financial-planner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit 5 - Financial Planning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import requests as req\n",
    "import pandas as pd\n",
    "from alpaca_trade_api import TimeFrame, REST\n",
    "from MCForecastTools import MCSimulation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Personal Finance Planner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Crypto Prices Using the `requests` Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of crypto assets\n",
    "my_btc = 1.2\n",
    "my_eth = 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crypto API URLs\n",
    "btc_url = \"https://api.alternative.me/v2/ticker/Bitcoin/?convert=CAD\"\n",
    "eth_url = \"https://api.alternative.me/v2/ticker/Ethereum/?convert=CAD\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 1.2 BTC is $60486.12\n",
      "The current value of your 5.3 ETH is $19892.53\n"
     ]
    }
   ],
   "source": [
    "# Fetch current BTC price\n",
    "response = req.get(btc_url)\n",
    "btc_data = response.json()\n",
    "btc_price = btc_data['data']['1']['quotes']['CAD']['price']\n",
    "\n",
    "# Fetch current ETH price\n",
    "response = req.get(eth_url)\n",
    "eth_data = response.json()\n",
    "eth_price = eth_data['data']['1027']['quotes']['CAD']['price']\n",
    "\n",
    "# Compute current value of my crypto\n",
    "my_btc_value = my_btc * btc_price\n",
    "my_eth_value = my_eth * eth_price\n",
    "\n",
    "# Print current crypto wallet balance\n",
    "print(f\"The current value of your {my_btc} BTC is ${my_btc_value:0.2f}\")\n",
    "print(f\"The current value of your {my_eth} ETH is ${my_eth_value:0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of shares\n",
    "my_agg = 200\n",
    "my_spy = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Alpaca API object\n",
    "apca = REST()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-23 04:00:00+00:00</th>\n",
       "      <td>105.98</td>\n",
       "      <td>106.01</td>\n",
       "      <td>105.90</td>\n",
       "      <td>105.95</td>\n",
       "      <td>2620804</td>\n",
       "      <td>11475</td>\n",
       "      <td>105.953540</td>\n",
       "      <td>AGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24 04:00:00+00:00</th>\n",
       "      <td>105.85</td>\n",
       "      <td>105.92</td>\n",
       "      <td>105.78</td>\n",
       "      <td>105.81</td>\n",
       "      <td>3442319</td>\n",
       "      <td>16110</td>\n",
       "      <td>105.846798</td>\n",
       "      <td>AGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25 04:00:00+00:00</th>\n",
       "      <td>105.72</td>\n",
       "      <td>105.74</td>\n",
       "      <td>105.60</td>\n",
       "      <td>105.67</td>\n",
       "      <td>3984953</td>\n",
       "      <td>13988</td>\n",
       "      <td>105.715560</td>\n",
       "      <td>AGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26 04:00:00+00:00</th>\n",
       "      <td>105.83</td>\n",
       "      <td>105.90</td>\n",
       "      <td>105.76</td>\n",
       "      <td>105.85</td>\n",
       "      <td>4870388</td>\n",
       "      <td>13589</td>\n",
       "      <td>105.829017</td>\n",
       "      <td>AGG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27 04:00:00+00:00</th>\n",
       "      <td>105.96</td>\n",
       "      <td>106.04</td>\n",
       "      <td>105.94</td>\n",
       "      <td>105.98</td>\n",
       "      <td>3806396</td>\n",
       "      <td>9173</td>\n",
       "      <td>105.988632</td>\n",
       "      <td>AGG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             open    high     low   close   volume  \\\n",
       "timestamp                                                            \n",
       "2018-04-23 04:00:00+00:00  105.98  106.01  105.90  105.95  2620804   \n",
       "2018-04-24 04:00:00+00:00  105.85  105.92  105.78  105.81  3442319   \n",
       "2018-04-25 04:00:00+00:00  105.72  105.74  105.60  105.67  3984953   \n",
       "2018-04-26 04:00:00+00:00  105.83  105.90  105.76  105.85  4870388   \n",
       "2018-04-27 04:00:00+00:00  105.96  106.04  105.94  105.98  3806396   \n",
       "\n",
       "                           trade_count        vwap symbol  \n",
       "timestamp                                                  \n",
       "2018-04-23 04:00:00+00:00        11475  105.953540    AGG  \n",
       "2018-04-24 04:00:00+00:00        16110  105.846798    AGG  \n",
       "2018-04-25 04:00:00+00:00        13988  105.715560    AGG  \n",
       "2018-04-26 04:00:00+00:00        13589  105.829017    AGG  \n",
       "2018-04-27 04:00:00+00:00         9173  105.988632    AGG  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format current date as ISO format\n",
    "yesterday = pd.Timestamp.now(tz=\"America/New_York\") - pd.Timedelta(days=1)\n",
    "\n",
    "start = (yesterday - pd.Timedelta(days=1460)).strftime('%Y-%m-%d')\n",
    "end = yesterday.strftime('%Y-%m-%d')\n",
    "\n",
    "# Set the tickers\n",
    "tickers = [\"AGG\", \"SPY\"]\n",
    "\n",
    "# Set timeframe to '1D' for Alpaca API\n",
    "timeframe = \"1DAY\"\n",
    "\n",
    "# Get current closing prices for SPY and AGG\n",
    "df_tickers = apca.get_bars(tickers, TimeFrame.Day, start, end).df\n",
    "\n",
    "# Preview DataFrame\n",
    "df_tickers.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current AGG closing price: $103.04\n",
      "Current SPY closing price: $438.06\n"
     ]
    }
   ],
   "source": [
    "# Pick AGG and SPY close prices\n",
    "df_closing_prices = pd.DataFrame()\n",
    "df_closing_prices = pd.pivot_table(df_tickers, values='close', columns='symbol', index=df_tickers.index)\n",
    "df_closing_prices.sort_values(by='timestamp', ascending=False, inplace=True)\n",
    "\n",
    "df_closing_prices.index = pd.to_datetime(df_closing_prices.index.strftime('%Y-%m-%d'))\n",
    "df_closing_prices.index.name = 'date'\n",
    "df_closing_prices.columns = ['AGG', 'SPY']\n",
    "\n",
    "agg_close_price, spy_close_price = df_closing_prices.iloc[0]\n",
    "\n",
    "# Print AGG and SPY close prices\n",
    "print(f\"Current AGG closing price: ${agg_close_price}\")\n",
    "print(f\"Current SPY closing price: ${spy_close_price}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 50 SPY shares is $21903.00\n",
      "The current value of your 200 AGG shares is $20608.00\n"
     ]
    }
   ],
   "source": [
    "# Compute the current value of shares\n",
    "my_spy_value = my_spy * spy_close_price\n",
    "my_agg_value = my_agg * agg_close_price\n",
    "\n",
    "# Print current value of shares\n",
    "print(f\"The current value of your {my_spy} SPY shares is ${my_spy_value:0.2f}\")\n",
    "print(f\"The current value of your {my_agg} AGG shares is ${my_agg_value:0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Savings Health Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crypto</th>\n",
       "      <td>80378.654354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shares</th>\n",
       "      <td>42511.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              amount\n",
       "crypto  80378.654354\n",
       "shares  42511.000000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set monthly household income\n",
    "monthly_income = 12000\n",
    "\n",
    "# Consolidate financial assets data\n",
    "df_savings = pd.DataFrame(\n",
    "  {\n",
    "    'amount': [\n",
    "      my_btc_value + my_eth_value,\n",
    "      my_spy_value + my_agg_value\n",
    "    ]\n",
    "  }, \n",
    "  index=['crypto', 'shares']\n",
    ")\n",
    "\n",
    "# Display savings DataFrame\n",
    "df_savings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='amount'>], dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot savings pie chart\n",
    "df_savings.plot.pie(labels=['crypto', 'shares'], autopct='%.0f%%', subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congratulations! You have enough money in this fund.\n"
     ]
    }
   ],
   "source": [
    "# Set ideal emergency fund\n",
    "emergency_fund = monthly_income * 3\n",
    "\n",
    "# Calculate total amount of savings\n",
    "total_savings = df_savings.iloc[:, 0].sum()\n",
    "total_savings = round(total_savings, 2)\n",
    "\n",
    "# Validate saving health\n",
    "if total_savings > emergency_fund:\n",
    "  print(f\"Congratulations! You have enough money in this fund.\")\n",
    "elif total_savings == emergency_fund:\n",
    "  print(f\"You have exactly enough savings! Congratulations on reaching this financial goal.\")\n",
    "else:\n",
    "  print(f\"Almost there! You are {emergency_fund - total_savings} away from reaching this financial goal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Retirement Planning\n",
    "\n",
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set start and end dates of five years back from today.\n",
    "# Sample results may vary from the solution based on the time frame chosen\n",
    "yesterday = pd.Timestamp.now(tz=\"America/New_York\") - pd.Timedelta(days=1)\n",
    "\n",
    "start = (yesterday - pd.Timedelta(days=365*5)).strftime('%Y-%m-%d')\n",
    "end = yesterday.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AGG</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>109.18</td>\n",
       "      <td>237.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>108.82</td>\n",
       "      <td>238.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>108.96</td>\n",
       "      <td>238.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>109.06</td>\n",
       "      <td>238.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>109.25</td>\n",
       "      <td>238.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AGG     SPY\n",
       "             close   close\n",
       "date                      \n",
       "2017-04-24  109.18  237.17\n",
       "2017-04-25  108.82  238.55\n",
       "2017-04-26  108.96  238.40\n",
       "2017-04-27  109.06  238.60\n",
       "2017-04-28  109.25  238.08"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get 5 years' worth of historical data for SPY and AGG\n",
    "# (use a limit=1000 parameter to call the most recent 1000 days of data)\n",
    "df_stock_data = apca.get_bars(tickers, TimeFrame.Day, start, end, adjustment='raw').df\n",
    "df_stock_data.index = pd.DatetimeIndex(df_stock_data.index.date, name='date')\n",
    "\n",
    "df_stock_data = df_stock_data[['close', 'symbol']]\n",
    "df_stock_data = pd.pivot_table(df_stock_data, values='close', columns='symbol', index=df_stock_data.index)\n",
    "\n",
    "multi_index_columns = pd.MultiIndex.from_tuples(\n",
    "  [(ticker, \"close\") for ticker in tickers]\n",
    ")\n",
    "\n",
    "df_stock_data.columns = multi_index_columns\n",
    "df_stock_data\n",
    "\n",
    "# Display sample data\n",
    "df_stock_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirtyyear = MCSimulation(\n",
    "  portfolio_data = df_stock_data,\n",
    "  weights = [.40,.60],\n",
    "  num_simulation = 500,\n",
    "  num_trading_days = 252*30\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AGG</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>109.18</td>\n",
       "      <td>237.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>108.82</td>\n",
       "      <td>238.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>108.96</td>\n",
       "      <td>238.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>109.06</td>\n",
       "      <td>238.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>109.25</td>\n",
       "      <td>238.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               AGG     SPY\n",
       "             close   close\n",
       "date                      \n",
       "2017-04-24  109.18  237.17\n",
       "2017-04-25  108.82  238.55\n",
       "2017-04-26  108.96  238.40\n",
       "2017-04-27  109.06  238.60\n",
       "2017-04-28  109.25  238.08"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the simulation input data\n",
    "df_stock_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 0.\n",
      "Running Monte Carlo simulation number 10.\n",
      "Running Monte Carlo simulation number 20.\n",
      "Running Monte Carlo simulation number 30.\n",
      "Running Monte Carlo simulation number 40.\n",
      "Running Monte Carlo simulation number 50.\n",
      "Running Monte Carlo simulation number 60.\n",
      "Running Monte Carlo simulation number 70.\n",
      "Running Monte Carlo simulation number 80.\n",
      "Running Monte Carlo simulation number 90.\n",
      "Running Monte Carlo simulation number 100.\n",
      "Running Monte Carlo simulation number 110.\n",
      "Running Monte Carlo simulation number 120.\n",
      "Running Monte Carlo simulation number 130.\n",
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n",
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n",
      "Running Monte Carlo simulation number 200.\n",
      "Running Monte Carlo simulation number 210.\n",
      "Running Monte Carlo simulation number 220.\n",
      "Running Monte Carlo simulation number 230.\n",
      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
      "Running Monte Carlo simulation number 270.\n",
      "Running Monte Carlo simulation number 280.\n",
      "Running Monte Carlo simulation number 290.\n",
      "Running Monte Carlo simulation number 300.\n",
      "Running Monte Carlo simulation number 310.\n",
      "Running Monte Carlo simulation number 320.\n",
      "Running Monte Carlo simulation number 330.\n",
      "Running Monte Carlo simulation number 340.\n",
      "Running Monte Carlo simulation number 350.\n",
      "Running Monte Carlo simulation number 360.\n",
      "Running Monte Carlo simulation number 370.\n",
      "Running Monte Carlo simulation number 380.\n",
      "Running Monte Carlo simulation number 390.\n",
      "Running Monte Carlo simulation number 400.\n",
      "Running Monte Carlo simulation number 410.\n",
      "Running Monte Carlo simulation number 420.\n",
      "Running Monte Carlo simulation number 430.\n",
      "Running Monte Carlo simulation number 440.\n",
      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
      "Running Monte Carlo simulation number 470.\n",
      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "      <td>1.007875</td>\n",
       "      <td>0.994717</td>\n",
       "      <td>0.996652</td>\n",
       "      <td>1.005092</td>\n",
       "      <td>0.999925</td>\n",
       "      <td>1.002554</td>\n",
       "      <td>0.989970</td>\n",
       "      <td>0.997209</td>\n",
       "      <td>0.995667</td>\n",
       "      <td>0.993126</td>\n",
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       "      <td>0.993092</td>\n",
       "      <td>1.005170</td>\n",
       "      <td>0.995859</td>\n",
       "      <td>0.992622</td>\n",
       "      <td>1.011765</td>\n",
       "      <td>1.012767</td>\n",
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       "      <td>0.990723</td>\n",
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       "      <td>0.997831</td>\n",
       "      <td>1.003736</td>\n",
       "      <td>0.989561</td>\n",
       "      <td>1.003376</td>\n",
       "      <td>0.987890</td>\n",
       "      <td>0.988854</td>\n",
       "      <td>0.993288</td>\n",
       "      <td>0.998471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.995811</td>\n",
       "      <td>1.003288</td>\n",
       "      <td>0.997057</td>\n",
       "      <td>0.981850</td>\n",
       "      <td>1.006798</td>\n",
       "      <td>1.026072</td>\n",
       "      <td>1.014824</td>\n",
       "      <td>0.994484</td>\n",
       "      <td>1.014006</td>\n",
       "      <td>1.008014</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.009160</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>0.998838</td>\n",
       "      <td>1.008746</td>\n",
       "      <td>0.978788</td>\n",
       "      <td>1.000904</td>\n",
       "      <td>0.983041</td>\n",
       "      <td>0.990805</td>\n",
       "      <td>0.999247</td>\n",
       "      <td>0.998173</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991554</td>\n",
       "      <td>0.999930</td>\n",
       "      <td>0.998706</td>\n",
       "      <td>0.981251</td>\n",
       "      <td>1.014114</td>\n",
       "      <td>1.029001</td>\n",
       "      <td>1.014547</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>1.004635</td>\n",
       "      <td>1.013815</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.003375</td>\n",
       "      <td>0.961657</td>\n",
       "      <td>1.000964</td>\n",
       "      <td>1.021114</td>\n",
       "      <td>0.975670</td>\n",
       "      <td>1.004329</td>\n",
       "      <td>0.984479</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>1.009091</td>\n",
       "      <td>0.999288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988127</td>\n",
       "      <td>1.001109</td>\n",
       "      <td>0.990562</td>\n",
       "      <td>0.980085</td>\n",
       "      <td>1.002523</td>\n",
       "      <td>1.030143</td>\n",
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       "      <th>7556</th>\n",
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       "      <td>10.641276</td>\n",
       "      <td>16.218515</td>\n",
       "      <td>8.640286</td>\n",
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       "      <td>29.437940</td>\n",
       "      <td>7.243875</td>\n",
       "      <td>2.870255</td>\n",
       "      <td>...</td>\n",
       "      <td>8.785396</td>\n",
       "      <td>4.478131</td>\n",
       "      <td>7.676828</td>\n",
       "      <td>9.532752</td>\n",
       "      <td>6.550597</td>\n",
       "      <td>2.024691</td>\n",
       "      <td>4.378252</td>\n",
       "      <td>8.203479</td>\n",
       "      <td>7.776899</td>\n",
       "      <td>3.740661</td>\n",
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       "    <tr>\n",
       "      <th>7557</th>\n",
       "      <td>9.275151</td>\n",
       "      <td>3.857040</td>\n",
       "      <td>10.642430</td>\n",
       "      <td>16.167681</td>\n",
       "      <td>8.751759</td>\n",
       "      <td>30.983772</td>\n",
       "      <td>4.248663</td>\n",
       "      <td>29.622765</td>\n",
       "      <td>7.205423</td>\n",
       "      <td>2.871968</td>\n",
       "      <td>...</td>\n",
       "      <td>8.748801</td>\n",
       "      <td>4.447899</td>\n",
       "      <td>7.665902</td>\n",
       "      <td>9.568951</td>\n",
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       "      <th>7558</th>\n",
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       "      <td>8.842629</td>\n",
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       "      <td>4.197018</td>\n",
       "      <td>29.353061</td>\n",
       "      <td>7.167961</td>\n",
       "      <td>2.871120</td>\n",
       "      <td>...</td>\n",
       "      <td>8.707915</td>\n",
       "      <td>4.448923</td>\n",
       "      <td>7.768801</td>\n",
       "      <td>9.451219</td>\n",
       "      <td>6.509955</td>\n",
       "      <td>2.011160</td>\n",
       "      <td>4.345987</td>\n",
       "      <td>8.113144</td>\n",
       "      <td>7.726472</td>\n",
       "      <td>3.830629</td>\n",
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       "    <tr>\n",
       "      <th>7559</th>\n",
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       "      <td>10.538599</td>\n",
       "      <td>16.052955</td>\n",
       "      <td>8.874929</td>\n",
       "      <td>30.773818</td>\n",
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       "      <td>29.528277</td>\n",
       "      <td>7.028552</td>\n",
       "      <td>2.897621</td>\n",
       "      <td>...</td>\n",
       "      <td>8.773101</td>\n",
       "      <td>4.436369</td>\n",
       "      <td>7.797311</td>\n",
       "      <td>9.442880</td>\n",
       "      <td>6.499909</td>\n",
       "      <td>2.016738</td>\n",
       "      <td>4.311741</td>\n",
       "      <td>8.200099</td>\n",
       "      <td>7.733388</td>\n",
       "      <td>3.829451</td>\n",
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       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>9.275449</td>\n",
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       "      <td>29.496387</td>\n",
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       "      <td>2.889308</td>\n",
       "      <td>...</td>\n",
       "      <td>8.662111</td>\n",
       "      <td>4.400386</td>\n",
       "      <td>7.796497</td>\n",
       "      <td>9.471845</td>\n",
       "      <td>6.514442</td>\n",
       "      <td>2.009240</td>\n",
       "      <td>4.295741</td>\n",
       "      <td>8.115977</td>\n",
       "      <td>7.748593</td>\n",
       "      <td>3.870787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7561 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1          2          3         4          5         6    \\\n",
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       "2     1.023815  0.979873   0.997831   1.003736  0.989561   1.003376  0.987890   \n",
       "3     1.009160  0.972044   0.998838   1.008746  0.978788   1.000904  0.983041   \n",
       "4     1.003375  0.961657   1.000964   1.021114  0.975670   1.004329  0.984479   \n",
       "...        ...       ...        ...        ...       ...        ...       ...   \n",
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       "7558  9.242721  3.883234  10.636497  16.214236  8.842629  30.990712  4.197018   \n",
       "7559  9.172286  3.887944  10.538599  16.052955  8.874929  30.773818  4.159165   \n",
       "7560  9.275449  3.856478  10.520506  16.079060  8.952238  30.804845  4.138270   \n",
       "\n",
       "            7         8         9    ...       490       491       492  \\\n",
       "0      1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1      0.997209  0.995667  0.993126  ...  0.993092  1.005170  0.995859   \n",
       "2      0.988854  0.993288  0.998471  ...  0.995811  1.003288  0.997057   \n",
       "3      0.990805  0.999247  0.998173  ...  0.991554  0.999930  0.998706   \n",
       "4      0.999934  1.009091  0.999288  ...  0.988127  1.001109  0.990562   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "7556  29.437940  7.243875  2.870255  ...  8.785396  4.478131  7.676828   \n",
       "7557  29.622765  7.205423  2.871968  ...  8.748801  4.447899  7.665902   \n",
       "7558  29.353061  7.167961  2.871120  ...  8.707915  4.448923  7.768801   \n",
       "7559  29.528277  7.028552  2.897621  ...  8.773101  4.436369  7.797311   \n",
       "7560  29.496387  6.988143  2.889308  ...  8.662111  4.400386  7.796497   \n",
       "\n",
       "           493       494       495       496       497       498       499  \n",
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       "1     0.992622  1.011765  1.012767  1.023599  0.990723  1.000214  1.008186  \n",
       "2     0.981850  1.006798  1.026072  1.014824  0.994484  1.014006  1.008014  \n",
       "3     0.981251  1.014114  1.029001  1.014547  0.999911  1.004635  1.013815  \n",
       "4     0.980085  1.002523  1.030143  1.020042  1.001132  0.992500  1.014394  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "7556  9.532752  6.550597  2.024691  4.378252  8.203479  7.776899  3.740661  \n",
       "7557  9.568951  6.574195  2.018098  4.377856  8.135941  7.712407  3.808168  \n",
       "7558  9.451219  6.509955  2.011160  4.345987  8.113144  7.726472  3.830629  \n",
       "7559  9.442880  6.499909  2.016738  4.311741  8.200099  7.733388  3.829451  \n",
       "7560  9.471845  6.514442  2.009240  4.295741  8.115977  7.748593  3.870787  \n",
       "\n",
       "[7561 rows x 500 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "MC_thirtyyear.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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39kZNCWW3Ub69PlvyL5SWdbzx/prc3AVKU5jYyji5xx7210wRAa0XQojmabkOI4SIQf1uFxhJD6KOZ7gx9vMI/h1/AQwXQgxFaVX9huJSyplSyiNQAvBmlGazw7QrCAkhEoQQRwohIoQQFmOp3mRgplHkc5Q66lQhRARKPbZWSrnZyH8L+D8hRKYQoivqIr3RRl8nCiHOEkIkCsU4lFps8Z4cUBusBo4RQiQJIdKBf7ZTNhY131hvSJVXtsovQU0VhmMJ6gXxHyGEVQgxFTge9UXWLkKIsYZGxGq00WygGYSUMh9lGPigcV2Go9St77Yu2waPo7483jQejBjX53EhxHBDJbkLOM/42v4bv2012WrgFONLpS8B1XA4YlHaLodx/c9pkVeGUn+26UNHSrnKKPcKMFNKWW1kLQVqhRA3CiEijeMaKoQYu4fHsjfXt737BUOr+RFwvxAi1rgm/4f6GmqTva3Xov5m1O/4P4ba+UfUiyJOCGESQvQRaiVn8zF0E0LYWjSxmo5f1z+SWJQQV4F6QT7QTtmXgSuM35kQQkQLIY41PiqWoh7uDxnpEcYLEFod/28996jp1P5CiPON+8hq/P4HCSFsQi3bjje0GbWEeQ4YPIH6Lb8qhEg3xnw2Slj4d7P24w/+XbR7f4ehvWvQLlLKuaiFHhe2SH4HON54T5mNczBVKLcCAvW+eRV1fxahjH/3dOwXogx1g7Q1xjur5fvqWpTtT7OG5EPUsyJWCNENNX32jVH9XWPchxqCyT0os4k91Qi1JhaljaoWalXhnc0ZUsqdqGnDu437bBLq+bU3fIIa/0Tjd3E3HVQYCCHsQrkg+AJl0P16i7HXG2PPpJUiwNB8fYL6YF4qpcwz2usihDjBOI9Oo409cgGwO42QFWWY1mwsfQ3KeHWLMbAylBr3fuOAxhM8v/wiag5yHUoV/62RFo4q1I2yFfXjfwd4RLZYHvgbeBtlCJmLeuB/2E7ZG1Av4DrUj7Z12btQQkS1EOKMlhnGXOkJKHuLcpRB8wUtBMP2iDP6qyJgif9oG2XPRn35FqKE0TulmhvfLVLN2U9EfdEuEULUAbNQ89PbjGKXom7CCpR2amFH2m6DJ1BfRiUo1WZ71/Mq4B5jTHfQ4kvYeLDcj1puWS2EaMum4H3UV8Z7Lep6UT/4kSijxXLUSyGcHUmb7OX1fRVl71EthPiijTLXoASsHajl0O8Br3VgSHtbr5lHgMuEEGko1b8NtVqpCvXAaZ5imY36gi0WQpQbaXtyXf9I3kL9Xnahxt7mh5OUcjnq3n4GdYzbUNPvLe+RvqgpmAKUuh3CH/9en3vjZTcD9awsRE0/NBu8grI1yhVqeuAKAtMBrdupQE0DRRjHXoESyM6XUrZ+bv1Rv4uO3N8tx9zmNeggt2HYlBjt5aM0greg3lP5qGeXCSWYdEHZxEiUcfrFQohme6kHgduMsd8QrjOhPvDPoNW0mMFZxvjrUPfhf6UyH2nmH6iXciHKSNl/j0hlw3IF6ndTihICrtqD89AWT6IMm8tRv4UfWuWfg3pPV6KEpLf2phNj/NegPgKLUOegFCWItMV/jGd7pdHvCmBii6m6u1E2wjUoWeGzMG28ibK5ertFmgmlZCk02p6CcS4NQbN+d8fTvJJAo9FoDjiEEG8B26SU9+zrsWg0BypCTXNVo0xKcnZT/Lf00wM19ZUupaz9vdr9y4TY0Gg0mj1BKHufAShth0aj+R0RQhxvTI1Ho2Yv1hFYrPBH9GdCaTw/+D2FINCCkEajOXApRn2lfrqbchqNZs85ETUdVYjy9XTWb1iV1y6GsFWL8jN0526K73n7empMo9FoNBpNZ0VrhPZzhFq594lQgVE3CSEOFmr1209CiK3G/8R9PU6NRqPRaP6KaI3Qfo4Q4k2UR9NXjGWKUagVEpVSyoeEEDehwhTc2F47KSkpMisr648fsEaj0RxArFixolxKmbqvx6H549CC0H6MUJ6l16BipskW6VtQMd+KhHKIN1dK2W6ogjFjxsjly5f/sQPWaDSaAwwhxAop5Zh9PQ7NH4eeGtu/6Y3yjfG6EGKVEOIVw2isi+EErzkGS9hQCEKIy4QQy4UQy8vKyv68UWs0Go1G8xdBC0L7N82RqZ+XUo5COXC7qaOVpZQvSSnHSCnHpKZqza5Go9FoNK3RgtD+TQFQIKVcYux/ghKMSowpMYz/pftofBqNRqPR/KXRgtB+jJSyGMgXQjTb/0xHudL/ikC8nQsx4ttoNBqNRqPZM8JFWtbsX1wDvGusGNuBipdjAj4SQlyCiot0+j4cn0aj0Wg0f1m0ILSfI6VcDYRbsTD9Tx6KRqPRaDQHHHpqTKPRaDQaTadFC0IajUaj2e/ZtqKUpnrXvh6G5gBEC0IajUaj2a9prHUx8+X1fP/Cun09FM0BiBaENBqNRrNf43F5ASjaVrOPR6I5ENGCkEaj0Wj2a3xeHQpK88ehBSGNRqPR7Hc01DiZ9cZG3C4vbkMjpNH8Eejl8xqNRqPZ7/jyiVVUFTfSpXc8yZkx+3o4mgMYrRHSaDQazX7Fzg0VVBU3AuBq8vDZIyv28Yg0BzJaENJoNBrNfsWvn2zzb+9cXxGU5/NpeyHN74sWhDQajUazX1FV1ODfLs4JXinm8/r+7OFoDnC0IKTRaDSa/YqsYcn+bZ8nWAOkV5Bpfm+0IKTRaDSa/YrcdRVt5nlcWiOk+X3RgpBGo9Fo/jLUVzn29RA0BxhaENJoNBrNX4ayvLp9PQTNAYYWhDQajUaz33PS9aMAmPvuln08Es2BhhaENBqNRrNfYY+yMGxqt6A0k0W/rjR/DPrO0mg0Gs1+hdftw2w1cep/RgcSpWyxKXXYDc3vhhaENBqNRrNPefaK2Tx7xWz/vtfjw2wRWGyBV1R0gt2//dyVc3jp2nm4HJ4/dZyaAxMtCGk0Go1mv8Dl8ODzSaQEs8WEyaxeUTGJduJSIrFFBofHrCxsCNeMRrNHaEFIo9FoNPuMllqdnesrmPXmRkAJQhHRVgC6DUoCIKFLVFDd5d/l/jmD1BzQ6OjzGo1Go9ln/PLRVv/2j69s8G+bLSai4mycdfs44tMiAbBYg7/d+4/r8ucMUnNAozVCGo1Go9lnbFpYFDa9sdYFQHJmDBarGYDCrdXttjU/u4zDHp2L06MNqTUdRwtCGo1Go9nviIqz7baM1xMcbuOurzeQU95AXkXjHzUszQGIFoQ0Go1Gs8+IjLWGTW+qd4WkjTi8e9C+t1VAVqtJvdI8Ph2YVdNxtCCk0Wg0mn3CliXFpHSLCZs38OCMkDRXY/By+RXf5wbtm0wCgKOfWvD7DFDTKdCCkEaj0Wj2CT+/vpH8TVWYLCIkL6aF36Bm0rLigvbrq5xB+/mVekpMs+foVWP7OUKIXKAO8AIeKeUYIUQS8CGQBeQCZ0gpq/bVGDUajWZP8bWYvjKbTfhaGTibraHf6eYwAlNL6p3awaJmz9Eaob8Gh0kpR0opxxj7NwGzpJT9gFnGvkaj0fxlqC1r8m9LGWrTI0So0NPsYDE5MzCd5mwKL/y4vb6w6RpNa7Qg9NfkROBNY/tN4KR9NxSNRqPZc4JWfEk449axnHDtyHbrlO6sBaBiV70/rbHGGbbskh2VrNhZ+ZvHqTnw0YLQ/o8EfhRCrBBCXGakdZFSFgEY/9PCVRRCXCaEWC6EWF5WVvYnDVej0Wh2T12Fw7/t9fhI7R5L98FJId6jWxJOSxRGmQTAea8u4dTnF/3mcWoOfLSN0P7PIVLKQiFEGvCTEGJzRytKKV8CXgIYM2aMXk+q0Wj2CxZ9sZ2VP+z077cUZk6/eQxuZ3iHiH3HpLFmVn5Qmtcd0Cz1TYthW2l962oaTbtojdB+jpSy0PhfCnwOjANKhBAZAMb/0n03Qo1Go9kzWgpBrbFFWIiOD10xBpDeK56Jp/Tl7DvGc/K/RgGw8LNt/nyP14fV3L5BtUbTGi0I7ccIIaKFELHN28AMYD3wFXChUexC4Mt9M0KNRqP5cxk1owdJXaNJ6BINBEJxALi9ErdXK781e4aeGtu/6QJ8bsyLW4D3pJQ/CCGWAR8JIS4B8oDT9+EYNRqNpsOEWyGWmBG9x+1ExdkQArKGJfvT9Eoxzd6gBaH9GCnlDmBEmPQKYPqfPyKNRqP5bbS06QHoMSSJscf12qu2pISVM/M4+OS+gAqtMa5XEktzKluUkWGNrDWaZrQgpNFoNJo/Dbcr2BD6+GtG/n5te31EGpHqm9lZ0UhWyp5rnDSdB20jpNFoNJo/DY/rj5u+8nglPZODl99PfXTuH9af5sBAC0IajUaj+VOQUvLhfUsB6DcmjTNuHfu7tV3rcNPk9rImv5qcB4/hb4f1xtMjGgms2KkjEGnaRgtCGo1Go/lTWPLVDpxGBPneo9JI7R77u7X99cJsANYU1CCE4DuLG8+gBJxHZjK3pPp360dz4KEFIY1Go9H8Kaz4PuA/yOf7/abIGpYs5aCrTuOgki3YLOq1lmvykVxfzUkr5yHqtUZI0zZaENJoNBrNn47vd/T3U/zx1wBM2bWKRTdN86dHuF2k11UxKK3tsB0ajRaENBqNRvOnE5sYsdd1fT4PK1aeQ3RkNjF1ebi++QSAGXnLafz39fgMX0UWr1qhZrdZf/uANQcsevm8RqPRaP4UEtOjqCpuBCBzQOJet5Nf8DrV1UsY//2qkLz6OXPY9sjNMPYsLMb0m82qBSFN22iNkEaj0Wj+FCw28+4LdYCiok+JWNW2k0Tva1/Ss6iArjXlAKRF7r32SXPgowUhjUaj0fzhZC8tpiyvDoDug5P2uH5OeQPD75rJrtKVACS93L6W54FnH8Gx002JL4b0KC0IadpGT41pNBqN5g/np9c2AhCbFMHRVwzb4/qHPToXgY/N61VoxTiLFeFpWyuUXFXJOm8Geb4EzObfRxOlOTDRGiGNRqPR/GkkdInEupdTZFaz27/t7tH+qrPKmHgAamQklXnOvepP0znQgpBGo9Fo/jz2MgDqBQf3xGry+PddvdoWhGqtUfxt2s3+/cR0vXxe0zZ6akyj0Wg0fxp7Gwi+wenFagpohPCGL+ezS54YeUZQWmSMbe861XQKtCCk0Wg0mj8ER4Obd25fhMcd8CIt9kISklLy6coCUiMDgpDJEVymcZyX6ou8NBBNzQcxQXl1LjcJVvse96vpHOipMY1Go9H8Ibz6rwU4Gz14gwShPW8nr1L5Hmo5NSaaBFIEpsdqT1EqIgcRjC3dFFT/3V+373mnmk6DFoQ0Go1G86dxyOn99rjOrqomgKCpMVMjuPoEBCGfsUL+cW5k/pgJQfV3zJ23FyPVdBa0IKTRaDSaP42EvYj7lVvRCEgmd1voTzM1gYyAnWmpKsFwK5Qr+lAvgv0GHRfbuLfD1XQCtI2QRqPRaP4QrHYzbmfAqvmIvw3eq3Z2VjQwqetipnYPCEKiSeDr6uOzE8dxUsIcLD4feT/fAkdBeV0kAGfbV2IXXkaccPpvOxDNAY0WhDQajUbzh9BaEOo/Ln2v2smtaGBq91+D0kz1ICPhxEEzWbr0VHbmJjBHpGDJrvGX6bUrj1pHNe6i4TBkyN4dhOaAR0+NaTQajeYPISYpMEU1dHLmXrezs6KRXvF5gQQ3mJwCb6ykuqorAGs86r8lp95frEtJCcLdhKNxx173rTnw0YKQRqPRaP4QWq4Wq61o2ut2cisagvZNRlO+aNi69WAAKmV0SD2zV2mjzAPT9rpvzYGPFoQ0Go1G84fgbAys8tqVXb1XbczPLsPh9pFT08OfJgxBSO7G7loaS/Ujk7P2qm9N50ALQhqNRqP5Q3A2Bfz+jDu+1161ccFrSzln848ccnMxSc/E8+aGszA1KQmnzNy2zVGmqZotsSqmWXLy5L3qW9M50MbSGo1Go/lD8Hp8pPeOo3hHLT2HJu91O1MLVgEQsbGJgxry2SaGk8om6olvs05/cxlC+trM12ia0YLQXwAhhBlYDuySUh4nhEgCPgSygFzgDCll1b4boUaj0QQjpcTnkXQbmMSp/xmz122cteVnVqb1p3t9GQBH7lzmzy+uTsZnVobSEbhxY8KL0gIN2LHhtx+EplOgp8b+GlwHtPQZfxMwS0rZD5hl7Gs0Gs1+g8+jvD6bLXv/mnHn53Phph84ccevYfPnyUPI8yWyxpuJA6tfCAKwmNqIyqrRtEILQvs5QohuwLHAKy2STwTeNLbfBE76k4el0Wg07ZK9rATYe0Goad06ts84st0yXkx42niNRXodYdM1mtZoQWj/50ngP0DLye4uUsoiAON/2LWhQojLhBDLhRDLy8rK/vCBajSazkN5QR2Nta6wefVVDma/pZTYZuteRFkFck8/I2i/MTKSD886k6XjxvrTssxVSBlof6Ilh7PsKznetoHUknwArnz53b3qX9N50ILQfowQ4jigVEq5Ym/qSylfklKOkVKOSU1N/Z1Hp9FoOjMf3reMD+5dEjZv/gfZ/u2d6yr2uG1Xfn5I2tcnngBATu/e/rRUUwM+AoJQlHARIbwMK9xKfooypI6Ka9ugWqMBLQjt7xwCnCCEyAU+AKYJId4BSoQQGQDG/9J9N0SNRtNZaapzB+1vWlhIZWEDiekBBz95Gyv3qM2ab75l+xEzgtKqEhKC9mWL7ZaCkFUoxfmwdev2qE9N50avGtuPkVLeDNwMIISYCtwgpTxPCPEIcCHwkPH/y301Ro1G0/moLgkfzX32W5tD0k69cfQetV14ww0haT8eFWwrVHDxRTy6M4GxsoQlnp7+dAvKQDqhpgZI2aN+NZ0XrRH6a/IQcIQQYitwhLGv0Wg0fwp1VaGGyF5PqM+eCSf1pktW3G/qy2sKfU2tt1rJi0+nspVrabtQglCzxmjUUcf/pr41nQMtCP1FkFLOlVIeZ2xXSCmnSyn7Gf/3TPes0Wg0vwFbROhkgtsRulx91IyeCNExY+nS0lLqq6tD0heccnJIWm1tLRcf3I3WLccIZby9KzEWgG3LFneob03nRgtCGo1Go9kjwml/vn1ubUiaydTxFWPPPfccjz75ZEh6iSW8BcffDu6BV4Zvf20PtZC2rqKMn195tsNj0HROtCCk0Wg0mj2iZVT54h01uBweinfU/O79lEUGr/jqvqO/f7uqqirEh9D62C6c+cGHjNlR5E9b89P3uJ3ap5CmbbQgpNFoNJo94qunVvu3P314BS//c/5vas/nCx8TrCAukwiLWpmWUD4KR5QKsuqVgqe+XMQstxKMzpnQA+fYFA7ZuR6AtLpgY+41P373m8anObDRq8Y0Go1Gs0/xeAJR6qsSErBd+X98tr6MxKpueOVSUpLzEMUqgnzvmDg+KYlirTOwKmzIup+Y/e7LfDc84GPI5vbgsqpXnMli/ZOORPNXRGuENBqNRtNhygvqfvc23e6AP6Ls/v05bVMU75l7Upm5BrfXgskUiCHm2biZtebuQfWzPvkAgLimgKfrtNpGvGYLSfVNjDjiqN99zJoDBy0IaTQajabDfPrf9h3dn3zDQbtto7KwgYWfbcNVWMimgYPY+fPP/rwdvXr5t7d4ldFzQ0MMgyZmAFAUbaY1Np8SpGwtNEsxbkhyRzNheyGyrn63Y9J0XvTUmEaj0Wg6ROHWKjzu8PY8zXTtm7Dbdt6/R4XmSNu0AYDqn3+G5GSkhLecgVhiJsMjUGRUFdMuGMTmJUXgC12mb/N6kMConSX+tPVD+jFpuWrf19AIiYm7HZemc6I1QhqNRqPpEC1DasSlRPzm9r6bu5RVqf3YZlZaHjfB2h6zEWu6V++VAETU/YrHHGrvY/F58Amw+gLBN7anDcTsVUJTfWHBbx6r5sBFC0IajUajaZfGWhefPrwcR0NAEDrnrgn0GpHCCf8cyYn/HBlUPqNvPEMO7dpum+lj3uLx0WdxyyGXs8OIJSZqnUFlPJiYeMj72KyNvH79FVQ5l+CyBnuTPqfgA34Y0YeaSHtQutXr8QtCuzZ+vieHq+lk6KkxjUaj0bTLpoWFFO+opXhHrT/NbDFxzJXD/fsWm4khkzMBOOWG0PhiS5edSHLSofTpcwM9hyVQkboNtrUqY+4VtO+WZsxmDw0bTqayUMUxk9VVkBook+yuAmBxv24cs2a7Pz2ztJiquHhiHE3QpeOOHTWdDy0IaTQajaZdasqadlvm8v9NbTPP53NSV7eeurr19OlzAyUFq3mj+nx//jJ3dzJN1aRHNrK1RUD72KQivE4TW38JBHP1ugM2Qu7+cfhyBSbZMh694uR5P/q3iw+6imG7PQJNZ0VPjWk0Go2mTaqKG9j0a1FQ2vn3H7xHbTQ17QpJK20IqHU2eNP50T2QKE9MUBmT1cm6NwYEpX2XpiLRJ2R4eP2NO4DQVWSt2eG077aMpvOiBSGNRqPp5NSWN1FeEH6J+eePrQxJi0uO7FC7efmvU1m1CKczIEgt+GUSr4k4nL5Q4SSxf8BDdUpkOSXZqUH5XkzUWVRA1WNWzKd7aTFmkzLaHpVXBsDXk6YH1XlvxgkMEHryQ9M2WhDSaDSaTs7bty3iw/uWhg2m2nKlGMCACekdarOsrIx33t7IypXnsXrN3/3pLlcRu3zhBalGjzKEfmrqTdg8VtwieIXYguRD/NunrVC+hw7enA1ARpWyX5q8aklQnX7VcQz2aUFI0zZaENJoNBoNAPkbK5FSMvOV9fzw0jq2LCkOys/oE8/hFw1utw2fz8n6Ddfz7LPP4nJFU1Q4ACkDHp/DmPP4aXRHYTO5sDriEE2JuE0BQcjk87EjKsu/H+FVbSY0NmESgVfZ9m49eeTcS/37vSa9jrMx4GhRo2mNFpM1Go2mkyKlZO2cgI8diVoqv215KQDbV5b58y57agpW++7tcaprVlJS8hUm09n4fBZs9uAAqCWNaW3W/TlvKon2KpxmD2YJeZGZPN3rSgD+u/QRGvooGyJ331hargObuD0gsD140VX0T4qHd18GwBcj6dovYbfj1nRetEZIo9FoOilVxY388tFW/77Vbua759eFLdsRIQhA+pT2xWdMR23aODUo3ywCq76en/UoDyQ/Rz9zQOCKsTVgc9WSZw2epvOMOMK/7e0Tx66UgEAVVxeIf1YRl8Dz3QPBV70xaqm/RtMW+u7QaDSaTsrqn/OC9r0eH6W5tW2U7hglpd+EpDVPh60pG8yvheMAmJ63HF/yaNwJyWSYAn1GCAebP+oT2nBjQ9BukzVUMDv73qeQJhPr5geMs2UUSNl+WBBN50YLQhqNRtNJab0sfvEX29so2TGk9FJU9ElIutdrZVt1L/636gq+3nE0ALN6jCG+Zgc5u3pjJaAlclSr1WRjq5YHtfGtTAnaT6uuDumn2NASSSmpO8qLN0aCCWrrwmu5NBrQgpBGo9FoDMrz24jS3kHHzCUl3wJQVRW8sszliuSLbccEpU2qXI/dpTRSSaaAHVG+qTsAfRp3BJVfmBTwJ3TTG88R1xTs5HF7VhYAkzevIXtJCVUzIil5WK14q6/b1LED0HRKtLG0RqPRaIJI7RFLtwGJdO2fQH2Vkx5DkjpUz+NRtjrr1x0RlL5t63g2VQY7Rhxry6c4bQQAkQQv0QcVSLUtjlyywL9987U3cs3Hb3HXxdcC4IowMSBiNtISWKnm8zk6NH5N50RrhDQajaaT0zpAalleHRNP7UvWsBSGTs7ssANFj7eexsa4kPTy6tAArFUpKWwaqPwCmQQMrNsCwCmFX6g0lGGRuZVAdMHG7/3bj577dxYPGsmCKZMpSMsAwI0Vhy0ba6WL6ly1yiwhYVyHxq/pnGhBSKPRaDopZquJUTN6MPXcgfQelbr7Crth+/aHWbH8RP9+RoYSbkp9waEzUoSagmuMV1NjkbmbOaJ8NtfkPE+m4YU6yqumyw6pWhRUt0ddiX87tkEZUDdaA16qR+Vn02PWBrrcZaObuZbp88sxC9tvPjbNgYsWhDQajaYT4vNJvG6ff1n8hBMDS86PvXp4W9XaZPOWO0LSundbBcBP7uBpMdnK6Ej4vLTGKj1ck/M8w2vXB6UfXLTBv+20KQHnrYlHB/JXLcTaoFaJ1XdXrzjLhu/RaNpC2whpNBpNJ2Tt7HwATGYllCSmR/vzsoalhK0Tjvz8N6monE9FxdygdEt1OZve6ktUt54QHCmDHuaqoP3WglBDRDSWo07B/sXbCODivLewj3dx4vOBKPSlsZHsOigxqJ7V7ab3pp3+fe+cAfhMlTTF9EbrhDRtoTVCGo1G0wn59ZNtQLCzwSuensoljx3abj23u5blK06nvkE5Yszeek9YISiyKBcAUbLGnz7Duplz7Sv4vzlvE19R7k9vLQi9dtZ1RC+d59+P8TYQNyfYQ/Wy3pnk2bKC0tbNPTFov/vHOcx3Hseuyg4ue9N0SrQgtB8jhIgQQiwVQqwRQmwQQtxtpCcJIX4SQmw1/ifuri2NRqMJR5de8f5ts9VERLS1ndJQXjGbmpqVbNlyZ0jejs1qFZgnIaBRqpMBm56u5jqswofN5aImuYXWyevji4zA8nqnLQJPYbCzx8TGwMqvwuQUXMldKLUH2zVFuUJXh82N6EMDMSHpGk0zWhDav3EC06SUI4CRwFFCiAnATcAsKWU/YJaxr9FoNB3ijZt+9W/bI/fMQsLlUuEwqquDo7y7XRZ2lbZtWzSxMaAZElKSUFXo30/sU80dxzzu3x+yZWW7Y+haUU78IAfDdgUcQPZoKqTKFn4CzFVZHjZdowFtI7RfI6WUQLOHM6vxJ4ETgalG+pvAXODGP3l4Go3mL0pDtdO/nZgRtUd16+o2AmCxxCKlJGduDxqjM6ls6usvE5mXDcD62EFEepXjw8imakD1ZfZ6Sdy6jepxall9jylFVO+I9dc/et4X7Y6h7J9ubGYX43I2Mqgol4ebHsIsPCzKSCUZKDvNTOonXhoj1bL/prqO2zxpOh9aI7SfI4QwCyFWA6XAT1LKJUAXKWURgPE/bDhnIcRlQojlQojlZWVl4YpoNJpOTESMFSH2zH6mpOQrQDlP3Lp0ISU1g4OEIABXk5ttUb2ZkzKV77qoFV0J7mp/flRTE0JKfxAyn8dE3pzMdvsdUFQJwCn/fR53f4nN3ohZShKaGrDhwWk3YdumXml1PSPY0auX6gNIsCbv0TFqOhdaENrPkVJ6pZQjgW7AOCHE0D2o+5KUcoyUckxq6m/3EaLRaA4w5J5XiYjopqr64OvHHwwJv+GT8GrPi/i+y5FB6b0zjg7ajzqpkuhta4navp71b/XfzTglkW7lWDHR8CNU7PGxKG0RXuGloDEOKQSxM5UrAE+kicboKCIdDhIq+tPYwveQRtMaLQj9RZBSVqOmwI4CSoQQGQDG/9J9NzKNRvNXQsqA9BOXErHH9ePilB1Q3lw1reWOS0JK2OlNxCfhLefYsPWmfH4rADank8ZxXmyxXkweN+ZWBs4pQypD6g4/J9u/XZDaE4B3zPkURhdSWb8Cl7k3nxYHrAMayydT7FIaJmd1I5vT9HoSTdtoQWg/RgiRKoRIMLYjgcOBzcBXwIVGsQuBL/fJADUazV8Oj9vn3x41o+ce1/caHp+rthqrzUxmdvoSmePuywZvetg6gytyMCOZOmcOR/4wk5rTvFgiw8cSy5wY0N54I6KQwkRkfkB4c9mChTdLVxffV91JSmHAcLpy81GMylfR609Y9hjDTbV7fJyazoMWhPZvMoA5Qoi1wDKUjdA3wEPAEUKIrcARxr5Go9HsltIcJRRMPLUvfUeHNS9sk1273qeiYm6zaQ/SsC+q8imj5IbGUA/RAMVRKmhrl5JSopqaSO13J9aoUEFowKnbEaZA2429BlM/8CBs2cGvqtL1Cf7t2UlKcOq1ZkGLEia+H3Csfy/aXtzhY9R0PvSqsf0YKeVaYFSY9Apg+p8/Io1G81dnzrvKO3Np7p5rSTZvuY3Gsggqs+MptqfxcddTOdW3ljVeNQ21uZWDw2buX/hy0P5jszZy/mBJ/ZhoYpareGHJA6uITFER4+N61FNTEAje6umqJK+/3/IgAGlDqyE/sNotuksgDMfCMX8HwBHdw5/mcwVWyWk0rdEaIY1Go+lEJGWoUBo9hiTtVf38+ems2zqIj7ueCsAKT7d2y3//xQ1k1QVrZCKXmajZlsT2nF4AZFTV0X1KoEyPwwqJ713n3098U32zV8QncrF8EQhEpx8d5aH7lKdoHKe0UUuThgGwxRbQTkWnZuz5gWo6DVoQ0mg0mk5Eag/lr6f/+PD2PC2R0ofP5wbw/28qj+STrqf4y+T6QgWqU7fObbPNeksE1y75nIGP1zOiQmly+hcrA+kF889nwfzz6XKbjWFzlSAU0dTkr9sUZeNwfgQgw6oEofOTXeCDqKVqxdhwlwWPCZafMYK7/n4dAPFDR+72WDWdFz01ptFoNJ0AKSVIcDm8WKwmzObdfwfPntMPgJjogdQ3bN5N6QBnZs+iLDKe6fkrQvJiPA4k8P2IPoGxAdaZUaBMjVgz9CB65eQAkFgZCNB6atU8ZtksTI/z4AOEoRXq+o9gj9Lb01WYkHmjJ/BE+dM8ZdMhNjRtowUhjUaj6QQ8d+UcABLTo4JWjrWkpmYVK1aexfBhL5CScpg/fU+EIIBYdxM3L383bJ7DYqY20h6Sbl9ghxlqu6BbN3J69wbA2+j2l3GlVJFXamZ6nIcb041l92EOpckecG60ZLCFCPOeuwnQdB60IKTRaDSdiKrixjbzlq84DYA1a/9O165nh+TX5MbgEu0HZf3865sBqIy0E+X28EXvyZyVPcefP3tIVlD5cdsL+faUU4PS3C1ihqVWBfwKTWQBX7Zy4GhqCGw/csP9VMXH0W9rYDrNGnXtHnvP1nQutI2QRqPRHMAU59TgdoVf1t6ShoZtuBssFC1LRUooLHw/pEzOzO58nzYjbP3+9Vv5/osbiPAqDU6j3cLsIVkk2nLDlnclpOCOSwJLaKDURnMjlfZKhNfL0B3KmeJlNz/Al2W1FLgCry3RCKbqgJDzXZ/eLEqxsGJ4wEljQfJoZHX+bo9f03nRGiGNRqPZDygt/QGHs4ge3S/+3dqsLW/i0/+G2um0xtXUyPxZx5L9WS88TRbiutcTna60KtILpWuTKVqqfA75REAQudC+jEZs1JQ6OSp7TVCb2w1vzuk19bRGmkw4M7IAKKxuCsn/vvtPSCE4actp/rTrM+/hzl0BoSdqgYmE9wOvsO9HB/I2RAeHFPo4fxdnJHRv/yRoOi1aENJoNJp9jMNRxLr1VwOQlnY0Efbdr+jqCE117rDpvUYEorEXZm/m/dtvAPr503yegFCx7o0B+DwB4adPww4KIrtxqm0NgzZvotDqJd3tYNCugGdnnxA0RDRrelpNS0mJNAdePZuGDAnKTqwbSlnPkwA490E1Pbeja3fiTdtpjl7vkRD9s5WWwdJW9Wt7iXxiU6gwptE0o6fGNBqNZh+zaVMgTpbP28S27Y/Q2Jjzm9t1NYV6b5bSx6a5t7Dmp+8ADCEomPz5Gax+cRDSB4jgyKzzUiYDYBceum/LJqmy0h/lHaA4bTRVEQmsjVP+fKze4Gm5Y9buwNElVDtzzT/+wc0XXIDNoQyb37n9On9efX8zd+xS6XdmNGER4EsMbnfpwNDA0nEVbzAl93XSSreE5Gk0zWhBSKPRaPYxFkvAi/L6Ddezc+cLLFp8+G9u96v/rQ7aP/vO8Zx5iwqaOu+d11mx/JKw9Vx1Spuz5uVB+NxmHCYbs1Km4DAF7Hms+FjcN5NRO0s4eu2OQLqniRpbDPNSDiXikBNJq1XG2XnJgWOMDDMZUXnKqew45ljGV/4LpCSzPBBLujgjl1qfel0lWpTQZW8VdkMKC9dtcXB6nvJOvWveYXyz5R3WObNZkbOknbOk6exoQUij0Wj2IT6fk9Ky7/37dXXr/rC+YpMjaKwpBMDtaGLuIyVB+S5hpcjeJaTeurhhbIwdzNxkpQ2KFQ5MbhW2wmkxB5VNrtxIRYTyXm0fNZ0Uw1C7IjqwhN3qDp2y85QqwSdylYk5V50TlLd9WEDjFPuNma5XBQSy+msmU3NLLyJkHOfnurlxk5PieVMw46O/2405sgBRk9XOWdF0drSNkEaj0exDSktn/iHtetyhK8WsNjMNNQEHhRLIj+hGd0cBAlicOI418cP5W96bRHsDy+ydqV3BA1tjlB1RnYwgslBpgdwWMziDBZuuzgImVi7i5CGTKXUpDY3bHBCYbC4XwufjjI8+BuDDs85s8zheuukEfq3/1r8f+50Zn7Dw68H3MmuYm8fylT3Vdy3qSGlCiICDIZ+1FI2mLbRGSKPRaPYBHk8DPp+HnNxn2izj9YauqOoojTVKAOlzUHCEeZcr4Jdnbdwwvsw4nsqhSfgQFESq4KkNZmWU/HSvK3k380wcMvSb2exQDnzWdg+1zUmqb2J0zWoa/vN//rRop8u/PXXuPCbPCfgWytzwZdhjyD4snSN6fMUpK2/hkhQn90cq4awyaSBuWxwLRvQNW6/ac7l/2+aTFHeVYctpNKA1QhqNRrNPmDd/OCkph9PYGFhtNWXyanJyniYisjvZ2XfhdJYQEdENk2nPH9X11WrqavAhGRx8cm88Lh9SSlbMehKIB2B+8iQAvvOeRXWvgLBQZ80gxVUBQKUticpWyqVDKgLR3hvtoX6AXMZ0ma9FnLDy2Cigwr+fXlbu3560LuD3pyX95xTzyzFmemZsJSPSS9d/qb6ctnh8AmLCL4qjwXssidbnAbi9opK8gT3CF9Ro0BohjUaj2WeUl//s35522DYsllj69bsFi1nZ2CxaPJ1Fi6ftVdufP7oSgOgEO/GpUSRnxpCX8xHV25UQ1OfYnf6y1Y5gjclPKeMpbmUrZCWwAu3SlR/7t4fml4X07TNWzDvcSguUfcHZNNit5KbuecT772q9ZIx9C1MgGD1bBpyD0yqId7et6WleyHZSfQOnHvK3Pe5X03nQGiGNRqP5k/H5gpe1Dxxwf1AYiIiITP+2w7ELj6cOkymCwsIP6dr1rD3SEMUkBuJ6fXHne/7t6C5NsDF8HbfJyuqkI4LSUkwNFPmUEGX1ujhmzfZwVQGQQqi/Ncrwe02Tm2ghyB41mqwff2p3vM+MOIWSqEQaLRHctOwdBpXUIF1x2GZHANX+cruSLGEFIRMV+EjGJQdiJZtV1nhGJ7btY0ij0YKQRqPR/Mn4fMFTQfaIYAeK8fGjgvZralaxeo3yOO3x1JKVdVVImz+8uI7tq0K1M/YoFRtMSomnRagNk1WSEWejqNYVUgdge0RwxHavNNHdVIUHE0mONuakDJIanUhrJI0WO99nTcBrMiMBk08ZMK9L7s2wih1BdbzA88NP5tusg8EQCi846nasJZCZHQnRMFVcy8V3PIw0xzJjk4PbN6jz+GpvG6sTzBRHCr5f/BX13hP4b/zBHFM0lCX15zJ88ZtYJ1zY7pg1nRc9NabRaDR/Mi5XRdC+zRo8ZWQy2YiO7u/fzy94w79dsCs0qruUMqwQ1JKm2hr/dmpWd6ZP245HhgYj7W5zhq0/sW4zL312Py9/cX+7/TTTo0nyxuBjeGXo8bzpHIuw2LEZK8heG3JMSPk53Ufzbe9D/EJQM2NcFjZlWpHA3Cn/Iy+9K/mpVi5yQJ96JVj1XN1AnvNGPlp/NkL8CEB0UwpL6s8FYHbB5g6NWdM50YKQRqPR/Mls2nxT0L7FEhtSZvy47xg16h0AGhsD9jxOZ7H63+Rh5svrcTS4WTu7IGw/5993sH/b0aBWeVkifVzw3+fxeH2U1QWEnkR7FTePe5yYxpqgNk63r2aqdRuX//whAGafj/bwmJQgM3z9eiwtpgCdCUlMnr8AgDprFKceex+XTv8PT084kTndRvHY6EC0+74JAW1R44B4PpkUS3bX4Kj3kS1mxdwSLts0guqqUbxT/gQAZ9ZP4cQEKwKY10aoEY0G9NSYRqPR/OlUVy8N2o+M7BlSRghBYsIEAJqacomMzKKpKZeoqD4AvHL9fAAcjXWUFW0BQldGxaVE+rdLc7YB0G+GkiBu+Dg4SOp5gz4metuhDK2DTYmB9GjhplfZpg4d147hk2kq38iQwnJcJjOf953iz/MmBoyvi6KT8ZnMNFojKIhN47tWodUmZiyBsr40CMmSAcoRY32fCE5shK+rPGyODXbi6JYgK4/DHftmyJhOSLDyZW5Wh8av6ZxojZBGo9H8iWzf/ljQflbPqxAi/KO4pQF183Sa1ZpIztoyLJFVJPSZS8yw8+g1416i0pSwYomqoOeJ17E8NR+nJ2AT9O3/HgGgrkTZ1ZgaVN5Vvb7h6cNuZGTaemp2TCZL9OQKbyF9TOWcwkp65uZy2Nx5HTq2fr1PJz9ZabfeGXhkUJ6lOuCg0WcKFmRacuaAz3h/49mc3GDn3PqAN+p7jCX2ryxt4qVljUF1mpVDi+rC2wHFdtHG0pq20RohjUaj+RPJ3fmcf3vqlPWYTBFIKZn99mY2Lywia3gKx141PFDB3ROsO/F61frxuqoyKsvfo+/x/wtqN2ngDxx/zcWMeWwOzHsIgFfmb2F83B0MG/YcmUO7sWt9AcnD8gD4bKsKrzG6n7Kpqc6Z6G/L5LNyqC2HMz/4sMPHtXHwINZF/IIcPI7CgnoOz1vOx/0DS/+XOntwEnDrwZe22YanVwwzes4lbYkKseEx5CWzL3h12ODa9qfnWrMuMXH3hTSdFi0IaTQazZ/EosXBS9LNZjV19cl/l1OSUwtA7tpyfF4fPq/kxWvnMfCMnUF1fKadmNOChSCAmPSNLNnxIRDwJL0q+1MG9l/FihVnsmu98k0kTLDszoUh9Z013QI7IjQ8RzhWDZ2GzbGLmrQk8nuoqbkdvmTOqgoYbp9ZsoC6RjOnblNaJXcb2iBfnBVP/3iQEIHShNVGKk2ZrQ255ytcLMLFBOxB6fNqy5gSpzxeu/MX0y0zNNq9RtOMnhrTaDSaP4Hy8jk0NgaMgMeNU/Gz3r1zsV8IambNrAIqi5Rxc8XmGR3uY+Gm4Cmsn3MPom5XFNtmG9NhNi/1i68guVG13dsR6HeJzcensdWUpc8nP6qCnjk5Ie2v7paK15iu2541hlv6HsMNQy9lR/csf5m+m7b4t7vWl3HRoi+5Zs1ndG1QU3vlUQlBbbqGJuBNj8Q1IZVz5RuYdxzuz9tqGEhHeZVG6KFBwQLP4zhYgI+JsW8EpVf7EtiwRRlmewpXcvwRo8OcLY1GoQUhjUaj+RPI3fl8iz0TsTEDAaguaQwpu/CzbbgdSitTmz/On+5xxISUbcm7m08HYEDiVn/a1m+yKFuXDEBS/xpm1w5nmlktY2+KggXzz8fnE3xfNI7J0WsBSDY1MmHJUlqTM3gIn5x5BgCPTzgv0K9ztH8p/hnrZ/nT/7fgRbVhicCU2AuAouiUQIMSRlVIzIMSQAgs+Q1sWB4IwLqxu50kp4+Zc5Xg1mAJXlrfvCbtYvMp/rT5EW6WurPpselt6r64nCsGTCXWUtTOWdN0drQgtB8jhOguhJgjhNgkhNgghLjOSE8SQvwkhNhq/NcT4BrNfk5NzQr/9sgRr4Ytc+mTk/3b1aVKQBpw0Fh/2q5fr+5QX9cOe8G//UX68QD8mDKNOxtu5csWoTIqfCq46q+/nMdZ5s3+uBRWV8DJ4uLx4/3briS18st81H/Z4Qj2jv2OcwxzXH3ITuiGiEoGYSbaWQ1AzFGPED3lZkrSBgHg6RZFhA+urYngmE0u/v15NfgkX244CVDGz8sSK9mVYmFGcaCf5A3hBZpqrDwT18RGq4cldg83/foUAE5bHN7EJEw1v3bovGk6J1oQ2r/xAP+SUg4CJgBXCyEGAzcBs6SU/YBZxr5Go9lPaWrK829PGD+T5OTJ+Lw+Gg2vzpFxNq56/jBsEQGzzbnvqimmsccMCLRT0ZfNH70c1PbK0mF06/seKSnTyYguZnTaKja/GYjKvisyk1e7X8iW2AG05iCL8j9k9ng4+6N3OO3jTwCIagxoqXb2yuKXSYfw9fHH+dMWR0QSjp2+JBJMVmJmPEjsic+DVZUTFjWl9caky3H3iyMlPZZraiOxIzg81sKJCVaW/1TPOKEMuGcNj+SHGeoYbtgc8HU0sGgOqwxB7nNvI3ZPIK/JBN9Gu0lrrPSn/TrxAU6vSiY7eVDY8Wo0oI2l92uklEVAkbFdJ4TYBGQCJwJTjWJvAnOBG/fBEDUaTQfYvPk2/3Z0tHrBz31vC5t+VRqO8cf38i+VzxqWTO66gOdpe5QFiyWOCHtXzr5jPKX5hexSM0V4fSaeXX0pn+bU8eS0Qsrrk8hwKYeLl+58lZd7XgJAoyUq7LgGmMtASk775FNAOUvM2FWI26ZscxYcOomd3kQ8mYISXyw/OQag9DUBjdHh5pX87D0IACF9pHgDGpzYY58K6m/X4AS8mXZ6bVJR6TOsgmhzYLrrjKZexEQKHu8Xwd+3Oblie3D4D6vPyWvVC0lMGMO/v/kX4y02zjj2XiLdDo7JXcyIyp3Epqh4aE5bvL+eLIiCrLCnQKPRgtBfBSFEFjAKWAJ0MYQkpJRFQoi09upqNJp9Q1NTPkJYaHIozUtCQsDep1kIAoiKs/m3j7xsKC9eo4yehclNRcVcDp20zBCUTKzafJS/bKNHaVyKa5yc9fmVICC/XgVsjfCFjyEGMNW6Dbc0ERdbStS24CVZkxcsYN4UNUW3Iro3c9x9W9UOCC6jLfncbPmI5a7RVJslca5GIjLH0hbrMpVmqChRvXpcrWKmjoxS6fNm1YetX+IuwhSZzmw8/Ft6iXU3EeNq5OPv7ggUMk7rrxMf8Cc5FiyDSR03Otd0LrQg9BdACBEDfAr8U0pZK0RofKA26l0GXAbQo0eo11mNRvPHkZPzNDtyngxKG9D/LkDFBmtJRt8E/7bFGlhenjbiY9asncPo0R9RkP8WJaXf+POyev6DJRtD/fwMr13n347wNuEwB6axzrWvwCqU4GOzOKirT+OYuaqN3Nh0rpx+A1avm971RYywraDU1L5x9jBLMdE0Um1Wx9Pf2YS9/1FBZQoiBZ90t3FNdmAaKzfdyrZ0K55kC+wKtjUKx1M4+CFRUHbknaQs2cn3b9zgz3vtpwd3W39TVAbTdltK01nRNkL7OUIIK0oIeldK+ZmRXCKEyDDyM4DScHWllC9JKcdIKcekpqb+OQPWaDTkF7wVIgQBxMQoOx1HfXDsq4hoK16Pm/yN66ivquSw89SKssR+cwBYseKMICEIoE+f6ykwPxvSx6D6LX5Pyy2FoMOs2/xCEIDLE4FoETfsyulKuHCbrWyJ78EXfSazytY7pP3ml0YaPuzSiq/F5/TEMN/W9wyN4J1eNsYfGRxP7YtDYji1A0IQwMe4qOip/CCVj+9J4XMumkaosce6m9qtW27ykRDXp0P9aDonWhDajxFK9fMqsElK+XiLrK+AZl/yFwJf/tlj02g6O9In8XmDp5V8PsmWJcVkZ9/dbt3q0tCX9/KvP+eju2/mxSsuIK2HKyhgamuunf0A/W79jufmFwelD6zbjCs1k/pBY0hqsHHr2qcYZ97JLeWf01uW0Sd7K2nFgTqppeobam7PUSF9fDhgekjakaYK5hPHo6ZaPiOB852Tua5JRaMfmxLNiRW5/rKXj1bTYLkWGdIOwISKgBD0z1ERYctUd5tN0dCXAPB1CTbQrjsxUN+dLpEE+vGaAgFaN9m8LLCHH4NGA3pqbH/nEOB8YJ0QYrWRdgvwEPCREOISIA84fd8MT6PpnBTvqOHTh9Vy+DNuHUtqd6XtePmGT5BeK/1OVOWs1kTc7ioApk/b7q//2SOBpfSn3qic/f3ywVv+tJqyEvqMzkIIK1IGtEcuXyw2Ux0Nnhgg9OXuiU3EFaHiamXVVTDvsMM4Z9VMBmzJZldGBr9MmRxUvjQ9HY/ZzH9Hnduh477ep9qe4At4oX7Cm8kcUwGfdkskx3kYA+p8HDbZSl2ElZMONlEbFd6T9COrHf7tq5LPA14BIH/0I3Rf8W8ASga9DULSw3Ek2WT6yzcSSZzHQdHjLqQhQ+VVd6NHQgEJz9tYmPS4/yu/MMLHmOiGDh2fpnOiBaH9GCnlL7S0TAwm9HNNo9H8KXzaQpD56P5lnH7zGJwNHvoeF/BkUZ0zkWPPfA2zvYGfX9/Ai5/P5Zy7JxCbFNB+XPnsVExmEz5vcEiLLx6+l2vefiNICHpi5RWsL29jGbiUdHMUMjGh0J+0YrQSsFaPGsWALdkhQhDAPGdvonqMJ97TQI0lmgGymKn5y3mxR2CpvBkvXsyMMecT4x0StvvY+J4s7hLF4i6wfGYddZHqGK1b6vCMDXZz9vrqbQwrCUSi/79xjVxm8lDW9xOcMQU0Jm+gYNQT2Ou7gZAsYQLZfYYGtVFCBtVJTr6LOJ5+bGEKc+iRoAzSFyc/6xeCvh70LFUJ2YzsdQMaTVtoQUij0Wj2lFbKmI8fXEbq8E9JHhhIS+i1kFdvWIAtwozL8BL91i0LEQKiE+yk947HZFav7MrCgpAuqkqzg/a3VfWmre+ia3JfwGuLoDExIDDUx8X5t5vs9pA6DmkmRybz3AjllTkCNzdZnqFXHwcvugOCkE9ChqmGPjIpbN8AV4wLLM9vaCgEBoCUnLtDcp+xiCzO4WC0fQHDSiYE1Z2fkMZ88SnDe6/kRr4h63bIP7WS6v472chI/if+7S97vPsTvraexm3iEVAmQ8zhCKZIZUvl89iC2r540BpeLrNTVtN2tHuNRgtCGo1GswesmxsqtFhjSkgeODMorWanWirfLAQ1IyU0VDuJjLXi8fowmwSF2Zv8+Rn9BlC0dQtblyyGRLh/yfXsqu+K02tnuHkXSaYm5hpL2s0+D3ZvExJBYyutSUs29VNG2gM3bqJ/djZfn3A8js0OaGEL7cBK7IBa7t58IUN91bxg6sESRy43R8Vwlq8Lp0olZKxv8jIwEt5M+4o5cUs5r+ERfxuJTh+71r7A1+YrWepIIbd3DZDEhfJlZth/QHitKN+wikcG2sFYBbtWHIT1tlRcFTVsyL4DsuGF4+K5/ZtKvh0dxcV9rkEIL19zWsjxObATgROvM3iVW4pFcnOGg6aq8A4gNRrQxtIajUazR8z/IDskzWR2h6QVLf1bu+34vD763vo9t3+5nuxli2noNZijb3+INYNPp8KaxJKPZlNfFImnwUy32l0ArPVm+oWg3g07uEAs4G+F7+OzhWp8WrJ1qJpSG7F2LaXWeF53jackIiGozNRuC6jpBvmZObxgUu42xkdk8XzvHziVgKal0OXj+EHXMCdtJudnlnDb8ICQUWU38eOdgyif9hhmey1vj1XxxSwoYTCyOuCTSALZ3TYGjeGRs6/EZY3271/3TQ0AR65qJIlKEsw1Icdm8kku4V0A3I1qGk6YXaQM/cJfJiNxVbvnR9O50YKQRqPR7CV/e3QStrhd9DryHn+acEwh1vMsyMB0zGk3jeHqF4I92cz3KOHmncV5bCksxRcRxSMfzuLDtRXMSVH2PNu+yuKw3AWMrFmD3ecMqh9v9yEjY6gfMKpdbVBrrpiuppoWdR0WlH7uIBVeoyx6RVD6gG3nBe0XZn3PkXFubu3qJM6e4E+Pcav5wjfEZdhiyul34r/8eaNYDkD3FQEH+AL43zIY4tjsT1vTfxCLx90ZMmaLD3xeU9DE4JCqcvoVlnPrx1Xc/lEV3657kLw5ykYrodcCUgZ/6y9rrhqGRtMWWhDSaDSaDuJ2Baa5pl80iMgYG72PusufNmb0p0w75jVGTj4cgIQuUVz9wjS6ZCl7ncufmczUi/pw7D8H8XqLGFovRx2LRwq/F+eiiAye7nUlX3Y5lmh3PZ92PRmnKVjrMyC6brfjLfTGMc/VG6c0IyW8OPSEkDLvfn83w8RWTEIJMunulJAyzWw54iIOHvsZR8e7uY97uEYE4p7NXBqY3qsnhh84FoBT5QckUkVkVUAbdNxkZVMUWdOHN+dlsmKmWsbvsljxtNAItST70xfZ/NHLPLz1VQ4tLWB6wd85a0HgFdZnU2DcVdum4/UJXi6zsa1RsG772rZPkqbTo22ENBqNpoPkb1ABPQ87fyBbInx8/M1GTu51PCUlXwMQG6tWVdkiLBx3zQi/ANTM22+/zc6dO2Hg4SFtV8hQASAvqgdvdT8nJP0o2yYiRNvOCO0V9QzYtI03xlwKQLEzlkMit/Fz3+DwF+OLNnDrxEvJlV3577JruXjIG7y6XflAku5GhFUJLFXdZlE68F0Q4ENwvvgkqJ1/vfMyzl9nwSEfAHC5eNOf90uNi2nZXRlQFYi3VhwZbLwsUdNrUc7d+/upW3kyTyT8l0x7Nc+1U27rJy8yNHkOK2yCU6Lzd9uupvOiNUIajUbTAYp31PD9i+uQSHZUN3LdB6t49ZcdfiEIBCbDkd/y5ctx26uIiLZS2eDCYzheXJ5TzhJ3D95YHWrrUuCND0kDqLcEe2ROclWSbgofiwvgM8cQTvrpW75NCzhJbMLGz02DAQnCw2df30zsoJvYOO1tdsappezZVX1J3HCBv07DrLv826WD3gGTOoYn+U9In8f9OpsFQ0xMkbNC8pyuKsZXPRCSXmkNSeLQjQFHk6Vxbb+evqy+kedKPgub5zGrNs5NuZrbrE/zf41rybZ3LCyRpnOiNUIajUbTAW56diklUV5GOM28tGQZr864LSh/+rRtgIoj9s03KhyGRwrecY4B4MurD+Fb1+CQdvtHNpLdFMU6b1cADrVuZ9Kor3hw6fVhxzE8qrzNMRZ5Y7ho9VeURcRTGREXkh+R8THWhJVc2GKZ/4TEmaTvOh6bqZGhjPGnF/dPIa7/Bzhj8kFI7BsEwg0rRo0LajOhroZbLjCzLVMQVfguZAa7OMusCwSXXR21hU+zttG97FS+85bQlPYLZ5Qdw6JqC2/8XMtXUUr4eX1aLBfP3v3UXziuSP47X1TeQ4JF9Tswci7fVt67V21pOgdaENJoNJoOMNeqorlPG9qFJ7KuaLPc7Nmz/ds1MuA88cRnfw0qd0RaA0//4xTevnU695sDWpbupmr6JuTw6oxrueTH//nTHUdmYipqpOevxQwqyyG6voGoxkbmT52CU5r5ZujBODY7mDlyEAIfQkiQMKVgFb90H47AhzVhZch412fMZ7pzIEc1jfSnNf7yON57NlJFYFVXzEsJWK310CIaxwPPPszgnG1ccM5U7MyjpmoQLRxAE1/6X97cdqs6F+Z6bsp6gfLur3D7h5VAEh/0m8eumnxGyavJr/QwpALKY030LQ5dhdcRRkV/zremQ+mf8lpQendXxV61p+kcaEFIo9F0ShxuL28uzOXiQ3phs7RvJfDIzMDKpvrk0DmdW3+5lenGorAFCxb407d6Q4MdW/Hy6bm9GD5sGB5PHbV5sfRO38GOSOXUxyZ8PPjztSQmVeOY0dXvZwfAlxHFmbO+QgAFmZnMnzoFgPedB8EKJ80OF1+Z8U817uX/4MYv3uWCv70OwD/zA44PAZBwfNWUICHIsfYDvOWbWxfjzqufIbkmsKLs/MWfMK5uBbdc0RdXzlRcZUcDYC/djm3ELI5bNYJ6W6/AOUx5jPLuryB8ATug09fcSIIjDQCXkZxS5+PQjSr8hl3AUfFWKjw+rKIYq+kt5tReybDI71nZeCoA32XM45iiKZyZfB0uq4MfOZe1DGY4m8inK1nsYkNmesh10Gia0YKQRqPplFzxzgrmbikjymbm/IOzcHt9mIXAZAoIHttK61myo4Jn5wTihM3bsIDJLZwjXz/3PmpccaTPWQ3ApcKEWfoYOuIg3lgSbBR8qm0Nc919Se7ioGDXe6xdeg/Ql6OKf2LplH8hdm0AYJKtilpPVJAQ1IzHbMYREcENl99IkrOOQ7e1vSJqxuD5zJn6DAO5DACBRCK4cNl9RHhiqO73PReVnRhUx1k4i6Knvf6l6k+V2Li6sjtLBkegwh9CclMVX/Sr5PDxkrU/XhVUP7bP3VAn6FtxGt2t/f0eoFcmlhJb/gL/nHWGv2yzENQWR8ZZAC8jYk7E7euG1VRAv8hfVHuGIPStfBLSn+Q1TiePQAy0VzibIrqwsWtPjrRXt9uPpnOjBSGNRtPpcHl8zN1SBkCtw4PD7eXIJ+ezs6KRhTdNo2tCJBX1Tg5/fF5QvRP6fMeJfX6guiqdsrJJFBdH0uSKwpcZScTMXTgnpvHy5BM4efWvIULQefblSAQn7PyED65pNFIND9H4qKhpwuyxgRWcFivvjZ+BSXrwieDH9Ixn3vFvl5DKzq3BYSWa8Xls9KmW2I8NxD9LxEpUfTciPbFsst/LTWUB/0c/xyxg3IcfUvyU2y8ESR8cscnEhYc9HNR2RUQCM2pmYcLnT4vKegaTvQi7W3DmgokQBQON1WFfJc5FCklE46/AGXSEuMjllHiSGRP7fwBYTcEeva9OPzlov6UQBFCEMgIfXLiT9OBwZxpNEFoQ0mg0nY45W0r924/M3MIjM7f49yc+NJs7jhvMPd8E7GNO6P09vqZ4TuzzAwDr1h3hzzsrcjVvNkwEwFzYiGdAPF9kjcVeogQtgeTCCOVQ0JGTS4y3kda8nXkW1TVeUvom8EqvkXjM6tHcWggKh8diZUrOchId9XzVZxLOyWngg4L3bsURk0j/U64N9LPpSUyY+N7zFINru0CPQDsTc+Kont4U1HbZqiSuMWXzXe1GVsUFDL2HbpnF0A0DuDbzQnXc8TWYI5Wgcurc7kTYDyLGBFGGdu31tC9VOW/otOLh8U8yIFIJnOXuLD6seJTtifN53P6/kLKtacKOExu1xLZbbo4l2fBqpNGEogUhjUbTqTjrpUUs3lHZbpmWQtChmQs5se/3/n2fL9ieaEmvIZg2KkNqS249viQ7tpUB41yJ4BvnIE4v+pxYR+iy9yaTnWpbIt2aCtjWd3zY8Vz76avYGzw8csHlIXmuQ7tw83vvcNsV/6J7kpuHnr4J8xgTExbfT+FzrkBBKTAZHlOOTrkupB3f8rdpuDfgMPLjFUm80JBN+hQlpPTN20RZRiY11jhmzJvPSz3+hqdGCTbWKBVn7aR5XbF7zGD3MD0uIPQ4TMp5ZLxD2Uz91O91tqes5vlXnQyYVuIvl2LN5er00FhiLZkZHUV/l4ubUpMZVBjsY2lBTxOlpo9Jd4zmkKIsf3rirq3ttqnp3Gg/QhqNplPRnhA0fWCwzcohXRdz0ZAP/Ps5OaN4f+P11EYEjI7Xdu8bVKelEORNtuNNjaBP1UYsTcFCkDsmASkEX3c5FqvPxUjLJtri5J9/5phFc4PSUtcEpoqmPf8+C0eMYWv3LD4ZMZn4jy1IU8Ao+Z3CGAb89HrYtht+voO6Ly5DOmuxbwi8EgatSGFuYmA5/fYu/bjk1Uf51wu3UWyPx2Oyku4RnF5vw1t4BqaG7gz0NnHQgAT6xlb56/0vXcUBO6+mlsQmdX6rI5VGzjetp7/cpxWhvoaame58hEKZxKE9MrkhLYUTunVlsy00kGpel+mU9Xibdf3/GUj0+bDIhDbb1mi0Rkij0Rzw1DS6MZnA0sr2+POrJnLycwsBePjU4ZwxtjtZN6kYVSMy4zk/KxBR3uOxsq1oFHMmjsBBBEk1VVQkpKhw8m3gHqPCPgxdGxByorv1JrfWTURmF1zSRFVTIlOTV/HJuItC6j/739voVRQQeL644TKqY2PpWVwIwOU33U92z95Bdd4/8kTeP/JE3mw4ExOCbzmR61YeDCGmRG4ETnz1xQBIIWka62MLAyiYl8HY2I28ZEx9AVhXVFBuS2Znny1s7LUT28bTOb9eRXv/hhcw5/no22sNsAYSv6LAcShefHyfaLgNqJ/IEXkXA1AToaYNM3wVYIJ5tZdS7B4UNLpSGc+khCNxlZwEwETnM4jt9cT0vw+fK5G4nMvBpjQ9Pb2p9Pdm8EJkwDv3tuQMrnj3bRyRA1l5SLiro9EotCCk0Wj+skgpKSwspGvXrogwK6xAGUaPuucHhpmLGGUt5KIIeMOhQk2M6pHIj1enUlf2JKNGqrAXL5w3mjdnPc8lg99FCImU8MuC8wF4e/IM3uIscujFbQmPAiBqdu/zpjypCz0KcwB41DyNbqm1TJXbabCaqJ3ai68JLDO3bKjCWlDP5+tuwJ4b0NAUPeYi4191xDcEHA2+8NCtTHv+/bB9/mqZSl3eJXzQ08YHx8PymXXUW2DG1BjSHZKlyw8DIPMs2PRBV1b+I4vNph68Iq6CqdDSE4995i4E8FX6sVh7KXuntD63MOITJ1eeG8GrUnJzRSV9jaGVmZTG7M3Urxhe6gVrP/7l/IkXUSE/TqqIoNp1KP1MbwCwvvGokPFPMl+LqyRYOJLeGHzueMSO6zjCvk6NraKEI6KVE8d+hTsZuXE91WndOWTDKtaMVMbgIvmnsOdIowE9NabRaP7CrFu3jpdffpm77767zTKr8qq4IGIFo6yF/rTnzj2I9XcfCUD+1rPJycnjnnse5Psf3uCwfmb+Puwd5ZAQKCvLYmdSFwp6xjDIcDDYixx/W/YlSrtx5q6PSXRVMr5qKRMrF3FiydeMXL8Ys9fLhydcAsCb3c5lqj2HqbYd2CqK+fCQ44LGallXhaWgkYfidhBZnuVPf/Xfh+K0w2XXmLn5wsBqtO19+3DvC4/595OKn/Zvv2S/kvd7BtRAY46M5bbkzZhwUWF3BPW74p+jOK9sEffN/zDsOfzP8vf4/osbuH/17f60GrvgynMjsPmsmKSNe1KSGdarB1d1SeW8DCXAVFlq+L/GKK6qruPFko/8de9s2MAT7jf8+8sj63k/qZAT3A/zvS2Lcd364GoMCEGxg24iRjgx4aNh282cZghBAM7kLjxm+4Vn7PO45c1XGVhxMBM2dcNrOoNKkw8fkp5hvGxrNM1ojZBGo/lL4nA4+Pbbb9st43K5uPblnzi6ReB2F2aOHpru1yDlOnszr+h0elPIksW5eL2HE9nC/GRp7lF8P/5g3pWntttXiquCFFcF46oDTgfTCmxMqChmfr8RNBw8g/riaLqZVZwxUR0cKkPUurAUNvBNtxUkFEyDww6i7ovLuOiORyhOSWPAD1W8MKuAO0+t44qrzSR4sxhVPQYk3PRRCS9PvBeTr4aMXR9SlHlm2DEuHj6KggXTQ9LPKw6cx+J5U4Ly+h3yLdMKVmKye/nuVImQZqRx7p7KuZH+DmXnc8KAa3ELD5t9w7l9u1qp5vMJvpbH0b0yoPE6NuG+oPa/q72KnvGrmeMYSwGJXFn7ANQFa9ky6rsw0R7qL+kdx0F4CAiG40XAPmtmpIu1dmX8/fddUSF1NZpmtCCk0Wj+kjz00ENB+wUFBWRkZGA2B16Mjz7xP462B4yUc0wZ9PIVUVNTQ0JCAtu2PcWtEY/AEIhtamBU/lacK0/HbbGyNrMPm7p1xz3ejpCB1VQ7anrw667xdPNtQeQ1UWZRK6FMSI4q+xmEBCmI61lHXWQSAJO3rgGfifkk4nNM47WI2Tj7DuOcpbN5b5xySW0ubOLJbU+RwB3+vmJPeok3NrholKV0sVwLR8K971zGg6ebuPnj7RRmNBJXW8uyPh9h8pmxeCRvPPINRz4dEISuynbyXH8lCV6f91bQOZuTOJbDqpa1e563/nosvx6byCGxVbyqzIlwYOUn04N+IQjgqy3/owofx1PPxTSwgFj6VA2giiYcru6qnrWIF80jeRAlLG5tmsSqiDRqfcEaKmRgxVlkjxeZWDop7NhaCkEA1486nEyPkzPrbX4hCKCX0KvGNG2jBSGNRnNA8MorrwBw+OGHM2nSJJ7/bBauFiu14oZN5/8GdeHzj97jySefZED891TZ+8MgpQGpi4xmfv+RzO8/MqTtq3nKv33/khsCGRZlLBzjracwKp21I8ZxqPcn1srhDN5ZhGEqg9VnYaJLvcxNhrtCBxa+GaH6nlHk5obyfJL6BoSgZmK9NmJbWDqb4rpx88fKgLprkQosethaWN/TR2plFDZPPXf7bubQObcQ2ZhNovM2jviiB2XHpnNC+dygtp/rfpZfEPoychwnNi0Ne24PMVaB+aQJt4yg3PUuowj1CXQ8gfOdLWr51RdFnDOKFCPt6egEYrwH86D1VQDO8l1OhTCT5G4I2+9JcT8RXzoqbN4RX33PGzPGhqTvsvh4PCEgWB2EmapGHWtM0zZaENJoNH85CgoCK6luuummIO3Qzz//jMMSTcnaBUF1rjp2DI9dfiErppzEsl6DmSYjqcnrye6wep288vOZvGs5gSZPYM7MPTgBb/do7DN38c+JL/Be/N/YKIaxcfsgrNtqWR4NGO/jucRiIWDMfYjtCM4/OLDC6YG1DiAwloZ5DxI95eaw44medgcNs+7EV1dEQp9GMsZWs/Xrrtyzs5qUwQX4Tofqit7EeSA2cjXxcY0cnLoZWsQPmz76FRKqq8h1dSV9yjwu2+Ykq8GHs+GfWMVOzh74FX/b4eIw57XYTIHwIs+XfArA2SnX0Oh5AYALJ7h4d0kJHtk9aJzS8jgfmlfwbPnnANTYdgFJ1BPFAMcbeDHhMbR3lVKdi79te58f0w+jICad06PmEe1KCGrzzA8+ZNXIkaQXF5PUWAvACKeZSQ4rz8a30ioBNxPBhq7vsj7aFZKn0TSjBSGNRvOXobGxkSVbClg092cAFrl7cvDD8zmxVblffvgyaP+yC8/n7Rv+wdMX3eJPmy2O9Mseo3M3syJrYKCCTxLxU2FQGy2FIABvd/XynjBuHfnxvdkohqn0PrFYt6mXtAWY2UIIejPLxtMD7LQmyjQbsygn3qqmrorS4nnJ9jMTvAOYZPqYaPNsmrxjqXDfCYBt4PHEND1K6lClgck6VmI1qW2TGQ7e+AtlpjVsTqhlfBhlS6ktiTOXv8UUyyRM9hgifWp8ZaiVcI+tcZLjK6XCdCsptruxmnYGeQl4v/xpEkz12ApX8474jHR7NndwIzhGMFDkkyO7MN20giLXAH+dVyMTmSNjeNFdQG79FhYnhTqPPO7IZP5WsoOahm587Au26xm6RtkIjdqQTeRhd1IRMZN/VweuyQczH6IwfQL/N2KqP+1wrMyxVPHvqIFoNG0hZDs+MDQHDmPGjJHLly/f18PQaH4Td911V9D+m44xSAQDusRy+ZTerP3qlZA6kTu3YGmso+ionryTdWnYdv/9wm0AVCSk8NpZ/8S6ohxzubPNcZwQ+S0fTb6szfyE1XnUZqbhS40AYO7PdcR41cqtlowr9/Dswmy6x18Z0oZXmnl/x9Gc1+cbf9p816v09nXBRBVdI85vs//d4ZORFDo/3m25jVEL+FfP9+niTOG9XA8fV4ZfnXd1+smc6byTrTKDlRFXUOHuwZdVd9HkU0G+psS9wJLK/lTXrQZUNPsiezqfdj2Zs3Z9hEn6uKrbWaRYHybCvBqXrx/1plKq3N35yHcyTns9Z36gVrTFnvSSv98vq93EmGDCrptJGHw5btmPzXj5Ow18SQzJmHjHPp+oGjfXPnnXXp0rIcQKKeWY3ZfU/FXRGiGNRvOXwOfzBe1LqcJXAGwpqeOO937lnMYCXGkq+GaON4nh2T8CEJHobFMI+k/JXTR/DiZXl3P1q/fzareLgspcMvRtts/tgc3nxm2yYIn0hbTTkuqRPYL2j50Sw2WNXwCBQKHnOD7j2hURdI//b9g2zMIbJAQBuG2vguMWIszhDZwrXDcSaV5ElHl+m2OrcZ9HnfesNvOjTLNp9CkD7p/j8gE4eeXttCc2OXyxfGi/29iO5oOKp4LyZ3oGQt1K/74AujqL+W/JbEamFhMlNpLm+ZbGQhslOcn0mLaVJOD9ijdwpS0ksUJd69ruk4Oiip2YYNgpxT2K27iIAzHzC4Hl8g7c1Jsz2hm9prOjBSGNRvOXYPba3KD9chkdtH9QzWrstcV4YhPxRUaTnr8mkNklYJ9zj/wP9lwz5dW9GDRoPva0JrZ3705dfgwgmd812A3x8b1/ICsvlxzZjRF5xXjjq3l1ktLg3Lu2iduHq+mZzxfUE+OGI6bFhIy9wSp4Ij4gBL1y7w0cN3kZRASXq0i0srV3NCNXNBJBwK6lxJxIF28VU3yLyI84jySqAXD7uiMoxmJSy82bfIfS5DsUs6jAbtoAwG2p13JfWSCAaWshKM12HR5fCj5MeGUvCtylzK09FIDNWTlM2HlCq6ORdLVuoNrblUafWhX3aqma0uthW0me66Cg0tW2T4nYtTPknBzb63j62R8g5xsb9Q0x1BM4b4WLE4gYE0dl8gqkyYPD3pWVo65nas89m+L6yLYQBFQl1u5RPU3nQgtC+zFCiNeA44BSKeVQIy0J+BDIAnKBM6SUVW21odH81ampqeHXHdW8/OlPjLDAMnc3jsyycdL0GdyZmsAbv+bifPM2oowl2FG5m8BkJtbnxWQ24/N62TxgmL+9FMqIz6qlG9n+tHHddpKdmUT0kEaent0PgHsmPkBZYwojUtfz+ZLJLBhgZnbquRzlepSSnkrjc0Sxh6OL6mgw1RPtUy/yL+bXc9LkGK7Z4uTCXFfIdNhX//o7404IXc49e1Iy0ojWvvDQWKYtCKx0GnHI5xTPn4rFJOllCEEARZ6n2DrtCobMuhuPTPenrxS382LaU5RYd1Fg+4L7lM9Huk2azeKfGwFY6yglz5GI17ALAsi0bmJXi1AX6SVjGFE0rcUoJSAodA/lgtSLiTHV8FzJZ/7c1kKQo+rxIFlvUv96Hi+dwuq4EfS0f8zSSBjbEPoaKivK4Nfy+/Bm/ALAtDkvsWZUYFruuxo3M+IsWIRASun3CfWZbQmVop7znJPZZC6g1tQEwKG2+cC/Q/rRaEALQvs7bwDPAC2df9wEzJJSPiSEuMnYv3EfjE2j+cORUvLEE08AMMJ4Wr3y73PISAgY0l46KpHXXg+sGHom6wquyVUrmnxeL4X9u/NxhrKnuUS+QDwB7cCEZVUsHpuIo7eP2Khqrl15hj/PHlFCv8hicpbEMWdaHiZzI9HJ85nvCywbt0h4NvFZvknfwMO51zOsqR9WTznLZwZsL+9fPJN7xh6G02zjRnkvnvOqoJWCYtbklKB9KQSzJqdwk/MZKizxCCQ+BCYC7SrB6TLSNp0fsmKruzOG+/JuBeDMfv8mWxzGxSNv46nldYCZb6J/xFt9WMj53tUq3ldLIejF2Cb6uc1Mc6il/JPcDzPVtJr+Ia3AoZHV/FT4WlBaQpyLJ73HsCRxCG/nPEDKmkqaIpIABxL46Czl++jkTz5j0cQHSHR/ThnJ9Nq+g5iGBg755QYw7IOmxphp+upKsESCu4FVp/2TGlMTlYbB+DsRgalBOw489QeHGaVGo9CC0H6MlHK+ECKrVfKJwFRj+01gLloQ0hxAPPFTNpP7pzK6ZyL3PvBwSH6sNFNb3kThlqU01lSzdtbMoPzri9fhAeXYsLeJbw8LCDfTUDGnvD4Tl85+Chlt5kXftRADBQ4T7krl6yci8x1uaTidhoQzsRyUjceuXvddcy+lsPfzABxe7ObmHk+xOnoLAP/JesLfz0vb76C7S2loXo97m1dMLyOksmga5awEYEP/GFZlT8eJnbKlsYwd9yXzZz2Ad/psDkOtinvI/g9/mwvHJTBpaRWNkSbmyy9J+cXEc4fO5p78UIGmJR9ufUT9X9QEhgNCW9FhNLVbK5hYE7zkjWGlw4sEXCYfNcTwpW8SXWIceAScWW8hWlp4Nq6JrfZsukTGYmuqw2myYfe5+CzpODb4+oCUpKxR5yDS4cAnBB+fGbhGc0+4hL4UscyWDED/7IDmzuesQ5htWIvXsK1bF5YeMplK33qSTEUhY47wNHG55V0icVKYduweHK2ms6EFob8eXaSURQBSyiIhRFpbBYUQlwGXAfTo0aOtYhrN70pprYMIm5m4CKU5cbi9RFjNu6ml+Hh5Pk/N2spTs7by/qUT8LmDX9fXX389b96oopk7qh4Pqe/ucTnLDk1g2tJf6T1tM+eJT/157xghMryYuMD8MRyh0i/mQyJm7gq00TcGR5/At0WzEARQmPWyf3t8hZdXYgJTXH/39EGmbWL5FgsJH91BQ3wPfPXFvISHoqcDmpxIpzK0rky04s41MXHxL6weOYJzx38KhwMMYLMcxJUE4oYBOCPMzDm4J6mLH2ZokwAk9/wUEIKOHnQVdp+VL7YoQ+Ua+3fEO48JPclAk2HrXRlZxKrMnzmxcgz1lUob5DI1ECsjia3YTHnSYMrjvDxiUvZKvW0mdhBsKH5u9TKO+OUDzj3ydl7++WEGRMSS2VDOqi79WJEyiO3xXRlfvIkNog8Adyx5PXBuzWY+Pf20oPaqbFUsIzDb3+2wh3GueR93zlwa59yDdNTw3hQT5kNOByDJNDTk+K7jKRItPuaW9GJFZTdiEpK4POyZ0Gi0IHRAI6V8CXgJ1PL5fTwcTSdh3AOziLKZ2XjPUTz67SaeWbCDl04fyYzRme3We2ZpLvc1VGOLt2GqcfHk6x8yyALlvigunngGBx3Zky1LVIwHr3tH2DaePEZNEc2dfh+3yduD8prNpa8ueBaCZ5LwZEZh3tWIL8WOt0/8bo9xeJWXkwrc7KpP59uxhcywRjG0+zqogyNfUbGsfTV5ANQf4wMpMTVCt2+8kAElKTZin7UzYdsSAIojgvv8lUOVICQlQsLB8/pR4b6tzfHc3EMJP06Tm6MHXcXXuU18LM/jEvNshHcaM+MXcmTNREAtOW/mpz5P8uIz9awcOQESILYuj9ErHyHumMepn/MsAPkxqZxRX8aZR9/NDnuwgfp7RNMjZRp1fMC7M+8FIKpBuR0YVbKVUSUBQbFHXTHb4zM5uFgFrq2Mi+GnYwKampSqWsoTg4Ojjvt5JpWmFSSNvx53zlykQ8VpG5ljZ0OaF3NtJe5EFeJEuJ0Ij5umqjWsashilSeTXqXVHFO0nU39Q71gazTNaD9C+znG1Ng3LYyltwBTDW1QBjBXSjmgvTZA+xHS/PEsz63ktBcWhc0b7VC2JVc9f5jfsLWZdXWNHLE8Oyht3KZ1HFSqPBpvcXdhUkXwLe5u+Bmvay1xPeqI711H/vxBOPsP5X9Tj/aXEfVuTOUOsJp4LO16Ui0VfFs0g/e6dlw38Pat13L9IX/j46//y2HPv+9Pf6/kUZJfX4m5wse5R/yH08u/ZeiRW0h/pBvxNcErpCJOqqNXRF1QWs6PKTgqla2NTwimP/eeP++Knz8Bq4XLfD/RJPoxM34lJ5Q+2+YYn8h4mx8TAud9Tl4Bz3iv8+/78PF5r895cOd1UN6HnS7JCwdfR3JFV66fPR6ru46cXscDMHXeNZhk264Bzjr6TmpsMWBcw+Zl6o2/PIa3fEvYOiIqBWG2gsmKKTqVyHGXUyHq+NweCOfRa/sOxi1bRkmXNCqSk+lSXEJNfDxbYoU/wOvonCK61DYGte02m/jqpJPwWK3EbFpOyztLSMnRa5XAvPDgiVzy+qttHld7aD9CBz5aENrPCSMIPQJUtDCWTpJS/md37WhBSPNHk3VT+5Hgb6iOQCCY8fchyIxIuiRF8tL87TxmCxg6m3xeLlvwtX+/zhzD+xOncc0XBVRGFLOj5xBGbFpJdPlMHP3sTJi2GoDCrQOo6Wfmfu4m2lvHi+a/AXDlrEdweZUn5zP6f8Zbva7xt/2a51TeLE9gXnroC/Knq8/D7PMiAE+axH3RNNKyz0a6Gsg+8j9gDR8by7ZJELnChC1PUHaLh4HZdWQWhzpm3PRBBs06qqLkVM65Ty1vv2LOZ2AyEUUj5zomIwnWFJkooyD6HXp7tpBIE2/Ge3kiWQkjJ+ecTKOlkRhP6PL9GksdfQsC2pf/DXifa7ecHVJuW/kPXLb+65D01tx/wqPcbYrF3kL0qPsijINJk4XYE54LSpJIXo2Y7d8fua2YAcvn+fdtg07EtelLiuKjWZWV3qKixCQlR63L8Sd5TSaElMwc3geHycrnA0/g1Oj5VOfEcdyiBSQ3KsFp+bHdOP+xn3Z7XOHQgtCBjxaE9mOEEO+jDKNTgBLgTuAL4COgB5AHnC6lrNxdW1oQ0vwWXB4fNoupzfxv1xZx9XsBh3mTeiXzS05woMtLau0k+UzssHj5NEbZnLjGpuBLspNZVcrxaxfiM0di8gbsgl6YclLY/h6uvYbM2MKwea255Men6BO/g61iEK7xaholZvlHRKapF77ERF3SJZgLB9HYP5UxG9fwwCcPYCkTeBPsJE56xt/Wjkn/wR1Vuts+D1lcSYQrVLNSTyR9p/zASQt+It1bz7z+I9nUtRcA/1y6lp51DsZ4k0gUW3D4JgfVPXbgP/CJ8NqaY/KOIdIbHAKkuzeZfHMFGa5UaqoG8l2Ui/FOC33cb7G0rg+j4oLbfzyuHgl8+5Wyj2oy2zj/yNu5ZcMzHFRQDd6AQGdOH463dBP43EGenm3Vr1IxdwkiMhnZVEHsSS/hQ7LNXERXbxJR2JhtXU+uucxfp9ljtClzHN5dS/2i1Y9Ds/yxyKq62Yh0d8Feugt7Sg8O/3kWHxqrzDxSYBGSua7e3H54YIl93mcHM3b2Ks6fcRtTG7fwxEt3hT13u0MLQgc+WhDqJGhBSNNRXC4XFosFk0kJPq/9ksM932zkvb+P55xXlvjLzb1hKlkp0dz06Vo+WKY8EHfzmDi7XmlgJJKa4XGcffogpj02DzNwSY2dl+KdZJkqqB7dleKk4GXjY3M2ktxQS0VUIqt69MZjCbbtMJU7MEsPb6Rc2O4xVHhgU30MEULw+vLbaPJG4jxS2ShFln1JTNMnQeXTto+hS+4Ajty5hOH2TVTe5MbsiqHv3GeIs7yNwzsWlxzIlhkXAbAtbzh9e6jYV8InmfaLEvoq460k1bhpTZHzFUzUM+KIkfQrcoOE3hUrmTl8AgDJTh8z54ZqmTZG7mBwU2/eS/mOt1O/Cclv5tScU0PS/uY4DBMmVjS6uS6qCavXzVdfq0CuXpOVeZOfJKqpmPKoLhwWbWGTxcvtzWvJpAQhSKQSz8CHuaZsMOPf3ITNE3pstiGnYe83w79fU/Ar9vQRRFhi8DmqyfXlMzspVHgcsXo1iVVVdCkpxdnHR8W/PCFlLEs85AyMp2tME8u+mIo7NbydWdeum+jTN/T5tmD++biliYOqCzn1qRfCn7zdoAWhAx9tLK3RaPw0NTXx3/+Ghny4KAKefGMnLSOkT310LqN6JLAqr9qfNsEReKQIBDddfhAFBQVIAR4JL8U7SRYNeIYnhghBAMt6DQ5JO+W7t/nsmPOJKyvHtdLJyX2/UTpS4NrsR3HuBKvZwyVD32FQynoiTJBkgUkJyqfMmOk3chEBG5xoh9IEmbwwbOV4KuQg6knk9qVqmXnhc+pl33fu03SLOA6AOMuHlDljSbsrBnMpdGUz3afWEZMePO3VUgjKI5NG33hiXBcDgs+q3dz+UWA11ILBw/3bw0tDBQyAu7o9T525AUTYbABSm5SWKy8mlySXoHf5DGRjF77Gi3SU8WViBCA4LF9p7Bz2RBYefB8AXRO6ckSk0rqkY+LMXW/xYebpDPDkUUgiYzJfZBR19Bm9nGXze3HI1l0h/Xt3fAN9J4NQrhPjuynP3FJKGmbdReHAPpAUel0Hbt7CwkPG472zoM1j84y30B0lIE44dTZLl5xM335LSEgo5tdfzgXAbHaFFYIA+vZbRF7ecOanZxEqKmo0Ci0IaTQaAFbmVVFVEH41FsBgSyn5vgRiUzPJLqlniLmIXfl1YER/OqPeRk+PGQnkdLGQFVHD7B9nciUJOGeoL/mYreWMbtjBV10O7dCYBjas5UfroUTM3OUPOHFcbxU/7G+lr+CJsmGV1Vw6+B1Gpa0H4BFuZrUYw3Q5k9P4gNe4HLdQWqon3BeRPT+VmDo71mg3ZnsujXVVTF6/naaRPqouU1oJW30GkaYFQWNJtdfRZLaSdmQtzhpriBDUkkLHm5hIJgao8Pj4pT5U2zF5o4MlvdZy2w/9ODJWGU5/mzCfeG8Mo+sHc8rA/wNg0o7TcJkdLO2pNEIHlR1EnDuORnMTy1OX0beuD1/7ulJZNpYbaiJpqeMXEUk8MfM6HhhzLhflrqYk9SB2ZIzz5y+OX8Ai4WXwxh1UyBrSzF6uzXmOwfGl/CPxFl6sWU9uQgT2TT66GkKQe2AM1s31/ja8TQ4sm/6GZ7ASNl075uJc+x4+IfCazWweEioEAXxy7slMPOTDkPTa2hTi4srD1hk3/nP/9qCeP9C1rArXmOBzm3G1lTtuElzZ3UVGxjYyMraxbN2QsO1pNKAFIY2m0+N0OlmbW8zpr69nknUHfVu4/PnZ1Y/jhnXBuXUh0ufjSFs2k4dnsOiHItwR6ku+2BdLRvkIZszohSvezJn+EBBxIX3V90vhKzomBAFsjh7OixP+zvWL7sAnzVw78nnuLoygymsiquFJmvIuIyrFxbD0jbiwUUwGq4WaxVhY52ZttYe6xHqIhQvkK6RZ6mC4mZzNIxlx6lzmbBvDVxEbKZo4lGP6rsLktdJ/1stEm78n0aacOeZVpZJhr8Aa5aPHFGWOF5EY/PJ9NyaL9ZYxnFS/i8z6fwM2f94v9d6Q41JegODiTTtJEAP4strNkAgTL6V8icvShEma6UYXjl10MwKBRNKzKR2PvQITasryvPqpbKk4hXlCMsxn5m+1tpB+AOZNfopDgFUjxwWll6ctRJrU2Aq6JXP098v4tetg/j75Z56NTGdS3C3kvJFMTD8HORWpLB2SyejcPFalj+HsiC9JHtjAlk/T8blNVGXHMD/ibiavVcLS0rFjyOnTJ6i/rl03k9F1C2vXHoHbFUVm5qag/OwtB1NS0pfN8ZsZWHM00dGVHDT6W3ZsH02DtYFhPTYHlU/pWYYroKAk/TorJrfgncNMbGmlQvNFJIc9NxoNaBuhToO2EdKEw+v1cu+99wal1fjs/OgagAczTizkPHgM1dXVPPXUU220AoeddBmPr5rP4IJtvHXw0XjNHXOgeLZ8i2P5khWM4wlxIxGyiRflBWxecCwPTrkIgKvnfUKTtDNh7KfcVJAOJjdmeym1mx7CPiWS2ogkbJ4K4gv/SX3CuTTFHUVq3vlB/VSm38ct2z7jhZiVYUahSHYnMNFRwAPlwWsPhvXqgUVKVuXmh9QZntUdKQTfbwqsjHL5JLvcPl4bFsmIFQ1YDTno5XE3YBKSr7b8jxK3j8UNoQJSSxIGf0GXId+y5eOAE8fK5OWk1w3gsKgYrsLF094ofq4Lbudb8rh+2atkNlQwZ+ozrZsFoCmymPr4bDI3rKTaasKVmsnAvF1Y7D62Wby8cURem+OasSSNx9KWk12XzKeNh3HSnIVB+euGDmHj0ICTw8TEXQwZPAdhDrxrFi08g4MnfgTAjfmROFsJLnWb7yVuwB1IIbF5bTidiXz81C68cRJzraDwOVdQ+deWR1BiFZQkBdpJ8kZwv2cXkwob+N5yGkffppfPa8KjBaFOghaEOg8ej4f77ruPXr16ceGFF+Lz+di6dSubN2/mhBNOQAjhLxOOdZ50Vni68+mVExndM9GfXl5ezjPPBF6slbZK4hypWExhNB4NtRR27cXMvm5iK1+nMuMhvNZQQ9d3ZbDlRklxJudkr2G13cYj3U5gdsYFvLLoHq5uUNNEwlKNNXEJ0joed7dSYuteC2nTa07E7N2DOMRScpDTyb9LfAz1BU/JPNX0T97pNxOHtYEhTien19az0W4jQkreio8DCQMdWTyRqzxYeKXkm5rQabAf+r9KbrIyrn5h3XUk5ucxJ3Fqx8fYgi4WiBa5dG+EeeZuYAoo9l+OaeT9bwLBRcsP/hdl1i4UmaKZMv+fzBzWA589Bp8ZXMkZeGPiidq+HrPLgUTy7cHFlCe6wnUb6L/SztGLA8va4xocTNq2i/roaPK7d2ftyBFB5Q+d/Ha77f3fzkjOnudjTS/B+iwT9dv+Q2IDzLT9h82mQ8hfk8eo7cF+mMpH+fh2suDj3g/TYEnH3rgEd8QgfOY4diyYwetFKhBscdJivhjwPsO3TOTdB15s/8S2gRaEDny0INRJ0IJQ5+HFF1+kqCg09lIz511wAW+99RYtF8O/PeFIUuuqiHU0sj6zN0+kdWFzBNzZpytCCFZ8n8uceSuZ1dtMcWo0/UvyOVrO5temg8isrQnbz0e9Z2GW1f79zIZ+RLsFEljeexoX2mYz1bqa3Oz+XFK8kFKzmV/tvbmjS6jtjatqAtITgz315w6fhydy/s1ARy9O738D9eaAI77hDf1YG72VBK+X1xa56JdZFrb+KyVv45TKJ8+Lh36A9CzCnXQrjfaeXPzDrVxquzuofJTpGz6Wh+KpigpKr7WX896oe0HAYyVlzGhUK7M2Nh7DnNpLmRT9GhnWYaxv6EKM+UOW1x2NtIaGjWiPIRteJbY+n6imMpwmCyed8BD9avI53LwWc1keJimV7ZAQ+Cw2GvoND6ofnb2azw7dSX1UQIg78+dulCY6mTNanZ+xJWPYFVOIQBBXUs30VRE0pHantnsfJv7yKwsnHeKva/LaSS4bT2SXdfSc8j9/+sbF4xk8IbDy8JMSO2N+uJP0kmX0zv2WlSNGsGXgIO4RKnzKpg+6Bo2zPsvFG0MiqUqdSkZtHz6dMgWHLXAnT1o9i8O2jAo5P6sGzOSV60MXAXQELQgd+GhBqJOgBaHOQXFxMS+80PFlwvmJqSzqPZTKmHju7ZvJ7dtCVwWFw+rYRELpAwBEu6OJEyfgspjpUrkKV8yxdKsu44deW4lo+KXddmzSxqM5Vsri8rk3JanD4w7HPfnXsy5yAx+n/IjVZ+GrLeoF/G3CfJ7J+MBf7qPPxpDadSYpXUKXqy+ovYS1jceFbd9qhWOig5fzO3wSl4S8OBML6710qVHasUnp13NF13jq7QHN1JwtF+HwjSPW/CHx1rdp9MbzYvZQJnU5k4yo3njx8W/XUvrHNmIREqsrDqsrHpe9ioneTPrJTL4Ko20CmDb3av/25afcyjTbtrDlTE0N+CJbhMlwO8Fq9+/O7jobJBy67VwirJWY60upSJEM2rSJfgVNzJkeiEZvtTbhdtuhhUidkFBEamouSTE12GIDAmbZ+hOp2KjOa/e4fxB9lBJ2N38UmPYDSC9aRBOr2TRiGAetXMDgbcoObWN3eOloM+lNRzIuf88DqFYOXsHt1/579wXDoAWhAx8tCHUStCB0gOLzgquez7/7iZED+7B2xy5WLV/Osccey6raRrYuWcRHY6bhtNpIqq/hjBVz/FVLYhP5/KApABybGs/t9g18/XoR9x8xdrfdJhX8A5OvjtrU6/GZ4/HYegXlmzwVJBf+E4ABVQPZkrDZvwQ8oc7KSQvUl352t3oWDg92vAgwvXocBzUM5tO0jeywLg3Kq+pyNxml93NExcGcUdeLlMZx+Bw1mCLiaVj6PLlyEz1KfdiHnoE5pT+Nc+7BgweLD+J7N9J1XHVQe6saTmSHYzyfjU1kxmqI9XzNUfHVVLsq+bzmQQDGRplY1ejDAxwcbUYASRYRdhpsuu0xMuM3c4v1HJI98fQqP4jDYi2YjFAR+S4fKxuVwDTIWsWqUhWE1BOTSFO3viCMZ7I0kbyrL66697AIKwOrPPTYuY1tvU8kr4fy25OV+x2Z+d9x0Q02vMLLcXnHYffZQ8YUjqi6LOb0+IbxVeG1T7UN84iJnIKplR9Nq7WJPn2WkZoWCCWyauXR9OixjuSU8Evh066JZOXIG6iPVcGfzfZavM4YoG0nnWmlK0io3sqqIccR7Q71lt0eXuGhy0lXgTOWRKuXH3adyeN/v3WP2mhGC0IHPloQ6iRoQeivi6upEVtki+mWumKo3UXVy6fwFBeHlPempfP/7Z15fFxXefe/566zakaj0S7L8u7EjmM7cUJWSBMgG3v7lr1QaHjbQBsolFAoW1tKWNqytLQphIQ37FuBJCQQICGbl3hf4lWyte/S7PfOXc77x8iWFUm2EwJRrPv9fO5HM2fuPfObO6O5vznPc56z4+Kr2ZKdPuKheh5/8Wiljs439IspXtoIwL39f8x4/eR3wUdH1tFysIZUsplH8ufx9m2VRNMfX/8Wltu3cd+y6bk5x9GsA1QPVvKPFhgqt9TmUBV49yPv55vax/jNroumHXO4Oc+Wc8Yoaz6i/QN8OlfgvFDlAn3HIoOvLPWRSqVy8it6HF6VuZl4YpTaT2kIf2qibfPlo0RqbToer8cdUFAMHy2hMprewIbz//fEfveP3cpu1vHL85Nkq1T+aWs3zT/++xOPP3XlbfQpz+wCvPTwD2nt/jU7WuvorY5jJv4KoYSo1aBOU9hrTVaG9p0uyvnv8+J9nUQdh7vWXsZ4pJVHateQKuS4uG8nNdphDFxeG3oZ1qavAPDzCwT3XFJHtVvLeeOzTwvPSIOHdck1MkPEmxquqxm4FEVWcov21T1Oe+0mLh654JSvzTQLXHTxj874XPykV2djQeWuf6u85rIe5dHLPjNln2PqR9h13s0Mpqq5am+MxQMzj3idTG+15K4/SrGg/X28efs/UAjZ+HoXpubQumQTibYn+KxyK2/wvskD1qt5ScdG6o4YvOYLd56x9pMJjNDZT2CE5gmBEXphcmjT4/z0Xz/F4nUbuPaV72bkG3t5NHQ7+1k66zH3rr6Erpr6KW0XD+8j4mUJHxqhzerjo3olgdUFHouFcNbFcIWGAKx+nXxvhP4tdTP2/+OXv4mjC5by3q9+gsbwYvo3vJHXdjt8rOWL7Azv56J9KTqaCmworuEtG1Xujw5yTdNbeMJ7jNxJq5EvjK3iWH4vCgo+lYulp0hUXyA0n9pVo6S6ruX8xNUAlFS456ovkd7dyeDGyWKMy4ZLrLrgVkwlxMP6PkzlIYbkKhQvhjuyhpsbXjPtNfRY30My1Rz07f0m5rLrebT/21Ql3kZGTl+xvJJpIxEnjWR45UOopUP80fapa635wFBVhD3LXwXhNYTsErLYw+V7Tj97SQLFSIRocTK3qazrHGyNccd1CS4evnjWYzfWbaQnOj3Eqfgqf775NlSpIWao0GibQzhGlkh+ISP1kzPBdBscc3rS89HxVj731PV8+ZLJUOyIo/CFQZMVIY8tRY1C+1/jW438/CeVsNQ/Xf1JLnKrOXf3f/DjC9fzo1ffeGIBV4B1HT20dRcxnDzLhxbydOSCA3zmRev4a+8j1OISyfjEYqNs3vwalix5ksamg7QfuYCenqm1i1qHSvz5fwQ5QgEzExiheUJghF54ZAYHuP+Dt3FZfeVC7uCzVX+IPerU/9nNbSvZ1rqCuFWkoTzEoUQbAJ87cBtv6r/vxCVvOKXT1Rzm/D1ZimGV4ZTODeYSMnUfqIS2pORVD3yL5Uen1ncBGDNDVNuTi6O2xVZzce30XI1dow+zJvXiWV/Tr3rvJqYnubh2ah5OR243m4fvozG8hCGri0WrQiyuXsnQsu+j2UnKscq6Yk19FuccqhTzu7N9PaNOI0boPEqpRuKuoI2FrJ6olFylfYMq7XvTNGwZ/QIxo4Vf5xXujlkMqxLnJF/wgfHJNbuWmgoRBbb4A+SS+5ETYatX+w8wVmxkxzEbXxG8dE8Hulcxc7l0C/Hh2asln4ynKLiaRlEYVNt5fARvfM0neY25Z8p+qmPh6aEZ+7i/5X4GDv89anw34YYfYUoVWy9xWcfrOK//Sn604nZuPPR2DH/S2I0M/oI/2fcTxpLL2L72lml9Hjd8mmtz5WN/y+D7BN6Symyyjz3+QbrzMy114WM2/BCjemvlnheiPPRSzNoHEWolQfyvGqr4BF8ET0J4ahm7Nd2HGYvE6d8r0W2LG656HB/4kfKnvJU7eBn3n/Z8lsshNm38kyltQvgkPI9bPvnPpz1+JgIjdPYTGKF5QmCEnn/sjgxD/71rWnvDBzeAgK3ffYjND91J2a9c6P900Qfx8PkFRf4Zn1fo+6hRK+GubKiab190JfKkX9NX3HOYl5s/402h+5DS4aaGOnaGTJYYCrXpa0gxSps6QJIx6hjko/6HOaKs5aIdj3DJ1oemrSPVky7RZKVRijY/vPwYuajHDY/Vs7jUxHUt7/w9nqmZqQ29AkNKPAGfq04yLMK8+PAbiYUPsTfzNlaHVBaGFHQEquinwaxoFMBK6+t8VrmLbX1VpLRXoagzJ2a7ahHFN1CkRlIZolDTT696+qn41997L/Fcntir/xuB4HZ7kDf8/CMnHvcUhf0rV9LS3c1AQz2fvmgNqcQ2NoxMDRNOJiALTrWuRkbL043GS7qvIf3yj5A3xhl+4DOkY4O0XVPJa3p6IvJx7oxk+cZ9H5rStuPctzFaN5kbdk+kTK3qsGOpzqfb3oepV5Kb3//wJxizKyUVmtID/N3qLyAlxIwCAsl7uyNIBK65DM0+OFE4UqEUv4Y3Jca4XfnrKc/7x853uKX3LpYenRz5Ov+S7/Me/bMsYvYq57Nx25b30Dveyh8bT9K2eA8tLXsRAo4ebuQdN506cX82AiN09hMYoXlCYISeHdKX9Px95Qu04UMXoSVOnYjqDBUZ+PzWE/cTr1hM5mezfaF7JLSvElPv4T8HfoSnWCi+SVpVWBdRMYTHXZGHpx2VScb49pqrp4QUjvNfe/4WT/p8PDHzEgXZmnfhGKv52699btbX8MjaMXJpjbuOTK8ztLUqwwXZxIn7t9bexvJdLnsWZ/mH8fdRbU4Nybl+GSEUVKHhSx9FVEJKjjnGaNu9GMUGCjV7aNlxCwB+rh8RSmIf+BnO4V+gt12Jml6J1rQepM+C6Gt4R0Mdm8PTR0fu7+rhztKfsiC2lc82Ta5gn87Xc1X7m6gpTA21SCTDDY9gluoAiR2enOU07of4eXklNjqvNnaTVCxisWGWrXyM/3riZo55NazW+ihKAx/BCnWQh+3FjIrpOUU/vOfD7LpgLccWtaFpNqlUDyMjLXiegao6NDfvIxobw7ajNDdPrZ68u38ByXIVvq8Squ7nqcwCFh54Oapu0fqSf53lHaxQGmmj67fvxXcqIcDumiN8/ZoN/OyWPydqF8kkovzTh25iz5E2fKERbXL4j7rJ2Wdb2cAoKV7KAwC8i69TKEUQY2X8pvC0z99d8v/wvfIV3Gu+50TbF+VN/LW4fcp+Ea/IG/p/zgeO3kHSzTMbu1fGUaTEsm6k/sCbKTm95I59lOjRJqT0+G7L+RypamBT4zm8u+O77F/Wwmuv/jHn782SHp009Rk0vjN8Du/6cmCEAmYmMELzhMAIPTMGvrQdp2f6l3Tzpy5HKFMvANKT+JZL3z9unNLuTfxvKQCujcSn37D59ZoGdtT6XP3YHcS9MvvFYhCVsIperqI6cw5SKTNUs/1EX6maLlategjbCmOGKhf5T8t/YLeytnKcb3FF4dPsHjsyRYMiBV/q+BB31f6UzfE9mGWFNzy4YNrr0oRBvO1cXsbLz+j8bIrt5vFjv+C9NX9bOQfIGfNOTmZw2fcYa7uP9IhNNCvYuPsfaVr+A1oiW8mYGpHPxVGKlZDMij/pQ5koTu0AY+0RUouLPBky+YvG+lM9zSl558bPUw73MRQdIKIWAcnixU+SydZhleJsyaxkszs9NwUkX3vZ35y494tjL+EHB1+JJxXU6GEUYwR37CIkCpMjOZKFyhhXGUdQFBddt6aslSWlQIjf0/evo4Dun36/M+Qv+Rr2aJj123Nc7Fr8+7Utz7iPiFfiPZ3f5L2d0wss7uVCMktGufTI1B8NOfc1ZNx3nLbv7rX/Tjm5jSs3jiKpvAO2v5Ks+0Zsfz2bvC/wus/+4BlrhsAIzQcCIzRPCIzQJMWdg6AIQstTCFWAIvDGbaQvUaMavZ/YOO0YqZzwKgCEV9VgLKwic1/HtH19KTmiWDyVkQitTM3az/Cxhk/yhkcfxDemJ+ACCN9ncXs7XQsW4Goar/jZTxDNNgffWk8q3TPT4A/3Z67ht5bLy/qjvGtgMi/ix6lf8Y3aezBS/8l3n5hanLAz/xRxPUXCqD0xOnOmlIXD3y38N24aeB3VboJb2m4jq1VCde/rfSsvzbyocq58F4SCmOh/Z+QApm+QchNUt76XcKfO3p5LOG/sQerW5k5cuHwEdzfeyN8tfz8PPvkOflp3FbenmoiPfn2alrGaj+FGllRGJaRHbdfbpjwuUYmHPkp66Accje+e8tgy0+PmutkXTH02bMnpbIhPDS0WSnGi4dwsR8zMv2bWsiNxK6vYwyvL93COsYPi8BIi6SMz7t/4bh18gauauKtKICC8U8GtlQx90EFGZjxsVnxfo//IFRwJW1zR8gQAd/S8la2FC9jgPMHbVk3WY+qliTImC+ng3/ggW9nA+4/dyU1936EQ1njViv9BIMmrUfY+8aoZn2+8SmPzOSm2bbkOy69m3fYnWdFfJnbVRyt6gLwGDfKXFL2XTjk2q8G1L4lRViv/HNW2z8Kiw47qyqjtzQdt3ny0zK8aNA6N/YzbPv6pZ3YyJgiM0NlPYITmCfPNCHn5Mtb+MSLr606M4JR78wx+cftpjpyKsTjBtr2jHLErLiiqSNaEVRKqQnfZp7Psk/WhThOcu3gbO2vvJV49RmfneQwNLqZt0TZMo8iBA5djKzb7k/tZOb4S0zdRXZfG3j62ta1gzVN7efH2Sr0cNyUZ/CeHoi846NazS72MbcpVjOd0Fj3+GJ3KzN/JvyWOcppRmTNlvPlhpOJg9l7DvVd+mn2ZLjaWzuy7orH4Hq588n8Aj/suLJOLnFpTQvX5RJOFJxXeLr6Jh066+50IOfNSD1bfa3DGK7OmvLSJs74G86F+RNlHCXUivRjSSeHVh6jPdnPbFZ+kv1DLV4Z02mLDvD196iUkTsU/D36AD9d99lkf70n47S8+ShuCRS//BO2lNezb8l5+cGkcKeBl2/Nc0O5MO27l/rupH9yK4pdnfYc3r6ujdSRLQ6fFf77zetbU7iS8s0hJaKghn+X1/VT9UKP9nDV0Fpq4OLmGUbsLn/vorK3ip7nruMn5Ca+p33Gizx8vWE1VW99kGExKwpZPKawSKnloOwx2H2wBU/LOppnXcTsUbuU7DddR5RX4m8672bcsRk86hKKDa6s89a0l0CIo61ewSlnLO17cOK2PmFsgr0Vn6P3M+KOu/8e33vr5Z3VsYITOfgIjNE84W42Qly+Te7ib/CM9CEPBXFTJX7EOPIO1pk7B8ZBPEZtvhSZzDIxynLKRQ9NsEqbNkvPu5cktr8X3Kr9G/eIoo+ExciGLcdkF0ifkLaG35RW8+oFvASD8PHYqybeuejnZqIOvJgkVHkfxxjDsfUgvjDVwI0ZmAUVmnsr+dN4x8CBvr38tAP349Fk9HOyrPF9r9BxE4kJeZDbRL30ahIInPSxfwVRAEwJfSn6T/jafqzt9PoWnVjNe92HM0jZqsj/C9kucKsH3OAsNj3fV2kROMSB194jBEtPju2MGudi7YEvbxCMSmHlBV4HPBfU7+Mvz7zytht8eXoXoXUtZL/Fgw0OsH74ARQqyVV2cL2IMpA7QLyyiiqSvrHCF14DTdQGDOCTLyRNaDKNEOn2MJUsr/1ulXC3h+BBHO1fR1roXgFDHNWSNUdof/wuQ2syCZuHcp+6kYWDLtPZ/+bO/5ObvfwNX9+h6hUbjOoUlj3yOnvO/SL5+0pCo5TjhsWU07/zraX2cjCH2UWN8GlVUFpz1ZZSC91IUCoTVR3FlM4Yyc7VqqIT5xpy/QVfa8bxfEQ6VeTi1gY8tvpmOyNQw2p/Ku7mUR/gWb2WTuGyWHs+cz/V8HD+X4p8X/xWuUFmS91id8fhW22Q+360Pf41bPv6lZ9V/YITOfgIjNE94oRoh6flITyIUgdAmr5x+2aP3Y49XrovPkMQNi4ld1oTdlSXzk3acfBbzxVHccZ3tY1/CbPoN3V1ruLNQZDjae+K4hdlWVowuomnfUQBWvP4w93pwtL2Ka7ZWjEpHQ4FF/c/8l+uWxHo2pi5GkxaumHma9HFe2/u/FNUwy4rtLEtfx8NaJ/ct7saXEaQXoTxyBaGGn+A7CbzSQtblmti4sAn3nMpsH9XpJVHaiDr+I0CgC8kF3dezouul3HHJF1BlpWJwRJEUTypW6MT+iDXVrWxgE60cI830hOwHhhsYEhmeLMI5VUvIaE2sd8pck3zolK/pJ/2tEOrjVcnpoyEAPz50A8c6l/F/y9/EXhklsegow4+v44P5twOS+sgQn7p8enL3nbyTR7mS/8uXuZDNlNwQm/a8GjU7c4jy6XRGO2kttM74WOPRFIYboxBbgBTyRIHCa6s0TGVmQ/ho3mXElVyy8R944kX/CIDqlvC0MJqAGxIVXVL6J0KLAJY7REirnbwf/wzvb34lm1ovQD4txHlNv8Mn9w2Trf4O6cHZZ/fp4iCOXD7DIw5w+vPjG79EKVfCVV9eZnB3m4E7y+sGuHTI4fHa0/f7buc/Wa89gkAikOi4HGYZNQzxbd7CRWyknj5C2BwpJbmj8xYKKxumdyQli/M+vWGFl+x9nDtvuXn6PmdAYITOfgIjNE94IRoh6fr0fOSxKW3m8mrsg9NHe4xFVUQ3NDD2vYPoLTGq37iCjV/fxm2LdnBJ8RGe6NvAzWu/iqlOvdC22wqbChpDrqDdVkmPG1x4IElvjc1TC7Os6qhiWWeSvStWsrAvQ1VuHEc3sIwQNeOVWUaDNY3c/bq/BOCCXY9R1kOc/9RmGgcrRe00YaAJDbVssyh9CdurFvBfapKFZYtjxtNNj8Ss/wlCKaNG21H0cZAKpns9pfBhFLcyq8jXlqO4B8/4XAok6yMea8Meq8IeM12vLB/GPUFnWeGi6PQV5V3HRNOfo9ya4RDueIT7+l7Ck6VV9JPCQyHaejtr9SirY2XW1D+Fh8pn+Air2MNlPMzdvI2V7OPF/IYk41O6HCHFVi7iIeelHDPaTvn0y/s7SefHOVLbTFNmmHDZpikzwp6mRbQNFkjafdy/+mIubt9HbW6MVKaKeHYJ4zU70MsJYrklCKlSrQrOCytUa7MPb5XKFuGT3mcv00Vp03/iKgZ2wwUoKy9na0sdx6IKN/Q6tDwtBCkBT4Am4fsLdH7ZoLEtdWYjS7dvLrIi65HwD1Cq+jcuedE3AYi5RfJahE/572ORN0Ci+yWkDr2ekgp5TdBgVTQc/5h4VPKvTR++usTgv5ee2TIeAD9/KE+tXekvr8LLr4phq4I6y+fz20s0lnwOmLv5+ZKv8ob6mRfw3Xv3UpySxq9edDkHWzKYY5soHvkrpIyAlFw5+iiuUHms6XKWW/DuUJQLrMkP+S/Dv+LtH/v4GWs+mcAInf0ERmie8EIxQr7tMfjFbejNMUq7Zp4CfpyOc4f5i7EfskFL0xbNsjDRwaJEJ7lcDYePXEi3tLBcA0Uq7KzfRFaZzA1JWkmkU2R5V5RVR6tm7P9I6woOt61k17mnX3vrVLzknv/FzBT5zeJrMMMu5SEQfuX/LiUKtBp9dNDExZnf0LHiECPmCM7TDFtISGo1n0FXIa5KPtI4WdzwwaxGu1zKkdwxil4aVams3dWg+Vwac7kyfvplC54JizsKJHIuvgKZuI7h+CzotTiWrOKh1jZakpOjaIoviRQ9khmH9GiZ6jFnxtWlXFS+0PxmPrv0z6l2MozpiRn2enb8n/YH+d7ia56z/tb0dZGNRFiRifBAa5grBx0KmsBSBDf0Oly7/yilfZ/l0IrleNlB/uXGaxiuv25KH+ePuRyMq5S06Y70y/s+z//WXc6D6YtZn93LtqqZl9F4bVeZ63tdLBVuzP49IXU7D1ZfwpvXfPqZvSDpwykS5xUp8WfK1p/ShwQhWJD3uP3JErW25EBcwfRcFhQFe8KdLCgnMX2DqAzP2s0hpcg/L/wKdfZCrM5lnDv2BD2igdW5Ayf2EQgMJcSSqnWcV33FKWVZXgHbK7Et8Rhv+djsJSNORWCEzn4CIzRPmMtGyO7Mkn+4G6/gUD6aPdHepQzygFGZ8SM8FdOuw1Ms4oUWFCeGQKdgCn67KswC5xCjWj0H0inGow6WoYOY/NWsO0VSGYtcRMdTdWzz1OGnExz//xCCFqufG479mtFEiu1V52ArBl1mAwjB0n3HcNJRUhmLnnSCoWSUZLaTTKwJAajlDky/lsZMOxd3HECg4WFT5UVpaDyEonggBQta91AsJkgkBp/9CfWZdS3LvsPnUNMnqcuHOGqdjx2tYdhLUlPMsDC6i4WNv2RT+CqMvlpu5L8Y1tPElGEGw3U0jo9R8Gr5oPceYq7FXym/oKe8mlXJH7G/qhVXqPzd8vfRFa4ku3571/uJe0UOhVupdrNcmN3Dn63+F+rKo+yNnkNnOM15uYNktBid4aYTGkO2j2VOvoBLnypQMh22L06i+D4xu0Q2PHP40XAdbty+g6WdjZieIKX2kffqqY9ux1dayeZbyflwpEHnoTVhFE+y5miZ+y6c7O+S4R08kV7L8p4sB5ur0DyJqz43Seiz8br+X3M0XM+u+HIcZXr4SJEu/sTn+R/3jvLSHh1NVmbyGVJHZZiQuomSdwUSjUOxMP+xzOSROpNE2SdjKDSUPN7e7nBtn8OTsQ4ebV7JjxcYhF05oyEzPHliRtYULb7kxj27GUgV6PF+QMQt87Hum1hhtT3n5wVA4iFQcUUXmpxe+mEmOnK72TP2CEUvhy/g8qvexiXv+uNn9fyBETr7CYzQPOF5MUITny3f9vA9H3vUZvSeI+R68vRrCvvHLTaRR5ojpLVRjLBFyHHQ7Wri2WWESj1YmomvJciGdCg8gmZ10tHSyu4VKxhIJSjFFp1GxFQ9MVeS1wQxr8irB3/F0sIA148+yJ7oUq4feZRdsWXsiK/kT/ofoN9Ms8jqJS9DxIQ1rTsXhcfkBRwTC0iKfrbJ2dd/AojGRqiKD2OYRZLJfqqqTj3i9XQsK4ppFhFCsmPHy8lla9F1m3h8iJXnPIqqujiOgZQCRfEZ6F+CVYrR27MUJi6uEhiNVtGXqEFIScgpE3FsTKvI/sY2sqEo6bEMxXiETCRKspDHcH06alsYj4YwHI+yrqL4Ev8U+SDPhtaxLNccOcT6cZW92lFWe22MaBa99E/bt85PkM41sUhRMaRJUq+iI7+fxbGFjDpF0kbtDM8wG3l0eQhHrEO6ZYRmAOCNduCNtaMveBGuGWXIFIwbgrAH7XKIkeg2lrY9Qcqy6K6GPcY53KW8kzhZMiSpd4dZnTnCL9KX8djmN7G41M2wWcX9qRezP9LGNZ2/ZUH4GF3nqUgFhkQt++01fFt9LeP65Mwp0x/HFwaOmH0uvOpLluV8cnpF479tKyGBvQmFppLkmlMsZuqKStgr7MEjaZW+sMIruy1CUqWgwr1NOheOeiwu5PCEQD3DOfklNUNeHSUvDIZD93JdaT+m6MAnjComf/D40sSnCl+4FJw3UvAmR8+OX5+EEEgpERMjUwOuR1+hg2OFA1jlTqpUgwWx89AaN3DrepPumE5rdzuXbvsNC/qOIW+6lfdfffkZ6X46gRE6+wmM0Dzh926EpITuLQxs/CZbewoMDi1CN0tECmUSfo42fxu7RSMHs2VCWo784hrGI1XsSl2Ch45XrVPtDVPtDtJuLGZYbabaH2K/vpabOu/ikJ7kYGIdA6FGEDqGD1FXEnY9hk2JL1SQEks/fe5E0spR6/UwrNZQY49SFmEUz6YsingqrCwXqTa6KPs6KVnm3OgWjg2tQtVNavw8ImcyLGo44izA0l3cWpvBWIq8iJOWQ4T9EgvdTvJmmMMsR/EkUoVaBuliIeexk7VsZTkHGKSO0oHr+FLbpcQo0DTmsGhcpTqfRS0WqLaKbG9u5OcrFmHrBjGrSN4Ms7L/GLrnsrT9AM397ZhWCEq9HF56LsOpBgYaFhEr23SlaiiaIUrTcpGePeGyhek4ZMNRfEVB8zw2dPZgWh51+XbSeYtjNUn2Ni2mZJis6O8kE44yHEvQMjbEkqEebE0nncvgGTFUJ3/KuWZhabDCTrNCtKG4PlH1zJLRB0rHqA9PFke0vSK+71CyhkjFZ1601i0NoYUnTZQvHexQLwcTPyAje1E0CKeLqKZHpqMKI17GGg0RqS0hfUE4bTFiqQwLSafhERMKXq6N6lEFvd2j6ESwrBBJa2ouTFeoiYyRIFeVQDQrWGaU7kQLfc1LwPMRvkSMl5E1JmRdZJXO8UQvxXLwQzqa63D+kW30NLRiGWEu2fQgD176Chyjks/TONhFX90CEmXJ/2wucm94M79auobuRA1NpX4aSkNcv/mnrCkdZlXVACm1MK1+1bCaYJTrSBbX4cg2JFGk0ocS+jq/qatmXIthuGWWDLcz5kf5+Hm3gCdRfRfCBhfndrB+YBePKucTLZfwygqRUgnDzTOmxYgVspR1k2RuFN0vE8tmUf3JAl6+oqL4HrnqKgbSjaglj9rRAWLF2es1SVVQv76Ot7z/9IvdzkRghM5+AiM0T3gujJBdKvPY/Y+y9dff5ZDegxav4lh6DeFymVVHnqJUHWWUNLGyT7NfRSw/hKpAOJEkn2ylPpvA9rPk8egWHhE3hipLhFWXXCxC2hnnwcVLGQmHyZk6PbEIVWUPS1UoGNMNTtKyKekKtqpjyDJpJ89qq5t+PcmucCsvLm0HN85rBvowXZPhyCC/rK0jY2oMinr6RDO6LOMI43c6L2dCo+xhmNrTPlfElRRnCFMcZ2nO43B8+vTxZKlysRgPT42J6a7LuSNFdtZXsW6kzIo8FFTJlUM+B+MKjSWfsAcFTdAfFiwfPsZgxKHRypF2BaP6UqpsSa3lYpZKdCh9tIfGMUWIISU7TcdxVrhNtPppqmWUXWonBWGRkjF2asdY7S6gwU2wkDoEgiOij2GRxR06Ql/pSGXmkWPhhcIgFNRCdopRirk2engB9ZE2cs4YPcVDGEqIsm9VqlsLB9PMYZUV8BMkCxa2pmLrKomiTT6i4yiTn6e4nqLGbGTI7sKNDGNndZACVehI6eMzPXH8dyGrxqjy8gzrKTyhUl8emnE/H8FAqA5H6MT9PDoOMbuAq6iUzAhhu4iQEkX6FMIxQnaJQiSG6nkovk9n8yJ+c8l1nFdsJ+6XKDsaO2tXMhZOgJRovkt9ZhDXreT3WGYYXyiErSLZeJJFnQdZe3AXEjDsIj2Ni0iNVvK/Wns6UKRPyQwTtiuVzouhCL6iELJKaP5zc85cRaUYjVGVyzBY04DqudSMTx9JLUUi9DS10tx3jGRDBm3MJRa3aPNGEQ0e5159B9XrT51PNBuBETr7CYzQPOHZGqFPfP4DjKYaGdYSDIVSlNUw1VaRkFdE+j6WFqYvnkJIl7weYTgcB0CTLlVeAUOWyaoxikoYIf1pU32F9BFS4itTL+6NxQFiMo9OmaIWxpcqG+wtLHcOkhaDVMcGMI3p4arjuK6Opk2fiu26OlYuhjO4EmHH0Y0sg+UoKcXGjcKY6XHMaKA7u4rwWBHN8hiuWYJBlirXxlfCSHOM6kgf9fGDxAbDtPYdJV5QsapacPXlpBC0x3U64iUu7NVoHo+ju2mK+k7KPY9yOGKyp249w4lmVKLEc2kaxgVaPkwqsxNF9jCQXkgxrrKrrQ3ph3n1gSGarUGKnktaDeH7wzy2ciWqnqIjHiGr6fSHFFZlPW7odclpsCznEzqpGralHoBSHqlJfJnFLfeC6yKKEuH6KEYKXV+EpregRNNIp4jQI3i5ftR4A+7wIYQeAt/DGz2Mne+j2+4gnFzOuMwSqlmBIQyG3RGiZoqQ6zNq96OFqlme2MBA6SiG6xE1Ulh+iYG+R3HGDqP4Es33iVtlPKFgui7hhhL4MJxJYLge1YUSmi8ri3gqkrJQKa2SiBLkdY2Y7yCzGl5eJZkro0qJVCVOs8Srldi6ir3Sx7vQnZI/JSVkOuKMdVYxrlfDiMTWQmxuu4yyb5DIj2MZYTJV1QzW1rNhYCNLMgcYMuvRIi7n6E+RNgcZLNeDBQlvHGvYxBo3KVTFCSslTLdEvKFItLFIKFnGl4KuXDNdhVaqam0Oi2V00sSgV8ey3bsZH4xR54ygax4Rp0jILiGFgioEhucxlExjWAVczSBiFQhbRcKlAqrn4qoaZcMkXCogqBgJzfewjNCJBXoVKUFKVN9D806dSO+oGrYRIlaavtxMIRzHcGyONS+hrecwjhqiaMYZrKljNJ5GCIXG4R6ckIllhmjs6SReGMNVNMJZj5pCCeFD2i7SmBtiTI+DIklYRcZCMTpj9eSMCL2xOpZkehiKJ4kqZWJ+mZp8FkWF4ZqFjC1MkF6/i14rSW++mn0jK+jMtpwoAimkz1fesoFrV88wxf4MCIzQ2U9ghOYJz9YIve5n/8NjsamzpkxZwhaTMz8ScoxqRkkzRAiLIhFScpQCURxpYPo2jWoPjtDJkCRLFW20I1EZJ0mIEjWMMEaKF/MrmumZUYtf1MAXeAWNci6E2eHi+wr0GMihGI4RwYpE8aSL7mmUIiEinsSuBz8kcHN1VB/1UKRBItOOq4UphesAH18xcbUQpj1G2BoiVBojbA1jlPMo8rmddTUbTlRit0iKrsDxIVJQCFkSIhKvGrQsCBe8iETLCbQsKGUBQkGJ1qFUNYNQkHYGWS5UZgMBvpUBpzjluXwBjg5lHcoRsEzwXUCFw4sk0hEojsBXBdRKkuMqXkyy+lGP/laFnmaFzXWCYd0nXJa0DEM+IilUCWwPMnFIugpOxMPPq1RJFa+qjBqXaDoUiuC6grINflKy3JCYRwVeRrB4TLLkUCVv5Ugz+HHINQgKiqTPU3BiYWxhUqsnyBppcp5HlR9n3eF+VqY6cFQFqUrCCywiYz7S8FFiDhHFJz1WRro6TxkNaIpNRk+h5HS2e2nGqSLpCYQ/jqGWGaEBSTM7mleQD4dIWAXW5Z8i4llYIYOH6tZTJ/qp8UcYUWowhU2JMCUitNFBgnFMLMapJksCz1coKlEyJDhmtVL2Q0hVIDwJtocAFM+Dkg8ZByVXMfJ+0kBGVIQj8WMawvJAShKyhGL7OLZOwhEUxkYp1FYjCx5ht8Dawm5CjoUtdFTPR7gewpUoQhKVZYQZwTMilCI1KKqK5peRnktKFolHw9jRNPcX0sRCJpoiuWRRipGSx/27e6mJh4iZGhFTYzBroyuAIpA+LKuP4/o+UkKx7BE1VQazNotrozQmwhiaQltNlPMXJFhaF8PUVKSUZC2X0UIZRcBw3mZ/f46aqEG25GK7Hl1jJTxfsrNrHCEqeUOqEKiKwPV96uIhFqTChHWVRw8Pc+XyWhbVRNFUhatX1qE8y5y2wAid/QRG6AWKEOJa4AtUyux+VUp5yjmzz9YIffqT78fSQOpZFrrj+L6NUDxKrkM5GaJxdAS97KEXwdMUNGEiR0xinTaxvIMiFHwpcXDwF9j4JQ2lLHAXeFiGQbEcxY5ohOIFVM9HkRJHESQzZWRWIWdGKekRIopFVlfoqY/SY4TpJsaYHiIfHsdHwxUxBDaKq2IqBronGJO14HiYfgkhfFQvgpTVVFnQag/jmyopa5Rmux/bipKwiyTyJfy8wFY1hAJOlcpwSjAS8ogVbUZDHrGSIGlHGTKb2RNtIuGP0MAx0naBlrxFslhA8yRhrzISEREOdVoW1xAUQzBo6MQiJYRuYxUVNKky3KQQSihUqxq2ZpBmlJBq4+oKpu2jl33GSDFSbqa6lCdkucTKJWpGhhkdj1O0I4TLNobuElJtpKZQUMLkRBRbM7EjJr6iYkUjZMNxSkoIQ7gQCuNFI4wa9WTkMIbdjaV4ZEMmWVliUCj0+0PYShhFltCFQHd9fFkm5oRoKiaJ6RoNmk9KM4moVbi6oN/ppF+OogERobFQRmgpRxnywcHBERpoCsdUg2ipimgpTtj1aHQkKwhjKj6jNfX4DQvp8BJkiFGVGUI/vBX8PHpMIeLYJMtD9DgpbDPJkJHiQLgZw/FwGpspuZIGtcCKtI7lCYqWjWfEOGyr7LMVXFMhJMEQgnqpEkdgl21KniASMWhJh4lrGiaQqDJxqRTyrNWhYGgcGCsxULAhpBGJ6YRjBgtjIc6JhujKWuwcL9AYNqhRVNx8Gc3x6fY8kghCCHK2S23MpCkZRgGqowbVER1norTCUK5Ss0lXFXKWy1DeJltyyFkuJcfD8XxKZY+C7ZK3XRakIvgTx57TWEU8pFFyPHRVYVldDCEEVWGN2ph5Iuk44PQERujsJzBCL0CEECpwEHgp0A1sAd4gpdw32zF/qFljZdfCc8fwPAswkF4Ex5XEo1E0XSdTypKRKpbQyboeR0s2A7aDJgRhReABJqBrKp6U5ByPhK4hfZ/CqI0VVbAVQdF2iVgumnAYK+QouS6GYVKlKoQVge9YuH6JZEgjGg7TaSsMWT7JUAwDG2d0DFVCnW5R4+cgXIMtTByho3llSmoI0zSpwSdWGsMtjpJ1BAVPUAjXkYpEcICyaZAN6YRDYRojYWpUFdP1GM86FByP8cwQ7ngXXrlIVJYxwkk830WRPnVhqFGLhH0L33XIO2WqQgamGcKRGk5VPdFQGDNWjZpaSr/r4/k+IelQF4kjfA/f98mVSoRDYSKmgeu62LZNuVzGNE1CoRCK8swWVw0ICJgkMEJnP89s4ZuAucJFwGEpZTuAEOI7wKuAWY3QHwpDC4E2fdHE4ySjSZIn3d+QeAbLUTQ/a1l/eE5MPKoHVj8nXbadKC8zEZacSPitMSYTsDVNQ9M0otFnv0BlQEBAwHwi+Kn4wqQZ6Drpfjcz2AQhxE1CiCeFEE8ODc08MyUgICAgIGA+ExihFyYzBfinxTillLdLKS+UUl5YW/tMCswFBAQEBATMDwIj9MKkGzi51nwL0DvLvgEBAQEBAQGzEBihFyZbgGVCiEVCCAN4PfDT51lTQEBAQEDAC44gWfoFiJTSFUK8G3iAyvT5O6SUe59nWQEBAQEBAS84AiP0AkVKeR9w3/OtIyAgICAg4IVMEBoLCAgICAgImLcERiggICAgICBg3hJUlp4nCCGGgGPP8vA0MH3J57nDXNcHc19joO93Y67rg7mvca7qWyilDOqPnMUERijgtAghnpzLJebnuj6Y+xoDfb8bc10fzH2Nc11fwNlLEBoLCAgICAgImLcERiggICAgICBg3hIYoYAz4fbnW8BpmOv6YO5rDPT9bsx1fTD3Nc51fQFnKUGOUEBAQEBAQMC8JRgRCggICAgICJi3BEYoICAgICAgYN4SGKGAUyKEuFYIcUAIcVgIcesf8HnvEEIMCiH2nNSWEkL8UghxaOJv9UmPfWhC4wEhxMtPar9ACLF74rEvCiHEc6RvgRDiN0KIp4QQe4UQfzOXNAohQkKIzUKInRP6PjGX9J3UtyqE2C6EuGeO6js60fcOIcSTc02jECIphPiBEGL/xGfxkrmiTwixYuK8Hd+yQohb5oq+gIATSCmDLdhm3Kgs6HoEWAwYwE7g3D/Qc18JrAf2nNT2GeDWidu3ArdN3D53QpsJLJrQrE48thm4BBDAz4HrniN9jcD6idtx4OCEjjmhcaKv2MRtHdgEvGiu6DtJ5/uAbwH3zLX3eKLvo0D6aW1zRiNwF/DOidsGkJxL+k7SqQL9wMK5qC/Y5vcWjAgFnIqLgMNSynYpZRn4DvCqP8QTSyl/C4w+rflVVL74mfj76pPavyOltKWUHcBh4CIhRCNQJaV8QkopgW+cdMzvqq9PSrlt4nYOeAponisaZYX8xF19YpNzRR+AEKIFuAH46knNc0bfKZgTGoUQVVR+MHwNQEpZllKOzxV9T+Nq4IiU8tgc1RcwjwmMUMCpaAa6TrrfPdH2fFEvpeyDihEB6ibaZ9PZPHH76e3PKUKINmAdlVGXOaNxIuy0AxgEfimlnFP6gH8H/g7wT2qbS/qgYh5/IYTYKoS4aY5pXAwMAV+fCC9+VQgRnUP6Tub1wLcnbs9FfQHzmMAIBZyKmeLwc7Hewmw6f+/6hRAx4IfALVLK7Kl2nUXL702jlNKTUq4FWqj8sl59it3/oPqEEDcCg1LKrWd6yCw6ft/v8WVSyvXAdcDNQogrT7HvH1qjRiV8/BUp5TqgQCXUNBvPyzkUQhjAK4Hvn27XWXS8UL6HAl6gBEYo4FR0AwtOut8C9D5PWgAGJobJmfg7ONE+m87uidtPb39OEELoVEzQN6WUP5qLGgEmwiUPAdfOIX2XAa8UQhylEnL9IyHE3XNIHwBSyt6Jv4PAj6mEi+eKxm6ge2KkD+AHVIzRXNF3nOuAbVLKgYn7c01fwDwnMEIBp2ILsEwIsWjiV93rgZ8+j3p+CvzZxO0/A35yUvvrhRCmEGIRsAzYPDHsnhNCvGhilslbTzrmd2Kiv68BT0kp/3WuaRRC1AohkhO3w8A1wP65ok9K+SEpZYuUso3K5+rXUso3zxV9AEKIqBAifvw28DJgz1zRKKXsB7qEECsmmq4G9s0VfSfxBibDYsd1zCV9AfOd5ztbO9jm9gZcT2VG1BHgw3/A5/020Ac4VH4RvgOoAX4FHJr4mzpp/w9PaDzASTNKgAupXLyOAF9mopr6c6DvcirD87uAHRPb9XNFI7AG2D6hbw/w0Yn2OaHvaVpfwuSssTmjj0oOzs6Jbe/xz/8c07gWeHLiff5foHqO6YsAI0DipLY5oy/Ygk1KGSyxERAQEBAQEDB/CUJjAQEBAQEBAfOWwAgFBAQEBAQEzFsCIxQQEBAQEBAwbwmMUEBAQEBAQMC8JTBCAQEBAQEBAfOWwAgFBAQEBAQEzFsCIxQQEBAQEBAwb/n/ppXUVef6MIQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "line_plot = MC_thirtyyear.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "dist_plot = MC_thirtyyear.plot_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retirement Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean             11.033681\n",
      "std               8.357423\n",
      "min               1.247331\n",
      "25%               5.450098\n",
      "50%               8.208772\n",
      "75%              13.924686\n",
      "max              47.235571\n",
      "95% CI Lower      2.632999\n",
      "95% CI Upper     35.493324\n",
      "Name: 7560, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "summary_stats = MC_thirtyyear.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expected portfolio return at the `95%` lower and upper confidence intervals based on a `$20,000` initial investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $20000 in the portfolio over the next 30 years will end within in the range of $52659.97 and $709866.49\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $20,000\n",
    "ci_lower = round(summary_stats[8]*initial_investment,2)\n",
    "ci_upper = round(summary_stats[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower} and ${ci_upper}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the expected portfolio return at the `95%` lower and upper confidence intervals based on a `50%` increase in the initial investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $30000 in the portfolio over the next 30 years will end within in the range of $78989.96 and $1064799.73\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 20000 * 1.5\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $30,000\n",
    "ci_lower = round(summary_stats[8]*initial_investment,2)\n",
    "ci_upper = round(summary_stats[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${int(initial_investment)} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower} and ${ci_upper}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional Challenge - Early Retirement\n",
    "\n",
    "\n",
    "### Five Years Retirement Option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "MC_fiveyear = MCSimulation(\n",
    "  portfolio_data = df_stock_data,\n",
    "  weights = [.40,.60],\n",
    "  num_simulation = 500,\n",
    "  num_trading_days = 252*5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 0.\n",
      "Running Monte Carlo simulation number 10.\n",
      "Running Monte Carlo simulation number 20.\n",
      "Running Monte Carlo simulation number 30.\n",
      "Running Monte Carlo simulation number 40.\n",
      "Running Monte Carlo simulation number 50.\n",
      "Running Monte Carlo simulation number 60.\n",
      "Running Monte Carlo simulation number 70.\n",
      "Running Monte Carlo simulation number 80.\n",
      "Running Monte Carlo simulation number 90.\n",
      "Running Monte Carlo simulation number 100.\n",
      "Running Monte Carlo simulation number 110.\n",
      "Running Monte Carlo simulation number 120.\n",
      "Running Monte Carlo simulation number 130.\n",
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n",
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n",
      "Running Monte Carlo simulation number 200.\n",
      "Running Monte Carlo simulation number 210.\n",
      "Running Monte Carlo simulation number 220.\n",
      "Running Monte Carlo simulation number 230.\n",
      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
      "Running Monte Carlo simulation number 270.\n",
      "Running Monte Carlo simulation number 280.\n",
      "Running Monte Carlo simulation number 290.\n",
      "Running Monte Carlo simulation number 300.\n",
      "Running Monte Carlo simulation number 310.\n",
      "Running Monte Carlo simulation number 320.\n",
      "Running Monte Carlo simulation number 330.\n",
      "Running Monte Carlo simulation number 340.\n",
      "Running Monte Carlo simulation number 350.\n",
      "Running Monte Carlo simulation number 360.\n",
      "Running Monte Carlo simulation number 370.\n",
      "Running Monte Carlo simulation number 380.\n",
      "Running Monte Carlo simulation number 390.\n",
      "Running Monte Carlo simulation number 400.\n",
      "Running Monte Carlo simulation number 410.\n",
      "Running Monte Carlo simulation number 420.\n",
      "Running Monte Carlo simulation number 430.\n",
      "Running Monte Carlo simulation number 440.\n",
      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
      "Running Monte Carlo simulation number 470.\n",
      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.009159</td>\n",
       "      <td>1.001486</td>\n",
       "      <td>0.997737</td>\n",
       "      <td>1.002011</td>\n",
       "      <td>1.001539</td>\n",
       "      <td>0.990146</td>\n",
       "      <td>0.990678</td>\n",
       "      <td>1.000432</td>\n",
       "      <td>0.995300</td>\n",
       "      <td>0.994266</td>\n",
       "      <td>...</td>\n",
       "      <td>1.012740</td>\n",
       "      <td>0.997453</td>\n",
       "      <td>0.984563</td>\n",
       "      <td>1.000325</td>\n",
       "      <td>0.992148</td>\n",
       "      <td>1.002226</td>\n",
       "      <td>0.992744</td>\n",
       "      <td>1.018185</td>\n",
       "      <td>0.994034</td>\n",
       "      <td>1.004812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.019612</td>\n",
       "      <td>1.006094</td>\n",
       "      <td>0.992414</td>\n",
       "      <td>0.997041</td>\n",
       "      <td>1.016474</td>\n",
       "      <td>1.001476</td>\n",
       "      <td>0.986698</td>\n",
       "      <td>1.004393</td>\n",
       "      <td>0.984932</td>\n",
       "      <td>0.979219</td>\n",
       "      <td>...</td>\n",
       "      <td>1.021214</td>\n",
       "      <td>0.995638</td>\n",
       "      <td>0.983159</td>\n",
       "      <td>1.007234</td>\n",
       "      <td>1.006031</td>\n",
       "      <td>1.000047</td>\n",
       "      <td>1.008970</td>\n",
       "      <td>1.018636</td>\n",
       "      <td>1.002323</td>\n",
       "      <td>1.009075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.019021</td>\n",
       "      <td>0.997866</td>\n",
       "      <td>0.983200</td>\n",
       "      <td>1.003559</td>\n",
       "      <td>1.017054</td>\n",
       "      <td>0.996372</td>\n",
       "      <td>0.981493</td>\n",
       "      <td>1.003749</td>\n",
       "      <td>1.001333</td>\n",
       "      <td>0.967209</td>\n",
       "      <td>...</td>\n",
       "      <td>1.012702</td>\n",
       "      <td>0.987066</td>\n",
       "      <td>0.980192</td>\n",
       "      <td>1.017292</td>\n",
       "      <td>0.994583</td>\n",
       "      <td>1.002001</td>\n",
       "      <td>0.998644</td>\n",
       "      <td>1.023598</td>\n",
       "      <td>0.995784</td>\n",
       "      <td>1.016778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.022346</td>\n",
       "      <td>0.991193</td>\n",
       "      <td>0.970899</td>\n",
       "      <td>1.001835</td>\n",
       "      <td>1.019165</td>\n",
       "      <td>1.004003</td>\n",
       "      <td>0.978110</td>\n",
       "      <td>1.004995</td>\n",
       "      <td>0.999437</td>\n",
       "      <td>0.964625</td>\n",
       "      <td>...</td>\n",
       "      <td>1.009149</td>\n",
       "      <td>0.985031</td>\n",
       "      <td>1.001434</td>\n",
       "      <td>1.009709</td>\n",
       "      <td>0.983672</td>\n",
       "      <td>1.003505</td>\n",
       "      <td>1.004665</td>\n",
       "      <td>1.041544</td>\n",
       "      <td>0.996326</td>\n",
       "      <td>1.007216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1.651127</td>\n",
       "      <td>1.104504</td>\n",
       "      <td>0.917418</td>\n",
       "      <td>1.189521</td>\n",
       "      <td>1.185848</td>\n",
       "      <td>0.907238</td>\n",
       "      <td>1.344937</td>\n",
       "      <td>1.579989</td>\n",
       "      <td>1.299695</td>\n",
       "      <td>1.413296</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291254</td>\n",
       "      <td>1.228680</td>\n",
       "      <td>0.866220</td>\n",
       "      <td>1.058393</td>\n",
       "      <td>1.314464</td>\n",
       "      <td>1.281090</td>\n",
       "      <td>1.571495</td>\n",
       "      <td>1.533573</td>\n",
       "      <td>1.839433</td>\n",
       "      <td>1.495204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1.656994</td>\n",
       "      <td>1.103482</td>\n",
       "      <td>0.909860</td>\n",
       "      <td>1.175337</td>\n",
       "      <td>1.177428</td>\n",
       "      <td>0.914413</td>\n",
       "      <td>1.346450</td>\n",
       "      <td>1.581631</td>\n",
       "      <td>1.303360</td>\n",
       "      <td>1.392679</td>\n",
       "      <td>...</td>\n",
       "      <td>2.305576</td>\n",
       "      <td>1.242411</td>\n",
       "      <td>0.878553</td>\n",
       "      <td>1.054199</td>\n",
       "      <td>1.325851</td>\n",
       "      <td>1.304054</td>\n",
       "      <td>1.597144</td>\n",
       "      <td>1.519299</td>\n",
       "      <td>1.830794</td>\n",
       "      <td>1.488721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>1.650918</td>\n",
       "      <td>1.102582</td>\n",
       "      <td>0.903688</td>\n",
       "      <td>1.188448</td>\n",
       "      <td>1.188129</td>\n",
       "      <td>0.907472</td>\n",
       "      <td>1.351720</td>\n",
       "      <td>1.578099</td>\n",
       "      <td>1.299615</td>\n",
       "      <td>1.413094</td>\n",
       "      <td>...</td>\n",
       "      <td>2.299123</td>\n",
       "      <td>1.233790</td>\n",
       "      <td>0.870077</td>\n",
       "      <td>1.051034</td>\n",
       "      <td>1.340770</td>\n",
       "      <td>1.318222</td>\n",
       "      <td>1.634346</td>\n",
       "      <td>1.528001</td>\n",
       "      <td>1.824863</td>\n",
       "      <td>1.491667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>1.648708</td>\n",
       "      <td>1.103921</td>\n",
       "      <td>0.901561</td>\n",
       "      <td>1.188127</td>\n",
       "      <td>1.188319</td>\n",
       "      <td>0.899580</td>\n",
       "      <td>1.359093</td>\n",
       "      <td>1.565750</td>\n",
       "      <td>1.281752</td>\n",
       "      <td>1.406341</td>\n",
       "      <td>...</td>\n",
       "      <td>2.284880</td>\n",
       "      <td>1.235653</td>\n",
       "      <td>0.875817</td>\n",
       "      <td>1.044793</td>\n",
       "      <td>1.344909</td>\n",
       "      <td>1.322169</td>\n",
       "      <td>1.660667</td>\n",
       "      <td>1.527061</td>\n",
       "      <td>1.830129</td>\n",
       "      <td>1.491037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>1.635743</td>\n",
       "      <td>1.112502</td>\n",
       "      <td>0.899130</td>\n",
       "      <td>1.196372</td>\n",
       "      <td>1.211093</td>\n",
       "      <td>0.905313</td>\n",
       "      <td>1.365851</td>\n",
       "      <td>1.568241</td>\n",
       "      <td>1.279204</td>\n",
       "      <td>1.396970</td>\n",
       "      <td>...</td>\n",
       "      <td>2.282316</td>\n",
       "      <td>1.247109</td>\n",
       "      <td>0.888399</td>\n",
       "      <td>1.047274</td>\n",
       "      <td>1.339073</td>\n",
       "      <td>1.328560</td>\n",
       "      <td>1.662429</td>\n",
       "      <td>1.531621</td>\n",
       "      <td>1.790643</td>\n",
       "      <td>1.508090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1261 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "0     1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1     1.009159  1.001486  0.997737  1.002011  1.001539  0.990146  0.990678   \n",
       "2     1.019612  1.006094  0.992414  0.997041  1.016474  1.001476  0.986698   \n",
       "3     1.019021  0.997866  0.983200  1.003559  1.017054  0.996372  0.981493   \n",
       "4     1.022346  0.991193  0.970899  1.001835  1.019165  1.004003  0.978110   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1256  1.651127  1.104504  0.917418  1.189521  1.185848  0.907238  1.344937   \n",
       "1257  1.656994  1.103482  0.909860  1.175337  1.177428  0.914413  1.346450   \n",
       "1258  1.650918  1.102582  0.903688  1.188448  1.188129  0.907472  1.351720   \n",
       "1259  1.648708  1.103921  0.901561  1.188127  1.188319  0.899580  1.359093   \n",
       "1260  1.635743  1.112502  0.899130  1.196372  1.211093  0.905313  1.365851   \n",
       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1     1.000432  0.995300  0.994266  ...  1.012740  0.997453  0.984563   \n",
       "2     1.004393  0.984932  0.979219  ...  1.021214  0.995638  0.983159   \n",
       "3     1.003749  1.001333  0.967209  ...  1.012702  0.987066  0.980192   \n",
       "4     1.004995  0.999437  0.964625  ...  1.009149  0.985031  1.001434   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "1256  1.579989  1.299695  1.413296  ...  2.291254  1.228680  0.866220   \n",
       "1257  1.581631  1.303360  1.392679  ...  2.305576  1.242411  0.878553   \n",
       "1258  1.578099  1.299615  1.413094  ...  2.299123  1.233790  0.870077   \n",
       "1259  1.565750  1.281752  1.406341  ...  2.284880  1.235653  0.875817   \n",
       "1260  1.568241  1.279204  1.396970  ...  2.282316  1.247109  0.888399   \n",
       "\n",
       "           493       494       495       496       497       498       499  \n",
       "0     1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
       "1     1.000325  0.992148  1.002226  0.992744  1.018185  0.994034  1.004812  \n",
       "2     1.007234  1.006031  1.000047  1.008970  1.018636  1.002323  1.009075  \n",
       "3     1.017292  0.994583  1.002001  0.998644  1.023598  0.995784  1.016778  \n",
       "4     1.009709  0.983672  1.003505  1.004665  1.041544  0.996326  1.007216  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "1256  1.058393  1.314464  1.281090  1.571495  1.533573  1.839433  1.495204  \n",
       "1257  1.054199  1.325851  1.304054  1.597144  1.519299  1.830794  1.488721  \n",
       "1258  1.051034  1.340770  1.318222  1.634346  1.528001  1.824863  1.491667  \n",
       "1259  1.044793  1.344909  1.322169  1.660667  1.527061  1.830129  1.491037  \n",
       "1260  1.047274  1.339073  1.328560  1.662429  1.531621  1.790643  1.508090  \n",
       "\n",
       "[1261 rows x 500 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 5 years cumulative returns\n",
    "MC_fiveyear.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "line_plot = MC_fiveyear.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "dist_plot = MC_fiveyear.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              1.515163\n",
      "std               0.396993\n",
      "min               0.610872\n",
      "25%               1.243524\n",
      "50%               1.479090\n",
      "75%               1.745344\n",
      "max               3.195873\n",
      "95% CI Lower      0.841545\n",
      "95% CI Upper      2.373158\n",
      "Name: 1260, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "summary_stats = MC_fiveyear.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $60000 in the portfolio over the next 5 years will end within in the range of $50492.68 and $142389.51\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 60000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000\n",
    "ci_lower_five = round(summary_stats[8]*initial_investment,2)\n",
    "ci_upper_five = round(summary_stats[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 5 years will end within in the range of\"\n",
    "      f\" ${ci_lower_five} and ${ci_upper_five}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ten Years Retirement Option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "MC_tenyear = MCSimulation(\n",
    "  portfolio_data = df_stock_data,\n",
    "  weights = [.40,.60],\n",
    "  num_simulation = 500,\n",
    "  num_trading_days = 252*10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Monte Carlo simulation number 0.\n",
      "Running Monte Carlo simulation number 10.\n",
      "Running Monte Carlo simulation number 20.\n",
      "Running Monte Carlo simulation number 30.\n",
      "Running Monte Carlo simulation number 40.\n",
      "Running Monte Carlo simulation number 50.\n",
      "Running Monte Carlo simulation number 60.\n",
      "Running Monte Carlo simulation number 70.\n",
      "Running Monte Carlo simulation number 80.\n",
      "Running Monte Carlo simulation number 90.\n",
      "Running Monte Carlo simulation number 100.\n",
      "Running Monte Carlo simulation number 110.\n",
      "Running Monte Carlo simulation number 120.\n",
      "Running Monte Carlo simulation number 130.\n",
      "Running Monte Carlo simulation number 140.\n",
      "Running Monte Carlo simulation number 150.\n",
      "Running Monte Carlo simulation number 160.\n",
      "Running Monte Carlo simulation number 170.\n",
      "Running Monte Carlo simulation number 180.\n",
      "Running Monte Carlo simulation number 190.\n",
      "Running Monte Carlo simulation number 200.\n",
      "Running Monte Carlo simulation number 210.\n",
      "Running Monte Carlo simulation number 220.\n",
      "Running Monte Carlo simulation number 230.\n",
      "Running Monte Carlo simulation number 240.\n",
      "Running Monte Carlo simulation number 250.\n",
      "Running Monte Carlo simulation number 260.\n",
      "Running Monte Carlo simulation number 270.\n",
      "Running Monte Carlo simulation number 280.\n",
      "Running Monte Carlo simulation number 290.\n",
      "Running Monte Carlo simulation number 300.\n",
      "Running Monte Carlo simulation number 310.\n",
      "Running Monte Carlo simulation number 320.\n",
      "Running Monte Carlo simulation number 330.\n",
      "Running Monte Carlo simulation number 340.\n",
      "Running Monte Carlo simulation number 350.\n",
      "Running Monte Carlo simulation number 360.\n",
      "Running Monte Carlo simulation number 370.\n",
      "Running Monte Carlo simulation number 380.\n",
      "Running Monte Carlo simulation number 390.\n",
      "Running Monte Carlo simulation number 400.\n",
      "Running Monte Carlo simulation number 410.\n",
      "Running Monte Carlo simulation number 420.\n",
      "Running Monte Carlo simulation number 430.\n",
      "Running Monte Carlo simulation number 440.\n",
      "Running Monte Carlo simulation number 450.\n",
      "Running Monte Carlo simulation number 460.\n",
      "Running Monte Carlo simulation number 470.\n",
      "Running Monte Carlo simulation number 480.\n",
      "Running Monte Carlo simulation number 490.\n"
     ]
    },
    {
     "data": {
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       "      <td>1.004461</td>\n",
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       "      <td>0.995909</td>\n",
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       "      <td>0.999457</td>\n",
       "      <td>0.998956</td>\n",
       "      <td>0.985431</td>\n",
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       "      <td>1.003519</td>\n",
       "      <td>0.998576</td>\n",
       "      <td>0.994383</td>\n",
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       "      <td>1.012211</td>\n",
       "      <td>0.995078</td>\n",
       "      <td>0.985230</td>\n",
       "      <td>0.998126</td>\n",
       "      <td>1.005286</td>\n",
       "      <td>1.002472</td>\n",
       "      <td>0.988318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.995967</td>\n",
       "      <td>1.003812</td>\n",
       "      <td>0.989895</td>\n",
       "      <td>0.995703</td>\n",
       "      <td>1.001700</td>\n",
       "      <td>1.009211</td>\n",
       "      <td>1.003694</td>\n",
       "      <td>1.000523</td>\n",
       "      <td>1.007224</td>\n",
       "      <td>0.999643</td>\n",
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       "      <td>0.990367</td>\n",
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       "      <td>0.989177</td>\n",
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       "      <td>0.998598</td>\n",
       "      <td>0.994591</td>\n",
       "      <td>0.999326</td>\n",
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       "      <th>2520</th>\n",
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       "           0         1         2         3         4         5         6    \\\n",
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       "4     1.023989  0.980078  1.001383  1.025581  0.996721  0.988897  1.027242   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
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       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
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       "2     1.005286  1.002472  0.988318  ...  0.995967  1.003812  0.989895   \n",
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       "4     0.998598  0.994591  0.999326  ...  0.995344  1.006337  0.994049   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2516  1.318760  1.396761  3.672156  ...  2.748881  3.728692  1.776786   \n",
       "2517  1.331577  1.403674  3.702261  ...  2.754990  3.709032  1.785310   \n",
       "2518  1.335039  1.415493  3.726298  ...  2.736516  3.695662  1.770086   \n",
       "2519  1.334569  1.405494  3.733300  ...  2.729325  3.754521  1.785414   \n",
       "2520  1.327561  1.410768  3.707188  ...  2.741562  3.761993  1.768690   \n",
       "\n",
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       "2     0.995703  1.001700  1.009211  1.003694  1.000523  1.007224  0.999643  \n",
       "3     0.995960  1.005480  1.020558  0.998717  0.991650  0.997899  0.998909  \n",
       "4     0.988401  1.006588  1.012150  1.008616  0.983807  0.998884  1.004120  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2516  3.121573  2.634626  3.474752  1.915230  2.019231  1.782593  1.361464  \n",
       "2517  3.164270  2.633865  3.436416  1.922911  1.994776  1.788144  1.349832  \n",
       "2518  3.173217  2.639695  3.471892  1.902744  2.016963  1.791319  1.362857  \n",
       "2519  3.175139  2.648112  3.509310  1.890211  2.013939  1.819652  1.382264  \n",
       "2520  3.194000  2.639282  3.537799  1.885779  2.015234  1.830808  1.377150  \n",
       "\n",
       "[2521 rows x 500 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 10 years cumulative returns\n",
    "MC_tenyear.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "line_plot = MC_tenyear.plot_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "dist_plot = MC_tenyear.plot_distribution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count           500.000000\n",
      "mean              2.229220\n",
      "std               0.855731\n",
      "min               0.712961\n",
      "25%               1.592158\n",
      "50%               2.085196\n",
      "75%               2.740781\n",
      "max               6.905930\n",
      "95% CI Lower      1.000891\n",
      "95% CI Upper      4.167085\n",
      "Name: 2520, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "summary_stats = MC_tenyear.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $60000 in the portfolio over the next 10 years will end within in the range of $60053.45 and $250025.1\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment = 60000\n",
    "\n",
    "# Use the lower and upper `95%` confidence intervals to calculate the range of the possible outcomes of our $60,000\n",
    "ci_lower_ten = round(summary_stats[8]*initial_investment,2)\n",
    "ci_upper_ten = round(summary_stats[9]*initial_investment,2)\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio\"\n",
    "      f\" over the next 10 years will end within in the range of\"\n",
    "      f\" ${ci_lower_ten} and ${ci_upper_ten}\")"
   ]
  }
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