Supplemental Analyses for Main Research.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This kernel covers the investigations that are related to main research questions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Variation Introduced due to Start Month Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pathlib\n",
    "import scipy.stats as stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "data_path = pathlib.Path(r'Preprocessed Data')\n",
    "from annual_calculations import *\n",
    "from trade_simulations import *\n",
    "from long_term_simulations import *\n",
    "df = pd.read_csv(data_path / 'SP500_whole.csv')\n",
    "df_gold = pd.read_csv(data_path / \"Gold_prices.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Variation Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/73 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 73/73 [00:00<00:00, 108.57it/s]\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>(%)Adjusted_Real_Returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>1</td>\n",
       "      <td>23.909041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>2</td>\n",
       "      <td>25.935916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>3</td>\n",
       "      <td>22.955203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>4</td>\n",
       "      <td>21.114372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>5</td>\n",
       "      <td>17.749239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>6</td>\n",
       "      <td>14.030462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>7</td>\n",
       "      <td>24.381948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>8</td>\n",
       "      <td>22.555467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>9</td>\n",
       "      <td>21.501445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>10</td>\n",
       "      <td>16.506491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>11</td>\n",
       "      <td>13.780571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1951)</th>\n",
       "      <td>12</td>\n",
       "      <td>17.388744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Month  (%)Adjusted_Real_Returns\n",
       "Period                                       \n",
       "(1950, 1951)      1                 23.909041\n",
       "(1950, 1951)      2                 25.935916\n",
       "(1950, 1951)      3                 22.955203\n",
       "(1950, 1951)      4                 21.114372\n",
       "(1950, 1951)      5                 17.749239\n",
       "(1950, 1951)      6                 14.030462\n",
       "(1950, 1951)      7                 24.381948\n",
       "(1950, 1951)      8                 22.555467\n",
       "(1950, 1951)      9                 21.501445\n",
       "(1950, 1951)     10                 16.506491\n",
       "(1950, 1951)     11                 13.780571\n",
       "(1950, 1951)     12                 17.388744"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display_df = compute_annual_returns_stocks_individually_display(df)\n",
    "display_df.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2. Correlation Between Mean Prices and Monthly Mean Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.1. Correlation for Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 73/73 [00:00<00:00, 94.98it/s] \n",
      "100%|██████████| 73/73 [00:00<00:00, 1736.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between two results: PearsonRResult(statistic=0.999162622312241, pvalue=2.623935255451459e-100)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "individual_calculation = compute_annual_returns_stocks_individually(df)\n",
    "direct_calculation = compute_annual_returns_stocks(df)\n",
    "\n",
    "print(\"The correlation between two results:\", stats.pearsonr(individual_calculation[\"(%)Adjusted_Real_Returns\"],direct_calculation[\"(%)Adjusted_Annual_Return_With_Dividends\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2.2. Correlation for Gold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 73/73 [00:00<00:00, 108.62it/s]\n",
      "100%|██████████| 73/73 [00:00<00:00, 1633.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation between two results: PearsonRResult(statistic=0.9984969154177242, pvalue=2.712077624414471e-91)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "individual_calculation = compute_annual_returns_gold_individually(df_gold)\n",
    "direct_calculation = compute_annual_returns_commodity(df_gold)\n",
    "\n",
    "print(\"The correlation between two results:\", stats.pearsonr(individual_calculation[\"(%)Adjusted_Real_Returns\"],direct_calculation[\"(%)Adjusted_Annual_Return\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Checking the Requirements for Gold-SP500 Annual Return Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 73/73 [00:00<00:00, 1155.00it/s]\n",
      "100%|██████████| 73/73 [00:00<00:00, 1010.37it/s]\n"
     ]
    }
   ],
   "source": [
    "annual_returns_stocks = compute_annual_returns_stocks(df)\n",
    "annual_returns_gold = compute_annual_returns_commodity(df_gold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1. Are the distributions normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test Statistic</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>0.885272</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP w.o. Div</th>\n",
       "      <td>0.993161</td>\n",
       "      <td>0.964988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP Div</th>\n",
       "      <td>0.989988</td>\n",
       "      <td>0.836647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Test Statistic   p-value\n",
       "Gold               0.885272  0.000007\n",
       "SP w.o. Div        0.993161  0.964988\n",
       "SP Div             0.989988  0.836647"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_gold_normality_results = stats.shapiro(annual_returns_gold[\"(%)Adjusted_Annual_Return\"])\n",
    "adjusted_SP_normality_results_nodivs = stats.shapiro(annual_returns_stocks[\"(%)Adjusted_Annual_Return_Without_Dividends\"])\n",
    "adjusted_SP_normality_results = stats.shapiro(annual_returns_stocks[\"(%)Adjusted_Annual_Return_With_Dividends\"])\n",
    "\n",
    "normality_df = pd.DataFrame(data = [adjusted_gold_normality_results,\n",
    "                                    adjusted_SP_normality_results_nodivs,\n",
    "                                    adjusted_SP_normality_results],\n",
    "                                    columns = [\"Test Statistic\", \"p-value\"],\n",
    "                                    index = [\"Gold\", \"SP w.o. Div\",\n",
    "                                             \"SP Div\"])\n",
    "normality_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Correlation Between SPY and Index Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10009135682276281, 0.00027883713416550064)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_vs_index = pd.read_csv(data_path / 'fund_vs_index.csv')\n",
    "fund_vs_index['Ratio'] = fund_vs_index.Close_fund / fund_vs_index.Close_index\n",
    "fund_vs_index['Ratio'].mean(), fund_vs_index['Ratio'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Does purchase price is impotant for twenty years of investment strategy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 54/54 [00:16<00:00,  3.24it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% Change with Dividend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1979, 1999)</th>\n",
       "      <td>2.736313e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1980, 2000)</th>\n",
       "      <td>2.224194e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1981, 2001)</th>\n",
       "      <td>1.957764e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1978, 1998)</th>\n",
       "      <td>1.803735e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1982, 2002)</th>\n",
       "      <td>1.740867e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1977, 1997)</th>\n",
       "      <td>1.702033e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1984, 2004)</th>\n",
       "      <td>1.544145e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1952, 1972)</th>\n",
       "      <td>1.454983e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1976, 1996)</th>\n",
       "      <td>1.359988e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1950, 1970)</th>\n",
       "      <td>1.355886e-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              % Change with Dividend\n",
       "Period                              \n",
       "(1979, 1999)            2.736313e-13\n",
       "(1980, 2000)            2.224194e-13\n",
       "(1981, 2001)            1.957764e-13\n",
       "(1978, 1998)            1.803735e-13\n",
       "(1982, 2002)            1.740867e-13\n",
       "(1977, 1997)            1.702033e-13\n",
       "(1984, 2004)            1.544145e-13\n",
       "(1952, 1972)            1.454983e-13\n",
       "(1976, 1996)            1.359988e-13\n",
       "(1950, 1970)            1.355886e-13"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buying_price_simulation_results_stocks = simulate_twenty_years_of_investment(df, sample_size=30)\n",
    "real_profit_deviation = pd.DataFrame(buying_price_simulation_results_stocks.groupby(\"Period\")[\"% Change with Dividend\"].std()).sort_values(by = \"% Change with Dividend\", ascending = False)\n",
    "real_profit_deviation.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/54 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 54/54 [00:00<00:00, 181.95it/s]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(1972, 1992)</th>\n",
       "      <td>28.116030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1973, 1993)</th>\n",
       "      <td>23.965821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1968, 1988)</th>\n",
       "      <td>20.200097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2002, 2022)</th>\n",
       "      <td>18.537673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1969, 1989)</th>\n",
       "      <td>18.357148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2000, 2020)</th>\n",
       "      <td>17.928357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1999, 2019)</th>\n",
       "      <td>17.288218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1997, 2017)</th>\n",
       "      <td>14.894577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2003, 2023)</th>\n",
       "      <td>14.500490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1993, 2013)</th>\n",
       "      <td>13.969068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               % Change\n",
       "Period                 \n",
       "(1972, 1992)  28.116030\n",
       "(1973, 1993)  23.965821\n",
       "(1968, 1988)  20.200097\n",
       "(2002, 2022)  18.537673\n",
       "(1969, 1989)  18.357148\n",
       "(2000, 2020)  17.928357\n",
       "(1999, 2019)  17.288218\n",
       "(1997, 2017)  14.894577\n",
       "(2003, 2023)  14.500490\n",
       "(1993, 2013)  13.969068"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buying_price_simulation_results_gold = simulate_twenty_years_of_investment_gold(df_gold, sample_size=30)\n",
    "real_profit_deviation_gold = pd.DataFrame(buying_price_simulation_results_gold.groupby(\"Period\")[\"% Change\"].std()).sort_values(by = \"% Change\", ascending = False)\n",
    "real_profit_deviation_gold.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Checking the Requirements for Gold-SP500 20 Year Return Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 54/54 [00:00<00:00, 86.68it/s]\n"
     ]
    }
   ],
   "source": [
    "result_stock = simulate_twenty_years_of_investment(df, sample_size=1)\n",
    "result_gold = pd.DataFrame(buying_price_simulation_results_gold.groupby(\"Period\")[\"% Change\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1. Are the distributions normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9077686071395874, pvalue=0.0005306893726810813)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(result_stock['% Change w.o. Dividend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lYMGCFCxYkLVr1/LWW29RsGBBR4vR1S1Ax44dczwXEhJCUlISp06dyvCa9Pj5+VGsWDGnh4iIiORNuSYcNWvWjB07dhAdHe141KtXj6effpro6GhuvvlmQkJCWLlypeM1SUlJrF27loYNGwJQt25dChUq5HRNXFwcO3fudFwjIiIi+VuuGXMUGBhIRESE07mAgABKlizpOD9o0CDGjx9PlSpVqFKlCuPHj6dIkSJ06tQJgKCgILp168aQIUMoWbIkJUqUYOjQodSoUSPNAG8RERHJn3JNOHLFiy++yIULF+jTpw+nTp2ifv36rFixgsDAQMc106ZNo2DBgrRv354LFy7QrFkzIiMj8fHx8WDlIiIi4i1slmVZni4it0lMTCQoKIiEhASNPxIREclOFy9CXBxUqpTtt3b1+zvXjDkSERGRPO7HH6FWLWjdGi5d8lgZCkciIiLiWWfPwoAB0KgR7N0LJ07A/v0eK0fhSERERDxnxQqIiIC33wbLgueegz17zDkPyVMDskVERPIaux3WrTPDcMqWNY0reWIO0cmTMGQIREaa44oV4f33oUULT1YFqOVIRETEa0VFmczQtCl06mT+rFjRnM/VFi2CatVMMLLZTJfajh1eEYxA4UhERMQrRUXBE0/AkSPO52NjzflcGZDi403xTzwBf/0Ft98O69fDm29C0aKers5B4UhERMTL2O0wcKAZgnO1lHODBpnrcgXLMq1EVauaVqOCBWH4cPjlF/DCHSoUjkRERLzMunVpW4xSsyw4fNhc5/UOHoRWrcxA69OnoU4d2LIFXnsNrrGRvCcpHImIiHiZuLjsvc4jkpPNDLSICFi50gShSZPgp5/MWkZeTLPVREREvEzZstl73Q23Zw907w4bNpjjRo3ggw/g1ls9W5eL1HIkIiLiZRo1grAwM5ErPTYbhIeb67zK5cswfrxpGdqwwQyynjED1qzJNcEIFI5ERES8jo+PmcAFaQNSyvH06V623tHPP8Ndd5mB1klJ8OCDsGsX9O4NBXJX3Mhd1YqIiOQT7drBF19AuXLO58PCzPl27TxTVxoXLsCwYSYYRUdDiRLwySewbBmUL+/p6jJFY45ERES8VLt20LatF6+QvW6dGVu0b5857tAB3noLypTxbF1ZpHAkIiLixXx8oEkTT1dxlTNn4OWXzXgiMKntvfdMkssDFI5ERETEdd9+Cz17moWWwLQcTZkCN93k0bKyk8KRiIiIXN+JE/Cvf5nxRACVKsHs2dCsmWfrygEKRyIiIjnIbvfiMUOusCwzArxfPzh2zEyXGzQIxo6FgABPV5cjFI5ERERySFSU2SMt9VYgYWFmmr7XzDa7lrg46NMHliwxx9WqwZw5cPfdHi0rp2kqv4iISA6IijKbz1+9R1psrDkfFeWZulxiWTB3rtkodskSs1HsqFFmLaM8HoxA4UhERCTb2e2mxciy0j6Xcm7QIHOd14mJgZYtoVs3SEiAevVMKBo9Gvz8PF3dDaFwJCIi4iK73eyEMX+++TOjcLNuXdoWo9Qsy0z2WrcuJ6rMJLvd9PdFRMB335mNYl9/HTZuhBo1PF3dDaUxRyIiIi5wZ/xQXJxr93T1uhy3e7dpKdq0yRw3bmw2iq1c2bN1eYhajkRERK7D3fFDZcu6dl9Xr8sxSUlm1lnt2iYYBQbCrFnw/ff5NhgB2CwrvR5RuZbExESCgoJISEigWLFini5HRERykN0OFStm3E1ms5kWpJiYf6bop7wmNjb9cUfpveaG27rVtBZt326OW7c2q1yHhXmooJzn6ve3Wo5ERESuITPjh3x8THcbmCCUWsrx9OkeCkYXLsCLL0L9+iYYlSoF8+bB0qV5Ohi5Q+FIRETkGjI7fqhdO7N2YrlyzufDwsx5j6xztHYt1KxptvtIToZOncx4o44d06a4fEwDskVERK4hK+OH2rUze7F6fIXsxER46SWYOdMclytnutAeeeQGF5I7KByJiIhcQ6NGprXneuOHGjVK//U+PtCkSY6WeG3LlkGvXv/0DfbsCZMmQVCQB4vybupWExERuQavHj90LcePwzPPmIHWR47ALbeYWWgzZyoYXYfCkYiIyHV45fihjFgWLFhgtv747DMoUACGDjWDr5s29XR1uYK61URERFzgNeOHriU21mwUu3SpOa5Rw2wUe+ednq0rl1E4EhERcZHHxw9lxLLMitZDh5rB14UKwYgR8PLL4Ovr6epyHYUjERGR3Oz33+GFF2D1anNcv75pLape3bN15WIacyQiIpIb2e0wdarpOlu9Gvz9zfGPPyoYZZFajkRERHKbnTvN1h+bN5vj+++H2bPh5ps9W1ceoZYjERGR3CIpCUaPhjp1TDAqVsyEou++UzDKRmo5EhERyQ02b4bnn4ddu8xxmzYwY0ba9QUky9RyJCIi4s3On4chQ6BBAxOMSpeGhQthyRIFoxyiliMRERFvtXo1dO8Of/xhjp95xizHXbKkR8vK69RyJCIi4m0SEqBHDzPQ+o8/zFLcy5bBJ58oGN0ACkciIiLeZOlSqFbNDLQG6N3bdKc99JBn68pHck04eu+996hZsybFihWjWLFiNGjQgG+//dbxvGVZjB49mtDQUPz9/WnSpAm7Ugat/c+lS5fo378/pUqVIiAggDZt2nAkZZdiERERTzp2DJ56yuxRcvQoVKkCa9eaQdfFinm6unwl14SjsLAwJk6cyNatW9m6dSv3338/bdu2dQSgyZMnM3XqVN555x22bNlCSEgILVq04MyZM457DBo0iMWLF7NgwQLWr1/P2bNnad26NXa73VMfS0RE8jvLMhvEVqtmBloXKAAvvgi//gr33efp6vIlm2VZlqeLyKwSJUowZcoUnn/+eUJDQxk0aBAvvfQSYFqJgoODmTRpEj179iQhIYHSpUvzySef0KFDBwCOHj1KeHg433zzDa1atXL5fRMTEwkKCiIhIYFiSvMiIpJZhw+bbrNly8xxzZpm64969TxbVx7l6vd3rmk5Ss1ut7NgwQLOnTtHgwYNiImJIT4+npYtWzqu8fPzo3HjxmzYsAGAbdu2cfnyZadrQkNDiYiIcFyTkUuXLpGYmOj0EBERybTkZJg502zzsWyZ2Rx27FjYulXByAvkqnC0Y8cOihYtip+fH7169WLx4sVUq1aN+Ph4AIKDg52uDw4OdjwXHx+Pr68vxYsXz/CajEyYMIGgoCDHIzw8PBs/lYiI5Cv790PTpqbF6MwZuPtu+OUXGDECChXydHVCLgtHt912G9HR0WzatInevXvTpUsXdu/e7XjeZrM5XW9ZVppzV3PlmmHDhpGQkOB4HD58OPMfQkRE8qcrV2DyZNN19sMPUKSIWbNo/Xoz3ki8Rq5aBNLX15fKlSsDUK9ePbZs2cKbb77pGGcUHx9P2bJlHdcfO3bM0ZoUEhJCUlISp06dcmo9OnbsGA0bNrzm+/r5+eHn55fdH0dERPKL7dvN1h/btpnj5s3h/fehUiXP1iXpylUtR1ezLItLly5RqVIlQkJCWLlypeO5pKQk1q5d6wg+devWpVChQk7XxMXFsXPnzuuGIxERkUy5dAlGjoS6dU0wCgoyA65XrFAw8mK5puXo3//+Nw8++CDh4eGcOXOGBQsWsGbNGpYvX47NZmPQoEGMHz+eKlWqUKVKFcaPH0+RIkXo1KkTAEFBQXTr1o0hQ4ZQsmRJSpQowdChQ6lRowbNmzf38KcTEZE8Z+NG6NYN9uwxx48+Cu++C6GhHi1Lri/XhKO//vqLzp07ExcXR1BQEDVr1mT58uW0aNECgBdffJELFy7Qp08fTp06Rf369VmxYgWBgYGOe0ybNo2CBQvSvn17Lly4QLNmzYiMjMTHx8dTH0tERDzMbod16yAuDsqWhUaNIEtfC+fOwfDh8NZbZg2jMmVMKHr8cbjOGFfxDrl6nSNP0TpHIiJ5Q1QUDBwIqTdLCAuDN9+Edu0yccPvvoMXXoCDB83xs8/C1KnaD81L5Ol1jkRERLIqKgqeeMI5GAHExprzUVFu3Oz0adOF1qKFCUbly8O338JHHykY5UIKRyIiku/Y7abFKL2+k5RzgwaZ665ryRIzFX/uXHPcrx/s3AkPPJBN1cqNpnAkIiL5zrp1aVuMUrMss7PHunXXuMlff0H79vDYY2bA0m23mRe8/TakGu8quY/CkYiI5DtxcVm4zrLgk09Ma9Hnn5vR28OGQXQ03HtvdpYpHpJrZquJiIhkl1TrBbt33aFD0KuXGU8EUKuWWbeoTp3sLE88TC1HIiKS7zRqZGalZTSz3maD8HBzHWA2ip0xw2wU++234OcH48fD5s0KRnmQwpGIiOQ7Pj5muj6kDUgpx9On/2+9o717oUkT6NsXzp6Fe+4xXWjDhmmj2DxK4UhERPKldu3giy+gXDnn82Fh5ny7Nldg4kS44w4z0DogwAy2/uEHuP12zxQtN4TGHImISL7Vrh20bZvOCtk7oqF+N/j5Z3Nhq1YwaxZUqODReuXGUDgSEZF8zcfH9JoBcPEivDIWJk0yixwVLw7TppmVrrX1R76hcCQiIgLw449mleu9e83xE0+YbrSQEM/WJTecxhyJiEj+dvYs9O9v+tP27oXgYFi0yKxhpGCUL6nlSERE8q///hd69DDrFwE89xy88YbpTpN8S+FIRETyn5MnYfBgszEsQMWKZsB1y5YeLUu8g7rVREQkf1m0yGz98dFHZpD1gAGwY4eCkTio5UhERPIsu/2fafoVfONo8Fk/bIujzJO33262/mjY0LNFitdROBIRkTwpKgoGDoQjRyy68BHT+Bc2TpPsU5ACL78EI0ZA4cKeLlO8kLrVREQkz4mKMjPxfY4cZDkPEMlzFOc026hDXfsWouq8pmAkGVI4EhGRPMVuh0EDkulrvc1OImjFCi5QmBeZRH1+4ldbLQYNMteJpEfdaiIikqds+3QP82O7cw8bAPiBRnTnA/Zzq7nAgsOHzVgkx8rYIqmo5UhERPKGy5dh/Hjqdq/FPWzgDEXpzQyasOafYJRKXJwHapRcwe2Wo8OHD2Oz2QgLCwNg8+bNzJs3j2rVqtGjR49sL1BEROS6fv7ZbP0RHY0P8A0P0ouZHKZ8hi8pW/bGlSe5i9stR506dWL16tUAxMfH06JFCzZv3sy///1vXn311WwvUEREJEMXLsCwYXDXXRAdDSVKkPzRJ/Qst4wjtvSDkc0G4eFmtxCR9Lgdjnbu3Mldd90FwH/+8x8iIiLYsGED8+bNIzIyMrvrExERSd/69VCrFkycaEZXt28Pe/ZQ4NlnePMtG2CCUGopx9Ong4/PDa1WchG3w9Hly5fx8/MD4LvvvqNNmzYA3H777cSpA1dERHLamTPQr59p+tm3z/SPLV4MCxdCmTIAtGsHX3wB5co5vzQszJxv184DdUuu4faYo+rVqzNz5kwefvhhVq5cydixYwE4evQoJUuWzPYCRUREHJYvh549HRvFxj3UjR8fe51SN91EI7tza1C7dtC27T8rZJcta/KUWozketwOR5MmTeKxxx5jypQpdOnShTvuuAOApUuXOrrbREREstWJE2aj2I8/BuBcmUp0S57Nwm+awTfmkrAwePNN51YhHx9N1xf32SzLstx9kd1uJzExkeLFizvOHTx4kCJFilDmf02aeVliYiJBQUEkJCRQrFgxT5cjIpJ3WZbZKLZvXzh2DGw29j88iNpfj+UcAU6XpownUreZZMTV7+9MrXNkWRbbtm1j1qxZnDlzBgBfX1+KFCmSuWpFRESuFhcHjz8OTz5pglG1atjXbeD+6KlpghGYHAVo9WvJMrfD0Z9//kmNGjVo27Ytffv25e+//wZg8uTJDB06NNsLFBGRfMayYO5cqFrVDLQuWBBeeQV+/pl1l+/myJFrvzRl9WuRzHI7HA0cOJB69epx6tQp/P39Hecfe+wxVq1ala3FiYhIPvPHH9CypVnQMSGBxNvq8e1r21jTdAz2gn4ur2qtydOSFW4PyF6/fj0//vgjvr6+TucrVKhAbGxsthUmIiL5iN0Ob78Nw4fD+fPYfQsz3v81xuwdiP1l81UVFgYvvODa7bT6tWSF2+EoOTkZezqduUeOHCEwMDBbihIRkXxk1y7TUvTTTwD8Xb0xDXd9wIGkyk6XxcbCqFFQsiScPPnPGKPUbDYTorT6tWSF291qLVq0YPr06Y5jm83G2bNnGTVqFA899FB21iYiInlZUhKMHQu1a5tgFBhI8nuzqHv6ew5QOc3lluW84rVWv5ac4nY4mjZtGmvXrqVatWpcvHiRTp06UbFiRWJjY5k0aVJO1CgiInnNli1Qr54ZaH35Mjz8MOzezQ+39+BwbMZfTZZlljwaPVqrX0vOcbtbLTQ0lOjoaObPn8/PP/9McnIy3bp14+mnn3YaoC0iIpLG+fMm2bzxBiQnQ6lS8NZb8NRTYLMR5+IssypV4OBBrX4tOcPtcATg7+/P888/z/PPP5/d9YiISF61di107w4HDpjjjh3NktalSzsucXUgddmyWv1aco7b4ejj/y3dnpFnn30208WIiEgelJgIL70EM2ea43Ll4L334JFH0lzaqJHpHouN1YBr8Ry3tw9JvWUIwOXLlzl//rxjheyTJ09ma4HeSNuHiIi4aNky6NULx8qNPXvCpEkQFJThS6Ki4IknzN9Tf0NpexDJqhzbPuTUqVNOj7Nnz7J3717uvfde5s+fn6WiRUQkj/j7b3j6aWjd2gSjW26B7783rUfXCEZggs8XX2jAtXhOpjaeTc/WrVt55pln+O2337Ljdl5NLUciIhmwLFi4EPr3h+PHoUABGDwYxowBN/fftNs14Fqyl6vf35kakJ0eHx8fjh49ml23ExGR3CY2Fnr3hq++MscRETBnDtx1V6ZupwHX4ilud6stXbrU6fHll18yc+ZMOnfuzD333JMTNQIwYcIE7rzzTgIDAylTpgyPPvooe/fudbrGsixGjx5NaGgo/v7+NGnShF27djldc+nSJfr370+pUqUICAigTZs2HLnWLoYiInJtlgWzZ0O1aiYYFSpkWoq2bct0MBLxJLe71QoUcM5TNpuN0qVLc//99/PGG29QNoc2tHnggQd46qmnuPPOO7ly5QrDhw9nx44d7N69m4CAAAAmTZrEuHHjiIyM5NZbb+W1117jhx9+YO/evY6tTXr37s1XX31FZGQkJUuWZMiQIZw8eZJt27bh42J7rbrVRET+5/ffzYZnq1eb47vuMq1FEREuvVxdZ3Ijufz9beVSx44dswBr7dq1lmVZVnJyshUSEmJNnDjRcc3FixetoKAga+bMmZZlWdbp06etQoUKWQsWLHBcExsbaxUoUMBavny5y++dkJBgAVZCQkI2fRoRkVzmyhXLeuMNy/L3tywwf06das67aNEiywoLMy9PeYSFmfMiOcHV72+3u9W8RUJCAgAlSpQAICYmhvj4eFq2bOm4xs/Pj8aNG7NhwwYAtm3bxuXLl52uCQ0NJSIiwnFNei5dukRiYqLTQ0Qk39q5Exo2hCFD4MIFaNoUduyAf/3L5WaflOn6V49qiI0156OicqBuERe5NCB78ODBLt9w6tSpmS7GVZZlMXjwYO69914i/td0Gx8fD0BwcLDTtcHBwfz555+Oa3x9fdOs1RQcHOx4fXomTJjAmDFjsvMjiIjkPklJMGECjBtn9kMrVsxsA9KtW9pdYK/BboeBA9Nf5DFlc9lBg6BtW3WxiWe4FI5++eUXl25mc+M/jqzo168f27dvZ/369detwbKs69Z1vWuGDRvmFBATExMJDw93s2oRkVxs82YTgnbuNMdt2sCMGWkXI3LBunVpW4xSsyw4fNhcp9lq4gkuhaPVKQPtvED//v1ZunQpP/zwA2FhYY7zISEhgGkdSj0o/NixY47WpJCQEJKSkjh16pRT69GxY8do2LBhhu/p5+eHn59fdn8UERHvd/48jBwJ06ebjWJLl4a334b27d1qLUotLi57rxPJbrlmzJFlWfTr14+oqCi+//57KlWq5PR8pUqVCAkJYeXKlY5zSUlJrF271hF86tatS6FChZyuiYuLY+fOndcMRyIi+dLq1VCjBkydaoLRM8/A7t3QoUOmgxG4t7msiCdkahHILVu28Pnnn3Po0CGSkpKcnovKoVF0ffv2Zd68eXz55ZcEBgY6xggFBQXh7++PzWZj0KBBjB8/nipVqlClShXGjx9PkSJF6NSpk+Pabt26MWTIEEqWLEmJEiUYOnQoNWrUoHnz5jlSt4hIrpOQAP/3f2btIjD7dsycCQ8/nC231+ay4vXcnQY3f/58q1ChQtbDDz9s+fr6Wq1bt7Zuu+02KygoyOratWumpta5Akj38eGHHzquSU5OtkaNGmWFhIRYfn5+1n333Wft2LHD6T4XLlyw+vXrZ5UoUcLy9/e3WrdubR06dMitWjSVX0TyrKVLLSs09J+59b16WVYO/Fu3aJFl2WzmkXoqf8o5TeeXnODq97fbi0DWrFmTnj170rdvXwIDA/n111+pVKkSPXv2pGzZsvliVpcWgRSRPOfvv2HAAFiwwBxXrgwffACNG2f51hkt9BgVZWatpR6cHR5uhjdpc1nJCa5+f7sdjgICAti1axcVK1akVKlSrF69mho1arBnzx7uv/9+4vLBCDqFIxHJMywL5s83wejECbNR7NChMHo0+PunuTyjoONOAAoLgzffNAFIK2TLjZRjG8+WKFGCM2fOAFCuXDl27txJjRo1OH36NOfPn898xSIicmMdPmw2il22zBzXrGm2/qhXz4SWNc6h5csv0w86HTuafJXe+ddfTzuuKGWhxy++MAFJ0/XF27gdjho1asTKlSupUaMG7du3Z+DAgXz//fesXLmSZs2a5USNIiKSnZKT4f334cUX4cwZ8PWFESPgpZfA1zfd1p6SJU3D0tWOHIEpU1w/D1roUbyfy91q0dHR1KpVi5MnT3Lx4kVCQ0NJTk7m9ddfZ/369VSuXJmRI0emWX06L1K3mojkWvv3m41i1641x3ffbVqLqlUD/tnWw70BF5m3erVajuTGyfYxRwUKFKB27dp0796dTp06ERQUlG3F5jYKRyKS61y5AtOmwSuvwMWLUKSI2Qqkb19H043dDhUrXnv16uw2b57pfhO5EVz9/nZ5Ecgff/yROnXq8PLLL1O2bFmeeeYZr1o5W0REMrB9OzRoYLrRLl6E5s3NNiADBjj1aV1vW4+coIUexRu5HI4aNGjA7NmziY+P57333uPIkSM0b96cW265hXHjxnHkRv8XJSIi13bpkmkpqlsXtm6Fm26CuXNhxQq4apcBuLHbddhsZtq+FnoUb+T29iH+/v506dKFNWvWsG/fPjp27MisWbOoVKkSDz30UE7UKCIi7tq0CerUgbFjTZfaY4+ZrT+eey7DrT9yqhXn6rdLOZ4+XYOxxTtlaW+1W265hZdffpnhw4dTrFgx/vvf/2ZXXSIikhnnzplpYA0bmjAUHGzmzEdFpZt+7HZYs8ZMxbfbzRT8LGyb5mCzmcf//R+UK+f8XFjYP9P4RbxRpvZWA1i7di1z585l0aJF+Pj40L59e7p165adtYmIiDu++87MRDt40Bx36WI2jS1RIt3LM5qynzLVPvV0nZTjq6f0h4fDU0+lv85RykrXEyZooUfJXdxaIfvw4cNERkYSGRlJTEwMDRs2pFu3brRv356AgICcrNOraLaaiHiV06dhyBAzngiwypdne9/32R3eKsNVrPfvN4tgX/0NcK0QNH26WZfInRWyRbxJtq+Q3aJFC1avXk3p0qV59tlnef7557ntttuypVgREcmkJUugTx+TSmw2fn+gLw//Op69LwU6LsloFev0pLQa+fubhqhjx9KGnfTWJfLx0XpFkne4HI78/f1ZtGgRrVu3xkf/d0BExLP++gv694fPPzfHt93G2s4f0HTkvWlag661WnV6LMu8xsdHaxBJ/uRyOFq6dGlO1iEiIq6wLPj0UzPo+uRJk2BefBH78Fd45vbC2bqydT7YR1wkXZkekC0iIjfYoUPQsycsX26Oa9Uy44xq12bdmuxfwFELNEp+laWp/CIicgMkJ8O770L16iYY+fmZKWCbN2OvWZs1a2DRoux7Oy3QKPmdWo5ERLzZ3r3QvTusXw/A6Rr3sr7LBxStexvHv4R//St7W4y0QKOIwpGIiHe6fBneeMPMt790icuFizKm8ETG7+iNNTTnGv1Tr08kkl+5FI7cGYzdpk2bTBcjIiLAL79At27mT+CvWq2oHz2LPy9WyNa3SVnTaMwYqFJF6xOJpHApHD366KNOxzabjdRrR9pSrTVvt9uzpzIRkfzm4kWzF9qkSWZVxeLFSX5jGvVGPssRMr+nhyurWIvIP1xqm01OTnY8VqxYQa1atfj22285ffo0CQkJfPPNN9SpU4flKTMoRETEPRs2QO3aMH482O1Yjz/Bjx/sYdQfXTgSm7lg1K8frF4NMTEwebLZVWT1apg375/zCkYiabm1fQhAREQEM2fO5N5773U6v27dOnr06MGePXuytUBvpO1DRCTbnD0L//43vPOO6eMKCWFT53d5cn67LA+0Xr1aq1aLpJbt24ek+P333wkKCkpzPigoiIMpmx2KiMj1rVgBPXrAn3+a4+ee46smb9C2a/EsLeZos5kuM03FF8kct6c83HnnnQwaNIi4VEunxsfHM2TIEO66665sLU5EJE86eRKeew5atYI//8SqWJFfp6zgs2Zz6TY068EINBVfJCvcbjmaO3cujz32GBUqVKB8+fIAHDp0iFtvvZUlS5Zkd30iInnLokXQt6/ZG81m48CD/Xk4ehz7/q9ottxeg6xFss7tcFS5cmW2b9/OypUr+e2337Asi2rVqtG8eXOnWWsiIpJKXJwZIR0VBUBC6O0senAO3ec2zHRLUXi4WQqpdGlze03FF8kebg/ITu3ixYv4+fnlu1CkAdki4jLLgo8+MktZnz7NZQoykZcZx3AuUThTtxwxApo1UxAScZer399ujzlKTk5m7NixlCtXjqJFixITEwPAyJEjmTNnTuYrFhHJaw4eNOOKnnsOTp9mK3Wpx1ZeYWymglHKnmejR5tZaApGIjnD7XD02muvERkZyeTJk/H19XWcr1GjBh988EG2FicikislJ8Pbb0NEBKxciVW4MOOLTeRuNrGdOzJ1Sw20Frlx3A5HH3/8Me+//z5PP/00Pqn+C61Zsya//fZbthYnIpLr7Nlj+rsGDIBz56BRIza//yvDE1/CnoXtLMPC4IsvNNBa5EZwOxzFxsZSuXLlNOeTk5O5fPlythQlIpLrXL4M48ZBrVpmteuiRWHGDOyr1vD1vlszdcvSpeHTT7WatciN5vb/jalevTrr1q2jQgXnDRA///xzateunW2FiYjkGj//bDaKjY42xw88ALNmEbW1PANvxu2VrlO60GbOVCAS8QS3w9GoUaPo3LkzsbGxJCcnExUVxd69e/n444/5+uuvc6JGERHvdOECvPoqTJli9kMrUYLfek4nOuIZ9kfaGD2aTE3T11pFIp6Vqan8//3vfxk/fjzbtm0jOTmZOnXq8Morr9CyZcucqNHraCq/iLB+vWkt2rcPgCMN2/NIzFtExwW7dRubzQSoMWOgShWtVSSSk3Jkb7UrV64wbtw4nn/+edauXZvlIkVEcp0zZ2DYMHj3XXNctiwbO8/gnimPqpVIJI9wa0B2wYIFmTJlCna7PafqERHxXt9+C9Wr/xOMunfHvmM37edlLhiNGKGB1iLeyO3Zas2bN2fNmjU5UIqIiJc6cQKefRYeeggOH4ZKleC772D2bNbtuMntAdcpmjVT95mIN3J7QPaDDz7IsGHD2LlzJ3Xr1iUgIMDp+TZt2mRbcSIiHmVZZnGhfv3g2DEzQGjgQHjtNfjfv31xce7f1mYz3WmNGmVzvSKSLdwOR7179wZg6tSpaZ6z2WzqchORvCEuDvr0gSVLzHG1ajBnDtx9t9NlZcu6d1utdC3i/TK1t1pGDwUjEcn1LAvmzjVhaMkSKFgQXnnFrGV0VTAC0/oTFvZP6LkerXQt4v0yv5Y9cPHiRQoXztyu0iIiXuePP6BnTzOeCKBePdNaVLNmhi/x8YE334QnnvhnWn4KTdMXyZ3cbjmy2+2MHTuWcuXKUbRoUf744w8ARo4cyZw5c7K9QBGRHGe3m36uGjXgu++wChfm915TWDBwI2tO1sRuN5esWQPz55s/k5L+OS5RAv7zHyhXzvm2YWGwaJFpeOrYEZo0UTASyQ3cDkfjxo0jMjKSyZMn4+vr6zhfo0YNPvjgg2wt7mo//PADjzzyCKGhodhsNpakjAX4H8uyGD16NKGhofj7+9OkSRN27drldM2lS5fo378/pUqVIiAggDZt2nAks1NNRCTXs+/YTULNe+Ff/4Lz5/m7WmPuu2kHlWcOpWPngjRtCsHB5tG0KXTqZP4sUsT5+F//gqlTzT5o8+ZpPzSR3MztcPTxxx/z/vvv8/TTT+OT6v8C1axZk99++y1bi7vauXPnuOOOO3jnnXfSfX7y5MlMnTqVd955hy1bthASEkKLFi04c+aM45pBgwaxePFiFixYwPr16zl79iytW7fWeCmR/CYpid0dx2KvWYug3ZtIJJCezCR49/esj3feXPvECfNI7ep/MmJjoUMHOHlSrUQiuZ7lpsKFC1sHDx60LMuyihYtav3++++WZVnWrl27rICAAHdvl2mAtXjxYsdxcnKyFRISYk2cONFx7uLFi1ZQUJA1c+ZMy7Is6/Tp01ahQoWsBQsWOK6JjY21ChQoYC1fvtzl905ISLAAKyEhIesfRERuvM2brdPla1iWGRJkfcXDVjkOpxxm+mGzWVZ4uGVdueLpDygi6XH1+9vtlqPq1auzbt26NOc///xzateunfW0lkkxMTHEx8c77e/m5+dH48aN2bBhAwDbtm3j8uXLTteEhoYSERHhuCY9ly5dIjEx0ekhIrnQ+fPw4otYd99N0KEd/E0pOjKPR/iKWMKyfHvLMmtEpvNPpIjkIm7PVhs1ahSdO3cmNjaW5ORkoqKi2Lt3Lx9//DFff/11TtTokvj4eACCg503fQwODubPP/90XOPr60vx4sXTXJPy+vRMmDCBMWPGZHPFInJDrV0L3bvDgQPYgPk8xQDe4jils/2tMrMwpIh4D7dbjh555BEWLlzIN998g81m45VXXmHPnj189dVXtGjRIidqdIvtqsVGLMtKc+5q17tm2LBhJCQkOB6HDx/OllpF5AZITITevc0goAMHoFw51g5ZSifm50gwAvcXhhQR75KpdY5atWpFq1atsruWLAkJCQFM61DZVP8yHTt2zNGaFBISQlJSEqdOnXJqPTp27BgNGzbM8N5+fn74+fnlUOUikmOWLYNevXBsftazJ0yahPVLELyR/W+nbUFE8ga3W468VaVKlQgJCWHlypWOc0lJSaxdu9YRfOrWrUuhQoWcromLi2Pnzp3XDEciksscPw7PPAOtW5tgdMst8P33MHMmBAW5vaq1K7QtiEje4VLLUfHixa/bNZXi5MmTWSroWs6ePcuBAwccxzExMURHR1OiRAnKly/PoEGDGD9+PFWqVKFKlSqMHz+eIkWK0KlTJwCCgoLo1q0bQ4YMoWTJkpQoUYKhQ4dSo0YNmjdvnmN1i8gNYlmwcCH0728CUoECMHiwWaK6SBHHZdda1To9JUuaP1NP5/fxcZ7OHxZmgpHWNRLJ/VwKR9OnT3f8/cSJE7z22mu0atWKBg0aALBx40b++9//MnLkyBwpMsXWrVtp2rSp43jw4MEAdOnShcjISF588UUuXLhAnz59OHXqFPXr12fFihUEBgY6XjNt2jQKFixI+/btuXDhAs2aNSMyMtJpzSYRyYViY81GsUuXmuOICLP1x113OV1mt5vZZJcuwejRMHv2P71uAOHh8MYbULq0GVidsuUHmNelnGvYEDZscL5G/4yI5A02y7re/2dy9vjjj9O0aVP69evndP6dd97hu+++S7NqdV6UmJhIUFAQCQkJFCtWzNPliORvlgUffABDh5rB14UKwYgR8PLL2H18nQLN8eNmJevUYahcOejRQ3ufieQHrn5/ux2OihYtSnR0NJUrO68gu3//fmrXrs3Zs2czV3EuonAk4iV+/x1eeMHs1QFQv75pLapenagoGDjQOQilJ2XEwBdfqEtMJK9z9fvb7QHZJUuWZPHixWnOL1myhJIpHfMiIjnJbjcbmdWoYYKRv785/vFHRzB64onrByP4Z7zRoEFptwQRkfzJ7an8Y8aMoVu3bqxZs8Yx5mjTpk0sX748xzeeFRFh507o1g02bzbH999vBg7dfDNgAs7AgdcfZJ1a6pWtmzTJ/pJFJHdxu+Woa9eubNiwgZtuuomoqCgWLVpEUFAQP/74I127ds2BEkVEgKQkM4K6Th0TjIoVM6Hou+8cwQhMwHGlxSg9WtlaRMDNlqPLly/To0cPRo4cyWeffZZTNYmIONu82bQW7dxpjtu0gRkzzGjqq2Ql4GhlaxEBN1uOChUqlO54IxGRHHH+PAwZAg0amGBUurRZx2jJknSDEWQu4NhsZgq/VrYWEchEt9pjjz2WL6bri4iHrV5tBlxPnQrJyWbF6927oX37dJe2ttthzRqz3FHp0q6vfq2VrUXkam4PyK5cuTJjx45lw4YN1K1bl4CAAKfnBwwYkG3FiUg+lJAA//d/ZjwRmKWnZ82Chx7K8CWuTttPj1a2FpGrub3OUaVKlTK+mc3GH3/8keWivJ3WORLJIV99ZTaKPXrUHPfpAxMmmMHXGUiZtu/Kv2QZrX6tFiOR/MHV72+3W45iYmKyVJiISBp//w0DBsCCBea4ShWz6vV996V7ecoWILGxZsXrawWj0qVh2jQzRElBSERc4XY4SnH8+HFsNpsWfhSRzLMsmD/fBKMTJ0xyGToURo0yCzumw90utL//NsFI6xeJiKvcGpB9+vRp+vbtS6lSpQgODqZMmTKUKlWKfv36cfr06RwqUUTypMOH4ZFH4OmnTTCqWRN++gkmTrxmMHJ15evUtH6RiLjD5ZajkydP0qBBA2JjY3n66aepWrUqlmWxZ88eIiMjWbVqFRs2bKB48eI5Wa+I5HbJyWaw9f/9H5w5A76+MHIkvPSS2TQ2A5lZ+TqF1i8SEXe4HI5effVVfH19+f333wkODk7zXMuWLXn11VeZNm1athcpInnE/v1mo9i1a81xgwZmo9iqVa/70sysfG2zmdloWr9IRNzhcrfakiVLeP3119MEI4CQkBAmT56sBSJFJH1XrsCUKabrbO1aKFIE3nzTJB4XghG43zWm9YtEJLNcbjmKi4ujevXqGT4fERFBfHx8thQlInnI9u1m64+tW81x8+bw/vtwjWVBrma3w19/ufe2Wr9IRDLL5XBUqlQpDh48SFhYWLrPx8TEaOaaiPzj0iUYN86sU3TlCtx0k1ntumvXay5fnTJNP2UdouPHzXR9V7rUNG1fRLKDy+HogQceYPjw4axcuRJfX1+n5y5dusTIkSN54IEHsr1AEcmFNm0yrUW7d5vjxx6Dd9+97sjozK50nZK1Zs5US5GIZJ3LK2QfOXKEevXq4efnR9++fbn99tsB2L17NzNmzODSpUts3bqV8PDwHC3YG2iFbJEMnDsHI0aY8USWBWXKmFD0+OPX3ezMnZWurxYeri40Ebk+V7+/3do+JCYmhj59+rBixQpSXmaz2WjRogXvvPMOlStXznrluYDCkUg6Vq0yM9FSVtHv0sXs1eFCd7vdDhUrZm5vtGnToH9/daGJyPXlyPYhlSpV4ttvv+XUqVPs378fMBvRlihRImvVikjudfq0WdV6zhxzXL68GXDdqtV1X5oyvmjVqswFI4DgYAUjEclemdo+pHjx4tx1113ZXYuI5DZLlpjNYVPm2ffrB+PHQ2DgdV+a2fFFV9MCjyKS3TK9t5qI5GN//WX6sj7/3BzfdpvZKPbee116eVbGF6XQAo8iklPc2ltNRPI5y4JPPoFq1Uww8vGBYcMgOtrlYJSVbUBSaIFHEclJajkSEdccOgS9esG335rjWrVg7lyoXdut22RmG5CraYFHEclJCkcicm3JyWYBoZdegrNnwc8PRo0yg7CvsVFsRtzdBiQ83Ex6K136n4UhtcCjiOQkhSMRydjevdC9O6xfb47vuceMLfrfOmeZ4eoA6hEjoFkzBSERufEUjkQkrStX4PXXYfRosw1IQABMnGhmphXI3FDFlGn7sbGmFej48fTHHaUMtB49WqFIRDxD4UhEnEVHm60/fv7ZHLdqBbNmQYUKmb6lq9P2NdBaRLyBZquJiHHxIgwfDvXqmWBUvDhERpoB2FkMRk884dog7LAw+OILDbQWEc9Sy5GIwI8/mrFFv/1mjp94At5+G0JCsnRbV6btly5ttgApV07ji0TEO6jlSCQ/O3sWBgwwqeS338xeHIsWmTWMshiMwLVp+3//bYJRkyYKRiLiHdRyJJJfrVgBPXrAn3+a4+eeM3PmixfP8q1TBl8vWuTa9e5O7xcRyUkKRyL5zcmTMGSIGU8EULGiGXDdsmW23D4ze6ZpfzQR8SYKRyL5yaJF0Lev2RvNZjP7o40bB0WLZsvt3d0zTfujiYg3UjgSyQ/i46Ffv3/6uW6/HebMgYYNXb5FSlfZ1atUp16/6F//ci8Ygabti4j3UTgSycssCz76yKSW06ehYEGzDciIEVC4sMu3Sa+rLCwMOnaE+fMzt1ea9kcTEW+lcCSSVx08aAZcr1xpjuvUMa1FtWq5dZuMusqOHIEpU9wvq18/ePxxTdsXEe+lqfwieU1yslmjKCLCBCM/P7P1x08/uR2MXFmnyF2PP65p+yLi3dRyJJKX7NljFnPcsMEcN2pkNoq99Va3bpMyjmjVqsx1maVHg69FJLdQOBLJCy5fNn1cY8ZAUpKZfTZ5MvTsec2NYtMbZP3ll+5Pxb8eDb4WkdxE4Ugkt/v5Z7NRbHS0OX7wQZg5E8qXd1ziaggqWRJOnMj+EjX4WkRyk3w75mjGjBlUqlSJwoULU7duXdatW+fpkkTcc+ECDBsGd91lglGJEvDJJ7BsmVMwiooy6zw2bQqdOpk/g4PN2J+rW4eyMxiVLg2ffgqrV0NMjIKRiOQe+bLlaOHChQwaNIgZM2Zwzz33MGvWLB588EF2795N+VRfKiJea/1601q0b585bt8e3n4be8kyrFv7TwvR8ePmqasHVOdE61CKlC60mTMViEQkd7JZVnbOQ8kd6tevT506dXjvvfcc56pWrcqjjz7KhAkTrvv6xMREgoKCSEhIoFixYjlZqoizM2dMa9G775rjsmVhxgx49NF01yJKWaQxJ4SHw1NPpV3nKDxcXWgi4p1c/f7Ody1HSUlJbNu2jZdfftnpfMuWLdmQMsPnKpcuXeLSpUuO48TExBytUSRdy5ebAdaHDpnjbt3g9dfhppsyXIsoJ4LRiBHQrNk/6xRNmJD+ytkiIrlVvgtHx48fx263Exwc7HQ+ODiY+Pj4dF8zYcIExowZcyPKEwGcB1CHFzlBw0WDKfDJxwBYlSrxa9/Z7AltRtloswNIdq9FlJ6UqfijRzuHHx8fs26RiEheke/CUQpbysCI/7EsK825FMOGDWPw4MGO48TERMLDw3O0Psm//ukes3iCL3iHfhTgGJbNxoGHB/Lwz6+xf2iA4/pSpczYopykqfgikp/ku3BUqlQpfHx80rQSHTt2LE1rUgo/Pz/8/PxuRHmSz6V0j4VYR4miL4+xBIBdVKObNYefvr47zWuyMxjZbKYF6uop/ZqKLyL5Sb6byu/r60vdunVZmbLf1P+sXLmShm7sUC6S3ex2GDjAoqs1l91U4zGWcJmCjOEV6vAzP5E2GGVWSktQyZLO58PCYNEi+OsvMwV/3jxNxReR/CfftRwBDB48mM6dO1OvXj0aNGjA+++/z6FDh+jVq5enS5N8bMvCP/gwtgfNWWWOqUc35rCDmlm+99Wz1lJagtq2zXgwtcYRiUh+lS/DUYcOHThx4gSvvvoqcXFxRERE8M0331ChQgVPlyb5SMqg6/hYO/U2vk29D4ZTkPNcoDAjGct0BmHP4n+iKS1E8+ebRRkVgkREri9frnOUVVrnSLIqZdB14JHdzKEbDdgEwBoa050P+J3Kmbpv6dLw99//HGvNIRGRf2idIxEPS28/Mx8fE4w6Pp7Ei0xiBK/hRxKJBDKU1/mA7liZGAqYMs3+wAHYsEFrDomIZIXCkUgOSG+16rAwmDoVIvttZTPduIPtAHzNw/RiJrGEOa5NmTWW3nF6z4FpIfL1VTeZiEhW5bvZaiLZwW6HNWvMWJ41a5wHO6dMx0+zqeuRC8S0f5Glx+pzB9v5m1J0ZB6P8JVTMAKzdlFqKbPIFi2CcuXSPvfFF+o6ExHJLmo5EnFTRq1Cb75pZn+lt1r1fazlA7pThQMAzKMjA3mT45RO9z2mTTMhKL3usWvNMBMRkaxTOBK5SkZjhYAM9zCLjTXnR492Dk2BJDKJl+jNTACOUI7evMfXPHLNGsqVy7h7TNt1iIjkLIUjyReuFXhSy0yrEJhzNpu5LsVDLGMmvQjH3GwWPXiRySQSlGGdKQOrGzXK7CcVEZGsUjiSPO9agSf1OB13W4WuZllw8iSU5DjTGcQzfAbAAW7hBWazhqZO119rYLW6yUREPEcDsiVPy2hwdErgiYoyx3b7tVuFwLlVKH0WHVjAb7aqPMNn2CnAFIZSk+2OYGSzmbWHPv9cA6tFRLyVFoHMBC0C6VmudpHZ7VCxYsatPSldWDEx5n5Nm6Z/nStCieU9etOGrwDYQQTdmcNm7nJ6P/gnALn6OUREJHtoEUjxWlkJBa52kYF5j+t1gx0+/E8trihRAk6dSt3CZNGdD3idoQSRSBKFKDhqBAeqvszRob5wVZ2pV6vWwGoREe+kcCQZyomWDXfCTXqvvdaYoKu7pFwNPCmfzxUDB5qxRzYbVLJ+ZzYvcD+rAdhEfc5Mm0OLQdV5DGjzhFqGRERyI3WrZUJOdKt5WxdLVkLMte6ZXri5urspPe50kaX83Nasca2rbPVq8/OuWNEErfT+i0h9/y+j7Ox84U2GJoygCBc4jz9TbhpHjfcH0O5JpR8REW/l6ve3wlEmZHc4yokgktV6MhtiMpKZcJOaO0Enpasq5T1dCTwpe5498YR5Lr1ZZF98Ae1u3QndusHmzQDEV7+fg/+ezZ0dblarkIiIl3P1+1uz1TzM1dlUN4ors7YGDXLeLsMV7oz/SY87XWQpfHz+mWGWEnBSpDdtvl07E4DSm0W2aH4S7baPhjp1TDAqVgxmzyZkx3fc3UnBSEQkL1E48qCcCiJZkdUQk5HMhJvUXB0TdPV11wo86bWAtWsHBw+aFqh588yfMQs389jYOjBmDFy+DG3awO7d0L172tQlIiK5ngZke5A7QeRGzWrKaojJSGbDTYpGjUyguV4XWXorS7dr595+ZI5ZZOfOwSuvmOal5GQoXRrefhvat1coEhHJwxSOPCingkhWZDXEZCQr4Qb+6SJ74onMrSzt9rT577+HF16AP/4wx888Y3aDLVXKjZuIiEhupG41D8qpIJIVKSEmo4aRlBWe3d37y93xP+lxt4ssU06fNqGoWTMTjMLC4Ouv4ZNPFIxERPIJhSMPyqkgkhXZEWIykh3hJt0xQTHZFIyWLoXq1eGDD8xxr16waxc8/HA23FxERHILTeXPhOycyu/S9HEPTee/enmB8HDnFZ4zy9vWdOLYMRgwABYuNMeVK5uA1LixB4sSEZHspnWOctCNWOcou4JIVnhdiMlulmWanwYOhBMnoEABGDrULIHt7+/p6kREJJspHOWg/LBCdp53+DD07g3LlpnjmjVhzhyoV8+zdYmISI7RxrO5jDYhvUGSk+H99+HFF+HMGfD1hZEj4aWXoFAhT1cnIiJeQOFI8o/9+81MtLVrzfHdd5vWomrVPFuXiIh4Fc1Wk7zvyhWYMsV0na1dC0WKmAFd69crGImISBpqOZK8bft2s1Hs1q3muFkzmD0bKlXybF0iIuK11HIkedOlS2brj7p1TTAKCjJdaCtXKhiJiMg1qeVI8p5Nm0xr0e7d5vjRR+HddyE01KNliYhI7qCWI8k7zp2Df/0LGjY0wahMGfj8c7OQlIKRiIi4SC1HkjesWmVmosXEmONnn4WpU6FkSc/WJSIiuY5ajiR3O30auneH5s1NMCpfHr79Fj76SMFIREQyReFIcq8vvzRT8efMMcf9+sHOnfDAA56tS0REcjV1q0nuc+wY9O8P//mPOb7tNrNR7L33erYuERHJE9RyJLmHZcGnn0LVqiYY+fjAsGEQHa1gJCIi2UYtR5I7HDoEvXqZ8UQAtWqZ7rQ6dTxaloiI5D1qORLvlpwMM2ZA9eomGPn5wfjxsHmzgpGIiOQItRyJ99q3z8xEW7fOHN9zjxlbdPvtnq1LRETyNLUcife5cgUmTTIbxa5bBwEB8Pbb8MMPCkYiIpLj1HIk3uXXX+H55+Hnn81xq1YwaxZUqODZukREJN9Qy5F4h4sXYcQIqFfPBKPixSEy0owzUjASEZEbSC1H4nkbNpiNYn/7zRw/8YTpRgsJ8WxdIiKSL6nlSDzn7FkYMMCsUfTbbyYMLVpkNotVMBIREQ/JNeFo3LhxNGzYkCJFinDTTTele82hQ4d45JFHCAgIoFSpUgwYMICkpCSna3bs2EHjxo3x9/enXLlyvPrqq1iWdQM+gThZsQIiIkwLkWXBc8/B7t3Qrp2nKxMRkXwu13SrJSUl8eSTT9KgQQPmpOyllYrdbufhhx+mdOnSrF+/nhMnTtClSxcsy+Ltt98GIDExkRYtWtC0aVO2bNnCvn376Nq1KwEBAQwZMuRGf6T86eRJGDLEjCcCqFgR3n8fWrTwZFUiIiIOuSYcjRkzBoDIlC/Vq6xYsYLdu3dz+PBhQkNDAXjjjTfo2rUr48aNo1ixYnz22WdcvHiRyMhI/Pz8iIiIYN++fUydOpXBgwdjs9lu1MfJnxYtgr594a+/wGYz+6ONGwdFi3q6MhEREYdc0612PRs3biQiIsIRjABatWrFpUuX2LZtm+Oaxo0b4+fn53TN0aNHOXjwYIb3vnTpEomJiU4PcUN8vBlk/cQTJhjdfjusXw9vvqlgJCIiXifPhKP4+HiCg4OdzhUvXhxfX1/i4+MzvCblOOWa9EyYMIGgoCDHIzw8PJurz6Msy3SfVatmWo0KFjTT9aOjoWFDT1cnIiKSLo+Go9GjR2Oz2a752Lp1q8v3S69bzLIsp/NXX5MyGPtaXWrDhg0jISHB8Th8+LDLNeVbBw/CAw+YgdanTkHdurB1K4wda/ZHExER8VIeHXPUr18/nnrqqWteU7FiRZfuFRISwk8//eR07tSpU1y+fNnROhQSEpKmhejYsWMAaVqUUvPz83PqipNrSE6Gd9+FYcPg3DkoXBjGjIHBg03LkYiIiJfz6LdVqVKlKFWqVLbcq0GDBowbN464uDjKli0LmEHafn5+1K1b13HNv//9b5KSkvD19XVcExoa6nIIk2vYs8dsFLthgzlu1MhsFHvrrZ6tS0RExA25ZszRoUOHiI6O5tChQ9jtdqKjo4mOjubs2bMAtGzZkmrVqtG5c2d++eUXVq1axdChQ3nhhRcoVqwYAJ06dcLPz4+uXbuyc+dOFi9ezPjx4zVTLasuX4bx46FWLROMAgNhxgxYs0bBSEREch2blUtWQOzatSsfffRRmvOrV6+mSZMmgAlQffr04fvvv8ff359OnTrx+uuvO3WJ7dixg759+7J582aKFy9Or169eOWVV9wKR4mJiQQFBZGQkOAIXvnWzz+brT+io83xgw+ajWI1aF1ERLyMq9/fuSYceROFI+DCBXj1VZgyBex2KFnSTM3v1MmsYSQiIuJlXP3+1ghZcd+6dWZs0b595rhDB3jrLShTxrN1iYiIZINcM+ZIvMCZM2aF6/vuM8EoNBSWLIEFCxSMREQkz1DLkbjm22+hZ09IWePphRdg8mTIYBNgERGR3ErhSK7txAn417/gk0/M8c03w+zZcP/9nq1LREQkh6hbTdJnWfD552brj08+gQIFzEKO27crGImISJ6mliNJ6+hRM7ZoyRJzXK0azJ0L9et7tCwREZEbQS1H8g/LgjlzTBhasgQKFYJRo8xaRgpGIiKST6jlSIw//oAePWDVKnN8550mKNWo4dm6REREbjC1HOV3djtMn25C0KpV4O8Pr78OGzcqGImISL6klqP8bPdus/XHpk3muEkTMxOtcmWPliUiIuJJajnKj5KSYOxYqF3bBKNixcx+aKtWKRiJiEi+p5aj/GbrVtNatH27OW7dGt57D8LCPFuXiIiIl1DLUX5x4QK8+KKZdbZ9O5QqBfPmwdKlCkYiIiKpqOUoP1i71mwUe+CAOe7UyQzCLl3ao2WJiIh4I7Uc5WWJidC7txlofeAAlCsHX30Fn32mYCQiIpIBhaO8atkyqF4dZs40xz17wq5dZoyRiIiIZEjdannN8eMwaJBpHQK45Rb44APTeiQiIiLXpZajvMKyYMECqFrVBKMCBWDoUDP4WsFIRETEZWo5ygtiY6FPHzPzDMzK1nPmmC1ARERExC1qOcrNLMusaF2tmglGhQrBmDFmLSMFIxERkUxRy1Fu9fvv8MILsHq1Oa5f37QWVa/u2bpERERyObUc5TZ2O0ydarrOVq82G8VOnQo//qhgJCIikg3UcpSb7Nxptv7YvNkc33+/6Va7+WbP1iUiIpKHqOUoN0hKMmOJ6tQxwSgoyEzP/+47BSMREZFsppYjb7d5s2kt2rnTHLdtCzNmQGioZ+sSERHJo9Ry5K3OnzfrFDVoYIJR6dKwcCEsXqxgJCIikoPUcuSNVq82G8X+8Yc5fuYZs1FsyZIeLUtERCQ/UMuRN0lIMHug3X+/CUZhYWaPtE8+UTASERG5QdRy5C0OHIDGjeHoUXPcuzdMnAjFinm2LhERkXxG4chbVKpkWooCAsxMtPvu83RFIiIi+ZLCkbfw8YEvvoBSpczCjiIiIuIRCkfeJDzc0xWIiIjkexqQLSIiIpKKwpGIiIhIKgpHIiIiIqkoHImIiIikonAkIiIikorCkYiIiEgqCkciIiIiqSgciYiIiKSicCQiIiKSisKRiIiISCoKRyIiIiKpKByJiIiIpKJwJCIiIpJKQU8XkBtZlgVAYmKihysRERERV6V8b6d8j2dE4SgTzpw5A0B4eLiHKxERERF3nTlzhqCgoAyft1nXi0+SRnJyMkePHiUwMBCbzebpclyWmJhIeHg4hw8fplixYp4uR9DvxBvpd+Jd9PvwPrn5d2JZFmfOnCE0NJQCBTIeWaSWo0woUKAAYWFhni4j04oVK5br/ged1+l34n30O/Eu+n14n9z6O7lWi1EKDcgWERERSUXhSERERCQVhaN8xM/Pj1GjRuHn5+fpUuR/9DvxPvqdeBf9PrxPfvidaEC2iIiISCpqORIRERFJReFIREREJBWFIxEREZFUFI5EREREUlE4yocOHjxIt27dqFSpEv7+/txyyy2MGjWKpKQkT5eWr40bN46GDRtSpEgRbrrpJk+Xky/NmDGDSpUqUbhwYerWrcu6des8XVK+9sMPP/DII48QGhqKzWZjyZIlni4pX5swYQJ33nkngYGBlClThkcffZS9e/d6uqwcoXCUD/32228kJycza9Ysdu3axbRp05g5cyb//ve/PV1avpaUlMSTTz5J7969PV1KvrRw4UIGDRrE8OHD+eWXX2jUqBEPPvgghw4d8nRp+da5c+e44447eOeddzxdigBr166lb9++bNq0iZUrV3LlyhVatmzJuXPnPF1attNUfgFgypQpvPfee/zxxx+eLiXfi4yMZNCgQZw+fdrTpeQr9evXp06dOrz33nuOc1WrVuXRRx9lwoQJHqxMAGw2G4sXL+bRRx/1dCnyP3///TdlypRh7dq13HfffZ4uJ1up5UgASEhIoESJEp4uQ8QjkpKS2LZtGy1btnQ637JlSzZs2OChqkS8W0JCAkCe/O5QOBJ+//133n77bXr16uXpUkQ84vjx49jtdoKDg53OBwcHEx8f76GqRLyXZVkMHjyYe++9l4iICE+Xk+0UjvKQ0aNHY7PZrvnYunWr02uOHj3KAw88wJNPPkn37t09VHnelZnfiXiOzWZzOrYsK805EYF+/fqxfft25s+f7+lSckRBTxcg2adfv3489dRT17ymYsWKjr8fPXqUpk2b0qBBA95///0cri5/cvd3Ip5RqlQpfHx80rQSHTt2LE1rkkh+179/f5YuXcoPP/xAWFiYp8vJEQpHeUipUqUoVaqUS9fGxsbStGlT6taty4cffkiBAmpEzAnu/E7Ec3x9falbty4rV67ksccec5xfuXIlbdu29WBlIt7Dsiz69+/P4sWLWbNmDZUqVfJ0STlG4SgfOnr0KE2aNKF8+fK8/vrr/P33347nQkJCPFhZ/nbo0CFOnjzJoUOHsNvtREdHA1C5cmWKFi3q2eLygcGDB9O5c2fq1avnaE09dOiQxuJ50NmzZzlw4IDjOCYmhujoaEqUKEH58uU9WFn+1LdvX+bNm8eXX35JYGCgo6U1KCgIf39/D1eXvTSVPx+KjIzkueeeS/c5/c/Bc7p27cpHH32U5vzq1atp0qTJjS8oH5oxYwaTJ08mLi6OiIgIpk2bluemKOcma9asoWnTpmnOd+nShcjIyBtfUD6X0fi7Dz/8kK5du97YYnKYwpGIiIhIKhpoIiIiIpKKwpGIiIhIKgpHIiIiIqkoHImIiIikonAkIiIikorCkYiIiEgqCkciIiIiqSgciYiTgwcPYrPZHCt05xYVK1Zk+vTp2Xa/Jk2aMGjQoGy7nyfYbDaWLFkC5N7fq4gnKByJ5CM2m+2aj9ywym1kZCQ33XRTmvNbtmyhR48eN74gLzB69Ghq1aqV5nxcXBwPPvjgjS9IJJfT3moi+UhcXJzj7wsXLuSVV15h7969jnP+/v6cOnXKE6Vht9ux2WyZ3gS5dOnS2VxR7qe9EkUyRy1HIvlISEiI4xEUFITNZktzLsUff/xB06ZNKVKkCHfccQcbN250uteGDRu477778Pf3Jzw8nAEDBnDu3DnH86dOneLZZ5+lePHiFClShAcffJD9+/c7nk9pAfr666+pVq0afn5+/PnnnyQlJfHiiy9Srlw5AgICqF+/PmvWrAHMXlvPPfccCQkJjtau0aNHA2m71U6fPk2PHj0IDg6mcOHCRERE8PXXXwNw4sQJOnbsSFhYGEWKFKFGjRrMnz/f7Z/nxIkTCQ4OJjAwkG7duvHyyy87teCk1zX36KOPOrXQffrpp9SrV4/AwEBCQkLo1KkTx44dczy/Zs0abDYbq1atol69ehQpUoSGDRs6Qm1kZCRjxozh119/dfxMUvYdS92tlp7du3fz0EMPUbRoUYKDg+ncuTPHjx93PP/FF19Qo0YN/P39KVmyJM2bN3f6HYvkVQpHIpKu4cOHM3ToUKKjo7n11lvp2LEjV65cAWDHjh20atWKdu3asX37dhYuXMj69evp16+f4/Vdu3Zl69atLF26lI0bN2JZFg899BCXL192XHP+/HkmTJjABx98wK5duyhTpgzPPfccP/74IwsWLGD79u08+eSTPPDAA+zfv5+GDRsyffp0ihUrRlxcHHFxcQwdOjRN7cnJyTz44INs2LCBTz/9lN27dzNx4kR8fHwAuHjxInXr1uXrr79m586d9OjRg86dO/PTTz+5/PP5z3/+w6hRoxg3bhxbt26lbNmyzJgxw+2fc1JSEmPHjuXXX39lyZIlxMTEpNu9OXz4cN544w22bt1KwYIFef755wHo0KEDQ4YMoXr16o6fSYcOHa77vnFxcTRu3JhatWqxdetWli9fzl9//UX79u0dz3fs2JHnn3+ePXv2sGbNGtq1a6fNqSV/sEQkX/rwww+toKCgNOdjYmIswPrggw8c53bt2mUB1p49eyzLsqzOnTtbPXr0cHrdunXrrAIFClgXLlyw9u3bZwHWjz/+6Hj++PHjlr+/v/Wf//zH8f6AFR0d7bjmwIEDls1ms2JjY53u3axZM2vYsGHXrLtChQrWtGnTLMuyrP/+979WgQIFrL1797r883jooYesIUOGOI4bN25sDRw4MMPrGzRoYPXq1cvpXP369a077rjjmvdo27at1aVLlwzvu3nzZguwzpw5Y1mWZa1evdoCrO+++85xzbJlyyzAunDhgmVZljVq1Cin900BWIsXL7Ys65/f6y+//GJZlmWNHDnSatmypdP1hw8ftgBr79691rZt2yzAOnjwYIa1iuRVajkSkXTVrFnT8feyZcsCOLp7tm3bRmRkJEWLFnU8WrVqRXJyMjExMezZs4eCBQtSv359xz1KlizJbbfdxp49exznfH19nd7n559/xrIsbr31Vqd7r127lt9//93l2qOjowkLC+PWW29N93m73c64ceOoWbMmJUuWpGjRoqxYsYJDhw65/B579uyhQYMGTueuPnbFL7/8Qtu2balQoQKBgYE0adIEIE0t1/p9ZMa2bdtYvXq108/59ttvB+D333/njjvuoFmzZtSoUYMnn3yS2bNne2w8msiNpgHZIpKuQoUKOf5us9kA012V8mfPnj0ZMGBAmteVL1+effv2pXtPy7Ic9wIzADz1cXJyMj4+Pmzbts3RBZaiaNGiLtfu7+9/zeffeOMNpk2bxvTp06lRowYBAQEMGjSIpKQkl9/DFQUKFEjTDZW6W/HcuXO0bNmSli1b8umnn1K6dGkOHTpEq1at0tRyrd9HZiQnJ/PII48wadKkNM+VLVsWHx8fVq5cyYYNG1ixYgVvv/02w4cP56effqJSpUqZfl+R3EDhSETcVqdOHXbt2kXlypXTfb5atWpcuXKFn376iYYNGwJmEPS+ffuoWrVqhvetXbs2drudY8eO0ahRo3Sv8fX1xW63X7O+mjVrcuTIEfbt25du69G6deto27YtzzzzDGCCwv79+69Z29WqVq3Kpk2bePbZZx3nNm3a5HRN6dKlnWYI2u12du7cSdOmTQH47bffOH78OBMnTiQ8PByArVu3ulxDCld+JlerU6cOixYtomLFihQsmP5Xgc1m45577uGee+7hlVdeoUKFCixevJjBgwe7XaNIbqJuNRFx20svvcTGjRvp27cv0dHR7N+/n6VLl9K/f38AqlSpQtu2bXnhhRdYv349v/76K8888wzlypWjbdu2Gd731ltv5emnn+bZZ58lKiqKmJgYtmzZwqRJk/jmm28AMyvt7NmzrFq1iuPHj3P+/Pk092ncuDH33Xcfjz/+OCtXriQmJoZvv/2W5cuXA1C5cmVHq8iePXvo2bMn8fHxbv0MBg4cyNy5c5k7dy779u1j1KhR7Nq1y+ma+++/n2XLlrFs2TJ+++03+vTpw+nTpx3Ply9fHl9fX95++23++OMPli5dytixY92qI+VnEhMTQ3R0NMePH+fSpUvXfU3fvn05efIkHTt2ZPPmzfzxxx+sWLGC559/Hrvdzk8//cT48ePZunUrhw4dIioqir///tutACmSWykciYjbatasydq1a9m/fz+NGjWidu3ajBw50jEWBuDDDz+kbt26tG7dmgYNGmBZFt98841T91B6PvzwQ5599lmGDBnCbbfdRps2bfjpp58cLSsNGzakV69edOjQgdKlSzN58uR077No0SLuvPNOOnbsSLVq1XjxxRcdrSsjR46kTp06tGrViiZNmhASEsKjjz7q1s+gQ4cOvPLKK7z00kvUrVuXP//8k969eztd8/zzz9OlSxeeffZZGjduTKVKlRytRmBaliIjI/n888+pVq0aEydO5PXXX3erDoDHH3+cBx54gKZNm1K6dGmXliUIDQ3lxx9/xG6306pVKyIiIhg4cCBBQUEUKFCAYsWK8cMPP/DQQw9x6623MmLECN544w0tKin5gs26ukNcREQyZfTo0SxZskRbdIjkcmo5EhEREUlF4UhEREQkFXWriYiIiKSiliMRERGRVBSORERERFJROBIRERFJReFIREREJBWFIxEREZFUFI5EREREUlE4EhEREUlF4UhEREQkFYUjERERkVT+H1AIfUCivjsOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(result_stock['% Change w.o. Dividend'], plot = plt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9466741681098938, pvalue=0.017863910645246506)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(result_gold['% Change'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EBNUfiYiIFBLZ/f4utDVHIiIiIvlByZGIiIiIHSVHIiIiInaUHImIiIjYUXIkIiIiYkfJkYiIiIgdJUciIiIidpQciYiIiNhRciQiIiJiR8mRiIiIiB0lRyIiIiJ2lByJiIiI2FFyJCIiImJHyZGIiIiIHSVHIiIiInaUHImIiIjYUXIkIiIiYkfJkYiIiIgdJUciIiIidpQciYiIiNhRciQiIiJiR8mRiIiIiB0lRyIiIiJ2lByJiIiI2FFyJCIiImJHyZGIiIiIHSVHIiIiInaUHImIiIjYUXIkIiIiYkfJkYiIiIgdJUciIiIidpQciYiIiNhRciQiIiJiR8mRiIiIiB0lRyIiIiJ2lByJiIhIwZKa6tS3V3IkIiIiBcOFC/Dyy9Cli1PDKLTJ0dixY7FYLAwaNMjWZhgGkZGRBAYG4uHhQXh4OHv27HF4XVJSEgMGDMDX1xdPT0/atm3LsWPHbnH0IiIiYmMYsGwZ1KkDU6fCkiXw889OC6dQJkfbtm1j7ty51KtXz6F9/PjxTJ48mRkzZrBt2zYCAgJo2bIlFy5csPUZNGgQy5YtY8mSJWzcuJGLFy/Spk0brFbrrf4YIiIi8tdf0LYtdOgAx47B7bfD119D/fpOC6nQJUcXL16kS5cufPDBB5QrV87WbhgGU6dOZfjw4XTo0IGQkBA+/vhjLl++zKJFiwBISEhg3rx5TJo0iRYtWnDPPffw6aefsmvXLr799ltnfSQREZHiJyUFJkwwR4u++gpKloThw2H3bnjoIaeGVuiSo379+vHII4/QokULh/bY2Fji4+OJiIiwtbm7u9O0aVM2bdoEwI4dO0hJSXHoExgYSEhIiK1PRpKSkkhMTHR4iIiISC5t2gRhYfDqq3D5MjzwAOzcCe+8Ax4ezo6ucCVHS5Ys4eeff2bs2LHpnouPjwfA39/fod3f39/2XHx8PG5ubg4jTtf3ycjYsWPx9va2PYKDg2/2o4iIiBQ/585B795w//2waxf4+MBHH8G6deYIUgFRaJKjo0ePMnDgQD799FNKlSqVaT+LxeJwbRhGurbr3ajPsGHDSEhIsD2OHj2as+BFRESKM8OAzz6DWrVg7lyz7dlnYd8+888bfE/faoUmOdqxYwenTp0iLCwMV1dXXF1dWb9+Pe+99x6urq62EaPrR4BOnTpley4gIIDk5GTOnTuXaZ+MuLu7U7ZsWYeHiIiIZMMff0DLlvDMM3DqFNSuDevXmyNGvr7Oji5DhSY5at68Obt27WLnzp22R4MGDejSpQs7d+7k9ttvJyAggJiYGNtrkpOTWb9+PY0bNwYgLCyMkiVLOvSJi4tj9+7dtj4iIiKSB65ehZEjITQUvvsOSpUya4p27jRrjAowV2cHkF1eXl6EhIQ4tHl6euLj42NrHzRoEFFRUdSoUYMaNWoQFRVF6dKl6dy5MwDe3t707NmTIUOG4OPjQ/ny5Rk6dCihoaHpCrxFREQkl77/Hl580Rw1AmjVCt5/H+64w7lxZVOhSY6y49VXX+XKlSv07duXc+fO0bBhQ9asWYOXl5etz5QpU3B1deWpp57iypUrNG/enAULFuDi4uLEyEVERIqAU6dgyBD49FPzOiDA3NTxqacKXF1RViyGYRjODqKwSUxMxNvbm4SEBNUfiYiIpKbChx/Ca6/B+fNmItS3L4wZA97ezo7OJrvf30Vq5EhERERusV27oE8fc+8igLvvhjlz4F//cmpYN6PQFGSLiIhIAXLpkjlSVL++mRiVKQNTpsC2bYU6MQKNHImIiEhOffUV9O9vnosG5rlo06ZBUJBz48ojSo5EREQke44dg4EDITravK5c2VyF1qaNc+PKY5pWExERkaxdu2auOqtd20yMXFzMc9F+/73IJUagkSMRERHJyrZt5nlov/xiXjdqZBZch4Y6N658pJEjERERSS8hwawratjQTIxuu808F23jxiKdGIFGjkRERMSeYcC//w2DBkFcnNn2zDMwaRJUqODU0G4VJUciIiJi+vNP6NcPVq82r2vUgFmzoHlz58Z1i2laTUREpLhLToaoKKhb10yM3NwgMhJ++63YJUagkSMREZHibcMGc4fr3383rx980BwtqlnTuXE5kUaOREREiqMzZ+C55+CBB8zEyM8PPvkEvv22WCdGoORIRESkeDEMWLAAatWC+fPNthdegP37zcJri8Wp4RUEmlYTEREpLvbuNafQfvjBvA4NhdmzoXFj58ZVwGjkSEREpKi7cgXefBPuustMjEqXhvHjYccOJUYZ0MiRiIhIUfbNN9C3r7lMH8zjPmbMgCpVnBtXAaaRIxERkaIoLg6efhoeeshMjCpVMs9FW7lSidENKDkSEREpSqxWeP99s+D688+hRAl4+WWz3uixx1RwnQ2aVhMRESkqfvnFPCR22zbz+t57zUNi77nHuXEVMkqORERECrsLF+Dtt+G99yA1FcqWhbFjzUTJxSXDl1it5v6PcXFQsSI0aZJp12JHyZGIiEhhZRiwfDm89BIcO2a2Pf00TJ5sZjyZiI6GgQP/eQlAUBBMmwYdOuRvyIWBao5EREQKo7/+grZtzWzm2DG4/XbzXLTFi2+YGD3xhGNiBHD8uNkeHZ3PcRcCSo5EREQKMKsV1q0zc55168B6NQUmTIA6deCrr6BkSXMPo927oVWrG95r4EBzwOl6aW2DBpn9ijNNq4mIiBRQ109/NWIT80r2oXbKLrOhaVPzkNjatbN1vw0b0o8Y2TMMOHrU7BcefnOxF2YaORIRESmA7Ke/ynGWObzAJu6ndsouzuDD9v4LYO3abCdGYBZf52W/okrJkYiISAHzz/SXQRc+ZR+1eIEPAJjHc9RiPx1WdMeamrM9i7IoRcpVv6JK02oiIiL5KDdL5jdsAI9jf/AtL9Kc7wHYQx36MJuNNAHg71xMfzVpYq5KO34847oji8V8vkmT7N+zKNLIkYiISD6JjoaqVaFZM+jc2fyzatUbrAi7ehWf6ZHsIpTmfM8VSjGMKO7hF1tilCan018uLuZyfUi/UXba9dSp2u9IyZGIiEg+yNWS+e++g3r1CI0eiTvJfM1D1GUP4xhGCm7puudm+qtDB/jyS/OoNXtBQWa79jkCi2FkNLAmWUlMTMTb25uEhATKli3r7HBERKSAsVrNEaLMVoalTV/Fxv5vlObkSRgyBD77DACjYkVeTJrG3LNPYJC+rijd63MZY3HbITu739+qORIREclj2V4yvz6V8IMfwmuvwfnzZtbTrx+Wd94h4jtv5j4BFhzrg/Jq+svFpXgv18+KptVERETyWHZqgULYRd0+/2eef3b+PNSvDz/9BNOng7e3pr+cSCNHIiIieSyrWqDSXGIEIxnMZFwPWKFMGXjnHejXD1wdv5Y7dIB27Yrf9JezKTkSERHJY5ktmX+Er5hBf6ryFwBGh8exvDct/fCQHU1/3XqaVhMREclj1y+Zr8QxltKBr3iUqvzFYaqw6Y2vsCzNYN5MnE7JkYiISD7o0AGWfn6Nt8tOZS+16cAyUnBllter/PrZHhqPecTZIUomCk1yNGvWLOrVq0fZsmUpW7YsjRo14uuvv7Y9bxgGkZGRBAYG4uHhQXh4OHv27HG4R1JSEgMGDMDX1xdPT0/atm3LsayWE4iIiOTWTz/x2Nh/EZnwMl5c5HTNxuyc9zMvnHuXdp09nR2dZKHQJEdBQUGMGzeO7du3s337dh588EHatWtnS4DGjx/P5MmTmTFjBtu2bSMgIICWLVty4cIF2z0GDRrEsmXLWLJkCRs3buTixYu0adMGq9XqrI8lIiJFTUIC9O8P990Hv/wC5crB3Ln47d3Avc+Fqpi6MDAKsXLlyhkffvihkZqaagQEBBjjxo2zPXf16lXD29vbmD17tmEYhnH+/HmjZMmSxpIlS2x9jh8/bpQoUcJYvXp1jt43ISHBAIyEhIS8+SAiIlL4paYaxpIlhhEQYBhmHbZhdO1qGCdPOjsy+Z/sfn8XmpEje1arlSVLlnDp0iUaNWpEbGws8fHxRERE2Pq4u7vTtGlTNm3aBMCOHTtISUlx6BMYGEhISIitT2aSkpJITEx0eIiIiNgcOgStW8PTT0N8PNSsaR4FsnAhVKjg7OgkhwpVcrRr1y7KlCmDu7s7ffr0YdmyZdSpU4f4+HgA/P39Hfr7+/vbnouPj8fNzY1y5cpl2iczY8eOxdvb2/YIDg7Ow08lIiKFVnIyjBkDISHwzTfg7g4jR8Jvv8GDDzo7OsmlQrXP0Z133snOnTs5f/48S5cupXv37qxfv972vOW6I4YNw0jXdr3s9Bk2bBiDBw+2XScmJipBEhEp7n74Afr0gb17zevmzWHWLKhRw7lxyU0rVCNHbm5uVK9enQYNGjB27Fjuuusupk2bRkBAAEC6EaBTp07ZRpMCAgJITk7m3LlzmfbJjLu7u22VXNpDRESKqTNn4LnnoGlTMzGqUME8MDYmRolREVGokqPrGYZBUlIS1apVIyAggJiYGNtzycnJrF+/nsaNGwMQFhZGyZIlHfrExcWxe/duWx8REZFMGQbMnw+1apl/gnku2r590LnzPyfCSqFXaKbV3njjDVq3bk1wcDAXLlxgyZIlrFu3jtWrV2OxWBg0aBBRUVHUqFGDGjVqEBUVRenSpencuTMA3t7e9OzZkyFDhuDj40P58uUZOnQooaGhtGjRwsmfTkRECrTffzen0DZsMK9DQ2HOHGjUyLlxSb4oNMnRyZMn6dq1K3FxcXh7e1OvXj1Wr15Ny5YtAXj11Ve5cuUKffv25dy5czRs2JA1a9bg5eVlu8eUKVNwdXXlqaee4sqVKzRv3pwFCxbgok0nREQkI5cvmwXXEyZASgqULm0WXA8cCCVLOjs6yScWw7A/Ek+yIzExEW9vbxISElR/JCJSVK1eDX37Qmysef3oozB9OlSp4ty4JNey+/1dqGuORERE8tyJE9Cxo7lvUWwsBAXBsmWwYoUSo2Ki0EyriYiI5Aer1Swlij9uJeynWVRfMBxLYiKUKGFOn40cCf8r0UjrGxcHFStCkyboOJAiKMfJ0dGjR7FYLAQFBQHw008/sWjRIurUqcMLL7yQ5wGKiIjkl+hoM//xO/Yzc+hNDbYDcLbGvyj/+Wy45550fe3PKw8KgmnToEOHWx255KccT6t17tyZtWvXAua+Qi1btuSnn37ijTfeYNSoUXkeoIiISH6IjoYej19gyLFBbONe7mU7CZSlH+9T4cAmomMdE6MnnnBMjACOHzfbo6NvcfCSr3KcHO3evZt//etfAHzxxRe2s8kWLVrEggUL8jo+ERGRPGe9ZrD6+aX8Tm0GMQ0XUllCR2qxj5n0JdXiwqBB5jSa1WqOGGW0fCmtLa2vFA05nlZLSUnB3d0dgG+//Za2bdsCUKtWLeLi4vI2OhERkbx2+DDnO/Vn7tlVABzidvoykzW0snUxDDh69J9tja4fMbJn3zc8PB/jllsmxyNHdevWZfbs2WzYsIGYmBgeeughAE6cOIGPj0+eBygiIpInUlJg/HioWxefLatIpiSjeZMQdjskRvbi4sxHdmh8oOjI8cjRu+++y2OPPcaECRPo3r07d911FwArV660TbeJiIgUKJs2mUd97N4NwPm7mtLo11nso3aWL6tYMftvkZO+UrDlahNIq9VKYmIi5cqVs7UdPnyY0qVLU6FChTwNsCDSJpAiIoWD9fRZTj77OoGrPgDA8PHBMmkS1i7dqFrNwvHjGdcSWSzmSrS0/R+rViVbfbWsv2DL100gDcNgx44dzJkzhwsXLgDg5uZG6dKlcxetiIhIDlmtsG4dLF5s/ulQEG0YbHvpE84F1LIlRvN4jnpu+4n26o6Lq4Vp08yu158Xm3Y9daqZ7Li4kO2+UjTkODn666+/CA0NpV27dvTr14/Tp08DMH78eIYOHZrnAYqIiFwvOtoczWnWDDp3Nv+sWvV/S+r37+fUXS24d3o3fFNPs4c6NOEHejGPPfE+tqX3HTrAl19CpUqO9w4KMtvt9y7KSV8p/HI8rda+fXu8vLyYN28ePj4+/Prrr9x+++2sX7+eXr16ceDAgfyKtcDQtJqIiPOk7Tl0/bdXKa7yOuN403UsLteSuUIpRvE2kxhCCm62ftdPg+Vk12vtkF24Zff7O8cF2Rs3buTHH3/Ezc3Nob1KlSocP34855GKiIhkU2Z7Dj3Id8ziRWpyAK7Bf2lNf2YQy+3p7nH90nsXl+wvwc9JXym8cjytlpqaijWDna6OHTuG1//OnhEREckPGzY47jlUgZN8wjN8RwtqcoATVORJvuARVmWYGNnT0nvJTI6To5YtWzJ16lTbtcVi4eLFi4wYMYKHH344L2MTERFxkJbQWEjleeayj1o8w2ekYmE6/anNXr7kScCS5X1AS+8lczmeVpsyZQrNmjWjTp06XL16lc6dO3PgwAF8fX1ZvHhxfsQoIiICmAlNKL8xmz40ZjMAO6hPb+awgwa2fn5+cOZM1kvvmzS5VVFLYZPj5CgwMJCdO3eyePFifv75Z1JTU+nZsyddunTBw8MjP2IUERGBS5d44KuR/MxkXLFygTK8yTu8Tz+s//s6S0t8Jk+Gp54yr+0TJC29l+zI1SaQxZ1Wq4mI3GL/+Q/07w9HjgDwJY8ziGkc55+19WmJT9rS+uhos3jbvkYpONhMjLT0vnjKt9VqCxcuzPL5bt265fSWIiIi6Vit8NPSowRNHEjwtmVmY5Uq8P77lEh6BMtAwC7xCQpyTHw6dIB27bT0XnIuxyNH9keGAKSkpHD58mXbDtlnz57N0wALIo0ciYjkr2X/vsZvz09ncMLbeHGRFFyZ6zWEoNlv0a6zJ6A9hyTn8m3k6Ny5c+naDhw4wIsvvsgrr7yS09uJiIg4WPvuT1R9vTePsROAH2lMH2az52IoPANfljJHhbTnkOSXPKs52r59O8888wz79u3Li9sVaBo5EhHJG/ajP0FeCTT+6g0sc2ZRAoOzlONVxvMRz2H8b+cZHfIqNyPfRo4y4+LiwokTJ/LqdiIiUkRkNv31T8G0QUc+Zwov40I8AAvpylAmcpoKDve6fndrkfyQ4+Ro5cqVDteGYRAXF8eMGTO4//778ywwEREp/DJaMRYUBJ06wcSJUM04xNf04yG+AWA/NenDbNbRLMv7andryU85To7at2/vcG2xWPDz8+PBBx9k0qRJeRWXiIgUcpkdEHvsGEybkMQbTGA4Y/DgKldxJ4o3eJfXSMb9hvfW7taSn3KcHKWmpuZHHCIiUohdP3XWuHHGB8QCPMB6ZtOH2pg1qjG0oC8zOUiNG76PdreWWyHPao5ERKR4ymjqzNfXPL7Dng9nmMhQevAxACepwMtMYTGdyM5ZaNrdWm6VbCVHgwcPzvYNJ0+enOtgRESkYLt+hOjMGfOYjutHiOwTIwup9GABE3gFH86SioU59OYNojiP49559vz84PTpf66v3+RRJL9kKzn65ZdfsnUzi+XGmb+IiBROGY0QubhkPHWWpg57mMWLPMAGAH6lHr2Zw1buy/Q1aVNnBw/Cpk3a5FFuvWwlR2vXrs3vOEREpADLrLjaas24vweXeZN3eIUJlOQalyjNCEYyjYFco6StX1YHw7q5abm+OEcJZwcgIiIFm9WaeXF1Rh7ia3YTwhuMpSTXWEFbarOXSQzlGiWxWMwk6JVXoFIlx9cGBf1zcKyIs+SqIHvbtm38+9//5siRIyQnJzs8Fx0dnSeBiYiI89jXFp086TiVlpmKnGAqg3iKfwNwlCAGMJ1Nfu0zrR0aO1bno0nBk+PkaMmSJXTr1o2IiAhiYmKIiIjgwIEDxMfH89hjj+VHjCIikk8y2r16xYr0tUVZKYGVF5lFFG9Qlgtcw4VpDCSSkZQLLsOxLGqHdD6aFEQ5To6ioqKYMmUK/fr1w8vLi2nTplGtWjV69+5NRe3KJSJSaGRUYO3jA3//nf173MPPzKE397IdgK38i97M4TfL3QB8PFW1Q1L45Ljm6NChQzzyyCMAuLu7c+nSJSwWCy+//DJz587N8wBFRCTvpRVYXz86lN3EqAwXmMIgtnEv97KdBMrSl/dpzCZ+5W7VDkmhluORo/Lly3PhwgUAKlWqxO7duwkNDeX8+fNcvnw5zwMUEZG8ldMCa0cGj7GM93iJII4DsJinKTNnMk/VrEgT1Q5JEZDj5KhJkybExMQQGhrKU089xcCBA/n++++JiYmhefPm+RGjiIjkoQ0bsl9PZK8Kh5nOAB7lKwAOcTsjfGfSYU4rHtUIkRQh2Z5W27lzJwAzZszg6aefBmDYsGEMHTqUkydP0qFDB+bNm5cvQQKMHTuWe++9Fy8vLypUqED79u3Zv3+/Qx/DMIiMjCQwMBAPDw/Cw8PZs2ePQ5+kpCQGDBiAr68vnp6etG3blmO5+VdCRKSQyumJ9q6k8Crv8jt1eJSvsLqUZHf74RxfvZuP41tp6kyKnGwnR/Xr1ycsLIzPP/8cT09P88UlSvDqq6+ycuVKJk+eTLlymW8Df7PWr19Pv3792LJlCzExMVy7do2IiAguXbpk6zN+/HgmT57MjBkz2LZtGwEBAbRs2dI2DQgwaNAgli1bxpIlS9i4cSMXL16kTZs2WDPbyUxEpIiwWmHdOvj99+y/pjE/8jP1eZfXKc0VNrs3hZ2/ErLsHR5o5aGpMymajGzatGmT0atXL6Ns2bKGh4eH0aVLF+P777/P7svz3KlTpwzAWL9+vWEYhpGammoEBAQY48aNs/W5evWq4e3tbcyePdswDMM4f/68UbJkSWPJkiW2PsePHzdKlChhrF69OtvvnZCQYABGQkJCHn0aEZH8tXSpYQQFGYZZaXTjRzn+NubSy9ZwGh+jOwuMpV+mOvujiORadr+/sz1y1KhRIz744APi4+OZNWsWx44do0WLFtxxxx2MGTPmlk9NJSQkAGaBOEBsbCzx8fFERETY+ri7u9O0aVM2bdoEwI4dO0hJSXHoExgYSEhIiK1PRpKSkkhMTHR4iIgUFpmtTMuIBYNn+IT9llo8z4cAzOM5HgzcT9ul3enwuM7QlKIvx0v5PTw86N69O+vWreOPP/6gU6dOzJkzh2rVqvHwww/nR4zpGIbB4MGD+b//+z9CQkIAiI+PB8Df39+hr7+/v+25+Ph43Nzc0k3/2ffJyNixY/H29rY9goOD8/LjiIjkm5ysTKvJfja4t+ATuuFnnOZSlTp8+/YP3LF2Hr8c8VFtkRQbN3W22h133MHrr7/O8OHDKVu2LN98801exZWl/v3789tvv7F48eJ0z1ksjv9VYxhGurbr3ajPsGHDSEhIsD2OHj2au8BFRG6x7KxMc+cq3zWJZG/Jetyf9D2UKgVRUXj+8QstRjYhPFzL8qV4ydXZamAWSH/00UcsXboUFxcXnnrqKXr27JmXsWVowIABrFy5kh9++IGgoCBbe0BAAGCODtnv1H3q1CnbaFJAQADJycmcO3fOYfTo1KlTNG7cONP3dHd3x93dPa8/iohIvrA/EuRGxdfN+ZaZ9KXmhgNmQ+vWMGMG3H57/gcqUkDlaOTo6NGjjB49mjvuuINmzZpx6NAhpk+fzokTJ/jggw+477778itODMOgf//+REdH8/3331OtWjWH56tVq0ZAQAAxMTG2tuTkZNavX29LfMLCwihZsqRDn7i4OHbv3p1lciQiUlhER0PVqtCsGXTuDO+8k3G/CpzkE57hW1pSkwMk+VSEL76AVauUGEmxl+2Ro5YtW7J27Vr8/Pzo1q0bzz33HHfeeWd+xuagX79+LFq0iBUrVuDl5WWrEfL29sbDwwOLxcKgQYOIioqiRo0a1KhRg6ioKEqXLk3nzp1tfXv27MmQIUPw8fGhfPnyDB06lNDQUFq0aHHLPouISF6xHyU6cAAiI7OuL7KQyvN8wDhepxznScXCx2X60+3AO1Cu7C2LW6Qgy3Zy5OHhwdKlS2nTpg0uTph8njVrFgDh151eOH/+fHr06AHAq6++ypUrV+jbty/nzp2jYcOGrFmzBi8vL1v/KVOm4OrqylNPPcWVK1do3rw5CxYscMpnEhG5GRkdHJuVUH5jNn1ozGYAdlCfPsxh2McNcMm/bepECh2LYeTudJ3iLDExEW9vbxISEihbVv+lJSK3Xtry/Oz8C16aS0QSyctMwRUriXjxJu+wMqgfk6e5aBWaFBvZ/f7OdUG2iIg4R06W5z/KSqYzgCocAWBPrcc50H8aHepWYooOhxXJkJIjEZFCJjvL84M4ynu8xGMsByCWqvRnBq/MeoT24fkeokihdlP7HImIyK2X1cGxLlzjZSazl9o8xnJScGUcrxHCHnYFP0KTJrcuTpHCSiNHIiKFjN1Wbg7u5Sfm0Jt72AnARu6nD7P53WKeJDB1qqbRRLIjW8nRypUrs33Dtm3b5joYERG5sSZNICgIjh836468OU8Ub9CH2ZTA4CzleJXxfMRzGJQgOMhMjFR4LZI92VqtVqKE4+ybxWLB/mX2R29YrdY8DK9g0mo1EXGGdHsajTB4miVM5mUCOAnAQroylIn0H1mBGjXMUaYmKrwWAfJ4tVpqaqrt799++y2vvfYaUVFRNGrUCIvFwqZNm3jzzTeJioq6+chFRIoZ+6QnLZkBx7YzZ+Dll/8pxL6dQ3xbsi8PpqwBYD816cNsDgU3Y/ZUjRKJ3Iwc73MUEhLC7Nmz+b//+z+H9g0bNvDCCy+wd+/ePA2wINLIkYjklYw2cvTxMf/8++/0/d1I4hUmMJwxeHCVq7izpdkbnOzxGv6V3TVKJJKFfNvn6NChQ3h7e6dr9/b25vDhwzm9nYhIsZXZRo4ZJUUAD7Ce2fShNvsAiKEF/ZjJ1YM1iO2ipEgkr+R4Kf+9997LoEGDiLNbSxofH8+QIUP417/+lafBiYgUVTnZyNGHM8ynB+sJpzb7OEkFOvMZEazhADU4etScghORvJHj5Oijjz7i1KlTVKlSherVq1O9enUqV65MXFwc8+bNy48YRUSKnOxs5GghlWf5iP3cSQ8+JhULs+hDLfaxmM7AP4thstr7SERyJsfTatWrV+e3334jJiaGffv2YRgGderUoUWLFg6r1kREJHM3SmbqsIdZvMgDmENCv1KP3sxhK/dl2D+zvY9EJOdytQmkxWIhIiKCBx54AHd3dyVFIiLZlLYy7fffM37eg8u8yTu8wgRKco2LeDKCkUxjINYM/sm2WMw9j7TztUjeyXFylJqaypgxY5g9ezYnT57kjz/+4Pbbb+ett96iatWq9OzZMz/iFBEplK7fm+iDDzKfTnuIr3mfftxOLADLacdLvMdRKmfYP+2/S7XztUjeynFy9M477/Dxxx8zfvx4nn/+eVt7aGgoU6ZMUXIkIsXW9fsVXb83UWYqcoKpDOIp/g3AEYIZwHRW0i7L1wVp52uRfJHj5GjhwoXMnTuX5s2b06dPH1t7vXr12LdvX54GJyJSWGS0X9GNlMBKX2YyhuGU5QLXcGEaA3mv/EguWcqA3ZL+4GCYNAn8/Bw3i9SIkUjey3FydPz4capXr56uPTU1lZSUlDwJSkSkoEt3lEdk9pblp7mHn5lDb+5lOwBbaMiOXrMJ63I3f2awQ7YSIZFbJ8fJUd26ddmwYQNVqlRxaP/3v//NPffck2eBiYgUVLkZJUrjRSKjeJsBTMeFVM7jzTDGMpcX+PRBF8LD/+lr/3cRuXVynByNGDGCrl27cvz4cVJTU4mOjmb//v0sXLiQr776Kj9iFBEpMDLb1frGDDoQzXu8RCVOALCITgxmMicJALQcX6SgyPHZagDffPMNUVFR7Nixg9TUVOrXr8/bb79NREREfsRY4OhsNZHiyWqFqlVzPmJUhcO8Tz8e4b8AHOQO+jKTGMx/M9OW48fGaupMJD/ly9lq165dY8yYMTz33HOsX7/+poMUESksrFaYPj1niZErKQxmMiMYSWmukExJxvE6YxnGVTwALccXKYhydHyIq6srEyZMwGq15lc8IiIFTnS0OWL08svZf01jfuRn6vMur1OaK6wlnHr8xghG2RIjMEeMvvxSy/FFCpIc1xy1aNGCdevW0aNHj3wIR0SkYMlpjVE5zvIur/E8HwJwGl+GMIlP6ApYCAqC55+HGjW0Ck2koMpxctS6dWuGDRvG7t27CQsLw9PT0+H5tm3b5llwIiLOZLWaq9KylxgZPMOnTGIIFTgNwGLPnpR+712eu92H1lqSL1Jo5Lggu0SJzGfiLBZLsZhyU0G2SPGwbh00a3bjfjXZzyxe5EHWApBQqQ5/vjqbev2aKBESKUDypSAbzM0eRUSKg7i4rJ935yrDGMvrjMOdZKxupXCJfBvvIUO4x83t1gQpInkux8mRvatXr1KqVKm8ikVEpEDJat+hFsQwk77U4CAAxkOtcXl/Btx++y2KTkTyS45WqwFYrVZGjx5NpUqVKFOmDH/++ScAb731FvPmzcvzAEVEnKVJE3M1WdpyewB/4vmMzsQQQQ0OcrJERaxLvsDy31VKjESKiBwnR2PGjGHBggWMHz8eN7th49DQUD788MM8DU5ExJlcXGDaNPPvJUilN7PZRy06s5hULExnAFs/3odLxycdMygRKdRynBwtXLiQuXPn0qVLF1zsKg3r1avHvn378jQ4ERFnsFrNYuzFi6F8efhm/K9sc2vMbF7kNhLYQX3a+v9EpaXv0fYZLcoQKWpyXHN0/Phxqlevnq49NTWVlJSUPAlKRMRZ7A+V9eQikUQyiKm4YuVaaS9+fXw0F7v3Y0W4q1aiiRRROU6O6taty4YNG6hSpYpD+7///W/uueeePAtMRORWs9/w8VFWMoP+VOYoAF/yOKWnTuPh5ys5OUoRyW85To5GjBhB165dOX78OKmpqURHR7N//34WLlzIV199lR8xiojkG6sVNmyA48fN40EqGUeZzgDaswKAWKrSnxl8bXmEoNEQ+5w2cRQp6nJcc/Too4/y+eef89///heLxcLbb7/N3r17+c9//kPLli3zI0YRkXyRdmZas2bQ/ZlrPHN6MnupTXtWkIIrY3mduuzhvzyCYcDRo2YiJSJFW672OWrVqhWtWrXK61hERG4Z+ym0e/mJOfTmHnYCsJH76cNs9hCS7nU32hhSRAq/HI8ciYgUdmlnppU1zvM+fdnCfdzDTs5Sjl58wAP8kGFiBFlvDCkiRUO2kqNy5cpRvnz5bD3y0w8//MCjjz5KYGAgFouF5cuXOzxvGAaRkZEEBgbi4eFBeHg4e/bsceiTlJTEgAED8PX1xdPTk7Zt23Ls2LF8jVtECg6rFaa/Z3D/sSXspTZ9mUUJDBbSlVrsYx69MDL4p9FigeBgc2NIESnasjWtNnXqVNvf//77b9555x1atWpFo0aNANi8eTPffPMNb731Vr4EmebSpUvcddddPPvsszz++OPpnh8/fjyTJ09mwYIF1KxZk3feeYeWLVuyf/9+vLy8ABg0aBD/+c9/WLJkCT4+PgwZMoQ2bdqwY8cOh32bRKToiY6Gyf0O8VZ8XwaxBoB93MmLzGIdmZ8wm7a/49SpKsYWKRaMHOrQoYMxffr0dO3Tp0832rVrl9Pb5RpgLFu2zHadmppqBAQEGOPGjbO1Xb161fD29jZmz55tGIZhnD9/3ihZsqSxZMkSW5/jx48bJUqUMFavXp3t905ISDAAIyEh4eY/iIjkm2vXDGPtWsNYtMgw3nnrqvEmo40ruBsGGFdwN95klOHGVcOsPMr8ERxsGEuXOvvTiMjNyu73d45rjr755hseeuihdO2tWrXi22+/vflsLZdiY2OJj48nIiLC1ubu7k7Tpk3ZtGkTADt27CAlJcWhT2BgICEhIbY+GUlKSiIxMdHhISIFm/1KtDmd19Fh9N2M5i1KkUQMLQhlF+/wFsm4Z/h6Pz/49FNYuxZiY6FDh1sbv4g4T46TIx8fH5YtW5auffny5fj4+ORJULkRHx8PgL+/v0O7v7+/7bn4+Hjc3NwoV65cpn0yMnbsWLy9vW2P4ODgPI5eRG6W/ZEfo0aZK9GuHjvNfHqwjmbUZh/x+NOJRUSwhoPUyPA+Fov5mD0bunSB8HBNpYkUNzleyj9y5Eh69uzJunXrbDVHW7ZsYfXq1QXi4FnLdYc/GoaRru16N+ozbNgwBg8ebLtOTExUgiRSgNgf+QFgIZVnmc94XsWHs6RiYTZ9eIMoErgty3sFBZm1RRopEim+cpwc9ejRg9q1a/Pee+8RHR2NYRjUqVOHH3/8kYYNG+ZHjNkSEBAAmKNDFe3W2p46dco2mhQQEEBycjLnzp1zGD06deoUjRs3zvTe7u7uuLtnPPQuIs5lv18RQB32MJs+NGEjADu5iz7MZiv33fBeU6bAgAEaKRIp7nI0rZaSksKzzz6Ln58fn332GT///DO//PILn332mVMTI4Bq1aoREBBATEyMrS05OZn169fbEp+wsDBKlizp0CcuLo7du3dnmRyJSMGUtl+RYYAHlxnDG+zkbpqwkYt4MoSJNGD7DROjtGX6SoxEBHI4clSyZEmWLVuW70v2M3Px4kUOHjxou46NjWXnzp2UL1+eypUrM2jQIKKioqhRowY1atQgKiqK0qVL07lzZwC8vb3p2bMnQ4YMwcfHh/LlyzN06FBCQ0Np0aKFUz6TiORc2nlo331nTqU9xNe8Tz9uJxaA5bTjJd7jKJVveC8t0xeRdHK6DK5Hjx7GpEmTcreG7iatXbvWANI9unfvbhiGuZx/xIgRRkBAgOHu7m488MADxq5duxzuceXKFaN///5G+fLlDQ8PD6NNmzbGkSNHchSHlvKLOM/SpYYRFGQusa/IceMLnrCtuf+LYKMty2+4NF/L9EWKp+x+f1sMI22mPnvGjBnDxIkTad68OWFhYXh6ejo8/9JLL+VN1laAJSYm4u3tTUJCAmXLlnV2OCLFRlp9kcWw0peZjGE4ZbnANVyYyiAiieQSZTJ9vcVipkQjR0KNGuZRIE2aaMRIpLjI7vd3jpOjatWqZX4zi4U///wzJ7crlJQcidx6Vqu5b1GFYzuYQ28asAOALTSkD7P5lbtveI/gYK1EEynOsvv9nePVarGxsTcVmIhITlmtMGdCIkOPvUV/ZuBCKufxZhhjmcsLpJJ+6EejRCKSWzlOjtKcOXMGi8Xi1I0fRaToi15qsPqFaEacfYlKnABgEZ0YzGROEpDp67RfkYjkVo6W8p8/f55+/frh6+uLv78/FSpUwNfXl/79+3P+/Pl8ClFEiqvVsw/j/kQb5p59gkqc4ADVackaurAo08TozTd15IeI3Jxs1xydPXuWRo0acfz4cbp06ULt2rUxDIO9e/eyaNEigoOD2bRpU7qjOYoi1RyJ5LOUFFInTiZp+Eg8jCskU5JxvE4Ub5BEqQxfYrGYo0WxsZo6E5GM5XnN0ahRo3Bzc+PQoUPpzi8bNWoUERERjBo1iilTpuQ+ahGRH3+EPn0osXs3HsBawnmRWeynVqYv0V5FIpKXsj2ttnz5ciZOnJguMQLzWI7x48dneCCtiEi2nD0Lzz8P//d/sHs3V7186cbHPMj3WSZGYI4YffmlptFEJG9kOzmKi4ujbt26mT4fEhKS5cn2IiIZMgxYuBDuvBPSDq/u1Yvtn+zjE7oBWR8cPWWK6otEJG9lOzny9fXl8OHDmT4fGxurlWsikjP790Pz5tC9O5w5A3XrYl23gXVdPuCviz74+f0zZXY9nYcmIvkl2zVHDz30EMOHDycmJgY3NzeH55KSknjrrbd46KGH8jxAESmCrl6FqCh4911ITgYPDxgxgmVVX+alZ9w4dizrl6vGSETyU7ZXqx07dowGDRrg7u5Ov379qFXLrAH4/fffmTlzJklJSWzfvp3g4OB8Dbgg0Go1kZvw7bfw4ouQdoj0ww/DjBlE/1KNJ54wZ9luRDtdi0hu5PlqtaCgIDZv3kzfvn0ZNmwYaTmVxWKhZcuWzJgxo1gkRiKSSydPwuDBsGiReR0YCNOmweOPY021MPCBrBMjPz+zvqhSJe10LSL5K0c7ZFerVo2vv/6ac+fOceDAAQCqV69O+fLl8yU4ESkCUlNh7lx4/XVISIASJaB/fxg9Gv73X24bNnDDqbTTp83EKDw8/0MWkeItV8eHlCtXjn/96195HYuIFDW//gp9+sCWLeZ1WBjMng0NGjh0i4vL3u2y209E5Gbk6PgQEZFsuXgRhg41k6EtW8DLC957D7ZuTZcYgXkobHZkt5+IyM3I9cGzIiIZWrHCXF9/9Kh5/cQTZvV0pUrpulqt5pTa8eNmTdGZMxnXHaUdDdKkSf6GLiICSo5EJK8cOQIvvWQmRwBVq8L775ur0TIQHQ0DB9641kjL9kXkVtO0mojcnGvXYNIkqFPHTIxcXc3i6z17skyMnnjixokR6GgQEbn1NHIkIrm3ZYtZcP3rr+b1/febBdchIZm+xGo1R4y0bF9ECiqNHIlIzp0/b27k2LixmRiVKwcffAA//JBlYgQ5X7avxEhEbjWNHIlI9hkGLFkCL79sbuoI5rloEyaYwz3ZoGX7IlLQKTkSkew5eBD69oWYGPP6zjvNKbQc7sqoZfsiUtBpWk1EspaUZO5mHRJiJkbu7ub1r7/marvqJk3MIuu0VWjXs1jMs9O0bF9EnEXJkYhkbt06uOsuePttM0lq2RJ274Y33zSTpFxwcTGPVIP0CZKW7YtIQaDkSETSO33arCVq1gz27wd/f1i8GL75BqpXv+nbd+hgLs+/fl9ILdsXkYJANUci8o/UVJg/H159Fc6eNYdy+vSBqCi47bY8fasOHaBdO3P1WlycWWOkZfsiUhAoORIR0549ZiK0caN5fdddZsH1fffl21u6uOSqbElEJF8pORIp7i5fNgusJ040d7v29IRRo8yjQFzz7p+ItHPUNEokIgWdkiOR4uzrr6FfP4iNNa/btzerpStXztO3yegctaAg861UXyQiBY0KskWKoxMn4MknzbPPYmPNtfMrVsCyZfmSGGV0jtrx42Z7dHSevp2IyE1TciRSnFitMH061KplLgtzcYGhQ+H336Ft2zx9m3Xr4LPPzDKmjM5RS2sbNMjsLyJSUGhaTaS42LEDevc2/wRo2BDmzDELr/NQRlNomTEMOHrUrEVSYbaIFBRKjkSKusREeOstmDHDXKrv7Q3jxsELL0CJmx88ti+0PnAAIiMzHinKis5RE5GCRMmRSFFlGLB0qTmMc+KE2dapE0yeDAEBefIWORklyorOURORgkTJkUhRFBsL/fvDf/9rXlevDjNnmsd/5JG0QuucjhLZs1jMVWs6R01EChIVZIsUJSkp5pRZ3bpmYuTmZp6LtmtXniZGVqs5YnSziRHoHDURKXg0ciRSVGzcaC4N27PHvA4Ph1mzzJVpeWzDhpufSgsKMhMj7XMkIgVNsR05mjlzJtWqVaNUqVKEhYWxYcMGZ4ckkjt//w29eplzU3v2gK8vfPwxfP99viRGkPsCaj8/+PRTWLvWnPlTYiQiBVGxTI4+//xzBg0axPDhw/nll19o0qQJrVu35siRI84OTST7DAMWLjQToHnzzLZevWD/fujW7Z95q3yQ0wJqi8V8zJ4NXbqYg1qaShORgqpYJkeTJ0+mZ8+e9OrVi9q1azN16lSCg4OZNWuWs0MTyZ59++DBB6F7dzhzxqwx2rABPvgAypfPl7dM29hx8WLz70FB2c+/goLMPSc1UiQihUGxqzlKTk5mx44dvP766w7tERERbNq0KcPXJCUlkZSUZLtOTEzM1xhFMnXlCowdaxZdp6SAhweMGAEvv2wWX+eTjJbs+/iYg1cWi2Nhdtr1yJFQo4YOmRWRwqfYJUdnzpzBarXi7+/v0O7v7098fHyGrxk7diwjR468FeGJZC4mBl58EQ4dMq9bt4b334dq1fL1bTNbsn/2rPln+fJm2VMaFVqLSGFX7JKjNJbr5gMMw0jXlmbYsGEMHjzYdp2YmEhwcHC+xidiEx8Pgweb81kAgYHmcfaPP55vdUVpu14fP24OSmV2NprFYg5effstnDqlUSIRKRqKXXLk6+uLi4tLulGiU6dOpRtNSuPu7o67u/utCE/kH6mp5tlnw4ZBQoJ51Ef//jB6NJQtmydvYX/0R1pis2JFzs5GO3bMTIY6dcqTkEREnK7YJUdubm6EhYURExPDY489ZmuPiYmhXbt2ToxMxM7OneaeRVu3mtdhYWaiFBZmJjTrHBOatJGajJIdF5fsJ0E+Po5TZNmls9FEpCgpdskRwODBg+natSsNGjSgUaNGzJ07lyNHjtCnTx9nhybF3cWLZoH1tGlmRuPlBWPGQN++4OKSYWF0UJDZHTJ+rlMnc0YuO0lQbhIj0NloIlK0WAzjZg4AKLxmzpzJ+PHjiYuLIyQkhClTpvDAAw9k67WJiYl4e3uTkJBA2Tya3hBhxQoYMACOHjWvn3wSpkyBSpWAzAujr18tdiulnY0WG6s6IxEp+LL7/V1sk6OboeRI8tSRI/DSS2ZyBFC1Ktb33meD18O2abDGjeGOO27+yI68lFYLrv2LRKSwyO73d7GcVhMpEK5dM+fDRoyAS5fA1RVeeYUVoW/Sv29ph0TI19fc67Eg0ZJ9ESmqlByJOMPWrdC7N/z6q3n9f/8Hs2cTvb9uhlNnBSUx8vP7Z6ZPS/ZFpKhSciRyK50/D2+8YR4yZhjmDooTJkCPHliNEgx8yHn1Q1lJm0KbPVsjRSJS9BXLs9VEbjnDMJeM1aoFs2aZ1927m2ekPfcclCjBhg3OrylKS4J8fBzbdTaaiBQnGjkSyW8HD5pL8WNizOu0BCk83KFbbvcKsl+tlt2Va2n9rl/Sn1ZH1K5dxvsliYgUB0qORPJLUhKMH2/uU5SUBO7u8Oab8Mor4O6ebmPGChWyd1s/Pzh9+p/rtIQG0u9zFBwMTz+dfp+j7CRB1+VuIiLFhpby54KW8ssNrVuH0acPlv37ATjboCXen87E5c7qQMan3FeqBFevmge6ZvT/yrQ9hQ4ehE2bbn6HbI0EiUhxo32O8pGSI8k02Th9GoYOhYULAYjHn5eZwhKeJijIYtvJ+kabOV4/PaY9hUREbp72ORLJJxmN+gRXSmV524+ov+RVOHeOVCzMpg9vEEUCtwHmCfePP27W+WR1yn358uZJ9xlNgykxEhHJf0qORK6T1RRURkd41GU3s4/3of6sHwHYU/IunkuZw080dLhv2muyOr/MMMznv/3WfE9Ng4mI3HpKjkTsZHWwa7t25nNpSY4Hl3mbUQxhEiW5xkU8Ge85iqhLL2G9yf9rnTplHhgrIiK3npIjkf/J7GDX48fN9sjIf5Kmh1nFDPpTjcMALKM9A5nG0UuV8yQWnXIvIuI8So5EMKfS7EeF7KXVAk2bBoEcZxoDeYKlABwhmAFMZyXt8iSOtBVpTZrkye1ERCQXtEO2FAtWK6xbZ+73s26deW3vRrtTWwwrXc6+x15q8wRLuYYLExhKHX5Plxj5+f2zuizdfSxmQbbFkr5P2vXUqaovEhFxJiVHUuRFR0PVqtCsGXTubP5ZtarZniar3anD2M5WGvIeAynLBTZzH2Hs4FUmcIkytn4Wi7np4syZ/1zbS7ueO9dckl+pkuPzOqJDRKRg0LSaFDo52dDwRnVEaclIRjU+XiTyDm/Sj/dxIZVz3MaPbcbR7qvnMSwlIIN9iNKW23/5ZcaF3fbL8XVEh4hIwaRNIHNBm0A6T1arya4fcbFazRGizKbL0up7YmPN66pVzaTJMAweZynTGEglTgDwGZ2ZFDiZbUf8WbEi42M6rt+HSLtSi4gULNohOx8pObo5uU0aMhsFymz36HXrzCm0G1m71jxHLDoahj4eywz68TBfA3CA6vRlFt9ZWjjcX4mPiEjhox2ypUDKyciPveysJhs0yJyqSktSsnvKfVwckJJChz8m0dZtFK7JV0jCjXG8zliGUSG4FF9OdYzPxUUHs4qIFFVKjuSWyW79T0ZutJrMMODoUbNfWtKS3b2Cap3ZCPf0gT17cAWM8GbsfHYWNUveyWqNComIFDtKjuSWyM3Ij70cjQL9T5Mm5qiUWUeUvq8PfzPd8zXueWme2eDnB5MmYXnmGRpaLNcd/iEiIsWFlvJLpm60N1BO5GTkJyPZHQWy7+fiYk7XwfXL6g26sZC91KLTpf8lRr16wb590LVr5psUiYhIsaDkSDKUnb2BciI3Iz/20kaBstpcMTg4/c7Sacvq0/YUupN9fM+DfEx3/DgDISGwcSN88AGUL5+9IEVEpEhTciTppNUGXT/Sk1YblJsEKTcjP/YyHwW68c7SHTrA4X1Xie36Nntc6tGMdRgeHjBuHPz8M9x/f/aCExGRYkHJkTi4UW0QmLVBOZ1iy+3Ij73rR4HS3HBn6ZgYXO4Koeono3GxpsDDD2PZswdeew1KlszZBxERkSJPyZE4uNnaoMzczMiPvQ4d4PBhc2+iRYvMP2NjM0mM4uPNOcGICDh0CAIDzSzqq6+gWrWcfQARESk2tFqtgCgomwrebG1QVrJ7rMaN3HCPodRUmDMHhg2DhAQoUQIGDIBRo0CbdoqIyA0oOSoAcrsxYn642dqgG+nQIZ/PFPv1V+jdG7ZuNa8bNIDZsyEsLI/eQEREijodH5ILeXl8SE6PxMhvaeeRZbY3kP15ZAVqY8SLFyEy0hyCslrBywuiouDFFwtYoCIi4izZ/f5WzZET5Vfx883Iq9qgW2rFCqhTByZNMn9YTz5p7lnUv38BC1RERAoDJUdOlF/Fzzcr16vCbrUjR6B9e/Nx9KhZZP3f/8IXX5jF1yIiIrmgmiMnys/i55uV77VBN+PaNXN4a8QIuHQJXF3hlVfgzTehdGlnRyciIoWckiMnyu/i55tVIE+e37IF+vQxC68B/u//zILrunWdG5eIiBQZmlZzorzYGLHYOH/eLK5u3NhMjMqXhw8/hPXrlRiJiEieUnLkRIWy+PlWMwzz5NtatcwRIsOA7t3NguuePc09jERERPKQvlmcrNAUPzvDwYPQqpW5y/XJk2aCtHYtLFgAfn7Ojk5ERIqoQpMcjRkzhsaNG1O6dGluu+22DPscOXKERx99FE9PT3x9fXnppZdITk526LNr1y6aNm2Kh4cHlSpVYtSoUTh7q6ccHYlRHCQlwejREBICMTHg7m5e79xZAIugRESkqCk0BdnJyck8+eSTNGrUiHnz5qV73mq18sgjj+Dn58fGjRv5+++/6d69O4ZhMH36dMDc/Klly5Y0a9aMbdu28ccff9CjRw88PT0ZMmTIrf5IDgpk8bMzrF1r1hbt329eR0TA++9D9erOjUtERIqNQpMcjRw5EoAFCxZk+PyaNWv4/fffOXr0KIH/2+Nm0qRJ9OjRgzFjxlC2bFk+++wzrl69yoIFC3B3dyckJIQ//viDyZMnM3jwYCyZVUZL/jt9GoYOhYULzWt/f7PgqmPHzCvWRURE8kGhmVa7kc2bNxMSEmJLjABatWpFUlISO3bssPVp2rQp7u7uDn1OnDjB4cOHb3XIAuYhsR9+CHfeaSZGFgv07WsWXD/9tBIjERG55QrNyNGNxMfH4+/v79BWrlw53NzciI+Pt/WpWrWqQ5+018THx1OtWrUM752UlERSUpLtOjExMQ8jL8Z27zb3LPrxR/P67rvNFWkNGzo1LBERKd6cOnIUGRmJxWLJ8rF9+/Zs3y+jaTHDMBzar++TVoyd1ZTa2LFj8fb2tj2Cg4OzHZNk4PJleP11uOceMzHy9ITJk2HbNiVGIiLidE4dOerfvz9PP/10ln2uH+nJTEBAAFu3bnVoO3fuHCkpKbbRoYCAANsoUppTp04BpBt1sjds2DAGDx5su05MTFSClFurVpkHwqZNY7ZvD++9Z+52KSIiUgA4NTny9fXF19c3T+7VqFEjxowZQ1xcHBX/d97GmjVrcHd3JywszNbnjTfeIDk5GTc3N1ufwMDALJMwd3d3hzolyYXjx2HgQFi61LyuXBmmT4e2bZ0bl4iIyHUKTUH2kSNH2LlzJ0eOHMFqtbJz50527tzJxYsXAYiIiKBOnTp07dqVX375he+++46hQ4fy/PPPU7ZsWQA6d+6Mu7s7PXr0YPfu3SxbtoyoqCitVMtPVqu5DXitWmZi5OJirkrbs0eJkYiIFEgWw9k7IGZTjx49+Pjjj9O1r127lvD/bRB05MgR+vbty/fff4+HhwedO3dm4sSJDqM+u3btol+/fvz000+UK1eOPn368Pbbb+coOUpMTMTb25uEhARb4iUZ2L4deveGn382r++7D+bMgXr1nBuXiIgUS9n9/i40yVFBouToBhIT4c03zc0bU1Phtttg3Dh4/nmdhSYiIk6T3e/vIrOUXwoAwzAPhBs4EOLizLYuXWDSJHNTRxERkUJAyZHkjdhY6NcPvv7avK5RA2bOhBYtnBuXiIhIDmmOQ25OcrI5ZVa3rpkYubnB22/Db78pMRIRkUJJI0eSexs3mjtc79ljXjdrBrNmmUeBiIiIFFIaOZKc+/tv6NULmjQxEyM/P/NctO++U2IkIiKFnpIjyT7DgI8/NvcsmjfPbHv+efOQ2K5ddUisiIgUCZpWk+zZtw9efBHWrTOvQ0LMQ2Lvv9+pYYmIiOQ1jRxJ1q5cMQus69UzEyMPD3j3XXNjRyVGIiJSBGnkSDIXEwN9+8LBg+b1I4/AjBmQzcOARURECiONHEl68fHQuTNERJiJUWCgubnjf/6jxEhERIo8JUfyj9RUcyl+rVqweLF51MfAgbB3Lzz+uAquRUSkWNC0mph27jT3LNq61bxu0MAsuA4Lc2pYIiIit5pGjoq7ixdhyBAzGdq6Fby8YPp02LJFiZGIiBRLGjkqzpYvhwED4Ngx8/rJJ2HqVLPGSEREpJhSclQcHTliJkUrV5rX1arB++9D69bOjUtERKQA0LRacZKSAhMnQu3aZmLk6grDhsHu3UqMRERE/kcjR8XFli3Quzf89pt53aSJuTKtbl3nxiUiIlLAaOSoqDt/3jz2o3FjMzEqX948F23dOiVGIiIiGdDIUVFlGLBkCbz8Mpw8abb16AETJoCvr1NDExERKciUHBVFBw+ax37ExJjXtWqZexY1bercuERERAoBTasVJUlJMHo0hISYiVGpUvDOO+YGj0qMREREskUjR0XF2rVmbdH+/eZ1RATMnAl33OHcuERERAoZjRwVdqdPQ/fu8OCDZmLk72+ei7Z6tRIjERGRXFByVFilpsKHH8Kdd8LCheahsH37wr598PTTOiRWREQklzStVhjt3m0eEvvjj+b13XebBdcNGzo1LBERkaJAI0eFyeXL8PrrcM89ZmLk6QmTJ8O2bUqMRERE8ohGjgqLVaugf384fNi8bt8e3nsPgoOdGZWIiEiRo5Gjgu74cXjiCWjTxkyMKleGFStg2TIlRiIiIvlAyVFBZbXCtGnmBo5Ll4KLCwwdCnv2QNu2zo5ORESkyNK0WkG0fbt5SOzPP5vX990Hc+ZAvXrOjUtERKQY0MhRQZKQAAMGwL/+ZSZGt91mrkL78UclRiIiIreIRo4KioMH4YEHIC7OvO7SBSZNMjd1FBERkVtGyVFBUa0aBAVBmTLmsR8tWjg7IhERkWJJyVFB4eJiFl77+ZkHxoqIiIhTKDkqSLQ0X0RExOlUkC0iIiJiR8mRiIiIiJ1CkRwdPnyYnj17Uq1aNTw8PLjjjjsYMWIEycnJDv2OHDnCo48+iqenJ76+vrz00kvp+uzatYumTZvi4eFBpUqVGDVqFIZh3MqPIyIiIgVYoag52rdvH6mpqcyZM4fq1auze/dunn/+eS5dusTEiRMBsFqtPPLII/j5+bFx40b+/vtvunfvjmEYTJ8+HYDExERatmxJs2bN2LZtG3/88Qc9evTA09OTIUOGOPMjioiISAFhMQrpsMmECROYNWsWf/75JwBff/01bdq04ejRowQGBgKwZMkSevTowalTpyhbtiyzZs1i2LBhnDx5End3dwDGjRvH9OnTOXbsGBaLJVvvnZiYiLe3NwkJCZQtWzZ/PqCIiIjkqex+fxeKabWMJCQkUL58edv15s2bCQkJsSVGAK1atSIpKYkdO3bY+jRt2tSWGKX1OXHiBIfTTrvPQFJSEomJiQ4PERERKZoKZXJ06NAhpk+fTp8+fWxt8fHx+F+3m3S5cuVwc3MjPj4+0z5p12l9MjJ27Fi8vb1tj2AtuRcRESmynJocRUZGYrFYsnxs377d4TUnTpzgoYce4sknn6RXr14Oz2U0LWYYhkP79X3SZhWzmlIbNmwYCQkJtsfRo0dz/FlFRESkcHBqQXb//v15+umns+xTtWpV299PnDhBs2bNaNSoEXPnznXoFxAQwNatWx3azp07R0pKim10KCAgIN0I0alTpwDSjSjZc3d3d5iKExERkaLLqcmRr68vvr6+2ep7/PhxmjVrRlhYGPPnz6dECcdBr0aNGjFmzBji4uKoWLEiAGvWrMHd3Z2wsDBbnzfeeIPk5GTc3NxsfQIDAx2SMBERESm+CkXN0YkTJwgPDyc4OJiJEydy+vRp4uPjHUaBIiIiqFOnDl27duWXX37hu+++Y+jQoTz//PO2ivTOnTvj7u5Ojx492L17N8uWLSMqKorBgwdne6WaiIiIFG2FYp+jNWvWcPDgQQ4ePEhQUJDDc2k1Qy4uLqxatYq+ffty//334+HhQefOnW37IAF4e3sTExNDv379aNCgAeXKlWPw4MEMHjz4ln4eERERKbgK7T5HzqR9jkRERAqf7H5/F4qRo4ImLZ/UfkciIiKFR9r39o3GhZQc5cKFCxcAtN+RiIhIIXThwgW8vb0zfV7TarmQmprKiRMn8PLyKlSF3ImJiQQHB3P06FFNBxYQ+p0UPPqdFCz6fRQ8hfl3YhgGFy5cIDAwMN2qd3saOcqFEiVKpCsML0zKli1b6P4HXdTpd1Lw6HdSsOj3UfAU1t9JViNGaQrFUn4RERGRW0XJkYiIiIgdJUfFiLu7OyNGjNBRKAWIficFj34nBYt+HwVPcfidqCBbRERExI5GjkRERETsKDkSERERsaPkSERERMSOkiMRERERO0qOiqHDhw/Ts2dPqlWrhoeHB3fccQcjRowgOTnZ2aEVa2PGjKFx48aULl2a2267zdnhFEszZ86kWrVqlCpVirCwMDZs2ODskIq1H374gUcffZTAwEAsFgvLly93dkjF2tixY7n33nvx8vKiQoUKtG/fnv379zs7rHyh5KgY2rdvH6mpqcyZM4c9e/YwZcoUZs+ezRtvvOHs0Iq15ORknnzySV588UVnh1Isff755wwaNIjhw4fzyy+/0KRJE1q3bs2RI0ecHVqxdenSJe666y5mzJjh7FAEWL9+Pf369WPLli3ExMRw7do1IiIiuHTpkrNDy3Nayi8ATJgwgVmzZvHnn386O5Rib8GCBQwaNIjz5887O5RipWHDhtSvX59Zs2bZ2mrXrk379u0ZO3asEyMTAIvFwrJly2jfvr2zQ5H/OX36NBUqVGD9+vU88MADzg4nT2nkSABISEigfPnyzg5DxCmSk5PZsWMHERERDu0RERFs2rTJSVGJFGwJCQkARfK7Q8mRcOjQIaZPn06fPn2cHYqIU5w5cwar1Yq/v79Du7+/P/Hx8U6KSqTgMgyDwYMH83//93+EhIQ4O5w8p+SoCImMjMRisWT52L59u8NrTpw4wUMPPcSTTz5Jr169nBR50ZWb34k4j8Vicbg2DCNdm4hA//79+e2331i8eLGzQ8kXrs4OQPJO//79efrpp7PsU7VqVdvfT5w4QbNmzWjUqBFz587N5+iKp5z+TsQ5fH19cXFxSTdKdOrUqXSjSSLF3YABA1i5ciU//PADQUFBzg4nXyg5KkJ8fX3x9fXNVt/jx4/TrFkzwsLCmD9/PiVKaBAxP+TkdyLO4+bmRlhYGDExMTz22GO29piYGNq1a+fEyEQKDsMwGDBgAMuWLWPdunVUq1bN2SHlGyVHxdCJEycIDw+ncuXKTJw4kdOnT9ueCwgIcGJkxduRI0c4e/YsR44cwWq1snPnTgCqV69OmTJlnBtcMTB48GC6du1KgwYNbKOpR44cUS2eE128eJGDBw/armNjY9m5cyfly5encuXKToyseOrXrx+LFi1ixYoVeHl52UZavb298fDwcHJ0eUtL+YuhBQsW8Oyzz2b4nP7n4Dw9evTg448/Tte+du1awsPDb31AxdDMmTMZP348cXFxhISEMGXKlCK3RLkwWbduHc2aNUvX3r17dxYsWHDrAyrmMqu/mz9/Pj169Li1weQzJUciIiIidlRoIiIiImJHyZGIiIiIHSVHIiIiInaUHImIiIjYUXIkIiIiYkfJkYiIiIgdJUciIiIidpQciYiDw4cPY7FYbDt0FxZVq1Zl6tSpeXa/8PBwBg0alGf3cwaLxcLy5cuBwvt7FXEGJUcixYjFYsnyURh2uV2wYAG33XZbuvZt27bxwgsv3PqACoDIyEjuvvvudO1xcXG0bt361gckUsjpbDWRYiQuLs72988//5y3336b/fv329o8PDw4d+6cM0LDarVisVhyfQiyn59fHkdU+OmsRJHc0ciRSDESEBBge3h7e2OxWNK1pfnzzz9p1qwZpUuX5q677mLz5s0O99q0aRMPPPAAHh4eBAcH89JLL3Hp0iXb8+fOnaNbt26UK1eO0qVL07p1aw4cOGB7Pm0E6KuvvqJOnTq4u7vz119/kZyczKuvvkqlSpXw9PSkYcOGrFu3DjDP2nr22WdJSEiwjXZFRkYC6afVzp8/zwsvvIC/vz+lSpUiJCSEr776CoC///6bTp06ERQUROnSpQkNDWXx4sU5/nmOGzcOf39/vLy86NmzJ6+//rrDCE5GU3Pt27d3GKH79NNPadCgAV5eXgQEBNC5c2dOnTple37dunVYLBa+++47GjRoQOnSpWncuLEtqV2wYAEjR47k119/tf1M0s4ds59Wy8jvv//Oww8/TJkyZfD396dr166cOXPG9vyXX35JaGgoHh4e+Pj40KJFC4ffsUhRpeRIRDI0fPhwhg4dys6dO6lZsyadOnXi2rVrAOzatYtWrVrRoUMHfvvtNz7//HM2btxI//79ba/v0aMH27dvZ+XKlWzevBnDMHj44YdJSUmx9bl8+TJjx47lww8/ZM+ePVSoUIFnn32WH3/8kSVLlvDbb7/x5JNP8tBDD3HgwAEaN27M1KlTKVu2LHFxccTFxTF06NB0saemptK6dWs2bdrEp59+yu+//864ceNwcXEB4OrVq4SFhfHVV1+xe/duXnjhBbp27crWrVuz/fP54osvGDFiBGPGjGH79u1UrFiRmTNn5vjnnJyczOjRo/n1119Zvnw5sbGxGU5vDh8+nEmTJrF9+3ZcXV157rnnAOjYsSNDhgyhbt26tp9Jx44db/i+cXFxNG3alLvvvpvt27ezevVqTp48yVNPPWV7vlOnTjz33HPs3buXdevW0aFDBx1OLcWDISLF0vz58w1vb+907bGxsQZgfPjhh7a2PXv2GICxd+9ewzAMo2vXrsYLL7zg8LoNGzYYJUqUMK5cuWL88ccfBmD8+OOPtufPnDljeHh4GF988YXt/QFj586dtj4HDx40LBaLcfz4cYd7N2/e3Bg2bFiWcVepUsWYMmWKYRiG8c033xglSpQw9u/fn+2fx8MPP2wMGTLEdt20aVNj4MCBmfZv1KiR0adPH4e2hg0bGnfddVeW92jXrp3RvXv3TO/7008/GYBx4cIFwzAMY+3atQZgfPvtt7Y+q1atMgDjypUrhmEYxogRIxzeNw1gLFu2zDCMf36vv/zyi2EYhvHWW28ZERERDv2PHj1qAMb+/fuNHTt2GIBx+PDhTGMVKao0ciQiGapXr57t7xUrVgSwTffs2LGDBQsWUKZMGdujVatWpKamEhsby969e3F1daVhw4a2e/j4+HDnnXeyd+9eW5ubm5vD+/z8888YhkHNmjUd7r1+/XoOHTqU7dh37txJUFAQNWvWzPB5q9XKmDFjqFevHj4+PpQpU4Y1a9Zw5MiRbL/H3r17adSokUPb9dfZ8csvv9CuXTuqVKmCl5cX4eHhAOliyer3kRs7duxg7dq1Dj/nWrVqAXDo0CHuuusumjdvTmhoKE8++SQffPCB0+rRRG41FWSLSIZKlixp+7vFYgHM6aq0P3v37s1LL72U7nWVK1fmjz/+yPCehmHY7gVmAbj9dWpqKi4uLuzYscM2BZamTJky2Y7dw8Mjy+cnTZrElClTmDp1KqGhoXh6ejJo0CCSk5Oz/R7ZUaJEiXTTUPbTipcuXSIiIoKIiAg+/fRT/Pz8OHLkCK1atUoXS1a/j9xITU3l0Ucf5d133033XMWKFXFxcSEmJoZNmzaxZs0apk+fzvDhw9m6dSvVqlXL9fuKFAZKjkQkx+rXr8+ePXuoXr16hs/XqVOHa9eusXXrVho3bgyYRdB//PEHtWvXzvS+99xzD1arlVOnTtGkSZMM+7i5uWG1WrOMr169ehw7dow//vgjw9GjDRs20K5dO5555hnATBQOHDiQZWzXq127Nlu2bKFbt262ti1btjj08fPzc1ghaLVa2b17N82aNQNg3759nDlzhnHjxhEcHAzA9u3bsx1Dmuz8TK5Xv359li5dStWqVXF1zfirwGKxcP/993P//ffz9ttvU6VKFZYtW8bgwYNzHKNIYaJpNRHJsddee43NmzfTr18/du7cyYEDB1i5ciUDBgwAoEaNGrRr147nn3+ejRs38uuvv/LMM89QqVIl2rVrl+l9a9asSZcuXejWrRvR0dHExsaybds23n33Xf773/8C5qq0ixcv8t1333HmzBkuX76c7j5NmzblgQce4PHHHycmJobY2Fi+/vprVq9eDUD16tVtoyJ79+6ld+/exMfH5+hnMHDgQD766CM++ugj/vjjD0aMGMGePXsc+jz44IOsWrWKVatWsW/fPvr27cv58+dtz1euXBk3NzemT5/On3/+ycqVKxk9enSO4kj7mcTGxrJz507OnDlDUlLSDV/Tr18/zp49S6dOnfjpp5/4888/WbNmDc899xxWq5WtW7cSFRXF9u3bOXLkCNHR0Zw+fTpHCaRIYaXkSERyrF69eqxfv54DBw7QpEkT7rnnHt566y1bLQzA/PnzCQsLo02bNjRq1AjDMPjvf//rMD2Ukfnz59OtWzeGDBnCnXfeSdu2bdm6dattZKVx48b06dOHjh074ufnx/jx4zO8z9KlS7n33nvp1KkTderU4dVXX7WNrrz11lvUr1+fVq1aER4eTkBAAO3bt8/Rz6Bjx468/fbbvPbaa4SFhfHXX3/x4osvOvR57rnn6N69O926daNp06ZUq1bNNmoE5sjSggUL+Pe//02dOnUYN24cEydOzFEcAI8//jgPPfQQzZo1w8/PL1vbEgQGBvLjjz9itVpp1aoVISEhDBw4EG9vb0qUKEHZsmX54YcfePjhh6lZsyZvvvkmkyZN0qaSUixYjOsnxEVEJFciIyNZvny5jugQKeQ0ciQiIiJiR8mRiIiIiB1Nq4mIiIjY0ciRiIiIiB0lRyIiIiJ2lByJiIiI2FFyJCIiImJHyZGIiIiIHSVHIiIiInaUHImIiIjYUXIkIiIiYkfJkYiIiIid/wdzJIicw+MepgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(result_gold['% Change'], plot = plt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.8654451370239258, pvalue=2.2246933440328576e-05)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(result_stock['% Change with Dividend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(result_stock['% Change with Dividend'], plot = plt);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Checking the Requirements of T-test for Trading vs Buy-Hold Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "trade_result = simulate_trade_EMA(df,verbose=False,EMA1 = 5, EMA2 = 8)\n",
    "trade_result2 = simulate_trade_EMA(df,EMA1=3,EMA2=5)\n",
    "control_result = simulate_control_group(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "joinDF = pd.concat([control_result[\"(%)Annual_Return_Without_Dividends\"],\n",
    "                    trade_result[\"(%)Annual_Return_Without_Dividends\"],\n",
    "                    trade_result2[\"(%)Annual_Return_Without_Dividends\"]],\n",
    "                    axis = 1, join = 'inner'\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9727676510810852, pvalue=0.11500608175992966)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(control_result[\"(%)Annual_Return_Without_Dividends\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(control_result[\"(%)Annual_Return_Without_Dividends\"], plot = plt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9898917078971863, pvalue=0.8314666748046875)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(trade_result[\"(%)Annual_Return_Without_Dividends\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(trade_result[\"(%)Annual_Return_Without_Dividends\"], plot = plt);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.9605090022087097, pvalue=0.022189654409885406)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.shapiro(trade_result2[\"(%)Annual_Return_Without_Dividends\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(trade_result2[\"(%)Annual_Return_Without_Dividends\"], plot = plt);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}