AlphaWave Data Machine Learning for Stock Trading PCA and Clustering example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning for ESG Stock Trading:  PCA and Clustering\n",
    "**Hugh Donnelly, CFA**<br>\n",
    "*AlphaWave Data*\n",
    "\n",
    "**April 2021**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In developing a Pairs Trading strategy, finding valid, eligible pairs that exhibit unconditional mean-reverting behavior is of critical importance. We walk through an example implementation of finding eligible pairs and then perform a backtest on a selected pair. We show how popular algorithms from Machine Learning can help us navigate a very high-dimensional search space to find tradable pairs.\n",
    "\n",
    "Jupyter Notebooks are available on [Google Colab](https://colab.research.google.com/drive/1r2ZyFqDn8X9w2qOereU5T7xwr94acesA?usp=sharing) and [Github](https://github.com/AlphaWaveData/Jupyter-Notebooks/blob/master/AlphaWave%20Data%20Machine%20Learning%20for%20Stock%20Trading%20PCA%20and%20Clustering%20example.ipynb).\n",
    "\n",
    "For this project, we use several Python-based scientific computing technologies listed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.cluster import KMeans, DBSCAN\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from statsmodels.tsa.stattools import coint\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "\n",
    "import pymc3 as pm\n",
    "\n",
    "import theano as th\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define the Stock Universe\n",
    "\n",
    "We start by specifying that we will constrain our search for pairs to a large and liquid single stock universe. To achieve this, we create a function that scrapes the tickers of the S&P 500 and then cleans the tickers by replacing those containing a `.` with a `-` so we can easily use them in [AlphaWave Data's APIs](https://www.alphawavedata.com/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scrape the S&P 500 tickers from Wikipedia\n",
    "\n",
    "def get_tickers():\n",
    "    wiki_page = requests.get('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies').text\n",
    "    sp_data = pd.read_html(wiki_page)\n",
    "    ticker_df = sp_data[0]\n",
    "    ticker_options = ticker_df['Symbol']\n",
    "    return ticker_options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "505\n"
     ]
    }
   ],
   "source": [
    "# Run the ticker scrape function\n",
    "# Let's convert the get_tickers() output to a list and \n",
    "# replace tickers that have '.' with '-' so we can use AlphaWave Data APIs\n",
    "\n",
    "stock_tickers = get_tickers()\n",
    "stock_tickers = stock_tickers.to_list()\n",
    "for ticker in range(len(stock_tickers)):\n",
    "    stock_tickers[ticker] = stock_tickers[ticker].upper().replace(\".\", \"-\")\n",
    "\n",
    "print (len(stock_tickers))\n",
    "# stock_tickers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Retrieve ESG and Market Cap Data\n",
    "Let's use some fundamental and environmental, social and governance (ESG) classification data to search for pairs. When we look for pairs (or model anything in quantitative finance), it is generally good to have an \"economic prior\", as this helps mitigate overfitting. We will start with the following priors:\n",
    "\n",
    "- Stocks that share loadings to common factors (defined below) in the past should be related in the future.\n",
    "- Stocks of similar market caps should be related in the future.\n",
    "- ESG Risk Ratings assess the degree to which a company's enterprise business value is at risk driven by environmental, social and governance issues. Within the [Alphawave Data ESG Risk Ratings for Stocks API](https://rapidapi.com/alphawave/api/esg-risk-ratings-for-stocks), the `controversy score` identifies companies involved in incidents and events that may negatively impact stakeholders, the environment or the company's operations. Controversies are graded on a scale from A to F with F denoting the most serious controversies with the largest potential impact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the [AlphaWave Data Stock Analysis API](https://rapidapi.com/alphawave/api/stock-analysis/endpoints) to pull a company's Market Cap from the [Key Statistics](https://rapidapi.com/alphawave/api/stock-analysis?endpoint=apiendpoint_dff4b882-4be4-4169-a700-04275c92bdce) endpoint and the [AlphaWave Data ESG Risk Ratings for Stocks API](https://rapidapi.com/alphawave/api/esg-risk-ratings-for-stocks) to retrieve the `controversy score`.\n",
    "\n",
    "To call these APIs with Python, you can choose one of the supported Python code snippets provided in the API console. The following is an example of how to invoke the APIs with Python Requests. You will need to insert your own <b>x-rapidapi-host</b> and <b>x-rapidapi-key</b> information in the code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Controversy Score</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MMM</td>\n",
       "      <td>C</td>\n",
       "      <td>117560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABT</td>\n",
       "      <td>C</td>\n",
       "      <td>195290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABBV</td>\n",
       "      <td>C</td>\n",
       "      <td>203860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ACN</td>\n",
       "      <td>B</td>\n",
       "      <td>181340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ATVI</td>\n",
       "      <td>B</td>\n",
       "      <td>76260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XLNX</td>\n",
       "      <td>A</td>\n",
       "      <td>31580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XYL</td>\n",
       "      <td>B</td>\n",
       "      <td>21270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YUM</td>\n",
       "      <td>C</td>\n",
       "      <td>35660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZBH</td>\n",
       "      <td>B</td>\n",
       "      <td>32970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZTS</td>\n",
       "      <td>A</td>\n",
       "      <td>86540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>447 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Controversy Score  Market Cap\n",
       "MMM                  C   117560000\n",
       "ABT                  C   195290000\n",
       "ABBV                 C   203860000\n",
       "ACN                  B   181340000\n",
       "ATVI                 B    76260000\n",
       "...                ...         ...\n",
       "XLNX                 A    31580000\n",
       "XYL                  B    21270000\n",
       "YUM                  C    35660000\n",
       "ZBH                  B    32970000\n",
       "ZTS                  A    86540000\n",
       "\n",
       "[447 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch controversy score and market cap data\n",
    "\n",
    "key_stats_url = \"https://stock-analysis.p.rapidapi.com/api/v1/resources/key-stats\"\n",
    "\n",
    "key_stats_headers = {\n",
    "    'x-rapidapi-key': \"YOUR_X-RAPIDAPI-KEY_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\",\n",
    "    'x-rapidapi-host': \"YOUR_X-RAPIDAPI-HOST_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\"\n",
    "    }\n",
    "\n",
    "esg_url = \"https://esg-risk-ratings-for-stocks.p.rapidapi.com/api/v1/resources/esg\"\n",
    "\n",
    "esg_headers = {\n",
    "    'x-rapidapi-key': \"YOUR_X-RAPIDAPI-KEY_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\",\n",
    "    'x-rapidapi-host': \"YOUR_X-RAPIDAPI-HOST_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\"\n",
    "    }\n",
    "\n",
    "esg_data = []\n",
    "\n",
    "for ticker in stock_tickers:\n",
    "    querystring = {\"ticker\":ticker}\n",
    "    \n",
    "    try:\n",
    "        # Market Cap\n",
    "        key_stats_response = requests.request(\"GET\", key_stats_url, headers=key_stats_headers, params=querystring)\n",
    "        key_stats_df = pd.DataFrame.from_dict(key_stats_response.json())\n",
    "        key_stats_df = key_stats_df.transpose()\n",
    "        \n",
    "        market_cap_str = key_stats_df.loc[r'Market cap (intra-day) '][0]\n",
    "\n",
    "        market_cap_lst = market_cap_str.split('.')\n",
    "        if market_cap_str[len(market_cap_str)-1] == 'T':\n",
    "            market_cap_length = len(market_cap_lst[1])-1\n",
    "            market_cap_lst[1] = market_cap_lst[1].replace('T',(9-market_cap_length)*'0')\n",
    "            market_cap_int = int(''.join(market_cap_lst))\n",
    "        if market_cap_str[len(market_cap_str)-1] == 'B':\n",
    "            market_cap_length = len(market_cap_lst[1])-1\n",
    "            market_cap_lst[1] = market_cap_lst[1].replace('B',(6-market_cap_length)*'0')\n",
    "            market_cap_int = int(''.join(market_cap_lst))\n",
    "        \n",
    "        # Controversy Score\n",
    "        esg_score_response = requests.request(\"GET\", esg_url, headers=esg_headers, params=querystring)\n",
    "        esg_score_df = pd.DataFrame.from_dict(esg_score_response.json())\n",
    "        esg_score_df = esg_score_df.transpose()\n",
    "        controversy_datapoint = esg_score_df['Controversy Score']\n",
    "        \n",
    "        # Create Dataframe\n",
    "        df = pd.DataFrame({'Controversy Score': controversy_datapoint,\n",
    "                           'Market Cap': market_cap_int},\n",
    "                          index=[ticker])\n",
    "\n",
    "        esg_data.append(df)\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "result_esg_df = pd.concat(esg_data, ignore_index=False)\n",
    "result_esg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this analysis, we replace the `controversy score` ratings with decimal scores ranging from zero to one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the categorical data with numerical scores per the docs\n",
    "result_esg_df['Controversy Score'] = result_esg_df['Controversy Score'].astype(str)\n",
    "controversy_dict = {u'A': 0.1,\n",
    "               u'B': 0.3,\n",
    "               u'C': 0.7,\n",
    "               u'D': 0.9,\n",
    "               u'F': 1.0}\n",
    "result_esg_df = result_esg_df.replace({'Controversy Score': controversy_dict})\n",
    "\n",
    "result_esg_df['Controversy Score'] = result_esg_df['Controversy Score'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(447, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Controversy Score</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MMM</td>\n",
       "      <td>0.7</td>\n",
       "      <td>117560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABT</td>\n",
       "      <td>0.7</td>\n",
       "      <td>195290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABBV</td>\n",
       "      <td>0.7</td>\n",
       "      <td>203860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ACN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>181340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ATVI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Controversy Score  Market Cap\n",
       "MMM                 0.7   117560000\n",
       "ABT                 0.7   195290000\n",
       "ABBV                0.7   203860000\n",
       "ACN                 0.3   181340000\n",
       "ATVI                0.3    76260000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the shape and top of the dataframe\n",
    "\n",
    "print (result_esg_df.shape)\n",
    "result_esg_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove zero values to create our desired stock ticker list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Controversy Score</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>MMM</td>\n",
       "      <td>0.7</td>\n",
       "      <td>117560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABT</td>\n",
       "      <td>0.7</td>\n",
       "      <td>195290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABBV</td>\n",
       "      <td>0.7</td>\n",
       "      <td>203860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ACN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>181340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ATVI</td>\n",
       "      <td>0.3</td>\n",
       "      <td>76260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Controversy Score  Market Cap\n",
       "MMM                 0.7   117560000\n",
       "ABT                 0.7   195290000\n",
       "ABBV                0.7   203860000\n",
       "ACN                 0.3   181340000\n",
       "ATVI                0.3    76260000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove Controversy Scores and Market Cap with 0 as value\n",
    "\n",
    "result_esg_df = result_esg_df.loc[~((result_esg_df['Controversy Score'] == 0) | (result_esg_df['Market Cap'] == 0))]\n",
    "\n",
    "# print (result_esg_df.shape)\n",
    "result_esg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Controversy Score</th>\n",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>405.000000</td>\n",
       "      <td>4.050000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.377284</td>\n",
       "      <td>8.491556e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.233097</td>\n",
       "      <td>2.053946e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5.510000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.817000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>3.292000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>7.207000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.240000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Controversy Score    Market Cap\n",
       "count         405.000000  4.050000e+02\n",
       "mean            0.377284  8.491556e+07\n",
       "std             0.233097  2.053946e+08\n",
       "min             0.100000  5.510000e+06\n",
       "25%             0.300000  1.817000e+07\n",
       "50%             0.300000  3.292000e+07\n",
       "75%             0.700000  7.207000e+07\n",
       "max             1.000000  2.240000e+09"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_esg_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "405"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the index of the dataframe that removes 0 values as the clean stock ticker list\n",
    "# for the prices dataframe so that the symbols match between both dataframes\n",
    "\n",
    "clean_stock_tickers = list(result_esg_df.index)\n",
    "len(clean_stock_tickers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Retrieve Stock Price Data\n",
    "\n",
    "We can use the [2 Year Historical Daily Prices](https://rapidapi.com/alphawave/api/stock-prices2?endpoint=apiendpoint_33fa1878-1727-4775-beeb-f6b0da5314fd) endpoint from the [AlphaWave Data Stock Prices API](https://rapidapi.com/alphawave/api/stock-prices2/endpoints) to pull in the two year historical prices.  From this, we are going to calculate the daily returns for each stock selected.\n",
    "\n",
    "To call this API with Python, you can choose one of the supported Python code snippets provided in the API console. The following is an example of how to invoke the API with Python Requests. You will need to insert your own <b>x-rapidapi-host</b> and <b>x-rapidapi-key</b> information in the code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>AFL</th>\n",
       "      <th>A</th>\n",
       "      <th>APD</th>\n",
       "      <th>...</th>\n",
       "      <th>WY</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WLTW</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>-0.003680</td>\n",
       "      <td>0.016481</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>-0.003926</td>\n",
       "      <td>0.010869</td>\n",
       "      <td>-0.002698</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.013274</td>\n",
       "      <td>0.015130</td>\n",
       "      <td>0.010905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012849</td>\n",
       "      <td>0.001877</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.015535</td>\n",
       "      <td>-0.029038</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-0.005011</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.019283</td>\n",
       "      <td>0.005728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>-0.018152</td>\n",
       "      <td>-0.010307</td>\n",
       "      <td>-0.007445</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>-0.020398</td>\n",
       "      <td>-0.014971</td>\n",
       "      <td>-0.012850</td>\n",
       "      <td>-0.003176</td>\n",
       "      <td>-0.013121</td>\n",
       "      <td>-0.017688</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005970</td>\n",
       "      <td>0.006771</td>\n",
       "      <td>-0.012354</td>\n",
       "      <td>-0.048660</td>\n",
       "      <td>0.011700</td>\n",
       "      <td>-0.010619</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>-0.023470</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>-0.000491</td>\n",
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       "    <tr>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>-0.007094</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>-0.010590</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>0.018006</td>\n",
       "      <td>0.008876</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>0.009423</td>\n",
       "      <td>0.027208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006757</td>\n",
       "      <td>0.018245</td>\n",
       "      <td>-0.018585</td>\n",
       "      <td>0.017848</td>\n",
       "      <td>-0.003969</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>-0.045023</td>\n",
       "      <td>-0.001962</td>\n",
       "      <td>0.016416</td>\n",
       "      <td>0.013361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>-0.001142</td>\n",
       "      <td>-0.013434</td>\n",
       "      <td>-0.048436</td>\n",
       "      <td>0.021241</td>\n",
       "      <td>-0.021046</td>\n",
       "      <td>-0.007038</td>\n",
       "      <td>0.004776</td>\n",
       "      <td>0.013939</td>\n",
       "      <td>0.009295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012094</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>0.007115</td>\n",
       "      <td>0.012366</td>\n",
       "      <td>0.011803</td>\n",
       "      <td>0.034574</td>\n",
       "      <td>0.009632</td>\n",
       "      <td>0.008928</td>\n",
       "      <td>0.005817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>-0.011770</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>-0.004068</td>\n",
       "      <td>0.021421</td>\n",
       "      <td>-0.006723</td>\n",
       "      <td>-0.007840</td>\n",
       "      <td>-0.007088</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>-0.017130</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006721</td>\n",
       "      <td>-0.017408</td>\n",
       "      <td>-0.007611</td>\n",
       "      <td>-0.006341</td>\n",
       "      <td>-0.040717</td>\n",
       "      <td>-0.001237</td>\n",
       "      <td>-0.026856</td>\n",
       "      <td>-0.003018</td>\n",
       "      <td>0.009975</td>\n",
       "      <td>-0.004048</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",
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       "    <tr>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.007341</td>\n",
       "      <td>-0.001926</td>\n",
       "      <td>0.005581</td>\n",
       "      <td>-0.009857</td>\n",
       "      <td>0.002151</td>\n",
       "      <td>-0.002466</td>\n",
       "      <td>-0.010467</td>\n",
       "      <td>0.005230</td>\n",
       "      <td>-0.013478</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009686</td>\n",
       "      <td>-0.006434</td>\n",
       "      <td>-0.013582</td>\n",
       "      <td>-0.010987</td>\n",
       "      <td>-0.012779</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>-0.003654</td>\n",
       "      <td>0.003150</td>\n",
       "      <td>0.004133</td>\n",
       "      <td>-0.006347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-23</td>\n",
       "      <td>0.006471</td>\n",
       "      <td>-0.001538</td>\n",
       "      <td>0.005445</td>\n",
       "      <td>-0.007257</td>\n",
       "      <td>0.016854</td>\n",
       "      <td>-0.002746</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.008122</td>\n",
       "      <td>0.015982</td>\n",
       "      <td>0.019527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009781</td>\n",
       "      <td>0.024658</td>\n",
       "      <td>0.015921</td>\n",
       "      <td>-0.001115</td>\n",
       "      <td>-0.001849</td>\n",
       "      <td>-0.000562</td>\n",
       "      <td>0.012835</td>\n",
       "      <td>-0.000849</td>\n",
       "      <td>0.001353</td>\n",
       "      <td>0.008695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>-0.012710</td>\n",
       "      <td>-0.005920</td>\n",
       "      <td>-0.007610</td>\n",
       "      <td>0.013115</td>\n",
       "      <td>-0.000271</td>\n",
       "      <td>-0.013066</td>\n",
       "      <td>-0.003180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>-0.000413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016754</td>\n",
       "      <td>-0.011802</td>\n",
       "      <td>0.015248</td>\n",
       "      <td>-0.006827</td>\n",
       "      <td>0.006847</td>\n",
       "      <td>-0.006753</td>\n",
       "      <td>-0.000362</td>\n",
       "      <td>-0.017329</td>\n",
       "      <td>-0.010697</td>\n",
       "      <td>-0.004339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>-0.025948</td>\n",
       "      <td>-0.007179</td>\n",
       "      <td>0.002521</td>\n",
       "      <td>-0.028014</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>-0.014534</td>\n",
       "      <td>0.002998</td>\n",
       "      <td>-0.006262</td>\n",
       "      <td>-0.001344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013903</td>\n",
       "      <td>0.013722</td>\n",
       "      <td>-0.001252</td>\n",
       "      <td>0.021401</td>\n",
       "      <td>0.022000</td>\n",
       "      <td>-0.008499</td>\n",
       "      <td>0.008330</td>\n",
       "      <td>0.006311</td>\n",
       "      <td>0.003585</td>\n",
       "      <td>-0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>0.008588</td>\n",
       "      <td>-0.005136</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>-0.003384</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>-0.004562</td>\n",
       "      <td>0.005216</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>-0.010734</td>\n",
       "      <td>-0.000656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009269</td>\n",
       "      <td>-0.016210</td>\n",
       "      <td>0.019632</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.019948</td>\n",
       "      <td>-0.004714</td>\n",
       "      <td>0.001616</td>\n",
       "      <td>0.013057</td>\n",
       "      <td>0.010660</td>\n",
       "      <td>0.012430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504 rows × 405 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MMM       ABT       ACN      ATVI      ADBE       AAP  \\\n",
       "2019-04-30 -0.003680  0.016481  0.008057 -0.003926  0.010869 -0.002698   \n",
       "2019-05-01 -0.018152 -0.010307 -0.007445  0.007260 -0.020398 -0.014971   \n",
       "2019-05-02 -0.007094  0.000508 -0.010590  0.020387 -0.013093  0.018006   \n",
       "2019-05-03  0.002544 -0.001142 -0.013434 -0.048436  0.021241 -0.021046   \n",
       "2019-05-06 -0.011770  0.004829 -0.004068  0.021421 -0.006723 -0.007840   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2021-04-22  0.000100  0.007341 -0.001926  0.005581 -0.009857  0.002151   \n",
       "2021-04-23  0.006471 -0.001538  0.005445 -0.007257  0.016854 -0.002746   \n",
       "2021-04-26 -0.012710 -0.005920 -0.007610  0.013115 -0.000271 -0.013066   \n",
       "2021-04-27 -0.025948 -0.007179  0.002521 -0.028014  0.003141  0.000710   \n",
       "2021-04-28  0.008588 -0.005136  0.001929 -0.003384 -0.000976 -0.004562   \n",
       "\n",
       "                 AES       AFL         A       APD  ...        WY       WHR  \\\n",
       "2019-04-30  0.002166  0.013274  0.015130  0.010905  ...  0.012849  0.001877   \n",
       "2019-05-01 -0.012850 -0.003176 -0.013121 -0.017688  ... -0.005970  0.006771   \n",
       "2019-05-02  0.008876  0.000597  0.009423  0.027208  ... -0.006757  0.018245   \n",
       "2019-05-03 -0.007038  0.004776  0.013939  0.009295  ...  0.012094  0.001054   \n",
       "2019-05-06 -0.007088  0.000594  0.000757 -0.017130  ... -0.006721 -0.017408   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2021-04-22 -0.002466 -0.010467  0.005230 -0.013478  ... -0.009686 -0.006434   \n",
       "2021-04-23 -0.000706  0.008122  0.015982  0.019527  ...  0.009781  0.024658   \n",
       "2021-04-26 -0.003180  0.000000  0.004829 -0.000413  ...  0.016754 -0.011802   \n",
       "2021-04-27 -0.014534  0.002998 -0.006262 -0.001344  ...  0.013903  0.013722   \n",
       "2021-04-28  0.005216  0.001308 -0.010734 -0.000656  ...  0.009269 -0.016210   \n",
       "\n",
       "                 WMB      WLTW      WYNN       XEL       XYL       YUM  \\\n",
       "2019-04-30  0.001414  0.015535 -0.029038  0.018936 -0.005011  0.005684   \n",
       "2019-05-01 -0.012354 -0.048660  0.011700 -0.010619 -0.001319 -0.023470   \n",
       "2019-05-02 -0.018585  0.017848 -0.003969  0.000358 -0.045023 -0.001962   \n",
       "2019-05-03  0.004734  0.007115  0.012366  0.011803  0.034574  0.009632   \n",
       "2019-05-06 -0.007611 -0.006341 -0.040717 -0.001237 -0.026856 -0.003018   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2021-04-22 -0.013582 -0.010987 -0.012779 -0.002804 -0.003654  0.003150   \n",
       "2021-04-23  0.015921 -0.001115 -0.001849 -0.000562  0.012835 -0.000849   \n",
       "2021-04-26  0.015248 -0.006827  0.006847 -0.006753 -0.000362 -0.017329   \n",
       "2021-04-27 -0.001252  0.021401  0.022000 -0.008499  0.008330  0.006311   \n",
       "2021-04-28  0.019632  0.040000  0.019948 -0.004714  0.001616  0.013057   \n",
       "\n",
       "                 ZBH       ZTS  \n",
       "2019-04-30  0.019283  0.005728  \n",
       "2019-05-01 -0.015752 -0.000491  \n",
       "2019-05-02  0.016416  0.013361  \n",
       "2019-05-03  0.008928  0.005817  \n",
       "2019-05-06  0.009975 -0.004048  \n",
       "...              ...       ...  \n",
       "2021-04-22  0.004133 -0.006347  \n",
       "2021-04-23  0.001353  0.008695  \n",
       "2021-04-26 -0.010697 -0.004339  \n",
       "2021-04-27  0.003585 -0.000294  \n",
       "2021-04-28  0.010660  0.012430  \n",
       "\n",
       "[504 rows x 405 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch 2 year daily return data\n",
    "\n",
    "url = \"https://stock-prices2.p.rapidapi.com/api/v1/resources/stock-prices/2y\"\n",
    "\n",
    "headers = {\n",
    "    'x-rapidapi-host': \"YOUR_X-RAPIDAPI-HOST_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\",\n",
    "    'x-rapidapi-key': \"YOUR_X-RAPIDAPI-KEY_WILL_COPY_DIRECTLY_FROM_RAPIDAPI_PYTHON_CODE_SNIPPETS\"\n",
    "    }\n",
    "\n",
    "stock_frames = []\n",
    "\n",
    "for ticker in clean_stock_tickers:\n",
    "    querystring = {\"ticker\":ticker}\n",
    "    stock_daily_price_response = requests.request(\"GET\", url, headers=headers, params=querystring)\n",
    "\n",
    "    # Create Stock Prices DataFrame\n",
    "    stock_daily_price_df = pd.DataFrame.from_dict(stock_daily_price_response.json())\n",
    "    stock_daily_price_df = stock_daily_price_df.transpose()\n",
    "    stock_daily_price_df = stock_daily_price_df.rename(columns={'Close':ticker})\n",
    "    stock_daily_price_df = stock_daily_price_df[{ticker}]\n",
    "    stock_frames.append(stock_daily_price_df)\n",
    "\n",
    "combined_stock_price_df = pd.concat(stock_frames, axis=1, sort=True)\n",
    "\n",
    "pct_change_combined_stock_df = combined_stock_price_df.pct_change()\n",
    "\n",
    "returns = pct_change_combined_stock_df.dropna()\n",
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(504, 405)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>AFL</th>\n",
       "      <th>A</th>\n",
       "      <th>APD</th>\n",
       "      <th>...</th>\n",
       "      <th>WY</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WLTW</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>-0.003680</td>\n",
       "      <td>0.016481</td>\n",
       "      <td>0.008057</td>\n",
       "      <td>-0.003926</td>\n",
       "      <td>0.010869</td>\n",
       "      <td>-0.002698</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.013274</td>\n",
       "      <td>0.015130</td>\n",
       "      <td>0.010905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012849</td>\n",
       "      <td>0.001877</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.015535</td>\n",
       "      <td>-0.029038</td>\n",
       "      <td>0.018936</td>\n",
       "      <td>-0.005011</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.019283</td>\n",
       "      <td>0.005728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>-0.018152</td>\n",
       "      <td>-0.010307</td>\n",
       "      <td>-0.007445</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>-0.020398</td>\n",
       "      <td>-0.014971</td>\n",
       "      <td>-0.012850</td>\n",
       "      <td>-0.003176</td>\n",
       "      <td>-0.013121</td>\n",
       "      <td>-0.017688</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005970</td>\n",
       "      <td>0.006771</td>\n",
       "      <td>-0.012354</td>\n",
       "      <td>-0.048660</td>\n",
       "      <td>0.011700</td>\n",
       "      <td>-0.010619</td>\n",
       "      <td>-0.001319</td>\n",
       "      <td>-0.023470</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>-0.000491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>-0.007094</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>-0.010590</td>\n",
       "      <td>0.020387</td>\n",
       "      <td>-0.013093</td>\n",
       "      <td>0.018006</td>\n",
       "      <td>0.008876</td>\n",
       "      <td>0.000597</td>\n",
       "      <td>0.009423</td>\n",
       "      <td>0.027208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006757</td>\n",
       "      <td>0.018245</td>\n",
       "      <td>-0.018585</td>\n",
       "      <td>0.017848</td>\n",
       "      <td>-0.003969</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>-0.045023</td>\n",
       "      <td>-0.001962</td>\n",
       "      <td>0.016416</td>\n",
       "      <td>0.013361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>-0.001142</td>\n",
       "      <td>-0.013434</td>\n",
       "      <td>-0.048436</td>\n",
       "      <td>0.021241</td>\n",
       "      <td>-0.021046</td>\n",
       "      <td>-0.007038</td>\n",
       "      <td>0.004776</td>\n",
       "      <td>0.013939</td>\n",
       "      <td>0.009295</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012094</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>0.004734</td>\n",
       "      <td>0.007115</td>\n",
       "      <td>0.012366</td>\n",
       "      <td>0.011803</td>\n",
       "      <td>0.034574</td>\n",
       "      <td>0.009632</td>\n",
       "      <td>0.008928</td>\n",
       "      <td>0.005817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>-0.011770</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>-0.004068</td>\n",
       "      <td>0.021421</td>\n",
       "      <td>-0.006723</td>\n",
       "      <td>-0.007840</td>\n",
       "      <td>-0.007088</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>-0.017130</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006721</td>\n",
       "      <td>-0.017408</td>\n",
       "      <td>-0.007611</td>\n",
       "      <td>-0.006341</td>\n",
       "      <td>-0.040717</td>\n",
       "      <td>-0.001237</td>\n",
       "      <td>-0.026856</td>\n",
       "      <td>-0.003018</td>\n",
       "      <td>0.009975</td>\n",
       "      <td>-0.004048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 405 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MMM       ABT       ACN      ATVI      ADBE       AAP  \\\n",
       "2019-04-30 -0.003680  0.016481  0.008057 -0.003926  0.010869 -0.002698   \n",
       "2019-05-01 -0.018152 -0.010307 -0.007445  0.007260 -0.020398 -0.014971   \n",
       "2019-05-02 -0.007094  0.000508 -0.010590  0.020387 -0.013093  0.018006   \n",
       "2019-05-03  0.002544 -0.001142 -0.013434 -0.048436  0.021241 -0.021046   \n",
       "2019-05-06 -0.011770  0.004829 -0.004068  0.021421 -0.006723 -0.007840   \n",
       "\n",
       "                 AES       AFL         A       APD  ...        WY       WHR  \\\n",
       "2019-04-30  0.002166  0.013274  0.015130  0.010905  ...  0.012849  0.001877   \n",
       "2019-05-01 -0.012850 -0.003176 -0.013121 -0.017688  ... -0.005970  0.006771   \n",
       "2019-05-02  0.008876  0.000597  0.009423  0.027208  ... -0.006757  0.018245   \n",
       "2019-05-03 -0.007038  0.004776  0.013939  0.009295  ...  0.012094  0.001054   \n",
       "2019-05-06 -0.007088  0.000594  0.000757 -0.017130  ... -0.006721 -0.017408   \n",
       "\n",
       "                 WMB      WLTW      WYNN       XEL       XYL       YUM  \\\n",
       "2019-04-30  0.001414  0.015535 -0.029038  0.018936 -0.005011  0.005684   \n",
       "2019-05-01 -0.012354 -0.048660  0.011700 -0.010619 -0.001319 -0.023470   \n",
       "2019-05-02 -0.018585  0.017848 -0.003969  0.000358 -0.045023 -0.001962   \n",
       "2019-05-03  0.004734  0.007115  0.012366  0.011803  0.034574  0.009632   \n",
       "2019-05-06 -0.007611 -0.006341 -0.040717 -0.001237 -0.026856 -0.003018   \n",
       "\n",
       "                 ZBH       ZTS  \n",
       "2019-04-30  0.019283  0.005728  \n",
       "2019-05-01 -0.015752 -0.000491  \n",
       "2019-05-02  0.016416  0.013361  \n",
       "2019-05-03  0.008928  0.005817  \n",
       "2019-05-06  0.009975 -0.004048  \n",
       "\n",
       "[5 rows x 405 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (returns.shape)\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose a stock to confirm the daily returns data in a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c250ec550>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns[['AAPL']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can only work with stocks that have the full return series\n",
    "returns = returns.iloc[1:,:].dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(503, 405)\n"
     ]
    }
   ],
   "source": [
    "print (returns.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Find Candidate Pairs\n",
    "Given the pricing data, fundamental data, and ESG data, we will first classify stocks into clusters.  Within clusters, we then look for strong mean-reverting pair relationships.\n",
    "\n",
    "The first hypothesis above is that \"Stocks that share loadings to common factors in the past should be related in the future\". Common factors are things like sector/industry membership and widely known ranking schemes like momentum and value. We could specify the common factors *a priori* to well known factors, or alternatively, we could let the data speak for itself. We take the latter approach. We use PCA to reduce the dimensionality of the returns data and extract the historical latent common factor loadings for each stock. For a nice visual introduction to what PCA is doing, take a look [here](http://setosa.io/ev/principal-component-analysis/).\n",
    "\n",
    "We take these features, add in the fundamental and ESG features, and then use the `DBSCAN` **unsupervised** [clustering algorithm](http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html#dbscan) which is available in [`scikit-learn`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html). `DBSCAN` has advantages over `KMeans` in this use case, specifically:\n",
    "\n",
    "- `DBSCAN` does not cluster *all* stocks; it leaves out stocks which do not neatly fit into a cluster.\n",
    "- As a result, you do not need to specify the number of clusters.\n",
    "\n",
    "The clustering algorithm will give us sensible *candidate* pairs. We will need to do some validation in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a PCA Decomposition and DBSCAN Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=15, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_PRIN_COMPONENTS = 15\n",
    "pca = PCA(n_components=N_PRIN_COMPONENTS)\n",
    "pca.fit(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(405, 15)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_.T.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have reduced data now with the first `N_PRIN_COMPONENTS` principal component loadings. Let's add some fundamental and ESG values as well to make the model more robust."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(405, 17)\n"
     ]
    }
   ],
   "source": [
    "X = np.hstack(\n",
    "    (pca.components_.T,\n",
    "     result_esg_df['Market Cap'][returns.columns].values[:, np.newaxis],\n",
    "     result_esg_df['Controversy Score'][returns.columns].values[:, np.newaxis])\n",
    ")\n",
    "\n",
    "print (X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(405, 17)\n"
     ]
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit_transform(X)\n",
    "print (X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DBSCAN(algorithm='auto', eps=1.9, leaf_size=30, metric='euclidean',\n",
      "       metric_params=None, min_samples=5, n_jobs=None, p=None)\n",
      "\n",
      "Clusters discovered: 4\n"
     ]
    }
   ],
   "source": [
    "# clf = DBSCAN(eps=1.9, min_samples=3)\n",
    "clf = DBSCAN(eps=1.9, min_samples=5)\n",
    "print (clf)\n",
    "\n",
    "clf.fit(X)\n",
    "labels = clf.labels_\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "print (\"\\nClusters discovered: %d\" % n_clusters_)\n",
    "\n",
    "clustered = clf.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total pairs possible in universe: 81810 \n"
     ]
    }
   ],
   "source": [
    "# the initial dimensionality of the search was\n",
    "ticker_count = len(returns.columns)\n",
    "print (\"Total pairs possible in universe: %d \" % (ticker_count*(ticker_count-1)/2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "clustered_series = pd.Series(index=returns.columns, data=clustered.flatten())\n",
    "clustered_series_all = pd.Series(index=returns.columns, data=clustered.flatten())\n",
    "clustered_series = clustered_series[clustered_series != -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clusters formed: 4\n",
      "Pairs to evaluate: 522\n"
     ]
    }
   ],
   "source": [
    "CLUSTER_SIZE_LIMIT = 9999\n",
    "counts = clustered_series.value_counts()\n",
    "ticker_count_reduced = counts[(counts>1) & (counts<=CLUSTER_SIZE_LIMIT)]\n",
    "print (\"Clusters formed: %d\" % len(ticker_count_reduced))\n",
    "print (\"Pairs to evaluate: %d\" % (ticker_count_reduced*(ticker_count_reduced-1)).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have reduced the search space for pairs from more than 80,000 to approximately 500.\n",
    "\n",
    "### 4.b Cluster Visualization\n",
    "We have found 4 clusters. The data are clustered in 17 dimensions. As an attempt to visualize what has happened in 2d, we can try with [T-SNE](https://distill.pub/2016/misread-tsne/). T-SNE is an algorithm for visualizing very high dimension data in 2d, created in part by Geoff Hinton. We visualize the discovered pairs to help us gain confidence that the `DBSCAN` output is sensible; i.e., we want to see that T-SNE and DBSCAN both find our clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tsne = TSNE(learning_rate=1000, perplexity=25, random_state=1337).fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(1, facecolor='white')\n",
    "plt.figure(figsize=(16, 8), facecolor='white')\n",
    "plt.clf()\n",
    "plt.axis('off')\n",
    "\n",
    "plt.scatter(\n",
    "    X_tsne[(labels!=-1), 0],\n",
    "    X_tsne[(labels!=-1), 1],\n",
    "    s=100,\n",
    "    alpha=0.85,\n",
    "    c=labels[labels!=-1],\n",
    "    cmap=cm.Paired\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    X_tsne[(clustered_series_all==-1).values, 0],\n",
    "    X_tsne[(clustered_series_all==-1).values, 1],\n",
    "    s=100,\n",
    "    alpha=0.05\n",
    ")\n",
    "\n",
    "plt.title('T-SNE of all Stocks with DBSCAN Clusters Noted');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see how many stocks we found in each cluster and then visualize the normalized time series of the members of a handful of the smaller clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.barh(\n",
    "    range(len(clustered_series.value_counts())),\n",
    "    clustered_series.value_counts()\n",
    ")\n",
    "plt.title('Cluster Member Counts')\n",
    "plt.xlabel('Stocks in Cluster')\n",
    "plt.ylabel('Cluster Number');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To again visualize if our clustering is doing anything sensible, let's look at a few clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iLa2HRGsJ1dai+/04J0/GPm4cwfL9fAYPAoMhWGqBoXHvC4FdVryEEGcD9wEXSymPTh+7/0XK5qu/m97Y93MXzYb/XI4uoOms65DDTyFh7BUw487e9f40Bra+p/531QJw1+vrqexIoM0n0XXl5un1ltPYNJ81a6/Zz5sxOFIIllcA4CguxpSYSMr552MvHoN+kKPVpZQqot7rBcCan491yBAAdI8Hze3CZLPv1jvNnJYGUu/lenww0INBtM5OdCHYMWIEjrFjCW7ZclCvuS8MhmBZCYwRQowQQtiAK4G34isIIY4HnkUJleZ+2jD4yhJVThc9BFve7X0o5IPgbrx4Nr8NnyofjorhCYix5yO+twCsTjjrl3DNfyF/Mgw5FoQJLv83TLke2nZAxScx65snlEQoGjDZLWCCwcYjKvWHwb4TrlXWdevQnjmrOTnloHuFaZ2dBLdtI9Tt3my3Y4qmwA9VVyvPL+vuozSERR2T4TBvvvkmQgi2RAf8qqoqnE5nLCX+pEmTePFF5XxSVFTEscceGyu/9dZb99xPlwuAoN3OUquFyNhiIi0thJuaDuwDGCQOOI5FShkRQtwCfACYgeellJuEEL8GVkkp30KZvpKA16KSvlpKefGBXtvgCKA9zuz0ylVw5v1w+t3q/ROTlVD4SZ8U9yEvvHUr5E2i8dyb2Fn+S6YlFPWuM/ps9dLCyhSWWgBNm0ALwouzSOZ5XDhwBVMIh9pw2Iegab7Y6ZFIJ1br3tNrGByZhGprEQkJmONiSMxpaSqGRMqDFs8i/f5e7012O5jNIERsLbFbePRL1EQmw2HmzJnD9OnTeeWVV2LxKaNGjWLdunX9nlpaWkpW1sBSFEm/n5Ddjm5SukF7fj52wL+2/7YPNYMSICmlfA94r0/ZL+P+P3swrmNwBNJeCROvgsRM+OwJKH0Ihp0EyUPAoxY80SJgjvup7fgY/O3Ib/2T6uZHsTvySUgY0X/7ZqsSKgBJObHiHNlOOxm4Q8mxBfx4wRIMthiC5StMuLYOW0FBLwFizctDBoNo7e1YMjMPzoXjrucYNy4mRITNhgwGETZbzDTW7+kWCyDwuFwsW7aM0tJSLr744kEPfNQDAbzJydCqfvvNwDCHA//aNYN6nf3FiLw32H+0MHjqIW0YnHQz2JJg+dPw4iyw9sQe0F4O2WPB1w56BMrmgSONrsxMPFWbGDf2IUymAaTqSMqN/Ttb/pkSRw33+L9Fl3cbmZmn9xIsoVALUDyIN2twIDQ/8ii24cNI++Y3B1Q/XFPTywwGYC3IB6Dz9f+C1Mm6+ea9N7TgXmj8csD9NAcDmCJq4V2s7AnKtQX8SE3HZLMhyifDeb/r93whBMJqYf477zBz5kyKi4vJyMhgzZo1ZGRkUF5ezqRJk2L1n3jiCWbMmAHAmWeeidlsBuC73/0ud9xxR7/XkOEw4T6m3oamJsaMLSawbVu/5xxqDMFisP+E1AIn9mRwpsMZ98LUm2DuNVD9GYw9Xy26v/8zmHoDLPwVtKlkgpz0IzrdanaVkTGwRJJ6YlZsUbDEpGzgaVoqtbX/ZtjQG9A0b0/XDDfkIwYpJW1//zvAgASLlJJQXR0JJ5/Uq9yarwRLy2OPAZB5ww39emftVx+RyhNMSoTJhHDY+69o2rsJTlgszH3zTX7y858DcOWVVzJnzhx+9KMfDYopTA8ECEfv22w2k5+fT0tLC7bhw/GtWrXX8w8FhmAx2H/CUXu0LU47ScyE695VmkxCFjw7A8o/Vi8Asw3yJsJpd9FR/gscjgKczp6wJ0/Iwx9W/oEqVxU/n/ZzSjJLYsfKI27G9OlCRthEIFBHJOJB03rs44ZgOXLQ4nZL3Nv6iB4I0PirXyF9PmyFfTSWqGDpJlxXh62oaM8X341msct1Xa7Ygr05NQ3b0N4x3JHaOrTODmxFRZj72c8+nnZPF58sX07ZjTcihEDTNIQQ/PCHPxxQX/baV7+foN2O3W4nOTmZY445hoULF6IXDiXy9juDco0DxchubLD/hKOmp3izF4DJBKmFYHXAD5bA136hyoUJftECN36EdKbT2fkFaWkn9jp17ta5zNsxj3Ut61i4c2GvY8t8DcwK39erLN2vnAwjEXdMYxHCQjBkBNgeKYR29gTBaq17Fvju99/HNV85lVoLew/uppTeGRiCVVWD00FAxqWv708LsuYNwZqXhykxcZdjfXnjg/e5etYsdu7cSVVVFTU1NYwYMYLa2tpB6WswGESaTCRG+9Id8+fOyjywYOVBxBAsBvtPt8Zide6+jtUBp/xYeXhd0xPr4vXtIBxuJz1tWq/qZW1lFCYVMip1FNs7tvc6Nm9tA+u1no2bmiz55ISUg0Ao1EKHaxNztnwTjzYmusZicLiRUtL2j3/E3rc8+RTeL77YbX3d17NOZi3cNYFH4dNPMexFlfE4NJiCJRrQaBs2DEtmxi7HhdmMJTNzQN5or86fz0Vf+1ovl/fLLruMhx9+OLbG0v36y1/+Eqtz5plnxsqvvfba3bYf1HWElNjtylyXn5+P1Wrl4/p6fM49PIuHEMMUZrD/xDSWvcziLHYVlxJHZ4caXPpqLFvbt1KSWYLFZGFdc48tutpdzcYdQ8hM7WLVaY/wh01/5D5XHgXeeqqBLzf+mC9bE/mo+ja+aJnKvy/54IBvz+DA8a9aRdeiRQDYi4vpnDuXzrlzKfzr0ySfeeYu9cN1dbH/bYWFhKqqqLh4Frn33Ufjr37FiDffwFFSgjk1lVBFj6u76913cZSUYI9L/7JPaBrCZMKcsmteun3l4/nzVTyJritXZdR2xHuKTakaoJCUUhI2mbAKERNySUlJfOc73+Gll15i3UnTYN3hT+1iaCwG+09MsOz7LKmjcwV2+xCczmGxMlfQRbWnmrHpYxmbPpYGbwNt/jber3qf89+4kEgwnTGdqax+dTR11lwqzMkMkw2ENTPBYCPtupppuv1O2ryHbt8Og93TtXQZACPefIOi119jxBv/xZSQgPvdnugEGYkQ6VCpecI1ylyUcsEFqt777yNDIRp/9SsA/NGFb/uYMbGtgbuWLKH+zrto+GUswmGfkRGtt0v8gRAVJgcjrYuMRNBNJizRa3QzbNgwJkyYQFNu7m7OPLQYgsVg/xmIKawfpJR0dq4kLe3E2KyrM9DJe5VqsJlROINTC04F4J5P7+H+pfcjw+mAhTS/iUhQ5+vmS1mnB7CLCLrHAYBf6+nHKxvHHeDNGQwG3uXLcU6ejKOkBJPNhmP8eJJnzqRr8WJkKESkvZ3Kyy9n+8mnICMRQrU1JJ42g4JH/gSA1tHRq71u85J93DiC27bh/uBDam9V+ePizWj7itQiCIt57xUHQHfsS3d0/GASCQZBCKzRa4S0EI+vfhxX0EVRUREHN5HMwDFMYQb7z+4W7/dCMNREKNRMaurxgBIqM+YqX/6SjBLGZ45HSkmWM4sVjSuIdBUzgetZCeRYLVhMZgrrx1NqVeYui8cMaeDTVT8kUN+1Z88dg4OPjEQIbtlC+jW9c7cln30WrjfewPX2OzT/8Y9onZ0ABLdtI9LQiPPY4wiWl2NOTycQvwUwoLUrQWMfW4zu81F3222xY+G6+l1S+UhNA5Op37WRUE0Nus+HY+xY5WpsHhzB0t1OpKkJc2oqJtuBbacsNY1gTQ0hXY85MFhsNnxhH+2Bdp7b9Bx1XXU8OPVBCguPjB1JDI3FYP8JRQWLbd8Ei9+v3DoTnCrafmPbxtixWycrO7QQgmfOfobbJ9+Bv+Z6VkY3ZpB2E6UECFZZOWanGrCEXz3Ifk1pLthNdIaOjEXM/2VCO3ciQyEcY4tp+MUvaHvuOQASTzkF4XTScN99aB4PaZdfDoBv1Sq0jg4s2dlUXHAh2085Ff/69QBqMy2bLea6nDh1KqaUFITNRuaNN5B9+23oLlcv12aAwObNMfNaXzSXCxkOq6zFEQ0xSKYwESegBmNXSa2jg06TmS6HA3cohNB1rA4H9d56hBBMzpnM5w2f43A6uPHGGw/4eoOBIVgM9p+YKWzfBEvAr9xPnU4Vp7CpVW0x/MzZzzBtyCmsrmpn+oMLeej1Dj78TMWxDAub+GaXje2+ICvsEWyFVhyRNAC0oBIiQU15yUi7GXfYcWD3ZnDABLcqbcM2ahSu+W/RMecVAEwOB0nTpwNKyAx58AEsubkxN+P4vU5kIMCojxYyZtlSrHl5aJ1KY7EVFVG8/DPGrl5Fzl134RivNq2N9xTr3lFRc7v2mJQ00tCIDIcQ5sEZDuNziQ2GYAm4XOhxZrqUYBAsZkJaCKfFySWjL6Ez2MmzG5494GsNFoZgMRg4QQ944zYx2s/Fe5+/GjDhcKiAt01tmyhKKeLUglP5+5IKLntmObX+EEsq2lhR2U6yycQsn40RETMtZh0p4O1QAJvuICJNhIJKsIVRucGk3Yw/fGDmB4MDx/PRRwi7HRDIUIhwbS1dS5aweVwJljyVb8s+cgRCCFJnzSKwSU0wvEuXAGBOT2fYv/6JrbAQIQTmjAwi7T1rLsJsjsWcWHJVe5HmnuTp8YvnfVPKx2/jG2lXv2nTXgIfB4qwWLBGF9EPVLDIcBi/yYwAkk0mkkMhrHm5bGnfgpQSi7AwLU+57D+97mlcwcFf19kfDMFiMHD+dQH8cWRPEFa3xmIZuGDR9TCNjfNxOPIwmWyE9TArG1dyQu4JALy6sqZX/VleG99rt+GQykbusYPZJKjxhxGY8AonekDNEMMoDcbu0IlEBslebrBfhKqrcb+3gIzvXUdwR088UtNDswHQfX4yrruOzB/8AIDUSy+J1fEt/xyAkW/NJ/GknrQu5oyMXUxd3VhyVJBgJH7n2bigR6lpvYIg4/8HsBYUDIqrcaw/2dm8tagUW07OPqXNnzhxIueccw6VlZU0NzcT9nqJWC04bDaShwwhuagItwjENDCLyUJ+Uj6PnvEoAKsaj4yULoZgMdg7EbXPNw3K3s3zM5VQCXuVUDEN/GdUXfM8gUBNLH5lbdNavCEvJVUz2FHVSVWbj+l+C7d1OviOx05x2IwdwcW3TmJFFkwuyeGtW05Fd6hrenEgwjqabsIXUQLGZgsjdUEoou+2HwYHl26TlDk5hba//wNzRgaWvDxCO3eq8qQkcu+9B0s0LX7f1CymhATMWVls8foZ/ekGPm5z83YYgrsRLOa0NITVSmDL1tig29fdt9trTEpJcHvv4Nu9pWnZH157fwGnTp3KK6+8EivrzhXW/YoPhCwtLWX9+vVMmTKF2bNnE4lEaO/qQppM2OPMg+5Qz2ZnIhBk80WnMdGpEq7+bOnPBv0+9gdDsPwPoWkB/P7afdsEq+wteCgH1rzYU1bzOXxwH7Tu2GczWEvLByQllXDMeOVO+mntpxzTciqLPtc4+xkV8zA8YsaGYIhm4uJbJ3Hx7ZNIKUrm04ifYwtTGJNh4+xzR/HPryXikw6S8OOLJBCMWJEC7FalSXUFI7vth8HBJVyvNpFt/uMfCZWXk/+735I0/dTY8XjNov3fL7Hzmu/E3gubjeKVXyCE4MmdzXRpOvdvr6XemYTs6Oj39yuEwJyZieuNN9Cjbr59tZJuwaJ7fSrZZHdKfJN50JJZdtPV1cXyNWt45uGHewmWgXDSSSdRWVmJ2WSK7bdis/ckxQxpIVLsKQxLGQZeD2xvof3On5Njy8Qf8e+u2UPKUe1u3O4N8dqqGm6aMRLTALKSHsnsbPNis5jIS923gVzTgjQ2vkFe3jcpr/gTNTX/ZPTonzF82AC9R5aqTLK8HY0avvp1tff8qud2qRqJeJAystt9UFpbS3G71zNq5N2xsk9rP+W0put52tlji87RBG3jEintdPOj8WpG+1m5yjGVVb6MPz7/Di9ffz9k2wmakkmWPprCTkIRM5gFyVqIDhx0dLnISMwe2H3+jxBpaaH9pZdJv+LyXZI6Dibhut67kyfOmIFtxAi6li0jUt8QEzwATbOVeWzIb35D61/+ory9zGY6wxEWd6g97qv8IVxJyQhNQ3e7Maem7npvbWqtRPf71RpKROPRmhfYIZvRQyHYLjE5nchgEBnRMCU4kbqOMJmgeuDjw7iMcdxz4j17rDNv3jzOOeMMRg8dOicTQQ4AACAASURBVOC0+d3Mnz+fkpISMpOS8NbXIzIzY+n0NV1D0zWcFifJtmTQQZglcuUaHm8awjO3n8xGNnK4Oao1ll+9tYnfLtjCispd1edb56zlx3MOf+qDgeAJhLn4yWVM/30pd7+2nrDWY+LRdLlHDaSi4hG2bL2fpqa38Hp3ANDZuXJgF/a29d7L4rLnYMzX4bw/wLCTd6m+dNl0ln12BlJKGhvn43Ktw+frSbtRWfUkCQmjGDr0OgBqPDUEaywEfUno0ec6RRdkTs1m2NQcKgMhbn9lLQ0uP0u2t2KSGrULX6c5K6+ni9Y0kvHhizgJRSxgEnRbys967As0zdBa4qm7517ann0W10HOghuurwezGUtODiPmqS16bUOHMmbRIlIvuSQmWOKDGpNOOw1zWhq24cMBmF3RQEtIfX8S6ExW32xkN+YwuhfKpURzu5Hd373JpFyAdR0ZiSAjEYTVAkKo8oOwG+WcOXO4/JJLkRGNK664gjlz5gC7msLihUp3rjC3283dd98N4TC2UIikpKRYHE4ouv221RTVsCSkDPMRGm/FUt3I7+xXDPq97A9HtcZS0aL2xy5rcHPyqJ4d59q6gry1Xv2w//St47APUsTtwUBKyZ8+2IrLH+Ybkwt4bXUtX1S1c9nkQi48Lo+Ln1zGjTNGcPvZu25qJaWksWk+AO0dywkEVMJGn69i1wvputJCGtbDrCdV2RfPgh6Gc3+r9ls5NrqXxrQfqNeq58Hfgab5+HTJVHQ9AMCXG2+hpeX9WNNnfa0cXQ/i8ZQxbOh1mM3KFfiTHUs4d+sN1DiUYBwaNnHbiUVccP4ohMNMeYuXf3++k3nr1Hf1jVw/3qZElp98HgDDvDpttiQKgt2CRSDMglR6BK0v6CM5YfAWZb/q+FevBvYwOA8CUkqCFRWgaaRfcw2OceP46dYaCuw2bivKxTF+PK5589h6/gUEEpPo9t/TOtoJNzaScOKJSCn5sNVFcflGWjNyaU/PpjMpOVqvE/rZcDTnnntwv/suYbMZra0NTCbuLLoeR0kJejis3J9NJtD1XrtDDjZtbW0sWrSIjRs2gK6jRwM095Y2v7S0lIyMDBobG0lOTCQSzZsWb6YLa0p42sw2pYVJsKdGOK54J9vK83C/++5Buad95agVLLouqW5Xs6F1NZ29jn1Y1hT7f0Oti6lFu2YzPVJYU93BC8t3cu3Jw/n1rAmkOW08v6ySRxduo7rdR1cwQunWln4Fi99fFduXpKNjOZGIJ1peja6HMZms0LwZtBC8cg24ounNs4rh87+qPVWGnQwn7+aBmHI9Pt9OVsQJFaCXUAGoqvor5RVqTSUlpccMULZuJ4XaNOoKrNAS4uGbpzBjTDYLnnyEnV+u4+Y7fsapoyfzVGk5E4emckr12zx7+neozlYmnPEdQVptSUpjCTsJRwQmkyA1bs3eFzqyBYuUkua/rCXptEISj8/Z+wkHer1gEIDApoGZS2QkotYj9mENwv322wQ3bwbAPmY0XRGNF+uVmeqS3DSGXXM1WpeHjXNfZ0hFBbaJEwmtX0+4vgHd48GSnc06j5+mUIQTqysJOhJpT8/GlaS+R62jf6GY+b3ryPzedWxatQo9une9NeqGbLJaMSUkoPt8CKvtoAkVgNdff51rr72Wp373O8J1ddiLiznz61/fJW2+lBKfz4fD0RNzFeleF+rsjLlEC5MJX9hHjaeGiK6OW01WwpWVgKS65FQWpwzhpLy38CxZfNDua18YFFOYEGKmEGKrEGKHEOLefo6fJoRYI4SICCEGtjfpPhAIa7y7oaGXSajdF8ITUF/Cos1NNHsCrN7ZzsVPLuXjzcrX3SRg8dYjO736sh1tCAF3fn0sAD+dOZapRWoNY8l21Xd/qH9zT6dLzU5zcy8iGGxE07wkJZVgDYTw+ypB1+Dpk+DZ03qECsDCX0Q36sqE77zZq01dD/Z6X13zXEyonDj1rVj5mWeUkZt7EUBMqJjNiaSmKbdiqUvMO9LR7CG2enzYLSZmjMnG29lB2ZJSvJ0dVG1Yy8wJebz94+k8dMmxtGwrI5zUs8NecrCLZkcySQToCibh91kwmQRDzT3eQIFQj8A7EtHcIcINXjpe3br3ygdIvJeUf9VqGn/z0F4dOXZ8/RzKzzt/l/Lg9u2Eqqv7OQP8GzciomlMPMNHMHpJjzn17eZOhMlE67Xf46pf/okLH32OFy68XrUZTSpZLRJ4rbEdi5TcFJrEeKHSlESiloW9aVumxESsBQXYRo7Ekt3zezFHPdA4yMutc+bM4dJLL+0RXpHIbtPmP/roo7TH3U+3YDGF41yjpaTB24BEkmpPJS8pD+EPogeDmG06csRInGfdgzbEhO7Z/3xpg8kBCxYhhBl4CjgPGA98Wwgxvk+1auA64D8Her3+eGLRdn70nzV8tLknOKq1Sw2Ad587Fn9Y4+XPq/nrJ+VsqHXx0eYmTivO5tTRWcxfX4euHxmb4/THZ+WtjM9LITVBzRgdVjN3naOETJNb3WNdh3/XHElSp7FxHhZLGpmZZ8TKRzc6mLGincQ/Te1ZkAe1GZc1EezR2f3w6WonyDivL4+njCVLT+aLlbNoaHiDmpp/0dLyAYmJYzhx6lskJo7BZHIwZsz9mEx2CvKvjJ17xumbOG3GKuw29aCvWrKdvLZiHMf7cAUiDElVs7btKz6LnbN5ySLqtm5m7QfvsPil52lt6aQu0UxWQOdfn3vJEmG81gRMQvLejq8T9FmQAY0MCaFjlfD1Bo8ML5n+CDd5afyt2j7AnLKbrXAPgMC2bdTeeht6QAnXvoKg4+WXcc2bv8c2Ig0NhPvOtHWdiosupuLCi/o9R2ttRdjtmBIS+MKZHCsf6rCxtEOZp89cuRVpMuF1JvCGR5l3Wh5/HIA3qzfwcn0rI9vbySeNGS0RJje20h0Y350vLBgM0tEnSWU3lvR0zAm9M0KYU1Mxp6djzcvr95zB4pNPPmHmzJmx1C4yEuHWW29lwYIF+P3+2PrKypUrueGGGwiHw2zZsoXU1NQewRJSz7YlJ4eucBeBSIDchFwKkwvJcGSgu6NWGAv4UjKYeWwBO4qOQ5iPjLFsMPTBE4EdUsoKACHEK8AsoKy7gpSyKnpsUAMLNF2yvraTsnrl1/3+xka+Pl5FvLZ61CLXCcPTmVCQSunWZlo9PbPtY/JTKMlL4dY5a5mzspqrpw0fzK71i67LXbzTuoIRnly0g5tmjCAzqffgsqqqnc8r2rkjaubqaPTiavEzbFjP/ieZCRptPnD5w6Ql9ESbt7aV0tGxnLHFvyYhmpreGtLJWLuo5wJrX1J/b1oEBSfAaXep6PqKxWq/+j7xKeUVfyISceHxuCjb3OPZNWrknSQnqw24zjxjU6w8IUEZwh2Ogti6Sjdb19TjsbUzbFI6bAkwbogagOq3byExPYOsocPZuWEtr/36Z2jRh81vd9LktPLjrUEmuHRqc7uosEQ/My36EOuSpAixvcmPZI3Fv7lnpmpOHfxMAS2PPU5XaSmNiYnoXR5CfTy1ANzvvUfqBefHNIzdEWlrw5Kp1ikDGzYAIEP959INNzUjw2HsY4tZ2xXAIuDzk8bzbE0zL9W3URtQ52VaLdgDIRrG9h7oF409laCEEQ1NrDGHGduczOVtDfyrpAC/zU6wpRkpJY888gihUIgHHnhgQJ+HEAJbwa6bhx00ohpLX7fnbvS46P9urcVms2GOrgPZhg7FnJpKnasKq9lKmj2t51y/D2GStNvS+VbquezQdNqGzWD8mDVxI+/hYzBMYQVAfLh0bbRsnxFCfF8IsUoIsaqlpX8T1ZZGd8z19IlF2/nG059RGjVnLdvRs+1pt8aSlWTnlFFZbKh1Ue8K8PTVk3nyquO5+fRRXHRcHscVpjK3T7T3waCm3cfIn7/H+xsbe5XfMXcdzywu570vG3Y5Z966OpLsFm46bQSbltTxnwdW8O5TG7AHen6QJelqxrt6Zwcb63pyIrU0v4/FkkJ+/rdwOIchpGRIcxAhNcq/diF1w3qcGcgay+o136a6+jmwJ0PJhbsIFSk1OjtXk59/JSec8GqvY8nJx/Z7z3Z7LmPH/oYTJvf249ciOp0VYWrTt7CzUZkncurX0rB9KxuaWlh8xqWEoqYAIVQ/xp48g7xxagY61qPhdHzBuIb5+KMCyxFNGO4/MYuksIj9sn2h3qa7IwU9OPh7dfSlO0WJ68038Sz8iGBZnxHHZMK7ZAnlF1y4171DAlu24N+0CRmJ0LVkKQDmODNTPKGKCmQgQOoFF7DG7eP45EQKHTZmpCfj1yVTlqt+3JWdyemfvILs81trT1ED6OimNtZYK1lo20CQMImhAK1pGfiqa6ipqSEUFWzxA/SRRLcpbHefbXe/4zMvh0IhzLoOQsS+v4AWIMma1KueDIfBJAiY1IRgg6sLMebrNIyNe64PI4MhWPqzWO6XPial/JuUcoqUckr3Ps59mfn4Eq76+wo8gTD/WdGj2p85NptGdwCXT6nV3YIlO8nO5VMKueDYPF64/kTOPzaPC4/LJ9VpRQhByZAUGlwHf1bbLQz/9mk5AO5AmO8+/wULo44Eje5d+1DZ6mV0ThJOq5ll/92B1aFm5R89V8b3ThrOzBFLmDVqAUlWHze8sIoLn1jKsh1t6HqYltaPycr6GqbS32F/eBRT13SS0wGB3AlQeBLlBRKZmA15k4iYBZ2dX7B9x8M0Nr5FReVf2Lrt14CKTWlo+C8eTxma1kVa6hTSUk/g9NN6dndMTBy92/suLLgqlhOsm8YKFyJsxl4U4d31ypShf7mY/9x/J69MmE5p7gh8k1T+o8sf+C0X/eRnXHDD9whlKSE09bThaOfk0+brYmKb+qk5RAhpMyFTbDgCHjB3ayxHnmBxl9ZQ/6vPCGxVs1RrYRK6b/DdorW2nolW3uzZ5N5/HxDnZRQd2MI1Nbjff59wQwNtzz3fE7keN2C7/vsGVZd9k5bHHycQXZhnNyZkzeNBOJ1Ervg2q91epqerAfLktN7R7Zs2N3Fs0wbOWTyPpy+7uudaySkU+espdPlp1JPx6HaCIkJqIERtzhAC1dVUVva4sfv9B27u1KXEHelfAER0SXg/hJcwmVSMzF40ltzcXLKyeoS0ye/Hn+akrGMLm1o3oekaNnMfjTKiE7JYYwPtkoqdjC4aziVi9j7382AwGKawWmBo3PtCYFede5C5Y+562r0hLpmUT2F6AlOK0ind2sKWRjfTRmbS2hXCahakOC2kJlh56urJ/baTm+qgtStIWNOxmk2srGonrOlMLEzDH9bISjpw2/cXle3c81+1gLmx3s2sp5YxNN3J4m0tXD1tGIu3tVDVtuuiW2WLl2kjM/F2hggHNKZd0UKA+VSvKWJW6qV4cz4jEOjkgZN/y4qGE3ht+yUs2dHC+MytRCIuctJOx/PatfwjPZUbO92sljr3pHVxcU0Zp1pNuG+aS2riMYRCPdrSprI7Yv+PHnUXZZvvpaXlfZxOZSrsTsVisSRTWPhdAv4a5V22D2xZV4smNFIKiqjd6qc4Uk96WNmMuxLVGs+iognccdevaOryM/HEUxAVn7Ax6RhOX7+Wmjdf4JPppxKxHs+xXWpQsRNGmgUvLveSIr2YhRokAuEjZesjRbjJi/uDKgBClW4s2U5sBUn4NyqvKRnWEdbBCS8L1dSScNJJ5P/+91hzc3C/pzZSs40aSbDPPieBDV/S9vd/ENyyheRzz8VWWIDu6xmwu8/1rVlLuEH9XjS3Gyllr5m07vdDJII5O5t3W13owCW5ar0r2WLmpsIs2sIaYxMctDzzJNuFRnPSOpaOnkxe+m3QuJnjPWW8u+5HvCru4/3QOBBBfphcyuTiFdStL+TE7WXsjMti7PV6SUzcy/bYe6ElFKExGCbPbsUV0Shy2rBGNalKfxCfpjMh2Yl5X2NeLJbdmsK0qCYjhMAS56VmBZqdYYiTZTZTj2CR4RBSgsuRBB41IV3Z3MrdJxzLKaMy2blvPTwoDIZgWQmMEUKMAOqAK4GrDrTRUERn9c4OThjeE8UdiQsM/GhzE1dPG8bsS5UZpik649/c4CYr2c4zi5Vm0N8GP/EMSXEgJbR4ggxJcfCtZ5YDYLOYCGs6z313Cl8bp9ZtwpqOxST22mZf/vZpT9xIKKKzvqaT9TWdnFiUwexLj+W7z3/BzjZvr3P8IY16V4CizAS+XFxL6shPccl/A5B7/ApaI28jpA9T4CymjJ/MpLEe1raUs3y7jW+M+gyTyYGnKYH5Kck8n5ZKQJios1oIojOv+nMmDoFVay5n2onvEQ4rrWHkiDuoqHws1oe2tk9paVGbafn9O7FaM3E6e6ycY4v3fSvYlhoPWz9toTZ1C2s2KZPHRM8GSmZeTPa06fyxSQmCRR1daFs3UtK4E5/PR/vahawb+jXufuExMpuamLR2He2ZGQTGqu/GQQiTCUpcGnUpGpnmVjqx4D9AwdIWilATCDEpZd+2BtgdnsW1CIcZS6aTcF0XpiQbJqcV3R/BX9ZG24tl5N4+GeuQAxsoZThMuL6elPPPV0Jl4ULqfnInmExYkp0EAcf48eihEKEdO/CuWBHLABxpblKCxasW2k2pqbE0KVpnJ5GGhlhCSOn3I6KL5DIcJrBZtWHJzmad20eOzcLYxJ71td+M6dmI6kbLp6w4VWltCa4KSvUf03rWBYzrrERKE9n2bGwZi7HnLqC680Te+fA6ZqasxxwM0rRlCyPGhYBteL29n539IRzV0hqCyuLRHtbItSvB4ouOOx3hCFm2fZtECbMZzeUiGAhiH9Nbs9d1HVM0xiV+THGkpGDWmnoFbsZrLFqzMt2HLKrMpEXY7g/haW3isSsmMef7+9TFg8IBT42klBHgFuADYDPwqpRykxDi10KIiwGEEFOFELXAt4BnhRCbdt+iotEd4Psvrurl7dTXZPWNyT0/0pxkOykOC+UtXj6Kmpe63XL3xJBUpZE0uQOsru7xMJk0NA0p4dWVyiOm2RNgzH0LeG1V/5sG7Q5dl6ze2c7XxuXw4R2n9ervw9+YAMDwzASqWn29BGdFq3qok3w6az6oIG+KEiol436PqetmhFlpOHUb8wk1X8LwYT+gJLOajQ0BGls3kJp6PC/O/5ClTvVQz09KoyI5k2x7PmE9wjqvKne51xEMqs8rJ+c8Tj1lCVlZZwHw5cYfAZLi4gfUZ5XbvxdQN+/9dQP/eXDFHutsX9mE1CWlo18m1CIxo+EcksVcr+Rdm0rT8e8larBxOdXg+uGHH7Ki1U5lYga2iHrwR1VUMHXlKhxtqq5dhBEmgSbD0FrMkGhK/0Bo/9KWv9LQxsVrtnPu6q3MXL1tv9roj2ClC8foNBJPVPEV5hQbJqcFdIl3pVp/a3p8DYHt/Xs77Q0ZDlN72+00/vo3oGlsmzCRHb4ADffdD0DqBTMxN6nswYmnn8bI+fMQCQm90sp3ayR6l/oNOo/rWUMLVahJknOy2v1TcyvHGRmJUPmty9l5lZpT2kaOZJs3EBMqwc/+QXjLB7TX1/HIFReypnQRrc4giWETyb4MBJJEq4tOSzLupgjbvDPptFmw5y4AYGNEmdFqE5SJfPqiUgqHzqNwaBlud++EkoOBK84sZo86gvi1/TCHRTURPbirqVvX9ViqFoiuKUiJbrfiEwKn1Mm0RtfJ3GEimo6UkojLT9hmpSshgWSvm7M76mhLzWTVgrfJTTky9iEaFJ1bSvmelLJYSjlKSjk7WvZLKeVb0f9XSikLpZSJUspMKeUxe2szGNFp84ao6+xRx6uis/q0BCv3njeulzYjhGBEdhKVrV4qW71kJNp4+caTdmm3LznJ6otocgdU2hABGx88l1d/cDJXTRvGku0tUS1Dzdjmrtq3hf73NjbQ4Qsza1I+xbnJpCVYmT46i89/dhajc5Qn1PTRWXQFI3ywqSdwc2U0DY21yoctWZUX5F9FXt5l5GZeHqsX9uRSvqYFqzWFU0cXoksTT39xHH9ddR7F4gtWOx3o4RS8Zo0azUdt3Vj0SBJrveqz8/kqYoLFbs/F4chn4nF/o2Tc72LXKMi/nFNOXsyoUT/d471Wrm+lo8FLJKQeyrefWMfc2V8gdcmKtyvoaPTSWO5CZvnRvOOo1jPJNXXRkpLKh8dM49maFia6QyzTPyXF14XHkcCls2aRa+4kI8WPBIY2N5H6jW+QdcuPAEgvV9HJ3RqLJiNIaWVYm1qP8e2nYLl9Sw1fuLzUBtT54UFwSdfcQbSOILbhqTjHZ4IAc3JUsADhxp6Zt3vR/jmU+FauxPPBB3S+9hpvzTibb1rSuWLtDnRNhxEjGHLd19E1dS/WnByE2Yzz2N7OF4GyMvRAgJ3XXQeAN7pYH48lugaquZRgCW7b1ks4OY47lm2+AMUJDuhq5u9PvMa/Z/+eLR/+F4DSZx7F74iQZHYidPV7cVi9+C1OHEE/7siZfJLSkwK+WVOz8y9HjKZydDFpLhfm6D48bs/H+/VZddMV0WiLiwVzmk34dR09OqntFjHtYY323cSM9cebb76JvaiIrVFhfOmllzJv3rzY8WnTpvHYYz0Wgh/efDOLX3uN10rf5v+u+iHJuiTX7OC5XzzNNRfNpHPLVqTfj9TBlZiMVeqYdJ3xGWlELFY+W70Sd+uREZd3xOYK6055/mVtz8Y19VEh8/Yt07n59FG7nDMyK5GKli4qWr2Myk7EZtn77eWnqTiN6nYfG2o7Kc5NJsmuHvRTRmXiDWlsa/LEXJoT7ftmPfzrJ+WU5KVwwbHKpXLFz8/in9+bGnM7drX4OLkgnfxUB2+v71maWl7RRn6yHc/mTkpOU/c9b5GH+avmkz00g8oP72fDku/zpeajpdZNq7+VD3zLERYXKxqnUN5Rym+L2hASitznxdrVg7loXWOo1yV2x3Cqq/9BW9tizOZELJaexdX8/G9xzPhHGT36XkwmO05nIWbz7tebQoGeB655p/qsqje101rTReWGVla9W8U7T22geacHV3ojWttpOAlxBlsoy0nDFFExSMVVyikgOehHpmVxLJtZMW4Uv590JWNqKrGEQyRMnUr2LbfQmT+EhA51LTshTEiSLAtwbnqV6V98AkBH8MB38APo2ovX1EAI1SgNwDYsGXOyjczrjiFpegGmBPWb0jp6HA0s6fu3ttf58SL+PusKGn94C3+/8joAgs3N4O3iyROmE27bioyo56JbOGTf+uNebbgXLMC/Zg1aSyv9IgSd0d0gdbd6PiOtvet2lRyDV9MZk+jAt/RvBHUrHaEEli/4MFZHTwyRmJKFLtR929PV7P0Yewl5jhKqbHXokUSkFETMqo5T97PmwgsBMNWrslCohiUvvMCOaLqafaXc1/O5D3XYyLVZQCoNRUqJFmc1qQkM3LQ6Z84cTpkyhdfeV5koTj75ZD77TMVptbW1kZCQwKpVPcJzxcqVTJs0iTDqWUrRdGb/5jesXr2SuX/+M4lSIxxN8+JKSMIW8CPMZk4uGQfAghPPpfSl5/frMxhsjljB0j1b+L+X13DWI58APQGBObsJJhuZlUi9K0BZvZuRWQPbXyEj0cao7EQefm8Ln2xt4Zj8nqypJXlqIbmswc2GWrW43OLZNy+jmnYfJxalY4lGd9ktZqxxW6C+9shcPlt2CuNyYXuzhxUVbVz4xBI+2NTEGLMNm91MQuEGnm+x83Lix/yi7Bfct/YumiIRFqWsY+HYf7HE9h5vfvk2q1rLmFj8NBeO+SctuSs4PhBgZvLJePw9jgHHRcage4vx6246bSrQsqPzcxKcRbv0fciQWQwfdtOA7rO9vme2vXNjGwFvz4C+4BnluOBu8SPR+cDyPq5QDsWWdtJqymi3vEdm/Z1ML3uVzA4lXEf6rXisTp7eWsH7WSpR3/dLP0XYbCR/7UwAIumZ2KODgp0wk9lBju2f2GtWMvUzNTPs2M/U+X0fDM9uPIb2hXCL+h6suWqm7RybgSXN3ms9JeOqcVjzEtH9+9fv+SH4z8xLuG7idLpMZl5d+F9e+5nS7upyhuCqWoceUZMac5pa43JO7nFsMSUlobV3ENi2B/Nf3EDbbQoL1/c4gDiPP56WkWo9IdHbSe1H/wQgyRLkmLHZnJhZg31EDY1OiTBbkCb12TZHJ1sFQmmbbpMXqSUitUSEWf2+EiNdrMtR62o7KwQvtNkQdY1k/fZ3VP50zxmHd0fUgRCHWZBhs5BgNiF0L55ICL337UZvP6rJ6FosxUpfurq6WLZsGc/+8Y+8vkCZ806NEyxLly7l7LPPpq2tDSklm7ZtwuqwkpGnvMOcUvLYMy+zYNFSXn/ySZLSbZjMEj0YRFoFmtmCLRjAkZDISUOyOSczhYqCUXy4j+PTweIrkSusvMXLtiYPje4AGYm23SaNHJ2jhElXMMKonIEvfp4wPJ3yFvXD7Q6wBCjKVG389PUNsbKqVu8unjDdvLuhgdvnruWcY4Yw+5IJ3PTiKtyBCHlp/ae6DwUi5E15EYuzk2xbOYvbCvnBu7+mqysdmMqEFp1hZ7zE8+VL2RCwMkQMoVE2srzzM7ryPXiymkCHupRtLPnIBwVQLl2UW1xkRODppjaeOLYQf812vJW3ILUELps+mfJSiV4AP9u0lD8VT8Di30hm5ukD/rz6Y+dmNWNtTKpk7UJo6DPbzRqaRGtNF+vGL8AXLAYEEyI1pKano0c2YAKGZvqgxcY5x52ODCSxREgeylWz02fW+Bm/40uc06fHUqb/P3XnHWBVea393y6nlznTKzBDbw5NQECxgQVrNHZjw9hSNeUmxkSjMdGrsaCJMXajWADLVYqKDRCQOtRhgIEpTJ/T+9nt+2Mf5syA0Zjku9e8/zDss8/Z7373u1d51lrP0vPzsdanMAwz3diOgRIfuDciqa8voH9Q38yRaHrsn8DXjxxqTxLRbUG0D3zt59UdewAAIABJREFUpMIcLm4d7EV0yKQbQyQbAjhGfT0euxWDhgKQ1g2O2beH4jcW933WXlSK0bEHi1sl6bcibX0cjn0eQRAY/MILCKJAx69/Q6apicBzz3/5hSQJNA0tW/l+WBGV/up2fBdcwJ5s+rRn+2sEUg7OrLyeYttmisUFxEsFbqk2E0kbw43cFLqIvzgWcUhZhqDNxqNKfOruIG7RMDQnGEKfYrGrCbbmVWPYLRjbVbYNF7GuMuEfR+rotOPO3/+edP2eo44PuBVdRzDAIgk0I2AAshInAIRECRkrFlHog0MPjBmNettPUFMtYKiMKzoa2X/rrbc444wzGDtrFgX5+WzdvZvJZ57Jzp07yWQyfPbZZ0yZMoWOjg62bVjPe1tXMWHaRGJ2U66s27id+v1trF66CLdupd1RjODSKen1k3A4EDCwqhkki4xNFHnumBqmf7yZDTXjv/y5/S+Nb6zHIgpCXzU2mDT3Tb3xLw1O9Y+5HPs1iCV/PGckPz1tJPvuPZMzxpf1HZdEoU9ZAZw7oYKkorHgw/1H/ca+riivbWpF0QyWbu/gwifWsrHJfOnKs3QlrfUB9qzPWXbBjgQWjwkB5Ys70XQVzfsRjoolvHzBBEqr3mVBci2fxiwMjdRw78R7mXtoLg7VQYOvAb9uvlD+vFYaC80WAHKWf/62oB+Xu5zWRCcJZwt6qgpBKeD0qRX4FC/O2PEAhLxnkeedRGVVro7g6w7DMNi+poU2714+HPEiaW+Eji05L2nM7FK+/YtjKb46ylrXCoToNIqEGN7OPRTOPQfBMIWQ1SbyndRsJgwp4eL/volfPf8XLvxoOae0hDmmPYzm78DRr5cFhYWIuoGWFskjznh9P4megfn+kWyMxPgadQiLOo8OnIe/Brb+94bam0QuPtrIEAQByWvOW/bZEJ0yRkbH/9wutPg/DuXpqRRGJkNtq1njccWKtxALCylctozF555JS1kFW6wFHDq1kqrZfmxtb0IqArqGa/o0hAmTSZSbAl/tysX7mHTsUdfKO99sJXw40H8YonFMmozoctGbLXAdt/dV8m134bUWkjZOIyUIXFZeNeC3JiZMKAcjgjOyFKeu8a2fX4BhVTE0B4bmQnYdwF7xKpZMlLhoZdukfKbuM7jnRQ2hI0uDouv/dLGkLICULckz+pXh6dn4jwTY+wXxU5oB2X17mHG4/3jllVe49NJLES0WLrn0UhYtX45Nkhg3bhybN2/ms88+Y9KkSYyrHcnba9+nbkMdE6dOJOo0rf1BQ4YiYvDxetPb12SZkDOPWIGLXm8+Dl1DAHQBXm94HUkQOL0oj7aywf/U/f+7xzfWYxlX4WXmsCL2dEYZU+6lvsN0uU8a9fcbN5X0UzrjK45uBPT3RoXPwfdPGfGFn718/XQ+2tPNqxtauO/CY1A0nSdXNXLt8dUs297BL97YwY67TmPuw6sAM5tsaLGLN7a09f2GHFFY8t+b6Txg4tFDJxRjdcj0tHUiWVJoaRdlrq2Itgl932lpXUrdkHdpiVqY2j2FQfEh/HDDD7lt3m1Ux6p56sBTAJTHy+lwdYA9zkmWkTyydyUHLRaGKwp1DoPVbasRrBJgMGGQjycbHiafKextP5u8cXX8btNjvHLWK9htOYX6dUdPd4hM0OBAzTai9gB/G/k7Tt5/Bd2D9rLHUscplbN57r126nrqGO6tZWtCZoLUjUXN0DN2HMIaE7fuiHXgcDsJPHoXYirOnM8/Zc7nIJXWYTjM5+k4JmeRidnKbyUh8auChagxkX2f5+7DECGRMUiEQzz1veu46Df3UjFyzD91j6F/QzdKtTeBY9wXV6uX/GgyRrYaX3TmUlqjH7bgO9eMJ2qRNLH1HXjnDEH4gsZ1SlsbV6x4mwn79/DQ5fOZvnsbBbfcQiwS4JjK/SAIXDveLKBrSs41y5jvGwRV0+CSl7h/xQEqwyGOP+J3F1bN4PKtuViApaIC59SZhJcsIb3fNLLUbtNAspRlKZXS2b4hicG4LDkUYEHeGRy07cKCC6/djhbPsEFs7PtcMBK8nP84xykj0MQUaE4QFQQpiSWvjkzUB5zAphkOepICc+tySsCaShMO52KyAGW33/6F691/7I4lcUgigx02DMMgpsRoieSKr1XrYEqdTiyiwJ6Ymd1VahXxZ8Mte4N78Vg9ZkdH+tHm79yJIAgmJZGm8YCqMnPmTD755BNisRgen4dR08bz6tMLadi1l4uvuRhdALsuUlHg48F772bejTdSes89TL34EgxBoNdVSEaQ8SkpJIuFYKid+zfcx8zSGZxeVcYz/n899frfMb6xHgvAj+aM4IenjuCl+dP6jpV6vjyd7p7zxvHDU4b/Q4H7f2SUeu1cNm0wb3//eJxWmZtOHEYio7FiRye/fcekpnj845wHU5nv4OYjEgvqXt3fp1QAooEUmaTKtk9NLyPlP4EqdzuiLefN1GVeYl1cpipZzuB4NQICCTnBPdvvoTmTK4EqS+YE6U8PfIIEDM82PAoI5ks3vmg0Fu8OKiobeXXvq/jEBAkNlLTpET6y+ZEB893UuYn/afwf/tHxwVZTqd5w0lXcf8L9PH76Au69+2YevuYeUpYYy5qWUddjBuXzU+cDAhUEcdWM5P4DOSy/S+lB8qaJbdxJ/liR0gvOxzp4EFrXdpSm1YgeD/bxOcUiZ7F2NSkO+FeQTavVpiskVIlYMICqZAh2/PN1u5F/0WMxVB09riLlfXF8UHJZkAvMvS04cvZebG17X2ym88HNRD9qRe05upg29tln9D7zLPlRc599f7HJA7ft4D7e+MOd9HYPtCGXFc+GQSa7AYc2wKMTuL7+Oq6o/IgjR11yYO2G49gpxNZ7EN2lZFrM7DUtGARB6GMQ7g114FFjpNLmu9BpN9dvnWJ6RBZNRdEV5tsvJyDk7sceW8Mey26eqnsKVUgi6jYMNYdcKHIjo4zdJIUUr56UU65xuwNXMknz/qPRhK8aumEgZqHtcDo8QKkACHocTU9hEQRkUaDYKuMWs/tBMKHXaCZKXImjGzoLX1vIpZdeyrp169i3bx/NjY1UV1ayZs0aZs2axVNPPcXYsWORLAIjx41k18btdBzqYMSobJ2LJiPpGiOqq3n5oYe47pe/pKG+HqckkRHM5ygrGURZxqILPGXMoGvJDsZ7vl532f+f4xutWPIcFm6bO3IAOeOQoi8vVPvOjGpuy7L/ftUwDOMoC6f/iEQidHd3DzhWW5VHnsPC1tYgmSzu/uSnuQLIRFplRKmHirycAnTrA63LiD9FV1MERTHrFjb6YygZB3PGbEE0ZLyiwXuZIDFd4KSCUzn+8uNZVbaK/xp0M5dEZvJh24ecGj2VuYfmUpQyLeCn5jzFkLxq874EiQX5eTRP+DZ/nftXbqi9AXvlQlaFTfp6l2RCaIEWE844GDnIBW9eyM+fuZe73rmPa9+7ll+t+dU/1D97b3AvK+tWowsqkfZu7E12ZlbOxC7bqd9Uz488P+KSUZew7FvLuHvm3TQ0+bCiUpnuxFo6B001YacRySH0CH6UfcvBgIKLz6Lgd/fimGTWS+Rf9R1q3liC5MkJGXnwIHRBIN5r7o/DQWl3eZbOJxUipUno2YwuTfnnM8T+VcWixcxri55/oMAui+Xbx5m8T2p3Ej2lYmTTuA3t6NTn1vnX0758BZZslbc1S2UzwjgNh+TGEY8OOH+B+1qY/z7cFYazHwY1SZXejrNQ4d1pJ/PkuFzNUqfoQP6vOyiYfx2+iy6i+EdmkFxwFqH29uB/5hnU3l4Eh8OkMAH80SCFmSiydBEJNUrTaZX0onOsCKIhcl/zrczoGs+c7ROQjP61HOY6fbz/YwxRRcbC96qauT5ewJXhCIqtjTv0O0mSxGHRaajOcmUNN+G0/Rs2fPX6ZoeiKaTVNBom1AUQV462+CXVz6FIE4qWYazLjlfM0BE3jUDVUonFXgOCSG+yl654Fy++/CKnnXma+bmqIkgS58+dyyuLFvX1s588ZTIYaWRZpqSgkMnjx2MzRGTdwDBycO60ceN44KFHuPTCC+hsytHYSJkUkixjE3SC1SswhGX4JBHvv8mg/lfHNxYKO3JMqMpj26Ewl0/792CIoVCIRYsX0XaojVtvvZW8bEBYVVUEQWDfvn28+qqZUnnbbbfhcrl47733mD59OrVVeaza24vWr7ahRhHptMN3Z5vB01U/PxkD+Oit/TSuPMTZP5iAJIm89fBWlv15O2Nm5zNo9gLWxSTe8m1B6C2ix9JIuWCl2JaiLinj1GzMq53H24G36XH00PnXpTgA6QyB4uJilF4FKRriAuUCpHYJTVWQgO3nPsBT2x/h2QlXM7VsKv6kv2+eYwvHolk7IFOFnhzCtwZ/jzdb/kQ33eyT90K/VhcbOzcyuypX1AnmyyiLcl/ywgcHPqA8OIx4UQN128zrHG63+uGHZn3BHefeYb5Ajgp+Gl5GtRjC2xvg4Cg3kmpSi4xJ1rDP0UzCvxtBMrBcfB+IIqW//CWeuXNxn3rqUQkTjqIiuktKsB5SKa2NoCvZl8puWm5Pr7if8254gHltcS4HYqGvhgnUfs90Qkpk9gcBHjvHR1j517LC9JiJm0junNDI6DrvdIe4oDR/IMFgFhKzVrhJ7fKjBlIDFdIX1NR8PPk4dtcM5+qlS9hePYSxra1YrHlI9jyGlNSSiCzl4s7l1LuGscM1gj2Dqij/cCvn6zZuHnUeteSofA5UFbFcn063q4BLG1YSsHvZO2UqZ9WaKfORT0wvRXTkozTvovuBbL+dw/x+yRC9wTZ8uskR16120SQ10Wj10WnppUTJZ0R6MD/uvPLwDR11Pz2Yxo9sWBjrO8TwQJzVKRlN1HHW/R6j8H7KMhqrTxOp22MlNnoIM3ZupWvPHsaefPLAtc9kQFURj6DR72hvwKoARUP7PJYvSsqxiBZUTSGcCVPiLKE5YiIGoigDEkkdRDGPWMY0kp5/+3nyxXxIZfur2GzccsUVyCWlpJ0OmtubiVqiBElh1UU+WrYMwm1E4jqS1cAxcxbnjMlBtt/+9oVcccVlpHWdPbGUqTwUBcnj7Vu7ZPFWUrt7qLbb+PeXi3798c1Qb//AeOaaqXzy05MGUMP3H1/VsAhA1VV2+XehairzF89ngWUBSwctZc/BXNbI4sWLueeee/qUio7OXavv4r3t7/GXxr/wynuvMHGQr69w05LNVZySlvlDURkTi7386aaPaN7ei57WSDbHyKuyEKOLipE52uuOQ2Y67AcRU2DUZ0QOKRqDXTHmFh9HhV7BlNQ0frnxBsas+TOXFU1lgq+dq2s2c45nOkvVpTib6rH5OxAQWLr0XfTwIZSpN7FRNtdiVIHpufkseYwrNDNXbqy9kZjbz/UR08ovl3JxnSPHuwcG9kUPp8PMWTyHc986F3/ST1pL01QXoChZTEry02IdwgGtgMbGxr71A/jd737HkiVLWLhmLxoiIxxJhlmHU++VKdJ7EBGZmjSL9DbHGsn4rH2Wr+Tz4Zkz56gX3jAMbDYbXaWlqFEJXRXQNfMcsR/a5EikaddAF0X2bvhq1oS0kRNyrgNx8hI6gm7QnVZo+xo1DEcOLdvGQXTnFMSDBzv5Xn0LH/gjA8611ZhGjn10AYJNQgumULtycNGRHouh69z93R/x5smn404lydgkDg6uwTrSbNBVhZOYYmNBw33UfLCF/IiZsWeIAm+qSc5euIU7lav5k3ouAL+Y6eLBiyawtuIYOueWcKt1MZuaA1z21/Xsaejt4zoT7L4B87CUZmMpax6iR3TjzliJa1FWDV7PC3ueYFHNH2l2dOLScwJ+59QHOf6El5kaG/qF62YRTQ9GVg3Kst5YJBXFb2Qo1TTyE2mWzNLBY5YDGGmFdCZDOp0mGo2abZL37yd94MBRMqI4DHkJKA76cbWa8Jfe7/nn2/MZUziGISGZmi4DLRvIF7OM2x5bLkFIlzz0H4phzltV1Sw1iwCGjqIoJOUcEmBDRvJ5kB06BQ4VqyjhTsRR+nGH2WymzLOJIqPddqqy7lXKSKMaIn/Zdg0pZxcbDp1OfuYbwJnPf5BiKXLbqC764hTi9R3rOX3J6TQEvrwL3/KDy7n03Uu56p2r2Gsxsf2UnGJrixnrUFWVPdnq4VNOOYXbb7+dsDfMB70f8F/b/4uD3oPsZjczhuaoqR+oGcStITs1qkQikqGrKdsb5smdPH3batr2huiWt/H666/z2GOPoXl70b1N+EYvIxQvJqCZj2CP3ENCF6i06Mw75iZmtswkoSW4MRTiwlic2zcuYU55I0X2BFe21xNVoyhqBDEZx9rbgZ00FjR60jKr16/m+MzxuCQXW9as4qGrL+K+Yb/kyTlPUu33Up7vxG6YQrhzl4yY3QZzvWfzvUG3AVAZGsn7Te/Tm8ylDb9U/xKBVICmSBMnvX4Sx750LOFDGZKuNrxeLx9FSlilDGPJkiU0NjbS6KmlTjW5xXbs2MGrK9cjozHDk6baU8vuAhk3h6jOq+aEi88CwB3V2eVVB3hZXzRe2PUCF6y9gLT1cDMlAUPJKhZ7blsXxsznoQsimcRXs1in+3kDFkyaDZti8GIozJR1uzEMg0hvknTi68FqehYKkzw5w2h7NHnUNQEcE4spv2M61ko3coEdNZBC6adYOCL1WfOba+XNcnsV2GLESodirTkRkTZ8tjGEFS/fD34XX6gbZ7+6JuwSmenFPK+fzgPqJWBxMUQO8e0pVYgC3CAv5fvy27ywtol1B/wsWb4XDHAeW4pcOWXAPGyjTEPGaFrDIXsZZUkrq6Q1vFJo1m6kxQxN1jaCvjh582pQrCEs+TsRBJhhs1KWNj0eG6ZlIOgWXGoFhQWzsWZ0SrO1RGuddTRJBqMyGfJ081zRairuE9esIZFM4ff7iUaj1Psb+licSebgwP5KJj8aRsqmKvevSymxlyAKInrCXC9dUzEMAwODAkcBPlv/zFMJl60Ir82LLMokjAQxOYaiKiYXmCiArpNW0qhC7hqybpDox86cEO040mkybjdhhx3riBHZa2umMSWKZLLPT5ICZHQLWrebaE8hmjVKXnoL34TxH6NYvmw8tuUxOuId3L/x/i89b2/QVCY7wmYK33fHm8V/+3vNgF9bNmXykksuYfbs2VitVjKVA63UXtU/IJW5bVMPIyeVMHJ6KbFAitZdRwvEeMa0phSlgaBrJ8WTX8LiCLO22WQMntA5imHhoYyOjmBU/ExSqUoMw6BLbGdeNAylOcoN3YDhgTqu227DrpgJkraeNsoOmNjyZ9sPUNNWQ2lbKffccw//s/Ij4sOPYc/69dQ6R/PmfXdRtrkJW/a9ammJ8Pyk1/n2tp9x65ifcdMp17JoyrtMbZ2HZmi8v+sjlu5bjqqrrG1fy+SSycysmAmAYIgUxStQbWGqanJZdbGMzuyTTmZ1j406tYI777yTM888kx6xAFkwMBr2kHIX4pchkzrI6ILRFA43FVBRGPxeaAgebSQYhkFw0SJiaz7jiW1PmNeymS+prgq5wj+PSP5Y0+LLT5gxNF2USCa+um1rul+66hCHiCUTZdLeXEC4J6PytzvW8caDX+8F1vpBYV1phZt2NdERD3NT62vEjmiYJQhCH2RmKpYkmeacV3Okx5LJpvweVixFUgiHbFrQeZZXkAQno/OOo9bfjUeNImnmms3sR2N/2+W1vHnLLMirhLAJdV11XK75nZy1wKuya2wfkY/krURw5bI0HRMnwKFNBLv2EZOdDEpCjxwaMFcDg8GewdjHFhIu3NZ33OUKUZI2Dba7qlL8ynYNnpbv4cXJMeP/hFcvpSw777cKP6ZQ1bgqHMUlmN5dYbZltqxr2DIZVFE0ixvJQZh6JGckKerRhYSGpqHoCpIg4VJdA1oGA4ipDJphCniraEU+EjaTfQzyDMJtdaOjo4gKip41QEQRQ9dJ6QONG4eWQo+YsVbDAFskgyEIGEoaMNAxyCSTdDcdIJV9vulEAskmIwk6uiHyhu0uLtizh8Gf38MJ+g6+CeM/XrHsC+5je69ZwNgYahzwmaqr7OjJLXRDTwNOxYk740Y2ZC6yFCMAXVmurNZW84UaPNiM47RGW9mm5Ta/W3HTmenEKovcMW8Mc3WF4dMKOfPGY8gvdaKkNXZ82oYiR6mcKlExwmfmxAvgdIaYOGkFEycup6i4hfa20bSqpoAYfsjNxMAkTqifT7F4LYtWLwKgVPIjZeIYM7/P7pgpePelB5NUZeYZJp4784qr8FaUUznWxLNjuIjLuViCBCCItPYG2LPmEwA8MRspOYLVgEhSoeHtEEWJKjzZrKTlqTcIOcw1afywjtTjEg8v/Qs9hyIcE5/Bgyc+yNPDXue6DfdTnCwjKSX4uDPnuvvLZ/CbdTmrrLMxxISy0QRVmSohij1lZ6dPQlSDRFPdjM4o8NgU7ugI4ktAT57Abv/RLn3mYBOdv/4Nrddfj5atHYjYTQHRHwoDEW+NKVAKEqaVuvKEc4havjpw3t970ASBqVv+wO//fGffsT0Bc237Mw38I0OPKgg2CcEi8mBTJ291h7hq26P8YPkLFL32CEr/upF+Qyq0o3YnybRGsY81Ba/Rb45pXaez0/yuN0samWcvYlaxuT4pywEyusL4/FnMq/oulXIeomzGoK6qKKT1xAl4ZZFmi8FOWaO3cCwEzCDxr+dU0m4tpttSQLXQyWQkqjIGUoEdqcjcK96zL0f0mgwVzmnT4bUraXWYe7EonqbDcTR31WVjLsNS5ECZ1Ul9zyga2sfhcgWZ0TONq9PDcQhJikt2kzEsLI7djbT4u1gSQQo1HTF769NSKRyGQU1GQdYlQkpOUVhUhaTNiSKB3C80ZmR54+JKnN7w0eudqq9HVxQ8Fg9W3YqmaSj9Ej7EdE5RWETLAMXikaU+kkp7v26pmm4qIkQRTdfRsu0csomL2NDxCMnscxWQFI1IfiEuOY0k6KiZDNGAqRDTsZhJQplJI1kFVF1C1XJ7eqcYZeL+gV7k/9X4j1csC/csxCbZuHLMlQRSATJazvr76/a/cvmyy9nQsYEPmz9kXfc6fBkfp7Wdxk+iZ1H+xk0UKeDXAmiaRkdHBz6fD5fLRSAV4Pr3rkcQBJ6Z9QwXd81nUHgEYTnE/rpOBrUGqHTWsSvwMe3t7dS3bcBAR5UShIq2Utf6MSdePZQZV5WhqipDh5qC1+0JIAgGgwefQ3dBFyVhF66gKfwqZwisbn6TcEOYmBzjJKvp5se9owgnTchn0LEn02Efz1B3ADCYcto5fPfhp5g5y6ycj+FE0XJdKh31m5DiEXpiCQ7t2UW5Yxjzqr6Lq3wbqhwmLYC/zRSSnnw7hmHw/M7nGOpJUqT46HF0Mt3hYOzHI5jUeAaOlSPZubSL+ncDWHQrBwvqWDp4KavEhxHtJk79flOaQ/2YqGc8vZYHn9iIYcAYNcnY4lm8XWWhIGUyIZ+49gW6P+5i+js2EGBHtcCjWx5l8t8m8+S2J/t+J9PcBIAm0PeCRxzZLoKqSIdhKl/Rl4/sMD+/bc1CPr75MuqH1/I/J079yv00ULGAPWV6PJXZw7u6ol/0ta8cmY4YcpEp0N3bt/HxzZcx4aNddG/No+qpN9n//R984ffk/JyQctZma2D6eSw372rmPNHHHc88xoKHfguA3XYNGeMcDKK82v4oCT2n9E+rvBqnw4yFlNksWESBE/I9LOwI8LOGQ3y/5DLo2gkHPkF6cCgnTX2O2plv8m2pgQW4GBXRsJQ6ufL51QB4TzuHmjeWUHbnb7Aa7RDtoOUEs52CETxI2J1TwEX2Ih4/5XHm1Zixn1hsJw9u/R7/vfNGEoJIrxjHY5j3W1ZaTEa3IaPBnncRMzFEoChrjE1Im4okT0jhUd28a8vFSQXAkk4RtdsZ3NOv2DGjktEyNIeasIaiX9iN0KroyNmcJimTIXyoX1xO1foyJS2iBVkUqLJbGW6XsaOT0Q1U3cAu556ZLuhomoYgiuiGgY6OqENBxLy6TcjN4nB4xyKDU0rjs6YId3ehpMx3SUmn0DUNXdMRRJ3eWBFWcvKuxniYytZvRv+h/1jFYhgGD21+iMV7F3Nm9ZlUWEwrqSuRs0Te2fcOAI9tfYwff/JjANyim/Kycs4syRYrqimSljDxeNxULO5CDm7r4b4N99GT7OHJuU+SHxpCwYFaSmKD0AWdP7/xFDs2mRs5Egvx7rvv0thWT9xzkIwt5z53drfT0LQNp9PJuPEDCzZLx0/CbwQY1mJDtyiQibP9wOeQhEZPI5Mcr3NNyy4oHU9vxCCsmEpG2vcONSeeh0XUcVYM4rTH1mMYBi45y8Lrj1C5N9Z3neOKz6JAc5JBpLe1hSH5ZhB/bmwWyAn0spxJJ1lEDkYOcmPXRVwUOI18JY9Vni3cVfUEVXaVweExSDaBLe+1EA+ZVlO3J5shY++gIn8j52PBIYtIwPG+TVgLPwbgaUxBMEccjmIvoF65AyHwMrWpNHn1Nvy7PST9Vgrnz2fyKZea96IrPF73eN/8lNZWuvPgzh8UcDiDO+I6rFgElGy8CpsVQUwTs+by+iVdp6246CuTPPpDYZWJ3N8vaeb6/y7oJ2X5ev149IxGpiWKbZi5B2rfeA0AR1cOW2/r+eKY0uHaFgC5JNv3JKtYmpJplvWG6ZEsnLppbd95oj0fA4Owbhoje1IDM9qqY+Z9FWSD0KcWePs++1wsMf948TwwdCJZSO33J17GYbacgFfmuO1/RjM0tGgGa1UV+ZdcAh/fC6KFRocZa3EGWwnZcx7Li/Ne5MRBpgGkqlG6+/VR2dA1BUHQGDzErMtKJPehakev8x87QwxNlHBKPEmP4cVNkpKMma3W6zHvRxcgLx5GVgcKWVUx600KogauFIQ9AsoR7FCSDmrM9LYdySSufnMUdYOOmAk7WiQLiqLgQScWDJKBuxkGAAAgAElEQVSOmgZHWtexCv2acmGYDb1E0YyTCAayDu4UDNU8iAZ0Gb7DJ5vzT5nvr0XUcUjme+3M86GpKumEOZ+ULjNSbKNQyEGkE1Md2ORFR63Z/8X4j1UsuwO7eW6nSW43RZjC2vfMF6szblrrwWSQQ4lDeDPevuI8gB+kg9x0zWXkH1xKUK2gSlFJWaIcOHCAQCBA9x6N1575lBUHVjArcwb7Xsjw/tNm+5ghxcORdZl9xRuIeA70ubXt7WbhXcYaRPcG8WXJ/V5//XX27t3LyJEjaQt+Tqzf+72111RMlT0OKsfXYg2HEXSBkDXE98+7jCuiYbP46tvP0rRtM7vDpaQ1CVs6gPChaZm+bT2GJn+C9nAK4j0YgoTR3YOcFTxWZKrd4xnpGAeCQCAcYaM3xDp5L3bDhiimiVszXHH3cVzw08kYmkHyif2cGzwJYbybn557O3OGzmGzp55ny97AIcDUM2uYecFwpp1Tw7X3n0C6OIw37UONjgZHMz/FwepRQ3hfsLOtfDG2kvdYYI0xBQkLQGQna2vbkBUznnWfawyhkBlDcs2aReENN3DHcXew/AKTuE8SpL5snExLK2+fYGWvK4IjZTCmxSDmyMVYtKxiEZ0eIE3ImcvUkXQNTZbQv4L+PqVonL36Q36/ZBkXrP6077jRG2Smz40GrB+eJmzLDGB0/tLf3O0HzcA+zMe+eAp7FvaSErkN0VPwxRX5Uj/FIhyuUchCLi+0Hc0+HM8vQ7Tn8QMifJSQqRqTz2m/PY7P+l3rF/Upbvu0jo9/vYFMSuWs4pzRk0TkkK2EpGjlurH35O5BtnLiHA+fFkt8FskKNy2GEsx6pt27oPEj9Nl30rQ5gEs10OKdxOQ05RadP40eii25m2SylVWrj6Wp6c80RcxiSUkw2NJdiySpDBtqxmzC4Tps2sD13ewqYKIa5WetFZRrGi1GKR4hyVkdV/DbvVdQcOJ9vDzfQUYGeypNUSKnrKMOAUEzSKfjONMCGYcTi00gcwQ66tMcuGNxbKkU8hGdHw/DcKIgIosyPT099Pb2omkactYgSesGuqbjy/hYuXQlQ8uHUl9fz869e5k5bx7nnXIeU8fMZOy8eRx36hmceunNZAwLuxoaOfWyG6k9+2yOO3EO9zz8FIZh4LFpeIqLUQw/ZY4YyXAQSZCRv4BGBsAldHzh8f/t8R+nWBpDjWzu2sy7je9iFa2suHAFyZYkTs205g4rlr3dZqB+XGAcdsyX88c9KicFN8MHdyKm/HwSvhlPupCYpLPo7UXYbQ7qfTt5feJ9CIZI5bbJtO8zg49lc0RedD5EibuEVk8rK4a8y8rqldgr+uGplgRJgsyYMYPa2tq+415vECNRz4a46WKndXh2xzO4EzLulMzMU85DjgYwMKgYW8i09c+YX5z/AY0tYTYvfRuLXSAl2lio5nL0b7Mu4X3rz6hrbIdEL7iKmXL2BVww+2fMy0zmW6lpKDNl3Fl4IVEzhpCQYJfcSo8QwSVCLKXhK3FSMtRDcq+fvJBp5ZfMGcnMypk8eOKDnFV6Jms926h1ChQ3R5hwfDlTz6rBsKg0G004MiVoyWrCtm5uqrmHX4fu46dDH+qb56+G/Y5RVe38t7yfbrGOlkGmMHqq26Bi1JVkDrZQ/OMfMfiZp5GyeH2Vp4o7Z9yJZmh0JsxnmmlpZs8QmTyLl3tf1PDFDGIW0xMy+gfv8wqQhAwZKSc1pGyRpJL+8nqUZFrlJwufZsbKv5HZ+nzu2QaCvD5hGBgGdz9xK6ManiAe+seYZMPvN2Mpd2Ebns+Cpg5K/WbRrRE35xv1usiPx77wu4ehMPuofIRsaruhGSi6wasdAYosMpZsHKDb5yQ+6Xz8cogG124kVaBiuA9voYPC8blMRqcGl6eGMVzv4NW7N2BJ6vxqaDkXZFsIb62cy6pzXjGr84HLm3KW/8ZCmfD7fzTXSo0RazeFtx5oYlV3NR+tjNFtEyhO6fQWhDFskCcZKPGd7Nh5C3Xb5qMoQZpb/srBoMmH9e0JGq3RSkRRQVHN38to1r7Yw+FRV2RCnTMtJhKxUxhJKUEkFWoFB3myj7McNlSLqQRs8SxppK+CkNuOAbg7I1g0A8HpIGG46fUKhJ1wsFRAE8ESS2LNZHAeTvTIpr3rooD0JTRkTpsVMEjpupliDCx/cznHTj+W119/nWPGjuXTd99lycdLmDvnJO67+242fvgm77z6LGomybnX3sqtN36X7e++y+cr32bt5u38+bWViIaKSoxCxVwLQU3htubhMHLPJCWXcEgewoLMJX9/gv/L4z9OsZz/9vlcs+IaNndtZkrpFMocZTQ1NeFQTYHYp1h6TMVS5a7CkzQt12P07Gape5mU6OGQMoa0YX6vzLKDycNOoqF4E4qU5o/HLmD+T09DsgjEXUEWWh8DAap91WiCRlJOEiPGeu/6AaR1giAwbdo01HE5a6c9+RQJHTqsk1kWkrmnw0FPqpdhbS5KTpjCoLHHYFEVThpezc8LXQj7V0LJOJTisXz03JN4yio5b/BuAiXHMf1HL7FUNJXLadJmRopt2Db9BT3Qha57OeGCq5BadCr0fCpOHokwsxbRlYOEpiomxUZQjJMvW0ikBQzD4Ow3z+a5JW+wSoxQd02YWz9qYN6jq2kPJZkyeCopMc1z1c/zW+UPdPx1K1pK4XvLbkYRVLqjY9HDkxjR4iVAgDXerRyUWhnV7GbaLlNYhRpf5L3km+ybLlLv34YuepiY7CERNJ+NY5JJ3W6oKumDZvB4iNfMSmqONGMYButC22j1Zriudj6nrtyCV7MStuSC9+6E+bK5BlUiCjpav+58claxJI7g/OpuGljfkP47XogRCiMYBlXd5v4a27SXWPCrFYuh6miBFI7xRQiSgG/1pzjSue/Z8zN01JTgzvY12RiO82ZXjgRTkEVKb5tC/uWjubGlnTVFEtuUNIM+3UZQ1biuqojCsHl+p9uFx1GGnyRzRpkQiS8Ln5WOzOejiEJdRiekm7DNKHcVzliSdW/u5wdDSnl4VBU2QWDLzF+xtyCXiViR1PtgwfbwDjTVnGtSi5HpNq/tP1DPRv8gUt0KPXYRR0rDe8ZOMoZOnmh+VxAkEolcgk1HqAKfXaTEa0E3JM678DwyGVOxPLvrCrwMzOLrKRzCbn0QYjYu0Vl+KqJg4BZ3YZ9iBq0dndXkCRq6bGBVQRdBQyRtyyPoAUv28VpsMgKmMvF7BQwBNOlo6E2urETyeFBsUp9i8Vg9A/ZMYWEhbpcLSdeJpTNomkYinmDr51u59+F7Wbx4MQgiuiBgCAaCAYIkI+oZFMHCB0vfYdaxEzh51vEIgoHPbeXx+37NfQ8/BoJIXiwHJ9okFVEK4zJSJCQPacPF3tqXWXfGUh7Rz+F9+XtHb8L/g/GNrbzvSnRx5bIreWneS33H+hcv1QfquXbMtTQ3N5NOp6koqcCm2vp4fg6GDoIBtxR3oDTUontXMTmRTX3UMuxXRxK1BQgbTiDECMtGrF27iHr9/KDmPI4fM435782nfqKZ9qpkhUhKTfHk3CfZE9iDpmss2LqA0eJozj7lbFauXMnYsWPpjHdyz/p7uJALKSpqptTeRYs0lhPKT+OhzTsBmL6rgJGtbhqOPZ0fLtrFtIpBhNpbwb8Ehp4MV73FxkUvE+npYsbcSQw+5OfAqHkMK3aTmn42rPu4by28h1aTEQwwnEQX1gNQdN14kpUuTrrnA24ygKxu8WmVIO0nJMSptFhoCttojXQRCQV5IJt59uO2oby73RTuZzyyCskWgwpY5TVTbB8KPc2pb5zMBmUT5b3T2BueTnmqk7MiY+nd3UbQozCYEqbMO59VrzxP3cgwnYUp9g2Kg+6HHpAd07Hlq7QteBy5uBjHBNPD63n0UfxPPY112DDK7/s1YHqpUkcv950eZ7hYzoUjLkS0Och3FRHO8qvpqoAcz6ABgsNUVnq/vjdSFk5L9fNYDu3YzGu/u5OTL72Uyd8yq8AjwS/2HIxolEB7nDM/z2WrJWN/P1Ca2h9EaY/jGG9CXIfZi0d/+hGRvHzyM360pIhk13EZEp5oBFXXuXxbI1FNp+azVQwK+imcfx2WEif+jMrSUJSlU5zMSedw/wtL8/mw14TWUlaZPLGQeFxGbTCVqidLyV9a4yWqg+CyUpnKpQBXC+2sWfkak0+7C3GPwsiQyhZXnAJ7ztuzWhK89pnCmSc6CGYPO2QVu9yDUxhFfNM6El1mrM1nLabLBhndoEqOEtGdTGmL44gMpWruVezb//u+3w2m8in12fE5dCCN5LCSCZvw3qauScwSB6bOWtyl3Khfzb36W+SLMdzDjiPdYaG8shXP1GFEP9+Pv7eMCsPA4VbpTuax6dMwoe69iBIYho49mxkmWAJksAyoKdmlMsArEex2BMlUhHomDaqKIkuU10iUnm/KA5/Ph81mxt9saZW0rhOLx3j//feZPWc21cOr8fl8bN21k+oxZuxJMABJQDZUFMHKnoYGptSOQUBEEEHWUgypqSEajRKQ8nGrAaxZ49UlK2CEMFfMR0aP4i4s5ezaUu58cycLgrOBP/zdffm/Nb6xiqU32cu2nm0klAR22Y4oiDRFmgac0/hZIy+GXwRgwoQJuHe6ORg6SHusnTfb3yRfkyjavZCfV52E1NY54GZ747MomiJT35WHXT/Eax43c3oWghdGrH8a9fOF7CtyoPTj3vHZfDRHmplZMZOZFTN5v8nshnft969lZP5IfD4fI0eO5OG6XLvR6pqt+FUb35r+BCcuyXVyrOq2c7A8zvL1psU33uJG8veAux3GnkcsGGDD24sZPX44Mw8tAMBTaxYRVow+Gdbl7mWEFCMjqshKKenGMHKRg/gQN1etaUCtdvNkqYML6+2MS7r5MSnOVFXCUoJR1lI+Q+Bv25ZTGz6Ww3zMC1bmOIkiKRVSPjxmbgSWjI/3fetYl9yOLEpYg1MBge9kPuOqh58g0N5GuLuTwqrBRP29iAiUBmymUgHOHXYu74XdjMgUk1IjZPbvoOL++9DjcdSeHgIvmM8z09iIe8UqSqtL2dG9jXW7duAU4K+nPEaeLS/7PPJIW03F4m9wo2Up8gW7WZ8xgb2kMAX6YSgs1c8jCW9dBkDX+ncgq1iCnQO54Q4PoWkP4cWLOGF7jjQz2RsFBrJCG4ZBz1+3kzloegzWKlPJiV4ruq5T3biPWHkhJd1daEkrsl1HsghYVYXdXb1Es/ET2+2/oBsouO5aBEGgI51TYiv1nMdTtHUzDz5qCmvZXYRNspHIaIxvM4Pkh1PIi6rcjJ5ZTu1JVURe3AkxlagYosxRTbVtD8sff5TZ2tmMH23jFV8CSzxnvefn2Vhx8BEck68naXcy0dfOiRd9i0+XhykGOl5tIaGsBEYwZPAk/DaRalXlYNp8d8pVjZmtdbRtno3gs2JkYZyMWMioIi/lQitgJRSPoypBCksuAiCPgSndVlcpfm83VwVuR0DnT2UFdIrFONKduMuKCRt7qXBm6WZECDk8KGIcUMwiSVFElSR0UcTK0TEKQRRzxZSAgM5hFjFBEDAMUynowSDKAR18PuR+FfJOq4VkRgFB4J133uE7N30HVVA5/1vn89pbb/OTcVmONQQEwUBDoNeRT0rTQBAQdKMvkKMppkcVVjU6jCoshoHPIVGYaSdgOAgLHrwpK5IsUDOuCIdV4phSLwdb/j734f/m+LcoFkEQzgAexXwKTxuGcd8Rn9uAF4EpgB+4xDCMpn/kt6cvnM64wnG8ctYr7PEPbNjjiXooKSmhurqa4uJi3IqblmgLi3YsQjVU5oWzVlv3JhAVPiwdR23nfor1NNv16fzgOxew84XNjOtysalsE5sGm5bfqEwGt5rkW4NmsDBtittrx19LnjWPR7Y8wrIDy5g3dB7FTjPQ2JPsYVTBKMaPH08oFWLJviWcO+xc9vkbmGWLo7pmsDs08HbX1PailLoga5SlrF7sHfvBkQFPOR37G9AUhdHqdnRc/NhyK48Wl5Pc5Sf+t3ZE6Qoc0jo0owiPtAl0gbXS8QxVwHfuMO490MlmUYVReahAuuB44qpMzyE/iqLjlyOckJjEs4T427b3OStyat/cdANGRxvY4zEtrEFCiEONPyEvbx3x3lMprnmcsC1IXjqPqO7Cp4Q4ff5NWGx2SmuGUVpjwm1iFooq89tpLTUx4kvH/5BntnZwY/sCEr0mTCP6fOybZZK1i14vZXf/lo5f3k5q925GFbhYE/yAlF3jrNQoimtG983T5yjAEAUTwkj1S++xmwwNFknjcNKzlBUYqX5QmJDlWToMaiz983baUl+sWCx1K1DqVuD11vQdSze3ALnC0ERdN4ZBn1IBSDWa3oHkseJvPURBJETz6EqUJnO+VreKzWF6Sdes3grFOYp5MKvq5aIi2o9osXxuiY9bBpXQMm0KBiCIErOrbwYg3i9BwZn1lERJ5NSrxtAYbKDn2D9RtPl0ht9wId2v7OEY/VQ+bn4RqmBcSIMhoPSDeoo8Drr1JPZkDHuewOzyZsRjr+XDlY+QDnxKbcGJrOo6AbsUwx9T0UQ7BeIW/tRjKrXybMxB3raQE3+9G00z+P3v/0Bck5goHWTeJ9czS/wlwcQxOJQwtuZuRHS8wkAozOMsQ3dszz4zkWHFbiJSIc50D82xFnQ9SantIIfzQu1ihvFzB1PgtRGMRZBlCDvcgEFxLIxhGIRs5vNxK25KRDv05GAne76CUGmSoKqBAEp7P3ZsXQfDGKBYrKKAgYAoy3z66afsrt9tFlRqBhICP/r1z811cLkQoh3EpOlIisLYqiF8vnUDN3xLRbRnM/4O9uJ2uhBkAyMjkwKiSQE7g+jAoNCWAVXA4bXiyLI5jK/2sbl9YEHq/9X4l2MsgiBIwJ+AM4GxwGWCIIw94rT5QNAwjOHAw8CXl8gfMXb5d7GiaQUHAiaL8Nh0mlm9oxleMZxbbrmFefPm4fF48CgegpkgHx38CE/Gw42RbFZIJgZahljPUJZ2Ps6LPX8hXhpHEARK8guZ0HwRw+KmZWnTdX5XaFbWX+YZycJ5C3n17Fe5bcptXDzqYsYVjuOPm/+Ibug4soVm/WlPXtnzCkk1yXdGnENFTR2iqNOoWLj5w5sH3NOzVy/mkRNf7vt/WHQip7Lz9ZQROGRaXo70jbSnX+NnqZn0PLGNxld28lalhX2Wy/lLyUP8raoWGR0ZjQcmzGbntSOQh+fxYqv5glxVnI8DWOIW+WtvmJnDCkmoAnExQ3EoK3YzpajpciyAhE5lso3jA2txqabAq5GDGJliQj3nkjZcjGi7GICSRDlBzYZPjVAz8ehmUL/sTfPB+fO55KxbAPBavWzJ5qyeHNhA7GAca00NgWef6/tO5YMP4Dv/fLxnn01i3XpqPtlH1KajyALfvug3A34/322mxqqHvUpZQHQ6ESzmcxHEnHB85nf/RXV7K4l+fF9CVnga2QZPTdt7yUS/vDo/L3KQQLGZzaS052ocDEUn8GoDwdcGMgak9poeqeSxsvcDs27HN8iOZ4jpdXhHxfHazP3zE7vBhUVeFt6ba7EbazShmM4jFMvtQ8updWdp9gHRXYogmkKuuSxn6QuiQDy+H00zr/fz975F3LqO0JylWIqd5M8Zgk20UFtgxu1K2k2WgUlxDa9irk9JoZsZ376cao9I3O7m7Vl/oGJrL8vHn8+BqCnoi+yVDHKNYrsvq+DTq/vmYMXJdkZjJ4Mk2UgkUmiGSFSBKdlapknCfsKJBEWdIcZsXMK10nKunmje3+u9D/Be6Keo75YjOXNM4tVFTuLWQtyKn/PePg9VC+KzHkQ1zP1Q7NTwZLpRA34MBCzZLDO7kkEURUQlg1WVsOsgGzLqEZX0/fnpRHu/JJ2sweRIJun/DWv2/NcXLeaqq66iuamZ97e8z+otq6kaPJjPN240fzcLy0ZkF4O6Orj89DNZs2ErH61bS9zqIJlMceudv+G2m39ESaa4j6IzLUDUYu5WByBbxAFzHFHm4V/vGvTvGf+O4P00YL9hGAcM08d9FTjviHPOA17I/r0YOFX4IhrRvzMqFJXbV/+Stc0fUayqvNbexdlRlREjctaix+MhP20Gig8oByhO+8gnwgpree6HuiYQ14votjoYN90UDgV5Pqy6ne+0zwVgfjjCOoediLuYIdFejik+xiRwTAbxPDKR23wT6U50M+HFCfx2nZn2ezBsQkearrFo7yJOr5pKV8MtnJFnCrG1ftPrGd3kwZ4Wqep2MLiwhkTKwenCBu4JP01EMXDJ5vn76pto2dtAns2JTgWCXUJQm2lY8TiR937OnwaluH6ak5eq89iWl7Nwt+SNZVFXiEe3HSJtEfmO1cV/jx/CFLcTw2chklI5pjKPlGRCRQfEVootEsc459BsSFTaLTwwOsjZybUMPfM8Lo68z0XWrbiFnDB2GWlWpwZz7cY5TP08QdKwUmbTESWJ5mSaPfHD/Fc6b/WEqSurYe4JF3PFmCt49vRn+aRpD1WpDqpiOoktO/GcfjqJLVvIv/JKhix8GfdsMxPJeaypqE7tLum7dm1xLtPOfHbmvacspkDVRTuC3Q5ZhS/0c2JcqSQjWw4S7wcpHU491vR+QX7ji+ImA7dqz2AzsK32a6mQafviwkml1TwuuiwEd5tCW670Ix4bZf0vZLCD5DaVxsndPZy3ZAflh3L9QA7sMr30jrTCmWs/4b4P6rjBsFPtsNH565yiNfql5gYqddYPfofyk2R6QnWsXX86H+ww2QMGW7NemuSFP8/E0fsiRr6dCudQNEOnrWMDz60L8efPEoyImgJQFdLMvOhyRoyehN9dybK8WQDEC52k9QQZLUWpfQgTC05mYYUEaY1wql+QPjUOR8VYvERY/9kqDhw4QNJMPmd41FQso8UWArEItmwMrEwIki/paIZEjzqM/alZpJtlRCnGz88u4IbZQ9nSvYFOwUK+HkCNjCYubUUUNHTRVAJyNlnH0BMYCAhAYSxMkRLD47AjaireuIgvLiEaxgBqHZM3MvvcDQNRDWEpLSbhdKJmywns/4+69wyQo7rSv3+3Uuee7sk5KWchISEhiQwiimCSyYhkAw5/bK/BAds4h3XCXtuwgBPGNmCiCSYYIQFCgHJOo5Emp57u6VhdVff9UK3pGYHT2vsuPl+mu6ZCV9W996TnPCebHefFGAUE2S8feogV556HEAIFBYnkzDPP5InHnnDPbaYxhcqIdD12n9fLw3f/gG/ecw/zTzidqadcytFz5nHzNTdhFvzpkEfBkpIhCzxqDienonvGF+FMGNPt9v9a/hWhsDrg0JjvHcAxf2kfKaUlhIgDZcC7gfhjZMVIkssSI6QVhZU1VWxJ7mFewa2uoY+aMYrF7/dTL+tpVVs5mD/IirL5MPAaou8yBsO/pEzpp0v3ccbnW2mtaB49rjJaRi9Q1jSXV9p/SEoRzL3494TfuB8OFKyueAd0rof0APN3/5nW0lb2x/ezdcBNxO+JuUTVb/W+RX+mj9N9XmSBZj3twN5EFzP3hVnW00x6+zAVTS04juTXa9v5aud/078uDHMVgmHXqnzl8T+SyHuZE3WVX/nKmew/7ToOVzp8/fnH+NDF17r3jdsc6JWFn0VkYIOWZf1IGkWHLyx1wzZHlwVZk0ghhMMJuVd4POwDx2S9cYDqbAVdnTqDWLRYvWw+0Aa1zezevw9qGwhl4kyuLeGZAjz+9iWlfP71FAeCdeTSkryiMyPiTqhj1rrAgTXHTOUH7WMpMwS3L7ydvCNZnR3mgqF1JIxzQP0jnqlTIJ8nePxx+OfNGz0ictGFBJcuQa+r44GetzFU410Mx2Vl9dABpl5o8KV6EF4PHPZY1PE1Kx4zR3aMYunpcSdmf6JYIKg476FYdB/ki55MsnEO9vpnkcPF4WsedBVIzWePYeTPh0i+3oVnUoTcnmHUMi9CEchD7QyFS/DmDxFTFRxN4igCPZglp4bpXLWZTGx8I7uht9+BlVdzYCjOf/zKZSE4vfVH2EfXEn/0UQCcoCAw+4Ojx/iiPjaKF7nm2LPZuuebKALe6XqB5XO+TqvhPpP8vh3Qtw3x8jYqLn2DrgcyJG3IEeTArvtI+Kr46L7ZPNid4dmBbo5ZMIsyr5eEI3i9bwgCIbKG+9xH8kPUBSaxtURhY5WBum+IuG5ybibFmbkSPpa7knvCexFdYLxwBxNpIyIvJ0ySsrg7h6aKQ2xOp0ZrRSxU/HYSUwYYq9hLshVMb8pxQsM0Zv1iFleYNZxHjrndR+P3PeLuXzCcsHNoXg+2qeJzTKQKJUEffn+A+OAQUU8GQ7HpywZQLBNb85DzeAgZKVRdujFS2yIX78eT7UVTNErrWpDCIFcg/nTGFFAaha6e9/3xOVr9BSJNBJZicc3112ApWdKY/OL7X2KXvwVvrshOMWPCFJ75xS8Z8aoEIhV404XShMOKRe8imatGSvBqJo7pfZdimVVXQmnAoJ3/e/lXeCzv5XkcWYX29+yDEOJGIcTbQoi3p+QsvjQwxAwzT71ZPLw+byErpjKrSqO2tnZ0u+LkmdxUy+x9sznz4JnM97mDa8Su4Pd9P+CRoS+Sro2OUyoAtRWVrG55GOO4WtqmnMpN1VXMKJ8BdfNhpBt+OA++NwN+fyUAqr+cJ857YpSGHlzU0mN7HuOl9pdo9Chsje2lrP5GrOAi3klpOEiau/2ceNUN3Hrf7zjlU1/hk49s4uWtBxnZU0DtxPoJ6gUuI8uNmTYGykBxEIynVZ/y5z8xY5+bRD5KraJ16bNUNS+gJi/o1iTdXsE0WyWoazDSS/OO34EiuCr4Ios33c5K+VuMAVdTlCl5Oi0LE8kkxYXSej1F3vmLr7iSU089ER8mM+jg0uWLaSkP0FZ3DK+VLqbMHOSUyWVkxzDunrBu57i+8QOFJll7EgyDC6UAACAASURBVMMkFQ+LSiOkthzAN3s26dffQHi945QKuIlUvc5FqR1dffS7vBWA0kqX0y1rFOo7VB3F4x1VLIoyfoh5zRyZMTmWPlXj2x/6CgejRa9IsccrFuEtQRhFVm11wqkoE2cQC5cgki7LgmPaJN/oQq8JoIaMUWp83/Qy6r68hKpb5rrX7+/iUGUNgfgggbSPkgLE1VBNssFy9PZtNHT8mZxu8NTSk3ll3jFE33qT1f3DvNFW9GIG/nAv3V90veWUofPG1IVole54fDOVpyISQ0OSTbdhJdbx275SLFshnotTXoDUlvUXFb83uZadER8b0jZCLSedjdER28mMFfNpLYyTTCZD10aXBXw4EEKVDqYC9068HqENktTgY/N8kLWZTxwHWJDMkHXm0i9DvJ5yIwfz2EqYFJX0s0TZhpAOsnExTaKXe95qJZ0pPmvDHCHnjGc0r0o2E8vGRqME/YUcx0PG15im7OYX9vEkLLClgpF38ARNfBGLKhEiIz2EwhFUTcfMZTGUAp+ckaZKjxNQ8+gBy1UqeuG6uQQyVci7OBZicA9C11GjUZRgEJnPI5P9MNKDmk0w0+wkYKdHecMUoWALm4SeQGCjFgKvCjbeQjM2pdDmwtI0pACvZWBpCokSg4Qu8GhgKHkqfAP4tQxB3QEEmjF++fbq6r+sX9U/K/8KxdIBNIz5Xg8c2QN2dB8hhAaUMK6llCtSynuklEdLKY928wau7HGK7Wib8zai6ViM4TaXDvSw/OoCLtz9MVSpoksdn+WeXkyuwsFHrzmHsqowR0p1oJpt1WsY0vv5ZUU1WtlEwkYY6gs5g6HxxJYkeyE9xH31K7i78gQCioeuVBd3vn4nv931Wyb5w9w34OGyNT8lGTmfF7sClMUNbrnte+wLT+bXG/o46yfreGxDJ2fVZTBH3LusHI4T0PNkLI2px53KhGYvJUYVeqWP9G43br+zqRX/l91q6B995wsgJR8PlLDvjfOY9vT1HOuVSEWAIrispsx9Po9eR/PuPwCwrMUd7JOcnRj9XW7yUXctriaRojw3wPETGvj07bezcuVKbrrpJqbOmk3TtBl8qWYf3/3AHHSPh4uOrmdLZ4KkFuSk/lc46rQzWZ8oWvSWhEdmt/DAgNuTpbsA8dy8cw0As2qmk92yBf/ChSSeeYbw8uUogfduifDXJNIyBc2GTEGx2CKL8HlHSZeO9Fh8uRzpMR7LxhLXB3x7QhEQoDhjchmaj8BJX0T63eJCY/IZ+GddROWkUgYipajpYQ78YS9dd76OHctRcrbbUyS0rI7w8mYCC6oRuoLi1zGzFpF4Lx2VNdRjotXliNZkcBQXZDDi9eHpa8OXHeSZE27ju5dfz8FFS/CPJNizcRPRRBHto+7fxMgzLqJtb1UEv+aO61VOlnYtRqt9B+dH8zipjXRnAqzNZXl4QLI/vh+PcMg4UJ5JQONiKGmAfX9m4ZVTsfw6QnWfSWXLBKomTB695tq1a8nuLYJnphZgwemAH9lQQ7tfIW4ofHlCLR852c1XtuQtnsu6c3ebMgVz1gd5FbfNuC5Njlb3IDUfYsJJ6MLGQ554wg1nl6oZ1FycnBw/LspT9cRyMXYMut7x1Ilnj/5vl1PPPdZZ5KVElDa5zN+mgyj0dPFaJrYjsSwbOYZcUisYICE7RuAw3MMXAdWA9CCGMr4yUiQ6MGqqUUOu8SpjHa4RGtuPaiYpt+JkC2ARXSvCth0NDg9JTToYVh4E6P4Cs4RiIwVgSbqlTVc8QyZvU1Jo6xz2GVT6B5CFVtHqe3SLvG5py7u2/V/Iv0KxvAVMEkK0CCEM4FLgyIbpTwJXFz5fCLws/47OXC+zmHZqSU27bHTbXFOFyumQi7svExiKxaHdXbQqcF3U3LZuLKlTNruo8xrqxyBuDr0Fv7mE2lgXhmKwuX8z2wa2MbO8oMRq5sDMC4v7L7geu34+cnAfPPNJAo9/mBPe/CXHOxoguTSaI6hIlDEwxi++8SXi/jyLwwu5Zzfc+psNfOWPOxhO57nv6qP59mIPjuXWMnjiJooSJqeFOeWqazjTv4o0kxFGku5bbwXA+clPabzwA/gXLwLg2d8/hfmFj8J5P4P0AF956xyE4yBiOS6fUQPP3U5u8+tUre1Bz5scNG1oOQ5FwFBdIyJvIjKuDTBVO0hYVznhipUIIWhsbKSmxrUyhRBc/LFPMmWBe92VS1o4a1YN/3n+NL5493/iDQZZM+yGgqpz/XyutYalW++hpt3tn96RiGE98RG27FlHwM5SKypdpVZZgZNMjt7PPypqMEhYDZL2uorFI4TrsaiFMMQRo9tjmmSzRahu5vAkt6wiY/CYfhxYGTZ7biGluhNfGO5CUtlQwmBJFD0TY/h1N39mNIbwTnBj70JXCZ/YUKRgAVLDWfyZEYbCIXxYmIaCLeFFnw9VOOysmshwuJXe466nJN/APdth+kLXuFmz9h0mFFia1WkrSNUVveX+kH9UsWRHVIxqd8Gd5LGRuS72J90x7yg5rn7uan4yqHNHp58djuQ+VZCvPQp6t1FeH2Llt5dSN3kSqh5m0QcuZfv2Ys3OqlWrKEkXa3xa212PWfo0xDnLWDPRfQdTogG29LstHGodhxfTLkJwfXcO4wM/5WWOxUKlxJBMMgYR0WbwucokSAat8HqmlliQjb/LY6lINTCcHaa9u4tLNt5BWc3pfC5/Lb9UzmW5+S36iWIDpurmL4y8xF8o7vQLjUQ2T99ADPGeFJRjxEy5ysVMjhZkFl/mAPRsRiRd8IaUKmgeCFaD7sdn50bJTNUxib6MtEeNZY+dQ89bKIqkmwDxuihZzUBTDXDkuJ6afi2NonjQtTpy8VpsM4juUd+z22U08N6NEP//ln9asUgpLeBW4HlgB/B7KeU2IcRdQogVhd3uA8qEEHuB24Db/55zt9HIE9GbIVXscthKCKKuVpbDBzGzFo8/WCwWvHReKZOji/Bn+kja5dQ2VBKtc0Mjsw9bpo4Df7gedj+Hd909HFd/HA/tfIi+TB8Lq12LClWHC++Dy34Plz9C5rhbaNN3IDJDsPXR0evNz5qAQBdwW1WWhJlAPeKFZ3rr+dUbbeO2HdsYIP87t6lW+Qm1oEj0fSkiE+bCjsdRHT+69JN+3SUsvPfcS5hUUYYQgsh5bq967ysPYXV3M3LIA8EqQnaayw6s4upwCca6e+m750H2P1NFdpPG0q1v06ZGuav5Rl4tPYYPztaxLIcqJUaZM0yVmqKpMoDY/fzffC9eXeXHl8/jA8e0Eoi4i8Ka/kHmJHayce2F3Bp1YNU3qPW7z/263f2sYBFvlsxmtl/DOuiGdRSf+3+9uua9L/R3SFgJkDrcXEYIN8dSPx950a+IaeOJP71mlqxZVCzZQrMzLZ/HPhzKc8bjajZ1e0a7+clCD4/yygADkSieTBxHgi0lZdfM4K9JujuGgkTx2KgO5DwKL49o3Bkq5XWvQcYQrJ//CY7+5q20zq0g2Z9l+dzpZLw+Jh06QE3ChZEq9QtJTjmOmN9LRtcwdY2gdxaOlGQlKKVtDGUj7rJpdrFnZDwHWY/lTvkPV1fyfTq4V8vBcDvYbkOqqtYKvNEb8VQ08NhjjwEwfboL8tSdYnFpNOUqOmko9Ods2ktc5ba7/0V+ufN3nJDNkCmpZiiv48GiJ5GlL5HlhhtuRAQrCakmTcoARBqhoLAXV7yNt5BDbQ2YkB3GHKNYKptClGZq6M/0M7Q7RzRTjb41ResZH6N74Wfc4zw9CCA2hhtNlS43hiFUuoYzWNkUQh2fnwBQxkzbTDJJcgxCMG29Ox19GHVomT4srRrCNThKAJGwUApjRRyRBdBRkRJKzSxa3kKognKlmnhSZVgrJRR0SxjGtj2XVhJpG5gZC+noRKsDlFT4eD/Lv4TSRUr5jJRyspRygpTyq4Vtd0opnyx8zkopL5JSTpRSLpRS7v/rZ4SECLFi5SdoFsvYvy7Bjzrz/LSnD8VfiWm4k2XzE29x78dfxbeziN0OOnH0dBmlagcxq57aqgrO/3/zWXbJZMrrQ2CZblI+dgCMEOx5gcsnnM/U0qncdexdnDvxCEDb5OUw6VRGtvwXndVeumoL4bTjb4eJp7JgwMUt/GrIgwO05VSOLp/HsvBCji1fyDHbolR372OP5yq2lX+GK9QXAFC2vEzbUwVUiNGJN5rHN5wmhR/n6U/QbS/Djh8i8/arbLjoMh46/Twm+t18jFo+frFIPP8n5Me3QuUM/tN4lW8c38zQD7/K4I4iCePR7Vv4dc3Z/JfTwLXT72KWt5+Q14NmaNy11LW0azuegocuGR9i/FsiJan2t1g/kuVDTzxIz/owPP8ZcCwqTvk84QJkeX14BltDkzi5rgGzrc1tfFRYzPWa6r92hb8qET1MsoD9l6aJ4nOfqZixAqmMXzx8uRzmmFBYtuDSaFYOp0DHLo5I3p+3VpIqcSe7UlIPQNSrM1ASxZNNYgzsJLb7BeR7hCXGSqq70OXRcJ9H1lAYLnCbDSoCIzhEtNpLz94NaEacWE+anY8dwDN1KpMPtuGPFUK7igdVq2XtxFpemerG0zVvkA5tgKQxzN0HzuRTr95FhSbx2f10ZUrf49cU5cHkXkzHguGDDAwM0JnZhpW3efTeFwGYxxZOrSjWdpyx5Q0+bQ4yMeohYKVBE/QmcgyaFkiHn2/+HpPCdXx5YJC4z82RtXhcJbSpI05dXR22r4xTzRdpyu9zFYvHHacLyjZRKdy5HOp+HT2xb1woLFLlx7C89CR7GMi5objYwRFWLm3hlGlunqwmtA0PMJQsGhCOgJzMogsN25FojoXH8+7ePLbQQQ/gSEE87yWZhZSlM2x6SeS9pAoM1wgVK1SPUFzl4mRzo+gwO5XDNhWq+7uQUmLLI8JoeDETGgxlQUJeU0ggsQqFvLmUTQqJKSVeBDWaRCg2ZtogNZxDM1w0mKL+S5bu/zV53/46T0kTO9eKURLIsmw5SzJZkoMaD33PtXjjbQcAKNWKOIhU7wGSsRgRrZNBq4lgwI8vaDD7xHrE5t/BVyrgz18Dbwlc8Qjk0xy95xUePudhzp90PsrgPtj3Mrz0ZfjRAvj+LMgMk+h+EUtXaGsNw6fbYcnHYO8LtORNbig0m3okZtBnCTxvdDDht70sezbGtPYwl+svogmHQPIAX9Ef4M7lTaRWFZlzdTGA7rNxMoIDHSbD5tdJOysx9/2RjD/AncecyKU1pfgLg0krL3btAxh5/nl2HX0MA3ur4OBanF0vM7TTh6e5htannkQrjzCtcx8ZtYCWEgIG99FSXwOKQs8BN6zRoBfgs0PvrfcHTIuDmSP4sbY/zhtPfJb527cw9fW9xHYHYZub0xl8cTe/2bAGbYzRtjziJ7nmNfT6euwBd8HSqsYXBf4jEvWUkPQWILSZDOEzTi/+8wjP0WOa5K1iqNIsBCY0qWDbDhtbDHq8R9QyALlINYHTvo5eMxdTZtDsPPGIu2Crb34fz45H/ia5ZarPDZmFCk3Ych6Fw9RUjhT4yvtRlTae+M5X2Pz8N7DNvWxZ1UnZUXOZ2nGAY/btJ68qaB4NXYTdLoOKwBP5CHjT3DT5C/x6/hfI2K7xMZgpQxEwkK54z98D0KLbJMjzjtcD7/yc1U/+isr9PyGvDzMiXCDHCl4kuuozXHHZpSxbtozFXpWbpwS5se9LvPTOSlQdHt/QyaBpoeX2Ec8Nc2JZE5G8Q9JwFcsEXxZFwOYOdy4PjKUUDteAx/VYpgf3Usn4Ar+xobBwuQ8hFfoSAyRGXAWdGHBzIvObSllw7C/pKXkHLwIbcGTRy5D+IQQCn3RQpIPXGG905Ox6Ep56qJiMFSiCgkbyHrK2BgKyjg6KBuWTSOcshAAjbCEMVyk4+TwUyCKVrE122za3Eh/QlChN+TxadmxDOjBVjT6cUb8mCXQVQm9BVcUfcoE7uieEx6+NMim83+V9q1hGhnJsfrkDO+8w87g6tsRcVNZQqoxk1o8UGgElxjTfiyyPfBdT0eg0p+MMdeNPH0QRDoNWc/GEsXZ47Cb386G1MOtisuZUuu0HsXesKe738LXwq/Nh9XfAXw7DB+E/pxBXXIsxq2Tp+uMK+FoxfHN6Lslk1WJnofr7skw7IS1Lb79r/fo1E475MDS7NRpX1abof/jl0eMVn4Hmt7EyKqXJevKylaAxgnVwPb896Qwun9rCNyfXj+6vVRYXCzXqhqJkNsvInizk0wx95VbsnEr15z+HZ9IkPJOnUNXrWniK45AUBv0jQxx1rFuPcGjITQxXawUkV3exzcCTfcOc9vYu9qSyHLduBwsLkOJR2fsiG5wpfPUn3xnd5Ew+j+HwdfT/8G4iz7/IoePn8N0pDTwydwLlzz9LdvNmSlasIN/d46JrvP/zyVLqL2PEW1zUw2eeOfpZKkegZswcpl3cVxrus4v46rHzkqcWBjHy7jvL1xUtTdNKovjL2JfYyB8P3su3br+TZCgy7tzmyHgm3iMlPeDG48Naob3sGMUiHYk3GsdKuwaTAKzMq9jmDpg6F8WyqNmxgaymogX9hEt09MDJOJE6HEVQpUfedb0tA9MYyERJm9G/+JsWBi00JKv9Pnj9h5zZ8W1OZC2R6u04WpZgvAjnn/jsJZw8fxI3XnASnp+fBkBzthtNk6w7MMShZBZ/6jVUoTKlkAAf0QtweY/D5KoQmzrccaYmx1C7BypHPZZgzkITDnJMo6ycLNZmBKOuxzAYj+HkiotzLuOGz+4++Qd8/7wfoavuM+yVdWSF4ZI+6iaOahKVkqAeQcdGSrBkBaZTgyO8OIVaIEsp9D4qJO014SCFgi0FODaO6sE6rEAUMIOuwWYPDo564T0DA1z1qU9x+oKTWLFkBTddcgVdew9Q3djCXT+6G0/YQvM4dMWTzG2p4Mufd4tiN+zbxTUXnsWly5dx4glzuOWWT2AYFYRLQ5RU+N8FMX6/yvtWsSiFgOfi8ycw99QGhvKTeE6/jzeTlwEKKTtKyBPjpJIfuwf4SxkiiJ6KUVbwYMQJQXjuM27464+3jb/Ask8w8soh7HwJmSEXzcNIL/RugYZFcP1LcO0z4Csl7rMYLtFpGi4hMmyyJ3wISxVYU04hr6lM3p+ipeB6l9g2S4J9zK7PchhR7dNtmHURHHgVgP6778YeyVIyCeo/fRsKGXSfg5NXUMwaQtqj5Ga6g3xwyTK+MKF2tPgKQC0p5g6iV7ocV77588nuOUDSnEn/JoPQFD/+JScB4Jk0hUDc5M5cG89/8eNc9vwTbPc1MGHHDwh7DVTp0JhrR5m+AvQAm97+A8/u30ksb/GhbQfYPJLh4zsPMlSozTkMHyY1AO1vENqWQyoKlde4KbVMy4fo+flLoOvku7vBsristoyl0RCZDRtRo1HKb7mZfE83WvX/PAwGEPGXjyoWS9c466fnsHqLW38kj4ije3NZ8mNg0UlN48S3XieP25UP3HCZrSj0f7aYa+kd3sJrvY/xzuCfMJ0M3s5tjGhHWLyDMd5LUnF3XOSHXHhvVB3BkZAzFEZMd0G1pMDQMmTb1xNNZgins0hnmHzqWdZsdBfZvKqzrb4axWMQrI6iGnPwi0uwazoJ5Yrx9sM0NfvjzWwZmAHKu2uxlUKoc0tGZarP5vehEnpUFY/jelN63i3krKtswZIF7yLRBfefDvteGneucMB9DqqRwJP6MxdOvpBAyr3XmOIaXxGPYHZ9CZs7hsmYNsIqIK/OvwfmfNANSQNTSt1aHHHBvTDFNRDy0seckxvwlxgYPtcDMWwfHss/+huSQ+75qgPVzKqYBbqCLgUx4TCk2G5HSdPB0VKEFQND8SJyaaTQMA1JGz72YJNJux0a04k4CCg30jj+ICFPBkeIQhscyYG+YTQnz7DpZdgMEZJumNMaGMCxXLbwSz/2MZYtWMDOV5/hsTfXcOcdn6R7YIiWhgaefXUViibRAzaPP/0cEyZPRQIqgm/ceTtXXH8zq199jLfeepybbvogHs9f9jrfr/K+VSy25TDnlAbmLW8iVOZD1RX2HSrF1nwIFVJWBL93gMP2pz71bHo8NgE1Rrl+gLxQOK37UVj7Y2hbBQO7oXwKfHIP/EcbhGuQeXcSZrIzwLZg/ytkPQqZk2/DqZ3F7r1fpe+Cz/D2URFAoa7lJibtT2NpClvPuYBt08voqHXd4NpCKKTKcv/Oa/HwiWlrOLduG6JiMuxxk+LSgZHV6/BX5Kg5s4Zgu5t0NANu5X+n1czZ885lzaZt2IrCHScuQjmSakJR0JsaUYJBSq+6mqnbt1H5yU8AcOgPQyAFNb94YXR/va4Oadqc8/RLaP393PDE72gzahHbHuG2M1r5/OfuYKXnD1A9mzVLv8rypk9xbXuWaWu24gDnVkZ4ZwyceEPHXnj7Afj2BPqGB5m7fgP9S4/Df7q7MBy8diXSNCm7/jpwHDKbN48em926Fe+smQghMNsOYDQ3/VPjpCRYNtqsaTikcSh4iG+841LVSXGEYjHNcUnRyNAAd95/Nw3tO7GtQr9yM0feMz5RG8jl6UjvZuOEYpgmbYyP0effQ7Ec2DLAzz/9God2DGEViimDmTRWVgUhGDbdxTGhKITIEbfS1EyYhN8qKr+erhTG3b/lz3NOIunzo3gNlK4RzirRMARUtwaQY4gTZWFKvz3UzFM9rYTGeHNWaiKNvXO5JOF6TZZnOkv9GlJxuLOidBQnVUaMaDRKSYkfVeTZU13O8DmPQ6ITXhlHA8iUcI49Xz2DqdWDgMN5E89DKzTY2jPkQcUmqAtm10cYTue55oF1XGd+koNz/h/MuQRUbdRjUQYLAJdII5S6xp4h0iy5cCLXfnMpnoJi8VjjFctIQbFIR7L7rR7MtIOCg4VC9nB1u+kgC0zGgixCZshrDhk9OVqEaNh5hmMxrFwOR1ERAqqDKvsND4ZmoKIgJQTsOEE1jxAR/FoVDhGMkFX4DYJV69ahaxo3XHwxum0jlTALZkygsrYBn8fDtIktvL3JRdw9+dTTnHa2C8ZpCqdJxvqZWl+Lrrv3NGvWTIT49/BSxsr7lt1YSgiXHS50E0Sq/Ax2JDno34U3HyDplFElt6ACzpwPoiz/OsMdZ6H1mMwNPElPqIrq3gLD4xO3QrIHjvsUFPilHMuhR32YoG8eWrYMUv2w72U2zI6S3n8zFNIMhykFZs74Hr6qs/HNXsnEjgfYu8+lOxts8pP1qFT1FniIpKQnE6S6520AJoaHyDedCxt/g6ycw8j6Xdg5lciEBBhekBZfaPgFp8Z7KeNNDg68xY8/9yw5w6CvspqTIu+uvQFoefhhFJ8PobsLnP+oo2j8+c/puesuSs45GzVSTNoeLjRMvli0NuPDhYk5tN8N9wGZSDOXxSYDktL8MAktxKRggNtbaniir7ioblj1U05td/m9NloLaEhtw3v22egNRWh3+c0fJnDssQz+5Ke0X34FkYsuwn/0fHJ79hA69RScdJr8oUOUnH/e3zUe/pIE/CVYhXl3uEYgL924tkQZh8nxmjmsMcAEF9AIRjaNVfDGvLkcGWHwQvsJXM3rJL0wHMgBAUy9uIBbjM+pvJdi6S9U4z/5g420FupQyoljJVUaD1SRsQzwQFxVCNkmhmUz75QzWH2wWAwp7WHaOpuwGSEoVFAFWKAJQUQVlEbLxnctERaTy2F3fyUJ28OsiX20ActLr+eRHRNpVV7nU0aM54ZuYOqxLbw49BXOiNg8gY+9us6kfJ4yhtE0DSU/ggCkP8OO/VUsNkKQGYKSRpxlt6E8/XHC6W50VSGe2gsoHNpwiNoB91msPWgSEia9vT2cf5YbrnuzbYhrjj2JxhUuiq596yBeXVIFsNcFDHT1+ampX4gA0krVKKz2sMdydv25RLItOHmDdNwc9Vhee3Qvm146xDFXl+IPKmRTknceeZbXu/ahY2ErKopjgDAR0iGDMSa7AYbjtg5GSixFw08ORyjYUkNDxZEOqsgRrq1j9rnnUVqgjrGlH8a0CN62by9HFZB0SDCkhd/JkRPuXP3AORfw2yeep7qiDKFqNNdHSAy04zDAzTdfzsUXn8PChXNYvvwsVq684V3j6t9B3rceC0C4vBhrrWh0rZre4AG2Vq9mT2YZwYLrrtTNh1XfhMZiTNjQxsDxkm4i8jBMGSDesYn+iQ/RcfS3cGQJDOwit/9Z0r5CTUSiCV+seL7wyGIODaWxhUZDw9VomjtRpBB01XgpCxQWVSkYHmNNAei7noaRbg6+WkHnGnfB95x5CzI5SJ82i59NaObak2dgqSpT3noazbEJZDOoEyb+xWejhsOjSuWwBBYdw4Rn/kj5h8cTXupjGAqCJ7nhsVS28Hy6Nrj5J2CztwlTSu6d0cz28E72rj6dP1YlaPF7+OKEWm4rV5iUOsCG8DTwl8F1L9CnuOyv049fhlZWRuXtn6b1mWeo+OhHxyma4YcfpuvTt+ObM4foZZeRK5AresbQ8vxPJKAHcA7nKgrhU6ugMOQRw9uby7lxcgDHwSpYqrqZIZ1wFyevmSUtvPx21wX85Njp3HSryj0rCjUkjgAhUTQHr5nhF2dewK9PdxWjFRufdO7aO8yuN3tGvytOBltRqBAJzLRKY/kSspYby48rCiEnz9z2XiIzZyN9xXEvnRg713Yj7SFKDQ0xhvDUEGDgoX8M9aBQshzb4t63tEqoLnE/D25xj/OJHDpQ4Z3Iht1R9uYU5hZqH77nW8z+6GQqGWTu3LlurRhgaYKOXTHWlddjAlTPRGlwWZuiaTdfkkzvR6WcN197k5CVIa8odMooQZHDsiym1xQNpHPn1hZegeTpH23ike+Nz9s99tODbO2fz1t1D3BIK7Jue/yuYjmj/iwCTphodQBFFQx0poj3Z9j7di+GT8MXNij1F5P+9ug4cHBUiUAiFWWcUgFQFY3DJSsSgRQqNhKlZWdU+wAAIABJREFUcLxAIS81bFtFNTUUUYCh4yXlFA05OQa3LB2YouVRpU2+oFhOO+50/vTqBu55/DVOP+c8wj4DQ3Xf4WWXncm6dY9zwfkXsHr1Wo49dim53BGAmX8Ded96LIoqRj0WgOMvncy0xTX89+pPYakmP6kf4JTdhRvoXA+bfkP50R/klIZaftXVi2/KWfDGj+HiX8Lvr3JPEi2GXQY63dqXvK+frC+H/MUVDFYZgE7T63cRbXUpRto6vomwvQys28ey4X4+cepkPnLyJJoab2Df/m8DYJgnsv/AmzDVdYUzoSZgDP16qp9Y6EukN/5sdJPW+yJKbCdbQq5FMhII8sbMo1i26W1MvxcjneXoG1b+S57lYY8FIHjiCSRffpkuqvnI4nv42tavEBp2Fcs7ohRIsigSgBnn433u09D5JjQt5EONlbDuCXoTvTxTfzZ9yzfy64E0pXv3EauqxihUIZddc03xHisrKb/lFsJnnI41MEhm40ZKr70GxeMh+aqbB/FM/MvK8+8Rv+Znd50gp6vsmVIOdDOgDNCZ7ATbM25fbz43ugTLZC9Sul6HbmZJDLnKw2uaZFV3oX2yciUh3S25yhpxckaCphNTRCclWLu2n5+fcxFVg/1c8dzj71Isj31n/bjvwsqQ9PmZJjs5aFUROePLmL9wQ4fDikrUTKKlshgN9UyvrKevcx9ZVaAoMXCSQN5tsD20B3BJMP2KYPX6OBPG6k8ly5xyDQrtuLv2bYIK8NquW3e4snxiXSVruvIQEkT89ZSp3awuO8Bq4On0IIuPmk6v+jt4DvKaYGf/Tu6qTXFpWZTPNi+FsKscIuYgadvByffgdUoxDINIJktMDzCS9jG5xOHiFRcjhODHl83j0fUdzKl3jbJYz2GereJCvH/mz6BHYag7RUZOQveM4eIqeCxm2iKXtghGvZTWBtj2aifbXnVRdydfMw2pDOMroL6mnHclOjbTlIOkDZWYEaEuOUjSCLM/W2zXDNCKimUlSDk5Bo1SpgXT9OcGKM034wgHTapYMk/GSmIrrlrK2RkM1UtAK8EuINpap0zhiWcLoW8pUAqhwVwBUux4vLTOmsN/3XMPT/15FdvXvoJhlKJpYSwrQU1NJddddyMfvvk2Zs6cydatW5lf6JD57yLvW4+lvD5IaW3R6tAMlUizgaW6qJ39+b18bOZxyEW3gOkGA47b/ya9msZpjXVEagvcUxVT4ZJfu9w/VcUitp7+F1DyfrA1BlufwKaUkeYTwVGJHD2P0kun4jmpkurt11G163KI5/hhbBP717ohrqamG6HvbDbfP5nON0qpSBgEHYcr+rPM+o9H4ZIH3ep9wJQTGH5nvNWhDW5ESo0ny08ikMlw+7Ys/33eB3ng4itovuerNPz3fxNYdCSX5/9M1HAYpaQENRKh5KyzcDSNyHCMh40pvKNVws6nQfXwSgpafAYVhg6hKog2u7mUBy+Ghy6DA6uZl+8iZsPyzYf4VlsP1R3tyL/gWQkhqPjIrXgmTiSw6BjKP3QTSoGHLLdnD8IwMBr/OW4jv+4n5RP87uy5dNcVKDaE5PRHT8eR7/ZYCqUjxGOd6AXosSeXJTGSKuyTJau9u3r5jI39fPnBPNFJbsijfsjNByQC7jXz8Xc3WJJAT0RFKHmCZiexSARVSPTpx4DuxZTumBhWFdAk3vnzELpORUMjJ23dT1V9E2ZmH47tIhIVK86AWjS2Bkp34E3pHFCKwbALpz7MvnVFxKFPddFLnkK8sNrnKtNwOEIs5X5OKWVM8BZh2I/4PSjfaqbmORepZGmCvNddHLd6DGheim0ESQudSD7GVZv3ozgjeGwPt37kJsLpPD1aKXlUvN0+Vv+sn9W/382y2gj3X7NgFJjTW+hb03pUMTk9HF48+vDyWRvdO6bfSUGx5DIWmaSJN6BR3Tq+CLZ1rnsuIQTlQXes5VGxpIJig5l139NA7t1IRBuJrnhH2ygoRoC80FAQjOjuM9aETkiPElFdpZSz08RyvTg42J4ged3D6cedTS6f5/5HHgEHyKdZu2kHBw+5yq9XwFU33sLH7/gi5WUlozkUn6+RF/+0i2zKQDf89PT0MDg4SN0Yw/DfRd63Hksyn+RX23/F2a1nk8qnqA/Vj/azX1a3jNWdq7lyyWcRNYuQ35tPxl6Cb2gtL1y1noyQsPUp90ShaqiYAp89ByklmUScgR27yAf2o7cvYERrJ1u2l00TFjHsXYuRqua3v/kCJa/U0Ne2j5bgbBafdDGZrXHmRZehDrhuuxAKGx/bB6h07d5Js9/DG+17aTcr0PwlMO1s8EWRa35AbNtZWAOvIvwBZNpdwCxRQ9o6gbUehaN2beXCgUmcmggwtfFN1Hm3g/KvTdhNeOaPqKGQu5hXVDC5QBEyqEeQ+19kztKn6RtO8h8tY1BaE06Ct+8f5UvLJVQWbJzMVUc9wr76Jn7zs+8CEFxx9ruu97ckt3cvxsQJ71kB/Y+IX3PDjvGQl0H9yFqSd8OND+fuu/sO4i3QpHvMDPu2dsOCKry5HDn13cVzWT/44m4TNEVAc/4AABmPF0tRycWGScZyo5DYzpYYL08p5UBJCTf2dBP8Vo7ds9zC1opFbjjSwlUsMUXF9EgOfeJjPPNf1yC3DXMyEF33Dr215TiWm3Pp8Q8wwyoq8ZDQKUXnQU8R6jwhuot4R5HjKxzxgYQwFudNDXFRaRlsUCkrCZE2uyiVQZ7r3k6TYbEu5SrUt0vKYIwHZmkKeX+BG0wrhapZnPTwiZTWVnGFOciaWJwKJ0WJ8DHQvYpWW9KGiwgLOwpW3mbzyx0k+jOcdUuRRaPvQALDp1HZFOLxbV/irNuXk15T6C6ZtTCzNoZ3TEsDXUFRBLGeNLmURbQmMAq/bZxRytKLJmGMUUS1ER+qIuhNZEnhJWBnqRAOFoKU9FHuGyKWjWAXDJCM4hCVOmohRGrrXpwCvT+aoE+JEch7QYLf8TGiSwYUqAhHECMKiieMobj7//reH/PZL36F/7z/Pnw+g6aGOj71RTcv6wiFiVOmMXHKNDQlyeFxKoTgpRdX8R+fvoNA0B3X3/72t6n+J5GT/xfyvlUsQ7EBup7Zxll1P2HEHGHjlRtHFct1FYu4a9GdlD9yPcQ7GBk8moR1NRF5N9W5lOuZjHS7XoqnGNvd8vKfeOGeu1m8ZD5ypsXE4y9n25v3kfduY7jePXd+xEvOSdN3oI29/hZIbqbzhb0k9HJWBE+iXnNDALl00UWXjoPluIPDZsyi1LyEvqf8mLle7J5NqOVT0Gd4cHJRejLnYQnoKvOxZOObKB6LiVdcjtp4JM3av0a0sqLbr1dWsrSAw+81ykioQfpUN4d1Ze2Y8MCZ34H517rP8jcX07uhBLs7zrVdj447d8m0qfyjktuzh8AxC/8HdzJe/Lo7AXO6JKWNZwxwKsL4KnJk+t3FXpESCuGv3X2dozUr3myGfe0DPPXQZwhm0rxe/W56lkwAonGAAJAiEMpQ0Xk3ydBU4sEQyV0d/OKO17jxB8eje1TuXzhh9Nghn4OecUjWuB642ujy0TnCVSxJVSBsifP0dfy0CWY1OZz8BpQlC96Gvods2mF3JM/hjMMzkdWcObwMB8l2rTgWM46gunoG56S2YSNonjGF9dvXowjBbSc1U7rNAiNARci12HN5D3vSCRaVFZVyj9fPn5dVcuJqN5xraQKn3CWgTBseUBSGskMMGQrVyQGE46LMqowwffuepRUYDC+EQQg5giUXTmT98+30tY/vWdN/KElFQxDDq9FpzsYMtJIcdltQJGM5zKyFP1zMVwoh8AQ0Du1wPbiKhhA1E0rwBXWaZ5UjlPE5E4DSgEE2b5PM+ighTVBCHwGkEAR1i3hOYh+miVNBOIKoUUE/Nrv7RohqHpJIslInyRBxfYTqQDWGN0hXZxyI0JPtpEFU4VWKodeqmkYe+tH3sDIOnrAFGnTIKt5+7DH2jUF5asLm2mtXsnKlu+3LX/gGX/3SN4lUjc/T/rvJ+zYUVp0v5/KBszALpIHr+9bTnXIThTVPfZzy7q0FapY2TGcaACn7TOjfBW2rkW/8hAHzjtEOfgCdmzYw/5hq8HSAFGzOTUXTqpBqkcYjpfSy39/EI1Ou59mq02n3NWBm0ngTB2mPvU2pEiI/kKF3/3jW41eZw+bhal5q+sS47fnOJObeF5BmGmPyWXjn3IzReh4SGIj2YGkaTXYeq3sDnsb3RoD9q0WrrET29+FTBH1zrqHr+tVc+qcn+TlJKgydwfvuZ/exSxh+4ilk1UwyqUqc6mNID/iJXHoJemPDuPN5Jk/+C1d6b7GTSayeHoy/Ak74e+Wwx2IJC2tMzYaBAapG43HjSbTVQvhrx1BulLZcz2f44YUzCGbccEdOMxDqCIqvyOiQLURlA8INvXhKTEqHNqCZe+mPluLJuRb+7nXFhP1hMXe625QCE4/aMB0pJY5iIgvWckoKPlpIAW5pFry+vJxQxkTTNNIjw/hMi7YShXvLHuGbaj9dc5p5yujggRYvMbuoWA6ZCiO+DJ+/9Ro+/+ErMB0Tv+Fnzpw5bpuJfAp0P5VhdxGcGz2ePktQp0suieZYEsgzmIuTx2Eg6hpJeU1AlYuwzMssG/uKBbRRcxAj6yqd1pJqnK4NOAI2trtw4fkLqpl7SiNHndZEOmGSGXHnmmM7DHYmKW8IjXol+axNqtDVNBnLkj/CYwEorQ2SjrvnKG8IIhRBy5yK91QqALqq0FjqJ08xvGkSwK+bSEdFGUNGebjf2+HVwJEaERmlA4d4psDSoGiUekuxxnJ5OToj0v3dKSQJJH6lFMcogBTyAgVJoNDTR45hR9VVOYp6k1JiW857shb/u8n71mM5LNPTrWwI7uRrb36NpXVLEVJSYdmw6hvgKUEGqsl1FTr6yRpk/wbE4F4comRzc8k+sI2auxahGjq+wB7sSa+QAZxEBTe9sJ3/V6Mzs9iOg4HtEd6OzKc3p/Kjy47iow/aTBvZxbH9bzFh7RPkZ3l57gtrcVrcRWxzeCazE1tpbawldvkarmwpJd/bS3b7doLLjkfaeayu1wiefBKh5ceQfL2LRxp0vj/dS8sB1yJsCfrJb+048tb/10SrqSb12mtU6hr9gTq6lRA3PfYQPPYQcusWBu+/H3toiNhvHwLbovtznx89Nnjc8ZRefTX5ri4OXXc9AEbTP1aLkj/kgrj/2fwKFD0WS7EwlaKBECRIXnjgiJ4solBdfTDjJWS6ysCXyY7bJ6sa+Jt/imIMjm4bWO7g/ZiNlhzE9isYJSatbT4yAUlftIyaIff9vfLgLvI5m6gySEy43l9zWzsZj4eSQIpsTMdr+MlkMqDkkGYZwtNPUqqckEqzOhjExmFjtcmxQGmohL7YIH7TYjggkFWSpwY9sMpVDE3xFC3l2dE+7y+M6MDPuKHsBjyqh9z+HD7dx/nnn+/uYKbB8FNZ8FgunXQjJymN9Hd9ncVBmz8lg8hUnhFbsHv+NK45MEgg3UiDV4XEQQadNFc+e+Xoc9mjjhAe/BEA6USO8v5+Un6VoVQYhMurBlDR4OaievbHaZlTQaw3jZ13qGgIjuZR8jmbVEFpjAzlkI4cl2MBqGoO07krRrTaPy7s9ddECDEukpBxNEJaCmlr4xRLynKVQtcYbuGOMTVF5d5qyvwlCCEYGdPi2jErGQSiwKHCsR5UPMKlAJCOANUgYGWxhSAA5DWVnGWjF5SZlbfJJPJIR6Lqf3dz3fetvG9Voyy88I+b1/KNxV9n7/Bentz3JBV2YYh0vAXTV2Ce/hQSH0ZLGIkfp+cADOzBDkwfPdehlzbQdfAV7AnFPtxDWZUlqYNs7CpaMh1bbqB/Vyk3X7CUB65ZwNmza/n4adOYe+rpXHTBpQCku9dTrdez9603yCheFnzgMtT6yaw47VyWVIbRc1n2nXoaHR++mcSf/ky+bRVOMk7plVfgm+2arA+0GGSBHc1uuGRCNITZ0TGuG93/phj19TjpND+55SoyPT0M9BSbPg3e/wD24CDe2bPJbtrM4H/fN+7YwKJj8LS0EFyyBP+CBailpQjtH7NP8p1uElOvr/8be/5tOeyxbI9upy3chsf2MNWZio1NUGhH0oWh2q7HMmj78BRCYdGROB969Nej++RUY5xSAUi1SlDA1nPc+OL3+f3wBUw9UMLCHQH6o2X4M0OceFEzAK89speSAkKoNmcyr30rO5om0GD2YpoeHEfy2MbdCOGgW26yOY7K10UTr1WfSnMehsIWHcFyagtUMZaq0FUGE6samVEbRhE2dyz4Lu1DaaLRttEGUoflubbnsB2bXUO78I6hSMFMgR6gusSLELCrJ8mk6CS+1+vlMft4hgtAmGFbcNT8B2m3VbbHO8HvRgW0IyC6mz3F754tYVqySeJhHU9dGQEJjZNdSpnq1hJCpV7WPrEf6UgGDrnhs/KGEHrBKzGzLtqrcUbZaBuDd3ksNe77bpn7j1Wjq2MUi4mOV03jWDpe290eNkbIWYKBMYqmFGW0eBLAUEJoisZw2qQzlsFQxvTuAeJj9s0WPgsFHBukHiQjAjgoqEg8Ba9EFYLEQIZEf4ZM0h2Puud9b+//TXnfKhaBwApBZV+IBW82owqVoewQ1daYBO3sS8i2ZUCBwHw3wWX3DsLgHnKeaVjGMIMtT5JY38627beCYjPS30wy52fzGzP53As/5NLNbhOt+IEQA6+vod9TwSWLJnDiVNeN+ejJk/jq+bMoKRA1ahXlRD11CAQHffWcu3AiN13/WbxPxYg9spvs1q3IQlK454ufJbf9D3hnzCWwaBGiPsTOkEKfV7CsbTdn7tjE3JCflsWLIZ9n5KXxdBl/S8yOTpKr1/ztHY+Qwwu6L5PGWLWK53YWw3r93/0uSjhMzZfvcq9x4AAVt91G8OT/j73zjpOjrv//8zNle7neL3fJpRdSuBBCSY4O0jsqEkWaDRVUAoj4FdQoUb9YUPlRVESFAApfAiEECBI6BAgkIb1ervfbvjPz++Mzu3t7uTSSwAXu9Xjc43ZnZ2ZnZmc+78+7vV4nUH7nnSieTOx32F/uZ9R/X2RfEd8uZ/eOiv2vdtFVHdVS0mGwmBpDR6eLLl7Oad9Jk0W1PZaoUHHb0rDBUC8XL1mYOT7vzs2OUbtZJmKXIr/ZPg2HItCSCZpz89GTMcQ3TqfuZBnO1EImJ65eygM3f5WqzTt4b9Q4KmKNJA0v9yzbyM1PyXBSoS7DJZ2oGAVuvK/8Py5OhGl0JFg2uZeS5e9TEktSQhJDFQwrqmbhtccyrjDCDluVsdNcRY3TpNST6Vdaum0pP3ntJ6xuX82W7j5itfFecHgJunWOGVnAv9+pp8RTSsQSvNH8ATF7gt5pCFyuTMjzmc3PMFx38mN3tpf5bh+10WMOn49OktZ8N4bfQ3V5gJpp8jnSHCozzh5B+44QK5ftoGVbD6quZHke8ahBPJqkqMqfpoXX+xmWkbXFzP78aKafXr3Tb7Q79GWv8DgVFGFhJBw4TYWaAARsBua+pqIAgdZnu0g8QdKIsqMzgiKgwNNGsaeFFJ9lSx9PJ+UDCwXMpEJ0Rw9WIo4pFFQFKvLcFHk6sGIm0VCCZCKzbX9lyEMRg/oMlFn5eKeXkFjXzegcGccvSSbh/Hvh8mdg+LFE13fiqAyg24lRoyMCLWuIKFW0DX+S1lGPkShchrA7GBZvOpdvvziPI9+WD9uwTWvZcl8lmxbLQS7py8elq5jxOA233MLWK69ix4030f7XvwKgGz24hMrTVVfzVtWpVOZ5iG2SJYyJxhCx9ZJnadjf/orZ3QVmEs+MI0maFie8vYZLj5KJwy889Fd+qcdZVDsaz+HT0AoL6X3xv/t0fbZc9iW2XXklZmT3BIj9oZdnPIWRZpzOBpkDSBFaBk49FdeYMThG1oCqknPeuVT+4fcETjk5az9CVffZWwFIbK9H8flQgsE9r7wXcJJdHqzblTl3lq8n0W9dNSmNflfEgbuph4Fg5K7aaVnY7gNpCmfipq7cGHoyTsidKQF2vvw4AN/901+5+bd/RgklMb2CBSd8jrJoC6aay9IPt+Askn0O4/JknqkLQaxD5ipmeMKMyYuzujqBYsG0D7eQKJDfP7xYern3XlrGyrax5LiaaYr3MNLjwm2XIuc78lmydQmPrZMs02V92HrpbQZb8+PUiSXUd0YgKZv7IskIvaaCR7F4utvFU5ueyroGk1UP3rw1WcvWOjPXvrojRlKBrlwPTT0xSnOyS3pHTivCHXDw4j/W8N6SbeSXeVFUJe2VhLtislPdpeHLkwarf7hL1RQmzq5A0/etmtCnKuwQhfQ4HBR62gGBZXsrsYiJJgyEMNOREpCDY7474+mEY720dW0laVrku1pxKAncWpQy7zb8jowImsOCiJUkbkazxLjcyTiGEGhCeioerVeKhNnwBB3klngGFPA61DCoDUtRcSlagQMrbnBWmSxpDZqm7E0ZdiRmOEFiew/OkUF21MsbPmnkQyJMnDJC+SsB6K56DuFIkL/hbLxlMwnGehnRupXcSy/FdLkZtj4ElmBtcDyxySdjJZO0/O+ddC54hNBLL9FlCx4pHg+GTSZ4V+1I/jXdybZvfJNkuwxhmb0JQm++j3D5UPMzCW335Ik81drFunCml2XUts0ETpUU70JR0CsrSbZkdC/6I/TGG9Rf/z02nHEGls3Qm9whixm6Fy5k29e/QdfChbvcvi/6egqfj3Vza1AODpV/+iPlv/l1mnes6LrrKb7hBrR++i/7i/iWLejl5QfsAXKJfoalb9ij33ekkvebmwI4egfuaNZMa6dlIXsQagxlDIvmTaAlEywfM5FOu9Nb6ZR5sxHbtqXXK3l+MSXd63BZcfAWsTb+MHpAJsNnDhuNYkG3IlAicoIyYkuYc90xvKWZ82jMkR3jPo8T00zQ0vI4q9pGM6LgHSwERaqL6V3TCcaCfKXmK+nt7jvlPh48/UFIxiARlSwUPundV+ZK77OjF3KdclLRYQi+nB+jKWEx96WMHp9LdVFkCYRikqPK2XW+sCWgsfhZWYiStjjteQ6SIkZHKE5ePzVDVVc47/ppjKotQggYPlkauFTop7dT/h4Ot4rPNkqKemDuEU1VcFp+LIeFaYQwkx5AgADDTpc4bK9XEeB1tiHyLQpz3VTnSw8vnHDSEZXXyaVl5+UCDpudQcg+/wQWUSOMaWYbwKSioamCnh45Nll9Pvf4HZ+KMBgMYsMiBKg976NtlLHvCxLDmVc0mys7u9Md9NE1HWBBfe96Hv3VrSRFgoRVScIchtHoJ+FrYHtnKaYuZ/RGOI/rzhrPTypCKFgEzz4L9YgZmEk3D5eexzN5s6kNNbPpootov+++LMnc4ptvJu8rX8FobUHxbMa9vAXz5lvofe45uh66l8hb92JZJuE33kPxltCxYC3CIwfkpt4efr25kZK4vBm94RB5Z56Jow/liZafT7I9O67fF1svm0P3woXE128g/Nbb8hrZYamGH95C7/PP0/HgP/bq2ipeL/6TTgQgvnETZb3dIASuCRMInHYaakCGc/zHH0feZV/a3a72GUZPD+HXX8czffoB26dbycyMD2s7DIfSpwJoF4ZFVcGZzBb1SkHfmRCYUDJlWGztGCVG3OVETyZoKCzm3F/dw2sTphBvbcEUFmuqZFVUuNagvruJOc9Ij/dFvYaQWp/e77SKKhymSkSA11Y9VE0Y1RDh0hEZ/qnt/hjfKozx7vIzWbnqOjZse5Zw0ovfKz1vXzJEYkuCE3ecyMlVGc+ytriWAncB3H0czB8N0S7Z24Xs8wBo6IpS7pOTjeakwqpEEb+r+3XW+UeNKK12gOcbhTFmbD2dSkN20OdhUhg1cMVNWvPkte+MxMnx7NxomlPs4eQrJnL17+uo/Vw1kMmjhDpShkXDa/cDhToPDJ2JUARmUsHjGY4RCxDvzUNRBU63nvYaUsZCVRMUOsNEElswjTB+l4ZuayHFTR1NSe4kVxz0VVLp30GhswdNMTisuojZp5/GtLPOYcb553Pn3/6GaZrEVR3JX2qL0/UxLINdvGtfMGjNoy42ov/fmWBWALMwnvoVp49IkFDHEGuycA6D0JuNqHkuNjVI+oyOWBPuytPpLD4RY51s6FrRfgwVOQsACBsllOa4OTLeRIfLhWvcOAKlxRQk36TJVcz40gAzn/hfYvXbKfzOt8n/6leJvP8+CIFn6lQSO3bQ9Z//0PWPn+Ga9hWsuHzw4zZzsVF5BGbHZhzjzsbojOE5+js807OYW/KqcfaGueXe31PZtAN3NEruPX/OOl81Pw/jzTcHvBZmOItmkK1z5lD+v7/B6rc8tmYNlmHsVdNhxe9+R8sf/kDr735PfOPGvU7Cx7dvRysqQnF8NG3tnueew4rHCZ5x+kfafiD4PLnQ20xeNI9R3aPoKMrkSHYyLHbyXlcFLqN/oExiIMMSsQeA1kgeqnctnmH3sbGpnGAis4+2YC7x91+n69ZqKm6M01wdRDlHY8Wv7iTfLmK9T0mieTantynLLUU3VSJC4I6ZmFMvRXnn7+h2zN1SLIQp+LAkRJ0taNbc/BRdsVJU71qWK5IttVBPklI5yQ3m8u+z/k3ClHLDWBY0r8ycTNqw2LQvXRFOqDqBD9o+AGBpZzc/L5250zV4xwgzCyjULabWn8zq4hXg7CZkKXhso+gbeRHVxdOJLjbJ8ezcaJqC2mcQTTU5ZjwWjcPqKti+up1xR5cNuP2+QlEknT2Wi0Q4F82hEihwydJlmz8ux9lNJOlAqJkimlisCUVx2G5Nig9MBwQORwHxeIt9Ph4cmo4qeohj4XS5+efiV6hBobFpK1d880q6e3q44vs/wqErxFMpFUvF7XccMM9ssGDwmki7kU0TjYBFwqyCLcto6v4lLXe9R3RtB7GNXTDawbbGl5lw2VrrCM0sAAAgAElEQVTaHStItBm0rQ/Tpchu5ROnHEH4w4sRhkZclySU0ZWrcI0Zg9A09KJC3NEQa249gccvHgX12ymaewMF11yD0HU806bhmSqJFt90B8j/5S/lPpbfL3XCnZn4enzt0wD8d/osnirV0MsquGWOJIT88r//ydEr3mZYUwOFXR04qquzTlfLL8Do7MRK7jyqpWRPy+bPp+JPf0SvrKT+O98FwDd7tvxfV4cZCqXJHfcGeXO+jKOqCjMaJf+rX93j+kZXFxtOPIkdP7hhj+tGVqyg418P7WQUw6+9jpqTg+uww/b6OPeEoEPmajRL44ILLujnsWSvq9leiqqDw0wQHaDLvt2/80OezrEkTTzD7gOgWVfJ780MQq05uWi9Yf7Y3oEvHKbT5eGNR4JEG9qJ5coqsebAtqz9+l1+NFOj1+7DsKZ+mbDHhW5LJZt2GqolKA2Y210NQHfcj+rJ/NZtOzJVkC6Xi5G5IxmXP05qEaW48lKwQ2Eeh0bQrdPQGWXO+DlcOelK5h4hw19vNmYmOcdXHk+5r5xNSdi8/igal0uOswmdUwA4LBqjokHO9odNuR1vznkA5Lj3bvIhFIHmVOm1PRanW8Ob4+TCG6cfMMXEVJ9LT5tdsFHoRtPVdA4n2lmJUJwUeFpwaTLCoes5GEaIRLITnx6i1CvD4Pk+J4HARJxOuzBB8yOEgmo3GWuij1SBZVJZXMUf/ud2/vzPf5JUVAR9vR0Vf54LbzCb1+5Qx6D1WFKUJqLqCJwJN5FNswhqf0t/3P3cVpJOgwX//BEVx21DdxskDnsBVnwOb9JHo08+wBX55bzRkMfJW09h3bgAvS++SHTVKoLnSG37lMyv0tVJ+I03APDOkBxdlmXRkTTI0zXWhKKc9+56Li8v4od/f4Atl34J4XDgP+0cuv/zTwCMtvUkgkXcOFPOsmrrFRQzyamdzVxBFO2ii+h8+GEAVH9Gjx6kxwJgdHSgFWaXUvatonJPmYLi9rB1zhwASn96O4mmJoSm07t0KfH163HtpmGx4Ue34p15pAx5+byMWPgkViyG4vXucpsUep6XxJ09ixYR3/69naq6up54AqOzk8Dpp7Pli5diJRKEXl5Gxe9+l14n/OabeKbXIpQDN6cJ6DJ0F3AFmDhxIo4Pdh0K02zDrToNnMk47a4AZaFMCHLxtNE8eURmwD67+lL+vf7fRBUFIVTa/ZnEflQ4GHnqh5n3OZIdN6+rC18kRJcaTM+GC2w+L0OPUWkOo8VsJmqHXjRTJ2Rfj4l3N/Fq0IlmN9M9er6LdT0JvHY/zto1w6kcthndfzZCyDxNXXEd9a9lEyqm8c4DsLofm4M/IwVdGnSxozOCrupcO+1aGnobmPfGPK594dr0OrXFtdx5/J18ZdFX+P2O97hivZ3D2Xo09xT/lTHNYXJME8tfjnB46WyXnvzuPJb+cDhVejvk9djb/pS9Qef/bSC+I4RpmBhJM13M0ZbOZVgYSQvTMDH1JKaVRLUgJASKksA0s3MpxUoIR1kQzqpBCAWvd7T0aABVyQFaUJWMYUkICzdQM3YahmXR3NFJgSOA5gMsLb3tpw379XQLIfKEEM8KIdbZ/wfUQRVCLBJCdAohntzrnZsGHPdDuPxpvEdUYVBEzJyY/ji+pZuGrg2YJPGWyIdQCchZoUDQq8rAQHFOMT0OgUAhd81zbLv6GsxwGO8sKROsFcgHMtnaRuT9DxAeT7qT/Febmxi/7AMaYnHWheQNtjYUxVNbS82zi6lZ9DSeqdl0JtvGZKg8PlduYioKM3RB5e9+R+lP/meXp6vly3xMsi07z5JoaGD7NdLrSZUJe46Yjveooyj89rVoBQW4J0zAUSXLQGObN+/6ksZidD78MPXfvQ6j075WmrZXRgWg93lJbig8Hjaceio9S5dmfb7jBzfQ9LOfE3r1NaxEAjU/n8gHmRBMz/MvkKivx3vUUXv1fXsLvyaNtFPYlUS7ybFodu+KKcBlxGlzZbMdvDWyOE17/vmxn+fqSd8C00lXwkmj+C4JMmSTPapAq+nTKBeUg39hZxu+cBhDzQyQhWZSclI5wjgTDq7UruRfx/8LAN3U6VUE75ojiCQFHVohut2YV5of4r0RCoV2HmBVYxFN3M+O6FEIrZtSbzkXei5Mf4/aPwz6rp13G3UKnPQTSXOUW53+eEyJnzc2t7N8qwwflvpKuXjMxVm7KPZIQ3TrzFu55vCr0XQ5bATy81m74+soyHtXFEoJhM6wHL5z3HtvWPLKvCSickB2uA+8sFXfzvzsznbRp9dJ6veoAoTQEFm16soA64OqOuntiNHZHMZMqsS6y1D7VCkmlFQ/i4aJ7AOy7KKHZLj0U9FlPxD2d2owF3jOsqx5Qoi59vuB4iR3AB7g6n3ae44cLHWb5ThqHpn1cUe4gc6CIM5AgqSVg+bpxFDiKKZOvCyEG8jzF1M0tpeOt9vwNq7DQIpQGUcfQ2s8ideueEq2thBdtQrX2LEIVSVumszfLMtwp76yiql+mSj325K0qcS7XppNEPduII/LyvL5246MgSj3ZXo/im+6Ca2oiP7QbI8l2ZptWBpvuz39WrX5voQQDLsvu3FRcbnQykqJb95M56OP0vb/7kErLkYN+HGMqKHg6qtINGUaISMfrMR3zNE7HceuYFkW4XffIXDWmRR997tsOPkUup96Cn9dHQDxPuJU7fffjxIMknPeubTdd38679N+3304qqvJOf/8vf7evUFJsATqITdHzmucfTib4kKkH/OEpqHboTDLtCiMdLKiYCSTUsqFQNgeC71Jja9O/CoONCzDTX3cx03PlOAb08KRuZfweuvj9PSLmCWL5SAyftM6NNPAE+sFl4xljWyL0+Lzk1S60aLFVFdXM6FScpK5LUFIUVhoyPv7rbYyLtTWM3JjiLjDAZaLoK3l8XDn0Ty8uAfowT2sm0JXPi+//DKVlZVs27aNor73VjIGjSvgqGvh5NvksqOupe/IeOyoQh5/dwfn3fUKb9x8AkV+FzfPuJlzR53LJU/KpuBCj/Sgq4PVXD35ahqv62L1yw0kYgbr3pyJOmyB5EEZJYsGOsPyGg+UvN8VRk0vZvuH0rilWIwPBHLOrNnjOrFwgq6WCIEik3hCRjp8vrEIoRGPN6NpfkAhFmvE7a7cSdExRVOjOVQsQ0dXlXS4K2lJI/LBttUoikpxQSEIm4g2AQ7XkGEZCGcDdfbrvwJLGcCwWJb1nBCirv/yPSJHDt5anhswiRgZ0kILk3jdM9SVyPyDI3AOZs9f6HbvwAquJ1i5ChIQffkNLj7vWLbODhE9cy7vH3cSZ3zjm5yzfB0reiN80YpxBVB/3fVY4TC5l0oN+b5SvADv9Mj3HYnsHIhrnOxILvjmN6l/7DGWHlbLL0ryOPYH3+HK70gqlPJgJuy1qyqrlDcS37KZSG4OrnHj6HnuOXqff568yy8nb85leyzPdVRVEd+0meh7K4hv2SJDaMkksATnqFGoOTnpdZN9jMzuYIZChF57DeeoURgtrbinTEEvLcV79NFEV2XCQn0r0qIrV1J43XWofh8YBsm2NrTCQqJr1hA4/XOIj5j43xVKc2SjoD9XXuf+HkvqXdyho9lVYXk9XbiMBDty82BzZl9hm8jy4YatFHuLSRgmluFGKBEUZxNCWIzOHcPyFhdhu+z2WusO8tpaeScwlV6Xm1NXSc/O3xpishmlw+NCHQsr1SIsYjgTTnJzM859KxVERRf3GacBSTqR9CdV2yNsG6NDHHxCRQ0JgvTS627FVfYQKAmSnS4SiQRnn302O3bsYPjwjJgdjR/IpHNFbWZZv3to1uhMKfmaxh6K/C6EEEzIn8Bpw0/j6U1PU5OTPTiXDA9SMjzI9g/biUeTOEecBa/fCZMuAvp4LPsQCht5eBEvPCDDiqlk/seFjDeTOV7F7oVyOqW3ZhgmbnfVbp/BcFcMRVVQFBVhV41ZCFraWvnO3O/x+S9fiS4UhO2xYIkhj2UXKLYsqwHAsqwGIcTOU/F9gBDiKuAqgHEVQciXrrXQFVS/htFTQm/uSlbFFjPSOAW/bVSczhKC+UfS3PMXtkZewVUtu+QD/6eybck1DLvvXgoTCbZ1dfJozXhWbmxghc0c+09TY05uLnqHnC29MmU6r6/ZxrNtMk789szxfOfDrbzUIRugdsSyK4m0wkLGfSip9H96wpnsaOtmUiKCe80qcnq66PQHqSjIY0/QiotRc3JonvcLrEQC97RpRJbLarec889DLy7ewx7AOXwEHf+QA3zp7bcRPP98mn95B+33309k+ds4x2TCdr0vvkjTL37BiCceR98FLbdlWWz96hVE3s2QDronS9pz14QJ9P73vyQaGmi96y46FzxCzkUXYcXjeKZPJ+f88+ixQ2fbrriSkh/fitnTs8+ElXuDFF9YIl3xlRkgdkT8HH9sGy+MHk7Bo6AnZIK4vEP2m7QHsnNdEduw+EwT47GvoZ/3R4TlQvWsx1m4GICJRTW417roFPL7ZvAahl/h7WQtHYEgNavkfUlcUN7Ty6/Pj1DXq/Oa0w/EcBtucvoYeVV4iCoWeLbgr7qbdVvHk1I+drgcEIcc4JjlrTwQ/CZfLMv8XrH2GLNmzaKgoICCggJ4814It8Ps70O91A6ivI9h6Yciv4vnrp/NCb96kTWNPcwckY9mV2zNO3Yetxx5C36Hf8BtK8bmUTE2D4zxcOw3042XHR/BsDhcGid+ZTxbV7Z97A2C6e+zNPt9tmEzDJP2+hCKKsgt9ab1ZEA+I3334wk6QKhEIjEuOuVYzGQCl6pz2gXn8fkrvi0HXGHauTeB8lk1LEKIJcBAI8/NB/pgLMu6G7gboLa21krdqABacQCjp5P66XcQBJ5e4aEWOGL6kzidRXSFWmneDE3iA4qD7bgAfWsJ0ELrI4+imiadgRxenlzLf7e34FQE646dxClvreW3P/wZP9+yCtdXLue41z4EO4w1LeCh3OXguuoSFJqocOk82NDORe+u556Jw2mJJ3iiuZNrq4rpShq81NHDFL+HhB0W+tP9v+PVojLyfzNvj+cuhEAvK0t7ASmjkn/11Thr9uzOA3iPOTptWPwnn4wQguIbfkBszRrCb72N4vODpqG43fQsloNk+PXXCZ59dtZ+up9+mtimTeRccEGWUYGMh+YcPRpMk9a7/kjngkdwH344xTfdiOLKVPFotjGMrV3Lli98UW5/EAxLykOJm3aYq0+PwdZQLv7yViJlOgmdNFV+eZcsE+0OuFlbUsHoRlkgEdfloOg3TdQV/4ATf0SJFaUF0HyS0n1qWTU+zUu70UXC1NCVJKrD5LiWZaysrabyKRlCbQlafP9KFcMBeb09PFQi7ytfwpflsTiFC6Ek0L1yQvR+8RYSO+yiVpebM0L5zHAaaJaVZVQAAkqAWbNmyZwkAhZeJz849jrY/pasAAvsvmS3ptBHgc/B7QtXc/vC1bxzy0nkeh0oQtmlUcmCqoMvM6dc19xDvteBex+748fMKGHMjI9feySdSjEFLlc5qpqdc4z2JmzmYQsjYaL08ahSnGa+XCduvwMhBJGwQkfHu2zvHYbLMim2NDbYMwXTQnb426zWqvbpKjNOYY+GxbKsE3f1mRCiSQhRansrpWTp8R5YOMp9RNdnKNBLR2wiYeXYsVBBnk1n4S0J4/JFWds9h2M3yAqs8FOSmuK9aTNI2r0aE3xuHIrCJL+bl5OFFJ7+dRY0yv3/eUIVw1xOihxy3Zk5PmZO8bGkrZsHG9r5b0cvP9/YQH00zuK2bhpiCR5t6iBkmFxUkkf8HTkYz7j1hxze2ZnFr7U7uCZNIrpqFRV/+D1t992P79hjKbhm79NS3pmy90AvK0s3OQK4p0ym9U9/RispxlFejnA6ifXYdCYDVGfVf1cOTq7xsoS1dN7PaZh7I97Zs9LVXHqZHKx6ly1D8fmo+vsDO800B/KEnGP3XbtlTxgWkLm4qUWyLLyvYTFSpZ0mmKqCYhiYSYPccDdRTQc3/OqEi5nRvZU5Tz6CqsRwmFYmKHLXDM5wC+4ncz1L/LnkOHPZltxBT9xHnksWQlSub2TEG+28dkQRo1d2s6FEIeoUDI8nCCnyuGZ4ZxBMBLM8Fpfd4OlwtWEB250h/hnwc1l3D8S6ue6U/6V5yRyiA8zkR+SOQFEUeORyiGVoRWhcIT2Witqdwl8DYWxJgGXrZe/X65vaOXXiRx/g397SweFVuYcMNUnKAzFNcDlkdCEeTZKIGmgOhXgkE/42DRNQ+7yX95eiKunzFXZFq0KcuBAk+tC2WGB7LApun/6pDYXt71k9AcyxX88BHt/P/aURicZ47sXXee6hf3L/LXPRxwdJeDJ5gXJfI07fbIQQhN98k/UzZiNe8lMwQT7kRcp4VMPgN5dcTszmcuoYVs1J+XKAqPHIBG+500FjPIFhWbzXE8arKpxZmMNU21vpixPzAzQeN4U5ZfncX9/KYjtc9rcdbYQMk8N8bs4pziX64YcIlwvXxInp5PbeoOj732PE/z2B/4QTqH7w7/tkVAAUt5vqRx6h+uGHspY7x4wF0yT035dwTZyY9iQAjPZsvRLLzJDhdT8hVTi9Rx1F9YIFlM+fn/4sVbSQbGjAWVMz4CCi5edTfNNNDH/sUdTcXIpvuhHV59unc9ob1OTUsPDchVw+8XIAxnnGkRe1iyFS52UJTEUgLJNEKIQ3HiXicOF1GvQm3ehjTufEu/6BIuK4rMw1INpFoM81AcBIMMroRtHCdMUys1tnu0WwNUlQifPayHK6vfLxKo3pNNsVYhXJCgKBAFqfZtQUc4DiqicvmocwXDylSQPsjJk0tzyNO2TyrkPex34jYzgnD5sMXfWw8t/Q1KcJcs3T0L4Ryqbs1TWcXp0J1766oXWvthkIrb0xtrSFqa0esEB0UKKvHkrqf2dTmFBXjO7WKMmEmS6BNg0ra9uMYcnc/0rKsAiTuKmxTc0YfFUI22MR+PPdh4zx3Vfsr2GZB5wkhFgHnGS/RwhRK4S4J7WSEOIlYAFwghBiuxDilD3tuLtxK6seuIWXn32e9rUf8MBffs+WvPcAMJHu+WGjvgBAz5IlmL29FCy0NbwTbkrt5qVwfgGtXrl+cOwYfj9uGGcW5jCnTCYtS506hgVNsQSreqOM9bqymFAHws01ZYz2uHAqghNtQzXJ52bx9DGM8boIv/EG7qlT9pmgUfX5cI4atU/b9Id74oSduL1cY8dkXk+amPUdyfZsJt94n3Ll7qeeQgkG0QoLcU+amNV7o+blpZPwjpG7DtXlXfYlXOPHM+rlZeRddtku19tfDAsMQ7FjGg7VwREtstAjPdc0hfRYTINIVzfueJSI7sTrMOhNuPn6+jgvL/sQlTguK3vw2MmwvPoHxjW+CkBLXHqjzqhBICbDbJWObvI9EWrzHMwZ+zVuGH0NzXY1Yeum1uwEO+AUch+G1o4v6UMYLtYhPRpn3KSzYQklzXEedxehWrBge+Y3O2r8UfDO38EyobcJUvmBtyWFDMFsUbZdYXofQ/DBju7drLl7fNggPeGJZQeGYPTjgFAEiipIxOxwlZGi7NewLMvWhVGzPkvBMOS90dewpHI0ubYnaxlyQuBTFBwqKI44DuehrRC5J+yXYbEsq82yrBMsyxpl/2+3l79lWdYVfdY71rKsQsuy3JZlVViW9cye9u3KiTH+CxvI1zehV8WJbnqF7eGXAZh6+DMcOeMZ8vNkUjLFnaV3JxFxeL91PL6krOK6ZMJIXps0DYD8USMJ6hr/b2I1tUE50yyzvZKfb2rglc5exnnd7AkBTWVx7WhWHzOJ79sa8TNz5Ezc6OwktnYt3iP2X3b3QEGvrEwbAc/UqRR973pGvfIyamHBTh5L12OSEdc1SYqn5Vxw/oCzKqEoaXkA98SJO30+0PofF1RVRbXDD0k7LCYsC0sRKKZJpKMLTyJK1OHArUHcdGJiYmoRFBI4+xkWvc/7K8ZfC9vfJM82Nuu6S3m/dRxHvt3J2DK7XHbaF/ly1Vuc53yf7834OsMcIZrs/hKX4WJ0vzyTI5ExzCN9I9EtDzHVIIYDb8gi2NyKO55ksdfLJLWIcjMzAy7OLYDlduOwmZCMFbpXkk0CePdOt6S2Oo+LaiuYWB6gsSvTFLhyRxfrdsEC3R+d4ThPrpCFCyOLDrxnejDh8urEI0mMpIlp501cvkzxgaYrCEWkDQnIsFi4O46iKlkhrVT/i1ON2+o1Ak0R+JMCzRkFy8ThPHQ8uo+CQdt5r6jyxx17YabHoGuLl2RCJc9fiKIUE9+yhdY/30105UocI0ZIzqtmQYN1DuHWVoJAYUkxi794OS9OncH/jB+30/eUOeXNs6BRDgpH5+7dA+GyK2cm+z08MqWG6bahiqxcCZaFe8rehSA+DghFYfjj/8GKRtPJdy0vDy03j2RHxrBEPlhJ2/1/IXj+eZT++MdEV67EZVeB7Q5+m6V5sEBRFBQ7OZr2WCwFUxEohkGkuxtPPErU5STVgL0x+UN8phshkjsZlrAdg79w9IV8e/qVRJf9kTybYfrFhqk8v6GG77hsETlhURmoBntct5Jxelvr2WLnACdVT2JsvzzT56pzeG/NVJy+zcwqmMUbzVuIqBG2UM7oxk0EelWaVZW4o4fDA9OBt7ivoYnOE38CG56H7u0w/mxYZUeiR50Eq/4jX/v2rlDToSn88oLJzHv6Q+5dthHTtFAUwem/lXo/m+ftntutO5pg+k+XkLBn9IX+Q4uixOHWCHfHs3RRFFXgy3XR2xFFc6gIZCJf1RTcfgcdjWGMpIkv15U1+erbWKkIE8NS0ITkKtOccZKmslOBwKcNh1TmKFgVIhL1oigKlmmy5dIvpWfY+V+V8fUx/nnc+aXPE7Up6AtKirl76kiGHTWDqYGd3c9KlwOnPXAsmzGWc4pydlpnTzgm14/TnpHHVsvS49QAPljgHD58p2NS8/Iw7FCYGY/TcONcmRf5wQ8Quo57ypTdxoDL7riDwuuuQ8sdXLMvVVXThoU8+Xs29RZJj8UyiHSE8doeyyRzMyoGrqW9dJdvRlESO4XC6sIRdKHJjnTLQu3eTq49c1W969ECmco5RbfoXZIRbOvpbCHeuYMtmgvNcHLuWefu1B1f4NU5vXMEJ2w/jtKiUpx4QI2yxZIFEoFeg/dtQa3xxbJAYXo0xklFU+C9f4KnAA7/cmaHZVMzr7371gFQEnCSMCxuW7iKXy7K0NWYA0gJ9MW7WzvTRgU45HIHKY/DSJrEQrIyUFEFnoCDwko/qqakr0GoM0brth6MpEmgwI0n0L8vK/P7pholFWTIzSKKqrgOueuzrxi0HksKTt/xTJl4O6+9ehRCQNQWJYqtWZOlX/Lf6UcxUgjUHU0YnR0U33M3cU2jIuDDr6ncO3H4Tvu2kkkcmzbx2pHj8KsqPk0lvr1exqsti82f/wKlt92G//jj9vp4o6tWy6qsnH03UB83tLxcoitleXPP008TW7eeirvuQt1LAa7gmWcczMP7yFAUJR0KUydOgLff5q3GqZQnliEUk+7mXryJCIamcUbHC2xSHcR0nbhbhUgChwmbJ3+f6vfuAKA8abB85h2QNwa6tqNbMXINO9xRIPnTsB1rzacReuUVsIUWe9ub0HubWOPWyE3mEwhkU8gA6Lr0mgWCqqoq3Ku8CKWH18URnMTLdCuC7xbLvNnk4bMzG8ZDsO0NGDE7O5fS17B4dsEhtguUBGUhwf0vb85afs+yjYwq9nPcmIEN1Vo7XHb+tAqmDBv8935/KKrUZultj/ZZJn/jVANlIN8ttel7EulEv9Oz8xAqhEJu7hQmTBhFOAaqqnH+hV/g+qu/xTOLl/A/P/4tQuisX7+e8vJy3G43hx12GJdffjlnn312Vg5u/vz5nHjiLgtzBy0Gt2FxXs3R07+PEIKuSC45ng586wK03XsflilDEcMf/w9LogZXbWjiX3kFBDdtYuk9f6EScCST+PrQc4ffeosdc2+k6m9/pWPBAtr++CcAapYsSRMqbjrvPMzubhzV1RhtbXT95z97bVgsyyLy3nu4Jkw4sNfhIEGvqqL7mcWYkQg9z7+AVliIr272njcc5FAUBYFAWIK4zZKtYhK3NDxGgo7WEEWJGIYmB/Rc0UPMobM6NBpL2YFuCqyqo8E2LADE7IR2ixSUy+mX0DeQ89Q1p53L8AcfTS8PPHcDomsF23MqmamOGPB4UxViDoeDkpISPIoHocZZMHwxRfXHUOqQPU1mIkhhQRUcdjGseAg6NkPXNphxTZoKX55QVea1um+PeHFgYDbhnz0lvZdNP//cgLPttU09FPgc/OqiPYdOByOEEKiqgpE0+yzLXkfmXHSEIgh1xhCKGDj/KFTcbievvvok9d1+6pu6+eG3rsCMdHLTD7/EGadfiMORR11dHfPnz6e2VuaKly5dyrHHHsuTT+49peJgxaAOhZWXH57+4ZrtBGfNXz6k+Y476FmyBOeYMbjGjGGFT86QthaV0rtxE0k7HPXH876Y9cN3/OshEtu3s+mii9NGBSCyXCb/jd4QZretsbJFCijFN23c6+NNbN1Kor4ez5EzPuopf6xwT5wIhkF01SpCL7+Mr272x5pkP1hIhZoUSyFuT0BqlTWYQshG2e4o7mQMp90M2W75iek6d669ClMYqJbAm9evjyMuE+ambVg6ycXfx7Z0KnIA31RcTsmPf8yzbg+/zs3B1/QGq5wOLAFTiwYedFOGpbKyElVV8drlxyYGqyucdNnlq67Wb8lZdJ2t7JhqhiyfBk4/fP4huPAvWSST+4oKW1Xy4tqBq8nW7CKRv7apl1FFe9FMOYjRv6dkV+GqPfWeyByLgsNRhBAW+QWF/OLXt3P3Pf8Py7JQlEMr//RRMGg9FlX1UlWcqZ6ZVftTWnasINXwH31vBXmXy7xKSjde1BAAACAASURBVPJ3a0kZ019YxHBgyfSjyTn/POqv/x6lt9+GUNV0BZTR2oqam8uoZS+xdsaRhN95h+BZZ6W7zIvm3kDw9NPpfOQRWn77O5IdHag5OXQ9+ii+E04YMKfQsWABvUtfBDjg7L0HC66JsvKre9EzmL29abqWQx2KbRwVSyFmyvT9N7Qn+C/jCRleQuE4ChYuzdb/IEFcyvphCBPNUvAEs0u2zWg3SqQTXv8T3fhoIZ9RiRjLnfbERzjJJ0moq5Xcq77GdX+V5KHf7ujkl8ErgScYXz6egZAyLFVV0tPwOnuxdcHo9TnobpfnU+GXnGjofRK/7jyosNU4x/Qpovjio5DI5rvbGxT6nTx//Wyq870seHsb/VMrb2xqZ2xJgK5wAk0VeO3qh8auKMeOOrAS1gcCTz/9NI2NjXu1rmVaGIaFENKo7Ep8q6iwmOmTjtnj/hRFTyuvVA+vwDQNWlra8ft3nX996aWXmNKn8OfRRx+lZi+ZNwYTBq1h8XhG4HZnZk0TKkcS2tbM1j7reI+WA/i6cJRTCwI0F2biv8kTT+LyX/2U7uXLCZx2Kl2PPyFj3zbUQAChqrgmTST6vlTOi76/AoCcCy5A9fnwzppFy52/pff553EMH0HDD28h+NbblM37edaxdi9aROMtPwJA8Xh2EvEarNCLi9CHDaPjgQcADgqP1yeBlMeiWipxM9M17RVRoqaLWFJ6MX5dxtP9IkLMDoslFRPVUtHd2bNvI9yJsuF5lM4tPM65jGcdN7e1cL7dr9GuOMAIEe9py+KPalZVttrhlcq8gb0At93Am4qtH6HX8m5bA11KkpZAhG5FwWNaXH60PcA4+hiWU34mKVX6Y9RHj8uPKJSVkbqqEEuaXDVrBO9v7+LNze1saQuzeGUj1/7rHXxOnce/eTSlARetvTEKDrFKsP4QiiwL3pv1gCzOsAHXE3paj0dNEU/alPy7wqclFDZoDctASIWnqh9+iNi69XhnziSUNNgciXF6YQ7Ljj2OpxNx7qw7lVvGDyf+pzsBCL/5Fj3PPgvI0tieRYtwjpFNg84RNXQ9/jiWZRFduxa9sjLdHe4aPx69ooLuhU/hszvo+ysiAkTeyVQF6ZWVh1TFR86559By528BcI4c+QkfzYFBX48lbmRElywFNMPAMqWn4tPkb+klSsxOoCeEhcMETc8erMWG52DZHViKxmazgkp2MC3WzZ88x3FNeDmrHE401Ymnp4fVDT14TYuQIvibv4DORDcOU6UwMHBPyYgRI7jyyispL5d5vpNnnszL94R5KfA+20PL2ZQzgWi8g5k1diJe71Pd6D54ifKUYTnjsFJu+tw4TvnNf1m6ppl7l8lKhWgixgsfNpPndZA0LQp9g8+wnHbaaQd8n5ZlEemJ75HeXwgVy+5k2bZlM4qiUFxUekiNDx8Vh1RAPbGjAXQd18SJ5Jx/HkJRWNLeTdKCujw/BSVF/PLks4k5nBypWhg2Y3H3M8+AEPhmz6bsp7dTefefKb3tJ4Ckmjd7e+l9YSk9Ty/KmrULIQieew6hV16h48EH5ULLHOC46tOv1bzBVXq7J+Rccgn6sGG4J0/ea06zwY7sHEuGjdpSBKppoBjSU/E55H+viBBz6CjCICksdFRUVSWiZHqatAaZQBdmEgONpLsQgUXh2oUA/LbIwRWlxQRFNx1/PJUSm57/77lOtOA7OAx32jPpD0VR0kYFoLy8nBuuuQwznkvMiLBOc5IwfOSl9E365sHcB+9+0+1QUEpieFi+hw0tUkvk+6eMQQh4/sNmvv6gvDaHWu/KR4UQAk/AibYHkk1pQATtba384PpbueqqS3A4Dq3x4aNi0BoWKx4n8v77dD72bzZfeilbr7yK2Nq16MXF6QTzso4efrahgUKHxhFBL9UueWPn6So13dKo5H7hCyQbG8GyCJ5zNorXi2/WrHRJrWN4NQDbv/51+b66Kus4ci+6CDQt7S0l6nfsdKxZy/ZQ7z/YoOXmUvPMIqoe/PsnfSgHDGmPhUzyHsASAs0wSAqZwNCdMkzmI0rE4cC0FGKKQLNLlZ8fdh2/5cs0kSnZbR9+pnwRkIagP93LyOIOjlZXpvtcABStB5/l3FndcTeoyveiJGTorCWxDg1vms4+CwfRsJw2SeZ0crzSe6vKy0w8vnDEMPK9Tt7YlGmwLRiEHssnhUgkwpQpUzij7liu/vw51M0+kdtu+w1O5+77ilI5ltTfI4888jEd8YHFoA2FxdZvYPOFF+EcO5bYh5lGLc90mahsiMX5/HsbKXfp3D2hGlUICmw24nOKcjGapLseOOMMjK4uuhcuxDFAEsxRlTEkenk5uRdemPW5VljIiH8/Ru+yl4lv3EDngkfoef4F/McfR8eCBfQ8vYjoqlU4x40jtno1emXFAb8WBxtCCNhHXrPBjJRhUS2VRB+PBQVUw6DZnvhrLjn4+4gQcrpBSEOj2dzGiZwa2ukhhAdog7KpbBj7Tdi0CD1vGDTtbFg6LDkBMfpFO3xi3zqtHZpCbck01iTGEdNX41R2wQjhOnihsP85awJfr6sh4JLXY0J5pgcn1+ugyO9kVUOGV+yz4rHsDQw7BNvW3kBXwqRAD6Dr2R7r0n7S3nV1dXR1dfFpwOAdTewHNvbhh+RcfDGdDz8MloUoKaEjkeSv9W0kLYuHJtdQ5ZY39CWleYQNk2uriom9/zoAWlERJT+6Be/RRw9I8KhXVJD7hS+QaG6ifP78LD2RFJyjRuEcNYqWu+4CpHdTs+jpdMIewH/88eR/5cv4TzjhgF+KIewbskJhViZ5bykC1TCw4hFMBKpD3mNeESGku8AW7lJtGWCPHRqUhgWSvjIWPr0IAFdRDawGt2WhWBamHTdfY5dzxYWgJuSlQ3XS7mrHr+57Ke6xowp549UxuEpXo2qRgVc6yDmWVPkxwAnjssXmCv1OaOjzfshj2Qm6GcQfj6J7PlvXZtCGwvrCU3s42JU2Tzp8jFv2AQsa26nL86eNCkChQ+eGEaW4VYVks5SG0YoKUW399V2RKZb86BYqf//7AY1KX+Recgn+k04CoOV3vwegbP589MpKvDOPJHjWWSjeTzcH0KGAvsn7ZJZhAdU0ye3qJOJ0IhQwAmX4iNKru9EVmXPRhHRpUjmRlGHpMTLUHe48SbfS/45qtY1aTAgUS6UwWggWTND2vWn2rMllqAnZVJlQdiF1pH18A1bApXPDqWOZd54sU8+1FSJHFfm4/qTRBA6gVv2nBaYBlql9anVXdoVBf7aKx4Nn+vS0yuGdM+oAqI8lOKNw4NlafNu2dKWTcgD11bW8PMrm34FWWkr3Qpm0DZx2KiOfXYyndtfyr0P4ZKBYCkkyORbTTt7n9HQTs8lHrZwKvCLK/7mOwyNkE6Qq5AQj5bGEkQZm1QZZ7F5XV4fH6+VFZvCudjin92YqBTtsw5IQAo+qMqFjAudtPo+Jvj0zQPdHWY6ba448ikTPeGb4v7bP2x8MfK2uhkuOkHw1PVFptG8+fRzfOmHUZ6LaaV/hy3Pi8ulp2v3PCga1YSn7xTxGv/UmekkJJbf9hNFvvYlpc3AdnePj9MIMp1Xrn++m4dYfY5kmnQtkwksr2jcCvr2B4nRScqsMgfnq6hD7kJAdwseDVHxbtVQMa+fkfW6oG8Nl3/o5lfiQYSa3kBVPmt0ZnfJYDPsxSQ2bs2fPxu128wJH8UZyND9sa+f6tg6OcpfTYXtLcSFwqjoO3YFA4PuIAmfX1I1kpvd6LpzQL8Sqf/IVfHNPG8t3ThzF7NF7R83/WYSmqwQ+xYJeu8Kg9l0dVVUsae/hw1CUbw4rolso9BgmP6op4+vDMkYj9OqrtPzmNwC4D5tE+K230IqKqHrgbwfluPx1dYx69RWEPkBj2hA+caQMS3+PBRUUyyIY6oGgkD0GvmI8RAELl9pLCHDYlOYpw2KlVTVsbRch0t5MDAcey+LL3T1sdOWxXpXJ+5gQuBAkEjJvU1a2e935XcGlq9z/lQG0fa5bBcn4R9rngcKoYj/fKT60aVyGcHAwaA2LXlGOo6aGK5dvJGpaFDo0xvvkg17lzoS3LMui+Y756BUVGD099L60jMj775M/57Ksiq8DjcFGFT+EDHLt30axFAz65FjsWaMnHsXQVSyhIXQPqrDQMNCdkvojX5HlxRUVFcycOZNtr8py8o0pymIyRidG5l7s2tFGu0vFAhII+mY/KioOcLXgQSwzHsIQ9heDNhTW5PZS/bY0KgC/2tzERpsTrKqPFn2yuZnoqlXkfelSXOPH0bNoESQSeGYcGkSQQzjwyM/PZ+7cueT4c7I8FmGrSbqTMYRmgaKDXQLqJIHibEG3LIIO2aOi6zqnnHIKre4afs7XWcdwhg2TxsVp66PE+pgPEbZICkHogvuJCUHUXcH48ZIfLLiXUgRD+HRAVdWsfpR58+Zx7rnnMmXKFEaOHEkwGEx/9sorr1BXV8eYMWOYPHky06dP5913393zlwxiDFqPpSWeIN+uBJs7vIR5mxp5vFlqSFe5nViWRdPPf47ikgODa9Ik4vX1hF99DRQF99Rpn9ixD+GTh8vlQhNaVo4lY1jiRFQXKA6ETejoJIHhaKc6kYBgtoaJx+OhLRJh3LhxnHvuufa+BEVFRXS0t6dlKp2mnPB0DJtO/G2Bqvk5//zz09sM4bMDt9u9S+OwdOlS5s+fvxMn2IMPPkhtbS33338/3//+93nWpqE6FLFfHosQIk8I8awQYp39fyf/XAgxRQjxqhBipRBihRDi4n35Do+qcFaR3O0L7d3k6Sp+TSW2di0df3uAtrvvBsA5egyuMVLy1XP44ai+obLfzzp0oWP08VgUpY/WhgKoOkKTuRInCSKOHqoTSUxXXtZ+UqG1goICHH2qDK+88kpuvOmmzPcZspqsOSJLg52q7LbXh3JxQ9gHzJw5k/r6+j2vOIixvx7LXOA5y7LmCSHm2u9v6LdOGLjMsqx1Qogy4G0hxDOWZXXuaecTfW7+OL6KYS4HmoCoaTHOK0MPKYp6AL1qGKrPS/DMM1CDAbyzZu3naQ3h0wBNaFmGpW9hjlAtaVhSoTARJ6Ik8RmgOrL7mVIVXf25vlIGY2npNXQ2bKTHLALW8PCahwFwaAeu1H0IHw1r195GT+/qA7pPv28co0ffstt1UpQuKdx4441cfPHezakXLVrEOeecs1/H+Eljfw3L2UCd/fqvwFL6GRbLstb2eb1DCNEMFAJ7NCw3jShllFc+5FUuJxsiMYYpEN++nd4XX0QtLMB/4onkXvJ5AITDgf8QlPEcwsGBpmhYwkqrOyqiD3+XYmEJjaXLXqMO6bH0pCjz+3kY6bLjPkzJfRGunM27DW5UU9KbPLXpKQBc2u4bbofw6cXuQmG7whe/+EVCoRCGYbB8+fKDdGQfD/bXsBRbltUAYFlWgxBit40jQogjAAewYRefXwVcBeAZOYbR3syDOcorDUvek4+z4R/3gRAUfO1rFF77rf08hSF8WqHb1CxxIXBblgx/2VBUi6gB9c2tADiIkxAWqqWlhbdSKCiQAla7Cmnl2L1Vupn9ufNj7IofwsDYk2cxmPDggw8yefJk5s6dyze+8Q0ee+yxT/qQPjL2aFiEEEuAkgE+unlfvkgIUQo8AMyxrAG45wHLsu4G7gaora21KvpUf91SU8ZZRbmMuPaB1Mr4jqvbl0MYwmcMDkXeP3EBbivbY1FVE0OoJO1HwCGiWAIUS9vJgEydOhVVVZk0adKA35PKwQwZliHsL3Rd5/bbb6empobVq1czbtyu1SYHM/ZoWCzL2mVsSQjRJIQotb2VUmBAQiMhRABYCPzQsqzXPsqB1nhc1HhcfKjrWIkEjhEjcE3cd5qMIXx2oNk69AkhAAtVZCQNVMXEIGNYdFV23w8UClMUJSte3h8jRoxgxowZGIbB422Pp5e79YH1V4bw6Uf/HMupp57KvHnz9mpbt9vN9ddfz/z587n33nsP1iEeVOxvKOwJYA4wz/7/eP8VhBAO4N/A3yzLWrA/X2aZJlY8jveooyj/7Z2fOZqEIewb+obCILsqTFFMusIJkkhKHk1Iw6Kbyk6hsD3B6XRy2mmnEY/HufGfN6aXDyXvP7vYVT4OJNdcna1Im0J/Cv3rr7/+IBzVx4f9bZCcB5wkhFgHnGS/RwhRK4S4x17nImAW8GUhxLv2366nf7uB0dEBySS+449PywcPYQi7gkOVA3ssZVj6eiyaiYGS9lhUm9nYMYDHsrfov11CJHax5hCG8OnGfnkslmW1ATsJkFiW9RZwhf3678ABkSdMNjUBkgp/CEPYE3RFDvR/TH6OX/IEpqWgIL0WVTEwcKY9FlWRrA66qXxkw9Lfg+5N9H7UQx/CEA5pDFpKl4GQsA2LXly8hzWHMISMYXnEOprFxuFofZP3iolQHfhyCjCAhqL35TaWSl5e3kC72ycUm8XMmT5nv/czhCEcihi0hsWMROhZsiRrWbK5BTg4dPhD+PQhVRX2jdkVoOpZORZNNbFUHdXpZanHzXa37EHRLP2AGJa/nPIX8r35e15xCEP4FGLQGpb4ho1s/+a3sPokwZJNTSAEmt1XMIQh7A6pHMuG158ix+dFE5l7SVOTWIqO5XTS3EdTx6v70tLG+4O8nP03TkMYwqGKQUtCmULPs8+ieL14jzmGRHMTakH+kA7KEPYKqVCYIQziloKmmsRTVWCKCYrGAvX/2FaQMQKB/dSQn9Q2iUZP4070L0MYwmcJg96w1H/nuwB4Zh4JholeOBQGG8LeIeWxmMIkbqloIglIY6MpBqgOtrEtaxuPY//IS39x4S9obGwcKoX/jKN/Q+0ll1zC3LlzefLJJ7nlllswTZNEIsG3v/1trr766k/wSA8OBr1hAUmJH35V9lX6jjvuEz6aIRwq8NjMxQklQTSpSmNiQ1OTvOSK7bSNU90/fq/KykoqKyv3ax9DOPQxEFdYIvH/2zv3KCmqM4H/vurueTIIM8MzwzATZYMICDKLOyCsET1LTJSsKzl4XCUsZCLEk8jLgMQNMUvOiBgBs3sUMT5ycrLuJu5GN7IROImaIGElQQEh4eEoKKIMCIzMDPO4+0dV99T09HO6e7qn5/ud06erbt3H11/dqq++e29/1UJNTQ27du2irKyM5uZm6urq0iNgisnYORaAwcuXkTduHMPXPhgITesdoh6LEhuDcwfja/NxOvc0ze0WuQUdb5P0WG1sLjzVpYzP0sCRSmo4f/48ra2tlJTYizpyc3P53Oc+l2apUkPGeiye4oFcMmsWJfPn2wnOS7/yRo9Oo1RKb8Lr8VLcXEx9Xj1NZweQm9NhWN7PhU+lnTF5Y3i76W0AvvbJWaRU43tlE/cfOs6+hsak1jm2Xz7fHxX5VdPhwubffPPNjBw5khkzZvClL32J2267DcvK6Of7bpGxhsU3fHin1V/Fc+dybuvLDLjlljRKpfQmLMti4MWBnCw4yYV2Cx8dhuVAgT3XMqaww7DcfeYs+4aqYVESJ1zY/M2bN7N37162bdvGunXr2Lp1K08//XTPC5hiMtawBDNk5QoGf/teJAutu5IaPB4P3na7izcYwSvtvFg5hZve2cG7uV48RqjMrwzktwDxaHyvbCKaZ5EOxo0bx7hx47jjjjuorKzMSsPSq+7SalSUeLAsC8vYfeacE9L+mfEzuXXWAzSLkCMWhcGrwDRwpJIiGhoaOgWb3LNnDyNHjkyfQCmk13gsihIvbsPS/5J+cA4K5CLNeG3Dgod+vs7BTEXfoaIkgVBh81etWsXatWv5+te/Tn5+PoWFhVnprYAaFiWLcRuW68cNhd9DvjRjjAQMS7DHooZFSQbhwua/9NJLPSxJetCxJSVr8Xg8WE4Xb/fZQ1wFNOOjlYsi5IiHotyiTmXEp8uNFSVR1LAoWYvbY/n3T/7EBREKaMInrbbHYnm7vOXR8qnHoiiJokNhStbiNiwvnP0j+cUDKDzZxNm2QttjsXxd3r2iQ2GKkjjqsShZi2VZeExHpOJzlkWhp5EcWmkSIVd8XV5DbOlQmKIkjHosSlbj91gAPEA/q5ECmrgoQq43F6/Xy6VnL+X2ll2AzrEoSjJQj0XJWtrb2zsbFmMo9DRRJI00i5Drzcfr9TLh9ARmn/8UUI9FUZJBQoZFRIpFZKuIHHK+B4bIM1JEdovIHhHZLyJ3JdKmosRKsGHxYnsshQGPpUCHwpSU4PF4mDBhAmPHjmX27NlcuHABABFh6dKlgXzr1q1j9erVfPLJJ5SUlGCcmIivv/46IsLx48cBOHv2LMXFxbS3t3dtLANJ1GNZAWw3xowCtjv7wZwAphhjJgBXAytEZHiC7SpKVNra2gLLjcH2WPpJI4XSSLMl5OaEMCw5aliUxPHHCtu3bx85OTk89thjgB3R+Pnnn+fUqc6RtQcMGMDQoUM5cOAAADt27GDixIns2LEDgJ07d3L11Vf3moCViUo5C3jG2X4G+HJwBmPMRWOM/8UXuUloU1FioqSkJGiORSi0muhHE80i5OX062JYPLoqTEky06ZN4/DhwwB4vV5qamp45JFHuuSbOnVqwJDs2LGDxYsXd9qfMmVKzwmdIIlO3g8xxpwAMMacEJGQL0sRkRHAr4DLgOXGmA/C5KsBagDKy8sTFE3p6wwaNIhbv3wrW9/YCoDH8lJ68QynGGAbltyigGGpy7uCiqb9eLy6niWb+N6L+3n7g3NJrXPM8P5896YrYsrb2trKli1bmDlzZiDtG9/4BuPHj+fee+/tlHfKlCm8+uqrLFiwgKNHjzJ79mwef/xxwDYsK1euTN6PSDFRvQcR2SYi+0J8ZsXaiDHmmDFmPLZhmSsiQ8Lk22SMqTLGVA0aNCj2X6EoYbik6JLAttfyUfHBSYa1n6JZhPycwoBh+bncxFruwuPxhKtKUWLGHyusqqqK8vJy5vvfKwX079+fO++8k40bN3Yq4/dY3nnnHSoqKsjLy8MYQ0NDA7t372by5Mk9/TO6TdTHM2PM9eGOichJERnmeCvDgI+i1PWBiOwHpgE/j1taRYmTXE/H0JZleflM00d4PoVWscjz5QXeTd/Q2AzkdxkaU3o3sXoWySbc+1j83HPPPVx11VXMmzcvkDZq1CjOnDnDiy++SHV1NQCTJk3iqaeeorKykn79+oWrLuNIdL7jBWCusz0X+GVwBhEpE5F8Z3sgMBX4c4LtKkpM5LrmTNotL2IZjM9eWeM2On7UY1F6guLiYr7yla/w5JNPdkqvrq5mw4YNAcNSXV3N+vXre9X8CiRuWGqBG0TkEHCDs4+IVInIZifP5cAfRORN4BVgnTFmb4LtKkpMuA3LRREsj6HdZ0eeVcOipJOlS5d2WR02depUjh07RlVVFWAblqNHj/Y6w5KQ32+MqQdmhEh/A1jgbG8FxifSjqJ0lxzXi7sutLSCZWjzqseipJaGhoao6UOGDAn8v8XP8uXLWb58eWC/oqIi8N+W3oQu/VWyGq+n49mpRYSCsWMw+Xa39xuW6667LpBHDYuiJI4aFiWrcf+hrFUsrFwvZ3LsCfsc5/3206dPD+RRw6IoiaOGRclqOhsWgbZmHr/Ew4A2i0lDJkXMryhK99C1lUpWE+yx0NZCvU+4siWX0vzSwLE5c+YEwmkoipIYaliUrKaLx9LaTAvglc6eyejRoxk9enQPS6co2Yn6/UpW03UorIVWAS86l6IoqUINi5LVWJbFZ899FuiYYwnlsShKMgkXNj9cem/6V30s6NWlZD0T6ydS0lRCqwBtF2kRUY9FSSnhwuaHS8821LAofQLLWHx68SKtzRfsoTBRw6L0DO6w+bGkZwM6ea/0CcQIrQLGtNImooalr7BlBXyY5AhSQ8fBF2pjyhoqbH6k9GxBDYvSJ7CwaBVoFzs8hk8Ni5JC/GHzwfZM/GHzw6VnG2pYlD6BZSzaBFqw/3XvFe36fYIYPYtkEy5sfrRw+tmCzrEofQIxQht2vDAAr6WGRVFShRoWpU9gYfFRTgt78uzAk17xpVkiRengwoULlJWVBT4//OEP0y1SQuhjm9InsIz9DPXNIfYrr33qsSgpJJaw+W7a29tTKU6Pox6L0icQZ27Fj9dSj0VRUoUaFqVP0GK1dNpXw6IoqUMNi5L1LFq0iGaruVOaTw2LoqQMNSxK1jN48GBafMEeS06Y3IqiJEpChkVEikVkq4gccr4HRsjbX0TeF5EfJdKmonSHJqup036ORz0WRUkViXosK4DtxphRwHZnPxzfB15JsD1F6RalLaWd9r1WbpokUZTsJ1HDMgt4xtl+BvhyqEwiMgkYArycYHuK0i2mn5vO5DNlgX2fV4fClNQRLWy+/1Nba0cGaG1t5b777mPUqFGBY2vWrEnnT0iIRBfzDzHGnAAwxpwQkcHBGUTEAh4G7gBmRKpMRGqAGoDy8vIERVOUDrztXoY2DQCOA+DzqMeipA536Jbbb7+dxx57jCVLloQN6fKd73yHDz/8kL1795KXl8f58+d5+OGHe1rspBHVsIjINmBoiEOrYmxjEfCSMeaYiETMaIzZBGwCqKqqMjHWryhRMcbgbc8L7Od41bAoPcO0adN46623wh6/cOECTzzxBHV1deTl2X20qKiI1atX95CEySeqYTHGXB/umIicFJFhjrcyDPgoRLZqYJqILAL6ATki0mCMiTQfoyhJxRiDpy0/sO/z5kXIrWQLD+56kIOnDya1ztHFo/n25G/HlDc4PL47ujHAypUrufzyyykvL6eoqCipcqaTRIfCXgDmArXO9y+DMxhjbvdvi8hXgSo1KkpPY4zB21YQ2Pepx6KkkHDh8UMNhQV7M0899RQbNmygvr6eHTt2MGLEiJ4ROokkalhqgf8QkfnAe8BsABGpAu4yxixIsH5FSRrtuIfC8iPkVLKFWD2LZBNPePzLLruM9957j/Pnz1NUSaQmlgAACoVJREFUVMS8efOYN28eY8eOpa2tLcWSpoaEVoUZY+qNMTOMMaOc79NO+huhjIox5mljzN2JtKko3cEYQ6vrPfc6FKZkCgUFBcyfP5+7776bpib7/1ZtbW1cvHgxzZJ1Hw3xqvQJjDHgCkSZ61OPRel5gudYZs6cSW1tLWvWrOH+++9n7NixFBUVkZ+fz9y5cxk+fHgape0+aliUPonPpx6LkjrChccPN7Tl8/mora0N/K+lt6OxwpQ+ge2xdJDjKwiTU1GURFHDovQJ/IblXz/8iBsbPsWrHouipAwdClP6FNMbm5je2MQ5ny43VpRUoR6L0icIHgqzNFaYoqQMNSxKn0ANi6L0HGpYlD5BV8OiQ2GKkirUsCh9Eo9PPRYlNRw7dozKykpOnz4NwJkzZ6isrOSVV14hPz+/U9j8Z599FoCKigpOnTqVTrGTik7eK32C/Px8GhsbA/ueHF0VpqSGESNGsHDhQlasWMGmTZtYsWIFNTU1jBw5kksvvTTmUC+9GfVYlD7B/Pnz+eIXv0i70+UtfTWxkkIWL17Mzp07Wb9+Pb/73e9YunRpukXqUdRjUfoEpaWllJaW0jLstzQe2EJhlHcDKdnBhz/4Ac0Hkhs2P/fy0Qy9776IeXw+Hw899BAzZ87k5ZdfJifHHno9cuRIp5Aujz76KNOmTUuqfJmAGhalT+EruxJf2ZXpFkPpA2zZsoVhw4axb98+brjhBoA+MxSmhkVRlKwlmmeRKvbs2cPWrVvZuXMn11xzDXPmzEmLHOlC51gURVGSiDGGhQsXsn79esrLy1m+fDnLli1Lt1g9ihoWRVGUJPLEE09QXl4eGP5atGgRBw8e5N133w3Msfg/GzduDJQbP348ZWVllJWVsWTJknSJnxR0KExRFCWJ1NTUUFNTE9j3eDzs3r0boNOSdzd1dXU9IVqPoR6LoiiKklTUsCiKoihJRQ2LoihZR3BsuGwkk3+jGhZFUbKKvLw86uvrM/rGmyjGGOrr68nLy8zQRJKpyheR88Cf0y1HhlAKZE+Euu6jeuhAdWHTRQ+DBg3yrlmzpqKioiJfsjTCgjGGurq6xlWrVtV9/PHHrU6yXxcjjTGD0iheRhuWN4wxVemWIxNQXdioHjpQXdioHjrIJF3oUJiiKIqSVNSwKIqiKEklkw3LpnQLkEGoLmxUDx2oLmxUDx1kjC4ydo5FURRF6Z1ksseiKIqi9ELUsCiKoijJxRgT0wcYAfwGOADsB77lpBcDW4FDzvdAJ3008DrQDCwLqutbwD6nnnsitDkT+78sh4EVIY4/CjSEKVsA/Ao46LRT6zo2EtgOvAX8FiiLVQ+ZpgvgOuCPTh3PAN4w5e92yhqg1JU+EPgvRxe7gLEp1MPtTjtvATuAK2M91658c516DwFzo53rEOXXAMeC+w2wBHjbkW079n8BUtknUqWLImCP63MKWB+m/CRgr9PORjqGxp9zla8D9mS4Hv4X+AT4n1j6fIjyP3Xa2Qf8GPA56dcCZ126+Oc06eHHwEfAvihtpup+Gfe1Ec+FMwy4ytV5/wKMAdb6fwSwAnjQ2R4M/DX2hbzMVc9Y5wQWYEdX3gaMCtGeBzgCfBbIAd4ExriOVwE/iaKozzvbOcBrwBec/f+k40K8DvhJrHrIJF1ge5zHgL9y8j0AzA8j80SgAvtG4TYsDwHfdbZHA9tTqIcpdFxIXwD+EMu5drVXDBx1vgc62wMjnesQdfyNI3ewYfk8UOBsLwSeS3GfSIkuQuTbDUwPI/MuoBoQYEsonQEPE98NtUf14OSdAdxEV8MSss+HKH+jowMBfgYsdNKvDa6zp/Xg7E8HriKCYYmmLxK7X8Z9bcStMJcgvwRuwLaQw1zK/HNQvtV0vpnOBja79u8H7g1RfzXwa9f+SmClS4m/IcQNIoK8G4CvOdv7cbwUpzOd664e0qkLYBBw2JU+DXgpiqydLjLsp5RrXPtHgCGp1IOTPhB4P9q5DipzG/C4a/9x4LZI5zqCrGH7DfYN6fc90SdSqQtgFPaDh4QoPww4GK4+17VxjBAPO5miB9fxawljBIL7fBR5FwNrotXZU3pwpVUQ2bCk7H4ZlB7TtdGtORYRqXAa+AP2TegEgPM9OErxfcB0ESkRkQLsp4URIfJ9BrtT+znupIHt4r7gbzcGeQdgP9Fsd5LeBP7B2f57oEhESmKpK0TdFaRPF6cAn4j4/217a5jykXgTuMX5LZOxhwnL4qyjO3qYj/2UDJHPtZuo+UKc6+7gli1uMkUX2MbiOePcEUKUPx6l/DTgpDHmUIjyUekhPSQNEfEBd2APrfmpFpE3RWSLiFzRzXor6L4eYiWV98u4ZYv7RV8i0g/4BfZ8wLl4Y/EYYw6IyIPY44sN2De21hBZQ1VsRGQ49pP+tTHK68V2bzcaY446ycuAH4nIV4FXgffDyBCt7rTqwhhjRGQO8IiI5AIvhykfiVpgg4jswR5v/1O8dcSrBxH5PHYHvcafFCJbqJthxHxhznVciMg/Yg8b/G03y2eELhzmYN8oQzYdQ/nbsPUZNz2oh2Tyb8CrxpjXnP0/Ys8nNIjIjcB/Y3uBMZMEPcTcVIi0ZN0v/cdivjbi8lgci/4L4KfGmOed5JMiMsw5Pgx7kikixpgnjTFXGWOmA6eBQyIyQkT2OJ+7sC2u++m7DPgA2/JfBhwWkTqgQEQOi4jHVf4BV7lNwCFjzHpX+x8YY24xxkwEVjlpZ3uhLjDGvG6MmWaMmYxtJA857f/aKb85SvvnjDHzjDETgDuxh9feSZUeRGQ8sBmYZYypd5JD/j4Rudqlh5sj6cGh07mO0CfC/ZbrsfvDzcaY5lh14CqfMboQkSuxF3LsdvaDdXGczp5pcHkvtif7XIbrIW5CXRsi8l3svh94J7BzbTQ42y9hjw6UxtFOMvQQru4eu1867cV3bcQxRijAswStMMGe/HVPRq0NOr6ariuhBjvf5dirEEJNOnqxJyQr6ZiMuiJEvkhj5f+CfWKtoPRSfxr2hPoDseoh03ThKp+L7bpeF0X2OjrPsQwAcpztrwHPpkoPzm88DEzp5rkuxjZ6A53PO0BxpHMdQfbgyfuJ2PNL3ZpPyCRdOMdrge9Fkfn/sBcz+Cfvb3Qdmwm8kul6cOW/lm7OsQALsFdi5QelD6Vjpdxk4D3/fk/pwVWugshzLKm8X8Z9bcTTYa7BdkXfomP53Y1ACfYN7ZDz7b/Qh2Jb0XPYSwGPA/2dY69hL197E5gRoc0bsVdTHAFWhckTbpVDmSPvAZe8C5xjtzry/gX7CSE3zosnY3ThdNQD2JOCkZYrf9NptxX7SWazk17tyHsQeJ4Qhi2JetgMnHHlfSOec+3k+yfsC/AwMC/auQ5Rfq2jh3bne7WTvg046Sr/Qor7REp04Tp2FBgdReYq7Hm+I8CPcN00gaeBu+LRQRr18BrwMdDonNO/i9TnQ5RvddrotKwYe25iP/a1uZMwN/0e0MPPgBNAi/N7wq38TNX9Mu5rQ0O6KIqiKElF/3mvKIqiJBU1LIqiKEpSUcOiKIqiJBU1LIqiKEpSUcOiKIqiJBU1LIqiKEpSUcOiKIqiJJX/B+XWC0NJbHWEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the number of stocks in each cluster\n",
    "counts = clustered_series.value_counts()\n",
    "\n",
    "# let's visualize some clusters\n",
    "cluster_vis_list = list(counts[(counts<20) & (counts>1)].index)[::-1]\n",
    "\n",
    "# plot a handful of the smallest clusters\n",
    "for clust in cluster_vis_list[0:min(len(cluster_vis_list), 3)]:\n",
    "    tickers = list(clustered_series[clustered_series==clust].index)\n",
    "    means = np.log(combined_stock_price_df[tickers].mean())\n",
    "    data = np.log(combined_stock_price_df[tickers]).sub(means)\n",
    "    data.plot(title='Stock Time Series for Cluster %d' % clust)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might be interested to see how a cluster looks for a particular stock. Large industrial companies share similar regulatory oversight and economic sensitivities. We indeed see that our clustering has found an Industrials stock cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AME    3\n",
       "CSX    3\n",
       "DE     3\n",
       "NSC    3\n",
       "UNP    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_cluster = clustered_series.loc['CSX']\n",
    "clustered_series[clustered_series == which_cluster]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c25a259d0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tickers = list(clustered_series[clustered_series==which_cluster].index)\n",
    "means = np.log(combined_stock_price_df[tickers].mean())\n",
    "data = np.log(combined_stock_price_df[tickers]).sub(means)\n",
    "data.plot(legend=False, title=\"Stock Time Series for Cluster %d\" % which_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have sensible clusters of common stocks, we can validate the cointegration relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_cointegrated_pairs(data, significance=0.05):\n",
    "    n = data.shape[1]\n",
    "    score_matrix = np.zeros((n, n))\n",
    "    pvalue_matrix = np.ones((n, n))\n",
    "    keys = data.keys()\n",
    "    pairs = []\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            S1 = data[keys[i]]\n",
    "            S2 = data[keys[j]]\n",
    "            result = coint(S1, S2)\n",
    "            score = result[0]\n",
    "            pvalue = result[1]\n",
    "            score_matrix[i, j] = score\n",
    "            pvalue_matrix[i, j] = pvalue\n",
    "            if pvalue < significance:\n",
    "                pairs.append((keys[i], keys[j]))\n",
    "    return score_matrix, pvalue_matrix, pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_dict = {}\n",
    "for i, which_clust in enumerate(ticker_count_reduced.index):\n",
    "    tickers = clustered_series[clustered_series == which_clust].index\n",
    "    score_matrix, pvalue_matrix, pairs = find_cointegrated_pairs(\n",
    "        combined_stock_price_df[tickers].dropna()\n",
    "    )\n",
    "    cluster_dict[which_clust] = {}\n",
    "    cluster_dict[which_clust]['score_matrix'] = score_matrix\n",
    "    cluster_dict[which_clust]['pvalue_matrix'] = pvalue_matrix\n",
    "    cluster_dict[which_clust]['pairs'] = pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = []\n",
    "for clust in cluster_dict.keys():\n",
    "    pairs.extend(cluster_dict[clust]['pairs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the pairs with a validated cointegration relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('MO', 'HSY'),\n",
       " ('COST', 'PG'),\n",
       " ('COST', 'WMT'),\n",
       " ('HSY', 'PM'),\n",
       " ('HSY', 'RSG'),\n",
       " ('HSY', 'TGT'),\n",
       " ('MDLZ', 'PM'),\n",
       " ('MDLZ', 'PG'),\n",
       " ('MDLZ', 'RSG'),\n",
       " ('MDLZ', 'TGT'),\n",
       " ('PEP', 'PM'),\n",
       " ('PEP', 'PG'),\n",
       " ('PEP', 'RSG'),\n",
       " ('PEP', 'TGT'),\n",
       " ('PEP', 'VZ'),\n",
       " ('PEP', 'WMT'),\n",
       " ('PFE', 'PM'),\n",
       " ('PFE', 'RSG'),\n",
       " ('VZ', 'WMT'),\n",
       " ('LNT', 'CMS'),\n",
       " ('AEP', 'ATO'),\n",
       " ('AEP', 'ETR'),\n",
       " ('AEP', 'PPL'),\n",
       " ('DTE', 'PEG'),\n",
       " ('AME', 'UNP'),\n",
       " ('CSX', 'DE'),\n",
       " ('CSX', 'NSC')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 27 pairs.\n"
     ]
    }
   ],
   "source": [
    "print (\"We found %d pairs.\" % len(pairs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In those pairs, there are 25 unique tickers.\n"
     ]
    }
   ],
   "source": [
    "print (\"In those pairs, there are %d unique tickers.\" % len(np.unique(pairs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.c Pair Visualization\n",
    "For the pairs we found and validated, let's visualize them in 2d space with T-SNE again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stocks = np.unique(pairs)\n",
    "X_df = pd.DataFrame(index=returns.T.index, data=X)\n",
    "in_pairs_series = clustered_series.loc[stocks]\n",
    "stocks = list(np.unique(pairs))\n",
    "X_pairs = X_df.loc[stocks]\n",
    "\n",
    "X_tsne = TSNE(learning_rate=50, perplexity=3, random_state=1337).fit_transform(X_pairs)\n",
    "\n",
    "# plt.figure(1, facecolor='white')\n",
    "plt.figure(figsize=(16, 8), facecolor='white')\n",
    "plt.clf()\n",
    "plt.axis('off')\n",
    "for pair in pairs:\n",
    "    ticker1 = pair[0]\n",
    "    loc1 = X_pairs.index.get_loc(pair[0])\n",
    "    x1, y1 = X_tsne[loc1, :]\n",
    "\n",
    "    ticker2 = pair[1]\n",
    "    loc2 = X_pairs.index.get_loc(pair[1])\n",
    "    x2, y2 = X_tsne[loc2, :]\n",
    "      \n",
    "    plt.plot([x1, x2], [y1, y2], 'k-', alpha=0.3, c='gray')\n",
    "\n",
    "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], s=220, alpha=0.9, c=in_pairs_series.values.ravel(), cmap=cm.Paired)\n",
    "plt.title('T-SNE Visualization of Validated Pairs')\n",
    "\n",
    "\n",
    "# Add the participant names as text labels for each point\n",
    "for index, (x_pos, y_pos, label) in enumerate(zip(X_tsne[:, 0], X_tsne[:, 1], pairs)):\n",
    "\n",
    "    dx = x_pos - X_tsne[:, 0]\n",
    "    dx[index] = 1\n",
    "    dy = y_pos - X_tsne[:, 1]\n",
    "    dy[index] = 1\n",
    "    this_dx = dx[np.argmin(np.abs(dy))]\n",
    "    this_dy = dy[np.argmin(np.abs(dx))]\n",
    "    if this_dx > 0:\n",
    "        horizontalalignment = 'left'\n",
    "        x_pos = x_pos + 5\n",
    "    else:\n",
    "        horizontalalignment = 'right'\n",
    "        x_pos = x_pos - 5\n",
    "    if this_dy > 0:\n",
    "        verticalalignment = 'bottom'\n",
    "        y_pos = y_pos + 5\n",
    "    else:\n",
    "        verticalalignment = 'top'\n",
    "        y_pos = y_pos - 5\n",
    "    \n",
    "    plt.text(x_pos, y_pos, label, size=10,\n",
    "         horizontalalignment=horizontalalignment,\n",
    "         verticalalignment=verticalalignment,\n",
    "         bbox=dict(facecolor='w',\n",
    "                   alpha=.6))\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We have found a nice number of pairs to use in a pairs trading strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Pairs Trading Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.a Historical Stock Prices\n",
    "\n",
    "To examine how well our identified pairs trade algorithmically, we will first reload the historical stock prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ATVI</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AAP</th>\n",
       "      <th>AES</th>\n",
       "      <th>AFL</th>\n",
       "      <th>A</th>\n",
       "      <th>APD</th>\n",
       "      <th>...</th>\n",
       "      <th>WY</th>\n",
       "      <th>WHR</th>\n",
       "      <th>WMB</th>\n",
       "      <th>WLTW</th>\n",
       "      <th>WYNN</th>\n",
       "      <th>XEL</th>\n",
       "      <th>XYL</th>\n",
       "      <th>YUM</th>\n",
       "      <th>ZBH</th>\n",
       "      <th>ZTS</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>2019-04-29</td>\n",
       "      <td>177.170822</td>\n",
       "      <td>75.875763</td>\n",
       "      <td>176.433182</td>\n",
       "      <td>47.852070</td>\n",
       "      <td>286.140015</td>\n",
       "      <td>165.076157</td>\n",
       "      <td>16.146397</td>\n",
       "      <td>47.306011</td>\n",
       "      <td>76.098206</td>\n",
       "      <td>195.120132</td>\n",
       "      <td>...</td>\n",
       "      <td>24.866016</td>\n",
       "      <td>129.702393</td>\n",
       "      <td>24.468395</td>\n",
       "      <td>176.627579</td>\n",
       "      <td>143.701981</td>\n",
       "      <td>52.583630</td>\n",
       "      <td>81.727402</td>\n",
       "      <td>100.136955</td>\n",
       "      <td>119.042313</td>\n",
       "      <td>100.104774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>176.518814</td>\n",
       "      <td>77.126305</td>\n",
       "      <td>177.854675</td>\n",
       "      <td>47.664215</td>\n",
       "      <td>289.250000</td>\n",
       "      <td>164.630768</td>\n",
       "      <td>16.181368</td>\n",
       "      <td>47.933960</td>\n",
       "      <td>77.249573</td>\n",
       "      <td>197.247955</td>\n",
       "      <td>...</td>\n",
       "      <td>25.185532</td>\n",
       "      <td>129.945801</td>\n",
       "      <td>24.502991</td>\n",
       "      <td>179.371536</td>\n",
       "      <td>139.529129</td>\n",
       "      <td>53.579346</td>\n",
       "      <td>81.317879</td>\n",
       "      <td>100.706123</td>\n",
       "      <td>121.337830</td>\n",
       "      <td>100.678154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>173.314636</td>\n",
       "      <td>76.331390</td>\n",
       "      <td>176.530518</td>\n",
       "      <td>48.010258</td>\n",
       "      <td>283.350006</td>\n",
       "      <td>162.166031</td>\n",
       "      <td>15.973430</td>\n",
       "      <td>47.781731</td>\n",
       "      <td>76.235962</td>\n",
       "      <td>193.759033</td>\n",
       "      <td>...</td>\n",
       "      <td>25.035170</td>\n",
       "      <td>130.825699</td>\n",
       "      <td>24.200274</td>\n",
       "      <td>170.643311</td>\n",
       "      <td>141.161575</td>\n",
       "      <td>53.010361</td>\n",
       "      <td>81.210625</td>\n",
       "      <td>98.342590</td>\n",
       "      <td>119.426537</td>\n",
       "      <td>100.628731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>172.085114</td>\n",
       "      <td>76.370163</td>\n",
       "      <td>174.661133</td>\n",
       "      <td>48.989048</td>\n",
       "      <td>279.640015</td>\n",
       "      <td>165.086060</td>\n",
       "      <td>16.115202</td>\n",
       "      <td>47.810272</td>\n",
       "      <td>76.954338</td>\n",
       "      <td>199.030746</td>\n",
       "      <td>...</td>\n",
       "      <td>24.866016</td>\n",
       "      <td>133.212662</td>\n",
       "      <td>23.750519</td>\n",
       "      <td>173.688934</td>\n",
       "      <td>140.601318</td>\n",
       "      <td>53.029331</td>\n",
       "      <td>77.554253</td>\n",
       "      <td>98.149635</td>\n",
       "      <td>121.387100</td>\n",
       "      <td>101.973213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>172.522873</td>\n",
       "      <td>76.282928</td>\n",
       "      <td>172.314682</td>\n",
       "      <td>46.616219</td>\n",
       "      <td>285.579987</td>\n",
       "      <td>161.611694</td>\n",
       "      <td>16.001785</td>\n",
       "      <td>48.038628</td>\n",
       "      <td>78.026978</td>\n",
       "      <td>200.880646</td>\n",
       "      <td>...</td>\n",
       "      <td>25.166739</td>\n",
       "      <td>133.353088</td>\n",
       "      <td>23.862953</td>\n",
       "      <td>174.924728</td>\n",
       "      <td>142.340012</td>\n",
       "      <td>53.655212</td>\n",
       "      <td>80.235596</td>\n",
       "      <td>99.095055</td>\n",
       "      <td>122.470810</td>\n",
       "      <td>102.566360</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>2021-04-22</td>\n",
       "      <td>200.899994</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>290.160004</td>\n",
       "      <td>93.699997</td>\n",
       "      <td>507.290009</td>\n",
       "      <td>200.300003</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>52.939999</td>\n",
       "      <td>134.529999</td>\n",
       "      <td>284.739990</td>\n",
       "      <td>...</td>\n",
       "      <td>37.830002</td>\n",
       "      <td>233.190002</td>\n",
       "      <td>23.240000</td>\n",
       "      <td>233.149994</td>\n",
       "      <td>124.379997</td>\n",
       "      <td>71.120003</td>\n",
       "      <td>109.080002</td>\n",
       "      <td>117.820000</td>\n",
       "      <td>177.380005</td>\n",
       "      <td>169.070007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-23</td>\n",
       "      <td>202.199997</td>\n",
       "      <td>123.309998</td>\n",
       "      <td>291.739990</td>\n",
       "      <td>93.019997</td>\n",
       "      <td>515.840027</td>\n",
       "      <td>199.750000</td>\n",
       "      <td>28.299999</td>\n",
       "      <td>53.369999</td>\n",
       "      <td>136.679993</td>\n",
       "      <td>290.299988</td>\n",
       "      <td>...</td>\n",
       "      <td>38.200001</td>\n",
       "      <td>238.940002</td>\n",
       "      <td>23.610001</td>\n",
       "      <td>232.889999</td>\n",
       "      <td>124.150002</td>\n",
       "      <td>71.080002</td>\n",
       "      <td>110.480003</td>\n",
       "      <td>117.720001</td>\n",
       "      <td>177.619995</td>\n",
       "      <td>170.539993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>199.630005</td>\n",
       "      <td>122.580002</td>\n",
       "      <td>289.519989</td>\n",
       "      <td>94.239998</td>\n",
       "      <td>515.700012</td>\n",
       "      <td>197.139999</td>\n",
       "      <td>28.209999</td>\n",
       "      <td>53.369999</td>\n",
       "      <td>137.339996</td>\n",
       "      <td>290.179993</td>\n",
       "      <td>...</td>\n",
       "      <td>38.840000</td>\n",
       "      <td>236.119995</td>\n",
       "      <td>23.969999</td>\n",
       "      <td>231.300003</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>70.599998</td>\n",
       "      <td>110.440002</td>\n",
       "      <td>115.680000</td>\n",
       "      <td>175.720001</td>\n",
       "      <td>169.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>194.449997</td>\n",
       "      <td>121.699997</td>\n",
       "      <td>290.250000</td>\n",
       "      <td>91.599998</td>\n",
       "      <td>517.320007</td>\n",
       "      <td>197.279999</td>\n",
       "      <td>27.799999</td>\n",
       "      <td>53.529999</td>\n",
       "      <td>136.479996</td>\n",
       "      <td>289.790009</td>\n",
       "      <td>...</td>\n",
       "      <td>39.380001</td>\n",
       "      <td>239.360001</td>\n",
       "      <td>23.940001</td>\n",
       "      <td>236.250000</td>\n",
       "      <td>127.750000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>111.360001</td>\n",
       "      <td>116.410004</td>\n",
       "      <td>176.350006</td>\n",
       "      <td>169.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>196.119995</td>\n",
       "      <td>121.074997</td>\n",
       "      <td>290.809998</td>\n",
       "      <td>91.290001</td>\n",
       "      <td>516.815002</td>\n",
       "      <td>196.380005</td>\n",
       "      <td>27.945000</td>\n",
       "      <td>53.599998</td>\n",
       "      <td>135.014999</td>\n",
       "      <td>289.600006</td>\n",
       "      <td>...</td>\n",
       "      <td>39.744999</td>\n",
       "      <td>235.479996</td>\n",
       "      <td>24.410000</td>\n",
       "      <td>245.699997</td>\n",
       "      <td>130.298401</td>\n",
       "      <td>69.669998</td>\n",
       "      <td>111.540001</td>\n",
       "      <td>117.930000</td>\n",
       "      <td>178.229904</td>\n",
       "      <td>171.860001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 405 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   MMM         ABT         ACN       ATVI        ADBE  \\\n",
       "Date                                                                    \n",
       "2019-04-29  177.170822   75.875763  176.433182  47.852070  286.140015   \n",
       "2019-04-30  176.518814   77.126305  177.854675  47.664215  289.250000   \n",
       "2019-05-01  173.314636   76.331390  176.530518  48.010258  283.350006   \n",
       "2019-05-02  172.085114   76.370163  174.661133  48.989048  279.640015   \n",
       "2019-05-03  172.522873   76.282928  172.314682  46.616219  285.579987   \n",
       "...                ...         ...         ...        ...         ...   \n",
       "2021-04-22  200.899994  123.500000  290.160004  93.699997  507.290009   \n",
       "2021-04-23  202.199997  123.309998  291.739990  93.019997  515.840027   \n",
       "2021-04-26  199.630005  122.580002  289.519989  94.239998  515.700012   \n",
       "2021-04-27  194.449997  121.699997  290.250000  91.599998  517.320007   \n",
       "2021-04-28  196.119995  121.074997  290.809998  91.290001  516.815002   \n",
       "\n",
       "                   AAP        AES        AFL           A         APD  ...  \\\n",
       "Date                                                                  ...   \n",
       "2019-04-29  165.076157  16.146397  47.306011   76.098206  195.120132  ...   \n",
       "2019-04-30  164.630768  16.181368  47.933960   77.249573  197.247955  ...   \n",
       "2019-05-01  162.166031  15.973430  47.781731   76.235962  193.759033  ...   \n",
       "2019-05-02  165.086060  16.115202  47.810272   76.954338  199.030746  ...   \n",
       "2019-05-03  161.611694  16.001785  48.038628   78.026978  200.880646  ...   \n",
       "...                ...        ...        ...         ...         ...  ...   \n",
       "2021-04-22  200.300003  28.320000  52.939999  134.529999  284.739990  ...   \n",
       "2021-04-23  199.750000  28.299999  53.369999  136.679993  290.299988  ...   \n",
       "2021-04-26  197.139999  28.209999  53.369999  137.339996  290.179993  ...   \n",
       "2021-04-27  197.279999  27.799999  53.529999  136.479996  289.790009  ...   \n",
       "2021-04-28  196.380005  27.945000  53.599998  135.014999  289.600006  ...   \n",
       "\n",
       "                   WY         WHR        WMB        WLTW        WYNN  \\\n",
       "Date                                                                   \n",
       "2019-04-29  24.866016  129.702393  24.468395  176.627579  143.701981   \n",
       "2019-04-30  25.185532  129.945801  24.502991  179.371536  139.529129   \n",
       "2019-05-01  25.035170  130.825699  24.200274  170.643311  141.161575   \n",
       "2019-05-02  24.866016  133.212662  23.750519  173.688934  140.601318   \n",
       "2019-05-03  25.166739  133.353088  23.862953  174.924728  142.340012   \n",
       "...               ...         ...        ...         ...         ...   \n",
       "2021-04-22  37.830002  233.190002  23.240000  233.149994  124.379997   \n",
       "2021-04-23  38.200001  238.940002  23.610001  232.889999  124.150002   \n",
       "2021-04-26  38.840000  236.119995  23.969999  231.300003  125.000000   \n",
       "2021-04-27  39.380001  239.360001  23.940001  236.250000  127.750000   \n",
       "2021-04-28  39.744999  235.479996  24.410000  245.699997  130.298401   \n",
       "\n",
       "                  XEL         XYL         YUM         ZBH         ZTS  \n",
       "Date                                                                   \n",
       "2019-04-29  52.583630   81.727402  100.136955  119.042313  100.104774  \n",
       "2019-04-30  53.579346   81.317879  100.706123  121.337830  100.678154  \n",
       "2019-05-01  53.010361   81.210625   98.342590  119.426537  100.628731  \n",
       "2019-05-02  53.029331   77.554253   98.149635  121.387100  101.973213  \n",
       "2019-05-03  53.655212   80.235596   99.095055  122.470810  102.566360  \n",
       "...               ...         ...         ...         ...         ...  \n",
       "2021-04-22  71.120003  109.080002  117.820000  177.380005  169.070007  \n",
       "2021-04-23  71.080002  110.480003  117.720001  177.619995  170.539993  \n",
       "2021-04-26  70.599998  110.440002  115.680000  175.720001  169.800003  \n",
       "2021-04-27  70.000000  111.360001  116.410004  176.350006  169.750000  \n",
       "2021-04-28  69.669998  111.540001  117.930000  178.229904  171.860001  \n",
       "\n",
       "[505 rows x 405 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data = combined_stock_price_df.dropna()\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `LNT` (Alliant Energy Corporation) and `CMS` (CMS Energy Corporation) were identified as being a good pairs trading candidate, we define them as `symbol_one` and `symbol_two` in our trading algorithm below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_one = 'LNT'\n",
    "symbol_two = 'CMS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>LNT</th>\n",
       "      <th>CMS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>44.020252</td>\n",
       "      <td>51.775612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>44.923862</td>\n",
       "      <td>52.647541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>44.495831</td>\n",
       "      <td>52.107315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-02</td>\n",
       "      <td>44.400723</td>\n",
       "      <td>52.091099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>44.914356</td>\n",
       "      <td>52.740089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>56.849998</td>\n",
       "      <td>64.510002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-23</td>\n",
       "      <td>56.439999</td>\n",
       "      <td>64.129997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-26</td>\n",
       "      <td>56.240002</td>\n",
       "      <td>63.950001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-27</td>\n",
       "      <td>55.520000</td>\n",
       "      <td>63.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2021-04-28</td>\n",
       "      <td>55.340000</td>\n",
       "      <td>63.110001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LNT        CMS\n",
       "Date                            \n",
       "2019-04-29  44.020252  51.775612\n",
       "2019-04-30  44.923862  52.647541\n",
       "2019-05-01  44.495831  52.107315\n",
       "2019-05-02  44.400723  52.091099\n",
       "2019-05-03  44.914356  52.740089\n",
       "...               ...        ...\n",
       "2021-04-22  56.849998  64.510002\n",
       "2021-04-23  56.439999  64.129997\n",
       "2021-04-26  56.240002  63.950001\n",
       "2021-04-27  55.520000  63.320000\n",
       "2021-04-28  55.340000  63.110001\n",
       "\n",
       "[505 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_data = stock_data[[symbol_one,symbol_two]]\n",
    "stock_data.index.name = 'Date'\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on price data since January 1, 2020 in order to capture the coronavirus sell-off in March 2020 and subsequent stock market recovery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock1_name, stock2_name = symbol_one,symbol_two\n",
    "orig_data = stock_data.loc['2020-01-01':,]\n",
    "data = orig_data.diff().cumsum()\n",
    "data1 = data[stock1_name].ffill().fillna(0).values\n",
    "data2 = data[stock2_name].ffill().fillna(0).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now plot the historical stock prices for `LNT` and `CMS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "ax = plt.gca()\n",
    "plt.title(\"Potentially Cointegrated Stocks\")\n",
    "orig_data[stock1_name].plot(ax=ax,color=sns.color_palette()[1],linewidth=2)\n",
    "orig_data[stock2_name].plot(ax=ax,color=sns.color_palette()[2],linewidth=2)\n",
    "plt.ylabel(\"Price (USD)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These companies do indeed seem to have related price series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.b Bayesian Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We take a Bayesian approach to pairs trading using probabilistic programming, which is a form of Bayesian machine learning. Unlike simpler frequentist cointegration tests, our Bayesian approach allows us to monitor the relationship between a pair of equities over time, which allows us to follow pairs whose cointegration parameters change steadily or abruptly. When combined with a simple mean-reversion trading algorithm, we demonstrate this to be a viable theoretical trading strategy, ready for further evaluation and risk management.\n",
    "\n",
    "To learn more about this Bayesian approach to pairs trading, you can read AlphaWave Data's article titled [Bayesian Pairs Trading using Corporate Supply Chain Data](https://medium.com/@hdonnelly6/bayesian-pairs-trading-using-corporate-supply-chain-data-8b96305686d).\n",
    "\n",
    "We will use a Bayesian probabilistic programming package called [PyMC3](https://docs.pymc.io/). Its simple syntax is excellent for prototyping as seen with the model description in the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as model:\n",
    "    \n",
    "    # inject external stock data\n",
    "    stock1 = th.shared(data1)\n",
    "    stock2 = th.shared(data2)\n",
    "    \n",
    "    # define our cointegration variables\n",
    "    beta_sigma = pm.Exponential('beta_sigma', 50.)\n",
    "    beta = pm.GaussianRandomWalk('beta', sd=beta_sigma,\n",
    "                                 shape=data1.shape[0])\n",
    "    \n",
    "    # with our assumptions, cointegration can be reframed as a regression problem\n",
    "    stock2_regression = beta * stock1\n",
    "\n",
    "    # Assume prices are Normally distributed, the mean comes from the regression.\n",
    "    sd = pm.HalfNormal('sd', sd=.1)\n",
    "    likelihood = pm.Normal('y',\n",
    "                           mu=stock2_regression,\n",
    "                           sd=sd,\n",
    "                           observed=stock2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sd, beta, beta_sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [12000/12000 00:31<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 31 seconds.\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    stock1.set_value(data1)\n",
    "    stock2.set_value(data2)\n",
    "    trace = pm.sample(2000,tune=1000,cores=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the $\\beta$ distribution from the model over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_beta = trace[beta].T.mean(axis=1)\n",
    "\n",
    "plt.figure(figsize = (8,4))\n",
    "ax = plt.gca()\n",
    "plt.title(\"Beta Distribution over Time\")\n",
    "pd.Series(rolling_beta,index=orig_data.index).plot(ax=ax,color='r',zorder=1e6,linewidth=2)\n",
    "for orbit in trace[beta][:500]:\n",
    "    pd.Series(orbit,index=orig_data.index).plot(ax=ax,color=sns.color_palette()[0],alpha=0.05)\n",
    "plt.legend(['Beta Mean','Beta Orbit'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that $\\beta$ appears to shift between somewhat fixed regimes, and often does so abruptly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.c Trading Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing that two stocks may or may not be cointegrated does not explicitly define a trading strategy. For that we present the following simple mean-reversion style trading algorithm, which capitalizes on the assumed mean-reverting behavior of a cointegrated portfolio of stocks. We trade whenever our portfolio is moving back toward its mean value. When the algorithm is not trading, we dynamically update $\\beta$ and its other parameters, to adapt to potentially changing cointegration conditions. Once a trade begins, we are forced to trade the two stocks at a fixed rate, and so our $\\beta$ becomes locked for the duration of the trade. The algorithm's exact implementation is as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Define a \"signal\", which should mean-revert to zero if $\\beta$ remains relatively stationary.\n",
    "2. Define a \"smoothed signal\", a 15-day moving average of the \"signal\".\n",
    "2. If we are not trading...\n",
    " * Update $\\beta$ so that it does not remain fixed while we aren't trading.\n",
    " * If the smoothed signal is above zero _and_ moving downward, short our portfolio.\n",
    " * If the smoothed signal is below zero _and_ moving upward, go long on our portfolio.\n",
    "3. If we are trading long...\n",
    " * If the smoothed signal goes below its start value, close the trade; we may be diverging from the mean.\n",
    " * If the smoothed signal rises through the zero line, we've reached the mean. Close the trade.\n",
    "4. If we are trading short...\n",
    " * If the smoothed signal goes above its start value, close the trade; we may be diverging from the mean.\n",
    " * If the smoothed signal falls through the zero line, we've reached the mean. Close the trade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getStrategyPortfolioWeights(rolling_beta,stock_name1,stock_name2,data,smoothing_window=15):\n",
    "\n",
    "    data1 = data[stock_name1].ffill().fillna(0).values\n",
    "    data2 = data[stock_name2].ffill().fillna(0).values\n",
    "\n",
    "    # initial signal rebalance\n",
    "    fixed_beta = rolling_beta[smoothing_window]\n",
    "    signal = fixed_beta*data1 - data2\n",
    "    smoothed_signal = pd.Series(signal).rolling(smoothing_window).mean()\n",
    "    d_smoothed_signal = smoothed_signal.diff()\n",
    "    trading = \"not\"\n",
    "    trading_start = 0\n",
    "\n",
    "    leverage = 0*data.copy()\n",
    "    for i in range(smoothing_window,data1.shape[0]):\n",
    "        leverage.iloc[i,:] = leverage.iloc[i-1,:]\n",
    "\n",
    "        if trading==\"not\":\n",
    "\n",
    "            # dynamically rebalance the signal when not trading\n",
    "            fixed_beta = rolling_beta[i]\n",
    "            signal = fixed_beta*data1 - data2\n",
    "            smoothed_signal = pd.Series(signal).rolling(smoothing_window).mean()\n",
    "            d_smoothed_signal = smoothed_signal.diff()\n",
    "\n",
    "            if smoothed_signal[i]>0 and d_smoothed_signal[i]<0:\n",
    "\n",
    "                leverage.iloc[i,0] = -fixed_beta / (abs(fixed_beta)+1)\n",
    "                leverage.iloc[i,1] = 1 / (abs(fixed_beta)+1)\n",
    "\n",
    "                trading = \"short\"\n",
    "                trading_start = smoothed_signal[i]\n",
    "\n",
    "            elif smoothed_signal[i]<0 and d_smoothed_signal[i]>0:\n",
    "\n",
    "                fixed_beta = rolling_beta[i]\n",
    "                leverage.iloc[i,0] = fixed_beta / (abs(fixed_beta)+1)\n",
    "                leverage.iloc[i,1] = -1 / (abs(fixed_beta)+1)\n",
    "\n",
    "                trading = \"long\"\n",
    "                trading_start = smoothed_signal[i]\n",
    "\n",
    "            else:\n",
    "                leverage.iloc[i,0] = 0\n",
    "                leverage.iloc[i,1] = 0\n",
    "\n",
    "\n",
    "        elif trading==\"long\":\n",
    "\n",
    "            # a failed trade\n",
    "            if smoothed_signal[i] < trading_start:\n",
    "                leverage.iloc[i,0] = 0\n",
    "                leverage.iloc[i,1] = 0\n",
    "                trading = \"not\"\n",
    "\n",
    "            # a successful trade\n",
    "            if smoothed_signal[i]>0:\n",
    "                leverage.iloc[i,0] = 0\n",
    "                leverage.iloc[i,1] = 0\n",
    "                trading = \"not\"\n",
    "\n",
    "        elif trading==\"short\":\n",
    "\n",
    "            # a failed trade\n",
    "            if smoothed_signal[i] > trading_start:\n",
    "                leverage.iloc[i,0] = 0\n",
    "                leverage.iloc[i,1] = 0\n",
    "                trading = \"not\"\n",
    "\n",
    "            # a successful trade\n",
    "            if smoothed_signal[i]<0:\n",
    "                leverage.iloc[i,0] = 0\n",
    "                leverage.iloc[i,1] = 0\n",
    "                trading = \"not\"\n",
    "                \n",
    "    return leverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.d Backtesting & Performance in Market Drops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a long-short algorithm, the expectation is that this algorithm would perform well during market drops. The backtest here includes the coronavirus sell-off in March 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolioWeights = getStrategyPortfolioWeights(rolling_beta,stock1_name, stock2_name,data).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(pricingDF,leverageDF,start_cash):\n",
    "    \"\"\"Backtests pricing based on some given set of leverage. Leverage works such that it happens \"overnight\",\n",
    "    so leverage for \"today\" is applied to yesterday's close price. This algo can handle NaNs in pricing data\n",
    "    before a stock exists, but ffill() should be used for NaNs that occur after the stock has existed, even\n",
    "    if that stock ceases to exist later.\"\"\"\n",
    "    \n",
    "    pricing = pricingDF.values\n",
    "    leverage = leverageDF.values\n",
    "    \n",
    "    shares = np.zeros_like(pricing)\n",
    "    cash = np.zeros(pricing.shape[0])\n",
    "    cash[0] = start_cash\n",
    "    curr_price = np.zeros(pricing.shape[1])\n",
    "    curr_price_div = np.zeros(pricing.shape[1])\n",
    "    \n",
    "    for t in range(1,pricing.shape[0]):\n",
    "        \n",
    "        if np.any(leverage[t]!=leverage[t-1]):\n",
    "\n",
    "            # handle non-existent stock values\n",
    "            curr_price[:] = pricing[t-1]     # you can multiply with this one\n",
    "            curr_price[np.isnan(curr_price)] = 0\n",
    "            trading_allowed = (curr_price!=0)\n",
    "            curr_price_div[:] = curr_price    # you can divide with this one\n",
    "            curr_price_div[~trading_allowed] = 1\n",
    "            \n",
    "            # determine new positions (warning: leverage to non-trading_allowed stocks is just lost)\n",
    "            portfolio_value = (shares[t-1]*curr_price).sum()+cash[t-1]\n",
    "            target_shares = trading_allowed * (portfolio_value*leverage[t]) // curr_price_div\n",
    "            \n",
    "            # rebalance\n",
    "            shares[t] = target_shares\n",
    "            cash[t] = cash[t-1] - ((shares[t]-shares[t-1])*curr_price).sum()\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            # maintain positions\n",
    "            shares[t] = shares[t-1]\n",
    "            cash[t] = cash[t-1]\n",
    "    \n",
    "    returns = (shares*np.nan_to_num(pricing)).sum(axis=1)+cash\n",
    "    pct_returns = (returns-start_cash)/start_cash\n",
    "    return (\n",
    "        pd.DataFrame( shares, index=pricingDF.index, columns=pricingDF.columns ),\n",
    "        pd.Series( cash, index=pricingDF.index ),\n",
    "        pd.Series( pct_returns, index=pricingDF.index)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shares, cash, returns = backtest( orig_data, portfolioWeights, 1e6 )\n",
    "\n",
    "plt.figure(figsize = (16,8))\n",
    "ax = plt.gca()\n",
    "plt.title(\"Return Profile of Algorithm\")\n",
    "plt.ylabel(\"Percent Returns\")\n",
    "returns.plot(ax=ax,linewidth=3)\n",
    "vals = ax.get_yticks()\n",
    "ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we might have hoped, performance through market drops is strong. Returns are somewhat outsized due to our portfolio only being two stocks. For a finalized version of this algorithm, we might trade a hundred pairs or more to reduce volatility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Conclusions & Potential Future Directions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After using PCA to reduce the dimensionality of the returns data and extract the historical latent common factor loadings for each stock, we added the fundamental and ESG features, and then used the `DBSCAN` **unsupervised** [clustering algorithm](http://hdbscan.readthedocs.io/en/latest/comparing_clustering_algorithms.html#dbscan), which is available in [`scikit-learn`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html), to identify stock pairs. `DBSCAN`'s advantage is that it leaves out stocks which do not neatly fit into a cluster. As a result, you do not need to specify the number of clusters when using `DBSCAN`.\n",
    "\n",
    "We also demonstrated a robust prototype for what would be built into a more sophisticated pairs trading algorithm. There are many places where this algorithm and approach could be improved, including expanding the portfolio, creating criteria for when $\\beta$ is suitable to trade over, backtesting over more periods, using a Bayesian model with fewer simplifying assumptions, and investigating potential nonlinear relationships between stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by AlphaWave Data, Inc. (\"AlphaWave Data\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, AlphaWave Data, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to AlphaWave Data, Inc. at the time of publication. AlphaWave Data makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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