0.000000  0.012334   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  0.000000 -0.008097   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  0.000000  0.004219   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  0.000000 -0.012382   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.001440  0.002383 -0.001834 -0.006447 -0.000369 -0.003363   \n",
       "2019-10-15 -0.002335  0.026701  0.005779  0.003229  0.010166 -0.003156   \n",
       "2019-10-16 -0.004045 -0.002443 -0.024236 -0.015744  0.000244  0.009331   \n",
       "2019-10-17  0.003875  0.007310 -0.012769  0.003771 -0.002012  0.004310   \n",
       "2019-10-18  0.004791  0.004965 -0.013913 -0.005284 -0.001772  0.012184   \n",
       "\n",
       "                 AES       ALB       AME      AMGN  ...       VMC       WBA  \\\n",
       "Date                                                ...                       \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  ...  0.000000  0.000000   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2019-10-14 -0.001250 -0.019785 -0.001696  0.010355  ... -0.005236 -0.005945   \n",
       "2019-10-15  0.001874  0.004578  0.007891  0.008686  ...  0.002179  0.015622   \n",
       "2019-10-16  0.006843 -0.004282 -0.005856 -0.007110  ...  0.001496  0.002656   \n",
       "2019-10-17  0.006796  0.009426 -0.000339  0.001721  ...  0.003526  0.023148   \n",
       "2019-10-18  0.008584 -0.016107 -0.001809 -0.004135  ... -0.011506 -0.011507   \n",
       "\n",
       "                 WEC       WHR       WMB       WMT       XEL       XOM  \\\n",
       "Date                                                                     \n",
       "1970-01-02  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1970-01-05  0.000000  0.000000  0.000000  0.000000  0.000000  0.016000   \n",
       "1970-01-06  0.000000  0.000000  0.000000  0.000000  0.000000 -0.002066   \n",
       "1970-01-07  0.000000  0.000000  0.000000  0.000000  0.000000 -0.005903   \n",
       "1970-01-08  0.000000  0.000000  0.000000  0.000000  0.000000  0.001918   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2019-10-14 -0.011687  0.001579  0.001298 -0.008939 -0.000790  0.002895   \n",
       "2019-10-15 -0.005950  0.000946  0.001297  0.003016 -0.002056  0.003463   \n",
       "2019-10-16  0.006381  0.005783 -0.014796 -0.000921  0.005370 -0.017291   \n",
       "2019-10-17  0.001508  0.003941 -0.000439  0.003511  0.002831 -0.001320   \n",
       "2019-10-18  0.000646  0.005541  0.004813 -0.005858  0.005326 -0.007809   \n",
       "\n",
       "                XRAY       XRX  \n",
       "Date                            \n",
       "1970-01-02  0.000000  0.000000  \n",
       "1970-01-05  0.000000  0.000000  \n",
       "1970-01-06  0.000000  0.000000  \n",
       "1970-01-07  0.000000  0.000000  \n",
       "1970-01-08  0.000000  0.000000  \n",
       "...              ...       ...  \n",
       "2019-10-14 -0.011524 -0.005113  \n",
       "2019-10-15  0.004663  0.019628  \n",
       "2019-10-16  0.003158 -0.013156  \n",
       "2019-10-17  0.019108  0.004066  \n",
       "2019-10-18 -0.000910  0.007748  \n",
       "\n",
       "[12563 rows x 149 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = prices.astype(float).apply(np.log).diff(1)\n",
    "rs.index = pd.to_datetime(rs.index)\n",
    "rs = rs.T.sort_index().T\n",
    "rs = rs.fillna(0)\n",
    "\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007-07-02 00:00:00\n",
      "2007-08-01 00:00:00\n",
      "2007-09-04 00:00:00\n",
      "2007-10-01 00:00:00\n",
      "2007-11-01 00:00:00\n",
      "2007-12-03 00:00:00\n",
      "2008-01-02 00:00:00\n",
      "2008-02-01 00:00:00\n",
      "2008-03-03 00:00:00\n",
      "2008-04-01 00:00:00\n",
      "2008-05-01 00:00:00\n",
      "2008-06-02 00:00:00\n",
      "2008-07-01 00:00:00\n",
      "2008-08-01 00:00:00\n",
      "2008-09-02 00:00:00\n",
      "2008-10-01 00:00:00\n",
      "2008-11-03 00:00:00\n",
      "2008-12-01 00:00:00\n",
      "2009-01-02 00:00:00\n",
      "2009-02-02 00:00:00\n",
      "2009-03-02 00:00:00\n",
      "2009-04-01 00:00:00\n",
      "2009-05-01 00:00:00\n",
      "2009-06-01 00:00:00\n",
      "2009-07-01 00:00:00\n",
      "2009-08-03 00:00:00\n",
      "2009-09-01 00:00:00\n",
      "2009-10-01 00:00:00\n",
      "2009-11-02 00:00:00\n",
      "2009-12-01 00:00:00\n",
      "2010-01-04 00:00:00\n",
      "2010-02-01 00:00:00\n",
      "2010-03-01 00:00:00\n",
      "2010-04-01 00:00:00\n",
      "2010-05-03 00:00:00\n",
      "2010-06-01 00:00:00\n",
      "2010-07-01 00:00:00\n",
      "2010-08-02 00:00:00\n",
      "2010-09-01 00:00:00\n",
      "2010-10-01 00:00:00\n",
      "2010-11-01 00:00:00\n",
      "2010-12-01 00:00:00\n",
      "2011-01-03 00:00:00\n",
      "2011-02-01 00:00:00\n",
      "2011-03-01 00:00:00\n",
      "2011-04-01 00:00:00\n",
      "2011-05-02 00:00:00\n",
      "2011-06-01 00:00:00\n",
      "2011-07-01 00:00:00\n",
      "2011-08-01 00:00:00\n",
      "2011-09-01 00:00:00\n",
      "2011-10-03 00:00:00\n",
      "2011-11-01 00:00:00\n",
      "2011-12-01 00:00:00\n",
      "2012-01-03 00:00:00\n",
      "2012-02-01 00:00:00\n",
      "2012-03-01 00:00:00\n",
      "2012-04-02 00:00:00\n",
      "2012-05-01 00:00:00\n",
      "2012-06-01 00:00:00\n",
      "2012-07-02 00:00:00\n",
      "2012-08-01 00:00:00\n",
      "2012-09-04 00:00:00\n",
      "2012-10-01 00:00:00\n",
      "2012-11-01 00:00:00\n",
      "2012-12-03 00:00:00\n",
      "2013-01-02 00:00:00\n",
      "2013-02-01 00:00:00\n",
      "2013-03-01 00:00:00\n",
      "2013-04-01 00:00:00\n",
      "2013-05-01 00:00:00\n",
      "2013-06-03 00:00:00\n",
      "2013-07-01 00:00:00\n",
      "2013-08-01 00:00:00\n",
      "2013-09-03 00:00:00\n",
      "2013-10-01 00:00:00\n",
      "2013-11-01 00:00:00\n",
      "2013-12-02 00:00:00\n",
      "2014-01-02 00:00:00\n",
      "2014-02-03 00:00:00\n",
      "2014-03-03 00:00:00\n",
      "2014-04-01 00:00:00\n",
      "2014-05-01 00:00:00\n",
      "2014-06-02 00:00:00\n",
      "2014-07-01 00:00:00\n",
      "2014-08-01 00:00:00\n",
      "2014-09-02 00:00:00\n",
      "2014-10-01 00:00:00\n",
      "2014-11-03 00:00:00\n",
      "2014-12-01 00:00:00\n",
      "2015-01-02 00:00:00\n",
      "2015-02-02 00:00:00\n",
      "2015-03-02 00:00:00\n",
      "2015-04-01 00:00:00\n",
      "2015-05-01 00:00:00\n",
      "2015-06-01 00:00:00\n",
      "2015-07-01 00:00:00\n",
      "2015-08-03 00:00:00\n",
      "2015-09-01 00:00:00\n",
      "2015-10-01 00:00:00\n",
      "2015-11-02 00:00:00\n",
      "2015-12-01 00:00:00\n",
      "2016-01-04 00:00:00\n",
      "2016-02-01 00:00:00\n",
      "2016-03-01 00:00:00\n",
      "2016-04-01 00:00:00\n",
      "2016-05-02 00:00:00\n",
      "2016-06-01 00:00:00\n",
      "2016-07-01 00:00:00\n",
      "2016-08-01 00:00:00\n",
      "2016-09-01 00:00:00\n",
      "2016-10-03 00:00:00\n",
      "2016-11-01 00:00:00\n",
      "2016-12-01 00:00:00\n",
      "2017-01-03 00:00:00\n",
      "2017-02-01 00:00:00\n",
      "2017-03-01 00:00:00\n",
      "2017-04-03 00:00:00\n",
      "2017-05-01 00:00:00\n",
      "2017-06-01 00:00:00\n",
      "2017-07-03 00:00:00\n",
      "2017-08-01 00:00:00\n",
      "2017-09-01 00:00:00\n",
      "2017-10-02 00:00:00\n",
      "2017-11-01 00:00:00\n",
      "2017-12-01 00:00:00\n",
      "2018-01-02 00:00:00\n",
      "2018-02-06 00:00:00\n",
      "2018-03-01 00:00:00\n",
      "2018-04-03 00:00:00\n",
      "2018-05-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "for t in preds.index:\n",
    "    \n",
    "    print(t)\n",
    "    \n",
    "    past_rs = rs.loc[t+rd(years=-1):t][pos.loc[t][pos.loc[t]!=0].index]\n",
    "    cov = past_rs.cov()\n",
    "    w_opt = compute_w_opt (cov, past_rs)\n",
    "\n",
    "    pos.loc[t][pos.loc[t][pos.loc[t]!=0].index] = w_opt*pos.loc[t][pos.loc[t][pos.loc[t]!=0].index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>ADM</th>\n",
       "      <th>ADP</th>\n",
       "      <th>AEP</th>\n",
       "      <th>AES</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AME</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>...</th>\n",
       "      <th>VMC</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WEC</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XOM</th>\n",
       "      <th>XRAY</th>\n",
       "      <th>XRX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-07-02</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.968003e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.609744e-18</td>\n",
       "      <td>2.249720e-18</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.172073e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927226e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.423015e-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-03</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.968003e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.609744e-18</td>\n",
       "      <td>2.249720e-18</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.172073e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927226e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.423015e-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.968003e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.609744e-18</td>\n",
       "      <td>2.249720e-18</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.172073e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927226e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.423015e-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-06</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.968003e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.609744e-18</td>\n",
       "      <td>2.249720e-18</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.172073e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927226e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.423015e-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-07-09</td>\n",
       "      <td>0.006868</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.968003e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.609744e-18</td>\n",
       "      <td>2.249720e-18</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.172073e-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.927226e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.423015e-19</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.646813e-19</td>\n",
       "      <td>9.445002e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.112823e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858811e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486332e-18</td>\n",
       "      <td>9.675893e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115456e-18</td>\n",
       "      <td>0.044546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>1.944977e-02</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.646813e-19</td>\n",
       "      <td>9.445002e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.112823e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858811e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486332e-18</td>\n",
       "      <td>9.675893e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115456e-18</td>\n",
       "      <td>0.044546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>1.944977e-02</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.646813e-19</td>\n",
       "      <td>9.445002e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.112823e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858811e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486332e-18</td>\n",
       "      <td>9.675893e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115456e-18</td>\n",
       "      <td>0.044546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>1.944977e-02</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.646813e-19</td>\n",
       "      <td>9.445002e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.112823e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858811e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486332e-18</td>\n",
       "      <td>9.675893e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115456e-18</td>\n",
       "      <td>0.044546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>1.944977e-02</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.646813e-19</td>\n",
       "      <td>9.445002e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.112823e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.858811e-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.486332e-18</td>\n",
       "      <td>9.675893e-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.115456e-18</td>\n",
       "      <td>0.044546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.003124</td>\n",
       "      <td>1.944977e-02</td>\n",
       "      <td>0.003216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3098 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL           ABT          ADBE  ADM  ADP           AEP  \\\n",
       "Date                                                                       \n",
       "2007-07-02  0.006868  0.000000e+00  2.968003e-18  0.0  0.0  7.609744e-18   \n",
       "2007-07-03  0.006868  0.000000e+00  2.968003e-18  0.0  0.0  7.609744e-18   \n",
       "2007-07-05  0.006868  0.000000e+00  2.968003e-18  0.0  0.0  7.609744e-18   \n",
       "2007-07-06  0.006868  0.000000e+00  2.968003e-18  0.0  0.0  7.609744e-18   \n",
       "2007-07-09  0.006868  0.000000e+00  2.968003e-18  0.0  0.0  7.609744e-18   \n",
       "...              ...           ...           ...  ...  ...           ...   \n",
       "2019-10-14  0.000000  8.646813e-19  9.445002e-19  0.0  0.0  3.112823e-02   \n",
       "2019-10-15  0.000000  8.646813e-19  9.445002e-19  0.0  0.0  3.112823e-02   \n",
       "2019-10-16  0.000000  8.646813e-19  9.445002e-19  0.0  0.0  3.112823e-02   \n",
       "2019-10-17  0.000000  8.646813e-19  9.445002e-19  0.0  0.0  3.112823e-02   \n",
       "2019-10-18  0.000000  8.646813e-19  9.445002e-19  0.0  0.0  3.112823e-02   \n",
       "\n",
       "                     AES           ALB  AME      AMGN  ...           VMC  \\\n",
       "Date                                                   ...                 \n",
       "2007-07-02  2.249720e-18  0.000000e+00  0.0  0.000041  ...  0.000000e+00   \n",
       "2007-07-03  2.249720e-18  0.000000e+00  0.0  0.000041  ...  0.000000e+00   \n",
       "2007-07-05  2.249720e-18  0.000000e+00  0.0  0.000041  ...  0.000000e+00   \n",
       "2007-07-06  2.249720e-18  0.000000e+00  0.0  0.000041  ...  0.000000e+00   \n",
       "2007-07-09  2.249720e-18  0.000000e+00  0.0  0.000041  ...  0.000000e+00   \n",
       "...                  ...           ...  ...       ...  ...           ...   \n",
       "2019-10-14  0.000000e+00  3.858811e-18  0.0  0.000000  ...  1.486332e-18   \n",
       "2019-10-15  0.000000e+00  3.858811e-18  0.0  0.000000  ...  1.486332e-18   \n",
       "2019-10-16  0.000000e+00  3.858811e-18  0.0  0.000000  ...  1.486332e-18   \n",
       "2019-10-17  0.000000e+00  3.858811e-18  0.0  0.000000  ...  1.486332e-18   \n",
       "2019-10-18  0.000000e+00  3.858811e-18  0.0  0.000000  ...  1.486332e-18   \n",
       "\n",
       "                     WBA  WEC           WHR       WMB  WMT           XEL  \\\n",
       "Date                                                                       \n",
       "2007-07-02  1.172073e-03  0.0  0.000000e+00  0.000000  0.0  9.927226e-19   \n",
       "2007-07-03  1.172073e-03  0.0  0.000000e+00  0.000000  0.0  9.927226e-19   \n",
       "2007-07-05  1.172073e-03  0.0  0.000000e+00  0.000000  0.0  9.927226e-19   \n",
       "2007-07-06  1.172073e-03  0.0  0.000000e+00  0.000000  0.0  9.927226e-19   \n",
       "2007-07-09  1.172073e-03  0.0  0.000000e+00  0.000000  0.0  9.927226e-19   \n",
       "...                  ...  ...           ...       ...  ...           ...   \n",
       "2019-10-14  9.675893e-19  0.0  1.115456e-18  0.044546  0.0  0.000000e+00   \n",
       "2019-10-15  9.675893e-19  0.0  1.115456e-18  0.044546  0.0  0.000000e+00   \n",
       "2019-10-16  9.675893e-19  0.0  1.115456e-18  0.044546  0.0  0.000000e+00   \n",
       "2019-10-17  9.675893e-19  0.0  1.115456e-18  0.044546  0.0  0.000000e+00   \n",
       "2019-10-18  9.675893e-19  0.0  1.115456e-18  0.044546  0.0  0.000000e+00   \n",
       "\n",
       "                 XOM          XRAY       XRX  \n",
       "Date                                          \n",
       "2007-07-02  0.000000  1.423015e-19  0.000000  \n",
       "2007-07-03  0.000000  1.423015e-19  0.000000  \n",
       "2007-07-05  0.000000  1.423015e-19  0.000000  \n",
       "2007-07-06  0.000000  1.423015e-19  0.000000  \n",
       "2007-07-09  0.000000  1.423015e-19  0.000000  \n",
       "...              ...           ...       ...  \n",
       "2019-10-14  0.003124  1.944977e-02  0.003216  \n",
       "2019-10-15  0.003124  1.944977e-02  0.003216  \n",
       "2019-10-16  0.003124  1.944977e-02  0.003216  \n",
       "2019-10-17  0.003124  1.944977e-02  0.003216  \n",
       "2019-10-18  0.003124  1.944977e-02  0.003216  \n",
       "\n",
       "[3098 rows x 149 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = pos.reindex(rs.index)\n",
    "pos = pos.ffill()\n",
    "pos = pos.dropna()\n",
    "\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = rs.fillna(0)\n",
    "rs = rs.reindex(pos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sharpe: \n",
      " benchmark: 0.03881 \n",
      " portfolio: 0.05518\n",
      "Annual return: \n",
      " benchmark: 11.915 \n",
      " portfolio: 11.641\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_filtered_MV = compute_results (rs, pos, plot=True, savefig=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4u7vj7u7OzJkzOX78uEM/unXr5rR/ixcvpn///kyePFkCNiGEEELc8zRNY/GJxZbzqKSoO9eZPGQZs+ixtAfzj87n76t/O9w35Bj47uB3t71f+2L3WQI20IO03HzcfQD4M9r5xtdx6faz1ir7WVeEuSgXSnqWBCDyciQ///2zXdkLyfr0yODiwU7bzp3MJLciBW13E03TbvorKSmp0GWvR9myZZ1eu3TpEnFxcRiNRkqUKGEJctzd3RkyZAgGg4FLl6wzTEuWLGnXhoeHPiRrnmZ59epVKlQoeJcFZ+2Ys0rmVc78LGd1zc8HfYuDBQsWMGbMGNatW8fu3bt5+umn7cqYlStXzmn/VqxYgb+/P3369CnwvQghhBBC/JcdjjvMuxHvWkZsAJIyk/KpcfstOr6IJr80ISopio92fcRPR34C4PmGz/NB6w8s5dafXc/+2P15NXNTGHOMTNs7jRc2vEBKVgrrotbZ3XcWPJX2Kg3owfDpa6ft7sWkxvDp7k8BcHdxZ2zzsXi5edmVKeVlHVT5dPendjFDYmaiXsazFM54u3nn+35uZHqkKKLY2Fin1+rVq4e/vz9ubm789ddfuLg4xtLOAr68BAQE2AV5t1tGRgarV6/m66+/5vnnn7dcz8nJcVo+r6muX331FVOmTKFTp05s3bqVgICAW9JfIYQQQoi73eOrH3e4lnuz5jttwvYJdudH448CUKtULToGdaSyX2UGr9WX65y4duKWTe08nXiaXst6Wc5fC3+N7Zfsk6BUKOY4wPFAuQcsx0tOLGFUyCiUUkSnRPPQ4ocs98Y2H0vfmn0d6ptH2sySs5Mp7lEcsK5zK+lV0qEegK+7b77v6V870vZvFBsbS0REhOX83Llz7N27l2bNmtG+fXuMRiOJiYmEhIQ4vMwjXIXRoUMH4uPj79jm1JmZmRiNRjw9PS3XkpOTWbFiRZHaKV68OH/88QdKKbp06UJS0t31a5IQQgghxO1g1Ix259VL6stVImPujlVI/8T/w8mEk3nef7DSgwA0KtOICr56sGQeeboVbAM2wCFgC/AKwN3FcR+2Ep4laFWxFQBz/55Lg58akJadxo+H7LNLlvEp4/S5ubczeGT5I5ZjS9BmE9j93uN3y3Ex92J5vh+QoO22Kl26NE8++STz589n6dKldOvWjbJlyzJkyBBq1arF888/z4ABA/jkk0/YuHEjq1ev5tNPP2XYsGFFek6nTp3o0qULAwcO5PPPP2fTpk3MmzeP55577ha9M3slSpSgadOmTJw4kcWLF7N06VI6duxIiRIlitxWQEAA69ev59q1a3Tv3p20NOfZfIQQQggh/qt+vGIfNEwJ07daOpuUz2Zht0G6IZ3Xt7xO/5X96bPCupzlsVqP0bisvm/uvK7z8HTVf8hXStG3hj5ClVeGxhtVmG0FnI2SmeVOkPL78d85l3zOch5cPJjQiqGF6svltMt0XdKV/535H5fTLgP2QVtt/9p4uepTLBuXy3+fYZkeeRsFBQXx9ttv8+abb3L27FlCQkL49ddf8fLS/7C++eYbatasyaxZs3j33XcpXrw4devW5ZlnninSc5RSLF26lHHjxvHFF19w5coVKlasyMCBA2/F23Jq/vz5DB8+nMGDBxMQEMDLL79MWloaX3/9dZHbqlChAhs3buTBBx/kkUceYcWKFUUaeRRCCCGE+LeKSoziYPpBy/nC7gsp66Mvm8kwOuYKuF2yjdm0+rWVw7YDT9//NCMfGJnn8hfzOrDcfY9JjcHT1dOyLixHy+HXf34lpFxIkTJN7o3dC0DPaj0p51OOWYdmWe49VusxLqddZniD4XlVtwSYZinZKVxM0becnthyIn1qFC3Xwvnk83b7uuVeu7ag+wI2nNvAU/We4mVezrMdCdpukzlz5liOH3nkEadllFKMHDmSkSNH5tlOeHi4w7WwsDCH5Cje3t58/vnnfP75507bsU2rbzZkyBCGDBmSb7vgPCVp7n5Vr17dsiWArfHjx+f5PLPg4GCHZwQFBXHu3DmHskIIIYQQ/2W7L++2O68TUAdDjr45dKYxE03T7shWWF/t+8rpPnEerh759sccFGUaMi3XUrNT6bSoE+4u7ux5Yg9KKRYdX8THuz7GzcWNfU/uK3S/lpxYAkCNkjUYcv8Qu6DtnRbvFFg/R7PPwWDMMXIxVQ/aHqrykLMqRZL7s6lasirDS+YdRJrJ9EghhBBCCCHuUpvPbQbggbIPsKjHIgDcXNxwVa7kaDmWAO5WyB2UJWYmMmXPFJaeWMrsI7Od1lHkH0Cag7a9sXstae4vpVyyPO9K+hUA1p3Vsz0W9f3FpumJ/zKNmXbXXVThwp7mFZrbnZ9PPo8hx4C/l3+BGR4LknsUrygkaBNCCCGEEOIuNOyPYWyL3gbo69hspwlaRqxyBSc3Q46Wwzt/vsMDPz/AsfhjlusvbXyJ2Ydn827Eu3blzYlRwD4DozPm6ZEnr52k2xJ9r97k7GTLfXPQZfvcwrLdFHtQnUGAPk0S4Mk6Txaqjablm/JD5x94oeELAPwv6n9A3qn6c/uy3ZdUKVHF6b25D88tVBvOSNAmhBBCCCHEXWbpiaXsjNlpOQ/wtt/6yBz8mAOV5KxkZuyfQUxqzA0/e9XpVSw/tRyAfiv7WZKGHLhywKHs1+2/ZknPJfzS9Rc+bfMpLSq0yLdt29GmlOwUwD6T5PiI8cSkxliyLXq4FC6PQUJGAsP+0JP3+Xv5U8xDz8Y4tvlYvgj7gpFN8l5+lFuzCs3oUbWHfb/dCjdK1v6+9qzo7ZgxvWn5ptQLqFfoPuQmQZsQQgghhBB3EU3TmLpnquX82TLPOpQx7/918MpBXt74Mm0XtGX6gel8tvuzG54yuTvGfh3dK5teITol2qFcs/LNaB3YGqUUDcs05OEqDxfYtjlbolmOlkNSlnVbp2MJx+i0qJPlPHewmpfvDn7HwTg9YYtt+nwfdx86BHXAzaVoqTwqF69Mg9IN8ux3UV1NvwoZSbDqVfhrGuQYC65kQxKRCCGEEEIIcRfZfH6zZePsg4MPsmXLFocyxT31oO3LfV9yJvGM5fq6s+vYOn8rA+sM5NUmrxb6mbsu7WLFqRWMbznerj2AnTE7OXTlEKCPYjUs05BBdQY5rP8qjNwjVmnZaVzLuJZn+YLWyIEe+M07Os9ybg5ob1SzCs0sgeCNrEcDfcNv9syBSNP2DZ7FIDUOyjeAWgUnOJGgTQghhBBCiLvI78f1TZf93P3yzMRoDkzi0uIc7mUYM/jx8I90Ce5C3YC6BT4vMiaSZ9bpW0yZp0UC+rTCcH1aoTltfbvK7Rjfcnzh30wuuUeskrOS+SzyszzLO8tQmdv+2P125zcjyyNA1RJVLceFnR6Zl2frPwvnbNbprbIJqMcnQvLlfOtL0CaEEEIIIcRdIDU7lfPJ5/kz+k8AZnSakWdZcyZD2yQeuX2590u+6/Rdgc8d+sdQh2tlfcrS/r72Dtfr+NcpsL38GDX7aYEbzm2wHBdzL2ZZ52aWZkjDmGPE1cU1zzYvpV6yHL/U6CUG1r45exPbJhQp4VEi/8IbJ8K2ydD2DWj7Bqv6rGJXzC6CiweTmp1Kq8BWcDCPPd5Sr8Lkmvk2L2vahBBCCCGEuMOOXD1Ci/kt6L+yv+Wa7Zqq3C6n5j8yAxBxMcJhfVtqdiopWdbAKPx8uNO6Q+sNRSnF8l7L7a43Ld+0wOfmJ/fUxeMJxy3HG/pvoISnfXCUmp3K/iv2I2lmmqaRmJlo2TpgYO2BPN/wedxd3W+oj2a1/WtbjquWrGp/8+BCmPconNsBh5foARvAlk/gdDhBxYPoX7M/Tcs3JaxyGO4u7nDtrF6mvvXPGOUCn+Vq2wkJ2oQQQgghhLjDHl/1uMO1fDepLuR0vdFbRpOancpnuz/jyNUjPLL8EXos68Gcw3M4Fn+MmQdnWsr6uftZjs2p+6uWrMrElhN5OPhhfn74Z8fgpYiCSwTz0YMf0SlITzay7OQyANpXbo+vuy+uyjqiZk4oEpfuOAUU9JHE1r+1ZvN5fS87fy//onUmOwOMeU+/tE1eUrFYRfubS56FE3/Aj11gUa6RyiTHpC0YDZBout59KoS+rB/n2sw7LxK03Sbt27enYcOGGAz2v3YsXrwYpRTr16+3XDtw4AADBw4kMDAQDw8P/P396dixIwsWLLCrP2TIEJRSlpefnx9NmzZlyZIlhepTWFiYXX3b14ULFzh16hRKKX755Re7emvXrkUpxbBhw+yup6en4+7uzocffljUj0cIIYQQ4p6modmdt63UNt/ytsENQL+a/ZyW23huI1/v+5qf/v6JAasGcDH1InHpcUzeM5l+K/sRlRQFwLuh7zKryyxLvRola1iO+9Tow6dtP6VR2UZFeUt56l61O++3et/umre7Pt3TPGoGEFoxFLDff83WD4d/AKzZLv29ixC05RhheguYXBuWvwQRX0PC2TyLW6aFntgAlxy3PrCTdtXx2sV9oBmhVDB4+kGXDwrfV2RN220zffp0GjRowLRp03jttdcASElJYeTIkTz66KN06qT/2rBo0SIGDhxIq1at+OijjwgODiY+Pp41a9YwePBgUlJSeOaZZyzt1q5dm9mz9R3pk5KSmDNnDv3792fLli20bt26wH61a9fOaZBVtmxZPDw8KFeuHBERETzxxBOWexEREfj4+BAREWFXZ9euXRgMBlq1alX0D0gIIYQQ4h6Ve4PsL9t9WWDQVse/Djsu7bCcv9P8HRYdXwTA4LqDqRtQlze3vQnAP/H/5NlOcpa+Jq5fjX4opdg5cCcerh5FTpFfVL7uvvh7+VuCNGcZH80bWmcYMgrVZqFH2tLi4YfOkGDKkrnPNECxbqyezfH5bZaiv3b7lbj0OIKKB0FCFMzrW3D7qU5GBo/rm3TnVO9EtsGIp1uuNXrlGwB/5tmkBG23Se3atRk1ahTvvfcejz32GIGBgbz33nskJiYydaq+D0d0dDRDhgxh0KBB/Pjjj3ZD4r1792bUqFFcunTJrl1fX19atLBuYtixY0c2b97MihUrChW0+fv729XPLTQ01CE42759O4MHD+a7774jISGBUqX0v1ARERG4u7vTtOmNzXUWQgghhLiXnE3SR3gCvAKY0XEGdQIKTvbxfMPnmX1E/+G+c1Bnu0Qdtf1r065yO8t55OXIAtsz/7vTx92nSH2/ET5uPsSjB23VS1YHYF7XeSw6vogxTcfw7YFvAfhg5wc0LNPQ4XNxd3G3yy5Z6FT/x/8HV084vxdzEC4fgXL6Rtj3l77feu/qSfuy1TuCmxdE74Xki+BXUf+vs6Dtih44j99fnCW7NrByRGuq2N53Njpno9DTI5VSrkqpfUqpVabzKkqpnUqpE0qpBUopD9N1T9P5SdP94MI+479u3LhxBAQE8Oqrr3Lw4EGmTZvGhAkTqFhRnyP7/fffYzAYmDx5stM5zLVq1SIsLCzfZ7i4uODj40N2dsHpUQujVatWHDp0iORk/VcYo9HIrl27GDhwIOXKlWP79u2WshERETRq1Agfn9v3l10IIYQQ4t/OPNpUrWS1QgVsYB9cBRUPsrt3f+n78XIr/GbQE1pOKHTZm8k2k6Q5G2aDMg2Y2Goifh5+lPIqZbn/arjjnnO5k5bYBVj52TA+//unNjm/nmQ/eEKHd2HAPBh1VE/b3+ML/frB3+DQIvuyqVcA+CfFh5RMA28sPmh/38M33y4VZU3b/wFHbc4/AaZqmlYDSADMc/aeARI0TasOTDWVE4CPjw9ffvklv//+O7169aJu3bqMGDHCcn/r1q2EhITg71+0RZQGgwGDwUB8fDyff/45UVFR9OrVq1B1NU2z1De/jEbrX6CWLVuSk5PDzp07ATh8+DDp6emEhIQ4jMJt375dpkYKIYQQQhTR+eTzAPh5+BVQ0jlzALeoxyK+6fANVUpUwUXl/c/84OLBPFFHX/riolx4pMYj1/XcG2Wbqt9ZkNm7em/LcU6uhB1X0q5YEpRMajWJA4MPWAK/fMWdhBSbzJv9ZsNz28A2AMxIdKxnNMD+efbX/HMlZfEpbT1e/Azk2PTZ9Mw49OfsOhNvW5N4Y/5BdqGmRyqlKgHdgA+A131X4YkAACAASURBVJQ+DNQeMG+CMBcYD8wAepmOARYBXyullKZp9qsrb1Dwm6tvZnNFFvVxt+uq16tXL5o0acKePXvYtGkTbm7WP4KLFy/SqJHjAk/b5CMuLi64uFj/Eu7Zswd3d3e7+59++mmBI3JmS5YssasPEBQURFRUFABNmjTB09OTiIgIOnbsaBlN8/b2JjQ0lLVr1wJw7Ngxrl69KkGbEEIIIUQRnLp2ionbJwJQ0rPkdbXh6apnkqzlX4ta/rXyLTut3TTa3deOHC2H8r7lLdMS7zTze7BV2rs0IxqP4Kt9X1GtZDXL9eSsZNr/bt1Drme1nvlm2rTIMcLpzdbzp9fBfc3147fOWfdaiz9jXy8lFr5tbQ32ytWH1iP1hCImU9cfZ9HGQ/xlG3tF/gCNn4DsdH09HHBFs/4Z98mcwFLP9wA45DxBpkVh17R9AbwOmHsWAFzTNM0cTVwAAk3HgcB5AE3TDEqpRFP5Arpyb4iMjGTfvn0opQgPD6ddO+t8Y03THL5wkZGRdmvE+vbty6JF1uHWOnXq8NNPPwGQlpbGtm3bGDt2LAEBAQwZMgTAIWOlbaDYvn17PvnEfjDU09PT7rhJkyaWEbWIiAhCQ/VMPi1atGDChAkYjUbL/ZYtWxbtAxFCCCGEuAfEpsXyzf5vaBPYhg5BHZh7ZC5bLmzhSNwRQB/9ery2Y9r//DxS4xFWnVrFw1UeLlT5iS0nElY5DNBH2J6q91SRnnfdLu6HxAtQp3ueRbxcnY80NSnXBMBub7mjV4/alSlUwJadDl+FQNIF/bzHl9aAzcw8cnZ4EXR+H4qb0vxv/dx+dK7d26zPacLahfu5kpxJQloWh6OT8CBX0L1mtP6ykYw3c4Y2Zcjs3ezTavCV8RFGuC5hiqEfN5SIRCnVHYjVNG2PUirMfNlJUa0Q92zbHQ4MByhXrhzh4eF59qFEiRKWNVVmh8a2KajrRWY0GnF1zXu3dVu5+1MYOTk5DB8+nGbNmtGuXTs+/fRT+vbtS5Uq+jLE8uXLc/bsWbu2K1eubPlsRo4cicFgsNzPzs7Gy8uLWrWsv6g0btyYc+fOMWbMGB555BGUUhQvbr8oMykpyfJ+S5YsaVff2fsLCQlh7ty5JCYm8tdffzFu3DiSk5OpVasWWVlZREREsGXLFoKCgvDz87uuz+ZmycjIyPe7dKNSUlJuafvi302+HyI/8v0Q+ZHvx3/fyoSVrEtax5ITzrdmetrvaS4dvMQlLjncy+v7EaaF0aZSGw7vPFyoPmRFZbElekuR+n2jXIxZtNmmbya9O+QLUotZ0290Lt6ZdUnrADh7+Cxpx9Ic6l/O1oOlCwkXLJ/B9hRrToWhpYcW6u+O/9VIGpgDNmBPdBaX4/VRNx93haZp+KVkEWIuMKUO0RW7cqLGszQ8vh3z6rp0r/JEXlCM2BxJhtHuEWRR8KbeJT1d4NLfvNnMi493ZTA5ux/Ts7uTzo1Pj2wF9FRKdQW8gOLoI28llVJuptG2SsBFU/kLQGXgglLKDSgBxOduVNO0mcBMgJCQEC2/6XxHjx7Fz+/65vgWRXJy8i19zvTp0zl48CB79+6lVq1aLFy4kLfffptVq1YBevr9jz/+GIPBYMnI6OfnR7ly5QA9eHVzc7P00d3dHRcXF4c+N2rUiJkzZ5KZmUmZMmXYvXu33X1zeVdXV7v28hIWFsa0adPYs2cPZ86coX379vj5+eHn50fDhg05cOAAu3fvpnXr1rflzyk/Xl5eNG7c+Ja1Hx4eXuipp+LeI98PkR/5foj8yPfjvyndkM7729+nS3AXMo5nQJLzcvcH3E+fjn3ybMfu+6FpsGsmVAqBwCb5Pl/NVXb7vz3U5iGH5B23lKbBFGtilabqCNRrBWVqAhBGGAkZCVxNv0r1Us6naaYb0pk0bxKJOYm0adsGF+XCiYMn4CoMvX8orzV5zfmzU67Ashfg2jkY+BtMs8/3UKlFH/pO2WV3rU2Nevxkcx54cQ2BLfoA1/S38+Ryfr0UREAxDzKM+4v2WQArjS3oUD+QsLCGhAEf79KXexUUsEEhEpFomvaWpmmVNE0LBgYAmzRNGwRsBsy7+D0FLDcdrzCdY7q/6WavZ/s3io2NZezYsYwYMYIGDRrg6enJtGnTWL16NcuX6x/dsGHDcHV1ZcyYMTf0rMOHD+Pt7U1AQACgj5TZvorKvE5t8uTJVKhQgaAga4ai0NBQ1qxZw9GjR2VqpBBCCCGEjUXHF7Hy9Epe3vQyWy9szbNc1ZJV87znIGobrH0dZrUvsOiqPquY3HYyS3ouYXaX2bc3YAO4uBeSbUYOd8+CGaFw5bjlUimvUnkGbKBnlSzhWYLsnGxLlk1zApLSXqWdV4o7AZ9Xh5PrIe4YTHP8Qb95roANYOuJOL405ErKEnsUkvSxqZNe9Zi46m/+77eiB2xX3CvySvbLtKwW4PR+7fL5D3zcyD5tbwC/KaUmAfuAH0zXfwB+VkqdRB9hG3ADz/jPGD16NN7e3kyYYE2p2rVrV3r16sXIkSPp3LkzgYGBzJ49m0GDBnH69GmGDh1KcHAwKSkpREZGcvDgQXr27GnXbmpqKjt26Bsrpqens23bNmbNmsWLL75ol7AkL/Hx8Zb6turVq2cZNStbtizVqlVj7dq19Olj/ytQaGgoX3/9NZqmSRISIYQQQggbV9Kv2J03K9+MCS0nsPzUcgbUGsDpxNP88vcvvNrEMZ19nmzTzsefdsxgaOO+4vdxX/H7itrtG6NpEP4xVG4KV0873s8xwDdN4Y2z4F24xCvlfcqTmJnI5dTLlPYuzdUMfU+z0t5OgjZNg5/zHrUE2FdjBBzK8w3Yn2Yk6n128+Json0Gy1c61CDI34cmQaVYeziGK8mZXIv0paRKdWj1gktFNFyoUML5qFpyhsHpdbMiBW2apoUD4abj00AzJ2UygP5Fafe/buvWrfz888/Mnz/fYX3Zl19+Sd26dfnwww95//336d+/PzVq1OCTTz7hjTfe4MqVK/j5+dGoUSM++ugjnn76abv6//zzjyUxiJeXF1WqVGHixIm89loeQ8W5bN682VLf1rZt2+w2527VqhWnTp1yKBsaGqrPAfbzo379+oV6phBCCCHEf12GIYNtF7ZZzp+t/ywjGo9AKcVLjV4CIMA7gKblm+bVhHOXbaKNC5H5Bm233ckN8Etfh8spHT4iKykO/92TrRdPrIMGjxaq2fK+5TmWcIzolGjqla7HuaRzAJTzLedY+MR6SDzvvKFO72NIvUrfTdaZZ9XLFiO0agA/79A3ON9sbMz/uS211ok0jUt5FudSYrpdc70bVaRqmWIAvBBWjW0nrvBoxLs84bqBwW7r7crWy9RH58r4OWbJBPDzyj8su5GRNlFIbdq0Ia8ZokFBQaSm2kfjjRo14tdffy2w3Tlz5jBnzpzr7ldRFjzPnTuXuXPnOlwPDg7O870JIYQQQtyr5h6Zy8lrJwksFsiSnkvsNsO+bskxEDnbep4Se+Nt3iya5jRgA3h2ewB7kqqw+YUjBM6qp18sQt9rlqrJlgtbOHz1MG0qteFEwglclAt1/J1sRH7OtIdw7e76xtfjbaaEtnqF7SeukIN1auTcp5sRWNKbYQ9Woe1n4ezXqvNDvTk807wi/NjZUs7o6ce45Ucs58EBPgQH2G+IXbqYJ8e1yrxrGOoQtEXm1Absg7aOdcqy4aj+ORT3zj+JSVE21xZCCCGEEEIUIDU7lZ+P/gzA+Jbjb07AtuVTmFwLbFLfk3Am7/K3izEbIn+E3d87vf1384/ZHl+MLEMOEzbGcKjGi/qN9IT8281Mht8GweHFlPctD+j7s/0T/w8GzUC1ktWcf65/TtX/W8Y+O7rmX40BM7fz5A/WgO3whC4EltQ35A4K8OWDPvcD8P4eDwyBTSHQOiJnu4/aO93qsGJEa1xc7JPm2wZkR3KC7O6NyNJHV0vYBGffPWltv84tXNMmhBBCCCGEsPFX9F88v+F5ABqVaUTz8s0LqFEwz4wrsOMD64WaD8PxtXBqc96Vbof0a/BJkOP1Tu9DpRCSDa50nWWNdtb9fZn7XBOp7w4YMuzrnA4Hb3+o0EA/D/8Y/lkF/6zCd/B8AFKzUi3JSAJ9A3FgyLIe32da0vPEElg9isOhU9ix2JrQ/vvBIRTztA+FqpumOgKMXXqYV8uEUj46EoAkzRogNq8SQHEvx5Exfx8Py/HjWWPxJovLls0C9ADPdk85VxfFoudDWRh5ntFdajHR8R1ZSNAmhBBCCCFEAQ5dOcS6s+t4ufHLeLo6X5e0/ux6Xgu35hV4qt5Thdv4OT/GbBrve9vu0qlmE6l2fC2kxuVR6TZIi4dPqzhe7zSRiPIDKefjxbGYZMC+jxnogU1iUiKWiYupV+EnU0r+cXHg6g7H/7DUKeauB1Mp2Slk5eiBmburk+mEZ6x70AX/kAmspmppX5a+tJt3frTPFlnLychWsyr+luMFkedJd9GYZorDktCnQk55tCH1KznPwmk78pZEMYcdHjrVdVyDFxLsT0iwv8P13CRoE0IIIYQQIh8rT63k7T/1wOno1aN838X5VMB1Uessx03KNeHBSg/e+MMv7MYr07r+K/qRZTz8/d8c9wKyHbMU3jarnSe923oshsErd9pdG9G+Ov6+HkxY+Tfpmh7wljjyMzTuDdU7QvJFa+GrJ6FsHbh6wnLJPA0yNTuVKZFTAMg0Zto/+PgfMF9PbHJOK4d5ZOt0XCoNJ6wjN/O0SFtKKZ5vW41vt5wC4JBmDUpP5ASy4bU2VC97fXsSB/h6MG3A9e8lLGvahBBCCCHEPe9K2hXei3iPN7a+wcmEk5brey/vtQRsADtjdvLSxpdIy06zq5+j5RB5WZ9Kt6jHIuY8NCfPEbkiifrTevzgaHYZa5CFGwbNRU9Fb8y+8WcU1ZFlcGSp01tfnXBMw9+2ZhmGtqrCqQ+7ko51CqElcUnaVeu16S1gm02WSd+ylpG2vbF7uZSqb3mw61KufdbWv2c5jNXy34+ud6OKDuvRzLzcreHRGa289ZgKVCp1/WsTa5X3w9vD9brry0ibEEIIIYS4543eMpq9sXsB2HB2Aw3LNiTTmMmF5AsOZbde2Mr7O97nowc/AmB/7H6eXPskoKenr1mq5s3p1LH/wWbTWrZHf4K6vdi15BCgSMcTP9IhK7XQ+50VWU4O2O77e2YrzO3hWO6Z9aQfXMbP26PYrdW2u9WpbjnL9D9XF0W3JlXt90jTNMdpnhutq7s0Vw9L0GbLPE0SgJhDcOWo5XSmoRv9mlTCy92FX3acs1x/+P7yuCjFuz3q5fmWSxezDbStgd1ln5p4uRccdPVoWJGVBy46XI84ddVJ6cKToE0IIYQQQtyzso3ZbL+03RKwgR4Q7I7ZbVeuVWArulXpxrcHvuVc8jl2XtpJjpaDi3Lh631fW8qFVgi98XVsAOvegYivrOc1HyL6WjqL9uh7kKXgrQdtmUl60HbtHGwYD61fg/L339izc3Jg5QjY9wu0GwttX9evOwnYkl44yMSt17ia0pvNBn0z8cdCKtO6RmmupmTyVMtgu/IuHvZp8slMth9pyyU5NRXf3HXQRzYt4o5bDp/Ieos/c+rzTnk/hj1Y1S5om/FEkzyfY9avSSX2nkvAx8OVX3aco1/mu5RT10gs6WT9nhOf9m3AoOb3MWDmDrvrHes42VOuCCRoE0IIIYQQ/2nxGfH8cOgHnqjzBEoprmVeo7a/PiI0LmIcq0+vzre+m3Lj247fAtC1SlfaLmzLlfQrPP3H03zb8VsuplpHVkaFjLrxDu/7xS5gO1L3dQ7tvcybS6xDVAmaHxVUvD76deA3iDJt5H3lONzXHBo8BpWbXd/z98/T+wD6SF/5Bk4DweSWb9Jg6mG7a4Oa38e7Peri6eZ8VMrVM9cUw7SrsPb1PLtS3JhAZmq8w3W7qafJlwHIafosf26rD0DdCsXtynu4FW5VmJe7K1MebQTAthNxRF6tDRo8UMipjd4errSoGmB3rWppXz7r16BQ9fMia9qEEEIIIcS/UmJmIjGpMQWW+2TXJ/z09090XtyZTos60X9lf47EHeFc0jm7gK1p+aZO6z9W+zHLsauLq2W63p7Le2g6rynnk/XRr2W9llHCM//1VPm6dh4u7oPl+p5e+FWE8YlcKdvKLmBrVsWfBM00ZXDHDGvABnD5kL5n2g+dIDG68M/OTIb40/qxbXsAvz4Gp02ZGT38oNc3HH1iH/U3OQYiQQE+eQZsAB4eHvYX9lg3C38t63nndWIO4+ZiHWsKLh7MzE4z4fBi+KEzXDoAwF8x1ue2rK6vrXv2QX2EbExn+33bCqNHg4qWYx+Poo11fdinPsNa6+v4No0Oo5SvR8GV8iEjbUIIIYQQ4l+p9/LexKXHse2xbZy4doKapWraBU2apqGUYn/sfoe6A1YPcLj2ZJ0n6RLUBQ2NegH1eGXzK7Ss2JLXm9qPBLkox3GPSsUqUa1ktet/M3N76KNmtv5P7/fRq0a7y18/3phdn5myGMb+nX+br+zN+75ZdgZ8VMnx+oOjrElBlps2xQ59iYz7H2fQx5ucNqVp+T/KzTPXVMcI69TSJTltmMK3lvPdOTVp6nIclZ2Gt6s3yTnJAKzovUKfgjqjnV7wvJ6tcsVpfcpkcIB1NG9Ml9p0qluepsGlKKoa5axr6YqaRGRg8/uK/Lz8yEjbbdK+fXsaNmyIwWCwu7548WKUUqxfv95y7cCBAwwcOJDAwEA8PDzw9/enY8eOLFiwwK7+kCFDUEpZXn5+fjRt2pQlS5YUul+ZmZlMnTqVpk2b4ufnh5eXFzVq1ODVV1/lzJkzBdbP3Qfb159//kl6ejoeHh5MmjTJrt7Ro0dRStGxY0eHNgMDAxk+fHih34MQQggh7i2ZxkyWnlhKXLqewGLcX+N4+o+naf1ba45cPcKkHZNYeGwhzec356t9XxXQGvSr2Y8/+v5Bu/va8VjtxxhQewD1y9Rn86Ob+aD1Bw5B2lvN3nJoY2q7qTf2pnIHbABuniRnZPPJbutG1KtfaY2flzsG8z/jbdd25RZ/Sk9UYisjUd8XzSz9GnzgZL2VZ3G0No7TFp9dn0Xtcf8jPjXLsQ7Qt4mT4M9GTkBNxmcPZmSWKQjU9IB0lbGFQ9ljOZX1g+3TcbcZactrzeAVU9bIssW9LNc83FxoVsX/utYZ1i5vnWJZoYRXPiVvPRlpu02mT59OgwYNmDZtGq+9pu9rkZKSwsiRI3n00Ufp1KkTAIsWLWLgwIG0atWKjz76iODgYOLj41mzZg2DBw8mJSWFZ555xtJu7dq1mT1bH1ZOSkpizpw59O/fny1bttC6det8+5SWlkbnzp05dOgQI0aMYNKkSXh4eHD48GFmzZrFokWLOH/+fIHvzbYPturVq4e3tzeNGzcmIiLC7l5ERAQ+Pj7s3LkTo9GIq6v+68XZs2e5ePEirVq1KvC5QgghhLj3rDm9hje2vWF3LfxCuOV4wCr7EbSZB2fanft7+ROfYV0jteeJPXi4Fm3q2oOVHmRq2FReDX8VgA9bf2hZI3ddstMdrzV9FoDIqATLpbJ+ntSrWAJN0yjHtcK1/eNDMGQ1hH8E9R6B3waCMVNf81a7O+yd67TainIv8sq4jXzQfReDNuhr46JyyrE1x35KZMc6ZZn6WCPSs434erjh65l/eOHt4coc40MAvK3Np6zS30eSpu+b1iHzM151W8TnhkcZ4LpZr3T5EN7FnGTk9CyuJ2IxOa+Vxc1FMarTzcneWdNmpM02gLsTJGi7TWrXrs2oUaN47733eOyxxwgMDOS9994jMTGRqVP1X2aio6MZMmQIgwYN4scff7T7RaB3796MGjWKS5cu2bXr6+tLixbWXyY6duzI5s2bWbFiRYFB29ixY9m/fz87d+6kXj1r6tN27drx0ksv8cMPPxTqveXuQ24tW7Zk7ty5likKANu3b2fAgAHMmzePQ4cO0aiRvuDTHNxJ0CaEEEKI3P6J/8chYCusyn6VWfPIGgA6L+ps2e+rqAGbWfv72jO2+Vh83H3oWqXrdbVhEWtNV0/IM9DxPT0gAfac1YO25lX8mfa4vjmzUopsl1z99iwBmYnW84Aa+gbVMQdh0yTY9R3smG69v2um/ipmM8rWcoQlAcqk4/oo19hVJynV+09aB3kT9uUxu0fWKFuM754MwdVF4eflXqi36mWz3i1Z87YEbf9o+nTCU1ogL2f/HwA7c+rwPKsA8M5Kg9zJRGxG38ZlD8GvUj32PtOM4oXsS0Fs/y1+f+CdDdpkeuRtNG7cOAICAnj11Vc5ePAg06ZNY8KECVSsqC9y/P777zEYDEyePNnpEG6tWrUICwvL9xkuLi74+PiQnZ3/RotpaWnMnDmTF1980S5gs23n2WefLfyby0erVq1ISEjg6FHr/yBFREQQFhbmMAoXERFB2bJlqV69+k15thBCCCH+3Y4nHOehxQ8Rfj6cP6L+KHQ9L1f76WxP1n3SctytajcAHgp+6Lr75aJcGFB7AD2r9cTV5fo3TQas+5RV6wDdp4BXCVCK+TvP8fVmfaPvYQ9WpZzNtL+/XG2Spri4kz3mtPW81Uh4YpHpROnZJfOSomde5LWj0HEiNHuOpRVeIxbrGrAXl51j+VlrIPR822psHNWWlSNa45rHJtV5yTJap3NWc7EORlzT/Fgw3H4QYHNOY+YZOgDg7eZtvZGTAwuehHR9xHTGgzv42diZFlUDblrAZvbtE00Y27UODSrdor3wCkmCttvIx8eHL7/8kt9//51evXpRt25dRowYYbm/detWQkJC8Pf3L1K7BoMBg8FAfHw8n3/+OVFRUfTq1SvfOnv27LFMj7wZzH2wfZmZR83Mwdm1a9f4559/CA0NJTQ01CFoa9my5U3pkxBCCCH+HRIyEvK8NyVyCtEp0YzYNILT1/TA5JMHP2FT/01sfnSznkXQRJk2Q65Wohq7n9hN3xp9Aahesjo9q/W0lBt6/1A+bP0h41uOvwXvpojSE2DZC/qxjzVV/Nilh3h7qTVjZLtaZeyqbfa1ju7FBHbimZ9s9pkrWx/8KpjONPsRuDx8EpFE8Ntr+crzWUaf1QPCMV2sGRfHLdNT+z/XtipvPlybamWKFWqz6dxsk3vYSsOT5lUDuM/ffkuARcY2AHgbbZKxXNoPR1dYTqduigKgXHHbjbFvjofuL8+zbaretPY0o5Hk8HCyL8eiFZS1xca/d3rk+BtIp5oHvyI9v+AvvzO9evWiSZMm7Nmzh02bNuHmZv0juHjxomWaoC3bAMjFxQUXm53p9+zZg7u7u939Tz/9tMARuYsX9f1EKleubHc9JyeHnBzrLyC2/ctL7j6Ymb+IFSpUICgoiIiICIYNG8b27dsJCAigevXqhIaG8sYb+jSH1NRUDh48yOOPP17gM4UQQgjx75eWnUbbBW3JMGYwtvlYBtTW16PFpcfh7+WPi3KxG2HZdF7PWFi1ZFXK+OhBTGnv0hwYfMCSLCQ+Ix53F/3fJeNbjncamBX3KE6Pao4bRd9S8WdgwRP6JtjPrIejK+HsXxC91xRUKaih/5i+5tAl5u20bgo9JsQLN1f7sZaSvt6gJ1NkwRlPthqu8LVbL+5XUby00J3ujY/xSSG7tjJ4LDPCTwEweb2+UbWHmwtPt6rCT9ujuJyUaSlbP/DG/g1e3Mudg+M769MkbfLUNakeCMC5+DTLtc51y3Hgb300rXJWBru8Tf8ujbeOKm431rWM3tmORN6NNE3j7FNPkR65BwDflqHc9+OPhaorI223WWRkJPv27UMpRXh4uN092zVftuXd3d0tr0cffdTufp06ddi9eze7d+9my5YtTJw4kbFjxzJnzhxLGWcjYOaAKvfzevbsafe8w4f1X1WMRqPTUbTcfbB92WrVqpVlRC0iIoLQ0FAAWrRowZkzZ4iJiWHXrl0YDAYZaRNCCCHuEQuPLSTDqGdG/GDnBwAciTtCu4XtaPhTQxr/3JgN5zY41AsuHmx3bpvd0d/LHz+PIv0Uf3vsnQuXD+uJM6Y3h82T4PRm6yjY0LUk1ujNxWvpvDhPHzV74L6SRH3cjXqlHUe0SvlafzBP1/QRps8NjzEk+w1SjW4siHSeTM5Qug4Xh+wi5ulINJ/SRFfszIh/HJfKvNyuOt4erux8uyMtquqzwCqU8KJNzTIOZYuquJc7Hm4ufOn+tOWad5kgAB4N0bNPerm7UNnfh1hKkqW5MjI2ho6VwpjVeRYk6fvPHcupxNPZoy1tlC5280fabqbEZcstARtAasR2Yia+X6i6/+KRtusb6cpPcnIyfn637i95Tk4OL7zwAqGhoXTq1ImPP/6Yp556iqpV9SHXwMBALly4YFenbt26lgDoueeec2jTx8eHkJAQy3mbNm2IiYnh9ddf56mnnkIp5TAKpmkagYH6rxkXLlygZk1rhp0vvviC8ePHs2fPHp5/3rrBYbVq1Th79qzl/MyZMwQHBzvtgzMtW7Zk/vz5XL16le3bt1tS/VeuXJnAwEAiIiI4evQonp6eNGnSJN+2hBBCCPHfcDb5rN15j6U9iEqKspwbcgzkNjVsKl5ud/eIip1/1kDFRnB2e95lytbjixMBfDFjnd3l+c/mnejNNkvjVQqXJCOr9Rs0DG9A+rf6OrmudX/hbEI25iE7f18PSyr/rvUrWOr9NjzU6eDCjZqa3JFwFYRCY2BFPfvmJ30b8GqnmpQu5snciCg0XLiolSY45zJTG4yAMjVh/1IA1ri2Jx3rd6Fh5Zs/E+9m0QwGLr2lbxXh6u+PMV4fQUyYP59y495BS0vLr3rBQZtSygvYCniayi/SNO09pdQcoC1gjp6GaJq2X+l/ml8CXYE00/VC7Or33/ftt9+yb98+9u7dS61atfj555955ZVXWLVKz4rTpk0bPv74YxISRojLNgAAIABJREFUEihVSl/8aRsQFTagrFu3LleuXCEuLo4yZco4jHoBNGnSBB8fH9atW0f79u0t180JQFJSUuzKr1y5ksxM69C4OXlKYZlHz/7880927drFuHHjLPfM69qOHj1KSEgInp53968kQgghhLg5kmzStQN2AZutKiWqsLjnYsu0x3+NU5vgN5tlH25eUPMh+HsZVGmjZ2489Du0f4cv5pxwqJ7fmrEL8emMzn6ODi57WWbMP2O42TJjKOkGa3Cw5m/r1gdvPVyb3o0Daf7hRiqV8qZ6Wfu1Zzc7YDPbp9UA4FnT5tVKKSqU0KfEDmx+H5NWHyVaK00wl+HaWT1oS9QHOU5l6kHaa51qMqJ99VvWx5vhTH/TbDmlqLLod5I3beayaR/jU5274OLrm0/two20ZQLtNU1LUUq5A38qpdaa7o3RNG1RrvIPAzVMr+bADNN/72mxsbGMHTuWESNG0KCBvr/FtGnT6NatG8uXL6dXr14MGzaMzz77jDFjxvD9999f97MOHz6Mt7c3AQH6YlZno2A+Pj4MHz6cb775hqeeeoo6derk22b9+vWvuz8ADRo0wM/Pj5kzZ5Kenk7TptaMR6Ghofz+++8cP37cbg86IYQQQvy3JWTmnYBk5AMj6VOjD/5eRUvQdlc5vcV67OYFj8yCutaEKBgNEPYWOaWqAmvsqj5XQPILbw9XFhnbssjYNs8yu3Jq0czlGNE+tbmUksNbm5MB54Hgc22rARD1cbd8n3ureHs49svHw43fhrfg6I+VaMURiN4D1TtiPB+JK3BS02eO3cmALSsqiuQNGyjRty+uJfUMk7n7kh0dTaYpi3rZ0aNwr1iRUoMGWoK27ELsi1xg0Kbpi5/Mwy7upld+qU56AT+Z6u1QSpVUSlXQNO1SPnX+80aPHo23tzcTJkywXOvatSu9evVi5MiRdO7cmcDAQGbPns2gQYM4ffo0Q4cOJTg4mJSUFCIjIzl48CA9e/a0azc1NZUdO3YAkJ6ezrZt25g1axYvvviiXcISZz744AN27dpFaGgoL7/8Mg8++CBeXl5ER0czd+5cXF1d8fIqePqBbR9sVa9endKlSwPg6upKs2bNWLt2LY0bN8bHx5oZKDQ0lNGjR6NpmuzPJoQQQtwDZh+ezZYLW9hzWV/fs7D7QnZc2sGUPVMsZYbUG3LjqfTvpPQEOLhQP3b11JOPVLDfmBrX/2fvPKOiOP82fM3SewcBG2LFEg3YS+wmatTYuyYxJtZobIlo7D3GkqgxsUb/BnusiRp7L1iwN7AgKCBI7zvvh4HZXXZpihrzznUOx5mnzbM4q3PPrxmDkzdRcSlyU+uqxZj1STXsLPO2KlbxtOXonUj5/Mx3zagz66Dc93Wz8vT5/TtchBdY23tzKy0+17WqFX87boW1Sjty7oFk7bMxNyxLXGzM2CFKcW7EPYGImxglPiVCtOeWKCXUexuCLfHcOSIXL5Zj1OL/OYjK0oL0ZxGU3hiAytyc5CtXiFq6jKRAaYxgYoJTloGisHsuUEybIAhGQCBQFlgiiuJZQRAGATMEQfgeOAh8K4piKuAJaMvF0Ky28BxrDgQGAri5uekl5dDGzs6O+Pjcb7SiIjMz87Vc5+TJk6xbt46VK1ciCILONWbMmEGtWrWYNGkSEydO5MMPP+Tw4cMsWLCAsWPHEhUVhbW1NdWqVWPSpEn06dNHnp+eni6nzgcwNzenVKlS+Pv7M3To0AJ9lh07dvDbb7+xefNmFi5cSEZGBp6enjRu3JhTp07h5uaW5zo596DNr7/+Svfu3eVzPz8/Dh48iK+vr86a5cqVw8TEhLS0NKpVq/ZKfwcpKSl53kuvSkJCwmtdX+HdRrk/FPJCuT8U8uL/0/0RmR7Jj2EaceZl5sWzq8/wwotpntOYGTaTNvZtOH7s+Fvc5SsgilgmhVImeB3O8WHE2lbgcvVZiLej4fYRg1OCY6V09iVtVHT1jOfSuZM6/Ybuj2rGGhtKZScVty5pXqDXdkhBHX6DVEwJFV3hqf6zlbEAGVlL9PNOeyv332dlRWzUxmSK8OL+FY6E6AuZtEyRaFEKEYp8eIunabuoClxVewECU+qZv9G9G4VJksZ2w/8wvXdfbk++fFk+vuNXU2+e2tKSqCmTeaq1Vze9UbkjFKY+gCAI9sB2YBjwHHgKmAK/AvdFUZwqCMIeYJYoiiey5hwExoqiGJjLsvj5+YkXLlzI9bo3b97M132vKHjdiUgUXj+v+145cuRIvuUUFP7/otwfCnmh3B8KefH/5f6ITolm4smJHAs9JrcNqzGMgdUGvsVdFSGp8fB7e8mNL5tP/4JSuWfGTknPpN3PJ7jzLIHmlVxZ0U//gT+3+6P0t3sAGNioDONbV+LbrUFcCY1l66C6WJgY4fXdXr05ACGzWiOKMGPvTSp72NLx/eKF+5xvmC+mLeK3zO9J86iFaQlfOLuM3Zm1CWm8hGHNyr2RPaiTk3nQtRupd3VjD83KlSX17r1855v7+OC1batO29OZM4n5fR0ApbdswbJqlUBRFA1m9ytU9khRFF8IgnAE+FAUxR+ymlMFQVgNZOfbDAW0i38VB8IKcx0FBQUFBQUFBYX/Hj339ORJwhOdtl6Ver2l3RggMx2u/wneTcFKU+iaF4/g9t+gzoDaX0JubpuHZ+kKtgYjZcF2/kE0XX45jb2lCUfHNGHxwbscuR3B/chEeXjFYgXLApkTOwvJlXJ2p2r5jISDoz5AEAQEASa29Xmp671pzGxdIQZMw85B2DkAygphpNpb5DOzaHixbTvh48cb7PPaupWQLl1JvX07zzVSbtzQays2fjxu48YhpqWhssj7sxQke6QLkJ4l2CyA5sCc7Di1rGyRHYBrWVN2AkMFQQhASkAS+/89nk1BQUFBQUFB4f87Z8LP6Ai2g10O4mrp+hZ3lIPQQFiRlVG75gBoM186jrwDS7SsX/u+g56boGwL0M4fIIpwY4d0XLwWeDWEJv5y996r0uPwi6R03puim9ofYOyHFRjQIO/kI7nhZGVaoHE7htTH28U6/4H/MqwdJNGmjbcQRryTpeEJRcyLjRsNtqssLRFMTSmxbCkpN25g7FaMB126GBzrZKB0F4BgZISQj2CDglna3IG1WXFtKmCTKIq7BUE4lCXoBOAykF3Uay9Suv97SCn/Py3ANRQUFBQUFBQUFP7DfLH/C/nY28773yPYou5C4Bo4/bOm7fwK6ad6L3h8Tn/Ohq7g+yl8vFA6D70Aa9pARgpYF4PP9oFKRWxSOkP/uEB5NxtWn3yQ6xacrU0Z3Lhsobc+t1M1/rn5jE/e9yzQePt8kpv8W3FwdkN9X0AlaMK6TIRM3itu/9qumXr/Pknnz2PfpQsZz5/L7WX27iG4tZRh03XsGGkvHh6Y5FEOy7JmTVyGD3ul/RQke2QQUMNAe1MDw7OzTQ55pV0pKCgoKCgoKCj8q0jOSGbAvgEERQUx2m80/Sr3e6l13nd9n7UfrS3i3b0Cf42D+wcN913+n+bYriTEPtKcB66W6qw1/AaOzJIEG0A5jQVu/dmHHL8bxfG7UdISFiZ8WLkYGy88pkN1D1QqgW0XnzC9Q5WX2nrXmiXoWrNErv0V3Gy4/Swev1IOWJsbU8LhzVimihpPRxtiscIB3TrCpsZ5Z0ovDIlnzxG97nfcp0wh/fFjHnSX6utlREaR8ewZAOVOn8LYwQHXceNQWZjjoJVwLze89/2NScmSr5zhslAxbQoKCgoKCgoKCv//yFBnUPt/tRGzqj79cOEHGno2pIx9wd353K3cCU8MZ0aDGa9rm3mT+FyKU0tLhPQU6fj+YV3BVrGtZFlLjNCda18KRgRJx6II87wh6TkcnS39aPPhrKxhImeCNRaahuWc+bnn+9iaGzPjkyoYG6lIy1Az6ANvvULWRcXmQXV5EpNMJfeXi5X7t+Bpb0G0aIODoBFtvxX7ni/ymFMYMmNjedRPeglx9x9dAR+9bh1iejoqOzuMHRwAcPq0f65rCWZmiKmpGLu44DLqG0xLlSqSPSqiTUFBQUFBQUFBIU+mnJ4iC7Zs2u9oj6ulK1+//zXtvNuRqc5k3oV5mBuZM6j6IMyMzHTGx2fVCbMxfQuZugPXwq7hum1lGkOIJoslVTpDk/Fgbg9nl8GxeZq+dj9pjgUBBAOJSMztYWywnKRkzakHHL8bhUqA7z6qRO86peQC0sZGktXF1FhFObfX9/uwNTfB1v3ddInUxt7ShEQ0tYO9U9bhlWT3yqIt8dw54v/5h8wXL3Ido46LA8CkWLECrVl600Zi/rcBl6+HY+zklP+EAqKINgUFBQUFBQUFBR3i0uIkS1H4GYIig/jz3p8Gx0UkRbD62moikiJYdHGR3G5vZk//Kv0BSFenc+zxMRLSEzA3Mi9a0XYlQMrs2HAUXPkDStUHRy/9cRdW6bcFH5H+dKsCLaeDdxNNX9MJ0s+tPeBWGRxK686tOwT+maTb1vZHUBlx4m4Un605T1qmGoA5narRxS93F0aF/LG1MOGFlmzJxIiQqMQ8ZuSPOi2NR31zd/G1bd0aUa0m/u+/ATB2K1gMpnmFCrhPnfJKezOEItoUFBQUFBQUFBRkVlxdoSPAtFncZDFGKiOGHNSkL3ia+FRv/Jrra+hfpT9RyVG03NKSdHU6AKXtSqMSii4Oie1ZGfmigyXRBtBxBcQ8gJK1IWgT+LSXhF1ufLoXzO0M91VsY7i9wQgo1xJ+awoZydBnO3g35fyDaPquOos6yyjZvWYJOvv+u2ugvQvYmpsQha51M1Nd8FrTOUl/+pSQTzrqtZf47Tcef/EFgpkZHvPm8mLzFlm0WVR5ubjDokIRbQoKCgoKCgoKCgAkpSfxa9CvBvsGVhtI4xKNEQSBeR/MY8qpKSSkJ5CQnqA39nnKczbf2czU01N12kvbli6ijUbD/7RSq2cLNoBtA3THXpKKF2PlKrk/lqgFy7SKXWsJtpT0TPZdf0rDci44aqXRP3TrGZ+tuQBI8VULulXHr1QlVKNugbE5mEiueyuOB8uCrUFZZya3q/zKCSgUwMbcmHQxl9p4hUTMyCC4TVvUibqWOpeRI7Fu2ICSa9diZG+HYGSEWYXycr9102ZFc31RNHhPxMTEGBitoQhfdSjkRdOmTXnvvffIyMjQad+6dSuCIHDgwAG57cqVK/Ts2RNPT09MTU1xdHSkefPmbNy4UWd+//79s4ojSj82NjbUrFmTbdu2FXhfqampLFiwgJo1a2JjY4O5uTnlypVj5MiRhISE5Ds/5x60f06cOMGWLVsQBIHAwECD8y9cuIAgCGzatKnAe1ZQUFBQUFB4PTyIe0ByRrLBvmE1hskPmx+W/pBfWvyS51o5BRuAl50B18XCEvMQ5nrBkwuFm+dUFvw+ldwdm2e5r9X/mky1SEhUIruuhFFx4t98HXCZxQfvAtID9vG7kbJgA3jyIpmuy08zfvtVsLAHE3MSUzNY+M8d9l1/hrFK4Jx/M9YPqI25SdEIjf/vmJsYoRY0tiYjlcBvff0KtYaYns791m24VaWqjmAruXoVlW7dxPnLgQBY1a6FeYUKAFhUr45dp444Dfgc88qvXoh8y5YtqFQqBEFgwYIF1KtXj8mTJ5Oeno6jo2OecxVL2xti6dKlVKtWjcWLF/PNN98AkJCQwIgRI+jatSstWrQApL/Mnj17Ur9+fWbNmkXp0qWJjo5m79699O3bl4SEBD7//HN53YoVK7J69WoA4uLiWLNmDV26dOHo0aM0aNAgzz0lJSXRsmVLrl69yrBhw5g+fTqmpqZcu3aN3377jS1btvD48eN8P5v2HrSpXLkyfn5+2NjYEBAQgK+vr96YgIAArK2tadu2bb7XUVBQUFBQUHi9hCdIBaBru9dmRcsVvEh5wef7P6dV6VZ6Y41V+o+R29pt40z4Geaen2tw/eI2r+gqmJ4Cf+RIs16qATw8odtm6QTv94MTP2raOizRHNcZLCUicX+PabtusObUA53pa049YM2pB3jYmRMWm2JwKwHnH9Px/eLULO3AD/tvy3XYmlR0xdXG3OAchZfnhUpTk+3u9I9QqQpuwcyMj+dOzVo6bSYlS+K1eRNGdrm4xgKCIOAxo+iyna5cuVI+ztYDp0+fZsqU/GPgFNH2hqhYsSKjRo1i0qRJdOvWDU9PTyZNmkRsbCwLFiwA4MmTJ/Tv359evXqxatUqHdNphw4dGDVqFOHh4TrrWllZUadOHfm8efPmHD58mJ07d+Yr2vz9/bl8+TJnz56lcuXKcnuTJk0YMmSIzo2VFzn3kJMOHTqwadMm5s6dq/OZRFFk06ZNtG/fHkvLd7NuiIKCgoKCwn+JDbc2AFLxawB7c3u2tttqcKyDmYNeWzmHcpSxK6Mj2r6s9iXLg5ZjrDKmVrFaenMKRHoymFhIro4RN6S21j+ATwfJ2nVzJ5RpApaOUnybsQXYuoNXQ4i4BRXbkGpTHDOk5w+MTBA8qvPoeZKeYNNGW7At6l4dH3dbyrpaU3/2IcJiU+i6/DRDmnhz5HakPG5yu8qGllJ4RX4z7UOxlKeszGjN8nwEmyiKRC1dSsrVawimpqiTda3HRo6OeG3amKdgKyxqtZpbt27h4OCAu7u7Xn9YWBh/Z8XHvQyKaHuDTJw4kT/++IORI0cyYcIEFi9ezNy5c/HIqqC+YsUKMjIymD9/vkFf1woVKlAhy1ybGyqVCktLS9LT0/Mcl5SUxK+//sqQIUN0BJv2Ol98UTTVL3r06MG6des4deoU9evXl9tPnjzJ48eP6dGjR5FcR0FBQUFBQaHgJKYnYmVixamwU2y/u52JdSdyK/oWAH18+uQ738Pag/6V+7Pm+hoAtrfbDoCRyojLfS6z+c5m3CzdaFKyCX0r9yUlIwVXy4Jl4NMh5Bis/VgSaY/PSm0fzYNaX5CQmkFqciZOVTppxjtq1Y7zbgreTdl2MZTRm//mvRL2XHokpXdvU82df24807nU183KsedqOPcidOP0GpZzpn11T/ncRKuo85LD9+XjfSMa4WlvUfjPqJAvSWYudI2blP9AIPnCBaJ++tlgn9MXA3AZMQLBqGhcV589e8bDhw+pXbu23BYcHIyXl64r8Pfffw+AqakpV69epXPnzly9erXA11FE2xvE0tKSRYsW0aFDB86fP4+Pjw/Dhg2T+48dO4afn1++Pq05yY5zi4uLY9WqVTx48ID27dvnOScwMFB2jywKcsbqARgbS7dXixYtcHZ2JiAgQEe0BQQE4OjoWGR7UFBQUFBQUCgYBx4e4Jsj3zCxzkSmnZkGwN8PNFYANyu3Aq0zym8ULUq1wN3KHRdLF7ndSGVE94oaN0ZbU1tsTfMo8JwSB2Y2Ug20nJzKevjeO1rTVqImAD1+PcPVJ7EcHt2YPivP0rySm46lKzUjEzNjI9aceoBaRBZsAHuCJO8lFxszBjTwokIxG2p5OTK8WTnOBD9n2u4b3Hoq1ZabksN6NrxpOUZtvqLT1tm3OBWKvf4adKJaTeSPP2JZqxbWjRq99uv9W7A0LbhsiT90WK9NZWND2QP7UdnYFIlgO3ToEBs3bmTlypVkZmbq9B0/flxPtN24IVmI586dS/ny5QkKCtIx0tSsWZPz58/ner13VrRVXVv1rV7/ar+CK2Nt2rdvj6+vL4GBgRw6dEgWNiCZTatXr643R1sQqVQqVCrN253AwEBMTEx0+ufOnUvjxo3z3EdYWBgAJUro1g1Rq9Wo1Wr5XHt/uZFzD9mIoiiv0blzZzZv3szChQsxMjIiMzOTLVu20LlzZ4NzFRQUFBQUFF4fCwKl0IxswZYTE1XB/2+u5lLt5TciirCxN9zaLRWwfr8vqNWwaxiY2UL4FXh4Un+eqw+3n8Zz9UksAE1+OAJIsWhXQl+QlJrJ7WeS4OrsW5yg0Nhct3BsTBO56HU29cs68/eIRiSmZmBqrMLESDd3Xyff4jhZm9J/tfSQ3bGGJz90ee9lfwuFIvH0aZ6vWMnzFSupeP1akVmM/u1YmhbscyZfuUL0unV67Q7du2Fkb29gRuG4fv06bm5ujBgxIldL2aNHmhITmZmZ3L59m9OnTwPQqVMng3Osra3zvO47K9reVS5cuMClS5cQBIEjR47QpImmkKOhFKAXLlygZs2a8nmnTp3YsmWLfF6pUiV+//13QHJ5PH78OP7+/jg5OdG/f39A3wpmbGwsC6qc12vXrh179uyRz69evUqVKlXIzMyU52SvYWgPudGjRw9++eUXjh49StOmTTly5AjPnj1TXCMVFBQUFBReE+nqdIPiK1OdyeP4/BONvRHu/SMJNoCdw+Di7xD/FGLz2N8Xh/hf4FP8t18z2K1tTQPYEhgKQAlHC5pUcMW3lAPjtgaRki69pM4p2LSxMsv9UfmD8i6s6OuHjbkxtcs45b7fIkYdFycfJ1+6hKVf4bIovqu42JgVaFz02rWQkYFNixa4+Y9HnZRE3J69OPbN3+U3P0JDQ6levbpBDzNtJk6cSEJCAjVq1KB7d93EOdlhUTkpXjzvJD3vrGh7WUtXXsTHx2Nj8/rM2mq1mkGDBlG3bl1atGjB7Nmz6devH2XKSL7Xnp6ehIaG6szx8fGRTaVffvml3pqWlpb4aX1ZGzVqxNOnTxk7diz9+vVDEAQ9S5Yoinh6Sn7ZoaGhlC+vqUGxcOFCJk+eTGBgIF999ZXc7u3tzcOHD+XzkJAQSpcubXAPhmjYsCHFixcnICCApk2bEhAQgIeHB43+H5n1FRQUFBQU3hQPYh/QdXdXPq3yKYPeG6TTd/5Z7i5YAG3K5FJQ+nVwd7/ueWgue3OpBAMOgLE5my89xX97UKEvVcXDjqntpQLJccnpTNxxnekdXr5gsiAINPcpmBtpUZJw5Kh8HLtjx39atKXevYuxuwdG1lZ817oS9yISGNTYO9fxoigSt/cvAKybNsWkWDEAXIYNLZL9XL58WU+wdejQge+//57IyEjCw8Nlo8mcOXP05pctW1bHY+7YsWMMGTIEMzMzZs2axToDFsJs3lnR9i7yyy+/cOnSJS5evEiFChVYt24dw4cPZ/du6Q1To0aNmD17NjExMTg4SBmZtAVRQQWlj48PkZGRREVF4eLiYtA/1tfXF0tLS/bv30/Tpk3l9rJlywJSOQJtdu3aRWpqqnye21uC3BAEgW7durF69WoWLlzItm3b6Nevn86Nq6CgoKCgoPDqxKTE0H1Pd5Izkll6eSledl58WPpDACKTIg3WT8vG286b8bXHv5mNqtWQGJV7v1cjaDULimmE1ZMXyYzZohFsAQPrYGas4tutV3GzM6e0kyW/n35ICx83vvuoIrP/usX+G89wsjJlVkdNaE3vOqVoU81Dp4D2u8CjL78k8egx+Tz1zt23uJuCkxkXJ8WSFaLQeEzARp5OnoxtmzZ4zv8BT3sL/h6R+8v+lJs3Cfmko3xu07RJrmOziYuLIyoqiqCgIKZPn05gYCAzZsxg/HjD34F79+7pnB88eFDnOfraNcPW32xyirKGDRsSFFSwFxCKaHtDRERE4O/vz7Bhw6hWTfL9Xrx4MW3atGHHjh20b9+eAQMGMG/ePMaMGcOKFSte+lrXrl3DwsICJyfJVG/ICmZpacnAgQNZsmQJ/fr1o1KlSnmuWbXqq8cQ9ujRg/nz5zNmzBiio6MV10gFBQUFBYXXQJ+/+pCYrikePPHERCo7Vsbd2p2mmzUPmIPeG8SyK8t05vby6ZV3wpCiYvsguLJBc95zE3j6wu4RcHOX1NZ3JwgCsUnpLPjnDp/WL83Wi08A8C3lwMaBdTDOijXbN1LzMD+1fRU55OTXvn6o1aJeTS9BEN4ZwSaKIlZ7/+LmV4P0+tKfPn0LOyocsbt2ETZmLGbly+O14888hZs6KYm4v/7CulEjnk6eDEDcnj14zv8hz2uIajXhkybL5/ZdOueZzj8tLY358+cbFGf+/v40btyYevXq6bQfOnSIkSNHyuempqbUrVtXZ0y2F5ohqlevnmeJrPxQRNsbYvTo0VhYWOgUz2vdujXt27dnxIgRtGzZEk9PT1avXk2vXr0IDg7m008/pXTp0iQkJHDhwgWCgoJo166dzrqJiYmcOXMGgOTkZI4fP85vv/3G4MGD87VizZgxg3PnzlG3bl2GDh1Kw4YNMTc358mTJ6xduxYjIyPMzfMvDqm9B23Kli2Ls7OzfO7r60v58uVZtmwZ3t7eOrF6CgoKCgoKCkXDw7iHOucpmSm03t4aI0ETu/VtrW/pVakXH5T4gH0h+1h9fTUANiavP/shUfd0BJuoMuZUqhfvmzhg0XUdBG2CkrXlTJI/7L/NujMPWXPqAdZZMWafN/CSBZshtIVBYYow/5vIFp5p9+9jvXOnTl+506e4W7ce6vj4t7Q7DeqUFJIvX8bivfeI3bETdWIijv37kXwliPAJE0gLDgYg9c4d4vftw9jNjRcbN+EybCgmnpoyCreq10BM0S9kbuSgXw8wJ0nnzpOSZbEqNmUK9p90yHP8n3/+mas1DSSBpi3ali1bxuDBg+XzJUuW0K5dOywsdMs75JVMJL+YtfxQRNsb4NixY6xbt44NGzZga6v79mrRokX4+Pgwc+ZMpk2bRpcuXShXrhxz5sxh3LhxREZGYmNjQ/Xq1Zk1axafffaZzvxbt27JKt/c3BwvLy+mTp0qV1nPC0tLSw4dOsSSJUvYsGEDCxcuJCMjgxIlStCsWTOuXLkiu0vmhfYetFm3bh29e/fWaevevTtTp07VC8pUUFBQUFBQeHXmnNOPo8kmU5TSkpe2LU2vSr0AqOxUmetR1+UxNqZ5iLa0JDg2T8ro2GgMlNL/vz9f/pkCJ37Uadon1uar/92lX900prSvQmrlzvRfdZ6apVNxtDJl3RmNCE1IleKJqhUvuqLI/xZSbtwgbNy3OH72GcmXLxMMq1OEAAAgAElEQVS3ezcec2ajTknVGefm7y9bkdRJSYhqNcJbCjcRRZFHn35G8qVLmHh6kv5EsoRGzJtncPyTERpLlXExN1xHjAAgPSzMoGADyIyJIbjDJxSb4J9r/F7qfclt0a5TRxy6dc1zz+np6XTr1i3PMRMnTsTf318W/9pFsbt3785XX31V6BCfvOLVCoIi2t4AjRo10sm8qE2pUqVITEzUaatevTp//PFHvuuuWbOGNWvWvNLezMzM+Oabbwok8opiD1OmTNGxNiooKCgoKCgUDaIosv7megAaejZkUZNFvL/+fb1xvm6+Ouee1hprR66i7cEJWKOVoOT+Qei7A8o0LvgGr27RFWwtphF5+xT+dyQvorWnH7L2tEagnQ5+bnCZGiXt/5MFrEM6Sqngw7/7Tm4LHTpMZ0yJ5b9g/cEHAAiWlohJSaiTkjGytnpzG9UiIyKS5EuXAGTBZoiV0c/53FE3w2bs1m0YOzjg0LcvmbG5l2QASL11i4d9+lLp5g3d68fE8PzX34heLVmKzXLURtNbJzWVHTt2yOceHh4EBgZSLCthyfr16+nTR8oyqVKpaNOmDZs3b5Zj2bZs2ZJryv7c6NOnD6NHj8b+FcsN5CvaBEEwB44BZlnjt4iiOEkQBC8gAHAELgJ9RFFMEwTBDPgd8AWeA91EUXzwSrtUUFBQUFBQUCgkMSkxqAQVdmb/PasMQFJ6ElHJUbhbufPT5Z8oZVNK7pvVcBYmRiac6H6Cxpsak6HWZLxrUaqFzjrFbTRuWzamNpD4XBJXp7OKWptYQnqS/gZ+bw8eNaD1fCjuq9+fk62fa47LNIH6w1nyvBnP7zwo0Of9sHIxOvsWfysZG183mQVwcywd8AcWWvV8jaytyUhKQh0f98ZFW0xAAHF7/yLp3Ll8x/Z79IjzyUmUMTWjiZb7YEZkJM9mzSYzMRHL96X7x8LXF4sqVaS0/YCFny/JFwKlCQYMIBFz5xG7fbt8bl4t95qBCQkJeHp6EpdVMsHHx4egoCCMtOrc5QxD2rNnD0uXLiUkJASAFi10vzuG2L17NxMmTKB3797Y2dkxYMCAfOcUhIJY2lKBpqIoJgiCYAKcEAThL+AbYIEoigGCIPwCfA4sy/ozRhTFsoIgdAfmAHnbIBUUFBQUFBQUighRFFl0cRErr60E4ErfK6iE/1624gknJ3Dg4QGsTKx0Eo+UtCkpC1U7Mzsu9r7Ihlsb+P3673St0JX6nvV11nG3cpePrYzMYV0HeKqV0S6nYFOZgDpdOg67BGeWQvGa8Pc4qc3BCz77G2yK6c4zt4eUF9BsEjSUPHyeJ6bl+vnaVnNncOOyeDlbYWqswugdjU3LSUZUFEZOTrLrXXp4OPeaaBLE2LRqhW2rlqisrAgbO47M2FjienTXEWwARk5OZEREkBH1HBN3dxLPnOXJ6NEUm/Q9tgUQFy9L4qlTPJ2cu9eU5+pV+E+eTLOwcG5HRXE+Wbp/jtary7DNm/nGxYXPtKxuUYt/wnWcdO8Yu7jg2LcPiWfOYFGtGo6ffkrosGGk3b+PsYuL/l5OnJCPi02dglWtWvL5jh072L59O97e3owfP5758+fLgg0gICBAR7AB2NraMnHiRKZN0xSdX7p0KcnJyZibm+uFORmiTZs2tGlT9GUz8hVtouTXl53/3STrRwSaAj2z2tcCk5FEW/usY4AtwM+CIAhibv6BCgoKCgoKCgovyfIry9kdvJtVrVbhYulCaHwoq6+tZtOdTfIY/xP+zGwwU35I3nFvBypBxcfeH7+tbRcJBx4eANARbADOFs4654Ig0KtSLzmOLSemRqa0Kt2KZ4nPcI68pyvYtOmxESp8CDEP4dcPIDlGak+O0Qg2gJgQODIbPl6Y1f8CgjZKgs3IDOoNZ3dQGGeDo7kVLj1E//5ZLRqVd2Hd6QdM3HGdisVs+Lmnvmvnu0z6swgeDxxI6u3buY6xatCA4osWyuflz0qJ3o4cOSK3JSUl0blzZ77LyMQZiD90kLDRo0nLqqcbtWTpaxFtoijydOpUXvwRoNfnPn0aET/Mx8jbm4qdOhEeHs7PWv2RkZE4OzuTmppKAwcHHdEGEJFV08zM2xsTT0/K7PhT7iuzcwe3a7xPRmQkmQmJslVRFEUyIiMBXbdRURTx9/dn1qxZ8hpbt27lypUrOtfMLXN6zoQhwVmJVLST670NChTTJgiCERAIlAWWAPeBF6IoZtvaQ4Fsh2hP4DGAKIoZgiDEAk5AVI41BwIDAdzc3HRuxpzY2dkR/way42RmZr6R6yi8PlJSUvK8l16VhISE17q+wruNcn8o5IVyfxSekNQQItMjqWxRGSsjffevB6kP+Pmp9Gionco+J7uDd/Pk6RP6OfdjTdQaLiddBiA5OBlXE9fXs/lCUtj7I1WdmmtfqfRShb7X2tIWLCF2x3gcgKdujXlYqjtqlTE1zw8lwrURd8LNITxr3VqrcI04hs/NBVJ8W04CV3PapD7WCcFUvTZTbo638OTE4eMM/UdjvbMwhoSH1zgSJlACmNHAAmfzzP/U98U0KAiHpcvyHRda/T2CDXxu7fvjxIkT/PXXXzQo5s4ndnZErlqNSquWbkJ8PJd69ya1cmVSDCSKKzSiCBkZmN6/j4OWYEuuU5vUqlVJ9fXlGcDUKYz+9lvCw8N1ps+ZM0enflmbjh0hy+VRbWONKl5TG/hgSjLFDHx+JycnjMPDOb1tGxklS0BmJi7fjCLbfn4hMxOy5h04cEBHsAF6gq1p06ac0LLSaVO6dGlat25N3bp1mThxotxubm7+Vu/JAok2URQzgeqCINgD2wFD0jTbkmbIdq1nZRNF8VfgVwA/Pz+xcePGuV7/5s2bBS4s/SrEx8e/kesovD7Mzc2pUaPGa1v/yJEj5HWvKvz/Rrk/FPJCuT8KR7s/2xESGyKfn+pxChtTGzkNekhsCMP+HJbHCtC4eGOOhB4B4FLSJS49uqTTvypuFX93+pvtd7ejFtV0Kl+4BANFSWHvjycJT7JekesztV3uxbPzJSsRSLFOsynmmvW417w9HiojPFRGqNUiT14k42prhlm4DdxckOtSdc/ox/JkerzPoH903S2/+KAcrVuUf/k9/4sR1Woif/yR5ytW5jnOrnMnPKZPz7Vf+/74559/AHieIdlOtAUbgMmTJ5g8eYL5hUDs1q2n4vVrOiUQns2eQ2ZCPO6TJoGRUb6ZJ5+MGUv8gQOYV6hAclab08CBuH6jyQQZFhbGzZs3CcxKSlK5cmWCgoIMZlgMuXwZLgSiBiw6dyZ19Rq5r8fYscTGxmJra0t6ejrz5s2jadOm2Ff2ISE8nGrOTtg2bkziuXM80vrcjbUKXDdpoimq7e/vz4wZM+TzBQsWIIoiQ4YMwdQ09zp9LVu2BKSyVrNnzwYkMfc2/w0vVPZIURRfCIJwBKgD2AuCYJxlbSsOhGUNCwVKAKGCIBgDdkB00W1ZQUFBQUFB4b+KKIp8f+p7HcEGMOnUJBzNHdl4e2OB1xpXaxyflPuErw9/bbD/ScITBu4fyOnw0wB42XnxvpvGJS8tM43QhFDK2JV5iU/y+shUZ9J6W2v5vJ5HPXpVkopiV3CsULBF1GqIvAnO5eHCKjg8AzJSISMr7bqzlogyNiUmMY1vNgVy+Lbkjta+ugfz6gnoPfZOjoUldaS1DbDkumbGwEZlEEWRrz74d/1+s18MFAUJhw7pCDbn4cNwHjQIdWIiSWfPYlqmjFSHrVmzAq33xx9/yCLEuCB7VKuJ3f4nVvXqEjZ6DNbNmxGdlfU7dstW7Lt1w33K5FynxwQEELdLKnaerGWtcvy0v3y8detWevToQXq6FOdYuXJlDh06lGtKfDNXV9qGBBObmUnrefP4zlmKVZuSVSj8+PHjtGnThokTJzJnzhzs7Oy47e8PILuAxv31l2Yv/frKxylaZQOWLVvGV199pSPaRmSVGCgoFStWlI89PDwKNbeoyTcqVxAElywLG4IgWADNgZvAYaBz1rB+QHb+zJ1Z52T1H1Li2RQUFBQUFBQKwqP4R/x5TxPPkl0Q+kHcA4OCzcbUhq/f/5rTPU7TqZxkKfO09uR4t+MUtylO05JN+bXFr7leL1uwAcw6p3Gp2vdgH77rfWn/Z3t2B+9+5c9VlPx8+WfUolo+X95iOY2KN6K6a3UsjAuYCv/SOlhWD/aOhr/GQkqsRrABl0LjOHE3ilUnQjgXEk2NaQdkwQaw43IYLVfe112zVdbvb8A/uu1aRb2viZqU7N9+WBH/Nj5Ymr7dClTpYWFE/fILGc+fEzbuW25V8iFWKy18YYndtYvHQ4cS88cfRP2yHJBi1SreuI7L4MEIgoCRtTU2zZph5uWFTfPmBRKJw4cPp2fPnvL5jVzqmuUkfPx47jVuQtKFC0TM1q3j92LjRlkI5SQ1JEQv4Yhp6dJUuHwJYwcHHj16RPHixencubMs2EBKce/qmrvbsbW1NcFpaTzPzCQqTZOI5mii5CbZtm1bDhw4wMKFUmxfbGwspqVLA5D24AEAYtY8Ew8PnIdJFvfo6GhatWolr5edtTG7rNWkSZNy3VNuZKf/B+jSpUuh5xclBfmWuANrs+LaVMAmURR3C4JwAwgQBGE6cAnIfo2wElgnCMI9JAubUkVZQUFBQUFBAYDolGjuv7iPn5ufwQfV0PhQQBJjx7od49CjQ4w6OoqwhDC9sfU96rOs+TJ5ncn1JjO53mS9cXU96tLOux077+8EYGKdiUw7M01v3K3oWwTcCsBYZcyU05qH1YMPD9K2TNtCfc4rkVewN7OnlG2p/AcXgqeJT1lxdYV8Pr72+JdbaJ9kuSBwjV7XCyNHPll6Kt8lHqRa01GYzBznv7Gr15+/xLps+/kENUo6MG7cUywCfwG7ElC1MwQfZezKXZxW++BhZ87+bz5A9S/JBhm5aBGxO3YSuXCR3BY27lvCJ02mQuAFBCMj4v7ex/MVK7D7uC3xh49gZGuLx6yZZCYkYuKmESgPuvcg+bIUM5nwjxTrZ+zqSvHFi16pAHZ4eDg//fSTfL57927atm3LXCTrj0PfPjj27EnEokWk3Q+m1IYNiGmp3K1XP7clZZ58M4qSq1dhZGtLwsmTRC1bhv0nnyCma8pEOA34HJOSJbH96CNU5uZERUXh4+OjV2sYoGbNmnlez1or7f+xxETM27fjj+Bgnt6+JbdnuycCODo6YlqiBADpT6R/BzKfS058rt99i1HWeoMHD+bYsWMA1KlTB2NjSebMmDGDVq1a0bx583x/FzlRqVRs27aNkJCQ15IRsjAUJHtkEKAXJCSKYjBQy0B7CvB2paiCgoKCgoLCWyNDncHliMsUtymOWlTjYS09WN6JuUPfv/qSmJ7IipYrqO1eG9C4oy2/spyfL0uJRYpbF8dYZYy9mVSQNmeGRID5jecX2I1tWv1pjPYbjb2ZPYIgIIois8/NJiMrp5qjuSPRKdHMODtDb25CeoJeW148jn9M7729MRKMuNTnUpG52gEcCz2mc96jYg/DAzPS4MQCqNgGilXR7QsPgrTcE681T9T/HQA0rejKrI5VOXonkrFbpAyTF8XyzHFuwPn9McQmXwfgSmgs9pYmjGguxTxlZKq5QBU2ZSZgpBI4NLox5iZGBq/xponduZPYHTsN9okpKdyqXAWXEV/Lgi5FK6HG7f37ATAtVQr3GdNJOHlSFmzauHz9NSpLy5feY3x8vI6FrU2bNnoCxMStGKalS1N8gSbGUMw0L9D6Kdevc6dWbcwqViT1liSc5NpogMvXw3EeNAiQkvaNHDlStoJp06pVK8zMzGjaNPekQKAr2hLVapYLAnO2bMlzvJGDg3T92BekPXpEwvHjIAiYeZcF4Pbt22zcqLHET9eKDzQ3N9cRgYXlk08+eem5RcnbtUcrKCgoKCgo/OeYeHKijkvhd7W+o0WpFnyx/wtZfM05P4dmJZvRo2IPPtj4gd4a2QWfyzqU1eur4lSFcbXGYWVS8ILCKkGFg7mDfN6tYjc6lOtAm21tcLN0o65HXZYHLTc4NzY1tsDXAY2wyhQzCY0PpYRtCYJjg5lyagqCILCi5QqMVS/3CPYwTuPKVtZe/3cDwNF5cDjrofXITGj3syTcPGrA02uwvGGu6+9z7kdUqB2+pRyo7+3EuQfRnAmWrBqr+ksWlK5+JahZ2pEfD9xh15Uw/rkZobfOwn/u8ml9L0KiEumw5KTc7u1i9a8RbJkJCYRP/F6nzaFXL4xdXIjUEiXaFjhDpD18yMPefXTaKlwMJG7/fgQjY2zbvpqFRjtjoYeHB7t3S98t7UQagokx6enpbNmyhY8++gh7e3sEo9x/z2WPHQUR7n2g+e5lC7ac7IyK4rOs49GjR+sJNpVKxdOnT3ExUEfNENqulCBll8xm8uTJTJ48Wac/IiKCzCzRmxHzQoqty8zEql5dzMpI7raBgRqR+fDhQ0qWLFmgvbxL/PcqTf5Ladq0Ke+99x4ZGRk67Vu3bkUQBA4cOCC3XblyhZ49e+Lp6YmpqSmOjo40b96cjRs36szv378/giDIPzY2NtSsWZNt27YVeF+pqaksWLCAmjVrYmNjg7m5OeXKlWPkyJFy9fe8yN6DoQrxycnJ2NjYIAgCa7KCXqtUqcLHH+deF6dt27a51s1QUFBQUHg32Pdgn875rHOzWB60nOgUTV6yuzF3+eXKL0w6aTjOZOT7kpXG0dyROu515PZJdSfxR9s/qO5a3eC8wmBmZMZfnf5i7UdraeDZQK+/d6XeAMSlxen1aZOuTicxPZGk9CSmn5nO7HOz5b7r0dd5HPeY9n+252LERQKfBRIcG/zSe34U/0g+nl7fQLbB1HiNYMtm51D4tTGsbAm/t9O099oC7/UEF02yhYvPJTE5snl5vmlZgYCBdVnQ7T3++aaRzpJezlb0r5e36+d7U/brCDYAH/f8ixO/DtIjIgj+pCNxf/9N5osXUiHq4cMRU1MRLCwoe+woZY8cxm2CP85ffYnHvHl6a7j5+2NWvjzlz57BfeZMvX5jV1ccevWizK6dqCwtse/QAbuP2760pfXkyZM0btyYdu2kv7OPPvqIc+fOyf22trbsySoWfcfZma5du9KzZ08mTpzI2rVrdVPvm5jg9OWXlPx9LaU3BmDi6oqJmyvu0/XdhLXZHRfL599/z++//05cXJyOYCtWrBiHDh0iMzOzwIINwNfXF29vb/r06YODg+ZFSoMGDZg0aRK+vr4641NSUjiWZcHMjIoibMxYAFTWNhw8eJDevXtzKStr5ejRo/+Tgg0US9sbY+nSpVSrVo3FixfLAZEJCQmMGDGCrl27yqJny5Yt9OzZk/r16zNr1ixKly5NdHQ0e/fupW/fviQkJPD555/L61asWJHVq1cDEBcXx5o1a+jSpQtHjx6lQQP9/4C0SUpKomXLlly9epVhw4Yxffp0TE1NuXbtGr/99htbtmzh8eNc8glrYW1tzeHDh3n27Blubm5ye/abIG169OjB1KlTiYmJ0fmiAsTExLB//36dmhgKCgoKCu8OYQlhrL62mnR1ul5fdhKR8bXHM/Os5oH3xvMbemNnNphJCdsS8nmTEk04Ey4VGf6kbNG6KpmoTABwstAU++1Svgvty7anpE1J1t9cn6+lre/evtx7cQ9fN19OhumKlDFHx+iNT0pP0msrKFFJUtnbtR+upbJzZalRFOH0z+BcATbkEaHy+Kx8mNl1HaeoTo3WTQjZNoWqkZKV5XGqZL2sU8ZRHvtJDd1iw9mYGrDkrP2sFkP/d5H41AwDM6DiWxJt4d+NJ/XmTZ6MGKnX5z51CiY5EmfYfdwWy9q1CG7dBnVCAl5/bse8YkUc+0hC3r7jJ1g3asiDXr1If/gI56FDcR4yuMhcYVeuXCkn0shm/fr1ODpq/l6KFSvGmGvXCG79Ecu04q1+/llT1jrk669J/nsfZXbskK1SSUlJJCYmYmVlhVXDhggmJhg5OmLSpjW3HB1p1q8ff9Z4H+ukJKY+ewZAv379mDZNI/BmzpzJ8OHDsbIquLU7G3Nzc+7cuYNKpdJ5FsyOGbt+/brenB179jDCygpRK4Yu6exZWi//hTStZCalShVtDOm/CUW0vSEqVqzIqFGjmDRpEt26dcPT05NJkyYRGxvLgiz/4ydPntC/f3969erFqlWrdL74HTp0YNSoUXoFC62srKhTR/MGsnnz5hw+fJidO3fmK9r8/f25fPkyZ8+epXLlynJ7kyZNGDJkCCtX5l1TJJsKFSoQHx/P5s2bGTp0qNweEBBAu3bt2LBhg9zWo0cPJkyYwLZt23TEJ0hWx/T0dLp3V3LXKCgoKLxrXIq4xOf7PpcFW3mH8pSwKcHBR7qFl6u7VNepnxaRrOtaV8KmBB9763pk9KjYAzcrNyo7VcZI9Xpc6zytPeXjDHUG77m8h1pUIyCQkJ5AhjrDoEtjWmYa155LcU45BVtuTD0zlW3tJK+Y5IxkopKjCrzPF6kvgCyRmZ2c++5+2D9Bd6CrD1i7QfBh/UW8m7Eqqgoz9p6jm18JXlw1YnmWp12UaAeAsVH+zliO1roJ/1v6uPFBeRdOfteUapP3G5xTo4R9vuu+LNnJyg0JJ3Vq7pkW7XLxADJxdaXChfO5zjN2dqbsvn259heWoKAgJkyYwL59+2QhUqlSJWrXrs2HH36oI9gA3N3duXbtGju00t/npPzixSQ9f45xljhKT0/Hz8+P2NhYgoODMXNzo+zhQySkp+OQndL+iy8Ayf3y2KlT8nNm9kv1mTNn8t13373SZ80uB/DixQu5rXRWhsg6derILqG9e/dm/fr1LF++nM/e90W7mrKxuztp586iTfny/816f6C4R75RJk6ciJOTEyNHjiQoKIjFixczZcoUue7DihUryMjIYP58w4HVFSpUyLeon0qlwtLSUs9fOCdJSUn8+uuvDB48WEewaa/zRdaXtiB069aNgIAA+Tw+Pp69e/fqCbAyZcpQq1YtnbHZBAQE4OfnR7ly5Qp8XQUFBQWF18fv13/XE12GSMtMY/KpybJg87DyYHHTxSxsspA5DXVTjFubWONimbsrlaFsi4Ig0KxkM4pZFSvkJyg4KkHFsubLKO9Qnj4+feQ2WzPJMhSvlbjjdNhpOW5N2+VTmxkNZuBt5y2fO5prHrjvxtyVE6HU+l8tWm9rze3k2wXaZ7bVz97MHv4aBzPcYUNX3UGWTjD4NGk9t8Gnf0G5HEkYHL1YfOguABsvPOaWqLFqPscW0wIINgBPewvmdq4GwBcNvfi1rx8AtuYm3Jz6IcOblmVu52rcn9maY2OasPmrutQu45TXki9N2qNH3Krkw61KPsQfOULqvXskX71G7O493KxYSU6sIZiYYKLlPmdZSy+n3lvjvffeY9euXTqWo+vXr7N69WodT6ZsstPqh4XpZ1bNJl0UWbFxI8nJycycOZPt27dz8+ZNwsLCuHfvHgBXHj7UCDYtJk2aRO3atXVizoA8w1xeBXt7SdCvXbsWLy8vVq5cSdmymrjNkDiNm7LVB43wnP+DXmmB+vXzz5b5rqJY2t4glpaWLFq0iA4dOnD+/Hl8fHwYllVbAuDYsWP4+fnpvUnJj+w4t7i4OFatWsWDBw9o3759nnMCAwNl98iioEePHkyfPp1Hjx5RsmRJtm/fjoODAx98oB9c3qNHD0aPHk1ERIT8ZXv27BlHjhxh7ty5RbIfBQUFBQVd1KKaT//+lIbFGzKg6oA8x0YmRTLh5AROhWnSvldzrsa8D+bJmSC1mX1uthynNbHORD72/liuF5Yz86KVqRUuFrqiLTtzI0Dn8p15WzTwbKAX22Znakdsaixjjo5hWv1p2JnZMfDAQAAOdTlk0Er2ba1vaefdjuAXwdyPlWqZRadEY6wyJkMt/Z/98+Wf+d/N/8lzDscf5ku+zHN/Rx8fJT5dEo82JtZwYSWo9d0QU12rs+diKOO3X+XLRt6M7LUZwq/Aciku7aZnJ+KPx8jjH4maB99I0Z4T45rkuQ9tuvqVoItvcb2XzRamRnzTUlPou6STJSWdXj6DYm7EHzlC6p27RC5eLLeFfjXI4FjzatXw2iS56aaFPiFmwwacBxb8BfXrJDMzU6+tb9++ebpbPstyXcyPCRMm8PDhQ2bPnq3TXqVKFTZu3Mj8+fMNzuvRQ8pMOnbsWKpWrcq5c+fo3r07FSoUsIB7AZgzZw7jxo0DNJa2kiVLEhws/XsSFhbG9u3buXLlClhYQJagdVu8mE6dOhERIVnqg4OD8fLy0r/Af4h3VrTdrPh2k1VUunXzpea1b98eX19fAgMDOXTokFxDAqQbs3p1/cBq7eQjKpVKp8J8YGAgJiYmOv1z587N1yKX/VamRIkSOu1qtRq1WlOwU3t/eVGpUiWqVq3Kxo0bGTNmDAEBAXTt2lVnr9l069aNUaNGsXnzZoYMGQLA5s2bUavVdO3aVW+8goKCgsLLc/jRYUYdHSVbwS5GXGTLnS0saLyAmJQYyjuWx9nCGZDcyyadmsT2e9v11gmKCuKHCz/wY+Mf9fo239ksH7cs1VKnwHPjEo11aqJZm1jTsVxHVl9fTXJGMgCfVv6Utt5tCYkNoWaxvGs8vWlsTSVL29mnZ2m5taWcnAQgMjmSP279IZ9/UfULmpVsRiUn6RnFzMhM7vux8Y/YmtoyYL8kmH8N0i34ne3alxvhCeEMPaQJQVClxBoUbACjEnuze9MVABYdvEtiagZ1yhSj+Tc3+V9QLP4BuolQRFT0SvsOOxLp36w6rrYFSxWfTVGWNCgMsbv3EDZ6dIHHe/6gSS5iWtwTt7H68YZvi6CgIPm4U6dO/PTTT3kWqAb0nrG2bdtGx44dAWjcuDELFiygRo0aPH/+XE+wZdOtWzed459++olSpUrRpEkTHRH00Ucf8dFHHxX6c+XHmDFj8PPzw1AnC8sAACAASURBVMjIiIoVK+r1e3h4sGHDBipXrkxySgpkfebz58+zZ88eedx/XbDBOyza3lUuXLjApUtSzZYjR47QpInmbVZ2nZqc47WLFHbq1IktWrUsKlWqxO+//w5ILo/Hjx/H398fJycn+vfvD6CXsdLY2DhXv+927drpfAmuXr1KlSpVyMzM1PkPxZCY6969OwEBAXz++ef8888/fP/993pjQPLB/uCDD9i4caMs2jZu3EijRo0oXtxwsLOCgoKCQsHJUGcw8MBAbkffNpj58EnCE7rull6SOZk7sarVKsrYl+Hei3s6gq2Gaw0uRVySzw88PMCpsFPU86gnt12NvCofT603FXtz3ZglV0tXnC2cZYuUqZEpblZuHO12FGPBmJvRN+VYtWzxWCTcPwzrOkCJ2vDZPnhJYWFnZqdzvv7mes3xjfWyGK7jXofh7w/XGWttqqlH1aKUlHCsZamW7H+oH+91I+UGu+7v0ovny+bb49/Kx23LtIWNGvFIpXZQ6wvwasShW8/YveaCztwVJ0JYcULKCG2UVdDawsSILYPqsun8Y86GRHPyaVUApuWIU/u3Iooiz3JkcHT7fiI2zZoTtXQpRvb2uAwZDIA6JQXBzAyVmZmhpd46ycnJsqhq27atznNeXkyePJn9WbXiGjVqxCeffIKDgwMxMTEsWrSoUNm4y5cvL4euJCW9fKKcwiIIQr513cqWLYuRkRGJSYlgLUW1aWfRHD/+JQvMv2O8s6LtZS1deREfH4+NjU3+A18StVrNoEGDqFu3Li1atGD27Nn069ePMmXKAODp6UloaKjOHB8fH86fl4Jgv/xS323C0tISPz8/+bxRo0Y8ffqUsWPH0q9fPwRB0LHEgfQPnaenFHAdGhqqE7S5cOFCJk+eTGBgIF999ZXc7u3tzcOHmtowISEhshk7m+7duzN+/HhmzpyJp6cnderUISHBcEHSHj168OWXX8qf9+TJkyxbtszwL05BQUFBoVDcjr7N+af6CRTquNeRszBm8zzlOUsuL2F+4/lciZSsMypBRWDvQIxVxpwJP8OhR4dki9KXB77kRPcTspjpuVdT9PeTcoYzO35c5mNWX1+t05ZtjavmUi33D5KZIYmt3JKPqNVw9he4fwg6/QbZgjHkKPwvy83y8VlYWA2GXwQjE4gLh4CeULIutJqRr5izNc39uWBPyB7KO0j/hw6tMVSvv0v5LtyKvkXTEpqH0m/8vtETbZ7WnjxJeMKOezsMijZRFLkYcVE+n+UzAA6+rxnQbR0n70XhHpkgF73OjUy19AL21LdNcbAyZUp7O345ep/Zf0nZI11s/p3CJidp9++TGS251JbZtRMjZ2c52Yb7lMk6Y41M/71CVBRFOnfuzN69ewF0Yrjyo27duvJxdizX+fPniYqKolq13L9Xs2bN4s6dO3L2cZCynP9bMTU1xdvbm93h4TS1tsGqXj05w+T06dMV0aZQ9Pzyyy9cunSJixcvUqFCBdatW8fw4cPl1PiNGjVi9uzZOunwtUVZQQWlj48PkZGRREVF4eLiIos+bXx9fbG0tGT//v06bziy/7HIKbZ27dpFamqqfO5hIGDVy8uLWrVqsWDBAsaMydvloFOnTgwZMoRNmzYhiiJGRkZ07vz24hgUFBQU/ivEp8Uz+OBgnTZ7M3uWNltKVRfJmrLt7jYmndLUR7vw7AIxKTFMOT0FAP/a/nKmxDrudajjXkfHDbBBQAMm1Z2kI7g6luuY6556+/Rm9fXVVHcpRG21c7/B3izXN++mUk0xlRFkpoPKGEQ1LK0DUXekMXNK575W7COYXRJ6b4XVWS5eYRfBuSz4fQZ/DoGrm+G7x2CcJVpiHsChGdiFHwIbw7FYalHNrWhJ7JSxK6PXb25szowGM3Ta7M30syeO9hvNyCMjETHsInk67LR8bGdqCz9pBFt469U8Domm1wrdLHrn/JvhZGVGo7mHefIiWaevddViOFhphEwFN83zhbP1v1+0ZSYk8GigFFdo27o1Zu9IArOMjAzWr1/P33//zahRoxg4cCCXs+qPgeTtlLOwdEHJfm709vbG29s713GNGjVi8ODB2NjY8MMPP9C6dWvq1atHs2bNXuq6bwofHx/+vHOHLu3b0f/777mVlZOhevXqb809902jiLY3REREBP7+/gwbNkx++7F48WLatGnDjh07aN++PQMGDGDevHmMGTOGFStWvPS1rl27hoWFBU5OUoYmbUtcNpaWlgwcOJAlS5bQr1+/fE3oVatWLdC1R40axYYNG+jbt2+e4xwdHWnVqhUBAQGIokjLli3l/SooKCgoFI6niU8JfBZIy9It+enST3oZDVe1WkU5B82DbcdyHfnI6yPMjcypvq460SnRtNraSu7/yEs/dmVj2410262Jf8kWeNn0r9w/1/25WrpypOsRnVi3PAlcoxFsIFnSjs6ByNtw48+CrQFkVuuBUVCW2ExP0gi2bHaPlH6yWd/p/9g77/ia7jeOv8+9udmb7Cl2zKBGqMSqUqOoWXuUVrV0WNUWHbRG0RY1ihpFraoaP1RsLbFCzSZIgiRkJzfJHef3x0nuzXVvJEqLOu/Xy8s933XOua7kfs7zfT4P9FoDWbfgW8lV0MXVdHskwPDaw/nhzx8MOXluNm443RuRS48HawewNzUXc1A50CGkA9tipQe2VgorPO2l3KV767fdVd8lPiue40nGh6+/VOwHlyTjBg0qmmyyAY6azKvk6Yink5SXtuH1Jpy4lsaoH43bXGv6md5TNZ+nR7SJositiR+gvXkLm9DqeE+d+rgvqcwsWLCAt96SttCuW7fOpC8gIIAtW7Y8sABRKpXodLpSyzwVsWbNGpydpTxNd3d3jh07VsqMJ4NmzZqxZcsWDqWkEHH7NkeOHMHOzq7M9/1fQBZt/xLvvfcednZ2TJli/CXXvn17OnfuzOjRo3nhhRfw8/Nj2bJlvPrqq8TGxjJo0CCCg4PJzs7mxIkTnD17lk6dOpmsm5OTY/gPp1arOXjwIIsXL+aNN96waAJSnM8++4w//viDJk2a8Oabb/L8889ja2tLYmIiK1asQKlUYmv7YMnIPXr0KLOZSO/evXn11VcBWLly5QOdR0ZGRuZZQhRFfrz4I47WjnSq2AmdXseeG3u4lX2LLE2WwdRi7sm53MqR6nmOrDuSV6q8gk6vw8vB3C68SEDpRcl8qkiA9Kza01yAAKHlQpn2/DQmHLRcn6mCy/2NAAzFq3VayM80EzMGMhLhl7fN208sg5xk8/b7EHG8MaJDJw6GbkVx9sfSJ1w7CNNNDbpciplzgZQD+GbYm5xJOWPYamr2fmXehG8bgYs/vHFMMk+4fQ5+GgDOfkzrt9kg2jqEdDCYnRTPP7ydc5s2G6Q8uKJ8OAeVA263jDmEc3SWd6hUL1bA2sfFjo517GhSsRwDl/2Bm701g5ua/lt5FzMe8XR+skVb1q7/kVWYx+X13nsoHR+8uPO/iV6vZ8mSJXh5efHxxx+XOG7Tpk1/K2IUFxfHpUuXTLZKFufDDz9kzZo1TJo0Ca1Wa0iPedooMimJi4sz7Apr0qQJLi7mD1X+q8ii7V/gwIEDrFy50uTpRhFz584lNDSUzz//nE8++YTu3btTuXJlgwVqSkoKTk5O1K1bl2nTpjF48GCT+RcvXjT8R7W1taVChQpMnTqVd955p9Trsre357fffuPbb79lzZo1zJkzB61WS0BAAK1ateLMmTMPtLf6QencuTP29vbo9fpSSxTIyMjIPMucu3OOaX9MA6Chd0PWXFhjliMGGARbU7+mDK45GGvlg+fyjGs4rsS+SP9Ik2MnayeyCrLoUslyLpsZaddhZRdIvwGvRYF3TfP+pW0Mh+/6LKdGQQyD784qUbDVz1tAOSGTv0RfvrNfSGv9YS7p/ele8DGZOEC2SJM/mvK7rVG0bdI1o6vyUJku2bmYaOsX2o8x9aTIXNvgtgbRZjAcEUVIOgfbx4ImB+5cknLqEI1RvrtXIfkC37X+jv9d/x/vP/c++Top/aC4aOvzqzFXcH/8fkBy2eS45NSZXf8Nvj1suSbVp51rmrWVd7Rh26jnLY4XBIGDY1uQp9Fhb238aqjLyEDh5IRQykPgf4u7S5eSPGMmAG79+uEQHl7KjMfPli1bTDwJbGxs2LlzJwcOHGDMmDE4ODiQl5eHvf3fK4cQEBBg5gRenKlTpzL1KYpGlkSR2Cwqug1w9OjREkb/N5FF279A8+bNS7TyDQoKIicnx6Stbt26/Phj6U8Ely9fzvLlyx/q2mxsbHjnnXfKJPJKuob74ejoWOK9Ozg4mN27jIyMzLPOmZQz9N3elyY+TRgVNgprpTWHbx429BdFX+7HNy2/MeSklcbnzT5n4iEpkb9NUBtUClWJYx2tHfm9z+80WtMIMBac7lzpngdvOq0kVnT5EHcAGo8Ea3vYOBRSpbplbBommYFEjIVZ5nWf4vUebIyzJl6wZ3AJwZ86eYvIwJG7ovS0fWjuSGCk2bgk3OmR/yHjfU/x22071ulakCy6McLqF8OYb636M1L7g8k8bY/V2PxqrOM19rmxhte1yhvTBhxUhdGea4dgRQfTky970fzCD88lvNtiwu39YNt7WN+5BNbS+1nkJJ2iTjEML9BLtal8HX0h6zYA7Y9UBqTIzNutKtO+lg9Vvf++mVqAu6loyI+LI7ZDRxybNcN/wfx/XbjlnjyFLjODrF3/I2PzZuyfe47cohx9hYJygwb+q9fzd8jPz2f2bGOJjPbt2zNv3jwqVqxoUprp7wq2ZwlLEUK1Wm1h5H8XWbTJyMjIyMg8IVy4e4G+2yUr96O3jnL0VslPkm2UNrQNboufox874nYQWi6U7XHb6Vm1Z5kFG0DHih3xd/Jn1olZjKxrLnjuxV5lTz3PegY3QyeVk4mAAWDLCMnYo4jTa6Duq5BgtOkm+U/pz90rFs+zQCelA5wTTbfyZYp2/KYPY662Gxk4Wppq4IP21flsu+Q2/YdYna6Jxvzt6dreTNf2opXiJGmiE5n59owsEof1BxJV5QPeWHGUOa6WHzz6OPoYXlfISIJvnjOaopRGzHqo3Aa2vgVaNSrALjgINTpyNDkmpQKK0+DMZsiIByBZlAxNmoSUY0ybKhbHPwx5Z8+CTkf2/v3kHD2KY9Om6LJzUDjYP3LjB1GvJ+fQIXRZWWjiE1D5+XHro48Qi30pzy1mqha08gdUFgzRngREUWTt2rWcPHmSn3/+mStXruDs7Mzq1atp3759qakrMpYpX7481tbWFBQW134WkUWbjIyMjIzME4Aoiow9MLb0gYXMaznPUC/tjbqSW+THTT7+W1siwzzDWNV+VekDCwlwCjCItoY+DU3PqckzFWwA2UlwSIo4HAl4jfD4YoWl4w6YrX9d78lG3fN83qUWx6+lsvFcM7oVbmcMz/+abIyRiT6NAhGAIc0q0HLWfkP78OYhDAgPZljzEFYeu86HW85ZuBOBvfr6ACjRGVqXx+Qz+fBxwAp3jeWvSkV5aACuN2MgPcOkf5bdW7yrnmdscA+B1GJFrTcNMxnvJAqoBZHMgkyTotxF+Np74XN2o+E4D2nMzB51LF7f30Gfl8eViEj0Gab3Ej9kKN5TpnB78mRcOnfG55OpCKqSI7IPSspXX3F3cekGbEo3Nyru/h9Kx/uL9Yfl6tWrBAUFmZVM0ul0KJUllJ8oZOrUqSYOkIIgsG7dOl580ULEVabMCIKAr68v165dM7QV1SN+VpBFm4yMjIyMzGNGFEUG7RrEtcxrZZ4T5Bxk1mav+ne2WVV1rwqFuxyLnA/Jz4Id4+G0UfyJjt4I2bdN5n5ytQLvW9WlpfI0lkgWXYko+Ipq3s70aRRIgLsd75zqTaCQzGbd8yaCbWrnGvRvEmw43vNOBCdvpOFsa8WLNY2RsG71/Fh6MJZrdyV3xvfbVmXGrksm59WhpHX+l/RX7ubrvOaGds98W75IvkNQ+znGwb++Bxo1fg6+JObcpJJGY7JWTNAAvr7UGKVVAqOtNgFwK0PNSk1PxqpMXQOLKKfJI9nGmt3Xd3P+7nmzfmW2MadvnTYSAFd7FX6uZXTkLAVtWhpXW7YyiW4V53ahiUbGli1kbNlCpd/2orl5k9zjx3EfNAjFAxqXAejS0xG12hIFm+f77+PaoztKJyd06ekoXFz+EXv32NhYlixZQmRkJG3bSi6qH3/8MR9//DGCIJCUlIS3t7fZvO3bt9OunZSrmJaWxvz5800Em4ODA+vXr5cF2yOiuOnItm3baNGixWO8mn8fWbTJyMjIyMg8ZobvHk50UjQAo8JGMaTmEJbELOFMyhlaB7VmTvQcFrRZwJzoOQbzC1+Hx7c9LLRcqOG1IZ/r0BwTwTZf24lFd16ipzKKCSpjnnYW9sSLHiWufVhfAxDo0UAyVwhydyAFN7oXTDYZN7ljqIlgA8nqvpKneRTG3tqKDa+Hc+JaGvUCXfFwssHZ1oolh+K4XijkRraoyLf74CPtIJO5qTjRPicFfnoN1DngEgjHFwOweOBWzp1bzQtxRiG2TNuWGZciABBFo8B4OecDknAjBRdmqBaZnEO0sqNaQQEXbKyZeWKmxfclXmHcpjlOK9UoWz6oocWxf4fsvXtNBZsg4DVhPAXxCaRZcHi+2rJYXS9BgWvPHhTExWFfr57ZWEukb9nCrfGmTqTlRgzHKTKSa7164/fVbJzbGUs0KF3N69s9Kt5880127NjBtGnTDG1TpkxhypQp1KtXj4SEBIvz2rdvD8CRI0dYuHAhP/wg5URGRkayY8cO1Gq1oX6azMNT3NH8pZdeeoxX8ngoVbQJghAA/AB4A3pgkSiKcwVBmAwMA4oyZSeKori9cM4EYAigA94SRXHXw15oUWKujExJlGR4IiMjI/M4uaO+w8o/V5KXlceoFaMM7d+1/o5wv3BS81JNcteG1RqGIAgMr2N0nCsqXD289nBS81KZ1HjSP/87URTh8BxwCYBaptby1dyrGV4PrDEQjn4LBwvFhqM309WdWZjXHBC4LpqWG6gc6Mcf8dUZwG5D2zRNbyoJibRSnuRzzav4udrRp1EgAIHl7PFwsiElS3JY3PH289hYKQgu92BW7+UdbXixpjFa0q9JMP2aBBdG5lRo9Xq+3SeFDxsGu1MnwIVeDQO5Ma+YuUfxmm5AwOn1BBTbsvhK/kecEI3vzR59fcawkd/11UhCKnGwS9fATLR9ru5CP+EnNjuZCs7a5WtzIfUCGr0xkvep00eQB+tea0zdgEcnZLR3pdp+NlWqUG7IYByefx4rd+ma7WrXJvvgAQSVioyNm8zmqmNiUMfEkL13L669euJTQoHo7IMHSfnmG1TePmTtMv1qWG7YMDxHjwag+sULj+y+iqPRaFAqlVy+fBmtVkuNGjUYPXo0O3bsKHHOyZMnzdpatGjBvn37DMfhxVwsVSoVy5cvx9bW9oHLJsncn2f9/SxLpE0LvCuK4klBEJyAaEEQin7SfiWKoskjIUEQQoFeQA3AF9gjCEIVURR1/E1UKhVqtVp215G5L2q12mz/uYyMjMzj5K76Lq1/ao3Owq/AuafmEu4XzrGbUuTM3sqeNS+tua8Ya+DdgI2dNpbYb5H8bDi7DjxDJSfD8lVgyP/A7j4RgIu/wq6JkHZNOt44BGycYcBW8A3DQeXALy//gkqpwsXGBY4tNEx91WoGh3OMeT+79A1Mln77pfr0XKRhkiaLC/pAbAQNv+uro0MJWnB3sObQuBYm78PsHnXot/QPqng5mtQgexTUC5TehwKt0dr/uQpuvN+2GlqdntPcx5Hx9Crp/bxzmeEFo00E25BmFVh6CFrnf0mSaKxJl4kDv+ga01Ep/btf0ftxTB/KB/dssQQIijtKp/x8Pi3vzjupaQCsTq8B6PB3f3TfiTRJyaStXQuAS6eOuNxThselYwdcOkqumL6ffcaNwUPIOXLE0J+9d6/hdfradTi1aYNjU/NyBPHDpAhh3pmzZn2ur3R76PvQ6/UIgmDx/9DatWvp3bv3fee3bt2a6dOnc/HiRfr27WvS169fP0MkDaBr165s3rzZZExYWBjR0dFykOEfQhZtpSCK4i3gVuHrLEEQLgD3q8zXGVgrimI+ECcIwlWgIfC3iyl4enqSmJiIn58fdnZ28n8GGRNEUUStVpOYmIiXl3kBWRkZGZnHxYIzCywKNoBrGdcA+OFP6Ytgz6o9qeha8dGd/PoRSDoPN47CuWJC785l+CJYeu0eAvX6Q9PRIAig08CCplJ9sXvJz4RFkfBBEqhsCXYpXCM1FjJuAPChZiCHb0uCbXhECBGVPfhy1yUO3K5Fc6VUFNrF3oaOdfxYFV1YuuCeTRK1/c3zlp6v7MHRCS1xd3hwk5WyYm2l4OeRTVlyKI5XG0n5glZKBbnKUor33rmMVlQQpa9raGpfy5txL1ajvKMNX+yU2kLKO1DO0Zrj19IYpXmL8/pg+lntZpjmHTSi5a9jvbKyqJ1fQItcNR46HSsdBqG+K32ePBwfvgi2qNMh6nRcbd0aCkWjladnqfN8Z81EHR2N9m6qIdetOPFDhlL50EGsypcvcQ2Vvz9OrVqSf+Uq5Ue9iXWQeY5mWRgxYgSnT59m3rx5NGoklaJISEhAq9USVGzN+xW2njZtGiNHjsTJSRLo9evXp1u3bkRFRXHs2DE8PDzMTC/mzp1LQEAA8+ZJZjOVKlVi165d8nfUf5CuXbuya9cuwsLCHvelPBYeKKdNEIRgIAz4HWgKvCkIQn/gBFI0Lg1J0B0rNi2B+4u8UikqSH3z5k00Fp5EPSry8vKeeRX/tKJSqfDy8jIrXi4jIyPzuLiadpWNVySx1CaoDbuvS5tU6nvVJzopmlxtLiP2jDCYTjTwblDiWg9MdjKs6Ah67f3HpcbCnsmgsILwUZBw3CjYrB1JrjEYz1PzTOd85gUfp0siD2Ce8QvUSt0LgOToOKZ1FWxVSnqn5fLppr78TzmOVNERZ1srKt+Td9YlzI9TN9K4djeXyR1rWLxUH5dHY7hxP+oEuPJ1b9MvhPnWbnCPy/hCbUf6KnfjKOQBkIQb+UiCctJL1enbOAhrKwUONsaI49KBz2FvreT5L/ZRoNOzUNeJhYVlDWyRtn6+mK1mp6N0n5UKCqidL53YUycJtc2pRhFibfVw1vH5cXHEdX4Z8R4LdZW/f6lzrdzccGrdGvU5c8OUItI3b6b8MFOHTMHeHjE3l5Btv2BTqdLfu/BCtFotn376Kd999x2AQbAB+N9zDzY2NuTn5xuOw8PDOVIsUjh+/Hiz9W1tbXnxxRdLNBEJCAhg7ty5TJw4EZVKhbu7u8VxMo+OoUOH4u3tbbId9VmizKJNEARHYCMwWhTFTEEQFgCfID0j+wSYBQymqNKjKWbJRoIgvAa8BuDl5WVS4fxxkZ2djeM/bCMr889RUqLwoyQ7O/uJ+KzKPJnIn49nE7VejUbUoBN15Onz8FZ5IwgCkxImodVrqWtfl050oql7U9wd3REEgWgk05HDicai2QVXCoi6GvXQ1yPoddQ4/wXl7xFsBSoX/mgwB+esq9Q+95lJX/bhpZzIr0nTw6+iAuJdGjJK/y6nj+ooT3Ua2VzjW+EL44QprlytOIQ0t9o8V9i0ttDR8NOmdvg73eXY4YMAOGpELosBdMj/lHQcmfzHEYL0ULO8khAXSXi0dk+js5eAXrTn2rnjXHvod+HRkaM3jWgdpB7Ttb3xF5LpoPwdgAzREaUAX0Xa46y7wbHDUuQx564xynr9nFRn7MvnbRgdZerQ6OZgS47WhhkpKUS7hJGiu0uDvHyTMWmiI6dESegMrGFt9rPG6vp1bM6cJbd1K8TS0kl0OtzmzsP6HsGmc3bmjzt3oKw/xzQaPOzsUKjV5NWtS2a/vtgdPozTps2kzJrN9RPRFIRWx3HTJvTOLljn5iJaW3P0xg14iN/ZOTk5dOjQofSBhRQJtkaNGjF9+nQAfvzxRxYtWkTfvn2fip/b8u8XCWdnZ86ds1S+479PmUSbIAgqJMG2WhTFTQCiKCYV618MbCs8TAACik33B27eu6YoiouARQANGjQQi1eGf1xERUXxJFyHzJOL/BmRuR/y5+PZQ6vX0uXnLmZW/Z52nmTopFpX3cK6EVkp0vTzscJ0nb7V+9K6YeuHuJACSD4PPnXh5Aq4+7tJ955aM8it1IG3fjwF1GBhr7OE5+7loktTGv7UEMfc60QqjoM2B4DXkztxrnBb5x1c+DW/Dl7KfnykMroIVvprqck5Zmu74+tiS++XWqBUmD6/nXU2inMpIQC0binZdL/4ELf7bxJ3/pDBcm2SZhCrdNK2zu+0HQ2iTYWWRf0b0Kq66Rb9SKBS9SSqeTsRUJiDptXp+ejYbjLzJFGtEGBW74akrXTCgXxWX/+TbXU70fP6BpO13IRsRBQoFQKT+7Ux6VOfP8+1Ea8DEFC+HLq7qeRfvkTQjz+Sd/Ys6rNnSZkzF8Hensq/7SVr72/cumwsBO7YsiU+UyYjWFujdCllO+i9nDK9Fn3r1lzaJOV62e/fj/3+wtp5SVLJAvuaNYls2fLBzlEMnU5H165dTdrGjBnDV199hYODA23atGHLli3UqlWLmJgYk3Fr1qwhJET6HIaHhzNixAjq1q37VGxplH+/yJTFPVIAlgIXRFGcXazdpzDfDaALUCR7twJrBEGYjWREUhn445FetYyMjIyMzGNEL+oRRZHYjFiLtdWS1caaWk18mpj1v1v/XWZFzzIce9iXbIFvICEaFArwLdy+d+ssZCTA7bNwdD7kZ4BvPbgpud1leDbiqM+rXE7KZvZxPzh+yrDUiLXnAC/gKtdsAVEP+4zRt3Oi9MW2UQV3fo+TXAW/172Ip5DOCKtfLF5eMm7EjmuJQmH+BfitlpUZvc5yXbYnHVtbY9SqSLABXHWUwgAAIABJREFUxIrGOnDnxBDaVbKcv9Um1FTIWSkV/PrW8wxdcYJLSVlMbF8dDycb0kVH/IU7+GjyGXb8J4trAURPMqpdURRBFEn9fpmhLe0Ho7C+OW68iUmImJtLyvz5ZG77FQD7Bg0IXLEcoZSC0Q+CwsYG//nzSXjjDYv9tjUtb38tK8uXL2fr1q0ArFixgt69e6NSqZg1a5ZF8bV//36uXr1Kly5dTLYwWltbP7O5UTJPJ2WJtDUF+gExgiAU/cSdCPQWBKEu0tbHa8BwAFEUzwuCsB74E8l5cuTDOEfKyMjIyMg8SWTkZ9ByfUsK9KZbyxxVjjT3b872uO0m7e625rkuA2sOpIlvE175RbLS97ArRbSlxsKSwujE60fh/CY4MMN8XKFg03jWplnCCLJu2AAlm0FYolHeN3g523BsQisEQeDQlTscvJLCdwdimavtQndlFOWELAD0zgEoMuP5WDMAa6XComAD6FzXl2t3c6jo8fSlIGidAy2252DMsUux9sdWVXbhE+Buzw9DGvLnrUwiq3iQmlNAx4J3OGL7lsk4nSiwQ9+IDspjHNDV4qXaPrjaG81YbgwcRO7vv9+7vIHigq0Ig6gTBHw+/eSRCrYinFq2oPKhg6SuXIXKxxuVnz/xhfltdjUeXLRFR0fTqVMnpk6dymefSQ8XZs2aRf/+/Q1jSoqWRUREEBER8TfuQkbmyaIs7pGHsJyntt1CW9Gcz4DPSuqXkZGRkZF5WhmwY4CZYOtUsRMj6ozA3sqemDsxxGfFAzAlfAoqpeVSJCGuIYbXLwS/YOzIy4Qd46ByGwhuJrX9scTYv8A8cncvw6ynk6VPN2kLr1iORf0bYKdS8tOJeMZviqFrmB9fnO3FOJVk975R14wk3Pm+ay3Dl+BmlcvTrHJ5AsvZ88Hmc9TPX8g121cBUGRK93lAX5tv+pYctRAEgdGtq5R63U8i+d71mHh6CFf0Rk+1a9Nf4rUfTjD/cifaKKI54txKStJ/ALycbfFylszPXO2tuWVBXC/UdeQ7bUdO6iuzRdeUKTWkOnOiKHJ78hQTwSbY2VFuyBDufPON2TrWFSuiTUlBn5lpaAtc9j3WwcEPeNVlx6p8eTzHjDYc+3z2KTlHjuLU2nxf7Pz585k2bRrLly+nVSupaPfOnTupVq0aZ8+epXNhCYKhQ4ca5vTs2fMfu3YZmSeRB3KPlJGRkZGReVbQi3oUgqlD36HEQ/yV8ZfZ2AkNJ+BoLUWRtnfdTmpeKpn5mUZbfAuoFCp2vyI5StooC80ucu7AjELb/zNr7n+B7iFQpR2Ur0zBnVgSlf4E//4xB/2HE3XRVLBtfiOcsEBjXbZeDQPp1VCKIAWf6sRpsSLjrX5ksVYyd2hR1dz2/dVGQSRl5DHvt6v8rAuns1Jy3zulr0Sc6MMLNbzN5vwXcLSxYo1OEhINgtz4fpBkvfJa8xBe+bMXX9KL6sqHcy5WKgQcreGAzlgaAWChthNZ2PO9rh3WVgpaVJP+XTQJCaSvW2cY59anN+6DB2Pt74/HmyOJ69GTvLNSLTT/hQtwiowkNzqa669Ktcec2rbFoXHjB7rGxMREJkyYwMqVK7Gzs2PWrFm8/vrrZZ7v2q0brt3Ma7EdO3aMkSNHAlKdNAClUolOV/ImrZEjR+Ln91DG5DIyTx0P5xcrIyMjIyPzH+Tb09/SYn0LYtNjTdrfiXrH8HpmxEw87DyY0XyGQbAV4W7rfl/BVoS3gzfeDoViRxRh07D7TyhG+ovfsNP/LZaoI2hxugUt9vpTRf09/S82BKBrmB8H3m/BvvciTQSbJY7qa9C54FMuioHsead5iVvNhkdUxNnWijN6Yz25S3p/hkeEWBz/X6C4tf6G18NxtpUip9WKFfmu7feA5h0WsFEK9NdM4JJesqtfo21JFsZ8OjuVEkcb6Vl75g6p+JtN1apUv3gB748+wrqYzb1DEykaa1u7Nk6F5hX29etTcddOyg0bhtcEc4v74ly8eJHOnTsbClVXrVoVf39/Vq6Utlaq1WreeOMNVCoVU6ZMIScn52/f96pVq8zaShJs/fv3Z/fu3XxjIZooI/NfR460ycjIyMjI3MPCMwsB+Cr6K75u9TUAsRmxqLWSVfv7Dd6nbXBb2ga3fTQnLMiFpS9AUkyJQ4pHtwAaf59EHqYRNY1OqrDzUi0fvnylNlbK0p/NvlLfnw3Rkv16q2qeVPJ0KnGsg40VZz5+gc4TjcW303GiUx3fUs/ztFJSrlqRgAKo4OHw0Odp7GPFtlgNAwvG0UV50FDzrogMtYaC69dJW7+e1KXfA1B+uOVNmeVHDMc6MADHe1warYOC8Hz3HYtzioiPj6dp06akpqYa2i4Xc5osjlarZfLkyej1epo2bcratWtp2rQp3bp1w9XV9b7nmTBhgsF+H6BChQoEBgayv8htEpg+fTpdunShSpWnc2utjMyjRI60ycjIyMjIFEMUjaVFoxKiWBKzhFxNLovOLjK096z2N/Np1OmQGgc6TfETwuIWBsGmbzicfS/uISFiFgX1hpD99mXWVJrNWM1rTNZIxguLte3Jw8bSGQCY1zusTIINYGb3OszrHYaHkw1vtKhY6nhBEKge1hS9KEXjdpbrR6jPw20PfJJpUdWTEA8HBoYHm/WNb1eNhsHu9G5o2azkQehaWYrg3aIc83Uvk4U9Hk421PST3lt/Nzv+avuiQbC5dOuKUwmFnxV2drh264aV2/0jrBavo2tXE8FWnM2bN3Pp0iWDe2MRZ86coW3btixbtoyhQ4fi5ubG2cLtmZbYuHGjiWAD2LFjB1FRUaSkpFC3bl22bt3KuHHjZMEmI1OIHGmTkZGRkXmm2H19N58e+5RulbsxoMYAnK2dTbYDjj9ounVs7sm5zD0513DcpVIXYw5aWTj9IxyYQZjWCqKMESpaToLm78Pdq5ByEQDR0YeQA82BZMBH+nPkBCBtoVyla83v+upcEAMJdLfn5bq+VPBwIO5OLs8Fu/H13qsMbBpsVietNDrV8X2gaFmdIA9CTq4GoHsN76eiztXfIXv/fjJ37mLHG69jExBg1j8ioiIjIkoXumVBYeE9fKtVZZpXLs9XW08xfPVUkz7PMWMQFI/u2btarWb//v2cOHECgNOnT1OnTh1EUeSrr76iadOmNGrUCIAqVaqQl5fHypUrGTZsGD///LPZenXq1GHjxo0mNdVu375Nu3btOH3atPzDpEmTqFq1KgDly5fn1KlTyMjImCKLNhkZGRmZZ4qNlzeSmpfK4pjFLI5ZzISGE+hTvQ8AybnJBsv+NkFt2H19t9l8H0cfs7YS+fNn2DICALOsp1OrJdF2obDuWUAjvnCbCnfulricFisuiEEALOhbjxq+pqs+X7kM9d4eAQ2CjREcB5v/3lcJXWYmsR07oU1KAqTIlfdHH/7j5/305ZpM2nLOcNynYSBKhcAnQQUkJMQb2l06d8aq/IOVcriXxMRE+vTpw9ChQzl9+jSzZ8826a9Tpw4gRVbfecd8S6WNjQ21atW67zm6deuGSqXiueeeY8+ePQwcONBEsF28eJFLly7RoUOHh7oXGZlnAXl7pIyMjIzMM8G5O+eYcXwGh28eNmnfcnULp5JPodPrOHbrmKH906afsq/HPpOxnvaeDKoxqGwnzLoNv75Xcn9aHNyOgb1TANiQ35CFf5Qs2Ea1rGRyXNWr5Nyzf5pKHo54OUvRxrdbVX5s1/Go0KakkLZ2LaJG2rYa26mzQbABqM+fK2mqGZrkZMT7OB8Wkbl7N2k//si1V/tyrW9fFBkZ9G0cRJcwyRXxg3bVyI2KQpuaSva+KADsGzYkZNsv+Eyfdt+1r1y5wtatW1m1ahXXr1+3OKZXr14cOHCA/v37mwk2e3t7i3Pu5d5x/fv3p6CggObNmxvaNBoNR44cITAwkF27dgHg7OxMSkoKVatWpVOnTigeYcRQRua/yn/v8ZiMjIyMzDPB+kvr2R63nXbB7UrNMbuSdoXev/Y2aVMKSnSijgupF+i/oz+vVHmFq2lXAZjYaCL2KnvsVfbs67GPFutbADArYha2VramiydfhIx4yMsAR0/wrg13rsD2dyEnGdEzlCN1v8AveibxzmHcdanBy6cLXSIXNjMsMydeEmWtq3sxqmUlLidlEZOYwYR21dHq9TjaWBEW6Mr7P53li25lMxn5p1AoBH6faF5v62nlyvOSyFDHxGDl4YH29m2Tfs01y8LnXtQxMVzr3gO7BvVx69EDm6rVsK0q5WRl/baP/L+uUhB3DevgYFLuEUoOtnbQuTOfd6lF9/r+VDmyg4TPPzcOUCjw/nASNpVMxXsRx48fx8fHB6VSSWhoKFqt1tDXvn17rK2tWbZsGXfu3GHo0KEcOnTI4johISH3zUcrTs2aNencuTM///wzgwYN4vvvpXy7ffv20bx5cw4fNj4guXPnDgChoaGcP3++TOvLyMgYkUWbjIyMjMxTx83sm3xy7BMAopOiqepelbqedS2O3R67nXEHx5m0vV3vbQbUGEC9lfUMbRsubwDASmFl4grpbuuOj4MPWQVZBDkHmS6enw1L20B+JiXxeVorFm/NAoZDotT28j26b422JQmitLVxUb/6KBQCdQJc6d6gKI9KcjBsWc2L6A/blHiu/xq5J06gPn0au3r1sK9Xr/QJxRB1OrL27CXn6BHs69fHuUMHBEFAFEXUp0+TvS8KpzZtuNa9u2FOxsZNhtd29esTuOx7LtWugy43F1EUS8zdS9+wgYyft6I+J0Xk1CeiUZ+IBqDS/ihyDh7k1qT7b6+0P3SIvAsXsKtenfBK5bkyeIlJv8rHB5vKlqOau3fv5oUXXrDYB7B9u7Tld8uWLdja2pKXlwdIjo1xcXEmY0eMGIGDQ9ncMAVBYMuWLWbtCoXCIArT0tJwd3c39IWHh5dpbRkZGVNk0SYjIyMj81Sh0Wtou9HUar/fjn4sf3E59b3q88P5H4jNiOWjJh+hEBQcSDxgGFfJtRIzI2ZS0VUyjxhdbzRzTs4xWatTxU642xq/ZCoEBRs7bQTAybpwS+L1I5B5Ew7Pua9gA9iSVc2sbYW2DQOsjPly8aInoT7O/DCkIYoHNBF52sg+dBjNzURcX3kFQaFAFEXuLlqMWFBA/l9/oXRyROUfQMpXX5nMC1y+7L4FodM3bkLp4oxT69Zobt7k+uAhaK5dk/rWruPm+2OpFnOW1B9WkjxjBgB3Fy2yuJZ1cDDBq6X6YYJKhajRIBYUINiYG9CIBQX3FWRXIyLv93aYcHPsWEJ+kXIcxWKRMgArT/OC5+PHj2fRokWkpaWV+RxFgu2zzz5jwoQJnD17lpUrVzJr1iwAgoODy7xWWXBzcyMrKwsnJ+n/TuMHLOotIyMjIYs2GRkZGZmnii/++MJi+8CdA+lTrQ9rLq4BoHe13lR1r0p8pmTg0DKgJTMiZmCttDbMGVxzMJXdKvP2b2+jFaUvySPrjjRb2yDWAE6vgS2vm425E9geTXAEPgdMo3pZSjfaVvNi13kpR6qWnwsfJw7iR10rdtpITpXXRU+61vOjvOMDuFI+hWT8so2b778PQM7Ro6i8vLGrF2Ym0CyRNP0LKqxfR9qWLdj4+2PfuDHnFi1i8Rdf4ODoSH+NttQ1rkS2QHfXct5gwfvvYT1jJgCOEdJ2SVEU0dvaIGg06LOyUFgQbVl79pi1uQ8ZjD4rm/T16w1ttrVqkRdjXofPulJFPN56i8S33ib/ylW0aWkonZ3RpUs1+EJ2bCdl3jw8Ro0ymSeKIgsWLCAz0/yhQdOmTTl06BBJSUnMnj2bNWvWkJCQYOjv3r07EydOBCTDkRs3bhhEW1BQkNl6D4ujoyOzZ8/m4MGDdC8W2ZSRkSk7QvF6NI+LBg0aiEUWs4+TqKgoIiMjH/dlyDzByJ8Rmfshfz7+eZJykmi9wZhLtbf7Xvpt78fNnJtmYxe1WUQD7wY0XNUQrahlzyt78HLwsrhudkE21zKvEVouFIVwn1yx/GyY5mfW3C3/Y6LFqoBIZ8VhuikPIiIwRvMGXZvVYVKHUHbt3ccLLSMRBIHg8b8ioGet9af4CXeIyP+K395vRVC5hy/S/KRQkJBIXKdO6HNzCfx+KaJOT/ywYQ+0hkYUWZZ6l9fK/T2nxJnJyazPSOdzbx9aO5Vs3DIqMYF92dlsfb45lTUa/BfM51hqKgMGDOBbKxWVC8Va0KqV2DdogF6tRtRoUDo7c7lJOLrCSJfH6NGUe22YFEHU67kYWsPkPJV+28vd5cvJ2LgJfU4OLi+/jOe4sShdXblYPdTitVW/eMFie/fu3dmwYYPhOCwsDFEUuXTpEjt27CAiIsJk/K5du3ixsK7bunXr6NGjh6EvOzvbEAlLTk7Gw+PfcSGVKTvy75dnA0EQokVRbGCpT460ycjIyMg8sYiiSGZBJi42krX98vPLDX37e+7H3dad1S+tNhiFFOd08mlG/TYKrajF18G3RMEG4GjtSM3yNY0NWbch+QKERELxPKb/TTK8zLXzxV59k880fQoFG4DAz/pm/Kw3GowMalYBABulYMiJcrK1IitPS8+CjwDo9VzAPybY9Hl55MXEYFuzJkmfT0Ph7IRr167YVHw09cXuRdTpiB/xOjkHDxrabgx7DZsHLJL89Z0UNqRnEGitKrNoy9PrsS10IlyVlkpqo4Zk79zJ1KTb1LK1xUslFbA+kJ3NlynJ1LK15ZfMTPSF8zscLNxKGxZmWPOWn79BtF3v2w+7sDA0N2+iTUpCFRRoEGw+n32GazepJplOp2PixIk0avsC1Xf9DwChb18+nDePwMBAXv/9GIgiQuH1AGgCA1DdMNr634+tW7caBFuHDh1YsGABfn5+962X17ZtW3JycrCxsUGpVJr0OTo6sn79etLT02XBJiPzhCKLNhkZGRmZJ46iXSAbrmxg6tGpzImcw3dnv+NCqhR16Fu9ryHvrLxdeXpW7cm6S+tM1ph/Zr7h9bDaDxbh4dd34eI26DgX6g+U2rJuQ/QyAA7ravBq2gc4k0MmktiKrOpB57q+jFl3xmQpTyfzLXUbRoTTdo4x1y7E49EKNr1azc2xY9FlZaNJTEQTbyoGUpd+j8rXl5CdO1BYW5ewyoOfM+nzz0n/aYN5p05H/gXp3+67KpUZfvmKSbfXJ1PJ//NP0v/6i3Zr1+KmtOJ0npqRI0cS4OUFq9dYPOekW7cYNH4cHqtWo+nVk+c/+ghXpZJySiX5np78vnQpExISGDhwIG0uXKCOnR0vODnxa34+sQUFxBYUlHpfN7Uak2N1scLPmus3DK9dXu6MXq/n119/ZdmyZWzevBl7QeB4jx4UuJej6WefkluYT+bm5kbv3r0RRZHo6Gi2bt1KxIgR+E78wORcAYu+M7ueqKgoOnfubDj++uuv8ff3L/U+4P5W/vK2RRmZJxtZtMnIyMjIPHZ0eh3T/5jOocRDtA5qzfbY7dTxrGMobj06arTJ+NByplvJ+oX245e/fiFXm2u2djX3anSr3M3YoMmDmyfBMxTsXC1f0MVt0t+/vA01uoC1I/z2qaF7tOYNAINg2/xGOGGBUsFpe2srVh69zp3sfGb1qIPKgjV/VW8n/pjYioaf75Xux8es9HaZEPV6dBkZ5B47RkFCAm69+6BNSSa2XftS52pu3uRS7TpUO3sG4REIt9QVK8wEm/0PK7i0bBkBhXXGAFZs385q4MsKIRR4edK6/wA8evQwRLsArms0dOvWjW+++YaYmBjmzZ3LW+WlCNCIhHgiHR3Z5+bGlrhYyZlwwgQAvvf3Z+PGjbRr147XX38dhUKBr68vp0+fplOnTuzatYtotZp169axaNEi9u7dazjnqlWr6Nu3r8n1h4WFcbuYMLsfOWo1e/fu5eWXXza05YoiNdatMxvbp08fVq1aZXB1BLgxYADLL15Afe48dxcvxnPMaKwtmIIsWWJ0lRw9evQjNw6RkZF5MpFFm4yMjIzMY2fUb6M4mChtpyvaAlkk2CxRz8vU/j3IOYjDvQ9jpbDiq+iv+P6cVC+qT7U+TGg0wXTyvk/hyNfS67C+EDkBXIpFKs6sNR0/PdDkcKPueVJwo4qXI5eTspnxSm2DYANoW8ObtjW8S7tlPJ1tGdmiIplqLc0qP3jOll6t5lqPHuRfuWpoS5k12+JY+0aNEFQqfL+YjtLdnev9+hks6S/WroPvjBm4dOzwwNcAhVtYf/mFlDlzAXDr04c1aam8O2cONGpEiLU12yqEGMZn6HSIwOCrV+DqFZorlSaCrYhRhcYbtWrVYsndu2hFkSM5OUTFx3P69GkmRUaiKra9EGDQoEEMGmRe/Nza2podO3Zw69YtfH19AYiMjCQ2NhZRFBFFkdq1axvGv/vuu3z55Zeo1WoG+pnnMAI4RkSQvX+/4djpPjlzligu2AAuX74MgF3NGvjPnWNpCgcOHGD16tUATJkyhQ8++MDiOBkZmf8esmiTkZGRkXks3FHfQaVQcSPzhkGwlUa3yt0YXHMwfo7mX6StFNKvtME1BxtEm0memk4D6Tcku/4iTq0CQQGdCkWcOh02DwcgR7TBQcg3O89FfQD1g9zY+Ho4er34UBb977c1LwdQGqIoorlxgzsLFpoItnvxGP021sHBiPn5uBTbTgcQtGIFd+Yv4M633wKQumqliWhTnz9P1s6dpG/ajHPbtlh5epIyRxIS5YYPJ+t//8M6OBj/+d9ypWkzdKmphrmub47k82L5a7EFBUTn5lK/cGvekqVLGTJkiKH/wAHjNtEiZs6caWKk8efly7Rq1Yohb72Fh4cHbdo8eK06QRAMgg3A09MTz3ts9Hfu3Mnnn3/O0KFDUSgU2Nvbk1esP2T7r1gHB/Pjjz+Sl59Pv0+mkrFnLxX6mUboAC5cuED16tUNx1WqVCEiIoLFixdbvL6jR4+yevVqXn31VYv9er3e5D2ZNGkSCsXjK7AuIyPz7yKLNhkZGRmZf5UCXQEfHPqAndd2UtGlIs95PwfASyEv0S64HW/+9iYR/hHsT9hvNndwzcEEOhdGvkQR8rPA2gEURmMFFxsXpj0/jf3x+2kTVPjlviAXFjSBtGvmF3T2J3ALhnoDIDbK0Nwk/xua21zhG+FLQ9tJfSW+17XjWN/6AP96TTVddg53ly7h7oKFhja7+vWxf64Bucd+R336NADeU6fgVswd8F4EpRKPUW/i0uVl/mrdhrxz56WIU34+cZ1fpuD6dcPYtDWm+WR3v5PyrAri4sjYuNFEsM2uEMyScuUACAgIoG7duvzyyy8MT4hnhq8fMXlqxjRrRvfu3fnpp58sXlvTpk159913TdoqV67MjRtl26b4MLRt25a2bY01AAVB4LRCQbpOx2U7O/IyMji/Zg39+vUDoGFMDDtvJpKtN8YK27Vrx+rVq3Fzc2Pu3Lm8/fbbAAwYMIAxY8bQpEkTBg8ebHJeNzc30tLS+OSTT0oUbcWFrkqlkgWbjMwzhizaZGRkZGT+FQp0BSw/v5yvT31taPsr4y/+yvgLkIpah/uGEzNAqmV1Oe0yKoWKATsGkJafRmW3ykbBptPAJ4VbCkMiof/PJufqENKBDiGFkaPEk7DY1F1yvTaCL7W9OGH7OmjVsHcqnNsMmhwAPtQMJBMHtuXX5ZLwJbttxha2D6Jnowp4WDAX+SfRFxRwqW4YFBMHKl9f/GbPwq5uXUNbbnQ01hUqYOXubmkZM6z9/RHs7BDVanKPHyfx7dEGN8SyULyo9OCUZI5dughIYufrr7+mc+fOLF++nEGDBjEyUaoT9ln58jRr1sxEtL399ttcvXqVX3/9lRUrVpT5/P8GtuXKEfHXVTSiCPVMt+XWqlXL5HjOnDkGkQbg4mLMVZwwYQKCIDBw4EAWL17M0aNHAejRoweDBg2iXbt22BSrA7d8+XI+/vhjPvjgA+rUqcPy5csNffdurZSRkfnvU6poEwQhAPgB8Ab0wCJRFOcKguAOrAOCgWtAD1EU0wTJb3Yu0B7IBQaKonjyn7l8GRkZGZmngT3X9zAmakyJ/YNqDKKJTxOTtipu0ha7/T33czL5JBVdilnU/7HI+Do2Ctb0gh4rwMoGspOlCFy5wvHHl5qs+7b2LX7WNgYgQSyPv3BH6kiSxGKmaMcGXXPD+CuiHxt0zbGhgD/FIEb9jfyzh+X25Ckmgg2krXoKW1uTNvv69R94bVGtBuBG/wGGtnKj3uSkgwNjv/iCSY6OhIY35UKd2rwQFET+qVPcXWL6nq4PqWAQbBMnTqRv376GrYFFoqQIV1dXRowYwYULF2jSpAl6vZ5evXpha2uLKIr3ta1/HAQFBXH1asnbUAVBQBRF9u7dS8uWLU36evTowa+//srLL79suC9BEDh8+DC///4727ZtY+LEidwtLPidkpJimFv0ng0fPpz33nvP0P7nn3+abLuUkZF5NihLpE0LvCuK4klBEJyAaEEQdgMDgb2iKE4XBGE8MB4YB7QDKhf+aQQsKPxbRkZGRuYZJCM/gw8OmRomDKo5iGXnlhmOG3g3KPHLuiAI1PcqJkYOzITfPjEddHkHRE2XTEVmVpba3o8Fh3Jw5xIAB3U1eU3zDmqMQqdL/lSO275hstRX2ldQY8uJSa1ZcjCOhfv/4j3NCACGNKvAizV9Huj+/y650dHoc3JQurmRsWmTSZ/vjC/NBFtJZGZm8sMPPxAaGkqrVq3w8vJi7dq1REZGkp2djX1EBLnFDDWu9uhO6JtvGo57Apw4AUhbHmNjY3EfMoQrTcIBcBn5BpPfeguQDD8mT55sYhBib2/P6NGjmVOYE6dQKLC2tmbBggVm1/qkCTaQRNv9EEWRgIAAM8EGYGdnx/r1683aBUGgcePGNG4sPTwo2u5469YtYmJizCJ43xbmHr733nuyYJOReUYpVbSJongLuFX4OksQhAuAH9AZiCwctgLIjWZOAAAgAElEQVSIQhJtnYEfRKnIzjFBEFwFQfApXEdGRkZG5hlCp9cxcOdAEyv+/qH9GVNvDAFOAUw9OhWAAKeAsi1485SJYJtvN5w31IW1rA7Nlv4UcfcqKFWQcByAdzRvGATbu22q0K9JEHWn7iY4bw0tFKf4zvorpimGs1zXhE8616C8ow3j21Wjc11f2s2VjFJ6NyzjdT4EoiiSOHoMWbt2mbTbNaiPe//+2NWsiaqYoYYl9Ho9I0aM4MSJE6SnpxMXF2foS0pKokUL43ZRf19fdobWQBEfj/X4cXQZOrTEdePj41GpVKjVaqpd+BNdejo1CoUHQHZ2tpmjI8C0adO4ePEiYcWKVj8tBAYGmrXpdDrGjRvHzJkzAfDxeTghr1KpDIK1du3aZvlq6sJoaJGjpoyMzLPHA+W0CYIQDIQBvwNeRUJMFMVbgiAUWTD5AcWreCYUtpmINkEQXgNeA/Dy8iIqKurBr/4Rk52d/URch8yTi/wZkbkf8ufDnD/Vf3I1/Sr2Cnve9X4XT5Un5MD+/fvxwIPWzq3J0mURdzKO68J1s/nW+al43/6NRL92uKWdoeb5Lwx94XnzuJlXnuMKZ5ZZzzCbm7bpHdzSzwEQow8mBVdaBlrRMUSFmzKR038kUqOcgvN39ezTh1El7wcA7K3AKzeOqKhrhrW6Vlah0UH8+RMk/Pn3okFl+XwoMjPxGDvOYt/t6qFcs7aGy5elPyUgiiIbN24s0aXwXhJu3mREcDCfT5nCRwsXoNPpAFi/fj3ly5fn+++/JzExkX379hnm2NnZYWNjw9ixYw1W9XXq1OHw4cMlnmfcOOm+nrb/I6nFjFYCAgJYuHAhBw4coFmzZgbRptfrH/q+qlWrxvnz5w3rWSI2NpbY2NiHOo/M04n8+0VGkAJiZRgoCI7AfuAzURQ3CYKQLoqia7H+NFEU3QRB+BWYJoriocL2vcBYURSjS1q7QYMG4onCrRePk6ioKCIjIx/3Zcg8wcifEZn7IX8+jGy5uoUPDxtNKl4IeoFZkbPKvkDyRXD0hO3vwbmN4FUTks4Zuj/X9GaRrmPhkcg1W8uOe0W8nD+VTi91YnCzCibt6gIdL397mEtJWYa2ub3q0rmu5dpcfwd1TAxZu3Zx1d6BUC8vdOlpuHTtipWbsbZbztGjxL8x0pBfVoRtzZoUXL+O1/jxuHbrWuI5cnNzOXXqFOHh4axYscKsVllwcDCtW7fmm2++Qa/XM2nSJGbPno2jo6MhOjZgwABD4eaffvqJV155xWSNbdu20bFjR0oiISEBvxJqmj3NrFy5kv79+wOSIC5OUXRs5syZZo6XD8r27dt56aWXTNpCQkJMRFpZv7PJ/PeQf788GwiCEC2KYgNLfWWKtAmCoAI2AqtFUSzaWJ9UtO1REAQfILmwPQEovn/EH7j59y5dRkZGRuZpQy/qTQQbQK9qvUqfmHIJrh0EKzv42TTPrLhgA9irl1z8gsrZc/1uLpH5s9hlPR4VWhSC6RfbOdqu/Kmowk9NjLlJmuRkrFxdsbO2ZteY5pyOT+e7/X8xqmVlQn2dy3yvoiiSMmcud5csgcIIVdDqVSAI2NWsSco333J3kWSa4oZxy0nyjJn4TJuGa5eXKYiP58YgUwt4QaWiyu/HUBTWNhNFke+++w5PT0+6dOmCXq/nt99+4+TJk8TFxbFw4ULupUqVKnTv3h1RFJk6dSpKpbEswqxZs5g1SxLRgiCg0WgMgg2gW7duZut16NCBM2fOUKdOHeN1FppwtG3b9j8p2ABDCYDmzZub9a1cuZItW7YYRN3DYG9vj4+PD7duGTcmnTlzhujoaHr37l3myKmMjMx/k7K4RwrAUuCCKIrFkgXYCgwAphf+/XOx9jcFQViLZECSIeezycjIyPz3OZx4mE+OfWISUavjUYelbZdioyzFIv/UKvh5ZIndqZftSTopbe645eTOreblWBK/gaoZdtwYPIZXN0KN/KXoUPC68hfGqtYBMFXTj+917ejZwI+cLVvIPRlNxkajqYf35Mm4dn+FugGuLOhbdufF3BMnyNyxk7TVq836rr9qXmjZEkmffopDk8Zc693H0Gbl4UHwnt2s/vFHkk+cICwsDIVCwYEDBxgxQjJDGTx4MMePHycmJqbEtdu3b8+GDRuws7Mr8z0VkZaWVqIhSO3atYmNjSUkJAQwRn6mTZv2wOd5WvD09CQtLQ0HBwezvr59+9K3b9n+vctCYmIiU6ZMYcqUKURFReHo6EhERAQ3b8rPvmVknnVK3R4pCEIz4CAQg2T5DzARKa9tPRAI3AC6i6KYWijyvgFeRLL8HySK4n33PsrbI2WeFuTPiMz9eNY/H7VWmDre1fOsx4p2Zai5lXwB5je22JWbYs31vfe32Lfy9qZrxHiy8rUAqNByyOYtXMnmhYIvSbLyZa/TOTK//97ifM+xYyk32HQ7Yd6ly+QeP45bn94I95hCFCQk8lfr1mbrqIIC0Vw3LwBtW7Mmfl/N5tzEiQQ81xDXXj252jzCbJxbv35YDX8NDw+P+95vWdDr9WV2Yjx69Cjh4ZIT5LJlyxg4cGCpc8aOHcuMGVIeYcOGDTl27NgT6fz4NFH854der5eLZ8uY8Kz/fnlWeKjtkYW5aSX9JG5lYbwIlPy4VEZGRkbmP0OOJgd7K3uWn19u1hfiGlLyxL/2wZ6PwT0ECiRnSdHGiYzwiTjvnsiZ3RWxzcgteX4xtLdvs5XfGeEZgY+bHVGXUng+fy5O5HIXF/ZUzyFzqqlg858/n4Q33wS9nuz9+81E2/XevdHn5oIo4t7PGEnRZWZyrZfpVk+Xbl3xGjcOpbMz2rQ04jq/jDY52dAfsOg7rNzdyRwyBI/CL10OTZuSU8y0wzY0FPd336Ftu3ZlumeAFStWEB4ejoODA+np6VSpUoU1a9bQpEmTBxJQTZo0QavVcuzYMRo1KluFntatWxtEW/Xq1WXB9oiRBZuMjMy9yD8VZGRkZGRKJDk3GbVWMseIz4xnW+w29KK06WLvjb00/bEptX+ozezo2WZzG/vcEz0TRfg/e/cdHlXRNnD4d3aTTe+dhBYIvYQqRXqX0ItApCjSRAQRARUiHV9Emkj5KCIiiNKr9FAFAkhvgRBIgPTeNlvO98eGTZZNAgj6+src1+Xlnpk5c87GJfLsmXme8IMwxQl+7AqPL8G1rRC+DxmJLmnjCdxbmu6Pviw0YJtd9x06dPmauHr5e4tUZcoAkLNxA9vauLLm3fosc3tE3+sHSZQd+U+P6lgdPQgYAqOSy5dR/shhHFq2oPzBAwBknTnDjUqVybllqOeW9NNPhoANiJ05k+y8jH7Zly5xu/4b6BIMxbhLrV5F5Zs3KDFzJkpHwz44CxcXAo4dxf0Dw548t2HDULq4cPToUdLS0tizZw/Jycn4LVuKQ95eKYDY2rWwsrEpNjtcn6eCxR49elC+fHl8fHyoXLkySqWS/v37U758+SLnKIpSqaRx48ZYWDxfUukmTZoYXw8ZMuSFrycIgiC8mBdK+S8IgiD8+/wR9wdfnvqSxiUa069yP5ysnDj18BRuNm4M2T+EBiUa0LJkS6afNtRHS1Wn4mbtxqfHPjWba0vnLfTa2QudrDMN2h6chtXtzMY/MU/5LpflciDLDL26w6Tvs0ZDcczN4phfIEc/bU5ptyByHzzA0scHTUwMd9u0BSBx5So0MY8pfekypYGSUg61Ji8hI9JQo8xnxnSsq1QxzmvxVG2te126Yl29OjlP7RWL7NETr88mEjv7K2Ob2/Bh2OUtKSyMx0ej8PhoFI8ePSIwMJDLly+bjfl6zhz6DxxA1s1bNJw1y9i+Z88e2rZtS0xMDNHR0dSvX587d+7g7+9P7969GThwIEuXLi10j9XfxcbGhtTUVDIzM1+6RpkgCILwbCJoEwRBeM2tvrqae6n3uJd6j59u/EQJ+xI8zHho7D/58CQnH+Yv5bsYd5HHmfn5pTxtPRlafShvV3obgEO9DqHRa3CycjIMeHjeLGDT1x6I9vIWVFpDqv1TWYakw8Nu7KZsWgwA9z74jKiajQmIy+DX89H0rutHaTdDoKLKK3isKlkSj9EfEb9wkVkx6sa3TqLLe+3cu7dJwAaGzIdekycRO32Gsa1gwOY6cABJPxhqtxUM2JTOzriPGGE8lmW50OWBiYmJvPHGG0RHR5v1ASxdtoxxd++yePFiUtca9v6tWbOGDnlLJH19fY0ZGQMCAgDo1q0bXbp0+Ucsn3N0dMTR8fkzbQqCIAh/ngjaBEEQXnOp6lTjaxnZJGArzG+Rvxlff930a9qXbW/S72bjln+gyYYfTeuLHfQYyPun2uJIYy5bD+WBzoMqNyL4VHURp9thxnEdRvRFsrQEYHrXalhZFB6o2DVpSvzCRUXer9fnn+PS5+1C+1yDg3Hp3ZuM48eJmzeP3Dt3ce7VC+9pU5EkCVXZssRMmWocb+HjQ7ldO1FYGbJh6vV62rdvz4EDhqWWJUuWRK1WU6NGDQ4fPoxer8fT05OhQ4cyc+ZMkzpbERERjBo1isWLFwMwadIkBg4cWOT7eOKfELAJgiAIfy8RtAmCILzGppyawh9xfwBQ37s+Z2POFjlWISmM+9kAOvp3NAvYjFKi4PfFcMZQP0xv40Z3i2+5GA9EGYakYU/VzJV89Mcm+kUfMjndf/cuY8AGYG2ppCg21ariNWkSsTNmYOHhgTY+3tjnu3Ahju3aFnkuGGqiObRsiUPLlmZ9Ln36oCpdmqhhw7GpWZNSq1YiqVTs2rWLM2fOMGPGDJPxUVGGN3fw4EFj25IlS+jRowetWrUyZn978mTuScAGmBXEFgRBEIQnxNd1giAIr5FzMeeYfWY2ESkRyLLM5vDNAFR3r87cZnMJcAlgTO0xjKo1CoA2pdtQwq4ElVwrcarvKUrYlTDO1dyvedEX+qGTMWADWJXRyBCwFVA29RGbdobQNNqw38u+RQuce/XEb+kSrMqVe6H35fpOMJVv3iDg+DEcO3cytju0Nkty/MLsGjak0uVLlP5xLZJKxeeff06nTp3MAjZHR0cmTpxodv5bb71l1ta2rWkgOXbsWGPtM0EQBEF4mnjSJgiC8Jr4I+4P3t1neJqz/uZ6k76lrZfiZOXEls75haeH1hgKQK4uF0mSsFRYsqnzJtptboeERGPfxqYXuLIJtg6DoaGQfM/YvET1LnPS8uuaudur2N/YitgR+RknnUOm4tOv9yt5n96ff45NYCBOQUFIyqKf0P0ZFy5cKLSQ9JkzZ6hXrx5gSKH/n//8h9OnT/PDDz8UWuB6165dTJkyhVl5CUiqVav2Su9TEARB+HcRQZsgCMJ/yX/O/od1N9ZRxa0KpRxKkZabRnDlYJr6GVLaq3VqVl1ZhaXCkkHVBmGpsHzGjEU7/fg0Q/YXnZrdmDSkECqlyvjaQeXA9i7b0eg1OKgc8gdp1bB5sOH1sjcB0Nj7MtruK/bcV2KrUrJpeCOqlHAkdedOHo34yOQa3j27/ol3VTilszOu/fq91ByhoaFs3bqV6dOnG5NtpKSkUKdOHeOYbdu2sXDhQqZNm0b9+vWN7Z07d6Zz587Fzm9pacnUqVP59ddfCQ8Pp3v37sWOFwRBEF5vImgTBEH4G6l1aiwkC/ToWXdjHQDXE69zPfE6AFfir3Cq3ynAENT9evtXALztvOlUrlPhkxYjJjOGPff2MP/8fABcrV3pULYDO+/uJC03DYB3qxazl+r+KXAqCc4ljU0eth6GF3o9hK2E7CQINX/6tCmlAnsSDE+6+tQrRSUXS6KGjyAjrxaZqlw5Sq1cgeXfnDJerVYTFhZG1apVcXFxKXTM+PHjCQsLY8eOHVy8eBFHR0d69Ohh7N+9ezdvvfUWXbp0+dP3YWFhweXLl5FludCncYIgCILwhAjaBEEQ/mJZmizmnZ/Hxlsbnzk2XZPO58c/Jz47ntOPTxvbQ6NCnyto0+l16GU9FgoLfrj2A6uvriZZnWzs/77d9/g7+zOxfoG9V1lJcHoZuJUH9/KQmQBhq8DJD47NAb960G4WrGpjGD/qAti4wPKmkBplcv1Iy/KU0dwB4IDe8FSqcXk3Pqnvwa1atY3j7Jo1pcSsWVi4ufF3CwkJYc6cOQDUrFmT77//nlq1ahn7d+/eTViYIYtlZGQkzs7OJucPGjSo0H1qf4a1tfUrmUcQBEH4dxNBmyAIwl9Eq9ey7NIyll9eXuSYck7lsLGwIcAlgOScZEKjQ9kZsdNs3P77+5lyagpTGk0x69PoNWTkZmBtYU3XbV1J16QzpeEUvjn/jck4O0s7/J0LSXZx6ls4Mc+8/YnosPyADeDYXLC0MQnY9KUb82F4Hfbm1Ocby2X4kMRxfQ2W96/Dm+mRPGjW1DhWVbo0fvPno7C1Lfqaf4HIyEgOHTrEggULjG2XLl3iiy++YMuWLVhbWxMXF0dQUFCx84woUKNNEARBEP4OImgTBEH4C2j0Gs7FnDML2LZ32U6X7flL6jYEbcDGwrA0Tq1TU3ddXZPxSkmJvcqeVHUqm8M3807ld7iffp8fr//InKZz8LT1ZM7ZOWwO34y9pb3xqdonRz8xu6e+lfqa32j0+SIDNlmGzBgrrJw0WNrmp/rnUn4SkxyVK9trLmPCcQ1lUmNonnaRsSU/wFqrZpziLv4jF/AgJsY43nv6NFx69Srip/Zi0tLSOHnyJLVr18bLy6vIcVlZWfznP/9h2rRphfbv3bsXGxsbpk+fzqlTp4ztycnJnD9/ntat85Oo/PDDDyb71wRBEATh7yCCNkEQhFfsxMMTjDho+jTmncrv0K9SP0o6lmR07dEsvLAQwBiwAVgprUzOaeLbhAFVB1DHqw6N1jciR5dDyKkQriRcAWBr+Fbe8HmDn2/9DGCyDPJpE+pNoEeFHhB7HW7/BtV7Gvaq/ZyfsONHu4H0z/wBvUYiPKIUtnFpZDy0wdoll7LtErioL0eg4q5xfKiyEdMfdKPipWPsvZSfddIxN5Nacbd5I/YG2rw2haMj/jt3YFlMcPUikpOTcXV1BcDGxoadO3fSqlXh6f1nz55tlp7/wIEDNGrUCDs7O2Pb5MmTja/Pnj2Ls7MzrVq1QqvVEhAQgFqtplu3bq/k/gVBEAThRYigTRAE4RVKyE4wC9jerfYuY+uMNR6/XfFtwmLC6FrePGPi8JrD2Xl3J+veWoe7jbuxfWPQRrps72IM2AAWX1zM4ouLzeZ4WhnHMgRXeBvp5Hw4MtPQeGgqAHodIEsM041BG66k8uWy2GnUgIYMDAFlTrKKg4dqcMijDh4Vk/G1SgJgS3wgi0Pnm7+HK9tNjiVra0r93/JXFrBduXKFGjVqGI+zs7Np3bo1P//8M927d2fatGlcv36djRs3YmFhwaZNm4xj161bR/v27XHL20u3efNmkwQjAAMGDDCm7wdQKpVcuXIFSZKw/ZuXdAqCIAgCiKBNEAThlUnTpdFnVx+z9iB/0z1SDioHlrcpZJ9b2mNGVh/GyMCRhmNZhvQYyEmhrEelIq9bIlHGIRtu+UkgyyyoMxOGf459ter4jxiF/vdQYt9vj4vLRRSWCnKSVCRcs0ebo0SbbcjuOJbik6T4xicwIH4fadetedixApbJWrpdPGYyxq5RQzJP/Z5/3LgxfkuXoFCpnp7uT4uKijIJ2Arq08f0Z3/58mWSk5O5efMmYHg693RSke7du6PX69m8eTO9evVi+fLlDB482Gzugk/kBEEQBOHvJoI2QRCEV2R78nZis2IBcLZyZkbjGaRr0qngUqHwExZUh5QHpm0V34K+GwyvTy6Ag1MAkAbsMA7xtfflYcZDAKre1zNpgx6lDD81V9D2gh6PtLzMkEcvkXD0feN5yXg+1/u4XvkNdB26sPxCPIF1K9Jn4RjsNTnGfofdGQB4k4OkUuH2/vu4DhqI0tERXUoKtxs0BMCpe7eXDtgiIiJYsmQJH3/8Mdu3b2fkyJHGvp49e7JhwwZ++eUXgoODzc4tWFPto48+MgvYnpAkiZ49eyLL8kvdqyAIgiD8VUTQJgiC8Ipk6DKMr2t71qZZyWb5nVo1xF6DhNvw6CJU624SsOl1kHLHDpuEA1h5zSVq9losLVKwtLNH5aBDMXcAY5sHszh7L59V/JAZByZS77ZM/yP5CUKCQwskC/mTyoceobK3NwC9ZBlJkrjX9QSWSgmb3VuJK7A3TNOhM1U+H4eFh4exTensjM+sWWjj43F8ibT4O3fuxNPTk/nz57Nx40a++cY0E+YHH3zAd999B0Dfvn1ZvXo1hw4dKnSuypUrm50vCIIgCP9LRNAmCILwCuRoc7ieYyiQ7ZOpYvApa2LPf43nyPeRzn8Ph6Yi6yErXoWFjQ7FkeVEHfNAnWJJ2fZx3NvnAbJkmOzgqrxZTZfkNTi8lQYAfMqiIu5DYamnZNMkLKx15KZbEHXMsHerfKcYFmd1YaltVzQKJe7ZqcTbGgpLty3nzNfeyVj6+WKZF7CB4QkUQFl3w33I/fqisLYiZtJkbgQ2o8vc2SiVCrN7cO7+4sk6srKy+OCDDwgNDeX+/fvFjrWxsTFJLCJJErt37+azzz7D39+f5cuXc/XqVQCqVq3K+fPnsbAQ/7sTBEEQ/nc98/9ikiStBoKAOFmWq+W1TQGGAPF5wz6XZXlPXt9nwGBAB3wky/K+v+C+BUH4F5JlmdvJt/F3MtQSs1BYGAOHwmj0Gnbd3cWGmxuY3WQ25ZzL/V23aubrsK9xS5Wpf1vm3cM5oN9BEpC2fgV23mpU9vbEX3Es9Nx7vz3fssWilGkTT8I1B3RqBZ61UrF11wBgaafDrXI6Nm4aRlh+TKhDTfZ83JJyHvbEp6u5EZOGh70V5TzssVEpn3kdSaHApWdPXHr2pPJL3bGphIQEKlWqRGJi4jPH3r17F39/81pzVlZWzJtnKF3QtGlTatasCcD333+PlZWV2XhBEARB+F/yPF89rgEWA2ufap8vy/Lcgg2SJFUB+gBVgRLAQUmSKsiyrHsF9yoIwr/c7LOz2XBzg/G4lEMpJtSfQE2PmuyL3Efb0m1xts7flzTv3DzW3VgHwNjQsbQo2YL47HjG1R2Hi7XL33LP2qQkYud8TYx+FyMv6qn2wHRflDZbSeq9Z2cczFaqmNRqNMuuTCfzsTXaGhY8TnBhU5kWXLQvTyPra3yVu4K0+zYoLGSS79gh6yR830zCxk1DyaZJxrliZBe8pWQkBVjWkPlNV5+D2jqUdrOlgpcDAN5O1ng7Wb/aH8ZzUqvVHDx4kDVr1hAQEEBYWFihAVv79u2JioqiRYsWLF68mJkzZxYasD2tRo0axMXFcfPmTZMskIIgCILwv+qZQZssy8ckSSrznPN1AX6WZVkN3JMk6Q5QH/i9+NMEQXjdZGmyOBZ9DJ2so0PZDswJm2MSsAE8SH/AyEP5iScSshP4IPAD4/Hpx6eNryNSI4hIjQAgMTuRZW2W/cXvAHQZmdxt1x59ejofPtVXqmUCWbFWJFxzMLalW9qwtFF/fncpzVesYAeNmDBmNMTHUaVCKbYolLwXEsebXOUbuTc6fyXVfB3JTsxiW44LC5yXYu2cDoBrxQwkBUjmqxPpqJ7FO8qDbNI15SGG/Wbu9ioW9639V/0onkt4eDj9+vXj3LlzhfavXbuW/v37I+ftpSvo22+/faFreXh44FFgr50gCIIg/C97mUX+H0qSNAA4B3wiy3Iy4AucLjAmOq9NEITXTEJ2Ajvv7qR7QHecrJxM+mRZ5u1dbxOZFgnApBOT0MraQmYxdSfljvG1Tq/jQdqDQsediTmDXtajKCyieUk5N27w8JNxSCoVTl27oE9PN+m3983Gu24qljZ67Dxz8aieTsu0OVhk6nmra1O+71CV3ZcfM3K9Ycneam9H8M5fNnnBoianNNVpXdmLFpU8aF7REy8HK7p8dxJ9ooRCkjmsC6SlxUXjOTf0pbgve9FeGcanmqEk4sRCXX7tMXd7FWFftC52qelfKTU1lY8++oi1a59esJFv5syZ9O/fH+C/dp+CIAiC8E8lPU+K47wnbbsK7GnzAhIAGZgO+Miy/J4kSd8Bv8uyvC5v3CpgjyzLmwuZcygwFMDLy6vOzz///Ere0MvIyMjA3t7+v30bwj+Y+Iw8n2Ppx/g16VcAAqwCCFeHYyVZMcRjCBVtKnI07SibkjcVeu4M3xmcyzxHRZuK7EnZw5Xs/GLS7hbufOn7JZHqSL6JMWQD9LDwoJlDM7P5ZvnNwkHpwJ+lkTXYRjzA9eu5aEqXJnnUhyjS0nGfNs1s7N46EpFeEo1cLOijiwRgn64uH2k+xIFsapd2o28llTEYkWWZ4w+1VHJV4mlrGlg+TNdzL01H4xKm+/lydTL/d/ACXZSn+E7bhdPWowBYp23FJO1gLNBSRorhjuzHpAbWbAvX0KW8JbaWErYW4GL96gPY56HVahk8eDAPHpgG2GvXruX48eOsWLECgI0bN+Lp+XJ7+/7pxO8PoTji8yEUR3w+Xg8tWrQ4L8ty3cL6/lTQVlRfXhISZFmende3D5giy3KxyyPr1q0rF7Vc5u8UGhpK8+bN/9u3IfyDic/Is+2P3M8nRz8psn9M7TEsuLAAgFqetajgUoGNtwyFnftU7MMXDb4wGX8x7iLnYs+x6MIiZGQa+zbm5MOTxv5O/p2Y1WQWV+KvcCTqCPvv7+d+2n1G1x7N+9Xf50WlbN3GvWXz+d0xnhZX8n8/qipWwLl7D+JmzzY7Z1pfBVfLKDgbGYVN3u/UkbkfsVvfAFuVklMTW+Js+/IFpstM3A2Ar7MN1dKO0VxxkYXa7qhtvUnOMiQf6VHbj29613zpa72sc+fOMXLkSM6ePQtAyZIlmTt3LvXr18fS0hJfX8MijKNHj5Kenk5QUFBx0/0riN8fQnHE50Mojvh8vB4kSSoyaPtTyyMlSU9VQxMAACAASURBVPKRZflx3mE34Gre6x3AekmS5mFIRBIAnP0z1xAE4X+LLMvsu7+PT49+Wuy4JwEbwOQGkynnXI53q71LaFQonct1Nhsf6BlIoGcgN5Nusi9yn0nABtDcpQoc+JLqb35M9dofUcWtCh+Hfsx3F7+jT8U+2Kue75tJWa8n7ptvSFq1GmugxVP9ubduE7t4ERKgVcDlMhK1I2SS7aBzp3FsuLYT5AfoZIma6hW0qFmee30C0cugVLya5X4L+wSy4ewDQoKq8taibPbp69GnXkm+6lGD5MxcjtyKo1PNEq/kWn9GSkoKjx49omLFivTs2dMkdf/EiRPp3bu32TnNmjUzaxMEQRAEwdTzpPzfADQH3CVJiga+BJpLkhSIYXlkJDAMQJbla5Ik/QJcB7TASJE5UhD+3SJSIth9bzfnYs5xIe4CAO427hzseZArCVfov7d/oef92OFHAlwCAPC19yW4cjA8OA3HvwEbV7j8M1g7QeMxEBjMOKUPT9cPmevZnLab8hKVJN2F0o1pnRGHp40HcdnxxGbFFhm0ZZ4+TUboUVSlS6EqW5aHn36KLj6h0LEpduCcCVJ6JgBbu3vya0B+tsYlNg5w11DYebBmHLlKe/rUK4kkSShf4fasLoG+dAk0PKH6qnt1jocnMKK5ocyBi52K7rX9Xt3F8qSlpWFnZ4dSWXxJgOzsbCpUqEB8fDw2NjZkZ2cD4ODgwKlTp6hWzWyhhiAIgiAIz+l5skf2LaR5VSFtT8bPBGa+zE0JgvDPdy3xGqcenmLRH+Zlnqc2mopSoSTQM5CL/S+iVBj+wl/9h+oA1PasTaBnoOlJMVdgdTvTtpxUODQVDk3FG5hRpxeTks4AsKf7HnzWFnhyc2Mn2Sd/IzvRksRWbqCU2HBzA5MaTAJAn5lJ4po1KB0cyI2OJnntj0W+t0hPUMhgpYEvBijJtIYNc/K/f7Jv2YoRru4svbQUANetwwE4pKvFaWUdLoe0xdry2XXPXkaf+qXoU7/UK5nryJEjxiQgI0aMwMLCgqVLl9KiRQvWrVtH165dWbZsGbNmzcLGxobJkyejUqnYsmULo0eP5uHDhybzPQnY+vXrx08//fRK7lEQBEEQXmcvkz1SEITXTERKBJNOTuJqwlVkCt8P27NCT5r6NTUePwnYAL5q8hXLLi1jeuPpcHUznFwI6nSoPRAOfmkyT266ktT7NihVelzKZSEpoeHDeK4MvwI6LcReIefBHVIi7MlNtyA1Mr8W2oyHOj4bpCQpx/A0TJucTET7DuhSU4t9fy7BwQyucJwHOY/M+r5vraDb73q+6aak2eWVfJCSiluHSVwJW0KlXMN+st/09dg8stFfHrC9SmfOnKFly5bG40mTJhlfr1mzBoBNmzaxaVN+ohcHBwc0Gg2TJ08udu5+/fq92psVBEEQhNeUCNoEQXgmWZbZHL6Zqb9PNeuzVFgyu8lsNHoNzf2aG5Yj6nUQthLOrYb2syHqLGSn0LF2fzp22wnpMbDpvfxJCgRsY3I/wJU0Ohw4hX1uDgCxF5wBGY9mFyE7Bf2h2eQeWEncJUcyY8wLRJeLgfbnZXyq+xF3/DCJQ0aajTGjVPKJ9xEe5MQZm1xliTkxMRxwcmFjPRV760ogSQQl6gF4e+8M3n7yFnS1uOvSgqolnAqZ/J9BlmVWrFjBsmXLSExMJCQkhPfff/FkLRMnTjQ5rlKlCl27diUwMJBu3boREhJCYmIiHTp0eFW3LgiCIAivNRG0CYJQrFtJt5hwbAJ3U+8a28bWGYu7jTuNfRvjYuViWlcr7RHMq5x//GO3/NdnlkLfjbDhbWPTA70HpRTxAPyS2ZR6EQnUvnaikDuRSD5rRW73mqTeswUKTw+f62KBKlnLewf0HNFuJvHICpP+fbUl2l2Q2dJIQtevC5NbzECflcWBcxu5/GChcdzpvEyQCqBOTgwHSvmRpDSkza+fk2My58e5I9iqb8IQH8tC7+l5yLLMlStXKFeuHHZ2dvnvJzcXrVaLra1tMWcXT61Ws2/fPoYPH87jx4+N7QUDtpUrV9K/f38WL15MkyZNUCqVjB07lk6dOjF58mQcHR1p0KAB27dvN57TsWNHfvnlF7N7mzVr1p++V0EQBEEQzImgTRCEIun0OoYdGEZiTqKxbXPnzVRwqWA+WJYh/hYseaP4SQsEbGNzh7Nb34DGiqs0jb1ItQsPsMvOL6Dt8s47JDdsjnrSBKySE9FmK/MCtnwRlWrx1tafGLL2POfuJ+GaFcl32+YD0OJIksnYESOVJDpKrGsho1ZJ8HAXHRN6UserDqsz9puMtStQDsUCsNbrIS9os8pxBUWssf+R7I6fiw0NS7xY1pE2bdpw8OBBk7by5ctz+/ZtsrKy+O6775gwYQIAZ8+epV69ei80/7Fjx9i8eTOLFpnvOywoODiYwYMHAzB27Fhje2hoKADvvfceKpUKCwsL2rdvb2yfNm3aSwWTgiAIgiA8HxG0CYIAGJ70rLq6it/u/UZw5WC6lu/K7eTbxoDtqyZf0dG/o/mJl36Ge8fRR55AkRJpbB6U+ynlpUdMsiw8EcWw3DHUqNmGHQk30H+73qTPqlIlXN8JxqlHD7wlCVqdYOfAjyh/5oBxzGNHV6JtPHEfNR5Jklg50FDWZNGhcPTH56BINH3qtaGpgkRHQ1ClVuUHV4cfHKa6e3XupuQ/SdwdZb6nrZZazSNLC5pmZbNf34DdmjfYZTWJXFnJ6Lc70CiwqjGYKUxGRgaHDx/mt99+Y+nSpZQuXdokJf4Td+7cQaEwL4Rdv359YmJi8PLyMut7+PAh58+fJygoiA8//JDHjx8zatQoWrVqZTY2PT0de3t7ZFkmOjoaJycnHByKL0Lu4uJifH3kyBHmzZtHdnY2tWrVKvY8QRAEQRBeDRG0CYIAGLJBLrxgWB4YciqEkFMhxr4Al4D8gO3RRdCq4fhcCM9/OlUwzPg4dwQ+dTsjqZQ0ONGAONkJ15wMjlh8jIODmmO66rR/kEClLUPQP3Ufjp074Ttnjtn9dVi9gKoTtuOf+oibrqUILO3KR60CaFHRdJnkiObl0NRP5v7e/PbgcUo0loU/BVt7fS1rr68FDL8Q/7j3wKQ/UXbATUpnUkISb2Zl80ZODldK+xLt3pzKZ1bjJqWzyrvoLI63bt1iwYIFLFu2zKS9YMCmVCrR6XQMGjTImPyjMN7e3iQmJrJt2zZWr17N+vXrsbe3p1atWsTHx5uM3bZtm9n5v/zyC/b2hhIIkiRRsmTJIq9VnIJP4wRBEARB+OuJoE0Q/ktkWSZXn4uV0uq/dg/JF86Scuo4ai9njnjEFjmuimsVyEqCXwZA5PFi51yq7UTz3qPoEuiLLieHjlFhWC00JK6Ixo20KnZ4XE/Eg8Mm57kNeR/3Dz9Esix8X5iFUsHFWZ05Fh6PykJB8woepnvp8lgqFayxa8ubXDS2FQzY/DQaoou4hrbA66G5H2OJjt36BjiQxSmrUQRlZgGQ6FqXGV2r0b6aNzcep1HBy7wWXHZ2NhcvXqRRo0Ym7dWrV+fKlSvG4yd10J48XXs6aFuyZAnR0dHGfWJubm7GvtKlSxf6Pgrz+PHjQp/SCYIgCILwzyeCNkH4L9l4ayMzzxhKGtb2rM3S1kuxtfxr9wfJOh2SUklOeDj3OnU26WsNHH1HSanGbTjwIH+fVVvfpryfoYY5ZdFkKciMtQEZbNw1WDlq+T6tLbG3nPDKzeS6gw9VRw6hyZUj3JuxnZyrV3k6JHW8nml8rSpfDv+dOwsNvgpjo1LSrqq3SVtiYiI7duwgODgYlUoFQLqVN4NHK5n4i46V7fPT7y9/HEd1tZrL1lY8tLBgururyVwTE/P3wO3X18NOpWT9gLrcT8oieZc9DpKh/pi+ZD0kSaJJgAdNAjzM7nPdunXGumcF/fDDDwwYMIC4uDg6dOjAkCFDzJYm/vLLL+zcuZOlS5eaJCQZMmQIZcuWfa6f044dO5g0aRJTpkwhMzMTHx8fvL29n32iIAiCIAj/SCJoE4T/gt/u/WYM2AAuxF1gX+Q+gsoFYan48xkIi6JNTubRuE/JPHmy2HHT1+ng54P0qFee+z3q06vuu6gXtYGEWB6Eu5L52DS9/iM7NxpkXjUeN+AijNxLYc/s3D/8kITFi43HJf9vOXZvvmkSsG3fvp0hQ4ZQokQJLl26RPny5Vm4cCGNGzdm165ddOrUCUdHR+P4DRs2GGuBXbhwgczMTNLT03m/fRW+t5D4YpDpr7hAtRpbWaZxdg6HbW3M7rFZVjaHdYGM0XzAvdlvodbqsbZU0gjYcKAxpTTbWK9tQatKhQdADx48wNXVleTkZJP2jRs30rt3fiFwT09Pzp8/X+gcvXr1olevXmbtZcqUQaFQoNfrqVq1KteuXSv0/GbNmtGpUyc6depUaL8gCIIgCP97RNAmCH+j+2n3CdoaZDiQZSRAzgtaQk6FsOn2Jn7qWHjijj8jNzKS+EXfkrZnT6H9X/VUcLe8LcNzG1FnXt7+NK0W199v4vr7Te6wNm9k/pK8DL+yeHi7kn3+AiUyDUlKLLy80Maah2ql1nyPpbc3lqVLI0kSslZD4vL/o9Sqldg2bMjt27exsrKiTJkyLFq0iNGjRwMY92fduXOHjh3zk59MnTqVkJD8vXZz5841vl5cICB8pH+XeW/EM9bLg6CMTA7Y2tAwOwfbAhkha+WocdXpSFLmP4nz1Op4UzOekKAqSJJkUiR7v88IVoY3Bpey9HM0DV71ej3Hjh1j4MCBJu3Dhw+nefPmJgHby9i+fTtr167l66+/pnbt2iQlJTFz5kw+//xz1Go1Fy9epH79+q/kWoIgCIIg/HOIoE0Q/kIpD+4SNfh9ZEnG8kEsGdawwAayrKB8DFwtLbGinQK7vLJf8YmXSGqVhKu1a/ETF6COuIcuKRELLy9iZ83GJjAQm5o1sfD05F7PnshZhn1Yehsr7juqifSUuFlS4npJiT0fXUCl00JmHDmVy2O5dzJRx13JjjffZ5fs4Ytbz+7UG/0BAJqHD8k4cRJZq8Gld29krZbHX3xB+pFQyv6yEW2JEoSGhtKmdm2ysw3LCj0+/BCXvn2x9PKiefPmHD16FDAkxZALBFRFOXHCUL8tNTWVdevWceHChULHXfjjEm2qZ3MlL6nILEACIvTexOPMG4qbuOj1hD54yHfOTix3MRTE3qJtzpnPW6FOiSMkJIRBgwaxfPlyevXqxeRO1dhw1pF2ldxYuXIl7u7uBAUF0aFDB7O0/YMGDWLRokXPzMr4ooKCgggKMgT9N27c4NatWzRu3BgAKysr3njjGeUWBEEQBEH4nySCNkH4C6jDw4lfsoT0vb+Z/CGzzzH880S1+zIL/09ncu7Bc0H0Xn/KeKzPzSV93z5satZEVcqQpVDz6BHxi78j4/gxdPEJJudnHDlidj93e9Tls4A/QMq/my9KtEU1PX8/ljWACsq0SiQ7yRKdWoHKQUtmjBUHKgTRO8S0SLWlry8ub+c/QZIsLfGdNw9Zlhk7diwLFiwAwMHBgfT0dADGjRvHnDlzePvtt40BG2ASsN2+fZvbt2/ToEEDIiMjGT9+PEePHkWn03HgwAGz/W9vvvkmW7duxcMj/73k3PsDyA+YnpzxrmY80bIHHqQQqLjLMtUCOmdkstzFicZZ2Wy27U0fR2va9nyfAwcOMH36dADmzJlDUFAQ06dPp1b5/KWR/fr1MwnYrKysCAsLo3r16mb/DV41T09PPD0LLzAuCIIgCMK/iwjaBOEV0qWmoomN5V7nLoX2xzlLeNt6oXJzw8IylYwL0WZjql9IJjXiFg4+pdAmJnGrXy8s4pLRWygotXoVSknJ/f4DnvuernQIYHqFizwJXSq6VGRTlhWcXGk2Nku2om3uHFY7z6G0FMcM7Tvs96tLv0YNnnkdjUbD1atXadOmDYmJ+cW4nwRsYFjOWHBJY6tWrWjfvj2ffvopAKtWrSIgIICAgADAkCnx0KFDyLJMx44d2bt3r9l1u3Tpgru7uzHwW7t2rdkyxSe+CG6Pm4MNa07dx5XSJNxcTSltGocfRGOnlznbuznR0dEcOHDA7Nxdu3bRrl07k7b16/Pry7Vo0YJJkyb9LQGbIAiCIAivFxG0CcIz5Ny6Rcqvm7D09YWyZcg8dYq03/Zh37wZDi1bGsdlHD9B1JAhJudeLCuxor0Cn/I1mNPsa1wz4vE6MA3uGoKCh4nOpN03zxh5u3tXci3ANSP/D6lCqyd6wLuF3mOKLRypKbGvtgL/GJmg8p1p7d2U+WHfsKVkBCBRxaUSM+IT8bp4EPT5T7biZSeWaYM4ra/KY1Updk5ozYAVpYhLz+aLLtUZUdEDLwfrQq+r1+vp1KkTV69e5cED0/pmDg4OVK1aldOnTxd6bunSpdm8eTNOTk6MGzeO9PT0IpcTSpLEhg0bcHZ2NmmfM2cOY8aMMWkbMGAAAwcOZFDueNao5jBZMwhnMjitr8KPFb2xtlRSp7QrZ+8lUffSUnopj/I1/wdAdT9XWr5ZdIBacN9cQaGhoTRr1qzY4tqCIAiCIAh/lgjaBKEI+txcdMnJ3O8XjD7TkKbeC3gSmqT88gtekyaRtmsXjh07Ejtzpsn5XwYradSuP4dUpSEpAmnXeLi5y2SMb8MUvBukMP2RHwpZxi9HR8NzCpMllADHqkrUvy1jrclvW9PPi4MlEtBLoLWQaFWqFUkPDpHkKHGOXfgGdmFzXCwg4WblzKorJ7DXZJnMWzZnHTIKPmpZnjnVvPF3t8dGpeTAJy2QAaXCdCliUlIS69ev59q1a2bFogtavHgxI0eORKfTsX37dkqUKEGZMmXw8fEBwMnJicjISJNznrX/y8nJiczMTDZv3oxOp2PAgAHG2maF2X01lTJV1qOyUPBGWVcCHbXUqFqZTz75hP79+7Nj1Xzeq9GSe9nd+P7efc7rK/Cdo3kA7evrS25uLvHx8dy6dQuA+fPnc+HCBX788UcAqlSpUuy9C4IgCIIgvIzXLmjTxseTc/MWqTt2kLZzJ97TpqJLSsK6mmFJkyY2DkkhYeFhXnvpn0SWZfSpqciyjIWLy3/7dv5Vsv74g0efjkcTbb508WmxM2YAkH3RUMg520piTSuJS2UlMl2sWXD5OFL0LLPzsmUVK3VvsU9Xl2aKy/QuuxUdEkNdvGh4znSPm0YJizsrccyUWbnI0BfrDEc9E8jNKxq9LlVP1aPf06qUrzEb4pD9hqd+tgortkXeNwnYxuYO55JcjruzglAozGukFWyTZZm7d+8ybNgwDh8+bDb2iVq1ahEeHs7cuXMZNmwYAEqlku7duxvHbNiwgenTp7N9+/Yi5ymOra1tofXPnvbll18ydeo0qunucvHUYcqNHs3WXbsIDw9n+PDhDB8+PG/kDHaFnmb03UFIj01T6IeFhZGUlETbtm3RaDT4+fkRFxcHQPv27Rk1ahTz5s1DkiSTgteCIAiCIAiv2msVtOlzc4no3AVdgRpKMSFfGl87vPkmd/Ky05U7eACVn98Lza+JiSFt1y4svLxxaNUShW3+t/ayLD93AeHCyDodN6tWA8CuWVMyjx4z9tk1boylnx9ug98zJqoQXpz63j0eTZxIzqXLZn2l1/1I9rXrhP26jDX1UwmMkOly2jzbYUiwgvtehv/Oc3RelIg+btJ/RV+GfrmT6N6wMo3LuzPA342wsyepcuQXAHrnptF3vCNNr8rEuEholRDhDSsfx/K+jxdDRikpmSBzrbRkLBUAUCMpGgn4/nEsEzw8uGmVX+vNNycd55xUcmUlv+nrs0X3Jq079+cDf7dCA7aCZFmmTZs2HDp0yNhWqVIl2rVrR1RUFCVKlKBHjx40b978mT9fgD59+tCnT5/nGvsyDAk6ZEL3bAEMpQKKEtS8ARu27KJf70nGtm+++Ya6desajy0tLalVqxb79u2jVKlSVKpUCQB3d/e/5g0IgiAIgiAU8FoEbTk3b3Kva7dnjrPNC9gA7rZuQ8Xz51DY2ZmMkTUaUnfswLpaNawrVgQMhYszT57i8eTJyHmpzQGsq1VDHR6OrFYD4Dr4PZLDTiNptPiMGEnW6TOgUOA59mOTAK8w6rxlWYBJwAYYCyanbNyIx5jRuA0ejGT56gs0/1vFTJtG8voNZu02tWohq9XYNW6Ebd26XPOT+ZR0QEF4CZnbvjLJdhJVH8g0uabn56b5AdugTIkOcSfIlZUs0XVhr64+Nn41eKOsK5faVzIJlvzLV4a8hI/dElW0zYzBw0XPD04OrHUyFJIOyNWw4WEMfX29SbXPP9der2dZTJwxO6K/Rsuvjx4T5OfD/bzPwBvZOWhlBd0sl1KmbAAD6/rRoqJ51sHc3FyCg4PZtGkTwcHBNGnShGnTpvHo0SMALCwsqFu3LocPH8bGxrww9T+Jv7//C43v292QRl+hUKDVagv9gmXevHksX778uZ70CYIgCIIgvErS89RG+qvVrVtXPnfu3J86V5eWRvLPG4mfPx/y3kuZnzeAhSWZx4+Rdf4CmQWCMQCXAf3xnDCenPQUFHFJSBaWpO3aRcKSJSbjfBcuxKZWILrERHTJyTx4b7BJv+vg91DY2ZGw6Ns/de8m9/TOO3iMGYPS3jRIzI2MJGbGTJP34DlhAtmXL6GNjyf73PlC53P/YATuo0a91NO910HGsWNEDR1m0mbfogWen47Dyt8fWZaZEzaHy/GXuZyQ/wSucVY2J23NA5e5Gm/aRZ81Hn9i9SVTx36EvZX59yOyLBMVFYWruyf7ZvfAnhxqW9zDQzZkXtQBa50cKJurpXl2NmoJ3izlR07ePq4lMXHUy1FjLcts0LZgobY7W62+xEdKIkGhYJKHGy2zsmmVrqGPPI91n/TE66mi0FqtluvXr5ORkcH+/fuLfCLVpk0b9u/f/3w/1H8AWZYL3e/m7+9PREREkef16NGDTZs2/enrhoaGPvdTR+H1Iz4fQnHE50Mojvh8vB4kSTovy3Ldwvqe+aRNkqTVQBAQJ8tytbw2V2AjUAaIBHrLspwsGSKEhcBbQBYwSJblwqvfPmXMwh7cz7xJlIdEL9u2TAieV+x4WaMhsl8wOVeumPVF9ulb5HkeY8YQXq4E876tToK1luAy0zl/5gQajUyLYUOp/dZbZJ44QdzXc3k4enSx95C0arXJscLWFu/p01D5+hrvwbJMaXIeRaPMNexFyrDGLMkEQPK6dSSvW4dN3TqUWrmS5PUbSN+/37hX6omy27cZn/AB5Ny+TeLKlajv3EF9/YaxPWHJUqwqVMCxffti38O/RfrBg1j6+mJduTKyLJN58hSpW7Zg6euLY1BHVGXLolCp0KWlETf3G+TcXLLCwtA8fGicw3vKFJy6dkFhbQhsTj8+bdwXVtD3j2PJlaRCg7aCAdtaOjJz3GisLZWF3vNXX33F559/DkCJNoMZ8sGH5F74jE4KQ402JfBuqiFd/gdMZIn8Fe+lprHExRlXnY5q6lys876ocO2zhKOVvHj/Wwc6J66ml8UxlsXGAxAmVWPjp71xtVMZr52SkkKvXr3MikIXJTg4+LnG/VMU9WXF77//jp2dHZcvXyY1NZUOHTqY9M+fP//vuD1BEARBEIQX8jzLI9cAi4G1BdomAodkWf5KkqSJeccTgA5AQN4/bwBL8/5drOjo6wxbmlmgZS8XfZrjFx6DwsYa1wH5NalkWUbOyuJuly5oo/P/wp1mAyl2UMq0zrBRyVUrsW/cmNu3rnN+aw/2+lgAFlx/PJ3MMoZv5NU3LtAgYDSqsmVJ2/sbOVevms1jXbUqdk3eJHHZcpN2v+8W49CqlfF49/Q2/JB1BHgISKg0Svxj4JYvuKWDazok28MnW3X4x+TPk33uPLcCa5ld1755c7wmfWG2z866QgV858wBQJuQQETHIHSpqQAkb9z4rw3atElJ6FJSSN+/n5xbt0jf+1uRYxNXrCiyD0Dh5MTOBd3QKCIoGbGJszFnuRx/mcSc/FpjFrKMq07HZ4nJ1M0xLHe9cu8BGiBFqeC8tTUNs/Oj8RXat2j64XKzgE2v1/Ptt98yfvx4cnNzje2PDqzinCqGzLbvcD/OkATnQ4v8ZB3D+73NqK3OLEqZSOeMTDy0OtJlBw7ryzFBM4SwaiUAWD6yE5vO12LB7qmMsTDs5/rDLYh6eQFbUlISa9eu5eOPPy70Z1GpUiVCQ0MZNWoUqampzJ07919Rd0ySJDp27GgsRt2wYUP0ej1ffPEF27dv52ren3W/F9zHKgiCIAiC8Hd4ruWRkiSVAXYVeNJ2C2guy/JjSZJ8gFBZlitKkrQ87/WGp8cVN381axv51zJliuz3+Wo2jyd+VmjfLV9Y3ElJrEte4eAomRF7dBwKVHDbV8JGLXPLT2JRYiIp1RYTeXcM37k4FzoXwCh1L4YODSHn1i3udekKgHWVKqj8/fH6bCJKV1fW7fyOah4V8Vi/EyQJn5kzUDo4EHZpE7PPTidcpS9y/g7hZYgqdQsbnUT9bA3fuRmWQ755Vc9HO83Pc+zYEZfgftjWrg25mfyxYyzpD3+ndp0POBG+hywLG7q/8zMUeLIQO/srkn74AcnKioCTJ1Da2xd5P/9rtElJxM6aTdquXc8cqypThtyn0sqbUSr57B24W6LoZaRNs7JZHBtv3De2zfU9rHJT6JCxxWzsAo/pOAd2ok/9UsaATZZldu3aRWJiIu++W3idNSOFklM3orBQWXH5fgKP9i/gtlyS76dPIDlTjfabKvhISQCsrLQCl4BGdKvla5ZQ5NdzUXy56SxZWLH+/QY0Ku/Oxo0bzZKAdOnShSpVqtC+fXvq1av3j9+r9iLCwsIYNGgQW7Zswd/fH8si9nnev3+fmjVr0rp165daGgli+YpQPPH5EIojPh9CccTn4/VQ3PLIPxu0pciy7FygYU+oKgAAIABJREFUP1mWZRdJknYBX8myfCKv/RAwQZZlsw1rkiQNBYYCVLWyrvNrmTLcruCK16NknDKeb5/doZoSq9sq0FhItNQ0oWu5Xsy7Pp7IwtYfAjZ6PdnF1HUC6J+cxQ8rVEyaFIKPqyvHr/+IWh9PdZ8eJMZf4LH6Otu8kimRq2dMyTnYWBuCrpjYo6xM30jsU09Wqmbr8E+1J+ZuGVzs3HkrqAcRj5PQ6XWUcHPk8JX/cMjH8JdwC61MvXCZDjGVKVGyCtnNm6HUpkH6TRwVVlyN/Iav3FzN7vndlHTqO/dH610fVPYgSbjMnYvqzl1Shg9HHVjzuX6e/1TKmBiU8fFIajXOK1cVOka2sCBl5Ac4bPwFnbMzqcOHcUJzntSMGNrF+OJ86z7a3CyWe58j0ksi3gk66OoT66jjQlbRK3h/fviYKrka1nmMwzozGr1vA7x8y5Kclk789UNUyjxHU6VhiW643pd7zb5DpTQEULIss2fPHubOnVvo3EFBQbz99tvcuHGDWbPyywL4+fkZ638l5+iJuHmNnds2U7p0adwrVqdb9hb+kAPwbtgfDzsLTp8+zdWrV/npp5+oVKkSo0aNMtYN0+l0aDQaFixYwL59+0yu36pVKyZNmoQAGRkZWFlZFRnYvcg89v+iL0mEV0t8PoTiiM+HUBzx+Xg9tGjR4m8L2nYDs58K2sbLslx4tow8dWvXlsPCwkChYPGGENixmdYXi76vXfUkwioouFFKonl2WeYP3YKFwrDS89ajq8za9xnv1B9Om6odeefnHlxS3zabY037NaSlJ/HRybF0K9UZvwwHvk36CYCxSckk3KvMh3N20GZ9HVKVhe9JctXp+LHOt/x2bDLfOqab9fupYe9Q8z13BeVodOw/F8blG1fYqFwEgFKWmZVegUq1WvDBzSU8tHz+JJ/lc2VW9vgNzXdrSP7pJxzatsVv0cLnPv+fRJZlooePIOPoUZN2ydoa32/monR1RbJUYelbAn1GBqqSJY1j9kXuY9zRcQD42fvxdbOv6b+3P1q9ttBr2ev1tMjMxkmvw1Gvp0NGFm46HZflGqhafka9poa9T6GhoTRt2pQbN27g6uqKt7c3V6JT+P3yDdrXr0ZpD0O2x4ULFxISEkJaWprJdby9vTl79iwlC9zr7du3qVhgnyLAyJEjuXnzJj4+Pqxbt87YPu/bJZRs2Imafk44K3LYvXu32dM7GxsbVqxYQXBwMAMGDDAGgE+EhIQUmwJf+PPEN6FCccTnQyiO+HwIxRGfj9fDX/Gk7ZUujyyYPTI5LYE2m1ugznsg5psg0+2Unu0NFMQ5g1YJurwnGU0d3+DroEXYWhadLl+j1zB27yhCE04a22pK5Vg3YJvJuIepUbTf9pbxuG1GJu7pvqz3SXnmz+dpTWOaEi/fpH21AbwXNPC5zxuyoBmnXZKeOU7xyAp9CXWR/QOVgQzKbGLIqAnYNWqI+8iR2NSqRfK6ddg1aoRV+fLPfV9/J1mWST9wAIW1NYkrVpIVFmbSb+HlRZmfN2Dp41PkHAfvH+Tj0ML3bAHMi41nrFd+8fSSGg17ovM/ovekUtwu0xfXim9Sr0FTZFkmKyuL1NRUGjRoQFRUFGDIRBgeHm7MUijLMgcPHiQ+Pt4kcUdAQACzZs2iSpUqxidgBWk0GlQqw56zwMBALj6VfOZpUVFRDB48+JnZHKdNm0ZISIjx+NixYzRp0qTYc4SXI/6nKhRHfD6E4ojPh1Ac8fl4PbxU9sgi7AAGAl/l/Xt7gfYPJUn6GUMCktRnBWxPc3F0J2zQZW7G3OTao8tMvTqDxZ3zn3TV1JZlQe/VpKnT8Hd+di0mS4Ul33ZchlqnZv2N9ehkHV3LdzUb5+tUkkZ2DTmV+TsA++3twN48YCuZ6kodhypsU5ww6/OJt6Ws/g2+m7DoRd6y0Rfv7GDoptY8til8eSeAQi+xve8mDoUewtnBmaXnvyW2YqLJmB90F+nhk5+AJPPU72Se+h2X/v1J/vFHFHZ2VDxffIkFfWYmiatW52WgbIcsy2jj4klavYrUHTuxrV+fEl/PQaFSFTvP89JnZXG//wByrl0rtN+hbVvsGjXCpc/bhfZvv7Odvff2opAUXInLT83/VkYmewqUUfg0MZk2Wdkm576bmsaNXsepUKkaSqWCskBZICIigvnz5/Pjjz/yxx9/mF0zIiKCzZs3s2fPHsaPH8/OnTuZMGGCsb9t27bs2LEDKyurYt+7paUlW7duRa1WF5kgJDg4mJ9+MjwJLviU7omQkBDef/992rVrx40bN4xtT1y6dIkaNWoUex+CIAiCIAjCP9PzpPzfADQH3CVJiga+xBCs/SJJ0mDgAdArb/geDOn+72BI+f+MrAtFXpPKPpWp7FOZezG3WJvwK0E2zZnecz5KSYkkSbjbuL/QnFZKK96tVvzt9HHrS+sabZj2+zSTdq80ZzKkTI4O+x0rS8NfwN22TmFV2mbjGIccC/aPO/NC9/S0Mu5O/NDvIP/f3p3HR1Wfexz/PNkTskFIIBEMgqBsoiCItSLu3qqIuNZe3LiuxauovbZ1w1LrrqiAiq3VohWpiFosKhUUN0QpoCKuIPuqgOyB5Ll/nJOQSJggWWaSfN+vF6/MnHPm5Bl4mDnP+W2/mziMmdvLjT9akYM1X0fR4lSu6DyINm3aMOjCYM24/Px8zrnnl2T3z6xwrgmfPE7FycxhbdhNrmTTJnasXYsXFbH+hRdoOvD8svXhNk3/gPjsLNaNG1e24PRSdrXhtddYVlJMq4erv0bdpunTWXRh5f82LW+9hexzzsEqGYu4YP0CXpn/CrNWzWLGihkV9qWXlPDvRUtJcWd9XFzZ9PxdN7Xiu6tfZfKItvRvlc/gtevJjTuOW4YOY8mSJaSmpjJ16tTdxlpQUMCgQYMYPnw4GzZs4OyzzwbgySefrHDcsccey8SJE/d4jFT//sGNhNTUVE477bRd9t911120bNmS++67b5d9ffr0KevuOH36dJ5//nkGDdq5puDAgQNVsImIiIjUY/Vice0Vm1aQl5ZHnEWeRKS6Spuez/37fzF3+xIA/mffc7n66Bt3ObbESzj3ryfzlS2lV/KxDDvlJvIyc2oslu+3rOXmN0fSp/XhnNPl2IjHLlmyhJOuOg7rv7NAOPK7rVw1es8aUpv87HD2feIJtsydy7dnnPmT4kzIzaXVyBGkVlIUbFuwgDWPPIJv305iQQE/TJpE7uCrSG7XlsWXXU7xunUkFRZStHBh2WsSW7UiLi3o7lpw152kdOxY6e/9dv23nPriqbuN67mlyyk+/gUSsvIpHteHP+ZlcOXa9bzrV/P7391Ix44d+OzsFfwz8UTOveNfbN60abfn6tKlCyNHjmT69Ol06tSJU045hXHjxnHOObu2+l155ZUMGDCAY445ploLm7s7P/zwA4MGDaJjx44MGzaMzZs3k5eXx6Yw1gkTJtCjR49KW95mzZpF9+7d6devHw899BCFhYV7HYvsOXVfkUiUHxKJ8kMiUX40DtUe01bbqira6krpf4gZy2fwyOyRpMWncN8xD5KSkFLp8e6O47VeTO6J1WtWc+SQvqSk7qD4iCTiSpyxdxVX65yJhftiiYkUff0NAHFNmpBzyf+Q2Lo1y667vsKxBXfdSVbYQuTurL7/gSrXR/uxvN9cT065FqLKuDv3vnE3f1v69C77RqxYxYSMdFLcOb/XE9w98knWrVvHlZdeSN6Hd/Jm3BEMuaXiou2FhYUsLFc0ltevXz9uv/12OnToUDbmrPyH5tq1a+nfvz/Tpk0re01JSUm1irWqTJgwgaFDh9K7d29GjhxJQsLe9nCW2qAvVYlE+SGRKD8kEuVH41AbY9oatF75veiV36vK48wMo/Yu0H+K3Oa5fD5mLhPenMAtC2+hpNy6Xfs8+CBLr74agLTDe7P5/elVni/vtzfQ9JxziEtNxYuLwR0rVyCkH3EEiy+7nC1z5gCw7IbfknXaaWz76ivmn9pvj2JOKiwkqU0bUg/uRrMLLyRuD9YI++OEqxm3YWf3xf4bNvJiRjqpJSVk/Hw0l3/3DTtI4Pe3D+ell4KhlqU/Ydfuq6UF2+TJk+nWrRs5OTmUlJTsUTHUtGlT3nrrLYqKipgzZw5du3at1YIN4PTTT+f000+v1d8hIiIiIrEl+k1EUqOO63lc2eNnfpVDu39PJuO4nd0r84cOhd0sYQAQl57OgZ9+Qk65Isri4ysUbADx2dkUjn2Wpuf9smzb6pEjdynY2vzjH3T8fB5txj9P4Zi/kbR/O5qedx4HzJlNu9depfVjj9L8iisoTkpg6EvXM+6DMWzZuhkv2XWh8a3bipi55k0gWG5hNIdy88Dp3P7D/ly7qhWT3/6cj7e24qYnppQr1Co66aSTWL9+fYVtt912G8cddxy5ubnExcX95NarpKQkevbsSUpK5S2yIiIiIiLVoZa2BiajSQYFq5qwLG8TJRnrSWrVCoD2773L9sWLSSosZP8pU9j8wXRW3n0PzQYOpGjBAlI6Hkh8sxwyTjh+lwJtd8yMlrfcQlxmJt89+hhrHh5RYX/7998joWlTAFI7dwag3cSJACxct4Tlq9bQu/XBAAyfeBPj173G+HWvMezzuzm5aB/uvORVindsx92JT0hk3MNHsaB5CXEOTx7/IiMfHM3/jjiFxYsXs3LlSqDiFPjPPPMMubm5nHDCCUEMqak899xzZGZmsmzZMgoKChg1ahSXX3753v1li4iIiIjUARVtDdAJnQfw5OoxbE7azPKFX5Jf2IGEZs1IaNYMgMQWeWT160fmqafWSHe+3MGD+e7Rx8qeW1oahU89VVaw/diYdx/j7q+DAu93ba+lb9cBvLr6FUjcGcsrSUvpfW8Bd+Q0pdX2HdyQfBrPZ62lxBJpvzyXtvseUOm5S40fP54BAwYAsH79ev785z8zYMAAMjODWTbz8/OJhfGcIiIiIiJVUffIBujsnwddFt9PTWHGlOd2e1xNjb+yhARyy60v1ubZZ0nt2mW3xz/32aiyx3fMv58TX/o5q8KC7byiPLKKg66RN+fmsDkuji+Tk3hw64ssSEoktdiYcGPlU/LPmDGD6667jttuu62sYAPIzMzk2muvpU2bNtV5myIiIiIiUaGWtgaodWZr8rcksDx1B7krRvDlv/ehw3EXA7Dsi49ovu+BJKWmV3meou07mPOf9zmkx89ISNj9ODiA5pddSrMLzoe4uEoX3HZ3Nm3ewIixl7EwadfxaqUmjdlOm0M6M6f7vArbP04J1sfb9tkWKIG0tDSmTZtG9+7dGTlyJO5Oz5496dmzZ5XvS0RERESkPlHR1kBlF6WxPPUHbmyew9+m/4aPP3mZxF4X03HyQD5OPIiDbnw74uvXr1vL0uF96RC3kKlzr+b4i2+r8nfG7WYijvWbivj7mEH8Lfk/bEwIGnc7b0xkbvr2CsdtuHMpn37+KbwN+cc0J+f8lqSuj2NL1s4i76sJS+nduzfTpk0rW7h68ODBVcYmIiIiIlJfqWhroNKS8oAfWJMQzy9aF/Ds0ndpP3kqf8nK4MjN86p8/bwpzzAr+3tGNW3NyBWPArfh7nyzehMFWcksWbSAtvu1q7IF7tOPZzL7tbMYldeE8r1xF721nh1sJuHk5jQfn8Gb/3y/wuuWT1nD2q82UrSqCOKg7YVtWfnBSrbM38LUuVPLCjYRERERkYZORVsDtV96G2YWf132/Jf7tOTGNd8zvFlThjeD2cXFxJeb+t/d+XjeFyx6+XZSeg5k2TevMCovG4Bft8xj2NMPk9W8gD7vD6KIRDpYEe9mncoRQ3Zd5Bpg4aKFzJvyNKu+f4R78ipOSBI/azPvj58PJcA/VlTYt2PHDubOnUu3bt3Yunhr2favHwney5AhQzS1voiIiIg0KiraGqjWTfeHNf+usO325s3KHscPa8ai82cw+19/IWXt5/x8x3S62TbaxBkZb7/M49mZQHbZ8f/1zU2UfGM8nZVOi+Ji3krLoP+G11m1chl5LQoq/J5t27dTMqY7H2ZmMC6nYsGWsMmZ/eB8OnXqxGeffVZh36RJk4iPj+eggw5izZo1XHLJJfTq1Yu77rqLdevWccABBzB06NCa+QsSEREREaknVLQ1UF0794a3Ht3t/m0GPzxxBv0SFgPwRXIiV7YoYFW4RlufzVsqHN+zsDWnbtzEyxk7JzD5ICWZoU9fR951z1Y49u1x9zGkVcVCDuDb+75l65Kg9ez5559n48aN3HHHHdx444107dqVpHITmOTk5PDCCy8AcM011zBnzhy6du1KWlraT/lrEBERERGp91S0NVCd9+kYcf+hbfblnYWLubFZM75KSmJecsUZH6elpQJwVNpRvLX5LdysQsEGsDohge3b32LpwvnsU9gWgEULvmTZyhHwoxa2BXcuYNPnm7jssst49NGdxWRpYRZJSkoKhx12WJXHiYiIiIg0RFqnrYFKS0zjoaMfinjMxS1b8HJG+i4FW6mWyS35U78/RTzHY80yKPjrIcyY9i8APph0P/f8qGCbP2w+s16ehbtXKNhERERERKRqKtoasKP3PZpPLviE67tfX7btjCYnlz3+spJibf/s/bmo80XcevitjB8wnszkTB4/4fGy/dnJwTi3i7tcTEJJPPOSk5jUJI2eU37JZx9/yJatE8uOLd5STPrSdNZ9vo727dvXxlsUEREREWnw1D2yETg0/9Cyx30PO4nxU16p9Libe9/M2Qecvcv23vm9mTRgEkXFReyXtR8btm8gMymTBBIY/elobshrzrdr19Py5Zu4p/XOVraZF80kNTm15t+QiIiIiEgjopa2RqBTTidSE4LiqV1WOy7ucnGlx53Z4czdnqNVRivaZrfFzMhMygTgvE7nle1/pGkW65rMLXv+RJ8nVLCJiIiIiNQAFW2NgJnx+hmvM/aUsbTObM2QHkPos3Fni9iIY0bwzrnvEGc/LR1yUnPo165f2fMHmgXn7Fvci5779ayZ4EVEREREGrlqFW1m9q2ZfWJms83so3BbMzObbGZfhT+bVnUeqX3ZKdl0zulc9rxJhx5lj49qfRRZyVl7dd4//OwP9MytWKCd1+eivQtSRERERER2URMtbUe7+8HuXjpw6rfAG+7eHngjfC4xJim1SY2cJz4unl/3+HWFbZ1ad62Rc4uIiIiISO10jzwNeCp8/BTQvxZ+h1TT8YXHA9Att1u1z9WjRQ9ObHMiAPtl7Vc25k1ERERERKqvurNHOvC6mTnwmLuPBlq4+3IAd19uZnnVDVJqXp9WfXjmF8/QLrtdjZzv3qPuZejhQ4mPi8fMauScIiIiIiIC5u57/2KzAndfFhZmk4GrgJfdPbvcMWvdfZdxbWZ2KXApQIsWLXqMHTt2r+OoKRs3biQ9PT3aYUgMU45IJMoPiUT5IZEoPyQS5UfjcPTRR88sN+Ssgmq1tLn7svDnKjObAPQCVppZftjKlg+s2s1rRwOjAQ499FDv27dvdUKpEW+++SaxEIfELuWIRKL8kEiUHxKJ8kMiUX7IXo9pM7MmZpZR+hg4AfgUeBm4IDzsAuCl6gYpIiIiIiLSWFWnpa0FMCEcv5QA/N3dXzWzD4FxZjYIWAScVf0wRUREREREGqe9LtrcfT6wy9SD7v4dcGx1ghIREREREZFAbUz5LyIiIiIiIjVERZuIiIiIiEgMU9EmIiIiIiISw1S0iYiIiIiIxLBqLa5dY0GYrQYWRjsOoDmwJtpBSExTjkgkyg+JRPkhkSg/JBLlR+NQ6O65le2IiaItVpjZR7tbhVwElCMSmfJDIlF+SCTKD4lE+SHqHikiIiIiIhLDVLSJiIiIiIjEMBVtFY2OdgAS85QjEonyQyJRfkgkyg+JRPnRyGlMm4iIiIiISAxTS5uIiIiIiEgMU9EmIiIiIiISwxpd0WZmidGOQURERESkPDOzaMcgsavRFG1m1tvMxgL3mFmXaMcjscnM4sOf+uCUXSgvpCpmlhX+bDTfr7LnzKyzmaVEOw6JWanRDkBiV6P4UjGzs4BHgIlACnBtuF0XYAKAmR1hZk8BN5lZM9cMPVKOmR1mZo8DN5hZbrTjkdhiZnFmlmlmE4GHANy9JMphSQwxs4PM7B3gj0BOtOOR2BI2LIwHRprZCaU3kEXKaxRFG9Ae+Ke7Pw08AEE3SV2YC4CZtQVGAVOBQmCYmZ0c3agkFphZvJndQTDV8rtAd+BWM2sR3cgkloQF2gYgEdjHzM4BtbZJBTcBz7v76e6+FHTjWAJm1pfgGuQF4Avgv4Gm0YxJYlOD/EIxs7PN7FozOzzc9AUwwMz+D3gfKCC4m9EzakFKLOkBzHP3J4HrgNnAKWbWOqpRSSyIAxYBZ4X5cQ3QG3VhkV0dCKwBhgO/MrMMdy/RhXnjFrbCtgM2uvvwcNvxZpYNqDu+AHQFPnT3Z4AxBDd/NkY3JIlFDapoC++K3wLcEG563Mz6Edy9uBroA5zv7icBq4EzzKxldKKVaAm7IXQot+lDoJWZtXb3tQQtKuuA06MSoETVj/KjBHjW3b80s2R3XwYsAZpHL0KJtvI5Uu6C+2ugCFgQ/rnAzPZVj47Gp3x+hK2wq4AjzexkM3sRuJ6gG+1vwmOUI41IJdcgbwNnhdev/wHygVHh0B6RMg2qaHP3YuAA4Dp3vx+4FRgCdHD3N4CtBK1uAC8BBwGbohGr1D0zyzazV4DJwNlmlh7u2gq8A5wdPv8C+AzI0YDxxqOy/HD3YndfB+Du28wsA9gPWBbNWCU6KsmRJuUuuA8FfnD3ucBcgu+fR8wsUd0kG4fK8gPA3TcAfwWGAU+4+4nAn4HeZtY7agFLndrdNYi7zwZOAtoAV7p7X4KbxyeZWccohSsxqN5/kZjZ+WZ2VNjVAGAl0NTMEtx9PMGX57lhi9o3wJnhcYcQXKxL49EEeA24KnzcJ9y+GpgOdDWzXmHxvxQ4wt2VI43Hj/PjyEqOOQyY6+7LzCzdzNrXZYASdbv7DIGgG22GmT0H/B8wE/jS3bdrUpJGI1J+TCS4KC8dq/QRwfXKtjqMT6Jrt98x7j4DyAW+DTdNATJQw4KUUy+LNgvkm9lU4ALgVwRj1NIJxhR0BUpbUUYQdHMrBl4HeprZdOAs4PfhHTBpoMoV9Znh4O/RwDiCgr2Xme0TFmnTgVnAA2EedQYWmVla1IKXWldFfhxmZgXhcQnhS7KBxWZ2EUG32oOjEbfUnT3NEYKL8VxgBcFNwSuAA3SnvGHbg/zYB8DdPyboDjnYzJoTTDbRBfguSqFLHfgJ3zHJwHvAr8OXHkswy6huHEsZq29dqc0s3t2Lw/7At7j7f4cXVA8ByQQTSTwH3A585O6bzewfwFR3HxVekO/n7p9E7U1IrQrHmLQE/k4wJukbgrtaV7v7mvCYIwi6Q37k7mPKvfZ+oBXBLJLnu/sXSIPyE/Pjw3DW2dLXjiG4SfQU8EB4ISYNzN5+hphZ83L704Ekd/8+Cm9BalE1v2OuBdoSzGo9xN0/q+PwpZZV4/OjM0G36pbAdmCwu8+r+3cgsSqh6kNiQ1iY/QGIN7N/AZkErWe4+w4zG0xwh/N+gv8o5xIM5nyOYHD4zPDYjYAKtgaqXFGfASwtV9Q/QHCHawCAu79rZr0I7oRnASVhq+tvgDS1wDZMe5EfB5pZJkF+bAReAV5y9+ej9R6kdlXjM2SHu6+xYH0lD/NFGpjqfse4+/0WLDm0PXrvQmrLXuZHNrDN3eea2QVAvrvPj9qbkJhVL7pHmtlRBEVXU4IZuoYR3IU4Okz60hmabgPucfenCLpCnm9mswiKUxVqDZiZJZjZn4A/hflyAOWKeuB/gcPDfaUeJ+hGOxn42swKwoknVLA1MNXMjzeAb8ws393HqmBrmGrgM2R+uc8QjWFrYGrqOyY8XgVbA1MD+fFtOFxjiwo22Z16UbQRNC/f6+5XuPvjwKcEM7jdAjwCZYuYjgc2WzB1+4vAIOAMdz/H3TdHKXapZXtY1DtBS+3Qci89GbgSmAN0DadzlwamBvJjNkF+LK/DsKUO6TNEIlF+SCQ1+B2ztA7DlnqovnSPnAnMKG12JpgKtYu7/87MrjGzq9z9YTNrBWx398UA7r4imkFLnSkt6kv7hR9CxaK+R1jUTyD4EG3j7t8SDPA9zt2nRSdsqSPKD6mKckQiUX5IJMoPqRP1oqXN3Te7+7awYAM4nmCadoCLgI5mNhF4lmBhQmlcZgLjwrEkEBT1+7r7kwRjIK8Kuyu1AorDD0vc/SV9WDYKyg+pinJEIlF+SCTKD6kT9aJoK2Vm8eHdihbAy+HmDcDvgTuBvu5+T7Tik+jYm6I+nN1JGgHlh1RFOSKRKD8kEuWH1JX60j2yVAmQRLAW20FmNpxgjZOr3P2dqEYmUVc6axuVF/VdgAWlfcbD/uXSiCg/pCrKEYlE+SGRKD+kttWros3dPewr/CuC/sJ/dfe/RDksiR0q6iUS5YdURTkikSg/JBLlh9Sq+ri4ditgIHC/u2+LdjwSW8ysN/Be+EdFvVSg/JCqKEckEuWHRKL8kNpU74o2kUhU1Eskyg+pinJEIlF+SCTKD6lNKtpERERERERiWL2aPVJERERERKSxUdEmIiIiIiISw1S0iYiIiIiIxDAVbSIiIiIiIjFMRZuIiDRYZlZsZrPNbK6ZzTGza80s4nefmbUxs/PqKkYREZGqqGgTEZGGbIu7H+zunYHjgV8At1bxmjaAijYREYkZmvJfREQaLDPb6O7p5Z63BT4EmgOFwBigSbh7sLu/Z2bTgY7AAuAp4CHgTqAvkAyMdPfH6uxNiIhIo6eiTUREGqwfF23htrXAgcAGoMTdt5pZe+BZdz/UzPoC17v7KeHxlwJ57v5HM0sG3gXOcvcFdfpmRESk0UqIdgAiIiJ1zMKficAIMzsYKAY67OYPWTgSAAABA0lEQVT4E4CDzOzM8HkW0J6gJU5ERKTWqWgTEZFGI+weWQysIhjbthLoRjDGe+vuXgZc5e6v1UmQIiIiP6KJSEREpFEws1zgUWCEB2MDsoDl7l4CDATiw0M3ABnlXvoacIWZJYbn6WBmTRAREakjamkTEZGGLNXMZhN0hdxBMPHI/eG+UcB4MzsLmApsCrd/DOwwsznAk8CDBDNK/sfMDFgN9K+rNyAiIqKJSERERERERGKYukeKiIiIiIjEMBVtIiIiIiIiMUxFm4iIiIiISAxT0SYiIiIiIhLDVLSJiIiIiIjEMBVtIiIiIiIiMUxFm4iIiIiISAxT0SYiIiIiIhLD/h/NqMmACNPa0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "benchmark = 100*rs.cumsum().apply(np.exp).mean(1)\n",
    "\n",
    "benchmark.plot(linewidth=2, color='black')\n",
    "xgb_rs.plot(linewidth=2)\n",
    "graph_filtered_EW.plot(linewidth=2)\n",
    "graph_filtered_EV.plot(linewidth=2)\n",
    "graph_filtered_MV.plot(linewidth=2)\n",
    "\n",
    "plt.title('Performance')\n",
    "plt.legend(['benchmark', 'XGB-EW', 'XGB-G-EW', 'XGB-G-EV', 'XGB-G-MV'], fontsize=15)\n",
    "plt.grid()\n",
    "\n",
    "plt.savefig('fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}