backtest.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## Backtesting Dollar Cost Averaging on Degiro's ETF Core Selection Playground\n",
    "\n",
    "**Dollar Cost Averaging?**\n",
    "From Investopedia: \"Dollar-cost averaging involves investing the same amount of money in a target security at regular intervals over a certain period of time, regardless of price. By using dollar-cost averaging, investors may lower their average cost per share and reduce the impact of volatility on the their portfolios.\" With applying this strategy long-term, you hope that the average buying price of the ETF you are buying converges to the expected value.\n",
    "\n",
    "An example of this would be to buy 100$ worth of the S&P500 at the start of each month.\n",
    "\n",
    "**Why Degiro?**\n",
    "Degiro is a broker commonly used for buy-and-hold investors since they offer low transactions costs. They have a group of ETFs on which you only pay a 1€ handling fee on the first purchase of an ETF from that group per month. Which is very ideal for long term investing.\n",
    "\n",
    "The dataset used is up until 22 September 2024.\n",
    "\n",
    "Author: N. Yu (nick.j.k.yu@gmail.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2f1ef8-0ffa-461a-be77-470435f00fbf",
   "metadata": {},
   "source": [
    "### TODOs\n",
    " \n",
    " - [x] Account for dividends\n",
    " - [ ] Account for different currency of equity\n",
    " - [x] Account for inflation rate\n",
    " - [ ] Other metrics: e.g. XIRR, sharpe, max drawdown\n",
    " - [ ] Effect of increasing input with time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49fa6bc-870c-4381-8429-9ea2bb76767e",
   "metadata": {},
   "source": [
    "## 1. Get data\n",
    "\n",
    "We first need the list of ETFs on Degiro's Core Selection, they nicely provide this on their website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "646b3d28-9589-481a-9ed7-0d9d1a175705",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install requests pandas yfinance seaborn pyxirr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b59f7255-b719-4450-86c7-555c8df95de1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ‘core-selection-list-nl.csv’ already there; not retrieving.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -nc https://www.degiro.nl/assets/js/data/core-selection-list-nl.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e18a72-ccfe-4e41-b4c0-2a95d75eba4e",
   "metadata": {},
   "source": [
    "We will be using Yahoo finance to get historical data, but first we need the ticker symbol for each ETF.\n",
    "Only the ISIN is provided, we can query Yahoo Finance for the ticker using the ISIN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94d7ead5-2bbb-4129-84e9-3f1e029d008d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>isin</th>\n",
       "      <th>exchange</th>\n",
       "      <th>benchmark</th>\n",
       "      <th>region</th>\n",
       "      <th>esgRating</th>\n",
       "      <th>totalExpanseRation</th>\n",
       "      <th>translateView</th>\n",
       "      <th>linkKid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISHARES EURO STX50</td>\n",
       "      <td>IE0008471009</td>\n",
       "      <td>Euronext Amsterdam &amp; Tradegate</td>\n",
       "      <td>Euro STOXX 50</td>\n",
       "      <td>Europa</td>\n",
       "      <td>AA</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Bekijk</td>\n",
       "      <td>https://doc.morningstar.com/LatestDoc.aspx?cli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISHARES CHINA 25</td>\n",
       "      <td>IE00B02KXK85</td>\n",
       "      <td>Euronext Amsterdam &amp; Tradegate</td>\n",
       "      <td>FTSE China 25</td>\n",
       "      <td>China</td>\n",
       "      <td>A</td>\n",
       "      <td>0.74</td>\n",
       "      <td>Bekijk</td>\n",
       "      <td>https://doc.morningstar.com/LatestDoc.aspx?cli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ISHARES FTSE 100</td>\n",
       "      <td>IE0005042456</td>\n",
       "      <td>Euronext Amsterdam &amp; Tradegate</td>\n",
       "      <td>FTSE 100-index</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>AA</td>\n",
       "      <td>0.07</td>\n",
       "      <td>Bekijk</td>\n",
       "      <td>https://doc.morningstar.com/LatestDoc.aspx?cli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ISHARES ESTOXX DIV</td>\n",
       "      <td>IE00B0M62S72</td>\n",
       "      <td>Euronext Amsterdam &amp; Tradegate</td>\n",
       "      <td>STOXX Europe Select Dividend 30</td>\n",
       "      <td>Europa</td>\n",
       "      <td>AA</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Bekijk</td>\n",
       "      <td>https://doc.morningstar.com/LatestDoc.aspx?cli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ISHARES AEX</td>\n",
       "      <td>IE00B0M62Y33</td>\n",
       "      <td>Euronext Amsterdam &amp; Tradegate</td>\n",
       "      <td>AEX Total Net Return</td>\n",
       "      <td>Europa</td>\n",
       "      <td>AA</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Bekijk</td>\n",
       "      <td>https://doc.morningstar.com/LatestDoc.aspx?cli...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              product          isin                        exchange  \\\n",
       "1  ISHARES EURO STX50  IE0008471009  Euronext Amsterdam & Tradegate   \n",
       "2    ISHARES CHINA 25  IE00B02KXK85  Euronext Amsterdam & Tradegate   \n",
       "3    ISHARES FTSE 100  IE0005042456  Euronext Amsterdam & Tradegate   \n",
       "4  ISHARES ESTOXX DIV  IE00B0M62S72  Euronext Amsterdam & Tradegate   \n",
       "5         ISHARES AEX  IE00B0M62Y33  Euronext Amsterdam & Tradegate   \n",
       "\n",
       "                         benchmark          region esgRating  \\\n",
       "1                    Euro STOXX 50          Europa        AA   \n",
       "2                    FTSE China 25           China         A   \n",
       "3                   FTSE 100-index  United Kingdom        AA   \n",
       "4  STOXX Europe Select Dividend 30          Europa        AA   \n",
       "5             AEX Total Net Return          Europa        AA   \n",
       "\n",
       "  totalExpanseRation translateView  \\\n",
       "1                0.1        Bekijk   \n",
       "2               0.74        Bekijk   \n",
       "3               0.07        Bekijk   \n",
       "4                0.4        Bekijk   \n",
       "5                0.3        Bekijk   \n",
       "\n",
       "                                             linkKid  \n",
       "1  https://doc.morningstar.com/LatestDoc.aspx?cli...  \n",
       "2  https://doc.morningstar.com/LatestDoc.aspx?cli...  \n",
       "3  https://doc.morningstar.com/LatestDoc.aspx?cli...  \n",
       "4  https://doc.morningstar.com/LatestDoc.aspx?cli...  \n",
       "5  https://doc.morningstar.com/LatestDoc.aspx?cli...  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_core_selection = pd.read_csv('core-selection-list-nl.csv')\n",
    "\n",
    "# First row is an additional header, skip\n",
    "df_core_selection = df_core_selection.iloc[1:]\n",
    "\n",
    "df_core_selection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e19a78b5-1502-401f-95e3-592deda00f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "if Path('tickers.json').is_file():\n",
    "    with open('tickers.json', 'r') as f:\n",
    "        tickers = json.load(f)\n",
    "else:\n",
    "    user_agent_headers = {\n",
    "        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'\n",
    "    }\n",
    "    \n",
    "    url = 'https://query2.finance.yahoo.com/v1/finance/search?q={isin}'\n",
    "\n",
    "    tickers = {}\n",
    "    \n",
    "    with requests.Session() as s:\n",
    "        for isin in df_core_selection['isin']:\n",
    "            # Crude rate-limiting\n",
    "            time.sleep(0.1)\n",
    "            \n",
    "            response = s.get(url.format(isin=isin), headers=user_agent_headers)\n",
    "            quotes = response.json()['quotes']\n",
    "    \n",
    "            if len(quotes) == 0:\n",
    "                print(f'Could not find yf data for {isin}')\n",
    "                continue\n",
    "            \n",
    "            ticker = quotes[0]['symbol']\n",
    "            tickers[isin] = ticker\n",
    "    \n",
    "    with open('tickers.json', 'w+') as f:\n",
    "        json.dump(tickers, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72da9bb2-f722-4a8f-b731-82cfce800559",
   "metadata": {},
   "source": [
    "## 2. Get historical pricing data from Yahoo Finance\n",
    "\n",
    "We now have a dict of `ISIN: Ticker`, with `yfinance` we can get the pricing data from the inception date of the ETF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0b2858b-b3f0-410f-a53a-094149dec073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$FR0007052782.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE00B44T3H88.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE00BMC38736.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE000I8KRLL9.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE00BMDH1538.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE00BMDKNW35.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n",
      "$IE00BNG8L278.SG: possibly delisted; No price data found  (1d 1925-10-19 -> 2024-09-24)\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "from yfinance.exceptions import YFPricesMissingError\n",
    "\n",
    "for isin, ticker in tickers.items():\n",
    "    try:\n",
    "        df = yf.Ticker(ticker).history(period='max', raise_errors=True)\n",
    "        df.to_csv(f'data/{isin}-{ticker}.csv')\n",
    "    except YFPricesMissingError:\n",
    "        pass"
   ]
  },
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   "execution_count": 6,
   "id": "9f7a546b-ef2e-449f-8f4b-f1300f93714e",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "      <th>Capital Gains</th>\n",
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       "      <th>3113</th>\n",
       "      <td>2024-09-18 00:00:00+01:00</td>\n",
       "      <td>135.669998</td>\n",
       "      <td>135.820007</td>\n",
       "      <td>135.190002</td>\n",
       "      <td>135.199997</td>\n",
       "      <td>25748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3114</th>\n",
       "      <td>2024-09-19 00:00:00+01:00</td>\n",
       "      <td>136.899994</td>\n",
       "      <td>137.570007</td>\n",
       "      <td>136.673004</td>\n",
       "      <td>137.449997</td>\n",
       "      <td>15721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3115</th>\n",
       "      <td>2024-09-20 00:00:00+01:00</td>\n",
       "      <td>137.350006</td>\n",
       "      <td>137.899994</td>\n",
       "      <td>136.520004</td>\n",
       "      <td>136.600006</td>\n",
       "      <td>43344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3116</th>\n",
       "      <td>2024-09-23 00:00:00+01:00</td>\n",
       "      <td>137.259995</td>\n",
       "      <td>137.779999</td>\n",
       "      <td>136.649994</td>\n",
       "      <td>137.479996</td>\n",
       "      <td>19985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3117</th>\n",
       "      <td>2024-09-24 00:00:00+01:00</td>\n",
       "      <td>137.910004</td>\n",
       "      <td>138.110001</td>\n",
       "      <td>137.779999</td>\n",
       "      <td>138.070007</td>\n",
       "      <td>4061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                           Date        Open        High         Low  \\\n",
       "3113  2024-09-18 00:00:00+01:00  135.669998  135.820007  135.190002   \n",
       "3114  2024-09-19 00:00:00+01:00  136.899994  137.570007  136.673004   \n",
       "3115  2024-09-20 00:00:00+01:00  137.350006  137.899994  136.520004   \n",
       "3116  2024-09-23 00:00:00+01:00  137.259995  137.779999  136.649994   \n",
       "3117  2024-09-24 00:00:00+01:00  137.910004  138.110001  137.779999   \n",
       "\n",
       "           Close  Volume  Dividends  Stock Splits  Capital Gains  \n",
       "3113  135.199997   25748        0.0           0.0            0.0  \n",
       "3114  137.449997   15721        0.0           0.0            0.0  \n",
       "3115  136.600006   43344        0.0           0.0            0.0  \n",
       "3116  137.479996   19985        0.0           0.0            0.0  \n",
       "3117  138.070007    4061        0.0           0.0            0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vwrl_isin = 'IE00B3RBWM25'\n",
    "vwrl_ticker = tickers[vwrl_isin]\n",
    "df = pd.read_csv(f'data/{vwrl_isin}-{vwrl_ticker}.csv', parse_dates=['Date'])\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9c78cc9-214a-4c31-894c-1c305aebdff4",
   "metadata": {},
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    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "\n",
    "_ = df.plot(x='Date', y=['Open', 'Close', 'High'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5515dfc4-5e0c-4897-9896-ac5899d66ec2",
   "metadata": {},
   "source": [
    "## 3. Backtesting\n",
    "\n",
    "We use a very simple strategy.\n",
    "\n",
    "At the start of each month, we invest a set amount of cash. Using the total available cash, we buy as many equities as possible. \n",
    "\n",
    "Assumptions\n",
    "- All dividends are reinvested\n",
    "- The user is in the Netherlands, we use Consumer Price Index (CPI) data from the dutch Centraal Bureau voor de Statistiek (CBS)\n",
    "- We do not take currency exchange into account. We assume the user invests using the currency of the security.\n",
    "- We assume that we buy at the first day of the month at market open, so we use the open price as the buying price\n",
    "  \n",
    "We backtest and calculate the following metrics:\n",
    "- `equity_peak` the peak value of the portfolio during the entire duration of investing\n",
    "- `equity_final` the value of the total number of equities at the most recent date\n",
    "- `total_expense` the total amount of cash invested over the entire period\n",
    "- `BEP` break-even-price\n",
    "- `real_return` the return over the total amount of cash invested, which is defined as\n",
    "\n",
    "$$\n",
    "Nominal~Return = \\frac{\\#Equities \\cdot Price - Expense + Remaining~Cash}{Expense}\n",
    "$$\n",
    "\n",
    "$$\n",
    "Return = (\\frac{1 + Nominal~Return}{1 + Inflation~Rate} - 1) \\cdot 100\\%\n",
    "$$\n",
    "\n",
    "e.g. If you invested 100$ over a period of 12 months using some the following example stock data\n",
    "\n",
    "| **Month**    | **Stock Price (start of month)** | **Owned Stocks** | **Remaining Cash** |\n",
    "|--------------|-----------------|------------------|-------------------|\n",
    "| January      | 88.60           | 1                | 11.40             |\n",
    "| February     | 50.15           | 3                | 11.10             |\n",
    "| March        | 52.97           | 5                |  5.16             |\n",
    "| April        | 66.76           | 6                | 38.04             |\n",
    "| May          | 72.90           | 7                | 65.05             |\n",
    "| June         | 80.03           | 9                |  5.44             |\n",
    "| July         | 69.18           | 10               | 36.26             |\n",
    "| August       | 73.68           | 11               | 62.58             |\n",
    "| September    | 67.29           | 13               | 28.00             |\n",
    "| October      | 62.61           | 15               |  2.78             |\n",
    "| November     | 66.58           | 16               | 36.02             |\n",
    "| December     | 71.20           | 17               | 64,82             |\n",
    "\n",
    "Let's say the final price at the end of december is 75, and there is a transaction cost of 2$ and there is an annual inflation rate of 5% then you would have a return of\n",
    "\n",
    "$$Expense = \\# Months \\cdot (Invested + Transaction~Cost) = 12 \\cdot (100 + 2) = 1224$$\n",
    "$$\n",
    "Nominal~Return = \\frac{17 \\cdot 75 - 1224 + 64,82}{12 \\cdot 100} \\cdot 100\\% = \\frac{1275 - 1224 + 64,82}{1200} \\cdot 100\\% \\approx 9,65\\%\n",
    "$$\n",
    "$$\n",
    "Return = \\frac{1 + 0.0965}{1 + 0.05} - 1 \\approx 0,04428 \\cdot 100\\% \\approx 4,43 \\%\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "06698a51-59a8-4c3e-bdc3-73cd65a273e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>CPI_1</th>\n",
       "      <th>DerivedCPI_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2024</td>\n",
       "      <td>4</td>\n",
       "      <td>129.42</td>\n",
       "      <td>127.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2024</td>\n",
       "      <td>5</td>\n",
       "      <td>129.69</td>\n",
       "      <td>127.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2024</td>\n",
       "      <td>6</td>\n",
       "      <td>129.91</td>\n",
       "      <td>127.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2024</td>\n",
       "      <td>7</td>\n",
       "      <td>131.82</td>\n",
       "      <td>129.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2024</td>\n",
       "      <td>8</td>\n",
       "      <td>132.24</td>\n",
       "      <td>129.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Month   CPI_1  DerivedCPI_2\n",
       "339  2024      4  129.42        127.52\n",
       "340  2024      5  129.69        127.79\n",
       "341  2024      6  129.91        127.53\n",
       "342  2024      7  131.82        129.26\n",
       "343  2024      8  132.24        129.67"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To calculate the inflation rate, we use the Consumer Price Index as an approximation \n",
    "df_cpi = pd.read_csv('cpi_nl_all_items.csv', delimiter=';')\n",
    "df_cpi['Year'] = df_cpi['Periods'].apply(lambda p: int(p.split('MM')[0]))\n",
    "df_cpi['Month'] = df_cpi['Periods'].apply(lambda p: int(p.split('MM')[1]))\n",
    "\n",
    "df_cpi = df_cpi[['Year', 'Month', 'CPI_1', 'DerivedCPI_2']]\n",
    "df_cpi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bd721b1-8ebe-433d-99dc-404e63155a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'start': Timestamp('2012-05-22 00:00:00+0100', tz='UTC+01:00'),\n",
       " 'end': Timestamp('2024-09-24 00:00:00+0100', tz='UTC+01:00'),\n",
       " 'duration': Timedelta('4508 days 00:00:00'),\n",
       " 'equity_peak': 32732.07014465332,\n",
       " 'equity_final': np.float64(32684.670867919922),\n",
       " 'total_expense': 15045,\n",
       " 'BEP': 63.48101265822785,\n",
       " 'current': np.float64(137.91000366210938),\n",
       " 'unrealized_nominal_return': np.float64(17639.670867919922),\n",
       " 'nominal_return': np.float64(1.1724606758338267),\n",
       " 'inflation_rate': np.float64(0.32437953222347043),\n",
       " 'real_return': np.float64(0.6403611072020514)}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "from pyxirr import xirr\n",
    "\n",
    "def backtest(df, cash=0, monthly_input=500, transaction_cost=1, years_to_date=10, dividend_tax_cut=0.15):\n",
    "    total_expense = 0\n",
    "    equity = 0\n",
    "    equity_peak = 0\n",
    "    transactions = []\n",
    "    portfolio_worth = []\n",
    "    cash_flow = []\n",
    "    \n",
    "    metrics = {}\n",
    "\n",
    "    # Get starting date, threshold to earliest date available\n",
    "    # simplify ytd to 365\n",
    "    start_date = df.iloc[-1]['Date'] - datetime.timedelta(days=365*years_to_date)\n",
    "    if start_date < df.iloc[0]['Date']:\n",
    "        start_date = df.iloc[0]['Date']\n",
    "    \n",
    "    metrics['start'] = start_date\n",
    "    metrics['end'] = df.iloc[-1]['Date']\n",
    "    metrics['duration'] = metrics['end'] - metrics['start']\n",
    "    \n",
    "    current_month = None\n",
    "\n",
    "    # Filter rows on start date\n",
    "    df = df[df['Date'] > start_date]\n",
    "    \n",
    "    for row in df.to_dict(orient='records'):\n",
    "    \n",
    "        date = row['Date']\n",
    "\n",
    "        # edge case, no data\n",
    "        if row['Open'] == 0:\n",
    "            continue\n",
    "\n",
    "        # account for dividends\n",
    "        if row['Dividends'] > 0:\n",
    "            dividends = (1 - dividend_tax_cut) * equity * row['Dividends']\n",
    "\n",
    "            # reinvest dividends\n",
    "            cash += dividends\n",
    "            \n",
    "            cash_flow.append((date.date(), dividends))\n",
    "        \n",
    "        # detect new month, assume data is in ascending order\n",
    "        if date.month != current_month:\n",
    "            current_month = date.month\n",
    "    \n",
    "            # buy using available cash\n",
    "            cash += monthly_input\n",
    "            total_expense += monthly_input\n",
    "            cash_flow.append((date.date(), -monthly_input))\n",
    "    \n",
    "            to_buy = int((cash - transaction_cost) / row['Open'])\n",
    "\n",
    "            # If we have enough money to buy shares, buy as much as possible\n",
    "            if to_buy > 0:\n",
    "                cash = cash - transaction_cost - to_buy * row['Open']\n",
    "                equity += to_buy\n",
    "                total_expense += transaction_cost\n",
    "\n",
    "                transactions.append((date, to_buy, row['Open'], cash))\n",
    "    \n",
    "        portfolio_worth.append((date, equity, total_expense, equity * row['Open'], cash))\n",
    "        \n",
    "        equity_high = equity * row['High']\n",
    "        if equity_high > equity_peak:\n",
    "            equity_peak = equity_high\n",
    "    \n",
    "    metrics['equity_peak'] = equity_peak\n",
    "    metrics['equity_final'] = df.iloc[-1]['Open'] * equity\n",
    "    metrics['total_expense'] = total_expense\n",
    "    metrics['BEP'] = total_expense / equity\n",
    "    metrics['current'] = df.iloc[-1]['Open']\n",
    "    metrics['unrealized_nominal_return'] = metrics['equity_final'] - total_expense\n",
    "    metrics['nominal_return'] = ((metrics['equity_final'] - total_expense) / total_expense)\n",
    "\n",
    "    # Calculate inflation adjusted real values\n",
    "    cpi_start = float(df_cpi.loc[(df_cpi['Year'] == metrics['start'].year) & (df_cpi['Month'] == metrics['start'].month)]['DerivedCPI_2'].iloc[0])\n",
    "    cpi_end = df_cpi.iloc[-1]['DerivedCPI_2']\n",
    "    inflation_rate = (cpi_end - cpi_start) / cpi_start\n",
    "    metrics['inflation_rate'] = inflation_rate\n",
    "    metrics['real_return'] = ((1 + metrics['nominal_return']) / (1 + inflation_rate)) - 1\n",
    "\n",
    "    # Calculate XIRR\n",
    "    # TODO: inflation accounted cash flow\n",
    "    cash_flow.append((metrics['end'], metrics['equity_final']))\n",
    "    # metrics['XIRR'] = xirr(cash_flow)\n",
    "    \n",
    "    return metrics, transactions, portfolio_worth\n",
    "\n",
    "\n",
    "metrics, transactions, portfolio_worth = backtest(df, monthly_input=100, years_to_date=20)\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8665aa-41ad-4516-84bd-7c07258a3bb0",
   "metadata": {},
   "source": [
    "## 4. Visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4d14565-7531-4ccf-b5c2-ca7fa548942e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, axs = plt.subplots(nrows=3, sharex=True, figsize=(10, 8))\n",
    "\n",
    "axs[0].plot([r[0] for r in portfolio_worth], [r[3] for r in portfolio_worth], label='Portfolio')\n",
    "axs[0].plot([r[0] for r in portfolio_worth], [r[2] for r in portfolio_worth], label='Total Expense')\n",
    "axs[0].set_title('Portfolio')\n",
    "axs[0].legend()\n",
    "\n",
    "axs[1].set_title('VWRL Open price')\n",
    "df.plot(x='Date', y='Open', ax=axs[1], legend=None)\n",
    "axs[1].scatter(\n",
    "    [r[0] for r in transactions], \n",
    "    [float(df.loc[df['Date'] == r[0]]['Open'].iloc[0]) for r in transactions], \n",
    "    s=[r[1]*5 for r in transactions],\n",
    "    c='red', marker='v', zorder=2, linewidths=0.5, edgecolors='none', label='Buy'\n",
    ")\n",
    "axs[1].legend()\n",
    "\n",
    "axs[2].plot([r[0] for r in portfolio_worth], [r[4] for r in portfolio_worth], label='Cash')\n",
    "_ = axs[2].set_title('Cash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "814f5c32-562f-4513-b278-075bcc286817",
   "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>ticker</th>\n",
       "      <th>isin</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>equity_peak</th>\n",
       "      <th>equity_final</th>\n",
       "      <th>total_expense</th>\n",
       "      <th>BEP</th>\n",
       "      <th>current</th>\n",
       "      <th>unrealized_nominal_return</th>\n",
       "      <th>nominal_return</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>real_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EUEAA.XC</td>\n",
       "      <td>IE0008471009</td>\n",
       "      <td>2022-11-10 00:00:00+00:00</td>\n",
       "      <td>2024-09-19 00:00:00+01:00</td>\n",
       "      <td>678 days 23:00:00</td>\n",
       "      <td>13437.519836</td>\n",
       "      <td>13421.440491</td>\n",
       "      <td>11523</td>\n",
       "      <td>42.996269</td>\n",
       "      <td>50.080002</td>\n",
       "      <td>1898.440491</td>\n",
       "      <td>0.164752</td>\n",
       "      <td>0.039272</td>\n",
       "      <td>0.120738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FXC.L</td>\n",
       "      <td>IE00B02KXK85</td>\n",
       "      <td>2009-01-02 00:00:00+00:00</td>\n",
       "      <td>2024-09-24 00:00:00+01:00</td>\n",
       "      <td>5743 days 23:00:00</td>\n",
       "      <td>101673.000000</td>\n",
       "      <td>73092.000000</td>\n",
       "      <td>94512</td>\n",
       "      <td>7876.000000</td>\n",
       "      <td>6091.000000</td>\n",
       "      <td>-21420.000000</td>\n",
       "      <td>-0.226638</td>\n",
       "      <td>0.416539</td>\n",
       "      <td>-0.454048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISF.L</td>\n",
       "      <td>IE0005042456</td>\n",
       "      <td>2009-01-02 00:00:00+00:00</td>\n",
       "      <td>2024-09-24 00:00:00+01:00</td>\n",
       "      <td>5743 days 23:00:00</td>\n",
       "      <td>121954.096273</td>\n",
       "      <td>119576.448242</td>\n",
       "      <td>94646</td>\n",
       "      <td>639.500000</td>\n",
       "      <td>807.948975</td>\n",
       "      <td>24930.448242</td>\n",
       "      <td>0.263407</td>\n",
       "      <td>0.416539</td>\n",
       "      <td>-0.108103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDVY.AS</td>\n",
       "      <td>IE00B0M62S72</td>\n",
       "      <td>2008-01-02 00:00:00+01:00</td>\n",
       "      <td>2024-09-24 00:00:00+02:00</td>\n",
       "      <td>6109 days 23:00:00</td>\n",
       "      <td>222434.681780</td>\n",
       "      <td>221408.290255</td>\n",
       "      <td>100701</td>\n",
       "      <td>8.241345</td>\n",
       "      <td>18.120001</td>\n",
       "      <td>120707.290255</td>\n",
       "      <td>1.198670</td>\n",
       "      <td>0.436946</td>\n",
       "      <td>0.530099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IAEX.AS</td>\n",
       "      <td>IE00B0M62Y33</td>\n",
       "      <td>2008-01-02 00:00:00+01:00</td>\n",
       "      <td>2024-09-24 00:00:00+02:00</td>\n",
       "      <td>6109 days 23:00:00</td>\n",
       "      <td>322936.083826</td>\n",
       "      <td>312133.106850</td>\n",
       "      <td>100701</td>\n",
       "      <td>29.265039</td>\n",
       "      <td>90.709999</td>\n",
       "      <td>211432.106850</td>\n",
       "      <td>2.099603</td>\n",
       "      <td>0.436946</td>\n",
       "      <td>1.157077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ticker          isin                      start  \\\n",
       "0  EUEAA.XC  IE0008471009  2022-11-10 00:00:00+00:00   \n",
       "1     FXC.L  IE00B02KXK85  2009-01-02 00:00:00+00:00   \n",
       "2     ISF.L  IE0005042456  2009-01-02 00:00:00+00:00   \n",
       "3   IDVY.AS  IE00B0M62S72  2008-01-02 00:00:00+01:00   \n",
       "4   IAEX.AS  IE00B0M62Y33  2008-01-02 00:00:00+01:00   \n",
       "\n",
       "                         end           duration    equity_peak   equity_final  \\\n",
       "0  2024-09-19 00:00:00+01:00  678 days 23:00:00   13437.519836   13421.440491   \n",
       "1  2024-09-24 00:00:00+01:00 5743 days 23:00:00  101673.000000   73092.000000   \n",
       "2  2024-09-24 00:00:00+01:00 5743 days 23:00:00  121954.096273  119576.448242   \n",
       "3  2024-09-24 00:00:00+02:00 6109 days 23:00:00  222434.681780  221408.290255   \n",
       "4  2024-09-24 00:00:00+02:00 6109 days 23:00:00  322936.083826  312133.106850   \n",
       "\n",
       "   total_expense          BEP      current  unrealized_nominal_return  \\\n",
       "0          11523    42.996269    50.080002                1898.440491   \n",
       "1          94512  7876.000000  6091.000000              -21420.000000   \n",
       "2          94646   639.500000   807.948975               24930.448242   \n",
       "3         100701     8.241345    18.120001              120707.290255   \n",
       "4         100701    29.265039    90.709999              211432.106850   \n",
       "\n",
       "   nominal_return  inflation_rate  real_return  \n",
       "0        0.164752        0.039272     0.120738  \n",
       "1       -0.226638        0.416539    -0.454048  \n",
       "2        0.263407        0.416539    -0.108103  \n",
       "3        1.198670        0.436946     0.530099  \n",
       "4        2.099603        0.436946     1.157077  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "all_metrics = []\n",
    "\n",
    "for isin, ticker in tickers.items():\n",
    "    if not Path(f'data/{isin}-{ticker}.csv').is_file():\n",
    "        continue\n",
    "\n",
    "    df = pd.read_csv(f'data/{isin}-{ticker}.csv', parse_dates=['Date'])\n",
    "\n",
    "    # XXX: empty data\n",
    "    if len(df) == 0:\n",
    "        continue\n",
    "\n",
    "    metrics = {}\n",
    "    metrics['ticker'] = ticker\n",
    "    metrics['isin'] = isin\n",
    "    \n",
    "    m, _, _ = backtest(df, monthly_input=500, years_to_date=20)\n",
    "    metrics.update(m)\n",
    "    \n",
    "    all_metrics.append(metrics)\n",
    "\n",
    "df_all = pd.DataFrame(all_metrics)\n",
    "df_all.to_csv('metrics.csv')\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd64f79-38fd-4c77-87c1-93cac3a15da0",
   "metadata": {},
   "source": [
    "## Winners and losers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4fbc4302-af41-4a2c-bed4-0dfb8691c668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>ticker</th>\n",
       "      <th>nominal_return</th>\n",
       "      <th>BEP</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CSSPX.MI</td>\n",
       "      <td>2.269761</td>\n",
       "      <td>166.678846</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>XDWT.L</td>\n",
       "      <td>3.463259</td>\n",
       "      <td>20.092939</td>\n",
       "      <td>89.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CSNDX.SW</td>\n",
       "      <td>3.527070</td>\n",
       "      <td>251.155807</td>\n",
       "      <td>1137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>WTEC.L</td>\n",
       "      <td>4.267594</td>\n",
       "      <td>31.982343</td>\n",
       "      <td>168.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EQQQ.MI</td>\n",
       "      <td>5.170405</td>\n",
       "      <td>70.966173</td>\n",
       "      <td>437.890015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ticker  nominal_return         BEP      current\n",
       "21  CSSPX.MI        2.269761  166.678846   545.000000\n",
       "37    XDWT.L        3.463259   20.092939    89.680000\n",
       "31  CSNDX.SW        3.527070  251.155807  1137.000000\n",
       "38    WTEC.L        4.267594   31.982343   168.470001\n",
       "11   EQQQ.MI        5.170405   70.966173   437.890015"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.sort_values(by='nominal_return').iloc[-5:][['ticker', 'nominal_return', 'BEP', 'current']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e862272-26db-46d1-b330-19a5324dcbe7",
   "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>ticker</th>\n",
       "      <th>real_return</th>\n",
       "      <th>BEP</th>\n",
       "      <th>current</th>\n",
       "      <th>start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IUIT.L</td>\n",
       "      <td>1.379586</td>\n",
       "      <td>10.253826</td>\n",
       "      <td>31.690001</td>\n",
       "      <td>2015-11-20 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CSNDX.SW</td>\n",
       "      <td>2.217163</td>\n",
       "      <td>251.155807</td>\n",
       "      <td>1137.000000</td>\n",
       "      <td>2010-01-26 00:00:00+01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>XDWT.L</td>\n",
       "      <td>2.241000</td>\n",
       "      <td>20.092939</td>\n",
       "      <td>89.680000</td>\n",
       "      <td>2010-11-22 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>WTEC.L</td>\n",
       "      <td>2.745041</td>\n",
       "      <td>31.982343</td>\n",
       "      <td>168.470001</td>\n",
       "      <td>2009-02-13 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EQQQ.MI</td>\n",
       "      <td>3.294111</td>\n",
       "      <td>70.966173</td>\n",
       "      <td>437.890015</td>\n",
       "      <td>2008-01-02 00:00:00+01:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ticker  real_return         BEP      current                      start\n",
       "35    IUIT.L     1.379586   10.253826    31.690001  2015-11-20 00:00:00+00:00\n",
       "31  CSNDX.SW     2.217163  251.155807  1137.000000  2010-01-26 00:00:00+01:00\n",
       "37    XDWT.L     2.241000   20.092939    89.680000  2010-11-22 00:00:00+00:00\n",
       "38    WTEC.L     2.745041   31.982343   168.470001  2009-02-13 00:00:00+00:00\n",
       "11   EQQQ.MI     3.294111   70.966173   437.890015  2008-01-02 00:00:00+01:00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.sort_values(by='real_return').iloc[-5:][['ticker', 'real_return', 'BEP', 'current', 'start']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75ecb060-a3f0-42a0-a2a5-16db95aed175",
   "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>ticker</th>\n",
       "      <th>real_return</th>\n",
       "      <th>BEP</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>FOOD.L</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.677871</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FXC.L</td>\n",
       "      <td>-0.454048</td>\n",
       "      <td>7876.000000</td>\n",
       "      <td>6091.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>KWEB.L</td>\n",
       "      <td>-0.374059</td>\n",
       "      <td>24.719249</td>\n",
       "      <td>19.3720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>HTWO.L</td>\n",
       "      <td>-0.366709</td>\n",
       "      <td>5.772192</td>\n",
       "      <td>4.4395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>EXXY.DE</td>\n",
       "      <td>-0.244725</td>\n",
       "      <td>22.772727</td>\n",
       "      <td>24.7150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ticker  real_return          BEP    current\n",
       "63   FOOD.L    -1.000000     4.677871     0.0000\n",
       "1     FXC.L    -0.454048  7876.000000  6091.0000\n",
       "59   KWEB.L    -0.374059    24.719249    19.3720\n",
       "64   HTWO.L    -0.366709     5.772192     4.4395\n",
       "14  EXXY.DE    -0.244725    22.772727    24.7150"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.sort_values(by='real_return').iloc[:5][['ticker', 'real_return', 'BEP', 'current']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9789fcc6-a744-4e45-92e8-ec215a49e065",
   "metadata": {},
   "source": [
    "## Does increasing monthly input have an effect on return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "faacfb5f-bb54-4c63-8632-892e9154856a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Onz7tN51/9uzZCAgIwKeffor58+ef92dJTk5GcHAwvv32W/zmN7/p9HpmZiY+/fRTjBw5Enq9Xti+bNkybNiwAUuXLsXvf/97GI1G/O9//8PevXvx7LPP9jhviGEY/PnPf8bDDz+MJ598EnPnzsXRo0fxySefnPdnIoj+hoQBgiB6xYwZM/D555/j3XffxZtvvon6+nooFAoMHz4cL7/8spAuvHTpUqhUKnz44Yf44osvEBgYiLlz5+L3v/89JBIJ/vCHP5yXxW7SpEl477338Nprr+Ghhx6CRCLB1KlT8ec//1lkUVy9ejWef/55rFy5Ei6XC2PGjMErr7wiyhN44IEHIJVK8fnnn+O9995DYmIi1qxZg7Fjx/b6OhEEQRDEpUxUVBS+/vprvPXWW/jmm2/wzjvvwO12IzY2FnfffTcWL14sCh4EIHQj8sXixYvxl7/8RbTtq6++glQqxdy5c32+R61WY86cOVi3bp3QRvh8mTRpEjZu3Nip1BBghYGPPvoIV1xxhWh7SEgIPvnkE7z00kv45z//CbvdjtTUVKxZswZXXnnlOR2f72CwZs0arF+/HsnJyXjmmWfwpz/9qVefiyD6C8Z9Po1OCYIg+hi3293pgYMgCIIgCIIgiP6HMgYIgrgkIFGAIAiCIAiCIC4OJAwQBEEQBEEQBEEQxCCGhAGCIAiCIAiCIAiCGMSQMEAQBEEQBEEQBEEQgxgSBgiCIAiCIAiCIAhiEEPCAEEQBEEQBEEQBEEMYkgYIAiCIAiCIAiCIIhBjOxin8Bgwe12w+VyX+zTuGyRSBi6fn0AXcfeQ9ew99A17Bt6ex0lEobahPYxdK/vPfT3offQNew9dA37BrqOvedC3utJGLhAuFxu1Ne3XuzTuCyRySQwmQJgNrfB4XBd7NO5bKHr2HvoGvYeuoZ9Q19cx8DAAEilJAz0JXSv7x3096H30DXsPXQN+wa6jr3nQt/rqZSAIAiCIAiCIAiCIAYxJAwQBEEQBEEQBEEQxCCGhAGCIAiCIAiCIAiCGMSQMEAQBEEQBEEQBEEQgxgSBgiCIAiCuKx46623cOedd3Y5JisrC0uWLEFGRgYmTZqEv/3tb2hubhaN+e677zBv3jykp6fjhhtuwJ49e/rztAmCIAjikoW6EhAEQRAEcdmwdu1avPLKKxg3bpzfMbW1tVi2bBmuuuoq/P3vf0dDQwP++te/4vHHH8frr78OANi7dy8eeeQRPProo5gyZQq+/PJL3Hvvvfjf//6HhISEfv0MLpcLTqejX49xOeJyMWhvl8Jms8Lp7N8WZ1KpDBIJrY8RBEHwkDBAEARBEMQlT1VVFZ566ins27cPcXFxXY4tKytDZmYmnnnmGchkMsTHx+OWW27Byy+/LIx55513cNVVV2Hx4sUAgMceewxHjhzBBx98gGeeeaZfPoPb7YbZXA+LpaVf9j8QqK2VwOW6MK3N1Got9PrAHvf4JgiCGMiQMEAQBEEQxCVPVlYW5HI5NmzYgNdffx1lZWV+x44aNQr//ve/hZ8LCgqwfv16TJkyBQC7Yn/48GE8/vjjovdNnDgRP/zwQ/98AEAQBbRaExQKJU1IfSCVMv3uFnC73bDZrGhpaQAAGAxB/Xo8giCIywESBgiCIAiCuOSZNWsWZs2adc7vmzNnDoqLixEZGYnVq1cDAMxmM9ra2hAeHi4aGxoaisrKyj453464XE5BFNBq9f1yjIGATCaBw9H/jgGFQgkAaGlpgE5norICgiAGPSQMEARBEAQxYHnxxRdhsVjwwgsvYPHixVi/fj3a29sBAAqFQjRWqVTCarX26ngyme8Jps3m4I6p7NX+BzK8gYJhAHf/mgYAeP8uXJDJBsYjsVQqEf2XOHfoGvYNdB17z4W+hgPjryBBEARBEIQPRo4cCQBYvXo1pk+fji1btmD69OkAAJvNJhprtVqhVqvP+1gSCQOTKcDna+3tUtTWSiCTSfyKBwTLhXoIdrkkkEgkMBjUUKlUF+SYFwq9/vz/HRMsdA37BrqOvedCXUMSBgiCIAiCGFAUFhaipKQEM2bMELaFhYXBaDSiqqoKRqMRGo0G1dXVovdVV1cjLCzsvI/rcrlhNrf5fM1ms3LdCNwXxCp/OcIwrCjgdLouiGPA6XTD5XKhqakNFouz/w94AZBKJdDr1TCbLXA66d/Z+UDXsG+g69h7+uIa6vXqHoutJAwQBEEQBDGg2L17N1auXImdO3dCr2fr+UtKStDQ0ICEhAQwDIMxY8Zg//79uPnmm4X37du3r8s2iD3B36S/vwP1LhV27dqBIUMiER8/1Ofrhw8fxPLl9+GLLzYgImKI6DVeDPAlCtx660KUlp4Vbbvmmmvxl7/8vdfnPBDFGqfTNeA+04WGrmHfQNex91yoazjgvGxvvfUW7rzzzi7HZGVlYcmSJcjIyMCkSZPwt7/9Dc3NzaIx3333HebNm4f09HTccMMN2LNnT3+eNkEQBEEQ54nT6URNTY2QHXDttdfCaDTikUceQV5eHg4ePIjly5cjPT0dM2fOBAAsW7YM3377Ld5//30UFBRg5cqVyM7OxpIlSy7mR7msqayswGOPPYSGhvo+3a/FYkF5eRlWrnwF69dvFr4efPDPfXocgiCIwcyAEgbWrl2LV155pcsxtbW1WLZsGSIjI7Fu3TqsWbMGhw4dErUs2rt3Lx555BHceuut+PrrrzF58mTce++9KCgo6OdPQBAEQQw0Wix2ZBfXw30hvNGDlIqKCmRmZmLTpk0AAKPRiA8++AAA8Otf/xoPPPAAhg8fjnfffRdSqRQAkJmZiWeffRaffPIJFi5ciL179+LNN99EQkLCRfsclzv99W+8qKgALpcLaWnpCAoKFr60Wm2/HI8giIFLU4sV+WVNF/s0LkkGRClBVVUVnnrqKezbtw9xcXFdji0rK0NmZiaeeeYZyGQyxMfH45ZbbsHLL78sjHnnnXdw1VVXYfHixQCAxx57DEeOHMEHH3yAZ555pj8/CkEQBDHAeHfjKRwrqMMtMxMxd2KMsH3PyUp8ua0Ad80fhhFxgcL2plYbfjp0FtNHRSLIIA5Eq25oQ5BBBdnA0vXPmeeff170c1RUFHJzc0Xb4uPj8dZbb3W5nxtuuAE33HBDX5/egGbVqpewc+d2fP75emFbS0sLFiyYA5uN7eiwfPl9WLbsN7j77t/i2LEjWL36FRQU5CM6Ogbz5y8Q7W/RoutQWVnh81hPPPEU5s27DgUF+QgMDBLKQgiCIM6Xt785hewzDbh3wXBMGh7e/RsGEQNCGMjKyoJcLseGDRvw+uuvo6yszO/YUaNG4d///rfwc0FBAdavX48pU6YAAFwuFw4fPixyEADAxIkT8cMPP/TPByAIgiAuKm63G+t3FkEhl2LepFjRa9UNbbA73YgM9p023xXmVhuOF9YBAL7eUYjRScEID9TA7Xbjm93FaGi24j8bT+Gf90xEgErOjttegO3HKnC8oA5PLh4HGRcadDCnGmv+dxILpsRh0czEXn5i4lLB7XbDZr949bcKuQQM3yewB8ybtwCff/4Jjh07ilGjRgMAfvrpB+h0Wjz77Fv47W+X4l//Wonx4yehvLwMDz30e1xzzXw8+eTTKCoqwMqVz4r29847H8Ll8gT/yWQSoZaWdwQUFORDrVbjyScfxYkTx2A0mjB//gIsWnQrJJLBLZIRBHFunKlky8c/+TEPafFB0KrlF/mMLh0GhDAwa9YszJo165zfN2fOHBQXFyMyMhKrV68GAJjNZrS1tSE8XKwghYaGorKyslfnSe2Jzg/qg9o30HXsPXQNe09Pr6HT5cLZqhbEhuvOadJyvhSWN2HDrmIAQFSoFmOSQwAANrsT//roENraHfj7XRMQG64T3vN/3+Ugv7QRf741A0adpzd93tlGnCquxzWTY3E4r0YIUrM7XHh/Uzb+smQcSqpaUFnPptc3tdjw6U95+O31abA5nDiYUwMAKKlqwXf7SrBwGhvi9ssRVvRWyKX0b3GA4Ha78dzHhy+qrTUxyoAVt4/p8f9niYlJSEkZhu+//1YQBjZv3og5c+YhMJB1vuh0emg0Gnz44XsICgrCn/70GKRSKWJj41BdXYVVqzwLNCaTSbR/b2GAp6ioAM3NzZgx40osW3Yvjh8/ijVrVsFsNuOee+7rxacnCGKgUFrdgnabE4lRBr9j2trtaLM6AADNbXZ89lMe7r52+IU6xUueASEMnC8vvvgiLBYLXnjhBSxevBjr168XgosUCoVorFKphNVqPe9jddXbmOgZ1Ae1b6Dr2HsGwjXMKa5HoEGFUJPmohy/u2v4xU+n8eGmbNx8ZRIWz/PctM9UmPHyp4dxy5XJuCLdk2judLnxw74zyEgOQXiQ+G+txeqAQibpchJ9ghMFAODjH07jitFRUCtl2HOiAs1tdgDAh9/n4oXl0yCVMKhrsuCXw6Vwu4FPf8nHiiUTALATvbfX7EZVfRtcjAS5Z9gQtgVTh2LL/jPIK23C7qxq1DRaAABxEXqUVJqx60QlZo6LgcPlRpvVAYVcCpvdiQ07izBtTDQMWiVyShoAAHOnDBWu30D4tzjo6X/dq8+ZP38B3n57Df74x0dQXV2FEyeO47HH/tppXGFhPpKSUoRcBwBIS0sXjbnjjltQVeW7lOCRR57A1VdfgxdfXAWr1So4CBISEtHa2oIPPngXd911L7kGCGKQ43K7sfKTI2i3OfDyHzIFB15HapvYeZ6cEyB3nazE7PHRiAnT+RzvzfGCWnzyYx5un52MtKFB3Y5vbbfjv1tO44q0CIyID+x2PADsOlGBYIMKKTGm7gf3A4NaGBg5ciQAYPXq1Zg+fTq2bNmC6dOnAwBsNptorNVqhVp9/g9gXfU2JrqG+qD2DXQde89AuYbFlWY89Z/9iB+ix9/vmnBBj93Ta/jzgRIAwLpf8jE+JQThgayA8cWPuSgobcKqz44gKkgNnYYVcbcdKcO732YjzKTGs7+dDDnn0Kqsb8OT7+xFSrQJD/96NCTcqqjT5cIH3+UixKjCtVfEYcdRdjVeJmVQ22jB+xtO4NdXJeMX7jwA1gnw5ZYcXD0hBj8eKBGcALuPV2DLniKMSw1FQVkTqjgnwP+25cPtZud9szKGwKRV4IPvcvB/32ZBpWBvvwumxKGgrAnf7jmD1744ishgduIze1wUaprasf9UFV5cexAThoXB7QZSYoxQMG6YzZYL2tuY6B8YhsGK28dcVqUEADB79lysXv0Kdu/egYKCfAwbNgJxcfGoqCjvMJKB2y3+bDKZ+NHzxRdfhcPhEH6WShmhtSPvQJDL5ZDLxQ/6Q4cmwmKxoLnZDIPBeE7nTxDEwMLcakOLhRXx65ra/QoDNY2sMBAVooVSLkFOSSNKa1p6JAzszapCVYMF72w8hX/cMxF6jaLL8ftOVWFPVhWO5NXi6bsmIMTY9TyyrKYF736bDZlUgr8tGYeo0AsfrjrohIHCwkKUlJRgxowZwrawsDAYjUZUVVXBaDRCo9Ggurpa9L7q6mqEhYX16tjUw7N3UB/UvoGuY++53K/hkdwauMHW2dlsTkgkfb9kuftkBbKK6nH77BRoVJ5bTWOLFdkljUgaouvU193ldkPCsBPz0ppWAKwT4L8/nMbyRelwud04cpq12be2O/DlLwW4c04KAOBADvs3u6rBgo27i3HdFXEAgJ3HymGzu3CisA7bj5QjMz0CAKv8b+Ws+QwYVDdYIJdJcM+1w/HG/07i+31nMSY5BEfy2ONNGRmOXScq8cUvBRiVEIx9WVUAgFCTGtUNFnywOQfJUQbsPsGWnEklDJwu9vMlRRuh1ygwNT0C+7IqkVPSCJvdBrVShhFxJoyIM+Hw6RpU1LWhqYV1GEwYFgajVoGcMw0oq2nF/2oLAQCTR4SL/u1d7v8WCVYcUCqk3Q+8hNDpdJg2bQa2b9+KvLxc3HTTLQDQSWBISkrGpk3fwG63CxP7nJxs0Zjw8AjRzx1LCdxuN371qxswd+583HXXvcL27OwsBAUFkShAEBeRphYrth8rx/TRkdAHdD1RPl9qmyxQKWRdZgHwTgAAMLfZuhjHuvVCjCpIuWefplb/473hnX7NbXZ89H0ufndDWpeialU9O77d5sR/Np7CY7eN6fJ5q6yWfe5xOF14+5ss/HXJuAtehj7olgp2796N5cuXw2w2C9tKSkrQ0NCAhIQEMAyDMWPGYP/+/aL37du3D+PGjbvQp0sQBNEJm92JbUfL0NZuP6/3nypmLelOlxuNLedfIuUPl8uNT37Mw56sKnyzu0j02rsbs/HP9/dj874S0fZfDpfi/pe24UheDY4VsGF9YSY1pBIGR/NrkVVcj6JyM8xtdiGMb+vRMpRUNcNideBUsadv+re7i4Wb/5G8WmH7F1vz0cpds0O5NcL2z3/JBwCkxQdifGooxqWGwuV245XPj6Hd5oRJp8Sya4YhMcoAq92Jt77JQl4pWxP+p1tGISxQg6YWGz77OR8HcljBYPGcFOEhZuJwVlSWMAyWXpMKBXejH5sSArlMCrlMirvnDwf/fBEVEoDoUC10GgWWzGWFD7cbkEklGJcScu6/EILoB+bPX4Bt235GWVkZrrxyDgAIzsrCwny0tLRg4cJFsFgseO65Z1BcXIRdu3bgvffePqfjMAyDadNm4pNPPsZPP21BWVkp1q9fh//+90PcfTflCxDExeSHA2fx9Y4ivLPxVL+0K21us+HJ/+zD39/fj7Z2h99x/D0fYN0DfsdxjoFggxqGADYbqKmlZ8JAdaPnGIdya7Avu6rr8Q0ep3heaRM27y/pYjS7sMFTWtOKddsLe3RefcmAFwacTidqamqE7IBrr70WRqMRjzzyCPLy8nDw4EEsX74c6enpmDlzJgBg2bJl+Pbbb/H++++joKAAK1euRHZ2NpYsWXIxPwpBEAQAYPP+EnywORfvfpvd/WAAdocTH32fiyOna2C1O1FQ7gk6q/G60fUVRRVmtHI38J8OlQrHaLc5cJJL6F+/owjNXqr+z0fKYHe4sHbLaWH1f9roIZg5JhIA8OmPeTjEuQXGJAdjwrBQuN3Af7ecxsmiejicboQa1UiJNsLmcOGzn/JR02jB2eoWSBgGYSY1mtvsWLetEE6XSxAM5F5q/LiUUADAr69MglopFT7D2JQQSCQMlsxNhVTCIJ8TBZKiDAg1abCUm7zvOF6BxhbWCTBpRDj+9KtRWDhtKKame1ZEQ00aLL0mFTFhWsyZ4GldOHSIXnA5zBoTJWzPSArBlLRw4XNr/NgjCeJCM27cBBgMRkydOh06HWvDNRiMmD9/AdasWYX//OcNBAeHYNWqN1BdXYW77roDq1e/jCVL7j7nY9133+9x++2L8dZbq3HHHTfjs8/W4sEH/4wFCxb29cciCOIcqOYms1lF9TjB3d/7krKaVtjsLtSbrfh6h/+Jcp23Y6DV/6JJDScgBBtVgsOhK4cBj8XqEPKGrh4fDQBY+8PpLhdXeCGBXxz4enuh0BHB53hOSEiONgIAThbV+x3bXwz4UoKKigpceeWVeO6553DjjTfCaDTigw8+wPPPP49f//rXkEqluPLKK/H4448L4TiZmZl49tlnsWbNGrz88stITEzEm2++iYSEhIv8aQiCIIBT3M3iSF4tzla3ILqbOrT92dX45UgZ9mRVYsncVDi8LPy1Te1I6TDe7nDh//33MBQyCR6+dTSkXsFe246WobnNjvmTY0UWurqmdpjbbIiP0IseDhxON77eXoh7F4xAdnGDYK9vszqwYVcxbp+djNomC8q40oF6sxX1ZvZGOzoxGPoABfZmVaGstlVI8R+dFIzkKCOO5tXidGkT6szsA0FGcjCmpEXg7+8fwKHTNXBwtffJ0QZcnxmP//ffI9h6pAzBRhVaLHYEqGS4bXYy3vnmFGRSBqMS2TAhk06JG6clYO2W0wCA8amsYBAZHID5k2OF7gX89pQYE2ZkRAqlCWOSgiGXSRAXrkdceOe+65NGhGPSiM69k2+YOhTTR0fCqBXbMRfPTUFqrAkjE7oPOyKICwVb39+Ma6+9XrR9xYq/YcWKvwk/JyenYvVqsUtg4cJF53QsmUyGpUvvwdKl95z/CRME0efUN3sm5J/+lI/hcYGCq68v4O/vAPDz4VJMGRnu874qKiXoyjHAjQsxqIVxPXEM8O/TquVYNCMBuSWNOFPVjA++y8HyRemdSgpcbreQZ7Bw2lDY7E4cyavFOxtP4aml4yCXdS4f4x0DMzMisWh6ArSaC78QMOCEgeeff170c1RUFHJzc0Xb4uPj8dZbb3W5nxtuuAE33HBDX58eQRBEr7DZnSis8JRCfbunGPddn9ble7LPsKUD7TYnPv5B/PfQl2Mgv7QRheXsMXafqMTUUWz6f3ObDR9uzoUbQERQAMZytna3242XPjuKyvo2PPrrDEEYuHJsFH4+VIq9p6owe3w0jnPbE6ONyD/biK1HyjBrTKRwfmqlDBaujVCoSY3wQA0YhsHCqfH46IfTcLrckEoYjBwahACVHPMnx+LrHUWo44SEjKQQRIVqMWtsJH48WCqUJIxJDkFKjAmTR4RhT1YVvvilQBg/aXgYrHYnjAFK0Wr8zIxIFFWY4Xa7kRDpaX00f3IsjubXoq6pHeOHeXJnFk1PwLH8WjQ0WzEprfOkv6eYvNoe8shlUkwZGeFjNEFceMxmMw4fPoCff/4R4eHhGDfuwgaYEgRx6cAL+VIJg8r6NvxyuAyzuRX1rmhqseKFT49i4vAwwS3nC94JwIAtqftwcy6eXDyuU62+tzDAZwZ8ta0ARRVm/O6GNGhUcrjdbqHkINigEjrC8ALB3qxK/Hy4DMvmpSKiQ3cj/lkpxKiGTCrB3dcOwzP/dwDHCuqw60SlkF/E09hshcPpglTCIEivxJJrUlFQvh/lta34cmshfn1VUqfPyrsvwgLVPsWPC8GALyUgCIK4XDiaV4tnPz6Eci6Ahqe5zYbv9p1Ba7sdheVmOJxuqLiwsgPZ1aioa/W1OwDspJ2feAMQ7PFDgtmbHq9oe5NV7Bm/flcR7A4nADabgPcarNteAKeLXZGvqGsTVvPXbjmN4grWKjdvUqywMv7FL/k4zk3U75ibitFJwXC63PjilwIcy6/jxscgIZK9GY5ODBYU+GmjhyAyhD3fpCiDkDY8d2IMe3MHq+InchP4GzKHikKQxiSzAsYtMxOhVnr08DEpIWAYBjNGR2J0UrDoGkgkDO65djh+c90IoZMBwE7S/3LnOLzwuytg8DqGRiXDitvH4I83j8KIuJ61JSKIyxGn04nnn/8HTp/OwV//+sw5dzQgCGJg4HC6hEn1gsx4AMD6nUVCd4CuOFFYj/LaVvxveyGKvBY7OlLLOQZmZERCrZShuLIZW7kuQqJxHcIHXW43vt9/FqeKG/DF1gJuux02uwsMgEC9SriH80LCT4dLkV/WhLe/OSU83/B4hAH2mSMqRIvruc/8yU+nUW8WP0vxk/wggwpSiQR6jQLLrkkFAGw5eFaUiwSwpQr8tQw1Xpw20gAJAwRBEJcMm/adQX5pE77kbmI8G3YW44tfCvDxD6eFXvajEoORkRQMN4Bv95wRja9ttOCv/9mHLQfPorrBgoZmK2RSBpHBHgWcr3vn1fOiCrNQ3+Z9w6o3W7H1CNuCLMur3q2irg27uAT+k16lA2W1rXADiA7VwqRTYuG0eMikbEughmYrFDIJ0hKCceuVSZAwXLAgt99RicG4//o0LJgSh2u9VhCkEgnumjcMiZEG0Xa5TIo7rk6BVMJg+ughwgqCRiXDLTPZ0q/ESAMC9eyN3KBV4sZpQwEASoUUI+LOr0+wXCYR2g16E2xUI53s/sQAx2QyYfPmrfj006+RnJx6sU+HIIiLRGOzFW6wbX7nTYpBVIgWbVYH1u8o6va9/ETaDeDjH3Lh8hNcyDsGhg7RC/fvr7YVoMmrtt/ldnfIGLDB3GoTygm3HS1HbkkDarnJvVGnhFwmEYSBFosdDqdLCCY8U9ncKSC52ssxwDN3YgyGDtHDYnXi/e9yROGL/PhQr/GjEoMxI4PNTXr322whDBnwCAk6jVzUyelCQ8IAQRCDhhOFdT1uS3OhsTucKOZU86P5tSiraRFey+bEgP3ZVdiXzQbzpUQbhUny3qwqUVru7pOVKKttxVdbC7D3FJuamxhpwELuphoTpkViFLu6XtvUjupGC5796BD++eEh1DRahHAcXg3fuKcYFqsDWZxgwE9+1+8sgs3uxAluYs8r6QCQNpRdNQ82qDF7nCdMb1hcIJRyKYYEB2BGBlui4HK7EaRXITI4AIF6FW6YOrRTW6L4CD2euHMshndYjU9PCMLqP04THhh4rkiLwGO3ZeB3C8VlFjMzInHzzATct2CEzxo/giAIgric8Dep7gusNqffwLz6ZnZybtIpIZVIBHv8L0fKRM8wvvDODiiqaMaOY+VdjgvSqzAzIxJx4TpYrE589nO+MMZbBOB/9nYQAMD/fZeDcs5dGcI5DQPUcqFlYV1Tu+j5cP3OIpR6fYYaH8KAVCLB3fOHQS6TIKuoHtuOlnceb/KMB4BfzUxEmEmNhmYrPv7htLBdEBI6jL/QkDBAEMSg4GRRHV7+/BhWf3W81/vKLq4XrZ7zWKwOkYp9LvAlAjzfcWq1uc0mlBa43UAVZ9lPiTEiPkKPtKGBcLnd2OTlGuBdBTaHC99wQXmpsSaMSQ7Bw7eOxu8XjkSIgb35NDZbcfR0DZwuN1osdqz530m44QnaC+XS/D/8PhcNzVbIZRLce91wBOmVaGi24rt9JTh9thEA8NsFaYICn5Hoaas3f3KsMNHnA/4A1nrIW/vTE4PO25KsVEh9vjclxgSjVlyzL5EwuGZiLEYlBncaTxAEQVy+WG1OIafmUqC2yYI1/zuJ4kr/Vvne4HS58Pf39+O5jw+JJsZ9yRdb8/H0/x3Az4dLO73Gr/oH6tiJ9rBYEzKSguFyu/Hpz/ldti/k3xsXznY0+WpbYacSBJfbLYwLMqggkTBYPDcFDAPsPVUluBt5EYBvBdzcZkcNtwIfE6qFQatAVYMF67axXQ2Cucm9hGGg4wL++G5NaqUUoxKC4HC68d632UJJAV926S0MAGze0k3cwsRnP+cLggDvAAjtMF6pkOKe64ZDwjDYd6oKe0+xzkv+2e5ilhEAJAwQBDFIyC1pBAAUlJu7bBfTHW3tdrz46VH8mwvb82blJ0fw+Nt7Rb1rAWDd9kL89d19nWrQss804P1N2Wix2JHHtcCLCGJvCvtOVaGuqR153KRbrfSsbusDFAgPZMfxoT27TlSg3twOu8OJ/DLPQwi/kjAslrXNj4gLRLBRDZ1GDoVcAjfYNns8/LXhk4VvmBovnA/AOhU0KjlumMreCDfsLILd4UKgXon4CB0eu30MHlyULjgSAECjkuP+60fgqrFRmMYFGQKAXqPAnXOSERWiFbXoIwiCIIhzwe5w4Yl39uKZ/zsg5OJcbHYer8DBnGq89212l5Pk86W2qR0lVS0oKDOL7uN9yZkq9png6+2dJ+68YyBQ7xHgfzUrEVIJg6yieiFXyBd8aPCN04ciMiQALRY71m0Tl1GyTgA3GMYTzBsXrsesDPZ54aMfTsPucAklkTFhrMjgcrtRzD3LRIdqccdstvcS7wjgs4kAwBDA7reAe24KMaixeG4qNFyeweZ9JXC53KhrEmcMeHPV+GgkRxlgtTvx3rfZcLndHmHAhwMgYYgB114RCwD4+Hs2n8A7ePBiQsIAQRCDgmIvMcBXcE1PyTvbCLvDBTeAnw56FPSGZivOVDbDanNii9d2l8uNnw6dRVlNKzbvF9esffFLPnYcr8A3u4pxurQRABuwMyzWBKfLje8PlCCXEwYmDQ9HEjfZTok2CivkSVFGpMYY4XS58d2+EuSXmeFwumAIUCA+gg3yU8qlwvc8DMMIroEyzpGg82qNMyKeFRImDAtDVIjWaztr5Z88IhyRIQFCGGFafCAYhkF4oMbnavywuEDcNjsZCrnYvj9peDieuXuCKP+AIAiCIM6F+uZ2NDRbUdVgwcGcmot9OgCARq4NXmlNqxCy26f7b/Y4FL/ZxZb29TUN3DFa2x2CA1F4zcwLA57JcqhJI3Ql+O+W0z6dDG4vJ0CoUY07ZicDYLMAvIMI+dwAo1YpaoG4cNpQGAIUqKpvw3f7zgjjwkxqBHD1+YUV7GJLkEGFsSkhGJvscTF6r/obuPbABWXs+GCjGiadUiiLWL+zCKeK6+Fwsl2ReHeENxKGwV3zh0EhlyD3bCN+OljqM2PAm2uviEN8hB5tVgf+s/GUsNBEpQQEQQxKmttsQku8/sbtdotcAnuzqs7bbpjrlfC/82QF2riUf/6mArCrBG1cqEx5bSssVvZmveNYhRA2025zoKSKrV/bfqwc+ZxjIDnKiHmTYoXtvOKeHG3EHVenYOTQIFwzKUZ0TrxrYPuxcuzPZlf2U2NNuHH6UEgYBhlJwT77Cnur5mqlDEvnskFiMimD5GgjAPaGd+N0T/0+LwxIJAxumpYgbE+Lp9A9giAI4uLQ0uZZzf75SGfb+8XA7FWzvnFPcZ+7Brxr4htbbPj5cM8XPerN7UIZoD9cLjcamz3H+PlwqcgpWd/MlxKIS/auuyIOeo0cFXVt+HZX5yDCFosdNgcrGJh0KqGdcMcgwjqvMgJvNCoZbr2Snbhv3H0Gp7hOSkEGldCR6Ewl+3wVzC2A3DY7WShdHOK1EMGPP1vDj2ePdUVaONK5koK3vzkl7L9jm0SeUJMGt8xMBMCWX/DPmB1LD3hkUgl+c91wKORsOHM+9wwZZqJSAoIgBiHvfpuNf354UAi0646cMw34xwcHe1wG4HK5sedkJZpabahrakeLxQ6phEGYSQ2r3SmE8one43bD5er6xn26xCMMWG1O7DzB2vfyvYQBq92JbVyQTsftW4+wN+7CcrNw87PanWi3OaFWShEdqsXwOBNiw3Sw2V2CvSw52ojoUC0eumVUp/62qbEmJETqYXe4hPCbYbEmjIgLxMr7J2PZPN/J4cFeN6zUGCNGJwXjzquT8dsFaaLU/VEJQZg7MQZXj48WreyPSgzCpBFhQtYBQRAEQVwMmr1s7gVlvSsZ7Ak9CfzznrgXlpuR47Ww0BUOpwvvfHPKbyAfD+8Y4Ce8m/ae6fGixxvrT+L5tYdxNK/W75imVrbtn4RhkBYfyLUY9oT+1Zv58EHxxF2tlAlhx//9PqdT9hL/PkOAAnIuF+DmmYlQKaSiIELeCRCs77xKP2FYKEbEB8LhdAktmYMMnhaEvFOBt/6bdEo8+usM3D1/mMhByY/nf538RJ5hGCyZmwq1UiaUUPhb/eeZmRGJtPhAIS/KqFV0ckl6Ex6owW1XJYu2kWOAIIhBh9vtFpTqnT2si/vpcCmKKsz4347CHo3fd6oK72w8hbc3ZAllBJEhAZjJ1bJvO1ImUu9bLHY8vHoXVn11XLTd5XLj9XUn8M43p+B2uwXHwJS0cPa8Dp2Fy+UW3A+pMUZueymcLlen7IAfD5XC7vBs907fT4g0QCJhwDAM5k2OFbaHmtRCfZ0vGIYRXAM8/HkE6lV+0/dDvFT44XFsKcDMMVEYmxIiGscwDG6ZmYhbr0wShfwxDIN7rxuBvy4Z57N9H0EQBEFcCLwdAwCbjN9Tsorr8c3u4k696/3xy5EyPPDy9k696DtibmUnwPxEdGOH1sL+yClpwJ6sSnz0Q66oDV9H+FKFKWnhCA/UoMVix5YDZ3t0jLIatoTwi635fj83X0Zg1CmEFsNH8mqFibjgGNB3fj6Zmj4EcRE6tLU78LmXmAB4nADeJQhGrVLILvpyawFaLHbU+nEMAOzzx51XJwvCAsC6A3gHAI/3e2PDdZgyMkL0esfx3hkCJp0Sv+acCexrXU/aGYbBsnnDhHKG7oQEgG0dzZc5aNVyBKjk3byjfyFhgCCIC06duR3tNtZefySvBu227hXus5zt/kRhfY+S//O4mv3sMw2CvT4uXIcr0sIhk0pQUt2CogrPikLOmQY0tdpwvKBOVOJwtroFh07XYE9WJXYer0BjixVSCYNfXZmEAJUMNY3tOHy6RhAfbrsqGTqNHPVmKw7l1gglBjfPSIRJp0RTiw37TlUhnzu/66bEIYi7qSZHGYXjjk0OEZRj3tbfFSOHBiGWC94J1Cu7vYEB4pvc8DhTt+MJgiC6Y9euHSgq8i/gHj58EJmZ41BR0fVqqD+OHz+KadMmdNre1NSIp59+EnPnzsQ118zCSy/9P7S3+59UEQOLZgs7SeYndntPVQqlft2x9ofT+Hp7IXafqOzR+GP5tbDanPhqW4Hf8gC3242mVlasuGVmAqQSBtlnGoT0+67g7fsOpxvf7in2P67FU+PPBwV/f6CkU0hgRyxWh/AMVlHXhh3HfC/QNHATf5NOKWox/NlPebDanWjmxJhAHyv6EgmDxXNYt+KOYxWicktPC0KxoHDl2EhEhQSgtd2Br7YVCKJIkI/9A6x9f8GUOOHnYIMKeo1noi9hmC4XVQCPY8CzD/Gz05SR4UKL5liug0JXmHRKLL2GbWE4Kqn77kcMw2DJNanISArutMBzMSBhgCCIC04pp1QDgM3uwpEurGwAexPjg1xcbjd2Z3V/8+aTdAHgYC4bRBQXrodWLcf41FAAEGz9AESBN95teQq9tv93C9tzNiZMB61ajqlcwv4nP+XB4XRBq5azroSMSADA/3YUobrRAgbs5P6qcaxbYfP+EuQLDgMT7po3DONTQzF9tCexXyJhcMfsZMSEajFrTGS3n5dhGNw0YyikEgaThof3qPVfVKiWLa8I1AhdDgiCIM6XysoKPPbYQ2ho6FmJ2Lly/PhRPP74w3D5WOF88snHUFpagldffQP//Of/w549O/Hii8/1y3kQHsxtNuzPruq2DK+/4R0DoxNDEBkcAJvdhd0ne+ZIbOAm2D8cONujHIDmNnbiXlTRjByu41FHLFaHYGePj9Bj0ogwAMC3u7t3DTS1ehY/dhyvQC33/NMRXhgwahUYlxqK6FAtLFYnvtvb9TEaOyyu/G9nkc8SBL7rAF8qcD3XYrikugXfcu4HhUwirJB3JDHKgKvGs5lIH285LfwbqffhGAAAqUSCO65mOwhsP1ouiAm+HAM8cybEYHRiMEYnBosyBtj9KyGVdD3V7SwMiI/FMAweWJiGP986GlNGhne5L56xKSF4/aFpuGZibPeDwToF/nBTuhDaeDEhYYAgiAtOGRfyws9d9/mo9/emlBvPs/N4RZc3b4fThbPVrZ2282ovr3rvz64SQgK9hYEDOdUw8zd+L/dAK7f6kBDJ2gJnjYkEw3jsdglD9IIdXyZlhJCeyJAAaFQyTB8VCZVCivLaVli5TIHI4AAMiwvE/TekQacR36DShgbh73dN6JQp4I+0+CCsenCqKCywK0KMajxx51g8/KtRPRISCIIguqI/WrIBgMPhwGuv/RvLl9+H8PCITq+fPHkcR44cwl/+8jRSUlIxdux4PProX/D995tQU1PdL+dEsKzfWYQ312fh+wMl3Q/uR/iMAZ1GjpmcmP5Lh5JBX9jsTli51fOy2lYhyK4rvEMF/U3C+XwBtVIKhVyKeZNiwQA4ml+L0uoWn+/h4UsEAMDpcuOb3cU+xzVw44xaJRsUzNX1/3SotNPkX/Q+7pkl1KRGmEkNc6sN3+/v/Pvjx/HhgjqNQljV3sQJAyadssvnh8Xzh0GjlOFMZTO2H+eyA7iMAV9OgORoI6akhcMNzzNXx8m6NzKpBMsXpWP5onRIGEYkDHT1Ph6DVun1ve9MALlMiuFxgd2KDB3P63Lk8jxrgiD6lLZ2B/754UFs2Nk5PbavyC1pwBZOjecdA1dwdfonC+uFiTiPudWGPScr4XS5cJa7iSZFGaCQSVBR1yZayQdYJ0HOmQbYHS5U1LXB4XRBrZQKNwmphBHa7iVGGhAZEgCbw4U9WVVwud2Cw0AfoIDD6RbCb/jjqJWem0VCJNs2MNigxhivFjj8dkOAApNGeJTlRG67RiXDtFFDROP9JdyeL2qlDJJzmOTHR+g7WecIgrhwuN1uuO3Wi/d1jpP5Vatewi23XC/a1tLSglmzpuDmmxcAAJYvvw/vvvsWAODYsSP4zW+WYNasKViy5NfIz88TvXfRouuQmTlO+Jo0aYzw/aZN3wAALBYLjh49gn//ezUWLfpVp3M6duwIgoKCERcXL2zLyBgLhmFw/PjRc/p8xLlRw61m7z7ZMxt+f8E7BrQaOSaPCIdSLkVFXZvfFX2e5g7ZBD90U6PvXSIAACeL6lFS1TnokBcP9AHsxDMiKABjObfit92s6PPlkhOHsy6DXScqUd3Q5neckZu4pycEISFSD5vDhY1+xATAM+EPNqhw03S2u9Dm/SXC9o7jvO34V46NQqhRLYQv+ioj8MakU2Eht1jxFZcd4M8xwHPzrESRC6G7Y3gjFga6f7bxdgyE0LMQKC2KIAicKq5HYbkZZ6tbMGdCDJQK/ymq58u732ajtqkdgXqV4AAYmxKK0ppWnKlsxsGcasziggEBNhBn14lKNLawfYkBVkkONqiwJ6sKu05UImGIQRi/+0Ql3tuUjWmjhggT8dgwHeKH6PHd3hJEhWiFkBqGYTBjdCTWbjmNrUfLMDzOBIvVCYVMghunDcX/fZeDrUfKMSMjEhW1rIhx84xEfPh9LgDPRB8ArhobhUNcqUKC1/arx0ULwYre22ePi8aPB0vhcruR5LWdIIjBh9vtRtuGf8FVld/94H5CGpYE9YIneuwamjdvAT7//BMcO3YUo0aNBgD89NMP0Om0ePbZt/Db3y7Fv/61EuPHT0J5eRkeeuj3uOaa+XjyyadRVFSAlSufFe3vnXc+hMvl6b8uk0ng4FqZabWsmKvT6fDuux8BgCAWeFNTU43Q0DDRNrlcDr3egKqqrh1pRO/g6/jLalpRWt2CqFDtRTkPPmNAp5ZDrZRhclo4th4pwy+HSzEs1n+GDv8+hVwCu92FE4V1KKtthckU4HN8u80plAiMTgzG0fxabN5XgnsXjBCN4x0D3hPPayfH4mBONfZnV+GGqfF+W9M1cu8dkxyC1nY7ThbW45tdxbj72uHCGO+cAP4YDMPgxmkJeOGTI9h2tBxzJ8SIug8J++cEBZNWibEpIUiMNCC/rAnrdxZi6TXDhHENZk/GAI9cJsHNMxPw+tcnAXRuVeiLq8ZFYeuRMpTVtOLr7YVebQh9v1evUeCmGQn4cHMuTDollF0k+3fEcI6OAZVCCrlMArvDhWBjzwWIgQo5BgiCECzvdoer25Td86Gt3YFaLkRm98kKVNaxx4sKCcAkThHv2D6woIxdqd95ogJnq1k1PjpUi0wuUXbfqSrY7J6HyRyujeDerEohMTcmTIe5E2IwYVgoFk6L9949Jo8Ig0ImQVlNK7YcZDMFYsJ1mDQ8DAEqGerM7diwsxhusHa3qaMiMDYlBNNGR4raySRHGzFhWCiSogxIjPRY/qNCtZiaHoEQo0oIrgHYWrmrxkVBpZBiHLd6QBDE4IXB5VXGk5iYhJSUYfj++2+FbZs3b8ScOfMQGMi2LdXp9NBoNNiw4WsEBQXhT396DLGxcZgx40osXXq3aH8mkwlBQcE+v5TKnj2ot7e3Q6FQdNquUChgs3UfVkucP61eAX++2gD3JYdP13QqLeThHQN8SR6f9XP4dG2nlXBvzNzqf7hJg9FcWNwPPmz1nvHspF0pl+L6TPa5Yn92daccgCbBMeD5dxkTpkN6QhDcbv8lCABgbvGICjdksqvtu7MqUVXvcQ3w+1cppEK7QoBtVTw8zgSny431u3y7QOubPU4DvusQwOYZlHld33qhlED8/+GY5BAhFLkn7fWkEglu59rybT1Shibu83XlBJg2agjuvDoZ98wf5neML7zDB7vKJuBhGEYQE8gxQI4BgiAA0c3mSF4tMpJDuhh97pTXeur9+aBBtVKKIL0KE4aF4fOf85Ff2oTaRguCjWpY7U5Ucba5iro24bE5JkyHUJMaQXoV6sztOJxXg0nDWcs+Xwpgc7iwlwsnjAvXQadR4L7r0zqdk0Ylx4RhYdh5ogLbuBDCuHAdFHIppo4ags37SvDjIdZSGD9ED6lEggdvHgWTKQANDa0AWBsdwzA+9w8Ay+b5vqH9alYifjUrker6CWKQwzAM1AueABy27gf3FzLFOf8tmj9/Ad5+ew3++MdHUF1dhRMnjuOxx/7aaVxhYT6SklIglXpW/NLS0kVj7rjjFlRV+Q6Je+SRJ3D11dd0ez5KpRI2W+draLPZoFbTw35/wuf0AKxgf+P0oT0qZ7Pandi05wyuSAtHWA/Cb8tqW7F63QnoAxR48XdXdKrh5pP4+RbA0aFaJEUZkFfahB3HyrEgM77TPgFPkKBOI8fV46NxJK8WO49X4O7rfYsJfNmjPkCO2HAdRsSZkFXcgO/3n8XtV3t60pt9OAYA4NrJcTheUIddJyqxYEp8p8mx2+1GY6snVDDUpMGohCAcK6jDhl1F+M11rDOhkZu0e9fI89w4LQGnig9i98lKzJ0Yi8hgsfuhsUOJQGKUAWOTQ3DodA2+2FqAP948Ci632xNuqBN/BoZhcP/1I7DzRAWmj+4+HBkAUmNNmDAsFPuz2cwPuUwCndp/az4Jl9d0rugDPPvsiWMAYLMFapvayTEAcgwQBAGg0qt27VhBbZ+nC5fVdlb4I4O1YLhWMqmczW8f11awvLYV3mWvbrA2v1CjGhKGEZJhd3FWfZvdiYraNtF4oPvWMtO5EEJ+PN9reGZGJBhAOIehET0L/+spDMOQKEAQBADu74FcefG+zuNv0ezZc2Gz2bB79w58//0mDBs2QlTf7/Xp4HaLOwjIZOI1qRdffBXvv/9f4evDDz8Rvs/MnNaj8wkNDUNdnbi7jd1uh9nchOBgcmb1F263WyglkDAM6sztyC/tvh0fwLr7vtldjP9sPNWj8fyKvLnVhsOna0SvOZwuwbmg1XgmhnwI4dajZYL9vyN8xoAuQIHkaCNiw3SwO1zYvLfY53hzByfANZPY5Pkdx8tFWUm+HAMAOwlPiTbC6XJj877OzoR2mxM2O3uu/KT/hqmsa2DvqSphocVTDtDZKTN0iB4ZScFwu9m6/o74yg5YNINtqXi8oA7ZZxrQ0maHw+kGAzbcsCMGrRLzJ8cJQkxPuGVmolAWEKhX9ctzkFwmRahJDblMgiHBvstBOjJ3QiwykoJFmVGDFRIGCIJAVT17w2XA3iR70mf3XCjjwgYVMs+fnMgQzx/siR3KCfiwQW97XHSIVgjqm8KVE5wqbkBdUzvKalvhcruhVsogk7JjlHKp3/o9nqERekR71UPywkCIUY2RXvb/oUP6VhggCIK4nNHpdJg2bQa2b9+KrVt/wvz51wFApwf9pKRk5ORkw273rCrn5GSLxoSHRyAqKlr4io6OEb7XaHr2YD9q1BhUV1ehtNQTHHfkyCEAQHr6qPP6jET3WO1OOLmFhDHJrA2/uy5DPHzyfkG5WeQq9AfvCADErYYBTzkDA0Cr8kxUxyaHQq+Ro7HFhmP5vtsiC44BNeucuZprGbdpV5FPMUEQBjjL+rBYE2LDdLA5XPj5UGmncR0dAwBwLZfsv+1YeafuAfzPaqVUmETHhuuEif4Grjyg0asjgS8WzUiAhGFwNL8Wp882il7j2zN6CwNhgRrM4Fb/P/85X8gB0Aco+ixhP1CvwnVT4gCgk4uhL3n89jH4+7LxnTo9+WNsSgj+cFM6AlQ9FzkGKiQMEMQgxe12w+12o8ViF264fH0db/f35mx1C6p99NLNLWlAto9cguMFdfjpUCncbjfKuJv+rLEeWxjfIQAAxqWEQCZlUFbTirPVLUIrnylp4TBwanh0mGf1P8SoRmqMEW6wdXd8GcHQCB0ykkK48dpuE/8ZhsEMrg5RrZSJauX4IEQJwyA2rGvnAUEQxGBj/vwF2LbtZ5SVleHKK+cAgGDbLyzMR0tLCxYuXASLxYLnnnsGxcVF2LVrB9577+0+P5cRI9IwcuQoPPXUE8jOzsLhwwfxwgvPYu7c+QgJIcdAf8G7BaQSBtNGsw68AznVflfnvWn1mujv4NrYdYW3MJBT0oiKOo+Y0MJN7gPUctF9Xy6TYCrXCejnw2Ixgce7NAAAxg8LhVGrQL3Zin1ZnUUOM+cw0HsF/l0zKQYA2yaQb33ozzEAAMPjTEiMNMDucOG7vWLXQJOQLyCe8PN5Bgeyq1Fa0+Kx+fsRBiKCAjBtFLuI8sUv+UL3EafLJYgWpg7vvS4zDmqlFGeqmvEd52Yw9SBc8FyYOzEGDyxMw+2zk7sffJ4YtUpEBPWf8DCQIWGAIAYA1Q1tWPO/k8IEnKeqvg1/e3cf9meLb25VDW343b+34/Nf8oXgwUC9Uli5P9pBGDC32fCvDw/i2Y8Owe7w3PCtdide/uIYXvrsmNB+BmBFh7c3ZGHtltPIKWkUzmt8aigShujBgG09yKNRyZGewIoSe7IqBcdAbLgOc8azN9wxnGjBw7sGdp2oQEklKwzEhOlw7RVxiAjSCMFD3TElLRyZIyPwq1mJorrItKGBuD4zHovnpvRLlwaCIIjLmXHjJsBgMGLq1OnQ6Vjx1GAwYv78BVizZhX+8583EBwcglWr3kB1dRXuuusOrF79MpYsububPZ87DMPg2WdfQETEECxffh/+9rfHMXHiFXj44cf7/FiEB36lPkAlw7BYE/QBCrRY7D0KMW7xyibYfbKyWzHBWxgAgK1Hyju95svWPn30EDAMkH2mwaczoblDaKFMKsFszjWweX9Jp3aeHR0DADAuJRQhRhVa2x3YzokcXTkGGIYRJvpbj5aJXAN8vkDH98WE6TAuJQRuABt2FgnvMfgoJeBZkBkPhVyCgnKz0D2pqcUGt5sVc3QdjqHXKDCPK404mMNmAfS1MCBhGIxNCe3z/RJ9AwkDBDEA+OlQGQ7mVGP9TnEC7b7sKpTWtOKbDv1ss4rqYbU7sfVouZDwG2bSIC0+CFIJg8r6NpEaX1rdApuDVZmzz3hu+JV1bbDZXXC53cJNBADqzVa0WdkHhp8Plwo3yIggDZYvSsfflo5HTIdV+Mkj2NyAvVmVwjlFh2oxZ0I03nh4OtKGBonGj0sJhVIhRXWDBfu4MJuYMB2iQ7X4128mCfvrDoVcirvmD8M0blWBR8LduDtuJwiCIACLxYLm5mZce+31ou0rVvwNP/+8G3/84yMAgOTkVKxe/TZ+/nkXPvlkHRYuXISdOw8iIuL8/rbOm3cddu482Gm7yRSIf/7z/2HLlh3YuPFH/PnPj0OppMlHf8Kv+mtUckglEkzgOu3s9bHS3vm9nm4GzW12HMuv63I8P/lP4Er7dp2oEDoT8ZN773wBnmCDGqMT2YWFH72s/p5je8IHeWaOiYJCLsWZymbkljSKxnscBp5JtUTCYO4EdhHjh/0lcDhdXQoDgH/XgNCRwMeEf0FmPBgAB3NrhPIAf44B/jV+ceWrbQVwOF1CGYFBq/AZEnn1+GgEeQUiduxIQAxsSBggiAFADWfxP1VUD6fLo7pXN7Dby2pahXaBgCdTwGpzYttRVt0OD9RAo5IhNcYIADjqVY9XUecJ9juY6wn9KfcSDw54CQPezgVepQ42qKBSyKDTKHyGAqYnBCFAJUNjiw2t7Q5IGAYRQQFgGMZnD1ulQorx3EOIhRMhugsbJAiCIHqH2WzG1q0/4fnn/4Hw8HCMGzfhYp8ScZHwdgwAwMQRrOvwcF6NYKn3/152Mh8RxGYB7eymnIAXBiYOD0OwQYU2q0N47mjmXvOXcs/nBuw+UdHJedDRMQCwzoMrx7Hv+eHAWdH4juGDPFNGRkCvkaPObMUvh8uE7AV/de7+XAONrf6zA6JCtIKz05Mx0HUd/dyJMdBp5KhqsGDHsXJPRwI/goJcJsXNMxOEn016EtcGEyQMEMQAgK/9b7M6UFhu7rQdgCh4p8qrC8EZzobPtwsazdXoe+cMVHoJA0fzagXxwdtVUFBuRm0Tezxfdr3ugmbkMgnGDwsTfo4I0kAu6/pPVCZXTgCwQkFP+ukSBEEQ54/T6cTzz/8Dp0/n4K9/fYY6rAxi+FaFGi60bWiEHiFGFWx2F47k1XT1VmGCPodbaT9eWCek5fuCdyfoNApM5/IM+BDCFh+r/t4kRxsRHaqFzeHCjmNiAUJwAHR474JpbCeAY/m1opbOnlIC8XiFXCqICXxAYIBK1uVzjLdrYNPeMwCApm5KBK6fGi9a6Td2Y8lXK2VYMIUVINbvKhbKR7t63/jUUKHc0zugmRj4kDBAEJc5brdbcAwAwIlCj9WfdwwAHYWBziGC4YHspJq33BWUNgk3zIp6r5Afi12w1nm3CASAgznsgwAvDPAdAgAgMqT7m8sVXvb/ntyMkqIMghgQHartUe9kgiAI4vwxmUzYvHkrPv30ayQnp17s0yH6mXpzO04U+rb5d3QMMAyDScPZ+/iebsoJ+Il+QqQBiVEGuN3A7pMVfsfzrgCtRo7M9CGQShgUlJtRUtXseU3tf3V+Njdp//FQqZBnYPVqDdhxZT8qVIdRicFwA/jeyzXQMXzQmyvHREKtlArXxdeYjufFuwa2HWU7FAhOgADfE/cwkwaZ6Z5FEX/jvJk+eghCTWqYW21C2YI/xwB/Xn+8eRQe+XUG0uIDu90/MXAgYYAgLnOaWm2iQMCT3A3cYnUIyjbAlhmwrYVcQj9g73k07xgIMqgQE6aFGx4xgS8l4FsM8uUBfCkBLybwtj5++/TRngBA7/aE/kiI1CPUyE70o3ogDDAMg1lcyODwWFO34wmCIAiC6Dkffp+Llz8/hiOnOzsA2qy8Y8DTWviKNFYYOFlUJ6x+d8TldgsdDbRqOaZy7r+dxys6hf3xCAGDKjkMAQqMTWHdjVuPlqOlzX/4IM/E4aHQaeRoaLbiMPdZ+HwBmVQClY+Q4fmTY4XzYp+1nELpoq9Jv0YlFzoaAf7zBbzp6BoQuhl0FSo4JQ4BKhmiQrQ9CkeWSSW4aTpbHsDnP3UX/qdWsoGS5AgaXJAwQBCXObxbQKNkb8zFlc0wt9qE7QEqGQL1StgcLpzIr0VtYzucLjfkMgnS4tlAP6mEQbDBEzDDT/SP5tWi3eYQ7H38TfLQ6RrYHS7BkXDdlDgwDFBUYUZto0VwDEwfPQShRjUkDIOhEfpuPwvDMLhtdhJGDg3ClLSehQfOHh+Nvywei/mT43o0niAIgiAGK1uPlGH9ziK/E/CO8M8Su09WdnqNXxnXePV/DwvUICFSD7cb2HvKt2ugrd0B/ugBKhnGD2PDhKsaLMgrbeo03u12d5r8z+AWHvZkVaKGK2P0V0oAsLXzfLeiHw+yIYRCNoFG7nMCnBJjxNAhejicLvx0qFTII5BKGOGZqyOzx0dDwZUPdOcYADpkDRwpF0oyjV28N1CvwnO/nYy/LB7b7f55xqWEIN7rOay7EgRicELCAEFc5vCT89hwHWLC2FX2rOJ6YXtYoAbpXKL/wewqIV8g1KjGaK4FYKhJDanE8+cgg8sZyCqqR0kV2yFAp5FjXEoo1EoZzK027MmqhNPlhlIuRVy4DinRRgBsUE+7zQmphEF4oAZ//vVorLhzjOBI6I70hGA8dMsoGLqwuXnDMAwShhi6zSMgCIIgiMGMy+3G2i2nsX5nkSiPqCv4yf+xgjphtZynrUMpAc8VaawDwJeYwO6TnWArFVJutV4mdDTomAEAAO02pxDmx3ceSIkxIiJIA6vNiYIy9rN0JQwAwMyMSEglDPLLmlBUYfbZetAbhmFwzUQ2A+GXw6WCSKIPUPhdSddrFJjBCRBDgrp3SgIe14DD6RJKG7p7BtKq5T6Dmf3BMAxu8Q4V7OEzFjG4oCdpgriEaWqxYn92FVwdlP1j+bV49I3dyCttFG5UIUYVRnICwMnCOiF4MNSkxsgEdvuB7CoheCbUpMYVI8IxNT0CN05LEO0/JkyLIL0KNodLaO8TEaiBTCoR3ATf7ikGAIQHacAwjBAcuJXrchBqUkMmlSDYoEbCEEOfXheCIAiCIM4Nq9cE+2BudTejWfjJv8PpEiz4PK3tnUsJADa8TiZlcLa6BSVVzZ32ybcq1Ho5Daams4GCB3KrOwkQfBmBXCYRJsMMwwiuAR5/GQM8Bq0SE7hnlS0Hz3p1JPAvKGQkhSDMpEZruwMbudbP/oQEnptnJuDBRelCsGJ3eLsGANb631Fs6QtSYkyYPS4aSVEGDB3SvYuTGHyQMEAQF5AWi12o7/dm69EyrPzvYaHejefzX/Lx5vos7DwuDuTZk1WJ2qZ2/HSoFDWNbBvCEKNaCIk5WVQvpOiGGtUYHhsIuVSC6vo2HDnN5gaEBWqgVEixbN4woVaPh2EYjElmtx3icgPCOeV7XCq7nT/uEK7V0NjkEDAMhFCf7roQEARBEARx4fCecB/Krem2nMDucAr3dADYny0WE4ScAJV4Yq1VyzGKW0Tw5RrgJ/oBas/kNyFSj4ggDWx2F/ZnV/kc3zFD4IqR4SK3oLYbxwAAzB7PZgAcyK7G2WreEel/oi+RMJjDuQayihvY8QFdH0cqkWBUYnCP6v95eNcAwGYT9Fdt/6+vSsKKO8ZCcQ5uA2LwQMIAQfQDR/JqsMfHzXD1V8fx5H/2idoIAsCWA2eRU9LY6aZbWsPW6nt3FAA8k/KsonpUc6UBIUY1EiINUCmkaG6zC+0GQ01qKBVSpHLhfFlFbNeC8G6s/WOS2Zs6/9jA9xpOiw8U3eyGcAKAPkCB1BhTp+0EQRAEQVx82ryEgdqmdqFU0B98GQHPqeJ60QKGJ2Og8+o2H0K491SV0OLY8z5OGPASFBiGEVwDOzoshrT6EQYCVHJMGBYq/KzrInyQJy5cj6QoA5wuN7YdZdsddleCMCUtXNSe0NCNY+B8YBgGN04bCgnDID5C1+f7J4ieQMIAQfQxTpcLb63PwjsbTwmTdoCt7SsoN8PmcAmdAwA2VKfOzE70s880iPZV28RuzylpFN1Y+XCa1nYHCivY2roQI2vdHx7HugZ4hT3UyE7oRyUFifYdxrX580dSlFF0s+SFBLlMilEJnn1FeNXQjU/13KBJGCAIghh87Nq1A0VFhX5fP3z4IDIzx6GionMteU84fvwopk2b0Gn7Dz98h8zMcZ2+zvc4A5G2DhP97soJvDMEYsN0cLrcOJhb4/V65wk+z8ihQdCq5TC32pBVJH628TfRn5wWDqmEQWG5GWU1HtGi2c94AEI9v1op9dlZwBd860Ib19Gpu5BAuUyKq7j3AICuB6GC50NqrAnP/3YS7p4/vF/2TxDdQcIAQXhhsTp6nNTrj6YWm3CzyT3bKGxvbrUJtX25JZ7tre0OIWwmt6QBLm5MW7tdsP1ZrA4UV7B1eu02h1AXBwD86YZyE/20oeKes/x2PhvAs71rx4BEwiAjyfMe3jEAAGNTQn1uH5MSAglnf6NSAoIgiMFFZWUFHnvsITQ01PfL/o8fP4rHH38Yrg4r0ACQn5+HjIyxWL9+s+grNDSsX87lcqRj7X535QS8w0CtlGHCcPa+v5/rNOD2ajnoyzEgk0owaTh77XefFDsAPKUE4om+IUCBdG7hwds14K+UAAAShhhw73XDcf/1aT2232ckByNI7wnf64nTYOaYSCHfoLuMgd4QbFSfUwkCQfQlJAwQBEd1QxseXLUT73+X06v98K39AIja7tR7bc892yjcjOs4VwDAigR8zVut13aAtfD52g6wrQp5xZ7PGQAAlUIqrPqHmjSICmW7FijlUhi76JHLw+cM8CGCPCOHBsKkUyLYoBKEB4C9WS6bl4qbpg9FZIi22/0TBEEQA4feCuv+cDgceO21f2P58vsQHh7hc0xhYT4SEpIQFBQs+pJKaZLFwwsD8RE6yKQMKuvbhPbCvvA4BuSYkMpO8k+fbURDs1XUKcCXYwBgMwAA4Ehercit0OqnmwEATB3FlhPsPlkJO7fIIrQq9GP5nzQiHGlDg3y+5gupRIIrx56bAyBAJcfCqfHQquWdFmAIYqBAwgBBcBRWmOFwulDUwxY+/vArDJg9E3pzq03oDlBnFk/0+XKCug4CAB96I7TL8bpBhhg9k/Ngg1pYxQ81qkUK+jgujTfUpO6Rsj4iPhBXjonCr2YlQiLxjFcpZHj6rgn4+7LxojaHADBlZATmT47rdt8EQRDE5ceqVS/hlluuF21raWnBrFlTcPPNCwAAy5ffh3fffQsAcOzYEfzmN0swa9YULFnya+Tn54neu2jRdSLr/6RJY4TvN236BgBgsVhw9OgR/Pvfq7Fo0a98nldBQT7i4uL6+NMOLHhhIFCnEsoOD+XW+B3f5tV1IMigQmKUAW4A+7OrhIm+VMJAIfc9nYgN02FIcADsDpeobIHPGPDlAEgfGoRAvRItFjsOce9p4cf7ESDOh6mjIgQHQKCuZ637rp4Qg1UPTkUULXwQA5S+74VBEJcp5hY2UKfd5uzVfrydAVX1bWhqtcEQoEC92Soal3u2ERFBAYIAwIAN+sspacDciTGCMyAmVIuS6hYUlDWh3eZALRc8mBRtxNnqFlQ3WBDSIS9g5NAgVNS1IbRDwODVE2Pxy8GzmDi8Z9ZKqUSC269O9vmarxs6QRAEcW643W7YXPbuB/YTCon8nBLQ581bgM8//wTHjh3FqFGjAQA//fQDdDotnn32Lfz2t0vxr3+txPjxk1BeXoaHHvo9rrlmPp588mkUFRVg5cpnRft7550P4XJ57rsymQQObqVYq2UnYDqdDu+++xEACGKBN2azGTU11Th27CjWrfsCTU1NGD58BO6/fzliYmLP6XpcjjicLrzwyRGEmTS49/oRfsd5lwaMijbieEEdDubWYIFXqzxf4zVKdrowcVgY8kubsD+7CsO4QOMAlczvvx+GYXBFWji+3FqAnScqMI1zAwilBD4m+hIJg2npQ/C/nUXYeqQMk0aEd+sYOB8CVHI8sDAN5bWtiA6liT5BACQMEIRAUxsvDDi6Gdk1jc1iASC/tBFjU0JR38xO6KUSBk6XG6dLGjFjdKTgGEiNNSH7TANyzzbC4XQJ24fFmdBmdaC2qR2nzzaihgseDDGoYdIp8ePBUkR1qOe/ZmIMWi12UVgOAESH6fDaQ9OEhy6CIAji4uF2u/Hvw2tQ2HTmop3DUEMc/jTm/h6LA4mJSUhJGYbvv/9WEAY2b96IOXPmITCQXYXW6fTQaDT48MP3EBQUhD/96TFIpVLExsahuroKq1b9W9ifyWQS7d9bGOgpRUUFAACXy4Unnvg7rNZ2fPDBe/jd7+7Bhx9+isDAntvML0cq69qQV9qEvNImLJw2FCaT74wfb2FgdFIwpBIGpTUtqGpoQ5iP3CHeFaDmLP/jUkPx3x9Po6iiGcWVbO6RpptV/CvSwrFuWyHyS5tQUdeKiKAAtFq4UgK172nI1FFDsGFXMU6XNqGstrXLjIHekDY06JxKEAhioEOlBATBwTsGrPbeOgbYCb1Myj5k8eUEvGNgJHcT4nMG+BKDUYnBCFDJYLU5UVzZLDgGgg1qDI9jH5xOFTcIjoFgowo3ThuKpdekYvZ4sQBg0Cpx97XDERtOLW8IgiAubfqnX3l/Mn/+Avz884+w2WwoLT2LEyeOY968BZ3GFRbmIykpRVTnn5aWLhpzxx23YPbsqcLXzJlThO9/+OG7Hp3PqFEZ2LjxR/z97/9CauowjBqVgWeffQFut8unw+BywNxmE8KIu8PitaBxOM9/aYDFyj7fqJVSaNVypMYYAfgvJ2jrkAVgCFBgOOcU+Plwqeg1fxi1SozkavJ3nmADBf11JeAx6ZQYlcg+K207UiYIAz0JCSQI4vwhxwBBcPCOAYfTDYfTBZn0/HQz3jEwcmgQjuTVIq+0EYBHMBifGooThXVoaLaiptEiOANCDCqkxphw6HQNss80CC0Jgwwq6DRybD9WgazieuERMtighkohE6x5BEEQg4W33noLO3fuxEcffeR3TF5eHl544QUcO3YMEokE48ePx+OPP44hQ9i/mU6nExkZGbBaxS6v3//+9/jDH/7Qr+fPwzAM/jTm/suqlAAAZs+ei9WrX8Hu3TtQUJCPYcNGIC4u3kdrQAZut3j1XyYTP3q++OKrcDg8E1uplIHTyU6IeQdCTzAajaKfVSoVIiIiUVPTdUu+S5GiCjP++eFBTE0fgqXXpHY7np/wA8Dh3BrcOmeYn3F8FwF2gj02JRRZxQ04mFONeZM6l1y0WbmMAaXndzZpRDiyihtQUtUi2ldXZKYPwbGCOuw+UYkbpw0VMgb8hRYCbBvCI3m12HWyUlho6djFgCCIvoUcAwTBwTsGgN7lDPAZAxO4oL8zlS1otzkEx0BYoAbxQ/QA2LaFddz2QL0KqZwSn3OmQcgeCDaoMCzWBAZAWU2rEFoYYlSd9zkSBEFcrqxduxavvPJKl2MaGhqwbNkyqFQqfPTRR3jnnXdQX1+Pe+65RxACiouLYbVasX79euzcuVP4uuuuuy7Ap/DAMAyUUsVF+zpXUQBga/6nTZuB7du3YuvWnzB//nXCZ/EmKSkZOTnZsNs9wkdOTrZoTHh4BKKiooWv6OgY4XuNpmdtb9evX4d5866ExWIRtrW2tuDs2TOIjx96zp/vYlNa3QK3mw35czi7L6vwLoHMLWmEudXmc5xFKCVgHRwZySFgAM6laOk03tOO0DMhH5McArnMM33ozjEAAKMSg6DTyNHUasPx/DqPE6GLif6I+EAEG1SwWD0tminbiCD6FxIGCIKDdwwA554z8MP+Evx0qBRutxuNLexDZ0KkHkF6JVxuN/JLm4TtgXolUqKNAICTRfXCDTyIEwAAIK+0UWjnE6RXQadRICaMLQtwcCspwQYSBgiCGDxUVVXhvvvuw4svvtht+vyPP/6ItrY2rFy5EsnJyUhLS8MLL7yAgoICHD58GACQm5sLrVaL1NRUhISECF8BAT2bjA525s9fgG3bfkZZWRmuvHIOAECtZoNwCwvz0dLSgoULF8FiseC5555BcXERdu3agffee7vPz2XSpCvgcrnwj3/8DYWFBcjJOYW//OVRGI0mXHPNdX1+vP6GX5xotzmR79XdyB98dgAAuNxuHMyu9D2uXRwmaAhQIIUrJziQ09lZ0SoIA57Jv1opQ0ZSsPCzpgfCgEwqweQRbOvC7w+cBV8g0ZWoIGEYTB8tdkSSMEAQ/QsJAwQB9kba3OpZ0bD6cQzklTYKK/Y8LRY7Pv05H2u3nEZZbaswcTdqlUiKMgIA9udUw+1mgwf1Xjfio/m1AAClXIoAlQwRQRoYAhTCPrRqOdTcDXx4vCegyaBVQC6j3swEQQwesrKyIJfLsWHDBowaNarLsZMnT8aaNWugUnkEVAnXWtVsZlvS5ubmIiEhof9OeIAzbtwEGAxGTJ06HTodK1wbDEbMn78Aa9aswn/+8waCg0OwatUbqK6uwl133YHVq1/GkiV39/m5hIWF49VX34DF0obf/e5uPPjg/dBqdVi16k0olT1rRdef5Jc14Z8fHkR+WfeTfEC8OHG8sK778VbxM8vek76FAYtX+CDPeM7duD+7szDQsSsBz6Th4cL3XZUDeJOZHgEAOH22EQCgUki7LdnMTB8CKdcqWSphoFLQcw9B9CeUMUAQYCf3Lrcn5MdXKUFDsxXPrz0Mk06JF+6/QrBMNnh1ITjA3Vj1AQrIpBIkRRux91SVEOxj0ikhYRgkRhogYRjYueTlQL1S2N+wWBP2nqoCwLoFeIbHBeK7vSUAgBCjuD0hQRDEQGfWrFmYNWtWj8ZGRUUhKipKtO3tt9+GSqXC+PHjAQCnT5+Gw+HA3XffjZycHISFhWHJkiW4/vrr+/zcByIWiwXNzc249lrx9Vqx4m9YseJvws/JyalYvVrsEli4cNF5H3fevOswb15nF0BKSipefvn1895vf7I/uwqF5WZsOXAWiZGGbsd7P4OcKKjDLTMTuxzPT/gTIvUoKDPjcG41bPZUSDqUdrT5EAbGpoRg7Q+ncaayuVN3AosPxwAApA0NhFYtR4vF3qNSAgCICtEiPkKHogq2m0FPBAVDgAIZySE4mFMNrebcszAIgjg3SBggCIjzBQCg3UdnguqGNrjdbHeB6gYLwgLZmydfIgB4rHgmHbtCkRTFPgDwN+1AbrtKIUNchA6F5ezKlbcAkOolDHiXCyRHGSCXSWB3uBBCZQQEQRA95qOPPsLHH3+MJ598Ugi0y8vLg8vlwvLlyxEeHo5t27ZhxYoVsNvtWLTo/CeuMpnvVVCXa2BMasxmMw4fPoCff/4R4eHhGDduQp/tm5/3MQzg7lkgf58glTJ+f299gc3OLgJkn2mARMp0mrB3xOrVrrGsthWNLVYEd7EgYHWwzyzDYgPR2GxDnbkd2SWNGJUgbsXHP4voAhTC5w3UqzA8zoSTRfU4lFuDBZnxwvhWH+MB9t/4/Mmx+Hp7IUYMDerxtZs+OhJFFTnsPjXyHr1vzoRoHMqpRmSwtl9/R95IOSeD9DxDqAkWuo6950JfQxIGiEGLy+1G7pkGRIfp0NQhqKejLQ8AGr3Eg/yyJkEY8HYM8GUGJi0rAAwJDoBGKRNU+kAvASAl2ugRBgxiYYDHe7tcJkVSlAGnihsQbCDHAEEQRHe43W68+uqreOONN3D//ffjzjvvFF7buHEjnE6nkCmQmpqK8vJyvPvuu+ctDEgkjN8e8u3tUtTWSvp9EtrfMIwbzz//DxiNJvzrX/8Pcnnf27sv1EOwy8VAIpHAYNCIyk76/Djcf1ssdjS0OZDIlRn6Hd9BFMmraEZSfLDvwQC46kMEmTSYPDICG3cV4XhBHWaMi/GMcbkFJ8KQMD0MWk+JxczxMThZVI+DuTVYcl0aAPb/HQvXPWBImB6mDs8dd8wfgduuGQ6JpOeC15wpQ/HfLadhc7hg1Kn8/r/izSRTAP79kBZBBhVMugu7KKLX07NWX0DXsfdcqGtIwgAxaDlVVI9/f34M41JCkJEUInrNau8cPtjk5QwoKDdjyki2Xs7bMcBj0rM3XAnDIDHKgOMFdaLtAJASY8R3+9jSAG/BIMSgQpBehTpze6eAwYVTh0IuLaYWhQRBEN1gt9uxYsUKbNy4EStWrMDSpUtFr/uaCCYnJ2PDhg3nfUyXyw2zuc3nazabFS6XC06nGw5H90nzlyo6nQGbN28Vfu7Lz8IwrCjgdLouiGPA6XTD5XKhqakNFsv5dyPqjuZWz3PC3mNlCAro2kbf1MK1MTaqUdNowZ7j5Zg8LNT/eK4dMlwujBwaiI27irAvqxJ1dS3CxL3V4slRsrXb0OD1nDMs2gCphEFxhRlZedUYEhwAi9UhCBR2qx0NDX3zex6XGordJyuhlEvQ0NDao/cEBcgBh7PH43uLVCqBXq+G2WyBswddIQjf0HXsPX1xDfV6dY/FVhIGiAFPW7sDvxwpxcRhYSIrXgW3up97thFDh4hr/nxlDDR6uQoKvQKEGps7CwN8yQAAJEcbBWEg0EvtTooyCnbJIC/BgGEYXD0hGlsOnMWoRPEKQUKkAQ/e3HXoFkEQBAE8+uij2LJlC1566SXMnz9f9JrZbMZVV12Fxx9/HDfeeKOw/cSJE0hKSurVcf1NlJ3OC+iNv0zhxYALWUYAoN/FGouXC/FEYR3mTIjpYrSntn9cagi+21uCU0X1sLTb/YYO890GFDIJkqIMCFDJYG61IbekQQhBbuaeYeQyCeAW/ztVyaUYER+I4wV12HOyEtdnxgvjpRIGEvSdAHTdlDg0tdowfdSQS14gczpdl/w5Xg7Qdew9F+oaXr5eNj+89dZbIqugL/Ly8nDvvfdi4sSJmDx5MpYvX47y8nLhdafTifT0dKSkpIi+Xnvttf4+faIf2HWyAl9tK8TXOwpF2/k2gc1tdhRXmkWv+epK4O0YOFvTIqQG8yUG3iWDRi+LHp8zALAhgzxqpQzD4wIhYRjEhetFx5o9Lhor77+CQgYJgiB6gNPpRE1NDdrb2ZXTdevWYdOmTXjooYcwYcIE1NTUCF/t7e3Q6/WYNGkSXn75ZWzbtg3FxcV4++23sWHDBvzhD3+4yJ+GGGh4P1OcPtsEm48cI2/4xYmkSCOMWgVsDhdySxr9jueFB7VCBplUgvFc14AjebXCGF/Bg96MT2UdCfuzq+B2u0WtCvsy9C/MpMHDvxotKpskCOLSYEAJA2vXrsUrr7zS5ZiGhgYsW7YMKpUKH330Ed555x3U19fjnnvugdXKTvyKi4thtVqxfv167Ny5U/i66667LsCnIM4Xc5sNPxw4ixYvuxwANJjZ3yufhMvT3OZxAGQV1Ytes/hyDHhlDLjdnv3xGQMp0UbhdW/HQFy4XmjJ4x0yCAD3Xz8C/7hnAoYEU99sgiCI86WiogKZmZnYtGkTADY/AABWrlyJzMxM0Rc/5tlnn8W8efPw1FNP4brrrsOmTZuwatUqTJ06tV/P1X2hl8MJv1yo34V3oLHD6UJeN20LeWFArZQinQsQ5J2Hvsc7hPEAMDGNFQYOn64RPqOvVoXeZCSFQCZlUFHXhrKaVrRx+QKaHrYjJAji8mdAlBJUVVXhqaeewr59+xAXF9fl2B9//BFtbW1YuXKlUF/4wgsvYMaMGTh8+DAmT56M3NxcaLVapKamXoCzJ/qKLQfO4ts9Z9DcZsNN0z29qXkBoKq+DVabE0quD6651SMg8Mq4UatAY4vNp2OAzxLQaeRobrOjoKwJw2JNwvbxqaHI4RR9o5cwIJdJsOyaVFQ1tCE6VCvap0Ylp5suQRDEOfL888+Lfo6KikJubq7w83vvvdftPrRaLVasWIEVK1b0+fn5Qipl7z02mxUKhbKb0cSFwGZj799Saf8+Dlu5iXt0qBZnq1twqqgeI+IC/Y7nJ/oqhQzpCcHYfqwCxwvrcJuf8R3dAGNSQiGXSlDdYEFZbSuiQrSCq0DjRxjQqGQYOTQIR/JqsT+nCvER+i7HEwQx8BgQ/7dnZWVBLpdjw4YNeP3111FWVuZ37OTJk7FmzRpR6JBEwq7mms2snTw3NxcJCQk+309cutSbWQtpcWUHZwDnIHCDLQHgewib28SdCAAg1KRBY4tNuCl708Q5BjKSQrD9WDkKy81wOF1CScKoxGD8b2cRnE53J2fAZE69JwiCIAYnEokUarUWLS0NAACFQkl92X3gcjH9nsfgdrths1nR0tIAtVorPAf2F1bOMZCRFMwKA8UNXY7nHQMqpRTDYk2QShhUN1hQVd8mdEQSjbfy49nHeo1KjrShgTiSV4uDOdWICtGizco5AJT+u0iMHxbKCgPZ1Qgzabh9DYipAkEQPWBA/N8+a9YszJo1q0djo6KiEBUVJdr29ttvQ6VSYfz48QCA06dPw+Fw4O6770ZOTg7CwsKwZMkSXH/99b06z8u5PdHFpKc9PPlV/9KaFtG19i4tKKttFeramtvEJQcAEB6owemzjbA5XKJ92OxOQZEfPywU24+Vo6C8Ca3tDrjBhvMEm9T4xz0T4XS5oVFfei4A6ifbe+ga9h66hn0DXcfLE72eXSXmxQGiMxKJBC7XhQkqU6u1wu+kv3C7PW0CxySHYMOuYpRUNaO5zQadRtFpvMvtFlyLKoUMaqUMydFGZJ9pwPGCOszuIAy4XG5BeFArPJN+fpJ/6HQNbpg61JND0IUDYHRiMBQy1mnAixfkGCCIwcOg/7/9o48+wscff4wnn3wSgYHszSEvLw8ulwvLly9HeHg4tm3bhhUrVsBut/dLb2OiZ3TXw5PPBWhqsYGRyQQ7f0u7Z/W/ssEi/B58OQbiIg3YfqwcTjdgMgWgrd0OpUKGmga2g4FcJsEVo6Pw6hfH0dxmR2m9BQAQaFAhKFCLoEBtp31ealA/2d5D17D30DXsG+g6Xl4wDAODIQg6nQlOZ2dn2mBHKmVgMGjQ1NTW764BqVTW704BALA7PK0XQ4xqRIUEoLSmFdlnGjBhWFin8VabE/wnV3ET/fSEIE4YqMXs8dGi8d4OR5XC81ifkRQCqYRBWU0rKupauw0f5N8/KjEYB3KqcSCnGgAQQI4Bghg0DNr/291uN1599VW88cYbuP/++0WdDDZu3Ain04mAAHYCmZqaivLycrz77rvnLQx01duY6Jqe9vD0bht4Mq8aI+JZoafJa/vpMw1oaGiF1eYUFPlAnRL1zVYwDKDlLHbNrVYUldTjz2t2YVisCQumxAMADAEKtLa0IzZch4KyJmw9WAIA0GsUF6y/7vlC/WR7D13D3kPXsG+40L2Nib5FIpFAIum8WjzYkckkUKlUsFicl0V7s6qGNhwvqMPMjEghZLgj3sGDSrkUw+MCUVrTilPF9T6FAd5dwDBs+0GAFQY++zkfOSWNsFgdosk9P+GXSSVsK0KOALUcw+JMOFlYj4O5Nd2GD/JMGh6GAznVcHB/V9QkDBDEoGFQ/t9ut9uxYsUKbNy4EStWrMDSpUtFr3vnD/AkJydjw4YNvTru5XCTu5Tprodni8XjADhT2YyUaCOsdqdgsQOAs9UtsNocqOM6FShkEiRHG7H3VBV0ajkUXI9gi9WBogozbHYXThTUY0Iqe/M2apVwOFxIGKJHQVkTTnApwUat4rL5/VI/2d5D17D30DXsG+g6EsTF48utBTiUWwOlXIppo4b4HMMvQijkEkgkDIbHBeKHA2eRVdQAt9vdKWfCO3iQfy0iKABhgRpU1bfhZFG90FoQ8OQL+MoOGJcSipOF9TiUW93jMMG0oUHQKGWC4EClBAQxeBiUSwWPPvooNm/ejJdeeqmTKGA2mzFhwgSsW7dOtP3EiRNISkq6gGdJnAsOp0uonwOA0uoWAJ6OBFIJA5VCCofThYq6NjRzgYE6jQKx4ToAgD5AKdj2rDanEDbocrtxrIDtBWzQsis8CVyAoY17IDdqKWGaIAiCIAYT/LPEqeJ6v2OEvAA5+3yREm2ETMqgztyO6gZLp/FC8KBCPNHPSAwGABzNqxFtt/BCgo8JfEZSMCQMg5KqFpzhgpm7cwDIZRKMSw0Rfg6gzkkEMWgY8MKA0+lETU0N2tvZxPp169Zh06ZNeOihhzBhwgTU1NQIX+3t7dDr9Zg0aRJefvllbNu2DcXFxXj77bexYcMG/OEPf7jIn4bwh3fAIMB2HwA8AYM6jVxoFXi2qkXoJKAPkGNUYjDUSilGJgQKN+J2mxNNrZ4SBL5/sDGAFQAShuhFxzPpSBggCIIgiMGE1c4uDuSUNMLt9p2JwJcS8K2SlQqp0B3Jl6DgTxgYncQKA8cL6uD0CmcUQgUVnSf8Oo0CKTFGAJ6OTT1xAEwc7umkRF0JCGLwMOCFgYqKCmRmZmLTpk0A2PwAAFi5ciUyMzNFX/yYZ599FvPmzcNTTz2F6667Dps2bcKqVaswderUi/Y5iK7hhQHekFde2wqny+UlDCgQE8Y6A85UNQvBg3qNAuGBGrz24DTcPCPRIwzYPY4BwHOj5h0DgXqVSAwwaqlWlCAIgiAGE/yk39xqQ3md7xwp3jGglHsm2MPi2Aykk0W+hAFPKYE3CZF6aNVytLY7kHe2SdjuyQ7w3YZwnFfZATuu+4l+SrRReMYxBNDzDUEMFgacDPj888+Lfo6KikJubq7w83vvvdftPrRaLVasWIEVK1b0+fkR/UMLJwCEmtRoaLHCZnehusEilBLoNHLE8I6B6hYhZVfH3fAkElZSUHI3YqvNicYWKzpi8BIAEobocTCXtfRRKQFBEARBDC5sXhlGOWcaEBncufuULwfAyKGB+Hp7IU6daYDD6RIFF/LjO070pRIJ0hOCsPtkJY7m1wqtly1+hASeMUnB+Pj7XKHTQU+EAYmEwe9uSENeaROSo43djicIYmAw4B0DxOCAdwzoAhSIDGYFgNKaVp+OgZKqZphb2e0dlXDvG3dNY3un43gLAHzOAEClBARBEAQx2ODdAACQU9Lge4ydnbgrvZ4vYsJ00GvksNqcyCttEo33CAmdJ/CjhZyBWqF0gQ8f9DfhN2iVSPKa3Pc0TDAh0oC5E2M6hSMSBDFwIWGAGBAIwoBajuhQVrE/W92CZq5TgU4tx5DgAEglDFrbHSiuMrPbNWJhQCGTgL8HVjeytkDvW6K3kJAwxCMMkGOAIAiCIAYX3l2Pcksa4fKRM9AxfBAAJAyDtKFBAICThXWi8e1W3gHQuTRgRHwgZFIG1Y0WoXShu1ICABib4gkT7GocQRCDGxIGiAFBMycMBKjliArhHAPVLaLwQblMgiGcza+wnBUG9Bpx2i7DMMLNmA/0SYjyLQDERegQE6rFsFhTj6x5BEEQBEEMDBxOF5wuVgiQShi0WOxCRyRvOoYP8qQNZXMGTnQQBix+wgcB1hXAlxDw3Qn4UoKunkPGJoeAAcAwgFZNXQYIgvANCQPEgKBV5BjgSwlahOwB3hnA5wzwor7eR6iOUt6hRRCXBCyVMNB6CQkyqQRPLRuPP986uu8+CEEQBEEQlzze+QJ8HX5OSWOncUL4YEdhID4IDMOWPdabPaWL/sIHeTKS2NX/o/lsG2VLFw4DnkC9CvcuGIG75g2DhtoPEgThBxIGiMuWU8X1eP7jQyiv9WQJaDVyRHKOgdqmdlQ3sj2CddyEns8Z4NFrOgsD3jdjuUyC0YnBYACEBWog6VBrxzAM1d8RBEEQxCCDb1UolTAYEc+u/uec6Zwz0O6jlABgV+6HRrCtj727E/hrV8gzKoEtQSgsM6Op1SZkDHSXHTBxeBimjIzo+kMRBDGoIWGAuGzZebwCp0ubsOtEhZAxoFXJoVXLhTDA8tpWAF6OgTCtaB86X44Br5uxIUCBiKAAPHpbBpbfNLJfPgdBEARBEJcXfL6AQi5Fagxr78892wiXy+1zXEfHAAAhZ8C7nKA7YSBQr0JsuA5uAMfzaz1dCaikkSCIXkLCAHHZwosBZbWtHmGAcwbw5QQ8OmG7xzHAgC096Ii6gzAAACkxJoSaNH138gRBEARBXLYIJQJyCWLDtVAppLBYHSipbvY5rqNjAABGcsLAqeJ6OJysA6G7UgIAyOC6ExzJqxXykNRdjCcIgugJJAwQlzxWmxOnSxqE1jw8gjBQ04oWrvsAH6oTGSLuJcw7BjQqGUKMKnasRg6JpHMZgHfGgK8MAoIgCIIgBjeCE0AuhVQiQQqfM3CmUTSu3U/GAADEheugVcthsTqFUOTuHAMAMJrLPsoqrkdTqxUAdRsgCKL3kDBAXPJ8uDkHD7+6HScK60XbW9tZYaDO3I6GZrEwEB3icQxIGAYalUdJj+FcA/4m/d52PAO1ISQIgiCIQUfHxYiO2LyEAQBCt4CcEnHOQFelBBIJg7R4cXcCPjOgqy4D0aFaBBtUsDtcqDezwkBXDgOCIIieQMIAcclTUc/26s0vbRRtb7E4hO95Cx7vDIjyKiXQauSi0EA+Z8BX8CAgdgwYyDFAEARBEIOK9zZl47E39wjORF8IGQPchN87Z4B/JgG8wwd9T9z5coITBZwwYOu+ywDDMBiTHCLaplaRMEAQRO8gYYC45Gn1KhngcbpcQoseHgaeVN7wQA2kXJmATiPOERiXGopQoxoTh4f5PJ7KR8YAQRAEQRCDgxMFdahtasdJr1DAjlg7OAaiw7QIUMlgtTlxprK58zg/E32+o0FJdQsaW6w9KiUA0FkY6GY8QRBEd5AwQFwyOJwuNLXaOm1vbWcFgLLa1k7bvAlQezIDZFIJhgSzOQMdAwYjggLw/H2TMW3UEJ/nQcIAQRAEQQxebA52cn76bKPfMXy7Ql4YkDAMkrmcgWyvtoXWbhwA+gAF4sLZEseThfU9Ch8EgMRIg2jhg0oJCILoLSQMEJcMb63PwsOrd6GSKx0A2Bo/3jFQVd8m2PNafdj7tB0EgCguZ0Dnp2TAH943V72WhAGCIAiCGEzYuEn/6dImv2O8uxLw8Kv/p4o9mUhC+KCPrgQ8fDnBkbwaOJxstoGqmzBBiYRBBhdCKJNKIJfRIz1BEL2D/ooQlwwl1c1wud3I88oSsNldcHI9gZ0uN6o40aDV0tkx0FEYSI0xAujcoaA7lOQYIAiCIIhBicPpee4or22Fua2zkxHoXEoAACPiWGEgr7RJEA74cV2VBqQncDkDXqUL3ZUSAMCY5FAAgFZNbgGCIHoP/SUhLhnauPKAyjqPY4DvPMBTVtuKyBAtWrjtQ4IDUM6VGHQUBqakRyA+Qi+UFPQUKiUgCIIgiMEJ7xbgyTvbhLEpIZ3GCeGDXsJAqEmNIL0KdeZ25J5twPC4QMEB4C9jAADiI/TQaeRobmOfbeQyCaSS7tfu0oYG4obM+HN+ziEIgvAFOQaISwK32w0L16KnwksYaOuQJcCLAHwpQaBOiRCjCkBnYUDCMIgK1Qq5Az1Fxd3kNUoZ5DIK8yEIgiCISwFXNy0E+wI+X4DHX86AL8cAwzBCOUFWUYNQRtBxXEckEgbpXDkB0DO3AMA+5yzIjMe41NAejScIgugKEgaISwKr3Snc8L0zBjo6Bso50YBvIaRVy4UsAW2H7gPnC59JEKhX9sn+CIIgCILoHTWNFvxx1U58ubXgnN9rtTtFLQS7wmbvmTBgs/nuNuCdM8CXE8ikDGTSrh+5RyUGC9/3VBggCILoS0gYIC4JvJ0BNY0WT8igP8cAJxgEqOQYnxoKtVIm1Pb1lqGRetw0fSjuuDqlT/ZHEARBEETvKK5sRovFjj1Zlef0PofThb+8sxdPvbe/R44DvtuATMq6DUuqmzu1R2bH+Q4VHBZrAgO29JFf6OjKLcAzIj5QaLOspg4DBEFcBEgYIC4J2rxuuk6XGzWNFgAeASCGa+XDdyZo4cIHA9QyTBoRjtV/nCqo9L1FwjCYPzlOaDtEEARBEMTFxc5Z/BuarWj2Ewjoi+Y2O+rNVlTUtYkyjPzBOwaMWrZU0e0G8ss6dyfgBQSFXPworVXLERfBPrMczqsB0DMHgFopE547yDFAEMTFgIQB4pKgY5YAf/Pmt8dF6KFSSNnOBA0WIWMggMsVYJhzyxEgCIIgCOLygQ/xA4CSqpYev887M6DAxwS/03gvJwA/UfdVTuDPMQAAwzkH45HTrDCg7KEDYDRXTqCn4GOCIC4CJAwQlwRtHWx6FXxbQk4Y0GkUQupuRW2rKGOAIAiCIIiBjd3hyQgoqWru+fu8ugwUlJu7HW91eJwAvDCQe47CQBrnYGxssfkd44uZYyJxy8xELJw2tEfjCYIg+hISBohLgo71exV14iwBrVqOSE4YKK9tFWUMEARBEAQxsPEWBs6cgzBgc3gLAz13DChkUqRwwkBRublTKCH/sy/bf0KkQSQG9LQ0QCaVYO7EGEQEUftBgiAuPCQMEJcEfMkAH7zDB/bw27UaBSK57gNlta1CKQE5BgiCIAhi4GN3ejsGel5KYPcqJSivafUZJOiNjXMYKBVShBjVMGgVcLrcKKoQuw14x4DChxtAJpUgJcYo/NxTxwBBEMTFhIQB4pKALyWICWMDeyrr2uB2u8WOgRDOMVDXipZ2T/ggQRAEQRADG2/HQFV9G9ptXU/webwdA24AhRVdlxPwmQQKmQQMwwiugY7lBHwrQn+Tfu9OSRQmSBDE5QAJA8QlgYWb6MdH6MCAzRZobrN7OQY8pQSVdW3CDZkcAwRBEAQx8HF0mOCXVrf26H02r4wBACjsJoDQJnQbYCfz/gII+a4E/oSB4V6dkpQkDBAEcRlAwgBxSdBmZZ0BhgAFggwqAGzOAB8+qFXLEWhQQSlnOxMAAMOw7X0IgiAIghjYeDsGAKCkumc5A96lBED3AYS2DiUCvDCQX9YEB1fO4HS5hO/9TfqHBGlg0inZMVRKQBDEZQAJA8QlQZuVvRFrVHKEB2kAsDkDbXwpgUYBCcMggnsNYIMHJdSmkCAIgiAGPHzGgEzK3vd72pmALyXgWwAWlpvhdrv9jrd6lRIAwJDgAASoZLDZXThTyR7TavOIFEq570dphmGE7gQ6DbkbCYK49CFhgLgksHACgEYpQ0Qg15awrg2tFo9jAIBQTgAAASpyCxAEQRDEYIB3DPBZRGd6GEDIv29ohB4yqQQtFjuqGyx+x3csJZAwDFJiTACAnJIGAJ7gQYZhgwb9sXDaUNwyMxHTR0f26FwJgiAuJiQMEJcEfPigWiUTHAPFlc1wcaq+llPbh3gJA5QvQBAEQRCDA94xkDDEAAAoq2kR7PxdwYcJqpUyxIWzokJ+FzkD/KTf2wkwLJYTBs6wwoDN7gkeZLpwLhq1SsydGEPPKwRBXBaQMEBcEvAhg6xjgBMGuORgmZQR6vO8hYEAutESBEEQxKCADx+MCNZArZTC4XSjoq6t2/fZBQeABAmRegBsOYE/hIwBmScXIJUTBvJKm2B3uLzEA8oOIAhi4EDCAHFJwPcV1ihlQo4AXxcYoJILirxIGFCRMEAQBEEQgwHeMaCQSRAdyq789yRngH+WkMskgtugoAvHgM1LSOAZEqSBPkABm8OFwvImEgYIghiQkDBAXDTWbS/E8x8fgtXuFEoJNCoZ9AEKUbcBjVeWQJBBJdysA9SUMUAQBEEQgwG7MMGXIiZMCwA40yNhwOMASIhkhYGzNS1C22N/470n/QzDCOUE2WcaBGFAQcIAQRADCBIGiIvG9qNlOF3ahOziBjicbJaAWikD07H7gFfJANuZgHUNaMkxQBAEQRCDAkEYkEoQG8Y7BroPIOTfp5BJYNIpYdIp4XYDxZW+ywk6hg/yiIQBG9+qkB6jCYIYONBfNOKiwbcoLOSyBBgGUHH9gMMDxW0JvUniFP9wL/GAIAiCIIiBi92rJIDvTHC22hNS7A9+oi/n3IYJQ9icAX8BhELGQIc2hHzOQGG5Gc1tNgBUSkAQxMCChAHiomB3uIQ04SJOGNBwbgEAYsdAh7aEN81IwF/uHItxqaEX6GwJgiAIgriY8M8MMinrKpRJJbBYnaht9N96EADsDnGYIF9O4C+A0Co4DMST/hCDCkF6FZwuN04W1QMgYYAgiIEFCQPERcFicwjf890HvHMFwgP9dx9Qytk6QUkXLYIIgiAIghg4eGcMyKQSRIawzwndlRN4hw8CEAUQun24DWx+ggUZhsGwONY1kEXCAEEQAxASBoiLAt+FAABa2z3BgzxdOQYIgiAIghhc8F0J+Al+bA8DCL0zBgAgNlwLmZSBuc2OGh9uA3+lBIAnZ4DCBwmCGIiQMEBcFLyFAR6Nl2Mg1KQWHAEaChkkCIIgvHjrrbdw5513djkmLy8P9957LyZOnIjJkydj+fLlKC8vF41Zu3YtrrzySqSnp+O2227DqVOn+vO0iV5g77Dyz7csPFvdnWPAyb1PKvw3LpzNGTh9tnPOgM1PKQEApMaYRD+TY4AgiIEECQNEv/P61yfw7EeHhPpAALBYO7cJ8i4lkEklCDGqAJBjgCAIgvCwdu1avPLKK12OaWhowLJly6BSqfDRRx/hnXfeQX19Pe655x5YrVYAwNdff42VK1fiwQcfxLp16xAVFYVly5ahvr7+AnwK4lzxzhgAgNhwVhgorjD7LAngsQtdBjyPvEnRbDnB6dJG0ViXy+1xGPhwDJh0SpGjkboSEAQxkKC/aES/4nS5cCi3BvllTaisbxO2+3QMdBAAMpJCoJBLMJQLCiIIgiAGL1VVVbjvvvvw4osvIi4ursuxP/74I9ra2rBy5UokJycjLS0NL7zwAgoKCnD48GEAwJtvvok77rgDCxYsQGJiIp599lmo1Wp88cUXF+DTEN60tdu7fN3tdosyBgAgJlQLqYQtCag3W/2+19ahlAAAkqOMAIC8s40dxnoWLfyVCfDdCQByDBAEMbAgYYDoV9ptnptsTYOnls93KYG4ZOCWWYlY/cdpiAwO6DSWIAiCGFxkZWVBLpdjw4YNGDVqVJdjJ0+ejDVr1kClUgnbJBL2kcdsNqOurg7FxcWYPHmy8LpMJsO4ceNw4MCB/vkAhE+OnK7B71/ZgR8OnPU7xuH0OALkUvb3qJBLhecDvruRL+wdSgkAIDHKAAZAVYMFTS0eUYFvbciO9/2IPMyrnIAyBgiCGEiQMED0K+1eJQNVXsJAWw8cAwBbUkAQBEEQs2bNwmuvvYbo6Ohux0ZFRWHSpEmibW+//TZUKhXGjx+PyspKAEBERIRoTGhoqPAacWHgwwNPFNT6HcO7BQDxhD1+CJsVUFTpXxjw5RgIUMmFrgZ5pZ6cAe/gQX+dj7wdAyoSBgiCGEBQ8TbRr7R7tSX0Tv9t54QBpUIKK+cq8A4fJAiCIIi+4qOPPsLHH3+MJ598EoGBgSgsLAQAKBQK0TilUilkEJwvMj8rzYRvXJwZoLSmFVJuMUDacVHAa46uUkrBcJP2hEgDth0tR3FFs9/rzosKapVMNCYlxoTSmlbklzdhUlo4AMDJZRUo5VK/+zPqlIgN1+FMZTN0AYpL7vft9xoSPYauYd9A17H3XOhrSDMxol+xeJUSVDd6lxKw2+PCdMjlavzUJAwQBEEQfYjb7carr76KN954A/fff7/QyYAvMbDZbKLxVqsVarX6vI8nkTAwmaj87VyQchb/plYbnNyEX68X/w7snDIgl0kQGKgVto9ODQO+zcaZqmYYDBpIJJ1X+XnHQEiwFiaDZ79jhoXhp0OlKCg3C7+z2hb234NKKevy9/iHW0Zj/6kqTB0bDZXi0nx26XgNiXOHrmHfQNex91yoa3hp/jUjBgzejoHqBk/4IF9KEBfhEQZ8lRIQBEEQxPlgt9uxYsUKbNy4EStWrMDSpUuF1/gSgurqaiQkJAjbq6urERYWdt7HdLncMJvbuh9ICLS0ehwa2QW1uGJ0FMxmC5xenYxq61oBsPkCDQ2twnadUgKFTIK2dgeyC2owpEMmkcPpgouzJLS1WiFxefYZGcg+aBeWNaG8sglqpUw4jkzCiI7TkVC9EtdOioGl1QpLa+8cJn2NVCqBXq/udA2JnkPXsG+g69h7+uIa6vXqHjsOaCZG9CveGQN1TVY4nC7IpBJBMAjUqaAPUMDcaqNSAoIgCKLPePTRR7Flyxa89NJLmD9/vui1oKAgxMfHY9++fUIAocPhwMGDB3Hbbbf16rgOBz0Anwt2r04AxRVmXDEacDpdouvIP0vIZJJO1zcmXIf80ibknW1EqFG8quYddCyB+Hej1ygQbFChtqkduWcakDY0SBivkEkv+99jx2tInDt0DfsGuo6950JdwwFX9PHWW28JVkF/5OXl4d5778XEiRMxefJkLF++HOXl5aIxa9euxZVXXon09HTcdtttOHXqVH+e9oDgp0Ol2HNSHNpk8XIMuNxu1JnbAXgcA2qlDNddEYeMpGAkROov3MkSBEEQAwan04mamhq0t7P3mHXr1mHTpk146KGHMGHCBNTU1Ahf/Ji77roL77//Pr7++mvk5+fjiSeeQHt7OxYtWnQxP8qgw+7wdBworW7xOcbBrZTJpZ1LBYZGsM8OxRXNnV6z+Qkt5EmONgIATpc2suO9wgcJgiAGGwPqL9/atWvxyiuvdDmmoaEBy5Ytg0qlwkcffYR33nkH9fX1uOeee4TAoa+//horV67Egw8+iHXr1iEqKgrLli1DfX39BfgUlyfNbTas3XIa723KFqUHe7crBDwtCy1ewsCVY6Pwh5vSRa2ECIIgCKKnVFRUIDMzE5s2bQIAbNy4EQCwcuVKZGZmir74MbfccguWL1+OV155BTfddBPKysrw/vvvIzAw8KJ9jsGIw8see9aPMMA/V8h8PCfERegA+O5MYLfzrQolQmChN4IwcJbtTMC3K6Q2hARBDEYGhHe7qqoKTz31FPbt24e4uLgux/74449oa2vDypUrhfChF154ATNmzMDhw4cxefJkvPnmm7jjjjuwYMECAMCzzz6Lq666Cl988QV++9vf9vfHuSzhHQBOlxv15naEBWoAdBYG+ABC3haoVtLNlyAIgjg3nn/+edHPUVFRyM3NFX5+7733erSfu+++G3fffXefnhtxbngLA2U1rT7raHlhQO6jTjaecwyUVDUL5Yo8vloVepMUZQAAFFWYYXe4YOXKGvyNJwiCGMgMiL98WVlZkMvl2LBhA0aNGtXl2MmTJ2PNmjWCKAAAEgl7GcxmM+rq6lBcXCzUHAKATCbDuHHjcODAgf75AAMAq5cAUNPUuS0hTzXnGPAuJSAIgiAIYnBi9xIC7E4Xyms7h/4JwoCPCXuoUY0AlQwOpxulNS09fh8AhAdqoNPIYXe4cKayGTbuWUZJjgGCIAYhA2JWNmvWLMyaNatHY6OiohAVFSXa9vbbb0OlUmH8+PGoqKgA4Eks5gkNDUVOTk6vzvNS63Xbl9idnhrBerNV+KxW7qas08jR3GZHTZMFMplEKCXoSQ9g6oPaN9B17D10DXsPXcO+ga4jMVBwej0/AEBxuRlpcUbRtq4yBhiGQVyEHllF9SiqaEZcuCevyCY4AHxP9BmGQVKUEYdP1+B0aSOcXAcDKiUgCGIwMiCEgd7w0Ucf4eOPP8aTTz6JwMBAFBYWAgAUCoVonFKpFDIIzoeB3ttYXuVR6ZvbHcJn5dcBEqOMOHK6BrVNVhgMGqHEIDxUB5NO1XF3PqE+qH0DXcfeQ9ew99A17BvoOhKXO7xjQKuWo8ViR1FFUydhoLuV//gIHSsMlJsxMyNS2M6XEsi7CBNMjjKwwsDZRkSHagFQ+CBBEIOTQSsMuN1uvPrqq3jjjTdw//33C50M+BIDm80mGm+1WqFWn/8D2EDvbVxb77H+na1qFvr/mptZMWVIsAZHTgOVda0or2oSxtrabWhwiHMIOkJ9UPsGuo69h65h76Fr2Ddc6N7GBNFf8C244iP0OFFYh+IKHyGCvGPAz8o/nzPQMYDQbu86YwAAkrgAwvzSJqHdIZUSEAQxGBmUwoDdbseKFSuwceNGrFixAkuXLhVe40sIqqurkZCQIGyvrq5GWFhYr447kHt4tlk8WQI1DW3CZ21rtwMAIgI1kEoY2B0unK1k3QUyKQMJmB5fF+qD2jfQdew9dA17D13DvoGuI3G5w5cJxIXr/AsDQlcCf44BVhgor21Fu80BlYJ9vOVLCbrqehQTpoVSIUWb1YFC7tgUPkgQxGBkUP7le/TRR7F582a89NJLIlEAAIKCghAfH499+/YJ2xwOBw4ePIjx48df4DO9fLDaPav+tU3twvd8yUCASo5gA+vG4BV9/sZNEARBEMTghM8o4tsO1jRY0MotKghjHP4zBgDAqFXCpFPC7QbOVDZ3el9XE32pROLpTlDOCQPkGCAIYhAy4IUB5/9n777j47jrxP+/pmxV77Il997i7jSn2XE6IYV6HKGEy8GFhMtxgYNAuON3QI6abw4IOTq5HCUEQggppJHEKXbiEne5F8nqXVun/f6Y3ZXWkqxqq72fPPyQdnZmd3Yidubznvfn/bYs6uvriUbdweof/vAHnnrqKe666y7WrFlDfX196l9ynY9//OP8/Oc/549//CMHDx7ki1/8ItFolPe85z0j+VFGtWi8M2OgPWykHicDA36vlmpheLTaPWkHpSOBEEIIMaElp8JkZ3gpyHZvIFTWpXcm6JxK0Ptla2o6QXVnYCDeR22CpPlT8wBIlkGUwIAQYiIa94GB6upq1q5dy1NPPQXAk08+CcA3v/lN1q5dm/Yvuc773vc+7rzzTu6//35uvvlmqqqq+PnPf05+fv6IfY7R5GBlKw0tkbRlXTMGoDNrIJIIEPi9OiV5icBAImNAWhUKIYQQE1tq0K+plCeK/52oa09fJ5Ux0PuAfUYi4+BIl6kIRuLapK+B/rypuWmPZSqBEGIiGncjs/vuuy/tcXl5ORUVFanHP/vZz/r1Orfeeiu33nrrsO7beNDUFuUb/7uFyYUZ/H+fODe1PJkZkNTQEqW8KDO1PODTKM13i/rUNIZTy4QQQggxcSVrZOiaytTiTN452MCJuo4e1+lfxkBnYKC/GQPTS7PweTViiWsWKT4ohJiIJCQqBqSpPYYDnGwMpQoGAamTaVJ9awTbcVLL/V6d4sRUgmSqnmQMCCGEEBObmagxoGsKU0qSGQPpgYFkVoGu91xjANzAgIKbsdja4XZEivejxgC4dQbmluemHku7QiHERCTffGJAklMGHAdaEifersv1ROurhpZoWrDA79UoTUwlSJLAgBBCCDGxJW8y6JrKlMRUgsq6DmzHSa3TOZWg98vWgE+nrMjd/mBVW2K7xFSC03QlSJrfZTpBf9YXQojxRgIDYkDiXWoJNLV1BgaSUwYmF7qD/4bWSGqZpip4dJW8bF9aOp8EBoQQQoiJy3YcLDuRMaCrlBYE0TWVaNxK63CUCgz0MWCfnegucLCqBej/VAKA+dPyUr/7vBIYEEJMPBIYEAPStchgY1vnSTuZHVCeiNY3tEZTnQn8Xg1FUVAVheK8QGobqTEghBBCTFxWlymJHk1FU1WmlrpFBCu7TCdITSXopV1h0uwyt87AwapWdzsjMZWgH1MDppZkprolZfjlxoUQYuKRwIAYkLjReRJv6hIYiCYCBmVFGYCbMRCJdbYqTOo6nUAyBoQQQoiJyzA7pwskpyJOTxQR7BoY6E/xQYDZZW7GwLGadgzTIj6AqQSaqvKpGxfz4SvnUXzK1EchhJgIZGQmBiQ9Y6BLjYFExkBZoZsxEIlZNLS6LQ39XQIAJfkSGBBCCCEEaUWMtUQ2wIzJbmDgRA8ZA30FBopyA2QHPbSFDY7VdHROQehnMcFF0/NZNL3fuy+EEOOKZAyIAelaY6Cxy/y/ZMAgO8NDTqYX6Dypd80YKOk6lcArgQEhhBBiokoGBjTVnW4IMDN517+2PbVef2sMKIrCrLJknYHWfnclEEIIIYEBMUCx3qYSdOn9W5zrDv6P1yYDA5IxIIQQQoh0qY4EXQbuM8tyAbdWUUfEADoDA33VGICuBQhbMYz+TyUQQoiJTgIDYkC6Zgw0tEVxHAfHcVJTCfxePVVg8HidG+0PdK0x0CUwEJTAgBBCCDFhGZZbY6BrG8LMgCd1HZHMGjD7OZUAOusMdM0Y6M92Qggx0ck3pRiQroGBWNwiEjMxLTvVb7hrxkBrRxxIzxjICnrIDHhSvwshhBBiYkoWFdROyQSYnuhMcKzGDQykphJofV+2Ti/NQlMV2kJxaprCgEwlEEKI/pBvSjEgXYsPgluAMDmNAMDnVSnqUkcA0msMKIrCJ65byAfXz0mbViCEEEKIiSWVCXDKgD/ZmaBbYKAfUwI8upYKLCSvTzwemUoghBB9kVxuMSBdawwANLZFCfjcE65Hd3sQF+emD/j9p0wZOGdWAcwqOLM7KoQQQohRLVVj4NTAwCkZA53r9V1jAGBWWQ6HTralHkvGgBBC9E2+KcWAxE/NGGiNpuoL+BIR+eJTMga61hgQQgghxPhn2TaV9R04iamGPTETNQZODQxMSwQG6loihKNGl4yB/l22JusMJEmNASGE6Jt8U4oBSU4lyMvyAW5ngqiRLDzoBgAy/HpaxwG/BAaEEEKICeUPrxzm3p9uZktFfa/rGKmigumZAFlBLwXZfgCO1XZ0Wa9/l62zTgkMSFcCIYTomwQGxIDEE1MJJhW40wUa27pkDCQCAIqipAoQQvepBEIIIYQY3+pb3JbGFcdbel2ns/hg98vRrtMJBlJjANybF4U5/tRjj0cud4UQoi/yTSkGJJkxMLkgA4Cmtlhnq8IuxX26FiCUjAEhhBBiYrESd/mrGjp6Xae34oPQOZ3g0MnW1DJPP2sMQPp0AqkxIIQQfZNvSjEgycDApEI3MNDYZSqBt0tgIC1jwCsZA0IIIcREkqwfcLIh1Oc6p9YYgM7AwMHKLoGBAQzwZ6UFBuQGhRBC9EUCA2JA4qmMAXcqQUt7jFDEANIzA7oWIEx2LRBCCCHExJDMBmgLG7SH46ddp6duA9NK3MBAa6hz254CCL1JZgzomoqq9j/TQAghJiq5lSsGJNmusCDHj9ejEjdsTjaGgc4aAwBFkjEghBBCTFiW3dmN4GRDiHlTvd3WOV1RwewML3lZPprbY4A7wFeU/g/wp5ZksmHVFHIzu7+vEEKI7iRjQPSbbTup6L7Po1GY4w7+q+rd+YP+XqcSSMaAEEIIMZEkawwAVNb3PJ0geU2hqT1fjiYLEEL3zgV9URSFD14+h6vPmzag7YQQYqKSwIDot2R9AUgGBtyKv8kTfteMgbxsHyX5QQqy/WQGPGd3R4UQQggxopL1A6D3OgOm2XO7wqTkdALouUChEEKI4SM53qLfkvUFFNy0v2RgIBIzATdYkKQqCl/9+GpsZ2BzAoUQQggx9pl2Z8ZAVW+BgdMUH4TOAoQwsMKDQgghBk4CA6LfYonIvtejoShKaipB0qm1BPrbb1gIIYQQ48upGQOO43SrEdBZfLDvwIAu1xRCCHFGSfhV9Fs87mYM+Dzun00yYyDJJ7UEhBBCCEF6jYGOiEFb2Oi2zumKDwLkZvrISRQP9PTQuUAIIcTwkcCA6LeY6QYGvIkpA4W56YGBrsUHhRBCCDFxde1KAHAyUai4q2RWgXaadoLJOgMylUAIIc4s+ZYV/daZMZAIDJwylUAyBoQQQojx5ak3j3H/o+9gmHbfK3eRnCZQnOdeK1T2UGfA7CNjADo7E0jxQSGEOLPOyrfsggULzsbbiDMsZnTWGADI8OtprQglMCCEEBOXnOvHpxe3VrLjUCMHK1sGtF0yG2Bq4o5/T50J+qoxALB4RgEApQUZA3p/IYQQA3NWAgOO4/S9khj14mZ6jYFTCxDKVAIhhJi45Fw/PiUH+PWt0QFtl6wxMK0kE+i5M0GyXeHpAgOzy3P41qcu4MNXzh3Q+wshhBiYs9KV4NQqtEmO4/DSSy+xbds2WltbycnJYdmyZVx22WWoqqSMnQ0dEYPfvXSQtUsmMXdK7mnXjcXTawyAW4CwMjFvUDIGhBBi4pJz/fiUHODXNUcGtF23jIH67p0JOtsVnr6wYMEpxY6FEEIMvxFtV3jPPfdQVlbGhg0byMzMpKOjg1dffZXnn3+eb3zjGyO5axPG2/vq2LijmtaOeJ+BgbiZPpUA0gsQSsaAEEKIU8m5fmyzE5kg9S39DwzYtpParqwwA1VRCMdMWjri5GX5UusZ/ZhKIIQQ4uwYlsDAq6++ykUXXTTg7Y4dO8bXv/71tGXnnHMOH/rQh4Zjt0Q/NLW7qYHN7bEen7dthyffOMrc8lxiRvpUAkgvQCgZA0IIMX7JuX5ishJ39esGEBiw7M5ChQGfTnFegJqmMCcbQmmBAasfxQeFEEKcHUMKDDQ1NeHxePjxj3+cdrHw/e9/n09/+tN9br9s2TK++MUvctFFF5GVlUV7ezuvvfYa55xzzlB2SwxAMiDQGuo5MHDoZCuPv3qEyYUZrJxbBHR2JQAo6pLe55fAgBBCjDtyrp/Ykm0H6wcwlSA5RQDcaQJlhRnUNIWpqu9g0Yz81HOSMSCEEKPHkAIDe/bs4dFHH6WiooJPfvKTzJo1i+nTp/Pss8/262Lh7rvvZt++fWzbto3jx4+TnZ3N3//93zN//vyh7JYYgJaOOADtYQPTsrudnMNRE3BTCJMZA12nEiTn/SmKnNiFEGI8knP9xOU4TiowEI6ZhKIGGX5Pn9sltwHQVJXJhRls2V/frQChaSZrDMj1gxBCjLQhBQbWrl3L2rVref7557nssss4fPgwBw8e5Fvf+la/X2P+/PnMnz+fiooKDh48SDweH8ouiQFq6TKFoC0UJz87vcBPsq6AYdo0trnTDrpmDEwuzGDulFyKcvy9Fp4SQggxdsm5fuKyT+k0UdccYcakvgMDyTaEqqKgqgplRW6rwVNbFna2K5TrByGEGGnDEqK9/PLL0TSNOXPmcPXVV/f7LsCtt94KwC9+8Qu+973v0dbWxsMPP8x3vvOd4dgt0Q9dawskswe6iieyBABqGsMAeLvUGNA1lX/70ApuvW7hGdxLIYQQI03O9ROPbacHBvpbgPDUAf/kwkRgoDGU1tZSphIIIcToMaiMgRMnTvDYY4/hOA4zZ85k3rx5zJ49G10f2MsZhgHA888/z69+9StUVeWDH/wgH/zgBwezW2KAYoZFOGamHrd2dK8zkMwYAKhpcgMDPuk+IIQQ456c60XXWgHQ/8BAsmChlggMlOYH0VSFSMyiuT2Wyk6U4oNCCDF6DOqb+FOf+hRHjx4F4K9//Suf/vSnWb58Oddff/2AXufQoUN87nOf48SJE2lphbFYz4XwxPBqOSUQ0BI6fcZAcs6gBAaEEGL8k3O9OHUqwUAzBjTVvczUNZWS/CBAWp0Bw5IaA0IIMVoMKmOgpqaGP//5z2lzykOhEAcOHBjQ6/zud79L/a5pWup1PvOZzwxmt8QAtZzSorCvjIEkrwQGhBBi3BvN5/qHHnqIjRs38vDDD/e5rm3b3HbbbSxdupQ77rgj7bkrrriCY8eOpS278cYbue+++wa9b+OJZXWvMdAfZmrA3/m3M7kwg5MNIarqQyyZWeCuZ0qNASGEGC0GFRhYt24du3btYsmSJallGRkZLFu2bECvU1ZW1m1ZRkYGl1xyyWB2SwxQ86kZA33UGEjyeSSyL4QQ491oPdc/8sgj3H///axatarPdePxOPfeey+vvvoqS5cuTXsuHA5z4sQJHnroIRYtWpRa7vf7T32ZCcsaZI2B5HZdMwGmFGXw9j44UdeRWmZKjQEhhBg1BhUYME2Tz3zmM9xxxx1cdNFFFBYWDmj74Zq3KIampd0NBCiAQ88ZA0ZPGQO6ZAwIIcR4N9rO9bW1tXzlK19h06ZNTJ8+vc/1t27dyr333ks0GiU7O7vb8wcPHsS2bZYvX05OTs6g9mm8s+z0a4CmtliPrY1PlZpK0DUwUJIFwPG69tQyKT4ohBCjx6C+iZcsWcK5557Lww8/zPr161m7di233nprv1sXDde8RTE0yRoDkxLVgvudMeCVwIAQQox3o+1cv3v3bjweD0888US3u/89efnll7nooot4/PHHycrK6vZ8RUUFhYWFEhQ4jVRtIa+Gz6PhAA2t0b6366EN4dTiTACqG8IYpoVtOyRLGEjxQSGEGHkDDtvbts3s2bP58Ic/jK7r2LbNsWPH2L9/P/v37+/XawzXvEUxNMlWhdNKsjjZEKIlJDUGhBBCjM5z/bp161i3bl2/17/rrrtO+3xFRQXBYJA777yTrVu3kpeXx80338wtt9yCqspAFTrbFeqqQl6Wj8r6EHXNYUoThQR7Yya209TO//Z5WT4yAx46IgZVDSEmFWSknpMaA0IIMfIGHBhQVZXPfOYzbN26NfV4xowZzJgxgyuvvLJfrzFc8xbF0CRrDEyflMUbu2toC8WxbQe1y4m8x4wBiewLIcS4NhHO9QcOHKCtrY0rr7yS22+/nS1btvCtb32L1tbWIRVG1MfTOTIR1NE0ldKCIJX1Iepbo31+xmRlAo+upq07tSSLPUebqKpPDwz4fTq6pqamHmgytWDQ5BgOnRzD4SHHcejO9jEc1ES/iy66iC1btrBy5cpBvelQ5y2K4dHSJWNAARwH2sNxcjJ9qXV6zBiQqQRCCDHujfdz/Y9//GNisVhqmsG8efPo6OjgwQcf5I477hhU1oCqKuTlZfS94hjRFDIAd4A/bVIOb++rpyVk9PkZ/f5W96fPk7bu3Gl57DnaRG1LlIzMziKPhQWZaZkl2dmB4fwYE5Icw6GTYzg85DgO3dk6hoMKDFiWxZ133smnP/1pLr/8coqKiga0/ZIlS/D5fDz88MP8+7//O1lZWcybN4/58+dz9913D2aXxAA5jpOqKZCf7SMrw0tbKE5LxymBgR4zBiQwIIQQ4914P9d7vV68Xm/asrlz5xIOh2ltbSUvL2/Ar2nbDm1t4eHaxRHX3OJ+FgXIDXoAOFbdSnNz6LTbtba53Qsc20lbtzTXDQZUHGuiodHtTuDRVFoS76NpKtnZAdraIqk6BWJg5BgOnRzD4SHHceiG4xhmZwf6nXEwqMDAmjVryMnJ4bHHHuOb3/wmGRkZzJ8/n3nz5vV5sh+OeYti6DoiRqpqcG6mj9xEYKA1FAM6izQluxIk5wUCeKRdoRBCjHvj+VzvOA4bNmzghhtu4NOf/nRq+c6dOykqKhpUUCDJ7CHTbqxK3hxQVYWiXPeOVU1juM/PmNxOU5W0dcsSxY6P13UQiZkA6LrS7fUsyx5Xx3EkyDEcOjmGw0OO49CdrWM4qMDALbfckvrdcRyOHTtGRUVFv072wzFv8XQeeughNm7cyMMPP9znurZtc9ttt7F06VLuuOOOtOeuuOIKjh07lrbsxhtv5L777hvyPo4GyWyBrKAHXVPdLIG6jm6dCWKG+0c4qSDIgcpWvB4VVZEiQUIIMd6N5nP9qSzLoqmpiaysLPx+f5/rK4rChg0b+OlPf8rMmTNZvHgxb7zxBj/5yU+45557hn3/xiq7SxHBkjw3MNDYFu2zZaHZQ1cCgNKCILqmEotbVDeGE+vIzQYhhBgNBhUY+PCHP8y8efOYN28ec+fOZc6cOUyfPr3fJ/uhzlvszSOPPML999/PqlWr+lw3Ho9z77338uqrr3ZrexQOhzlx4gQPPfQQixYtSi3vz8XGWJHsSJCXmDaQk+mmUyZbGCYZphv1L81PBAZkGoEQQkwIo/Vc35Pq6mrWr1/PN77xDW666aZ+bfPZz36WzMxMvvvd71JTU0N5eTn33HMP73vf+87w3o4dXbsLZGd48Xk1YnGL+pZIWvHAbttZie1OGfRrqkp5UQZHa9o5fLINkMCAEEKMFoMKDHziE5+goqKCTZs28fDDD3P48GFKS0uZO3cuP/zhD/vcfqjzFk9VW1vLV77yFTZt2sT06dP7XH/r1q3ce++9RKNRsrOzuz1/8OBBbNtm+fLl47a/cTIAkJvlBgZyEwGC1lMyBpLFB5MXAH4pPCiEEBPCaDvXd3Vq9l55eTkVFRW9rv/iiy92W6brOrfffju33377sO3XeNOZMaCiKG7WwPHaDmqawqcNDCTnwupq9wzDqSWZicCAW6BQWhUKIcToMKjAwCWXXMIll1ySelxfX89Xv/pVysvL+7X9UOYt9mT37t14PB6eeOIJfvCDH1BVVXXa9V9++WUuuugibr/9dq6//vpuz1dUVFBYWDhugwLQ2ZEgGRDI7SVjIDlPcOH0PFbPL2b+tMHPuxRCCDF2jLZzvTj7rMSd/2Qb45K8IMdrO6htipx2u1SmQQ+D/qklWUA1R2raAckYEEKI0WJQgYFTFRUV8e1vf7vfUfehzFvsybp161i3bl2/17/rrrtO+3xFRQXBYJA777yTrVu3kpeXx80338wtt9wyqPZFSaOpt3FrqLMjga6rFOT4U8u77mcyYyA7w8sd7znn7O8o0gd1uMhxHDo5hkMnx3B4jMRxHOlzvTj7LNu9BkgO8Evy3ToDdc2n77zQWWOg+9/n1GK3wHEs7t548Mh3gRBCjAqDCgw8+OCDqXmHZWVlgNv2p6879UlDnbd4ph04cIC2tjauvPJKbr/9drZs2cK3vvUtWltb+cxnPjOo1xxtvY3bo2414PLSbPLyMphWlgu4RQmT+2nbTqorQVFhVmrawUiRPqjDQ47j0MkxHDo5hsPjTB7H8X6uF32zEnf+9S4ZAwC1zafPGEhmGug93EwpK8pAAZzE49F000QIISayQQUGGhsb+fnPf87+/fuxLIvZs2cTCoVSFw59Geq8xTPtxz/+MbFYjKwsN6o9b948Ojo6ePDBB7njjjsGlTUw2nob1yei/T5Nobk5hO64p+jm9igNje1oqkosMY0AIBKO4pjmiOyr9EEdHnIch06O4dDJMRweZ6O38Xg/14u+JQMDqakE+cnAQB8ZA6dkGnQV8OkU5wVSwYWe6hAIIYQ4+wYVGPjSl76U+r22tpaDBw+iKAqrV6/u1/ZDnbd4pnm9Xrxeb9qyuXPnEg6HaW1tHXR/49HUw7O5LQpAVsCDadoEfTqaqmDZDo0tUfKz/USinYEAle59hs826YM6POQ4Dp0cw6GTYzg8zuRxHO/netG3rsUHgVTLwqa2GHHDwuvpuSBxKmOgl8DTlJKszsCAZAwIIcSoMKhv4/e+972p30tKSrjwwgu54IIL8Hg8g9qJ5LzFAwcODGr74eQ4Dpdffjnf//7305bv3LmToqKiQQcFRhPTsmkLG0BnVwJVVVItC5OtDJOFB3VNSd0tEEIIMTGM53O96B+rS7tCgMyAh6DPvadU19L7dILOGgM9XztMLc5M/S7FB4UQYnQY0LdxU1MT4Lbz68kHPvCBfr3Ogw8+yIsvvpg2T3Eg8xYHwrIs6uvriUaj/VpfURQ2bNjAT3/6U5566imOHz/Ob3/7W37yk59w5513Dvv+jYRkS0JNVcgKdF7g5SWCBKnAQOIulEeXFoVCCDFRjMVzvTgzktNUkoEBRVFS0wlqGnufTmBa6QGFU7mdCVxSfFAIIUaHAU0lWL9+PcFgEMMw+NrXvsbcuXNTRYXi8Xi/Kw0Pdd7iQFRXV7N+/Xq+8Y1vcNNNN/Vrm89+9rNkZmby3e9+l5qaGsrLy7nnnnt43/veN+z7NxKaOzpbFSpK50k7L8sPtHXLGPB65KQthBATxVg814szw+qh7eCkgiBHqtuoaeo9MGCdpisBwNSSLhkDMpVACCFGhQEFBrZt28aJEye49tprKSoq4s033+SXv/wlR48eRVEUrrvuun69zlDnLZ7Offfdl/a4vLycioqKXtd/8cUXuy3TdZ3bb7+93y2ZxpqWxMA/75QuA3mZiYyBjvSMAZ9kDAghxIQxFs714uw4tfggQGkiY6D6dBkDqYBCz4P+nAwv2UEPbWFDig8KIcQoMeDig1OmTOGPf/wjs2bNSi0zDINQKERubm6/XuO9730vjz76KODOWywpKRnoboghSGUMnBoYOHUqQSJjwCMZA0IIMaHIuV5A1+KD6RkDADVNoV63s/qoMaAoClNKsth9pEkyBoQQYpQY1Lfx9OnT+d3vfse9997Lt7/9bTweT78uFIZr3qIYmmTGQG5meueFVGAg0bEgbrgndq9kDAghxIQj53ph9hAYKC3IAKCmKYyTaHXcbbs+uhIAzJjk1hnI8A+umKUQQojhNajAwJe//GWefPJJFi5cyK9//WsANm7cyC9+8YvTbrd+/XouvPDC1LzFRx99lB07dhCNRmlra+v3vEUxNC0dvUwlyDp1KkGixoBE84UQYsKRc73oLD7YeR1QnBtAVRQiMYvWULzH7Uw7vWhhT65YPZUPrJvNhtVThnGPhRBCDNagRnwvvPACP/rRj/jABz6AmjhZLFq0iP/7v/877Xbbtm3jN7/5DaqqpuYtfvGLX2TVqlVceOGFXHnllYPZHTFAyakCyZoCSfmpqQRxHMfpzBjopU+xEEKI8UvO9cJ2utcY8Ogqhbl+oPc6A5bVvWjhqTIDHq5YM5WcDG+v6wghhDh7BlxjACA3N5dwOEwwGEwty8vLo6Ghoc9th2Peohia5kS7wtxTAgPJmgOmZdMRMTAkY0AIISYsOdcLq5e2g5Pyg9Q1R6hpDLFgWl637cxkjQFVrh+EEGKsGNQ39gc+8AH+9V//lcbGxlS7u8OHD5Odnd2v7Qc7b1EMj96mEuiaSnbQnevX3B4jlsoYkBO7EEJMNHKuFz21KwSYlKgz0FvGQH9qDAghhBhdBvyNbds2M2fOZOnSpWzYsIFwOMyXv/xlPv7xj/P3f//3/XqNwc5bFEMXiZnE4m4mwKkZAwB5WW56YFN7LJUx4JHig0IIMaHIuV5Al8DAKRkDpanOBL1MJbBP35VACCHE6DPgqQSqqnLXXXexdetWPvShD/HGG2/Q2trK9ddf3+/exC+88AIvvfQSwWCQ73znO4A7b/GrX/0qH/3oRwe6S2IAkvUFAj4dn7f7gD8vy8ex2nZa2mPETckYEEKIiUjO9QK6BgbSrwOSLQv7rjEg1w9CCDFWDKrGwEUXXcSWLVtYuXIl7373uwe8/VDmLYqh6W0aQVJyeVN7TNoVCiHEBCbnemEn7vyrp2YM5Lv/TRvbosQMC98pRYpTNQYkY0AIIcaMQYVyLcvizjvv5Ne//jX19fUD3n6o8xbF4CUzBnIze64CnCxA6GYMJIoPSsaAEEJMOHKuF8k7//opgYGsoJfMgFuTqLaH6QRSY0AIIcaeQWUMrFmzhpycHB577DG++c1vkpGRwfz585k3bx533333abdNzltsaWlhw4YNxONxvvzlL/Pqq6/2e96iGLxUxkAP9QWgs2VhU3uUnAz3d8kYEEKIiUfO9cJyeq4xAG6dgYOVrZxsDDG1JCvtOTORadDTdkIIIUanQQUGbrnlltTvjuNw7NgxKioq2L9/f5/bDse8RTF4Le2JVoW9TCVIBgaa22MEfO6fh2QMCCHExCPnepHMGDh1KgHA5IIMNzDQ0D1jQGoMCCHE2DOowEBXiqIwffp0pk+fzpVXXtmvbYY6b1EMXnNHcipBL4GBHLcrQWNrlMKcAAAeXU7sQggxkcm5fmKy7d4H+GWFbsvCkw2hbs9JjQEhhBh7RmTEN9R5i2Lw+io+mMwYiJs2Te1RgG5FhYQQQoi+yLl+7OutXSHA5NMEBpLb6arcWBBCiLFiyBkDgzGUeYtiaJLFB3sLDHh0jZwML62hOLVNkcQyObELIYQYGDnXj32nqxWQDAzUNUcwTDvtWkEyBoQQYuw5K4EBJ1G8Jmko8xbF4Nm2Q2tHosZAL1MJAPKz/bSG4qkTu1cyBoQQQvRBzvXjT3IqQU81BnIzvQR8OpGYSW1TmPLizNQ2yT8FqTEghBBjx1kJDOzbt6/X5wYzb1EMTns4ju04KApkZ3h6Xa8g28eR6s7HXskYEEII0Qc5148/qSKCPQQGFEWhrDCDg1WtVDWEUoGB5E2F3rYTQggxOsmIbwJJFh7MyfCinWbeX362P+2xtCsUQgghJp7OdoU9XzNMLgwCUNWlzoBpdWaO6JIxIIQQY4Z8Y08gyfoCp5tGAFCQc0pgQNoVCiGEEBPO6TIGACYXulkC1V0DA3aXjAGpMSCEEGOGjPgmkJZEfYHeCg8mFUjGgBBCCDHhWcnig70M8HvKGOgaTFAVCQwIIcRYIYGBCSSVMTDAwIBHMgaEEEKICed0xQcByhIZA8nOBACWdfpgghBCiNFJRnwTSEs/pxLkZ6c/75OMASGEEGLCsRKBAb2XwIDbmUDDdhxqm8IAmKlt5BJTCCHGEvnWnkBaEsUH8/oIDGQGPGl1BSRjQAghhJh4koGB3ooPKorC5MIMoHM6QbIrgS4ZA0IIMabIiG8CSXYlyM3ynnY9RVFS0wl0TZU5gkIIIcQElKwX0NtUAoDyInc6QWV9R9o2mnQkEEKIMUW+tSeQ5FSCvjIGoLNloVeXPxEhhBBivNl2oJ53Djacdp1U8cF+BAaq6tMzBk63jRBCiNFHRn0TRNywCEVNoO+uBAAFiToD0qpQCCGEGF8M0+LBx3fxgz/uJBa3el3Ptk/frhCgvMidSnCizs0Y6JxKINcPQggxlsi39gSRrC/g1VUCPr3P9TszBqTwoBBCCDGeGKaNaTmYlkN9a6TX9VI1Bk5TL6AskTHQ2BYlEjM7CxZKjQEhhEjjJLKwkuxQM44ZH6G96a7vEaIYF1o63D+63CwfSj9qBiRrDEjGgBBCCDG+JAfvAA0t0dR0gN7WO12NgcyAh7wsH83tMarqQ5hSY0AIMQCObaKoPQ9JHcfBqtqN4stEK5p+ZvfDsYmFmlEDWSgoKEYMu/EYeuk8VE3H7mjCPL4dffoK1GAuVuNxjIqN6DNXo5fOwazcRXzXc+hTzsEz/xLMg28Qf+cptNJ5eJdfh3HgdeLbn0QtmIpvxbsxj23D2PMSSiAL74rr8Sy4NHUc7HALiu4Fvefv5jNFAgMTRPMA6gsAzCrLQVMVppZkncndEkIIIcRZZncJDPQrY6CPegFlRRk0t8c4Ud9BboZb4FgyBoQYOeaJnTjxCJ5ZawCI73kJu/E43hXXo2bkDeo1HcdBURSceBjFG3SXmXGsukNoxbNQdC92uBXz4Bvo05ZBweTu+1W5i9imR9EmzcW36iaMQ5uIvfFr1Pxy/Bd9FK1gSuf7xUJEX/0l5uHNAHjmX4JvzXtR/O5g2W6pBkDNneS+9vEdOPEw+kz3Mxt7XsDuaMS79BoU3UvsrT9gN1e5xyC7mNjGX2HVHcK79FrCRVP4v3d+xW5v+h19ACpAARTHcX+efBpVVVFsC8UBdedWlF2JxyqolZUoVU+jOA5qtoMS3oHy2g4UB5RJmag0ouz8GSqglOe6r3n0SdQTf0XLKgQjitLRxDIlm8vf/Z+D+m81WBIYmCCSgYHcftQXACjND3L/nWv7Ne1ACCGEEGNHl7gADS3RXtdLdRjopV1hUnlRJrsON1FZ30FmwB106H1sI4Q4M+xwC5Fn7wfbQs0uQglkE3vtV+A4mIffwn/JrejTlwNg1R/Bqj+KZ+6FKLoX4+AbmCd24V1+LWp2CfG3/0j0yFs8M306W2O1XBnzsvrIfvS5F+JdvIHoyz/DbjzOptISXs0JcF5TGxfWN7Fr9xM8N7mYmK4BCooDGFGIR1AyQWnfivLSVnewPTkDhWaUt76H6s9CDeSgWAZOewOKZaKU57mD5/Z3UF7ahZZdDKaB09GA6oCakY/iWBBqcdc79DgKKsRCKDioL2xCUXUUwx0LKZt/gKIkBvJZoBx+kn01Xjq8vU+fdgAnmXGtJJac+h2nDsP061iiIGzQS4eicPnQX3FAZNQ3QSRrDPQ3YwAgw+85U7sjhBBCiBFidZnn2tBLxoDjONhO/zIGpiQ7E9R1MLssx91GMgaEOC3Ltthev5MZOdPI9+dhOzY76nfTFGtBcUefKI7NwoIFFGcUAbD/6OvUWCFUfxYODvbJChRVRyud4w52AfPYdqwMD+BB3/kYij8LK8ObeE0DXv8R+rFFqJn5mBWvgWOjHngOLacYq2ov4KDUbEfNzMdpreWN3CAHQkcAeEwLU12YybzKt6DyLRRgd1EWmzIdsMI8maOzw1fAcb8GVhhOrW3q6WPwbEcglPhO8ijg6WEsEmt0fwYS7dcdt/ApGckxju3+85wy5vH7T/vWJY7Oh6ddSe7uVzHrD6MtuBTPovXEdj+PUbkLfd5FaHPXYhzdgnF8O+rMVWjli7FaThI/ug2tfBHkleEYYYyqvSiFUyGYi21bWC1VkJGPo3vd/27xCI7mwVHAdhwcM0b82DaMY9vAl4E2dy1zZqw9/bE6AyQwMEEkAwP9zRgQQgjhchwbUPpVn+WM7odtgxFB8WWM6H6IsS9tKkEvGQNd6xD0NcgvS3YmqA9hmtKVQIikZPp98ne74Shq3mQsVeOnO37FjqZ9ZGp+Pr3iH3np0PNsatrd7TW8B57ktrk3c+jE2zwdPdbzG7XuSH9ckp34pQ6idVCak/68VQmtlVCanDJsgn0SJnVdLwYZuQB4bIdzOqJsyQ7wem6Q13ODaS+nAEvao+zM8rtBAeCitjgrWtpxUNw77h4v3mXXoZYvxjajmMd3oOSXoWSX4OBg1FQQ3/kcdiwECiiT5+NZuB50jzt4tk2Mo1sxDr+Fo2no8y7G8QYwDryG4zjo8y8GXybG0S3YRhRt5irwZmDWHcCOdqCVLcDRPFgdTdiRVtT8Ke7A3IwTNC3OnXU5Pt2LM+tisOIoujtmyrrwI+nHbuEG919SsAgmL01fJ392+uO8mT39V0s3aQXOahNUFUUZme9PCQxMEKmpBJneEd4TIYQYOCcWwg41o+ZOQuklXc9uq8OqP4rdXodWPBt98vzO7R0HJ9SM3XISu7UWNXcS2uQFaYN9Jx7Bbq3Bbqnu8q8Gu60GxRvEs/gK9GlLcdoasFur3XVba7FbakBV8cy/FM/sc7FDze7y1hqwDDxz16IVzXDfw4gmnnOft9vc34l2oE9fgWfhZWDbieV17s+2OpzWWuz2BrBNvGveh77qujN7wMW4ljaVoDWSNnhJrdNlJbWPoNikggxURSESM6lvdQMNfWUZCHG2GIc340Q7CM9cwaHWozgAjuMOVt1HFAUKmZEzFYCOeIidR3fR1hHGsmx3ncT/HWzbBFUj05PB4qxpUF2BOXke25v3E205iVm9D61kDkp2EWbtAcxj76AWz0Cbcg7Gkbew64+g+LM5VDKZfaEq9/2sKP/11v/DAVTHYVEohuoN4sQjNOgqJ/0efrD/96lU9jnhOB47seeKgqO4nwdNh4w87LZ6UHXQvThGGFBwNB2tZA6OouBEO7BaqnBsCyXLnWpgt9XixKMo2cUovgzsSCtOPAwZeWQEcrm2YDmTGmpYWljA32rfJm7FwYiDx4tX83HFtEtZnDOTfW3Hee743zhv0irOy52J8frDhA9uQS2YQmD97ai5pZ3/YQrmpv+HypuFM+tS4rueR82dhGfm6u7/MUuWYi99j5sdkQySL7gu/Tts+sXp20y5YEB/L4qigD4yN1IVbWSH5hIYmCBSUwkkY0AIMUiOY+O01mE1HsNuPA4eP96F61InZ3fw3YTdeAKr8Tjg4Jl/KWqw8w6EHWkn0nqY6NEDWNEwnrkXoGYVdb6HEcNursJursJqrsJuqsRursIJNQOgZJfgW34daB7sjkb0soWohdOIv/1H4tv+nLa/2qR5KJkFqUE+RvqdUbV4Jlrh9NTzTril988eaSP+1u+Jv/X7XteJb/kj8S1/7Lbc2P0Cal45TrQdJ9La+/bvPEX8nad6fd79UF7UzPzTryNEH7pmA0TjFqGoSWbA0+s6fRUS9OgqkwqCVDWEOFrTlthGMgbE8HDiYfD4B3UXNb7nRWIbf0WNV+NH1c8SVZxe172p/FKWkMED9RtpjvX+XZ20KAY3VdXz06nFnNS7vG5tA9Qmfs/zglEFh90gAIVZgAOhKnTb4X31Hbya4+eE34PqOHyoQ2dRUwcYNe76s8/lYbOS3boBwA2Zc7mksJzYpkfB4yVwxZ0o/iyiL/wQu/kk4KbZe865Gq14JtHnfwCAd+UN+FbckNpFx4jiRNvTz789BAjTlMNKYGX5ub2usqBgLgsSA35VVyl53xdoPHwAO6OoX4NexRvEt+L6066j+rsXRh/pjL7xQgIDE4DjODS3u+0KB1JjQAgxevV1AnfiEayGY9gtJ9FK5qRV+gV3AG41HsduOoGaV4Y+aV7685aB3XwSu+GYGwhoOI7VdKLb4NrY8Sz6jBXYrbVYjScgHk57Pr79KTyz1rh30RtP4ERa6Xq5Fd/2Z/RZayAewWqqxGlvIHVr5lSajtNWS/Tln3ZuDyg5pTit7kWUWjQTNSMX8/gOrOqK9O0VFTW7GCW7COtkBXbdYey6w+mrBLJRcyeh5kxCzS1N/F6KVXeI+DtPY7fXo+aUoOaUuv9y3Z92ay3xHU9jN1aiZBUm1inBibZjHnoLu7my8z38WSg5JajZJan1wA0gWDX73cF/TnFiX4s718suRsnIR5GibmKIumYDANS3RE4bGOir+CBAeXGmGxiobgekK4EYHmblLiLPPoCaO4nANZ9FDWT3vVGCcfgtYhsfpk1T+fmkXKKKQ76tUpg3FVDcALcRw8qbxJFoPX+o/BvPmxZtuka+FqC4rcV9IVVz7+BH2kn+Ve/P8LLbp3BwWgExzSHDspkZjoPmQbHcQbwCqJkFbmA7MSVNL56J3VaPFm5lteFhwdX3suTEO7yw/y/MyZjMoqv/GSfSSuzN36IWz8S77Dpuswxe2v5/FGVPZtncKwDQZ65G0X2pCv3BG79C7PVHMPa9AoqKd+FlKJkFqHnl2OFmPAsuTTs2iseP4kmfc38mBteKoqDll+GYPVT7F6OOBAYmgFDUxLTc/0PmSGBAiEEzju8k3OjFyU9Pf+tzkO7Y2C3VWDUHEv/2gxnDu/xdeBatB8fBbjqBVXsQJ9KOZ95FqFmFndvbJnZTJVbdYazaQ9h1h7DbG9BnrsJ33gfctL/GE4nqwoex649gN1eTGmArCp4Fl6HmTnJT7RuOYrecdFMPE/TpK9AmL3CDCY3H3DsP9qlVgwDNg5o/Ba1gKlbtfuzmk+6FSJKioeZNQs2f4qbL1x/GqHg17SX0vFKU3DLsaAireh/mgdfTnlcC2ah5Zaj55ah5ZWj55ah5k0FRie96DvPA6yi+TPAFsU7scIMCqob/4o/hmesW67E7Gt33VVTUvMmouZPdgbWW7BHcirHvZTBjqcG/mjup1/n7ak4Jnjm9pyNqRTPwzD6v55TsNe/Fqj+CmpGPmlPS63t4Zp2LE48k7ozJoEqcOacGBhpao8yYlD7g6hoY6M+f49TiTDbtqaUj4g6KNMkYmBAMy+B4exUzc6alzaU3bJMttdsJmWFs28ZybGzHSvy0sRwLu8vvyeXuYxvbtjDjYYzaA9glQWxasV/5T8idjK0qWI6NFWnDUhQc3Ytlm1jRdrfsnNePZZvYZhx7ViFWYr+KDItPnagns8UDHh92rdvuzjnZzF+KctiY7aVN18gxLG47eoJc00bJyMMJNQFNoGp45l+Ksfcl9gV0Hp6UQ0xTCdjwiapmyksWEtjwaeI7/4qx41k8S6/Ct+w6rKYqjF3Pos+5EH3SPBzbwjqxE7V4Jmogm+zcSdywcD2K5gbnFG+AwJWfSR1jXfeyYdVH0467mlmQ9ljRffgv/jj67AtAdYPgAMEbvgSWmQogCHE6EhiYAFoS9QUyAx48upyoheiL4zjYre5AXs0pRZ80D+PwZqLP/5AOwDNrDfqiy7HrDmFV78es2Y/iDeA//0Po05e7fX0bjmLV7HcDAbUHIRbq9j6x1x8hvus5N4XdjKeWx3c8g3fpNThGBLvuMFb9UbDi3bY3D76JeXSrO4DvYRCvZOSjZhZg1R7A2PNi9+eDuW6woLoC8+hW97W68mWgFUxFLZzm/iyYhppbmprj79gW5oHXsVuq3YF8wRR3cJ24uHEcB/PI21h1h1BzStHyy/EWTyW/uIDmZrdImVm5G7NyJ2pmIWp+mfs6p7kj5Fv+LnzL35V6bLdUY+x/DX36crTiWanlamYBvpU39Po6ajCnz3TFwehpQK9mFnS7iOt1e29guHdJiG5s55TAQEv3zgRW4oaCpvav8OaUkvSBhy41BsY9x3H4n12/Yk9jBVdNW8e7Zl1FfPcLRLc8zi9nTmFfvGHobxI4ZagSOtl9HTPxM3mJayb+nrv8DRb68/nUvKvJqPspdtMJd6E3iF62EPPI21xT14Lim8yR7CzeW3OSXNPGd84VeFa9h+jGh7GOb3cH3tOXo89czZLDb/EP0+byevM+rppyMWULY2ils1FUHd/yd+Fddl3q/zdafhnaxR9P7YuiaujTlqV9hOR5c6i61tYBNzMAaTIm+kkCAxNAs9QXEOOA4zhgxlFObT+TfN62sFtq0gauac/HI1gn92FW7sKqP4w2aR6+VTeh6G5BTrujEatqD2bVHqyTe9Pmm+uzzsU8ui312Di0GePQ5vTXj4WI/PX/oeaXu8XobDPteTQvWskstNI5aCVzsNvqiG1+FKetzn3eG0ArnuUWwKs71H2uujeIVjwTrXgWWvFM8PiJvfFr7IajACi+TLfAUZH7Ty2agRrMBcA8uZf49r+AqqEVTk88Pz31vNVURXzbn3GMKFrhNNTCqWgF01AyC047IFBUDc+8i3p/XlHwzFydVkBIOSU4qZcvQi9f1Otr9EXNnYRvzXsGvb0Ymx566CE2btzIww8/3Oe6tm1z2223sXTpUu644460555++mn++7//m8rKSmbOnMnnP/95zj///DO126OGdepUgtbunQmSWQX9LSI4tTh93q9kDIx/L1e9zp5Gd8rWs8deYobjZdrrj7Ax28e+eAMeVJaXLkNTNFQUlHArekYequZBjYZwqvag50/BUzQTp6YC58RONF8W/kXrsQ68gdJSg+bPIrDyRlTHxtz6BEqoBU33oRpRVHD72OOgOaB5g6i6H6WjERXwFs0keNk/onl8ZOhBNFXDfveXiDzzPexwK8Gr/wW1eBbG7uexG4/z/nPfj+LPRF3VRtBoJJIzE8tyCFx6a1o2mD55Pvrk+SwBlkxJzLfPSz82kvUlxiIJDEwAnR0JJDAgzj7HjINl9Jo+bbc3JO5Wb8Fub8B/4d+jT1vubmub7h35Y9sxj2/HaavHc86V+Na8F0XVsMMtWCd2Yp7YgVm5C+IR1JLZBNb/E0owF7vhqBsIqNyFVXsInM676nb9EazK3WjFszBP7ukcoCdpOmrBVOy6I5iHNgGgTz2H4nUfpPbJH2G1N6CVzEGfNA+tdA7GkbcxdjyL3eTOJVcC2Wilc935/aVzUAunoqjpX7n6tOXu3fTcyah5k1AUFcexMfa+jHnkbdTsYrSSWW66YU5pt8JL2g33Yje4FY6VrMJeL0T0yQvQJy/o9b+Rll9GYP0ne31eiNHkkUce4f7772fVqlV9rhuPx7n33nt59dVXWbo0vZ3Um2++yd13383nPvc5LrzwQn7/+99z22238fjjjzNr1qxeXnF86DaVoKeMgWRgoJ+1ArIzvORmemnpcLObpMbA2GC31+OEWtBK5+A4DpGNDxOrqUC55KNYWYUYdpy4bRC3DAzLIG7HiVsGYTPC4wf/AkBpRgk1oVp+deRpFhRmsC3bnbt+bV0rl06djj7rXKIv/Q/mwTdQC6YRWP9Jwn/5lpuif/w4+lwFc/9buNPfIlD/qBtc9wQIXvnPaPll7r4WLiLyl2+5U+EA76qbULwBYq8/ApqX4NWfQcksIPrSj0FRCFz+Tyje9LZ6anYxwfd8zb0uSdxo8C7ekL5ORi6BvDKizSGSU/JkoC8mAgkMTACdHQmkVaEYPnZrrTvfvIcK6Y4ZdwfrhzZjHt8OjoP/sn/AM3ONm6bffBLz6BY3GNCQ3pM38uwDeJZcgRNqxjyxE4z0C1ZjxzPuHH3b6rYtgF17kNBjX3YfnJK+r2SXoJcvQs2bTHzLn7CbTnSmFCoqavFM9MkL0CYvQCuZjaJ7MU/uJfrKz1F8mWRu+Cf8pUVkv+//S/XqTtJKZuOZcwF280m0ohluy58+LiTUzPxux09JFA3yLrzstNu6u6ympc8LMZ7V1tbyla98hU2bNjF9+vQ+19+6dSv33nsv0WiU7Ozu01N+/OMfc/nll3PLLbcA8PnPf55t27bxy1/+kq9+9avDvfujSrfigz1kDCQDA321KuxqakkWLR1uVXTpSnBmJefxx6wYMSue+mli4AkpNLa2EY5HicU73OexiVlx4la8c/14mEjzCWIKGHv9xB0LAxsKgF0/7td+zM+aym3LP8E3//Yf1GgWb+e406EWK1mc21ZH9KX/QT+xA/PgGwDYjccIPfolN1DvDUA8grl/IwD67POx6g65gXpFIXD5p1JBAQA1I4/Au/6N2Gv/i5pXhnf5u9zidqVzUTz+VCHX4LV3n3afFVUFVW6WCXEqCQxMAC2SMSD6yXEc7LpDGPs3Yh5+G7VwGoENt6ci7na4BfPgJoyDr7uDct1LYMMd6FOWuMGAyl2YhzdjHtverXp99PkfYs7aitVwBKe1tvOJxEldn74Cu7kaY9/fMHY+2/m0Pwtt6lL0qUvBNom+8ou0SvJq0Qz0KeegT1mC4s8k8vwP3UrDAJ4AetlCtPJF6OWLU8V4APQZq4hvfwoUBb1sAVrpvB7nd+uTF5Dx/v9y98XTfYpCV1rBVLSCqf061kKIgdm9ezcej4cnnniCH/zgB1RVVZ12/ZdffpmLLrqI22+/neuvT68nYds2W7du5d/+7d/Slp977rn89a9/HfZ9H22sRI0Bn1cjFrdobI1gO05aEKAzY6D/A/wpxZnsOOQGBvo7BWGiilsGISNExIwStaJEzM5/UfOUx1ak23NRK4btDEOld2/ivOYY3Z7SHfB6AnhVD3q0A48Rw5dRgNefjVZ7mDzD5PLq/Tix3/HR47W8nZOBb8Fl5OaWs7J4KTa/xDzweqrArGfxBsyDb+JE28HjJ/juL2MeeJ349ifRJi/Af+mtOJF2Ym/9Hn3qUvQp53TbJzWQTeDyf0pbphVOG/pxEEJIYGAiSKb15UqNgQnNDrfh5AZ7fi7ajrn/dYyKlxN9cF1W1W7CT34T76L1GIc2YVXtTqtkjxkn8sz96NOWYVbtSbu7r2Tko89ag2fmaowDb2Dsfh7z0Jvuk5qOVrYIffoK9GnLU8XmHMdBLZyGefANN1gwbRlq0cy09mxq/lSMfX9z58qXL0YN5qR9luC7v+QWs/NnoxbP7LHeAIAazMV/wd/169hJCqEQI2/dunWsW7eu3+vfddddvT7X1tZGOBymtLQ0bXlxcTE1NTWD3sexIpkxUJjjp7ohjGk5tLTHyM/ubF9m2Z3FB/trWklnnQHJGOjdO/W7+cmuh4dnYA94UfF5gngd8ISa8dkQKJyGp6MFvb0Rn+Pg82SQOf8SfN5MfJoXve4Iyu6X8KISKJyOXn0Aj+MQKJxJ1tqPYvzlmyiRNrSyhWhFM4nveTLxbs0owVy3Do+igmNj7HmRXOC6xe9LqzvjXHIrUTOOeeRt9Fnn4jv/7/DMv5j4tr/gWXAJWt5ktDXvwTP/YpTMQhRVRcnII3DpPwzLcRFCDIwEBiaAZI2BPMkYGJccI+be4T+xE+/Sq9P60TuWiXnkbYw9L2LV7MeYvRLv+n8CNBzHxqrag7Hv5c7K9gCaF33mKvSyRcTe/A12w9G0vvFqyWw8s89Hn76C2Ju/xTz0JubRLQAoGXnoM1bjmbXGHZQn5sSrRTNR88uxag+hT12CXr6kx7vziqL0mUav5ZehXfChXp9XdC+e6SsHcgiFEBNMNOpmNHm96VPsfD4fsVhsSK+tj4HuP8lgp1fXKMz1U9ccoak9RnF+sNs6mqr0+zPNmNw5ZcPr0QZ8LJLZCeO5cKFlWzx+6C/Yjo2qqAR0/yn/AgR0P/7EY1+kg2BWMcFADr5wB/bGR8jIn0LBZf8I7zyLs/0ZVCBw/vuJ7XoBu73ZfaPaMJgxUFQUfxZOpBZd20vmtf+CE26l7blf4cTjBM57H76lVxJ67kGspkoy138SLacY87p/pf3xr2NV7cGq2gOAXrYAs8otzqsEc8i6/vN0PP3/sFtr8c6/iMCiS075tCr6VZ/GaqxEKyh3rwmKp+G7Mv2OP/mljCYT4e/wbJDjOHRn+xhKYGACSHYlkKkE44sdasbY/TzxvX9LzaWPnNxL8LrPoWTkY+x9CWPv33Aibaltwge3YBgPoBTOxKh4BScxFxTclHzPvIvxzD43NXVALZpO5K8PoCgq+uzz8Mw+Py0d37/uNuIFU3AibegzVrrz8pXuX16KouBdcCksuPSMHAshhBgIn889H8bj6W1AY7EYgcDgW0aqqkJeXs+FVkeTQNA9L3i9GnnZbmCgPWal7XugORE88Wj9/kw5OUECPo1IzCIr0zfoY5GdPX7bdr585E3qwg1keTP4/nX/ScDTmaXhmAZt2/5KcNYKPPmT6Nj9KnXP/RpPYTllH7uPmt99g2hHCDr2kbH5UUIVm0jmc0Te+C0AenYhWmYesZMHAMg5911kLryQk7/6EuaJndhbHsVoqsGJh/FNnkPpZe9BUTXyP/iFtMr75C0i4+Z/peZ33wDbInPxxRRdfweNz/6U0L43Kb7xLgLT5pH38f8ienwPwTkrUbRehhX5vRe/Hc3G89/h2STHcejO1jGUwMA4Z1o27SH3wkfaFY4PVv1R4jufxTy0OVVlX8kuRvFlYtcfJvzkN8EyIJGiqARz8cy/BE/hFMIv/g/GsXfg2Dvui3mDeOacj2fexT3O0dPyyshMzK/viaKo+JZdO/wfUgghzqDc3FyCwSB1dendSOrq6igpKRn069q2Q1tbeKi7d8a1tbnTvmzLJj9RmPhIZQvNcwtT67S2dn6O5ub0Qq6nU16UyYHKVuJxc0DbgXtXLDs7QFtbBMsanjT70cSyLR7d5Vbyv3zaJYTrmmh6+3G8Cy5BL5pO5M1HiW79M82ZfyLrpi/T9twvATAaKjnxsy9gNZ4AVQPbIrQ3MW9/+nLQvRgH3e45wYs/Qu7M+VT+4h4Ujw9lyXWEPT6C628j9Oz3aXvrKXdnNA++iz9OSw+FJ1Py55J5zV0YVXvRV15PS0sE7dy/I2vNB4gqaqJqvwoli4m3xYChZduMFuP97/BskeM4dMNxDLOzA/3OOJDAwDjXForj4KYCZgY9I707ArA7GolvexIn3ILv4o+l5tcDWI0nsJur0GeuTpsb7zgOVvU+4tv+nErpA9AmzcOz5Ar0qcvBihN+6tvYtQc7n1u4Hn3GChRVR9dVsgrzqf3Tf6NmF+OZfwn6jFUounSrEEJMLIqisGLFCjZv3sx73/ve1PJNmzb1qw3i6ZzasWQ0MhL7qCoKhYkq8jWNobR9jxud6wzkM12+agqW7bBgau6gj4Vl2WPiOHZlOzbt8Q5aY220xttoibW6v8faaEksa440E7aiZGp+1k46n9Dffop5eDPxw1sIXHUX0XeecV+ro5G2330ZJ9qOEsjGiXa4QQHAu/xdOEYUY8czoHnxnv93KP5sHMdtxadNXYqelUH2330T03KwFAVMG3XaKryrbiL+9h/c11l5A072pD6PszJ5Md7Ji7EA0tYdW/99BmMs/h2ORnIch+5sHcNxFxh46KGH2LhxIw8//HCf69q2zW233cbSpUu544470p57+umn+e///m8qKyuZOXMmn//85zn//PPP1G6fMc2pjgTeAbUcEsPPiXYQ2/oExp4X3f68gPNciMC1d2O31hB/+/HUXH3PyX34LvoIANaJd4htezI14EfR0GetwbvkSrSi6Z1voPoJXv1ZjINvoJXOQcuf0m0fgjOWkvuR/ydf0EKIccWyLJqamsjKysLv9/e9AfCxj32M2267jYULF3LxxRfz2GOPsXfvXr72ta+d4b0decnig6qqUJzrBgbqWtJbw6aKD2oDu3ZYPb+Y1fOL+15xjIlbBs3RZpqiLTRFm2mKNtPY5ffWeFu/iwluaAqh1xwkcngzAE64hfCfvgaWgZpXht1a41buB3wX/j12Wx3xzb9HySzAu/QaUDW3Y0/BFNSsIgAC6z+V9h6KqqLY6fvjXf4uUFWIhfGec9VQD4kQYpwZV4GBRx55hPvvv79f0f54PM69997Lq6++ytKlS9Oee/PNN7n77rv53Oc+x4UXXsjvf/97brvtNh5//HFmzRpbPcNbkvUFZBrBsHAsAxQFRe3+fx2zchexTb8F1UPwms+i+Ny5lY4Zx9j9PLFtf4a4e+GlTZqH1Xgcq2Y/4cfuxW6pTryKewFm7PsbYGPVH+1svafpeOZdgnfp1ahZhfRE8QbwLux/1W4hhBgPqqurWb9+Pd/4xje46aab+rXN2rVr+frXv84Pf/hDvve97zF79mx+9KMfjbnz/GCkWhGqCsV5icBAcyRtjnnXdSYCx3HoMELURxqoCzdQH26gPtJIfaSRpmgzHUbf0yIUFLIcldyMQnKC+WRWVpAZaiPHk0nBnIsJvPVHckybgO0Qefq7QOJ6oPYQWO60T/8lt2LVHSL2+iNok+ahz1gNgJo7CS1/SirLbzDT+BRFwbfsugFvJ4SYGMZFYKC2tpavfOUrbNq0ienTp/e5/tatW7n33nuJRqNkZ2d3e/7HP/4xl19+ObfccgsAn//859m2bRu//OUv+epXvzrcu39GSUeCgXEsA1S9x/Z0dqSN8KP3oGTmE3zXF1E87jG1OxqJvfFrzCNvp9aNvPQ/BK74DObhTcQ2/z5V5E8tmILv3PejlS3CqtxF5JnvpoIC+sw1eFe+G6tqD7HXH8HY94r7YroPz8J1eM+5EjWYe2YPgBBCjAH33Xdf2uPy8nIqKip6Xf/FF1/scfkNN9zADTfcMJy7NibYTmfGQFGuHwWIxi3awwbZGe7A07LGZ2AgakapDtVRF66nLuIGAOraq6mPNRO1jdNu69O85PvzuvzLTf2e581C/dN9qG3VqEV+vEsuIFr5cmLLMNT/ERwLbco5WJW73RpBqob/kk9gHn+H2Ov/iz53LVrxTPdfyWzUnNLU9Yh02xFCnGnjIjCwe/duPB4PTzzxBD/4wQ+oqqo67fovv/wyF110EbfffjvXX3992nO2bbN161b+7d/+LW35ueeey1//+tdh3/czTToS9I9jW8S3/4X41ifQpy7Fv+HT3YID5oHXcaLtONF2Yq8/gm/th4nveIb4tj+DGQdFRZ9zIeahN7COv0Po1/+KE2oC3DZ+vtU3o8++AEV1C4DoU5bgX/9PWFV78Cxal0r91/LKcKIdGPs34pm7Fu/iDSj+zLN7QIQQQoxbqakEioJH18jP9tHYFqOuOZIKDCSDB2M1MBC3DGrCtVR31FIdquVkqIbqUC1N0eZet1GAXF8uRcFCigMFFAULKQoUpAb/vrYm7LqDeOZdlFYHCMA4+CbRNreYpV1/JNXmV5tyDtaJHW4gwOPHf+knMCo2Et/8O7xLrkTNLsK7+HL0KUtQsgpSr6cVzRj+gyKEEKcxLgID69atY926/qdP33XXXb0+19bWRjgcprQ0vadqcXExNTU1g95HGJnexm2JjgQFOf4x0Vu5J2e6h6fVWkv4+R+5qXyAeXQLVsVL+BZcQrxiI1rBVLTimRj7N6a2MSpewazciRNyLzD0SfMIXPRh9MKpxMrmEn7pp25QwOPHv+I6/Odcmcow6Eqfey7MPbf78vNuhvNuHtbPKf1kh06O4dDJMRwechzFUNinTBMozgvS2BajtjnM7PIcYGxlDETMCD7Nh6qoxC2DH7zzEw61HMXB6XH9bG8WpcFiioKFFLS3krP/TQoNi8KMUnJu/nxq0O/EQjjRdtSsUhzbJvTcAzhtdTiRVnwr3p16PcexiW97EgC1ZLZbE8gywRsksO4f3fpCO5/Fu/Qa1EA2vmXX4Jm5Oi0QoOYMvhuGEEIMh3ERGBhO0Wiib683vVK7z+cjFht8G5aR6m3cHnGL3JWXZo+J3sqncyZ6eLbv/Bstz/wYJx5F9QUJzl5Jx+5Xib7+G8w9L2I0VKJ4/BRd90/YTZUomoes5Rtoe/spnFAzWkYu+Zd/hMxFF3VmGFxwDa0esDqayF59HXpm7rDv91BIP9mhk2M4dHIMh4ccRzEYyfoBSiowEGDvsWbqmjsLEJqp4oOjM/hkWAa/qfgjFc0HaY61sCB/Lp9e9gl2NuzhYMsRADI8QSZllFIaiVHqzaZ89iVMyiwh05OoAeQ4hP/w79iJmyjEqzD2vIh38Qb3ub98C7vxGIFr7naDBImMgPjWJ9BnrELLKwPAOv4OdnMlePwEr7qL6N9+gnlsG95zrkLxZeA77wN45l2Mmjc5tf9qdtFZPFpCCNE3CQycwudz7+rG4/G05bFYjEBg8BdgI9XbuL7ZfU+POrA+xKPJmeiD6sQjhF/5JfH9bh9gffI8MtZ/EiUzD72lEbNqD0ZDpbuuEaXu8e+5681YgbrqvfgsBUXT8S+7BtMboKXllP+2sy9BAdoNYJQcd+knO3RyDIdOjuHwONu9jcX4kogLdMkY6N6ZoOt0g9EgZIR5eO/vWFF8DmtKV7Ctfidv1nTW9tnbtJ/K9pNsqXsHgMunXsINs67Brj9C+HG3PlSg/AL0LjdJ7Poj2I3HQNPxrng38bceI/bWH9BnrsFuqsRuOApAdOMvUbyJ7Tx+MKJEX/4ZwevvAUVxiwsD3oXrUHwZ+C//J6yaA2iT5wNu0T8tv+xMHyIhhBgSCQycIjc3l2AwSF1dXdryuro6SkqGluY1Ei3iksUHs4PeMd+ibqA9PM2qPcR3PIN32bXok+Z1vk7DMSLP/8CN/CsK3pU34F32LmxVBQt8l9yK/cKDqNkleM+5kvCT/wUxd3Cvz7kQy1bwrnb7Xnfv6zv6ST/ZoZNjOHRyDIeHHEcxGKcO+otzgwDUNXcGuVNdCQbYrvBMeaP6LXY27OFwy1GWF5/D9vpdAFxcdj6tsTbeadjN88dfYXfjPgDWlK5AUZS0aYCxN3+DdsOXURQ3IGbsfQlIFP9dei3mkbexG465RYMT7QIBnNZad1KCqhO87t8IP3kfdt0hjN3PoxZMwa47DJoHz5IrAVA0D3rZwrNwVIQQYvjIrYJTKIrCihUr2Lx5c9ryTZs29asN4mgSiZlE4xYAuZnePtYeX+K7XyDy1LexTuwg+sKDOHH3YsfY/xrhP/0nTlsdSmYBgXd9Ed+Kd6cKAgKomQVkvPtLBC77B7SCqfgv+TgASmYBWtniEfk8QgghxHCxEtMEkqe+ri0LO9cZXTUGdtTvASBkhtla+w57Gt0uFBdMXsMFupuW/1btVkzbpDRYzOSMUhzLwDi0yX0BRcGuP4J58E3ArR9gHHSf8yy4DEVV8V/4YQDM/a9iHXczD7yrOttfeuacj1Y0Hd+57wMg9tbvib35W/e5eRejBnPO5CEQQogzatxnDFiWRVNTE1lZWfj9/n5t87GPfYzbbruNhQsXcvHFF/PYY4+xd+9evva1r53hvR1eLYmOBAGfht877v9TA253gdjr/4ex5wV3gebFCbcQe/M3oOoYe9yWVdqUcwis+0cUX991FzzTV6Le8GWUQHZaAEEIIYQYi7pNJch1AwOhqElHxCAz4BlVxQc74iEOtx5NPX7swJ8xbIMCfz6l7e1kb/wd+dMLaUoUWV5ZstTNFji2HWIhlIw8PAsuI/72H4ht/j36jFUYB14HK46aV45WMhsArWQ2+ty1mPs3Ag7alHPwLn8XdnMV5okdeJdeC4BnwaWYhzZhVVe40w0UDe/Sq8/uQRFCiGE27kc51dXVrF27lqeeeqrf26xdu5avf/3r/PrXv+bGG2/kzTff5Ec/+hGzZs06g3s6/Frax2erQsdxsMMt3ZfHw0Se+V4qKOBd8x4CV7sdKIx9r6SCAt4V7yZw1T/3KyiQpBXPQs2SQkFCCCHGvtRUgkSw2+fVUpmFtU1uhl1nVsHIBwZ2Nu7FwSHHmwW4WQMAy4oWYx3ahAqs6VLrZ2XJMsDNEgTwzD7fLQSYWYATaiK+89nUNALPwkvT2hP71rwXPG6gxLvkChRFwb/uk2R+5AeouW7HKkVR8V/0MdA8AOhzzkPNKjxzB0AIIc6CcXcb+b777kt7XF5eTkVFRa/rv/jiiz0uv+GGG7jhhhuGc9fOuuaO8RcYcIwYkb8+gFW1B/+lt+KZuxYAO9xC5KnvYDedAN2L/7J/xDNjJUBn9N8bJLDuNvSpy0bwEwghhBAjy0rVGOhcVpofpKUjTk1TmFllOV1aGo78PaSdDe40ggsmn8u+pv0caTsOwNLChZhvuMWBV7VHeDM3yNTiBZQEi7AjbVgndgLudYCie/GtvpnoS/9D/O3HwbFA9+KZc0Hae6nBHILXfR67rQ693J0+6AYO0gMkam4p/os/hlHxKr4u0w2EEGKsGneBAdGppcPtrJCXNT4CA048QuTp72DV7Acg9uZv0actx4l2EH7q2zjt9SiBbAJXfxatcFpqO//aWzBK56CXLZS7/kIIISY8x0lmDHQOdksLMth3vIWaVMbA6Cg+GLcM9ibqCZxTtJAcXzZH2o6T481mSihCLNqO4sskO7OAfzt6DE+2mw1oHnwDHAu1aAZaok2gPvs81F3PYde77Qw9s89D8Qa7vadWNB2taHqf++aZc0G3wIIQQoxVEhgYx5IdCcZaYCC27c9YJ3biX/8p1Iw8AOxYhPY/fwur9iB4Ayi+TJz2eqIv/wyr9gBOpA0lu5jgNf+Kml2c9nqK7sU7/5KR+ChCCCHEqNNTYcHSfHeAXNN4SmBgBNoV2o7NoZajbKl7hx31u4nbBnm+XKZkljEpo5SmaDNz82Zh73KnCugzVqLPWEnk6e9i7H4e7+IrOqcRzL0w9bqKouI7/4NEnvi6+9yCdWf9swkhxGglgYFxbCzWGIjvfJb4W4+5v7/9R/yXfBzHiFHzu/vdoIAvg+C1d+NE2ok8/R3Mo1sAUAumErj6s1IRWAghhOjDqe0KoUtgYIQyBhzH4URbFX85vpuNRzfTFG1JPefVvFw1fR2KouBRdN4962oc2yR05AEA9Fnnok1egFY6F6tmP9GXHsJuPA6qhmfWeWnvo5fOxbf2I2Cb/coKEEKIiUICA+NYyxirMWAc3kzsjd90Pt7/Kp5F64lsfhSzcjd4AwSv/ixa4XQA9GnLMY9tQ5s0n8CVd/aYDiiEEEKIdJ3FB7tOJXDPobXNEWzbSXUlONPFBxsjzWyu2cJbtdupDdellvs1H0uLFrO8eAnz8ubgTRT6S7JO7sOJdaD4s9AmzUNRFLxr3kvkia9hVbtTD/Rpy1H8md3e07vwsjP6mYQQYiySwMA4liw+OBamEpjVFURf/B/AwbNwPU6oCfPYNsJ/+hpYcRSPn8xr/xWKZqa28a//FFZ1Bdrk+SinXDAIIYQQomeW030qQWG2H11TMS2bhrboGS0+aNkWOxr28NrJTexrOoCD+166qrNi8mKWF5zD/Nx5acEAx3Ewj25BK5qBmlmAeWiTu82MVSiq5v5eOgdt6lKs4+8A6dMIhBBCnJ4EBsYp23FoTRQfTLYgGq2spioiz/4/sE306SvxXfAh7JZqzOPbwYqD5qH0/V8gmj0D07RT2ym6F33KkpHbcSGEEGIM6iljQFUVSvICVDWEqGkMp9oV6sOYMdAaa+PVqjfYWLWJdqMjtXxWxGBN6QrOXfE+JhcX0twcSjvfA5hHtxJ97vuouZMJ3vTvGEe3uvs3a03aer7VNxM+sQslmIMm1whCCNFvEhgYp9rDBpbtoCiQM0oCA1ZTFRgRtJLZqWV2tJ3IM9+FeBi1ZDb+df+Ioqpo+WV4llyJUfEqmRs+RWDaYqLNoRHceyGEEGJ86KnGALh1BqoaQtQ0hTtbGg5DYOB4eyV/O/Eab9dux3IsALK9WZw3aRUrjlSQW7UdpXEz/hUfSNvOsQxQdRRFwTz8lrvvLSeJvvJziIVQAtlopfPSttEKphK8+asoXj+KKpe5QgjRX/KNOU4lCw9mZ3hHRQ9iq+Uk4ce/Clac4E3/gVYwFce2iL7wIE5Ho9tR4Mp/RtE7gxj+8z6A79z34/FoI7jnQghx5jmOk+iVfnpxK45XGx3BXjF22T10JYDOOgNdAwNDKT54sOUITx95nn3NB1LLZuZM57Ipa1lauAgVhY6NTwLgRNuJVbwKxe8GwKo7RPhPX8ezZAO+1e/BTEwPgEQrQkCfsRqlh2scLb9s0PsshBATlQQGxqnmUdSRwDHjRJ9/EEx3n+LbniRw+T8R2/x7rKo9oPsIXHFnjwWC+nOhLIQQAK2xdhxsAnoAr+rOTa7qqGZ/yyEK/PksLpiPoigca6skbsXx6z78uh+/5ieg+/CoHmrDdexp2k8oHnKf0334tc6fTdFm9jbtx7RNVpYsoyyzlH1NB2mMNuHTvPg19zXDZoR9TQdoi7ezpHAhc3Jncrj1KCdDtXhVD37dh0/zYTkWB5oPc6K9Cl3VE9v78Gs+fMmfmg9VUTnSeoyGaBM3zr6Wq2ZK8TQxeMkaA6dmA3S2LAyRk7h+GGi7QsdxONByiKeOPM+BlsPu+ygqK4rP4bIpa5mePbVzP+qPQKwzGzC2/RmcC68DwNj3KjgWxs7nUHNKwYigBLLBtnFi7jSEU6cRCCGEGDwJDIxTyY4EeaMgMBB78zfYTSfAlwGxEObht4htfQJjx9MA+C+9FS2/fIT3UojxxbRNVEVFVXrPGHIch5OhGqJmjIDuT/wL4NO8PQblHMfheHslzbFWgrofv+4noAUIePwEND8N0Sa21e2kLlyPT/OlXtOfem0/mb4gJWoeRtShIxbhnfrdHG87gUfzJF6vcxvbsdnduI+DLUfQVS0xgPenBvQBzV1PUzT2txyiJlSb2lcFBY/mIW7FU8tyvNmYtknIDPd4PBSUVBG0/thWv7Nf61V1VPMML/S5nmEbGLaRNvf6VKqiku3N6vc+CtGT000lADdjIDPoZqZoWv+zDk+0V/GHg39hf/NBd1tF4/xJq7hi2mUUBPK7rW9W7nbXK1uE3XAMu62O0L43cUqXYp5IZAg4FrHX/xcAfdoKlOxC4pt/jxLMRSudM4BPLYQQ4nQkMDBOpTIGRrgjgXl8B8aeFwEIrP8Uxu4XMI9tI/72HwDwLr0Gz0yJ+IvxzbAMmmIt+DU/QU8ATx/zXsNGhKNtx/GoHoKeAEE9kBqwG7bBtrqd7G7ch6ZqBHT3+aDuJ+AJoqLwTv0udjbuxXbs1GA66AmkBv5BPYBX81LRdIC6SEO391cVNTVAD3jc9w7ofirbT9IYbTpTh+m0DNsgYkZpjvW+joI7yHES/4tbcTyqh9m5MzjRXkVrvA2AoB4gx5dN1IwRtaJEzVhqG13RmJM3i+JgYeL5GFEzmlrXr/mZnz8HB4dN1W/TboSYmTONKZllxG2DWGJ9VdGYkzeTTE8Gb9Vs42SohhnZU5meMxXbsYklXtt2bKZnT2VW7gwAYlaMiBlNvE7M/WnFMCyDssxJzM6dgV/3n/HjLca3nooPQudUgpaOOOGo0eM6PWmOtvDnw8+yuWZr6v9HF0w+lyumXUqu4sGs3I0zY0W3Of9WlRsY0KctxymdQ3zL47S88Sd8F+XihJpBUcGxwTLd9aavQJs8H+JRtMkLUE4T+BRCCDEwEhgYp1KtCkew8KATCxF95WcAeBZfgV6+GMUbxDy2DXDvEHhXv2fE9k+I07Fsiz1NFbTHOwh6ggT1AFn+DCxfAYYJmqOn3VWPmFE212ylNlzXZbAeoDpUyxvVbxE2I6l1ParHfT4x6Hd/uu/RYYTZXr8Twza67VMyA8C0zX5/jqgVJWpFaY619Pi8R/UkBslRwmYE27GxHZuQGXbvrEfT1/eqHiZnTkoNmMNmJHVXXlVU5uXNZk7uTOKJgXzUjBIxo0TMiPu7FSVqxQgbERQU5uXNZkHBXHAcd73EQD1iRjFtk9m5M1hUMB9N1VKvlfppxYiYEeKWQXnmJBYWzCOgB4jbhjuYt2Lk+XLxah4M26Si6QB+3c+M7KloamftEsdxiFlxolY0FTTpj3fNvBLbsdH7CPSsKV3Rr9cT4myxeqkxkOH3kB300BY2qKwP9bhOV4Zt8tyxl/jrsZcwEt9Lq0qWcf3Mq1IZAtGNv8LY8yLe5e/Ct/rm1LaOGcOqcWsP6OWLwJ9J/J2niNccwn7jN+7yqUtx4mGs6grw+NHKFqBoHnxr5NpBCCGGmwQGxqnkVIKRzBiIvv5/OOEWlJxSfGvciwGteCaexRuwmyoJrP9Uj0WDhOiJ4zi0Gx14VB2/5u+1/kRDpJHNNVtRUAh6gmToAfenJ0hQD5LhCaAqKm9Uv82rVW8St+KpAXpyHZ/uZUf9bhqjzb3uj6ZoiUF9MBEAqCFq9X4726N6MG0TBwfDNmiNG6k72D0p9OejqiphI5I2YLcdmwJ/PmtKl+PTfEQSg/OIGSFsRIhZMabnTOXc0pVke7Pc5WaEiNG5XiQxsC4JFrG0aFHqDrTjOIkBfSQ1mA8bnb9nebNYWDAP3ykDZ8u2iFhRdEXr8262rqvk5WXQ1NSBYVqnneowWD7Ni0/zktNlmUfVWVy4oMf1FUVJTE8Y2PdlX1M1hBitessYAJhcmEHb8RbaQm7Ar7fAwIHmw/y64g/UhusAmJUzg5vmXJtWQwDAPOFOuTH2vIR3+btSRYat6gqwTZSMfJScUhRFwbfgYmI7n8es3OO+99SlqHllRP7yX3jmXYSieYbh0wshhOiJBAbGqWRXgpGqMWAe3YZ54DVQFAKXfgKlywW3/4IPjcg+ibPLsE3aYm0EPYHTDuSTjrYdZ2/jAfy6LzVIz/BkkOEJ0Bxt5S9H/sqh1qOAOyBz13GfTw7oDdtge/0ubMc+7Xudqre76ZmeDKZlT0kNkMNmhJAZxrItLMei3ehImw9eGixmSeFCYlacsBkmbEbwqV7OnbSSRQXzARJ32aPu80Zy0O7+TAYAlhUtZnr21NQxSw7Yw0YYwzYoDBT0e0Ca5e1e1LM3iqKkBtW5vpy+N0jQVI1MNaPf6yffSwbVQoyMRFygW40BgEmFGew73pJ6fGpgIGyE+ePBp3i9ejPgfse8d871rChe2u173u5owmmvB8CJdWAe2oRn3kVAZ30BvXxRajvf0quJ7XrRnT6AmzGgZuSR+ZEfgHTjEEKIM0oCA+NUS4cb6R+JjAEn2kH01Z8D4D3narSS2Wd9H8SZYdgmr1VtojHalBiUu3fiM7v8HtSDvF27nScOPZ0aNKcP5N279hl65zbH26vY3s9CbgC2Y9NhhOgwQj0+Pz9vDgWBPEJGhLDhpsSHDXdQn0x7LwwUcPnUS5iaVUbIcAfxYSPsbmOGKc0oZnXJCrxd7lDpukpubpDahmbaoqHObcwImZ4MZuVM7zMAEvQECXqCQPdCXL3pOmAXQoihsm134N1TNsDkgvQgX9fig/ubD/HLPb+hJdYKwIWTz+WGWVcnvtO6s2r2pz2O73oOfe5aFEVJ1RfQyhZ1vld2ERkLzie05zXUwmmoGXkAaTcXhBBCnBkSGBiHDNOiI+LOT84bgcBA9LWHcSJtqHmT8a684ay/v+iZYZuYVs9z09+p38WfDj1NODHATR/suz+9qoeXTmzssVhdb1RFTaW/n24gD27huHOKFqErGqHEYD5khAkbYSzH5rxJq7hq+jqCepBw4rnk88n141acRQULmJEztdf3MWyTqBklwxMc1B1rRVHw6T7y/B7yyB3w9kIIMdKSNQZ6+gosK0wPDKiKgmVb/OXIc/z12Es4OBQHC/nQ/PcyO1E0s9f3qa4AQJ+7FvPQZuzG41g1+1FzSrCbKgHQyhambZN/2YeItbfhWXT5YD+eEEKIQZDAwDjUnMgW8OgqQd/Z/U9sHn8H89AmUFT8l3wiNZdQnBmWbXG8vQpNUVN3332aL+2utWVbPHP0Bf567CVMx8Kv+dLu9lu2xf6WQ6n12+O9t0oDyPZmsapkGREzmhich+hI/AwZYRwc/Jqfq2es59LyC7Edp8tAPkTIiKTWTf7TVI1Lyi+gLHNSj+/pOE7aZ/JqOQNKde/Ko+p4BpBeL4QQ401yKoHWQ52fyacEBsJOG9/b+luOtB0H4PxJq3nPnOv7VZPDqkkEBqavQFE1jH0vY+x+Hn3acgDUgmmogey0bTy5JWS9625Mc2BTwoQQQgyNBAbGoa71BfpKax4K8/g7KMEctMLpADhmnOhrbq9hz5Ir0IpnnrH3nihsx6Yh0ohH9bh37bukkjdFm/nZrv/jSNuxtG00RUsN+jM8QTqMcFp/92ii/VnXtnOqorJh6qUsLz6ny6Dd/dnR5Wd55mSunH4ZAT3Q6/5GzSgezZvWkm8oA3ngjP4dCyHERJMqPtjDd2tW0ENmwJPKPHyx4Qnq4tX4NT9/N/8mVpYsS1vfiXZgtVSjl85Jf49IG3bzSQD00rmo2UUY+17GPLIFJ+Zmj+nlixBCCDE6SGBgHDobHQnMqj1EnvkeeANk/t13UbwB4tufxGmvR8nIxydTCHoUMSNUtlcT0P3ufPxEin5PA9+WWCs/3fW/HG7tHPh7VD1RWT9Ic6yFiBnFq3oI6AFCZhjTNrEci7Z4O23x9tR2AT3AhxbcxPmzllFV30BrtCM18I+YUebmzer1bv1AqIra61xTIYQQo0Nv7QrBDcROLgiyv7IVVJO6eDUAd6+6ndKMkm7rR1/5OebRLfg33IFnxsrO90jUF1DzylH8mWj+TLTJC7BO7sWqSnQdKJPAgBBCjBYSGBiHmhMZA7mZZyaN33EcYpsfdR/EIxj7X0Ofeg7x7U8B4Lvg71A8p29ZNhEdbTvOg+/8vNs8e13RCHgCZOjBRFs9t6f93qb9tMXb0RQNBwfbsTFsk9Z4W6rN3bSsKXx88d9RGChIVa5PT9MPEbcMFhbMoyAjlyxfBiUZCgW+gpE4BEIIIUYB20m2K+z+nOM4TCrKYH9lK0rAPV9lejJ6DAo4jo150h3kG3teTA8MJOoLaJPmppZ5Fl+OdXKv+0DzoJ2SZSCEEGLkSGBgHEpmDJypwoPmkbew64+kHsd3P+9WF7ZNtPLF6NNXnmbr8StiRvlNxR841naCgB4gqAcIeNyfXs3DxqpNGLZBpicDRVEIGxEsx8J0LNrjHT3O7Z+cUco/LLmFokABMSuWKrIXNiI4OMzJnYmeSNnvWrk+3593tj++EEKIMSI1leCUjIHWWDsP7fgFDd5WUNegBtzMs8m91X9prYV4BACrajd2ez1qVpH7uPYgAFppZ2BAn7ocJbMAp6MRrXSu1CESQohRRAID41BzlxoDw82xTWJvPQaA55yrMPa+jNNag9laA4qK77wPjsv54BVNB2mINOLX/QS6/HMfBzBtkx9s/ynH2k+c9nUW5s/j1sUfwq/73cwLK07EjCTa5YVT7fXCZgRd1Tl/0upUgSd/4v0KBtDmTgghhDhVMjCgdTlfN0aa+e/t/0N9pBEANasJNegGBsoyStO2d2wbRVWx6g6nLTcqNuJbdSOOEcNucKfBdc0KUFQV36obif7tJ3jmXzz8H0wIIcSgSWBgHEoWHzwTNQbM/a/jtNai+LPwrXg32DbGrr8C4FlwGVp+2bC/50h75uiL/PnwM/1aN8MT5O/mvwdNUQkbEbfPvRkhYkTID+RxSdkFaKoGuHf4/boPv+4jz597Bj+BEEII0alzKoEbGHAchwd3/CwVFABQs5pRAm4m2+TMzsBA9JVfYBzeTMaN92IlsgeVrCKc9nqMilfxrng3Vv1hcGyUjHzUzPSpa565a9Fnn4eiyiWoEEKMJvKtPA61JNoV5g5zxoBjm8S2/RkA77JrULwBvIsvx9jzInh8eFfdMKzvd6bUhRt44vAzrClZzjlFi4hbcV6peoO2WLubiq/7Ein5PmpDdTxz7EUA5ubNxnYsImaUqBklkvjn4F5g5fly+aelH0+7gBJCCCFGG+uUqQTtRgfVoVoUFK6beQV/Pvws3twWbN2tMTApkTHgODbGoU1gRIjveckNAAC+FdcTffM3OKEmrMpdWA1HAdBKZvf4/hIUEEKI0Ue+mccZx3FoPkM1BswDb7hdB/xZeBasA0DNLiZ403+geLyo/qxhfb8zIWyEeXDHz6gLN7CzfjefWfFJnjv2N3Y07D7tdtfM2MC1MzZ0W56cDhC1omR5MlPZAEIIIcRodWqNgdpQPQD5/lxWlyznz4efhWALSiLwPSlReNBpbwDDrSlgHnwDJx4G3DoCnjkXYOx6DmPvSzi2lVguxQWFEGKskMDAOBOKmhimDQxvVwLHtjqzBZZejeLpDDqM1ukDETPCayc3Yzs2HtWDrupsqd1OXbgBBQXTsbh/64+wHAtd1blo8nmYjkXMihGz4sTMGHHbYHnRYi6bclGP79F1OoAQQggxFqSmEiRqDNSF3cBAcbCIfH8eeb5cmmMtABT681PnOKuhs32uE3G74+DLQMkuxrPwMoxdz2Ee3w6ae/2hlUhgQAghxgoJDIwzyY4EmQEPHn347l6bh9/CaatzswUWrh+21z1TbMfmp7seYW/T/m7PeTUvn176Cf5v3++pCdcBcMuC97OyZOnZ3k0hhBDirLNsBy2/mv3te5gx6TxqE4GBkmARiqIwO3cGb9VuA2ByRgl2tB3Vn5UqKIiiQCK4oBXNQFEUtNzJaJMXuO0IzRjoXtSC8hH5fEIIIQauhw62YixLFR4czmwBxyH+zlMAeBZvSMsWGK2ePvI8e5v241E9nFe6ilUly1hauIhlRYu5femtzMqdzj+e81EWF8zn7+e/V4ICQgghJgyLGJ5ZO/hz5R9pi7enBQYAZufOSK1bVHOM0K/vxm6rw2p0AwOeeZeknteKOtf1LFzXubx4ltQSEEKIMUS+sceZ5jPQkcCq2o3deBx0L94uJ/2R9nbNNh4/9DS2Y6MoCgpuSqSiKDRHWwD44LybOHfSyh63Lw4W8qmlHz9buyuEEEKMCranA0VxS+cebj2WNpUAYE7uzNS6JY01YEQx9m9MZQx45q3FajiK3XAUbdL81Lr69OUowVyccEuvhQeFEEKMThIYGGeSUwnyBtmRwLFN4jueQS9bjFY0HYD4O08D4Jl/CYo/c1j2c6jqwg08su/3xG2j13UuKb+w16CAEEIIMVE53lDq9wPNh2iINgGdGQPFwSIK/fk0RVsoj7qdjoy9f3PrCigKasEUAld+BrvxGHr5otRrKaqO7/wPEt/xDJ55PdfmEUIIMTpJYGCcaR5iq0Jj79+Ib/49RvYrZLz/PuymSqyq3aCoeJdcMZy7OmiWbfGrPb8lbhvMyZ3JzXPehYODk5jvaDsOPs0rbQOFEEKIHtjeUGou6Zbad7AdG6/mJdeXA7iZd3cs/wdaD71B/sHfA53FBtWcSSi6D0X3oWbkdXttz6xz8cw696x8DiGEEMNHAgPjTLLGwGBbFRr7XgHAaavDqq7APPAGAPqMVahZRcOzk73Y3biPzTVbMWwTwzYwLRPDNjEdglChyQAAQ+dJREFUExuLqBHDsE3ilkHUiuLX/Hx4wfspCHS/MBFCCCFEL7pkDLQbHQCUBApREl0KAAoDBWR2dHBqXp5aOO1s7KEQQoizTAID40xzx+BrDFgNR91aAgnxHc9gVe0B3KKDZ1JrrJ2f7vpfYla839t8YN6NEhQQQgghBsoX7rYoWV/ArK7AibbjmbEKu6kScG8OmEfeBkArnHr29lMIIcRZI4GBcSaVMTCIqQTJbAE1rxy7uRLr+Dvu44KpZ7yI0BOHnyZmxSnLnMRFZefjUXU8qo6uevB7vOTnZBINmSiOhkfVCXqCZHlHR70DIYQQYqxwHAclERjI9+XTFOusL+A4NpG/PgCxEOpN/54KDHiWXIFVexAn3IJaPGukdl0IIcQZJIGBccSybdpCiRoDA8wYcMw4xkF32oDv/A8Se+PX2M2JC4JF69PSC4eT4zgcaTvOm9XunYgPzruJGTnpaYq6rpKXl0FzcwjTtM/IfgghhBATQciIoHjca4WVRct5rvIFIBEY6GiEmDvNwNj9Ak64BQAtfwqBq+7CbjqBXjp3RPZbCCHEmSWBgXGktSOOA2iqQlbQM6BtzaNbIB5BySpEK1uAZ/5FxN74Nfgy8Mw+b0j7VRuq40DLYdri7bTFO9yfsbbE43YM2wRgTemKbkEBIYQQQgyfulAjAE7cy/y8uanAQHFGEXbzydR6xv7XAFCyClG8AbTCaWhSX0AIIcYtCQyMI8n6AjmZXtQB3uE3EkUGPXMuQFFUPAsuxW6tRStfjKIPrpAhQNgI860tPyBiRk67XmGggHfPunrQ7yOEEEKIvtWFGwCwY0GmZpWR58vFcixKg8XYh3d2rui4GXpqXvlI7KYQQoizTAID40hLu5saOND6AnakDatyFwB6IjtA0X34194y5H36W+VrRMwIub4cFhXMI9ub5f7zZXf+7s3Eq3mH/F5CCCGEOL36iBsYcKIZeHSdz6++E9tx8Gpeoi2JjAFFTQUGtHwJDAghxEQggYFxpGWQHQnMI2+DY6MWTkPLnTxs+xM1o/zthJuKeNPsa1lZsmzYXlsIIcTE9dBDD7Fx40YefvjhXtdpbm7mP//zP3nllVdQFIVrr72Wz33ucwQCgdQ6V1xxBceOHUvb7sYbb+S+++47Y/s+0uojiakE0SCqCll6ZyFfKzGVQJ9zIeb+VwFQJTAghBATggQGxpHmREeC3AFmDJgH3wQYci2BU71a9SYhM0xxsJDlxecM62sLIYSYmB555BHuv/9+Vq1addr17rzzTiKRCL/4xS9oa2vjnnvuIRwO81//9V8AhMNhTpw4wUMPPcSiRYtS2/n9/jO6/yOtMZoIDMSCKEB048M48TD+S/8hVWPAu/hyrMqdOJE2tOKZI7i3QgghzhYJDIwjyYyBvAFkDNjtDVg1+wEFfdbQAgPH2k6wt2k/J9pPUtlxkobEXYkrpl6GqqhDem0hhBATW21tLV/5ylfYtGkT06dPP+2627ZtY/PmzTz11FPMmuW21/vqV7/KJz7xCf7lX/6FkpISDh48iG3bLF++nJycnLPwCUaHhkRggFgGxEMYe9zig+aUJWBEQFFQ8yYTvO7fsKNtqNnFI7i3QgghzhYZrY0jnRkD/Z+vbx55CwBt0jzUjLzBv3e0he9u+SF/Pvws2+t3poICs3Kms6Z0xaBfVwghhADYvXs3Ho+HJ554gqVLl5523bfffpuioqJUUABgzZo1KIrCli1bAKioqKCwsHBCBQUaI810GB0AKEYQp6Um9Vxs65/c5dklKJoHNbdUWhMKIcQEIhkD40gqY2AAUwnMY9sB0GesHNJ7v1r1JqZjURIs5oLJqynPnEx55mQyvRlDel0hhBACYN26daxbt65f69bW1jJp0qS0ZV6vl9zcXKqrqwE3MBAMBrnzzjvZunUreXl53Hzzzdxyyy2o6uDvm+j66Lzn4jgOv9n/BwCstjxUxwsddZ3Pt9YCoOeXjdhn0DQ17acYODmGQyfHcHjIcRy6s30MJTAwjgy0+KAT7cCqOQCAPnXZoN/XsE1eO7kJgOtnXsmy4iWDfi0hhBBiqCKRCF5v9+w5n89HLOaeKw8cOEBbWxtXXnklt99+O1u2bOFb3/oWra2tfOYznxnU+6qqQl7e6AyI/+3IG+xprEBXdaJHFxHQVLzRBsKnrJcxadqIf4bs7EDfK4nTkmM4dHIMh4ccx6E7W8dQAgPjRDRuEolZQP+LD5ondrjdCPLKUbOLBv3e2+p20GGEyPXlsKRw4aBfRwghhBgOfr+feDzebXksFiMYDALw4x//mFgsRlZWFgDz5s2jo6ODBx98kDvuuGNQWQO27dDWdupQe+RFzCi/2PooAJeUXsqTUR3FD6GaEwAoviBOzN3veLCY5ubQiOynpqlkZwdoa4tgWfaI7MNYJ8dw6OQYDg85jkM3HMcwOzvQ74yDcRcYmKgtjJL1BfxejYCvf/9ZU9MIpi0b9Ps2RZt58YTb0mjt5PPQVG3QryWEEEIMh9LSUp5//vm0ZfF4nJaWFoqL3WJ6Xq+3W1bB3LlzCYfDtLa2kpc3uLo7pjn6LoDfOvkOYTNCcbCQVfnn8SRvo6oKVrNbY8Cz+EriW/7orpxdOuKfwbLsEd+HsU6O4dDJMRwechyH7mwdw3EVGJjILYxaOtw7I/3tSOBYJuaJncDAAwM1oVq21e3inYZdnGivAkBTNC4sWzOg1xFCCCHOhNWrV/Ptb3+bY8eOMW3aNAA2b94MwMqVK3Echw0bNnDDDTfw6U9/OrXdzp07KSoqGnRQYLTaXOMWXDyvdBU4CgCaCnZbIjAw53ys+sM4sRBqXvmI7acQQoiRMy4CA9LCCFpSHQn6FxiwqivAiKAEslH70aO4Pd7B27Xb2VSzJRUMAFBQmJU7nSumXUa2N2twOy+EEEIMgWVZNDU1kZWVhd/vZ+nSpaxYsYK77rqLf//3fyccDnPvvfdyww03UFJSAsCGDRv46U9/ysyZM1m8eDFvvPEGP/nJT7jnnntG+NMMr8ZIMwdaDgOwunQ5bS0OAHlqCCwTVA0ls5DgVXeN5G4KIYQYYeMiMNC1hdEPfvADqqqqel23rxZG11xzzZhsYZQqPNjf+gLHtwOgT12KovQ+78S0TR7Z93vert2O7bgpLKqisjB/LkuLFrOkcCFZ3syh7bwQYlyxbRvLMnt5TiEa1YjHY1iWc5b3bPzo6zhqmj6kyvpjTXV1NevXr+cb3/gGN910E4qi8P3vf5//+I//4CMf+Qg+n4+rrrqKL3zhC6ltPvvZz5KZmcl3v/tdampqKC8v55577uF973vfCH6S4fdW7TYA5uTOJN+fR7PdCkCR0gaAml2CMoH+VoQQQvRsXAQGxkoLozMpWWOgP1MJHMdJ1RfQpi0/7bpbat9hc81WAKZlTeHcSStZWbxU2hAKIbpxHIe2tiYikY7TrtfQoGLbMt9wqPo6joFAJtnZ+SiKchb36uw4td5PeXk5FRUVacsKCgp44IEHen0NXde5/fbbuf3228/IPo4GjuOkzuFrSt22xLbtBpIKVTdAoOaWjszOCSGEGFXGRWBgIEaqhRGc2d7GrSG3xkB+jq/P97EaK3Ha60Hz4J+2BOU0629vcOsQXDPzct49+6rh2+EBkD6ow0OO49DJMTy95uYGIpEQmZl5eL2+HgekiuK2dLNtB0cSBgbtdMfRcRzi8RgdHS2J9nmFI7OTYsQdb6+kNlyHR9VZnmglnAwMFCiJwECOBAaEEEJMwMDASLUwOtO9jdsjBgBTSnP6fJ/mPbsBCExfQn5xfq/rhY0Iexr3A7B+7vnk5Uhf4/FAjuPQyTHszrIsqqtD5OTkkZU1dqZhjVfBYABNU+joaCE7uxxNk44xE1EyW+CcwkUEdLeAciowgBsYUHJKRmbnhBBCjCoTLjAwUi2MznRv4/rmCABelT77D7ftfRMApfyc0667uXorpm1SmlFMhpUtfY3HODmOQyfHsHfxeBzLstE072lb6iiKexwty5aMgSHoz3HUNC+WZdPQ0NZjptxAehuLsceyLd6u3Q7AmtIVncsTfzD5tACSMSCEEMI14QIDI9nC6Ez1n7QdJ1V8MCvgOe372OFWrFq3OrFSvvS0626p2QHAssLFieJWI3sVL31Qh4ccx6GTY9hdMlDS13z25CBWggJD05/jmPxvIX+vE9Pepv10GCEyPRksyJ+bWm7bDhoW2bQDEhgQQgjhGve3CizLor6+nmg0CpDWwmjHjh28+eabaS2MFEVJtTB66qmnOH78OL/97W/5yU9+wp133jnCn6ZnHWEDy3ZQgOyM7neFurKOvwM4qIXTUTN6D3KEjTC7G91CTsuKzxnGvRVCCCHEmZacRrCqZBma2jmVxLahQO1AAfD4UQLZI7ODQgghRpVxnzEwEVoYJTsSZGd40ftIC021KZy2rMfnbcfmzeotPHH4aQzboChQQHnmpB7XFUKIiea1115l8uQyZsyY2ePzW7e+zZ13fpJHH32CSZMm9/t1P/CBG6msPJG27Oqrr+Oee/59KLsrJqiIGWFHg1tPqOs0AgDLdijUEtkC2UXjsmuFEEKIgRt3gYGJ2MKoOTGNIDfz9K0KHTOOWbkLAL2HNoX14UYe2fcoB1rcqQZFgQI+svADctEghBBATU01n//8XTzwwI96DQwMRiQS4eTJKr75zfuZN29+arnP5x+29xATy7a6XRi2SUmwmKlZ5WnP2Y5Dgeq2FFWzikdi94QQQoxC4y4wMBEl6wvkZZ0+MGCd3AtmHCUjH7Vgamq57dj8rfI1njj0DIZt4FU9XDvzCi4tvxBdlT8RIYQAtw3gmXDkyCFs22bx4nPIzpa0bjF0m2u2AHBu6YpuwX27S8aAkl101vdNCCHE6CSjvnGgpT2ZMXD6+gLmse2AO40geaEQNsI8uOPnHG49BsDcvNl8aP7NFAYKztwOCyHEKPXAA99h48ZX+N3v/pRa1tHRwfXXX0k87n7X3nnnJ/nYx/6BW2/9R955Zxvf//79HDp0kClTpnLttdenvd573vMuamqqe3yvL37xK1xzzbs4dOgg+fkFEhQQw6Ip2pzK/Ftd2j070LYdCtTEVIIsCQwIIYRwSWBgHEjWGMg9TcaA4zid9QWmLkstf+74yxxuPYZf83HT7Ou4YPIamToghBg2juMQN9Ir4lu2c9aq5Hs96oC+06655np+97tf884721m6dBkAL7zwV7KyMvn61x/iH//xo3zta99k9erzOHmyirvu+jRXX30tX/rSf3DkyCG++c2vp73ej3/8K2zb6vG9MjMzATh06CCBQIAvfelz7Nz5Drm5eVx77fW85z0fQFXHfY1gMczeqtkGwJzcmeT7uxcZtmyHouRUgmyZSiCEEMIlgYFxoKUjDkDeaWoM2I3HcELNoPvQJrtzWA3b5PWTbqvGDy98P8uKFp/5nRVCTBiO4/CN/93KwarWEduH2eU5fOFD3dOpe11/9hzmzVvAs8/+JRUYeOaZJ7nyymvIz88HICsrm2AwyK9+9TMKCgr4l3/5PJqmMW3adOrqanngge+mXq8/LW6PHDlEe3s7l166no997DZ27NjOD3/4AG1tbXziE58c+IcWE5bjOKluBGtKV/a4jm3bFGjJwIBkDAghhHBJYGAc6E/GgHnUvYOgly9G0d0pB9vrdtJhhMj15bCkYMGZ31EhxMQzBhOQrr32ev7nf37IP//z3dTV1bJz5w4+//kvd1vv8OGDzJkzD03rbAW3eHF6e9e///v3UVvb81SCu+/+IldccTXf/vYDxGKxVAbBrFmzCYU6+OUvf8rHP36bZA2IfjvRXkVNuA6PqrO8eEnac3aoGTQdNd6OTzFxACWzcGR2VAghxKgjgYFxIFV88DQZAz21KXyl6g0A1k4+N63HsRBCDAdFUfjCh1Z0m0qg6+qonUoAsGHDVXz/+/fz+uuvcujQQRYsWMT06TOorj55ypoKjnPqZ0s/rX772/8P0zR7fJ9kBoLH48Hj8aQ9N3PmbCKRCO3tbeTk5A5o/8XElcwWOKdwEQG9s6uF3VZP6LEvo/iz8E5y62CE1CyyNbkMFEII4ZIzwhhnmDYdEQPoPWPA7mjCbjgGKGhTlwJQ1VHN4dajqIrKBZPXnK3dFUJMMIqi4POmBx51XUVTR28qQVZWFhdffCmvvPI3Dhyo4Oab3wfQLcAwZ85cnnrqzxiGkRrY79u3N22d0tJJp30vx3F4//tv4KqrruXjH78ttXzv3t0UFBRIUED0m2VbvFXrZgeuKV2R9lzs7T+AEcUxohR43XU6tNyzvYtCCCFGMclPHOOS2QK6ppLh7znOk8wWUEtmoQbcqtfJbIGlRYvJ8UklbCGE6Oraa6/n5ZdfpKqqivXrrwQgEAgA7hSCjo4ObrzxPUQiEb7xja9y9OgRXnvtVX72s/8Z0PsoisLFF1/Gr3/9v7zwwnNUVVXypz/9gf/7v19x661SX0D0396m/XQYITI9GSzIn5tabjWewDz4ZupxYdM7AIT03LO9i0IIIUYxyRgY41LTCLK8vabLdm1TCBAxo7yVSDe8uOz8M76PQggx1qxatYacnFyWLFlKVlYWADk5uVx77fX88IcPUFl5gn/+57t54IEHeeCB7/Lxj/89JSUlfOQjt/Kd79w3oPf65Cc/TWZmJg899H3q6+uYNGkyn/nMv3L99TeeiY8mxqnkNIJVJcvSpgfGNj8KOCjZxThtdaiO2yUjLIEBIYQQXUhgYIxLFh7srb6AY8SwTu4BQJ/q9jN+q2YrMStOabCYObkzz86OCiHEGOLO72/nuuvenbb8C1+4ly984d7U47lz5/P976dnCdx443sG9F66rvPRj36Cj370E4PfYTGhRcwIOxp2A+nTCMzqCqwTO0DRCFz5GcJPfB1iIQBCntyR2FUhhBCjlAQGxriWPjoSmFW7wDJRsopQ8ybjOE5qGsFFZecPuCiXEEKMZ21tbWzd+hYvvvg8paWlrFolNVjE6LetbheGbVISLGZqVjng1q9wswXAM/9itLwy9GnLMfdvBCDi6buVphBCiIlDagyMcS0dcQBye8kYMI9uB9xpBIqicKj1KNWhWryqh3MnrehxGyGEmKgsy+K++/4/9u/fx5e//FUJnooxYXPNFsDNFkj+zZrHtmHXHgTNi3elm/nimbkqtU3YK4EBIYQQnSRjYIxrTtUY6B4YcGwbK9Wm0J1G8Erl6wCsLl1OQA+cnZ0UQogxIi8vj2ee+dtI74YQ/dYUbeZAy2EAVpe453rHtom/9XsAvEuuQA3mAqCVLaY5MJUTrQ62FhyR/RVCCDE6ScbAGJesMdBTxoBdfxgn2g7eANqkubTF29levwuAi8ouOKv7KYQQQojh91aN235wTu5MCgJuFoB54DXs5pPgy8C79OrUuoqms2nKLfy04zJUTS4BhRBCdJKzwhjXcpqMgVQ3gvIlKKrO6yffwnIsZmRPZUrW5LO5m0IIIYQYZo7jpLoRrCld6S4z48S2PA6Ab9l1KL6MtG1s2wFAVWWajBBCiE4SGBjDHMfpLD6Y6e32vHnMvYugT1+O7dhsrHL7GF8kLQqFEEKIMe9EexU14To8qs7y4iUAGHtewuloRMnIx7NofbdtrERgQJPAgBBCiC4kMDCGhWMmcdMGuk8lsNvqsJurQFHRp5zD7sZ9NMdayPAEWVF8zkjsrhBCCCGGUTJb4JzCRQR0P048THzbnwHwrnw3it79poHjXjZIYU0hhBBpJDAwhiWzBTL8Ol6PlvZcchqBVjoXxZfBK5Vui8LzJ63Go3nO6n4KIYQQYnhZtsXbtdsBtxsBQHzHMzixDtTcSXjmru15O0cyBoQQQnQngYExLNmRILen+gKpbgTLqA83srdpPwBrJ5931vZPCCGEEGfG3qb9tBsdZHoyWJA/FzvcSnzHswB4V9+Momo9bmfbbsqA1BgQQgjRlQQGxrBkR4K8U6YROPEw1skKwA0MbDz5Jg4OC/PnURQsOOv7KYQQQojhlZxGsKpkGZqqEd/6BJgx1KKZ6NNX9rpdIi4gGQNCCCHSSGBgDGvpiAPdMwbMEzvBsVBzJ2FlFvBG9VsAXFwuRQeFEGIoXnvtVY4cOdzr81u3vs3atauorj45qNffsWM7F1+8ptvy1v+/vTsPi6r6/wD+nhl2WUUFEXfFBQFBWUxBQVMUJTPta+WvUjO1hac0RbNFzS0yU1GyRevb8i0tzTTJylwrN0TRElNESZTFDUdkGWbm/P6YuDIOm8ywzvv1PDw5955759zDxGfmM+eez608LFjwGiIjwzF8eATeffdtFBUV1ug5qPErVBfi5LW/AOhuI9Aqc1GSuhcAYB08rtL1A0oXH5RzjQEiIiqDiYFG7G5FgnsSA6XrC7TrjeTck7hTUgAXa2d4u3av6y4SETUZ2dlZiI19GTdv3qiV8588eQJz5syUpnqX9dprscjM/AerVr2PRYvexsGDvyEubmmt9IMavuO5f6JEq4abXSu0c/BEcdIWQGig8OwFC48elR6rFSxXSEREhpgYaMTy/l1jwKXMjAGh1UB96SQAXZnCA5d1iw4OaBMCuYy/biKimhL/fqAyNbVajfj4FYiJmQZ399YG+//88ySOHz+GefMWoFu37ujTJxCzZ8/Djz/uwNWrubXSJ2rYjmQfA/DvbIHr/0CdpitHbB00rspjtSxXSERE5bCo7w5QzZW3xoAm+xxQfAcya3tcsbXFBeU/UMgUeMAjsL66SURmTAgBqFX3bJNDqA2/Fa8VFlb3VZZt9ep38dtv+7Fp0/fStvz8fERHD4NKpfubGxMzDRMnTsHkyVORknIca9asxPnzaWjbth2ioqL1zjd27ChkZ2eV+1yvvvomRowYhcLCQpw4cRwrVqxBdnYWlixZoNcuJeU4XF1boEOHjtI2f/8+kMlkOHnyBAYPHlrt66PG70bRTZzL093OEujmj+K9nwAALDoHQ9GifZXHa6VbCWqvj0RE1PgwMdCI3a1KcLdOsTrjOABA0c4Pv2UdBgD0btkLjlYOdd9BIjJrQggUbFsMbU5avfVB4dYVttGvVjs5MGJENDZt+gopKSfg59cbAPDrrz/DwcEeS5Z8gKlTn8bixXEIDAzBlSuX8fLLL2D48Ci89toCXLhwHnFxS/TO99FHn0Gr1ZT7XPb29gAABwcHrF//OQAgMXG7QburV3PRqpWb3jZLS0s4OTkhJyenWtdFTcfRbF2c7+rcCU43c1B46SQgU8C675hqHS+tMcDMABERlcHEQCOl0WqhvKP7Fq50xoAQQlpfoKStN45m7gAAhLbhooNEVD9kaFwfPrp06Ypu3Xrgp592SImBnTt/wLBhI9C8eXMAgIODI+zs7PDZZxvg6uqKGTNioVAo0L59B+Tm5mD16hXS+VxcXIzuU1FREaysrAy2W1lZS7MYyDwIIaRqBIFu/ig+sgkAYNljIORObpUdKuEaA0REVB4mBhop5Z0SCKFbVdihme4No/ZWFoQyB5Bb4JhFMVTaErRu5oYuzh2rOBsRkenJZDLYRr9qcCuBhYUc6gZ6KwEAREVF48MPE/DSS7OQm5uDU6dOIjb2dYN26elp6Nq1GxSKu/Xie/Xy1WszYcKjyMkp/1aCWbNexdChw6vsj7W1NVQqlcF2laoYtra2VR5PTcel25eRXZALS7kFfIq10OamAxZWsAqIrvrgf2lZlYCIiMrBxEAjVbq+gJO9lRTcNf/OFpB7dMNv/y5MFNqm332/KSYiMhWZTAZY6ldOkVnIIZPVUWKgBh58MBJr1qzEH38cwPnzaejRwxsdOnQspwShDELoX4eFhX5YXb58FdRqdbnPUzoDoSqtWrnhwIF9ettKSkpw69YttGjRqlrnoKahdLaAj2tPKJK2QwvAymcY5HbO1T6HhosPEhFROZgYaKTKq0hQehtBhnt7ZOclwUphhSD3gProHhFRo+Xg4ICwsEHYv38vzp37G4888igAGCRZu3b1QmLidpSUlMDS0hIAcOZMql6b8qoM3C8/vwC8/348MjMvwdOzLQDg+HFd8tfX18/o81PjoNFqkJRzAgDQR2MNbd4VwLoZrPyqnnVSlpZrDBARUTlYv66RKp0x4Fy6vkBRPjQ55wAAf8huA9CVMbK1sKmfDhIRNWJRUdHYt283Ll++jMGDhwGANG0/PT0N+fn5ePjhsSgsLMTSpQtx8eIF/P77AWzY8KHJ++Lt3Qs+Pn54881XkZr6F5KTk/DOO0swfHgUWrbkjAFzkXrjLG6X5MPeshk6/qUrRWztPxIyK7v7Oo+0xgBnExIRURlMDDRS0oyBfxMD6n9SACGQ7+qJlDxdgiCMiw4SEdVI375BcHJyRmjoQDg46Kq6ODk5IyoqGgkJq/Hxx++jRYuWWL36feTm5mDSpAlYs+Y9PPXUZJP3RSaTYcmSd9C6tQdiYqbhjTfmIDj4Acye/arJn4sartLbCPwVLpDfuQFZs+aw7Dn4vs+j5a0ERERUDt5K0Ejl3dYvVVhapjDJrRW0qkx0cuqANvbGT2ElIjJHhYWFuH37NkaOfEhv+9y5b2Du3Dekx15e3bFmjf4sgYcfHlvj5x0xYhRGjBhlsN3FpTkWLXpbb1udLuJI9apQXYST1/4CAPhd1CX/rfuMhszCsFpFVViukIiIysPEQCN1M//urQRCUwJ15p/QADikvQkACG0TUo+9IyJqnJRKJZKTj2L37l1wd3dH375B9d0lIpzIPYUSrRqt5DZok58LubMHLLz61+hcpbcScMYAERGVxcRAI1W6xoCLgzU0WX8DJUX428UFeeo7sLdsBv9WvlWcgYiI7qXRaLBs2VtwdnbBwoVLWdWFGgTpNoLrNyEDYBX4CGRyReUHVaD0VgIZEwNERFQGEwONVF6+rqa1i4M11Gd0txEcauEEiAL0ax0ISzl/tURE98vFxQU7d+6t724QSW4U3cTZvPMAgN637kDeqjMsOtxfxaGreYU4dDoHni2b3S1XyKQXERGVwU+PjVCxSoPCYl1dbKdmVlBnnMA1SwXOigLIIMMA3kZARETUJBzN1iX/OxaWwEWthXXQuGrNZNEKgT/Tr2N38mWcOn8dAoCVpRzOzXSLFnONASIiKouJgUaotCKBtZUC1neyUZB/HYdaOgIAvF27oYVt8/rsHhEREZmAEEK6jSBAWQhFWx9YeHSv9Jj8whIcOHkFe49fxtW8Ir19qhItbvx7KyITA0REVBYTA42QtL6AvTXUGcdRIgOOOdoCEAhliUIiIqIm4dLty8guyIWFVsDnTjGsH6y44sWFLCV2H8vE4dRcqDW6ahV21hYY4Nsag/zbYOWmFOTmFUr7uPggERGVxcRAI3S3IoHuNoIUexsUygRcbVzQ07VbPfeOiIiITKF0tkDPO8Ww7xQMRYv2evtVJRocSc3F7uRMXMy+LW1v52aPiABPBPd0g7WlbpFCVycb5OYVSm3kXGOAiIjKYGKgESq9laC1XQm0uek45OkCABjgEQK5TF6fXSMiIqp1H3zwAX777Td8/vnnFba5efMmFi1ahP3790MmkyEqKgqzZ8+Gra2t1ObHH39EfHw8MjMz0alTJ8TGxqJfv4Yx806j1eBoVhIAwD9fBevwMdK+3JsF2Hv8Cg6cvII7Rbo1hywUMgR2b4WIAE908nA0WIeghZON3mPeSkBERGUxMdAIld5K0EX2DzKtLZBpYwkLmQL9PALruWdERES168svv8TKlSvRt2/fStvFxMSgsLAQn376KZRKJebNm4eCggK8/fbbAIBDhw5h1qxZmD17Nvr3749vv/0Wzz77LLZu3YrOnTvXxaVUKvXGWeRritBMrYV3uwcA+5Y4kXYNe5Iv48903WKCAODqaINB/h4I9fOAo51VhedjYoCIiCrDxEAjlPdvYsCj6Dx2O+m++ejdygcOVvb12S0iIqJak5OTgzfffBOHDx9Ghw4dKm17/PhxHDlyBImJidKH/IULF+KZZ57BjBkz4Obmho8++ghDhgzBk08+CQCIjY3F8ePH8d///hcLFy6s7cup0qHzuwAAfndK8IdtAH754CCu3bq7mGCvTs0R4e8J386u1fqQ38LJVu8x1xggIqKyOO+8EcrLV8ESaljfSUeKve4bgLA2D9Rzr4iImr7ffz+ACxfSK9yfnJyEAQP6IivrSo3Of/LkCYSFBRls//nnHzFgQF+9n5CQgBo/T2P0119/wdLSEtu2bYOfn1+lbZOSktCyZUu9b/6DgoIgk8lw7NgxaLVaJCcnG9w2EBwcjKNHj9ZK/+9HgeoOTuVfAgCUXPXAV7/n4tqtIjSzscCwoLZYOjUEMx7tjd5dW1T7m3/Xe2cMMC9ARERlcMZAI3TzdjG8LLNxvJkCJXIZ2jRrjU5O7as+kIiIaiw7OwuxsS9j9ep16Nixk8nPf/LkCcyZMxNardZgX1raOfj798H8+YulbRYWctjbO5m8Hw1VREQEIiIiqtU2JycHrVu31ttmZWUFZ2dnZGVlQalUoqCgAO7u7nptWrVqhezsbKP6aWFh/HcuBw7ugFoGuBZrcOSmLzq0dsCQPm0R7H13McH75e5qp/fYylJhkr6akkIh1/sv3T+OofE4hqbBcTReXY8hEwONjFYI5OUXY7DNPzj477TAUM8Qg0WGiIjItIQQVTeqAbVajfffX43NmzehU6cuUCpvGbRJT09D585d4eraQtpmYSGHWm2YRCCgsLAQVlaG99tbW1ujuLgYRUW6Kfn3tindX1NyuQwuLs1qfHypLp5eaPnnUXTW9MTLMQ+ia1tno+O8o5MdLBQyqDW613Hz5s3gcs/tBQ2Fo2PD7FdjwjE0HsfQNDiOxqurMWRioJHJLyyBVquFvVM2rlnZwVpuiUA3//ruFhFRuYQQUGlL9LZpcPfDSW2zklve1weq1avfxW+/7cemTd9L2/Lz8xEdPQwqle4DY0zMNEycOAWTJ09FSspxrFmzEufPp6Ft23aIiorWO9/YsaOQnZ1V7nO9+uqbGDFiFAoLC3HixHGsWLEG2dlZWLJkgUHb8+fTMGBAWLWvw9zZ2NhApVIZbC8uLoadnR2sra0BwKBNcXGxXtWC+6XVCiiVBTU+vlS3jgFY1DFAepyXZ/w5AaC5ow1yb+pKFt5WFkJezuyU+qRQyOHoaAulshAaTcPqW2PBMTQex9A0OI7GM8UYOjraVnvGQZNLDDT1EkZ5t4vRVnEdJ110v+Ag9wDYWNhUcRQRUd0TQmBFcgLSb2XUWx86OXXAjIDp1U4OjBgRjU2bvkJKygn4+fUGAPz6689wcLDHkiUfYOrUp7F4cRwCA0Nw5cplvPzyCxg+PAqvvbYAFy6cR1zcEr3zffTRZ9BqNeU+l729bsFYBwcHrF+vi1mJidsN2imVSly9mouUlBPYsuUb3Lp1Cz17euPFF1+Ch0fb6g6FWXF3d8euXbv0tqlUKuTl5aFVq1ZwdnaGnZ0dcnNz9drk5ubCzc3NqOduyLM4XMskBrRa0WD7qtFoG2zfGguOofE4hqbBcTReXY1hk7rpo7SEUVViYmKQkZGBTz/9FKtWrcK+ffswf/58aX9pCaPx48fju+++Q79+/fDss8/i/Pnztdf5arp5uxhd7DKQ2kz3bUeYZ/967hERUWUa121OXbp0RbduPfDTTzukbTt3/oBhw0agefPmAAAHB0fY2dlh27bv4OrqihkzYtG+fQcMGjQYTz89We98Li4ucHVtUe6PtXX1kroXLuhij1arxauvzsfChUtRXKzC1KmTcOPGdRNdedMSGBiI7OxsZGTcTUodOXIEANCnTx/IZDIEBARI20odPny4yjKIjVnZkoWsSkBERGU1iRkD5lTCKC+/GOqW2dDKFOhk1Rwe9u5VH0REVA9kMhlmBEw3uJWg7H3Ote1+byUAgKioaHz4YQJeemkWcnNzcOrUScTGvm7QLj09DV27doNCcXcxuF69fPXaTJjwKHJyyr+VYNasVzF06PAq++Pn548fftgFJycn6VqWLHkHjzwyEomJ2zFhwtP3cXVNk0ajwY0bN+Dg4AAbGxv4+fkhICAAL7/8MubPn4+CggK88cYbGD16tDQjYOLEiXj22WfRs2dPhIWFYfPmzUhNTcXixYureLbGq2xioLrVDIiIyDw0icRA2RJGa9euxeXLlytsW1UJo8jISCQnJ2POnDl6xwUHB+Pnn3+utWuorvwb2fjbWffvsPYD67UvRERVkclksFboL/BmYSGHAg13WuGDD0ZizZqV+OOPAzh/Pg09enijQ4eO5ZQGlEEI/euwsNAPq8uXr4JarS73eUpnIFSHs7Oz3mMbGxt4eHjg6tXc8g8wM1lZWRg8eDCWLl2KMWPGQCaTYc2aNViwYAGeeuopWFtbIzIyEnPnzpWOGTBgAJYsWYKEhAS899576NKlC9atW6f3/qCpaVFmsUE5Fy0mIqIymkRioLGUMDKFPOUhKB0VsNMA/m0C67s7RERNjoODA8LCBmH//r04d+5vPPLIowBgMPOga1cvJCZuR0lJCSwtLQEAZ86k6rVxd9ePNzXx/fdb8MEHa7F58w/SWjh37uTjn3/+MVjs0FwsW7ZM77Gnpyf+/vtvvW2urq5YvXp1pecZPXo0Ro8eberuNViunDFAREQVaBKJgftRXyWMANPUNr6q0CUnvOEGm3KuoyliHVTT4Dgaj2NYMa22eh8ySj9by2RALVX/M4moqGjMmTMDQgCDBw8DAOlDeXp6Gry8uuPhh8di8+ZNWLp0IZ58chIuX87Ehg0fmrwvISEP4P334/HWW2/gmWemQaUqxrp1a+Di4owRI0ZVebxCIWtw9eqpfrR01r2G5TIZEwNERKTH7BID9VXCyFS1jR/oGAbLjN/x1MhpJjlfY8I6qKbBcTQex9BQUZEC167Jq/0htKEnV0JCQuDs7AJfXz+4uDgBAFxdm2PUqIeQkLAaly9nYubM2Vi79gOsXLkckydPgJubOyZOfAbvvLMUCoW8Rh/GSz+slT22TRsPrFmzDgkJ8XjuuckQAggODsHrr8+HnV3Fr0WtVga5XA4nJzvY2LB6DQEuDtZ4OKwTbCwVvJWAiIj0mF1ioL5KGJmqtvEDvYfhgd66b69u3rxj9PkaA9ZBNQ2Oo/E4hhVTqYqh1Wqh0VReAk0m042jRqNt0DMGCgoKoFQqMWJEtN71xMa+Li1EqFZr0bmzF+Lj9WcJPPTQI9L++xUZORKRkSMNju3SpRtWrFgjPa7OOGo0AlqtFrduFaCw0LBk4v3UNqamY9QDHeq7C0RE1ACZXWIgMDAQy5cvR0ZGBtq3bw+g4hJG48aNk44zRQkj1vA0DuugmgbH0XgcQ0OaalYZKP0Q21CTAkqlEsnJR7F79y64u7ujb9+g+u5Sue5nHKtK1hARERE1+a8KNBoNrl69Kq0dULaE0cmTJ3Ho0KFySxjt2LEDn3zyCc6fP4+4uDikpqbiqaeeqs9LISKiWqbRaLBs2Vs4e/YMXn994X2XOiQiIiJqjJp8YiArKwsDBgxAYmIiAEgljDw9PfHUU0/hpZdeQlhYGObPny8dU1rC6KuvvsLDDz+MQ4cONfkSRkREBLi4uGDnzr34+uvv4OXVvb67Q0RERFQnmtytBCxhRERERERERFR9TX7GABER1R3RUBcPMEP8XRAREVF1MTFARERGk8sVAACt1nD1e6ofpb+L0t8NERERUUWYGCAiIqPJ5XLI5QoUFRlflpVMo6ioAHK5AnI5Qz0RERFVrsmtMUBERHVPJpPB3t4ZSuV15OdbwsrKpsIV/bVaWbXLG1LFKhpHIQRUqiIUFd2Bo6MrKysQERFRlZgYICIik7C1bYaSkmLk598CkFdhO7lcDq1WW2f9aqoqH0cZbG3tYWvbrE77RERERI0TEwNERGQSMpkMTk6ucHBwhkZT/loDCoUMTk52uHWrgLMGjFDVOCoUCq4tQERERNXGxAAREZmU7r728j+UWljIYWNjg8JCDdRqzhqoKY4jERERmRJXJCIiIiIiIiIyY0wMEBEREREREZkxJgaIiIiIiIiIzJhMCMHVn+qAEAJaLYe6phQKOTQa3kdrLI6j8TiGxuMYmoax4yiXy1jK0MQY643Hvw/G4xgaj2NoGhxH49VlrGdigIiIiIiIiMiM8VYCIiIiIiIiIjPGxAARERERERGRGWNigIiIiIiIiMiMMTFAREREREREZMaYGCAiIiIiIiIyY0wMEBEREREREZkxJgaIiIiIiIiIzBgTA0RERERERERmjIkBIiIiIiIiIjPGxAARERERERGRGWNigIiIiIiIiMiMMTFAREREREREZMaYGCAiIiIiIiIyY0wMUL3Jy8vDG2+8gbCwMAQEBOCxxx5DUlKStP/gwYMYM2YM/Pz8EBkZiR07dugdX1xcjAULFqBfv37w9/fHzJkzcePGjbq+jAbjwoUL8Pf3x5YtW6RtqampmDBhAnr37o2IiAh89tlnesdotVqsXr0aoaGh6N27N6ZMmYJLly7VddcbhK1bt2LEiBHw8fFBVFQUfvzxR2lfZmYmpk6dioCAAAwYMAArV66ERqPRO/7LL7/E4MGD4evri8cffxynT5+u60uoV2q1GqtWrUJ4eDj8/f3xxBNP4MSJE9J+vhYr98EHH+D//u//9LaZYsyqOgdRbWOsNy3GeuMw1huHsd54DTreC6J6MnHiRDFy5Ehx9OhRkZ6eLhYsWCB8fX3F+fPnRVpamvDx8RErVqwQaWlp4uOPPxY9e/YUf/zxh3T8nDlzxJAhQ8TRo0dFSkqKGD16tHjiiSfq8Yrqj0qlEmPGjBFeXl5i8+bNQgghbty4IYKDg8XcuXNFWlqa+Pbbb4WPj4/49ttvpePi4+NFcHCw2LNnj0hNTRWTJk0SQ4cOFcXFxfV1KfVi69atomfPnuKLL74QGRkZIiEhQXTv3l0kJycLlUolhg4dKp599lnx999/i19++UUEBQWJVatWScdv2bJF+Pr6iu+//16cO3dOzJo1SwQFBYnr16/X41XVrdWrV4v+/fuLAwcOiIsXL4p58+aJPn36iJycHL4Wq/DFF1+I7t27iwkTJkjbTDFm1TkHUW1jrDcdxnrjMNYbj7HeOA093jMxQPXi4sWLwsvLSyQlJUnbtFqtGDJkiFi5cqV4/fXXxdixY/WOmTFjhpg0aZIQQojs7GzRvXt3sXfvXml/enq68PLyEsnJyXVzEQ3Iu+++K5588km9Nwvr1q0TAwYMECUlJXrthg4dKoQQori4WPj7+4svv/xS2n/r1i3h6+srtm/fXrcXUI+0Wq0IDw8Xy5Yt09s+adIksW7dOrF9+3bRq1cvkZeXJ+37+uuvRUBAgPQHeejQoSIuLk7aX1JSIgYOHCjWrVtXNxfRAERHR4ulS5dKj2/fvi28vLzETz/9xNdiBbKzs8XUqVNF7969RWRkpN4bBVOMWVXnIKptjPWmxVhfc4z1psFYXzONJd7zVgKqFy4uLvjwww/h4+MjbZPJZJDJZFAqlUhKSkK/fv30jgkJCcGxY8cghMCxY8ekbaU6duwINzc3HD16tG4uooE4evQoNm7ciGXLlultT0pKQlBQECwsLKRtISEhuHjxIq5du4YzZ87gzp07euPs6OiInj17mtUYXrhwAZcvX8aoUaP0tq9fvx5Tp05FUlISvL294eTkJO0LCQlBfn4+UlNTcf36dVy8eFFvHC0sLNC3b1+zGkdXV1fs2bMHmZmZ0Gg02LhxI6ysrNC9e3e+Fivw119/wdLSEtu2bYOfn5/ePlOMWVXnIKptjPWmw1hvHMZ602Csr5nGEu+ZGKB64ejoiIEDB8LKykra9tNPPyEjIwOhoaHIzs6Gu7u73jGtWrVCYWEhbt68iZycHLi4uMDa2tqgTXZ2dp1cQ0OgVCoxe/ZsvPbaa2jdurXevorGEACysrKkcbr3OHMbwwsXLgAACgoKMHnyZPTr1w/jxo3D7t27AXAcq2vevHmwtLTE4MGD4ePjg/feew+rV69Gu3btOIYViIiIQHx8PNq2bWuwzxRjVtU5iGobY71pMNYbj7HeNBjra6axxHsmBqhBSE5Oxty5czF06FAMGjQIRUVFem8kAEiPVSoVCgsLDfYDgLW1NYqLi+ukzw3B/Pnz4e/vb5ABB1DuGJa+uSouLkZhYSEAlNvGnMYwPz8fABAbG4uRI0diw4YN6N+/P5577jkcPHiQ41hNaWlpcHBwwNq1a7Fx40aMGTMGr7zyClJTUzmGNWCKMavqHER1jbG+ZhjrjcdYbxqM9abXkOK9RdVNiGrXrl278MorryAgIADLly8HoHsxq1QqvXalj21tbWFjY2OwH9C9+G1tbWu/0w3A1q1bkZSUhO3bt5e7v7wxKv3jYGdnBxsbGwC6cS39d2kbcxlDALC0tAQATJ48GQ8//DAAoEePHjh9+jQ++eST+xrHe9uYyzhmZWVh5syZ+PTTT9G3b18AgI+PD9LS0hAfH8/XYg2YYsyqOgdRXWKsrxnGetNgrDceY33taEjxnjMGqF598cUXePHFFxEeHo5169ZJ2a3WrVsjNzdXr21ubi7s7Ozg4OAAd3d35OXlGfxPkJubCzc3tzrrf33avHkzrl+/jkGDBsHf3x/+/v4AgDfffBPPPPMM3N3dyx1DAHBzc5OmJJXXxlzGEIB0rV5eXnrbu3TpgszMTI5jNaSkpKCkpETvPmIA8PPzQ0ZGBsewBkwxZlWdg6iuMNbXHGO9aTDWG4+xvnY0pHjPxADVm//9739466238MQTT2DFihV6U2D69u2LI0eO6LU/dOgQAgICIJfL0adPH2i1WmlhIkB3/1hOTg4CAwPr7Brq0/Lly5GYmIitW7dKPwAQExODxYsXIzAwEMeOHdOrwXvo0CF07NgRrq6u6N69O+zt7XH48GFpv1KpxOnTp81mDAHA29sbzZo1Q0pKit72s2fPol27dggMDMTp06elaYiAbhybNWuG7t27w9XVFR07dtQbR7VajaSkJLMZx9L72v7++2+97WfPnkWHDh34WqwBU4xZVecgqguM9cZhrDcNxnrjMdbXjgYV72tQcYHIaOnp6cLb21s8//zzIjc3V+9HqVSKs2fPCm9vb/HOO++ItLQ0sX79eoPaxjNmzBARERHi0KFDUm3jsuU/zFHZEkbXrl0TgYGBIjY2Vpw7d05s3rxZ+Pj4iC1btkjtV6xYIYKCgsSuXbv06qKqVKr6uoR6sXbtWuHv7y+2b9+uV9v40KFDoqioSAwZMkRMnjxZpKamSrWN4+PjpeM3btwofH19xZYtW6TaxsHBwWZT21ij0YjHHntMREZGioMHD4oLFy6I9957T/To0UOcOHGCr8VqiI2N1fv7ZYoxq845iGoTY33tYKyvGcZ64zDWm0ZDjvdMDFC9eP/994WXl1e5P7GxsUIIIfbt2ydGjhwpevXqJSIjI8WOHTv0znHnzh0xb9480bdvX9G3b18xY8YMcePGjfq4nAaj7JsFIYRISUkRjz76qOjVq5cIDw8Xn3/+uV57tVot4uLiREhIiOjdu7eYMmWKuHTpUl13u0HYsGGDiIiIEN7e3iI6Olr88ssv0r6LFy+KiRMnCh8fHzFgwACxcuVKodFo9I7/+OOPRVhYmPD19RWPP/64OH36dF1fQr3Ky8sT8+fPF4MGDRL+/v7iP//5jzh8+LC0n6/Fyt37RkEI04xZVecgqk2M9bWDsb7mGOuNw1hvvIYc72VCCGH8JAgiIiIiIiIiaoy4xgARERERERGRGWNigIiIiIiIiMiMMTFAREREREREZMaYGCAiIiIiIiIyY0wMEBEREREREZkxJgaIiIiIiIiIzBgTA0RU5xpDldTa7mNjGAMiIqKaagxxjrGe6C4mBoiamPj4eHTr1q2+u1EulUqFJUuWYPv27Qb7Pv30U0ycOBEAEBERgW7dumHmzJkVnuvRRx9Ft27dEB8fb/J+njt3Do899pjeNmOfa9q0afjmm2/KPb9KpUJkZCROnDhR4/MTEZH5YKw3HmM9kT4mBoiozuTm5uK///0v1Gq1wb59+/Zh4MCB0mO5XI49e/aguLjYoG1mZiZSUlJqrZ87d+7E8ePHTXa+LVu2ICcnB4888ki557eyssIrr7yC2NhYFBUVmex5iYiI6hpjPWM9NU5MDBBRvSsoKEBSUhLCwsKkbQEBAbhz5w72799v0D4xMRE9evSoyy7WWFFREZYvX45p06ZBLq/4T+6QIUNgaWmJr776qg57R0REVDcY6xnrqWFjYoCoiduyZQt69uyJlJQU/Oc//4GPjw/Cw8Oxfv16qc2wYcMQExNjcOxDDz2E6dOnS4937dqFMWPGwMfHB/3798eiRYtQUFAg7S8qKsL8+fMRFhaGXr16ITIyUnqezMxMDB48GAAwd+5cRERESMcdPHgQrVq1QqdOnaRtbdu2Ra9evbBz506DfiUmJiIqKspg++3bt7F06VIMGTIEPj4+GDlyJL799lu9NhEREVi9ejXefvttPPDAA/D19cXkyZNx8eJFALrpmWvWrAFgOKUwPz8f8+bNQ1BQEPz9/RETE4Nr166VM+p3bd68GcXFxQgPD6/y/KNGjcInn3wClUpV6TmJiIjKYqxnrCcyFhMDRGZAq9XipZdewogRI/Dhhx8iICAAcXFxOHDgAAAgOjoa+/btQ35+vnTM+fPncebMGTz00EMAgO3bt+P5559Hp06dsHbtWrzwwgvYtm0bnnvuOWlxnSVLlmD//v2IjY3F+vXrMXjwYMTFxWHz5s1o1aqVFCSnT58u/RswnFpYasSIEQZTDNPT03HmzBmDNwtFRUV4/PHHsX37djzzzDNISEhAnz59MG/ePKxbt06v7WeffYb09HQsXboUixYtwp9//onY2FgAwLhx4zB27FgAwMaNGzFu3Di940pKSrBq1SrMnDkTu3fvxsKFCysd+23btmHQoEGwsrKq8vyRkZHIycnBkSNHKj0nERHRvRjrGeuJjGFR3x0gotonhMBzzz0nBaY+ffrgl19+wd69exEaGoro6GjEx8dj165dGD16NADghx9+gKOjIyIiIiCEwPLlyxEaGorly5dL5+3QoQOefvpp7Nu3D4MGDcKRI0fQv39/KZAHBwfDzs4Orq6usLKykqYEtmvXDj179pTOc+DAAcyfP9+g38OHD8c777yD/fv348EHHwSg+wbB398fHh4eem23bNmCs2fP4uuvv4a/vz8AIDQ0FGq1GgkJCRg/fjycnZ0BAI6OjkhISIBCoQAA/PPPP4iPj8fNmzfh7u4Od3d3AEDv3r31nsPHxwdxcXEAgH79+iElJQX79u2rcNzz8/Nx6tQpDB8+XNpW2fnbt28PJycnHDx4EAMGDKjwvERERPdirGesJzIGZwwQmYnSAAroFsBp3ry5NDWwbdu2CAgIQGJiotRmx44diIyMhJWVFdLT05GdnY2IiAio1WrpJzAwEPb29vj9998B6N4cbNq0CVOmTMEXX3yBS5cu4fnnn8egQYMq7Ne5c+dw/fp1BAcHG+zz8PBA79699aYYJiYmYuTIkQZtjxw5gjZt2uhdJ6D7hqS4uFhvASMfHx/pjQIAKXgXFhZW2E9A9yarLE9PTyiVygrbZ2VlQaPRwNPTs9LzluXh4YHMzMxqtyciIirFWM9YT1RTTAwQmQkbGxu9x3K5XK++7kMPPYQ//vgDN2/exKlTp5CRkSFNLczLywMALFiwAN7e3no/+fn5yM3NBQDMmzcPL730EjIzM/HWW29hyJAhGD9+PM6cOVNhv/bt24egoCCD/pUaPny4NMXwzJkzuHjxIiIjIw3a3bp1Cy1btjTY3qJFCwDQC+q2trYGYwHopmFWxs7OzuC4ymoU3759u9zjKmNra6s3zZOIiKi6GOsZ64lqircSEBEAXVBetGgRdu3ahfT0dLRp00bKmjs6OgIAZs+ejaCgIINjnZycAOi+nZg+fTqmT5+OK1euYM+ePUhISMDMmTOxY8eOcp+37NTB8kRGRmLZsmU4cOAATp06hZCQELi6upbbh4yMDIPtV69eBQC4uLhUMQKmV/qclX3TcC+lUmkwdZKIiMgUGOtNj7GemgrOGCAiALo3BOHh4fj111/x008/ITo6GjKZDADQqVMnuLq6IjMzEz4+PtKPm5sb3n33XZw+fRpFRUUYNmwYNmzYAEA3Te6JJ55AVFQUrly5AgB6U/oA3X15ycnJeqWL7uXm5oY+ffpg586d+PHHH8tdoRgAAgMDcfnyZYOaxNu2bYOlpSV8fX2rPRaVlRq6H25ublAoFMjOzq7W+YUQyMnJQZs2bUzy/ERERGUx1t/FWE+kjzMGiEgSHR2NmJgYaDQaaWohoAvyL7/8Mt544w0oFAqEh4dDqVQiISEBOTk58Pb2ho2NDby9vbFmzRpYWlqiW7duuHDhAr777jsMGzYMAODg4ABAV7Koc+fOUmBs3759pf0aPnw4li5dCplMhqFDh5bbZsyYMfjf//6H559/HjExMfD09MTu3buxefNmvPDCC9I3IdVR2vaHH36An58f2rZtW+1jy7Kzs0NAQACOHTuGp59+usrznz17Frdv30ZoaGiNno+IiKgqjPU6jPVE+jhjgIgkAwcOhIODA3x8fNCxY0e9fePGjcO7776L5ORkTJs2DfPnz4enpyc+//xzKdgtXLgQY8aMwYYNGzBp0iQkJCRg7Nix0irE9vb2mDhxInbt2oUpU6Zgz549lX6DUCoyMhJarRahoaHSG4572dra4vPPP0d4eDhWrVqF6dOn49ixY1i8eDFefPHF+xqHoUOHwsfHB3PmzNGrAV0Tw4YNw+HDh/XKMFV0/v3796Nly5YICAgw6jmJiIgqwlivw1hPpE8mKltNg4iIjFJYWIghQ4Zg1qxZUnmo8gghMGzYMDz++ON63zgQERFRw8ZYT00BZwwQEdUiW1tbvPjii1i/fj00Gk2F7X7++WdoNBqMHz++DntHRERExmKsp6aAiQEiolo2fvx4uLu745tvvil3v0qlwooVKxAXF1dhKSciIiJquBjrqbHjrQREREREREREZowzBoiIiIiIiIjMGBMDRERERERERGaMiQEiIiIiIiIiM8bEABEREREREZEZY2KAiIiIiIiIyIwxMUBERERERERkxpgYICIiIiIiIjJjTAwQERERERERmTEmBoiIiIiIiIjM2P8DGJJz2ZTPxfIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vwrl_isin = 'IE00B3RBWM25'\n",
    "sp500_isin = 'IE00B3XXRP09'\n",
    "\n",
    "_, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "\n",
    "df = pd.read_csv(f'data/{sp500_isin}-{tickers[sp500_isin]}.csv', parse_dates=['Date'])\n",
    "for ytd in (5, 10, 15):\n",
    "    base, _, _ = backtest(df, monthly_input=50, years_to_date=ytd)\n",
    "    metrics = [\n",
    "        (50, 1)\n",
    "    ]\n",
    "    for monthly_input in range(100, 1000, 5):\n",
    "        ret, _, _ = backtest(df, monthly_input=monthly_input, years_to_date=ytd)\n",
    "        metrics.append((monthly_input, ret['real_return'] / base['real_return']))\n",
    "    \n",
    "    axs[0].plot([x[0] for x in metrics], [x[1] for x in metrics], label=f'ytd={ytd}')\n",
    "\n",
    "axs[0].set_title('S&P500')\n",
    "axs[0].set_ylabel(r'$\\frac{return_t}{return_{50}}$')\n",
    "axs[0].set_xlabel('Invest/Month (t)')\n",
    "axs[0].legend()\n",
    "\n",
    "df = pd.read_csv(f'data/{vwrl_isin}-{tickers[vwrl_isin]}.csv', parse_dates=['Date'])\n",
    "for ytd in (5, 10, 15):\n",
    "    base, _, _ = backtest(df, monthly_input=50, years_to_date=ytd)\n",
    "    metrics = [\n",
    "        (50, 1)\n",
    "    ]\n",
    "    for monthly_input in range(100, 1000, 5):\n",
    "        ret, _, _ = backtest(df, monthly_input=monthly_input, years_to_date=ytd)\n",
    "        metrics.append((monthly_input, ret['real_return'] / base['real_return']))\n",
    "    \n",
    "    axs[1].plot([x[0] for x in metrics], [x[1] for x in metrics], label=f'ytd={ytd}')\n",
    "\n",
    "axs[1].set_title('FTSE All-World')\n",
    "axs[1].set_ylabel(r'$\\frac{return_t}{return_{50}}$')\n",
    "axs[1].set_xlabel('Invest/Month (t)')\n",
    "axs[1].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb54df4-fa60-4ff2-8495-20cd294b9a43",
   "metadata": {},
   "source": [
    "It seems like increasing input does increase the relative return. The function follows a logarithmic curve, so it eventually converges to a maximum return. Something to investigate is using an investment value relative to the price of the security. But for the S&P500 and All-world, which are priced <120, anywhere >400 seems to be the sweet spot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95e3eeb-f885-4972-bfaf-964f47d8dd1c",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Using simple backtesting, we can see that the Invesco EQQQ (Nasdaq-100) seems to have the historically strongest growth, outperforming the S&P500 even. However, QQQs P/E ratio is quite high, which is a bit riskier. It is also much less diversified, with a lot in tech."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9dac0ea2-bfe2-4f58-ac9d-2a1a4d6b3aa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NbConvertApp] Converting notebook backtest.ipynb to html\n",
      "[NbConvertApp] WARNING | Alternative text is missing on 3 image(s).\n",
      "[NbConvertApp] Writing 622497 bytes to backtest.html\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbconvert --to html backtest.ipynb"
   ]
  }
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