pairs_trading.ipynb

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    "<h2>\n",
    "Exploratory Statistics of Pairs Trading\n",
    "</h2>\n",
    "<p>\n",
    "The Jupyter notebook explores pairs trading strategies.\n",
    "This document is available on the GitHub repository https://github.com/IanLKaplan/pairs_trading\n",
    "</p>\n",
    "<blockquote>\n",
    "<p>\n",
    "Pairs trading is an approach that takes advantage of the\n",
    "mispricing between two (or more) co-moving assets, by\n",
    "taking a long position in one (many) and shorting the\n",
    "other(s), betting that the relationship will hold and that\n",
    "prices will converge back to an equilibrium level.\n",
    "</p>\n",
    "<p>\n",
    "<i>Definitive Guide to Pairs Trading</i> availabel from <a href=\"https://hudsonthames.org/\">Hudson and Thames</a>\n",
    "</p>\n",
    "</blockquote>\n",
    "<p>\n",
    "Pairs trading is sometimes referred to as a statistical arbitrage trading strategy.\n",
    "</p>\n",
    "<blockquote>\n",
    "<p>\n",
    "Statistical arbitrage and pairs trading tries to solve this problem using price relativity. If two assets share the same\n",
    "characteristics and risk exposures, then we can assume that their behavior would be similar as well. This has\n",
    "the benefit of not having to estimate the intrinsic value of an asset but rather just if it is under or overvalued\n",
    "relative to a peer(s). We only have to focus on the relationship between the two, and if the spread happens\n",
    "to widen, it could be that one of the securities is overpriced, the other is underpriced, or the mispricing is a\n",
    "combination of both.\n",
    "</p>\n",
    "<p>\n",
    "<i>Definitive Guide to Pairs Trading</i> available from <a href=\"https://hudsonthames.org/\">Hudson and Thames</a>\n",
    "</p>\n",
    "</blockquote>\n",
    "<p>\n",
    "Pairs trading algorithms have been reported to yield portfolios with Sharpe ratios in excess of 1.0 and returns of 10% or\n",
    "higher. Pairs trading takes both long and short positions, so the portfolio tends to be market neutral. A pairs trading portfolio\n",
    "can have drawdowns, but the drawdowns should be less than a benchmark like the S&P 500 because of the market neutral nature of the\n",
    "portfolio. The lower, market neutral, structure of a pairs trading portfolio means that the portfolio will have a lower return\n",
    "than a comparable benchmark like the S&P 500.\n",
    "</p>\n",
    "<p>\n",
    "Markets tend toward efficiency and many quantitative approaches fade over time as they are adopted by hedge funds. Pairs trading\n",
    "goes back to the mid-1980s. Surprisingly, pairs trading still seems to be profitable. One reason for this could be that there are a vast\n",
    "number of possible pairs and the pairs portfolio is a faction of the pairs universe. This could\n",
    "leave unexploited pairs in the market. Pairs trading may also be difficult to scale to a level that would be attractive to institutional\n",
    "traders, like hedge funds, so the strategy has not been arbitraged out of the market.\n",
    "</p>\n",
    "<p>\n",
    "Mathematical finance often uses models that are based on normal distributions, constant means and standard deviations. Actual market\n",
    "data is often not normally distributed and changes constantly. The statistics used to select stocks for pairs trading assume\n",
    "that the pair distribution has a constant mean and standard deviation (e.g., the pairs spread is a stationary time series). As this notebook\n",
    "shows, this assumption is statistically valid about forty percent of the time.\n",
    "</p>\n",
    "<p>\n",
    "The statistics that predict a successful pair will not be accurate in all time periods. For the strategy to be successful, the prediction\n",
    "must be right more often than not. To minimize the risk in any particular trade, this suggests that trading a larger portfolio will\n",
    "be more successful than trading a small portfolio.\n",
    "</p>\n",
    "<h3>\n",
    "Overview\n",
    "</h3>\n",
    "<p>\n",
    "The primary references used for this notebook are the books <i>Pairs Trading</i> by Ganapathy Vidyamurthy and <i>Algorithmic\n",
    "Trading</i> by Ernest P. Chan.\n",
    "</p>\n",
    "<p>\n",
    "The pairs trading strategy attempts to find a pair of stocks where the weighted spread between the stock close prices is\n",
    "mean reverting.\n",
    "</p>\n",
    "<p>\n",
    "Implementing the pairs trading strategy involves two logical steps:\n",
    "</p>\n",
    "<ol>\n",
    "<li>\n",
    "<p>\n",
    "Pairs selection: Identify a pair of stocks that are likely to have mean reverting behavior using a lookback period.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "Trading the stocks using the long/short strategy over the trading period. This involves building a trading signal\n",
    "from the weighted spread formed from the close prices of the stock pair. When the trading signal is above or below\n",
    "the mean by some threshold value, a long and short position are taken in the two stocks.\n",
    "</p>\n",
    "</li>\n",
    "</ol>\n",
    "<h2>\n",
    "Pairs Selection\n",
    "</h2>\n",
    "<h3>\n",
    "S&P 500 Industry Sectors\n",
    "</h3>\n",
    "<p>\n",
    "In this notebook, pairs are selected from the S&P 500 stock universe. These stocks have a high trading volume, with a small\n",
    "bid-ask spread. S&P 500 stocks are also easier to short, with lower borrowing fees and a lower chance of a short position being called\n",
    "back.\n",
    "</p>\n",
    "<p>\n",
    "In pairs selection we are trying to find pairs that are cointegrated, where the price spread has mean reverting behavior. Just as there\n",
    "can be spurious correlation there can be spurious cointegration, so the stock pair should\n",
    "have some logical connection. In the book <i>Pairs Trading</i> (Vidyamurthy) the author discusses using factor models to select pairs with\n",
    "similar factor characteristics.\n",
    "</p>\n",
    "<p>\n",
    "Factor models are often built using company fundamental factors like earnings, corporate debt and cash flow. These factors\n",
    "tend to be generic. Many companies in completely different industry sectors may have similar fundamental factors.  When selecting pairs\n",
    "we would like to select stocks that are affected by similar market forces. For example, energy sector stocks tend to be\n",
    "affected by the same economic and market forces. Factors affecting companies outside the energy sector can be much more complicated.\n",
    "In many cases the company factors that affect S&P 500 companies are broad economic factors that are not obviously useful in choosing pairs\n",
    "for mean reversion trading.\n",
    "</p>\n",
    "<p>\n",
    "In lieu of specific factors, the S&P 500 industry sector is used as the set from which pairs are drawn.\n",
    "Although not perfect, the industry sector will tend to select stocks with similar behavior, while reducing the universe of\n",
    "stocks from which pairs are selected.\n",
    "</p>\n",
    "<p>\n",
    "Reducing the universe of stock pairs is important because, even with modern computing power, it would be difficult\n",
    "to test all possible stock pairs in the S&P 500, since the number of pairs grows exponentially with N, the number of stocks.\n",
    "</p>\n",
    "\n",
    "\\$ number \\space of \\space pairs = \\frac{N^2 - N}{2} $\n",
    "\n",
    "<p>\n",
    "The S&P 500 component stocks (in 2022) and their related industries have been downloaded from barchart.com.  The files are included in this\n",
    "GitHub repository.\n",
    "</p>\n",
    "<p>\n",
    "The S&P 500 industry sectors are:\n",
    "</p>\n",
    "<ol>\n",
    "<li>\n",
    "Consumer discretionary\n",
    "</li>\n",
    "<li>\n",
    "Consumer staples\n",
    "</li>\n",
    "<li>\n",
    "Energy\n",
    "</li>\n",
    "<li>\n",
    "Financials\n",
    "</li>\n",
    "<li>\n",
    "Health care\n",
    "</li>\n",
    "<li>\n",
    "Industrials\n",
    "</li>\n",
    "<li>\n",
    "Infotech\n",
    "</li>\n",
    "<li>\n",
    "Materials\n",
    "</li>\n",
    "<li>\n",
    "Real estate\n",
    "</li>\n",
    "<li>\n",
    "Communication services\n",
    "</li>\n",
    "<li>\n",
    "Utilities\n",
    "</li>\n",
    "</ol>\n",
    "<p>\n",
    "The stocks from the Barchart list include stocks for the same company, with different classes (e.g., class A, class B or class C stocks).\n",
    "The list of stocks is filtered to remove all but the lowest stock class (e.g., if there is a class B stock, the class A stock is removed\n",
    "from the list).\n",
    "</p>\n",
    "<h3>\n",
    "Stock Market Close Data\n",
    "</h3>\n",
    "<p>\n",
    "The data used to model pairs trading in this notebook uses close price data for all of the S&P 500 stocks from the start date to yesterday\n",
    "(e.g., one day in the past). In other models (see Stock Market Cash Trigger and ETF Rotation) the close price data was downloaded the first time the\n",
    "notebook was run and stored in temporary files.  The first notebook run incurred the initial overhead of downloading\n",
    "the data, but subsequent runs could read the data from local temporary files.\n",
    "</p>\n",
    "<p>\n",
    "For the S&P 500 stock universe, downloading all of the close price data the first time the notebook is run would have an unacceptable\n",
    "overhead. To avoid this, the data is downloaded once and stored in local files. When the notebook is run at later times, only the\n",
    "data between the end of the last date in the file and the current end date will be downloaded.\n",
    "</p>\n",
    "<p>\n",
    "There are stocks in the S&P 500 list that were listed on the stock exchange later than the start date.  These\n",
    "stocks are filtered out, so the final stock set does not include all of the S&P 500 stocks.\n",
    "</p>\n",
    "<p>\n",
    "Filtering stocks in this way can create a survivorship bias. This should not be a problem for back testing pairs trading\n",
    "algorithms through the historical time period. The purpose of this backtest is to understand the pairs trading behavior.\n",
    "The results do not depend on the stock universe, only on the pairs selected.\n",
    "</p>\n"
   ]
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     "text": [
      "\n",
      "1 Failed download:\n",
      "- CTXS: No data found, symbol may be delisted\n",
      "\n",
      "1 Failed download:\n",
      "- NLOK: No data found, symbol may be delisted\n",
      "\n",
      "1 Failed download:\n",
      "- NLSN: No data found, symbol may be delisted\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#\n",
    "# To generate a python file from the notebook use jupytext:\n",
    "# pip install jupytext --upgrade\n",
    "# jupytext --to py pairs_trading.ipynb\n",
    "#\n",
    "\n",
    "import os\n",
    "from datetime import datetime\n",
    "# pathos is used as a more robust multiprocessing library\n",
    "# pip install pathos\n",
    "from pathos.multiprocessing import Pool\n",
    "from typing import List, Tuple, Dict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from numpy import log\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "from tabulate import tabulate\n",
    "\n",
    "from coint_analysis.coint_analysis_result import CointAnalysisResult, CointInfo\n",
    "from coint_data_io.coint_matrix_io import CointMatrixIO\n",
    "from pairs.pairs import get_pairs\n",
    "#\n",
    "# Local libraries\n",
    "#\n",
    "from plot_ts.plot_time_series import plot_ts, plot_two_ts\n",
    "from read_market_data.MarketData import MarketData, read_s_and_p_stock_info, extract_sectors\n",
    "\n",
    "# Apply the default theme\n",
    "sns.set_theme()\n",
    "\n",
    "from s_and_p_filter import s_and_p_directory, s_and_p_stock_file\n",
    "s_and_p_file = s_and_p_directory + os.path.sep + s_and_p_stock_file\n",
    "\n",
    "start_date_str = '2007-01-03'\n",
    "start_date: datetime = datetime.fromisoformat(start_date_str)\n",
    "\n",
    "trading_days = 252\n",
    "\n",
    "\n",
    "def calc_pair_counts(sector_info: dict) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Return a DataFrame\n",
    "    :param sector_info: a dictionary where the key is the sector name and the\n",
    "                        value is a list of stock symbols for that sector\n",
    "    :return: a DataFrame where the index is the sector names and the columns are \"num stocks\" and \"num pairs\"\n",
    "             \"num stocks\" is the number of stocks in the sector. \"num pairs\" is the number of unique pairs\n",
    "             that can be formed from the set of sector stocks.  The last row is the sum of the columns.\n",
    "    \"\"\"\n",
    "    column_label = ['num stocks', 'num pairs']\n",
    "    sectors = list(sector_info.keys())\n",
    "    counts_l: list = list()\n",
    "    n_l: list = list()\n",
    "    for sector in sectors:\n",
    "        n = len(sector_info[sector])\n",
    "        n_l.append(n)\n",
    "        count = ((n ** 2.0) - n) / 2.0\n",
    "        counts_l.append(count)\n",
    "    num_stocks = sum(n_l)\n",
    "    info_df = pd.DataFrame(n_l)\n",
    "    info_df = pd.concat([info_df, pd.DataFrame(counts_l)], axis=1)\n",
    "    info_df.columns = column_label\n",
    "    sum_pairs = sum(counts_l)\n",
    "    num_stocks_df = pd.DataFrame([num_stocks])\n",
    "    sum_df = pd.DataFrame([sum_pairs])\n",
    "    row_df = pd.concat([num_stocks_df, sum_df], axis=1)\n",
    "    row_df.columns = column_label\n",
    "    info_df = pd.concat([info_df, row_df], axis=0)\n",
    "    sectors.append('Sum')\n",
    "    info_df.index = sectors\n",
    "    return info_df\n",
    "\n",
    "\n",
    "# stock_info_df: a DataFrame with columns Symbol, Name, Sector\n",
    "stock_info_df = read_s_and_p_stock_info(s_and_p_file)\n",
    "stock_l: list = list(set(stock_info_df['Symbol']))\n",
    "stock_l.sort()\n",
    "market_data = MarketData(start_date=start_date)\n",
    "\n",
    "# Get close prices for the S&P 500 list\n",
    "close_prices_df = market_data.get_close_data(stock_l)\n",
    "final_stock_list = list(close_prices_df.columns)\n",
    "mask = stock_info_df['Symbol'].isin(final_stock_list)\n",
    "# Some stocks were listed on the stock exchange later than start_date. In this case the stock will not\n",
    "# be returned by MarketData.get_close_data(). final_stock_info has the Symbol, Name, Sector for the\n",
    "# set of stocks that it was possible to obtain close prices for the date range.\n",
    "final_stock_info_df = stock_info_df[mask]\n",
    "\n",
    "sectors = extract_sectors(final_stock_info_df)\n",
    "pairs_info_df = calc_pair_counts(sectors)\n"
   ]
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    "<p>\n",
    "The table below shows the number of unique pairs for each S&P 500 sector and the total number of pairs. By drawing pairs from sectors,\n",
    "rather than the whole S&P 500 set of stocks, the number of possible pairs is reduced from 124,750.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════════════════════════╤══════════════╤═════════════╕\n",
      "│                        │   num stocks │   num pairs │\n",
      "╞════════════════════════╪══════════════╪═════════════╡\n",
      "│ materials              │           22 │         231 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ information-technology │           54 │        1431 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ consumer-discretionary │           47 │        1081 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ health-care            │           52 │        1326 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ communication-services │           16 │         120 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ energies               │           16 │         120 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ consumer-staples       │           28 │         378 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ utilities              │           24 │         276 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ industrials            │           58 │        1653 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ financials             │           58 │        1653 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ real-estate            │           28 │         378 │\n",
      "├────────────────────────┼──────────────┼─────────────┤\n",
      "│ Sum                    │          403 │        8647 │\n",
      "╘════════════════════════╧══════════════╧═════════════╛\n"
     ]
    }
   ],
   "source": [
    "print(tabulate(pairs_info_df, headers=[*pairs_info_df.columns], tablefmt='fancy_grid'))\n",
    "\n",
    "# An experiment to replace tabulate tables. Unfortunately, this table does not display when the notebook is examined\n",
    "# on GitHub.\n",
    "#\n",
    "# import plotly.graph_objects as go\n",
    "#\n",
    "# fig = go.Figure(data=[go.Table(\n",
    "#     header=dict(values=['S&P 500 sector', *pairs_info_df.columns],\n",
    "#                 fill_color='paleturquoise',\n",
    "#                 align='left'),\n",
    "#     cells=dict(values=[pairs_info_df.index, pairs_info_df['num stocks'], pairs_info_df['num pairs']],\n",
    "#                fill_color='lavender',\n",
    "#                align='left'))\n",
    "# ])\n",
    "#\n",
    "# fig.show()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>\n",
    "Lookback Time Period\n",
    "</h3>\n",
    "<p>\n",
    "Pairs are selected for trading using a lookback period. The longer the lookback period, the\n",
    "less error there will be in the selection statistics, assuming that the data is stable\n",
    "(e.g., constant mean and standard deviation).  Stock price time series are not stable over time, however. The mean and the\n",
    "standard deviation change, as do other statistics like correlation and cointegration (mean reversion).\n",
    "</p>\n",
    "<p>\n",
    "In using a lookback period to choose trading pairs we are making the assumption that the past\n",
    "will resemble the future trading period (as we shall see, this is not true in the majority of cases).\n",
    "The longer the lookback period, the less likely it is that the statistics will match\n",
    "the trading period. This creates a tension between statistical accuracy and statistics that are more likely to reflect\n",
    "the future trading period.\n",
    "</p>\n",
    "<p>\n",
    "A half year period is used for the lookback period. In practice the statistics for a year period (252 trading days) and a\n",
    "six month period (126 trading days) seem to be similar. We assume that the six month period will more accurately resemble the\n",
    "future trading period.\n",
    "</p>\n",
    "\n",
    "<h2>\n",
    "Mean Reversion\n",
    "</h2>\n",
    "<p>\n",
    "A single stock price series is rarely stationary and mean reverting. In pairs trading, we are looking for stock pairs where the stock price spread time series is stationary and mean in the in-sample (lookback) period. We are making a bet that the spread time series will also be stationary and mean reverting in the out-of-sample trading period.\n",
    "</p>\n",
    "<p>\n",
    "When a pair forms a mean reverting, stationary time series, it is referred to as a cointegrated time series.\n",
    "</p>\n",
    "<blockquote>\n",
    "<p>\n",
    "This (linear) price data combination of n different time series into one price data series is called cointegration and the resulting\n",
    "price series w.r.t. financial data is called a cointegrated pair.\n",
    "</p>\n",
    "<p>\n",
    "Cointegration Analysis of Financial Time Series Data by Johannes Steffen, Pascal Held and Rudolf Kruse, 2014\n",
    "</p>\n",
    "<p>\n",
    "https://www.inf.ovgu.de/inf_media/downloads/forschung/technical_reports_und_preprints/2014/02_2014.pdf\n",
    "</p>\n",
    "</blockquote>\n",
    "<p>\n",
    "In the equation below, <i>s</i> is the stationary mean reverting weighted spread time series, P<sub>A</sub> is the price series for stock A,\n",
    "P<sub>B</sub> is the price series for stock B and β is the weight factor (for one share of stock A there will be β shares of\n",
    "stock B).\n",
    "</p>\n",
    "\n",
    "\\$ s = P_A - \\beta P_B $\n",
    "\n",
    "\\$ s = \\mu  \\space when \\space P_A = \\beta P_B $\n",
    "\n",
    "\\$ s > \\mu + \\delta \\space when \\space P_A > \\beta P_B \\space (short \\space P_A, \\space long \\space P_B) $\n",
    "\n",
    "\\$ s < \\mu - \\delta \\space when \\space P_A < \\beta P_B \\space (long \\space P_A, \\space sort \\space P_B) $\n",
    "\n",
    "<p>\n",
    "When s is above the mean at some level, delta (perhaps one standard deviation), a short position will be taken in stock A and a long position will be taken in β shares of stock B. When s is below the mean at some level (perhaps one standard deviation) a long position will be taken in stock A and a short position will be taken in β shares of stock B.\n",
    "</p>\n",
    "<p>\n",
    "In identifying a pair for pairs trading, a determination is made on whether <i>s</i> is mean reverting.  The process of determining\n",
    "mean reversion will also provide the value of β.\n",
    "</p>\n",
    "<h2>\n",
    "Testing for Cointegration and Mean Reversion\n",
    "</h2>\n",
    "<p>\n",
    "Highly correlated pairs are selected from a common industry sector.  Once a pair with high correlation is identified, the next step\n",
    "is to test whether the pair is cointegrated and mean reverting. Two tests are commonly used to test for mean reversion:\n",
    "</p>\n",
    "<ol>\n",
    "<li>\n",
    "Engle-Granger Test: Linear Regression and the Augmented Dickey-Fuller (ADF) test\n",
    "</li>\n",
    "<li>\n",
    "The Johansen Test\n",
    "</li>\n",
    "</ol>\n",
    "<p>\n",
    "Each of these tests has advantages and disadvantages, which will be discussed below.\n",
    "</p>\n",
    "<h3>\n",
    "Engle-Granger Test: Linear Regression and the Augmented Dickey-Fuller Test\n",
    "</h3>\n",
    "<p>\n",
    "Two linear regressions are performed on the price series of the stock pair. The residuals of the regression with the highest\n",
    "slope are tested with the Augmented Dickey-Fuller (ADF) test to determine whether mean reversion is likely.\n",
    "</p>\n",
    "<p>\n",
    "Linear regression is designed to provide a measure of the effect of a dependent variable (on the x-axis) on the independent\n",
    "variable (on the y-axis). An example might be body mass index (a measure of body fat) on the x-axis and blood cholesterol on the y-axis.\n",
    "</p>\n",
    "<p>\n",
    "In selecting pairs, we pick pairs with a high correlation from the same industry sector. This means that some\n",
    "process is acting on both stocks. However, the movement of one stock does not necessarily cause movement in the other stock. Also,\n",
    "both stock price series are driven by an underlying random process. This means that linear regression is not perfectly suited\n",
    "for analyzing pairs. Two linear regressions are performed since we don't know which stock to pick as the dependent variable. The regression\n",
    "with the highest slope is used to analyze mean reversion and build the cointegrated time series.\n",
    "</p>\n",
    "<p>\n",
    "The result of the (Engle) Granger test the linear regression slope is the weight value for the equation above. A linear regression\n",
    "intercept is also available, along with the confidence level of the cointegration process (e.g., 90%, 95% and 99% confidence).\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CointData:\n",
    "    \"\"\"\n",
    "    cointegrated: boolean - true if the time series were cointegrated, false otherwise\n",
    "    weight: float - in the stationary cointegrtion equation x = A - w * B\n",
    "    confidence: int - confidence level, in percent: 90, 95, 99\n",
    "    asset_a: str - the symbol for the time series A in the above equation\n",
    "    asset_b: str - the symbol for the time series B in the above equation\n",
    "    \"\"\"\n",
    "    def __init__(self,\n",
    "                 cointegrated: bool,\n",
    "                 confidence: int,\n",
    "                 weight: float,\n",
    "                 asset_a: str,\n",
    "                 asset_b: str):\n",
    "        self.cointegrated = cointegrated\n",
    "        self.confidence = confidence\n",
    "        self.weight = weight\n",
    "        self.asset_a = asset_a\n",
    "        self.asset_b = asset_b\n",
    "        self.intercept = np.NAN\n",
    "\n",
    "\n",
    "    def __str__(self):\n",
    "        if self.has_intercept():\n",
    "            s = f'cointegrated: {self.cointegrated}, confidence: {self.confidence}, weight: {self.weight}, intercept: {self.get_intercept()} ({self.asset_a}, {self.asset_b})'\n",
    "        else:\n",
    "            s = f'cointegrated: {self.cointegrated}, confidence: {self.confidence}, weight: {self.weight}, ({self.asset_a}, {self.asset_b})'\n",
    "        return s\n",
    "\n",
    "    def set_intercept(self, intercept: float) -> None:\n",
    "        self.intercept = intercept\n",
    "\n",
    "    def get_intercept(self) -> float:\n",
    "        return self.intercept\n",
    "\n",
    "    def has_intercept(self) -> bool:\n",
    "        return not np.isnan(self.intercept)\n",
    "\n",
    "\n",
    "class PairStatistics:\n",
    "    def __init__(self):\n",
    "        self.decimals = 2\n",
    "        pass\n",
    "\n",
    "    def correlation(self, data_a_df: pd.DataFrame, data_b_df: pd.DataFrame) -> float:\n",
    "        data_a = np.array(data_a_df).flatten()\n",
    "        data_b = np.array(data_b_df).flatten()\n",
    "        c = np.corrcoef(data_a, data_b)\n",
    "        cor_v = round(c[0, 1], 2)\n",
    "        return cor_v\n",
    "\n",
    "    def find_interval(self, coint_stat: float, critical_vals: dict) -> Tuple[bool, int]:\n",
    "        \"\"\"\n",
    "        :param coint_stat: the ADF statistic\n",
    "        :param critical_vals: a dictionary defining the ADF intervals {'1%': -3.49, '5%': -2.89, '10%': -2.58}. The\n",
    "                              dictionary values may be either positive or negative.\n",
    "        :return: if the adf_stat is in the critical value range, return True and the integer value of the interval\n",
    "                 (e.g., 1, 5, 10). Or False and 0\n",
    "        \"\"\"\n",
    "        cointegrated = False\n",
    "        interval_key = ''\n",
    "        interval = 0\n",
    "        abs_coint_stat = abs(coint_stat)\n",
    "        for key, value in critical_vals.items():\n",
    "            abs_value = abs(value)\n",
    "            if abs_coint_stat > abs_value and abs_value > interval:\n",
    "                interval = abs_value\n",
    "                interval_key = key\n",
    "                cointegrated = True\n",
    "        key_val = int(interval_key.replace('%','')) if cointegrated else 0\n",
    "        return cointegrated, key_val\n",
    "\n",
    "    def engle_granger_coint(self, data_a: pd.DataFrame, data_b: pd.DataFrame) -> CointData:\n",
    "        sym_a = data_a.columns[0]\n",
    "        sym_b = data_b.columns[0]\n",
    "        data_b_const = sm.add_constant(data_b)\n",
    "        # x = I + b * A\n",
    "        result_ab = sm.OLS(data_a, data_b_const).fit()\n",
    "        data_a_const = sm.add_constant(data_a)\n",
    "        # x = I + b * B\n",
    "        result_ba = sm.OLS(data_b, data_a_const).fit()\n",
    "        slope_ab = result_ab.params[data_b.columns[0]]\n",
    "        slope_ba = result_ba.params[data_a.columns[0]]\n",
    "        result = result_ab\n",
    "        slope = slope_ab\n",
    "        if slope_ab < slope_ba:\n",
    "            t = sym_a\n",
    "            sym_a = sym_b\n",
    "            sym_b = t\n",
    "            result = result_ba\n",
    "            slope = slope_ba\n",
    "        intercept = round(result.params['const'], self.decimals)\n",
    "        slope = round(slope, self.decimals)\n",
    "        residuals = result.resid\n",
    "        adf_result = adfuller(residuals)\n",
    "        adf_stat = round(adf_result[0], self.decimals)\n",
    "        critical_vals = adf_result[4]\n",
    "        cointegrated, interval = self.find_interval(adf_stat, critical_vals)\n",
    "        coint_data = CointData(cointegrated=cointegrated, confidence=interval, weight=slope, asset_a=sym_a, asset_b=sym_b)\n",
    "        coint_data.set_intercept(intercept=intercept)\n",
    "        return coint_data\n",
    "\n",
    "    def johansen_coint(self, data_a: pd.DataFrame, data_b: pd.DataFrame) -> CointData:\n",
    "        ts_df = pd.concat([data_a, data_b], axis=1)\n",
    "        johansen_rslt = coint_johansen(ts_df, 0, 1)\n",
    "        hedge_val = round(np.abs(johansen_rslt.evec[0, 0] / johansen_rslt.evec[1, 0]), 2)\n",
    "        critical_vals = pd.DataFrame(johansen_rslt.trace_stat_crit_vals[0]).transpose()\n",
    "        critical_vals.columns = ['10%', '5%', '1%']\n",
    "        critical_vals_dict = critical_vals.to_dict('records')[0]\n",
    "        trace_stat = johansen_rslt.trace_stat[0]\n",
    "        cointegrated, interval = self.find_interval(coint_stat=trace_stat, critical_vals=critical_vals_dict)\n",
    "        sym_a = data_a.columns[0]\n",
    "        sym_b = data_b.columns[0]\n",
    "        coint_data = CointData(cointegrated=cointegrated, confidence=interval, weight=hedge_val, asset_a=sym_a,\n",
    "                               asset_b=sym_b)\n",
    "        return coint_data\n",
    "\n",
    "    def compute_halflife(self, z_df: pd.DataFrame) -> int:\n",
    "        \"\"\"\n",
    "        Calculate the half-life of a mean reverting stationary series where the series\n",
    "        is an Ornstein–Uhlenbeck process\n",
    "        From example 7.5 in Quantitative Trading by Ernest P. Chan\n",
    "        \"\"\"\n",
    "        prevz = z_df.shift()\n",
    "        dz = z_df - prevz\n",
    "        dz = dz[1:]\n",
    "        prevz = prevz[1:]\n",
    "        result = sm.OLS(dz, prevz - np.mean(prevz)).fit()\n",
    "        theta = result.params\n",
    "        halflife_f = -np.log(2) / theta\n",
    "        halflife = round(halflife_f, 0)\n",
    "        return int(halflife)\n",
    "\n",
    "    def stationary_series(self, data_a: pd.DataFrame, data_b: pd.DataFrame, coint_data: CointData) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        compute the stationary time series x = A - w * B  or x = A - i - w * B if there is an intercept i\n",
    "        \"\"\"\n",
    "        data_df = pd.concat([data_a, data_b], axis=1)\n",
    "        data_a_df = pd.DataFrame(data_df[coint_data.asset_a])\n",
    "        data_b_df = pd.DataFrame(data_df[coint_data.asset_b])\n",
    "        if coint_data.has_intercept():\n",
    "            stationary_a = data_a_df.values - coint_data.get_intercept() - coint_data.weight * data_b_df.values\n",
    "        else:\n",
    "            stationary_a = data_a_df.values - coint_data.weight * data_b_df.values\n",
    "        stationary_df = pd.DataFrame(stationary_a.flatten())\n",
    "        stationary_df.index = data_df.index\n",
    "        return stationary_df\n",
    "\n",
    "\n",
    "def plot_stationary_ts(stationary_df: pd.DataFrame, plus_delta: float, minus_delta: float, title: str) -> None:\n",
    "    stationary_df.plot(grid=True, title=title, figsize=(10, 6))\n",
    "    stat_mean = stationary_df.mean()[0]\n",
    "    plt.axhline(y=stat_mean, color='black', linewidth=2)\n",
    "    plt.axhline(y=stat_mean + plus_delta, color='red', linewidth=1, linestyle='--')\n",
    "    plt.axhline(y=stat_mean - minus_delta, color='green', linewidth=1, linestyle='--')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "half_year = int(trading_days/2)\n",
    "\n",
    "close_index = close_prices_df.index\n",
    "\n",
    "d2007_ix = 0\n",
    "\n",
    "d2007_close = pd.DataFrame(close_prices_df[['AAPL', 'MPWR', 'YUM']]).iloc[d2007_ix:d2007_ix+half_year]\n",
    "\n",
    "d2007_cor = round(d2007_close.corr().iloc[0,[1,2]], 2)\n",
    "cor_df = pd.DataFrame([d2007_cor])\n",
    "cor_df.index = ['2007']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>\n",
    "Example: AAPL/MPWR\n",
    "</h3>\n",
    "<p>\n",
    "AAPL (Apple Inc) and MPWR (Monolithic Power Systems, Inc) are in the technology industry sector.  YUM (Yum brands) is a food company in a\n",
    "different industry sector. The correlations of MPWR and YUM with AAPL in the first half of 2007 are shown below.  AAPL and MPWR are both technology sector\n",
    "stocks, while YUM brands is a food company. Other than overall stock market dynamics, we would not expect AAPL and YUM to be correlated, so this\n",
    "appears to be an example of false correlation.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒══════╤════════╤═══════╕\n",
      "│      │   MPWR │   YUM │\n",
      "╞══════╪════════╪═══════╡\n",
      "│ 2007 │   0.91 │  0.88 │\n",
      "╘══════╧════════╧═══════╛\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(tabulate(cor_df, headers=[*cor_df.columns], tablefmt='fancy_grid'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "Monolithic Power Systems, Inc. (MPWR) stock grew at a rate that was similar to Apple's, although their everall market\n",
    "capitalization is a faction of Apples now. I was unfamiliar with MPWR until I wrote this notebook. Bloomberg describes\n",
    "MPWR's business as:\n",
    "</p>\n",
    "<blockquote>\n",
    "Monolithic Power Systems, Inc. designs and manufactures power management solutions. The Company provides power conversion, LED lighting, load switches, cigarette lighter adapters, chargers, position sensors, analog input, and other electrical components. Monolithic Power Systems serves customers globally.\n",
    "</blockquote>\n",
    "<p>\n",
    "A brief digression: Apple is now (2022) one of the most\n",
    "valuable companies in the world. A number of investment funds bought Apple shares and were able to beat the overall market\n",
    "for the years when Apple had exceptional growth.\n",
    "</p>\n",
    "<p>\n",
    "My father invested in such a fund. Every quarter they sent out a \"market outlook\"\n",
    "newsletter. This newsletter was filled with blather to make fund investors think that the people managing the fund had special insight into the\n",
    "stock market (which justified the fees the fund charged). In fact, their only insight was their position in Apple stock.\n",
    "When Apple's share price plateaued, so did the funds returns.\n",
    "</p>\n",
    "<p>\n",
    "Pairs may have high correlation without being cointegrated.  The (Engle) Granger test shows that in the first half of 2007\n",
    "AAPL and MPWR were cointegrated at the 99% level.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger test for cointegration (AAPL/MPWR): cointegrated: True, confidence: 1, weight: 3.7, intercept: 1.05 (MPWR, AAPL)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pair_stat = PairStatistics()\n",
    "\n",
    "d2007_aapl = pd.DataFrame(d2007_close['AAPL'])\n",
    "d2007_mpwr = pd.DataFrame(d2007_close['MPWR'])\n",
    "d2007_yum = pd.DataFrame(d2007_close['YUM'])\n",
    "\n",
    "coint_data_granger_aapl_mpwr = pair_stat.engle_granger_coint(data_a=d2007_aapl, data_b=d2007_mpwr)\n",
    "print(f'Granger test for cointegration (AAPL/MPWR): {coint_data_granger_aapl_mpwr}')\n",
    "\n",
    "data_a = pd.DataFrame(d2007_close[coint_data_granger_aapl_mpwr.asset_a])\n",
    "data_b = pd.DataFrame(d2007_close[coint_data_granger_aapl_mpwr.asset_b])\n",
    "\n",
    "stationary_df = pair_stat.stationary_series(data_a=data_a, data_b=data_b, coint_data=coint_data_granger_aapl_mpwr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The confidence level represents the error percentage. So 1 = 1% error or 99% confidence, 5 = 5% or 95% confidence and 10 = 10% or\n",
    "90% confidence.\n",
    "</p>\n",
    "<p>\n",
    "The plot below shows the stationary spread time series formed by\n",
    "</p>\n",
    "\n",
    "\\$  s = MPWR - intercept - 3.7 * AAPL $\n",
    "\n",
    "<p>\n",
    "The dotted lines are a plus one and minus one standard deviation. The intercept adjusts the time series so that the mean is zero.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "std_dev = stationary_df.std()[0]\n",
    "plot_stationary_ts(stationary_df=stationary_df, plus_delta=std_dev, minus_delta=std_dev, title='Granger AAPL/MPWR stationary time series, 1st half of 2007' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "AAPL and MPWR are both technology stocks that have some overlapping businesses (MPWR's products are used by companies like Apple). We would\n",
    "expect that the two stocks might be cointegrated.\n",
    "</p>\n",
    "<p>\n",
    "The test for cointegration is performed in a lookback period. The next period is the trading period where the close prices of the\n",
    "pair are combined with the weight (and perhaps the intercept) to form what we hope will be a stationary mean reverting time series\n",
    "that can be profitably traded.\n",
    "</p>\n",
    "<p>\n",
    "The test below applies the Granger test to the second half of 2007 to see if mean reversion persisted. As the test results shows,\n",
    "this is not the case.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger test for cointegration (AAPL/MPWR): second half of 2007 cointegrated: False, confidence: 0, weight: 1.09, intercept: 14.56 (MPWR, AAPL)\n"
     ]
    }
   ],
   "source": [
    "second_half_start = d2007_ix+half_year\n",
    "d2007_aapl_2 = pd.DataFrame(close_prices_df['AAPL']).iloc[second_half_start:second_half_start+half_year]\n",
    "d2007_mpwr_2 = pd.DataFrame(close_prices_df['MPWR']).iloc[second_half_start:second_half_start+half_year]\n",
    "\n",
    "coint_data_granger_aapl_mpwr_2 = pair_stat.engle_granger_coint(data_a=d2007_aapl_2, data_b=d2007_mpwr_2)\n",
    "\n",
    "print(f'Granger test for cointegration (AAPL/MPWR): second half of 2007 {coint_data_granger_aapl_mpwr_2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The pair AAPL and YUM (Yum Brands, a food company) would not be expected to be cointegrated (although they have a surprisingly\n",
    "high correlation). As expected, the Granger test does not show cointegration and mean reversion.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger test for cointegration (AAPL/YUM): cointegrated: False, confidence: 0, weight: 2.45, intercept: 13.6 (YUM, AAPL)\n"
     ]
    }
   ],
   "source": [
    "coint_data_granger_aapl_yum = pair_stat.engle_granger_coint(data_a=d2007_aapl, data_b=d2007_yum)\n",
    "print(f'Granger test for cointegration (AAPL/YUM): {coint_data_granger_aapl_yum}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>\n",
    "Correlation and Cointegration\n",
    "</h3>\n",
    "<p>\n",
    "In choosing pairs for pairs trading, we are looking for a stock pair that is influenced by similar factors. Industry sector and high\n",
    "correlation can be used as a first filter for pairs.\n",
    "</p>\n",
    "<p>\n",
    "The tests for cointegration may find that a pair with a low correlation value is cointegrated and mean reverting. One\n",
    "example is AAPL and technology sector stock GPN (Global Payments Inc.)  For the first half of 2007, AAPL and GPN have\n",
    "a low correlation.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒══════╤════════════════════════╕\n",
      "│      │   Correlation AAPL/GPN │\n",
      "╞══════╪════════════════════════╡\n",
      "│ 2007 │                   0.32 │\n",
      "╘══════╧════════════════════════╛\n"
     ]
    }
   ],
   "source": [
    "d2007_gpn = pd.DataFrame(close_prices_df['GPN']).iloc[d2007_ix:d2007_ix+half_year]\n",
    "\n",
    "cor_df = pd.DataFrame([pair_stat.correlation(data_a_df=d2007_aapl, data_b_df=d2007_gpn)])\n",
    "cor_df.index = ['2007']\n",
    "cor_df.columns = ['Correlation AAPL/GPN']\n",
    "print(tabulate(cor_df, headers=[*cor_df.columns], tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The normalized close prices for the two stocks in the first half of 2007 are shown below.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 2 Axes>",
      "image/png": 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W81bg6tati7Zt2+b7c26thw8f4u2330alSpXwyiuvGG/PysoCUHCfpdyTKz8mMzPT5O+RRx9b2M+HwWDAxo0bUa9ePdSoUaNEXwcREcBKHxG5kAkTJmDdunWYM2dOgdW+qKgoKBQKkwtTQHr33d/fP1//VVHDRx4d6y4HJblilfd2g8GAlJQU4xa3EydOYObMmTh16hQyMjJMHp+SkmLVoIq9e/fi559/xtixY9GzZ0/j7Tdv3gQAvPjiiwU+T+5di46OBoACpyJWq1bNrAvyAwcOID4+Ho0aNcKtW7eMt7dq1QobN27Eu+++C4Ui973ETZs2QavVIiIiwuTxjRo1wvr16wuses6cORO+vr5QqVQICQnJ97001+7du9GuXTvjJFT590HeApnXL7/8Ap1Oh8jISHz99df57vfw8MCsWbMASBf6lSpVKnaL6KRJk6yq9lmqoJ/hd955B1OmTEGnTp1Qv359dOzYEQMGDCjwKAFLPPqzD0iVxuTkZOPHmZmZmD17NlavXo3Y2FiIomi8ryRveOQl/yzn3Y4tq1GjBvbv31/ioTbp6ekYO3Ys0tLSsHjxYpPtr3Kwe7SXF8gNe/JjPD09odVqC/wcRQ2OOnr0KGJjY51quioRuTaGPiJyGXmrfXlHnT/K3NH2RfVb5Q0v5twuX9zevn0bo0aNQvXq1TFlyhSEhoZCrVZjz549WLBggck2QnPduXMH7777Ltq2bYvJkycX+Hn/85//FLjdrLihJJaQq3mPrkF29OhRk+2z8va0YcOGFfj4O3fu5AsizZs3L3ZrX3EyMjKMA05k4eHhUKlUBQ63kYeyFPZ7pVQqTSY3mqNFixZo2bIl5s2bh+eee86i51qioGmvffr0QfPmzbFt2zYcOHAAv/32G+bOnYuZM2cWeVxGcQr7/ckb7D7//HOsXr0aL774Ipo0aQI/Pz8IgoA333zT5HHOLDs7G5MmTcKlS5fw22+/oXbt2ib3BwYGQqPR4MGDB/meK99Wvnx5ANIbTnq9Hg8fPjTZ4pmdnY3ExETj4x61fv16KBQK9O3b11ZfFhE95hj6iMiljB8/HuvWrStwkmdYWBgMBgNu3bplsiUqLi4OycnJZvetlcTOnTuRnZ2NX3/91aRaKG+ztFRmZiYmTZoEPz8/fPvtt/lCpxyaypYtW2QwkdeSt+Imu3HjRrHrSE9Px86dO9GnTx+TSqPsiy++wPr1642h786dOzh58iRGjBiBFi1amDzWYDDgvffew/r16zFhwoRiP7elDh8+jOzsbDz55JPG27y9vdGyZUscO3YMsbGxdjv6I69JkyZh5MiRWLp0qd0/16PKly+P559/Hs8//zwePnyIgQMHYtasWcbQZ8sz//KS+/by9hNmZWXZrMoH5P4sF/Rze/36dQQFBVld5TMYDHj//fdx6NAhfP/998Y3BPJSKBSoXbs2zp07l+++M2fOoHLlysbKstzneu7cOZPAfe7cORgMBtStWzffa2RnZ2Pr1q1o2bJlqfycEtHjgT19RORSwsPD8fTTT2PZsmX53mmXL6r++OMPk9vnz59vcr89ydWQR7e1rVq1yqrX++STT3Dz5k389NNPxvO88urQoQN8fX0xe/bsAreRyQNIypcvj4iICKxZs8bkAvzAgQO4evVqsevYtm0b0tPT8fzzz6NXr175fnXu3Blbt241bnmTq3yvvPJKvsf26dPHOMXTHvbs2YMGDRrkm9T42muvQa/X45133sl3CDYAm1eiWrZsaaz2ydv+7E2v1+cLWGXLlkX58uVNtiN6eXnZNIjJCqoGLly4EHq93mafQ/5ZXrt2rcnW0suXL+PAgQMl+nP++eefY9OmTfjkk0/Qo0ePQh/Xs2dPnD17FmfPnjXedv36dRw+fNhkYEzr1q0RGBiY7wiPJUuWwMvLyzhdNq89e/YgOTmZZ/MRkU2x0kdELmfcuHH4+++/cePGDZNpjHXr1sXAgQOxbNkyJCcno0WLFjh79izWrFmDbt26mWw9tJd27dpBrVZj3LhxeO6555CWloYVK1agbNmyBW4HK8ru3buxdu1a9OzZE5cuXcKlS5eM9/n4+KBbt27w9fXFtGnT8N577+GZZ55Bnz59UKZMGURHR2PPnj144okn8PHHHwMA3nrrLYwdOxbDhw/HoEGDkJiYiEWLFqFWrVoF9rrltX79egQGBhZ6QHqXLl2wfPly7N69Gz169MD69esRERFRYB+Y/PjPP/8c58+fR/369S36fQGksxD37duX7/YXXngBe/fuxTPPPJPvvubNm+P//u//8MUXX6Bnz57o168fqlevjuzsbNy8eRPr16+HWq22aqx/YSZOnIgXXnjBZq9XHPkw+p49e6Ju3brw9vbGwYMHcfbsWZPqW/369bFp0yZ8+eWXaNiwIby9vc0+q68onTp1wt9//w1fX1/UrFkTp06dwsGDB606cqMo7733Hl599VUMHToUzz77rPHIBj8/P5NJupZYsGABFi9ejKZNm8LT0xN///23yf3du3c3VhCHDx+OFStWYOzYsRg9ejRUKhUWLFiAsmXLGgdHAdIW8tdffx2fffYZXn/9dXTo0AHHjx/HunXr8Oabbxb4+7J+/XpoNJoCK+pERNZi6CMil1OlShU8/fTTWLNmTb77vvjiC1SqVAlr1qzB9u3bERwcjLFjx1p9IWip6tWr48cff8T333+Pr7/+GsHBwRg2bBjKlCmDDz74wKLXkqt0W7ZswZYtW0zuCwsLQ7du3QAA/fr1Q/ny5TFnzhz89ttvyM7ORoUKFdC8eXOT8PPkk0/ihx9+wPfff4///e9/CA8Px5dffokdO3bg6NGjha7j4cOHOHToEPr27VtoX1ebNm3g5eWFdevWISwsDNevXy9y62bnzp3x+eefY926dVaFvsIOP2/evDmioqJMtnbmNXz4cDRt2hQLFizAP//8gwcPHkCtVqNy5coYOHAghg0bZvXwmIK0atUKLVu2LPL315Y8PT0xbNgwHDhwAFu3boUoiggPD8cnn3xickD48OHDcfHiRaxevRoLFixAWFiYTULfhx9+CIVCgfXr1yMrKwtPPPEE5s+fbzL90hbatm2LefPm4ccff8SPP/4IlUqFFi1a4N1337V6YE1kZCQA4OTJkzh58mS++3fs2GEMfb6+vli4cCFmzJiBX3/9FQaDAa1atcLUqVPz9aQ+//zzUKvV+P3337Fz506EhoZi6tSpBQ5fSk1Nxe7du9GpUyerBj4RERVGEF2ls5qIiKgYc+fOxYIFC7B//3679a0RERG5Gvb0ERGR2wgLC8PUqVMZ+IiIiPJgpY+IiIiIiMiNsdJHRERERETkxhj6iIiIiIiI3BhDHxERERERkRtj6CMiIiIiInJjDH1ERERERERujIezF4GDTYmIiIiICIBLHwfE0FeMuLhURy8BKpUCQUE+SEhIg05ncPRyiIiIiIgeK8HBvo5eQolweycREREREZEbY+gjIiIiIiJyYwx9REREREREboyhj4iIiIiIyI0x9BEREREREbkxhj4iIiIiIiI3xtBHRERERETkxhj6iIiIiIiI3BhDHxERERERkRtj6CMiIiIiInJjDH1ERERERERujKGPiIiIiIjIjTH0ERERERERuTGGPiIiIiIiIjfG0EdEREREROTGGPqIiIiIiIjcGEMfERERERGRG3Oq0Hf1KjBuHNCkCaBSAQ0amPc8UQS++goIDwe8vIA2bYDDh+26VCIiIiIiIrNcjb+KcRvGocmsJlB9pkKDX8wLOqIo4qv9XyH8u3B4TfdCm9/a4PBdy4OOU4W+8+eBjRuBmjWBevXMf97XXwOffAK8+SawYQMQGgr06AFcv26/tRIREREREZnj/P3z2HhlI2qWqYl65cwPOl8f+Bqf7P4Eb7Z+ExuGbUCobyh6LOyB6wmWBR1BFEXR0kXbi8EAKHJi6KhRwPHjwLlzRT8nMxOoUAF47TVgxgzptuxsoHZtoE8f4JdfrF+PKIqIi0u1/gVsRKVSICjIBwkJadDpDI5eDhERERHRYyU42BeCIFj9fINogEKQgs6otaNwPPo4zk0oOuhk6jJR4ZsKeK3Fa5jRVQo62fps1J5ZG31q9cEvfc0POk5V6VNYsZqDB4HkZGDIkNzbNBrgmWeATZtstzYiIiIiIiJryIHPEgfvHERyVjKG1M8NOhqlBs9EPINNVywLOk4V+qwRGSn9t25d09sjIoDbt4GMjNJfU2nRG/RwokItEREREZHN6PWOXoFjRcZJQadusGnQiQiOwO2k28jQmh90VDZdmQMkJAAeHoCnp+ntQUHSgJeEBGm4S0G6du1a6Otu2bIFSqUSHh6O/y1SKKRSslqthFIp5fSkrCS0WdQcLSu2xoK+Cx25PCIiIiIim4qLA15+2RPdu+swcaLO0csBAERHR2PkyJGF3r9jxw6bfr6EjAR4KD3gqTINOkFeQRAhIiEzAV7qQoLOIxyfaJycv795v5Glwdc39xt+7PpBRKVGYd/dPU61RiIiIiKikvL3B3btAgB1zi8qCZcPfUFBQFaWNNAlb7UvIQEQBOn+whSXxkVRRHKy4/eHKhQCfH09kZqaCYNB2s55LvoiAECr1zrFGomIiIiIbGXdOiW+/VaD9u11+OILraOXAz8/T1SsWNHm1byiBHkFIUufhUxdpkm1LyEjAQIEBHkWEXQe4fKhT+7lu3QJaNw49/bIyNxz+0oiK8vx5WSVStrSqdXqjdM7r8VLY1qz9dlOsUYiIiIiIls5d06BkyeB+vUNTnGt6+dX+p9T7uW7FHcJjUNyg05kXCTCA8LN3toJuMEgl7ZtpfLvihW5t2m1wOrV0pEN7upm8g0AgNbg+Hc+iIiIiIhsKS5OmmkRHPz4Di1sW7kt/D38seJCbtDR6rVYHbkafWpZFnScqtKXnp57zMKtW9JRDCtXSh937AiUKwd07Srdd/WqdLunJzB1KjBtmnR/w4bS2XwPHwLvvOOQL6NU3EySQp/OoIMoiiU6N4SIiIiIyJm4W+hL16Ybj1m4lXQLyVnJWHlBCjodq3REOZ9y6PpnV9xKvIWrr0tBx1Pliantp2La7mko510ODSs0xC/HfsHD9Id4p61lQcepQt/9+8Dgwaa3yR/v2gV06iSNbtU9UuF9/31pUuc33wAPHgBNmgBbtgDVq5fGqkufKIrGSh8gVfs0So0DV0REREREZDvuFvrup93H4BWmQUf+eNeLu9DJpxP0Bj10BtOg83679yGKIr459A0epD1Ak5Am2DJiC6oHWRZ0BJEHvRVKFEXExaU6ehlQqRQICvJBQkIadDoD4jMfou7v1Yz3X381Gr5qXweukIiIiIjIdjp08MalS0qsXJmOJ590/IF9wcG+Lr2zzuV7+h5H8tZOmU7Pvj4iIiIich/uVulzNIY+F3Qr+abJx9kc5kJEREREbkKvB+LjpdBXtixDny0w9LmgfJU+hj4iIiIichPx8QJEkaHPlhj6XFDeIS6AdFYfEREREZE7kLd2liljgMqpxk66LoY+F/To9s5Hp/wQEREREbkq9vPZHkOfC3p0e2e2gZU+IiIiInIPDH22x9DnYjJ1mYhJiwYAeCo9AbCnj4iIiIjcx8OH7OezNYY+F3M7+RZEiPBR+6K8dwUA7OkjIiIiIvfBSp/tMfS5mFs5Q1yq+leDWqkGwJ4+IiIiInIfDx4w9NkaQ5+LkSd3Vg2oBo1CA4A9fURERETkPljpsz2GPhcjD3Gp6l8NKoVc6WNPHxERERG5h7g4KaIw9NkOQ5+LkY9rqOJfFZqc7Z3ZeoY+IiIiInIP8iCXcuUY+myFoc/F5N3eKVf6tKz0EREREZGbkLd3cnqn7TD0uRCDaDBW+qr65/b0adnTR0RERERuICsLSE6We/oMDl6N+2DocyH30u4hS58FpaBEmG8lqBQqAICW2zuJiIiI3FJcnIBWrXzw6acejl5KqZC3dqpUIgICHLwYN8LQ50JuJl0HAFTyqwy1Ug2NUq70MfQRERERuaNDh5S4cUOBOXPUiI939GrsL+/WTgWTis3wt9KF3MgzuRMAe/qIiIiI3FxMjBSCtFoB69apHbwa++NxDfbB0OdCjMc1BFQHAGjk0KdnTx8RERGRO4qJyb1cX7VK5cCVlA4OcbEPhj4XIoe+Kv5VAeSt9OkctSQiIiIisiO50gcAR46ocOuWUMSjXR8rffbB0OdCHt3emdvTx0ofERERkTu6d08KQWq1FIJWr3bvLZ5y6OMZfbbF0OdCbiXlntEHsKePiIiIyF50OuCrrzT47TfHhqx796TL9UGDpJ1dK1eqILpxHoqLk75eVvpsi6HPRSRnJeNh5kMAQNWc7Z25PX0MfURERES2IorA++974NtvPfDhhx7IynLcOuRK3yuvZMPTU8SVK0qcPeu+l/DykQ0Mfbblvj8xbuZa/DUAQLBXMHw1fgBY6SMiIiKyh2++0WDhQqmNxmAQTPrqSlNyMpCeLn3uWrUM6NlTqvatWOG+WzxzB7nwYHZbYuhzEdcSpNBXJaefD2BPHxEREZGtLVyoxn//Kx2ErtFI1aa8EzRLk/x5AwNFeHkBgwZJb/SvWaOCXu+QJdkdB7nYB0Ofi7ieIB3MXjVP6FMppLG9rPQRERERldw//yjx7rtS4HvzzSy0bCklq6gox1T65ApjaKhU9erSRY+gIBH37yuwb5/SIWuyJ1Fk6LMXhj4XIW/vrBJQ1XibRpFT6WNPHxEREVGJHDumwNixXjAYBAwfno0pU7JRsaIUPKKjHXPJLPfzhYRI69BogP79peu+Vavcb4tnWhqQkcHQZw8MfS5C3t5pUulTsqePiIiIqKQuXlRgxAhvZGQI6NZNh//+NwuCAISFSRW26GhHVfqkS3W50gfkTvHcsEGF9HTzXkcUga1blcYhKc5KXp+XlwgfHwcvxs0w9LkI4/bOgOrG24zTO9nTR0RERGSxhATg//7PA926eSMhQcATT+gxd24G1DlFtNBQudLn2O2dcqUPAFq21CM83IC0NAFbtqjMep3Nm1UYMcIbo0d72mWdtpJ3a6fg3PnU5TD0uQCtXovbSbcB5B7XAOSd3qlzxLKIiIiIXFJ2NjB7thqtWvli9mwNtFoBXbrosGhRhkmFSa70RUU5anunXOnLDX2CkDvQxdwtnocOKXP+q8KJE857+Z87uZNbO23Neb/rZHQn5Tb0oh5eKi9U8A4x3m6c3qlnpY+IiIjIHJs2qdChgw/+7/88kZgoICJCj6VL07F0aUa+PjK5p89RRzY8OshFJm/x3LnTvC2bp0/nXvLPmqWx4Qptiwez2w9Dnwu4mXQTAFDFvyqEPLVuNc/pIyIiIjLbpk0qjBrlhRs3FChXzoD//S8TO3ako0uXgs8/qFhRCltxcQpkZpbmSiW5oc80BNWubUCjRnrodAI2bix6i6fBAJw9mzvpc/16FW7fds69k5zcaT8MfS7gZpLUz1ctTz8fkBv6shn6iIiIiIokisAPP0hVrsGDtThyJA0jR2qhKiIzBQYC3t6O6evTanNDUN6ePlmPHlK17/Dhoo9uuH5dQFqaAC8vEe3b62AwCJg71zmrfbmhjwez2xpDnwu4kXQDgOlxDUBu6NMx9BEREREV6cgRJU6eVMLDQ8S0aVnw9S3+OYKQW2Ur7QPaY2MFiKIAtVossMeteXOpOnn8eNGh7/Rp6f769Q2YOFFqCfrrLzWSk228YBtgpc9+GPpcwK1kKfRVC6hmcrs6p6cvmz19REREREWaNUt6s3zwYC3KlTM/VMhbPEv7gPa8kzsVBVyxN2smhb6bNxV48KDwtcmhr3FjPTp31qNOHT1SUwUsWuR85/xxkIv9MPS5gITMBABAzaDaJrerFdJ+BB2ndxIREREV6vp1AZs3S9dNY8datkMqLMwxlT55cmdBWzsBICAAqFu3+GrfmTPS6zRqpIcgAOPGSV//3LkaaJ1ss5gc+iwJ5WQehj4X8EHr/8MXnb9Ax8qdTG5XK1jpIyIiIirO3LkaiKKArl11qFPHsn4xR1f6Hp3cmVfuFs+CL+kNBuDMGSkQNmokvc6gQVoEBxsQFaXAhg3mnfNXWri9034Y+lxAm7B2+PDJD6EQTL9d7OkjIiIiKlpCArBkiXTNNH685W+Uy8c2REeX7mWzXFksrNIHFN/Xd+OGgNRUAZ6eojHsenoCo0dL146//qqB6CT5ShRhPH6Coc/2GPpcmFopT+9kpY+IiIioIAsXapCeLqBePT06dCj4aIaiyJW+0p7emdvTV1SlT7rv1CllgVs18w5xyTuldNQoLTw9RZw6pcSRI0UPgiktSUmATseePnth6HNhuZU+9vQRERERPSo7G5g3L7fKJ1iR23IrfaUb+u7dK/iMvrxq1jQgMFBERoaA8+fzX9bLoa9RI9OwGxwsYvBgKSX+8otzDHSRt3b6+4vw8HDwYtwQQ58LY08fERERUeHWrlXh3j0FKlQwYOBA694kDwuTqmnx8Qqkp9tydUWTt3cWFfoUitwpngVt8ZSHuDRunL/CKQ902bJFhYsXHR8J4uKkNbDKZx+O/w6T1djTR0RERFQwUZR61gDglVe00Fh5Hrm/f+4B7XL1zd5EMfdzFbW9Eyi8r6+gIS551aplQK9eWoiigJEjvRAbW7qVzEfJx06wn88+GPpcWG5PH0MfERERUV779ytx/rwS3t4iXnjB+l1RgpBb7YuKKp1L56QkICOj+O2dQG7oO3bMNPTdvCkgJUWAh4dY6MTS//0vC1WrGnD7tgLDh3shJcUGi7dS7uROy6arknkY+lwYK31EREREBZOrfM89p0VQUMleS+7rK61jG+StnYGBIry8in7sE0/ooVCIuHNHYVKtk6t89esboC6kba9cORHLlqUjONiAs2eVeOklL2Q7qGuIkzvti6HPhamV7OkjIiIietTduwK2b5fGVY4ZU/LrJDn0ldYB7blDXIqvevn5AXXrSo/LW+0rbIjLo6pVE7F4cQa8vUXs3avCG294wuCAYhsPZrcvhj4XplZIf5mJEKE3WD6CmIiIiMgdrV4tlbbatdOhevWSh4jSPqA9t5/PvLUX1NcnD3EpqJ/vUU2aGPD77xlQqUSsWqXG55+X/vhMOfRxkIt9MPS5MI0ityOZZ/URERERSVatkt4YHzTINsdalfYB7bmTO80ruT3a1yeKuds7C5rcWZAuXfT49ttMAMDPP2swd27pHuWQ29PH0GcPDH0uTKXI/cPIvj4iIiIi4Px5BS5eVEKjEdGvn22uj+RBLqV1Vl/uwezmBaCWLaVgd+aMAtnZ0hCXpCQBGk3hQ1wK8txzOnz0URYA4PPPPZCVZeHCS4Chz74Y+lyYOk/oy9Yz9BERERGtXCldH3XvrkNAgG1es7QrfffuFX9GX17VqokoW9aArCwBZ88qjFW+evUMFh9VMWlSNgICRGRmCrh8ufSiAge52BdDnwtTKpRQCNK3kJU+IiIietwZDMDq1dLWzmeftc3WTiC3py8xUUBams1etlBypc/c7Z2CADRrJj32+HElTp+W+/ksn/kgCEC9etLzbH1oe3Y2sHOnEtpHLlt1OiA+nqHPnhj6XJzc18eePiIiInrcHTyoREyMAgEBIrp1s13o8/cHfH3lCZ723+KZG/rMD0B5+/rkyZ2NG1s3hrNePel5Fy4oi3mkZb75RoPnnvPGlCmmg2Li4wWIogBBEFGmDEOfPTD0uTi5r0/LSh8RERE95laulKp8Tz+thYeNB1DmTvC07+WzVpvb32ZuTx8AtGiRG/rOnrVsiMujckOf7b5WvR5YulS6bl20SI1//819bfnrLVNGhNK2OZNyMPS5OI0yJ/Sxp4+IiIgeY5mZwPr10nWRLbd2ynLP6rNvpS82Vqp6qdWiRccXNGmih1IpIiZGgcREaYiLfH6fpeTtneaEvsxMmBwKX5gDB5TGXkVRFDBliif0OZmUQ1zsj6HPxbHSR0RERARs26ZCSoqASpUMaNXK9ucXyxM87V3pyzu5U2HBp/Lxya3QAUBEhOVDXGTyxM/79xXGQFaYceM80by5D06dKnqx8oCd3r218PMTceqUEn/9Jd3GIS72x9Dn4uSePq2ePX1ERET0+JK3dj7zjNaisGQuub/O3sc2yNUwS7Z2yuS+PsC6IS4yX1+galUp+BU1zCUrC9i+XYWsLAEzZxaeMDMygA0bpO/P+PFavP++dBbE9OkeiI9npa80MPS5OJVC+gOkNdh+GwMRERGRK0hIkMIHYJ+tnQAQFlY6xzZYOrkzL7mvD7B+iIvMnC2eZ88qkJ0trXfjRhVu3So4EG/dqkJqqoDKlQ1o2VKP0aO1iIjQIyFBwJdfejD0lQKGPhenUeZU+ji9k4iIiB5T69apodUKqF9fb3UfW3HkQS72rvTFxFh2Rl9etqr0AeZN8Dx+PPc+g0HA3LkFV/tWrTKtwqpUwFdfSdW+P/9UY/du6X5LehjJMgx9Lo49fURERPS4k0PFs8/a73pIHuRSej19lofXKlVE9OqlRZs2OtSvX9JKX/ETPOXQ16GDVF396y81kpJMHxMfX3AVtk0bPZ55RgtRFHDypPQ6rPTZD0Ofi9PIoY89fURERPQYunNHwOHDKgiCiGeesV+7izzIJTlZQGqq3T4N7t2z/LgGmSAAf/6Zib//zoBaXbJ1yNs7L11SGKdsPurYMSmsvflmNiIi9EhLE/Dnn6bVvnXr1NDpBDRsqDcOiJFNm5YFH5/cr5Ohz34Y+lxcbqWPPX1ERETkHr76SoPnn/dCVlbxj129WroWat9eb9WWSHP5+gJ+fvbv6yvJ9k5bqlJFhLe3iMxMATdu5N/SGhUlICZGAaVSRNOmeowbJxUg5s1TQ5un4CoP2Bk0KH8VNiRExDvv5H6TGfrsh6HPxbGnj4iIiNxJVhYwc6YG27apcPRo8Sd1HzwoPaZvX/u/AZ57bIN9+vpEMW+lzz69ieZSKmHsjyyor0/e2lm/vgE+PsAzz+hQrpwBMTEK/P23FPRu3RJw9GjRVdgxY7Ro2lQPf38RderY/qgNkjD0uTj29BEREZE7uXhRAa1WCj7mHA4uP6akg0vMYe8D2pOSgIwMeXqn46teRU3wlEOfPDzGwwN4+WXpenTWLA1EMbcK26GDvtDtqmo1sG5dOs6cSUVQkM2/BMrB0Oficnv6GPqIiIjI9Z0+nVtVKmpyJCCd7xYbK13O2mtqZ17yBE97DXORt3YGBorw8rLLp7BIUcNc5H6+vBNDX3xRCy8vEWfOKHHwoNK4tbO4ATseHoC3t61WTQVh6HNxKiUrfUREROQ+zpzJvTwtrtInHxxetaoBvr52XRaA3EqfvY5tKMkZffZQ2LENmZnSGX2AaegrW1bEkCHSNel773ngyhUlPD3FUtl6S0Vj6HNxGgV7+oiIiMh95K30FTU5EsgNfRERpdMLlntWn30uoUsyudMe5N/X27cVSEnJvf30aSW0WgHlyhlQpYrpWuWBLleuSN/HXr108PMrnfVS4Rj6XJxKIZXNWekjIiIiV5eVlRvkFIrCJ0fK5EqgXJGyN/tX+uTJnc5R6QsKyl2L/H0BgGPHcqt8wiO/FTVqiOjZM7eyV9DUTip9DH0uTp7emc2ePiIiInJxkZHSEJfAQBGNGxc+OVIm31daoS8szL5HNuQezO4clT6g4C2euUNcCv59nzBBqvYFBxvQuTMncjoDhj4Xp84Z5KJjpY+IiIhcnLy1s1EjPerXL3xyJADo9VJIBGB8rL3JVa+UFMFku6Ot3LvnHGf05fXoBE9RzA19LVoU/Pvepo0eS5akY+XKDGg0BT6EShlDn4uTQ182e/qIiIjIxclDXBo31hc5ORIAbtwQkJkpwNtbzNdXZi++vkBAgPS5Hp3geeqUAqtXqyCWYClyT5+zbO8Ecit98vbOO3cE3L+vgEolonHjwsN21676UqvAUvFUjl4AlYyx0qfnVCQiIiJybWfOyJU+A8qVk9JTYds7L16Ubq9b1wBl8We420zFigYkJSkRHS2gbl3pAPLp0z2wdq10TRYSko62bS2vPOr1wK1bzlfpi4jI3d4pirlHNTRsaHCKYyXIPAx9Lk4t9/Sx0kdEREQuLDvb9KD1wEAp+MiTIx+dAHn+fOlO7pRVrCji4kVpa+m+fSrMnatGdnbuNJNTpxRWhb7jx5VIShIQECAag5YzqFnTALVaREqKgLt3hXyHspNr4PZOF6fOmd7Jnj4iIiJyZZcuKZCdLYWeqlXFQidHykp7cqdMPrZh2jRP/PyzBtnZAjp00BkPIC/uQPnCbNsmPa9rVx1UTlSW0WiAWrVyt9oy9Lkmhj4Xp1ZweicRERG5vrxDXORjAAo7HDzvbaUd+uQJngBQu7Yef/0lDSzp10+Xsy7rLq+3bpWSXo8ezteyI/8enzihxLlz0tdX2BAXck5O9D4CWYPTO4mIiMgdnD4tb+3MDXH16umxY4cqX5BKTZW2fQKlv71z0CAtzpxRoGNHPUaO1BqrcvKUy8uXFdBqAbXa/Ne8dUtAZKQSSqWIzp2dMfTpAaixbJkaer2AkBCDSfgl58fQ5+LY00dERETuQB7iknciZGETPOXtnqGhBpQpU0oLzFGliogFCzLz3V65sghfXxGpqQKuX1egTh3zK5Dbt0uX5C1b6hEUZLOl2oz8fZAPjy/oUHZybtze6eLY00dERESuTqs1HeIikweaXLyoNDkKQd7a6UwDTxQKaZIoYPkWzy1bnHdrJ5B/Cy37+VwPQ5+LY08fERERubrISAWysgT4+4uoVi033T06OVKWO8TFucLHoweZmyM1FTh4UAqxPXo419cjq1BBRJkyucGPoc/1MPS5OPb0ERERkas7e1a6JG3Y0HTb4KOTI2WOmtxZnKIGzxRmzx4VsrMFVK1qQM2azvX1yAQh92vTaESTvktyDQx9Lk6tlEIfe/qIiIjIVeVO7swfJh4NUqKYezC784Y+8y+x807tdOY+Oflra9jQAE9PBy+GLMbQ5+JyK33OuQeciIiIqDgFDXGRPbplMipKQHKyALVadLrKmLzWqCgFkpKKf7zBkHs+n7P288n699fC21vE8OHcXeaKOL3TxeX29LHSR0RERK5HpwPOn5cCXcGhz7R6Jv+3Zk0DNJpSWqSZ/P2BSpUMuHtXgYsXlWjduujet1OnFIiLU8DXVyz2sY7WooUBN2+mOnoZZCVW+lycvL2TPX1ERETkii5dUiAzU4Cvr+kQF5kc+q5dUyAjw3GHsptLXpccZIsib+3s3FnndAGW3AtDn4uTt3dmM/QRERGRCzpzJveoBkUBV6by5EiDQcCVKwqnHeIis2SCZ95+PiJ7YuhzcZzeSURERK6sqCEugOnkyAsXFMaD2evXd87tkPJa5WEzhYmOFnDunBKCIKJrV+f8Wsh9MPS5OLWSPX1ERETkuuTQV1A/n0wOUv/+q8TVq85e6ZNDnwKGIpYoV/maNTMgODj/tlYiW2Loc3FqhfQXBqd3EhERkavR6XK3QRZ19ltEhHTfhg0q6PUCgoJEVKjgnEGpenUDPDxEpKUJuH278DMYtm2TruF69uQ1HNkfQ5+L08jTO3lOHxEREbmYy5cVyMgQ4OMjokaNwkOf3CcXF6cwfuysZ9qpVEDt2kUf0p6eDuzbJ93XvTtDH9mf04W+yEige3fAxwcICQHeew/INiPPPHwIjBsHhIdLz23QAJg1y/7rdTQVe/qIiIjIRclDXBo2LHiIi6xOHQMEIbey56xbO2XFHdK+b58SmZkCKlUyGKuY5N4i4yLRfWF3+MzwQcg3IXhv23tmtWc9TH+IcRvGIfy7cPjM8EGDXxpg1nHLQ45TndOXkAB06QLUqgWsXg1ERQFvvSW9G/LTT0U/d/BgKTDOmCEFv02bgPHjAaUSePXV0lm/I2hyjmzI1jP0ERERkWuRK2ENGxYdfLy9gerVRVy7JpX3nD/06QGojUNnHrVqlXT91r27zmkrlmQ7CRkJ6PJHF9QqWwurh6xGVEoU3tryFtK16fipT9EhZ/CKwYiMi8SMrjMQHhCOTVc2YfzG8VAKSrzazPyQ41Shb9YsIDkZWLMGKFNGuk2nAyZMAD74AKhYseDn3bsH7NoFzJ8PjBol3dalC3DsGLB0qXuHPlb6iIiIyFVFRUmJp0qV4kNcvXp6XLuWu73TmeVW+vJv77xzR8D69dIl+MiRvH57HMw6PgvJWclYM3QNynhJIUdn0GHCxgn4oMMHqOhXcMi5l3oPu27uwvz+8zGqySgAQJdqXXAs+hiWnl9qUehzqu2dmzcD3brlBj4AGDIEMBiArVsLf542589LQIDp7QEBgOicPb42w54+IiIiclXR0dKlaMWKxV+wydsgBUFEnTrOXumT1nf9uoD0dNP75s7VQK8X0KGDDg0aOPfXQbax+epmdKvezRj4AGBI/SEwiAZsvVZ4yNHm7OQL8DANOQEeARAtDDlOFfoiI4G6dU1vCwwEQkOl+wpTuTLQo4e0tfPCBSAlBVi+XAqKr71m1yU7XG6lT2fxN5+IiIjIkaKjpUpfxYrFh5+GDaXqXo0aBnh723VZJVaunIjgYANEUcClS7mX2ykpwKJF0rXbhAl8w/5xERkXibrBpiEn0DMQoX6hiIwrPORUDqiMHjV6YMb+Gbjw4AJSslKw/PxybL22Fa+1sCzkONX2zoQEKeQ9KigIiI8v+rmrVwNDhwL160sfK5XAzJnAoEGFP6dr166F3rdlyxYolUp4eDj+t0ihkP5CVKuVUCpNc7qv6JX7OLVo7PEjIiIicmZ6PVCxooCQEKB2bQU8PIquRfTtC3z/vRZNm+qd4vqsOAMGiDhxAnj4UA0PDymw/v23ErVqCahSxYBevQQoFM7/dVCu6OhojBw5stD7d+zYUeDtCZkJCPQMzHd7kGcQ4jOKDjmrh6zG0JVDUf8XKeQoBSVm9p6JQfWKCDkFcIufNFEEXnoJuHIFWLxYqgxu2wZMniwFxuees/61/f29in9QKfH19cx3m9Iz950xLx8VfDTOs14iIiKiohw/Lv+fedcvb7wBAK7xBvfcufL/aYy3vfKK9EvabMdrNiqaKIp46e+XcCX+ChY/sxihfqHYdm0bJm+ZjCCvIDzXwPyQ41ShLygISErKf3tCgmmf36M2bgRWrADOnAEaNpRu69QJuH8fePvtwkNfYWlcJooikpMzzFu8HSkUAnx9PZGamgmDwXQLp1af28gcl5gEvadT7dglIiIiKtC5cwImTvRESIgBS5dmOXo5NvfPP0p89ZUGTzyhx7ffZmPnTgU++8wDQUEili7NhIeHo1dIlvDz80TFihWLzQ8FCfIMQlJm/pCTkJlg0uf3qI1XNmLFhRU4M+4MGlaQQk6nqp1wP+0+3t76tuuGvrp18/fuJSUBMTH5e/3yunBB2s7ZoIHp7U2bAvPmSUc+WLv3OyvL8QdmqlRSkNNq9dDpTPe8523jS8vIhLfg+PUSERERFefCBRVOngRatTI4xfWWrQUHG3DypAa3bgmYMUOHzz7zxsmTwDvvZAPQIcv9cq5b8/Oz/rl1g+si8qFpyEnKTEJMSky+Xr+8Ljy4AKWgRIPypiGnaWhTzDs5D+nadHirzQs5TlUW6t0b2L4dSEzMvW3FCkChkAa1FKZKFWlf+JkzprefOAGUL2994HMFgiBAnTPMRcsJnkREROQi5OMazJnc6Ypq1TJAqRQRH6/AunUqnDyphIeHiJde4jENj5veNXtj+/XtSMxMNN624sIKKAQFetQoPORUCagCvajHmVjTkHMi+gTK+5Q3O/ABThb6xo2TUvSAAdLkzfnzgXfflW7Pe0Zf165AzZq5H/fpIx3I/uyzwKJFwI4dwPvvAwsWAJMmlfZXUfpyQx//EiEiIiLXYMlxDa7I01OaNAoAH34o7eUcPFiLcuXc8+ulwo1rPg5+Gj8MWDoAW69txfyT8/Hutncxrvk4kzP6uv7ZFTV/zA05fWr1QXhAOJ5d8SwWnVmEHdd34P1t72PB6QWY1NKykONU2zuDgqTANmmSFPz8/KRm1+nTTR+n10uHtsv8/KTnffihFPYSE4Fq1YBvvwUmTizNr8Ax1EoNoEs3nuVBRERE5Ozk4xrCwtz3rLp69Qy4fFmJ+/elgDt2LK/VHkdBXkHY8cIOTNo8CQOWDoCfhx9eafoKpnc1DTl6gx46Q27I8fPww44XduDDnR/i/e3vIzEzEdUCq+HbHt9iYkvLQo5ThT4AiIiQtngWZffu/LfVrAksW2aXJTk9dc64X1b6iIiIyFXIlb7QUPetfNWrZ8DatdL/d+2qc/pD5cl+IspFYPsLRYec3aN257utZpmaWPZsyUOOU23vJOuoFdIoYPb0ERERkat4PCp9uVPWx4/ndRo5jtNV+shy7OkjIiIiV6LVArGx7j3IBQBattQjONiAevUM6NBBX/wTiOyEoc8NqJU5oY89fUREROQC7t0TIIoC1GoRwcHuG/oCA4GzZ9MgioAgOHo19Dhj6HMDrPQRERGRK8nbz6dw82YjpdLRKyBiT59bYE8fERERuRK5n69iRfft5yNyJgx9biB3eqeumEcSEREROZ67H8xO5GwY+tyAWilV+rL1rPQRERGR84uJkS5B3XlyJ5EzYehzA5qc7Z069vQRERGRC2Clj6h0MfS5AVXO9k5W+oiIiMgVyJU+hj6i0sHQ5wY0SrnSx54+IiIicn5ypY/bO4lKB0OfG1DlHNmQzemdRERE5OSys4EHD6TQFxrKSh9RaWDocwOanNDHnj4iIiJydvLB7B4e7n0wO5EzYehzA8ZKn56hj4iIiJxb3oPZBcHBiyF6TDD0uYHcnj6GPiIiInJuPJidqPQx9LkB9vQRERGRq4iK4uROotLG0OcG2NNHREREroKVPqLSx9DnBtjTR0RERK4iN/Sx0kdUWhj63AB7+oiIiMhVyINceEYfUelh6HMDKoUKAHv6iIiIyPnJB7Oz0kdUehj63IBGIVf6dA5eCREREVHhsrKAuDgOciEqbQx9bkCllHv6WOkjIiIi5xUTI1X5PD1FlCnD0EdUWhj63IA8vVPLnj4iIiJyYjExPJidyBEY+tyAiqGPiIiIXIDcz8chLkSli6HPDcjTO7Xc3klEREROTJ7cyX4+otLF0OcG1Kz0ERERkQvgwexEjsHQ5wbk0JfN0EdEREROjAezEzkGQ58bkEMfD2cnIiIiZ5a7vZOVPqLSxNDnBtQ5PX08soGIiIicGSt9RI7B0OcG1AoVAB7OTkRERM4rMzP3YHZO7yQqXQx9bkCtYKWPiIiInJt8MLuXl4jAQMeuhehxw9DnBtjTR0RERM4u73ENPJidqHQx9LkBtVKe3slKHxERETkn+WB2DnEhKn0MfW4gt9LHnj4iIiJyTjExPJidyFEY+twAe/qIiIjI2cmVPg5xISp9DH1ugD19RERE5OxY6SNyHIY+N5Db08fQR0RERM6JPX1EjsPQ5wZY6SMiIiJnx4PZiRyHoc8NqJXs6SMiIiLnlZEBxMfL2ztZ6SMqbQx9bkCtUAEARIjQG/QOXg0RERGRqSNHlAAAb28RAQEOXgzRY4ihzw1ocqZ3Ajyrj4iIiJzLpUsKvPqqFwCgb18dD2YncgCGPjegyunpA9jXR0RERM4jOlrAc895ISlJQPPmevz3v5mOXhLRY4mhzw2o84S+bD1DHxERETleUhIwbJgXoqIUqFVLj0WL0uHt7ehVET2eGPrcgFKhhEKQvpWs9BEREZGjZWYCL7zghYsXlahQwYClSzNQpoyjV0X0+GLocxNyXx97+oiIiMiR9Hrgtdc8ceiQCn5+IpYsyUDlyjymgciRGPrchNzXp2Wlj4iIiBzoP//RYP16NdRqEQsWZKBBAx7RQORoDH1uQqPMCX3s6SMiIiIHWr5cuib5738z0aEDj5IicgYMfW6ClT4iIiJytNRUICpKurzs3Vvn4NUQkYyhz03IPX1aPXv6iIiIyDp79yrx5pseiI217jC9K1ekS8vy5Q0ICrLlyoioJFSOXgDZhkohfSu1Br6rRkRERJYTReDddz1x44YCJ08qsW5dOvz9LXuNS5ek0Fe7Nvv4iJwJK31uQqPMqfRxeicRERFZ4eRJBW7ckC4NL1xQYtQoL2RlWfYaly8z9BE5I4Y+N8GePiIiIiqJlSula4nmzfXw9RWxf78KkyZ5wmBBfrtyRQmAoY/I2TD0uQmNHPrY00dEREQW0mqBtWulVpG33srC/PkZUKtFrF2rxiefeJj9OtzeSeScGPrcRG6ljz19REREZJm9e5WIi1MgONiAjh316NhRjx9/zAQAzJ6twS+/qIt9jYwM4NYtaQAMQx+Rc2HocxPs6SMiIiJrrVghhbr+/XVQ5+S7QYN0+OQTKfhNm+aJVauKnv937ZoCoiggKEhEuXKiXddLRJZh6HMT7OkjIiIia6SmAv/8IwW6Z581vY6YMEGLsWOlN5TfecezyMEu8hCXWrX0EKw78YGI7IShz03k9vQx9BEREZH5Nm9WIT1dQLVqBjzxhOm2TEEAPv00C2XLGpCWJuDs2cIvHeXQV6cOt3YSORuGPjehUrLSR0RERJaTp3YOGqQtsEKnUADNmklB7vhxZaGvw+MaiJwXQ5+b0CjY00dERESWuX9fwJ49UpB7dGtnXi1a6AGYF/pq1WLoI3I2DH1uQqWQ9uKz0kdERETmWrtWBYNBQLNmelSvXvjwlebNpdB37FjBoU+rBa5f5/ZOImfF0Ocm5Omd2ezpIyIiIjOtWpW7tbMoTZrooVSKiIlRICoq/x7QGzcU0OkE+PiIqFiRkzuJnA1Dn5tQ5wxy0bHSR0RERGa4elXAyZNKKJUi+vcv+pxfHx+gXr3C+/ryHsrOyZ1Ezoehz03IoS+bPX1ERERkBnmAS6dOerPO1Suqr+/KFQ5xIXJmDH1uwljp0xf9Th0RERGRKOZu7SxqgEteRfX1cXInkXNj6HMTarmnj5U+IiIiKsaJEwrcuqWAt7eIXr3Me8NYDn1nzyqQmWl6X+72Tr1N10lEtsHQ5ybUOdM72dNHRERExdm+Xbpu6NFDBx8f855TpYqI4GADtFoBp0/nVvv0euDaNVb6iJwZQ5+bUCs4vZOIiIjMc+CAFNqefNL8ypwg5O3ry72EvH1bQGamAE9PEeHhnNxJ5IwY+twEp3cSERGROdLSgH//lUJfu3aWzQJo3lyq5OXt65P7+WrUMEBZ+NntRORADH1ugj19REREZI6jR5XQagWEhRlQtapllbm8EzzFnKdeviwlPR7KTuS8GPrcBHv6iIiIyBzy1s727fUWn6nXuLEeKpWI+/cVuHNHejIndxI5P4Y+N8GePiIiIjLH/v3SG8WWbu0EAC8voGFD00Pa5TP6atVi6CNyVgx9boI9fURERFSc5GTg1Cnp8q99e+uOV8h7Xp8o5h7XwO2dRM6Loc9NqJVS6GNPHxERERXm8GElDAYBVasaUKmSdZM25dB3/LgS0dEC0tIEqFQiqlVj6CNyVgx9biK30mf5Vg0iIiJ6PMhbOzt0sP56QR7mcv68wnheX/XqBqjVJV8fEdmHqiRPjo2NxbFjx/Dw4UP07NkTISEh0Ov1SElJgZ+fH5Sc21tqcnv6WOkjIiKigu3fLx/VYN3WTgAICxMREmLAvXsKLF8uXUpyiAuRc7Mq9ImiiK+++gp//fUXdDodBEFA7dq1ERISgvT0dHTp0gWvv/46Ro0aZePlUmHk7Z3s6SMiIqKCJCRI1TmgZKFPEKQtnhs2KLB1K0MfkSuwanvnvHnz8Oeff2L06NGYP38+RDF3T7ifnx969OiBrVu32myRVDx5e2c2Qx8REREV4OBBFURRQO3aelSoYF0/n0zu69PppGMbGPqInJtVoW/FihUYMGAA3nrrLdStWzff/XXq1MHNmzdLujayAKd3EhERUVFssbVTJvf1yRj6iJybVaEvJiYGTZs2LfR+Ly8vpKamWr0ospxayZ4+IiIiKlzeQ9lLqlEjAzQaqVqoUIioUYOhj8iZWRX6ypYti5iYmELvP3/+PEJDQ61eFFlOrZD21HN6JxERET3q/n0BkZFS6GvbtuShz8Mj95D28HARXl4lfkkisiOrQl/37t2xdOlS3Llzx3ibIEh7uvfv3481a9agV69etlkhmUUjT+/kOX1ERET0iIMHpcBXv74eZcuWrJ9PJvf1cWsnkfOzanrn66+/jiNHjqB///5o3rw5BEHA3Llz8cMPP+DUqVOIiIjAuHHjbL1WKoKKPX1ERERUiH37bLe1UzZ6dDbOnlVgzBi+4Uzk7Kyq9Pn5+WH58uV45ZVXEBsbCw8PDxw7dgwpKSl47bXXsHjxYnixzl+qNDlHNmTrGfqIiIjI1IED0vv87dvbrg2kWjURa9dm4MknbRckicg+rD6c3dPTExMmTMCECRNsuR6yEit9REREVJDoaAHXryugUIho04YBjehxZFWlT6fTFTmdMzU1FTodB4qUJvb0ERERUUHkoxoaNzbA39/BiyEih7Aq9H3xxRd47rnnCr1/2LBh+Oqrr6xeFFkut9KngyjapkGbiIiIXJ+8tbNdO74hT/S4sir07du3Dz179iz0/p49e2Lv3r1WL4osJ/f0AYCWWzyJiIgIQEYGsGeP7Ye4EJFrsSr03b9/HxUqVCj0/vLlyyM2NtaqBUVGAt27Az4+QEgI8N57QLaZOxajooAXXwTKlQO8vICICOCvv6xahsuRK30AQx8REREBej0wbpwnoqMVCAoS0aoVQx+Ro0TGRaL7wu7wmeGDkG9C8N6295CtNy/kRCVH4cW1L6Lcf8vBa7oXIn6OwF9nLAs5Vg1yCQwMxI0bNwq9/9q1a/D19bX4dRMSgC5dgFq1gNWrpRD31ltAejrw009FPzcmBmjTBqhTB5gzB/D3B86fB7KyLF6GS5J7+gBAq88G1D4OXA0RERE5kigCU6d6YPNmNTw8RCxYkAEfXhoQOURCRgK6/NEFtcrWwuohqxGVEoW3tryFdG06fupTdMiJSYlBm9/aoE5wHcx5ag78Pfxx/sF5ZOktCzlWhb4OHTpg6dKl6NevH+rVq2dy3/nz57F8+XKrDmefNQtITgbWrAHKlJFu0+mACROADz4AKlYs/LnvvQdUrgz88w+glHYxoGtXi5fgslSK3G+l1sA9+0RERI+z77/XYMECDQRBxC+/ZHJqJ5EDzTo+C8lZyVgzdA3KeEkhR2fQYcLGCfigwweo6Fd4yHlv+3uoHFAZ/zz/D5QKKeR0rW55yLFqe+cbb7wBX19fDB48GJMmTcIPP/yAH374ARMnTsSQIUPg6+uLN954w+LX3bwZ6NYtN/ABwJAhgMEAbN1a+POSk4Hly6VwKAe+x40gCFDnbPHUcoInERHRY2vJEhW+/NIDADBjRhb69eObwUSOtPnqZnSr3s0Y+ABgSP0hMIgGbL1WeMhJzkrG8vPLMaH5BGPgs5ZVoa9ChQpYtWoVnnrqKRw6dAi//vorfv31Vxw+fBj9+vXDypUrERISYvHrRkYCdeua3hYYCISGSvcV5t9/pb4/tRro2FH6b0gI8P77gPYxam/LDX2P0RdNRERERtu3K/HWW54AgEmTsvDyy7wmIHK0yLhI1A02DTmBnoEI9QtFZFzhIeffmH+Rrc+GWqlGxwUdof5cjZBvQvD+tveh1Vv2Z9vqw9nLly+Pr7/+GqIoIj4+HgBQpkwZCIJg7UsiIUEKeY8KCgJyPkWB7t2T/vvKK8CrrwLTpgFHjwIffwwoFMCXXxb8vK5F7P/csmULlEolPDys/i2yGYVC+j1Vq5VQKgvP6WqlBtClQ1AanGLdREREZJ4HD4C1a1V45hkdypa17jUuXxbw008eaNRIQI8eWkydaoAg8HqAyFaio6MxcuTIQu/fsWNHgbcnZCYg0DMw3+1BnkGIzyg85NxLlULOK+tewatPvIppHafhaNRRfLz7YygEBb7sVkjIKUCJ/yYQBAFlrf3byUYMBum/3boB//uf9P+dOwMpKcA330jhz8vLutf297fyiXbg6+tZ5P3ysQ0e3kqnWjcREREVzd8fePttAFAX99BCNW8OHDggf6Qu0WsRkeMZRCnkdKveDf/rKYWcztU6IyU7Bd8c/AYfd/wYXmrzrvnNCn0//fQTBEHA+PHjoVAo8FNxozQhhcHXXnvNrEXIgoKApKT8tyckmPb5FfQ8QJr8mVfXrsD06cDVq0DDhvmfV1gal4miiOTkjGJWbX8KhQBfX0+kpmbCYCj84HV5e2d8UjKSPR2/biIiIiretm0KTJ8u9eBVrmzAwoWWjx4XReDZZz3w8KECP/6YiUaNCr9eICLL+fl5omLFisXmh4IEeQYhKTN/yEnITDDp8yvoeQDQpZppyOlarSum75uOq/FX0bBCASGnABaFvldffRUajcZuoa9u3fy9e0lJ0nEMj/b65fXIANF8MjMtWoaJrCzHNz+rVNKWTq1WD53OUPjjBCn0pWdlOsW6iYiIqGgpKcCECT64f1/6+ORJBW7e1CM01LLQduuWgO3bFVCpRISHax+bI6uISoufn/XPrRtcF5EPTUNOUmYSYlJi8vX65VWvXNEhJ1NnfsgxK/RFPpLEHv3YVnr3BmbMABITc3v7VqyQ+vJ69Cj8eVWqSJW87duBiRNzb9+2TdrWWVwodBfqnO2dljZ2EhERkWP8978euH9fgerVDfDyEnH+vBL79ysxeLBlb94ePy5N9mvY0GB1SwsR2Ufvmr0xY/8MJGYmGnv7VlxYAYWgQI8ahYecKoFV0LB8Q2y/vh0TW+aGnG3Xt8FL5VVsKMzL4umd2dnZ2LFjh12C37hxUooeMEA6omH+fODdd6Xb857R17UrULOm6XOnTwfWrQMmT5bC3owZUj/fW2/hsTmMlNM7iYiIXEdkpAJz50r/ds+YkYlOnaSz9A4csHw0uxz6mjfneXxEzmZc83Hw0/hhwNIB2HptK+afnI93t72Lcc3HmZzR1/XPrqj5o2nImd5lOtZdWofJ/0zGtmvbMGPfDHxz8Bu81eYt+GjMDzkWhz61Wo033ngDJ0+etPSpxQoKAnbsAFQqKfhNmSJN5Pz2W9PH6fXSoe159esHLFkiVfueegqYMwf49FPg889tvkynpVZoAPCcPiIiImcnisAHH3hArxfQu7cWXbro0aGDdHGzf7/lc/bk0NeiBUMfkbMJ8grCjhd2QKVQYcDSAZiyYwpeafoKvu1pGnL0Bj10BtOQ069OPywZtATbr2/HU0uewpwTc/Bpp0/xeWfLQo4giqLFnb5PPfUU+vTpgwkTJlj6VJciiiLi4lIdvQyoVAoEBfkgISGtyJ6+Xis749/7J7CwzzL0rNq7FFdIRERElli7VoUxY7zg6Sli//40hIeLSE0Fatf2hU4n4NixVFSpYt4lWloaULOmL/R6Af/+m4pKlTjEhcjWgoN9S3Q0naNZdTj72LFj8ddff+H69eu2Xg+VgFopVfqy9az0EREROavUVOCTT6RpnW+8kY3wcCmk+foCTZtKb+5assXz9Gkl9HoBISEGhIUx8BFRflad03f69GkEBgaiX79+aNmyJcLCwuDpmf8MuY8++qjECyTzyT19Ovb0EREROa1vv9UgJkaBKlUMeO010zdq27fX4dgxJfbvV2H4cPOGueTt53PhQgQR2ZFVoW/RokXG/z906FCBjxEEgaGvlMmhj5U+IiIi5xQVJWD2bGlnzvTpmXj0PfP27fX47jtg/34lRBFmhbjjx6WNW+znI6LCWBX67HVkA5VMbqWPZ/QRERE5o1Wr1NBqBbRurUOPHvlDWvPmemg0Iu7dU+D6dQE1ahS9XVMUObmTiIpncU+fXq/HgwcPkMVTP52OsaeP0zuJiIicjigCK1dK77cPHVrwG7ReXrkVu337in9v/sYNAXFxCmg0Iho1KnzYGxE93swOfaIo4ttvv0WLFi3w5JNPolmzZnjttdeQmJhox+WRJdQK6R8H9vQRERE5n/PnFYiMVMLDQ8RTTxX+b3W7duaf15f3UHYPD9usk4jcj9mhb/Xq1ZgzZw78/f3Ro0cP1K5dGzt27MDUqVPtuT6ygHxOX7aeoY+IiMjZrFwptWH06KFDQEDhj2vfPjf0FXewFs/nIyJzmN3Tt2TJEtSrVw+LFy82Tur84osvsHjxYsTHx6NMmTJ2WySZh9M7iYiInJNeD6xeLV12DRpUdO/9E0/o4e0tIi5OgchIBSIiCt+2ydBHROYwu9J3584d9O/f3+RohuHDh8NgMODWrVt2WRxZhj19REREzungQSXu3VMgMFBE165Fhz6NBmjZsvgtnqmpwIUL0qUch7gQUVHMDn1JSUn5qnlBQUEAwKEuToI9fURERM5J3trZr5/WrN47eYvnvn2Fh76TJ5UwGARUqmRAaCgPZSeiwlk0vVPgiZ9OjT19REREzicjA9iwQXpjdvBg845Vat9eetyhQyroCyni8agGIjKXRef0/e9//8Ps2bONHxsM0h7zjz76CF5eXiaPFQQB69ats8ESyVzs6SMiInI+27apkJIiVeTkbZvFadTIAF9fEYmJAs6fVxR4HANDHxGZy+zQ16JFiwJv5wAX56FWSqGPPX1ERETOQz6bb9AgLRRm7rFSqYA2bfTYtk2F/fuV+UIfD2UnIkuYHfoWLlxoz3WQDeRW+szbOkJERET2FR8P7NghXW49+6xl/z63b6/LCX0qTJhguovn2jUBCQkCPD1FNGjAQ9mJqGgW9fSRczNO79Sz0kdEROQM1q1TQ6sV0KCBHnXqWBbO5GEuhw4pkZJiep9c5WvcWA+NxiZLJSI3xtDnRuTpnVr29BERETmFVavkKp/l/zbXr29AcLABaWkCWrf2wZ9/qqHLKRYeOyZv7WSVj4iKx9DnRuTpnQx9REREjnf7toAjR1QQBBEDB1reeqFQAL//nonq1Q148ECBd97xRJcu3ti5U8l+PiKyCEOfG5F7+hj6iIiIHE8+m699e73V5+i1bq3H3r1pmD49E0FBIiIjlXjuOW9cvMjQR0TmY+hzI5qcnj4te/qIiIgcJjZWwFtveeA//5H+XbZma2deGg3w6qtaHDmSivHjs6FWSwGyShUDKlTgoexEVDyLzukj56Yy9vRxeicREVFpS08Hfv1Vg5kzNUhPFwAAzzyjxaBBtvl3OTAQ+PTTLLz0UjYWLVKjc2dW+YjIPAx9bkRj7OljpY+IiKi0iCKwfLkKM2Z4ICZG2kTVrJken36aiZYtbT9opWpVER99xH/rich8ZoW+Ll26QBAEi15YEARs377dqkWRdVRyT5+ePX1ERESlZc0aFSZN8gIAhIcb8NFHWejfXwcLL52IiOzGrNDXsmXLfKHv3LlzuHLlCmrWrIlq1aoBAG7cuIGrV6+iVq1aaNCgge1XS0XSKDnIhYiIqLRt2CBdTg0bpsXXX2fC09PBCyIieoRZoe+rr74y+Xj79u3Yvn075s+fjzZt2pjcd+DAAUyePBlvvPGG7VZJZlFxeicREVGpMhiAAweky6kRI7IZ+IjIKVk1vfOHH37AiBEj8gU+AGjXrh2ef/55/PDDDyVeHFnG2NPH6Z1ERESl4sIFBRISBHh7i2jShAelE5Fzsir03bp1C4GBgYXeHxgYiNu3b1u7JrISK31ERESl68AB6by8Nm30UKsdvBgiokJYFfrCw8OxevVqpKWl5bsvNTUVq1atQuXKlUu8OLIMe/qIiIhK1/790tbOdu14XBIROS+rjmyYPHkyXn/9dfTu3RsDBw5ElSpVAEgVwDVr1uDhw4fc3ukArPQRERGVHp0OOHhQqvR16MAz84jIeVkV+rp164Y5c+bgm2++wezZs03ui4iIwPTp09GhQwebLJDMx54+IiKi0nP2rAIpKQICAkQ0aMB+PiJyXlYfzt6+fXu0b98eDx48QHR0NACgYsWKKFeunM0WR5ZRKaVvp9bALSZERET2Jm/tbNNGB6XSwYshIiqC1aFPVq5cOQY9J2Gs9BlY6SMiIrK3/fulpNe+Pbd2EpFzs2qQCwBER0fj448/Rs+ePdGyZUscO3YMABAfH48vvvgCFy5csNkiyTxq9vQRERGVCq0WOHJECn3t2jH0EZFzsyr0Xb16FQMHDsTmzZtRqVIlpKSkQKeTthSWKVMGJ06cwKJFi2y6UCqeOmd6p0E0QG/gP0BERET2cvKkAunpAsqWNSAigv18ROTcrNre+d///hd+fn5Yvnw5AKBt27Ym93fs2BGbN28u+erIInKlD5CqfUoFGwyIiIjsQe7na9tWD4XV+6aIiEqHVX9NHTt2DMOGDUOZMmUgCEK++ytWrIjY2NgSL44so87p6QPY10dERGRP8qHs3NpJRK7AqtAniiI8PT0LvT8+Ph4ajabQ+8k+Hq30ERERke1lZgJHj/J8PiJyHVaFvnr16mHPnj0F3qfT6bBx40Y0bty4RAsjyykVSigE6Vuq1TP0ERER2cOJE0pkZQkoX96AmjXZz0dEzs+q0DdmzBjs27cPn3zyCa5cuQIAePjwIQ4ePIjRo0fj+vXrGDNmjE0XSubhBE8iIiL72rcv96iGArpciIicjlWDXDp27Igvv/wSM2bMMA5zeffddyGKInx9ffH111+jRYsWNl0omUet0CBLn4Vs9vQRERHZhdzPx/P5iMhVWH04+4ABA9CjRw8cPHgQN2/ehMFgQHh4ONq3bw9fX19brpEsoFZI31KdXufglRAREbmftDTg33/lIS78t5aIXIPVoQ8AvL290a1bN1uthWxArZQG6LDSR0REZHtHjyqh1QqoVMmAqlVFRy+HiMgsVvX0de3aFUOHDsX169cLvH/79u3o2rVriRZG1pF7+nTs6SMiIrK5vEc1sJ+PiFyFVaEvKioK58+fx+DBg7F9+/Z896enpyM6OrrEiyPLyaEvm9M7iYiIbE4+lL19e27tJCLXYVXoA4CpU6eiRYsWmDRpEr7//nsbLolKgpU+IiIi+4iLE3D6tHTpxCEuRORKrA59/v7+mDVrFl577TXMmTMHY8aMQUpKii3XRlZgTx8REZF9LFighl4voEkTPcLC2M9HRK7D6tAnmzhxImbNmoXTp0/j2WefNZ7bR47BSh8REZHtZWYCv/8u/Rs7bhzfWCUi11Li0AcATz75JFauXAkvLy8MGTIEO3bssMXLkhXY00dERGR7q1apERenQFiYAf36sZ+PiFyLTUIfAFSuXBnLli1Djx49sGXLFlu9LFlIrWSlj4iIyJZEEZg1S/r39ZVXsqFWO3hBREQWsuqcvj///BM1atTId7uHhwe+/vpr9O7dGwkJCSVeHFlOrWBPHxERkS3t2qXEpUtK+PqKGDmSb6oSkeuxKvS1bNmyyPs7depkzcuSDagV0rdUZ+DWEyIiIlv49VfpDdXnn9fC39/BiyEisoJZoW/t2rUAgP79+0MQBOPHxRkwYICVyyJrGad36lnpIyIiKqnz5xXYs0cFhULEq6/y31Yick1mhb4pU6ZAEAT06dMHGo0GU6ZMKfY5giAw9DmAPMhFy54+IiKiEps9W3oztV8/HcLDeUwDEbkms0KfPI1To9GYfEzOJzf08d1IIiKikoiNFbBqlXSpxGMaiMiVmRX6wsLCivyYnEdu6GNPHxERUUn8/rsaWq2Ali11aNbM4OjlUCGiUu5i1ZXleKXhOHirvR29nBLZfWcnbiffwsh6oyAIgqOXQ27EqkEu5Lw0OT19Wvb0ERERWS0tDViwQPo3ddw4tkw4s08Ofoh119YgW5+Nd1oU34LkrO6k3MbITUORpc9CqE8oulft5eglkRsxK/S98MILFr+wIAj4448/LH4elYwqZ3one/qIiIist3y5GgkJAqpUMaB3b+6ecVYG0YC9d3cBAHbe3u7Soe/zQx8jS58FAPj19E8MfWRTZh3OLoqixb8MBm6DcARNzjl95+LOIE2b5uDVEBERuaZ166Q3UV95JRtKpYMXYwfn4s6i6/IO+OrI5xBF1x1Qcy7uDBKzEgEA/94/jsRM1zwn+mjMEay9uhoCBCgFJfZH7cXZB6cdvSxyI2ZV+hYuXGjvdZCN1C1TDwCw5eZmtF3cDNPafoEBNQdxXzgREZEFLl2S3hdv00bv4JXYXro2HWO2jsLVxCs4G3ca99Lu4X+dfoRS4Xrpdt/dvcb/N4gG7Ivai341+jtwRZYziAb834H3AQDPR7yAdF06Vl9ZgV9OzcSv3ec5eHXkLsyq9JHrGB4xEgt6LUa4XxXEpEVj7LbR6L+2N87GnXH00oiIiFxCXJyAuDjpEqlGDffbufTJwQ9xNfEKgjyCoBAUWBy5EGO2vWTcWuhK9kftAQD4awIAALvvuN6E+ZWXl+Hk/X/hq/bDlFb/hwlNJgEA/r62GtGpUTb7POfjznEX2GOsxKEvNTUV9+7dQ3R0dL5fVPoEQUCf6k9h37CjmNLyI3ipvHA45iC6r3gS0w5+5NJbOIiIiErDlSvS5VF4uAE+Pg5ejI39c2MT/jj/GwBgbs8/8FvPhdAoNFh/bS1e2PScS4UCrV6LQ9EHAQCvP/EmAGn6pStd66Rp0zD98KcAgMnN3kZ57/JoVK4J2lXsAJ1Bh7lnZtnk83x/4ht0Xt4W7+150yavR67H6tC3ePFi9OjRAy1atEDnzp3RtWvXfL/IcbxUXnir+Xs4OOwEBtR8BgbRgF9O/YhT9/919NKIiIic2uXL0uVRrVruVeWLTbuHN3e9BgCY0OR1PFmpE/pW74e/+q6At8obu+7swND1A5GU0yPn7P69fwLpujSU9SyL0Q3HQKPQ4E7KbVxLvOropZnt55M/ICYtGuF+VTCm0QTj7eObTAQALLywAKnZKQU+N1OXiWP3jkBvKHoL8torqzDjyGcAXC8Uk+1YFfqWLFmCzz77DOHh4Zg8eTJEUcSLL76IMWPGIDg4GHXr1sX06dNtvVayQphfJczpsQCdK0sh/NSDkw5eERERkXOTQ1/t2u4T+gyiAZN2jsPDzIdoENwIU1v9n/G+jpU7Y8XTfyPAIxBH7x3GwL+fKjRoOBN5a2e7sCfhq/ZFq4ptAbjOFs/o1Cj8fOoHAMAnbT+Hp8rTeF+3Kj1RM7AWkrOT8NfFP/M992HGQ/Rb0xN9V3fH85sGFxrUj907gkk7xxk/fpBxH1Gpd237hZBLsCr0LVq0CO3bt8e8efMwZMgQAEDHjh3x5ptvYtOmTUhLS0NiYqIt10kl1LhcUwDSlCsiIiIqnDzEpU4d9xniMvfMr9h9Zyc8lZ6Y1e03eCg9TO5vEdIKa/tvQrBXOZyLO4O1V1c7aKXm258zxKV92JMAgE6VuwAAdrlI6Pvi8DRk6DLQOrQtnqpuOnxGISgwrrFU7Ztz5lfoDLnHhsSkRqP/2l44nfNG/s7b29FrVRdcTbhi8hq3km/ixc3DkKXPQq+qfdAwuDEA4N/Y4/b8sshJWRX6bt++jc6dOwMA1Go1AECrlc6F8/Pzw7PPPovFixfbaIlkCw3LNQIAnOH4XyIioiLJPX3usr3zfNw5fH7oEwDAp+1moHaZOgU+rn5wAwypMwyA879JnKHLwLF7RwAAHSpJoU/e1XQgap/TD6X5N/Y4Vl5eBgECPm/3ZYFT1gfXeQ7BXsG4k3IbG6+vAwBcT7qGfmt64nLCJVT0CcO8Hn8gzLcSriVeRa9VXbDz9nYAQFJWIp7fOBhxGXFoGNwYv3Sfh+YhLQAAJxj6HktWhT4/Pz/o9dK7X76+vvDy8sK9e/eM9/v4+CAuLs42KySbkN/dufjwPLR6HtxORERUkORkICbGfbZ3iqKI13eOR7YhGz2q9MKo+i8X+fj6ZRsAAM4/PFcay7PasXtHkG3IRkWfMFQPqAkAqFe2Psp5lUe6Lt0YCJ2VPKBlcJ3n0Lh80wIf46Xywqj6rwAAfj01ExcensfTa3rhdsotVA+ogfXPbMHTNQdiy7O70SKkFZKzkzB847P4+eSPeGXLi7iccAmhPhWxqM8y+Kp90bR8MwDAyfsnSueLJKdiVeirVasWIiMjjR83btwYS5YsQWxsLGJiYrBs2TJUrVrVVmskG6jiXxX+mgBkG7JxKSGy+CcQERE9huQqX0iIAQEBDl6MDSRkxeNsnLTL59vOPxV7bm+9nNB34eF5px74se+u1M/XvtKTxq9JISiMWzx3397psLUVJ0ufhS03NwMAXqw/usjHvtTgVXgoPfDv/RPos6ob7qfHol7ZBlg3cAsq+4UDAMp7l8fq/hswvO5IGEQDPj30Efbc3QVvlQ8W9V2OUN+KAIBmFaRK35kHp0y2i9LjwarQ9/TTT+PKlSvIzs4GAEyaNAnXrl1Dp06d0KVLF9y4cQOTJ0+25TqphARBQMNgaYvnWW7xJCIiKpC7Te68nngNABDqUxHlvcsX+/haQbWhVqiRkp2MOym37b08q8lDXOR+Ppkr9PXtubMTqdoUhPpUNAaxwpTzLmfccpuuS0PzCi2xtv/GfN9LD6UHvuv8E6a3/xpKQQmFoMCcHr8br/0AoEZgTfhrApCuS8fF+Au2/8LIqamsedKgQYMwaNAg48fNmjXDxo0bsXPnTiiVSrRr1w7VqlWz2SLJNhqWa4wD0ftwJu4UhmGEo5dDRETkdC5dUgIA6tRxj9B3I+k6AKB6QA2zHq9RalArqA4uPDyHCw/PI9y/ij2XZ5XkrCSczDmC6tHQ1zEn9J2NO40H6Q9Qzrtcqa+vOOuv/Q0AeKr601AIxddfXmv6BjZc+xstQlphVo/f4av2LfBxgiDg1Ubj0T6sI7SGbDQq18TkfoWgQJPyT2Dv3V04GXvCJBCS+yvx4eyyypUr48UXX8SIESMY+JxUo3JSX9/ZB87dnE1EROQo7jbERQ591QKqm/2c3L6+s3ZZU0kdjjkIg2hAtYDqqORX2eS+8t7l0SAnzOy563xbPLP12fjn5iYAQL8aA8x6TvWAGrg4+gYW9V1eaODLK6JsvXyBT9asgtTXxwmej58Shz6DwYCkpCQkJibm+0XORR7mci7ubLEHeRIRET2Oco9rcI/Qdz1J2t5ZLdC8Sh9g2tfnjOR+vg5hnQq8X57iuftOyUKfKIr48shn+ProdJsNwdsftQdJWYko710BLUJamf08cyqC5mhavjkA4N/7DH2PG6u2d2q1WsydOxerVq3CvXv3YDAU/BfjxYsXS7Q4sq2agbXgpfJCui4N15OuoVZQbUcviYiIyGmkpwN37khDQdxhcicA3JQrff4WVPqCcyp9cc5Z6dsXJZ3PJx/V8KhOlbtg5snvsPvOToiiWOzwmsJcTriE7058AwA4HH0Q83r+ibJeZa1bdA55a2ff6v2gVChL9FrWaJpT6bsUH4nU7BT4avxKfQ3kGFaFvo8//hhr165F48aN0a1bN/j58QfGFSgVStQv2xDHY4/ibNxphj4iIqI8rl1TQBQFlCljQHCw806utIRc6atuRaXvRtJ1pGnT4KP2scvarBGXEYcLOcdJtK3YocDHtAxtDW+VN+6nx+LCw/PGEGupwzEHjf9/IHofeq7shD/7LEW9svWtej2tXotN19cDMH9rp61V8K6ASr6VcTf1Dk49OJmvJ5Lcl1Wh759//kH//v3x1Vdf2Xo9ZGcNyzXC8dijOPPgNJ6pNdjRyyEiInIa8tZOd6nyJWTGIzErEQBQ1d/8eQvlvcujnFd5PMi4j8j4C8VOmCxNB6P2AQAiytQvdEiLh9IDbSu2x/bbW7Hrzg6rQ9+RmEMAgGdqDca/scdxM/kG+qzqhl+6zUWf6k9Z/HoHovchISsBwV7BaB3a1qo12cITFZrjbuod/Bt7nKHvMWLVBmEvLy80btzY1muhUtAouAkAHttARET0KPm4BncJfXKVL9SnIrzV3hY9V65mOVtfn7y188lKHYt8XOfwkvf1yaFvWN0R2PLsLnSo1AnpujSM+mc4vj3+H4vPMZS3dvap9jRUCqvqLjbxRIWcvr5YHtL+OLEq9PXt2xe7d++28VKoNBgneMaddupDV4mIiEqbu4U+ayZ3yuoHNwTgfH19++7uBgC0Lyb0dcoZ5nIk5iDStGkWf56olLu4k3IbSkGJZiEtEORZBsueWo1XG44DAHx19Av87/jXZr+ezqDD5hvy1s7+Fq/Hlp4onzPBk8NcHitWhb53330Xfn5+GDt2LLZu3YozZ87g/Pnz+X6R86lTJgJqhRqJWYlOfegqERFRaXO30CcfzG5N6HPGSl90ahSuJ12DQlCgTTHbI2sG1kK4XxVk6bOM0z4tceSeVOVrGNzIeEyCSqHC9A7/wRftpPamOWd+Qbo23azXOxxzEHEZcSjjWQZtK7a3eD221LBcYygFJe6lxSA6Ncqha6HSY1VtOTs7G6IoYu/evdi7d2++++VJSZze6Xw0Sg3qlqmHs3GncebBaac8dJWIiKi0ZWcDN26413ENuZU+84e4yOqXlSp9Fx6eL9EETFvafGMDAKBp+Wbw9wgo8rGCIKBn1d6Ye3YWttzchF7V+lj0uQ5HS0NcWlXMHy5fbjgWc87Owu3km1h1ZTlG1htV7Outv7YWANC72lNQK9UWrcXWfNQ+qFumHs4/PIt/Y0+gom+YQ9dDpcOq0PfBBx9g+/bt6NOnDxo3bszpnS6mUbnGOBt3GufiTuOpGk87ejlEREQOd/26Anq9AF9fESEh7tH+cDPZ+u2dtYJqQ61QIzk7CXdT76CyX7itl2exlZeXAwAG1hxk1uN7VuuTE/o2wyAaLDrrTu7naxXSJt99SoUSoxu8imkHP8S8M7MxIuLFIkOx3qDHxuvOsbVT9kSF5lLou3+c14KPCatC3/79+zFixAh88MEHtl4PlYIGwY0AAGc4zIWIiAhA7tbOOnUMcIKilk3I2zurW1Hp0yg1qBVUBxcensP5uHMOD303kq7jROwxKAQF+tcyL/S1CW0Hf00A4jIe4N/Y42ge0tKs5yVkxuNi/AUAQKvQ/KEPAIbXHYH/HJ2Oi/HncSj6ANqGFb5l89i9I7ifHosAj0C0Dyu6F7G0NKvQHAsvzMe/sezre1xY1dPn6+uLKlW4LdBVycNczsQx9BEREQG5oa9WLffY2pmQGY+ErAQAQNUA849ryCu3r++czdZlrdVXVgAAnqzUCRW8K5j1HLVSja7h3QAA/9zYZPbnOnbvCACpL7CwYyECPYPwbO3nAABzz84q8vXkrZ29qvaBRqkxex321DRnmMup+yehN+gdvBoqDVaFviFDhmDDhg3Q6/lD4orqlW0AhaDA/fRYxKbdc/RyiIiIHC53iIt7XNvI/XwhPqFWH64u9/Wdd3DoE0URKy8vAwAMqjXEouf2zOnl23LT/NB3WN7aWUiVT/ZywzEApF7Duyl3CnyMQTRgw/V1AJxnaycA1A6qAx+1L9J1abiUEOno5VApsCr01ahRA5mZmRg4cCDmz5+PzZs3Y+vWrfl+kXPyUfugZmAtANLRDURERI87+WB29xviYnk/n8xZKn2nH5zEtcSr8FJ5oW/1fhY9t0vlblApVLiUEGk8t7A4xiEuxYS+iLL10D7sSRhEAxac+63Ax/xw4n+ISYuGn8YfHSt3sWjt9qRUKNGkXFMA4BbPx4RVPX1vvvmm8f+//rrgM0o4vdO5NQxujMsJl3DmwWl0q9LT0cshIiJyGJ1OGuQCuM/2TjngWNPPJ5PP6rueeA1p2jSrK4YltSpngEvPqr3hq7FseGCgZxDahLbDvqg92HpzM8Y1nljk4zN0GTj94CQAoHUxx0IA0iTP/VF7sejiArzd4n14qbyM9629sgpfHv0cAPBxm8/gofSwaO329kSF5jgQvQ8n75/AiHovOno5ZGdWhb4///zT1uugUtaoXBOsurIcZ+POOHopREREDnX7toCsLAFeXiIqV3aPyZ22qPSV9y6PYK9yiMt4gEvxF/FEhea2Wp7Z9AY91lxdBQAYVHuoVa/Rs2pv7Ivagy03ig99J2NPQGvQooJ3CKr4VzXrtSv7heNOym2subISwyNGApD6AiftlA5yH9v4NbxYf7RVa7cn+ft5gpW+x4LF2zuzsrIQGRkJQRDQsmXLIn+R82pYTprgeZYTPImI6DF36ZISAFCzpgFKpYMXYyO2CH0AUL9sAwCO6+vbF7UH99NjEeQRhM6Vu1r1Gj2q9gYgHZCekBlf5GMPx0hbO1uHtjXrbEKVQoVRDV4BAMw7OxuiKOJW8k28uHkYsvRZ6FW1D6a1+cKqddvbEznDXCLjLyBVm+rg1ZC9WRz6PDw88M033+DGjRv2WA+VkoY5xzbcTrlV7F+ARERE7uzKFffa2gkAN3K2d1pzMHte9XJCn6P6+uStnU/XfMbqyZdVA6ohokw96EU9dtzeVuRj5fP5Wlcsup8vr+cjRsJT6YlzcWew/dYWPL9xMOIy4tAwuDF+6T4PSoVzvpMQ6lsRoT4VYRANOHP/lKOXQ3Zm1SCXWrVqISoqytZrAQBERgLduwM+PkBICPDee0B2tmWv8f33gCAATz1llyW6hQCPQOO2hXNxZx27GCIiIgdytyEuiZkJiM95Q9fa4xpk9YNzKn1xpR/6MnQZxkPNB9W2bGrno3pWzZnieWNzoY/RG/Q4du8oAKCVGf18sjKeZY3re/Gf4biccAmhPhWxqM8y+Kp9S7Bq+2tWoQUA4HjsUQevxP1FxkWi+8Lu8Jnhg5BvQvDetveQrbcs5Hx/+HsInwp4arHlIceq0Pfmm29i6dKlOHjwoDVPL1RCAtClixTyVq8GZswA5swB3nrL/Ne4dw/49FOgfHmbLs0tNQzOOa+PWzyJiOgxlntcg3uEPnlrZwXvkBKHjtxK33mIYun2O269uRmp2hRU9gtHy5BWJXqtntWkLZ47bm8r9EL7/MOzSNWmwE/jj4gy9Sx6/ZcbjgUA6Aw6eKt8sKjPMoT6VizRmktDq9DWAHInlpJ9JGQkoMsfXZCtz8bqIasxo+sMzDkxB29tMT/k3Eu9h0/3fIryPtaFHKsGuSxatAiBgYF4+eWXUalSJVSqVAkeHqYTiQRBwK+//mrR686aBSQnA2vWAGXKSLfpdMCECcAHHwAVzfiz8957wNNPA7duWfSpH0uNyjXGhut/Y2nkItxOuWlyX4AmEK81fR0BHoEOWRsREVFpMBhyt3e6TehLtk0/HyCd56ZSqJCcnYS7qXdQ2S+8xK9pLvlsvmdqDYZCsKpOYdS0fDOU8yqPBxn3cTB6PzoVcHyCHHxahrSyeEtmg+CG6F6lJ3bf2YnZPX5Hw3KNS7Te0iIfS3H03hHoDXqn3Yrq6mYdn4XkrGSsGboGZbykkKMz6DBh4wR80OEDVPQrPuS8t+09PF3nadxKtC7kWBX6Ll++DAAIDQ2FXq/HrQISljnNr4/avBno1i038AHAkCHAuHHA1q3AqFFFP3//fmDtWuDSJWDYMIs//WNHLulfSogs8GDOMl5lip1yRURE5MqiogSkpwtQq0VUreoeoe96YsmPa5BplBrUCqyDi/HnceHh+VILffGZD439dyXd2gkACkGBnlV7Y9HFP7Dl5qYCQ9+Re4cBmHdUQ0Hm9/oLydnJCPYKLtFaS1OD4EbwUfsiOTsJF+MvoEHOMR1kW5uvbka36t2MgQ8AhtQfgnEbxmHrta0Y1WRUkc/ff3s/1kauxaWJlzBslXUhx6rQt3PnTqs+WXEiI4HRj0y0DQwEQkOl+4qi1wMTJwIffig9norXPuxJfNfpJ0Sl3jW5/UjMIeyL2oNrieYdYkpEROSKMjOB33+XhoNUr26AWu3gBdmIrSZ3yuoHN8DF+PM4H3cWPXMmYdrbuqtroTPo0CC4EeqWibDJa/as1kcKfTc2Y0b7/5oUKERRNPtQ9sJolBqXCnyANH20eYUW2HN3F47EHGLos5PIuEiMbmoacgI9AxHqF4rIuKJDjt6gx8RNE/Fhhw8R6md9yLEq9NlLQoIU8h4VFATEFzNg8pdfgLQ0IM+58cXq2rXw0b9btmyBUqmEh4fjf4sUCukvJbVaCaWyZNsbHvXoDyAALDz3B/ZF7cHd1FtO8fUTERHZkigCO3cqMHeuGvfuKdC0KTBypMFt/s27lSJNWK8dXNsmX1OjCo2w8vIyRCZcsPvvUWzaPay+tAo/n/wRADA04jmbfc5u1bvCS+WFu6l3cCXlovH4KgC4lnAVDzLuQ6PUoFXllvBQucfPgjnaV26PPXd34VjsYUxoPsHRy3Fq0dHRGDlyZKH379ixo8DbEzITEOgZmO/2IM8gxGcUHXJ+OfYL0rRpeLONBSGnACX6iT569Ch2796N6OhoAEDFihXRqVOnUj+j7/594OOPgT//BDTWTfMtlL+/l21fsAR8fT1L5fNEhNYGANxNveNUXz+5h6TMJHx/+Hv0q9MPT4Q+4ejlENFjauBA6Vcudc4v13c957iGRpXq2eTf8VZVmgP7gAvx5+xyXZCUmYTVF1dj8bnF2HljJwyitM22gk8FvNxiFPz9bPM5/eGF7jW6Y92ldZh++FP0rdUX4QHhCA8Ix757ewAALcNaonyZIJt8PlfRtVZnTD/0OQ7HHISfn6dVLVpkH/fT7uPj3R/jzwF/Wn1kicyq0JednY23334b27dvhyiK8Pf3BwAkJydj/vz56N69O/73v/9BbeE+iaAgICkp/+0JCaZ9fo/6+GOgUSOgQwcgMVG6TaeTfiUmAr6+QEFv2BSWxmWiKCI5OcPs9duLQiHA19cTqamZMBjsPzkrWCWVjm8m3kRiUlqJm6eJ8npj25v449x8fLn/S8zs/guGRrABl4hs68gRBebPV0GrNb1drxdw86b0b5qXl4hhw3QYMkQHz9J5T7VUJGYmIi49DgBQTlXRJtcx1b2lN4OvPLyCuw9i4e/hX+LXlG28th6jN76ITH2m8bYWoS0xuM5QPFNnEHzEQJtei/Wq0hfrLq3D5qubsflq/uMbWlRo7RTXfqUpwr8R1Ao1olOicfZuJKoGVHX0kpySn58nKlasWGx+KEiQZxCSMvOHnITMBJM+v0d9vOtjNKrQCB2qdEBiZiIAaQCMzqBDYmYifDW+UCnMi3NWhb6ff/4Z27Ztw+jRozF69GgEB0v7lx8+fIjff/8dv/32G37++WdMnjzZotetWzd/715SEhATI91XmMhIYO9eKTQ+KihIGhDTq5dFSzHKytJZ90QbUqmkf6C0Wj10Ovs3mZfThEApKJGlz8LdhGhU8Amx++ekx0NMajQWX1gEAMjSZ2HMPy/j9L0z+Kj1NE4MIyKbMBiAceN8cP16wW9YKhQinn9ei/fey0aFCtIbqVlZpblC+7r0QBq2V967AjSil02uYwJUZVHZLxx3Um7j8J0j6Fi5c4lfEwASMuPx+rbXkKnPRK3A2ni29lAMrPWsydmCtr4OG1B9MNI6ZuByfCTupt5FVOpdRKXcwcPMh1ApVOhTpZ9TXPuVJiU0aFSuCU7EHsPem3sRWreSo5fklPz8rH9u3eC6iHxoGnKSMpMQkxKDusGFh5zIuEjsvbUXQV/nDzlBXwdh8/Ob0aumeSHHqtC3fv16DBw4EO+9957J7WXLlsW7776Lhw8fYt26dRaHvt69pbP5EhNze/tWrAAUCqBHj8Kf9/33uRU+2eTJgJcX8OWXUhWQzKdSqFDRNwx3Um7jVvIthj6ymV9P/wStQYtWoW3QrmJ7fHviv/j51A+IjL+A2d1/h79HgKOXSEQubutWJa5fVyAgQMTs2RlQPvJ+UpUqBlStWrrnzZUmWw9xkbUIaYk7KbdxPPaozULfjCOfIy4jDnWC6mLHkP0l3r5mDpVChRfr559nkK5Nh0HUw1dTgit7F9YqtA1OxB7DkZhDGFp3uKOX43Z61+yNGftnIDEz0djbt+LCCigEBXrUKDzkfN/re2OFTzb5n8nwUnvhy65folEF80OOVaHvwYMHaFREkmrUqBE2btxo8euOGwfMnAkMGCCdyxcVBbz7rnR73jP6unaVzuG7elX6uEmT/K8VGCht6+zUyeJlEIBwvyq4k3Ibt1NuomVoyQ5EJQKk8dt/np8PAHiz2TvoEt4ddcvUwxu7JmDH7W3otaoLFvZZihqBtRy8UiJyZb/+KgWHF17IRpcuegevpvTJ/Xy2OK4hr+YVWmL1lZU4du+ITV7v39jj+PP87wCAr5/8tlQCX1G81d4O/fyO1jq0LX459SOOxBxy9FLc0rjm4zDz6EwMWDoAH3T4AFHJUXh327sY13ycyRl9Xf/siluJt3D1dSnkNAlpku+1Aj0D4avxRaeqnSxag1XNWiEhITh69Gih9x87dgwhIZZXh4KCgB07pP67AQOAKVOAV14Bvv3W9HF6vdSvR/ZT2V86h+dO8m0Hr4Tcxbwzs5GuS0PD4MboXLkbAGBArUFYP3ALKvqE4WriFfRa1RWX4y85eKVE5KpOnVLg0CEVVCoRL7+sLf4Jbsh+lT7pDeATsceNg1aspTfo8d7etyBCxODaz6FtWHtbLJFKQH6D/0riZcRlxDl4Ne4nyCsIO17YAZVChQFLB2DKjil4pekr+LanacjRG/TQGewTcqyq9A0YMAAzZ86En58fRo0ahSpVqkAQBNy8eRN//PEH/vnnH0yaNMmqBUVEANu3F/2Y3buLfx1zHkOFC/erAgC4nXLLwSshd5CqTcW8s7MAAG888ZbJZLBG5Zpg6+A9eGHTUPx7/wTGbX8ZmwftgIfSw1HLJSIXNWuWVC0aMECHihXddwtnUeTQZ+tKX72yDeCl8kJSViKuJFxGnTJFDFsoxoLzv+HMg1Pw1wTgk7Zf2HCVZK0ynmVRt0wEIuMv4kjMIfSt3s/RS3I7EeUisP2FokPO7lG7i30dcx5TEKsqfePGjcOAAQOwfPly9O3bF40aNULDhg3Rt29fLFu2DAMGDMC4ceOsWhA5h3D/nNCXzNBHJbfw/AIkZiWiRmBN9K3+dL77y3uXxx+9l6CMZxmcizuDr47wIoDocZWSAvz2mxrXr1s2Nv7uXQF//y29lz1+fLY9luYSbuRs77R1pU+tVKNp+WYAgOP3Ct/tVZzY9Fh8eeRzAMAHrT9Gee/yNlkflVzLEOlQem7xdE9WVfqUSiW++uorjBo1Cnv37kVUVBQAICwsDE8++STqFjVqk1wCK31kK1n6LPx6eiYAYGKTyYVO6azgE4LvOv+MFzcPw8+nfkDn8K54slKnUlwpETmDqVM9sXy5Gmq1iNGjtXj77SzjcLeizJungV4voH17HRo2tP+ka2eUnJVk3Jpn69AHSH19B6P349i9I3i+3gtWvcanBz9CcnYSGpdrihfr5R+oQo7TumIb/HnhdxyJOejopZAdlOhw9rp16zLguSm50heVehd6g57j9Mlqyy8twb20GFT0CcPgOs8V+dje1frihXqj8eeF3zFpxzjsGnoAZTzLltJKicjRYmIErF4tXZpotQJmz9Zg2TI13n47Cy+9pIWmkFkfKSnAwoXS2cDjxj3OVT5pa2c5r/J2mULZIrQVcBI4Hmtdpe9A1D6svLwMAgT858lveW3hZFqHtgUAnHlwGqnaVPiqfR28IrIlnrpNBQrxCYVaoYbOoENMWrSjl0MuSmfQYea/3wEAxjeZaNZ0tk/bTUfNwFqISYvGO7snQxQfz74cosfRb7+podMJaNNGh6VL0xERoUdiooD/+z9PdOjgg40bVSjor4TFi9VISRFQs6Ye3bo9fhM7ZfYa4iJrVqEFAOBywiUkZMZb9NyzD07jnT1vAABerD8aTSs0s/n6qGQq+VVGJd/K0It6/Bt73NHLIRszu9LXr59lDZ2CIGDdunUWL4icg0JQoJJfZdxIuo7bybdQya+yo5dELmj9tbW4mXwDZTzLYES9UWY9x0ftg1ndf0PvVV2x4frfWBK5CMMjRtp3oUTkcKmpwB9/SG8MjRunRZcuejz5ZDqWLFHjq680uHFDgZde8kLr1jp89lkWmjSRtnDqdMCcObnPUzzGb2cbj2sItO0QF1mwVzCqB9TA9aRr+Df2OLpWKeIQZQCiKOJg9H78+O+32HVnR85rlMMHrT62y/qo5FqGtsbdK3dwOPqgXVosdAYd9t7dhZahbVhJLGVm/9UYGBho1i+dTocrV67gypUr9lw3lQL29VFJJGYm4PsT3wAAXm00Hj5qH7Of26hcE0xp9X8AgA/2vYfriVftskYich7LlqmRlCSgWjUDevaURparVMDIkVocOZKGt97KgpeXiMOHVejRwwfjx3vi7l0BmzapcOeOAmXLGjB48ON5TIPsZvINAEBV/2p2+xzNQ1oCQJHn9RlEAzZeX48+q7ti4N99sevODigEBZ6pNRgbBm5BoGeQ3dZHJSNv8bTXMJePD0zFcxsGoffKLsbKNJUOsyt9CxcuLPL+Bw8eYO7cuVi2bBmUSiWefjr/hD5yLZzgSdbaH7UXk3aMQ1TqXfhrAvBygzEWv8ZrTV7HrtvbsT9qLybvmoh1A/+xw0qJyBno9cDs2VK1buzY7HzVOl9fYMqUbLzwghYzZnhg+XI1Vq1SY+NGFQIDpf2eo0Zp4eVV2it3LtGpUjuGPXfntAhpheWXluBY7LFCH/P6zvFYfmkJAMBT6YlhESMwvvEkVA2wXxgl22hdUQp9J2KPQavXQq1U2+y1Y9NjsfDCAgDApYRI9FzZCfN6/smhbaWkxJsg4uLiMGPGDHTv3h2LFy9Gnz59sHnzZnz55Ze2WB85ECt9j49svW0GH2TpszDt4EcY9Hc/RKXeRbWA6ljRb61V7+oqBAVmdpkFpaDE4ZiDrPYRubF//lHh5k0FgoJEDB1aeLWuYkURP/2Uie3b09CunQ6ZmQLu3VPAw0PESy893lU+ALiX04Mf6lPRbp+jeQWp0vdv7HHoDfn7Jy/FR2L5pSUQIOCNJ97G8ZHn8PWT3zLwuYjaQXUQ6BGIdF06zsadtulrzz79M7L0WWhUrgmeKN8MiVmJGLp+IH47O5v9+6XA6tD34MEDzJgxA926dcPixYvRu3dvY9gLDw+35RrJQVjpezx8cWgawueUx87b20r0OhcfXkDPlZ3xy6kfIULEyHqjsGPI/hI164f5VTK+A7j26uoSrY+InNesWVI14cUXs+Fjxk7wRo0MWL06A3/8kYH27XX45JMslC/Pi8aYtBgA9g19dctEwFfthzRtKi7GX8h3/6zTPwEAeld7Ch+2/oTn8LkYhaBAq1DpvL7DRWzx1Oq1uJ18C4ejD2Ll5WVYf+3vIoNbYmYCFpz7DQDwboupWDtgMwbXfg56UY+p+97FO3vesNkb0FQwi49sePDgAebMmYMVK1ZAp9Ph6aefxvjx41G5Mgd9uJvKflJ4v5Ny28ErIXtZc2Ulfjz5LQDg11M/oUt4d6teZ9P1DRi77SVk6bNQ1rMsvu38E3pX62uTNQ6s9Sx23dmBtVdX4a3m79nkNYnIefz7rwJHjqig0Yh4+WXzq3WCAPTurUPv3jo7rs51pGpTkZKdDAAI8Qmx2+dRKpR4okJz7L27C8fuHUGD4IbG++6n38eKS0sBAOObTLLbGsi+WoW2xZabm7Hj1lZUD6iBqNQ7uJtyN89/7yI2/R4Moul5mNPbf41XG40v8DV/PzcXqdoURJSpj+5VekIhKPBT19moV7YBPjv0f1h4YQEuPDyP/2v9KdpUbAdBEErjS32smB367t+/bwx7er0e/fv3x7hx4xj23Fi4f1UAQHRqFLL12WaN2yfXcSk+Em/uyv1Hee/d3YhOjUJF3zCLX+u/x75Elj4LnSt3xY9dZ6GCdwWbrbN3tb7QKDSIjL+Iiw8vIKJsPZu9NhE53qxZ0r8tzzyjQ4UKrNZZ616qVOXzVfvZ5Yy+vFqEtMTeu7tw/N5RvNTgFePtv5+bg2xDNppVaI6WIa3sugayn9Y5lb59UXuwL2pPoY/TKDQI86uEAE0ATj04ic8OfYz2YR3z/Tudpk3D3DO/AgBef+JNKARpo6EgCHit6euoU6YOxm57GSdij2HA333QrEILvP7EW+hZtbfxsVRyZoe+7t27Izs7GxERERg7diwqVaqE5ORknD9/vtDn1K9f3yaLJMco51UOXiovZOgyjP1Z5B5SspPx0j/PI12Xhg5hHZGlz8LRe4ex8vIyvP7EWxa9VnJWEi48PAcA+LHLrzYNfAAQ4BGILuHd8M/NTfj76iqGPiI3cueOgPXrpUuRsWO5taskYoz9fKF2/1wtCpjgma5Nx4Jz8wAA4xtPYqXGhTUu1xTtw55EZPxFVPKthDC/yjn/rYQw38oI8w1DJb9wBHsFQyEoIIoint84GNtvb8W4bS9jy7O74KnyNL7eXxf+wMPMh6jiXxX9az6T7/N1q9ITu4cexMx/v8OSyEU4EXsML24ehtpBdTCx6WQMqjXEpgNlHldmh76srCwAwIULFzB58uQiHyuKIgRBwMWLF0u0OHIsQRBQ2S8clxMu4XbyLYY+NyGKIibvmoiriVcQ6lMRs7r/jq03N+PovcNYFrkYk5q+adE/1sfuHYEIEVX9q6GCnbYU9a/5DP65uQlrr67G+y0/4sUEkZuYO1cDvV5Ax4461K9vKP4JVCg59IX42q+fTyYf0n4z+QYepD9AOe9yWH5pCeIz4xHuVwV9qlt2tjM5F7VSjdX9N5j9eEEQ8H2XX9BpWRtcjD+PLw5/gi/afw1AGhT3y6mZAICJTSdDpSg4elT2C8d/On6Hd1pMxdwzv+L3c3NxOeESXt85HkdiDuG7zj+V/At7zJkd+jiN8/EU7ldFCn2c4Ok2Zp3+GeuvrYVaocZvPf9EOe9yeLrmAHyw/11cSbyMk/dP4IkKzc1+PbnRW278toee1frAS+WF60nXcC7uDBqWa2y3z0WOlaZNg0ah4bu6j4HUVGDRIun7PH48q3wldc84xMX+lb4Aj0DULROByPiLOB57FD2r9sbsMz8DAMY0Gl/ohT25r/Le5fFjl18wfONgzDnzK7qEd0OX8O5YdXk5otOiUME7BEPrDDfrdT5s/QkmNZ2MPy7Mx7LIv4zT5KlkzP5TOXDgQHuug5xUZf+cYS7JHObiDg5FH8Bnh6RDzz9r96XxkF0/jT96V3sKq6+swLJLiy0KffIBrvKBrvbgq/ZFtyo9sf7aWqy5uoqhz009zHiIVn81QSW/yvh7wCYEeAQ6eklkR6dPK5GaKiAszIDOnfOP/ifLxJTCcQ15Na/QUgp9945CgIBriVfhrwnA8IiRpfL5yfl0q9ITLzccg9/OzsGkHeOxc+gB47C4cY0nmmz5LI6/RwAmNZ2MSU0n22m1jx92R1KRwv2qAgBup9x06DrIMtn6bBy7dwRrr6zCTyd/wNR97+CFTc/hxc3DoBf1GFRrCEY3eNXkOfI7cGuurESWPsusz5Olz8LJ+ycAAK0r2q/SBwADag4CAPx9dTXP83FTR+8dRnK21CM6essL0Op57po7u3ZNugSJiDCAO7ZLLiZnkEtIKVT6AOmQdkDa4v/raWn73gv1X7L7EBlybh+3+Rx1y0TgQcZ9PLW6O64lXkWgRyBerP+So5f22GP9nYoUnlPpu81Kn8vI1GXiqTU9cObBqQLvr1+2Ib7p9EO+vrgnK3VCqE9FxKRFY+vNf9CvRv9iP9fJ+/8iS5+FYK9yqB5Q0xbLL1S3Kj3go/bFnZTbOBF7zFilJPchDwQCgH13d+O9vW/i204z2cPppuTQV6MGe/lsoTQOZs+reZ5hLnpRD5VChVf+v737Dm+q/h44/r4Z3Xswyiyr7L2XCjIVAVHEjYIK7r333n4VxYk/wQWyRJG9RKZsECh7ltXS0t004/7+uL1pS/dO0/N6nj5KcnPzSZumOTnnc067+yrlvoXr8jZ58+XV0xgy50pOJB0HYEK7++TDABcgmT5RKL2OWvb0VR8fbnmX3bE78TX70bNub8Y0H8sjnZ/g/f6f8Ms1s1k8ZiW+5rzTj40GIze0uAmA3w78Uqz72nxmA6Dt56voN+beJm+GNh4OaNk+4X72xmlB38CGgzAoBn7eP4PPd35axasSFeXoUe0tSJMmEvSVh7OVuKcPoGlQM4I9g7GrWmnuqGZjSjXyR7ifNmFteanXawD4mHyY2G5SFa9IgGT6RBEaBmhB34W086Tb0vE2eVfxikRhdl7YzhdZb5K/GPgNw5tcW6Lb3xR1C1N2fMLKk8udHdkKk72fr2JLO3Wjmo9h7qHfWHBkPq/1eVvm97gZPdM3qcODDGw4iOfXPc0bG18mMqAJ1za9LtexyZlJLD72F+m2dEY3G0OAZ2BVLFmUwZEj2gdFkukrO7vDzoW08wDUrYTunQAGxUCX2t1YcXIZIMPYRW73tJ+Mh9GTpkHNCPUOrerlCCTTJ4oQ5BmMn1lLyZ9OPlXFqxGFybRn8siqB7CrdkY1u77EAR9Ai5AoOtXqjM1hY96h3wo91u6w82/WjKaKbOKS05UNBhDgEci51LPOgFO4h1RrKscSjwLQJqwdE9tPYkK7ewF4YOU97Di/jQxbBn8d/ZMJS++g9f815cGV9/HU34/S6cc2vLHxFc5nvekVrs9qhePHpbyzvMSmX8Cu2jEqRsK9a1Xa/faM6ANAv3pX0C6sfaXdr3B9BsXAXW0n0r/+lVW9FJFFgj5RKEVRnNm+k1m12cI1fbr9I/bH7yXUK5S3+31Y6vOMzWroMuvAr4Uetz9+H0mZifia/WgT1q7U91cSnkZPrsma//T74bmVcp+ickTH70NFpZZPbcK8wwB4o8+7XN1wMOm2dG5aOJq2PzTnriW38ueR37HYLTQPakGL4CiSM5OYsuMTuv7YlifXPMrRxCNV/GhEUU6dUrDZFLy9VerWlcZMZXU2RdvPV8unNkaDsdLud2K7+3il15t8cfU3lXafQojSkaBPFEkP+k7Ivj6XtTfuPz7Z9gEA7/T70PmmuTRGNx+D2WDmv7jdzj1W+dEzbV1rd6vUmUwjm10PwJ9HfsfmsFXa/YqKpT/X2oS2dV5mMpj4ZvD/0Tq0LZcsl0jKTCTCtx4PdHyElWPXse7mLawdt5kfh8+ia+3uWOwWZuz7nt6/dOGz7R9X1UMRxaA3cWnSxIFB3omUWWXv59P5mH14oNPDldYxVAhRevJSK4rU0F9m9bkym8PGo6sfwOawMSzyWmdQVFohXqEMbjwMgFmFNHTZfFZr4tIzonJKO3X96l1BqFcocelx/H1qVaXet6g4+n6+1jmCPgA/D39+G/E7L/Z8lQWjFrP9jr280vsN2oW1R1EUDIqBIY2H8df1y/lj1BL61b8Sh+pg7sHCy5NF1ZLOneVLn9FXp5I6dwohqh8J+kSRpIOna5u6cwq7YncQ6BnE+/0/LpcumvrMvrkHf8t3Zp+qqmyqhKHs+TEbzYxoOgqACUvv4Md9P8jcPjew1xn0tclzXS2fWjzc+XF6RfQpsHmPoij0jOjNM91eACDdll5xixVlJkFf+TqnZ/r8JOMmhMifBH2iSA2yyjtPJUnQ52oOJRzkgy1vA/BGn3eo7VunXM47sOEg6vjWJTb9AtP25N2rcSLpOOdSz2I2mOlUq0u53GdJPNP9RfrVu4I0WxpPrHmYOxffTFx6XKWvQ5QPVVXZd3EvQJn3h3qbvADy/bBCuA4Z11C+zlbyjD4hRPUjQZ8okmT6ysahOrj1rxu57a+xxKbFFnpsui2dp/9+jGvnDXZ+cluYNza+jMVuYUDDq53ZufJgNpp5rvtLAHy09b08AZW+n699eEd8zD7ldr/FFeodyuzrFvBa77fxMHiw5PgirpjZkxUnllb6WkTZnUo+SXJmEmaDmWZBzct0Lk+jFvRlSKbPpUmmr3zpe/pkb50QoiAS9IkiNQzQ9vTFZ8STkplcxaupfo4lHmH5iaUsO7GEIXOuZE/c7nyPO5tyhlG/D+OHvdP499wm3vv3rULPuzt2J0uOL8KgGHizz3vlPhz9ppa30C6sA8mZSXnWsrmKSjtzMigGJnd8kKU3rKFVSGti0y9wy1838vL656tsTaJ09Cxfi+CWeBg9ynQuL8n0FcuCw/P4etcXpFnTKv2+U1PhzBkJ+nJSVZX1Mf/wzubXuZB2ocS3P5cimT4hROEk6BNF8vcIINgzGICTydLMpaSOJx5z/v/plFOMmDeYP4/8nuuY7ee3MnjOley4sJ0AD23I9K/RP3Ew/kCB5/1gyzsAXN/8RpoFly07kh+DYuDNvu8C8OO+/2P/xX3O6zZVUROX/LQJa8vSG9ZwX4cHAPhq1+e51ipc396Le4D89/OVlGdW0JduS5e9ngXYcm4z9ywbz0vrn6PPr11ZcHhepX6v9NLOkBAHwcGVdrcuyaE6WHR0IcPnDWT0gmv4ZNuHvLz+uRKfJ7t7pwR9Qoj8SdAniqVhQGMATsq+vhI7njXfsG+9/lzVYCBptjQmLL2D9/59C4fq4LcDvzLy92GcTztHy5BWrLhxLcMir8WhOnh78+v5nnPXhR0sPb4Yg2Lgia5PV9jae0X04domI3GoDl5a/xyqqhKbFsvhS4cA6Fane4Xdd0l4mbx4o887dM7aXyhz2qoX57iGcpj36J1V3qmikunILPP53I3NYePpvx8HwGwwE5NymnuWjWf0gmsKHdFSnvSgr2nTmhuUZ9ozmRn9M/1+7c74Jbew7fxWPAxalvuvo39wKSOh2OdKyUwmxapV4dSRRi5CiAJI0CeKpYE+tkH29ZXYiaygr21Ye36+ZjaTOjwIaHvlBvzWlwdX3ofFbmFo4+Esun4FjQMjeb7HyxgUA4uO/cm281vynFPP8o1pPpamZdwDVZSXe72Oh8GDtadXs/zEEv49twmAliGtCPEKrdD7Lqn6Wc/T05KRrlb2FdK5s6T0TB+AxZZR5vO5m+/3fMPei3sI9gzm31t38VS35/AyerHhzDoGzu7Ls2ufICEjvkLXUNP3851PO0//mT14eNVkDl06SIBHII92fpLtd+yjdWhbLHYLcw/NLvb59Cyfv0cAfma/ilq2EKKak6BPFIs+oF0yfSWnB32NAhpjMph4vc/bfDbgSzwMHs43u491eZIfhv2Cn4c/AFEhLZ2NWd7c+Gqu0qsd57ex7MSSCs/y6RoHRjpLJ1/Z8ALrYv4GoEcV7ucrSPaHExL0VRep1lSOJR4FoE1o2TN9HgYPFLT9rel2CfpyOpd6lnez9ue+2Os16vnX56luz7H+lq2MaDoKh+rg+/++pefPnfjhv2nYHfYKWUdNDvocqoOHV07iaOIRwr1r8XKvN9hxx16e7/kytXxqcWur2wH4ef+MYp8zu3OnZPmEEAWToE8Uix70nZBMX4npQV/jrBJZgHEtb2X+qL8YGnkN04bM4LmszF5OT3V7Dk+jJ+vP/MPqUyucl3+4Vdtnd0OLm2gS1KzC1w/waJcnCPMO58ilw0zf+z0APer2rJT7Lon6/g0AOJV8qopXIoorOn4fKirh3rUI9wkv8/kURcHb5A3UvEzfzgvbC+0Q/PL650ixJtOldldubXWH8/IG/g2ZNmQG80YupFVIaxIsCTy99jGunt2fTWc2lPs6a/K4hml7vmb1qZV4Gb2YN3IhD3Z6BH+PAOf1Y1qMxcPgwX9xu9kdu7NY5zybIoPZhRBFk6BPFEtDPYOSVLwMyldfmenUyZejR8u3o2R1o6pqjkxfZK7rutXpwYxhvzoHjV+uvn8D7mp7DwBvbnoNh+pg+/mtLD+xFKNi5PFKyPLp/D0CeK6HNsLB5rABVdu5syANsoK+0xL0VRvZ+/nalts5PY2eAGTUoKBvT+wuBs+5kt6/dmH+oTl5rl9zahW/H56HQTHwfv9P8h1y37def1aOXcc7/T4g0DOIvRf3cN3vQ5m0/G7OpMSUyzpVFQ4frpmZvn0X9/L6xpcBeLXPW0SFtMxzTIhXKNc0GQEUP9vnHMwumT4hRCEk6BPF0tC/MaDN6itOl7cZM8zExBiYN89cwStzbRczLpJqTUFBoUHW6IuSeLTLE/h7BPBf3G5+PzyXD7doWb4bo8bRJLBpeS+3ULe0vJ3Wodob8/p+DZxZNVcie/qqn+z9fOUX9Hnpmb4aVN6548J2ABItl7hv+d1MXj6RRMslQBtf8ezaJwCY0PZe2oV3KPA8JoOJCe3uY+Mt27mj9d0oKMw7NIc+v3Zj14UdZV7nxYsKiYkKiqISGek+QZ9DdbDxzHqSLIn5Xp9uS2fy8glY7BYGNRrCXW0mFniuW7KysHMPzia9GPMmZTC7EKI4JOgTxVLfvwEKCsmZSby8/rlCP0FPScnes7F1q7GyluiSjmftVYrwq+fMPpREiFcoD3Z8BIAX1z3DipPLMCpGHuvyVLmusziMBiPv9f8YX7MfN7Usv0Hw5UnP9CVYEmSmZDWxNyvoa1OOQZ/+u5ZegzJ9esfaJoFNMSpG5h76jatm9WFDzDq+2PEpRxOPUMunNs90f6FY5wvzDuPDK//H8hv/plOtzqRaU3j2nyfLPNpB/9tQv76Kt3eZTuVS3tn8BiN/H0aPnzvy077pefZDvrnxFfbH7yPMO5z/XTW10Lmq/epfQUP/RiRlJrLwyIIi79s5mF06dwohCiFBn8hjzRojo0Z5c+BA9tPDx+zDY121QOPr3VMZMufKAtt7//efEVXV/qBt3WrE4T4f5pZYziYupXVvh/sJ965FXHocAGOjbiYysEk5rK7ketTtyeEJp4r9xrGy+XsEEOQZBMi+vupAVVXnYPbyzPTpe/oy7EVnSdyFHvRNbHcff4xeQuOASE6nnGL0gmv4aOt7ALze520CPANLdN724R2ZPuxXfEy+bDu/hfmH85aOlmidWSX/7rSfb8f5bUzZ8QmgVXc8vuYhhs4dwL9nNwOw8sQyvt3zFQBTBnxZ5N5Vg2Lg5la3AfDL/h+LvP9zkukTQhSDBH0ijx9+MLNhg4k338ydmXq2+4v8NHwWYd7h7I/fx5A5V/Llzs9xqLn/eO/alf20SkpSOHSo5j7NyiPo8zX78kS3ZwAwKkYe7fJkOays9IwG187eSoln9XEq+STJmUmYDWaaB7cot/Pqmb6a1Mjl2KWsTF9QM7rV6cGqseu4tdUdqKhYHVb61b+S0c1uKNW56/jW5ZHO2my/Nza+UqySw4K4W+fOTHsmj65+AIfqYGTT63mjzzv4ewSwK3YH184fxOTlE3l41f2AFpAPbDS4WOcdF3UrCgrrz/xT5NzRs7KnTwhRDDX33bgo0OnT2tNi6VITR47kLkEZ3HgYf9+0iSGNh5HpyOSVDc9z4x8jc23y37Urd1BQk0s8yyPoA7i91Xgmd3iIj678rMqyfNWFs4NnimT6KtveuP84nHCo2MfrWb7mwVF4GD3KbR1ezkxfzQj6HKqD40nHAJx7ff08/Pnkqs+ZPuxXbm11B58VUVJYlEkdH6S+XwNiUk7z5c4ppT5PcYK+NGsay48vKVNwWVn+t+1D9sfvI9QrlHf6f8h9HR5g0y07uLXVHSgozD30G7HpF2gV0pqXe71R7PPW86/PVQ0HAvDr/p8KPM7msHEh7TwgmT4hROEk6BN5nD6d/cbgq6/yvhEL9wlnxrCZfHjFp/iYfPgn5m+umNWL3w/NBWD3bu1p1bq1tqdh69aa+zQrr6DPbDTzWp+3uCVrhpMoWAM/6eBZFc6nnmPY3AEMmXsVF9MvFus2ey/uAcp3Px+AV9aA9prSvTMm5TQWuwWzwZynwdKwyGv45KrPqedfv0z34W3y5qVerwHw2faPnR0jS6qooC8hI57rF1zDrYvGMmX7J6VbbCXZG/cf/9v+IQDv9PuQMO8wQPsb+clVn7NkzCq61+lJbZ86fDlomvN5WVz6WI2Z0T87uyZfLjbtAg7VgVExEuZd9pEnQgj3VXPfjYt8paZCfHz20+K338xcvJj302FFUbijzV2sHPsPnWp1JtFyiXuX38V9S+7l4EmtgcaECVYAtmyRTF9Zgz5RfFLeWTUWHVtIhj2D5Mwkvsvav1SUitjPB+BlzAr6akim72hWaWejgMaYDKYKu59RzcbQtXZ30mxpvLXptRLf3m6HY8cKntF3LvUsI38fxvYL2wBYdXJ52RZcgWwOG4+svh+bw8bwyBGMbHZ9nmM61e7CwuuXsfvOA7QObVPi+xjSeDihXqGcTztX4PdC79xZ26eOy5feCyGqlgR9IpeYGO0p4e+v0r69nfR0henTCx670DSoOQtHL+fxrk9jUAzMPzoTJnUkpOPfDB2qfTJ58KCRS5cqY/WuxWK3OMteL5/RJypOA32mpAR9lWrxsYXO/5+25+tidU/dG5eV6SvHGX2QnemrKXv6cnburEiKovBmX21szKwDv5R4hENMjILFouDhodKgQe4uoCeSjjNi/hCi4/c7M1a7YneSnJlUqrXuvLCd+1fc4xxcXt6m7vyM3bE7CfIM4r3+HxVaOlvasloPowc3Rt0MwE8FzOxz7ueTzp1CiCJI0Cdy0Us769d3MHlyJgDTppmxWAq+jdlo5tnuL/LHqKUE0xiCThA/8io+O/gstfv+CbX2sG5LSiWs3rWcSjqJioqv2c9Z9iMqnj62Qbp3Vp5EyyXWxawFoJZPbS5ZLjF97/8VeptUayrHskaalHemzzMr05deUzJ9WUFfZFDFz+7sXLsrN7S4CYAX1z9bohEOemlnZKQDY46k1IH4aEbMH8KJpOM0CmjM4jEraRTQGLtq59+zm0q1znc2v8Gcg7N479+3SnX7whyMP8AHW94B4I0+71Lbt06534dOL/FcfnwJF9Iu5Lle79xZR/bzCSGKIEGfyEVv4lK/vsp119mIiHAQG2tg3ryiS4a61+3BFfu3wY67QFH5etcXnL/6Ori/PXcfiqDpd/W5YmZPJi69k293f8nu2J0F7lNwByeyGis0CmhcpgYKomT08s4LaedrzJ6uqrb8xFJsDhtRwS15occrAHy16/NCv//R8ftQUQn3rkUtn1rluh5nI5dq0AikPDg7d1Zwpk/3Ys9X8TZ5s/nsRv488nuxbqOqKruOXAD/M0REneZc6lnOpZ5lQ8w6Rv4+lHOpZ2kZ0oqFo5fRKKAxfev1B2BdzD8lXp/VbmVzVrA4//Ac55D68pBuS+eR1ZOx2C0MbDiIsVmZuIoSFdKSTrU6Y1ftLDr6Z57rz6ZI504hRPFI0CdyiYnJzvSZzTBxopbt++orD4rzge7+nYGw4HseqzOLoZHXEKF0gLQQAJIzk9gfv48/jsznhXXPcPXs/rSY1oixf47io63vOT/1dxfHZT9flQjxCsHH5APAmZTTBR53Mf0icw7OIsVa87LQ5W3RUa20c3iTaxnTYiwRvvU4n3aOWQd+KfA22fv5Sr7XqShezpENhZQouJHKKu/URfjV48FOjwLw+qZX8gwiz8/kFRN4OzMSnqjH6s6NaT89ivbToxi1YDjxGfF0rtWF30ctcmbNekf0BWDDmZIHfbtid5BmSwW0IG32gZklPkd+kjOTGLfwerad34q/RwAfXvFppXygN6LpaAD+PJp3UPtZyfQJIYpJgj6Ry6lT2lOiXj0twrv9diu+vir79xtZs6bwTeKpqThn8t3dZzgzhv3Kj33Xw/sX8f00mbVjtzLz2rk83+NlBjYchL9HACnWZNacWsV7/75Fr186c9+yu9gTt7tiH2QlkSYuVUNRlOyxDYWUeL616VXuX3EPA2b1Yeu5fytreW4n3ZbubDIxPHIEHkYP7u/4EACf7/hfgdl8fT9feZd2Qs6RDe6f6bM5bM7XmiaVUN6pe6DjI4R4hXAy6TirT60o9NhTySeZn9XdGYcRA0aMSvbXsMhrmXPdH4R4hTpvowd9pdnXtz4rO+id9TyYvvf7EpWh5udi+kWuXzCCjWfW4+8RwM/DfytzR9TiurbJdQCsj1lLXHpcruvOyYw+IUQxSdAnctEzfQ0aaJ3VAgPh1lu1Lpxffln4HK29ew04HAq1ajmoXVv7A9uqlQNfX5XUBD8cF1oyoOEgHu3yJL9eO5eDd59g9dgNvNv/I65qMBCH6mD+4bkM/K0v4xZez4aYdWX+Q12VJOirOsVp5rL1vBboHU86xoj5Q3j/37fduty4ovx9ajVptjTq+zWgfXhHAG5tfSehXqGcSDrOgsPz8tzmTEoMK7MCxfJu4gLZw9kzakCm73TyKawOK55GT+r5VU4QAuBj9nHu7ft5/4+FHjsz+mdUVDzPXAWv2/i9cxJnJyc4v6YP+wU/D/9ct6nnX5/GAZE4VAebzmwo0drWZ2UHH+n8BD4mHw4kRLP57MYSnSOnsylnGPn7UHbF7iDUK5T5IxfSM6J3qc9XUo0DI2kf3hGH6sjVMAlyZvok6BNCFE6CPpFL9p6+7Hba99yTicGgsmaNif37C37K7N6tZQI7dMi+rckEnTtrpT+Xj24wGoy0CWvL3W3vYdaI+awau57rm9+IQTGw6uQKRi0YzqgFw4u1H2NP7C5uWXgDe+P+K/ZjrWh60NdYgr5KV9TYhjRrGgcTDgAwNPIa7KqdD7e+y4j5gzl66XClrdMdLDqm7TMaFnmNs9TN1+zLPe0nA/DZ9k9yfXiz5dxmBs2+ghNJxwnzDuOqBleX+5pqUqZPL+1sHBCJQancP+m3ZDUZWXp8EbFpsfkeY3fYncPFLRsnAPmPa8iPvq9v/Zl1xV6T1W51Nn8ZGnkNY1qMBeCHvdOKfY6cjiYeYcT8IRxMOEBd3wj+GL3U+eFGZRrRZCRAnj2Uzu6dUt4phCiCBH3CyW6HM2f0PX3Zb9IaNVK55hotA5LfsHbdrl1aUNe+fe79HV276kPaCy8PbRvWjq8GTWPTLTsY32YCnkZPNp5Zzxc7Piv0dqqq8sSah1lxchkfbX2v0GMri6qqOTJ9Mq6hshXVwXPfxf9wqA7CvWsxfegvfDVoGgEegWw7v5UBv/VjZvTPlbncasvmsLHs+GIAhjW5Ntd1d7e9B1+zH/vj97L8xBJAy/iM/v0aYtMv0Dq0LUvGrCbcJ/dA6bNnFRISyrYub+fIBvfP9B2rxM6dl2sd2obOtbpgc9iYfTD/fXNrT6/hdMop/ExBsP96/P1VwsOLV8HRu17Wvr4SNHPZGbudNFsaIV4htAxpxR2t7wJg4ZEFeUojCxObFssfh+dz3fyhnEw+QWRgE/4cvZTmwS2KfY7ydG1TrcTzn9N/E59xEdD2GKZm7UmuIyMbhBBFkKBPOJ07p2C3K5jNqrM8UzdpktbQZe5cE+fP579xffdu7enUoUPpgj5d48BI3r/iE74epLV8/3bPV84/cvlZfmIJO2O1eVGrTi4n3QU69l3MuEiqNQUFhQYBDat6OTWOvqfvdEr+QZ++b7R9eAcUReH65jey5qYN9InoR5otlYdXTXabvaUVadPZDcRnxBPiFULPurnL3YK8grmr7UQAPtn2IS+vf56HV00m05HJ8MgRLLx+GQ0DGuW6zZkzCr17+9Kxox/vvutBSil77Hg6h7NX/WtBRTtayZ07L6dn+37eNz3fcvxfsko/u3vfBDZvmjVzUNzeJ/q+vt1xu0iyJBbrNvp+vl4RfTEoBjrU6kSnWp3JdGTya/RPBd7uZNIJft3/E4+ueoBev3SmzQ9NmbjsTi6knad1aFv+GL00z/O1MjUNak7r0LbYVTtLj2kftOidO/09AvAz+1XZ2oQQ1YMEfcJJb+JSt66K4bJnRrduDrp2tZOZqfB//5d3WHtaGhw4oAd9uUt3unTRgr4jRwxcvFj8TmfDIq+hbVh7Uq0pfLnz83yPUVWVD7a8m70OWxp/n1pd7PuoKMezOpFG+NVz7i8Slae+X9aevqT8yzv3xO4CoF1Yh+zb+Ddg7sg/GR45AoCPtrhG1tiV6S3kBzcehsmQd6zLfR0ewNPoybbzW/hql/Y7/GTXZ/l+6I/5vkn95x8jqakK6ekKH3/sSc+evvz0kxl70c0hc9GHs6fXgJEdld2583Kjm4/Bx+TDoUsH2XJZQ6SL6Red5b9NEu7W/lvM0k7QXj8jA5to+/rOFm9fnx709a3Xz3nZnW20stIZe7/Hoea9/693fUG3n9rzyOr7+SX6R45klXi3CmnNve0n8/vIv6jtU7vY664oI5rmLvHU9/NJExchRHFI0CecLm/icjl9WPsPP5hJS8t9nd7EJTzcQZ06uT/tDQmBZs20d23bthX/KacoCk91ew6A7/Z8zcX0vNm+ZSeWsCt2Bz4mX65vfiOQvceoKkkTl6rVMCu7ejb1TL7NWXbHZQV94R1yXW5QDDzX4yUUFBYd+1OyfYVQVZXFx/4CcAbKl6vtU5txLW8DwMfkw7QhM3i6+/MF7j3T9/3262ejcWMHFy4YePxxLwYM8Cmye3BOeqbPUgOGszuDvioo7wQty3RdM22kwC/7Z+S6bs7BmVgdVtqHdyTtaEcAmjYtftAHOfb1xRS9ry/TnsmWc5sB6B2RHfSNbHY9AR6BnEg6nutDQVVVef/ft3lp/XOoqHSp3Y2HOz3Oz8N/4+DdJ/h73Cbe7PseQV7BJVpzRRnRZBQAf59eTaLlkoxrEEKUiAR9wklv4qKPa7jc8OE2GjZ0EB9v4Lffcmf7cjZxya90p2tX7Q99cUs8dUMbD6d9eMesbN+UXNdpWb53AJjY7j5ubz0egGXHF1d5F0YJ+qpWLZ/aeBg8sKt25xsjXaY9k+iL+wCtvPNyUSEtGd18DAAf5sgii9x2xe4gJuU0PiZfrmhwVYHHvdLrdV7s+RqLx6xiRNNRhZ5Tf3246y4r69al8vrrGQQFaSNjxo71Yd68vNnE/Oh7+gobDu8OrHYrJ5NOAFWX6QO4tdWdAPx+eB4pmcmA9vqsl3be2uoODh/W/r6UNOjLntdXdNC388IO0mxphHqFEhXS0nm5r9mXsVHjAG18A4BDdfDS+mf5cKv2O/5c95dYdP0KXuz1KoMaD3WZQC+nFiFRRAW3xOqwsvT4YhnXIIQoEQn6hNPp09mD2fNjNMK992rZvq+/9sCR47CCmrjoSrqvT3d5ti/nRvwlxxexO3YnvmY/Jnd8iB51exHiFUJ8RnyxS4EqigR9VcugGIjwqwdoLe1zOpAQTaYjk0DPIBr6579H5/Euz6CgsPjYQmcpaEkkZyY53/y6K30g+4CGVzvnoeXHz8Ofhzs/RqvQ1oWeLzkZoqO1P0ndutnx8IBJk6xs3pzCqFHa2Jj58/OWluenpmT6TiWfwK7a8TZ5V2nL/u51etAsqDlptlR+zxrRsf3CVvbH78PL6MV1kTfw33/aa3+rVqUL+vbE7Sqyk/P6mLXaber1y5NNvqONVl669PgiTief4tHVD/DN7i8BeKffhzzW9alKGbReVtdmlXguPLIgR3mnZPqEEEWToE84ZY9rKLiz2i23WAkIUDlyxMDy5dkB3K5d+e/n03XrpgV927cbsZUwCTe40VA6hHcizZbqzPZdnuUL9Q7FZDAxpPFwIHuvUVWRoK/qFTSrL3s/X/sC3+S1CIlidPMbAPhga8myfSnWFHr90oUrf+vj1oGfXkY9/LKunaW1fbsRh0OhYUNHrkZSwcHZHzZt3WqgOKM7s/f0uXcjl+xxDU0qfVxDToqiZDd0ySrx1LN81zYdScyRENLSFAICVFq0KFnQV9cvgiaBTbP29RU+a08f7aAHijm1DGlFz7q9sat2hs4dwMzonzEqRj4f+DUT2t1bojVVJT1bvvrUSg4nHAKkc6cQongk6BNO+p6+gjJ9AH5+cPvt2qfu+viG9HQ4eDD/zp26qCgH/v4qaWlKobP+8qNl+54FYNqeb4hLj2Pxsb/4L253VpbvQeexw5toe4sWH/urSge7S9BX9eo7xzZcFvTF5W3ikp8nuj6DQTGw5Nhf7I7dWez73XxmAxfSznMy6Tifbv+4ZIuuJg4nHOJgwgFMBhODGg0pl3PqVQB6VUBO7do58PRUuXjRwLFjRWdj9Dl9Frt7j2xwdu6sov18OY2NuhmTwcS281vYfn4r8w7NAeC2Vnc692p26WLP0ySsOPpkNWVZX8joBm0/36Zcx1/uzqxs34W083gYPJg25EfGRt1c8gVVoVYhrWka1AyL3cI/MX8DkukTQhSPBH0CAFXN7t5ZWNAH2rB2k0ll/XoTu3cb2LfPgN2uEBbmoG7d/AMtg6HgIe3FMajRUDpmZfu+2PGpcx/GPe0mEeIV6jzuivpX4WPyJSblNLuyxjhUNovdwpmUGEBm9FWlBs4B7bnLO3frmb7w9oXevnlwC0Y3y8r2ZWWViyPnIOmvdn3u3HPlTvQsX996/Qn0DCqXcxYW9Hl6Qvv22utScV4/vPSRDTUk01eV+/l0tXxqMbjRMADuXX43qdYUIgOb0Cuij/Nnlt/Ptjj0IK6wfX3bL2wj3ZZOmHcYUcEt8z3m2qYjaeDfEF+zH79cO6fcstSVSVEUZ0MXnezpE0IUhwR9AoDEREhN1T5BL6iRiy4iQuW667QazS+/9HDu5yuoiYuutPv6IPfevqk7P+O/uN34mf1zZflAK+sa2GgQkL3nqLKdSjqJioqv2Y8w77AqWYPImenLDvrsDjt74/YA0D6sY5Hn0LN9S48vZteF4n2IoO8r8vcIwGK38MbGV0q4cte39rSWYRgaeU25nM/hgG3btNcFvRT8ciV5/fCqIY1c9NECrhD0Adza6nYATmZVOtzS8nYURSk0oC8O576+2F1cykjI9xh9gHvviH4Flm17Gj1ZPXY9227fQ//6V5ZqLa5AH92gk0yfEKI4JOgTQHaWLyzMgXfBPRmc9PENCxaYWLJE66hXUBMXnf5mrjSZPoCrGw2hU63OqGhB6b3tJxHsFZLnuOGR2qe3VTW64UTSMUAr7awOjQHcVXamL7u882jiEdJsafiYfGga1KzIczQLbu4cBVKcbF9yZhK7skpBv7r6OwyKgQVH5hW5F6m6OZ71HG8d2rZcznf4sIFLlxS8vVVat86/0qAkQV/2cPaMKi3zrmjHsuaBukJ5J8BVDa92BiBGxchNLW/hwgWFkycNKIrqnNlaUnV869I0qBkqaoG/S879fPXy7ufLKcAzMFd1SHXUNqy9c+uAUTES5h1etQsSQlQLEvQJIOd+vuK9QerQwUHv3jZsNoU1a/Sgr/CyUP36EycMWEqx1UZRFJ7u9jygZVHu6/BAvscNajQEs8HMwYQDzo3ulem47OdzCXqmLybltHMgs743r3VoW4yG4n348ETXpzEoBpadWMLOC9sLPXbz2Y04VAeNAyIZ1Hios5X9S+uezXcodHVktVuJycqeNiqg+2lJbd2q/Snq2NGOuYAGnfqHRvv3G0hJKfx8+sgGcN99fRa7hdMp2s/BVTJ9JoOJm1tpcxkHNR5KHd+6ziC9ZUsHAQGlP7c+d2/9mbz7+ix2C1uz5vP1ich/P587URTF2dCltk+dYr+WCSFqNgn6BJCzc2fx35jq2T5dQU1cdCEhKiaTFlTGx5cuAzaw0WC+HfwDv42Yn2+WD7RPcvWBvlWR7ZMmLq4hwrceBsWAxW4hNu0CkL2fL7/5fAVpGtScMc3HAvDZ9k8KPXZdVomZ/vx7tvuL+Jn92RW7g9kHZpb4MbiimJTT2FU7nkZPavvWKZdzFqf8r04dlQYNHDgcCtu3F/4mV8/0gfuObTiReByH6sDX7Ectn9pVvRynx7o8xXv9P+ajKz4DKPN+Pl1ffV9fPkPad5zX9/OF0yI4qkz3U12Mi7oVH5Mvfev3r+qlCCGqCQn6BJBd3lnUfr6cBg2yOwfthoY6irytwQChodoxcXGlL3sc2ex6utTuVugx2V08K39fnwR9rsFsNDtLzfQOnv/F7QaK7tx5uckdHwJg+YklJFkSCzzOua8oq8Qs3Cecx7s+DcBbm18jxVpEiqoaOJmsNaZp6N+o3MYE6EFfQfv5dMUt8TQbzM61lWZfX0oKvPaaJ3PmmHLNI83PxYsKH3zgwa+/Fm9wfHnJ2cTFlcrIPY2e3NV2IuE+WsmhnsUta9Cn7+v7L243P/w3LdfPVc/+9SlkP5+7aRESxX/jD/LpVVOreilCiGpCgj4BZJd3NmhQ/EyfwQD3369l+3r2tBfaxEWnB32xsRX7h3lo5DUoKGw7v5WzKWcq9L4upwd9jSXoq3J6iefp5FOoqsruuJJn+gDahLalRXAUFruFxcf+yveYJEui8/w554Td034SjQIacy71LJ/v+F8pHoVr0Z/fDQPKp7QzMRGio/WW/oW//hQ36FMUBS+jtjk5oxSZvs8+8+CLLzy4/35vhgzxYcOGvPdnscAXX5jp0cOXDz7w5JFHvJ2jayqDK3XuLEhmJs5GX0UF9EWp7VuHK+pfhYrK02sfo8uPbfls+8ckWRKd2b+i9vO5Gz8PfyntFEIUmwR9Asgu7yxJpg/gttus/PprGu+/X7x9M2FhZc/0FUdtn9p0rdMdgMXH83+TXhFUVc2R6ZNxDVWtvl9WB8+UU5xMPkGi5RJmg5mokFYlOo+iKIxsdj0ACw7Py/cYfT9fZGATIvzqOS/3NHrySq83AZi647M8IySqG30ERXllsvWunY0bOwgPL/z1Rw8ctm41FpmB8zJ5AiXP9KWmwvTp2gxSDw+VXbuMjBrlwx13eHHkiIKqwh9/mOjTx5fXXvMiKUnBw0Nb99dfF7AhsQJkz+hrUmn3WVJ79xrIyFAIDlZp2rTsDXWmD/uVt/u+T32/BsSmX+DNTa/S6cc2bDq7AcguqxZCCJGXBH0CgNOnS57pA1AUGDjQXuSbNV1lBX0AwyO1Es/KHN0Qlx5HqjUFBYUGAQ0r7X5F/vQOnqeSTrAnVivtbBnSGg+jR4nPNarZGADWnF5FQkZ8nusv38+X0zVNRtA7oi8Z9gze3/J2ie/blejdaRuWU9BXknb+bdo48PZWuXRJ4ciRwv98OTN9JZzVN2uWmYQEhUaNHGzblsr48ZkYjSpLlpjp18+XK67wYeJEb06eNFC7toNPP01n1iztPmbPNhf52rZvn4FffzVR1qaix7IyfZEunOnLOZS9PKoufcw+TGw/ic237uTzgV/TMqQVyZlJWB1Wwr1r0SyoednvRAgh3JQEfYKMDLhwoXSZvpLSg8OLFysh6MsavPvP6TVcMbMXt/51I0///Rifbf+YuQd/K3RvVmnpb4gj/OrhafQs9/OLkslZ3rknbidQ8tJOXfPgFrQJbYfNYeOvo3kbBOmDo3OWduoUReHlXq8DMOfgLOcew+qovPes6oFBccr/zObshlH6XrGCeOqZvhJ073Q44OuvtQ8E7rsvk9q1Vd5/38Lff6cxaJDWrTg62oiPj8pTT1nYtCmVm2+20bu3nY4d7WRkKPzwQ8HZvsREuOEGbx55xJvly8tWlpdd3ln06JGqUtb5fAUxG82MjbqZNTdt5MfhsxjRdBRv9H2nxuznE0KI0pCgT3DmjPaH0sdHJSSkYoO+7ExfxT/1IgOb0Ldef1RU9sfvZfmJpfywdxpvbnqVySsmMmB2P+LS48r1PqWJi2txzupLOeXs3NmulEEfwKisEs/fLyvxTLRcYk8++/ly6ly7K/3qX4nNYWPqzs9KvYaqVp7lnQ4Hzk6cxQ0MiruvrzSZvqVLTRw7ZiAwUGXcOKvz8hYtHPz8czpz56bxzDNasPfUU5n4+mrXK0p2N+PvvzeTUUBF6XvveTpf+/T5pqWRbksnJuU04Doz+vJT3AY9pWVQDAxpPIxpQ2Y452kKIYTInwR9Isd+Pke5lOAUpjLLOwFmj1jAP+P+Zea18/joys94vMtTjI26mTq+dTmZdJw7F99cqu5+BZGgz7U4yzuTT7HH2bmzfanPp+/rWxfzNxeyxkAAbMraz9cksCl1/SIKvP2jnZ8A4Od9M4hNiy31OqpKcmYSFzMuAtComI1c0tNhxQpjvrP1DhwwkJys4OOj0qpV8UrLu3XTjtMzhAXR9/SVZGTDl19qWbo77sjEzy/v9f362XniiUzq1Mn74di119qoV89BXJyBuXPzZvv++8/A999nX758edGdQQtyPFGrKAjwCCTURQeNnz2rcPq0AYNBpVOnign6hBBCFJ8EfaLEg9nLIjRUe5dTWUGf0WAkKqQlAxpeze2tx/Nsj5f4fODXzL3uTwI8AtlybjOPrJqMWtYNNsDZlDNsOLMekKDPVdTzrw9AqjWFC2nnMSgGWoe2LfX5GgdG0qlWZxyqg4VHFzgvX5+1n69PEY0k+tbrT+daXciwZ/Dt7i+Lfb8Wu4XNZzfx2fZPeOrvx6qsPPREVpYvxCsEf4/CJ207HDBnjtbw5JZbfBgzxidP4Kdngjp3tmMqZuJLz/QdOGAgKang47xMeqaveEHfjh0GNm0yYTKpTJxoLfoGlzGb4Z57tGzfV1+Zc+3ZU1V47jlPHA6FoUOt+PqqnD9vYPfu0v0Jzi7tbOKyJY36z7ZVK0e+AbQQQojKJUGfcM7oK8lg9tKq7ExfQZoHt+D/hv6EyWBi/uG5vLflrQKP/S9uD0uOLWJP7C4SMuJzBYgJGfH8uO8HRv9+DR1ntGLt6dUAJe4OKSqGt8mbMO9w57+bB7XA1+xbpnOOzGrokrOLp76fr08RLeMVReHhrGzftP++KXRf6b9nN/PmxlcZMX8Izb6rz4j5g3lz0ytM3zuNT7d9XKbHUFrFLe3ctMnIsGE+3H+/t7OSYMcOI/fc4401RzxVkv18uvBwlUaNHKiq4uz8mR+vrAHtxR3Z8NVX2l6+0aNt1K1bug+BbrvNip+fyoEDRlavzl7bnDkmNm824eOj8s47Fq680gbAsmWlK/F0Bn0uXNpZXkPZhRBClA8J+gQxMXrQV/GZPj3oq4xGLkXpV/8KPuj/PwA+3vo+s6J/cV6nqip/n1rNmAUjGPBbH+5YPI6Bs/sR9X1jIr+NoO+v3bh23mDa/tCcJ9Y8zPoz/6Ci0qNuLz668jOGNh5eRY9KXK6hf3YX1bZlKO3UjWw6GoBNZzZwNuUMlzIS2JO1X7BPRL8ibz80cjhRwS1Jzkzih73T8j3mx30/cO38QXy242M2n92IxW4hzDucrrW1MSSbzq4v8+MoDeeMPv/G+V5/9KjC+PFeXHedDzt2GPHzU3nhBQvz56fh7a2ycqWJJ57wcmbBSju4Ww8SCyrxjIlRMJEV9BUj03f6tMIff2gB2KRJmSVaS04BAXDrrVpUO3WqFkQmJ2uD3gEefzyTevVUBg/Wgr7ly0sX9B275PqdOyt6P58QQoiSKf1OcuE29HENlZnpS0tTSE3F2Qihqtza+g6OJh5hyo5PeHzNQ9T3b0B8xkU+2/4Ju2J3AGAymGgZ0ppzqWeJS48lzZbKwYQDznO0Dm3L9c1vZHTzMc49ZMJ11PdvyPYL2wBoH96xzOer51+f7nV68u+5TfxxZD6NAiJRUWkW1JzavnWKvL1BMfBQ58d4cOV9fLXrC+5pPxnvrFJEgLkHf+PJNY8A2tiRIY2H0aNuTyIDm5Jgiafl95EcTDjAxfSLhHpX7n6uk8nHgfwzfSkpMGKED7Gx2j6u22+38tRTmdSqpf3Of/ttOnfe6c3MmWbq1nUwaVImhw9nt/Qvia5d7cyZY863mcv8+SYmT/YiaKIfRBSvkcu333pgtyv062ejXbuyvQ7ee28m335rZu1aE3v3Gpg508yFCwaaNHFw331aQDlwoB1F0WYAnjun5LtHsDCuPpjdYsFZuiqZPiGEcA0S9Ikc5Z0Vn+nz9QVvb5X0dIW4OAVf34q/z6K80PMVjiUeZeHRBYxecI3zch+TD7e1vpNJHR50tv5Pt6VzNiWG0ymnOZ96jrZh7WkV2rqqli6KQf/ZQenHNVxudPMx/HtuE78fnkvXOj0A6F2MLJ/z9s1u4L1/3+JU8kl+2f8jE9rdC8DiY3/x4Mr7UFG5q+1E3u33Ua49WyFeoUQFt+RAQjT/ntvEsMhrCrqLCnEi8TgADfNp4rJ1q5HYWAPh4Q7mzk2nZcvcwdPgwXY++MDC44978cknnhw6pL3uNGtmJySkZOvQs0fbt2tD2g1ZNStr1xp58EEvHA6F+PPeWtBXxMiG5GT46SetwUpZsny6Bg1URoywsWCBmRde8GTzZi0wffvtDDyzprjUqqXSubODbduMLF9u4vbbi7+HUFVV54dOrlreuXu3gcxMhdBQB5GRVf8aL4QQQso7azyHI3tkQ2Vk+hQFQkNdY1+fzqAY+Hzg13Su1QWAIM8gnuz6LNtu38ubfd/LFTR4m7xpEtSM/vWv5MaocRLwVQMNcvz82oa1K5dzXtt0FAbFwLbzW/nj8Hyg6P18OZmNZh7opGXzpu78DKvdyppTq7hn6Z3YVTs3thjHO/0+zLdJR4+6vQGtvLSyFdadVs+69etnzxPw6W67zcrTT2tB2MKFWqDVtWvJX3datXLg46OSlKRw8KD2Z2zPHgPjx3tjtSo0bOgAm5Y9vZRSeKbv55/NJCcrNG9uZ+DA8slK6eMbNmwwYbcrDB9uZcCA3OceNEgv8SzZvL7Dlw4Rlx6Lp9GTNqHl83wuLZuNfIfMZ8/nq/iO0EIIIYpHgr4aLjZWITNTwWBQS1xiVFqu0swlJx+zD3Ou+4Mfh89i+x37eLr785VeOicqhh6gNApoTKBnULmcs7ZPbec8vrOpZ4CC5/MV5OaWtxHmHc6p5JO8suF5xi++hUxHJtc0uY5PB0zFoOT/8tyjbk8ANp+t3KDPoTqcXUMLC/qKKud74olMbr89O6NWmvI/k0nr+Anavr6TJxVuvtmblBSF3r1trFmTSrC/tqfu7/W2As8TF6fw5Zf6MHarM2NYVp07O+jeXbtfLy+V11/Pm23U9/X9/beJ9OKPEuSfmL8B6F6nJ14mr7IvtpQsFujXz5euXX2ZMyf3+InSNOgRQghRsSToq+H0/Xx166qY846WqhCu1MwlJz8Pf4Y0HoafWfqLu5P+9a9iYrv7eLvv++V63lFZXTxB6wpanP18OXmbvJnU4QEAvtvzNWm2NAY0vJqvBk3DZCi48r5nhJbp2x23i1RrailWXjoX0s6TYc/AoBio51c/13UOB85OmkW90VcUeO89CzfeaCUiwsGQIQUHZYXRg8Xly42MG+fNhQsGWrWyM316On5+MKC/FsztibawZ0/eP3WpqXDrrd6cPWugYUMHN95Y8jENhXn++UyCglReecVCw4Z5P1Br08ZBRISD9HSF9euLn+1bd3otoI3/qEr//WfgyBEDp04ZuP9+b4YO9WHjRiOqWvwPAIQQQlQeCfpquJyD2StLdqZPnn6i4nkYPXi73wcMajy0XM97TZPrMCram9ve9Yq/ny+n8W0mEOARCECviD58P+QnPI2ehd6mgX9D6vnVx+awsf381lLdb2kczyrtrO/XALMx9ydEhw4ZSExU8PZWad266NcSkwm++CKDHTtSqV27dBUGekCxZImZw4eN1KvnYObMdAK1byfNGmtBH0YLzzzjlSsTZbXCPfd4s2OHkZAQBzNnpuHtTbnq3dvOwYMpTJiQO5hUVRWbw4aiZJd4Fnd0g0N1sD4mK+irX7VB365d2nO/Xj0Hfn4qO3caGTnSh3HjvDl3zoDRqNKhgwR9QgjhKuRddw2X3bmz8jbbh4Vp775iY10r0ydESYR6hzI0q5HK0MbDSnWOAM9Apl79Dfd1eICfhs/Cx+xTrNvpJZ6bKrHE82Qx9vN17GgvUcVAWfZ7demSHcUFBanMnJmea76el1GL4oxe6WzdauS337TASlXhqac8WbHChLe3yk8/pdOsWeW8/tkddu5achstv48kJvm0M8u5fLkp371xl9sbt4cESwJ+Zn86Ze1Brip6d86bbrKyaVMqd96ZicGgsnq19n1u08ZR5d2ZhRBCZJOgr4bTM32V0cRF54p7+oQojc8GTGXJmFUMbDS41OcY3HgYb/R5B3+PgGLfxtnM5ezGUt9vSTln9OXbubPy2/OHhqr06mXD11dlxox0oqJyv4bp+91atNFKYF9/3ZPERHjvPQ9++cUDg0Hlm2/SS9VIprTe3/IWi479SVJmIv/E/E2fPna8vVViYgzs3Vv0n+N/srJ8vSJ6F1oCXBn0TF/79g5q1VL54AMLf/+dxtVXa4Hs6NHlWy4rhBCibGRkQw0XE1P5mT5X694pRGn5ewTQuXbXSr/fnllB37ZzW7DarXnKLSvCyaQTQOGZvspu3DFnTjrp6dpQ9Mt5GbWgr17DNKzN7Bw+bGTMGB9279bW+uGHFoYMqbz1Lj2+mE+2fej89/6L+xjXEvr3t7N0qYlly0y0bVv4yIh1p7UmLn3rXVGhay1KRgYcOKAFqTlLOKOiHPzySzopKVU/g1UIIURukumr4bJn9FXep93h4a7ZyEWI6iIqpCVBnkGk2VL5L253pdxnQZm+S5fgwAF9yHrlvY4AmM35B3yQnemzqBm8/bbWPVMP+J56ysJtt1VeJup44jEeWKHNYmyYFTRHx+8Dsrt4Ll9e+GewVruVjVnlvFW9n2/fPgM2mzaHLyIi7weGfn5lK90VQghR/iToq+FiYipvMLtOyjuFKBuDYqB7HX1fX+WUeBY0o2/7di2QatzY4fxAxxV4ZmX6LLYMrrzSzsiRWpB3++2ZPPlk2YewF1e6LZ27l95OUmYiXWt359OrvgDgQHw0kN3MZft2AxcuFPyauOPCdlKtKYR4hdAmtG3FL7wQevDcvr3M4RNCiOpCgr4aLCUFLl2qvMHsupxBX3GaFwgh8uqRNbphcyUEfRm2DM6lngWgUUBkrutcdSabd1amL8OeAcBnn2Xwxx9pfPCBpdICFVVVeWbt4/wXt5sw7zCmDZlB2zBtoPqZ1BgSLZeoU0frcqmqCitXFjy6YV3WfL4+9foXOMOxsuhNXKQ7pxBCVB+yp68G05u4BAWp+FXiaDp9T5/NppCYCEFBlXffQriLHnV6AfDvuY2oqopSgZHM6eRTqKj4mHwJ9QrNdZ0e9LnaTDZPU3amD8DbG3r2LJ81qqpKbHosMcmnOJ1ympiUUxgVI/X8GlDfvz71/RsQ7BnCT/unMzP6ZwyKga8H/R91/SIAiPCtx5nUGKLjo+lRtyeDBtnYtcvI0qUmbr45/7mF62JcYz4f5G7iIoQQonqQoK8GO3NGe5MYEVG5f7g9PSEgQCUpSSEuTiEoSNJ9QpRUx1qd8DJ6EZcex+FLh2ge3KLC7utE0jFAK+3MGVza7dnlna4W9OmNXNKzMn3l4Y/D83nn3zc4nXwKi91S6LE+Jh/nMc/3eIV+9bObr7QMbcWZ1BgOxO+nR92eDBtm48MPPVm0yMzvv9sYNSp34JduS2fLuc0A9KviJi4WC0RHS6ZPCCGqGynvrMH00k4981aZsjt4ylNQiNLwMHo4O4dW9Ly+E8lZnTsDG+e6/MABAykpCj4+Kq1auVbWx8ukzenLsKWX2zm/2PkpRy4dxmK3oKBQ1zeCrrW7M7Lp9YxoOorOtbpQy6c2AGm2NOyqneGRI3io06O5zhMV3ArIbubSrp2DiRO1fYYPPujFunW5yzy3nNuMxW6hjm9dmgY1K7fHUxr79xuwWhVCQhyVuhdcCCFE2UimrwZLSNCCvsDAyv/DHRamcuyYNHMRoix61O3JhjPr2Hx2I7e3Hl9h93Mi8TgAjfxzd+7URzV06WLH5GJ/TfRMX1EZueJyqA5n85UFoxbTpXY3PIwe+R5rsVs4kxJDXHosHcM75ym9bRXaGoDo+P3Oy954w8L58wp//mnmzju9WbAgjbZttUB63ens0s6KLOMtjpylndLERQghqg+XS7NER8OgQdqMnzp14OmnIbOIRmtnz2rHdewI/v5Qvz7ccgucOFEpS6629ExfcHBVBH3amxkJ+oQovcoa0n4yOf8Zfa66nw+yRzaUV6bveNIx0mxpeBo96V6nZ4EBH4Cn0ZPIwCZ0q9Mj3xmKLUNyZ/oAjEb44osMevWykZyscPPN3pw6pb0+/pPVxKV//SvL5bGUhTRxEUKI0omOi2bQj4PwfduXOh/W4enlT5NpLzzIOZt8lqeXP03Hrzri/44/9T+uzy1zb+HEpZIHOS4V9CUkwIABWpA3bx68/TZ88w08/njht9u2TTt+7FhYsAA+/hj27IHu3SE2tnLWXh3pQV9VZfpAgj4hyqJbne4YFAMnk45zNuVMhd1PQTP69EyfKwZ9njkyfWo5tAmOvqhl5VoEt8RoKLjLZnE0D45CQSEuPY7YtOw/Ul5eMH16Oi1b2jl/3sC4cd6cPJ/EzgvbAehTr1+Z7rc8SBMXIYQouYT0BAZMH0CmPZN5Y+fx9sC3+WbbNzy+tPAgZ9vZbczbP4+xbcayYNwCPh7yMXsu7KH7d92JTS1ZkONSBTlffQVJSTB/PoSEaJfZbHD//fD88xARkf/t+vbVMoQ5y4t694aGDWHGDHjiiYpfe3WkB31V0T1Tn+clQZ8QpefvEUDbsPbsjt3J5rMbGdV8TLnfh6qqOWb0ZY9riI+HI0e0zw27dHG9oE8f2QDa2AbvrD1+pbU/fi+QXZpZFr5mXxoGNOJE0nEOJOwn3CfceV1QEMycmc7w4T4cOmTk5me3Yu9mp3FAJA38G5b5vsvCYtH29AG0b+96P3MhhHBVX239iiRLEvNvmk+Itxbk2Bw27v/rfp7v9zwR/vkHOX0b9iX6wWhMhuwgp3eD3jT8pCEzds3gid7FD3JcKtO3eDFcfXV2wAda9s7hgGXLCr5dUBB59pPUrw/h4XCm4j78rvYSE7X/VkV5Z3YjFwn6hCiLHs4h7RXTzOWSJYHkzCSAXEHHtm1axqdZM3uu12xXoWf6IHtsQ1nomb5WIW3KfC7tPFn7+i7uy3NdRITKzJnpBAaqHLJppZ05u39WlehorYlLUJBKw4bSxEUIIYpr8eHFXN3kamfABzC2zVgcqoNlRwoOcoK8gnIFfAD1A+oT7hvOmeSSBTkuFfRFR0PLlrkvCwqCunW160ri4EG4cAFatSq35bkdvZFLVYxM0Ms7L16UoE+IsugZUbH7+vQsXy2f2viYfZyXZ+/nc80yP7PRjFHR1phRDmMbsjN95fNHpaUe9MXn/8etZUsHL79sgchVgGvM59u9Wy/ttEsTFyGEKIHouGhahuUOcoK8gqjrX5fouJIFOQcvHuRC6gVahZfs75FLlXcmJORfahgcrJUSFZeqwsMPa+WgN99c8HEDBw4s8LqlS5diNBrx9Kz6b5HBoP11NZuNGI3lF6dHRBjo1AmaNFEq/XG2aKHQqRPUqmVwie+xENVVv0Z9Adh/cS9J9njCfWoVevzs6Fl8suUjPr36c7rV7V7k+WPSTgEQGRiZ63c1OdlEp05w7bWqy/4Oe5u8SbGmYDdYy7RGi83CkUuHAehQt325PN62tbWM4cFL+ws8X59BsZC8C4Beda6q8u9zUpKRTp1gxAjX/ZkLIURFOnPmDLfffnuB169cuTLfyxMyEgjyCspzebBXMPHpxQ9yVFXl4cUPE+Efwc1tCwly8uGWr9qvvgorV8KSJVoX0LIICCjbPpDy5OfnVfRBJTBvnv5/5Xve4rjySti+HbRks+t8j4WobgICIulUpxM7zu3gmbVPMPvG2QW29d8Xu48Hl08mw5bB3YvvYNekXfn+EcrpvCUGgObhzXK9Hn73nf5/BXexrGpeZi9SrCmYvcr2Wr7r3EHsqp0gryCi6jYtl7EJ3Rp1BmD/xX34+3vle86Thk0AtK3VlnZNGpf5Psvq1Ve1LzBnfQkhhKhMr655lZXHVrLk1iX4epQsyHGpoC84OHufWU4JCRR7z8i338Lrr8O0aVBIIg8oOBrXqapKUlL5DfYtLYNBwc/Pi5SUDByO8ivFHDzYi8xMhVmz0qldu9xOWywJCTB6tDeKorJiRQbGsjXDE6JG+2TAFK6eeSVz98/ly03fcFubO/IcY7FZGDd7HBlZ+9tOJp5kwvx7+H749EKDmOgLBwGI8K7vfD08dEjhnnu88PVV+eMP1/391Wf1xV5KIMmr9K/l/57YBkDr0DYkJ5e9VBSgrrkhRsVIoiWR6DOHqedfP88xC/b9AcD5jQN4P9nKpEm2crnv0rBaYfhwL6xWhV9+SS+wsZoQQrgrf38vIiIiiowf8hPsFUxiRt4gJyEjIdc+v8J8u+1bXl/7OtOum8bAJkUEOflwqaCvZcu8e/cSE7U5fJfv9cvP/PkwebIW9N19d/msyWKpuj+yOpNJK+m0Wu3YbOWzfyY9HTZvVrLOb8NSPvOLi83TE3buVFFVhTNn7NSqJU0BhCit1kHtebb7S7y56RWeWvUEXcJ70CSwaa5jXln/Mnti9xDqFconV33BXUtuZd7BOQxoMIixUQWXiBxLOAZAfd9GztfDFSvM7NgBV1xhx2azYav6l8l8eRg8AUjOSCvTa/me8/8BEBXcqhz/JhhpGtSMgwkH2H3uP8I86uS61mK38OfhPwGIXXs9v+4xcNddVfeN3rPHwL//ak1cQkIq/2+GEEJUNX//0t+2ZVhLoi/mDnISMxI5m3w2z16//MzfP5/Jf03m9Stf5+5OpQtyXKqRy7BhsGIFXLqUfdns2WAwwODBhd92zRpt/94998BLL1XkKt2DPq7BaFTLXAJbGkYjhIRIMxchyssDHR+mT0Q/0myp3L98Ila71Xnd36dW8+WuKQD8b8BUhkYO5+luzwPw7NonOZ54rMDzOmf0+Wsz+lQVVqzQPi90xfl8OXlljWko64D2/Re1Ji5685XyEuUc0r4/z3VrTq0iKTORcK86cLIvu3cb8q2E0cXEKHTt6ssLL3iW6xp1+ny+du2kiYsQQpTUsGbDWHF0BZcyLjkvm71vNgbFwOCmhQc5a46v4ea5N3NP53t46YrSBzkuFfRNmqRF0aNGaSMa/u//4KmntMtzlpIMHAjNmmX/e/9+7TbNm8Ptt8OmTdlfR45U9qOoHvTOncHBapX9AZcB7UKUH6PByOcDvybQM4jtF7bx0dZ3AYjPuMiDK+8DYHybCQxpPAyAhzs/To+6vUixJnP/inuwOfJmkewOO6dTtEYujQIaA/Deex6sWGHCYFAZOtRFU3xZvIxaAGSxly0ttT9eG6vQKrR8xjXoWjqDvrxjG34/NBeA0S1G06wpOBwKGzcWXEf7449mTp408N13Zo4dK//X1F279Pl8rtmtVQghXNmkrpPw9/Bn1MxRLDuyjP/b8X88tfwpJnWdlGtG38AZA2n2WXaQsz92P6NmjqJ5aHNu73A7m05vcn4diS9ZkONSQV9wsNaAxWTSgrhnn4WJE+Hjj3MfZ7eTq5xo82atDPS//6BPH+jVK/vrjTcq9SFUG4mJVTeuQSdBnxDlq55/fT664lMA/rf9Izad2cDjqx/mfNo5WgRH8Wrvt5zHGg1Gpl79Lf4eAWw9/y+fbPsgz/nOpMZgc9jwMHhQx7cuP/xg5uOPtUDqgw8sdOjg2gFAeWT6kiyJxKScBqBlcDH2GZRAywJm9aXb0llyfBEAI5tdT58+WkZ1/fr8d2SoKsyda876f4Vvvin/5jr6uIYOHVw7uyuEEK4o2DuYlXesxGQwMWrmKJ5d+SwTO03k4yG5gxy7w57rQ9jNMZtJtCTy34X/6PN9H3pN6+X8emNtyYIcl9rTB9pcvRUrCj9mzZrc/x4/XvsSxadn+gIDq24NEvQJUf6uazaam08u59fon7j5rxtItaZgNpj5ctC0XHP2QBu2/sEVnzBp+QQ+2voePev2po5vXU4nnyIm5TTbz28FoL5/A5Yu8eTZZ7WA78knLdx+uzXPfbsaL5PWyCW9DEHf/qzSywjfegR5BZfLunR6pu9gwgEcqgODon0Ou+LEMlKtKTTwb0jX2t0528/O9Onwzz/5Z/q2bjVw4oQBg0HF4VD49VczzzxjyXcEUmlYrbBvn57pk6BPCCFKo1V4K1bcUXiQs2b8mlz/Ht9xPOM7ji+X+3e5oE9UDn3fZHCwZPqEcDdv9X2PjWfWczxJ26v3Qs9XaRfWPt9jr29+IytOLGPOwVmM+WNEvsfUUlozaZIXDofC7bdn8tRTmRW29vLkmdW9syzlndHO0s7y3c8HEBnYBA+DB2m2NE4mnaBxYCQACw5r83SuazoaRVHo3VsLtPbtM3LxokJoaO7X7TlztCzf9dfb2LfPwL59RmbM8ODhh8vn53TggAGLRSEgQCUyUppuCSFEdeRS5Z2i8uiNXAIDJegTwt34efjz1aBp+Jr9GNxoKJM6PFDo8e/2+5BmQc0B8DX70TKkFQMbDuKO1ndzb9NX2ffhF2RkKAwZYuO99yzVppGHd1amryzlnRXVxAXAZDDRPDgKyG7mkmJNYfmJJQCMbj4G0F4rW7XSAr8NG3Jn+6xWWLBA+/z2xhutTJqkBXrffWcms5xi8927s7N81eVnL4QQIjfJ9NVQetBXlZk+/dNqCfqEKH+da3dl311H8DR6OssGCxLgGcjfN20izZZKgEegc25fXJzCoEE+JMUY6NLFztdfp2OqRn81yiPTt78CM32glXjuvbiHA/H7GRo5nGXHF5NuSycysAntwjo4j+vb187+/UbWrTMyYkT2fo/Vq43ExxsID3fQr58dhwPeesvBuXMGfv/dxNixZW+2o3fulCYuQghRfUmmr4bSgz7XaOQiT0MhKoK3ybvIgE9nNpoJ9AzKNaj9hx/MxMQYaNLEwU8/pePjU8gJXFBZ9/SpqupsslLenTt1+r4+Pbj8Pau0c3SzMbl+Fnozl3Xrcmf6cpZ2mkzg4QETJmj7Lb/80gO1jC/xFy8qzJ+v3UeXLrKfTwghqit5t11DuVbQJ5k+IVyNqmYHFI89Zsmzj6w6KGum73zaORIsCRgVI82DWpTn0pxaZmUQo+P3k2i5xKoTywEY2WxMruN697ahKCqHDhk5f157zUxOhiVLtNTrmDHZjXXuvDMTHx+VvXuNeYLEknr7bQ8uXVJo08bOsGGuPaJDCCFEwSToq6H07p1VGfSFh2ulQhL0CeF6du40cPSoAW9vlWuuqZ5v9su6p29/VpavSWBTZ9awvOmZvsMJB1l45A8yHZm0DGmVp5w0KAjatdNeM9ev1wK5v/4ykZGh0KyZPdf4jOBguOmm7Gxfae3caeCnn7TA/513LNWqtFcIIURuEvTVUK40py85WcFSttnJQohypmf5hg614edXxYspJS+jPqcvo1S310suW1bQfj7Qxmb4mHzIdGTy2Q5tXtPIZtfne2zfvrlLPPXZfGPG2PI0WLnvvkwURWXFChMHD5b8T73DAc8+64WqKtx4o5WePaW0UwghqjMJ+mooV8j0BQSA2azd/8WLku0TwlXYbDB/vpbWueEG15/HVxBPPdNnL2XQl9W5s1UFdO7UGRQDUSHa0PdjiUcBGFVg0KdlXNetM3HunOKc25eztFPXpInKkCHa8V9/bS7xun75xcz27Ub8/FReflk+lRNCiOpOijVqKD3TF1y+s4ZLRFG0Dp7nzinExSlERFS/PUNCuKO1a43ExRkIDXVw5ZXVN8Pj5SzvLF3Qp49RqKgmLrqWIa3ZcWE7AO3COtA0a3zG5Xr2tGM0qhw/buDzzz1wOBS6dbPTuHH+r533329lyRIzv/1mpmVLB4bLPuZt2tTBFVfkHcOQkABvvqmVhT79tIXateW1WQghqjsJ+mogux0SE7X/r8o5faCVeJ47J/v6hHAlemnnyJE2zCVPErkML2cjl5IHfXaHnQN60Je1766iROU4f0GlnQB+ftCxo4Nt24x8951e2llwJrZHDzsdO9rZudPICy/kvyexZ08br79uoWPH7D2B777rSXy8gZYt7c5OoEIIIao3CfpqoKQkUNWqL++E7H19sbES9AnhClJSYNGi6l/aCWXL9J1IOkaGPQNvkzeNAiLLe2m5tMwR9BVU2qnr18/Gtm1GHA4Fk0ll5MiCm+woCnz0UQZTp3pgvexHabPB6tUmNm0yMXiwiTFjrLzwgoWEBIXp07WA8u23LdU66BdCCJFNgr4aSB/X4OOj4lH6xm7lQsY2COFaliwxkZam0Lixgy5dqvcwbn1kQ2n29O3L6tzZIrglRkPZxh4UpXvdnrQL60DbsHY0DGhU6LF9+tj53/+0/x8wwF7kKI127Rx8+WX+j//MGYW33/bkt9/MzJ1r5q+/TISHqzgcCqNGWZ2NY4QQQlR/0silBtKDvuDgqt+noQd90shFCNeQ3RHSmmevV3XjXYZM3/74rCYuFdi5U+dn9mPl2H/4dMDUIo/t1s3ubIBV1kxsRITK559nsHx5Kr1728jIUDh1yoCPj8qrr0rzFiGEcCeS6auBXKFzpy470yefPwhR1WJjFdas0bJa1b20E3Jk+koxp8/ZxCWkYpu4lJSPD7z8soV9+4zlNiy9QwcH8+ens2SJiR9+MHPLLVZprCWEEG5Ggr4ayBVm9OnCwmRAuxAlkZlJscuyrVYwmSh2xm7BAhN2u0KnTnaaNq3614ey0vf0Wewlz1rp4xpaVnATl9K47z4rUL5BuaLAsGG2cgskhRBCuBZJr9RArpnpk6BPiKLMm2eifn1/Zs8u+vO6o0cVWrXyo3dvXxYvNqEW49dd79rpDlk+yB7Onl7CTF+GLYOjiUcAaF3B4xqEEEKIyiBBXw2UPaNPgj4hqpOZM7WgbOrUolN9M2Z4kJSkcOSIgTvv9Ob6673Zvbvgl/yjRxW2bzdiNBbeEbI68TJ5AiXP9B1KOIBDdRDsGUwtn9oVsTQhhBCiUknQVwPpmb7AwCpeCLkbuRQnEyFETZWZCf/+q+2327vXyP79Bb982+0wf76WDRw61Iqnp8r69SYGDfLhwQe9OHMm74csepbviivs1KrlHr+MeqavpHv6Np/dCGhD2ZXq3s1GCCGEQIK+GsmVunfq7cbT0xVSU6t4McKlOBzw228m+vTxYeJELzIzq3pFVWv7diNpadkByNy5BZd4bthg5OxZA4GBKt9+m8HGjamMGWNFVRV++81Mp06+NGjgl+vro4+07KG7lHYCeGbt6ct0ZOJQizd+QlVVftw3HYBrmoyosLUJIYQQlUmCvhro0iXtv4GBVR/0+fpq8wJBSjxFtg0bjAwZ4sODD3pz6JCRP/4w88gjXjiq99i4Mlm3TsvyhYZq34R588wFfj/0gPC666x4ekL9+ipffpnB0qWp9OxpQ1UVLJbcX6qqUK+eg6FD3aO0E7IbuUDxxzZsOfcv++P34m3y5sYW4ypqaUIIIUSlku6dNZArZfpAK/E8eVIhLk6hcWPXWJOoGkeOKLz2midLlmilhn5+KmPHWpkxQxseXaeOyiuv1Mz5YevXa0Hfo49m8v77npw+bWDzZiO9euUeoJ2eDn/+qTdkyR3AderkYMGCdC5cULDlE9uFhal4elbM+quClzFH0GdPx8fsU+Rtpu+dBsCoZmMI8gqusLUJIYQQlUkyfTWQK41sAGnmIjS//GKiXz9fliwxYzSqjB+fyebNqbz7roWPP9ayNF984cE335ireKWVLz0dtmzRgr6rr7YxYoRWgjlnTt7P7ZYvN5GcrFC/voMePex5rlcUqF1bpV69vF/uFPABmAwmTAbte2SxFf1hQXzGRf44Mh+A8W0mVOjahBBCiMokQV8N5EojGyBnMxd5OtZkH3/sic2mcNVVNtasSeP99y2Eh2vPjXHjbLzwgvam/aWXPFmwoGYVKWzdaiQzU6FOHQdNmqjODN4ff5ixXBbL6IHgmDFWDPIrlT22wV50M5dZ0b9isVtoH96RjrU6V/TShBBCiEojbwlqIL2803WCPhnQXtNlZMCpU9rPf8qUDKKi8m5We/jhTO6+OxNVVXjgAS/nHreaQH+sffrYURTo3dtORISDxESFFSuyA+D4eFi5Ug/63GdvXlk4xzYUkelTVdVZ2nlnm7ula6cQQgi3IkFfDZOeDhkZrhX06R08Y2PlTVZNdfy4AVVV8PdXndm9yykKvPWWhWuvtZKZqXDnnd7s3VszXsLWrdMCuX79tEDOYIDRo7X/z1ni+ccfZqxWhbZt7bRsWYO73uRQ3LEN62LWcjTxCH5mf0Y3v6EyliaEEEJUmprxjkk46fv5jEYVf/8qXkwWvXnLrFlmoqPlKVkTHTmi/dybNnVQWILFaISpUzPo2dNGcrLCzTd7c/q0e39YkJICO3Zo358+fbL36OmjFZYvN5GYqF2md+10p7ELZeVZzAHt0/d+D8CNUTfhZ/ar8HUJIYQQlUneYdcwOUs7XaV66YYbrHTvbiMxUWHcOG9iYlxkYaLS5Az6iuLlBTNmpNOypZ1z5wyMG+dNQkJFr7Dq/PuvEZtNoWFDB40aZWdB27Rx0KqVncxMhT//NHPihMLmzSYURXVmAUWOPX2FZPrOp55j0bE/Abij9d2Vsi4hhBCiMknQV8PoQV9gYBUvJAdvb/jxx3RatLBz5oyBm2/2ds4SFDVDSYI+gKAg+PXXdOrWdXDwoJHbbvMhveg+HdXSP/9o2bucWT6dvm9vzhwT8+ZpXU379rVTt65rlG67An1PX4a94Dl9v+z/EZvDRrc6PWgT1rayliaEEEJUGgn6ahi9c6erzOjTBQfDzJnp1KnjIDrayJ13epNRvFnKwg0cOaI9L4sb9AHUq6cyc2Y6gYEqW7YYmTTJC3veuKja0+fz9e2bN3s3ZoxWxrlhg4np0/XZfFLamZOe6bMUMJzd7rDz474fABnTIIQQwn1J0FfD6Ht/XKWJS07162tv4v39VTZuNHH//e75Jl7kdfRoyTJ9ulatHMyYkY6np8rixWaefdYT1fWe2qWWmAi7d2vfm7598/4y1Kun0ru3FgyeOWPAy0vlmmuktDMnL5M2oL2gTN+qk8s5nXKKYM9gRjQdVYkrE0IIISqPBH01jJ7pCwx0zXfGrVtrb+I9PFQWLjTzwgvu9SZe5HXpEsTFaS9FTZqUvONkr152pk7NQFFUpk/34JNPPMp5hVVn40YjDodC06aOAks29Zl9AEOG2AgIqKzVVQ+exqygr4BMn97AZVzL25wBohBCCOFuJOirYfQ9fa5W3plTnz52vvhCexP//fcebNxYc+ax1UR6lq92bQd+pWyaOGKEjbff1rozvvuup3PmX3W3fr2+n6/g7N2IEVY8PLTfZ73cU2TLzvTl3fRptVtZeXI5ALe2uqNS1yWEEEJUJgn6apjsRi6uG/QBjBxpY+xY7Y3uokWmIo4W1dnhw9rLULNmZZsrN2GClZYttRLIQ4fc46Xtn3+0Dzz69Su4zjkwED75JINHHrEwaJDUQ1/OKyvTl99w9osZcdhVO0bFSLPg5pW9NCGEEKLSuMc7I1Fs1SHTpxs6VAv6li41SYmnG9MzfaUp7bxcvXraE+Xs2ap9aYuPh6Sksp3j4kWFffu0oK9378KDuRtvtPHCC5kYJSmeh57pS88n03ch7TwA4T61MCjy51AIIYT7kr9yNUzOOX2u7oorbHh4qJw4YXBmg4T7Kem4hsJERGjnqMpZjykp0KePLz17+jq7kpbGhg1aBNeqlZ3wcNf/fXVVhe3p04O+Wj61K3VNQgghRGWTd9I1THUK+vz8smeTLV0qKQx3Vb5Bn/a8PnOm6oK+DRuMXLxoIC7OwE03+XD+fOnWsm6d9pzPbz6fKD7vrEyfJZ/unRfSLgBQy7tWpa5JCCGEqGwS9NUwevfOoKCqXUdxDR6slXguXy77+tyRqpZ+XEN+6tXTznHmTNW9tK1bl/1cPXnSwK23epOSUvDxqqp1ME1IyP2l7+fLb1SDKD4vkzanTzJ9QgghajIJ+mqYxMTqk+kDGDRIC/r+/ddIQkIVL0aUu3PnFNLSFIxGlUaNyv6c1McaVGWmT8/QPfechbAwB7t3G7nrLm8yM/Meu3atkauv9qFFC3+ionJ/HT5sRFGy5/CJ0pHyTiGEEEKCvhrF4dAyClB9gr6GDVVatbJjtyusWiXZPnej79Vs1EjFbC77+fRMX0yMoUqa/8THw9692mO65RYrv/ySjo+Pyt9/m3j0US8cWcnMQ4cM3HabNzfc4MOePQWXLl9/va3aZOVdVWHD2Z3lnT5S3imEEMK9ybvoGiQ5GVS1emX6QMv27d9vZPlyE2PGSNbDnZTnfj7IzvSlpiokJ1Ppg8o3bDChqgpRUXZq11apXVvl++/Tue02b+bMMRMcrGK3w/TpZux2BZNJ5c47rTz+eGa+HXVN8gpdZtkjGyTTJ4QQouaSTF8Nou/n8/FR8fSs4sWUgD57bOVKEzaJ+dyKHvSVx7gGAF/f7A80YmIq/+Vt/fq8zVcGDLDz8cdawPHttx58/70HdrvC0KFW1q5N5Z13LISHq5hM5PkSZVd4pk+CPiGEEDWDBH01SHXq3JlT1652QkIcJCYq/PuvdPF0J+XZxEWnj22oin19+QV9AOPG2XjpJW04ePv2dubPT2PGjAyaNatev4vVkb6nL92W35w+Ke8UQghRM0jQV4PoQV9gYPV6o2k0wsCB2pvoZcsk/eFOyru8E3KObajcl7cLFxSio/WgL29K+qGHMtm3L4Vly9JkDEMl8nKObLDkujzVmkqKNRmQTJ8QQgj3J0FfDaIHffntHXJ12aMbJNPnLqxWOHFCe042a1b+mb7KHtCuD1Nv08ZOSEj+x4SFqRjkVbdSeTm7d+bO9MVmZfl8TD74mv0qfV1CCCFEZZK3HzVI9oy+6hf0XXWVDZNJ5dAhI0ePVl07flF+TpxQsNsVfHxU6tQpv+dkvXrauc6erdyXN5mr55oKyvTppZ3hPrVQFHlNEUII4d4k6KtBqtuMvpwCAqBXL+3NtAxqdw85m7iU53vuunWrJtO3fr32vOzbV7oNuRJ9OPvle/qkiYsQQoiaRIK+GiQ701e16ygtvcRT9vW5h4rYzwfZmb7SNnJZuNDErFkle46dOaNw9KgBg0F1fjghXIOXUWtVfHmm73zaOUCCPiGEEDWDBH01SGKi9t/quKcPtHl9ABs3GklKquLFiDKruKBP795Z8gHtKSlw331ePPSQN4cPFz9oXLdOK+3s0MFR6bMBReH0TF/ePX16pk86dwohhHB/EvTVIHqmr7p179Q1aaLSrJkdm01hzRrJ9lV3FTGuAXDuD0xLU5wfdBTXf/8ZsVq135OlS4v/HNNLO/Pr2imqlj6yweqwYndkZ2GzxzVIpk8IIYT7k6CvBqnO3Tt1gweXbHTDnj0G/vrLVOKMj6h4FZXp8/GBkBB9X1/JXuJ2784+viR7R/VMnzRxcT16IxfIPaBd9vQJIYSoSSToq0Gq65y+nPR9fUuXmkhJKfzYlBS44QYf7rrLmylTPCphdaK4UlLg3LnsRi7lTZ/Vd/Zsyfb17dqVPRJk82Yjly4VfZsTJxROnTJgMql07y5Bn6vRRzYAZNgk6BNCCFEzSdBXg7hDpq9HDzuRkQ4SExVmzTIXeuysWWZnSeubb3qWuDmHqDh6aWdYmKNCGgvpQV9pM31Go4rdrrBqVdHPmXXrtGM6d7bjJ+PeXI7RYMRs0F4rLLkyfVnlnd6yp08IIYT7k6CvBqnOIxt0RiPce28mAF9/7YG9gMSK3Q5ffaVl99q21Q567DEvVq2S4e6uIOe4hoqgD2gvSQfP1FQ4dEhb15gxxe8UK6Wdru/yZi4O1UFsuuzpE0IIUXNI0FdDZGRojS2gegd9AOPGWQkKUjl+3FBgs40lS0ycOGEgOFjljz/SGDPGis2mcPfd3uzcKU/7qpa9n69inovZYxuK/7P+7z8jDodC7doObrvNCsCqVSZshfRmUVUJ+qoDz6yxDelZ5Z2XLAlYHdrPOMwnvMrWJYQQQlQWefdbQ+hZPoNBxd+/ihdTRr6+cOedWrbvyy/zL/HUL7/zzkz8/ODTTzPo399GWprCLbd4c+xY5Q7uFrlVVBMXnT6gvSSZPr20s0MHB9262QkOVrl0SWHLloKzw0eOKJw/b8DTU6VrVwn6XJV3VqZPL+/USzuDPYOdAaEQQgjhziToqyGym7iAwQ1+6hMmWDGbVTZvNrF9e+4HtG2bgX//NWE2q0yYoH2a7+EB//d/6bRrZycuzsBNN/kQG+segd+JEwrR0YY8XyUdV1CZKmpcg640mT69iUv79naMRhg4sOgST30/X9eudry8CjxMVDE9sNMbuUgTFyGEEDWNG7z9F8WhNzSp7qWdujp1VEaP1t6U63v3dPq/r7/eRu3a2Y/X3x9++SWdhg0dHD9u4J57vKr9KIfp08106+ZH//6+eb46dvQjLs71AltVrdxMX3F/xnv26Jk+LWOnd4pdtqzgTJ9eXiylna7NuafPLkGfEEKImkmCvhpCz/pU586dl5s0SSvx/PNPE6dOacHNyZMKf/5pynV9TrVrq8yalYaPj8qGDSbmzKneHT3/+ENbv7+/SliYw/llMqmkpirs2OF6v+KxsQpJSQqKotK4cUU1ctGe5+npCgkJRR+flgYHDmSXdwJcdZUNk0nl0CEjR4/mDZ5XrzaycqUJo1FlxAgZyu7K8mb6tPLOcB/p3CmEEKJmcL13hKJC6Jm+6jyj73Jt2zro18+G3a7w7bdadu/bbz1wOBT697fRpk3+AUXTpiqPPaYFhK+95klycuH346rZwIwMnPvNlixJY9++VOfXtddqQcjBg673K66XdjZooFZYSaSXlzYOAopX4rl3rwGHQ6FWLQd16mg/8MBA6NlTy+CtWJH7w4HMTHjhBS2QmDjRSosWFRO8ivKRd0+fZPqEEELULK73jlBUCHeY0Zef++/XgreffjITE6Pw88/mXJcXZNKkTJo0cXDhgoEPPii4kUNiIlxzjQ/du/sye7YJhwu9t9+2zUhGhhaoNGuWe2HNm2v/PnjQ9UZU6IFoRZV26urW1ff1FV3iunu39n3Ss3y6QYO04PnyLrFffeXB4cNGwsMdPPWUpTyWKyqQPqBd9vQJIYSoqSToqyGyG7m4V9B31VV2WrSwk5KicPPN3qSkKERF2bnqqsL3WHl6wttva28Av/3WTHR03l+FjAy44w5vtm41cvy4gQce8GboUB82bnSNQCrnqADlsrgmKkoP+lzvV/y337KHmVekevW070FxBrTnbOKS05AhWtC3caPRmRE+c0bh44+1zPLLL1sICCivFYuK4mnSgr50uzanzzmYXco7hRBC1BCu945QVAh3zfQZDDBpktahMzpae+M+aZI1TxCUnwED7AwbZsVuV3juOc9cZZx2O9x/vxcbN5rw91d56CELfn4qO3caGTnShzvv9Mp3n1dlKmw+nF5uePCgwaXKU3N2Vh0/3lqh96Xv6zt7tuif065d2kth+/a5M31Nmqg0berAZlNYvVoLVl991ZO0NIXu3W2MHSt7+aoDPdNnsWlZ2VjJ9AkhhKhhJOirpjIy4LbbvHnpJc9ilRzqQZ+7dO/M6YYbrM79W2FhDsaMKX4w8cYbFry8VNavN/H779qbelWFF1/0ZOFCMx4eKtOnp/PSS5ls3pzKnXdmYjCoLF5spm9fX7p2zfs1aJBPhc8BTE2F7du1oK9Pn7yBR5MmDoxGleRkhXPnXKeDZ0GdVSuCHvQVlelLT8/OiOqdO3PK7uJpYt06I7//bsZgUHnnHUuxPlwQVc/LpJd36pk+CfqEEELULBL0VVNr1xpZtszE11978MorRQ8Xduegz8sLHnxQ28P30EOZJWoO0rChyiOPaLd95RVPUlJgyhQPpk3TgpPPP89wZtLCw1U++MDC33+ncfXVNmw2hZMnDXm+du0y8swzFTsO4t9/jVitCvXrO2jcOO8deXhAZKRrlXgW1Vm1vEVEaI+/qEzf3r0G7HaFsDCHcx9gTnrQt3Klkeef137Xxo+30q6dC23wFIXSgz6LPQOr3crFjIuABH1CCCFqjurdr74G++ef7B/d1197ULeug/vvLzjD5c5BH8DkyVauucZGw4Ylf3wPPJDJzJlmTpwwMG6cN//+q31v33gjg1Gj8mbRoqIc/PJLOocPKyQm5g4oEhMV7rjDmzVrTPz1l8nZRbO8rV+vZ/ny7ufTtWjh4PBhIwcPGrjiiqqfI1eczqrlSR/QXlSmT9/P16GDI9/vZffudgICVC5eNHDxIoSGOnj2WWneUp14ZpV3ptsyiEuPBcCoGAnxCqnKZQkhhBCVxjVSAKLE9Df9/fppQcWrr3oxd27+MXxKCs4Sv6CgSllepVMUaNRILVW5nZcXvPWW1tRFD/geeCCT++4rvEy0WTOVLl0cub4GDLA7s44vv+xJWlrJ11Mc69frQ8ELDir1fX36/LnycO6cwqOPevJ//2fGWoIteUlJFLuzannRB7SfPVv4gPbszp35B8ZmMwwcmP19fvHFTLf9PXJXOTN9emlnuE8tDIr8CRRCCFEzyF+8aig+XitJA5g6NYN779XeRD/8sBdr12Z3lrTZYPp0M927+zpnlekdDUVugwfbGTpUi2LGjLHy0kulz+Q8/HAmDRo4OH3awGefeZTXEp2Sk2HnTu3nmV8TF50e9B06VD6/5omJcNNN3vzyiwfPPOPFlVf6sGyZsVhlrD/9ZC52Z9XyopdqZmQoxMcX/GlAQU1cctKHr3fpYufmmyu2AY0of95GbU5fhi1D9vMJIYSokSToq4Y2bDChqtob6Nq1VV5/3cLIkVasVoXx473Zs8fAypVGrrrKh6ee8iIuzkCTJg5+/jmNBg3cs7yzPEydmsHMmWlMmZKBoQy/GT4+8PrrWtD4+ece5d7lc9MmI3a7QmSkw1nCmJ/yHNtgscD48d7s328kLMxBaKiDQ4eM3HabDzfcoD3nCmK1aqWdUPzOquXB0xPCw/UB7fnfaUZGdia0oEwfwDXX2Jg3L41Zs9LK9NwQVcPTpO3FzLCnZ49r8JZxDUIIIWoOeftSDeXczwXa2ILPP8+gTx8bKSkKw4f7cPPNPhw4YCQ4WOWttzJYuzaVQYOqfl+XK/Pz08Y4mMphp+vw4TauvNJGZqbCiy+Wb1MXfT9nYaWdoA0/VxRtL1pcXOkjLYcDHnzQi/XrTfj5qcyalc7mzak8+KAFDw+Vf/4xcfXVPjz8sFe+TVP+/NNETIyhxJ1Vy0N2B8/8H/++fdlNXPRj86MoWlZVZvJVT17OTJ9FMn1CCCFqJAn6qiF9Ppse9IGW1Zg+PZ1WrexYLApms8rkyZls3pzCPfdY8Sj/KkNRCEWBd97JwGxWWbHCxNKl5TfQXQ/6CyvtBC3jqGd2S1viqara3sQFC8yYzSo//JBOu3YOAgLg5Zcz2bAhldGjraiqwsyZZnr18uW99zxIScm+vT6m4e67rSXqrFoe9A6eennz5fQmLu3a5d/ERbgH58gGezoX0iXoE0IIUfNI0FfNXLigcOBA/vPZAgJg7tx03nwzg3XrUnntNYs0nKhCTZtqgTfAiy96kZ5e9nMmJMB//2m/tr17F5251Us8S9vM5YsvzHzzjRa0TZmSQf/+ue+zYUOVr7/OYPHiVLp1s5OWpvDRR5706uXLzz+bWb/eyM6dRry8Kn4Ye3707F1B5Z27dxdd2imqv5zD2Z3lnT5S3imEEKLmkJEN1Yye5WnTxk5IPt3Gw8JU7r1XGk24ikcfzWTOHDMnTxp44gkvunTJHVyYzXDttdZ8f5b5uXw/Z1GaN3ewfHnh+/oSEmDhQjOZlzXVPH9e4X//0/ZCvfZaBtdfX3A5aZcuDhYuTGPhQhOvv+7JiRMGHnvMC7NZW+ONN1oJC6v8/aRFDWjXM32FNXER1Z9nzkyflHcKIYSogSToq2b00s6iSvuEa/Dzg9des3DPPd7MmWNmzhxznmP+/NPEb7+lF6u8ML/S3sJERWnHFRb0vfyyF7Nm5V2XbtKkTCZPLvqDBEXRulwOHmzj++/NfPyxp3OO4aRJVfNBhN6tNr+9hhkZEB0tmb6aQM/0ZdgspNniAQn6hBBC1CwS9FUz69YVr4mHcB3XXWfj2DGLsywzp6VLTfz9t4mFC03OsQCFubyJT1H0sQ0FBX02m7YG0GbR+frmzsZ17mwvccDm6QmTJ1u56SYr33/vQf36Dpo3r5pMWmGZvv37DdhsCiEhDurXl6627szblNXIJWf3TinvFEIIUYNI0FeNxMQoHDtmwGBQ6dVLMhPVhaJoZZ75ee89Dz76yJOXX/ZkwAAbvr4Fn+fCBYXo6Pz3cxZED7bOnTOQlESe7pNbthi5dEkhOFjlp5/SMZZfvxlCQuDJJytnEHtB9EYu+oD2nNnUnKWd0sTFvXkatTLl+PSLpFq1LkOS6RNCCFGTSCOXakQv7evQwSGt493EQw9pg9xjYgx8+mnhLVY3bCh8P2d+AgKgbt2Cs33LlmVn+coz4HMVdeqoKIpKZqaSa2xFZibMm6c9dintdH9eWZm+BEsCAD4mH3zNflW5JCGEEKJSSdBXjaxdq/24pLTTffj4wBtvaIPcp04tfJB7afdzFlbiuWyZds4hQ9zzOeXhAeHhuTt4OhzwyCNebNpkwsdH5cYb3fOxi2z6cHZduE8tFEnvCiGEqEEk6KsmVBX++adk+7lE9TBsmI0BA7RB7i+8UPAg99Lu58wO+nKn8o4eVTh0yIjJpHLlle4b+NSrpwd92svdm296MHeuGZNJ5fvv053fH+G+vLOGs+uktFMIIURNI0FfNXHsGJw+bcBkUuneXYI+d6Io8NZb2iD3lStNLFmSe6ttSgq8844HR4+Wbj9nQZm+FSu0++nZ005gYBkegIvLHtCu8O23Zj7/XMv6fPxxBgMGyO9STXB5pk+CPiGEEDWNBH3VxOrV2n87d7bjJ1tR3E7T79NG9QAAGuVJREFUpir33681PXnpJU/S07XOmjNmmOnRw5dPPtHetN5yi7XE+zkLCvr0rp2DB7tvlg+yO3j++quZF1/Uvo8vvGBh3Dj3ftwim1eeTJ907hRCCFGzSPfOamLVKu2/Mp/PfT36aCazZ2uD3B97zIv9+w3s36+VZEZGOnj5ZQvDh5c8UNGDvlOnFFJTwdcXkpNh40bt3O4f9GmPf/du7fHefXcmDz9ctV1FReXyyhrOrpNMnxDCHTgcDux29/4bXlmMRhMGg3vnwiToqwZUNTvTJ0Gf+/L1hddftzBxojfz5mnD0oOCVJ580sL48VY8Cm/uWaDQUJWwMAdxcQaOHDHQvr2DNWtM2GwKzZrZadLEvWfU6Zk+gGuvtfLWWxYZ0VDDGBQDHgYPMh1asC9BnxCiOlNVlaSkeNLTU6p6KW7F29uPgIAQt230JUFfNXD4sMLZs+DpqdK1qwR97mzECBvXXGNl2TITEyZYefxxC0FBZT9v8+Za0HfggBb06aWdgwa5//OpWzc7Pj4q3brZmTo1wy1HU4iieZm8ycyUoE8IUf3pAZ+fXzAeHp5uG6RUFlVVycy0kJKijfUJDAyt4hVVDAn6qgG9a2f37g68vIo4WFRrigLTpmVgs1HqzF5+WrRwsHEjHDpkwG6HlStrRmknQP36Kvv3p+DlhWT4ajB9QDtAbQn6hBDVlMNhdwZ8fn4ytLm8eHhofyNSUhLw9w92y1JP93tEbkgP+qS0s2YwGMo34AOIitL2tR04YGD7dgMXLxoIDKw5nWC9vSXgq+m8TdnNXCTTJ4Sorux27e+2HqSI8qN/T911n6QEfdWAwwFGIwwbVjPeoIvy17y5FvQdOmRg2TItwT9ggA2zuSpXJUTlyZnpC/MOr8KVCCFE2UlJZ/lz9++pywV90dEwaJDW1KJOHXj6acgsRqM9VYV334WGDbVP9Xv1gk2bKn69lWHqVAsHDkDbtjJEWpSOnuk7dszAX3/p+/nc85MsIfLjlZXpC/EKwcNYzql0IYQQogjRcdEM+nEQvm/7UufDOjy9/Gky7UUHOaqq8u66d2n4SUO83/Km17RebDpd8iDHpYK+hAQYMEAL8ubNg7ffhm++gccfL/q2770Hr7wCjz0GCxdC3boweDAcPVrx665ovr7QtGlVr0JUZ7VrqwQEqNjtCocPGzEYVAYOlKBP1Bx6pk9KO4UQQlS2hPQEBkwfQKY9k3lj5/H2wLf5Zts3PL606CDnvfXv8cqaV3is52MsvHkhdf3qMvjHwRxNKFmQ41KNXL76CpKSYP58CAnRLrPZ4P774fnnISIi/9tlZMA778ATT2hBH0C/ftCiBXz4IUydWjnrF8JVKYpW4rltm94UyE5wcBUvSohKpO/pC5egTwghXM7GjeuZO3cW0dH7SE5OJiAgkJYtWzF48DAGDhyMwWDgrbdeZfHihc7bhIaGEhXVmvvue4CmTZsBsGjRn7z99mvUrVuPX3+di8mUHer89tsvfPbZx6xbt7XSH99XW78iyZLE/JvmE+KtBTk2h437/7qf5/s9T4R//kFOhi2Dd9a9wxO9nuCxXlqQ069RP1pMacGHGz5k6jXFD3JcKtO3eDFcfXV2wAcwdqy2p23ZsoJvt2GDFiyOHZt9mYcHXH89LFpUcesVojqJisreE1oTRjUIkZM+oL2Wd60qXokQQoicvv76C5566hE8PDx57LGn+fTTL3nssafx8/PnjTdeZsuWzc5jIyLq8dVX/8dXX33P5MkPc+zYER588F4uXozLdc6zZ2NYtmxxZT+UAi0+vJirm1ztDPgAxrYZi0N1sOxIwUHOhlMbSLIkMbZNdpDjYfTg+lbXs+hQyYIcl8r0RUfD3XfnviwoSCvVjI4u/HYALVvmvrxVKzh5EtLTtX1+QtRkLVpk7wkdMkRKO0XN4mnMCvok0yeEEC5jw4Z1/Pjj/3HXXfcwYcJ9ua4bMOBqbrxxXK5snaenJ23btgOgbdv21K0bwQMP3MPSpYu55Zbbncd17tyVGTP+jyFDhmN0gQG90XHR3N0pd5AT5BVEXf+6RMcVHOTo17UMyx3ktAprxcnEk6Rb0/E2Fy/IcamgLyGBfAdRBwdDfHzht/P0JM8Mu+BgrcFLQkL+Qd/AgQMLPOfSpUsxGo14elb9t8hg0LoJmc1GjEaXSs6KaqR/f+jUCerXd9C2rQFFkeeSqDnCfLVhu5HBjV3idV0IIUpDUeyAgqK4xyiiWbN+JjQ0jPHjJ+T7eNq0aZvnspzHtWrVCoBz587kunz8+Ik88shkVq5cxpAhwwq8fd7LFTw8jHh45P934syZM9x+++35XgewcuXKfC9PyEggyCsoz+XBXsHEpxcc5CSkJ+Bp9HRWqzhv5x2MikpCRkL1DPpcUUCA66QI/fxkMrsovauugu3bQavqdp3ntRCV4ZUBL9GyVgvu6zaRAE95/gshqqeMDAWDQfvKmQhQVZU0W1qVrcvH5FPikQc2m409e3Zx1VUD8fQsuquyoigoSu7Hff78OQDCw8MxGg3OREmLFs3p3/9KZsz4nqFDh2EwZF9XUALFbte+r35+XnhdnklyAy4V9AUHQ2Ji3ssTEnLv88vvdhaL1tAl588oIUGL2gtqWFFQNK5TVZWkpPRirLxi6U/AlJQMHA61qpcjhBDVToihNpPbPwwWSLJU/eu6EEKURmamBYdDxeFQsdu1bRuqqnLNvMFsObe5iFtXnO51e7Jw9NISBX4JCQlkZmYSHl7b+VhAezz6EHogK2AzoKoqqqpisWhjDmJiTvPee29hMpno27c/drvD+T7Zble54467mTDhdlasWM7AgYNyXJf/CDT9+5qSkkFmZt732/7+XkRERBQZP+Qn2CuYxIy8QU5CRkKufX55bucdjMVuIcOWkSvbl5CegIJCsFfxu/K5VNDXsmXevXuJiXD2bN79epffDuDAAejQIfvy6OjsuX2lZbFU/d4nk0n7RMJqtWOzyaw+IYQQQoiayGq1Ayqqqm1hAu2/ClVc65ljPcW+SdbxiqLkuu3q1St56aVnnf++/vobefzxZwA4duwoV1zR03ldWFg4L7/8JpGRzXKdQ1UhKqoVvXr1Yfr0aVx11dV57jf/9ahkZtpR1bzv//39S/b4cmoZ1pLoi7mDnMSMRM4mn82zX+/y2wEciDtAhzrZQU50XDQNAxsWu7QTXCzoGzZMm8136VL23r7Zs8Fg0GbuFaR3bwgI0I7Vgz6rVZv1N3x4Ra9aCCGEEEKIqqEoCn+OXlrtyjsDAgLx8PDgwoXzuS7v0qU73303A4Bnnsk9x65evfq89trbgEJYWBihoWGF3u+dd05k0qS7WLt2TYnWVt6GNRvG2+ve5lLGJefevtn7ZmNQDAxuWnCQ07tBbwI8A5i9b7Yz6LParcyLnsfw5iULclwq6Js0CaZMgVGjtLl8MTHw1FPa5Tln9A0cCCdOwOHD2r+9vOC55+DVVyE8HNq102bzXbwITz5ZFY9ECCGEEEKIyqEoCr5m36peRomYTCbatevAtm1bsNvtzi6bAQEBBAS0BsBsNue6jYeHBy1bti72fbRt245u3Xowffp3DB16TfktvoQmdZ3ElH+nMGrmKJ7v9zwxSTE8tfwpJnWdlGtG38AZAzlx6QSHH9aCHC+TF8/1fY5X17xKuE847Wq3Y+qWqVxMu8iTvUsW5LhU0BccDCtXwkMPaYGfvz9MnAhvvZX7OLtdG9qe0zPPaGnZDz+E2Fjo2BGWLoUmTSpr9UIIIYQQQojiuummW3n66Uf58cf/Y/z4iRVyH+PHT+SBB+7B3z+gQs5fHMHeway8YyUPLX6IUTNH4e/pz8ROE3lrYO4gx+6wY3PkDnKe6fMMqqry4cYPiU2NpWOdjiy9bSlNgksW5LhU0AfabL0VKwo/Zs2avJcpipbte+65ClmWEEIIIYQQohz17t2X224bz3fffcWhQwcYMGAwYWFhpKSksGvXDuLjL+LjU7YMZocOnejYsTPbtm0pp1WXTqvwVqy4o/AgZ834NXkuUxSF5/o9x3P9yhbkuFzQJ4QQQgghhKgZJk16kPbtOzJv3m98/PG7pKSkEBAQSFRUS5599mWuvrqQxh7FNH78RB599P5yWG31pahqSXvt1ByqqhIXl1LVy8BkMhAc7EtCQqp07xRCCCGEqKGs1kwuXjxLaGhdzOaiZ9uJ4ivqexsW5lfiZjWuJP/phEIIIYQQQggh3IIEfUIIIYQQQgjhxiToE0IIIYQQQgg3JkGfEEIIIYQQQrgxCfqEEEIIIYQQwo1J0CeEEEIIIUQ1Is33y5+7f08l6BNCCCGEEKIaMBqNAGRmWqp4Je5H/54aje45xtw9H5UQQgghhBBuxmAw4u3tR0pKAgAeHp7VenacK1BVlcxMCykpCXh7+2EwuGdOTII+IYQQQgghqomAgBAAZ+Anyoe3t5/ze+uOJOgTQgghhBCimlAUhcDAUPz9g7HbbVW9HLdgNJrcNsOnk6BPCCGEEEKIasZgMGAweFT1MkQ14d4hrRBCCCGEEELUcBL0CSGEEEIIIYQbk6BPCCGEEEIIIdyYorr7JMIycKVvjaIoLrUeIYQQQgghapLqPB5Dgj4hhBBCCCGEcGNS3imEEEIIIYQQbkyCPiGEEEIIIYRwYxL0CSGEEEIIIYQbk6BPCCGEEEIIIdyYBH1CCCGEEEII4cYk6BNCCCGEEEIINyZBnxBCCCGEEEK4MQn6hBBCCCGEEMKNSdAnhBBCCCGEEG5Mgj4hhBBCCCGEcGMS9AkhhBBCCCGEG5OgTwghhBBCCCHcmAR9QgghhBBCCOHGanTQt3jxYiZPnkz//v3p2LEjI0eOZM6cOaiqmuu42bNnM2TIENq1a8d1113H6tWr85wrOTmZ559/nu7du9OpUycefvhhLly4kOuYAQMGEBUVle/Xzp07i1zvkSNHuOuuu+jYsSN9+vTh/fffJzMzM9cxixYt4qGHHqJ///5ERUUxbdq0En1PVq1axXXXXUe7du0YMmQIc+fOzfM4H3roIQYMGED79u3p2bMnEydOZPfu3SW6HyGEEEIIIUTlMFX1AqrSDz/8QL169Xj22WcJDg5mw4YNvPTSS5w7d44HH3wQgL/++ouXXnqJSZMm0bNnTxYtWsSDDz7Izz//TMeOHZ3nevTRRzl8+DCvvvoqnp6e/O9//+Oee+5h7ty5mEzat/nzzz/PE6R9+OGHHDlyhLZt2xa61sTERO68804aN27MlClTOH/+PO+++y4ZGRm8/PLLzuOWLFnCqVOnuPLKK5k1a1aJvh9bt27lwQcf5IYbbuD5559n06ZNvPDCC/j6+jJ06FAAMjMz8fDwYPLkydSvX5+UlBSmT5/OnXfeybx584iMjCzRfQohhBBCCCEqlqJentaqQeLj4wkJCcl12UsvvcSiRYvYsmULBoOBIUOG0LZtWz766CPnMePGjcPf359vv/0WgB07djBu3DimTZtG3759ATh69CjDhw/n448/Zvjw4fnef1paGn369GHUqFG88sorha7166+/5quvvmL16tUEBQUBMGvWLF577TVWr15N7dq1AXA4HBgMWgI3KiqKp59+mgkTJhTr+zFhwgRSU1OZOXOm87InnniC/fv3s2jRogJvl5qaSo8ePXjwwQeZNGlSse5LCCGEEEIIUTlqdHnn5QEfQKtWrUhJSSEtLY1Tp05x/Phxhg0bluuY4cOHs3HjRmfWbu3atQQEBNCnTx/nMU2aNKFVq1asXbu2wPtfuXIlaWlpjBgxosi1rl27ll69ejkDPoBhw4bhcDhYv3698zI94CupzMxMNm/e7Mzo6YYPH86RI0c4ffp0gbf18fHB09MTq9VaqvsWQgghhBBCVJwaHfTlZ9u2bdSuXRs/Pz+OHj0KkKdksWnTplitVk6dOgVoWb3IyEgURcl1XJMmTZznyM/ChQupV68enTt3LnJdR48epUmTJrkuCwgIIDw8vND7KK6TJ09itVrz3EfTpk2d95+Tw+HAZrNx4cIF3n33XQwGA6NGjSrzOoQQQgghhBDlS4K+HLZu3cqiRYu4++67AW0fHWjBVU76v/Xrk5KS8Pf3z3O+wMBA5zGXS0hIYP369Vx77bXFWltSUlKedRR1HyVR3Meq+/TTT2nTpg39+vXjzz//5JtvvqFBgwZlXocQQgghhBCifEnQl+XcuXM89thj9OjRgzvuuKPC72/x4sVYrdY8QZ/dbsdmszm/KmLLZc7z22y2Up3jlltuYc6cOXz55Zd06NCBe++9l71795bzSoUQQgghhBBlVaO7d+qSkpK45557CAoKYsqUKc59cYGBgYA2piA8PDzX8TmvDwgI4Ny5c3nOm5iY6DzmcgsXLiQqKooWLVrkunz8+PH8+++/zn/PmDGDHj16EBAQQHJyconuoyBt2rTJ9e8DBw7keqw5Xf5YdbVr13Y2j7nyyiu54YYb+Oyzz/j6669LtBYhhBBCCCFExarxQV9GRgb33XcfycnJzJo1K1eZpr6/7fL9dEePHsVsNjvLGZs0acLGjRtRVTXXvr5jx47lCeoAzpw5w/bt23n88cfzXPfaa6+Rmprq/Le+nzC//YHJycnExsbm2YdXlDlz5uS5rGHDhpjNZo4ePUq/fv1yPVb9/gtiMBho1aoV27ZtK9E6hBBCCCGEEBWvRpd32mw2Hn30UY4ePcp3333nzFzpGjRoQOPGjVmyZEmuyxctWkSvXr3w8PAAoH///iQmJrJx40bnMceOHWPfvn30798/z/0uXLgQIN/9fE2aNKFdu3bOLz8/P+d9bNiwwZl5A20mn8FgyNU1tDhynr9du3YAeHh40KNHD5YuXZrnsTZt2pT69esXeD6bzcbu3btlT58QQgghhBAuqEZn+vQZd88++ywpKSns3LnTeV3r1q3x8PDgoYce4sknn6Rhw4b06NGDRYsWsXv3bn766SfnsZ06daJv3748//zzPPPMM3h6evLJJ58QFRXF4MGD89zvwoUL6dy5MxEREcVe67hx4/jxxx954IEHuO+++zh//jzvv/8+48aNyxWsHj58mMOHDzv/ffDgQZYsWYK3tzdXXHFFofcxefJk7rjjDl599VWGDRvG5s2bWbhwIZ988onzmFmzZrF792569+5NeHg4cXFxzJw5k2PHjhU5a1AIIYQQQghR+Wr0cPYBAwYQExOT73UrV650Zrdmz57Nt99+y5kzZ4iMjOTxxx/nqquuynV8cnIy77zzDsuXL8dms9G3b19efPHFPNnDw4cPc8011/DKK69wyy23lGi9R44c4Y033mDHjh34+voycuRIHnvsMWfGEWDKlCl8/vnneW5br149Vq1aVeR9rFy5kv/9738cO3aMiIgI7r33Xm644Qbn9du2bWPq1Kns37+fpKQkwsPDadeuHffffz8tW7Ys0eMRQgghhBBCVLwaHfQJIYQQQgghhLur0Xv6hBBCCCGEEMLdSdAnhBBCCCGEEG5Mgj4hhBBCCCGEcGMS9AkhhBBCCCGEG5OgTwghhBBCCCHcmAR9QgghhBBCCOHGJOgTQgghhBBCCDcmQZ8QQogaacqUKURFRVX1MoQQQogKJ0GfEEIIUQI///wz8+bNq+plCCGEEMUmQZ8QQghRAr/++ivz58+v6mUIIYQQxSZBnxBCCCGEEEK4MVNVL0AIIYSoaFu3buWdd97h4MGD1K5dm4kTJ+Y5Zu7cuSxYsIBDhw6RnJxMw4YNue2227jlllucxwwYMICYmBgA537A7t278+OPPwKQlJTElClTWLZsGRcvXqRu3brceOONTJw4EYNBPmcVQghRNSToE0II4dYOHDjAhAkTCAkJ4aGHHsJmszFlyhRCQ0NzHffrr7/SvHlzBgwYgMlkYvXq1bz22muoqsqtt94KwPPPP88bb7yBj48PkyZNAiAsLAyA9PR0brvtNs6fP8+4ceOoW7cuO3bs4OOPPyY2NpYXXnihch+4EEIIkUVRVVWt6kUIIYQQFeWBBx7gn3/+YcmSJURERABw5MgRRowYgd1u58CBAwBkZGTg5eWV67YTJkzgxIkTrFixwnnZtddeS3BwsDO7p5s6dSrffvst8+fPp3Hjxs7LP/roI6ZNm8bKlSupW7duBT1KIYQQomBSayKEEMJt2e121q1bx9VXX+0M+ACaNm1K3759cx2bM+BLTk4mPj6e7t27c+rUKZKTk4u8ryVLltClSxcCAgKIj493fvXu3Ru73c6WLVvK74EJIYQQJSDlnUIIIdxWfHw8GRkZNGrUKM91kZGR/P33385/b9u2jSlTprBz507S09NzHZucnIy/v3+h93XixAkOHDhAr169ClyLEEIIURUk6BNCCFHjnTx5kvHjx9OkSROeffZZ6tati9ls5u+//+aHH37A4XAUeQ6Hw0GfPn3ybRID5Cr5FEIIISqTBH1CCCHcVkhICF5eXpw4cSLPdceOHXP+/6pVq8jMzOTLL7/MVQa6efPmPLdTFCXf+2rYsCFpaWn07t27HFYuhBBClB/Z0yeEEMJtGY1G+vbty4oVKzhz5ozz8iNHjrBu3bpcxwHk7G2WnJzM3Llz85zT29ubpKSkPJcPGzaMHTt28M8//+S5LikpCZvNVqbHIoQQQpSWdO8UQgjh1qKjoxk7diyhoaHcfPPN2O12fvrpJ0JDQzlw4AAHDhzg6NGjXHfddURGRjJu3DhSU1OZPXs2Pj4+REdHs3LlSurXrw/Aa6+9xq+//srDDz9Mo0aNCAkJoVevXqSnp3Prrbdy4MABRo8eTZs2bUhPT+fgwYMsXbqUlStXEhISUsXfDSGEEDWRBH1CCCHc3pYtW5zD2evUqcPEiROJjY3l888/d45sWLVqFf/73/84fvw4YWFh3HzzzYSEhPD888/nCvri4uJ44YUX2LJlC6mpqbmGs6empvL111+zZMkSzpw5g5+fH40bN2bw4MHcfvvtmM3mKvseCCGEqLkk6BNCCCGEEEIINyZ7+oQQQgghhBDCjUnQJ4QQQgghhBBuTII+IYQQQgghhHBjEvQJIYQQQgghhBuToE8IIYQQQggh3JgEfUIIIYQQQgjhxiToE0IIIYQQQgg3JkGfEEIIIYQQQrgxCfqEEEIIIYQQwo1J0CeEEEIIIYQQbkyCPiGEEEIIIYRwYxL0CSGEEEIIIYQbk6BPCCGEEEIIIdzY/wOOKub0akPEYQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normalize_df(data_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    min_s = data_df.min()\n",
    "    max_s = data_df.max()\n",
    "    norm_df = (data_df - min_s) / (max_s - min_s)\n",
    "    return norm_df\n",
    "\n",
    "\n",
    "d2007_aapl_norm = normalize_df(d2007_aapl)\n",
    "d2007_gpn_norm = normalize_df(d2007_gpn)\n",
    "\n",
    "plot_two_ts(data_a=d2007_aapl_norm, data_b=d2007_gpn_norm, title='Normalized AAPL/GPN first half of 2007',x_label='date', y_label='Normalized Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p>\n",
    "The Granger test shows that AAPL and GPN are cointegrated with 99% confidence (1% error). The Johansen test (see below) also shows\n",
    "that AAPL and GPN are cointegrated with 99% confidence.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger test for cointegration (AAPL/GPN)  : cointegrated: True, confidence: 1, weight: 0.56, intercept: 17.52 (GPN, AAPL)\n"
     ]
    }
   ],
   "source": [
    "coint_data_granger_aapl_gpn = pair_stat.engle_granger_coint(data_a=d2007_aapl, data_b=d2007_gpn)\n",
    "print(f'Granger test for cointegration (AAPL/GPN)  : {coint_data_granger_aapl_gpn}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The plot below shows the stationary time series formed from AAPL and GPN close prices.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stationary_df = pair_stat.stationary_series(data_a=d2007_aapl, data_b=d2007_gpn, coint_data=coint_data_granger_aapl_gpn)\n",
    "stat_sd = stationary_df.std()[0]\n",
    "title='Granger AAPL/GPN stationary time series, 1st half of 2007'\n",
    "plot_stationary_ts(stationary_df=stationary_df, plus_delta=stat_sd, minus_delta=stat_sd, title=title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "Given their low correlation and unrelated businesses (computer and media hardware vs payments) this may be an example of spurious\n",
    "cointegration. If the cointegratoin is spurious, cointegration may be more likely to break down in a future time period and the\n",
    "pair may not be profitable to trade.\n",
    "</p>\n",
    "<p>\n",
    "The Granger test for the second half of 2007 (e.g., the six months following the period above) is shown below:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granger test for cointegration (AAPL/GPN): second half of 2007 cointegrated: False, confidence: 0, weight: 1.68, intercept: 11.46 (GPN, AAPL)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "d2007_gpn_2 = pd.DataFrame(close_prices_df['GPN']).iloc[second_half_start:second_half_start+half_year]\n",
    "coint_data_granger_aapl_gpn_2 = pair_stat.engle_granger_coint(data_a=d2007_aapl_2, data_b=d2007_gpn_2)\n",
    "\n",
    "print(f'Granger test for cointegration (AAPL/GPN): second half of 2007 {coint_data_granger_aapl_gpn_2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The Granger test shows that there is no cointegration in the six momnth period following the first half of 2007, which reinforces the idea\n",
    "that the previous cointegration was spurious.\n",
    "</p>\n",
    "<h3>\n",
    "The Johansen Test\n",
    "</h3>\n",
    "<p>\n",
    "The Granger linear regression based test can only be used for two assets. The Johansen test can be used on more than two assets.\n",
    "</p>\n",
    "<p>\n",
    "The Johansen test uses eigenvalue decomposition for the estimation of cointegration. In contrast to the Granger test which has two steps:\n",
    "linear regression and the ADF test, the Johansen test is a single step test that also provides the weight factors. There is no linear\n",
    "constant (regression intercept) as there is with the Granger test, so the Johansen result may not have a mean of zero.\n",
    "</p>\n",
    "<p>\n",
    "The Johansen test and the Granger test do not always agree. The Johansen test is applied to AAPL/MPWR for the close prices from the first\n",
    "half of 2007. The Johansen test shows no cointegration, although the Granger test showed cointegration at the 99% confidence level.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Johansen test for cointegration (AAPL/MPWR), first half of 2007 : cointegrated: False, confidence: 0, weight: 3.71, (AAPL, MPWR)\n"
     ]
    }
   ],
   "source": [
    "coint_data_johansen_aapl_mpwr = pair_stat.johansen_coint(data_a=d2007_aapl, data_b=d2007_mpwr)\n",
    "print(f'Johansen test for cointegration (AAPL/MPWR), first half of 2007 : {coint_data_johansen_aapl_mpwr}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "In the research papers on cointegration, there doesn't seem to be a consensus on whether the Granger or\n",
    "Johansen tests are better. Some authors suggest using both tests, but they don't provide any empirical insight into why this might be advantageous.\n",
    "</p>\n",
    "\n",
    "<h2>\n",
    "Correlation\n",
    "</h2>\n",
    "<p>\n",
    "After selecting stocks based on their industry sector, the next filter used is the pair correlation of the close prices.\n",
    "</p>\n",
    "<p>\n",
    "Stocks that are strongly correlated are more likely to also exhibit mean reversion since they have similar market behavior.\n",
    "This section examines the correlation statistics for the S&P 500 sector pairs.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def display_histogram(data_v: np.array, x_label: str, y_label: str) -> None:\n",
    "    num_bins = int(np.sqrt(data_v.shape[0])) * 4\n",
    "    fix, ax = plt.subplots(figsize=(10, 8))\n",
    "    ax.set_xlabel(x_label)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.grid(True)\n",
    "    ax.hist(data_v, bins=num_bins, color=\"blue\", ec=\"blue\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class SerialCorrelation:\n",
    "    class SerialCorrResult:\n",
    "        def __init__(self, pair: Tuple, corr_df: pd.DataFrame):\n",
    "            self.pair: Tuple = pair\n",
    "            self.corr_df: pd.DataFrame = corr_df\n",
    "\n",
    "    def __init__(self, stock_close_df: pd.DataFrame, pairs_list: List[Tuple], window: int):\n",
    "        self.stock_close_df = stock_close_df\n",
    "        self.pairs_list = pairs_list\n",
    "        self.window = window\n",
    "        self.index = self.stock_close_df.index\n",
    "\n",
    "    def calc_pair_serial_correlation(self, pair) -> SerialCorrResult:\n",
    "        stock_a_sym = pair[0]\n",
    "        stock_b_sym = pair[1]\n",
    "        stock_a_df = self.stock_close_df[stock_a_sym]\n",
    "        stock_b_df = self.stock_close_df[stock_b_sym]\n",
    "        corr_list = list()\n",
    "        date_list = list()\n",
    "        for ix in range(0, self.stock_close_df.shape[0], self.window):\n",
    "            stock_a_win = log(stock_a_df.iloc[ix:ix + self.window])\n",
    "            stock_b_win = log(stock_b_df.iloc[ix:ix + self.window])\n",
    "            c = np.corrcoef(stock_a_win, stock_b_win)\n",
    "            corr =  round(c[0, 1], 2)\n",
    "            corr_list.append(corr)\n",
    "            date_list.append(self.index[ix])\n",
    "        corr_df = pd.DataFrame(corr_list)\n",
    "        corr_df.index = date_list\n",
    "        serial_corr_result = self.SerialCorrResult(pair, corr_df)\n",
    "        return serial_corr_result\n",
    "\n",
    "    def build_corr_frame(self, corr_list: List[SerialCorrResult]) -> pd.DataFrame:\n",
    "        num_cols = len(corr_list)\n",
    "        num_rows = corr_list[0].corr_df.shape[0]\n",
    "        corr_m = np.zeros([num_rows, num_cols])\n",
    "        col_names = list()\n",
    "        for col_ix in range(num_cols):\n",
    "            pair = corr_list[col_ix].pair\n",
    "            col = f'{pair[0]}:{pair[1]}'\n",
    "            col_names.append(col)\n",
    "            corr_df = corr_list[col_ix].corr_df\n",
    "            for row_ix in range(num_rows):\n",
    "                corr_m[row_ix, col_ix] = corr_df.iloc[row_ix]\n",
    "        corr_df = pd.DataFrame(corr_m)\n",
    "        corr_df.columns = col_names\n",
    "        corr_df.index = corr_list[0].corr_df.index\n",
    "        return corr_df\n",
    "\n",
    "\n",
    "    def serial_correlation(self) -> pd.DataFrame:\n",
    "        # serial_corr_list = list()\n",
    "        # for pair in self.pairs_list:\n",
    "        #     serial_corr = self.calc_pair_serial_correlation(pair)\n",
    "        #     serial_corr_list.append(serial_corr)\n",
    "        with Pool() as mp_pool:\n",
    "            serial_corr_list = mp_pool.map(self.calc_pair_serial_correlation, self.pairs_list)\n",
    "        corr_df = self.build_corr_frame(serial_corr_list)\n",
    "        return corr_df\n",
    "\n",
    "\n",
    "pairs_list = get_pairs(sectors)\n",
    "\n",
    "serial_correlation = SerialCorrelation(close_prices_df, pairs_list, half_year)\n",
    "\n",
    "apple_tuple: Tuple = ('AAPL', 'MPWR')\n",
    "apple_corr_result: SerialCorrelation.SerialCorrResult = serial_correlation.calc_pair_serial_correlation(apple_tuple)\n",
    "apple_corr_df = apple_corr_result.corr_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The lookback period for pairs selection is six months (126 trading days). As a first step, all of the S&P 500 sector\n",
    "pairs will be tested for correlation over the lookback period.\n",
    "</p>\n",
    "<p>\n",
    "Correlation is calculated price for each stock pair.\n",
    "</p>\n",
    "<p>\n",
    "The windowed correlation is not stable. The plot below shows the correlation between two stocks, AAPL and MPWR, over windowed\n",
    "periods from the start date.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plot_ts(data_s=apple_corr_df[0], title=f'correlation between {apple_tuple[0]} and {apple_tuple[1]}',\n",
    "        x_label='Window Start Date', y_label=f'Correlation over {half_year} day window')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "Since correlation is not stable, a stock pair that is highly correlated in one time period may be uncorrelated (or negatively\n",
    "correlated) in the next time period.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "corr_df = serial_correlation.serial_correlation()\n",
    "\n",
    "\n",
    "def calc_corr_dist(corr_df: pd.DataFrame, cut_off: float) -> pd.DataFrame:\n",
    "    count_list = list()\n",
    "    for row_num, row in corr_df.iterrows():\n",
    "        count = 0\n",
    "        for val in row:\n",
    "            if val >= cut_off:\n",
    "                count = count + 1\n",
    "        count_list.append(count)\n",
    "    count_df = pd.DataFrame(count_list)\n",
    "    count_df.index = corr_df.index\n",
    "    return count_df\n",
    "\n",
    "\n",
    "cor_a = np.array(corr_df.values).ravel()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The histogram below shows the aggregate distribution of the pair correlation over all half year time periods.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "display_histogram(cor_a, 'Correlation between pairs', 'Count')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The pairs are selected from a common S&P industry sector, so there are a significant number of pairs that\n",
    "have a correlation above 0.75.\n",
    "</p>\n",
    "<p>\n",
    "There are a small number of pairs that have a strong negative correlation (a negative correlation -0.75 to approximately -0.9).\n",
    "Initially we look at pairs that have a strong positive correlation, but it may be unwise to ignore the negative correlations as\n",
    "well.\n",
    "</p>\n",
    "<p>\n",
    "The plot below shows the number of pairs, in a half year time period, with a correlation above a particular cutoff.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "correlation_cutoff = 0.75\n",
    "cor_dist_df = calc_corr_dist(corr_df, correlation_cutoff)\n",
    "\n",
    "\n",
    "spy_close_df = market_data.read_data('SPY')\n",
    "spy_close_df = pd.DataFrame(spy_close_df[spy_close_df.columns[0]])\n",
    "cor_dist_df.columns = ['Correlation']\n",
    "\n",
    "\n",
    "plot_two_ts(data_a=cor_dist_df, data_b=spy_close_df, title=f\"Number of pairs with a correlation >= {correlation_cutoff}, by time period and SPY\",\n",
    "            x_label='Window Start Date', y_label=f'Number of pairs in the {half_year} day window')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "In the plot above, about 75% of the pairs are highly correlated around 2008. This corresponds to the 2008–2009 stock market crash caused by the financial crisis. The high correlation around the crash lends validity to the financial market maxim that in a market crash, all assets become correlated.\n",
    "</p>\n",
    "<p>\n",
    "To the extent that correlation is a predictor for mean reversion, this also suggests that mean reversion statistics may be volatile.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CalcPairsCointegration:\n",
    "    def __init__(self, close_prices_df: pd.DataFrame):\n",
    "        self.close_prices_df = close_prices_df\n",
    "        self.pair_stat = PairStatistics()\n",
    "        self.coint_matrix_io = CointMatrixIO()\n",
    "\n",
    "    def compute_halflife(self, z_df: pd.DataFrame) -> int:\n",
    "        \"\"\"\n",
    "        Calculate the half-life of a mean reverting stationary series where the series\n",
    "        is an Ornstein–Uhlenbeck process\n",
    "        From example 7.5 in Quantitative Trading by Ernest P. Chan\n",
    "        \"\"\"\n",
    "        prevz = z_df.shift()\n",
    "        dz = z_df - prevz\n",
    "        dz = dz[1:]\n",
    "        prevz = prevz[1:]\n",
    "        norm_prevz = prevz - np.mean(prevz.values)\n",
    "        result = sm.OLS(dz, norm_prevz).fit()\n",
    "        theta = result.params\n",
    "        halflife_f = -np.log(2) / theta\n",
    "        halflife = round(halflife_f, 0)\n",
    "        return int(halflife)\n",
    "\n",
    "    def calc_pair_coint(self, pair_str: str, window_start: int, window: int) -> CointAnalysisResult:\n",
    "        pair_l = pair_str.split(':')\n",
    "        asset_a = pd.DataFrame(\n",
    "            self.close_prices_df[pair_l[0]].iloc[window_start:window_start + window])\n",
    "        asset_b = pd.DataFrame(\n",
    "            self.close_prices_df[pair_l[1]].iloc[window_start:window_start + window])\n",
    "        granger_coint = self.pair_stat.engle_granger_coint(asset_a, asset_b)\n",
    "        asset_a_str = asset_a.columns[0]\n",
    "        asset_b_str = asset_b.columns[0]\n",
    "        if asset_a_str != granger_coint.asset_a:\n",
    "            t = asset_a_str\n",
    "            asset_a_str = asset_b_str\n",
    "            asset_b_str = t\n",
    "        granger_pair_str = f'{asset_a_str}:{asset_b_str}'\n",
    "        granger_coint_info = CointInfo(pair_str=granger_pair_str,\n",
    "                                       confidence=granger_coint.confidence,\n",
    "                                       weight=granger_coint.weight,\n",
    "                                       has_intercept=True,\n",
    "                                       intercept=granger_coint.intercept)\n",
    "        johansen_coint = self.pair_stat.johansen_coint(asset_a, asset_b)\n",
    "        johansen_coint_info = CointInfo(pair_str=pair_str,\n",
    "                                        confidence=johansen_coint.confidence,\n",
    "                                        weight=johansen_coint.weight,\n",
    "                                        has_intercept=False,\n",
    "                                        intercept=np.NAN)\n",
    "        coint_result = CointAnalysisResult(granger_coint=granger_coint_info, johansen_coint=johansen_coint_info)\n",
    "        return coint_result\n",
    "\n",
    "    def calc_pairs_coint_dataframe(self, corr_df: pd.DataFrame, window: int) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        :param corr_df: a data frame of pairs correlation values, where the index is the date and\n",
    "                        the columns are the pairs\n",
    "        :param window:  the look back window\n",
    "        :return: a data frame of tuples composed of a correlation value and the granger and johansen cointegration\n",
    "                 objects.\n",
    "        \"\"\"\n",
    "        if self.coint_matrix_io.has_files():\n",
    "            coint_info_df = self.coint_matrix_io.read_files()\n",
    "        else:\n",
    "            coint_info_a = np.zeros(corr_df.shape, dtype='O')\n",
    "            pairs_l = list(corr_df.columns)\n",
    "            window_start = 0\n",
    "            for row_ix in range(corr_df.shape[0]):\n",
    "                print(f'CalcPairsCointegration::calc_pairs_coint_dataframe: processing row {row_ix}')\n",
    "                for col_ix in range(corr_df.shape[1]):\n",
    "                    pair_str = pairs_l[col_ix]\n",
    "                    coint_info = self.calc_pair_coint(pair_str=pair_str, window_start=window_start, window=window)\n",
    "                    correlation = corr_df.iloc[row_ix, col_ix]\n",
    "                    coint_info_a[row_ix, col_ix] = (correlation, coint_info)\n",
    "                window_start = window_start + window\n",
    "            coint_info_df = pd.DataFrame(coint_info_a)\n",
    "            coint_info_df.columns = corr_df.columns\n",
    "            coint_info_df.index = corr_df.index\n",
    "            self.coint_matrix_io.write_files(coint_info_df)\n",
    "        return coint_info_df\n",
    "\n",
    "\n",
    "class Statistics:\n",
    "    def __init__(self):\n",
    "        # Total number of pairs with correlation >= cutoff\n",
    "        self.total_correlation = 0\n",
    "        # Total for serial correlation where elem_n is greater than or equal to the cutoff\n",
    "        # and elem_n_1 is greater than or equal to cutoff 2\n",
    "        self.serial_correlation = 0\n",
    "        # Total pairs - the size of the data frame: shape[0] * shape[1]\n",
    "        self.total_pairs: int = 0\n",
    "        # Serial cointegrated pairs count by time period\n",
    "        self.pair_count_df = pd.DataFrame()\n",
    "        #\n",
    "        # Lists for correlation/cointegration distribution\n",
    "        #\n",
    "        # Correlation with Granger cointegration\n",
    "        self.corr_granger: List = list()\n",
    "        # Correlation with Johansen cointegration\n",
    "        self.corr_johansen: List = list()\n",
    "        # A list of correlation values where the value is associated with Granger OR Johansen cointegration\n",
    "        # The length of this list is the total cointegration number\n",
    "        self.corr_granger_or_johansen: List = list()\n",
    "        # A list of correlation values where the value is associated with Granger AND Johansen cointegration\n",
    "        self.corr_granger_and_johansen: List = list()\n",
    "        #\n",
    "        # Correlation/Cointegration counts\n",
    "        #\n",
    "        # Total number of pairs that have correlation >= cutoff and Granger cointegration\n",
    "        self.total_corr_granger: int = 0\n",
    "        # Total number of pairs that have correlation >= cutoff and Johansen cointegration\n",
    "        self.total_corr_johansen: int = 0\n",
    "        # Total number of pairs that have correlation >= cutoff and Granger OR Johansen cointegration\n",
    "        self.total_corr_granger_or_johansen = 0\n",
    "        # Total number of pairs that have correlation >= cutoff and Granger ANd Johansen cointegration\n",
    "        self.total_corr_granger_and_johansen = 0\n",
    "        #\n",
    "        # Serial cointegration\n",
    "        #\n",
    "        # Number of pairs with in-sample correlation >= cutoff, in-sample granger and serial cointegration\n",
    "        self.corr_granger_serial_coint: int = 0\n",
    "        # Number of pairs with in-sample correlation >= cutoff, in-smaple Johansen and serial cointegratoin\n",
    "        self.corr_johansen_serial_coint: int = 0\n",
    "        # Number of pairs with in-sample correlation >= cutoff, in-sample Granger or Johansen and serial cointegration\n",
    "        self.granger_or_johansen_serial_coint: int = 0\n",
    "        # Number of pairs with in-sample correlation >= cutoff, in-sample Granger AND Johansen and serial cointegration\n",
    "        self.granger_and_johansen_serial_coint: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample granger = 90\n",
    "        self.granger_coint_90: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample granger = 90 and serial cointegrated\n",
    "        self.granger_serial_coint_90: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample granger = 95\n",
    "        self.granger_coint_95: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample granger = 95 and serial cointegrated\n",
    "        self.granger_serial_coint_95: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample granger = 99\n",
    "        self.granger_coint_99: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample granger = 99 and serial cointegrated\n",
    "        self.granger_serial_coint_99: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample Johansen = 90\n",
    "        self.johansen_coint_90: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample johansen = 90 and serial cointegrated\n",
    "        self.johansen_serial_coint_90: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample Johansen = 95\n",
    "        self.johansen_coint_95: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample johansen = 95 and serial cointegrated\n",
    "        self.johansen_serial_coint_95: int = 0\n",
    "        # Number of pairs with correlation >= cutoff and in-sample Johansen = 99\n",
    "        self.johansen_coint_99: int = 0\n",
    "        # Number of pairs with correlation >= cutoff, in-sample johansen = 99 and serial cointegrated\n",
    "        self.johansen_serial_coint_99: int = 0\n",
    "\n",
    "\n",
    "\n",
    "class CalcStatistics:\n",
    "    def __init__(self, cutoff: float, cutoff_2: float) -> None:\n",
    "        self.cutoff = cutoff\n",
    "        self.cutoff_2 = cutoff_2\n",
    "\n",
    "    def add_coint_stats(self,\n",
    "                        correlation_n: float,\n",
    "                        is_n_granger_coint: bool,\n",
    "                        is_n_johansen_coint: bool,\n",
    "                        stats: Statistics) -> None:\n",
    "        \"\"\"\n",
    "        Collect the relationship between Granger and Johansen cointegration and correlation\n",
    "        in the in-sample data.\n",
    "        :param correlation_n: The correlation for the in-sample data\n",
    "        :param is_n_granger_coint: whether the in-sample data shows Granger cointegration\n",
    "        :param is_n_johansen_coint: whether the in-smaple data shows Johansen cointegration\n",
    "        :param stats: the statistics object\n",
    "        :return: nothing\n",
    "        \"\"\"\n",
    "        is_n_either_coint = is_n_granger_coint or is_n_johansen_coint\n",
    "        is_n_both_coint = is_n_granger_coint and is_n_johansen_coint\n",
    "        if is_n_granger_coint:\n",
    "            stats.corr_granger.append(correlation_n)\n",
    "        if is_n_johansen_coint:\n",
    "            stats.corr_johansen.append(correlation_n)\n",
    "        if is_n_either_coint:\n",
    "            stats.corr_granger_or_johansen.append(correlation_n)\n",
    "        if is_n_both_coint:\n",
    "            stats.corr_granger_and_johansen.append(correlation_n)\n",
    "\n",
    "    def correlation_and_cointegration(self,\n",
    "                                      is_n_granger_coint: bool,\n",
    "                                      is_n_johansen_coint: bool,\n",
    "                                      is_n_1_granger_coint: bool,\n",
    "                                      is_n_1_johansen_coint: bool,\n",
    "                                      stats: Statistics,\n",
    "                                      period,\n",
    "                                      pairs_dict: Dict) -> None:\n",
    "        n_1_cointegration = is_n_1_granger_coint or is_n_1_johansen_coint\n",
    "        # Total number of pairs that have correlation >= cutoff and Granger cointegration\n",
    "        if is_n_granger_coint:\n",
    "            stats.total_corr_granger += 1\n",
    "            count: int = pairs_dict[period]\n",
    "            pairs_dict[period] = count + 1\n",
    "            # Number of pairs with in-smaple correlation >= cutoff, in-sample granger and serial cointegration\n",
    "            if n_1_cointegration:\n",
    "                stats.corr_granger_serial_coint += 1\n",
    "        # Total number of pairs that have correlation >= cutoff and Johansen cointegration\n",
    "        if is_n_johansen_coint:\n",
    "            stats.total_corr_johansen += 1\n",
    "            if n_1_cointegration:\n",
    "                # Number of pairs with in-sample correlation >= cutoff, in-smaple Johansen and serial cointegratoin\n",
    "                stats.corr_johansen_serial_coint += 1\n",
    "        # Total number of pairs that have correlation >= cutoff and Granger OR Johansen cointegration\n",
    "        if is_n_granger_coint or is_n_johansen_coint:\n",
    "            stats.total_corr_granger_or_johansen += 1\n",
    "            if n_1_cointegration:\n",
    "                # Number of pairs with in-sample correlation >= cutoff, in-sample Granger or Johansen and serial cointegration\n",
    "                stats.granger_or_johansen_serial_coint += 1\n",
    "        if is_n_granger_coint and is_n_johansen_coint:\n",
    "            stats.total_corr_granger_and_johansen += 1\n",
    "            if n_1_cointegration:\n",
    "                # Number of pairs with in-sample correlation >= cutoff, in-sample Granger AND Johansen and serial cointegration\n",
    "                stats.granger_and_johansen_serial_coint += 1\n",
    "\n",
    "\n",
    "    def confidence_stats(self, elem_n_coint: CointAnalysisResult,\n",
    "                         elem_n_1_coint: CointAnalysisResult,\n",
    "                         stats: Statistics) -> None:\n",
    "        granger_n_conf: int = elem_n_coint.granger_coint.confidence\n",
    "        granger_n_1_conf: int = elem_n_1_coint.granger_coint.confidence\n",
    "        johansen_n_conf: int = elem_n_coint.johansen_coint.confidence\n",
    "        johansen_n_1_conf: int = elem_n_1_coint.johansen_coint.confidence\n",
    "        either_n_1_coint = granger_n_1_conf > 0 or johansen_n_1_conf > 0\n",
    "        match granger_n_conf:\n",
    "            case 10:\n",
    "                stats.granger_coint_90 += 1\n",
    "                if either_n_1_coint:\n",
    "                    # Number of pairs with correlation >= cutoff, in-sample granger = 90 and serial cointegrated\n",
    "                    stats.granger_serial_coint_90 += 1\n",
    "            case 5:\n",
    "                stats.granger_coint_95 += 1\n",
    "                if either_n_1_coint:\n",
    "                    stats.granger_serial_coint_95 += 1\n",
    "            case 1:\n",
    "                stats.granger_coint_99 += 1\n",
    "                if either_n_1_coint:\n",
    "                    stats.granger_serial_coint_99 += 1\n",
    "        match johansen_n_conf:\n",
    "            case 10:\n",
    "                stats.johansen_coint_90 += 1\n",
    "                if either_n_1_coint:\n",
    "                    stats.johansen_serial_coint_90 += 1\n",
    "            case 5:\n",
    "                stats.johansen_coint_95 += 1\n",
    "                if either_n_1_coint:\n",
    "                    stats.johansen_serial_coint_95 += 1\n",
    "            case 1:\n",
    "                stats.johansen_coint_99 += 1\n",
    "                if either_n_1_coint:\n",
    "                    stats.johansen_serial_coint_99 += 1\n",
    "\n",
    "    def col_stats(self,\n",
    "                  elem_n_tuple: Tuple,\n",
    "                  elem_n_1_tuple: Tuple,\n",
    "                  stats: Statistics,\n",
    "                  period,\n",
    "                  pairs_dict: Dict) -> None:\n",
    "        correlation_n = elem_n_tuple[0]\n",
    "        elem_n_coint: CointAnalysisResult = elem_n_tuple[1]\n",
    "        elem_n_1_coint: CointAnalysisResult = elem_n_1_tuple[1]\n",
    "        elem_n_granger = elem_n_coint.granger_coint\n",
    "        is_n_granger_coint = elem_n_granger.confidence > 0\n",
    "        elem_n_1_granger = elem_n_1_coint.granger_coint\n",
    "        is_n_1_granger_coint = elem_n_1_granger.confidence > 0\n",
    "        #\n",
    "        elem_n_johansen = elem_n_coint.johansen_coint\n",
    "        is_n_johansen_coint = elem_n_johansen.confidence > 0\n",
    "        elem_n_1_johansen = elem_n_1_coint.johansen_coint\n",
    "        is_n_1_johansen_coint = elem_n_1_johansen.confidence > 0\n",
    "        self.add_coint_stats(correlation_n,\n",
    "                             is_n_granger_coint,\n",
    "                             is_n_johansen_coint,\n",
    "                             stats)\n",
    "        if correlation_n >= self.cutoff:\n",
    "            stats.total_correlation += 1\n",
    "            correlation_n_1 = elem_n_1_tuple[0]\n",
    "            if correlation_n_1 >= self.cutoff_2:\n",
    "                stats.serial_correlation += 1\n",
    "            self.correlation_and_cointegration(is_n_granger_coint,\n",
    "                                               is_n_johansen_coint,\n",
    "                                               is_n_1_granger_coint,\n",
    "                                               is_n_1_johansen_coint,\n",
    "                                               stats,\n",
    "                                               period,\n",
    "                                               pairs_dict)\n",
    "            self.confidence_stats(elem_n_coint, elem_n_1_coint, stats)\n",
    "\n",
    "\n",
    "    def build_pairs_dict(self, coint_info_df: pd.DataFrame) -> Dict:\n",
    "        pairs_dict = dict()\n",
    "        index = coint_info_df.index\n",
    "        for time_stamp in index:\n",
    "            pairs_dict[time_stamp]: int = 0\n",
    "        return pairs_dict\n",
    "\n",
    "\n",
    "    def traverse(self, coint_info_df: pd.DataFrame) -> Statistics:\n",
    "        stats = Statistics()\n",
    "        pairs_dict: Dict = self.build_pairs_dict(coint_info_df)\n",
    "        index = coint_info_df.index\n",
    "        rows = coint_info_df.shape[0]\n",
    "        cols = coint_info_df.shape[1]\n",
    "        stats.total_pairs = rows * cols\n",
    "        for col_ix in range(cols):\n",
    "            for row_ix in range(rows-1):\n",
    "                elem_n_tuple: Tuple = coint_info_df.iloc[row_ix, col_ix]\n",
    "                elem_n_1_tuple: Tuple = coint_info_df.iloc[row_ix+1, col_ix]\n",
    "                period = index[row_ix]\n",
    "                self.col_stats(elem_n_tuple, elem_n_1_tuple, stats, period, pairs_dict)\n",
    "        counts = pairs_dict.values()\n",
    "        pair_count_df = pd.DataFrame(counts)\n",
    "        pair_count_df.index = index\n",
    "        pair_count_df.columns = ['Pairs Count']\n",
    "        stats.pair_count_df = pair_count_df.iloc[:-1]\n",
    "        return stats\n",
    "\n",
    "\n",
    "\n",
    "cointegration_calc = CalcPairsCointegration(close_prices_df=close_prices_df)\n",
    "coint_info_df = cointegration_calc.calc_pairs_coint_dataframe(corr_df=corr_df, window=half_year)\n",
    "\n",
    "calc_statistics = CalcStatistics(cutoff=correlation_cutoff, cutoff_2=correlation_cutoff-0.10)\n",
    "stats = calc_statistics.traverse(coint_info_df=coint_info_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>\n",
    "Correlation Statistics\n",
    "</h2>\n",
    "<p>\n",
    "This section looks at correlation statistics and their relation to cointegration:\n",
    "</p>\n",
    "<ul>\n",
    "<li>\n",
    "<p>\n",
    "Stability of pairs correlation. When there is high pairs correlation (correlation greater than or equal to 0.75) in the past six months,\n",
    "how often is there correlation in the next time period (correlation >= 0.60)?\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "Is pairs correlation related to cointegration? For example, is high correlation related to cointegration?\n",
    "</p>\n",
    "</li>\n",
    "</ul>\n",
    "<h3>\n",
    "Stability of Correlation\n",
    "</h3>\n",
    "<p>\n",
    "For pairs trading to be a profitable strategy the statistics that are observed over a past period must persist into the next time period often\n",
    "enough to be profitable.  If a pairs forms a stationary mean reverting time series in a past period, profitable pairs trading relies\n",
    "on this statistic holding over the out-of-sample trading period (often enough to be profitable).\n",
    "</p>\n",
    "<p>\n",
    "In this section I look at whether a strong correlation between pairs makes it likely that there will be a strong correlation in\n",
    "the next time period. This is an important statistic for pairs trading because correlation is related to cointegration. If correlation persists\n",
    "between periods then cointegration and mean reversion are more likely to persist. If correlation does not persist between time periods\n",
    "then cointegration may not be persistent.\n",
    "</p>\n",
    "<p>\n",
    "The table below shows the dependence between correlation in the past period and correlation in the next period. The correlation is the\n",
    "past period is at least 0.75. The correlation in the next period is at least 0.60.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒════╤═════════════════════════════╤══════════════════════════════╤═══════════╕\n",
      "│    │   Total Correlation >= 0.75 │   Serial Correlation >= 0.65 │   percent │\n",
      "╞════╪═════════════════════════════╪══════════════════════════════╪═══════════╡\n",
      "│  0 │                       85220 │                        41616 │     48.83 │\n",
      "╘════╧═════════════════════════════╧══════════════════════════════╧═══════════╛\n"
     ]
    }
   ],
   "source": [
    "correlation_percent = round((stats.serial_correlation/stats.total_correlation) * 100, 2)\n",
    "correlation_depend_df = pd.DataFrame([stats.total_correlation, stats.serial_correlation, correlation_percent]).transpose()\n",
    "correlation_depend_df.columns = [f'Total Correlation >= {correlation_cutoff}', f'Serial Correlation >= {correlation_cutoff-0.10}', 'percent']\n",
    "\n",
    "print(tabulate(correlation_depend_df, headers=[*correlation_depend_df.columns], tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "As the table above shows, about half (~49%) of the pairs with a high correlation are also highly correlated in the next time period.\n",
    "</p>\n",
    "\n",
    "<h3>\n",
    "Computation\n",
    "</h3>\n",
    "<p>\n",
    "For every pair the correlation, granger cointegration and johansen cointegration are calculated. The calculation is very compute intensive\n",
    "and Python is notorious for being slow compared to C++ and Java (Python does, however, have lots of support for statistics and data analysis).\n",
    "I have only succeeded in successfully doing this calculation in a single Python thread.  I tried to use Python's Pool parallelism which would\n",
    "allow me to run the calculation in parallel on the 16-CPU cores on my Linux system. Unfortunately this resulted in a segment fault\n",
    "causing the calculation to fail.\n",
    "</p>\n",
    "<p>\n",
    "The historical calculation is constant (since the historical data is constant). To avoid recalculating the correlation and cointegration\n",
    "data, the values are stored in files. A Pandas DataFrame is constructed from this data.\n",
    "</p>\n",
    "<p>\n",
    "The data in these files can be recalculated by removing the files and running the notebook.\n",
    "</p>\n",
    "\n",
    "<h3>\n",
    "Correlation and Cointegration\n",
    "</h3>\n",
    "<p>\n",
    "After filtering for the S&P 500 industry sector, the next filter that is used in pair selection is correlation (i.e., pairs with\n",
    "high correlation are selected for cointegration testing).\n",
    "</p>\n",
    "<p>\n",
    "The histograms below show the relationship between correlation and cointegration, in the same half-year, in-sample period.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_granger_a = np.array(stats.corr_granger)\n",
    "display_histogram(correlation_granger_a, 'Correlation vs Granger Cointegration', 'Frequency')\n",
    "\n",
    "correlation_johansen_a = np.array(stats.corr_johansen)\n",
    "display_histogram(correlation_granger_a, 'Correlation vs Johansen Cointegration', 'Frequency')\n",
    "\n",
    "correlation_and_cointegration_a = np.array(stats.corr_granger_or_johansen)\n",
    "display_histogram(correlation_and_cointegration_a, 'Correlation with Granger OR Johansen Cointegration', 'Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The histogram plot above shows that there is a strong relationship between correlation and cointegration. That is, highly correlated\n",
    "pairs are more likely to be cointegrated.\n",
    "</p>\n",
    "<p>\n",
    "The histogram also shows that there are cointegration is much more common with higher positive correlation.\n",
    "</p>\n",
    "<p>\n",
    "The histogram below shows the relationship between correlation and cointegration where cointegration meets both the Granger\n",
    "and Johansen tests (e.g., Granger AND Johansen cointegration).\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "correlation_granger_and_johansen_a = np.array(stats.corr_granger_and_johansen)\n",
    "display_histogram(correlation_granger_and_johansen_a, 'Correlation with Granger AND Johansen Cointegration', 'Frequency')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p>\n",
    "The histograms above show that correlation is an effective first filter for cointegration.\n",
    "</p>\n",
    "<p>\n",
    "The histogram below shows the number of pairs that are highly correlated with Granger cointegration for each in-sample\n",
    "time period.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='Date'>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x1000 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.pair_count_df.plot(kind='bar', figsize=(12,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "As we have seen, correlation increases in market crashes. The histogram above suggests that cointegration increases as well.\n",
    "</p>\n",
    "\n",
    "<h2>\n",
    "Stability of Cointegration\n",
    "</h2>\n",
    "<p>\n",
    "Cointegration for a pair is calculated over an in-sample look-back period. When a pair is found to be cointegrated, trading takes place\n",
    "in the out-of-sample period. For example, cointegration is calculated over a half year look-back period. Trading, using the cointegration spread,\n",
    "takes place over a three month out-of-sample period following the look-back period.\n",
    "</p>\n",
    "<p>\n",
    "For a cointegrated pair, the spread time series is a stationary time series with a constant mean and standard deviation.\n",
    "The spread time series is mean reverting, so that divergences from the mean return to the mean. The statistics of the spread time series,\n",
    "in an ideal world, are consistent between the look-back period and the trading period.\n",
    "</p>\n",
    "<p>\n",
    "In the real world, stock and stock pair behavior is constantly changing.  For pairs trading to succeed in the real world (as opposed to\n",
    "the ideal world), the mean reversion of the spread time series must persist (be stationary) between the look-back period and the trading\n",
    "period often enough to yield a profit.\n",
    "</p>\n",
    "<p>\n",
    "There is no way to know if a pair will remain stationary between the look-back period and the trading period. What can be examined\n",
    "are the historical frequences where there is stationarity between the look-back period and the trading period.\n",
    "</p>\n",
    "<p>\n",
    "There are a limited number of six month time periods. Looking at the stationarity for a single pair would not be statistically\n",
    "significant, but we can look over all 8863 pairs over 32 time periods.\n",
    "</p>\n",
    "<p>\n",
    "The histogram below shows the relationship between past correlation and serial cointegration. Here cointegration is either Granger\n",
    "or Johansen cointegration.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "coint_totals = np.array([# Total number of pairs that have correlation >= cutoff and Granger cointegration\n",
    "            stats.total_corr_granger,\n",
    "            # Total number of pairs that have correlation >= cutoff and Johansen cointegration\n",
    "            stats.total_corr_johansen,\n",
    "            # Total number of pairs that have correlation >= cutoff and Granger OR Johansen cointegration\n",
    "            stats.total_corr_granger_or_johansen,\n",
    "            # Total number of pairs that have correlation >= cutoff and Granger ANd Johansen cointegration\n",
    "            stats.total_corr_granger_and_johansen\n",
    "           ])\n",
    "serial_coint = np.array([# Number of pairs with in-sample correlation >= cutoff, in-sample granger and serial cointegration\n",
    "            stats.corr_granger_serial_coint,\n",
    "            # Number of pairs with in-sample correlation >= cutoff, in-smaple Johansen and serial cointegratoin\n",
    "            stats.corr_johansen_serial_coint,\n",
    "            # Number of pairs with in-sample correlation >= cutoff, in-sample Granger or Johansen and serial cointegration\n",
    "            stats.granger_or_johansen_serial_coint,\n",
    "            # Number of pairs with in-sample correlation >= cutoff, in-sample Granger AND Johansen and serial cointegration\n",
    "            stats.granger_and_johansen_serial_coint\n",
    "            ])\n",
    "\n",
    "coint_percent = ((serial_coint / coint_totals) * 100).round(2)\n",
    "\n",
    "coint_stats_df = pd.DataFrame([coint_totals, serial_coint, coint_percent]).transpose()\n",
    "coint_stats_df.columns = ['Coint Total', 'Serial Coint', 'Percent']\n",
    "coint_stats_df.index = ['Granger', 'Johansen', 'Granger or Johansen', 'Granger and Johansen']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The table below shows the relationship between cointegration in the in-sample six month period and cointegration in the six month out of\n",
    "sample (trading) period.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒══════════════════════╤═══════════════╤════════════════╤═══════════╕\n",
      "│                      │   Coint Total │   Serial Coint │   Percent │\n",
      "╞══════════════════════╪═══════════════╪════════════════╪═══════════╡\n",
      "│ Granger              │         36639 │          14511 │     39.61 │\n",
      "├──────────────────────┼───────────────┼────────────────┼───────────┤\n",
      "│ Johansen             │         22120 │           8805 │     39.81 │\n",
      "├──────────────────────┼───────────────┼────────────────┼───────────┤\n",
      "│ Granger or Johansen  │         42460 │          16770 │     39.5  │\n",
      "├──────────────────────┼───────────────┼────────────────┼───────────┤\n",
      "│ Granger and Johansen │         16299 │           6546 │     40.16 │\n",
      "╘══════════════════════╧═══════════════╧════════════════╧═══════════╛\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print(tabulate(coint_stats_df, headers=[*coint_stats_df.columns], tablefmt='fancy_grid'))\n",
    "\n",
    "coint_conf_a = np.array([ # Number of pairs with correlation >= cutoff, in-sample granger = 90\n",
    "                            stats.granger_coint_90,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample granger = 95\n",
    "                            stats.granger_coint_95,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample granger = 99\n",
    "                            stats.granger_coint_99,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 90\n",
    "                            stats.johansen_coint_90,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 95\n",
    "                            stats.johansen_coint_95,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 99\n",
    "                            stats.johansen_coint_99 ])\n",
    "\n",
    "coint_conf_serial_a = np.array([ # Number of pairs with correlation >= cutoff, in-sample granger = 90 and serial cointegrated\n",
    "                            stats.granger_serial_coint_90,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample granger = 95 and serial cointegrated\n",
    "                            stats.granger_serial_coint_95,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample granger = 99 and serial cointegrated\n",
    "                            stats.granger_serial_coint_99,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 90 and serial cointegrated\n",
    "                            stats.johansen_serial_coint_90,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 95 and serial cointegrated\n",
    "                            stats.johansen_serial_coint_95,\n",
    "                            # Number of pairs with correlation >= cutoff, in-sample johansen = 99 and serial cointegrated\n",
    "                            stats.johansen_serial_coint_99 ])\n",
    "\n",
    "coint_conf_percent = ((coint_conf_serial_a/coint_conf_a) * 100).round(2)\n",
    "\n",
    "\n",
    "coint_conf_df = pd.DataFrame([coint_conf_a, coint_conf_serial_a, coint_conf_percent]).transpose()\n",
    "coint_conf_df.columns = ['Cointegration', 'Serial Coint', 'Percent']\n",
    "coint_conf_df.index = ['Granger 90%', 'Granger 95%', 'Granger 99%', 'Johansen 90', 'Johansen 95', 'Johansen 99' ]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The table below shows the relationship between the cointegration confidence intervals in the in-sample period and\n",
    "cointegration in the out-of-sample trading period.\n",
    "</p>\n",
    "<p>\n",
    "The results in this table show that the in-sample confidence interval does not seem to predict out-of-sample cointegration.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════════════╤═════════════════╤════════════════╤═══════════╕\n",
      "│             │   Cointegration │   Serial Coint │   Percent │\n",
      "╞═════════════╪═════════════════╪════════════════╪═══════════╡\n",
      "│ Granger 90% │           12827 │           5004 │     39.01 │\n",
      "├─────────────┼─────────────────┼────────────────┼───────────┤\n",
      "│ Granger 95% │           15438 │           6080 │     39.38 │\n",
      "├─────────────┼─────────────────┼────────────────┼───────────┤\n",
      "│ Granger 99% │            8374 │           3427 │     40.92 │\n",
      "├─────────────┼─────────────────┼────────────────┼───────────┤\n",
      "│ Johansen 90 │            8680 │           3391 │     39.07 │\n",
      "├─────────────┼─────────────────┼────────────────┼───────────┤\n",
      "│ Johansen 95 │            9514 │           3852 │     40.49 │\n",
      "├─────────────┼─────────────────┼────────────────┼───────────┤\n",
      "│ Johansen 99 │            3926 │           1562 │     39.79 │\n",
      "╘═════════════╧═════════════════╧════════════════╧═══════════╛\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(tabulate(coint_conf_df, headers=[*coint_conf_df.columns], tablefmt='fancy_grid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>\n",
    "Stability of Cointegration: Conclusions\n",
    "</h3>\n",
    "<ul>\n",
    "<li>\n",
    "<p>\n",
    "The Granger and Johansen have close to the same results.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "Cointegration is persistent about 40% of the time for either Granger or Johansen cointegration, at all of the confidence levels.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "There are lots of in-sample pairs with high correlation and cointegration to choose from.\n",
    "</p>\n",
    "</li>\n",
    "</ul>\n",
    "<p>\n",
    "Since the Granger and Johansen tests produce very similar results. I prefer the Granger test because it is easier to understand and\n",
    "provides an intercept value, which tends to produce a spread series with a mean of zero. Higher cointegration confidence intervals do\n",
    "not deliver higher serial cointegration, so a simple test for a non-zero confidence interval can be used.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>\n",
    "Halflife for a Mean Reverting Ornstein – Uhlenbeck Process\n",
    "</h2>\n",
    "<p>\n",
    "A Ornstein – Uhlenbeck process is a random walk process with a tendency to move back toward the mean. Half-life of the mean-reversionis the\n",
    "average time it will take the process to get pulled half-way back to the mean.\n",
    "</p>\n",
    "<p>\n",
    "Cointegration from one time period to the next happens in only about 40% of the cointegrated pairs. This suggests that half-life will also\n",
    "be unstable between time periods.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class HalflifeCalculation:\n",
    "    def __init__(self, coint_info_df: pd.DataFrame, close_prices_df: pd.DataFrame, correlation_cutoff: float, window: int) -> None:\n",
    "        self.coint_info_df = coint_info_df\n",
    "        self.close_prices_df = close_prices_df\n",
    "        self.window = window\n",
    "        self.cutoff = correlation_cutoff\n",
    "\n",
    "    def half_life(self, spread_a: np.array) -> int:\n",
    "        \"\"\"\n",
    "        https://quant.stackexchange.com/questions/25086/calculating-half-life-of-mean-reverting-series-with-python/25119\n",
    "        :param spread_a:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        spread_lag = np.roll(spread_a, 1)\n",
    "        spread_lag[0] = 0\n",
    "        spread_ret = spread_a - spread_lag\n",
    "        spread_ret[0] = 0\n",
    "        # adds intercept terms to X variable for regression\n",
    "        spread_lag2 = sm.add_constant(spread_lag)\n",
    "        model = sm.OLS(spread_ret, spread_lag2)\n",
    "        res = model.fit()\n",
    "        halflife_f = -log(2) / res.params[1]\n",
    "        halflife_i = int(round(halflife_f))\n",
    "        return halflife_i\n",
    "\n",
    "    def calc_spread(self, coint_info: CointInfo, window_start: int) -> np.array:\n",
    "        pair_l = coint_info.pair_str.split(':')\n",
    "        if pair_l[0] in self.close_prices_df.columns and pair_l[1] in self.close_prices_df.columns:\n",
    "            asset_a_a = self.close_prices_df[pair_l[0]].iloc[window_start:window_start + self.window].values\n",
    "            asset_b_a = self.close_prices_df[pair_l[1]].iloc[window_start:window_start + self.window].values\n",
    "            spread_a = asset_a_a - coint_info.intercept - (coint_info.weight * asset_b_a)\n",
    "        else:\n",
    "            spread_a = np.zeros(0)\n",
    "        return spread_a\n",
    "\n",
    "    def half_life_distribution(self) -> np.array:\n",
    "        \"\"\"\n",
    "        CointInfo(pair_str=granger_pair_str,\n",
    "                  confidence=granger_coint.confidence,\n",
    "                  weight=granger_coint.weight,\n",
    "                  has_intercept=True,\n",
    "                  intercept=granger_coint.intercept)\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        rows = coint_info_df.shape[0]\n",
    "        cols = coint_info_df.shape[1]\n",
    "        halflife_l = list()\n",
    "        pairs = coint_info_df.columns\n",
    "        window_start = 0\n",
    "        for row_ix in range(rows):\n",
    "            for col_ix in range(cols):\n",
    "                elem_n_tuple: Tuple = coint_info_df.iloc[row_ix, col_ix]\n",
    "                correlation_n = elem_n_tuple[0]\n",
    "                if correlation_n >= self.cutoff:\n",
    "                    elem_n_coint: CointAnalysisResult = elem_n_tuple[1]\n",
    "                    elem_n_granger = elem_n_coint.granger_coint\n",
    "                    if elem_n_granger.confidence > 0:\n",
    "                        spread_a = self.calc_spread(elem_n_granger, window_start)\n",
    "                        if len(spread_a) > 0:\n",
    "                            half_life = self.half_life(spread_a)\n",
    "                            if half_life > 0:\n",
    "                                halflife_l.append(half_life)\n",
    "            window_start += self.window\n",
    "        halflife_a = np.array(halflife_l)\n",
    "        halflife_std = np.std(halflife_a)\n",
    "        # All halflife values will be grater than zero. Filter out halflife values that are over\n",
    "        # 8 standard deviations out.\n",
    "        halflife_filter = halflife_a <= (8 * halflife_std)\n",
    "        halflife_filtered_a = halflife_a[halflife_filter]\n",
    "        return halflife_filtered_a\n",
    "\n",
    "\n",
    "half_life_calc = HalflifeCalculation(coint_info_df=coint_info_df,\n",
    "                                     close_prices_df=close_prices_df,\n",
    "                                     correlation_cutoff=correlation_cutoff,\n",
    "                                     window=half_year)\n",
    "halflife_a = half_life_calc.half_life_distribution()\n",
    "halflife_df = pd.DataFrame(halflife_a)\n",
    "halflife_df.columns = ['Spread Halflife']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "The plot below shows the half-life distribution mean reverting spread for pairs with a correlation greater than or equal to 0.75.  Negative\n",
    "half-life values and values that are greater than eight standard deviations are filtered out.\n",
    "</p>\n",
    "<p>\n",
    "This distribution shows the half-life values for all time periods (from 2007 through 2022) for all highly correlated pairs. Given the number\n",
    "of values, we can assume statistical significance.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: ylabel='Frequency'>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "halflife_df.plot(kind='hist', xlabel='Spread Half-life', figsize=(10, 6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "I am skeptical about the validity of the half-life statistics.  The histogram plot shows most of the half-life values clustered around 4.\n",
    "I would have more confidence in a distribution that included a wider range of values that are less skewed.\n",
    "</p>\n",
    "<p>\n",
    "The half-life statistic may be useful in weeding out problematic spread time series. For example, a spread with a negative half-life can\n",
    "be discarded. Similarly, a half-life greater than 10 could also be discarded.\n",
    "</p>\n",
    "<h2>\n",
    "Pairs Trading and Delusions in Mathematical Finance\n",
    "</h2>\n",
    "<p>\n",
    "Mathematical finance often assumes normal distributions and statistics that are constant (constant mean and standard deviation, for example).\n",
    "If these assumptions were actually true, it would be much easier to make money (except for the fact that mathematical finance would assume\n",
    "that these opportunities would have already been arbitraged out).\n",
    "</p>\n",
    "<p>\n",
    "The assumptions made in mathematical finance often exist because they allow closed-form (e.g., equation-based) solutions to finance problems.\n",
    "The fact that these assumptions are wrong in most cases is often ignored.\n",
    "</p>\n",
    "<p>\n",
    "Often the correlation and cointegration statistics are not constant between the in-sample and out-of-sample periods. In the case of correlation,\n",
    "there can be wide variations between adjacent time periods. Cointegration is also not constant from one time period to another.\n",
    "In fact, in 60% of the cases, a pair that is cointegrated in one time period is not cointegrated in the following time period.\n",
    "</p>\n",
    "<h2>\n",
    "An Algorithm for Selecting Pairs\n",
    "</h2>\n",
    "<p>\n",
    "Based on the statistical observation from the data above, I can propose the following algorithm to select pairs. These criteria\n",
    "are applied to the in-sample data (the past half year). Trading takes place in the next time period (the out-of-sample) period.\n",
    "</p>\n",
    "<p>\n",
    "A three month trading period is used. This time period, rather than a six month time period, is chosen because a shorter time period\n",
    "is more likely to have consistent statistics with the in-sample time period. A shorter time period also allows the statistical tests\n",
    "to be run more often. The shorter time period limits the time that pairs are held. An open pair position that does not mean revert will\n",
    "be closed at the end of the three month trading period.\n",
    "</p>\n",
    "<p>\n",
    "On average only 40% of the pairs that are cointegrated in the in-sample time period will be co-integrated in the out-of-sample\n",
    "time period (e.g., the trading period). This suggests that a sufficient number of pairs must be traded to take advantage of\n",
    "the 40% cointegration that can be expected.\n",
    "</p>\n",
    "<p>\n",
    "For 60% of the pairs traded (on average) there will not be cointegration and mean reversion in the out-of-sample trading period.\n",
    "In order for the pairs trading strategy to be successful the profit from the mean reverting pairs must be higher\n",
    "than the losses from the pairs that do not mean revert.\n",
    "A market neutral weighting is used for all of the pairs that are traded, so there is some reason to expect that the pairs that\n",
    "do not mean will have, on average, a return near zero.\n",
    "</p>\n",
    "<p>\n",
    "The pairs trading strategy seeks to profit from the pairs that mean revert on average 40% of the time (while the other pairs are,\n",
    "we hope, market neutral).  This suggests that the strategy will do better if we make lots of small \"bets\" (perhaps trading\n",
    "100 pairs). Making lots of small bets also reduces the risk from company factors. For example, if one of the pairs selected included\n",
    "Tesla (TSLA) there could be losses due to the post Twitter purchase \"Elon Musk factor\". However, if the investment in the pair is small\n",
    "the loss would also be small.\n",
    "</p>\n",
    "<p>\n",
    "The algorithm for pairs trading is outlined below.\n",
    "</p>\n",
    "<ol>\n",
    "<li>\n",
    "Select pairs from an S&P 500 industry sector.\n",
    "</li>\n",
    "<li>\n",
    "Filter pairs for high correlation (correlation greater than or equal to 0.75).\n",
    "</li>\n",
    "<li>\n",
    "Use the Granger test to identify pairs that are cointegrated (at any confidence level)\n",
    "</li>\n",
    "</ol>\n",
    "<p>\n",
    "After selecting highly correlated, cointegrated pairs there will (historically) be between 700 and 1500 pairs, which are far more than\n",
    "can be traded by a retail trader.  To reduce the number of pairs the following additional filters are applied.\n",
    "</p>\n",
    "<ol>\n",
    "<li>\n",
    "Discard any pairs with a spread time series half-life that is negative or larger than 10.\n",
    "</li>\n",
    "<li>\n",
    "Select the top N pairs by the volatility of the pair spread (sorted by high volatility).\n",
    "</li>\n",
    "</ol>\n",
    "<p>\n",
    "High volatility pairs are selected based on the speculation that these stocks will yield the highest profit.\n",
    "</p>\n",
    "<h2>\n",
    "Disclaimer\n",
    "</h2>\n",
    "<p>\n",
    "This notebook is not financial advice, investment advice, or tax advice.\n",
    "The information in this notebook is for informational and recreational purposes only.\n",
    "Investment products discussed (ETFs, mutual funds, etc.) are for illustrative purposes\n",
    "only. This is not a recommendation to buy, sell, or otherwise transact in any of the\n",
    "products mentioned. Do your own due diligence. Past performance does not guarantee\n",
    "future returns.\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h2>\n",
    "References\n",
    "</h2>\n",
    "<ol>\n",
    "<li>\n",
    "<i>Pairs Trading: Quantitative Method and Analysis</i> by Ganapathy Vidyamurthy, 2004, Wiley Publishing\n",
    "</li>\n",
    "<li>\n",
    "Algorithmic Trading: Winning Strategies and Their Rationale by Ernie Chan, 2013, Wiley Publishing\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"https://medium.com/@financialnoob/granger-causality-test-in-pairs-trading-bf2fd939e575\">Granger causality test in pairs trading</a> by\n",
    "Alexander Pavlov (behind the Medium paywall).\n",
    "</p>\n",
    "<p>\n",
    "In this paper the Granger causality test is used to select stocks from the VBR Small-Cap ETF. The result is a yearly return of about\n",
    "28% and a Sharpe ratio of 1.7.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"https://letianzj.github.io/cointegration-pairs-trading.html\">Quantitative Trading and Systematic Investing by Letian Wang</a>\n",
    "</p>\n",
    "<p>\n",
    "This post includes a discussion on how the results of Johansen cointegration can be interpreted.\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"http://jonathankinlay.com/2019/02/pairs-trading-part-2-practical-considerations/\">Pairs Trading – Part 2: Practical Considerations</a> by Jonathan Kinlay  This is a practitioner's view of pairs trading. (email: jkinlay@gmail.com)\n",
    "</p>\n",
    "<p>\n",
    "This post discusses some of the challenges and pitfalls in pairs trading.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"https://www.sciencedirect.com/science/article/pii/S037843712100964X\">Applying Hurst Exponent in pair trading strategies\n",
    "on Nasdaq 100 index</a>\n",
    "by Quynh Bui and Robert Ślepaczuk\n",
    "</p>\n",
    "<p>\n",
    "The results in this article show that filtering pairs using the Hurst exponent value is superior to using\n",
    "cointegration. I've included this article in the references because it contradicts many articles that use\n",
    "cointegration. The overall results of unleveraged pairs trading shown in this article are poor.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"https://www.sciencedirect.com/science/article/pii/S2214845021000880\">Pairs trading: is it applicable to exchange-traded funds?</a>\n",
    "</p>\n",
    "<p>\n",
    "This article looks at pairs trading of ETFs. Some authors have suggested that cointegration is more persistent with ETFs.\n",
    "</p>\n",
    "</li>\n",
    "<li>\n",
    "<p>\n",
    "<a href=\"https://www.inf.ovgu.de/inf_media/downloads/forschung/technical_reports_und_preprints/2014/02_2014.pdf\">Cointegration Analysis\n",
    "of Financial Time Series Data by Johannes Steffen, Pascal Held and Rudolf Kruse, 2014</a>\n",
    "</p>\n",
    "<p>\n",
    "Pairs trading in the Foreign Exchange (FX) market.\n",
    "</p>\n",
    "</li>\n",
    "</ol>"
   ]
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