Meme_Stock_Portfolio_Management.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "id": "fb7159f6",
   "metadata": {},
   "source": [
    "# Does r/WallStreetBets Stocks Significantly Outperform?\n",
    "\n",
    "Thanks to the prevalence of COVID-19 in our everyday lives, it's getting increasingly difficult to return to normal for most people. While I've used this additional flexibility to pick up on old hobbies (gaming, music, etc.), others have used theirs to learn about financial markets. Knowing that I work in finance, some of my friends have reached out to me for financial pointers, while others have opted for the convenience of reading r/WallStreetBets.\n",
    "\n",
    "You may be asking, why am I -- someone who would be considered a \"sophiscated investor\" -- would even be interested in a platform such as r/WallStreetBets? For those of you who don't know, I've written a piece about the subreddit last year. However, it was never clearly explained the buzz around r/WallStreetBets.\n",
    "\n",
    "Due to the pandemic (the financial insecurity and flexibility it brought to millions of people), as well as the stimulus checks provided by the government and the rise of free trading platforms such as Robinhood, a lot of people who would typically not dabble with stocks are having fun with the stock market. They're also investing in all sorts of zany things like Dogecoin and GameStop. Institutional Investors (the \"smart money\") and the veterans in the financial media fail to understand this, and they're generally condescending and negative towards these new brand of retail investors.\n",
    "\n",
    "They call them idiots for taking risks in cryptocurrencies; they call them fools for believing companies in dying industries with falling revenues are great investments [2]. From this, the term \"Dumb Money\" was used to describe this new breed of investors; \"DOGE/GME to the moon,\" they frequently chant, much to the disdain and confusion of legacy investors and their friends in the legacy media [3].\n",
    "\n",
    "Recognizing the condescension, these retail investors decide to take their agency back using the self-described label known as Retards. Retards, if you don't know, is a rearrangement of the word tradeRS. Since they're not considered legitimate tradeRS in the eyes of the investment community, they'll just call themselves Retards, which is an anagram designed to reclaim the agency taken from retail investments. Sure, they may be considered \"Dumb Money,\" but they're going to make the investment decisions they want to without the influence and manipulation of institutional investors and their friends in the financial media.\n",
    "\n",
    "This is largely the energy behind drama involving r/WallStreetBets and the rest of the investment community.\n",
    "\n",
    "## Should You Listen To r/WallStreetBets Stock Picks?\n",
    "\n",
    "It is generally assumed -- rightly or wrongly -- that if you have a background in finance, you know what you're talking about. The barriers required to work within the industry seem to justify the claim. The most well-known front-end finance jobs require a bachelor's degree at an accredited four-year university, along with passing, at minimum, the Securities Industry Essentials Exam (or SIE) and either a FINRA Series 3 or 7 Exam.\n",
    "\n",
    "Jobs that are more analytically driven, such as Actuaries, may require candidates to have a statistical or mathematical background and pass several SOA (Society of Actuaries) Exams. While Quants (which is my domain) typically don't require examinations; however, some positions do encourage and require candidates to have at minimum a Masters of Science in a STEM field.\n",
    "\n",
    "So yes, it may be easy to see why the people who work on Wall Street are considered the \"Smart Money.\"\n",
    "\n",
    "However, this doesn't mean you need education and fancy certificates to make good investments. Warren Buffett, one of the greatest investors alive, began to invest on his own when he was only 11-years-old. While the man would never even look at any of the companies r/WallStreetBets are investing in, he established a system that allowed him to make sound investments using the resources available at the time; namely, a book published by Benjamin Graham called The Intelligent Investor. Speaking from personal experience, I started learning about finance and economics on my own time before I enrolled in university to pursue it as a career.\n",
    "\n",
    "Today, the resources available to help retail investors are potentially endless. Most of what you'll find on the internet is bunk; however, you can find invaluable information if you know where to look.\n",
    "\n",
    "This project aims to see if the Retards at r/WallStreetBets know where to look. Are they seeing things we aren't seeing or just larping as wall street speculators?\n",
    "\n",
    "## Building a Portfolio\n",
    "\n",
    "![02.PNG](attachment:02.PNG)\n",
    "\n",
    "We will construct a portfolio using popular stocks from the WallStreetBets community. In July 2021, I decided to find the most talk-about stocks in the r/WallStreetBets subreddit and narrowed the list down to 20 of the most popular equities on the platform. Six of these assets have recently gone public, and they will be excluded from the experiment.\n",
    "\n",
    "We're also going to include popular cryptocurrencies, such as Bitcoin and Dogecoin.\n",
    "\n",
    "Now that we've identified all of the asset classes we will use, we need to establish an interval for our analysis. At a minimum, we need five years' worth of information, so the timeframe will be from January 1st, 2017, to today.\n",
    "\n",
    "For the sake of simplicity and organzation, we're going to create a dictionary for our three financial instruments: `stocks`, `bonds` and `commodities`. Afterwards, we will assign each dictionary to their respective variables, and then assign these variables as elements for the creation a broader list called `tickers`. \n",
    "\n",
    "From there, using our clearly defined time interval, we will loop through each financial instrument in the list and use `DataReader` to extract the financial data from Yahoo Finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "537cdb01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data series for instrument AMC Entertainment with ticker = AMC\n",
      "Loading data series for instrument Cleveland-Cliffs with ticker = CLF\n",
      "Loading data series for instrument Bed Bath and Beyond, Inc. with ticker = BBBY\n",
      "Loading data series for instrument GameStop with ticker = GME\n",
      "Loading data series for instrument DuPoint de Nemours with ticker = DD\n",
      "Loading data series for instrument Macys with ticker = M\n",
      "Loading data series for instrument Clean Energy Fuels Corp. with ticker = CLNE\n",
      "Loading data series for instrument BlackBerry with ticker = BB\n",
      "Loading data series for instrument RingCentral with ticker = RNG\n",
      "Loading data series for instrument Workhorse Group with ticker = WKHS\n",
      "Loading data series for instrument Hostess Brands with ticker = TWNK\n",
      "Loading data series for instrument Senseonics with ticker = SENS\n",
      "Loading data series for instrument Cassava Sciences with ticker = SAVA\n",
      "Loading data series for instrument Ternium with ticker = TX\n",
      "Loading data series for instrument Liberty All-Star Equity with ticker = USA\n",
      "Loading data series for instrument US Steel Corp with ticker = X\n",
      "Loading data series for instrument HCA with ticker = HCA\n",
      "Loading data series for instrument VRTX with ticker = VRTX\n",
      "Loading data series for instrument Bitcoin with ticker = BTC-USD\n",
      "Loading data series for instrument Dogecoin with ticker = DOGE-USD\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>34.750000</td>\n",
       "      <td>33.900002</td>\n",
       "      <td>34.049999</td>\n",
       "      <td>34.750000</td>\n",
       "      <td>766100.0</td>\n",
       "      <td>27.085867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>34.799999</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.200001</td>\n",
       "      <td>547900.0</td>\n",
       "      <td>26.657169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>34.450001</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>443200.0</td>\n",
       "      <td>26.501278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>34.349998</td>\n",
       "      <td>33.700001</td>\n",
       "      <td>34.049999</td>\n",
       "      <td>33.799999</td>\n",
       "      <td>336700.0</td>\n",
       "      <td>26.345390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>34.049999</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>33.650002</td>\n",
       "      <td>33.299999</td>\n",
       "      <td>415100.0</td>\n",
       "      <td>25.955666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>7.970000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>6.830000</td>\n",
       "      <td>43284800.0</td>\n",
       "      <td>6.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>7.520000</td>\n",
       "      <td>6.980000</td>\n",
       "      <td>7.160000</td>\n",
       "      <td>7.450000</td>\n",
       "      <td>33360100.0</td>\n",
       "      <td>7.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>7.710000</td>\n",
       "      <td>7.110000</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>24459500.0</td>\n",
       "      <td>7.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>7.610000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>7.460000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>22502000.0</td>\n",
       "      <td>7.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>7.280000</td>\n",
       "      <td>6.820000</td>\n",
       "      <td>6.970000</td>\n",
       "      <td>6.970000</td>\n",
       "      <td>16373600.0</td>\n",
       "      <td>6.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1447 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close\n",
       "Date                                                                         \n",
       "2017-01-03  34.750000  33.900002  34.049999  34.750000    766100.0  27.085867\n",
       "2017-01-04  34.799999  34.000000  34.000000  34.200001    547900.0  26.657169\n",
       "2017-01-05  34.450001  33.500000  34.250000  34.000000    443200.0  26.501278\n",
       "2017-01-06  34.349998  33.700001  34.049999  33.799999    336700.0  26.345390\n",
       "2017-01-09  34.049999  33.250000  33.650002  33.299999    415100.0  25.955666\n",
       "...               ...        ...        ...        ...         ...        ...\n",
       "2022-09-26   7.970000   6.810000   7.650000   6.830000  43284800.0   6.830000\n",
       "2022-09-27   7.520000   6.980000   7.160000   7.450000  33360100.0   7.450000\n",
       "2022-09-28   7.710000   7.110000   7.300000   7.670000  24459500.0   7.670000\n",
       "2022-09-29   7.610000   6.900000   7.460000   7.100000  22502000.0   7.100000\n",
       "2022-09-30   7.280000   6.820000   6.970000   6.970000  16373600.0   6.970000\n",
       "\n",
       "[1447 rows x 6 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas_datareader import data as web\n",
    "from functools import reduce\n",
    "from tabulate import tabulate\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "def display(data):\n",
    "\tprint(tabulate(data, headers = 'keys', tablefmt = 'psql'))\n",
    "\treturn\n",
    "\n",
    "# 1 - Define `tickers` & `company names` for every instrument\n",
    "stocks      = {'AMC':'AMC Entertainment', 'CLF':'Cleveland-Cliffs', 'BBBY':'Bed Bath and Beyond, Inc.', 'GME': 'GameStop', 'DD':'DuPoint de Nemours', 'M' : 'Macys', 'CLNE': 'Clean Energy Fuels Corp.', 'BB':'BlackBerry', 'RNG':'RingCentral', 'WKHS':'Workhorse Group', 'TWNK':'Hostess Brands', 'SENS':'Senseonics', 'SAVA':'Cassava Sciences', 'TX':'Ternium', 'USA':'Liberty All-Star Equity','X': 'US Steel Corp'}\n",
    "bonds       = {'HCA' : 'HCA', 'VRTX' :  'VRTX'}\n",
    "commodities = {'BTC-USD' : 'Bitcoin', 'DOGE-USD' : 'Dogecoin'}\n",
    "instruments = {**stocks, **bonds, **commodities}\n",
    "tickers     = list(instruments.keys())\n",
    "instruments_data = {}\n",
    "N = len(tickers)\n",
    "\n",
    "# 2 - We will look at stock prices over the past years, starting at January 1, 2015\n",
    "# 01-01-2015 - 16-04-2020\n",
    "start = datetime.datetime(2017,1,1)\n",
    "end = datetime.datetime.today()\n",
    "\n",
    "# 3 - Let's get instruments data based on the tickers.\n",
    "# First argument is the series we want, second is the source (\"yahoo\" for Yahoo! Finance), third is the start date, fourth is the end date\n",
    "for ticker, instrument in instruments.items():\n",
    "    print(\"Loading data series for instrument {} with ticker = {}\".format(instruments[ticker], ticker))\n",
    "    instruments_data[ticker] = web.DataReader(ticker, data_source = 'yahoo', start=start, end = end)\n",
    "    \n",
    "instruments_data['AMC']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4092eb",
   "metadata": {},
   "source": [
    "As always, financial data comes with a plethora of nice but not necessary information. The only bits of information we want is the Adjusted Close prices or Adj. Close. Adjusted Close prices reflect the stocks' value, accounting for corporate actions (stock splits, dividend announcements, rights offerings, etc.)\n",
    "\n",
    "Afterward, we will use a list comprehension to count the number of trading days in our five-year time interval. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85b2f063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Trading Days</th>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>1447</td>\n",
       "      <td>2101</td>\n",
       "      <td>1789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AMC   CLF  BBBY   GME    DD     M  CLNE    BB   RNG  WKHS  \\\n",
       "Trading Days  1447  1447  1447  1447  1447  1447  1447  1447  1447  1447   \n",
       "\n",
       "              TWNK  SENS  SAVA    TX   USA     X   HCA  VRTX  BTC-USD  \\\n",
       "Trading Days  1447  1447  1447  1447  1447  1447  1447  1447     2101   \n",
       "\n",
       "              DOGE-USD  \n",
       "Trading Days      1789  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.3.1 - keep only 'adjusted close' prices\n",
    "for ticker, instrument in instruments.items():\n",
    "    instruments_data[ticker] = instruments_data[ticker][\"Adj Close\"]\n",
    "    \n",
    "    \n",
    "tr_days = [ len(instr) for _, instr in instruments_data.items() ]\n",
    "tr_days = pd.DataFrame(tr_days, index = tickers, columns = [\"Trading Days\"])\n",
    "\n",
    "tr_days.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d393ed",
   "metadata": {},
   "source": [
    "Typically, U.S. equities have approximately 252 trading days in an entire year, primarily due to weekends (obviously, the stock markets are not open on weekends) and U.S. Federal Holidays. Instruments such as Bitcoin and Commodities are traded Over-The-Counter (OTC), not on centralized exchanges such as the New York Stock Exchange (NYSE). These financial instruments are available for trading 24 hours a day, 7 days a week, and will have more trading days than equities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c582cab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stocks &amp; Bonds</th>\n",
       "      <td>251</td>\n",
       "      <td>251</td>\n",
       "      <td>252</td>\n",
       "      <td>253</td>\n",
       "      <td>252</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bitcoin</th>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>366</td>\n",
       "      <td>365</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dogecoin</th>\n",
       "      <td>53</td>\n",
       "      <td>365</td>\n",
       "      <td>365</td>\n",
       "      <td>366</td>\n",
       "      <td>365</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date            2017  2018  2019  2020  2021  2022\n",
       "Stocks & Bonds   251   251   252   253   252   188\n",
       "Bitcoin          365   365   365   366   365   275\n",
       "Dogecoin          53   365   365   366   365   275"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_days_stocks_bonds = instruments_data['AMC'].groupby([instruments_data['AMC'].index.year]).agg('count')\n",
    "tr_days_bitcoin = instruments_data['BTC-USD'].groupby([instruments_data['BTC-USD'].index.year]).agg('count')\n",
    "tr_days_palladium = instruments_data['DOGE-USD'].groupby([instruments_data['DOGE-USD'].index.year]).agg('count')\n",
    "\n",
    "tr_days_per_year = pd.DataFrame([tr_days_stocks_bonds, tr_days_bitcoin, tr_days_palladium], index=[\"Stocks & Bonds\", \"Bitcoin\", \"Dogecoin\"])\n",
    "\n",
    "tr_days_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "915fb757",
   "metadata": {},
   "source": [
    "The next thing that we need to do now is create one unified Dataframe consisting of all of our financial instruments. To do this, we will simply convert our dictionary into a list, then create a lambda function that merges all of the historical stock prices we've extracted using the `DataReader` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57113a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-09</th>\n",
       "      <td>9.159251</td>\n",
       "      <td>5.719453</td>\n",
       "      <td>17.938910</td>\n",
       "      <td>3.760898</td>\n",
       "      <td>90.349464</td>\n",
       "      <td>15.706628</td>\n",
       "      <td>2.10</td>\n",
       "      <td>10.76</td>\n",
       "      <td>47.549999</td>\n",
       "      <td>2.67</td>\n",
       "      <td>12.210000</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>23.101530</td>\n",
       "      <td>3.696440</td>\n",
       "      <td>26.200775</td>\n",
       "      <td>75.811691</td>\n",
       "      <td>148.169998</td>\n",
       "      <td>7143.580078</td>\n",
       "      <td>0.001415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>8.837873</td>\n",
       "      <td>5.681576</td>\n",
       "      <td>17.823177</td>\n",
       "      <td>3.518881</td>\n",
       "      <td>90.014984</td>\n",
       "      <td>16.093252</td>\n",
       "      <td>2.09</td>\n",
       "      <td>10.78</td>\n",
       "      <td>46.150002</td>\n",
       "      <td>2.74</td>\n",
       "      <td>12.510000</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.790000</td>\n",
       "      <td>23.474537</td>\n",
       "      <td>3.696440</td>\n",
       "      <td>26.548832</td>\n",
       "      <td>75.114777</td>\n",
       "      <td>148.740005</td>\n",
       "      <td>6618.140137</td>\n",
       "      <td>0.001163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>9.360112</td>\n",
       "      <td>5.738392</td>\n",
       "      <td>17.556093</td>\n",
       "      <td>3.469621</td>\n",
       "      <td>90.645363</td>\n",
       "      <td>15.569699</td>\n",
       "      <td>2.02</td>\n",
       "      <td>10.69</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.94</td>\n",
       "      <td>12.640000</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.780000</td>\n",
       "      <td>23.449669</td>\n",
       "      <td>3.684420</td>\n",
       "      <td>26.703527</td>\n",
       "      <td>74.188744</td>\n",
       "      <td>148.029999</td>\n",
       "      <td>6559.490234</td>\n",
       "      <td>0.001211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>9.802006</td>\n",
       "      <td>5.539536</td>\n",
       "      <td>18.001230</td>\n",
       "      <td>3.467479</td>\n",
       "      <td>88.692261</td>\n",
       "      <td>15.867723</td>\n",
       "      <td>1.97</td>\n",
       "      <td>10.40</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.630000</td>\n",
       "      <td>2.61</td>\n",
       "      <td>3.830000</td>\n",
       "      <td>22.513008</td>\n",
       "      <td>3.678408</td>\n",
       "      <td>25.785046</td>\n",
       "      <td>72.603951</td>\n",
       "      <td>147.820007</td>\n",
       "      <td>6635.750000</td>\n",
       "      <td>0.001184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-15</th>\n",
       "      <td>10.083210</td>\n",
       "      <td>5.833085</td>\n",
       "      <td>17.760857</td>\n",
       "      <td>3.426786</td>\n",
       "      <td>89.209625</td>\n",
       "      <td>16.093252</td>\n",
       "      <td>2.02</td>\n",
       "      <td>10.33</td>\n",
       "      <td>46.400002</td>\n",
       "      <td>3.01</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>2.62</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>22.521297</td>\n",
       "      <td>3.660378</td>\n",
       "      <td>26.210447</td>\n",
       "      <td>71.534691</td>\n",
       "      <td>148.660004</td>\n",
       "      <td>7315.540039</td>\n",
       "      <td>0.001339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>6.830000</td>\n",
       "      <td>13.080000</td>\n",
       "      <td>6.370000</td>\n",
       "      <td>24.480000</td>\n",
       "      <td>50.049999</td>\n",
       "      <td>15.210000</td>\n",
       "      <td>5.05</td>\n",
       "      <td>4.97</td>\n",
       "      <td>40.540001</td>\n",
       "      <td>2.69</td>\n",
       "      <td>23.580000</td>\n",
       "      <td>1.33</td>\n",
       "      <td>45.189999</td>\n",
       "      <td>26.750000</td>\n",
       "      <td>5.570000</td>\n",
       "      <td>18.200001</td>\n",
       "      <td>187.240005</td>\n",
       "      <td>276.779999</td>\n",
       "      <td>19222.671875</td>\n",
       "      <td>0.061089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>7.450000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>6.380000</td>\n",
       "      <td>26.129999</td>\n",
       "      <td>50.090000</td>\n",
       "      <td>15.820000</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.09</td>\n",
       "      <td>40.610001</td>\n",
       "      <td>2.72</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>1.33</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>27.350000</td>\n",
       "      <td>5.520000</td>\n",
       "      <td>18.639999</td>\n",
       "      <td>184.309998</td>\n",
       "      <td>284.739990</td>\n",
       "      <td>19110.546875</td>\n",
       "      <td>0.060609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>7.670000</td>\n",
       "      <td>13.980000</td>\n",
       "      <td>6.460000</td>\n",
       "      <td>27.309999</td>\n",
       "      <td>51.250000</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.99</td>\n",
       "      <td>42.720001</td>\n",
       "      <td>2.91</td>\n",
       "      <td>24.059999</td>\n",
       "      <td>1.39</td>\n",
       "      <td>43.840000</td>\n",
       "      <td>27.469999</td>\n",
       "      <td>5.690000</td>\n",
       "      <td>18.730000</td>\n",
       "      <td>189.940002</td>\n",
       "      <td>292.410004</td>\n",
       "      <td>19426.720703</td>\n",
       "      <td>0.060623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>13.770000</td>\n",
       "      <td>6.190000</td>\n",
       "      <td>25.459999</td>\n",
       "      <td>50.650002</td>\n",
       "      <td>15.840000</td>\n",
       "      <td>5.39</td>\n",
       "      <td>4.80</td>\n",
       "      <td>40.490002</td>\n",
       "      <td>2.68</td>\n",
       "      <td>23.860001</td>\n",
       "      <td>1.42</td>\n",
       "      <td>43.070000</td>\n",
       "      <td>27.230000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>18.540001</td>\n",
       "      <td>187.679993</td>\n",
       "      <td>292.799988</td>\n",
       "      <td>19573.050781</td>\n",
       "      <td>0.060728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>6.970000</td>\n",
       "      <td>13.470000</td>\n",
       "      <td>6.090000</td>\n",
       "      <td>25.129999</td>\n",
       "      <td>50.400002</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>5.34</td>\n",
       "      <td>4.70</td>\n",
       "      <td>39.959999</td>\n",
       "      <td>2.87</td>\n",
       "      <td>23.240000</td>\n",
       "      <td>1.32</td>\n",
       "      <td>41.820000</td>\n",
       "      <td>27.389999</td>\n",
       "      <td>5.530000</td>\n",
       "      <td>18.120001</td>\n",
       "      <td>183.789993</td>\n",
       "      <td>289.540009</td>\n",
       "      <td>19431.789062</td>\n",
       "      <td>0.061653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1231 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMC        CLF       BBBY        GME         DD          M  \\\n",
       "Date                                                                           \n",
       "2017-11-09   9.159251   5.719453  17.938910   3.760898  90.349464  15.706628   \n",
       "2017-11-10   8.837873   5.681576  17.823177   3.518881  90.014984  16.093252   \n",
       "2017-11-13   9.360112   5.738392  17.556093   3.469621  90.645363  15.569699   \n",
       "2017-11-14   9.802006   5.539536  18.001230   3.467479  88.692261  15.867723   \n",
       "2017-11-15  10.083210   5.833085  17.760857   3.426786  89.209625  16.093252   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2022-09-26   6.830000  13.080000   6.370000  24.480000  50.049999  15.210000   \n",
       "2022-09-27   7.450000  13.600000   6.380000  26.129999  50.090000  15.820000   \n",
       "2022-09-28   7.670000  13.980000   6.460000  27.309999  51.250000  16.530001   \n",
       "2022-09-29   7.100000  13.770000   6.190000  25.459999  50.650002  15.840000   \n",
       "2022-09-30   6.970000  13.470000   6.090000  25.129999  50.400002  15.670000   \n",
       "\n",
       "            CLNE     BB        RNG  WKHS       TWNK  SENS       SAVA  \\\n",
       "Date                                                                   \n",
       "2017-11-09  2.10  10.76  47.549999  2.67  12.210000  2.63   3.560000   \n",
       "2017-11-10  2.09  10.78  46.150002  2.74  12.510000  2.65   3.790000   \n",
       "2017-11-13  2.02  10.69  46.000000  2.94  12.640000  2.65   3.780000   \n",
       "2017-11-14  1.97  10.40  47.000000  3.00  12.630000  2.61   3.830000   \n",
       "2017-11-15  2.02  10.33  46.400002  3.01  12.380000  2.62   3.920000   \n",
       "...          ...    ...        ...   ...        ...   ...        ...   \n",
       "2022-09-26  5.05   4.97  40.540001  2.69  23.580000  1.33  45.189999   \n",
       "2022-09-27  5.38   5.09  40.610001  2.72  23.650000  1.33  46.180000   \n",
       "2022-09-28  5.68   4.99  42.720001  2.91  24.059999  1.39  43.840000   \n",
       "2022-09-29  5.39   4.80  40.490002  2.68  23.860001  1.42  43.070000   \n",
       "2022-09-30  5.34   4.70  39.959999  2.87  23.240000  1.32  41.820000   \n",
       "\n",
       "                   TX       USA          X         HCA        VRTX  \\\n",
       "Date                                                                 \n",
       "2017-11-09  23.101530  3.696440  26.200775   75.811691  148.169998   \n",
       "2017-11-10  23.474537  3.696440  26.548832   75.114777  148.740005   \n",
       "2017-11-13  23.449669  3.684420  26.703527   74.188744  148.029999   \n",
       "2017-11-14  22.513008  3.678408  25.785046   72.603951  147.820007   \n",
       "2017-11-15  22.521297  3.660378  26.210447   71.534691  148.660004   \n",
       "...               ...       ...        ...         ...         ...   \n",
       "2022-09-26  26.750000  5.570000  18.200001  187.240005  276.779999   \n",
       "2022-09-27  27.350000  5.520000  18.639999  184.309998  284.739990   \n",
       "2022-09-28  27.469999  5.690000  18.730000  189.940002  292.410004   \n",
       "2022-09-29  27.230000  5.550000  18.540001  187.679993  292.799988   \n",
       "2022-09-30  27.389999  5.530000  18.120001  183.789993  289.540009   \n",
       "\n",
       "                 BTC-USD  DOGE-USD  \n",
       "Date                                \n",
       "2017-11-09   7143.580078  0.001415  \n",
       "2017-11-10   6618.140137  0.001163  \n",
       "2017-11-13   6559.490234  0.001211  \n",
       "2017-11-14   6635.750000  0.001184  \n",
       "2017-11-15   7315.540039  0.001339  \n",
       "...                  ...       ...  \n",
       "2022-09-26  19222.671875  0.061089  \n",
       "2022-09-27  19110.546875  0.060609  \n",
       "2022-09-28  19426.720703  0.060623  \n",
       "2022-09-29  19573.050781  0.060728  \n",
       "2022-09-30  19431.789062  0.061653  \n",
       "\n",
       "[1231 rows x 20 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2.4 - Merging Dataframes\n",
    "'''\n",
    "    instruments_data = {'AAPL' : dataframe (1331 x 1),..., 'BTC-USD' : dataframe (1934 x 1), 'PA=F' : dataframe (1336 x 1)}\n",
    "    [*] So list(instruments_data.values()) : we only keep the dataframes in a list\n",
    "    [*] data_df = pd.concat(data, axis = 1).dropna() DID not wor because of different `commodities` sizes\n",
    "\n",
    "'''\n",
    "\n",
    "data = list(instruments_data.values())\n",
    "data_df = reduce(lambda x, y: pd.merge(x, y, left_index = True, right_index = True, how = 'outer'), data).dropna()\n",
    "data_df.columns = tickers\n",
    "\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf15d1b2",
   "metadata": {},
   "source": [
    "We will also need to create a DataFrame that normalizes the total number of trading days per year for our entire portfolio. This centralization will be based around a single asset. For the sake of simplicity, we will choose AMC, since this company has been in the news as of late. This centralization will become important for our model later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82d88de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Date</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All instruments (merged)</th>\n",
       "      <td>35</td>\n",
       "      <td>251</td>\n",
       "      <td>252</td>\n",
       "      <td>253</td>\n",
       "      <td>252</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date                      2017  2018  2019  2020  2021  2022\n",
       "All instruments (merged)    35   251   252   253   252   188"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_days_per_year = data_df['AMC'].groupby([data_df['AMC'].index.year]).agg('count')\n",
    "tr_days_per_year = pd.DataFrame([tr_days_per_year], index = [\"All instruments (merged)\"])\n",
    "\n",
    "tr_days_per_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98240811",
   "metadata": {},
   "source": [
    "We've taken the individual dataframes for all 20 instruments and we've developed a 'master' dataframe, which will be used for further analysis. We can plot all of the instruments together in a time to compare the trends for the last 5 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b35f2f07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "data_df.plot(ax = plt.gca(), grid = True)\n",
    "ax.set_title('Adjusted Close for all instruments')\n",
    "ax.set_facecolor((0.95, 0.95, 0.99))\n",
    "ax.grid(c = (0.75, 0.75, 0.99))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "367d6ef8",
   "metadata": {},
   "source": [
    "No surprise, Bitcoin has experienced some ups and downs, but still dwarfs all other assets we're looking at right now. Besides the mismatching in scaling, what is wrong with this chart? What's wrong is we're looking at all of the assets in absolute prices. While the absolute price is important when assessing value, we are more concerned with the relative change of an asset (as well as its volatility).\n",
    "\n",
    "Because crypto-currencies such as DOGE have been extremely volatile, we will plot their returns separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ed73d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x27efd13ce80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_df = data_df.drop(['BTC-USD', 'DOGE-USD'], axis = 1)\n",
    "simple_returns = stock_df.apply(lambda x: x /x[0] - 1)\n",
    "simple_returns.plot(grid = True, figsize = (10, 5)).axhline(y = 0, color = \"black\", lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc97f749",
   "metadata": {},
   "source": [
    "As we can see, returns are extremely volatile, even for equities. This should be no surprise, considering these were all stocks that were popular with r/WallStreetBets. Either way, we will need find a rate of return for all our investments. This rate of return can either be represented as APR or APY.\n",
    "\n",
    "### APR vs APY\n",
    "\n",
    "**Annual Percentage Rate** and **Annual Percentage Yield** are often used interchangably, considering they're both expressed in terms of percentages, but they're used for different purproses. APR measures the cost of a loan, while APY measure the return on an investment. From the lender's perspective APR is the metric to use, but we will end up using both in our analysis.\n",
    "\n",
    "However, one issue with using APR is that it doesn't take into account of compounding, while APY does. We will show how using **Logarithmic Return** is virtually the same as creating a compounding effect in our time series.\n",
    "\n",
    "### Logarithm Returns\n",
    "\n",
    "The effective annual interest rate is an essential tool that evaluates the real return on investment. If we assume the number of compounding periods in one year is $n$, the formula to convert the stated annual interest rate to the effective annual interest rate is \n",
    "\n",
    "$$r_{effective} = \\big(1+\\frac{r_{nominal}}{n}\\big)^{n}-1$$\n",
    "\n",
    "Now imagine the price of an asset is changing every millisecond. The period of compounding $n$ approaches infinity, and this is referred to as continuous compounding. The formula for continuous compounding is given below:\n",
    "\n",
    "$$lim_{n\\rightarrow\\infty}\\big(1+\\frac{r}{n}\\big)^{n}=e^{r}$$\n",
    "\n",
    "From the equation above, we know that if we assume continuous compounding:\n",
    "\n",
    "$$e^{r_{nominal}}=1+r_{r_{effective}}=\\frac{P_{n}}{P_{n-1}}$$\n",
    "\n",
    "Then we take $ln$ on both sides of the equation:\n",
    "\n",
    "$$r_{nominal}=ln\\bigg(\\frac{P_{n}}{P_{n-1}}\\bigg)=ln(P_{n})-ln(P_{n-1})$$\n",
    "\n",
    "Here we have the logarithmic return, or the continously compounded return. This return is actually very close to the effective interest rate. If we rearrange the following equation:\n",
    "\n",
    "$$e^{r_{nominal}}=1+r_{r_{effective}}$$\n",
    "\n",
    "we will have\n",
    "\n",
    "$${r_{effective}} = e^{r_{nominal}} - 1 \\approx r_{nominal} $$\n",
    "\n",
    "The second quality holds due to Taylor Expansion and the small interest rate. This is frequently used when calculating returns because once we take the logarithm of asset prices, we can calculate the logarithmic return by simple subtraction. \n",
    "\n",
    "### Why Use Logarithmic Returns?\n",
    "\n",
    "The problem with simple returns is that they're just not symmetric. Simple returns, whether they're positive or negative, then to equal the magnitude and do not cancel each other out, resulting in a net change.\n",
    "\n",
    "For example, if your portfolio goes up by 50% (say $\\$100$ to $\\$150$) and then declines by $50\\%$ (from $\\$150$ to $\\$75$), you're not back to where you started. While mathematically, you may have averaged a $0\\%$ return on your portfolio, it's not a very useful summary that you are now $25\\%$ below your principal investment.\n",
    "\n",
    "$$ \\frac{\\big(\\frac{(150-100)}{100}+\\frac{(75-150)}{150}\\big)}{2}=0\\% $$\n",
    "\n",
    "In other words, while you may have mathematically \"broken even\", in reality your portfolio is down $25\\%$. As the saying goes, the stock market takes the stairs up and the elevator down.\n",
    "\n",
    "Instead, we use Logarithmic returns in mathematical finance. One advantage of logarithmic returns is the symmetry involved. Log returns of equal manitude but opposite direction (positive and negative) will cancel each other out. This means that the same investment of $\\$$100$ will indeed show a return of $0\\%$, which is helpful for our analysis.\n",
    "\n",
    "Also, simple returns are not additive over time, and adding returns for multiple periods does not yield the total return over the total length of time. In other words, simple returns fail to satisfy the property of time value, which is fundamental for any financial analysis.\n",
    "\n",
    "For example, if your portfolio goes from $\\$100$ to $\\$110$ USD in one year and then $\\$112$ the next year, your simple annual returns would be $\\$10.0%$ to $\\$9.09%$, respectively. The sum of those returns would be $19.09\\%$ which is not your actual total return of $2%$. ($\\$19.09 \\neq \\$20$)\n",
    "\n",
    "On the other hand, logarithmic returns are additive over time. Consider the ordered sequence of $n$ trades. Calculating the compounding return, which is the running return of this sequence of trades over time:\n",
    "\n",
    "$$(1+R_{1})(1+R_{2})\\ldots(1+R_{n}) = \\prod^{n}_{i}(1+R_{i}) $$\n",
    "\n",
    "where $R_{i}$ is the return for each period. Taking the natural logarithm of this would give us the following:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "ln\\Big(\\prod^{n}_{i}(1+R_{i})\\Big) & = \\sum^{n}_{i}ln\\Big(1+R_{i}\\Big) \\\\\n",
    "& = ln(1+R_{i})+(1+R_{2})+ \\ldots + (1+R_{n}) \\\\\n",
    "& = ln(\\frac{P_{1}}{P_{0}})+ln(\\frac{P_{2}}{P_{1}})+ln(\\frac{P_{3}}{P_{2}})+\\ldots+ln(\\frac{P_{n-1}}{P_{n-2}})+ln(\\frac{P_{n}}{P_{n-1}}) \\\\\n",
    "& = ln(P_{1})-ln(P_{0})+ln(P_{2})-ln(P_{1})+\\ldots+ln(P_{n})-ln(P_{n-1}) \\\\\n",
    "& = ln(P_{n}) - ln(P_{0}) \\\\\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "As we can see, if we sum up the logarithmic returns $ln(\\frac{P_{i}}{P_{j}})$ for each period, we end up with $ln(P_{n})-ln(P_{0})=ln(\\frac{P_{n}}{P_{0}})$. In other words, the compound return over $n$ periods is merely the differene in log between initial and final periods.\n",
    "\n",
    "Using our portfolio example, if your portfolio goes from \\\\$10 to \\\\$11 in one year and then to \\\\$12 the next year, your logarithmic returns $r$ over the period of two years would be:\n",
    "\n",
    "$$r = ln(P_{2})-ln(P_{0})=ln(12)-ln(10)=0.18$$\n",
    "\n",
    "which is the same as summing the logarithmic returns of each individual period:\n",
    "\n",
    "$$ln(\\frac{P_{1}}{P_{0}})+ln(\\frac{P_{2}}{P_{1}})=ln(\\frac{11}{10})+ln(\\frac{12}{11})=0.18$$\n",
    "\n",
    "$P_{2}$ can also be expressed as $e$ raised to the logarithmic return $r$:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "P_{2} & = P_{0}(1+R_{1})(1+R_{2}) \\\\\n",
    "& = P_{0}\\prod^{2}_{i=1}(1+R_{i}) \\\\\n",
    "& = P_{0}e^{ln(\\frac{P_{2}}{P_{0}})}\\\\\n",
    "& = P_{0}e^{ln(\\frac{12}{10}})\\\\\n",
    "& = 12 \\\\\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "And, because the logarithmic returns are additive, the arithmetic mean of log returns is much more informative. The average log return, times the number of sub-periods, tells us the exact total compounded return over the period of time.\n",
    "\n",
    "### Logarithm Return\n",
    "\n",
    "Now that we understand the importance of log returns, we will calculate the log returns of our portfolio.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7e13b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10</th>\n",
       "      <td>-0.035088</td>\n",
       "      <td>-0.006623</td>\n",
       "      <td>-0.006451</td>\n",
       "      <td>-0.064351</td>\n",
       "      <td>-0.003702</td>\n",
       "      <td>0.024615</td>\n",
       "      <td>-0.004762</td>\n",
       "      <td>0.001859</td>\n",
       "      <td>-0.029443</td>\n",
       "      <td>0.026217</td>\n",
       "      <td>0.024570</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.064607</td>\n",
       "      <td>0.016146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013284</td>\n",
       "      <td>-0.009193</td>\n",
       "      <td>0.003847</td>\n",
       "      <td>-0.073554</td>\n",
       "      <td>-0.178092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-13</th>\n",
       "      <td>0.059091</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.014985</td>\n",
       "      <td>-0.013999</td>\n",
       "      <td>0.007003</td>\n",
       "      <td>-0.032532</td>\n",
       "      <td>-0.033493</td>\n",
       "      <td>-0.008349</td>\n",
       "      <td>-0.003250</td>\n",
       "      <td>0.072993</td>\n",
       "      <td>0.010392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002639</td>\n",
       "      <td>-0.001059</td>\n",
       "      <td>-0.003252</td>\n",
       "      <td>0.005827</td>\n",
       "      <td>-0.012328</td>\n",
       "      <td>-0.004773</td>\n",
       "      <td>-0.008862</td>\n",
       "      <td>0.041273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-14</th>\n",
       "      <td>0.047210</td>\n",
       "      <td>-0.034654</td>\n",
       "      <td>0.025355</td>\n",
       "      <td>-0.000617</td>\n",
       "      <td>-0.021547</td>\n",
       "      <td>0.019141</td>\n",
       "      <td>-0.024752</td>\n",
       "      <td>-0.027128</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>-0.000791</td>\n",
       "      <td>-0.015094</td>\n",
       "      <td>0.013228</td>\n",
       "      <td>-0.039943</td>\n",
       "      <td>-0.001632</td>\n",
       "      <td>-0.034396</td>\n",
       "      <td>-0.021362</td>\n",
       "      <td>-0.001419</td>\n",
       "      <td>0.011626</td>\n",
       "      <td>-0.022296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-15</th>\n",
       "      <td>0.028688</td>\n",
       "      <td>0.052992</td>\n",
       "      <td>-0.013353</td>\n",
       "      <td>-0.011736</td>\n",
       "      <td>0.005833</td>\n",
       "      <td>0.014213</td>\n",
       "      <td>0.025381</td>\n",
       "      <td>-0.006731</td>\n",
       "      <td>-0.012766</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>-0.019794</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>0.023499</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>-0.004902</td>\n",
       "      <td>0.016498</td>\n",
       "      <td>-0.014727</td>\n",
       "      <td>0.005683</td>\n",
       "      <td>0.102444</td>\n",
       "      <td>0.130912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>-0.145181</td>\n",
       "      <td>-0.014318</td>\n",
       "      <td>-0.044978</td>\n",
       "      <td>-0.022364</td>\n",
       "      <td>-0.013599</td>\n",
       "      <td>-0.032443</td>\n",
       "      <td>-0.052533</td>\n",
       "      <td>-0.019724</td>\n",
       "      <td>-0.006373</td>\n",
       "      <td>0.011278</td>\n",
       "      <td>-0.016681</td>\n",
       "      <td>0.007576</td>\n",
       "      <td>0.079293</td>\n",
       "      <td>-0.040531</td>\n",
       "      <td>-0.027923</td>\n",
       "      <td>-0.020979</td>\n",
       "      <td>-0.022245</td>\n",
       "      <td>-0.023532</td>\n",
       "      <td>-0.003885</td>\n",
       "      <td>-0.035477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>0.090776</td>\n",
       "      <td>0.039755</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>0.067402</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>0.040105</td>\n",
       "      <td>0.065347</td>\n",
       "      <td>0.024145</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.011152</td>\n",
       "      <td>0.002969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021908</td>\n",
       "      <td>0.022430</td>\n",
       "      <td>-0.008977</td>\n",
       "      <td>0.024176</td>\n",
       "      <td>-0.015648</td>\n",
       "      <td>0.028759</td>\n",
       "      <td>-0.005833</td>\n",
       "      <td>-0.007857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>0.029530</td>\n",
       "      <td>0.027941</td>\n",
       "      <td>0.012539</td>\n",
       "      <td>0.045159</td>\n",
       "      <td>0.023158</td>\n",
       "      <td>0.044880</td>\n",
       "      <td>0.055762</td>\n",
       "      <td>-0.019646</td>\n",
       "      <td>0.051958</td>\n",
       "      <td>0.069853</td>\n",
       "      <td>0.017336</td>\n",
       "      <td>0.045113</td>\n",
       "      <td>-0.050671</td>\n",
       "      <td>0.004388</td>\n",
       "      <td>0.030797</td>\n",
       "      <td>0.004828</td>\n",
       "      <td>0.030546</td>\n",
       "      <td>0.026937</td>\n",
       "      <td>0.016544</td>\n",
       "      <td>0.000231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>-0.074316</td>\n",
       "      <td>-0.015021</td>\n",
       "      <td>-0.041796</td>\n",
       "      <td>-0.067741</td>\n",
       "      <td>-0.011707</td>\n",
       "      <td>-0.041742</td>\n",
       "      <td>-0.051056</td>\n",
       "      <td>-0.038076</td>\n",
       "      <td>-0.052200</td>\n",
       "      <td>-0.079038</td>\n",
       "      <td>-0.008313</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>-0.017564</td>\n",
       "      <td>-0.008737</td>\n",
       "      <td>-0.024605</td>\n",
       "      <td>-0.010144</td>\n",
       "      <td>-0.011899</td>\n",
       "      <td>0.001334</td>\n",
       "      <td>0.007532</td>\n",
       "      <td>0.001732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>-0.018310</td>\n",
       "      <td>-0.021787</td>\n",
       "      <td>-0.016155</td>\n",
       "      <td>-0.012962</td>\n",
       "      <td>-0.004936</td>\n",
       "      <td>-0.010732</td>\n",
       "      <td>-0.009276</td>\n",
       "      <td>-0.020833</td>\n",
       "      <td>-0.013090</td>\n",
       "      <td>0.070895</td>\n",
       "      <td>-0.025985</td>\n",
       "      <td>-0.070422</td>\n",
       "      <td>-0.029023</td>\n",
       "      <td>0.005876</td>\n",
       "      <td>-0.003604</td>\n",
       "      <td>-0.022654</td>\n",
       "      <td>-0.020727</td>\n",
       "      <td>-0.011134</td>\n",
       "      <td>-0.007217</td>\n",
       "      <td>0.015232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1231 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AMC       CLF      BBBY       GME        DD         M  \\\n",
       "Date                                                                     \n",
       "2017-11-09       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-11-10 -0.035088 -0.006623 -0.006451 -0.064351 -0.003702  0.024615   \n",
       "2017-11-13  0.059091  0.010000 -0.014985 -0.013999  0.007003 -0.032532   \n",
       "2017-11-14  0.047210 -0.034654  0.025355 -0.000617 -0.021547  0.019141   \n",
       "2017-11-15  0.028688  0.052992 -0.013353 -0.011736  0.005833  0.014213   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2022-09-26 -0.145181 -0.014318 -0.044978 -0.022364 -0.013599 -0.032443   \n",
       "2022-09-27  0.090776  0.039755  0.001570  0.067402  0.000799  0.040105   \n",
       "2022-09-28  0.029530  0.027941  0.012539  0.045159  0.023158  0.044880   \n",
       "2022-09-29 -0.074316 -0.015021 -0.041796 -0.067741 -0.011707 -0.041742   \n",
       "2022-09-30 -0.018310 -0.021787 -0.016155 -0.012962 -0.004936 -0.010732   \n",
       "\n",
       "                CLNE        BB       RNG      WKHS      TWNK      SENS  \\\n",
       "Date                                                                     \n",
       "2017-11-09       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-11-10 -0.004762  0.001859 -0.029443  0.026217  0.024570  0.007605   \n",
       "2017-11-13 -0.033493 -0.008349 -0.003250  0.072993  0.010392  0.000000   \n",
       "2017-11-14 -0.024752 -0.027128  0.021739  0.020408 -0.000791 -0.015094   \n",
       "2017-11-15  0.025381 -0.006731 -0.012766  0.003333 -0.019794  0.003831   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2022-09-26 -0.052533 -0.019724 -0.006373  0.011278 -0.016681  0.007576   \n",
       "2022-09-27  0.065347  0.024145  0.001727  0.011152  0.002969  0.000000   \n",
       "2022-09-28  0.055762 -0.019646  0.051958  0.069853  0.017336  0.045113   \n",
       "2022-09-29 -0.051056 -0.038076 -0.052200 -0.079038 -0.008313  0.021583   \n",
       "2022-09-30 -0.009276 -0.020833 -0.013090  0.070895 -0.025985 -0.070422   \n",
       "\n",
       "                SAVA        TX       USA         X       HCA      VRTX  \\\n",
       "Date                                                                     \n",
       "2017-11-09       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2017-11-10  0.064607  0.016146  0.000000  0.013284 -0.009193  0.003847   \n",
       "2017-11-13 -0.002639 -0.001059 -0.003252  0.005827 -0.012328 -0.004773   \n",
       "2017-11-14  0.013228 -0.039943 -0.001632 -0.034396 -0.021362 -0.001419   \n",
       "2017-11-15  0.023499  0.000368 -0.004902  0.016498 -0.014727  0.005683   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2022-09-26  0.079293 -0.040531 -0.027923 -0.020979 -0.022245 -0.023532   \n",
       "2022-09-27  0.021908  0.022430 -0.008977  0.024176 -0.015648  0.028759   \n",
       "2022-09-28 -0.050671  0.004388  0.030797  0.004828  0.030546  0.026937   \n",
       "2022-09-29 -0.017564 -0.008737 -0.024605 -0.010144 -0.011899  0.001334   \n",
       "2022-09-30 -0.029023  0.005876 -0.003604 -0.022654 -0.020727 -0.011134   \n",
       "\n",
       "             BTC-USD  DOGE-USD  \n",
       "Date                            \n",
       "2017-11-09       NaN       NaN  \n",
       "2017-11-10 -0.073554 -0.178092  \n",
       "2017-11-13 -0.008862  0.041273  \n",
       "2017-11-14  0.011626 -0.022296  \n",
       "2017-11-15  0.102444  0.130912  \n",
       "...              ...       ...  \n",
       "2022-09-26 -0.003885 -0.035477  \n",
       "2022-09-27 -0.005833 -0.007857  \n",
       "2022-09-28  0.016544  0.000231  \n",
       "2022-09-29  0.007532  0.001732  \n",
       "2022-09-30 -0.007217  0.015232  \n",
       "\n",
       "[1231 rows x 20 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns = data_df.pct_change()\n",
    "log_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305a6b86",
   "metadata": {},
   "source": [
    "Now we can plot all of the returns on the same axis without scaling issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7164507",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x27efee91a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIjan2iqIO4QQowVQowWQowUQjyeiIYREREREVFqkyQJ//d//2fc9vl86N27N37yk58ksVXJFVUVRCIiIiIiopbq3Lkzdu7cCafTCQBYuXIlBg4cmORWJRcDMCIiIiIiipurrroKn332GQBg8eLFuO2225LcouRqURVEIiIiIiJKPasXLkD50cMxXWefk07GRVOmN7vcrbfeiscffxw/+clPsGPHDkybNg1r166NaVtSCTNgREREREQUN6NHj0ZBQQEWL16Mq666KtnNSTpmwIiIiIiI2rloMlXxdM011+DBBx/EmjVrUFVVldS2JBsDMCIiIiIiiqtp06ahW7duGDVqFNasWZPs5iQVuyASEREREVFcDRo0CPfee2+ym9EmMANGRERERERxYbfbQ+6bNGkSJk2alPjGtBHMgBERERERESUIAzAiIiIiIqIEYQBGRERERESUIAzAiIiIiIiIEoQBGBERERERUYIwACMiIiIiIkoQBmBERERERBQ3paWluPXWWzF8+HCMHz8eV111Ffbv34+RI0eGLDtlyhQMHDgQbrcbAFBZWYmhQ4cCAAoKCpCVlYXc3Fzjv//85z+JfCsxwXnAiIiIiIgoLoQQuP7663H33XdjyZIlAIDt27ejrKws4nOsViv+/e9/Y8aMGSGPDR8+HHl5efFqbkIwA0ZERERERHGxevVqpKWl4Z577jHuGzNmDAYPHhzxOffffz/+9re/wefzJaKJCccMGBERERFRO1f7ySF4ihtjus70AZ3R7erhTS6zc+dOjB8/vkXrHTJkCM4//3z897//xdVXXx3w2KFDh5Cbm2vcfumll3DBBRe0aP3JxgCMiIiIiIjalDlz5uDaa6/Fj3/844D720MXRAZgRERElDpEshtAlJqay1TFy4gRI/Dee++1+Hk/+MEPkJubi3feeScOrUoujgEjIiKi1CEluwFE1BIXX3wx3G43FixYYNy3Y8cOFBYWNvvc3//+93juuefi2bykYABGRERERERxIUkSli5dii+//BLDhw/HiBEjMGfOHPTr1w/79u3DoEGDjP/efffdgOeOGDEC48aNC7hPHwOm//fiiy8m8u3EBLsgEhERUepgF0SilDNgwICwXQm9Xm/IfTfddFPA7Q8++MD4e+jQoXA6nbFvYIIxA0ZERESpg10QiSjFMQAjIiIiIiJKEAZgRERElDrYBZGIUhwDMCIiIiIiogRhAEZERERERJQgDMCIiIiIiIgShAEYERERERHFhdVqRW5uLsaMGYNx48Zh/fr1AICCggJkZWUZj02cOBH79u1LcmsTgwEYERERERHFRVZWFvLy8rB9+3Y888wzmDNnjvHY8OHDjcfuvvtuPP3000lsaeIwACMiIiIiorirr69H9+7dW/xYe2NLdgOIiIiIiCi+li1bhtLS0pius1+/fpg8eXKTyzidTuTm5sLlcqGkpASrVq0yHjt06BByc3PR0NAAh8OBTZs2xbR9bRUzYERERJRyOB0YUWrQuyDu3bsXy5cvx1133QUh1F+w3gXx0KFDmD9/PqZPn57k1iYGM2BERERERO1cc5mqRJgwYQIqKytRUVER8tg111yDqVOnJqFViccMGBEREaUcKdkNIKIW27t3L2RZRs+ePUMeW7duHYYPH56EViUeM2BERESUctgFkSg16GPAAEAIgUWLFsFqtQLwjwETQiA9PR2vv/56EluaOAzAiIiIiIgoLmRZDnv/0KFD4XQ6E9yatoFdEImIiCjlsAsiEaUqBmBERESUctgFkYhSFQMwIiIiIiKiBGEARkRERERElCAMwIiIiIiIiBKEARgREREREVGCMAAjIiIiIqK4sFqtyM3NxciRI3H11VejtrYWAFBQUABJkvDSSy8Zy86cORMLFy40bs+bNw+nn346Ro0ahTFjxuCBBx6A1+tN8DuIPQZgREREREQUF1lZWcjLy8POnTvRo0cPvPLKK8Zjffr0wQsvvACPxxPyvFdffRVffPEFNm7ciPz8fHz//ffo06dPu5g7jAEYERERERHF3YQJE3D8+HHjdu/evXHJJZdg0aJFIcs+9dRT+Mc//oFu3boBANLT0zF79mx07do1Uc2NG1uyG0BERERERPG1f/8TaLDviek6u2SfgVNP/WNUy8qyjK+++go/+9nPAu7/3e9+h8mTJ2PatGnGffX19bDb7Rg2bFhM29tWMANGRERERERx4XQ6kZubi379+qGsrAyXXXZZwOMnn3wyzjnnHPzvf/+LuI4VK1YgNzcXQ4cOxfr16+Pd5LhjBoyIiIiIqJ2LNlMVa/oYMIfDgSuuuAKvvPIK7r333oBlHnnkEdx444248MILAQBdu3ZFdnY2jhw5gmHDhuGKK67AFVdcgZ/85Cdhx4ulGmbAiIiIiIgorjp16oQXX3wRzz//PHw+X8Bjp59+Os4880x88sknxn1z5szBjBkzjKqJQgi4XK5ENjlumAEjIiIiIqK4Gzt2LEaPHo3FixfjggsuCHjs97//PcaOHWvcnjFjBhobG3HOOecgIyMD2dnZOO+88wKWSVUMwIiIiIiIKC7sdnvAbXOWa+fOncbfY8aMgaIoxm1JkvDQQw/hoYcein8jE4xdEImIiIiIiBKEARgRERGlHJHsBhARnSAGYERERERERAnCAIyIiIhSjpTsBhARnSAGYERERJRy2AWRiFIVAzAiIiIiIqIEYQBGREREKYddEInavlmzZmH+/PnG7SuuuAI///nPjdu//e1vMW/ePIwcOdK477XXXsP48eNRU1ODKVOm4L333gtYZ3Z2NgBAURTce++9GDlyJEaNGoUf/vCHOHLkSHzfUIwwACMiIqKUwy6IRG3feeedh/Xr1wNQA6bKykrs2rXLeHz9+vWYOHGicfu///0vXnrpJaxYsQLdu3dvct1vv/02iouLsWPHDuTn52Pp0qXo1q1bXN5HrDEAIyIiohTC0IsoVUycOBEbNmwAAOzatQsjR45Ely5dUFNTA7fbjT179qBHjx4AgHfeeQd/+ctf8MUXX6BXr17NrrukpAT9+/eHxaKGM4MGDWo2aGsrbMluABEREVH02PmQ6ET88UARdtqdMV3nyOwsPPGDQREfHzBgAGw2G44dO4b169djwoQJOH78ODZs2ICcnByMGjUK6enpOHr0KGbOnIlt27ahX79+Aet46KGH8OSTT4as++abb8b555+PtWvX4pJLLsH//d//YezYsTF9f/HCDBgREREREcXFxIkTsX79eiMAmzBhgnH7vPPOAwD07t0bQ4YMwTvvvBPy/Llz5yIvL8/4Tzdo0CDs27cPzzzzDCwWCy655BJ89dVXiXpbrcIMGBEREaUQdkEkOhFNZariSR8Hlp+fj5EjR2Lw4MF4/vnn0bVrV0ydOhUA0KlTJ3z++ee44IIL0KdPH9xxxx1RrTsjIwOTJ0/G5MmT0bdvX3z44Ye45JJL4vl2YoIZMCIiIkoh7IJIlEomTpyITz/9FD169IDVakWPHj1QW1uLDRs2BBTg6NOnD5YvX45HHnkEK1asaHa9W7duRXFxMQC1wMeOHTtw0kknxe19xBIDMCIiIiIiiotRo0ahsrIS5557bsB9OTk5IcU2hg0bho8//hjTpk3Dd9991+R6y8vLcfXVV2PkyJEYPXo0bDYbZs6cGZf3EGvsgkhEREQphF0QiVKJ1WpFfX19wH0LFy40/h46dCh27txp3B4zZgyOHz8OADj77LND1me32wEAV155Ja688so4tDj+mAEjIiKiFMIuiESU2hiAERERERERJQgDMCIiIkoh7IJIRKmNARgREREREVGCMAAjIiIiIiJKEAZgRERERERECcIAjIiIiIiIYq6qqgq5ubnIzc1Fv379MHDgQOTm5mLYsGH461//aix3xRVX4Oc//7lx+7e//S3mzZuHgoICSJKEl156yXhs5syZRhn7KVOm4L333gMAVFdXY+zYsXjjjTcS8+ZagQEYERERERHFXM+ePZGXl4e8vDzcc889mDVrFvLy8jB37lxs3rwZAKAoCiorK7Fr1y7jeevXr8fEiRMBAH369MELL7wAj8cT8XXq6upwxRVXYPr06Zg6dWp831QMMAAjIiIiIqKEmThxIjZs2AAA2LVrF0aOHIkuXbqgpqYGbrcbe/bswbhx4wAAvXv3xiWXXIJFixaFXZfdbsfkyZNx++23Y8aMGQl7D61hS3YDiIiIiIgovh77ZBd2F9fHdJ1nDuiKP189osXPGzBgAGw2G44dO4b169djwoQJOH78ODZs2ICcnByMGjUK6enpxvK/+93vMHnyZEybNi1kXQ888AB+/vOfY9asWa16L4nEDBgRERGlHCE4HxhRKps4cSLWr19vBGATJkwwbp933nkBy5588sk455xz8L///S9kPRdffDE++ugjlJeXJ6rprcYMGBERERFRO3cimap4Ou+887B+/Xrk5+dj5MiRGDx4MJ5//nl07do17DiuRx55BDfeeCMuvPDCgPtvvfVWnHfeebjqqquwevVqdOnSJVFv4YQxA0ZEREQpR5KkZDeBiFph4sSJ+PTTT9GjRw9YrVb06NEDtbW12LBhg1GAw+z000/HmWeeiU8++STksVmzZuGSSy7BT3/60yaLdbQVDMCIiIgo5bALIlFqGzVqFCorK3HuuecG3JeTk4NevXqFfc7vf/97FBUVhX3s2WefxaBBg3DnnXdCUZS4tDlW2AWRiJJub6MTp3fOSnYziIiIKE4effTRgNtWqxX19YFFQfT5vXRDhw7Fzp07jdtjxowJCK6Cl0+FOcAAZsCIKMlWVNZh0nf78EFZTbKbQkQphF0QiShVMQAjoqTa1+gCAOyxO5PcEiJKJeyCSESpigEYERERpQ7GXUSU4hiAERERUepgz0MiSnEMwIiIiIiIiBKEARgRERGlDnZBJKIUxwCMiJKK4+iJqEXYBZEo5Tz11FMYMWIERo8ejdzcXGzatAmTJk3CaaedhtzcXOTm5uLGG28EoJar79SpE8rLy43nZ2dnN7muVMN5wIiIiIiIKC42bNiATz/9FFu3bkVGRgYqKyvh8XgAAG+99RbOOuuskOf06tULzz//PJ599tmo15VKmAEjIiKi1MGsOVFKKSkpQa9evZCRkQFADa4GDBjQ5HOmTZuGt99+G9XV1a1eV1vEDBgRERERUXu3bDZQmh/bdfYbBUz+S5OLXH755Xj88cdx6qmn4tJLL8Utt9yCCy+8EABwxx13ICsrCwBw2WWXYe7cuQDULofTpk3DCy+8gMceeyyqdaUSZsCIiIiIiCgusrOzsWXLFixYsAC9e/fGLbfcgoULFwJQuyDm5eUhLy/PCL509957LxYtWoSGhoao1pVKmAEjIiIiImrvmslUxZPVasWkSZMwadIkjBo1CosWLWr2Od26dcPtt9+OV155pdl1TZkyJU4tj49mM2CSJA2WJGm1JEm7JUnaJUnSfYloGFGybCnbgu9Lv092M4iIiIhS3r59+3DgwAHjdl5eHk466aSonvvAAw/gn//8J3w+X6vX1ZZEkwHzAfitEGKrJEldAGyRJGmlEGJ3nNtGlBRTlk8BAOTfHeN+0kREREQdjN1ux29+8xvU1tbCZrPhlFNOwYIFC3DjjTcGjAHr1asXvvzyy4Dn9urVC9dffz3+9re/NbmuVNNsACaEKAFQov3dIEnSHgADATAAI6JWkzinDxERUbs1fvx4rF+/PuT+NWvWhF3+0UcfDbg9b948zJs3r8l1pZoWjQGTJGkogLEAQmY8kyRpOoDpANC3b9+IH2p7ZLfbO9T77Sja4nfaHre1QyIDQBaOHTuGNYX7k90cMmmP2xu1XdFvb+pVm6PcZ1ArdJT9W05OTkARC4ofl8sV9TYVdQAmSVI2gPcB3C+EqA9+XAixAMACADjrrLPEpEmTol11yluzZg060vtt97RxoW3xO22P29r2gjLgSAmGDBmCScNTby6P9qw9bm/UdkW9ve37GgC4z6BW6Sj7tz179qBLly7JbkaHkJmZibFjx0a1bFRl6CVJSoMafL0lhPigFW0jIiIiIiLqsKKpgigB+BeAPUKIefFvEhEREVHTOHyUiFJVNBmw8wDcCeBiSZLytP+uinO7iIiIiCISyW4AEdEJiqYK4jrwQhMREREREVGrRTUGjIiIiKgt4ZVhotTx1FNPYcSIERg9ejRyc3OxaZNaUN3n86F3796YPXs2AKCgoACDBg2CoigBzzc/Z/78+cjMzERdXV1i30QMMQAjIiIiIqK42LBhAz799FNs3boVO3bswJdffonBgwcDAFauXIlTTz0V7777LoQQGDp0KIYMGYK1a9caz9+7dy8aGhpwzjnnAAAWL16MH/7wh/jgg9StC8gAjIiIiFIOx4ARpYaSkhL06tULGRkZAIBevXphwAB1ConFixfjvvvuw5AhQ7BhwwYAwG233YYlS5YYz1+yZAluvfVWAMChQ4dgt9vx5JNPYvHixQl+J7HToomYiYjihd2JiIiI4ufZ757F3uq9MV3n6T1Ox+/O/l2Ty1x++eV4/PHHceqpp+LSSy/FLbfcggsvvBAulwtffvkl/vnPf6K2thaLFy/GxIkTcfPNNyM3NxcvvfQSbDYb3n77bbz77rsA/MHYBRdcgH379qGsrAx9+/aN6XtKBGbAiIiIKOXwog1RasjOzsaWLVuwYMEC9O7dG7fccgsWLlyITz/9FBdddBGysrJwww034MMPP4Qsy+jbty9GjhyJr776Cnl5ebDZbBg5ciQANWN26623wmKx4IYbbjACs1TDDBgRERGlHHZBJGqZ5jJV8WS1WjFp0iRMmjQJo0aNwqJFi5Ceno5169Zh6NChAICqqiqsWrUKl112mdENsW/fvrjtttsAAPn5+Thw4AAuu+wyAIDH48GwYcMwc+bMZL2tE8YMGBERERERxcW+fftw4MAB43ZeXh569+6NtWvX4tixYygoKEBBQQFeeeUVY1zXT3/6U3z++ed4++23jfFfixcvxqOPPmosX1xcjOLiYhw9ejQp76s1GIARERFRymEXRKLUYLfbcffdd+PMM8/E6NGjsXv3blx44YW4+OKLjcIcAHDttdfik08+gdvtRrdu3TBhwgT07dsXJ598MgB1/Nf1118fsO7rr78+oGBHqmAXRCIiIko57IJIlBrGjx+P9evXh9x/9913B9zu0aMHKioqjNsffvhhwOOHDx8OWce8efNi08gEYwaMiIiIiIgoQRiAEVFSCV7HJiIiog6EARgREREREVGCMAAjIiIiIiJKEAZgRERERERECcIAjIiIiIiIKEEYgBERERERUcxVVVUhNzcXubm56NevHwYOHIjc3FwMHz4cw4YNQ3V1NQCgpqYGw4YNQ0FBQXIbnCAMwIiIiIiIKOZ69uyJvLw85OXl4Z577sGsWbOQl5eHQ4cOYcaMGZg9ezYAYPbs2Zg+fTqGDh2a3AYnCCdiJiIiIiKihJo1axbGjx+P+fPnY926dXj55ZeT3aSEYQBGRERERNTOlT79NNx79sZ0nRlnnI5+jzxyQs9NS0vD3LlzceWVV+KLL75AWlpaTNvWlrELIhG1CZIkJbsJRERElEDLli1D//79sXPnzmQ3JaGYASOiNkEIkewmEBERtVsnmqmKl7y8PKxcuRIbN27E+eefj1tvvRX9+/dPdrMSghkwIiIiIiJKGCEEZsyYgfnz52PIkCF46KGH8OCDDya7WQnDAIyIiIiIiBLmtddew5AhQ3DZZZcBAH71q19hz549+Prrr5PcssRgF0QiIiIiIoqrRx991Ph7+vTpmD59unHbarVi69atSWhVcjADRkRERERElCAMwIiIiIiIiBKEARgRERGlHNZNJaJUxQCMiIiIiIgoQRiAEVGbwImYiagluMcgolTFAIyIiIhSDrsgElGqYgBGRERERERxUVBQgJEjRwbc9+ijj+K5557Dxo0bcc455yA3NxdnnHFGQKl6ALj//vsxcOBAKIqSwBbHH+cBIyIiopTDLohEqe/uu+/GO++8gzFjxkCWZezbt894TFEULF26FIMHD8bXX3+Niy66KIktjS1mwIiIiCjlsAsiUeorLy9H//79AaiTMZ955pnGY2vWrMGIESMwY8YMLF68OFlNjAtmwIiIiIiI2rm17+xHZaE9puvsNTgbF9x86gk/f9asWTjttNMwadIkXHnllbj77ruRmZkJAFi8eDFuu+02XHvttXjkkUfg9XqRlpYWq6YnFTNgRERElDrY95AopUSqcixJEv70pz9h8+bNuPzyy/G///0PV155JQDA4/Hg888/x3XXXYeuXbvinHPOwYoVKxLZ7LhiBoyIiIiIqJ1rTaaqNXr27ImampqA+6qrqzFs2DAAwPDhwzFjxgz84he/QO/evVFVVYX169ejtrYWo0aNAgA4HA5kZWXhJz/5ScLbHw/MgBERERERUVxkZ2ejf//+WLVqFQA1+Fq+fDnOP/98fPbZZxBCHdF54MABWK1WdOvWDYsXL8brr7+OgoICFBQU4MiRI1i5ciUcDkcy30rMMAAjoqTiQHoiahn2QSRKNf/5z3/wxBNPIDc3FxdffDH+/Oc/Y/jw4fjvf/+L0047Dbm5ubjzzjvx1ltvwe12Y/ny5fjxj39sPL9z5844//zz8cknnyTxXcQOuyASEREREVHcnHnmmVi9enXI/UuWLAm7fHV1dch9H3zwQczblSzMgBERERERESUIAzAiIiIiIqIEYQBGRERERESUIAzAiIiIiIiIEoQBGBERERERUYIwACMiIqLUwSr0RJTiGIARERFR6hGcRZCorSssLMSwYcOMsvI1NTUYNmwYCgoKktuwJGMARkRtAi9qExERtS+DBw/GjBkzMHv2bADA7NmzMX36dAwdOjS5DUsyTsRMRG0Cr2UTUUtwn0GUGmbNmoXx48dj/vz5WLduHV5++eVkNynpGIAREREREbVzqxcuQPnRwzFdZ5+TTsZFU6Y3uUxaWhrmzp2LK6+8El988QXS0tJi2oZUxC6IREREREQUN8uWLUP//v2xc+fOZDelTWAGjIiIiFIOuyAStUxzmap4ycvLw8qVK7Fx40acf/75uPXWW9G/f/+ktKWtYAaMiIiIUgYDL6LUIYTAjBkzMH/+fAwZMgQPPfQQHnzwwWQ3K+kYgBEREVHKYeVUorbvtddew5AhQ3DZZZcBAH71q19hz549+Prrr5PcsuRiF0QiIiJKOcyEEbV906dPx/Tp/q6PVqsVW7duTWKL2gZmwIiIiCglbSjeALfsTnYziIhahAEYEbUJ7E5ERC1R46rB9JXT8Zfv/pLsphARtQgDMCIiIko5Tp+a+TpcG9t5jYiI4o0BGBEREaUcZs2JKFUxACMiIqLUwciLiFIcAzAiIiJKOayCSESpigEYERERpR5JTYUJhmJEbVp2dnbA7YULF2LmzJnG7f/85z8YOXIkRo0ahbFjx+K5554zHvP5fOjduzdmz56dsPYmAgMwIiIiShkjK48AYE9EovZg2bJlmD9/Pr744gvk5+dj48aNyMnJMR5fuXIlTj31VLz77rsQov1cbGEARkRE1A4IIfBN0TdQhJLspsTV6KpDAIB2dC5G1GE988wzeO655zBgwAAAQEZGBn7xi18Yjy9evBj33XcfhgwZgg0bNiSrmTFnS3YDiIiIqPV2V+/Gr7/6Nd644g2c1e+sZDeHiNqY2k8OwVPcGNN1pg/ojG5XD29yGafTidzcXON2dXU1rrnmGgDAzp07MX78+LDPc7lc+PLLL/HPf/4TtbW1WLx4MSZOnBizticTM2BElFS8iE0UG06vEwDglt1JbklitaduSUTtUVZWFvLy8oz/Hn/88aie9+mnn+Kiiy5CVlYWbrjhBnz44YeQZTnOrU0MZsCIiIjagY5SjEIYxTe02x3kfRO1VnOZqmQYMWIEtmzZgosvvjjkscWLF2PdunUYOnQoAKCqqgqrVq3CZZddluBWxh4zYERERO2ALNQrwwxIiChVzJkzBw899BBKS0sBAB6PB6+//jrq6+uxdu1aHDt2DAUFBSgoKMArr7yCxYsXJ7nFscEMGBERUTugF99glzwiShVXXXUVysrKcOmll0IIAUmSMG3aNCxduhQXX3wxMjIyjGWvvfZaPPzww3C73QH3pyIGYERERO2AEYB1sAxYR3u/RKnGbrcH3J4yZQqmTJli3J46dSqmTp0a8ry777474HaPHj1QUVERlzYmGrsgEhERtQPtvfy8zh9ucSYwIkpNDMCIiIjaAXY9JCJKDQzAiIiI2gGjCEcHCcREyB9ERKmBARgREVE7oAde7X1MlF6G3rjdzt8vEbU/DMCIqE3gaA6i1tEzYB2FxL0GEaUoBmBE1CbwGjZR6yjoWGXoO8a7JKL2iAEYERFRO6AoHbMMPRG1XRdddBFWrFgRcN/8+fMxefJkZGVlITc3F2eeeSbuuusulJWVITc3F7m5uejXrx8GDhxo3D569CiGDRuG6upqAEBNTQ2GDRuGgoKCJLyr1mMARkRE1A4YGbB2H4AFjQHrIBk/olR02223YcmSJQH3LVmyBHPmzMHw4cORl5eH/Px8FBUV4csvv0ReXh7y8vJwzz33YNasWcbtk046CTNmzMDs2bMBALNnz8b06dMxdOjQJLyr1mMARkRE1A50lHnAdAy7iNq+G2+8EZ999hk8Hg8AoKCgAMXFxRg8eLCxjNVqxdlnn43jx483ua5Zs2Zh48aNmD9/PtatW4cHH3wwrm2PJ1uyG0BEREStZwRgHSwyaf8ZP6LYWLZsGUpLS2O6zn79+mHy5MkRH+/RowfOPvtsLFu2DNdeey2WLFmCm2++GZKpmqnL5cKmTZvwwgsvNPlaaWlpmDt3Lq688kp88cUXSEtLi9n7SDRmwIiIiNoBPQBr7wGJYPFDopRi7oa4ZMkS3HbbbQCAQ4cOITc3F3379kX//v0xevToZte1bNky9O/fHzt37oxrm+ONGTAiIqJ2oKMEYME62vslOlFNZari6dprr8WsWbOwdetWOBwOjB8/HgUFBcYYsMrKSpx33nn4+OOPcc0110RcT15eHlauXImNGzfi/PPPx6233or+/fsn8J3EDjNgRERE7QDHgBFRW5SdnY2LLroI06ZNM7JfZr169cJf/vIXPPPMMxHXIYTAjBkzMH/+fAwZMgQPPfRQSo8BYwBGRG0CexURtY6RAWvnVQGFvrdo5++TqD257bbbsH379rABGABcd911cDgcWLt2bdjHX3vtNQwZMgSXXXYZAOBXv/oV9uzZg6+//jpubY4ndkEkIiJqB2QhA+g4XfI6xrskah+uu+66gItDQ4cODRjHJUkStm/fbtx+9NFHA54/ffp0TJ8+3bhttVqxdevW+DU4zpgBIyIiagf0k5uOEoDp2nvGj4jaHwZgRERE7YCeAesoOlqgSUTtBwMwIiKidsDIBLXzuERo8wcx8UVEqYoBGBERUTugoGOVoe8Y75KI2iMGYERERO1AqldBLBIWvFBQ1oJndMwxb0SU+hiAERERtQOpPg/YJqThmSMl8CrNBFR6Ffr4N4mIKC4YgBEREbUDqV6GXtEiq5a2P1UzfkQdhdVqRW5uLsaMGYNx48Zh/fr1yM/PR25uLnJzc9GjRw8MGzYMubm5uPTSS+H1ejF79mz84Ac/wLhx4zBhwgQsW7YsZL0LFy7EzJkzA+6bNGkSNm/eDAD497//jVGjRmH06NEYOXIkPvroIwDAlClTMGzYMIwZMwannnoq7rrrLhQVFcX/gzDhPGBElFQ8dyKKjVQvQ6/XcIy29Qy8iFJDVlYW8vLyAAArVqzAnDlz8PXXXxv3TZkyBT/5yU9w4403AgBmz56NkpIS7Ny5ExkZGSgrK2vxhMtFRUV46qmnsHXrVuTk5MBut6OiosJ4fO7cubjxxhshhMD8+fNx8cUXY+fOnUhPT4/Je24OM2BERETtgJEBS9HAxAjAWtj8VA04iTqi+vp6dO/ePeLjDocDr732Gl566SVkZGQAAPr27Yubb765Ra9TXl6OLl26IDs7GwCQnZ2NYcOGhSwnSRJmzZqFfv36hc2yxQszYERERO1AqgZeOn0EW3PvQhhdFaNT8dLLEIqMPvfdd6JNI2oX9u9/Ag32PTFdZ5fsM3DqqX9schmn04nc3Fy4XC6UlJRg1apVEZc9ePAghgwZgq5du7aqXWPGjEHfvn0xbNgwXHLJJfjpT3+Kq6++OuLy48aNw969e3Httde26nWj1WwGTJKkf0uSVC5J0s5ENCiVPb/5eYxaNCrZzSAiog4o1SdilqMIrMxBZrSZr8pXXkHVP15tTdOIqBX0Loh79+7F8uXLcdddd8XkgpGkzQkY7n6r1Yrly5fjvffew6mnnopZs2bh0UcfjbiuRF/AiiYDthDAywD+E9+mpL6FuxYmuwlERNRBpfoYMH8GrIn2m0+SUvNtEiVNc5mqRJgwYQIqKytRUVGBPn36hDx+yimn4NixY6ivrw/Jgi1duhSPPfYYAOD1119Hz549UVNTE7BMdXU1evXqBUANxM4++2ycffbZuOyyyzB16tSIQdi2bdtwySWXxOAdRqfZDJgQ4hsA1QloCxEREZ2g9jIGrMnASpYhpMBMWbICzsUlVTjqdCfltYlS1d69eyHLMnr27Bn28U6dOuFnP/sZ7rvvPng8HgBARUUF3n33XVx//fXIy8tDXl4ezjrrLPzwhz/Et99+i9LSUgDA5s2b4Xa7MXjwYBQXF2Pr1q3GevPy8nDSSSeFvJ4QAi+++CJKSkpw5ZVXxuEdh8cxYERERO1Aqs8DFk0VRKGEvsdkBJxCCMzaW4ieaTbsOn9kwl+fKJXoY8AA9bezaNEiWK3WiMs/+eST+MMf/oAzzzwTmZmZ6Ny5Mx5//PGQ5fr27YsXXngBV111FRRFQXZ2NhYvXgyLxQKv14sHH3wQxcXFyMzMRO/evfHqq/6uyA899BCeeOIJOBwOnHvuuVi9enXCKiACgBTNjkuSpKEAPhVCRNzLSJI0HcB0AOjbt+/4JUuWxKqNbZ7dbkd2djZ+c/Q3AICXTnopyS2i1mjL36O+rbUn74sMvIssXA8XbpFcyW4OmbTH7a09e6/6PXzd8DXu6HkHzs0+N9nNabEXPGnYkNYZ/0YtOoUf2gF4PHj/6+/x7qU/Rq63DMdLHkT/tP54ZMAjEdfb954ZAICyV/8Rs7YqArgd3QAAS6TamK2XEqej7N9ycnJwyimnJLsZHcLBgwdRV1cXcN9FF120RQhxVvCyMcuACSEWAFgAAGeddZaYNGlSrFbd5q1ZswaTJk0CFqm3O9J7b5fa8PdobGvtyJYjpUBBKYaedBImndw/2c0hk/a4vbVn3278FtgHnHbaaZj0g0nJbk6Lvbz6OwDA+RdcgK628FfHZXsj3v/6ewBAj+7dcbwE6Ny5c5PbqV7zLZbbslcRwNfbY75eSpyOsn/bs2cPunTpkuxmdAiZmZkYO3ZsVMtyHjAiIqJ2oN10QWyqZ45Q2kTtjbbRCiJKVdGUoV8MYAOA0yRJKpIk6WfxbxYRERG1hIJ2EoA1uZC/1H4yqz3KjL+IqBWa7YIohLgtEQ0hIiKiE6dnwFK3DH1L5wFLnngU/vjtO9vx49H9cPHpfWO+biJqW9gFkYiIqB0wArAUL0PfbAYsaPLVZLzfeOQa399ahGkLN8dhzUTU1jAAIyIiagdSfQyYMRFzk0PA/O9RXy4ZGT8lRYNcImobGIARERG1A6neBTGqDFhA4JO895naoS5RYlmtVuTm5mLEiBEYM2YMnn/+eSimiynr1q3D2WefjdNPPx2nn346FixYEPD8N998E6NHjzae//Of/xy1tbUA1Cqkp512GnJzc5Gbm4sbb7wxbBuCpxxYuHAhZs6cCQDYt28fJk2ahNzcXJxxxhmYPn06ALVSZk5ODsaOHYvTTjsNP/rRj/Dpp5/G5DPhRMxERETtQOoHYPoYsCbab86AxbtBTVBS8yMmSoqsrCzk5eUBAMrLy3H77bejvr4ejz32GEpLS3H77bfjww8/xLhx41BZWYkrrrgCAwcOxI9//GMsX74cf/vb37Bs2TIMHDgQsixj0aJFKCsrQ7du3QAAb731Fs46K2Sqrajde++9mDVrFq699loAQH5+vvHYBRdcYARdeXl5uO6665CVlYVLLrnkhF8PYAaMiIioXUj1MWBRZZUUBUJqvlhHvLEMPdGJ6dOnDxYsWICXX34ZQgi88sormDJlCsaNGwcA6NWrF/7617/iL3/5CwDgqaeewnPPPYeBAwcCULNp06ZNw2mnnRazNpWUlGDQoEHG7VGjRoVdLjc3F3/605/w8ssvt/o1mQEjIiJqB1J9DJh/HrDIy4R7LBkZvxSNcamD++OBIuy0O2O6zpHZWXjiB4OaX9Dk5JNPhizLKC8vx65du3D33XcHPH7WWWdh165dAIBdu3YZwVkkd9xxB7KysgAAl112GebOndui9syaNQsXX3wxJk6ciMsvvxxTp041smvBxo0b1+L1h8MMWByk6tVHIiJKXakegBlFOJpcKvTR9lAFkecNRM3Lz89Hbm4uhg8fjrffftu4/6233kJeXh7y8vJaFBxJWjZ96tSp2LNnD2666SasWbMG5557Ltxud9jnxOq3ygxYHChCgVWyJrsZRETUgaR6F0Q5innAoPg7/yXzfca6CiLHlFEitDRTFS+HDx+G1WpFnz59cOaZZ2LLli3G+CsA2LJlC0aMGAEAGDFiBLZu3YqLLroIo0aNQl5eHmbOnAmnM3ImT5ZljB8/HgBwzTXX4PHHH0dWVhY8Hg/S09MBANXV1ejVq5fxnAEDBmDatGmYNm0aRo4ciZ07d4Zd97Zt23DGGWe0+jNgBiwOUnUANBERpa5UL8Lhz4C1sA9iEjADRnRiKioqcM8992DmzJmQJAm//vWvsXDhQqNIR1VVFX73u9/h4YcfBgDMmTMHDz74IIqKiox1NBV8Aeo4MT0j9vjjjwMALrzwQrz55pvG89955x1cdNFFAIDly5fD6/UCAEpLS1FVVWWMOTPbsWMHnnjiCfz6179u3YcAZsDiIlUPfkRElLqUFC+OHtUYsDZTBZEZMKJoOZ1O5Obmwuv1wmaz4c4778QDDzwAAOjfvz/efPNN/OIXv0BDQwOEELj//vtx9dVXAwCuuuoqVFRUYPLkyZBlGd26dcPIkSNxxRVXGOs3jwHr1asXvvzyy5A2vPDCC/jlL3+JF198EUII3HXXXfjRj34EAPjiiy9w3333ITMzEwAwd+5c9OvXD3v37sXatWsxduxYOBwO9OnTBy+++GKrKyACDMDigztSIiJKMFmoIUyqXgSMagyYMP+ZvPcZ61dO1e+MKBqyLDf5+I9+9CN8//33ER+/++67Qwp16NasWRNVGwYOHBhxDq958+Zh3rx5IfdPmjQJdXV1Ua2/pdgFMQ64IyUiokTTu7Glane2qCZihjDK0CdTrDNWKfqVEdEJYgAWBwzAiIgo0VI9AxZNEQ4R5kp6Mt5vrOcBYwBG1LEwAIuDVL36SJQM/LUQxUaqH3uMLohRTgSm/5mM9y3HOgPGPSFRh8IALA64IyUiokTTM2CpqskuiIdWA267FmzpmbIklqGP8WuzCAfFU6pfnEkFLf2MGYDFATd0oujxggVRbKT6GLCINRwbq4D/Xgfs+gCijUQqsf6IU/U7o7YvMzMTVVVV3MbiSAiBqqoqo4piNFgFkYiIqB1ot2PAPA3qvz53YBfExDQrrFgX/G8jcSW1Q4MGDUJRUREqKiqS3ZR2LTMzE4MGRT/RNQOwOEjVg18qKm0sRa27Fqf3OD3ZTaETxF8LUWyk+hXuiPOAeV3aA0rAPGC6pBThiHkKLLarI9KlpaVh2LBhyW4GBWEAFgepfhBMJZe9dxkAIP/u/CS3hIgouRShBiepegyKOA+Yz6k9oHWxlPSbyRwDFuP1peh3RkQnhmPA4oAZMKLo8byDKDbaTxfEoPb73NofAgiXAUvCTiTm84DFdnVE1MYxAIuDVD34ESUTfzVErZPKxx6lqbFdXn8GzNwFMZnvNtZBHzNgRB0LA7A4SNXuH8TvjohSVyqXofeFmd/L/6ApAyYEhCQFPJyciZhji4ceoo6FARgRtQk8/yBqnVQuQ+8zNTnyGDAl4L0ldR6wGH/GqZy9JKKWYwAWB6l48CMVD4KJx0+cKDZSeQyY3GQXRL0Komgzx1dmwIioNRiARenVY+XYXNcY1bKpePAjIqLUZmTAUvAY5Gsqs+VzGY8EFOFI4tuMeRGO1PvKiKgVGIBF6fmCUnxQVhPVsql48CNVW7m62hHxsydqnfY7BsyfAYMQEJEmbE4gJcavziIcRB0LA7Ao+UT0O3ueSBJFj78WothI5XnA5KaarFdBhIAIk3pKxvvlPMxE1BoMwKLkE9HntZgBS1387pKHnzy1RWWNZSkT0BgBWAr+mnxNjQHTqyAKJSDyEW6X8XeB040vKuvi2MJAMZ+IOdZ9GomoTWMAFgUhBLwtGPybKgdroraAvxdqq47WH8Wl712Kf+/8d7KbEhU9AEtFTRbh8JnmARMKtB6IkMtKjUX+XVSJe/cci28jTWTut4ioFRiARUE/pDED1v7xu0sefvLU1hxvOA4A2FiyMcktiU4qB2CBY8CC9gZeUxEO83KKP+PnVpSAdcRbzDNgDOiIOhQGYFHwKnplqejwij5R9PhrobZOgtT8Qm1AKo8Ba3oeMJf/ARF+QbkF47RjIebzgKXeV0ZErcAALAp6V4Nod5DMoqQwfnVJw4+eqHVSeQxY010Q9QBMgZCVkOUERIvGaceC+bViEfAyA0bUsTAAi4I3hedWIWrr+Ksiig0l5h3jEqfpiZhNVRDhL0NvXlJGYidpNgdMTVZwjBL3g0QdCwOwKOhdI6I9tKVi9w9SMcgmIl2q7Q8UpZ10QQyZB8ztfyAw9aT9IxLfBdH0txyDVxYCsHY+AMmWuEqORJQ8tmQ3IBX42AWRKG6MbkT82VAbJUkpMgYM7bULoj8DBiW0CyLQsqliYsFcNT4mGTAh0GnIv6D4sgHc3voVElGbxgxYFIwALMrlU/HgRyp+d0SkS7X9QSqPAQucByxCFUShVkEUkr6cnywEEjmVlmLu/hiDq0f6Giw2e6vXRURtHwOwKPhaOAYsFbt/ECWL/nNJxZPG9uSDAx/g0fWPJrsZbVKqVUFMRQEl5EO6IJqKcJgyYOa0uZoBS9w+RARkwFiEg4hahgFYFPQDQ7RX13gimboYPFNH9ef1f8b7B95PdjOoFYwALAV3Y3IT8ZcRgCH8QC8BAZ+SvDFgvhi8MA89RB0Lx4BFwdvSfg3ckRJFTb9gwZ8NUeu0ny6IKueOHfCVl6NLcBdELSMp/OnzxHdBNLU3FtkrZsCIOhYGYFHgGLCOw/zdCSFSZvA9EcVeqmXEUzkAC9eNr+DmWwAAZ/zMp90jIIQc9vkJL8IR9NqtXl8io0ciSjp2QYyCz3+RDQBwxOHGu6XVEZdPxYMfUbKIoH+J2pwUuA4jEjwGKtbCZcD8D5ozYAi7nL9acWI+g4B5wGK8PiJq/xiARcE/Bkz9d3FJFR7YWxhx+VS7akp+IuAkgN8jEaUGcwGOVDwGBc4DFlwFUStDH6EIh4AwxpAl6p0HzAMWkyqIqVtAhYhajgFYFIK7ILqFaHKHyxN3oujx10JtVWv25fU+Gf+34zDK3N4YtiiygAAsBX9VEecBkwSgeP2PCAFoXcPN7zmaoQKxDEwD5wFr/XpldkEk6lAYgEXBpwTu2L1K09eqUvHgR5R0/NlQO/JOaTW+rKrHi0fLEvJ6ClI9AxY+AJMsAamxgAyYbDoQy0YXxCZeRIkuy+Q9fhylTzwJIUfuXBjQBTEGH7ccYWwbEbVPDMCiENwF0dtcX/PUO/ZRGKl4EpOS+DFTO6QfLxJVx0dW4n8C75UVrD9UGZd1RwrALNagHYQIvSEg/MfppnYoUe7Ti2fPQc1bb8G5bVvEZWLdBZFjwIg6FgZgUfCK0AyY+XYwZsBSF7+75OEnT23ViUzErG/PiarfkYh915e7y3D7a5twvNbZ+pXVlwDHtxo3zT3wzLGIZPX/bV+zGrXPz4XQPlTJWF6EFMsKK8ogx8iyNbF8YAYsFgEYx4ARdSQsQx+F4J2r18iIAZYwR1dmTtqGnQ0OZFotOKVT5gk9n8FYYvBTbls4/UJs6IeBEwneToS5C1u89l0NLrUcvNMTg2zby2cBHjvwaB2A4AyY/2/JlAGT62ohl4fPlEXVBTHaY7OkL95EAGb6OxYTMTMAI+pYmAGLgle/sqb961Ga7uqgsJpRm/DgviI8daikRc9h8Jw8DHjbBo5FCZUSGTBzABOn/ZjHGHQVg/V77AE3o+qCGPS+zJ9tLItwGN93E4ubL8zGovugzNMGw8PfPIx39r2T7GYQxRUDsCgE9y33GpNdhseT+LbBqSio8/Fksq1j4NW28Eq8X2v25cYzEzUGLAGBs8enbhsxLdinfcaRuvGZM2Ch34f/dizHgEUzcM+jhL52ayi88GFYdmQZntj4RLKbQRRXDMCiEHxlzaudn7BqbNsmCwFHCy8rmoMBBgaJxU87ecwntj7Fl8SWpLat9Y1o1C76JDoDlogy9F5ZD8BiuH5tkuXAecD8f5szYJJxLNY/VUm77Z8HrJkUWMva1sTy5qCLEzETUUsxAIuCUYZe2z/qGbBIV9p44t42+ISAM8qyw5Q8/LUknznoSnYGTPHIOP7YBjj3Vgc+UHUIcNvDP6kNaJRlXLP1AN4uVdutB7WWBIVgCQ3AYrmJeBwAIs8DFpABC3pfUpgMWCyKcPgzYJGX95oDMBbhIKIWYgAWheAdu9H1IMI+l10Q2wav0roMGCMD6ijcstv4O9ljwIRbhnD64KsKqrT30jjgrRuT0qZoipI0+hT4BNAoB3ZRT1Q9k4AT+DjtuzyyXoAqhi/gbQTQxDxgARmwyPtz2eiCGJmIttuKZFThiLiIVzEHYNGttinMgHVs+0ob8Praw8luBiUQA7AomKseAua+5uExA9Y2yAJwcmRzyuD5R/KYA7CkX4nXN4RwZ7XHNiS2KS3Ylzu0tFDwBbtkFOGIxrHp09GwanWLnqOPAYsN7ZPxqoF2pCqIljAZMCHpXQ/Vfz3WnpD1x5ougxhl06IIwGKeAeMOsCP7ePtxPPnZnmQ3gxKIZeijYJS31XbenqCJmYMxAGsbfEIYJ0XREhFOAih+eN6RfB7ZY/yd7AyY/rMTsUgrxEg0VRD1iz1GAKY1P1FXOVtShl4IgcZv1qLxm7U4Y2/0J30xHQOWlgV4HYBHzYCZv+5I84BJwa8rAa5OZ6Oi528AX9PFsUJW3JQoomZzwMgiHNRabq24AKcB6TiYAYuCUYZev22UoY+g7Zw3dGiyEHDKCruEpgh+S8njUUwBmJLkLoj6hmDOXifpN9ySfYcegOmBhD5GOFEnU+a27q3ei0W7FkVeWD6x79gfgJ3Q0wPZMrSVqmPAmi9DLyHcXsKXPjzgdpOX3Fq4HTX1/ZurIMbi85CFAITAnXX1QGNV61dIKcUTy98WpQQGYFGINBFzpN8JMydtg0+o34SLe7Q2jd9O8rXFLogBGbBktykKye6CaM6AbSzZiOc3Px85gDjBKhr+MvQx+NXaMtV/w3ZB9DPGgKV1CgmgtBA38L4meyD6H6z98EOUPfNM2MWMoLmJdcU6AyaEgr6yjIera4G9n7R6fZRa9N+WzPOVDoMBWBSMjJcIfzsYMy5tg35QbEkhDnZBTB5+2snTJrsgKikWgEXogpiwMvRBuR8BAZ8IP6WAONEATPZ3k2o1PQDTJmQOVwXxaG9gSz+tD2JaFsLvJYICsCZe0vy+S2bPQfWi/0RYsoVjwGKw95IVAZu+Gp+7yWWp/YnpxY026nDtYRTbi5PdjDaDY8Ci4L+iqf7rDbodjCfuiReu37Q+r4xDUdAzCW2i6PDXknxtKwDTqx2ZuyAmNwCLagyYondBDMqAJaoKYpigyit7kWZJC7ew+m8LG+eVm7742CJpWeq/Rhl6/0N6gPfQz20AumByQR0kazqE8IauRwoOwIKyZCdyQhtNGfpYV0GE7C+rr82NRh2H27i4keSGxNG1H10LAMi/Oz/JLWkbmAGLQqQy9JF+KAzAEi/cZ65/by2phBgwEXN73hMSmYQrQ/996ff4zdHfoNJZmZQ2tYUMWEv25f4iHOptYwxYzFsVXnAGDAgMrM1OOAOmTzIdizFPXu36bzNjwACo2TJJCinCISTJNCkzwrdNiCYejCCKwNQrBDIs6nIxqYKoAEa9EWbAOhyjC2I7Pe9w8aJCCAZgUWhxGfp2+gNqy4I/c0X4T51aOhcYJZYI+pcSz3yirmdS3t33LgB1PFEi+YtwmLaIJBUGMfYrUURRTu0AYWTAjKcmfiJmnbm4SuDCrc2Ate7XqnhkeCu1dYTpghjCmgFzEQ69DL0iWRB8GhOylhMJwKJY3hfrAAwKLEYXRJ6sdjTtvQvi4TrOcRaMAVgUgruUNFeGnpLP3D+/RWPAGAZQBxRQBVHLgOVk5AAA6tx1iW2MqQjHh9uO4+Z/bkiJDJhDDn8FO2EZsDCfkTmzaSZaXQWxlftJRQDQukZ69ImY/Q+HrN2aAUiWkEcUixXBn7AS/GxzW8Nk/k44G6gIZFrUU6hYTcRsMbogMgPW0RgBWDstwnGw9iAAYFD2oCS3pO1gABYFfxl6bQxYM2XoeRKfeMGfubk7i/MED7D8HhNDGBkDft7JEq4KYvfM7gCAGldNYhtjZMAU3P92Hr47Up20AKwlFSGDuyDqb8OSqDFgYdrqlcOMmQJOOAPm9sVonIow/mcaA2bu/h20vDVDS4A1H4A11QUx3D5GeMJkCaOYiNknzAHYiX8gr6w+iFN/vwyKovhPyDpwBqyjHgfaexn6AzUHAAADuwyMyfruW3UfxvxnTEzWlSwMwKJg7lIihGi+DH0H3YEkU3CwZL4ieaJVEIk6inBFOPQMWK27NrGN0fevqTYGLKgIh54lSmYGrNkuiC0UswyYEPAHYGoXxMAxYEFZxPqjCDcPmGKxhinCEe61Ij0YKQCL3HSd15QBa00Z+rkr9sEjKxBQILELYvKnwYiDSmclPj/8eZPLtPcuiAdq1QDMEqOwY1XhqpTfVlgFMQp6xksgsJtEpB8KMydJEPSRmw+IJzoGjMFYYnAMWPI1VQWx3l2f2MboGSRfGwjAjCAqiiqIwWXotfuTOgasuSIcLR4DFpsMmBpca22IpggHENBWYwyYxYbg68jBn4Jo4hYACHfk7n5NHQO8QiDTKoV9zROhCMEiHAhfTCbV/XLlL7G/Zj8uGHQBuqR3CbtMu++CWKN2QeT5sR8zYFEwH1A9poMcM2BtR0gXRNNOrCVdELlzoI4oXBdEWSt8kbSy9KbfsHICY5ZcPlerx6+dyBiw4AAsmV0QIwVgJ1yEwxej8c8K4M+AqWPAAsrQh3mKMI0Bq+ypTiwiW6wQwUU4gk9gmxsDdgJdEIUQ2FLvQJaeAYvBSbNPUZAmayEYM2DtSmFDYbPLuLUKo+0x/rJ77ChzlAHg+bFZhw7AVlTWocIToY+8iXliTfPcHyxD33Y0NQaMVRCJmhaQAQsKvBJ9QqSfQAvT71ZpQRXEbw9WwicruP3z23H+kvNb1Rb9vbdsHjD9ua166RYLFyhH6oIoTnCf6IlhBkyStDaYuiBa4T/WBqt3+SeVtndRswhCCtMFMfjJzVRBVNyhn5HxfUd4nx+V1wIANtVpwWP4xVrEp8iYVHaBdsON2k8OwX0kwQVw2oD2GIDp+9Sm3lu77oK4+mmc5VQvKrTHDOeJ6rABmEdRcHf+Edyw7VCzy5onYjZX1wuptkRtRkARDgZgRE0KNw9Y8L8JYxTh8P+G5SgDsILKRtzx+ias2ltuDPpuXVNaMAYsJAOW2DFg4a4sRyzCYQSWLROzk0TFPAbM3wVRPyEJXnvt6TNQZfcYj9gz1Emc5XBVEJvKgIUrwuFtIgMW4ftvDDqmhCvCce7G3Th/056wzw/HJysBEzHbvy1GxT93RP389qI9Zkh8Qr144FN8Afcrbjf2jslF/bJlxsUNuR2mwLI3vII3SssBtM8A+0R12ABMP7YfcDSf6jfP+2XOgEX6nbTHHUhbF/yZBxTh0K5MLzxeiUu+35vIZlEU+GtJPnOmZHPZZpTYS4yThWQdMIXpRxxtqfBGj9rmOmfzPRum/2czPtha1HQbWrAv1zNg5h4TQOD7iKeWZMCgd+k8wTFgrT5HNI8BM7ogCmNQevDqa7JOh4AESQKO9e2P4u691dWErYIYOQAzvhOf/0S4yS6IEXSxWQNuhwvACpweHHREP5bLJ/uMAEzEaAyYEAKvbn8VJfaSmKwvEdrjCbr+noLfm6+iAsLtRvnc52JXYbQJnqIieEtL4/cCUdB/n4pQUNqY3LYkW4cNwBTjKmXzjLLCAkEZsAjrZoo14YKvVIebB2yP3dnsAdF88GZXUpWQFVT9bw+8ZY3xfZ24rr3tUhQ3ZDm5Yz7MXRBf3f4qLn//cuOEPviqbdyFqYLo1LIkzdHjNJe3+YzZmv0V2FHUdBevE8mA6Sfj+u/Ffag26nW0RrhgMXIRDm3ZFgZg/i6Irfu1ioAqiP55wIwuiEGfu8PjP6ZWdu9p/K0GYIGnMXJwwBvQVm39psIbTRXhiHQ27NU2tBVnnaq+Zgx2Xj6fDJ8ltmPACuoL8EreK7h/zf0xWV8itOfzp+CLJJI2hlAoSkK6IB669DIcnHRR3NYfDT0IfWHrC7jsvctQ6axs8TpS6YJCUzpsANaSsrE+UxVEbxQn6MyAJV5oBiy0C2KjrLRqvpaOynPcDueOSlS/1/ouXeF09K9kz97fY+eue0/ouVvrGmOyvwk3Ya/e7S9iFiVe9Lfj85+IvZb/SlRP9Wknxs5mAjCvrJ7wNNfdpyWfrSNoHjC9K5ziTcwJZbgT18hFOE5wImZf7DJgklEFsVG7y18FMPhjb3TLEEICJMB2MjCufpf6HIsNwRmw4O6qAevStg/FHICdQBEOj3Z/jzQbJEQ3D5jdJ2O33RnxcZ8s49tTRqk3vLHJgOknu05f5NeNxJmfj9r33otJO1qiPZ8/hVzM0rczWfZ3QWzH7x/w76dWFKwAAJQ1lrV4HVNXTI1pm5KlwwZgLTn86EGXAhG2CIcQQJHLvxNn5iT5AopwaAfdBllu9kolv7sw9Ivl8X6ZDvrRu92lcDiOtvh5yyvqcNXWA/hfSXWr2xDuRF0ftxDxJD5OfIoPL/R7CwU4btx3tO5I2GWvfXkdHv9kt3Fbv3rs9DQd9Dg8+vi2pjc6owhHFJmi4HnA9DWLZE7ErITvinniZej17pWBn5vi8qFxW3nU6xFhqiD6zGXYgzg8svF5Ds0oxknOYvV1JatRkl7nkxXA6wKW3gPUFvpfR224+o8pAFPCZcC0dUbq+urRzgPSJQk2SQrZjsJlMabuPIKLv98XsWKi1+eFIzNTexOxyYC1ZBqFYAU33YySP/wxJu2IxqZaOwqc7nbZBVEX/N70YjhCUUznk+3sQBj0G9Lfn0ObfuL3637f4lXqFRVTXccNwJqa4d7XAJe7NHRZ4b/yBfi7IB6AFWdt2A1n9iXGcpRYoVUQ/X/rV6btPrVsSrQ7uGTuCD+vqMWVm/e3rZ1xnE4kO3rQKxQfZF9Di593xKmeOO6PYhxrc8IFWUYGLMEBWI27Fsu7f4sttnzjvkgn5tuL6vDvb9XgzFtWDumzjwCEZsC+L6hGg8sfjDj1ACyG47Oc2rqCe1ckqqpZuBPX4MxmvadeDcpaOBFzYX0hluxdYlylD44hHNsrUPP2Psh1UWZuzEU4fC5AkeETgM3oghi0freMNEmBYrXAavEXqwg3BkxWfED5LmD7YuDYxrBFOBSX/zcjfD68u7kQzywzFczQVxkpWNLWk2aRYJUCjzdAYJEOfR/+vVYx0R0hwJBlBRb9sViNATMKwUTeeX9cXotjzuTPO3bttoM4d+Me/OKLXyS7KXGjX9QyyOpt85jEdlczLKjbpb6fatQy34fqmi+E11514AAs8mMbN16Bb789z7jtNV3R9AUU4VD/tms7N3uPKeoJfgc/oWwLwlVBtGsDz5vav7WVgOeeXUeR1+AI6PKaNAlqQ7S/m4MOFyZs3I2t9Y3otzoPa6tbHry0JUL44JOT+x7cshtplrSA+/QDZKQsSrwI7RfqEqaTwii2wWNTpyLrxb+is8cZMAassNqBm17dgEc/9mfK9GId0WbAohFchEN/ZqJ+wWGLcAQFz+ctPg9z1s5p8Txgdy2/C09tegqQ9MIsQUGmQ91GhC+6z0vNOpmW9TRCNndBDFq+welClsUHZ1YWrBbFOHEJOwZMEYBdy8YJGRACNV264rnbfw6X9r6Fx4PPJ0zCC7dMAXw+rNhVhs/zw4wrifD9mzNgFkmCHNTiep//u3Bpy6Zpn7U7QlCnKD5YzEFpDFQ1qr+hSBlcRQjM2F0Qkyx6rOyr2ZfsJsRNSPdY7ZxEyApGVB3BL3d82P7K0AdXftR+U5G6tr+e/zq+OvpV3JvVFnTgACzyRu72BKY3AydiNo8BC/wXADyZuVGfSJa6vdjVRJ9wil5w4KR/ZzbJnwFrbGEf62QG0vort4kATNfC7krRauk7fPVYBY44PZi9X61g90F5TewblUCK8EGWHRAnWu49BpuIR/Ggc1rngPv0ClUxy4DVHAXeuglwNx1s6gdocwBmCfMeg3/znsOHAQAShJHhAoCdx9VCGzUO//twuLWLMc2NATuBiZj1i3t618NoQhIhBBYdr0SdN7qCJ97ycuyfMBGuff6T1bBFOEwnOfrjKwpWtHgesBqX/hsLn6FSHD645EZ4w8ypFc6y+gacM+F5eCSt7qGnMaALoq/WFfB+HC4vBNQxYB6rD2s6qWXoFYsVkAJPY3yKD7Brx3BFDcBe/ekd+OyCS/CZXW2fcLnw/ZmjsX70eAivF3VOT0BSUGqmC6I+FCHNIsEmhR5TzAGYfuFPb6U7wjp9PhlWPeALMybzRDz03lYAkbdjh6xAFv6AkuIrpAuilvkSiowJxfm47vA6KC3MTrd5pqDTKlmb3ae+tectfHH0i3i3qk1gABYF44qmEGHL0Mum9L434wdRZ1HGb9iFS75vv1d7WsqjKNjbeGIBafCPWv9+u9qsxhgwuy/wBCma9SRDhcdrbEPeoAPjraIbfr+/6dLZMZegj0QAqKxcDZ+v6WqL+nekn+Tk2CJ1UEsNeuDl89mT1gaPHBqAFTeq42xiVoSjYC1w4Aug6mCTi+nBgQtu9IaEc2D1z49k4opQ3MIqlIAuiF8XroeUVoUf9Mk27mtpBqy5MTRrqxuMiyXmC3bmf5tS4PTgd/uLsKKqPoqlAfuq1ZBralDz5lvGfeEyYOZ5wAIeFy3LgImgdxNS9Mjhxdulr+OzBXOjWt8ejxclmT3htGhjnrwO+IQwuiDWLi9A43f+YQAOrwdv5dhQni4hv7OMBq16nCKFFuEQYTJgwePEFLcbzoxMKBYLhNeHWoc3qCCLHj2H//Y82ueXJkmwQgo5ptSZAjA9MK9vVL8Le4QCMYqiwKKH6z4XYrHjPV6r7ksjbb/2DlL4oa0I7YIoG/920cZEKVFmkVOGab+TZkmDIhRsr9gecfEGT0O7Hgdo1oEDsOiXNcrQI3gMWGBXEwCAZIn6JD5B08OkjD8eOI5J3+1DqbvlXZ5Cx4Cpt7tYraFdENv4wWbUt7ug76YDqm5qf//reMvLtraK3oSgY/jbe9/G3cvujtnqM+QKbN/xc5SXLwOgBqLHXZFP/vUArFvKB2Dq9u711uP9/e8bg5OjFoPEpFt2o1Nap4D7Yp4Ba9BOqH1Nr0+vkuWCG6+hM55HZ0hhfrN6EBVMEiIgADv534/hyor30CndZtxnjAGLwZX/pWU1uGm7fxyDfjKrHxei2d80ynrJ/yiz8/pVZdO231wZenMFNr3rU7SMEyIpfKnshVYPXpr2B+w8djiq9em/3Wpbd62hdsgCAV0QvSX+CzFVUjkW9kjDxhwbCjP8bVcsFjUzZuKVAzNgAZ+LqQiHMyMTssUK4fWixuENDEKMKoiRM2A2CbBIEqxhinCYM2A79V4ueoGYCCfYHq/X2M4loSBSqbBvaxpQE2WmVJL05SIFYNp218aPie1FcHZL+PwBWLZH3U4UX4Kn/TgBpY2lWLJ3SXQLmzJgNosNHtmD2d/MDruoW3bDLbvDXkxqjzpuANaCq0vmMvQBB0i9u3bA0h32I22xfdX7Aq7QflurZgDMB69oBZ986JmjrjYrHLICRQjjal+011baQjbMfGB0Ja2bSPjXfXLTk9havjVmr5KuqF3FZEUNQEZ9uwvjN+wOWU5vTY12kp2TZgtZJpXoGbCtpevw6IZH8fzm51u4gta3wSN7kJ2WHXCfXrra/BuNZFfVLoxaNAqHa5s4AddPipvpXqWfzLokN3pp+9Nwe9VGtw+AQKdh87F836fG/RahBIwByz3oxRlltQEnyXrw1lxwZL4Se+SnN6B+2bKAx52yghm7AytY6hfW9BZE8/UY3Rej/TK1faRk8Qdg4U5aGouPwblTLdkeUALbqI7XUto+NGgnujxTXXd9dk5Ua6nXVjDh3DfxbU5uSBdEIQU2zpGmjonySUCN1RwoWQApaFJkRQD2MvhgxWfeLhBC+IM0cwCWmQnZYoHwqV0QzcH4kUo1+Ku2h99WPUIgXcvCWZvpgvi1PkZV+8waIxzfGp0+WE1Bl4TQCxUeRcENeYdw2/boAl09YI60WTX4AscttjWK4kVV9Trj9vO3/ARfLHgpiS1qnZDfqF6EQ1H8GbAWXhxJhntW3oOnNj2FalcUYweDArAyRxmK7EWYPHRyyKL1brUHADNg7Vyk7JMSZsB5pDFg+iYS+HOxnHAhh+oor2q1B4X1hbjxkxsxb8s84z79IBaLg4H+/XaxWeFUFOMEx/w6qcDcBbExSeWR4v1x6etPU9QAXDQz8W9w1670OI1NSxT9/bq9agBa7oy+nHeseGRPSAbMeCyKLojLDquByddFX0deKMoMmPCp+2A3/Ce/UphNv9EtA5IMa2YpXlv6iHG/RQSOAcvwAlZFgWyKGvQxYOEyYEIIHNryHTwup/8ijKLAtXs3jj/0cMCyFZ7IxwsjA9bEezXaY5Swj2Jh+DNgkikDFu6kpWb1Vyi48Ua1Xebf1Qme5EmSvzu+mT6WLrirXyQNpu/iUKfBgMcRUIRDfS3/upwW9XOWLRKcFgXm6EwgsHiMosiAvRwvDrkDP3OdghV1DihGRktbxqV3QZTgdnnglQV8WZux+thqAEBZvRrw1TSEL4bhVYSx31HL0Ac+rgdg6ZLk78WgZ8BkBf8sLMfgNdtxoMw/HtLp8cBi+lwlhG5begGPvIYos+RS099zY5gMWE1pMaqKCo3bkcbBJcLhIy8gL+9u1NR+b9yX/9WKpLWnpQ46XEYPHCB0HjD9s5UUBV08ehfE+J4HKjE4XNa41TGhUQVKpqCzj6+bkZXvnN45ZNF6jxqAMQPWzplPwtceeAPP7N6g3i+HjkEKHAPm3+D0g5B5DFhLuiCaba1rxJnrduLj8toWPzcV6fM47K7yZzj0rp6OGAQa+nfW1WaBQ1aM7BfQzBiwMF3+kslc3rgxWVfGInRBjDWbFoApoumMS/DXkkoBdTj6+5UU9WSvxdtdK74XxS2j8o2dcHtd6GwLPSACanDWXJuiKXdtjMtpJgOmNKjV6NySP1CzhplMy+HxGVX5Tin2328RClymLINiUceFmXcrDn0MWJgA7PDW7/DhXx/Hlk8/NE4whCzjeO++2DR6XMCylUHdILvZrEaRBT2bFVUA1tICQXrgZ/Vnf8POA2ZKDpvHnxhFOFp88UL7PILbo32OnrQMAMCjjz6KL76IPJDeHIBVpXUDPPaAMWACCNiunVZ1W/BZJDiCz1qkwAy47PMC9jIcz+yrrt8r45tx5+gNhfD5ArogOrRpHETv/+He1fdq69SKcET4PrxCIM2iLmORpIAA5vWiCnxWoV5M6ZZm9X+n2mfU6FPw54PF8AqBj3f4N9xqYcHuzifDp4Wh4TJgkSooRmQEYBG6IBoZMP99/75vOhb+dob/jiRmZByN6nhRr+fEqjR+sr0Y9y3Zhm+KvsHe6r2xbFqz3IqC8zftxYzdBcZ9kYpwAEC2V+uCGMfPu7BHFywfPRz1FbG5yBfV/HKmoPO86pFGcJVhzQhZVA/AmAFr58wHuhlFQ/BCWRaqvT4I4d/praysw015B42s1wGH27hSCYTPgIkTzIDla/3E19akdknt5vzjWDke2HvMuAqSZvVfvdS/kxMJwCJVQexis0IAqDJlF5U20LUwkuAry+YxYObPJeoxHPuWAw2tnLRQaToCkxUZ846UYlcUV2VlIbAzwnI2IwMWOQDzygqWlAYejM2fRK3Xh2+T+BtaemAp9lW3rLCO3gXRolX9i/bqn/FttGJzdu2pgmtfDZwOB7LTs8MuIyBCB4+fCLueAWs6AGvUTkoqLfWoHrICBy+8F+lhNgm722eMcTmlxP8hZPZYE5AByz/5FCy94i7IpoN6YxNjwA5sWg8AsGWYThBkGXc+Og+zfzErYNmKoF4LPdNsxu+0JXsx4zlRfpdKQ4PRLuO+MCctPivw3Q8kPPT1Q0FdEE8wANMyYF3LHFDcptfWjovu9EzjvvXr10dcTYNpv1adlqMV4fCfkIigtjmt/gyYPagkppACM2A+n8cf7EM9NvhsapBW9a9/Ye/IUVDcLjgzMqBYLHA1MQdWUxMxW2prUfnaa7BJgOJ1GdU9/3DgOL6ttSPTIiHTYsE7pTUocLqNDcJp+s6sFv973DtwOI50Goy8rqcDANItu5Bt/SjodVt2bJSayYA1yM1PSB6vDFhtWWmz6zYuZp/gRabvC6rx2Y4S/PqrX+OmT25q8fPrykux7JV5alDfQnrvla+r/cWVIhbhAIwMmIhjBqy4m7qPX7sltGt/S+jnW1ElG0xdELu4s4y/gwOweZvnYd1xtbtps8fAgGGdbfd8rjkdOADz/+2AetCwILAL4rSdBVhbYw848X1on78Cnb4KY1MRMtQiyC3fIGzawaat9sWOlc31jVhd3WB0azL/CPWDwIlkepoqwgEA5abCHk2d5ET13ZXuBB7NAQ6tbnE7mxMcfJq3B70LokWWUf/553D6nCixh5m7Rif7gMW3AIuublWb9Kvbkc7V6n0e/LWgFJds3o91zQQ/Lx4tw6Wb92NHgwPH9+7Gq/fcBTjVA0+6oj5XNHGy/9GhipD7zCcPU/KP4Ia8QyGfY6Un/oGZV/bi8Q2P44MDH7Toecb7VZza7cTuAwQEGmBHTkbk8TvNjQMzMmCRNhIh/BcCmgnAnB71cVdWFSpOXww5ox69CkNP1BweGeNrtuOkkk4Yftz/eGbOpoAKiX/65f3Yfua5cJl2+kYGLMxH7ahXsxdp6Rn+oEaWISyhh8uKoAxYz3QbHIoCIQT0vhSxzoB5fAp8di1b7PT32DAqNppW4bUCewdLWFGwIqAgR6QMmHmy6nB6eCoxrXQZRq4tR8076oUG4fUXunCnZ0ZVRrs+OADT5gHTc1nbXNshm96ISwvAfJKEekvQNhacAXPXAaZCNhUN/u1NadA+N7fHyIC5wxT6EeYiHFWHgPrigMe9QsBqt6Pi+XmwShJ8ez8D/j4hYJmuNqsxrODW7YeMAhvmIhw1Dq9WcMN/XHJa1GNiz/Tn0C3ttYB1elq6b2gmANN7hjR53hGHjExDVSX+ff90HN62Oarlo8q0hOGVlagL24RzdEcedn+zCnXl5VCEUAPpZgghsPTAUtRpAZW5gmvIPGCmTH2GdoFEPoEx8NHSt+v1B6tisr7gLpXhX9T/fnp4/QFYuiXd+FsRCt7Y9QYW7Fig3m5B0J/K2bIOHICZfhRCK2kLf0UyAND385HG3oR0QRReIIp5DoIJIWDVVhFuR6gootn5auLJ5XNhR8WOmKxLEUCtV4Zb64Zk/hG2pgtipACsqzZG4vgxf+Dc6i5rR79V/933ecuf66gGGiNXMWwIOtiFGwOWJvvgKSzEb776DS5///LIr6VfTGim7Hezmtn2Kj3+g9KNeU3Par+jQT1hPOb0oKbkOBprqmGrVQ8GNqF3QYy8U290hT5mzhrq8+rZgw5i/z5egVu3H45rBcxjDcfgE74WT1ysB2B6F8Sw5cS9tfBqY8RCRHFu4pbdqHXVhnlxwGlxwwsfumd0j/j8gEqIzlpg25uBq9E+1wWFFfigrCZMA+oBrahHc10QjQtbVtNrWkMrXdpdXoyr2YNTijqhf7WCjFNPBaB+HOYqiPoJrnnf6vBEngfM7dDGYpgq6JkzzubfZGXQGLAeaVZ1XiUh0KCfWDf5brX2RHMirDn1D8vw7fYCAED+wRL4jIybdoHG1F3TZ1OzYALCGOCuvqHQVn247ThGPfpFwLikYL295eivTZfgLdc+J4fP+M7cGRlRnTyZX0ENwOzaGDCtK2NjCRam+T9bl0XdFmSLhFpr4GlLSAYsaAxlhXkcl97LwuOBsFggWyxwOsOM89IDMEVAeWk8Kl86N+BhjyKQphVQsECCLFmAusKAZXJsVuPCqt2nGPtR85igzN15+Pyjh3F6dYGRDapOUy+EeAE82rMH9pUd8Ae4Me+C2HwVxJZWzIxGXbma/XI26F3OIry+sZ1aTigT521lqWm3Uy+M4cMnFbU4f9OekG7HwbaUbcF/1szG/K0vAgj85EO6IMqh6xI+H6o8PvRbnYe3iiMHSkedbn+Blyjpn0aDs+UZvcD1aOe+Qceq/xyvxNa6oGlkTEFnN68/Q55u9Z/7BQemzWbATB+q0qK+Bm0LAzAAsnbdTRYCR51uNEIdjN7ceY0/AFNJwgsBS4u7BMnCnwGrqwr9wf3gD8tw1YtrW7bSCIQQWFlZ16Ig5ImNT+COz+8wylK3RtbRQzjz8G44tavgAT9CrU1/L2x5/+SQeWm0m120AGzd0o/9jzW1nmi+vNacxH9yH/D+zyM+XB9UotjcBVHPDNpkGXJtLTaVbgLg33l9X9cYOG9YrMqH6+uM8IOo9kT3Oi8dLcOySjWI8Alh9HWXXOpBLk3LgLmbaLfLHfrtmY+x+riM+qCThiqPD14hogru67w+LK+IEOw04VCtGnyeaAAmtOqP4a7o7dr9W+ze/SAAoKroGGpKTVfktfff1NXZ13a8hts+uy30tQHUW9UT6m4Z3cI+12frh021pgP9xzOBj34NlOaHLFvs9iKvPkwXU1OXsOAiHE9ufDKgGI/Dpb6WedtXtK7KirYRKkJBXW0VMhQvumhxXdaYMQAAiwKcdfA747kW7fN1a59raWMpPq6eDktGadguiO5G7UKALPsze6btxqUo+OTQJ/jTt39ChccXcBDtoVXkdMgK7HrwZi83qp2ZybITm7fcgvr6fDj1Lnyygi1Hq1HrqsWj6x+Fyxe+CERjVS0AoLC4Gl/vV7PCeuBjbo/XqgZggH98BWA6qTZlwP67Ua3mWN4QeTvKVJz+bIT2W1OcPni1F/WkZ8LrdaJLl9BMta52+ZGALojqGDC1C6I5zC41VV5xacG43aZmwQIFZsCEK/ACV7hKhno3V2Gx4LsuxSGP+1emYHnP83HWOe8GFMnyCgU2bR02CZCl0AsEXWxWWLW2KhCmLoiKsSvNLD4GALj20DqjAEdFmnohpNhmw/tds3HzGwvw7HI12xirLogLdy7EvC3zsLFMvbDaVJwit6A42LfHv8W28m3NLueoqwWgBjZNvb6/C6J0QhMU+1o5ntzjUIMJ2efDMacHPgHUat/762sP49J5oUWHLGW7sPR4Kc7Z/xWAwNNBvQuiW1FQ75OhhPlsvV4Pjmj78jebCMD+fqwcvwqqwNosbcOrc7jwbP4n6Ldqa9hCQoDanf93+wrDHjN7l3pxx2oZbm/gcx/eX4Srth4IuK/Eftz4u5vs7/FkPvcL7pqpB2DljvLwPUrYBTG1mXdLPu0KmiyAO/Y4sBQ3A1CvbDVFv1JvDsAg2bDJntaijcInhLGjPrpnF6qLAyfalRWBvaWx6T61td6BO/OP4MLv9oZM8htJfqV6otXobXqC3Gh0On4MZ5QUoNarnliE64K4yx7+pKMpkTNg6ib+4UVXGo9FmwVp/js8gW4RdYWAI3IGLDhzEy4D1pjVCe5af4Dglt0ocXtw9dYDgdkH46Qv+m3R5ZXx9Od7YHebBuwr/oNgOIW10QUrTx32d5f0CQFZv4KsXWV0aBmetVWRB1w7w1x9NF9M0C9kBE9loN+OppLk9F1HMWXnkSbno/uwrAZnb9gd8NqH69TS0FF1yzDRAzBFVn9f4QIwj6cSDfY9AICVr72MVW/8M+DxlZV1OHfjHnxWURv2NQ7XHUaVK8zBXAjUW9XX7Z7ZHRYp9JBQM2Au7t5jem6ddkA1d2kzVWqpDTeGocF08SYoA7bl0DLsPqhWUWxcvx4Ni9WLJV7TZpsuB55kP/zNw3ix+C7cNPQO3NbtYgDAL09dh/9cbIFFANO2LTWWlbQLFB5tfUUNRfCgHtasgoDvb96Webh/9f1wayddihw+A/af747ikXWPYOnBpajw+tDP2oieRTNg8xSguykAMzJgzlqgMnRcoNNZiLq6zahvyDdOclbsKsMN/9iAud+9iPcPvI+b3nox9LME0EkLzDJlj3E9KFwGzGuT4NO+0gav6RgS5jd9sFwNPDPTIs+rl6G4jG6m+r+Kw4vGNPVFfBYbDh58DLljlyMjw44vv/wyZB21R+sgm7oRVmZ0xwF5XUAVRCFJsJn2ry6rus3UWkN/G8EZMNkduH+tcYT+jhtNGdKq7HDFGbQiHIpASUYvuKwZKDZ1VXTKAuk+LxwZmbBIUtgALCegQqU/iK9raDDemV17O1ahGF3VKtPVAMyjfb4Z1nocqVS/m5ZmwG5xD8ayPX/X6vr7Pb/lebyx8w2sL1WDJW8TxzpvCwKwe768B3ctuwueYjtEExMKN9bWwJedg2NlaqAe+YKwdgEEklrdsgUmb96PTeGHtUbNyID5fEYArn8HT362x/jNmGU51OPXyfWhF5L1i6U/31mAU9fmo84RenHA43XCo71GRnB3W5NKr6/JgLyhoQGeoIuj+nQMleIg3tz6CNLc+7Br30HsOf0MuPYG/g5WVTdgUXEV8sOM2X7gzXpcu1Gg5ngTQyA0f/vOPzl7N186FCkTApbA3k9Bx0z9GLj0wFL8ef2fYfeEfs7By6aiDheAvXS0DHvszrA/eKes4LhHoAFdAPi7IEaiaFG6/yTZB3fniXixvCveK6vBcftxCCHgVhTct+dYwA7czCeEf/JOiyVmFWqC7d//BLaVqX2uDzrcWF1d3+TywqdAKMI4CYk4vqMlFBmZXg+q3eoPKvAqiP87EY2t66OsH1C6hOm61OQYsKiCs1ZccXFUA02MpwnughhuDBgAzB96hn+VPgdqtROKI+YsSLSZGJ8H8KgnnR9sPY4F3xzGi1+ZrmI18YENtA/ER5+vju51TLxCGCe1Fi0DZpXVE0RnE5+P0xO6sw3IgEUIwOq02/YoutPo3Rib2tp/u68Qx1yegKuD+hxYLc2AKYoegEXOgAnFC7e7BLLsgsflgr068PehT/a6syG0iisAVDmrIk6obM6AhQvAQhujZ0/Cn6jXhRvDoM8BBoSMAbN76uF1qRcOKhf8G+h2EhpzrofT2gPbkYsF+BVOEWON5WXZjRUFailqizQIXdMmoS7bgiNKBT49xwKr4q8oBgASFPQXRZAV9XX1z8GSXhnQBfGNnW/gq2NfGV0QhaIYV2Jl05Xeb7963/i7wuNFV08+LIodWQ0rjEnBAzJgEtTffRCvt0Z7Hf92VK5dfHJqr7e3JPyFt87aBax0xQer1Z8VBACL6fM3Z8Ae2F8L2dpbvaHI8FqtsGeoXYLsX38N1FZr64n8e88QLkj6aYP2z4fV9ahJV28IiwX1DVvVhy0y1q1bh3f+MAcH1vnncmoI2p9U27rimHV/QBVEALCZfgduLQNWa2xypjL0IQFYrbqE9j66Hd4T8j7MVXEt4Y5r+l2KAo9FXf+/vj+KP3+0E4Ba1CmnoR7/9/h81Hp9kLUP461N/oxEV5vV1FVL4NRq9bGD36wwVu9xa9uiUACtUIg+BsyrLZRmtRtjGqvdLbsQe4/zNPXtKP7ftfkYp0hZRvsi8Z3AmKTyl7fBsT1yFtRRVwvn4B9g8/5DQNWhJua/81/YUVrYjm0NDhzp3LpzFo+2L5BlH6q1Y6xb23bOO74DfRurQs4ZLBab1mL9ftPnrW3TK6vUc69dYWrC+3wu4/wlrYmT0CqPL6B6ZbBFixZh9erAY7Meh9st6gUxSbjh2KyeE9avUPepd+44jKcOFeOIFhx6hcArqw9i7gp/gGbTh8aafqORxtqVN/ozzJ2FFVWDX0N9r5nY68lBxaDXIaSMkABM3+9WONVtyCVHvijPACxFyELgqcMluGrL/rAp7+PaztCrzSvS3E/Xp/hQX1+PgsJCWCAgmfqt7qotwwVfvo6X8v+Lr6rq8XZpNf5w4Hj49QhhDK4VkoSG6sgZktZYVfQtvi7yV6bSJ/aV7Y0Q3tCTxuN/+BY1HxzwX92ORaZXkWETCuoc6klfmnZwU4SA0/QD9jx3WkBXP1mRI55AhqMfULrYQk8SI+3snU4nfKar97GciFlxOKB4POr4mSaKEDQ02QXR/9j6voOMv10+l/FdFpmDfD2QaSaolF+6CHh6AAD/RYdahym70UQXxB6eHnCn+U+AIl87D9QoK5D1AEy7ypgh1CAwTYp8xdUdLgNmPmkzArDAz1EPyI5VrDWqDpotr6jDkDXb0eiTjc/Z6y7DjvxfweeLfNJj/n5OJAMmhAKjtLfsQLbDClt96G9R0aqzOl2FUHw+owuPsZ5mXqfCWQFZyCF97fUxYACwc+tO2LSCBnogZp6ZyaEVNrjf1oDnu3cDTJMA+09CJNR5w5woBWTAPMDLPwT++SMAgB3CuNBg6XoOKofmwpHzU9T0vh9/lf6Ir6VLINKtxvv0etXgUzJd1W/MMlXM6zQmYD4qqxV4AM9ifNbfAcAYf2pJrwg5aZAUwOvyl4OuK1fbnV7zPU5yqvvv2Vlvo6H7nfCmD0Olx4cu2sTAAjZ/BkxR0KBPWgwJcPoDMCEL2DeVwOPWAx6vUV33R04J3SHBF+HKth4wGgGY7DUNV9ICMNNTvVagKke9oNjgc0C2qQGYUBQsvvwa/OLe30N4PCj85T14ar1a8MHXRH+0dMUJqx54ay/8D3sdvNq4LMVigaIVWBJCAoSCwgP5+Pilvxj7oYagbdBlyYQLGVDg338ISEC5/0KkW9tGa8LtYIInYnbXos6Shm/RDQDQo7wo5CnmyZBFuB2bJMFttUDxeuDVArxtpQ3YeFj9zio9PuTYG1DTNQceIYwM2Ptf7zJW0dVqMXohyAAGNKjHdYvFYdzv8/rw1hXXonBgf2Oco0/7DbolCYCAxdpojGn8zeoHwnwAzbMJ/2dkvkAkLGoArv/+djQ44DJVsgQAX4QuasGEEPBkjlQ/TwVQwozX1TXWmnpqvDQOSp3/3CjTbTotNfYrosUZsFjQL8Z8U7AG7oMf46kD8+F01yKvpgy+Md3xwtcvhuxDnFqqPVxAone165eublNFQZnJVWdNQK3HjTVagJbWxEXvaq/cZODcw/k93BU7A+7Tt3VvurY/ChO8rKyqx0vHyo2LuT4h8NmOEnyz339eqtfHcZsybI4w+yyH1wGnqeeUBPX9ezr9EB/V9wYsGZBtvUPGfOndTSud6mvOefB+bNkffghMLM/TEq1DBWB66tipiLAbbqGrZQFYndeL8vJyZHRugFV4kWHqb72hrhGOnGvxr4quAICleb/BH1fdHnY9PgEj5axYLKhp/BC7dz8cdtkTtbKyDo9Kf8FKyT/7uJ6+3n/WWSi6976A5YV2EHZsLjN2fC29sh+sfnUh4PWhIjsHS10jIaQ0I6umX5EckKF+9i5LBlCy3XjuzFUzMf7N8RHXHXwVqtqldmfLCReARfi9Pvvss/jf//4X/RsKIz9/Jg4c/EvI/fvGjceRn14PuOvw/cl2HDj4TNjnNwRd5QvMgPkf85l2Ok6fEy7tuyw0B2BRfF++Ojesdf6ddJZ2ous0VZFrKgMmSzLcNjWLea5Yh77p0YVgdp9sjAGzaCe8mcKOL3E5vvEFzrU0r6DUOJgFtEtvg+kz0q8YNvhkeDxVqK7+FkII1Hm1IOPQSygqehP5DQ70W52Hg9ocQM8cKYFHCBxzeYzxOA21G1BRsQIfHg2t1KW/ov67/fTQpzhSdwSZkoAcxcTFxnrMczMpDpy7qycGf9uojq/y+A9c+kmt01EARfbBWV8fMLGw/hGEO14LIYwDWfBv2Kf44NUC3m2btxmBl35hRLb1MZa96J2LAABf2XxY2K1r4GuYykXXBm3DwqtANJQC1gzAkqZegKjcD5Rsh+JxoFECvPoAGVsOrGm1AIBMWxdjHR7Fher9nSGEBI9HDcA6Kf4TxYrJ/s8if+RD+GbsD7V2AXVZA7EMP8FgqzqZq96l2pJ1PCTbk+71HxJlWYZLGw9mgYJx9bsBIfBUrylwdbkcrs6TUOHxobNeFl2yGhmwep8M/ZqtIkkBGbDG70pQu/Qg7PuOqI+bMmCdFeAypEUMgoqcboh0Czr5XFh8+dX46z2/wj0lpajy+PwBmOmpPhtQ0b2b2jzF4Q9WFAUV3XuguGdvOBvVk8wBdnUbaSoDlq64YdHXof3WuptOIhXJAkXLNB7v0Qs+7SgqQYJY/zJK/vId6upDL0DVacGSOcFlcZmqF2pRZZU1zClLUBVExVuH7daB6FKnZncy3KFdqBpNFxyFJKGrL7Q3yLenDkZh8X64td9CvaLAK6sVLiu9XnTVxwoKqEU4AMz53x+M53ct3QK41eOQEAKSFqhndVKME1if14PXr7sV//zZNMjaOEevlkGpPqcrru/mhWR1osD2N6w6tgrC9F4VoWDW6ln4vtQ/SXEwpxU464ouqE47AwUF/0Bd3dbAapiSFoApCo7bG3D55v1YPfEqAMDycRJKu0ffBfGTijrU9fkdXNmXwC15mu6CGHQBSS72H+szTL9Bn7a/EsIXl2IgzfE4tTFgRz7FrB2PYou8DvLeB3B93hF8Pf5cNHbtDG9Qt/aiOi1r5vMhS3ahh6fWeEy/AGZT3Ojsc8BuOrYe79UHT/zsXryekYO92nEpvYkMWLXXF/FishAC13k/waiafwc+oF+s6VSDSdle2BD5YvBhLQDzKgJFNQ7jffp8jfBe4IOQhJHBBdQeZGmKwFdfNRjZz9LGUthMTdyTWYh5O+/HDWVfwKvtNyThiZwBc6jrOe/4FlTMn4dwmAFLEeb+suGq/ugnrz49AGsmApu5vxo77S5Y0z2wQEa6KQDTx3R5kAkJwIS6HRhWE37uBVkIOD3qVXYpHfBIO1BSvRFbyvTUrYBkjdwHNhrhxoboJ48/ffZVvJLVHc7dVSiavRaKw4vVFbV4s/9+lKZVwqFVKqqrPbHMnOL0Qa53o35FAeD24liPviiXekCxdjd2SHqGone61g3DmgHseMdYhz4/BKAGh3VfFARcYdNPAP+5/Z+Yt2UeXt/5BoDwGbCmTjCqayKPP1KEgv01+43bW+sdIWXXyyuWoeDYv8I+Xz6mduur7yxw7NjrYZcxd0F8UDwJr6xgz95HUF6xwsjMjNqzGRat8qA37STYvU6jslZgBqz5MWAi6CqlTSu1bZ5HSZgOEjVLD8B1wH/1UpZkuNLSkSbc6ARH5LEEioKnD72I0xvVLJFdVowB2HoGLBONeEP6Jb5RAgPtvx4pxeJS9aTbEaYbijk+1DNgbvsurF13Nrbl3YWjR/+BSod6Nd2FLHg8FXi1UN2x5xUth8sVfhC+x6N2m3vl6HGUmA40jrpanLfuc1hkGW5ZRom9BHPWzYFHcePhfi6k16/Cf1f8FQcuvhhyQ+TsGRAYgEFxIsNjQbpdAV49H3h3qv8hLWvjcB6F7PNBCAVyo3/dIZ/66qeBzerBt9HbCKdWgVAPwNYWrcWoRaOwumQNfNp+ywKLkd3QAzCk+TOtDp8j8EKH6Yq0+f7g7p+uxy+FtOEloEtfwJYRMHasse4YhGkiWyEAr0dtq0/yB1iW48V4c8j92FM1AR4tA9ZV9k8c7RwXGFiW9+ilrs/SFUKywo1MWIUX7z7xCGo3qlkKydoYcuDPNJ38Vdud8Hr9bT3FWYgzD+Th7GXLMXbnRvSrdKDWJyNLCw6EZIO7UA2qykzjB2UhoDTUQrar61Ia1cc82uSy5gDMJhw42ZKPtNrw1UTP/m4v3Bf1R0OXbCy4/nYcOmkYahUF1287iCcPqZken6lbt9cKyFpEJikOo7vesWNHcMq+9bD5vKjQAjA9iAjflUjg6hwPenSph9DWL0mA094Q0A1XzYCp2+p3p5yG705Su8BZJCtwaC289Q2oyww96avVAjCbXK+9GmBzu9DbpmBoun97cmsnpBlN7MMVrx1lSjfjdpapkImieFDYowvqTUFFvcWNG61b/CuoOgQBwJVuQ0NjHbzab8EuFHhkBXU+GT4BIwCThQKfZEWNxYKcXH+w17XBn3lzKgJfnqVmfOW0NEh61tt0TuLRehJ4JZtR1+3CLj7A5oTTtgf3rb4PgL+3QYOnAV8e+1K7P7zSTPU7PdLpPBw6/By2bL01KAOmBqmFJdtwz6dPAAB8NhvSOzXi31dY8c1IS0CvkHAO1x7G9C+m46hesMLWF9edfj9er1uKuvrtYZ8TnMGXK/xjJPWLIF0tCurq1OBSET4osgyfRaAhy4dt5dtw0TsXod5Tr87zprfxu9eA3R8jWk4lfJdtnduhPj66vgDLsjthbacsWKq3wqlNXSRsgNcnUN3ogcfnr3q6rvwkHKvshv+V/ALff3utsT49WFj4/Qwc+nYyHKbYwZ2udj2tkqzGhdhIGTBFCNT4fJBF+GETNdW18MpZqPNl4bkV+9Dg8kIIf67opB5VuK67F6dLR6G41d9j8BATvajT9oPb4WpsNAKw4pJ34L5Ohm+ggMfl/205ZAU5HoEcH1D7sVp5uaSxBI2ZP0CRdh721/4rsdFaiFf2PgW7rI2zhCXiGLAqVxW6WgT6/caDgh5bEA4DsBThMh34wx1jjmtzoOgZsGg+nH3Hd0OGFVbIRrljAEjX5/WCrdk5LMas34XHCtQrZbYcGcJWDZezBuP/cQZQU4C0Ht8g+9QnI875pLjlgGDE4XWEzNuztzHMVUBZgZBl1HTNwcKrb0LDGrWMrrfCidv3HMX80eNx79Bn4dWyB42NgVcJHfkV8BSGP8EUQsB9+DCceXmoW34EFQvUq86SUFCXpY6MFVIafIoPtV6fscPpo6XmnZYMHCz7AmXr1oSs27mrEg2rClH3+ZGA1wOAl/Nexhs731CrUQLIDnPFNNzPNb8iH5UZgQFmcGr73zv/jRs+vgF7XOrJ+5YGB27MO4TC+kL8ft3v4VW8qEdX3Cm9i38VhfZ/t2YEru+JQ8X4c1C3VHMXxFOxD17ZjeLit5Gf/ys4ZAWdGxtw5dcfYljBHliULqjt9ziWV3mMDFiJ2+sfkxhFBsztqEJVd/UzP1C0Ho66/Xgt7TlkuExjdrT1Ca+Cxk2lcGwth69OmzRYy4Blww4rZHiFwMHyhoAA7oG9x/DEtu8wreh9XFq1QXufMhR9XjO9CiL8v093UHeGDw8tR2HZFyFFNCxCxuBif7da/dvuWeG/WlZesQIOqCfrTmRCktKwr9EFCIHuRb/Fiq+vDttL0+NWf282eAPmkNq3YS1yt69Dr5pyvPfV39HgbcDJxQpyrAI9bALdrAr+Wvpf+IpL4NodOv7ELKA7pOKETZaQ5hJqewrUiw6Hag/B41JP1p1aAAYAcoOpqp0xWF3z9bPAp7MA+PvRA/7xT58e/hQAcPz4DvQd8T4e6OuCRVhg0QJwvY+/OQADYARyanvN3bj0bnhSSAYsSz+5ze4HWNMDuuA21KnjYvSMbr59OyqLtgEA3PAHYHuH9EN1pgP5lpvh8ai/0y6mAMxaMCXgNSu79QQAo8udF2kQIg3HduXDVqmeNEiSgAeBv/kMj/+CTW3pYTgdDnRyWdF91yAMrS5AJ+2K+KXrPsVNKzaq708LwDo1rMDhf70GSYiA8tD77RV4ZWMpDj69Bj5F4AutwqZP0rIjitcIYi5qWIx70h9Dz/JNAe3ylpWjdumHxu3iPv0CHt/vcBlBrL2Tv/KAGoCpf1sUJ/SKgRu//xZpPg9y6mtQZtdOnLXv3pxZxfEt8Drr0d0qcElXH86fVICH77gduzvXwWdvwN9/dhuqGk2ZWosFQgvAJm5djQnfqlN1SLBACCuO5b6MZ8eEnvTWQS08IVnU/YMlTcBaV445/T3I7Xt+QFfYDEVBT1sTAZjwoNqXaaSFj/f2vx/ZV438wX1QbbrQ5cjojPJe/Y3bYt3L0IfmuJz1cGlBq1MS8MoKKrXgrYtDz4AJyJIFPx40AP89a7CxnkwRfv8rMmyAlg2zSDJsngJk1n8Gn1X9brySDYrVf87gs/k/L/N4t23Fq5EhCePqy2MbHsOV7/uLTQH+/YH/KGgxuuCq61N/Y6VWG7b0vAwA0M9diQtOVXtF5J2Zi1srHTjucKPOqWejRMBJ//aK7dhQsgH/26NNTaGd+xwU6/HQ5vdRYT8W8hkEdEEEIJf7xxdlar/BAekCMCrE+qAoMtaNrsT7Fx3Hi1tfRKWzEjsqdmDz5s148sknUV9fD3z+IPDOnSGvF87m0s2YXTjbOK+SZTfmbX7OGMsLAG6nHfWdvLApMvK1idnrTUGw0skCtyxjzIK1+OV7eQCAWrken8h94euvwDHQAZ/N9F1q72dkvfp+7abP0aGNx4RQ1GkL4L+QH6zeJxs9ecIly1evW4UNmRI2WxXs3noL/rbkY3UuQ0nCW9fcgZxM9btMkzxwHDgE2LLgqM8J+F71MW8Lji3CmPp8o6R/RcVKAICl36nwmKag8dQW4ukDL6ufi1uGp9iO+g924aivEJMHD4QPQHF6nTGNhF4UCZIV7qBhGbJQCyBVOavQzaYgTQIwOPzF8bJ6J5bvbL4YSFvUoQIwp2lyxnCp2wJtksaWBGCFDgd8khaAmU7t9cyxXmFxRc+J+LznBc2v0AZY0tzIFNqVhfI9sHVRM2dF9iIcrDmI2WtnB1zFKn1+M4of3WDcnrpiKv629W9webxwl6pXlircYQ56Phnfb77euG0axmFosPkPrp7ywCqI1W/tRfkreRCKQNXxQmMCUwComP8CDl/1YxTcehu8FfXwVWqFDYSCOu0EQSANR5wOjF2/CyXaFWOlWr161ZiWiaODa7DHfn/oFR7tpt1pN90VtIx2JT8rTAAW0v306Hrc/vnt+HpAaElZg9eFbTvV7onlvsDP4Q+rZ+Oz/R9hR8UObMVZAIC3CwNP7G598kX86c5ZAfd9Xd2A7+sD19UQ0M3QBq/sf7xRVpBduwoAkO2oR5Y3C5AsKHTLRgZMAdRsjdcVWuzDUQ3Haz+EKPWPU/iydCkeHHUfZAn42crp2Lv7t7jMuhUnNfq7JSpObc6kBvXk3bGtHKXPqGW+LcICV1qaFoD54FUEfvziOry/brsx39n+RhcKy9SDWmdZm6fLnAFzOWAVPtSgh/GaxxrK8Ja4AROFOv3CZO9C5B2Yh0bTyWEPby2Kv7kYl6/6OVZseh6A2s14oDiKbLe/RLrL54JbO9FwIwuSZMU+uxOTq9TvOzut1tgmzFXGfFoGLNe+B13WP2/cX3GsAABg83nR919fwl54BBP2CvTTTgrTTb8fuUY90YjUV18pVgsWQEiQFBesioSjA36An+S+Aq+2LVz30XVwaSW5HY4COLUxGUpDaLep4Is9/8j7h9H9EAAKtKvUeiCWqaTjUHY/NKSfqQZg2l7Pqg3AltNMJ6aQsL7wC0zv5UK2RQBhxrpJWql//SJA/fpXUWvTAoIufYH0ztjvKINLO7FoqFcvQHi13+8hb6ERCCimsT0+7YQ0TbhRtnkVLvuuD3K8neHOLoQ3oxrnHjsH11RPwrI9f0eGLLD6bHVfq3eh9CIdikgHhAgY7+q2FOEPa3+PyX8bg+HHFWR6/PuLzKrtOFpTjSFlnXCK8yy4DzagoZO/W6SxnKlceq/Bw/CDWie2mkrxC0j4OOMrrM75DnkNDuzQCqb4oM+B5O/2KgDYvenotOFM9KvyV4it+/gjlMyZY9yuyulm/K1/Sv4LgBactac7BlRkwmuTjAmNbXKD0YXtqKJmhCUIrD9yCJU9exrjmIxrHK464LWL8f57NxrHwgyLF6OO/xm/HTIHDdUVcGZkwWXaxyqSBQcxFE/iMXR22dHVXqu2SLJAka34plcnlEgDQz5DIwMG9bvJGd6AtP1rsRJX4DXpV3BlTzKW7aYoQYXnA8mShEanf9vZdFboMrWmn+OQAi98Hv/x0bXjoFFR0uGT8X3dSQAAa7qAtbPLmHw7Wy/QoCg4mJ6JBqsFr43wV3xzO9zwidAeGBWZGbDox6s0ge6lf0Tn+k+Nx70WG0w1M+C2uoyCDuYuiL4jv8VD/fy/wY93vY8RX3lRW+YfK2NUaNa2UaczHZu3+rtUG2PAMoZD0S5WWGwCR7VsXEWPwTgoC9y+eAvGPPYFAOD5L/bj5n/6zzf0izLlWi8DfZxRdeZIfCz9FD/ZXhHQtV4IEZABa7RkYlon/3yWGdpvsJtVYCdGQ4YFQqgX7Ir6aK91WD2G+RQf9u5dCUCgpqYGr+V0xQfZ/gszTdlTvQcKFGwp2wKXz4U1X58Ja9kCPLDGP85uXY/D+GBSMY5bfNiVoWZ+d6eNMB5v6GxDo8sHb25PLMvRxi3VFCHDa0F6F22cnxaA3dTYExmlnwW0wSn7v+gXb52qfX7CKBZlCxOAyfX1qDKNha5ZsTxwgeI83LBtGp7u2RUv5Hjx/QAZ/Xc9hM92f4DtJztR3sOCHtoVNqvwoa7Rg8xxd8NqPxllhWUIJiQbhp18FD5Zgddbi7q6zejk8KHf6VnGOQEA5HzxG1xcr03/4RM4uPBd7Pv+A/woT+2NUGKzQZYEGiz6+GL9vVsDK7RCzWo5fA64ZJdxPO3Uwxl22pCfL/4c9/zvWyMDmUo6VAD2Ual/xxSuC+LBGvXxg9JpOIRTmu2CCACru5yMiuzuIQGYLPQMWBq8QuDukc9g2sgnm19hmpaWNcokNQLaRNGN3kb88ds/4rPDn2FP1R71SpSsQKnXBz2r76m4ej/yD6/EnKefQcarZwM730eVJ7TbR/2htahv9J+MQwgc7mxBXUhfa7Uxrg1VkO1q327FVKZccXix8IEZeHP2/cZ9O+rt+Cb3h5A69YRc609TW4SCWi0DBikNtR4PnIpAmXZS2VVRr9Y3pGeY1u+DN/0HcGZfAkUo8KACQvLhQ1nd8XjThmJfUKlhASskIcMabv6d4K9+8xshy6gfh0B+RT58sgti57uQa9UreVbt89jVeTgA4KHZWzH3XzLsFR4I2YJ0xYNOlsCfVlnP3vhq1EQoAGbhFbyOe1Dm8YZMBWAuQ++DDQ6P6UqhIx/daj4CAGQ3FKOTWz1IlnkVowgHABRVHgee6gtseUP7LFTOsk3YcHIVqg7/B28eL0NBYz0+cHfHMulq/K7fANRYJDh9tQCALO1fIQR8K/8Fm1QIuT50bJNP8mkZsAbY4INHCLh9Cv7vm4uAucPhlBXIAkivV7vj6AFYg3kMmNOB7MoqlMB/sn+4Ts1uXoivkCbcGIJjcHkb4TT9bh84uggA8FGXznhk3xvYc/BvgFyPX8uBv7Mqt3/7cyITCqy4pfhjPHz0ZXyEn0LAP1+MW1EAITD9649gr1G7gT147A30WvcsoA8MPloAQA3AXJnpsBcfQ3FPCSdpv/80CejSaMOOQb3hOn4cfz9ShoFrtqPep17Vc5aWoL6yAm8c2oRxe47DByvSFAsk4Uaaz4LDQ07Flm4jUS9lQXGqn5d+EdXpKIBT6xYnN6gXPJSAIvAwrqT4ALya9wqO1PkzxVdv2YMNtXajqlSDNQ1PS4/haekxNQDTuqFZtN+3z2rKtEg2fL59Ns7MUnBhF2/YAAzaIGu9FP2UsgzMOONP6kPZfVGS2RU3OHfgkd5qhspuL4FNTsPw8rOxafP3kAGItNDTa/0kRrZlYdNHB9HX2RcD6gZhV+4/ceTU9+GwOHFT1eXwdCrFeZ5tqNZOwvQAbJt0Fr6wqZPp5nQpwqRsdX/hsu3H2ryPMdjeA41ds5DhNRUdUdJwy2k7cGW3rjiv7/VQHCMw/FBo92JzFtNtcaOXRy3FnymAzi4HIEkYUJGFGkcl6txu9Her+zg9A3asstbIgHksGShxdoGkWNDFof6+3y2txgeZgWPuqk0BWLrbidv694Ce25eEBSOPdMXl3/eF1wYoWhfE20vex8laOWd9e7EoMjYcLcFXl10Kn0XCBQPWw1I6EQe+WwtolSTXOQuRru/OBFChqD0lyixFeHnq71HZ2b+vFhYLFuDX2CONhD3TH6xaJAuE14Ia4R9TaFarZcCs2vbTqbcbaXIlDuEHAACbFkoMTFMw1BK5Zh6gBoGm4ZO4Ise/naZ38aBzPwfqTNVxleJ+GL7bP45K8tTCJZku1mrFZly9s3F49CD8u0i9oCELDwaWFEARChosodvsQXsVlLSQu7EtKwfGN6D1FhWWTsbjPskGxTT2Z0Y/F67pph3fTBmw93ErCmy5xq8/x56GAVVZOPzlEvxs4fcYOvszONPV7zDDok247EtH/m7T/H1aF0QzYbXiqPYb1KcwOFznD1C/PVSJg9XFqHRWQq6rMy5s6+c/+kUgoV3IPuqx4s/fPIJFm9UpFaoqvsfgSUeMOco2dh6JzdmnGuvXM2By+kl4RvozvsM5aPApUGQZ6VqDqi3qa7rrNuHQwIPo0esYhBB4sUc3/Ll3z4CLtvO+nh/yHgGg2K52PX/1/d/hzSduxzEMwZCszgEXc490qwUA7LEBjdox3RyA/f6nnfHOwSVaw9XPrHupHeleCzK7qfvpaiUTXVzpOO/0QnT2rYWjthpFNisckoTsg7uwcsRQCAAHhgxTPzehGD09git0eoqOY//Z52D/hx8Y9xX+bk7AMijfDQGg1pZtfAcvjuyMOduewNrcWth8xeihZWBt8MHbww1LJ3V/vKkiTJdRyYr1o69Dl5O6oLJqDYSQceY+O3r5ViBLq3C4a+1xzO9zBuaPudB42vvnrkGnG33oX6UG+d9lahlE/dxI0s91rah312FIaRbO2aXuB2QhGxcOtVNi9MzxoqAydChOTc7f0Gn43/GXQ8U42sRcmG1RhwjACusLcdMnN+PNw/5xRMsKQ/uT6jPQA8Bf8fuoPpx6KQNZTrX7lcUUgPmMkyAbyk2VYsL11zVXT9Szb41SFhb1vwaKoxa2TgUAgIKKYli1frNOtwP2b4tx/Pff4FBnC/Z2sRgnyA7FiwJHCXYMOwV/HD4TrjXPwYN0BDuoHDMmoVbbBtx8fmfcXRxYbSZNUp/bYAVmv5OHon9uh1zj39C9Wunkhip/V6f/DTkNz9/+c3S9/GHI1eZywja40/QxBDY4tCxNtVs9YuZADTgaOqs/RIu3E5QGD2r7/Qn2HlPg8tZhS9XVOH76/3BEqFfcavs/gVt2VmFbvn+yasXWA4CAxxFavlQRAg6vAwV16ueKmoKQZQBgc9lm3P757Xj6iytQUbbcmADUqm0ZH/a5VL0tgEFVAHwCT367BKs3Tw3I1AS8tlVCudQPq6XLUOXxhczrUmcac+JDGhpcFdi2Tp3nqHfDR7BqV8y62mvQyat+jnudNjxywD/eoKhWqx62ZSGK03uj/4Vf49PyWrgbj+GAdBqKG/fjibW/wdXvnYe9ipp12thF/detbcOdfOrJoVxVh/T0F1Gd+SFEmAIYh3MOo7yTC521LojBZXF3NDggC4GBWpfGTloAVu+TUWhXD6IWlwPd9hajFAOM5zX69O7A6TgJBbBChlW44RQCQ44fQt/y47iqQv2+7RYL7untRvGxl3G952WcZA3sqmCel8qFLFR6HOjm3I2rz3wE70h34CBONQJhpyzDpsiwQMDjqcAmTPDPWWQvw2dl1Sg+qgY0NtkLV1Y6XFWVcGQAA/6fvbcMkKQ8975/d1W1+7jbzrq7Oy6Lu2sIQYIkQHAnQvCEBIJDcGdx2EUWVlh3352d2XHtae+q90PVdHXvzC7kPDnPm/Mcri8zXXrXbZf/L0uPB0xj+qps9mR7eeXzd7hjpx4e8fg7n/HS7ffwz4vO4olfncdztU00W4fQgU9XwNCwJgStft1iGFEUkp1dCDRdAVM1IpG9BGR9n0oGdQ9KQtsHhMMQsh8J+PBENO7/3LSuCxJsC0VTHrAai8ngJaQUOIBi7BdxwyoOpkAFsFsZx+60MNP6HgHN8MLsbWlly+6NLPKPZo9NV+KSrlzmq7qg+a3DgD8PNXLmrt+yvuRI3v9gvg7W0IfXOmEoERt926kvnky4bABW1cHFroe4PvcUYkQBjR3TrucC+90cVmd4CdPa/4F8AgCDx9VybCCOUEEVbczdXs6N7ddz1YaD8Xab+2FMSuCxxPDZ9O/MshdS1pApsFrVGCU2s70REcMVS9KVUCnrSiBpcTQEs2oHcVb0BJqfu5tjG75AAxrD+jzasXV1SgGTtDhNUV15lJM64PvlG3ZzS8nAjPc2+wKp/6VEguPy/PQoYJaE2Z64AmFLPZ5uBceyflQYAAE9Bj5rtI2QYexSJcEJ/d8D4OMn7kvl6iUQ2AyhVFUVbl9/Ltmddt70b+V3Tz+KZBhSrLEIqpBSPCw7bJZKEEg02ttoFSaPTScThEN/lga4CkO0oK8FuyGs/6YgwmnlBwZj6K50YJejSEqPV9Hsj4LxTZTPraUrTclXhZyR8C2JTuJKkP7H7qRoUgNJOVO5WtmhW+tjJV2c/s4TBpqrxHTD0t9DVnukT0VRk2wmRLnBllXJ9No0y9aMOmmVNpW8nm9J84B9wDy+Y1rqt5KU6HT72LZsCZ9vNHJebZmRL5q1m5ChXGqAJvpSwCR2Gh6whNEOJW8xnsHXo2kam+u7SBTfzuxXZ7Nt3oW0LNL34WKLoSQb3xRDQtHiKFqcDhz0X7uS+YtPZ/6G+/FXdTHUtZrRrGWxMjjj/T0gHAlF95R+zWzO2FnEMY98hcU4FzRQTxd0WnlcXMGq/IHsXWl69tKNrL+7/nc6GE/O5XzW3MGaJl0BranfCcBJS91MeW09N4gH+KV4ms2us/VnJOLEjbpzO7ShDG8eSm6blVrJ9OBqQmF5w3dMXL6QvGZ9n49JMra4wObTZaT3GgczrMY0oHz/1B0cXlrMnLJiAusW0O1w8PFIc31LcnfKI95jePtkXT2vLN3N7vV6btWSRZ+a32ooNF0LFqDFYqBphIWgJf8Wyhxm/lkPObo+JsuI1rCIBCGnAxB86vuOTza81+v6hFLIVnsZqwqtNDd9RgxLqlSTp3s7ke44C15az7v2I/jEoStgGhqPiau4xfMnLIbMssVqyJHSvvu7QjDcypzleQzepfdTUjUVMKvBlOwKXPDJJF7f/DoaGhoy3d5jUYUdWWniL3XNmWV4/gfQ/woFLMuRxcbWDShx0736dmdOr+u0tJAXFTmtjsP+SUnGsUcivRSwHjACTVj5sMkMFYqsa+HFxbt4/vtdqWM+zPNxY0deJiZw3YBreKFpO5ds9XLqdg+NX65FNpSZxoa9JL5+jSLbGZwyzcWZU1wkanazeNliSMh0aRLryobwRMlJaK1bUfsACO9QPJwjXjF/GxN9xT7esp5eWBPw8nyllZcTa/njSjMkq7s+rSZRLARNmwnYt3FTw18psp+FRWxNnZZzzTBFt6LQ0dnO1TufYe22vxp9oZ8POv38lcvZqA0j3Goyke5wA3u0Yo4t+xVfus2QNYCzl18KgCp5ibqmoQmFyLJnU+edBjNZdstNPHPRDM567TTanngLrc0ci1iaZbSmS7f0bgnHqQ/vImA3hBBjnOMpq6cBuKKGCWOjJNxAd1LV84z2obSIA1Qg1BXkya+389XmJiqu/4DGkGm6jWIj2N5I3DAMWGO7mOiPIg0O4E5IFHr04y1JC4ObPuKInXoS9Z40wXipbxgAd2yrZcWi57lN3MvjYjjW6AZcspUaw+ukyLpAHDY2R3fCCJ3bvokrCrI5pWLDfuFeNcmVCkHUEBS69qa+sz4WJ6GpFEZ1gaDHA/Z9Rzer1uvhsUqoG6cSpj7NAxZOmIik/dhCPQV8xkFs9amc8MFznPjRcxTFdIXf7pOosulj0l9dRUyFx5t0kTGBQiJNuAnjYO2mBbRrrSnrYqfmTXnAtoW6sSWjOJ3trLKO5GFxLRsc1QB0f/Yui77+GlmLkD24DUs8RtRu522RR7PPTa7RhkBYSVlxY8lkKlfqz1n5XDPjUNqcdjRgWyLLaJMTS0JC00BWTQVs7KTX+baumRnr/DzGlTyuXQ5CY1rRLACCa5dx2dN3Q6MJIiKAhs5mVOA9t4sOh8Q4sc0U8LQENkmk8kASaYiNkiaZhnlrFhqQlO04jPIAPeh3GrDAeSWPtVvpeP8Dur/7jqzoOo7zx5BIYE9G2XX/STz4nl4vq92ie7wbEyG223UPSA86Xk3DBhYM7cf6okrqfdmELQqqpbexKCkL7HYjTMUYN4tDFyR3W4oJdEkIzVxcR9QZ3yXlp47JhnK4gSF8wwyG7ypipruG6dkWZIsTl5bP8G2moJRVpferw4gHy7IVpoSJnn54cdWdGX0YsrXikFtIqEn6RRbq6G1CkG+vAGBo2M6UGafzRb6CJOtrwS2FdAVM05AbW2kI6/2lJAWu7IU6eiEQTlMadheaxgpJTRKPJVLXpYdRAuQoKuUtNpSwjcIOXdnSjL3eGm0nZMCOJyVMHibF0GJhdnf7cEZ9nNI8nSVM4m3lDMZKEzlz5RhKf9jOkHHfMiiq5zk6IiFUSUoZ+4SabniLs6ZoE7uooC9q30cBe4NTsHrMqAZFtuJMt/8cgDVrQCAvRKC/EeKZJubYfTFkq0rQYXqcJIdKMr1sgehkeHY9cr5K9sh22EdHaTCMeiMO24CntNt4o0R5faaX0u7oWxj0apaUbKHaJcZs8jNzRXnq/A6rnY88zox7HKlPML8lJmx0ob9TVeO4Irn87czfsMQwPk4QG+iZCioSrWRhs8KI0tcJyCpCWNH2qfsnqQmSNomJleCTVdpdRrqA105T2fO8sbuZ7jT+4ph8Ocs7d4BmKgs9bU1INtwEyaaZZnIJly3E1r0Yb2wZrWTx21HXke1vZo29OrPfjL0zrOiGm7yWvTg2bSMUjaNJ+YRds1PXNif1uRYUHsIPPpY6HknLTz277W004SDmnMAdmzdw+vzTWfvtu+xpq8EW07BJLjTTiUub0k/PYQ+FSBieIqLF9O/ozxHfFdItXMhGLpdVyMgxiRlLPuXMN/9qtEXC7kmkqnTIliQ5HeYLuhNdDN8umPe5zu8+mHMiv7/kttT56N4EkXYDGMM4dvHzP3DdG2toDMYQNh/NWab8mpRluhctYsudl7LhurNAU+mSJFQlm2ZHfwosKr/JD1PWbqWq1oUSr6VNGcAt3AuRbXiqVtAwfT5/LnqeL4SZi2cz5MCo10TO/q6ljqW209hgH8G2gmEEumrYeMdtxLObaBU5dAiPMfa98x+3Gvt6WJKIZtTxkwlG0mRITQ9BbA43I6kSN+w9hz2UoAFlVpUHlz+kj7FrOiH/CYS9R6Zutf6UOpb/QfQ/q7X/RXJZXLjshb02mwNREomWcN8IOVKaxypic5KUFd1Cn+4BS2M+W4NpCtjuDu56ay03v23m2FjToEB7rIcxI2dlVZPMoDl7mTSzgVjVD9Tb9Xc3tuxFJOsIJkzmX7t6IZ8+/wTzvilk/AbTQvp5UA/jKNVMRQMgROYm/07t2wApgauHctqjHPltAc02XQBY5drGPxteo0VpB6Bj0zYkZM6vHol2XznRv02kLl8mVhDk1mHns9L5MXtDurVXzTb7VJEsDOxazWm1z6EZ3pweD9heey7fiFnc4b6YzWnFSL/fuxvXJl2Ia7WaCc9gxp4nZVMxK1x8K4qRDJ0fNeqw7G1iyrJubttTQKD2XBLdpmK+J2BazB8yFvqngb9wrvc6ZuRbsCQtRJNRQkkLSSFjSyTwHPs3Ng2fzOKaHfyeS3lYu4zt8TgzF28geFcJ2tcPpp65RB6e0eZwVxd3fbCBVevv5KzBr9AcCac29y68VHc8RWWVnifkStZxUFmIETMW4R2sUZFlxnHXdr/AUmkzeYqgJhjmb7XTqY0cz9iaa8ht72B3OMoj2fqGv1bSLY4eZ3nK6GDT9LkQNgSRKdoCFjxxMJGdq1ktO5FU6JIz54Xe56DKrlQIIsClU5+lePpCvghMoCEaZ0soxh8rzucLeS6RjuFM97s4pf5DCrNrCf4iF6vcgVMJsZci/JruvWrY+JX+ze2dVLGV+7meN8XJhG0Wujx+nGmKqtVprmu36GZP0st67+X8hSs5R7xCEDMUKoKDycHleKjBqukCawgXIUOo+rC+nuPFq4wd9x57KAPgG28RYWysveMvTHvgHmxD4yyfMYVCuZaQFODNwrFsLi0my6bxVy5ngzIVb8j0FqXDYLvbngOgKbuAmND7vD7ppCkGRXvCBEZ30uk2vQRvbvmOsq4ydtCPdWKE3uc2/duzdm3HEQ0jf7/QlEc1jYW7tvPOzn4Ean30tyV5Y9BjdObeTH6rjdnffUrdM/fR0rieIbtUujEZ5eqS/oQMD7uCAlhQhYLXMBApWcfRIB/CmeINVGFla1yi7tpr2X3e+Yxy72aIJ0Cl1MTtXz2Ee2KUJdU6mmW74kUDdnQ2E25SCXRaSBj8t2t3C+2GB2lx5RA6A1lE02rc9VBW+QbGT3gbi8XcP8Jec9yDCKxxJ8uYwIucjTcRob8tiWw117PQVDQEd4k7+au4kpG2oRxZuBd1ymo685YiucOoShMaAkvVIKxxK3/nUja6dQExx1qILWYKDkmXj887x9Kxd1dKoI7mrEbJX4aKYKG1g6QATYhUaOfHuWWEFYWtbglF0ff9fqKUguZGEIKHD55Fu12fO7IqiFvbmNhyNZZEIztzs7j8qTsoqv2OuhxTAJPVJJ9ueQVnl15I1R5NC1vS4KbCCMcO1g1bg7p1hbTHAxZQWzg48BaSFMcbC2MXOj+RbUme+9t5fBjvxyHbjmNsfDAPid/wmu1YNMCNG6GoiGExBjo2gKbhSIRAMj1gyTQE2jYlyEJ5LmvFqF5jK2txOvdRwLqFB6snnvqtSDb8CcF3TGEVow4IbNVOgHRWn0wTc2yGAkaxyfvsOTFEWqi6JrpAlrmOB3mbE7HkZobaxhQLTsIoJLE4Ekho2OIWQj2h9T3PdewHYU/YU61PWDVGbPOhyXp7JLUbrApPB7ycId7gVU6jEy+yUZx5X8WzCw+qlmTbugc4eJWujNV58ijK/wev2O4kouj9F8bBb3mQdzieLFuMae4Es329jbI2NcKiwkmss81mlCNJrtquf7NTL+vwxy3fZVy/YuAfOLt/I/33uIkZBhAZwdyluSQlK06C5NBEM+Y69MiwjuGEZCdbHOXscGfycXtMoayrjLDQFTBXKMgxn77MW54b6cieTDD7fAbuykFOCuKGoiVpKklFpl+tRlGzxp6amtTzfln7AsNduha9OWIjKfvZfOt11KuNXP2WyshVe1Ht+6QCJFVi4TBxw/PoCjsRkoxqtRMRTtzGnlgcciCHNMpO30v+ICMUU+3GlaWyk0re4GSEVUUWFpJIxLDQHhec2+mgeorO67ZWDsl4t7O+Eymiv7fHkzescwMT2n4gkUjgPvyPFBafkLpelSTe/fo7TrvxMeYdfRmhZJTL83PQhI1meymHe+MUWzWOaLIwfVU2czaezWPyTWwTA+iW86ku2EFXoQ4ohDAVRU/cAJlJ87reot1CKGxFGryOnQPqsTftpTPrfSJj9XkRFG5UKUa3LdP4YI9ZyG88kdKgPtaLvOleT5musJlqYVOVVAhiTsTDHofCdeIhljCZUqtKRzyGQFBt5ONqRoTWkOBWbAeA7f9PpP8VChhAQilll/PoXscnJhf1cbXuAXNG+kb4s6QJLZok0WbNRiaRoYCF02o/eGOmVaH7qzpuU+o41rI8dcyahv7Ww7yihhWx3WJnMwPZTRmTfVsZ7aknKQfYvnUptc1v8fQuE7L7k2XbUIIRXBELFfXmQlrUobu35/EmL2rmwu0mk2HUGaFbLkzhtqS7iLkRhdwOK82KPl1Csr7pfeLfQEz5DGnp71g55wIuHDmc9ckBNNgDfCkO5ib3nfwt+xwWak6+aniVoyrvoMWSnXq2O2ZjuSXJvJIiurAjaUk86H2+zQizsmtx1tebIAIDnxd0u/R2FFoyGZwq+7Ekc+hXb5osP+83JCUcFXbrOVzucAhbAvLW7aY5YKHeY0FDJuKcSFTuI2gfqJNKsVsSDGsbSmf3Tua3DNLfKSBEjNayKhbu0cMAuwVYQ0txRTZQPfV9mr96MPWcr+TxGc9NGB6nXOU7fkguoyvaST56KEMnPrLse8jOriGImzzZ9Kj1G72WMtsm0FR+u+Mp0PTiiKXBPeysqefW0+/i1YIzADj/w9dBjRIzvq3HairZqlLPq27UldaIsYHd28/DooJdbN3yCWd+XMgx33hpUTrQ6ObzgZ+zo/8bHNUuKAiVgdBREHvyNDaix8i/UnAY1j0fENc0WqwBVsgDqLW18otvb+ShTffxz4nn8FdxJZH+LoZkbaSeQipVXVEP7dA9ZgX1tYhkJ3XCZNLdDjeq1cZ7YjJHFBfiaPURi5sb7yccQ4t9MouEDvucnlsWwU5NgZtBFTYKjILHYc3J+fU6wMo3QYkBic+JYqUOPdTEHRA8K47mm4GlrC/J5dshc3hVnEHSqrB4yFgKGvZw/PIga+UJfCNm8aTvsowx9rZ9mPrfEtEBdXaU9k8dW9Ac4NEuib2lDsQEC+nSo2irI29ikHb8tMrZdONEtenMv90T4OHzbmRb1PTVb/n8DRKv3kRtZx6T12VzVK4BQmEr5vDvCxi8dRvayvXMWRTitpdUCnaa3rPv+g2j2xANFU3GktD3IEnV1+Re58F87jk/df1GA8Yq6dO4h9v4tXgcp0hg3xNncXYVNdYCiqP1xCULd1VdzA/1FopXBDnmmyK9NhYQaB9No1UXcBu9WQi7q8/CuKIHtcuir4E2h5s2r7l/qYT43rqRB8R1zBfH4I+1c1GeSkg2DVEyGt3Vw1K/7W7TG7931GPEZ/2TYbsl/LlFDD34LZpHulgo5rLWyE+xK26cxr5klRxoFiuSFGea4zMmuJIEEl4uWnM3U5eUgRDIiTrkpO43fntoP64dofBhvr7m6h0akmwgvElx5tXr82I1o1FK4miSTHVwCP0UuDyniaODz1Dv92CPxTh04Rsc8vXHnLHgfQ76YQlRq53Fe83k/vw2kw/ZDd0hJytGsbM/0xIn4ok7waorxVN8yxjvWMTELZ+wYUoBsgH5LltVSqY2UD1vN66Em7jN7KsfshO4jlxP9ThdCezGiZNuXIEQtsJkKopDtnoIWHUh2heysJ5M41MP+dROtgtdyZWS6flacTOvWtiwJ+FRcQ1/EDfjwZlhCE2nx8UVWF0mj06P/pANL3Uy3xQQ/YM7iVqcHG0r4HhNBwJJWF00izyWMQHN1ltMimj6N6qqQFITWBMKEV+mq8xuj/Y5l+M46dGkqqdtpnxuLRGbfm+e6CBgkbGg8+93xIncze3MV84GTeOYTYUZz+rCi5oMsXb928QMBOGgy4dF3k1UCKJGCF1IuAkLFw3GXlialJjoMcYprRSGV+smKRT+Li6jDIWKbgOASdaF3Y6wCRcP4CrfgPBBWbMlVY/NokmUNjmICicuusmlkZY0BQxgI7oAfkv15TRlZ4KyjBga52SPRKukz52Eon+XLZFH0qg5WdA5kry2AhKGAiaTICErzPmhlNM+z6O+xdzX1gYLmBMwEAaFIOqcyIcTBtEthelXZyGqyAiH/hxfpy4D1UfjtMz/gLii0VT2PIsHTAEg5vEQwYnXCNEf1OhHica43PsP5k/XPUWaGier/wzu5VbeFKfgzB6MJyz4PTdznniZ7uYOPjl6Ei8MupC+aI51DWqeARoVTxBsbWFKbAWj2IrY8y0A7WlIT/HiIWzeU0eHCBAUHv7WsI7FhZeDkAgpHtqD07mEp4h6qgiXDuDDqeNSxtL0nEIwlRkAf9A0HGYFTRlsdvArVAQdeInaLNSM97DFp8t1IVxEbR102dLzxTWygn6+GDKVvNhUTotW8E7hwamzAgu1G1amflsSAjWa5K1l68kN+7isSgdUWpMcgVvWsCajXPFmDL89y2iznUHtg3h+1U0HrJv2n0g/SQETQhwmhNgkhNgqhLj+v7tR/25au3MPHfX+Ps+dKT3NTdrNvY7HhRXZ0Xf3pCtgADWOCrJoxZ6Wz9OVprwNasjMqdqg1OKXTeZhJUqupnth4lhREyIVxmEtCnG7uIcbxAMA7AweRGvxwzTI9azQvPiqzfdsJ0qktJpYVj62pMkMmrJ0ISwX/R3Hdb0FQDeZaEG+OUZIDGai47FWH+1T+uHr30WXU1fM6o2Qm+9iK3lryw886j+YjweU8W3uIJ61HkW73ZbxXNxZuApC3FG+gzbFn/JI9WvJ5agV/ThrfjmhDjtOQliMnLBdFp25W0U3TuvjqUdJmp2oESZoldQMBpdddCl1lQ/QMcpUMuPZrcSNTSVgwFfvPd5G0q3RtSPOqkE+dlS4GBI+ma6cy4j77Rz/jcbx3/bOdwpJTvxxJ40dK6gTuvAXlxU+dWxltyfBQUs3EjQYqbNzPppqoCbZTYt+zJaZTxA14qI1BBcsS6LFJQoMBawnLyImWbmCx9mkzOA5zmcVo1CUBB41QfXWK1jkzKay5XCO/2Es3s75NHR/CcBnhU7iJPEnNC56+VHyOvXndeJlwg4PIWu/FHKaTXWQ3aGlPGAnFCWZ6EoS6aghkjsAtexUHix8gY3ZDzF8xSssra/B0TGP47L07+3JAQOIG3mF7+TNpTBsFqXuP2k+o0d/yJSGdlQBNTY97MZSnc/U8qV0CR8lCV1Jbs9xcze30T4ol2XyQRl91llehnOyC+f0LQyKZJE7oBM1aWEb1XTjYrHl0Izrd6O/x6u1E8HB3/gVVyrPkGOUV+jUvMxbP5+hHY1MbE6wITiX88U/WcVofYyw0ulwkXQ4yTm4g2+9epz7NNf3zBj0AUd+9R4lIs4bnJJ6p80awCY58VcGccfM/DzJ8FjXFpSn8qW8Nf2pS+hzOl1ZBNhcXMKXZRMJC32t7qGULa6dRArKWD9gDFGbg10V1byzooZfLdxGRR0sl/IJ22wkHR5WabqBxp4MYfXGmHpCByNKJuJtdbE914elw9w/KsJ7qArrqISKJnPqd/o69kR7h5MAtGAjZLNTN9LHHqF7C61KOVuKBvGguA6AKXHdMvpY6Rk8UH2qebOm0ZWUsXeX0W1J82C6nfTl3NjMQB7nMiRLAhV4ZcJBvFZwrN6nWhLJmmCX0pi6PmHfzWYGZUQ9NMgF2LymEcPiCfI+x1CDrtxr9m62VQ7E59bn9Er0vovIej+EiBIsHswTp1/DsEG/IJFfjtfbjE1JUmxRGdWpg/K0K/rzrrTvwaJIZNmLeXv4ABYUOljr1wXRXV7T4quKBN+79HG3EkVIGmK6g3dnncipCV3wm9uxGFtA3ytKw4U4RIKq/M1YqiOEHS464yquqI+jtjk5fUQrliydF3kTEg/yG/7IDYzN0SHKL958NL6SClzjAmTnGDXJ+g3GN94MA7L5TAEqYMlj4xAzj/CScQFu8l9BcPgWFjCXIB5cdOvr35IkZhgRSwqrmDF4BsOPDOJ3BtiGGWo2KWIizvriZoSIGjd5qOLSUjU5ZVyMqT3JvE42ATv6Il+VOa+TfYTfR9JKHNhzY7x3zNk8lf9HBpT5eDUaoM7Yu3eLStpEVq/7exA6ZatKWW0LsmTHVpSfcY1lP+0Ly/YUCqJkV4lWu+jONnJq6CSJTL8Of+r6PaKMVpHPgD0CW1mmshLEw+m1M/h0Vxvdfn2ehrPdTN08ho4mKxE5sw3t+GkkD1/UyzVChwxPpnk4bMJcH1bbAPKLMxdjdYZgDasZyXniZdzuSvK79fmvALI9STdufPEQOTTRLgLEsLCTSs4Qb7BA6AJ4XFLottkznlk+YA8lpetpMDxge51FRKx2Xm/5IzZVXz+vH3kuq8f8gURP7S1ZQpSNR1hddHj9tL/8Qup5OxL9+R59HVm1KBZpKusqhzJxbRZ7xh7OyjFT2BjQx67Epe9/NcFu9jz2SKrUxdpSfe4m8koJ4cQf1udXSdZANMNI8A0ziQVDCFT+XlVI0AjHk7OqcYcxIxginbwgzmOJmMLmkSagRw+9OngedqO+lrSygWd/cS5vHXcaD596NbXWKN1Z62lPy92tv2I9U8Nm1MQaqZSo20Tc/nv+5XQJH9sKhqA6XYQcPoayGgCnvR9fMYu3OQGBlqGAOWPmXCiLmAqYkxCPczmXiqfpcHq4RjzKB+JY/duERLszSJfFlG+THplqprEjp5i9Q4t5uP+dvFxwPBYjz/vm2l9S0LQJb5nep+6Qxhlbqxm2zUJuPI8uY//dJIYQE16aCv5ATrA0JR95E5UMbRvK1q6rsPbKL/vPph9trRBCBh4DDgeGAKcJIYYc+K7/LOrcXceN763s85xMEhHrW2tuk7P7PK7QWyAZwhocFnOz88TSUI0kczK2OHfhcrWSHkvgSMjcx9VM1RYSRyHerRAzLGAtZLZhp3cUAO3yZKJqEblDTOY1ZsZirNYQZVNqiRSaG3XzwAHYtAiV6N6FE90vcJj2fq8QxDYDicqZ5gHbmlXGw+I3rJ4xh2BA95yMqNc34Qm1MsHpx/LCVJMxRixW3oln1iJpKswhf2onLqtGSLHj09oBCAxuJae8GCnLRbc9Bwch5HZ902qy6kqjnQiB7AWpZ0Ulje2GsBdQoLT0itS5LUL3Sm0VZkKrw2KO1bcGItQfKq5j09HFrL82n6ctF9Lu9tJdrr9vh38wn02/i6N36TkZSsK0sm+lPwuHnsKnvtMJqaZlvc4BQiTZWzKMFyYdyq7cXKa2+ilu1t+9PXlU6tqgPbPPVauETY5gsbahneimxREgV2tC0pJ0oG+qnXiJCgdrxCg+FkfyB6EbDOy2GOPajmFh/vGsGHES4YIBFK2zUmV4AGNCZd50mc/HDcfX1YEtofdtt/AwKdmfFqk/5cmdACwacjhXfF5J8d4R3Cj9gphRCGe1FuWZo07myRnTqZJLuH/U6Xw8+mwce0rwZDXwZu48AMMDps//eBrgy32Yxo096Iro1nwHJ074Q+p4S1aSRgNtL8cIB91YMID1Yji3iN+zjIkZfRYvsDFgkC7YHz6ohS0M4Cr749wifs+fuIGYcKCkhQDvRPc65NFABHvKMxY1Qhja8bOm80R+vUXh0R+62eTVLfU9Ss/D4jcIR4Jov2rWVo0mYoQOtvl8jBXLmBreRuuQ2dSIciojen8OKD+O2cMOx3VIhBkuMzekul7P3YlmO8g3YO53VE0mYL+Z6/kzD4vfZHzrCtdoPhZmjPseyuhwNJMzuI3sAXr7txdVsDPLSkjeSCjZjNs+ilD1CKLl1ayX9G+RRZJ+IwIkfQ34htUQlAvZWJRDv20mYMl1kYc5JzdJeYOGLSYhZH0PcR4A3rcmv4ivxk9I/f7GfiTvTNBzNE7VnsMTMY05IYsDEahiV78x9K9xUCesdHg9JNK8zjFX0kSATaM3OYmvxWw6XQ52FfZG0muf+FzG78W5O7lX3Nbrusa0Gt/fZo/nn+Js/sKVfMSRLNRmc9+5V7K3oIoYVlYYCtiXnklcNinMQs+3bBzgpd0b4JmyPSQkDZevib9zKc7wcA7aPoRvrdu4c7w+l6tcu7DaotjdmQpAibqbZrfOE7bSn9dzK/iiRBdeO4WfSHUe2wdVUOfL477SY+jCjdZWSMBRRPlRcTYcP4Cnjj0b/6DtRL36njaqbTwnd41n8tgYsi9C9UnbweWkIJbPUjGJlWIcH/ULscvfgqO0gYIJP/D12OHczl0sZyzZOSXYvQ4SKOymHFtOMqUw2b3QRKZy0SAKuUZ6lCfEpSwSM/DThkwSFSkV1nx70VQWVa9hSUkZpTPzaRb6uDm0bk4QJopbIG7yx2bVRN2MOx2piBB3PA8tzWhZpxSmDD59kdWVhtKbJub0cN10BSyMk5qAPtdv5V6+UU6jwWJGB+wWlVjTijlnUHERbjWBbJdxF1VlnNIVxN6TOaFYU+24Q9zDNeJRvh02F0lL4qKbJDKBZGY+WRAvh2/OImnLbEdcWJGcNgq7c+jurysniZxcSpttbF4xgKhsyhmSmmSDGMZV4q98Hjiiz8+JSeZa3O6sYj3DMs47FCeFOTA8ei4z2sfxDrqx01o9C0dC54kCGTy6saiiy00Oeq5uKzks3Wcv3x8FcdNtKDA/+Cfz7El6VEGWNXMv6imIHJUtaHmDOLjqSpKjz8b7nYnubMtTmS90MIqpfEW7r5Lt1SeyedRF+AtGM7foTLa49PmdK+nG8iWr15KQVY5uN415U6a+xJRpLxHGgdfY10IOFW9I59MOwnx0170osQRtBPAbcs4mRxcWm+kBrDjKDI98Z/Jpvb49arHTP6Hnzb9R6qBm9KHUOHXe+UZVGReP8/B9mkNxDSPZk2vOM3e0N1oxgOKIMGnWqyQkKRVW/oUyj2e4iC84mJLuPGTVVMa9QTPCyCGZz9zmlPlW6EbIWrcpn1o13aC5MM9Knc2US5WCMgJefR6tsoxKHR8c3ALASstu+o1to/PwPJbMPovjv1P5vnIACweNoCBkPr9OLuGvrieJ2fJ4fcYMgkYEV8IaoHjoD9wyuZKtfYCu/SfTgcpp9NAEYKumadsBhBAvA8cA6/87G/bvpApbkM8GDurznIRKJObE0Hewx+JErBamagv5VsykWNPDvzqEKXDv6wEDyE00pVCXADRLGoy6K43BTLuVMcC2rWZxElWNYSdCFi3EsfKmdDrvC12w7UGB6qGefLEu62A6x4A1pz117irfYzww5AryvLU0d5lCSk1BKdVsNt3OgJuulIDZQz1QwKqmpPhG0nB1L1OG0my0pXtoCYP3nkh1XiGXDdW/4wztGV4U5xJ3CMJKpkUr6lBA06daCDdetYsWKZc4VnJGr2Dl6DGsFsMp13aQsOoLvQm9/XWihNc003Jeo3TQpfbUEROsEL2LvLhiIbqtRkx9OJsePXNAjZUlfv3/a6bdTcSogeKwhNljhJt1KV6CJR4+um48R3X7edpthlxtYSDNTh/R+MGMmfRy6vjusjDVeXvo7tYZcKgiwe+/PodTR+oWnTb7pNS1MWtmHaGkkDhm6BLalXJuEn8CwB2FAlsdG4xQvh5FucciDzrqncUSZXN1PghBXLHw7JQjOHXJZ7gD+jjWugStTjcLB43iqNaP2ZPjN/txsJ9WKYcxXRvZ6atip6Wc1849iKKt3Tw3+iBEsh27FCHS30/QKED5fPUlALSUezlubxvb8wpTVj0XwZRHNZ62rWwQJgOvpYx8Gnh5yEgWCZMRt5JD3FiDidpCyII6YYIMaMCw4ErWukcBsCcvh7PEa+Rp9dwg38H9XJ8yWGwWgylvq0OyBthhxP3vohK31okbPZTPrXURFB4aPbrXISj5+GTSUD6szOLOhrdQpMwitwBRp0DqSrCVATi1bkLCxePiCjZoQ7l0zjK+z+pHltbC9M617LBX8IlzMwNz93CzeJz06JsKzwg08T0Rp5syaTsNFLI7u/f79kdvcyJzqz7BRZCP9gnp6s4HWx20O1wkZIWS+B62GjDe3ZKHdX4XX3Et5wQ+w1MyHMugON4tpqfc7t8NWLgit5zWZIyV/XVDhiO+f2SpbyfNZXFZES61m24D6KPGk8Xh2rsczTuEAg6ewiyM+upBh7Ezu4hr1qzgtdaDqB+QGUYcdaoQ6a3w9Qj1SyqHsFIZk3FOFTKvZU0gIJmKzq6Avs4mRL9nnXVoSph7ST4ndU2T4WWvo4TnxfmpPe+NgdVsEv/MeMf3vjwunvwyn6AreuvLA4wufI/aeC4LxVxW5Y7h1OwF1A2pA0YBYCOGhErU0QRphrShYg0fiyOp1Yq5VZge4nxtLw2ikJuy7+Ac7QkA6kURv9KeJDnKQuHIWrxMwUGYqLBzDY+m7t0+YBAfK9UcRDZfMYs8GjljyId8Pvx3qWvuHqjDXJ8WtfFP8WDq+CPaNWwc8hENnImbLhaIg8BglwdpH2GbuZa9zOs1JsXsYa9WTFIolLKbBgpSChPoBow7cuYRFB4uKPurOV7IWKxpec9hlZ40zaykKehd4X6MUvS8ZU2yYkmrqdUo5ePWetfB66HVjEz9n54D9iSXEMfKJjFEzwkUEquN8QIdzOHVOafwy+a/ZTwvS7RmoLT2UHbBVlyJOHFLiN35WyBtj+5LTuihnv0qnVxaCEUkSGAhasl8Vxde8nJnskb0Fq7rhseYQwOL0cPuWu0OXJOTLBw2nUcrTGNkVXMtW/N04+Ui27RezwEIpckEn3EYrSJT/mgkn4O0lTzR/0jmtuQQMRBK6l1dCMNrmJAcNBeUkBAWxjTDtt1DYTS8x7EUUseP0XLGsoVM1M9Oj59g7idorsx8sbAB3x9026iR27hzdIDt/gquls2am91V5jiMUFfypax73zYXDWNj+wO8r5yKNb8CgDx0L/q6LWs4piNOadw0PHwRP5SN9oFoQk75VD/Ny2VSh95HSSQs67ZTOdBHN276dQh+8EO9Q6D0M9e/6vjxMLkc114wlN/PhvdLHf/aYXi20qbPk+JSrs19wry3MxfDOZRBiifEq5wO6PJfD0WFHU0THNw2jneVCD0mudL+6wCdv0uSaZS/Xbkz9X+d1/yuqa2LWZ01mE+zvAxMbAEjgsRfPIoFviQWLZaKRgIY7/uK1Qxhr9zJ65zCW+JkGAhDt07hvTJ9jE7Zmhk91kNbqgcRNjqh0+Fmo7Mfm0Qejm1bIbePwn//oST6gkXPuECIE4HDNE270Ph9FjBR07TLDnDPj8MH/kw/08/0M/1MP9PP9DP9TD/Tz/Qz/b9LP2ia1ksz/CkesJ9EQoiLgYv/Xc/7mX6mn+ln+pl+pp/pZ/qZfqaf6Wf6f41+igJWC6T7fUuMYxmkadrfgb8DjBs3Tlu2bNm+l/z/Rmo0wQsv/p7fVpq5FFWJrdwpX8eLH17LgI4lPHjalQBcsvBtRoq9eKd/lro2mbBwmfwEnZIeJ1Gm7WS3qMh4xxu7nuS+smEsFXq4mVdtp1PyA5CtNdEi9DikM7WneUGcx9jwMtbahxIVDi7a+gKz+r3FhxzJC+J89kf/CJ/NtfYH6MZNTPQOYUin8aFlLHXqCrdNC3Prt09Q15WNo6uWUSctJoKdC8SLGfd41E66JC8erZMuYeR7aStYLUb3en6RVpNCpjv9+09wR0O8MmU2PqWN6fXf8GLhmRRodZz4w5c8Ou4Mjtde4U2hgxQcFPyQz9w6YlB+93waXHo8er/kGn77aQO/PHQO6gFKBhxV/wXvF8xhkLaOjWIoE1t/YHGWGfpx49+vxKm5SZZHcW6wcO1V9wJwg3Zbn3khD9VcSE5JG59tL6ImNITPhv8SgCG7t7C+TA/h8sc7aE8r1N0TvtIXDQptYnzTKt4oO4KQcDM6spwVdj1sanTLMlZk93aR/0a7ixc5hzpRym9rHmB1fCwfVc3Ybx/IaoKktP/lWxrbTY21LPX70M2LaK+E3KYg7xcdAsCotadzznonV538JADHay/zptDDPV/QTqChvh81m2Zhba9Frd+FAFqHTuTzcSO4OPgl/WzLWVtbQVNjFdk7vmTl1OP4oP8s5gQ38oXbDPkVmsaQ2l2sK6nIaOPE7etocfnZmq+Hf07bvJphe7fz4ozZdKUVbD2rYRMjt26kcMpz3M91LBcTEJrKxV+/TaG3DLvqpHn5G/zpQj3f7C9vfMimkmY+nTCK9UIP05uoLSKLFj4UvZFQzXaq+GmjTfSd+2lTI0QlOzNqlvFVqT6Gx9R+xDvFes7jzKZPyF3WyOuHn5lx38tfrcAnSfx5qI/v/NkUU8MmMYTjW1/D3zyJL/onOG3xQ9w76UEAhmsr8dLBSsakQucARjc+y4q8c5iqLWTCG8uoKCzinimz6MLDna23827WYSwUc1PXTwgvY4ljHCdt+JbXBk8F4PT1jzEqfxPJLIXtTRU8kXdFRluvfvVW/CWdyHuq+fSkAQxmPd73Bb8/Wr/u+NX38uaIGwDo37GBLT4dzeyiTW/wxEA9H+TQ6EIO2/M2k7b/Dilp4bAZgjanPp4V2jZ2in4Z6+fMTQt5YeDM1Bjsu66OXL2I0rZGWvvDq0XHMkZbQmBvlB+yRnDY9q+ZVfIub3qO4RMjV+487e98xqGMiq5k9pshdhXAQ3P0XIulH3exMmcvF40dgNCSqdDG2fHPyVXqGcFKPuJIfmA8ry1fzFFj9SLog5LrmbBmD8+NOiTVroOaOtnoj3NE8w7+Xmiu6Zk1XzNz3UvMzjuFs0ZXkRNrZaVtFAD+UDenLv2UH8oGsNSAn562dSXDandiiXSR622B4RFucetYVze88iT3ntI3Wtq+ZNPCnPfVx7xR8QbHtl/KEyMnp85N37mMryv0NvYPbuNW12/p7vbh/+h8HptdzFfZlRwZno9/lYwnGsbTEeYvRx+VMf9+vzLMPYPb6bAVcvzaz5i2eR0bAiW0eQIcFYdHJrnZQykNorBX2wCurPsB8fHnyLEgSj/B7w+6HYDT332al+adR0FrK8eu+YqNBWUsGKjvl04tyFDWsiU5iHbF/5P6oYf6N+xmS34ZI2KrWG3VQxIPVj/iZPECi5iBfbuPv/bTedKD2iU8xq9TecSg57S88u06Pk7upqxD43fzjkudu+KDP3Oq92h2TLuep+IXs9gyhVJ2keh20+zy0CayuT9yGTfZft8r1H9/NFxbSV7IwkpnHnPavuC1LL1t2eE2Whx6WPncrtV87hnR5/2Hah9k5Ium0+0frEcTTXTENmMbEuO+Ab/Sj3+xEO+Az8gr1jNKzhB67b6Z679h4ZDMMMXsZDMtcg7jtMUsE71zufKTDTTI+ZRpO1O1v/70WYzP7Zv5+3SzXYVaLWf+7a8EsxT+evJtB+wTt9pJUPIyUlvOKjHmgNcqWoKEAShyjPY674gTU+dGdLVx0cef4h2+iHMGPgjA3dq13GiE/ffQ5dr9fMLhbDJgDq4NPsqf3Hqw13mra3l6hM6rztz4Hi8M6puXHB38nPfcczl51z/xyyX8vWQ6l9Z9w1+KzP6c2dTNVWuC2Io/ZW/le5xv1euxzulezxeuIVzStoDHA7MynvtH7QruT95AvaKvr2HaKtaKkRyIHv/uYy6ZfGiv41fEHuBh61W9jk8Ofs93bl2GPWfnfJ6t0GWzw7X3UrzzishDnPTN+Uw/yMN47TuWislcsOY9LK1J9uZ7eGfQXCYklrFEGcflK57jy1EjWStGct3OTeRb/sJbRccyYMVChlty+fXwX3GY9j4fiaN6tQVIhS3et+wWrh93R6/zozrXsNLI2X7k+w8IRiOcd9udva77/5uE6Dvs9KdAhiwF+gshKoUQVuBU4N1/Y9v+26kjUkNb+ZKMY23RMPdsqeCaoYOQysw459k7vqJy+YKMa7VkknhakWY5Df42r1uvqr7NaUvlWwAk0uA9WzFRlPYaceSKHMetBZkQXUzBNn1zPVDMOEBC1YhjZbBq1hAb3/kJczf0Vnbr7Gbs8mS+pW1jPY5dG0hk27iv1sn8tUPwRIMZ93RJutIVTqs82ZOLtS8dtHgR5773GoO3b0HLn4kEjKtdwR5KaS3QmfalPITY2d6rD+oc2QzYtpaZ332IO7w0dbxNlbm/9FmU+IH7YXm+3s8bhZ4jpe7KrE8l+7rpTrbTtdOOp9OETw419A0xv2P1QKTVMt/YW1nj/o5xq97hiEWLkYKPUJTQbQ3u6N7U9d5QWy8h0dVmKrPtIS/Pl59MyEBKbFPM+dXq6o2oBUBzgKv4A6doL1DU5cQS7xtBy6u1c4n2MMeE1vQ6529+l0O/eoTq3ZvxtZtz4qH3rqUh9gLDvt1J9YI1WKMtTG36gFc6mlkemsMhP3xLVdNuTuA1bo/fzkXffIva6SbYlY1Fk+i2RxBCz8uRhMphS1aQfK+DXW8Moam9kNrs9/n7nFaWufSCrLtazM1mSl0Lx65YSP+oabMp0vZw8fI3OKfGRl6jHuNd1bCHiV+/jYhFM8oyAESSbTR16ihMfqNOnJMQQpNwOD4isOBxRCLKLQ038Lvmm9nR9C3Fa+dzI7cxRtPnl4sgdnrX5bFrIUbU66iRmpA4qPVTAP686QeWfWzGyc+Lvk6J0JOnfaFW7Jr+rOG7duDV9Dkm1FasEXMunrvrZc5f90fuKXyN3O4qJuz1ExSeFChAsfwOl+7w89d3IwzfKXH2a48xTFvJuTzBEZ+9zuXv30tBjZnnUxRxc+OiKzl+3atsy6+g6NvPKKSO0fyAf1ETJ2zNNKgscYxDaCruNeZcsLTH+VuolSUbmsn9qIZh2qrUuZnffYjcmqRrtYuGxE6O3/ss0/iK7PptqWuWeU0Yat8uEzWv345GBu7cwrSmGNd/NZLHIpPY1rqKz/a+wCVfPsmYjTrqVg1lFGl7GBjUk8yLtD0cudEEpunLqFHSpudllNc3cYt2I5eF/kH/Lc2c/v0CCvbAyhWHcWT0YyQtyXjtO2Z0LeUe9RrmdHzFtkAzsqVbT5pET/Ma2paFRYuhCRmn1o2sJXBEF3AMb1LJds6IP8vt3IC7xUQoO2TZciavM5ELH373ee5bLnj7CyuzNn/HWG0JWckWxq5fyqiPv6ChScLTNBZ7sBhpp2koUYzk/crmveQE2xi1bgkDtuqAAdnNq2ld28j6j9PQHNt2UtX5DAAXNT9idoqmclbz8xn95NJCdFmCIKAw1J3Bp7zdW7noxfuZvX4Zc35Yw3ffnsKqFUcwv2gZF66IcNFX71K8tBtPNEyHpR3RuImcuD7/K+t0wJv8iErCQCyMJ+wsLIrR7Gwmp7uTlXIT9XEtlRjfFx2/ogBLtBNJU+muMXNBKmu2MGL9Usb98AAxYUNOA9AJCTceOjhH+7s5Fo0f7/cd6SSH9Pb3KF8AyTZwEuYgPsYeMfeknM0u/lj/FtduM8FcYsLGC4EXUGPbaav7IePZWkMTb294Bk3VeXkSiQ58+EMtxCMGcmNw/yiNfZGLIJaYRBKFmNVcByWdZp3KBqvJw3xqa8b9XzGr1zMVLc5z2kn8Y+hdPDbsSV4Y8xWt0gryNZ2fve9/mzHrLkduq8xAoCzdsLr3s9QuJDVJPWbOqqQlmbR2AQCthsE5hJN6Cimgjn5qAeru+RnP8cdD2ANX8+akrQfsjzM3PoOq6nKX2Ltlv9cNbtG/xac1pY6lK18A962ME9Na2WUzC14PW1PXq5SBQhxvzMwt3OHSefcjy0KcustEAvR1Zd43pG1z6v/33LoRTE62styhI04vDphK+IDEBk79+ANaG5ZSt8JDzWYT7t1vlPZYazXllh6yE6Yw+Enqd7LbzLV3aKFe1wP45Pcyfnu79D2sY7U/dawstjj1f7fVlP0U1ZR5WztNDASx3Y4jCd5kmN1GYfVEy25cHYX46g2UVwPAaYmyA3uDnu/5RdsHVHdv4xIepbAywpYf2gFS82lsp1maCWBy1/epnDE51ve+cuVaUy7LrsumSfmfBcLxowqYpmkJ4DLgY2AD8KqmaesOfNd/FjWHHext0FFkDvteXxDH9Z/JX076C0PPnc0R1eZGNqP8B0qmtmTcn0BkQNlqaTBdw5ytnPbZ+3yV4864Jj0ZWUtT3j4XujXCaomAADkJ4R3fsTcuUgpYbkffiapNSpwYVnK0ZqqDG422BLlrzx9SAkYP1QkTBVHbq1KaW8q2sgiLixqoV+Er30YGhntvao5wd4byuFcU44m097ouX87lnPlv8siLr3P15hjHx7oYsWYjqpDZawBatK13886IpVi1CG1GErqsJZgVf5XDvn+dUcFXyU5TAruSCm0eiMt6P3gSwV7v1b8tMxHX1fkBkyKmMKKNKCZpoD+pabCkX+YfQl/0Rf+jeCU0ifakhEUk2RV4naUljzKKPaYQI5lFrK3dJnOyd6/BEl6Ns+sjlJiOMtnqyUSwapBNFIYedEe/1sYIbUXquK0N8jvbmMdbjO0cT782elF/bRPD46up2NDKLWNMC+TDW25hcsNylNCbWNyCQV1byFHM8T/Fu5RWdyfvVn7Ie6PW4qv/Nf/Y+Ce8g+dhHTiGG558lLse+hNFq04h8WYX/rUfYvu2hL17BzIwlseoqIVf9FvCBf2WsmJgFTSvoDPWRF1yM3HvqzzSqjPSqKQLjh1phYQvXL+X/K52CrvNNWYlxs0dj7JjwzPYO3UFTAp1IsVjuLetQexTbbSpo4OcSh1pr0cBs2kRHK7vuaf/1eQkO/F3R3D8cxfx142ab2PajevN2nY2eoNJ5NPAdfW69VHSkoxoreePiWuY1ODn7T1/SV03Y/WnTOZrAEbVLONWfsdx2muwZxuzErrSlte2mlx0xiknQth32vncvpXS5hjviOW0r38b0NE6i7QaAkqU+XueYGH9K+SPGEN+y15Oev9ZqtfsoX2bj2SthEjzcnYWTeNz27HUfxNAyHZ2Hz2A3ybu4WIew78pSc3nRYzctYKS4E4AfFobWYlmPF2mQJGT7KRTE3zj1kgGZSrRlavRdYuZsOrb1HVfDy3ih2/z2fp+Ke8OMZXKcBqiq5zUldC5e+cjr9rE1c/8mQu+XkhN50dcbCthRduXLM3ZytSsj2mW9L5MCgt2IuQZbbISQ+kDJjydBOBR7UQibvqrmwnsnAsIVDTkmIKj7juK17fwKx7kGN5kwxt5NK+dR83u4ZxvXYGs2jlj8Sc8+n0zqprAmrRRqumlAcrZwZOcRSJsCoPdTfnkxZsRsQ38TruVY7XX+KDwY6JJk7m3WXQjU033JuQNgpsaPuO2hd8y8+u3sSbi1ORKdH12C1qwMWNvtkf0PssKddHsDnDw1++StXMD7o3LyaeLFw+t4fPxTdylXctt2vU0+6LM+fwHLnz+evyvN3Bo6FkqEysJ7L2e/IQ+Lk5N3ydd8TCvjjsWgEZ7LZVRU4gTFFCcF2Zg0x4E0BBpx753JT+4VvJizgfImkpCqLw6qIEvir5kT0Ep934d5cr1W/A0vMSs5d8R3fEFOXufZFR9Iyc257OqYA/Lcpfxbf63TDhmLG5bdobHDCBLM4XWD2rSyomkKVnbSju55sXHcBVt4vOhkxgYyRQvLCSYoCymRDPqOKYJyAeivmDqtbBG0w/6/qyG9H7zxbsIL+um8cuBbGk1jRUTI1/zlaeJXYrOJy0xcx3YYzItrjgiKJClJGHhok6Ukh0LoRoFiVuciT7rgO2PnFo3Hf5mEshEnKZRIr/D3DubFZO39NSLBCiq39Wnpy2rq4nSuhALdtcxyHYVGoJ6JcoYluHROtiZ08we332MWVFF7nLT4OTt6q0ARCUZRW2jMU0BK63bxOgfdAU9bkTldOGlgXwG1wwiIqLsLMjsA61BV4LUtL7pQdDroYvffAnv6jhxo+TNLqmR/VHZ9uWMiSwgmQbwsC8FG7aw0dFInds0qtxQcTPSPkia6xo1WprNcV6GzneGdCbpdlsYoumGz6yaPRn3Tdz1fa939l++jWC0GUlLstVeYbZXXc83tsVs6Pieje1LOb/FNAzmrNOV7Z2x3kbonD1tWFo+5C9//QvD6nYxtkVvQ4FaywlL3seX6D1m10cvSf1/XfhmEpI+xuFOcy/rbniSK5fqhr4u2Vy/kmau0YaEqew17dSPF0aDKW+3JRTj0DHtWPI2ImsxGkQhNi3Mdt9WJnU/zsELnqPOu5K8pjDTv2tBtjroLNSdBD216Yq7MuXRsSt+oEDTZeGAkDPq1T2y7Q/8vuFO1q9ZCJrKmLVfcOW4xzhkTt/gMv+p9JNA8zVNm69p2gBN0/ppmnb3f3ej/t3Uv6AAv6xPoL0BvahmicPOwCwdacd6xMOpa4UE2/0lGffHJHuGC9FuMRf6NWMuYNbYIZxSUJwhOMYPsBkACDQDslflgpvuYdbkTzl17N0cmetjRpY+we37WDXusD5NTNhQUMmJ6hvS4FiQPKmVa/92CxNbvk5dm25JtsUj5M8YydfDGtjiquG8Yefx9jaJIaHdqWtmbvmYw794g+qdG3q1dbqUpKKxnjlLvkkd6zdzGpFho7CXDkEABf0PoXiXboFaaSATfikdQtiexE6UNsMDdkLLn/isZS+Vsw7lt9adPFBnQrJ6jboTwmCcF5bkclz7Ii6se2W//ehpfox1lXWE2j6iNPYdlmQXAclJQtE30UqbOW7taYiSgXh76v9HXnuKrBrdwlkdS6tfYXUjGbXdrEnTunaUx7wmoTiR1CAg4W59GjleR8yWyQjDwrQqhSTdknMfv2YSpsC7rKyKervO2CzhfNRW3TqbHW3gdO1ZPFoHv/d9wFnSN9imnE5BGvrQjM4VvBp8iSMrD6VbTOH9oUeyym2iSC3o+AXC4gIBne4EDsVN68lfIo55jKOn6euh2Gqh8oiriLTp68SlNHFBZA453Rpjfdm4LXH81gjN7gB7y3S0x02lMdyqhl3TeLaugcu79G9r95pePsnYYvxpirZbRLBI+hz3BHWmEbPYsCs6s+0xXmRHdGYX9wQ49aLT2fRWBZFlukKSEDIdIZmg5KSIIFO21tKW349PpgxDTClmQkgfdxf6GrIR6aXYAVi0OE6s/Grr7dxse50LzvkHsz6/BWs4j1Cyi5zWBkav/Z7mtVkcynz+pp0DwQ5K1d2cyMuoUZmRH3zP3Ruv5JSn65lUqTNQjTBvlb9Fnlfi7sm/5fOsJI7mbdiSenvsRBgjTaYr3kpSS5A3dBQAwVoXt9nMsXXazFDj7+UAP4yYyoqhEwn2H0WbzYczmsAVSjJpVA2nFK+kJa+AhEvfe8aKVbQoOal6KwDjXE30wP1N3FbD4Ki+5gINuqC8s6yZT8c3sLXITlTSCNa6SezDJXLqHmHszjuxGuPDljWM29lASWcHwYoH2BrfxIkTptGc1cCctU2cX1JFyN6NUMOpsZhXrxtRdlHOFu8G7l0ZZlhkN32RXbMwLlFNPO5gyeLjKdxjGFIETBv1Ig0Ta/jKm2ASiyisa6fbVUR+2ximd0zHpjVx6JQCqvfuYWKHla5OPbS8PKEr/vaExs7dFjZGZKKd+rzbsX0Mq1YeSof7BYaylpN4mRAa8TRDVJPLqLmU6EJJWChddTnFbflsKdUFnGSggl/+6lc0eD2sGGAKfUKLMFwaxyvj5pjH0EMvPbLp+X06NJ73a2uQzhhHoMGHx/BONNZtRm59i2FNLpKGhVpCI9DehNQUJm7V1+C7gQUMbtH30fNWLiYZqeXeWdeyx6kLbWuSZfTryuOX893UeYw6aIAc/QZVUvl48mzWbnoK61fPEpc7Gb/kA7Z2LEZKNlPd9BIOJUG3aCWiRKh31jOm4iAGBswIkB56cZUerloQrkVLW38iTbj7fvqx/ObiBA1WCS3axQB7ZoZDT9mXIarurbVbfppSkwZEnCI5maBpmY8dH5egGXbW4kgDbiXKls7ltKVBeI95Vec7bnsEVbHgb/k0de7Zw3bx3rS9ZEUiSJjfMsSdxGLA5UeERp9F7fZDnlCIxG4bcSwZpRiy02r1BSXTE+BXu/l23fG8uOtiRq3PjPDpoUB3I4O3BnFqGqO/tuFMWGhSVE7iJe5Er9Vnk9bhVV5hQJrQb+0D+bRbs0KyJSP9oUP5DHekIWM8o8KOKhQ+s7zK2dU39XqOombuw/20zWTvE/XijIaJWuyoRk1SVfRtjAXosDQhaKULX8bxY2prsQcXkLvnTt6QX+SLfvnU5ZipClMW/JAxdgCLNZUuyVR4d4kqykO78MWhw7aeQxIf8SftMnYPczLQpo9zZe1O/F26fOBL6pPKlmxmuLDTlVTJpZEOEcCntnGtdjeTkq/yycRGJEXB6fVxtVrJPStv5bWmFxg3+AnkWBu2ZG+0y8F1cUbGHWwtDHLnOpX+cV0ZFwmNwk27GdrZOyomz2UqroUblxOT9T3YmlYmqa3wbuJNPr30jWT2YboEm15JY7evlvl7niCcMCMjhmyux19chtVqZZgRFWYzeK+3QLCy6lNiFg2XpmKNa4Ta86iaoO87DRQgawkmdurRFVnxZn753H041tdxbvgpxmhL8FtsqbE6ds3zbLYVkLVwPWiCP993Fc7Op+gfi1FVkGmc/0+n/1lVy/6LpCUSdBj1oOYeMgcJGO42hWKHNTM0bUBQVwosFl0Q6lc0HSlNoXEYk3GCtoixfje+rCyGZeWh/QvdKdDQEGgiSWD4CEq8lQzx5fOPYZXMLNMF8TJ2cXnMgrxL33xihiCf785CGFbVgBYmkjwYgUZWZ3Of77ImY3jHjU/9vnrs1VTOOYt+YZPRVbZswbe7kZ3W8l73j8wq4Jk/3szNTz+WOlbev5jRr/8T75RjAbCVVlNU35Rx345heiyxjUgqBHFzU4y2pESBrZAWi59AzNxYz9yhL2iHUWi5MpDLo0ecgbPAhCIdqGVWP7CGVwKwNy4Rqf8Lc7adT0LItGXP5ohV28gfbJas606D1J3cZVpZXUOGMLBeH7thFUcwNKpvrFLeEJqNjbA9ZlqXfjnzV6n/E7Z+RF1TKPINZbZVwdbd2xLWQ37NdGvZiKJG9S0uW2uiOKeQVS3HUrj6YiyxJNEu/Zs9iV0M7/qIW9U7mTjqb/iUizht9MlgdeE16oz44kEsFge/n/F7cit0BtNm9afetS58WIYBocpfTtWgUaDYGDdEzxNzVPfDPjCXnFJ9/APSHr5rfJelodfJPtvMSwzKTlbn67klTVkJPs49hEsG38zoBAx163MykWagkAwBxIdKmeHNqcqZwB+P1sNSnGFdUY5brBTa9Llg13SlILv7Tcpa32d56UQsisxHVSegRnSmY9NCnOIYSxJIGMWtI/1nsmrEqeyuvownknp8u8XwesWxZqhf/TQ9ZCSJTEwovGLdydwh5yEsEsJYx7IKp731IMUNa/C1xJDQcBOk26bgcY0CICusEam3077Ag68rwmkj/6jfm6hHlVQGhR0ESqso9LoRaBR07TTaH2XSYc+k2uMsMPP17tJMa+O51eb8HWxzELdY+Wz60fgHDaJd8dJ/ezf2Gi+yRaPE20VUttIZcjNh9wI8vsMgAcJtKsSW0vGUKwUIVcMXUrli9i38Kv49o5bqAlxEJKjNjYDQ+H5YK13OLpp9mWEdIrGE93d9xtPdH3LTq79lS8E6gnbocOXybvlNhKccjezxcu6X3QzfrZLUdFu3ohoem64gBbW6F3la10K6ffMZtHkR44OZ+0cPnRGdTnZM9yLbo1kINPolCzgsNopQcZQjKuKMq9KVjS6Lm2ROPi2Vt1Bq/yWKlGDYIdcy/euFBN+7nJaw/i0VMX0tSokQq1ZnEdYEaz4tYvVTA8ixeAiHvHy10bT0J4BBWzdxbfQxLks+jJrvYmnTh6xuW0gyV9+3tnYuZ02/ThYe1E7jnBvZWFFN0OUmHRR4b45Ciebn1tYYn4w24aUBGj25dAXOZUDOVLa5TmGD62junqnnr24tCVIfiFCSP5JvTnmHqHINi1Q9XymKjXO/ep5RtSv4jXYXAHEpgXuH7plobtuGPRLFa3ewJG8J75a9y04xGL/VTUFbgrBbL1IrGZ5IDQl7RCJs0X932c32JyWNoernXFtxV4Zu4bF6sCqZNQ4BqjraOPndpzjhlWczjqd7wFRbNeMiUX7RHsYd6iLYlvkcC3HiIZlwt8FfFQc/hSRX7xxZR6cKKnTs9BCy6/y9vT1Js6qvkVgaC99uDP8FNNFdPQK7z4Ti1yTQBHi6ExlCvHfgQczo0EOphNZ3Pab9kRxW8HV0ERUOVqHnOx2+5jscabWNomn1ywJ2iX7NLczduYmKVjNCwxUyPQlF1u2sKTqEpthdbBtVjpZM0Cr0Egm5NHFI8CDGRPQ90pnmqVWSvb2HSSyIhKmcTEx8gBJdTlKK40lmel8C0S7O37uNhEggNLjwpT9zwsoXuboin4NW6oJ5bpuVp7XT+C13Ihv9Psuvy2JFTQ1EbXbDuIluGd8PteY10hyuzSgoPTP6PDevdfL0hvtA3cwXY7uwKl4Glg1OXfO876AUr+khQYKIJVOOGtWxinzbGTR6d/JW+FRu5g8kCjTmDyvg+9dP5uHPb8fTrcuXhcld2Lu+wN78VwZddh0xTTBMW85AbT2/3nkPo1lOMKn38/DTz+aivzzDgn4RHglsY9q82yl9VuOU+i8oaWpPvb+HV42e8Cav5R9PqytJqZqDP6qHBWqaRIGSx9Tmr3r1zdlDzXIa/8w/FGtUl596JCGv1o6q5GFrrcdBOFVKBkDxmYbmoFVfDNbQcrxDivi0/1ZCmik/Vu9uwuErRLhyOL5+kX6tEaafl22WS3EYync0XsjQUt27qAqZLFqI2HXjQnaoHncoCAgO3biE3yTvwxIopFDV3+eKBHnHVU6HowA0jUVDg3RUTuLRhibcyv5rA/4n0v8KBSy8bRNBr75xXVmRz/YZI5ieZbpa9w2AiSt6t3i9wxg39jWGDvkjyTTpzS8luEu7lst4IHVMkZ3E2L/Xa2LbXlYvMpN4exQwu8Xe69qIpDOGcpeHK2YOYEBuZvyrz5Gb2vStIkE0RwcQcaeFGgHkqIaXbOhQnO7MsDhp+q/pd+iN5m9VZcz4MRwxJlMoACj2ZKEFMy1QJS5dIBIGk7YW+Jj4cmYOilOR6fYdh40oQQPUo9GuC2GF9gKccT+OpLlgzio8jsfmPkahU98e8q0WZJuPXLeZz5bdtsNsg9SNtM8GGhMCOZGkNl+veWMfP4tx2mKytGb8dn3DshHn7oNO4o4iG9dXFpB9/nlIY/RNIrdgICdW6qAKis1DTOjMqcpjhvSVOqwMqF3BgFozdvqM8Q9z21GvYHHqdToGdpvt7KEyoxC20JIsaxvL3rDexqGs4fLKSm7f/BTe+ik45KVsydWF0Zb4Tv4QLOMu2wPIsgPREx5qcXIH13O1di8S1pRQMq7U3+u9QIYBodRjWomUnBxK//Y4xffrychn3PMAlz39KlVzf0Wu53POvvLX2PqZc6JbdtJ/+EhmnnIal538MJvyzuHtvIMQl3zN8rG393qvbGwxtTlD+WTKICb5XNzQr5TfjJ2K1Xs+fkkHY6ls3clhRZuZmL+HUaouwDi7m9jrrCJqCAZLRk3ho+m696OsrpFCWbfWvVc0S39HP71uygmTK8iv1tdQTz6ZvjZ1ifFI7R0ORVcAO4SfKreTteesYEBgAELKtFjX5IVZNXAgk7aZYcHDJsxjzLinGTXoKU550Ax9zR2upgQFd8s/AMj6VkNyODivn67Y5nfuTBsUc0ysWeb8GnPm26n/p1XpBUStWpSPJw/llDp9DlksFhJCJrstjpw0PWYxYWXs2sVMXPoVdwztxwOVxZxxg2mFHhGvZ1zlFAJBffOXbS6uGn8ysqqvQzXQI9yqdDuStOa0Iuw2smqvJqtWB+KQVD2wym2J4R7hJWzX+HKEIOIrp71lDCMHDkb2mHuWIEa39ygKnH4ASiLdrIu+zTVP30T+gvcYqQhWtHxCtLnvyHaBoOOHp5mxdQ9Hx8YStISZHR+KN56FQEYI+Lb5tzRvCNDWWgwISh0qJckoihFiJIfjkIwRkfU9wJfU9zOLGsNuSN2WQ/dwzsm/ZNjUqYhYFAH446eyKSIBAjnWzikLT+ecz8/ixBGj2B5cTVJLoPk0Xtnxexq0vXQ7kgS9ghy7zguUZJwxEVM4PtrdyKAppZw4bxoj/Jnheq+XHU7EM5cJxrT4xbDzUue6rPDR5AZG5OmC+QXTKqkx6g/FhZUdHeWc1/UaozDDmoduX03Vrk0o3ZvI6hIclBcg7J5GXI4jNAVZkbHHoqkwbYuwE3bNoKXkcfJbWkgaddXa8y5NPVOSNH7Z3omSxg8fnPUgsiRjlXq7nOqkoVykfURFKFOg75lvADHZyVP1jYyOJchvayWyIZMjN0dVvt1gI7+H17h6K3p9UavV1+uYXY1jVXWeMaRFH5czwoKcbp13JmWNG2p/y+2Nv0ETgitb2xkT6wIhmNBHmo0vmEBOU8Ccnlzu3/tPPlh2Dg2xWB8+9970xjq9f/u1NTBl6QpkLU6DKCRXbaS8tQFbNERWu84P4mneJ59seo3qCk3DacnWd3B06blCo+xt3Nk+hePVah47dzJ2j4/OpLnHHR49jdDAM1K/laiuvPXlt9OEBSlpKmC/GzobQZKoVcUbzZQ9frHVwrFNNxNo15WiQGcrrkQ3v60sxBPRiMohbhj5W3a+N5XOJ/oz3K/P5TOKi/nDuy8xbsMaYoode/dCAOSE6cmxJILc8bkJyGBRg3SFd6Z+B9qeZWPDx4DCsK7pqeNHjxrMr6tMgJibDx9E0ih47YjpfCKweTqexswIlildy7mt/zk8knc6IeEgLJxk29pwWT1U5DYwrnQ71ljC6DcVT9vTWKOb8VT0548z/8jRiae5hZtZX9uJqEnyVru+N7jc2SgWCycOOJESXyXC7sK+ReDdIWMPm+tjGKv4c+JMNCFzjMXGlmK9rx3dOp5AmzXA0splDIhu4kztqYy2W9O86pFYCGfHW1TVP8koh4dftZ7D0R2XA9DirMWe1OsL9pDitfPO6Gq+njCIPU6dP03ocPPYvIfYWNFFLG7mJjojEay+HKS8Yj7K1sMAw0bx8tLc2anrHAYvVz2VZHlNfIFsrZldxn4od+pexHFZe6jaG2TiD21IucVcEb+HK7Q/osUF3QkNqd7C84fVsHBUDLdjGkWJJFKi71y4/1T6X6GAaSU2wpIbq1CxSRJ2OfOzlXSEEsmCK6wvpuKi0/H5xiDLTrQ064oiyVSyI2PjlWVXnwpYnsGp7AlBXjw9aVYPQfS6PL3uObyghCEuO7cNPwiP3cJhg/Mzzvtd5amB06wuCq/W84F89ZmJQ79T3uIw7X1OLLWi7IuYJytMLzYFcU2DoZUF3Di0twu30NdbScwxvIbCYngLAnZcpaVY0uLuLUIQ8h2PDX0hSskuYrLu2cmz56IkfUiq+f0lp4xiRskMrMZ45Fn1Nhd5zdCWvG9NdMoj/L37OyIEm7VyPp40A/fll+M/+WSu4g88wi8otOvfMdjjpdCXx8UDB/PrigIco0axda7+DlmSEeW6RVi4slPhkEdXH5t6hyQEmwsHs7lwNBajrTZJIsuiIKMwq3UJV+x6oVfbBiX1vD07Ub5qmE2FEbZwOO9jt7hxHn01khPuGz+XNdV78Tb+CWvXZ2iSk4iaGSqBJJFLE2NZRtI/DLJ1JenUMfr4BZRMIWZ/ChiAe+ZMZK/OiBSrFZvTiZhzI7MfWoFvRCaCUliyYbFaGXf8GcwpG4dmFN8ldyCHFxf1Ytz9kvn0TxYSGzMZv0Xh7TH9qXTqQsTZ9xzONbccxJqpQxnt2o1TiVM8rJiF8iwmat/w5ClfELcP2SdIBA6v3811jz2ZUnZWFQ9G8vuJlOlepGAyiWzkXU5nIWXaDg5hvpljKc9leuXZALSKbEpdXvZHuwpCtHv8AFg3619XnFeNxeIlu2gmlvx8pp5yFifedBf5h5ihy5Jh/XYGEwinE6+hkPg79LyNTpH5TsVneluEx2RM5W79/2P8IYQkkTNJX+t6cJO+twzJMYWKmGRh1LrFlHpLybdZOK1/PoX9qlPnXVqMq8Zexc3/1Bm8EAKr3VzfllG6hXiaX1fEYoOHojjdyMkm5KS+v8hpRsbJo+ZxRNUpzP79M+wefSaHrQhREihA8hjr2u1EkMQa2cDcHH0f82SVM35jE2tKOvhqRDMDhItKbyvOjvT90aRtnSvxN3xPddNynNhYnd3Et8EEO6MrwRjnzng+NV8V0tBQjb29nZ3r9Hcl5EzjlVWF2XPcODr2GN8fZulg/bsUGbLmzWPqtGlULtc9gsNn3sRfm/T+WTU3F6EpCE3Bl23mdfrysihvaieu6vNraM4ofMb6E2gkE7oiMKDrE+4Zdxq+g8tR/Pozz7j7z6nn1Hv1cTxl1kNI6JEHPeSMyGiaYGKRgb45qpg/HG4iL9ZIeYh9nBaDvUlO+PB54rb+ZLck+P2AEv558O+ZgI56KssStngshTgrCYlg9kVokoNiTeP1I89h3YBRBH0TUoqEZuTWKsaRUk8pc8t10AFF6i22L5Iuw7Mswbg1G3gv62g+LT3G6BeT/lKiC3cWVXDZsm+Y9cN3FIeSZBugCnsSSYK1Kk6DH1gcvflRX9RsCfQ65iSGSw1x0NodVES6WfZxF78YUkiWAdiRkDR2rSmj+10LJ3QGubDDVCx+WZBZlFjR4MSBf0Gk5RE5LBa8Z/2TsZPO5qNOC3GtL1Umk0aOv4fnl5zD0Wv24uuO4qUnP1I3/uVLIc5787Fe93klUwFbNHo20+u/QEq00Jnw42p7CW/TQ5ySPYglrQOoQcVukVFkmQSCZd125i//JV+U2Mg59TFiI28DwN/4e6Z8+zuGZ+/t9T5beAVyIg14J1uPtohYNFxhvf+GJNdycLaX4/fobfN1Samx7smHm3frEMp/keDg2adwzr1/ZdhND3D/qGrOK87hkBwv02T9ypjVhr37W3JqLsxQwE7f+w7TXjdTJSzJIFKiEbuRLxZX46n8smjcRHm1uXORhWBmwM0Qt53zJpRjTGeONwy5tQyhPZZZBHt6+wqWeoeRFArdFl35H5MzEwwDhZBg9Mb1HKx9yDGtJliMEIpuPEoK2iOCX3U0cZX7MUKqER7v1OfxrZNv5b3jdLCMV6fpa1FJY3qftQ1jO7eyYGQls4eNIGLRF7pktN2qdWEdUUbU6cBNpqFckpKc0PAJZ+x9nzqLhkDDItuw25y8GlT5xJje349sRhaZykuWO5uJfjf9XXbGGYNYGXThMIy9kjEmdi2ErGm4AoX4An6+C4wGLUksqRvbS/Nm84uySgbYkvy+8nx+NehGHEMqcbnM9SlrKu+GtgNQ59jNRxPrGZdTgc2dxBlRsRYW4JPamcj3ONsayQt1ogUbU7n+DdZsPsqeCvb98/L/RPpfoYC5XNUEis7Gq/SNgielK2BCwhlWmTPuG3JzD0od1va5fvKkzxg/7q3UMVlxo4p9fWmQZ6AZ2e2ZXS3QQz3kPvbnXKuFLyYMop+xQK37MLaAu4LcUl2IVitmpMLLAtub+WxUDnYtjFdt5/iJt/GbgjZKCo7srYChKw3DLfoiUpMydrebPJuFK8oykQ8LPA5cMzMh0Xve2aOAKQFdqPYI0yNVZrcyyGVHNhiUNfwDwhCnLTYbweSRtKiZaEX69+rPzLfp4zW49HgA3MEO5LSNqa/+dhWMZrfaH4QgMHcuQpaZMvkrJk74kMNzfDgliXsGlPS6TxgbtkWy0CMeSEhggHCUeiuY5HMxK2AIlpIdJGtqbGySwCIJYpKVl9f8hhOadEVR0UwmOd6jN96mRagpqsShRnk5cTxl7EKR3UjjzqTolulsK9fbZ4usYqCzmritHxG1ty01GdX7qX3Ow3DoPXrfyRL3Dyxl/tgBzFy3MhXukR6CuK8C9i+RECkF2SplzunBbgezDM+yt6uNrD2/RkFmZnwICWfvBHFfrgN3wE6u1UJ5RGf44bIpRISLxWIaRba+1+sVuzaQ295KT9zKx5Nm0H/Bl9w5rJoT8gPMyvLi9foB8NPOvVzLwEA/Ts6Kc0qikxsnjWdMti6gZ2tNIPcN5x+1WNibEyHkyAbZQtbjCsGXSwlk5WZcN+n4UygfPgq8acA3hqfS2x1BcjiwOnTG7TRy3oLo/TTphFNxeH0Im4vCUlORGn340bj8AZyyxPppw3hw9CwAHEafR1WVeqvu+bIVmrDUv8t2YI9FUETf3ySj4bP5KE7TdRQjz8wVyEp5tHti/jWfD0nJfJaimsLUwOGH8/vpNzG+cALVg3TBpSSvEMluJ++3vyXrWV0gscS2gTFvSvtPJztRxBO21bxbW4clewAHF20hp7nvEOosWyGaTUPE2yi0nc78oiCH+i/lYP9dCGMPiKsS9dMmc9jb73DIl19SuymXL+qrkA/Wk8sbD59IzOFBXv8+L1x/EXPiEsdob1AmvqfTrQs0gwfcCoAkSTw48AJezz0Bq90Md3MOrmJV65esbv0KWU4DZaqYxNC6FsIoPH3o09w7/V78Rj8KTSVkjPUhzsEE7JlKQUH1AHJzdQUtZNfXSIXDxrcTB3Nwti5MZBWXMrCtlNDOSxmYY86x8SWmoBhVrCiNAjmtCx2lelhd1ObFGo9jlSTG+TwU+fT3KIqMrKok04wz1iVNWL+ux2dVqCsoY/6cE0FINBvorYoiWDfQTZcRCue2mAqu3AfUsg074Wab0UY7G5WiXtdMKtbnvayMQhk8CM3rxF/7a3y0A6CqccbsAcVoZ1Lb1yTTNwUVF5PbV3B1w9/M9kgJ8rQI1qSKKzcL/3HVyF4v3tYYLVWN7M2JIFQrWkKiYB801tKxejsHdunCv1WVaWm4B4tm7lMOSdKNYdOv4aqx16LuZx32kISG7Mql5OkYDq2Q3XmCkBEGZjWiLwJaiKkVLb1yWHPtpocv6c2nyZYku+7XlOQWIkhiCy/DPey4jHt6DFNvd2bzRvNgOhOG5zvZM1+jbCmtYfaDi1L3DK3vIKv2CtytTzMx14yGcBry1JqqMLmduiTvpYNqpw35kBy2d63GEZHpcOnrK6tCN2yWFhZwwohj9WfYLYweWk6WReHeASUZBvJoj5HXAOjIqbmAnJrzmTBpUoYCXxDvRgCFqs7rsqQwJwV6h3/ajX3+lVHVfDF+ELIQDN6re3FOcsDnw/shhZMEEqY38XjtZQriQepseciqSsiu7+Pjy4+DNJnKuUPjqr8/w8m+Y/DLKod44wihkO/M5612K9+syuaDPUNQNHPfcDt6e3Jfn24YQzRzTb41YwI3HXwSg7J8uCwu4iIzzC4a7+Ca2fezp3wOoe7MZyoiyWMb7+b+zX8k5Cgi5D0KZ+BUBnlNw3exzYLXnYe8D0pwjtvkR+ONV2pGVNWv8yJcl71T/46wIWPY7GQ79P1SqCHc7Xr4o9vq5pczXuGFYz7h6dJ5vJF/CLKsYHX5OK1JB1RPICPHmjkrz4cSWUx9dpQPbLNYJBzEVYG9rALZEJY7lEZy5Bye76971jRNoqSrEHHqS1DUu2TSfzL9r1DAAIJJ8Ci9BXYgpQQJSFnVhSUzzjxd/hVoOJ2VeL2m4KPIfdf6yDUUMJd/XwVMQ0VkKn/7oX0Zm12SUgKAto8SMixQgl2LUBndg92ez9Ah9yPL9j4VMACXU99sIu12bC6dmd5QVciX400Qh3y7hZJHHqH/d4t63S9ZZYRNRjiMUAMj/2eaF54YVsmCCYPYig70YA3pCFN+3wSETSaiTiKoHt7rmTZJF/J7vDgBm75ZJBSLnrCebNffbcnBFTK///oJ13P/nIeY3trI6E3rsAX8ADgcxbjdA7igNJftM0cwxrv/uizp/SSEYE6Jnu+kavD2mP68PEpnQDOWLGD6km+wpSlgAFHJ9Mr9QzuDv2LmT03I64+sJbBLCb45YiZPV51FxAgpkdPmjySgLKn/vvfgv9IdyKwr1UOb3qhkxyfFyBZbSrgFOKMom0qnjbtefZozPnwVn1tNecAuHH4hh1b0rgvyr9C+ilc65RlMM6utibeONAWfHwvF+TRbZ86dg45PHetZG2O8OlO5oVJnCH5DaBCSzK39iriqogDJbqfIbuWxIeU4ZImDDz4s4/k52bMZP+qvPHTwDDw2GzZrFg9ol3I714Oc6UlNjsrmwWqJ5074FU0l95G0FiNsXqSIoGs3WOz7qcHnK0FRdWGj1GCuWV1BhM2GbCgxwzRdkO2B65568plc+oQeunvSPY9y6ZMvATDn3F9wyd/08MYsi5LaA5yGcOKUJd7KO4j5cx6DaWY9lyOcfYdBv53Txo3bH+/znCTJKDYb3ty8lICmGkKuQGR4TwF0/BQJ8oeD21RGZ58yhFNvnoA7oAvn2eefh73a9L4dn+cHYF6enwGff8ZORwUViQStB9/DHfbf4OnqyEjm/5X2AA9ql2CXnbi9cSyuJLLopNmWhV+pwyJFkQwBN6FKhBUvvkgEuzE/VrQVY83TPdtT7n+C7x4+nZzODvZas8g/6ixO5iVKkmZJD7erKvW/kp/PXnemIWpgySg2dixhQ8d3GUrp6BOu4uY5R/BS8fmMKxiH2+pOecAgSadxbZboDVIBUDlZ9yAF0zyxlU5bav6f9+e/cuGfn+IvJx6Lz2kK+16rKWz987KZOL+TybtFP1/gKsBi0+dCmy+bkisuT11bYEQ0KEYb1ZSBT0NqiyGFklywdS3nvvca01bqZRxyj7kM1xnTGJ9XzfpsB3FD6XBbTQVMMR6jpOUSbbWY/yeMuRUpqiKaaypiUiCLyMHzsfzyBcKzZrHpyatotLXTYmSqXL5wL1NavLicOh+w2D2M6tyAI9m7rASASJpeK6uawJ40ASW87gTVBtJbRaAA98RCJK+XSIONbZ4oSVnD7s5lZt52xmbVwlEP8jn63uRzWqidPpTnV+t1oZSE0BUd1VwDzrTomnOHnZvy0O6Pcq0WhMWArfd6+WCsNRUG5g7pSoRHDTKlwoHNyCV0al3M8zRzyqCTqDlvAecOvYtfFOWw1anP35GFppJus3n47oY5fHKVbkDt4W+6oRHCBuiUPPVcWmI3pO6zWG3Mc9diCa/mwYOykJNtnD/0bH476lyzb435uaNIZewYPTS2Xc1ipMeJdVIOV475Gyv7t7O9qJvXDhnNMYdlKoP7I82YPz0hsAB5jjyEFkNocWTj+KHf6eGJhIq4t7GFkT59vZ7kDzLVnUCLBelymXOkn7N37uB1j9zEI3+6g1y/h8FZbk4eV8LTp5qG9xN4jeQ5S8na28bvOhs4bNECAPwupzm2is4PHCsl8mf8mlsLIxzhiyNJFkbljeKuaU9RtFKfyzaLQmfWRSQspXj6UMB6yJI2b4bnmPuS2+omaShgPZGkuY5s8l35xC1eJuyYk/EcmQR7Cu0saynG3v0N4fDRjI27yD3DDDv9dPxARpVMIS+WydecTnNen2ZTeGRZiK74FwBU2FRKlS588S6yE53kXXsNsttFYbYBJKaFUOJ6zpbH4kGWHTgcJfRIAooQWBSZRJcuE5Yn14Om8LuKImRVN1LeWfoPLinPYW5FEZLNxYD+OmhMbdU87jvsDH55sVGHTbWSm5Q4NNe/3/78T6X/NQpYZ0LdrwKmpCz6AiYYRS8tmYsj3ebWV6fJsisDJrOHCqz6O23xrozjX4hDiGGjj6iNXlQbybTm2CSBz6Pnu7i9vQvxHbfxC07dtjnj2P4s4iJNuLK7XMYxwWC3gyNydAubW5GRrFaUQIBnhlXy4dgBqXvcU4vIOnlAysOSaxSMvKKyHwGL/k7NcO/bIrrl0OMdj2TT+0Xpw5hplQS5ViX1TL8ic0aWkxPnP8vW4iBZe6/HueIzYsD4jfr73BY3Zww+g3xXPocnw/z5wbuQfb1zAH6MFKFkeIv+NHgAF5bkcGhO5rOevPJXPP3rSyg0vDR2Qyl5edgAkoZ9bsIeB7MnvJ66pzBnMvnsxSFi5FgVylxeIlKPAmaG1cgIVpb9mZsnP062Y//KYqzLSscOL7Klb08RQMXuj5l3UIKATe+ns4achdOy/43/p1C6R3b+mP68Mcq0ihYY/VFQWMTArIH8cHQJj/S3/iQFrGDGAmxZmSAw22eM4O3RuhB/RXkeNTNH4ujJH5ElflmWx3Vpcf09ZLNlMhN1n7VpsQTIo4EA7SBl9p92TD9e6Oei3Z9D0qILisIwAqhCYLHtJwTKV8Inyy/ipu5vcCq68Ft4+ZUIIRDG/HAbydqz+bzX7RarDYfnwCEUc7L1dpxckEXNrFEcPv2MDA+eZLxH3gdYaJLPyeU1/wRfb+8vgNXuwJebn1K2kmk5OgVOMxTOrsooKiSFB4bMy3iGbJHILs4M+ZPTBIkJfjf1s0dR6bQhFIWzRz/EWUPvwe7N5wt5MhFhT3k9AAbUljJs6TlYJBuByUHyR+uMuQcC/nOmpmD640kIG3X3emZncWtXSri1SBYumf5rypcuY8y7r+Nw6UJq+qxIN7589dvZrL0901CRl2fOTcWaOb+WlN2cRQAANthJREFUeeegphlf/IbSlSBBg11vUb7U9zqdeto5XPToUyS8+9+vst02DhtWkHFMCIGDEKd6duA3PPMCwfIzlzP/+PkMHVrBh7OO44fhk3BXmG0vTFfAZDkVgpjOikqOPJxz5r+JIxrBG+zimmMmcMm863G7B3PHXgeaYcl3Wcz9qcdI4LGY/fhNmsybMATnRF4psZxMT5h96lTkgB+EQDMUx+N4DYDRbWGU4mqOz+3iBO1lTs0L8uGKS9jxTaaRpYckw0AHkGh2kBU0hfCwxYUrT3+37Pfrfw0+8ct3YxxSOJuB1vGUuG9k785LYNx5DLjg71x6qZ6nJSsW8g2ACktUX8vemJln5JD/NZHqrv4lGQpYIi2+1xPVFUfP+NPg3PkpcCobMa6vrqLA7qS0fDR/Of5XXNGvkKsCg5lUMIMhgTGpZ9hlO4U+BwPy9fnRs74txlztNnKXLIVesu+5nqOrzOLCM93t+Jv+yE4jb3VO2RyKDeOTRGbIaWGevkePLprAsfkBLJKFhKKhylDsKeaRI+cxxPMT+Y7BLB4ebAITDckxwYh69pTfvPB3tkwfzpKWX3BZyxMMMgwYPSkPwflXU18SxGfTx9fahxHaGQ0xbNsGHE4HkiT4w4kjGVEa4I4KJw8qDwHgKa3iyxMP4biLzubaF/7O/CvPRbLbUyGIuNPqormcKVuoMGSucWUTU2BUDpuDqHsGbYX3YLP0bSx7r+w9TjjWNESmyyM22UaCTAWs3IhomdVvDjmhzD6eNulL3lszjIWNVdw1/n6sq9s4dkhBau6DbuC7cdKNuEQml87JNwFLlOFeXvTcxSLvSgDGjHmZKZO+oMoXIFA0gOwLdbnZY3hFA3JaKoqcvu/p75CFwCpLfBm2c3Hwavbs2gSqBXmf9aMJQZssI0sy1eUXMHfONt68+Lf0y/WR59E3F021oPQVSvY/gA7sH/9/iILJJB65bwWsZ9pZhICD7oDZN4Gyf0ANSfQWJ2XZxX1czW94OON4vtUCxLD1IfgkhOUnacChffJ/bJLElVUV7IrVcGaJ/txpp55NoEgXKm7oNw1LcXHGPfvzgPVsDBoCuytTeHp8aDmhZOa7D8vNFBIs+S4s+SYTHuV18n1HN/a0zXlm8i1WNO1CkEBDV2ZToU4DA7BPvZZKhw132lgJIbh/5ABeunQmHy9ZhKSCFAkTTRPr05UK96yZJBob9U3yXyQpzbsjEORYFe7q33vsvHZ9U3l0cDkfNncwya/33eiAi3eTU3A6XRw6735wlwEr9Xa5BzFDPE9SLgCOotpl44+dv+MW/zdYLCZSnRCQlJ1kZ1fh34/RIJ0OpIDpF8j85aC/8MXuL8iyZx342v3RRV+wu60RmkzLJ8AYX6aCmG/kaZSU6EyhpdzNs7EOzv0p7xACr5K5ItItykIIdARqo6iu9ON900OqmmnESFd4kTP7r681Kaw6c9d+RAEb0r2dIbE1fG3V9w/HtKkA5JZVICSJoXOP5mntNFobi4Hrf3L7e2iQy0H97FH7PV/Qrz/j553AqEOPyjxRNhFOfAoG9PY4Axx00a/w5xfS3v0tX9Z8SbHb3D/un3U/p71/Go3hRlyaVUdSc15C3vRrfrS9+9t3AJptWezKmcpDskQ8qVLvyqVS28YKoc/RQPMw/G3FIMGC5iqOKdENOFHVysRhr3D42u+ZK+nQ4KqmEpJ0IXPpcCvz6oMkGhpTwm0POT0uyjwQj+tCTCItRyddWbRbes8tT8D0iHlzM71jT549Lh1TBZ+lJ0JBQzPWS/F+vCGSJOPNzWP11GwS2o+ZKjJpx2zdO5NoMXNzUgLPlEtZK/S8Clvamh1dFqA820nu5Hk4hlajtfUO1/LMncv5N3kRoW+50D0qdTzXsIr7HDa6El0ZIYg5huLVlkirFZf23lSumSzvU30pk46uOpp1zeuYHnmeg7WPyZlyBVZXLkmpieN5DZu4Yr/g7lKiFXfbc3Tk68AzUnc3LofJX+o9leQeFsYRXYJrhK40yEa+Yn47/GHy3XywdgkJzUc8uhOA0tLMkG3bjY14X5pBt03PcdE0Kxj5pY59ogNEn8Uv0r41z0/MQAKWfF4kQ7mTVJWYNgCrPUb/8kqQFRyyQlsirte/S9v7evbIayYOAR5j4WYTTXTf9dczx11W3fj7i5mZoFt3T7ubu6fplYZ6lLUVjTqwS4mnhGyLjF0Sqf4fXzCepfVLKTAMPvlG/bL09350wkcH6IGfRiNzR7KgZkHGs2VVN6rbFIlQLMk0j5MbXXkM2L4xdZ9kUfDb/HREOzJ4ew8pshUSMfL2CSu/uHIAavkTaEYKguzW53nR735Hyz/+kTKogQA1QeH4djoS0zOUJdGH0Tvf6WBTNMG8rz7FMfi8XucBYnIMt7tvY0yBq4CEkezZo4BZjLaMKxnP6nAmEqLTWcJJ1z2FkCSyi0s57r4+H4tDceDbx6iUrtMosoVtDrNkUMCvo2r/YUhm3pjHUCoLrRqZxSR00jQNhB5ObFUkEprC63sFatSCZLMgC0F/f3+2tGfWBJP72DtTkRmaFfmneDL+A+l/kQcsiUfp+3M9ssSJ+QFeGlGlI5P1gUz44dgBKRhlq6X34pBlJ0V9TLl8w5pn8xfBbR29zv+UEMRb+hVxY1UhJxfoXgxV08i1Wnh+RBV+g+lNPO5kBkzUhT3vYYfiGD4s4xn7E4QuKNFd4zltDdjc+ySsS1Lq+T+Vbqwq4rnhlUzwm8966eA/sO5kEw5V1VcgACLZmz39YWApTw2r6HXcmhEqJvh1RZrlKc2T55k7l9K/9R1u9WOkpQlA+4Ze9UWD3Q6urihIWT5tisxV8V/xevF14NcteJdoD3OW9g8kycopjlWcZddDJ/o77WwTA3BV3p3hiexRilVNS1kZ95cPBbpVtk/qydOTFYrcRZw5pO9Qxp9ExWMJDNIRCPfNSUynHg9YT3/0MCT1JwqW3p+gcKboX7A2a2pvIdN8Tmb/9ZXLYh+gKySqEPtXeHtywGQFpwHJnTA8bzani6v/+S6lQ0fxw6Jj2bppyk9u+79CQpKYccZ5eHNye58cdgJY+7ZC9x8/mdyyCk4fdDqfnvgp/Y2aTgJBnjOPHAOZ1K1a9TxMyWZagA9A8gGuGejS91mHJBFLqOx2l/CrLhOUomcdfN+2lq1dJgCCFE2mUPQ8Lj1UOq5KxLFw8eUyzxznR+vx4Cl971+K4ieqQjRtWv7YendmmYYYZZ85cNCQfOYMMsGSUoaTNAUvP3wgtUOf+1n/4n7bQ30am+zelBBlS1uzlTkuFv5mNiXTJpB97rnIW/qusXTD5BsRnhqunjk5dSzHoY+D1/CEpHvALizJ5fAcHycVpCXXp60lzfDUygcIYQawK3Zum3IbkqRbuPMu+CVZp5+eyvfTtATPhI/jccwwKkt4Ld6mB8iuuxJrdBNuY2/ImjGD2knXUhauJSvWRkugP5JI4i2LIJQeIClzLCW7HZfBs3vyf3uRYiPPV0TMLnNYxWGoaQZZ5z570oyAe9+7e5FkeOuVQABZ7eDQtYsZ01HPofUyZ7cPIRDQ5Y2eKItgsm+BNNUGa6bxMp167rMrVrbfeySzB2YaEoQQqXt6rn1t82s4FSfZ9myEEBTaLFiM+fTkIU+y8qyV5BmlQHoUUIv8I0bBA5HBK4QQlLp15ff4/qZHaN9vtxlrzakoXFZeiJJm0FUsFnwGGqbUh7grUiknvdsrSQqynKmUZJ11Jv0XfJl2kQLxEP5+Icr/9tA+z+69lv88sITXr7+Uy197DrGfvQn2L69l2bP4+gy9HmsPCEePXCuEwCd6f0dOaTnZxaW9jgOpyBgAnz3T6ZA+l/fXnuEeJ8PTPJtFDh+KFmNWbnGf15seMAmLLIEmIVARIq57wCTBJSMv6XXXgRQwTbX0CQD0P4H+13jAupLJ/YYgCiF4dEjv+lfpNNrr5Hf9yrhjRwtuZ1Wv85KkUJB/DF/ldPJoazmvGoiEhcbm2rN52iRBVE0X8n984uRaLVxenk8oqTLI5WBmGoT+TyXLfsJfjsj180rtcpaFu3t5wP4rZJEEh+wTricJKUO4UTWB6JEMDAXs3OJMhKl9GYf+7PRvkCh32BBaGmDGv4lS+S8/YWz2JVkS2BSJYr8Ze3PthOtIGtDH+XlHkjSShXtq0RXuo1z17CU9vscPxw6g+AAK2L7C4L4k/hWl5gDkUWROzA8wLbD/+ZdvWEKdxnz/V0fFtR8vdSYZiuWPCHLppPURHlwUK8G5ZxMU/wQFbHAp3V/r7MPm2k9YqM9gcpKFe6ffy9Nrn2ZEzoiMSyRJIpHYTw7ZfwAJIShwFfQ63sMAPe4sYl3tuKb+NAXyQILi88OrWBsM45Al4kmN3aKEUJ2DnnqqkoEW1iAGAh+k7huxZj2hAt1LNmzYQ9zy2j/pjPmRlA7a3YKApIHhud/fHBFC8LdmGy0Jc6wP5K0DkP1lGb/7jZuIvB8Al1QOWI8ClNTwRX8aeMR/hcR+vP1uWaYjkTxg3mYPYpwQ8MTZ4ygxShHM6zePef0yw0zH5Y9jWPYwQgbcc7oHzC5LPD28EoDXDP6Xbpn2uu10RkBWFKzW/UeYpN417nVamhcgGeFyfv+E1N+gXEMz/tS1zq75ZmFw9P4PJlXsLhfdNj+72/U15xZt0FPjqg+eKCwWXFYFSIC6f4W52F2Mx+rhjzP/yK0fmHmE9n0EwaeHV/FafSvXbd6TOnZhXownG83vV3JzKX7gz7imTaP91QfpiH3IFYN+x5AlegiiMKIKep6toWUovvuS/QD7fY9BxCr/eP/vi5zbww+LbFa6Eqp5jSDlAXMYfH1/KQ//Kr1ytF4M22P1MK14Gt/UftPLqGO39Ch9pvK4c+sYiliLZFVSIYjd8e5ezw+ecDzeF19Ccvy02nK9SJIhYuQcWjPHJF1+yCoupbW2hmyfl7ZuPR3lQAqYLuvsZ78w5sH41iRX5WVz0QAzBN9qkXFoYcLix79n8emLM8bY63GA4Uh/ZFAZ1c40SPof2Rt7yGOxsnLaGLIsMi/uvyQqFkkylCYBQgUpjqbpCphd6QGf09G7Q4nQAfkImgXlXwz9/U+h/z0KWELdbwjiTyWbMTH2pzQNHapbb0tDJoRrgd2OTRIpy/73kwYzepFZTPhfaZFTlrh0H4TCn0oHWkA5peXklFX0ymv476IkIAyrjZbUDhhSlU4ZCtg+8L7/FWVpf6T1hLjtN9DlwPTY6WMYXGTm8rjdJqBJVdWVqf9nZXn4dqKJdtlDKQXMsASO9h44dv6nhCD+u+jHDBU9yJU91rOeHuwDxLFP2leA6ZuMh/2IB8ZqzSGZjJBMBvu0Rg6Oj4Y9K6Fs3zCd3s9SsnWB/9S778eyv3XiygHFDoqdAlcBN0y8odcl8r9xLP47SdsncKpnfRUWVOPy5+KaNOknPedAXqVsq5IyJsWSKq2Sh5qFRWCkHkj7rPF1jGMoy9jV5GUGuwBQFA+NsTEkkuGUZ0VFNb2jB+jveUOv4cHlD6Z+H5DJ90HH/ubm/Z4zjX1G+KHFgv+Qin/p+f8Kif18p0uW6EgkMzxg+1KPAoYGBw/J3+91AAOzBvLPo/7JCe+eAGSCcPRF6SvL57GzJxLBIitYf4LA63EPwuMelPodCExg5oxVKIobeCPz4n28VQGLQm00jlUS6fhElFo0MMoF7Ov57iGH2wok0PapbZVON068kZjhVU8rq5VZ0gZ9H6xyZO4Xp5cP58nGTRnHvIfrocGaUNnm24bP6wQDhl4YXtEedMACZ24vNM2M9lv3v+Z65vj+DLJ9XQuZyLn9XXZa4pkGrUKbhXv6F3OkAYRwIM/3j5HNAO5RCgpxWXsb+xShUPHaa4ie6CJjrcUNY+7cOdu4atPniIK/kp9Xhl/V29Qebe/1rPD06Uy8ef/r+EdJyKBFAAEHWAsn33IPezasxZJuKNlf5Ao98lrfURtCNo2b15blI6d5za0lbtxqI2H5x9fXvrng3rTQ+hMKMufXT1XAAHIMg8Ex/Y5hUlEmn/BavQQTUOjKRwhBVY6XXd26B0xTbchCYEvzOuY4ctjdtbvP+ZTsCQ/FhqOPkPH/CfS/QgHTNI2uRPJfC2/q6znG3x97ii3N2uiQbbw/pj8VxiZcaMu0PP0UD9i/gw60gIbOnMvQmXP/r7QDdIYve/R+sJb+dG/eviGIZPz6P+vHdAVOSwuB+K/QQT8ixKS/c1/lC+DoXD8fN3cy0PXTrHL7DUHsec9+rPT/HZRnVcixKJQ79LHqmd/7CvT7o39lPYgfsXpNn7YYVY2xc+dfKC+/uI+XGf2yDyOS+phLcpYO+d5XqIrZIAEnPg25A/d7SV95CP8WOnc+dDf++HUGuWfORA33jSIHML1YBxaY7NbDz3o8zKcPOp2Rub2Bf/5PaUihl9V1HXw/xMxlkveZMssjwxh63+fE7vkYYma4tyIJkqoGSUMBU1XK/vEMne+9hxzYv6B6wfALaI208tz65/T3/R8IjfuSqfDof0t9DiTn/0FY1k8k14zpmb9TocD7v0f7L+ydPcJ5ugesz+vSFL+A1wlNERRFxqL86/m5gKF8kQpT6yFhlPv4x4MJ7FMncfsIw9MjBLLxfaO1Vn47dio0vqnftB8wovKphTQ/dyfxnV/vtx2FaRDd6S3pET7TybKP8tuXh90kA+nQakVLxhGyJVWwvSeKxi4f2FBa7HcSa5lGVUlTr3M94/avesCK3CZoyk1VhXTvkxsuhOD8kj7Cnv8LlH3RhTjHj8M5dmyf54UQGSkWNsMDFknLPZw+cSBX7zqPfxbkcIb/DN7b/h4TCyf+W9qXQT08xOaFA+ztLn+AgZONtSlJoKopxbovOpAClh5WItkzn+EYmo2ntYam/8JW5rHYgQRCU3vx4Z+isO9Ld027q9exLLuPumCELJsfgHHl2dRthpgaR0u6kSRTAdPQUgpYX4a8iFHoeWRJHhfP6B2V9j+B/lcoYCFVRYVUXPh/lXo8Ej9mpE8HKZAlwfA+4E9T5/8PFYefSj8ln+n/FqkaKNkO8n89BiX3p7v+MzcBvd8GTZ7Bl7E3/q3fl1LA/i+Nzb50YkEWR+f5MxT5A5G8v1CGnhywf1MI4k8hqySxfMqQVIHqlAfsv+NlP0FgliQrVVW/7vtk0mBw+1gu+/KAuWfNJOfSX2IfuH/lCoBBRxzw9H+bB6xi6r90+Y/lSJZ4SlhzzhoWLFgApNX9E+L/LL9jP/TMeeNZX9+PX3x9d+rYvh6wYKuunN174kg+f+m71HFZFiRUFYyQRRUV+4AB/H/t3XmUZGWZ5/HfExEZkWtl1r6vVNZiUaxVxQ6FxaZAgx6hbZRCDjpHVBxtpR3paXv6YB/pGaGFUemRM3bbtqMHdGhH4TQ64kJDo6IiyDAsAyqbgiJVFFBbxjt/3HsjIyJjj3tvxI34fs7hVJEZy1uZb9x7n/s87/MOfqh+k5Diz3hYZVOS10josqXz9KX/+23tHTtdSwejD77W3XefUmU3+NaNDOqxV/dVvKkQOH5ygfRrqf5mEdOCG3q1SuEk6QM71kmf8v4+d8wLulauXa/5IxkNHTC91mhqvNyMp3kZmbHXpMWnnlPYhy2bssL5emLuSg1lc9IZV0uzlkgbzq740qOjg3ruye81PJTgsv8jA6MVSz2zZRezmVqnFfOOlMMDWb1y518pPWupLP1fJU1XB9S7/hjKpvXIn95Y8XvBDaCGMmBFx9egjE/yuiKPRnhOsXS6avBVyXUXHqEbvvOYNi+dHuObj1qmo1fO1sq5I5Lm6sFLHqz+Au0Ift9Fe7IdsubD+s1vv171Kav/+Va9fMe3Cmv/Kr5sjeJ9M5s+SZVNpsGNczV+Z0Zq4XAzMTAsabdSFc7WzWTAagmOt0GA5123eRkw87e2KM6AzR3ybnxWqk4IArBFY2NaOKu1Gzqd1hcBWFCvXG0NWKOCc0Wtk5lU2qSg0iMfOH6T/sNDP9Ltu0Zq35rsOUEA658EFtU+eZcrufDzL842nnCK9N32A7DiC7F2SxDD0GjwJdVYCxXMrZjL3oovQoKfYJPN3RrT7q+nEICVzsNKd6jTY2Oa//73t/mGEWbAIhZ8FvINboLbrInhrI5fs0i5708HEOUB2Ku7XpIkHX/I/JIm/kEGLJX3frZHLThKjbKSm2X1PyfP5hYpl99X93EpM/31umX6yi+8nZGX5upnHNqVHp15PL1+wwqdO3+31o1Uv0C5+vzD9JkbZm6LUPO9GsyAnTg5Tw/7f58z4v0MDjvyaK2eN6J79x3Q8/sPVH9yTeUZMC8AW//Az2UDA5p4xOvYlk2lZn6eh2ZLp15V8qWlN1yv/b/yylqrlXPWG0m1wKg8A1Zepljp1XKZjPa/8rwOvvJ84SJ/qFB61vqBL7jJ0EgGrPiit1bJY6etXTCqG/7kyBlf94KviAU/o8HpJQerVl2uVasur/qUwXXrNLhuXcXv3f7m27V7/25NuSkN7P2F8rm1FR9naZMNpGY2WRkZ0Jw941ILy/lnZUck7ZZVCsBCvDklTV+aZFIZ76ZDep8s7wVeuUxpBkyqfGx+7aBXwTGYTmbwJfVJALY/n9fa4ZwWVCgPaEZQwji3zusUXzxXOlguyA3osJGMbt8lvZTv3gX5UXnXstbWsRXftbv0hNKUc6gZMLVXghiXiz5+rX71wM/qPq7WYt/ITS8v6T4H/QvpsgxYlCFSUgOwQsepSCLpaePDEwraFaSqvFX5zzCdMh3MO6XyGR2d+bg+ecrpDb9f8We8kWPI6nd8RK/sr93NsOT1/dK4ZYPRB2CVjGbSOn9h7YvnkRayc8HPqt4asGLzRv3Oif4ekAtzA4U1o80rmxx+AJbym3uM+8e8rE3fRqt1NJ91xhnT/9NkybafeK06X5spQUynnPIqzTgEAWFQgthOw7fpfcDq/9yLPw/F+4OhSBAY5Grv39ioYK3dw79/WBPP/43Wz14v6aszH5hKyXKV5+nhmxbqO79/sen3DjZ2T1fYZimsa6HyG7KFDFjqoJzzG9YVBVQLhhfIZBUDwCMWHCFJMxoFJUlfBGArhnL612M21n9gHX+8eI4OOKeLFs+t+bjiDFi1g+XSwSFJeT3fxR3RojKvxZNu8V27kya9IM5KUtnh6HQJYqMWT67X4skaJXHB8Dt40V844IYYgoUWBAQZsIHyJijR/d6T0oSj3JELjtR9v72vUBISlfFs0YWM7dcfDs68G2tm2r59u9b5d5GDDNjBKaeJ9IqmNhpvtgTxfa+fbPi1JUl+ABZHCWKrgjnZzOeqXgniBYtmFzohpkZHld+zR2/cvFiv7j+o+aMhnPNmrAErbQoxu6gEsfaaq5maLdkOZmi15QTZsnNTzT1jzUmucgA21GJn2ZL3TjXfhGPT3E2FznQoE/yeBsMJwAKrxldp+dhyXbn1yorft7QpNVh5nl65ebmu+17zAVhQIZaOZsGAp+yGbNrScpaX2X6l5Hc/DK7znPSWybdo9fjqisf01eOroystjUlfBGBhSZvpkrJ26ZUMFB3wqx1rlw+PSdql56fiPbCds+ac+g+KWKst44tPGsGFU+HPkC6anXOFYKGb1s21o5MZsOP9/eAuXNTiBtC1tPs7DwKwskXtNS+Q2pTUDNh7j3ivzlp9lg6ZOKT+g9tw+aHv1j1+Cuzpo/+zfvPVawvfG1843R5/+/bthb+nUyn9dvde5Z30hs0zW+jXUhyAhdmEo/CaU7+X3IGSls7dppU5Wa8E8YYNK3T9Bq9t/5pvfkMHnnpKw/NGdOWZGyo+vmkzPvulAViwDUA2ZU2XPzdbghjkQ6sdNpopQQxKfEvWvAQZsHTp+plWtNKEo1fOg5GwcDNggaHMkG5/8+013taqZsBavRYa9edZpTVgYSm/IZtJZZSXt5YrKEEszoBNDE5ox4r4GsTFjQAsAsUtf6t9GCbH5kjapW3DM/emiEqn7xa0G9hkU0XrQ4INFK00EGvVCUtP0C2P3qJN8zbp/ufvL3ntpGv2giJMK4dyDW8zELugBDFTejFSfMf8v6xfptVD4WWpkxqApVNprZtded1CPR/e8mGtHl/d0GN3LNshPe3tq/T441s0Kul1Q9/ScZ/8StVmM0MDaeWdtPO4lfrgac2NsfhY1Gwb+kYM7H1Qc5+5QotyNTbF6bBMJqPJyUlt27at4ecEwWq1DJgVlf4NLFqkgUXNBcb1lMdU5RmwCb/DXDaV0sFmG300ebxMj+ekfQeVHa98nChvwtFIRq54XlpZF8R2ShBbaUNPAFZDoQlHuAFYXTUyYK0KNnRORbhgwMpSYMWZ3iADFqwBWzORzM6GzSAAi0C2ZA1YZXMHx3XPoQe1ZKKx/XR6SavBUnETjvLgqN2TxI4VO/TDi36o4YFh/ex5b11Vt5cgNiyhZW9VhV2CWJYBS8m7g/7RNYt18ZL6Ge9mJDUAa8clmy5p/MFFH7nXds/Xa4t+oFN1ozR6TdWnvHv7Gr1x8yJtWdVeljWsTl/FTJLl47vJ1goz09ve9ramnlPYmLvCPk2xKD8GuNJmHhNBBsxMU9ZcSXkzG7xL0lVbViv/6NO6aOOSit8vzoDNz2bqNOHwlMzF8jVgbZyXmsmABUE2AVgNxW3oY5RdPtZ0E7N6RtLRlyCWz9xKAdhAakA3nnajNs5pf9lQtyMAi0CugTVgkrRm/pYYRtN9Ws0sFd+1C04k5aWI7QjqjKPq9Ba3ws+mk004GvSuZfM0r2iPrWVxrJmZWCn9+t+k4dILdzPTIydtjuQtzUyTk5M68siZXbugktKyrVu3av73r6v7lAVjg1ow1lqJXyNNOG57023aP1VlT54+lba0BlIDDV3IR2FmBmyq5AxQ3IZ+v4v2RtrcbEZ/e+iqqt/PFc2xO7eub6jEuSQbW8iANdaGvubrNrEGrFBl0uK59dY/ujXUtWNdeUO0QhfEOMy9qHZw8thJm5v+aQ2lTCk5ZWL4TAef3+J1tyk3PVdOXHpi5GPoBt1/ZZZAxSUH7dyt6lUtlyCmZ5YgBqIoH+qVEkQl4A7m1ZPLCn//fydvjmd/vLOvlTa9SVoQ7522ZrMN/aR4qh5/zLGa/f0HpEzjewU2q5ESxBWzVkT2/kmVTqXrtqCPVnkIdlDponqTQ4ZyOmn2qI6cNay7/7BHUud2fAkyYOtHBjU/O6C9U/Vv8BWvRwzOQ4N+G/p2jo2FEsQG9vJrtwRx7ezKLdRbdemhl+quZ+7SofMOrf/guITcBTEsrWy5ZGYay2Sq3lR56/q3av2cOvtg1jE5ktPPXn61sI9ccQYsre5dJxsVArAIVNoHCdNaLkGs1IQjwi6IrTYL6T5d2QS+qpEGSybbDpBzo9L6s9p7DYSr5OZVWrrwi9Ki6C64SrogRlCC+JGtH6m7WXESDWWGSjbnjZ+V/Z9TWtPHjZFMWrcc4QUA977U2RLQoClXcDZppASx0s2AoTDWgKWCzGD9LEewZjuKm5ut2Lpoa8fXsc9QyIB18rMQntF0qur8/PNj/7zt179m3XKdv2C21vv7EpaUIDoCMISgZCNmIrAZwihBLC+PCDNblfdroHsmAwYkRdFHLp0y6XXR7vFS0gUxggvNt7/u7aG/Zje4/PDLtWvfro69fxAczNv1B/1u3NvnrNoNs+CrnTqam5kGbLodfiMliJVuKAYliO38O4JzaCMliPk858G6Ur0VgI2k0zoY4V6Pw+mUXj93OltYHIBlUgRgCEHJPmDkwGZoNVtVXDYxIwAL8edc2Acs4See9MSEDjz7bNOLyrteshJ6aEbRZy6KtvAz3665jZjhWTa2TMvGltV/YFT8Y8AVX/+Sdrvn9I+nVi/N64bD+EDKpgPBRgeUyUgHp7s7BiWI7bShDy54G1m7F9yI7JYMWFfq0hLEVo1lUtp9sPGN5ttVfCMg46IrNe9WBGARKFkD1gUH/27TarCUsYxMJic3fRLz/wi1BFHJ2Ii5nmWf/Yxe/ta3NbCkcncuoOsUZ8BiKAEOPuMZyyT+hks/Ce7BjOx7TXN/96SktPaX7QUWCDJPnfztZs0aCgRPWnqS7nrmLknSIbffpn2PPVb4XtAFsZ19CoMLXtfAXaygGRWfixo61IQjKqPptF5pYI1iWErWgBkZMIQg10Ab+n7W6gHdzDSQGtD+/P6ZXRAjOEkkPQAbWLRIc3Ze3OlhhC/ZvxbUUJKRijEDRvYrmcw5rf6NF0zsV+U799OZp5gGVcFAyhra/+tTp35Krx54VZKUXbFC2RXTDWAKXRDbOAAGF7wH85WD1WJBAMZno4Yey4AdOzGi5/YdqP/AkATz0TlT2sLbbzMpCMAikG1gI+Z+1k5gk01ntT+/f8bPNcwyCU48XY4SxJ52prtNd9jZiiPSDj7jcZQ7IkzTB4Hh/dI5P8zr0GPOrvjIRgKfqGXNZtyMzVQYVjadrVoeGEoTDv882VQAxm3k6oIMTo9kwD64KtwN0+sptKHPZ5XutaUSDSAAi0DpGjCUayewCUooyk8KYQa6LsJFqAhRF1xYIXw79XntdJ+XWXwdz4r3o0FymB+I7bwzr4XHVu6W2Q1HifIM2D8dtkaTw83d8S+sASMD1j0s5f2X7eSWDMlVyIDlc0r34TTjrBOBbNEBi2vEmUIJwPzXiKJlfGENGL+87kSA3CfiWwNGBixZXBCEFB8KqhwXuiEDNmCldR+nzW0+YzIYQgasEIC5+gHYloVbNJGb0Ds3v7P1N+x1qbSUG+NCr0WlAVj/RWAEYBHImHfXzYkuiGELOiEGwVEQLEWxD1jS14ABSRbHDZBCCSKd3hImOEZPB13VKheCgKWjTThS1lb3QqloDVgbL7N+treR7upZq+s+dmJwQne99a7W36wfpDJSR/fDS7ZCE458rq3mMklFABYBM1MuZdqbd1zCVxBGBiy4YArKJML8QUcR1AFoVgxNOMiAJZMfa5XEXFUS491wDh6wVNsXmGGUIJ6x6gzdMuuWQiCGNg2OS2PxrpvqJaUZsG74pMaLACwiWT8A68M5VVc7gU2wQDm4Ox7FQuFC+92uOHUD/cliuAESHEfIgCVLIdZq4hDdzPF8zTe/IRsMry32ybNHCwFUq8IoQZSkDXM2tPcCmHbmJ6Sp/Z0eRWIVtkXI55TqwxQYAVhEvHVgeUoQK2gnsClvwhEES2FeQBX2SOFX15WGjjpSL918s3KTk50eCiIV/QewkAEjAEukkqRXlRLEVlaM5taubWU4VV11SPt7MYbRhh4hG53f6REkWkkJYh9mKwjAIpINoV67V7WztiPIgAVZtLzC3ywyisYeCM/4eedpZNs2NpjucXGsAQveo3hDUHS/XNrrOtdMkX/ST8UpMw2mjGsK9Iyg+2y/liByhRmRIADrvylVXzuBTZABKzThcNGt16ILYncyM4KvHrZxwzUaHl4Ty3uRAUumwZTf9rv4uF8tA9ZDTVMHU6mu6OoIhKGwBmwq13aTmiTitl9Eglb0XMTP1M7PJOiCWMiARbBXSaENPeEzELslSy7QkiUXxPJehTVgNOFIlOmOh8XH6NqRVi+cigdT1GWgdwQBWEpZZfowA0YAFpFcyjhQVtFWAFbWBdG1VOFfW6EJRy+csQFURQYsmVzZn408thecNneWNo8NdXoYQCiCAGxAQ0oRgCEsWWq1qwqlBFHRlSCyDxjQH9gHLKEsaJRUdIzupVrDKj65YXmnhwCEZjgzLEnK2FhfltaSpIlINmV0K6oizCYcU27K+/8Qp3KhBLEPDwhAP2EfsGRK573f28DUVOFr1TZi7v2wDEimhSML9bnTP6fhA0f1ZRMOMmARyVqqJ2rOoxBGG/oZ+4ClyIABaA77gCXTwgPSvF/8RAt37Sl8bfTkkzs4IgCtOG7JcTpn88NaPW+k00OJHQFYRLKsAauqrQxYqjQDFmSrosiARdFZEUD3oA19Mpmkyccf1575iyVJs84+W4Pr13d2UABa8tE3buz0EDqCK8yIZFNGCVsVba0BS5c24YiiYUYUjT0AdB+acCTTnsUrJUm7/T9rrf+a7moLAN2D234RydEutqp2MktBBizKfcAoQQT6A2vAkumFo0/Uu584qHcuk5Y8cK+cfyMOAJKCGCEiWePyvao2fjDT+0aU7gMW5k+bEkSgP9AFMZmcc/rVrMXTXRDzNTJgQcNEzsgAughXmBHx2tBzwK+kndxgeRfEvMLfiJl9wID+QgCWTIVDdI0SxKG0d24Yz/A7BtA9KEGMyEmzx3SwD/YlaUU7wdKcwTkaygwVuiGevuJ03fzIzbrs0MvCGl7VdsYAekuhCyIliIkSHKJdcC6pUYJ4+txZ+tghS7RzydwYRgYAjSEAi8i5CyZ07oKJTg+jK7VTCnL+2vN13JLjCs04JgYndMu5t4Q1NEnFi7bJgAG9LLgZlDFOhUlSuEUWdMOtUYKYMtN7ViyIflAA0ARKEBG7djdiXj62PMTRVMcaMKC30YQj4RooQQSAbsQVJmLX7YFNFI09AHQv1oAl03QJIgEYgGTp7ith9KRuD2wKJYg04QB6WqELIhmwRCl0NkwFXRBpQw8gWQjAELtuD2wK+4B1+TgBtIeNmBOusB8kARiAZCEAQ+y6vQSRJhxAfyh0QSQASxQnV/4FAEiU7r4SRk/q9sCmkAHr8nECaE8QgAUbvCMZCm3og9JRShABJAwBGGLX7aV9wd3Vbs/UAWgPJYjJNN2G3j+XUIIIIGG4wkTsuj2zdN4h50mStiza0uGRAIgSbeiT6ZR18yRJm5eNS5quWgCApCAAQ+y6PbO0ZdEWPXjJg7HtNwagMwpdEMmAJcrRK+fol9ecrXWLvQCMNWAAkqa7r4TRk7o9AAPQH8iAJVxwLmENGICE4UoYANCf/GpoMmDJlBrMeX/OGuvwSACgObR+QuzIgAHoBin/HiRdEJNp8PDDtfCqqzT+R+d2eigA0BTOOogdARiAbsA+YMlmZpqz8+JODwMAmsaVMGLX7V0QAfQH2tADADqBAAyx6/Z9wAD0h0IGjCYcAIAYEYAhdpQgAugGZMAAAJ3AlTBiRwkigG5ABgwA0AkEYIgdJYgAukGhC6LRjwoAEB8CMMQuxbQD0A2CfcDIgAEAYsSVMGJHBgxANwjWo7IGDAAQJwIwxI41YAC6AU04AACdQACG2JEBA9ANCgEYJYgAgBgRgCE2G+ds7PQQAKCg0AWRDBgAIEa0fkJsbjrjJj318lOdHgYASJrOgGVSnAoBAPHhrIPYjOfGNZ4b7/QwAEASGTAAQGdQgggA6EsrZ63U2om1WjO+ptNDAQD0ETJgAIC+NG9onm4979ZODwMA0GfIgAEAAABATAjAAAAAACAmBGAAAAAAEBMCMAAAAACICQEYAAAAAMSEAAwAAAAAYkIABgAAAAAxIQADAAAAgJgQgAEAAABATAjAAAAAACAmBGAAAAAAEBMCMAAAAACICQEYAAAAAMSEAAwAAAAAYkIABgAAAAAxIQADAAAAgJgQgAEAAABATAjAAAAAACAmBGAAAAAAEBMCMAAAAACIiTnnwn9Rsxck/Sr0F+5e8yT9rtODQF9griFOzDfEifmGODHfEIeVzrn55V+MJADrN2Z2n3NuS6fHgd7HXEOcmG+IE/MNcWK+oZMoQQQAAACAmBCAAQAAAEBMCMDC8blODwB9g7mGODHfECfmG+LEfEPHsAYMAAAAAGJCBgwAAAAAYkIA1iAzs06PAQAAAECyEYA1bqjTA0B/MLO0/ydBPyLHfEPczIxrD0SOYxq6GQfBOszsWDP7mqTPmNkZwcUKEDYzO87MbpL0QTMbcyzQRITM7AQz+4Kk/2hmc5hviJKZbTOz90uScy7f6fGgd/lz7SZJHzGzGRvgAt2AAKwGM9su6bOS/qekRyS9XdLsDg4JPcrMTpH0aUl3Sloi6SozO7Ozo0KvMrM18o5t35W0UtLVZnZ2Z0eFXmVmH5B0q7xg/w3+17iZiVCZWdrMPiGvu+Hdko6S9JdmtrCzIwNmIgCrbbOkHzvnviTpi5IGJO3p7JDQo46SdLdz7suSrpa0UNJbzWxRZ4eFHrVV0sPOuX+Q9CFJ90s6x8yWd3JQ6FlPSDpH0uWSPipJzrkpSsQQgaclXegf2z4g6VixhARdiACsiF9uuK7oS3dJusDMPibpp5IWS/qsmV3QkQGiZ1SYa49KmjCzxc65P8gL9LOSzu/E+NBbzOxcM3ufmR3rf+nHkpab2XJ/vt0t6SVJb+7UGNE7Ksy32yQ94P+5JyhFlEQWDG0pmmtbnXNTkr7snHvUzHLOuWflBWTzOjxMYAYCMElmNmFmt0n6tqQLzWxUkpxz90s6S9IqSe9xzm2Xd6Fylplt7MxokWTV5pq8AGy3pC/4aw6XS/qZpFH/edwpRtPMbLGZfUPSn8krn/57MzvTOfeEpH+TdKH/0Eck/R9Jc8xssDOjRdLVmG9TkvLOub2SrpV0mZnNc84d7OR4kVwV5to/mtkZzrkXJck5t8/MxiStlvRsB4cKVEQA5hmRdIekK/y/nxR8wzn3I0nzJf3S/9KdksYkvRLvENEjyufayZLknHtM0p9K+oSkW5xzb5L0kKTt/vdpkIBWbJF0l3PuJOfc1ZKul/Qu/3t3SdpsZtv8C+RnJJ3gXyQDrSifb5+S9G6p5Bj2PUn3yjsGysy2xT9M9IBKc+3yssccI+kh59yzZjZqZpNxDxKopm8DMDPbaWanmNks59wz8hZt3ixpr6RjzGyJ/7icpHskvdd/6g5Jc/3HAXXVmWvbgrnmnNvvnPuuc+4r/lOPlvQvnRk1ksqfb9v9Y9d35K1fDfxe0mP+338oL8v6t34mdpOkX5vZcKwDRqLVmW8vSnrYf1xK8tZ+Sfq4vA51uyQdRYYfjWhirg34X5uQ9JSZXSqv7PqI+EYL1NZXAZh5FpvZdyVdIultkm70SyH2OudelfS/5aWzd0heGlvS/5I0amY/kPQnkt7nnHu+M/8KJEGTc+31Zc890cx+Ii8T+824x47kqTDfLpL0eUnDzrnnii5IFsvv5Oqc+41z7np5gdjn5XV5/Rt/bgJVtTjf8v7z1kr6H/LK+U90zv0dGX5U0+JcO+B/7TxJV8qrNPlj59wt8Y4eqK5vAjAzS/sH+TFJzzjndshLV78oLyMhSXLO3S2v3HC9v15nyDn3kLwP/jucczuccw/H/y9AUrQw1zaY2biZjfjfekLSXzjnznTO/TLWwSNxGpxvwb5Lp0v6qv+8Bf7X/kzSZc65Y5xzj8Q3ciRRC/Pta/7zgr3mdkv6mH8ufTDe0SNJ2phrQdON2+V1RLzUOfdAfCMH6st0egBRM2+vkaslpc3sdkmzJE1JhTa4/17Ss2Z2inPu+/7TbpJXIvFtSSvM7Ci/dOyJ+P8FSIoQ5tpKMzvaOfe0WDSMOpqdb2aWlfSCpEfN7K/ltZ3f7ndBfLlD/wwkREjz7VS/eoQKElQV0lw72d/WBehKPZ0BM29z25/IS0s/Lu8DfUDSqcHCX+dcXtJ/8v8LnC3pPfL2xtnsB19AVSHMtZ/Lm2tPxzdqJFWT8+2v/KcNSnqHvLUTY5JO84MvoKYQ59uLsQ4ciRPiXNsV68CBJvV6Biwv6Vrn3BclycyOlNeS9GOSbpR0tL8w+J8lvd7MVvklX3vlfYB/0JFRI4mYa4hTs/NtmaQlkv5J0nX+FhtAo5hviAtzDX2hpzNg8u6i3OynsyVv0e8Kf4f0tJld4d9JWSZpKlhv45z7OhfEaBJzDXFqZr7lnXNPO+d+5JzbyQUKWsB8Q1yYa+gLPR2AOededc7t89veSt4izRf8v18qaaOZfVPSlyX9VGLDW7SGuYY4NTnffiIx39A65hviwlxDv+j1EkRJhQWdTtJCeS3lJW/R+VWSDpX0ZLDOi3a4aAdzDXFiviFOzDfEhbmGXtfTGbAieUkDkn4n6TD/7slfyEtf/ytNNhAi5hrixHxDnJhviAtzDT3N+uXGgZkdK+ke/7+/d8799w4PCT2KuYY4Md8QJ+Yb4sJcQy/rpwBsmaSL5XXJ2dfp8aB3MdcQJ+Yb4sR8Q1yYa+hlfROAAQAAAECn9csaMAAAAADoOAIwAAAAAIgJARgAAAAAxIQADAAAAABiQgAGAAAAADEhAAMAJIKZTZnZ/Wb2kJn93Mw+ZGY1z2NmtsrMLoprjAAA1EMABgBIitecc0c45zZJOl3SGyT9ZZ3nrJJEAAYA6BrsAwYASAQz2+OcGy36/zWSfixpnqSVkr4oacT/9vucc/eY2b2SNkp6UtIXJN0g6RpJ2yXlJH3GOfffYvtHAAD6HgEYACARygMw/2svSVov6WVJeefcXjOblPRl59wWM9su6cPOuXP8x/87SQuccx83s5ykuyVd4Jx7MsZ/CgCgj2U6PQAAAEIwIOnTZnaEpClJ66o87gxJh5nZW/z/H5c0KS9DBgBA5AjAAACJ5JcgTkl6Xt5asN9KOlze+ua91Z4m6Qrn3B2xDBIAgDI04QAAJI6ZzZf0d5I+7bxa+nFJzznn8pIulpT2H/qypLGip94h6XIzG/BfZ52ZjQgAgJiQAQMAJMWQmd0vr9zwoLymG9f53/uspK+Z2U5J/yLpFf/rD0iaMrOfS/oHSdfL64z4UzMzSS9IOj+e4QMAQBMOAAAAAIgNJYgAAAAAEBMCMAAAAACICQEYAAAAAMSEAAwAAAAAYkIABgAAAAAxIQADAAAAgJgQgAEAAABATAjAAAAAACAm/x/JdKfsoYyCDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_returns.plot(grid = True, figsize = (15,10)).axhline(y = 0, color = \"black\", lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3fd8a25",
   "metadata": {},
   "source": [
    "Now that we have the log returns for each financial instrument, we can find the annualized returns for each financial instrument. We can get the annual returns by simply sum all the daily returns up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71088af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.319282</td>\n",
       "      <td>0.193701</td>\n",
       "      <td>0.117503</td>\n",
       "      <td>0.049852</td>\n",
       "      <td>0.020954</td>\n",
       "      <td>0.283965</td>\n",
       "      <td>-0.027666</td>\n",
       "      <td>0.048156</td>\n",
       "      <td>0.026506</td>\n",
       "      <td>-0.026505</td>\n",
       "      <td>0.199698</td>\n",
       "      <td>0.030662</td>\n",
       "      <td>0.124371</td>\n",
       "      <td>0.129098</td>\n",
       "      <td>0.052942</td>\n",
       "      <td>0.268554</td>\n",
       "      <td>0.105965</td>\n",
       "      <td>0.014491</td>\n",
       "      <td>0.838928</td>\n",
       "      <td>2.363588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.013513</td>\n",
       "      <td>0.198995</td>\n",
       "      <td>-0.514600</td>\n",
       "      <td>-0.134078</td>\n",
       "      <td>-0.225421</td>\n",
       "      <td>0.298659</td>\n",
       "      <td>0.024541</td>\n",
       "      <td>-0.367384</td>\n",
       "      <td>0.637784</td>\n",
       "      <td>-1.265451</td>\n",
       "      <td>-0.242209</td>\n",
       "      <td>0.181163</td>\n",
       "      <td>1.278481</td>\n",
       "      <td>-0.064875</td>\n",
       "      <td>-0.034156</td>\n",
       "      <td>-0.539472</td>\n",
       "      <td>0.393252</td>\n",
       "      <td>0.156808</td>\n",
       "      <td>-1.059485</td>\n",
       "      <td>-0.460700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.356007</td>\n",
       "      <td>0.259581</td>\n",
       "      <td>0.645607</td>\n",
       "      <td>-0.413062</td>\n",
       "      <td>-0.093506</td>\n",
       "      <td>-0.391751</td>\n",
       "      <td>0.463588</td>\n",
       "      <td>-0.006624</td>\n",
       "      <td>0.807603</td>\n",
       "      <td>3.392095</td>\n",
       "      <td>0.305427</td>\n",
       "      <td>-0.794182</td>\n",
       "      <td>2.244699</td>\n",
       "      <td>-0.099735</td>\n",
       "      <td>0.343089</td>\n",
       "      <td>-0.287362</td>\n",
       "      <td>0.220643</td>\n",
       "      <td>0.314603</td>\n",
       "      <td>0.889334</td>\n",
       "      <td>0.060381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-0.293860</td>\n",
       "      <td>0.912590</td>\n",
       "      <td>0.623036</td>\n",
       "      <td>1.791535</td>\n",
       "      <td>0.255500</td>\n",
       "      <td>0.055006</td>\n",
       "      <td>1.813528</td>\n",
       "      <td>0.282009</td>\n",
       "      <td>1.023098</td>\n",
       "      <td>2.587053</td>\n",
       "      <td>0.046794</td>\n",
       "      <td>0.544514</td>\n",
       "      <td>2.277395</td>\n",
       "      <td>0.427211</td>\n",
       "      <td>0.231289</td>\n",
       "      <td>0.698847</td>\n",
       "      <td>0.295764</td>\n",
       "      <td>0.185565</td>\n",
       "      <td>1.680002</td>\n",
       "      <td>1.304168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>5.790780</td>\n",
       "      <td>0.611148</td>\n",
       "      <td>0.461190</td>\n",
       "      <td>4.973412</td>\n",
       "      <td>0.186398</td>\n",
       "      <td>1.081416</td>\n",
       "      <td>0.199510</td>\n",
       "      <td>0.868557</td>\n",
       "      <td>-0.579882</td>\n",
       "      <td>-0.879537</td>\n",
       "      <td>0.357273</td>\n",
       "      <td>1.954119</td>\n",
       "      <td>3.653424</td>\n",
       "      <td>0.566393</td>\n",
       "      <td>0.315585</td>\n",
       "      <td>0.564664</td>\n",
       "      <td>0.491003</td>\n",
       "      <td>-0.039154</td>\n",
       "      <td>0.775613</td>\n",
       "      <td>7.859128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.782203</td>\n",
       "      <td>-0.337514</td>\n",
       "      <td>-0.039788</td>\n",
       "      <td>0.016453</td>\n",
       "      <td>-0.415544</td>\n",
       "      <td>-0.319002</td>\n",
       "      <td>0.071757</td>\n",
       "      <td>-0.567936</td>\n",
       "      <td>-1.319802</td>\n",
       "      <td>-0.049928</td>\n",
       "      <td>0.155768</td>\n",
       "      <td>-0.313453</td>\n",
       "      <td>0.404867</td>\n",
       "      <td>-0.338179</td>\n",
       "      <td>-0.309394</td>\n",
       "      <td>-0.146150</td>\n",
       "      <td>-0.251554</td>\n",
       "      <td>0.312780</td>\n",
       "      <td>-0.693345</td>\n",
       "      <td>-0.690936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMC       CLF      BBBY       GME        DD         M      CLNE  \\\n",
       "Date                                                                         \n",
       "2017  0.319282  0.193701  0.117503  0.049852  0.020954  0.283965 -0.027666   \n",
       "2018  0.013513  0.198995 -0.514600 -0.134078 -0.225421  0.298659  0.024541   \n",
       "2019 -0.356007  0.259581  0.645607 -0.413062 -0.093506 -0.391751  0.463588   \n",
       "2020 -0.293860  0.912590  0.623036  1.791535  0.255500  0.055006  1.813528   \n",
       "2021  5.790780  0.611148  0.461190  4.973412  0.186398  1.081416  0.199510   \n",
       "2022 -0.782203 -0.337514 -0.039788  0.016453 -0.415544 -0.319002  0.071757   \n",
       "\n",
       "            BB       RNG      WKHS      TWNK      SENS      SAVA        TX  \\\n",
       "Date                                                                         \n",
       "2017  0.048156  0.026506 -0.026505  0.199698  0.030662  0.124371  0.129098   \n",
       "2018 -0.367384  0.637784 -1.265451 -0.242209  0.181163  1.278481 -0.064875   \n",
       "2019 -0.006624  0.807603  3.392095  0.305427 -0.794182  2.244699 -0.099735   \n",
       "2020  0.282009  1.023098  2.587053  0.046794  0.544514  2.277395  0.427211   \n",
       "2021  0.868557 -0.579882 -0.879537  0.357273  1.954119  3.653424  0.566393   \n",
       "2022 -0.567936 -1.319802 -0.049928  0.155768 -0.313453  0.404867 -0.338179   \n",
       "\n",
       "           USA         X       HCA      VRTX   BTC-USD  DOGE-USD  \n",
       "Date                                                              \n",
       "2017  0.052942  0.268554  0.105965  0.014491  0.838928  2.363588  \n",
       "2018 -0.034156 -0.539472  0.393252  0.156808 -1.059485 -0.460700  \n",
       "2019  0.343089 -0.287362  0.220643  0.314603  0.889334  0.060381  \n",
       "2020  0.231289  0.698847  0.295764  0.185565  1.680002  1.304168  \n",
       "2021  0.315585  0.564664  0.491003 -0.039154  0.775613  7.859128  \n",
       "2022 -0.309394 -0.146150 -0.251554  0.312780 -0.693345 -0.690936  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "APR = log_returns.groupby([log_returns.index.year]).agg('sum')\n",
    "APR_avg = APR.mean()\n",
    "\n",
    "APR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7aa1b172",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average APR</th>\n",
       "      <td>0.781917</td>\n",
       "      <td>0.306417</td>\n",
       "      <td>0.215491</td>\n",
       "      <td>1.047352</td>\n",
       "      <td>-0.04527</td>\n",
       "      <td>0.168049</td>\n",
       "      <td>0.42421</td>\n",
       "      <td>0.042796</td>\n",
       "      <td>0.099218</td>\n",
       "      <td>0.626288</td>\n",
       "      <td>0.137125</td>\n",
       "      <td>0.267137</td>\n",
       "      <td>1.663873</td>\n",
       "      <td>0.103319</td>\n",
       "      <td>0.099892</td>\n",
       "      <td>0.09318</td>\n",
       "      <td>0.209179</td>\n",
       "      <td>0.157515</td>\n",
       "      <td>0.405174</td>\n",
       "      <td>1.739272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  AMC       CLF      BBBY       GME       DD         M  \\\n",
       "Average APR  0.781917  0.306417  0.215491  1.047352 -0.04527  0.168049   \n",
       "\n",
       "                CLNE        BB       RNG      WKHS      TWNK      SENS  \\\n",
       "Average APR  0.42421  0.042796  0.099218  0.626288  0.137125  0.267137   \n",
       "\n",
       "                 SAVA        TX       USA        X       HCA      VRTX  \\\n",
       "Average APR  1.663873  0.103319  0.099892  0.09318  0.209179  0.157515   \n",
       "\n",
       "              BTC-USD  DOGE-USD  \n",
       "Average APR  0.405174  1.739272  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(APR_avg, columns = ['Average APR']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6444ac",
   "metadata": {},
   "source": [
    "It may sound wrong to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by $[p_{0},p_{1},\\ldots,p_{n}]$. Then the cumulative rate of return is given the same equation we have shown above:\n",
    "\n",
    "$$1+r_{effective}\\approx1+r_{nominal}=ln(\\frac{P_{1}}{P_{0}})+ln(\\frac{P_{2}}{P_{1}})+ln(\\frac{P_{3}}{P_{2}})+\\ldots+ln(\\frac{P_{n-1}}{P_{n-2}})+ln(\\frac{P_{n}}{P_{n-1}})$$\n",
    "\n",
    "According to the equation above, we can sum up these logarithmic returns within a given period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic returns in quant finance.\n",
    "\n",
    "\n",
    "Now we have much more meaningful information rather than starring at ambiguous charts. Now we will derive the APY from the same data. We can calculate APY with the following formula:\n",
    "\n",
    "$$APY = \\big(1+\\frac{i_{nom}}{N}\\big)^{N}-1$$\n",
    "\n",
    "where $i_{nom}$ is the nominal interest rate and $N$ is the number of compounding periods of per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ba016be",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
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       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.374149</td>\n",
       "      <td>0.213085</td>\n",
       "      <td>0.124463</td>\n",
       "      <td>0.051078</td>\n",
       "      <td>0.021168</td>\n",
       "      <td>0.326865</td>\n",
       "      <td>-0.027297</td>\n",
       "      <td>0.049299</td>\n",
       "      <td>0.026850</td>\n",
       "      <td>-0.026167</td>\n",
       "      <td>0.220341</td>\n",
       "      <td>0.031123</td>\n",
       "      <td>0.132187</td>\n",
       "      <td>0.137531</td>\n",
       "      <td>0.054326</td>\n",
       "      <td>0.306732</td>\n",
       "      <td>0.111605</td>\n",
       "      <td>0.014593</td>\n",
       "      <td>1.291098</td>\n",
       "      <td>8.847332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.013604</td>\n",
       "      <td>0.220079</td>\n",
       "      <td>-0.402576</td>\n",
       "      <td>-0.125509</td>\n",
       "      <td>-0.201901</td>\n",
       "      <td>0.347811</td>\n",
       "      <td>0.024843</td>\n",
       "      <td>-0.307643</td>\n",
       "      <td>0.890752</td>\n",
       "      <td>-0.718789</td>\n",
       "      <td>-0.215200</td>\n",
       "      <td>0.198532</td>\n",
       "      <td>2.579546</td>\n",
       "      <td>-0.062823</td>\n",
       "      <td>-0.033582</td>\n",
       "      <td>-0.417282</td>\n",
       "      <td>0.481336</td>\n",
       "      <td>0.169713</td>\n",
       "      <td>-0.654142</td>\n",
       "      <td>-0.369425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-0.299709</td>\n",
       "      <td>0.296213</td>\n",
       "      <td>0.905571</td>\n",
       "      <td>-0.338603</td>\n",
       "      <td>-0.089284</td>\n",
       "      <td>-0.324333</td>\n",
       "      <td>0.589091</td>\n",
       "      <td>-0.006603</td>\n",
       "      <td>1.239632</td>\n",
       "      <td>28.063050</td>\n",
       "      <td>0.356953</td>\n",
       "      <td>-0.548616</td>\n",
       "      <td>8.344247</td>\n",
       "      <td>-0.094941</td>\n",
       "      <td>0.408966</td>\n",
       "      <td>-0.249883</td>\n",
       "      <td>0.246758</td>\n",
       "      <td>0.369446</td>\n",
       "      <td>1.429702</td>\n",
       "      <td>0.062234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-0.254747</td>\n",
       "      <td>1.486680</td>\n",
       "      <td>0.863152</td>\n",
       "      <td>4.960903</td>\n",
       "      <td>0.290941</td>\n",
       "      <td>0.056541</td>\n",
       "      <td>5.092503</td>\n",
       "      <td>0.325583</td>\n",
       "      <td>1.776065</td>\n",
       "      <td>12.117083</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>0.722762</td>\n",
       "      <td>8.652398</td>\n",
       "      <td>0.532424</td>\n",
       "      <td>0.260090</td>\n",
       "      <td>1.009495</td>\n",
       "      <td>0.343921</td>\n",
       "      <td>0.203817</td>\n",
       "      <td>4.335852</td>\n",
       "      <td>2.672300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>305.509241</td>\n",
       "      <td>0.841183</td>\n",
       "      <td>0.585292</td>\n",
       "      <td>136.685354</td>\n",
       "      <td>0.204819</td>\n",
       "      <td>1.942037</td>\n",
       "      <td>0.220707</td>\n",
       "      <td>1.379913</td>\n",
       "      <td>-0.440410</td>\n",
       "      <td>-0.585663</td>\n",
       "      <td>0.429065</td>\n",
       "      <td>6.004703</td>\n",
       "      <td>36.607141</td>\n",
       "      <td>0.760780</td>\n",
       "      <td>0.370790</td>\n",
       "      <td>0.757746</td>\n",
       "      <td>0.633174</td>\n",
       "      <td>-0.038401</td>\n",
       "      <td>1.169337</td>\n",
       "      <td>2295.327591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-0.543349</td>\n",
       "      <td>-0.286674</td>\n",
       "      <td>-0.039010</td>\n",
       "      <td>0.016588</td>\n",
       "      <td>-0.340322</td>\n",
       "      <td>-0.273323</td>\n",
       "      <td>0.074380</td>\n",
       "      <td>-0.433793</td>\n",
       "      <td>-0.734053</td>\n",
       "      <td>-0.048709</td>\n",
       "      <td>0.168480</td>\n",
       "      <td>-0.269273</td>\n",
       "      <td>0.498450</td>\n",
       "      <td>-0.287149</td>\n",
       "      <td>-0.266295</td>\n",
       "      <td>-0.136021</td>\n",
       "      <td>-0.222539</td>\n",
       "      <td>0.366865</td>\n",
       "      <td>-0.500739</td>\n",
       "      <td>-0.499530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AMC       CLF      BBBY         GME        DD         M  \\\n",
       "Date                                                                   \n",
       "2017    0.374149  0.213085  0.124463    0.051078  0.021168  0.326865   \n",
       "2018    0.013604  0.220079 -0.402576   -0.125509 -0.201901  0.347811   \n",
       "2019   -0.299709  0.296213  0.905571   -0.338603 -0.089284 -0.324333   \n",
       "2020   -0.254747  1.486680  0.863152    4.960903  0.290941  0.056541   \n",
       "2021  305.509241  0.841183  0.585292  136.685354  0.204819  1.942037   \n",
       "2022   -0.543349 -0.286674 -0.039010    0.016588 -0.340322 -0.273323   \n",
       "\n",
       "          CLNE        BB       RNG       WKHS      TWNK      SENS       SAVA  \\\n",
       "Date                                                                           \n",
       "2017 -0.027297  0.049299  0.026850  -0.026167  0.220341  0.031123   0.132187   \n",
       "2018  0.024843 -0.307643  0.890752  -0.718789 -0.215200  0.198532   2.579546   \n",
       "2019  0.589091 -0.006603  1.239632  28.063050  0.356953 -0.548616   8.344247   \n",
       "2020  5.092503  0.325583  1.776065  12.117083  0.047902  0.722762   8.652398   \n",
       "2021  0.220707  1.379913 -0.440410  -0.585663  0.429065  6.004703  36.607141   \n",
       "2022  0.074380 -0.433793 -0.734053  -0.048709  0.168480 -0.269273   0.498450   \n",
       "\n",
       "            TX       USA         X       HCA      VRTX   BTC-USD     DOGE-USD  \n",
       "Date                                                                           \n",
       "2017  0.137531  0.054326  0.306732  0.111605  0.014593  1.291098     8.847332  \n",
       "2018 -0.062823 -0.033582 -0.417282  0.481336  0.169713 -0.654142    -0.369425  \n",
       "2019 -0.094941  0.408966 -0.249883  0.246758  0.369446  1.429702     0.062234  \n",
       "2020  0.532424  0.260090  1.009495  0.343921  0.203817  4.335852     2.672300  \n",
       "2021  0.760780  0.370790  0.757746  0.633174 -0.038401  1.169337  2295.327591  \n",
       "2022 -0.287149 -0.266295 -0.136021 -0.222539  0.366865 -0.500739    -0.499530  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = np.array(tr_days_per_year.T)\n",
    "N_total = np.sum(N)\n",
    "APY = (1 + APR / N)**N-1\n",
    "APY_avg = (1 + APR_avg/N_total)**N_total - 1\n",
    "\n",
    "APY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1c417ae",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average APY</th>\n",
       "      <td>1.185117</td>\n",
       "      <td>0.358497</td>\n",
       "      <td>0.240448</td>\n",
       "      <td>1.848826</td>\n",
       "      <td>-0.044261</td>\n",
       "      <td>0.182981</td>\n",
       "      <td>0.52827</td>\n",
       "      <td>0.043725</td>\n",
       "      <td>0.104302</td>\n",
       "      <td>0.870356</td>\n",
       "      <td>0.146963</td>\n",
       "      <td>0.306182</td>\n",
       "      <td>4.273791</td>\n",
       "      <td>0.10884</td>\n",
       "      <td>0.105047</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>0.232644</td>\n",
       "      <td>0.170587</td>\n",
       "      <td>0.499464</td>\n",
       "      <td>4.68621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMC       CLF      BBBY       GME        DD         M  \\\n",
       "Average APY  1.185117  0.358497  0.240448  1.848826 -0.044261  0.182981   \n",
       "\n",
       "                CLNE        BB       RNG      WKHS      TWNK      SENS  \\\n",
       "Average APY  0.52827  0.043725  0.104302  0.870356  0.146963  0.306182   \n",
       "\n",
       "                 SAVA       TX       USA         X       HCA      VRTX  \\\n",
       "Average APY  4.273791  0.10884  0.105047  0.097656  0.232644  0.170587   \n",
       "\n",
       "              BTC-USD  DOGE-USD  \n",
       "Average APY  0.499464   4.68621  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(APY_avg, columns = ['Average APY']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5de6e17",
   "metadata": {},
   "source": [
    "As we can see, APY is larger than APR, as APY takes compounding into consideration. We will primarily be using APY to measure the returns of different investments.\n",
    "\n",
    "### Volatility / Variance / Standard Deviation\n",
    "\n",
    "By reading this notebook, I'll already assume that you understand concepts like volatility, variance, and standard deviation, so I won't spend too much time on it. Variance is a measurement of dispersion. In finance, variance is usually synonymous with risk. The higher the variance, the greater the risk. We represent the risk, or the reward standard deviation, with the symbol: sigma $(\\sigma)$.\n",
    "\n",
    "We calculate variance with the following equation:\n",
    "\n",
    "$$Var(R)=\\sigma^{2}=\\frac{\\sum^{n}_{i=1}(x_{i}-\\mu)^{2}}{n}$$\n",
    "\n",
    "Then, as always, the standard deviation is just the square root of the variance\n",
    "\n",
    "$$\\sigma = \\sqrt(\\sigma^{2})$$\n",
    "\n",
    "Calculating the variance for our dataset is just as simple as calculating the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb83a71d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.220565</td>\n",
       "      <td>0.183237</td>\n",
       "      <td>0.221731</td>\n",
       "      <td>0.131803</td>\n",
       "      <td>0.057254</td>\n",
       "      <td>0.161736</td>\n",
       "      <td>0.114966</td>\n",
       "      <td>0.154313</td>\n",
       "      <td>0.136685</td>\n",
       "      <td>0.182213</td>\n",
       "      <td>0.114372</td>\n",
       "      <td>0.205076</td>\n",
       "      <td>0.128029</td>\n",
       "      <td>0.086873</td>\n",
       "      <td>0.032355</td>\n",
       "      <td>0.116204</td>\n",
       "      <td>0.102872</td>\n",
       "      <td>0.081911</td>\n",
       "      <td>0.503675</td>\n",
       "      <td>1.048447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.432705</td>\n",
       "      <td>0.521096</td>\n",
       "      <td>0.460606</td>\n",
       "      <td>0.484174</td>\n",
       "      <td>0.276186</td>\n",
       "      <td>0.413381</td>\n",
       "      <td>0.617873</td>\n",
       "      <td>0.411403</td>\n",
       "      <td>0.458132</td>\n",
       "      <td>0.787518</td>\n",
       "      <td>0.345632</td>\n",
       "      <td>0.648267</td>\n",
       "      <td>2.745737</td>\n",
       "      <td>0.351978</td>\n",
       "      <td>0.188148</td>\n",
       "      <td>0.468649</td>\n",
       "      <td>0.257291</td>\n",
       "      <td>0.338878</td>\n",
       "      <td>0.768911</td>\n",
       "      <td>1.358696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.448191</td>\n",
       "      <td>0.531097</td>\n",
       "      <td>0.597720</td>\n",
       "      <td>0.716918</td>\n",
       "      <td>0.342987</td>\n",
       "      <td>0.414762</td>\n",
       "      <td>0.574677</td>\n",
       "      <td>0.429792</td>\n",
       "      <td>0.441143</td>\n",
       "      <td>2.425045</td>\n",
       "      <td>0.205089</td>\n",
       "      <td>0.691425</td>\n",
       "      <td>0.992082</td>\n",
       "      <td>0.346038</td>\n",
       "      <td>0.130597</td>\n",
       "      <td>0.583210</td>\n",
       "      <td>0.267603</td>\n",
       "      <td>0.268652</td>\n",
       "      <td>0.694175</td>\n",
       "      <td>0.649491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.403637</td>\n",
       "      <td>0.819863</td>\n",
       "      <td>1.075572</td>\n",
       "      <td>1.184659</td>\n",
       "      <td>0.511504</td>\n",
       "      <td>0.928136</td>\n",
       "      <td>1.124846</td>\n",
       "      <td>0.700887</td>\n",
       "      <td>0.650353</td>\n",
       "      <td>1.221019</td>\n",
       "      <td>0.282543</td>\n",
       "      <td>1.152877</td>\n",
       "      <td>2.096845</td>\n",
       "      <td>0.540436</td>\n",
       "      <td>0.461860</td>\n",
       "      <td>0.786865</td>\n",
       "      <td>0.608776</td>\n",
       "      <td>0.461328</td>\n",
       "      <td>0.724785</td>\n",
       "      <td>1.000601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>3.528605</td>\n",
       "      <td>0.649758</td>\n",
       "      <td>1.187698</td>\n",
       "      <td>2.694811</td>\n",
       "      <td>0.294038</td>\n",
       "      <td>0.679917</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>1.021758</td>\n",
       "      <td>0.499991</td>\n",
       "      <td>1.091671</td>\n",
       "      <td>0.221655</td>\n",
       "      <td>1.412210</td>\n",
       "      <td>2.151014</td>\n",
       "      <td>0.421381</td>\n",
       "      <td>0.221651</td>\n",
       "      <td>0.655333</td>\n",
       "      <td>0.271418</td>\n",
       "      <td>0.261513</td>\n",
       "      <td>0.785985</td>\n",
       "      <td>4.246155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>1.067504</td>\n",
       "      <td>0.532843</td>\n",
       "      <td>1.300501</td>\n",
       "      <td>0.923147</td>\n",
       "      <td>0.286076</td>\n",
       "      <td>0.589043</td>\n",
       "      <td>0.647308</td>\n",
       "      <td>0.488934</td>\n",
       "      <td>0.662463</td>\n",
       "      <td>0.860305</td>\n",
       "      <td>0.230416</td>\n",
       "      <td>0.879354</td>\n",
       "      <td>0.967522</td>\n",
       "      <td>0.409817</td>\n",
       "      <td>0.263274</td>\n",
       "      <td>0.491732</td>\n",
       "      <td>0.379241</td>\n",
       "      <td>0.268804</td>\n",
       "      <td>0.579754</td>\n",
       "      <td>0.788260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AMC       CLF      BBBY       GME        DD         M      CLNE  \\\n",
       "Date                                                                         \n",
       "2017  0.220565  0.183237  0.221731  0.131803  0.057254  0.161736  0.114966   \n",
       "2018  0.432705  0.521096  0.460606  0.484174  0.276186  0.413381  0.617873   \n",
       "2019  0.448191  0.531097  0.597720  0.716918  0.342987  0.414762  0.574677   \n",
       "2020  1.403637  0.819863  1.075572  1.184659  0.511504  0.928136  1.124846   \n",
       "2021  3.528605  0.649758  1.187698  2.694811  0.294038  0.679917  0.974039   \n",
       "2022  1.067504  0.532843  1.300501  0.923147  0.286076  0.589043  0.647308   \n",
       "\n",
       "            BB       RNG      WKHS      TWNK      SENS      SAVA        TX  \\\n",
       "Date                                                                         \n",
       "2017  0.154313  0.136685  0.182213  0.114372  0.205076  0.128029  0.086873   \n",
       "2018  0.411403  0.458132  0.787518  0.345632  0.648267  2.745737  0.351978   \n",
       "2019  0.429792  0.441143  2.425045  0.205089  0.691425  0.992082  0.346038   \n",
       "2020  0.700887  0.650353  1.221019  0.282543  1.152877  2.096845  0.540436   \n",
       "2021  1.021758  0.499991  1.091671  0.221655  1.412210  2.151014  0.421381   \n",
       "2022  0.488934  0.662463  0.860305  0.230416  0.879354  0.967522  0.409817   \n",
       "\n",
       "           USA         X       HCA      VRTX   BTC-USD  DOGE-USD  \n",
       "Date                                                              \n",
       "2017  0.032355  0.116204  0.102872  0.081911  0.503675  1.048447  \n",
       "2018  0.188148  0.468649  0.257291  0.338878  0.768911  1.358696  \n",
       "2019  0.130597  0.583210  0.267603  0.268652  0.694175  0.649491  \n",
       "2020  0.461860  0.786865  0.608776  0.461328  0.724785  1.000601  \n",
       "2021  0.221651  0.655333  0.271418  0.261513  0.785985  4.246155  \n",
       "2022  0.263274  0.491732  0.379241  0.268804  0.579754  0.788260  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STD = log_returns.groupby([log_returns.index.year]).agg('std') * np.sqrt(N)\n",
    "STD_avg = STD.mean()\n",
    "std = log_returns.std()\n",
    "\n",
    "STD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f850935a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average STD</th>\n",
       "      <td>1.183535</td>\n",
       "      <td>0.539649</td>\n",
       "      <td>0.807305</td>\n",
       "      <td>1.022585</td>\n",
       "      <td>0.294674</td>\n",
       "      <td>0.531163</td>\n",
       "      <td>0.675618</td>\n",
       "      <td>0.534514</td>\n",
       "      <td>0.474794</td>\n",
       "      <td>1.094628</td>\n",
       "      <td>0.233285</td>\n",
       "      <td>0.831535</td>\n",
       "      <td>1.513538</td>\n",
       "      <td>0.359421</td>\n",
       "      <td>0.216314</td>\n",
       "      <td>0.516999</td>\n",
       "      <td>0.314533</td>\n",
       "      <td>0.280181</td>\n",
       "      <td>0.676214</td>\n",
       "      <td>1.515275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMC       CLF      BBBY       GME        DD         M  \\\n",
       "Average STD  1.183535  0.539649  0.807305  1.022585  0.294674  0.531163   \n",
       "\n",
       "                 CLNE        BB       RNG      WKHS      TWNK      SENS  \\\n",
       "Average STD  0.675618  0.534514  0.474794  1.094628  0.233285  0.831535   \n",
       "\n",
       "                 SAVA        TX       USA         X       HCA      VRTX  \\\n",
       "Average STD  1.513538  0.359421  0.216314  0.516999  0.314533  0.280181   \n",
       "\n",
       "              BTC-USD  DOGE-USD  \n",
       "Average STD  0.676214  1.515275  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(STD_avg, columns = ['Average STD']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003cea0e",
   "metadata": {},
   "source": [
    "The following table shows the overall risk of each financial instrument. As we can see, AMC, GMEand DOGE is among the most volatile, which should surprise no one. TWNK and SAVA are also very volatile but are not as popular. As mentioned previously, the larger the standard deviation, the greater the risk. However, this is probably difficult to see from a table, and we can plot the values to see the dispersion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fefdf0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# configuration - generate different colors & sizes\n",
    "c = [y + x for y, x in zip(APY_avg, STD_avg)]\n",
    "c = list(map(lambda x : x /max(c), c))\n",
    "s = list(map(lambda x : x * 600, c))\n",
    "\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize = (16,12))\n",
    "ax.set_title(r\"Risk ($\\sigma$) vs Return ($APY$) of all  instruments\")\n",
    "ax.set_facecolor((0.95, 0.95, 0.99))\n",
    "ax.grid(c = (0.75, 0.75, 0.99))\n",
    "ax.set_xlabel(r\"Standard Deviation $\\sigma$\")\n",
    "ax.set_ylabel(r\"Annualized Percetaneg Yield $APY$ or $R_{effective}$\")\n",
    "ax.scatter(STD_avg, APY_avg, s = s , c = c , cmap = \"Blues\", alpha = 0.4, edgecolors=\"grey\", linewidth=2)\n",
    "ax.axhline(y = 0.0,xmin = 0 ,xmax = 5,c = \"blue\",linewidth = 1.5,zorder = 0,  linestyle = 'dashed')\n",
    "ax.axvline(x = 0.0,ymin = 0 ,ymax = 40,c = \"blue\",linewidth = 1.5,zorder = 0,  linestyle = 'dashed')\n",
    "for idx, instr in enumerate(list(STD.columns)):\n",
    "  ax.annotate(instr, (STD_avg[idx] + 0.01, APY_avg[idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdee55e",
   "metadata": {},
   "source": [
    "As we we can, DOGE and SAVA yield the greatest return on investment, but have the greatest amount of risk involved. We've also seen big gains on popular meme stocks such as GME and AMC. We haven't seen a lot of meme stocks with lots of volatility, which is intersting because assets that are discussed frequently on r/WallStreetBets tend to experience a lot of momentum. Regardless, these were the assets that were the most popular a year or so ago, so we're going to keep using these as our example.\n",
    "\n",
    "Now that we have a bigger idea of what we're dealing with, we're going to attempt to analyzing the systematic risk and the expected return of our meme stock portfolio, as well as creating the optimal portfolio by using a method known as CAPM.\n",
    "\n",
    "## Capital Asset Pricing Model\n",
    "\n",
    "The **Capital Asset Pricing Model** (CAPM) is a classic measure of the cost of capital. It is often used in finance to evaluate the price of assets and to assess the impact of the risk premium from the market at large.\n",
    "\n",
    "In general, portfolios and assets can face two types of risk: idiosyncratic and systematic risk. Idiosyncratic risk refers to the risks that are firm-specific and can be diversified away, such as supply-chain issues or faulty production. Systemic risk is market-wide and affects all market participants, such as a stalling economy or interest rate changes. Because all firms are exposed to systematic risk, it cannot be diversified away.\n",
    "\n",
    "### The Theory\n",
    "\n",
    "We can express the CAPM more clearly like so:\n",
    "\n",
    "$$E[R_{i}]=R_{F}+\\beta(E[R_{M}]-R_{F})$$\n",
    "\n",
    "where $R_{i}$ is the return of asset $i$, $R_{F}$ is the risk-free rate, and $R_{M}$ is the return of the market. The CAPM is one of the most basic measures of the cost of capital. It determiens the minimum return required to entice investors to hold a certain asset.\n",
    "\n",
    "To put it another way, CAPM says that the return of an asset should be the risk-free rate, which is what we would demand to account for inflation adn the time value of money, as well as something extra to compensate us for the amount of systematic risk we are exposed to.\n",
    "\n",
    "### CAPM Assumptions\n",
    "\n",
    "There are many different assumptions involved with CAPM, however, we are going to focus to two main assumptions:\n",
    "\n",
    "* We assumed that investors are able to trade without delay or cost and that everyone is able to borrow or lend money at the risk free rate.\n",
    "* We assumed that all investors are \"mean-variance optimizers.\" This essentially means that they would only demand portfolios with the highest return attainable for the given level of risk. These portfolios are along what we call the **efficient frontier.**\n",
    "\n",
    "## Descriptive/Financial Statistics Analyisis\n",
    "\n",
    "We're going to be calculating the alpha ($\\alpha$) and the beta ($\\beta$) of a given portfolio. What is the purpose of $\\alpha$ and $\\beta$? It can be derived from the expanded CAPM formula, which is shown here:\n",
    "\n",
    "$$E[R]-R_{f}=\\alpha+\\beta*(E[R_{m}]-R_{f})+\\epsilon$$\n",
    "\n",
    "$\\alpha$, a measurement of performance, can be derived from the following:\n",
    "\n",
    "$$\\alpha=(\\bar{E[R]}-R_{f})-\\beta*(\\bar{E[R_{m}]}-R_{f})$$\n",
    "\n",
    "$\\beta$, a measurement of systematic risk, which can be found with a simple regression. We can be derived $\\beta$ from the following formula:\n",
    "\n",
    "$$\\beta=\\rho_{i, m}\\frac{\\sigma}{\\sigma_{m}}$$\n",
    "\n",
    "where $\\rho_{i,m}$ is the covariance between two different assets, namely a single stock and the market index. \n",
    "\n",
    "$$\\rho_{i,m}=\\frac{Cov_{i,m}}{\\sigma*\\sigma_{m}}$$\n",
    "\n",
    "We're going to create two functions. Mainly for the purpose of visualization, so I won't go into detail explaining what they do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3916ddf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_statistic(statistic, title, limit = 0):\n",
    "  # configuration\n",
    "  fig, ax = plt.subplots(figsize = (15,8))\n",
    "  ax.set_facecolor((0.95, 0.95, 0.99))\n",
    "  ax.grid(c = (0.75, 0.75, 0.99), axis = 'y')\n",
    "  colors = sns.color_palette('Reds', n_colors = len(statistic))\n",
    "  # visualize\n",
    "  barlist = ax.bar(x = np.arange(len(statistic)), height =  statistic)\n",
    "  for b, c in zip(barlist, colors):\n",
    "    b.set_color(c)\n",
    "  ax.axhline(y = limit, xmin = -1 ,xmax = 1,c = \"blue\",linewidth = 1.5,zorder = 0,  linestyle = 'dashed')\n",
    "\n",
    "  # configure more\n",
    "  for i, v in enumerate(statistic):\n",
    "      ax.text( i - 0.22,v + 0.01 , str(round(v,3)), color = 'blue', fontweight='bold')\n",
    "  plt.xticks(np.arange(len(statistic)), instruments)\n",
    "  plt.title(r\"{} for every instrument (i) against market (m) S&P500\".format(title))\n",
    "  plt.xlabel(r\"Instrument\")\n",
    "  plt.ylabel(r\"{} value\".format(title))\n",
    "  plt.show()\n",
    "\n",
    "def visualize_model(alpha, beta, data, model):\n",
    "  fig, axs = plt.subplots(5,4, figsize = (14,10),  constrained_layout = True)\n",
    "  # fig.tight_layout()\n",
    "  idx = 0\n",
    "  R_m = data[\"^GSPC\"]\n",
    "  del data[\"^GSPC\"]\n",
    "  for a, b, instr in zip(alpha, beta, data):\n",
    "    i, j = int(idx / 4), idx % 4\n",
    "    axs[i, j].set_title(\"Model : {} fitted for '{}'\".format(model, instr))\n",
    "    axs[i, j].set_facecolor((0.95, 0.95, 0.99))\n",
    "    axs[i, j].grid(c = (0.75, 0.75, 0.99))\n",
    "    axs[i, j].set_xlabel(r\"Market (S&P500) log returns\")\n",
    "    axs[i, j].set_ylabel(r\"{} log returns\".format(instr))\n",
    "\n",
    "    R = data[instr]\n",
    "    y = a + b * R_m\n",
    "    axs[i, j].scatter(x = R_m, y = R, label = 'Returns'.format(instr))\n",
    "    axs[i, j].plot(R_m, y ,color = 'red', label = 'CAPM model')\n",
    "    idx += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3375728",
   "metadata": {},
   "source": [
    "We're also going to create a new list of tickers for the portfolio we are going to construct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48401bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AMC',\n",
       " 'CLF',\n",
       " 'BBBY',\n",
       " 'GME',\n",
       " 'DD',\n",
       " 'M',\n",
       " 'CLNE',\n",
       " 'BB',\n",
       " 'RNG',\n",
       " 'WKHS',\n",
       " 'TWNK',\n",
       " 'SENS',\n",
       " 'SAVA',\n",
       " 'TX',\n",
       " 'USA',\n",
       " 'X',\n",
       " 'HCA',\n",
       " 'VRTX',\n",
       " 'BTC-USD',\n",
       " 'DOGE-USD']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instruments = list(log_returns.columns)\n",
    "instruments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf393cd0",
   "metadata": {},
   "source": [
    "We need to incorporate the risk-free rate, $R_{f}$, into our model. We're going to use the `DataReader` method to import the 13-week Treasury Bill (IRX). We also need to import data for the S&P 500, which is a proxy for the market return, $R_{M}$.\n",
    "\n",
    "Afterwards, we will utilize the same methods we used to calculate annualized returns for our portfolio and we're going to include them in our average return/risk table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "38273de2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk-Free rate (Daily T-bill) = 3.179999828338623\n"
     ]
    }
   ],
   "source": [
    "# [*] Risk-Free Asset : 13 Week Tbill (^IRX). Get the most recent value\n",
    "risk_free = web.DataReader('^IRX', data_source = 'yahoo', start = start, end = end)['Adj Close']\n",
    "risk_free = float(risk_free.tail(1))\n",
    "\n",
    "print(\"Risk-Free rate (Daily T-bill) = {}\".format(risk_free))\n",
    "\n",
    "# [*] Market          : S&P 500 index (^GSPC) | Yahoo Finance for index pricing, '^GSPC' is the underlying for 'SPX' options.\n",
    "market = web.DataReader('^GSPC', data_source = 'yahoo', start=log_returns.index[0], end=end)['Adj Close']\n",
    "market = market.rename(\"^GSPC\")\n",
    "market_log_returns = market.pct_change()\n",
    "log_return_total = pd.concat([log_returns, market_log_returns], axis = 1).dropna()\n",
    "\n",
    "# Descriptive statistics\n",
    "# Return\n",
    "log_returns_total = pd.concat([log_returns, market_log_returns], axis=1).dropna()\n",
    "APR_total = log_returns_total.groupby([log_returns_total.index.year]).agg('sum')\n",
    "APR_avg_total = APR_total.mean()\n",
    "APR_avg_market = APR_avg_total['^GSPC']\n",
    "# RISK\n",
    "STD_total = log_return_total.groupby([log_return_total.index.year]).agg('std') * np.sqrt(N)\n",
    "STD_avg_total = STD_total.mean()\n",
    "STD_avg_market = STD_avg_total['^GSPC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "206c136f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>...</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average APR</th>\n",
       "      <td>0.781917</td>\n",
       "      <td>0.306417</td>\n",
       "      <td>0.215491</td>\n",
       "      <td>1.047352</td>\n",
       "      <td>-0.04527</td>\n",
       "      <td>0.168049</td>\n",
       "      <td>0.42421</td>\n",
       "      <td>0.042796</td>\n",
       "      <td>0.099218</td>\n",
       "      <td>0.626288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.267137</td>\n",
       "      <td>1.663873</td>\n",
       "      <td>0.103319</td>\n",
       "      <td>0.099892</td>\n",
       "      <td>0.09318</td>\n",
       "      <td>0.209179</td>\n",
       "      <td>0.157515</td>\n",
       "      <td>0.405174</td>\n",
       "      <td>1.739272</td>\n",
       "      <td>0.073327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMC       CLF      BBBY       GME       DD         M  \\\n",
       "Average APR  0.781917  0.306417  0.215491  1.047352 -0.04527  0.168049   \n",
       "\n",
       "                CLNE        BB       RNG      WKHS  ...      SENS      SAVA  \\\n",
       "Average APR  0.42421  0.042796  0.099218  0.626288  ...  0.267137  1.663873   \n",
       "\n",
       "                   TX       USA        X       HCA      VRTX   BTC-USD  \\\n",
       "Average APR  0.103319  0.099892  0.09318  0.209179  0.157515  0.405174   \n",
       "\n",
       "             DOGE-USD     ^GSPC  \n",
       "Average APR  1.739272  0.073327  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(APR_avg_total, columns = ['Average APR']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "379dc41f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>...</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average STD</th>\n",
       "      <td>1.183535</td>\n",
       "      <td>0.539649</td>\n",
       "      <td>0.807305</td>\n",
       "      <td>1.022585</td>\n",
       "      <td>0.294674</td>\n",
       "      <td>0.531163</td>\n",
       "      <td>0.675618</td>\n",
       "      <td>0.534514</td>\n",
       "      <td>0.474794</td>\n",
       "      <td>1.094628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.831535</td>\n",
       "      <td>1.513538</td>\n",
       "      <td>0.359421</td>\n",
       "      <td>0.216314</td>\n",
       "      <td>0.516999</td>\n",
       "      <td>0.314533</td>\n",
       "      <td>0.280181</td>\n",
       "      <td>0.676214</td>\n",
       "      <td>1.515275</td>\n",
       "      <td>0.16678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMC       CLF      BBBY       GME        DD         M  \\\n",
       "Average STD  1.183535  0.539649  0.807305  1.022585  0.294674  0.531163   \n",
       "\n",
       "                 CLNE        BB       RNG      WKHS  ...      SENS      SAVA  \\\n",
       "Average STD  0.675618  0.534514  0.474794  1.094628  ...  0.831535  1.513538   \n",
       "\n",
       "                   TX       USA         X       HCA      VRTX   BTC-USD  \\\n",
       "Average STD  0.359421  0.216314  0.516999  0.314533  0.280181  0.676214   \n",
       "\n",
       "             DOGE-USD    ^GSPC  \n",
       "Average STD  1.515275  0.16678  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(STD_avg_total, columns = ['Average STD']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b832a09",
   "metadata": {},
   "source": [
    "So we see that the risk-free rate is currently trending around 3.18%. We've calculated the market return for the last 5 years, which is roughly 7.81%. Compared to our selection of meme stocks, that may not seem like a lot. The vast majority of stocks in our portfolio yield a minimum of 10% or more, and GME and DOGE have yielded +100% within the last 5 years. However, it would be foolish to only look at the upside without considering the downside.\n",
    "\n",
    "Consider $\\sigma$, or proxy for our asset's annualized risk/volatility. The market risk is ~16%. Relative to its return, it may seem like it's risky to hold your money in the S&P 500; however, take a look at our meme stock portfolio. A lot of the stocks are very risky. The question is, how risky?\n",
    "\n",
    "We can visualize the alpha and beta with the previously created functions using the mathematical formulas we derived earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2386e990",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = log_returns.corrwith(market_log_returns)\n",
    "r_squared = corr ** 2\n",
    "\n",
    "def CAPM():\n",
    "  # 1 - Calculate average Risk Premium for every instrument  \n",
    "  # [*]  _\n",
    "  #     E[R] - R_f\n",
    "  # [*]   __\n",
    "  #     E[R_m] - R_f\n",
    "  APR_premium        = APR_avg - risk_free\n",
    "  APR_market_premium = APR_avg_market - risk_free\n",
    "\n",
    "  # 2 - Calculate α, β\n",
    "  beta  = corr *  STD_avg / STD_avg_market\n",
    "  alpha = APR_premium - beta * APR_market_premium \n",
    "  \n",
    "  return alpha, beta\n",
    "\n",
    "alpha, beta = CAPM()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40650e09",
   "metadata": {},
   "source": [
    "The baseline of $\\alpha$ is 0. An $\\alpha$ of 0 implies that the investment earned a return that matched the overall market return, as reflected by the selected benchmark index (or benchmark is the S&P 500). When $\\alpha$ is negative, the portfolio underperformed the benchmark index; positive $\\alpha$ indicates the opposite.\n",
    "\n",
    "So essentially, $\\alpha$ greater than 0 means that an asset has \"beat\" the market, but by how much? Since 0 is our benchmark, a 1 unit increase in $\\alpha$ means that a stock \"outperformed\" its benchmark by roughly 1%. Investors in mutual funds or ETFs often look for a fund with a high alpha to get a superior return on investment.\n",
    "\n",
    "What can we say about $\\beta$? Earlier, we mentioned that $\\beta$ is a measure of systematic risk, but mathematically it is derived from a regression analysis. This means that $\\beta$ also measures correlation, not just with other stocks but with the broader market. \n",
    "\n",
    "A $\\beta$ greater than 1 indicates that the asset is more volatile than the market; $\\beta$ less than 1 but greater than 0 indicates that the asset is less volatile than the broader market. If an asset has $\\beta$ less than 0 (very rare), it means that the asset is negatively correlated with the market. These assets offer a great hedge against market volatility. Of course, when $\\beta \\approx 0$, we can say that the asset is uncorrelated with the market; however, it is highly unlikely that you'll come across an asset like this, at least if the asset we're talking about stocks. Most research suggests that the vast majority of stocks are positively correlated with the market.\n",
    "\n",
    "So how does our meme stock portfolio perform?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ca2047cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average α</th>\n",
       "      <td>0.630232</td>\n",
       "      <td>2.404753</td>\n",
       "      <td>1.353394</td>\n",
       "      <td>0.722527</td>\n",
       "      <td>0.573957</td>\n",
       "      <td>1.512051</td>\n",
       "      <td>2.697135</td>\n",
       "      <td>0.94768</td>\n",
       "      <td>0.958793</td>\n",
       "      <td>-0.029312</td>\n",
       "      <td>-1.449014</td>\n",
       "      <td>1.626305</td>\n",
       "      <td>2.284017</td>\n",
       "      <td>0.361592</td>\n",
       "      <td>0.166555</td>\n",
       "      <td>1.393116</td>\n",
       "      <td>0.457841</td>\n",
       "      <td>-0.189046</td>\n",
       "      <td>0.478573</td>\n",
       "      <td>1.843163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AMC       CLF      BBBY       GME        DD         M  \\\n",
       "Average α  0.630232  2.404753  1.353394  0.722527  0.573957  1.512051   \n",
       "\n",
       "               CLNE       BB       RNG      WKHS      TWNK      SENS  \\\n",
       "Average α  2.697135  0.94768  0.958793 -0.029312 -1.449014  1.626305   \n",
       "\n",
       "               SAVA        TX       USA         X       HCA      VRTX  \\\n",
       "Average α  2.284017  0.361592  0.166555  1.393116  0.457841 -0.189046   \n",
       "\n",
       "            BTC-USD  DOGE-USD  \n",
       "Average α  0.478573  1.843163  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_statistic(alpha.values, \"Alpha α\")\n",
    "\n",
    "pd.DataFrame(alpha, columns = [\"Average α\"]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c81b1e3",
   "metadata": {},
   "source": [
    "As we can see, most of the stocks in our portfolio outperform the S&P 500 by a modest margin. Out of the 20 assets, 8 of them outperform our benchmark at least 1%, and 3 have underperformed our benchmark. If we average the $\\alpha$, it comes out to 0.9386, which means that our meme stock portfolio only outperforms the S&P by 0.93% (I got this figure by simply averaging the $\\alpha$ for each asset). So, should you invest in a portfolio like this? I suppose it \"depends\" on your income goals and overall suitability (after all, this isn't investment advice).\n",
    "\n",
    "If I'm a college student, probably majoring in economics/finance with an interest in quant finance, and I'm just experimenting with investment strategies, I might be grateful that my strategy is at least slightly better than the S&P. However, if I'm a grown adult looking generate wealth, I don't think I would be satisfied with 0.93%, which barely accounts for management fees.\n",
    "\n",
    "Granted, those who consider meme stocks a sound investment are usually self-taught and were first introduced to investing during the AMC/GME/DOGE craze. Needless to say, they are probably managing their own portfolios (no management fees for them). It may be difficult to quantitatively comprehend the idea of an $\\alpha$ 0.93, especially when so many assets can perform much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5ef38a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMC</th>\n",
       "      <th>CLF</th>\n",
       "      <th>BBBY</th>\n",
       "      <th>GME</th>\n",
       "      <th>DD</th>\n",
       "      <th>M</th>\n",
       "      <th>CLNE</th>\n",
       "      <th>BB</th>\n",
       "      <th>RNG</th>\n",
       "      <th>WKHS</th>\n",
       "      <th>TWNK</th>\n",
       "      <th>SENS</th>\n",
       "      <th>SAVA</th>\n",
       "      <th>TX</th>\n",
       "      <th>USA</th>\n",
       "      <th>X</th>\n",
       "      <th>HCA</th>\n",
       "      <th>VRTX</th>\n",
       "      <th>BTC-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Average β</th>\n",
       "      <td>0.974778</td>\n",
       "      <td>1.699032</td>\n",
       "      <td>1.38988</td>\n",
       "      <td>0.919046</td>\n",
       "      <td>1.222925</td>\n",
       "      <td>1.456221</td>\n",
       "      <td>1.75523</td>\n",
       "      <td>1.314874</td>\n",
       "      <td>1.30029</td>\n",
       "      <td>0.812574</td>\n",
       "      <td>0.513044</td>\n",
       "      <td>1.461103</td>\n",
       "      <td>1.22322</td>\n",
       "      <td>1.106738</td>\n",
       "      <td>1.045061</td>\n",
       "      <td>1.442037</td>\n",
       "      <td>1.103644</td>\n",
       "      <td>0.912049</td>\n",
       "      <td>1.047229</td>\n",
       "      <td>1.057045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AMC       CLF     BBBY       GME        DD         M     CLNE  \\\n",
       "Average β  0.974778  1.699032  1.38988  0.919046  1.222925  1.456221  1.75523   \n",
       "\n",
       "                 BB      RNG      WKHS      TWNK      SENS     SAVA        TX  \\\n",
       "Average β  1.314874  1.30029  0.812574  0.513044  1.461103  1.22322  1.106738   \n",
       "\n",
       "                USA         X       HCA      VRTX   BTC-USD  DOGE-USD  \n",
       "Average β  1.045061  1.442037  1.103644  0.912049  1.047229  1.057045  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_statistic(beta.values, \"Beta β\", limit = 1)\n",
    "\n",
    "pd.DataFrame(beta, columns = [\"Average β\"]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d563c6d1",
   "metadata": {},
   "source": [
    "As mentioned before, most assets (especially stocks) are positively correlated with the broader market. Most of the assets in our portfolio have $\\beta$ greater than 1, which means they are more volatile than the overall market. As we see, CLF has a $\\beta$ of 1.7, which means we can expect this stock to increase by 1.7% for every 1% increase in the broader market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "978c805d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(alpha/100, beta, data= log_return_total.copy(), model = 'CAPM')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750540bb",
   "metadata": {},
   "source": [
    "## Portfolio Optimization\n",
    "\n",
    "We have shown that our meme stock portfolio outperforms the S&P 500 by 0.93%, but that doesn't mean it will typically outperform our benchmark by this amount. This amount can change, based on the size of our portfolio, as well as how we weigh each asset. We can figure out how to best do this, by utilizing the **Modern Portfolio Theory** (MPT). The MPT is a method for selecting investments in order to maximize their overall returns within an acceptable level of risk.\n",
    "\n",
    "We've actually utilized this method on more traditional securities in a [different project](https://github.com/KidQuant/401K-Optimization-Using-Modern-Portfolio-Theory/blob/master/401K%20Optimization%20Using%20Modern%20Portfolio%20Theory.ipynb), so I won't go too much into the theory or mathematical concepts here. If you want to learn more about how the code works, you can review the notebook I've provided.\n",
    "\n",
    "One theoretical concept regarding MPT that I will address here is that MPT assumes that investors are rational and avoid risk whenever possible. However, we're looking at a portfolio primarily of meme stocks and other assets. One may not believe that an investor of this portfolio is risk-averse or rational; however, regardless of the assets one decides to invest in, they should be aware of the level of risk they take.\n",
    "\n",
    "For the purpose of this experiment, we're going to assume anyone who invests in this portfolio wants to maximize their return and minimize their risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fccf4e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipynb.fs.full.MPT_Functions import portfolio_annualized_performance\n",
    "from ipynb.fs.full.MPT_Functions import random_portfolios\n",
    "from ipynb.fs.full.MPT_Functions import display_simulated_ef_with_random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967460ce",
   "metadata": {},
   "source": [
    "What we did was we borrowed three functions from another notebook. If you wish to understand how they work, refer to the *401K Optimization Using Modern Portfolio Theory* project.\n",
    "\n",
    "We're going to find the potential returns for 100,000 potential portfolios. We'll use use the `portfolio_annualized_performance` to calculate the the portfolio returns, $R_{p}$, and the risk, $\\sigma$ for each theoretical portfolio. The `random_portfolios` function will create random portfolios by assigning weights for each asset. We're working with 20 different assets, so the possibilities are nearly endless.\n",
    "\n",
    "Finally, we will use the `display_simulated_ef_with_random` function to simulate 100,000 potential portfolio returns with their risk. The function will also provide the optimal asset allocation based on whether the investor is seeking the greatest return or the least amount of risk. Essentially, we are trying to find the most efficient portfolio possible.\n",
    "\n",
    "How do we measure the efficiency? We measure it with another formula known as the **Sharp Ratio**. The Sharpe ratio measures the performance of an investment compared to a risk-free asset, after adjusting for its risk. The sharpe ratio can be calculated by the following:\n",
    "\n",
    "$$Sharpe Ratio=\\frac{R_{p}-R_{f}}{\\sigma_{p}}$$\n",
    "\n",
    "The greater the Sharpe ratio, the better, as it indicates that an instrument's returns are large relative to its risk. Also, the greater the Sharpe ratio, the higher the earnings on average than the risk-free rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "631dd431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "Maximum Sharpe Ratio Portfolio Allocation\n",
      "\n",
      "Annualized Return: 0.84\n",
      "Annualized Volatility: 0.51\n",
      "\n",
      "\n",
      "Symbols      AMC   CLF  BBBY    GME    DD     M  CLNE   BB  RNG  WKHS   TWNK  \\\n",
      "allocation  6.43  6.71  0.85  10.85  0.01  0.71  4.36  5.7  0.5  4.69  11.16   \n",
      "\n",
      "Symbols     SENS   SAVA    TX   USA     X   HCA  VRTX  BTC-USD  DOGE-USD  \n",
      "allocation   0.8  10.03  1.69  3.11  2.81  5.05   3.7     9.66     11.17  \n",
      "--------------------------------------------------------------------------------\n",
      "Minimum Volatility Portfolio Allocation\n",
      "\n",
      "Annualized Return: 0.3\n",
      "Annualized Volatility: 0.3\n",
      "\n",
      "\n",
      "Symbols      AMC   CLF  BBBY   GME    DD     M  CLNE    BB  RNG  WKHS  TWNK  \\\n",
      "allocation  0.87  0.07  7.54  0.98  4.63  2.03  4.66  1.39  8.0  3.12  6.66   \n",
      "\n",
      "Symbols     SENS  SAVA     TX    USA     X   HCA  VRTX  BTC-USD  DOGE-USD  \n",
      "allocation  1.23   2.5  12.37  10.52  7.19  6.46  11.9     6.76      1.12  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = web.DataReader(instruments, data_source='yahoo', start=log_returns.index[0], end=end)['Adj Close']\n",
    "\n",
    "df\n",
    "    \n",
    "returns = log_returns\n",
    "mean_returns = returns.mean()\n",
    "cov_matrix = log_returns.cov()\n",
    "num_portfolios = 100000\n",
    "risk_free_rate = risk_free /100\n",
    "\n",
    "display_simulated_ef_with_random(mean_returns, cov_matrix, num_portfolios, risk_free_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44680c70",
   "metadata": {},
   "source": [
    "Now we've allocated all 20 of our assets based on our risk tolerance. As you can see, our program has plotted 100,000 unique portfolios, with annualized returns ($\\alpha$) on our y-axis and annualized volatility ($\\beta$) on our x-axis. It's possible to select a random portfolio inside the curve, but there will always be some portfolio out there, with the same number of assets, that will outperform in terms of returns and risk. The optimal or efficient portfolio will always exist somewhere on the edge of the curve, hence, the efficient frontier.\n",
    "\n",
    "If we want the least about of risk possible, using our selected 20 instruments, we should allocate most of our funds towards BBBY, RNG, TWNK, TX, USA, X, HCA, VRTX, and BTC. These 9 instruments will comprise 77.4% of the portfolio. By allocating the portfolio in this way and prioritizing risk, we will achieve an excess annualized return (our $\\alpha$) and volatility of 0.3.\n",
    "\n",
    "What if we only care about maximizing return? If we want the greatest return possible, using the same 20 instruments, we should allocate most of our funds towards AMC, CLF, GME, BB, TWNK, SAVA, HCA, BTC, and DOGE. These assets will comprise ~75% of our portfolio. Using this optimal portfolio allocation which prioritizes returns, our portfolio achieves an annualized return of 0.84, with a volatility of 0.51.\n",
    "\n",
    "So compared to the descriptive statistical analysis we've used earlier, we've actually **OVERSTATED** our return for this portfolio. Using the most optimal allocation method possible, with thousands of different possibilities, we find that our meme stock portfolio still barely *outperformes* our benchmark.\n",
    "\n",
    "In practical terms, it would be difficult for a serious investor to justify building a portfolio with these 20 assets when there are so many different investments out there that could do significantly better for the least amount of risk. This is especially true if you're trying to avoid investing in assets that appear to be overvalued, such as Bitcoin and GameStop.\n",
    "\n",
    "## Lessons From The Meme-Stock Craze\n",
    "\n",
    "I doubt this analysis will convince anyone apart of r/WallStreetBets, TradeRS, or anyone sympathetic to the meme-stock trading \"revolution.\" Much has been written about the heroic campaign by individual investors to slay giant institutional investors. It's easy to understand why this narrative is compelling, so I doubt anyone would want to listen to someone with institutional experiences, such as myself. Regardless, there are still valuable lessons that can be learned from the meme-stock craze, and the effort to democratize financial markets misses the market on many of these lessons.\n",
    "\n",
    "First, it's dangerous for investors to follow crowds in stock markets or any need. r/WallStreetBets initiated campaigns to inflate asset prices past their intrinsic value or their long-term fundaments, making opportunities such as GameStop, AMC, and Bed, Bath & Beyond appear to be attractive investment opportunities. But those who bought these stocks close to their peaks are already nursing losses as the shares have come down. Overpaying for stock prices that don't reflect business fundamentals isn't courageous. Many who bought into the hype are already learning this painful lesson on the risk of market fads.\n",
    "\n",
    "Second, the Federal Reserve has played an unwitting role creating an enviroment where the meme-stock challenge can happen. In the Fed's efforts to stabilize the economy, money has become virtually free. Ultralow interest rates encourages people to borrow and to take bigger risk to seek better returns. As a result, we've seen record sums of money being pumped into SPACS (special purpose  acquisition corporations) and private equity funds. As more money chases more opportunity, there are more instances of companies coming to market without being fully-vetted.\n",
    "\n",
    "Third, the cheap money also fuels record retail trading activity. We've seen this movie before, and it rarely ends well; for those of us who remember the dot-com frenzy in the late 1990s and the mortgage bubble that led to the 2008 financial crisis. With the Fed increasing interest rates to combat persistently high inflation, they will have to reluctantly deflate any bubbles that have emerged. We're venturing onto uncharted territory, where our rookie investors now have to learn to generate alpha in an environment without cheap money and a zero-lower bound.\n",
    "\n",
    "As such, quality is the best recipe for returns. Focusing on high-quality companies is a good defence against irrational market moves. And companies that enjoy strong organic growth drivers aren't beholden to the hypercompetitive M&A market for growth. Building an equity portfolio based on businesses with sustainable earnings growth is a recipe for consistent outperformance and reduced volatility, even in a world where smaller investors can mount powerful campaigns to shock market leaders."
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