financial-planner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unit 5 - Financial Planning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports\n",
    "import os\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "import alpaca_trade_api as tradeapi\n",
    "from MCForecastTools import MCSimulation\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load .env enviroment variables\n",
    "load_dotenv()"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set monthly household income\n",
    "monthly_income = 12000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of crypto assets\n",
    "my_btc = 1.2\n",
    "my_eth = 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Fetch current BTC price\n",
    "btc_response = requests.get(btc_url)\n",
    "btc_data = btc_response.json()\n",
    "\n",
    "# Check btc_data\n",
    "# print(json.dumps(btc_data, indent = 3))\n",
    "\n",
    "# Get current BTC price \n",
    "btc_price = btc_data[\"data\"][\"1\"][\"quotes\"][\"USD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch current ETH price\n",
    "eth_response = requests.get(eth_url)\n",
    "eth_data = eth_response.json()\n",
    "\n",
    "# Check eth_data\n",
    "# print(json.dumps(eth_data, indent = 3))\n",
    "\n",
    "# Get current ETH price\n",
    "eth_price = eth_data[\"data\"][\"1027\"][\"quotes\"][\"USD\"][\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute current value of my crpto\n",
    "my_btc_value = my_btc * btc_price\n",
    "my_eth_value = my_eth * eth_price\n",
    "total_crypto_wallet = my_btc_value + my_eth_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 1.2 BTC is $26,811.60\n",
      "The current value of your 5.3 ETH is $8,263.18\n",
      "The current value of your entire cryptocurrency wallet is $35,074.78\n"
     ]
    }
   ],
   "source": [
    "# Print current crypto wallet balance\n",
    "print(f\"The current value of your {my_btc} BTC is ${my_btc_value:,.2f}\")\n",
    "print(f\"The current value of your {my_eth} ETH is ${my_eth_value:,.2f}\")\n",
    "print(f\"The current value of your entire cryptocurrency wallet is ${total_crypto_wallet:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect Investments Data Using Alpaca: `SPY` (stocks) and `AGG` (bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set current amount of shares\n",
    "my_agg = 200\n",
    "my_spy = 110"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Alpaca API key and secret\n",
    "alpaca_api_key = os.getenv(\"ALPACA_API_KEY\")\n",
    "alpaca_secret_key = os.getenv(\"ALPACA_SECRET_KEY\")\n",
    "\n",
    "# Create the Alpaca API object\n",
    "alpaca = tradeapi.REST(alpaca_api_key, alpaca_secret_key, api_version=\"v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format current date as ISO format\n",
    "start_date = pd.Timestamp(\"2022-07-15\", tz = \"America/New_York\").isoformat()\n",
    "end_date = pd.Timestamp(\"2022-07-15\", tz = \"America/New_York\").isoformat()\n",
    "\n",
    "# Set the tickers\n",
    "ticker = [\"AGG\", \"SPY\"]\n",
    "\n",
    "# Set timeframe to \"1Day\" for Alpaca API\n",
    "timeframe = \"1Day\"\n",
    "\n",
    "# Run Alpaca to get last day's prices\n",
    "ticker1_df = alpaca.get_bars(\n",
    "    symbol=ticker,\n",
    "    timeframe=timeframe,\n",
    "    start=start_date,\n",
    "    end=end_date,\n",
    "    limit=1000\n",
    ").df\n",
    "\n",
    "# Preview ticker1_df\n",
    "# ticker1_df\n",
    "\n",
    "# Separate ticker data\n",
    "agg_ticker = ticker1_df[ticker1_df[\"symbol\"]==\"AGG\"].drop(\"symbol\", axis=1)\n",
    "spy_ticker = ticker1_df[ticker1_df[\"symbol\"]==\"SPY\"].drop(\"symbol\", axis = 1)\n",
    "\n",
    "# Concatenate the ticker DataFrames\n",
    "ticker_df = pd.concat([agg_ticker, spy_ticker], axis = 1, keys = [\"AGG\", \"SPY\"])\n",
    "\n",
    "# Preview ticker_df\n",
    "# ticker_df\n",
    "\n",
    "# Get current closing prices for SPY and AGG\n",
    "agg_close_price = ticker_df[\"AGG\"][\"close\"][0]\n",
    "spy_close_price = ticker_df[\"SPY\"][\"close\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current AGG closing price: $102.25\n",
      "Current SPY closing price: $385.13\n"
     ]
    }
   ],
   "source": [
    "# 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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current value of your 110 SPY shares is $42,364.30\n",
      "The current value of your 200 AGG shares is $20,450.00\n",
      "The current combined value of your stocks and bonds is $62,814.30\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",
    "total_stocks_bonds = my_spy_value + my_agg_value\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}\")\n",
    "print(f\"The current combined value of your stocks and bonds is ${total_stocks_bonds:0,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Savings Health Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crypto</th>\n",
       "      <td>35074.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stocks and bonds</th>\n",
       "      <td>62814.300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     amount\n",
       "crypto            35074.777\n",
       "stocks and bonds  62814.300"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Consolidate financial assets data\n",
    "savings_data = [total_crypto_wallet, total_stocks_bonds]\n",
    "\n",
    "# Create savings DataFrame\n",
    "savings_df = pd.DataFrame(savings_data, columns = [\"amount\"], index = [\"crypto\", \"stocks and bonds\"])\n",
    "\n",
    "# Display savings DataFrame\n",
    "display(savings_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot savings pie chart\n",
    "savings_df.plot.pie(y = 0, title = \"Total Savings\", figsize=(5,10));\n",
    "plt.legend(loc=\"upper left\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congratulations! You have more than 3 months of savings in your emergency fund.\n"
     ]
    }
   ],
   "source": [
    "# Set ideal emergency fund\n",
    "emergency_fund = monthly_income * 3\n",
    "\n",
    "# Calculate total amount of savings\n",
    "total_savings = sum(savings_data)\n",
    "\n",
    "# Validate saving health\n",
    "if total_savings > emergency_fund:\n",
    "    print(\"Congratulations! You have more than 3 months of savings in your emergency fund.\")\n",
    "elif total_savings == emergency_fund:\n",
    "    print(\"Keep going! You have exactly 3 months of savings in your emergency fund.\")\n",
    "else:\n",
    "    print(\"Don't worry, you're ${emergency_fund - savings_fund} away from your goal.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Retirement Planning\n",
    "\n",
    "### Monte Carlo Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set start and end dates of ten years back from today.\n",
    "start_date = pd.Timestamp('2012-07-16', tz='America/New_York').isoformat()\n",
    "end_date = pd.Timestamp('2022-07-16', tz='America/New_York').isoformat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 colspan=\"7\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"7\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\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>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",
       "    </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",
       "      <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>2015-12-01 05:00:00+00:00</th>\n",
       "      <td>108.54</td>\n",
       "      <td>108.8400</td>\n",
       "      <td>108.4300</td>\n",
       "      <td>108.82</td>\n",
       "      <td>4259986</td>\n",
       "      <td>15562</td>\n",
       "      <td>108.754152</td>\n",
       "      <td>209.42</td>\n",
       "      <td>210.8200</td>\n",
       "      <td>209.1100</td>\n",
       "      <td>210.68</td>\n",
       "      <td>97858418</td>\n",
       "      <td>337780</td>\n",
       "      <td>209.921410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02 05:00:00+00:00</th>\n",
       "      <td>108.73</td>\n",
       "      <td>108.7799</td>\n",
       "      <td>108.6100</td>\n",
       "      <td>108.67</td>\n",
       "      <td>2462787</td>\n",
       "      <td>11581</td>\n",
       "      <td>108.684692</td>\n",
       "      <td>210.60</td>\n",
       "      <td>211.0000</td>\n",
       "      <td>208.2300</td>\n",
       "      <td>208.54</td>\n",
       "      <td>108069059</td>\n",
       "      <td>367013</td>\n",
       "      <td>209.563055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03 05:00:00+00:00</th>\n",
       "      <td>108.41</td>\n",
       "      <td>108.4100</td>\n",
       "      <td>107.8100</td>\n",
       "      <td>107.89</td>\n",
       "      <td>4634020</td>\n",
       "      <td>16801</td>\n",
       "      <td>108.040315</td>\n",
       "      <td>208.90</td>\n",
       "      <td>209.1500</td>\n",
       "      <td>204.7511</td>\n",
       "      <td>205.58</td>\n",
       "      <td>166224154</td>\n",
       "      <td>546768</td>\n",
       "      <td>206.878936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04 05:00:00+00:00</th>\n",
       "      <td>108.05</td>\n",
       "      <td>108.3000</td>\n",
       "      <td>108.0000</td>\n",
       "      <td>108.24</td>\n",
       "      <td>2182057</td>\n",
       "      <td>9796</td>\n",
       "      <td>108.192232</td>\n",
       "      <td>206.10</td>\n",
       "      <td>209.9700</td>\n",
       "      <td>205.9300</td>\n",
       "      <td>209.66</td>\n",
       "      <td>192878747</td>\n",
       "      <td>556731</td>\n",
       "      <td>208.178631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07 05:00:00+00:00</th>\n",
       "      <td>108.30</td>\n",
       "      <td>108.5800</td>\n",
       "      <td>108.2300</td>\n",
       "      <td>108.40</td>\n",
       "      <td>2143773</td>\n",
       "      <td>9104</td>\n",
       "      <td>108.460067</td>\n",
       "      <td>209.20</td>\n",
       "      <td>209.7295</td>\n",
       "      <td>207.2000</td>\n",
       "      <td>208.27</td>\n",
       "      <td>102027111</td>\n",
       "      <td>374705</td>\n",
       "      <td>208.276128</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-11 04:00:00+00:00</th>\n",
       "      <td>101.67</td>\n",
       "      <td>101.9200</td>\n",
       "      <td>101.6029</td>\n",
       "      <td>101.70</td>\n",
       "      <td>5758310</td>\n",
       "      <td>17084</td>\n",
       "      <td>101.720884</td>\n",
       "      <td>385.91</td>\n",
       "      <td>386.8700</td>\n",
       "      <td>383.5000</td>\n",
       "      <td>384.23</td>\n",
       "      <td>58100363</td>\n",
       "      <td>473423</td>\n",
       "      <td>384.871994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-12 04:00:00+00:00</th>\n",
       "      <td>102.02</td>\n",
       "      <td>102.1900</td>\n",
       "      <td>101.8200</td>\n",
       "      <td>101.84</td>\n",
       "      <td>6664991</td>\n",
       "      <td>21057</td>\n",
       "      <td>102.039440</td>\n",
       "      <td>383.66</td>\n",
       "      <td>386.1600</td>\n",
       "      <td>378.9900</td>\n",
       "      <td>380.83</td>\n",
       "      <td>62027220</td>\n",
       "      <td>512244</td>\n",
       "      <td>382.617620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-13 04:00:00+00:00</th>\n",
       "      <td>101.23</td>\n",
       "      <td>102.2400</td>\n",
       "      <td>101.1900</td>\n",
       "      <td>102.21</td>\n",
       "      <td>6514247</td>\n",
       "      <td>26716</td>\n",
       "      <td>101.977478</td>\n",
       "      <td>375.09</td>\n",
       "      <td>381.9200</td>\n",
       "      <td>374.6580</td>\n",
       "      <td>378.83</td>\n",
       "      <td>83575452</td>\n",
       "      <td>671953</td>\n",
       "      <td>378.819688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-14 04:00:00+00:00</th>\n",
       "      <td>101.51</td>\n",
       "      <td>101.9800</td>\n",
       "      <td>101.3300</td>\n",
       "      <td>101.85</td>\n",
       "      <td>6224496</td>\n",
       "      <td>24722</td>\n",
       "      <td>101.747120</td>\n",
       "      <td>373.99</td>\n",
       "      <td>379.0498</td>\n",
       "      <td>371.0400</td>\n",
       "      <td>377.91</td>\n",
       "      <td>89349688</td>\n",
       "      <td>637416</td>\n",
       "      <td>375.589384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-15 04:00:00+00:00</th>\n",
       "      <td>101.94</td>\n",
       "      <td>102.3500</td>\n",
       "      <td>101.9000</td>\n",
       "      <td>102.25</td>\n",
       "      <td>8440151</td>\n",
       "      <td>27300</td>\n",
       "      <td>102.214854</td>\n",
       "      <td>382.62</td>\n",
       "      <td>385.2500</td>\n",
       "      <td>380.5400</td>\n",
       "      <td>385.13</td>\n",
       "      <td>79318992</td>\n",
       "      <td>488124</td>\n",
       "      <td>383.334989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1667 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                       \\\n",
       "                             open      high       low   close   volume   \n",
       "timestamp                                                                \n",
       "2015-12-01 05:00:00+00:00  108.54  108.8400  108.4300  108.82  4259986   \n",
       "2015-12-02 05:00:00+00:00  108.73  108.7799  108.6100  108.67  2462787   \n",
       "2015-12-03 05:00:00+00:00  108.41  108.4100  107.8100  107.89  4634020   \n",
       "2015-12-04 05:00:00+00:00  108.05  108.3000  108.0000  108.24  2182057   \n",
       "2015-12-07 05:00:00+00:00  108.30  108.5800  108.2300  108.40  2143773   \n",
       "...                           ...       ...       ...     ...      ...   \n",
       "2022-07-11 04:00:00+00:00  101.67  101.9200  101.6029  101.70  5758310   \n",
       "2022-07-12 04:00:00+00:00  102.02  102.1900  101.8200  101.84  6664991   \n",
       "2022-07-13 04:00:00+00:00  101.23  102.2400  101.1900  102.21  6514247   \n",
       "2022-07-14 04:00:00+00:00  101.51  101.9800  101.3300  101.85  6224496   \n",
       "2022-07-15 04:00:00+00:00  101.94  102.3500  101.9000  102.25  8440151   \n",
       "\n",
       "                                                      SPY                      \\\n",
       "                          trade_count        vwap    open      high       low   \n",
       "timestamp                                                                       \n",
       "2015-12-01 05:00:00+00:00       15562  108.754152  209.42  210.8200  209.1100   \n",
       "2015-12-02 05:00:00+00:00       11581  108.684692  210.60  211.0000  208.2300   \n",
       "2015-12-03 05:00:00+00:00       16801  108.040315  208.90  209.1500  204.7511   \n",
       "2015-12-04 05:00:00+00:00        9796  108.192232  206.10  209.9700  205.9300   \n",
       "2015-12-07 05:00:00+00:00        9104  108.460067  209.20  209.7295  207.2000   \n",
       "...                               ...         ...     ...       ...       ...   \n",
       "2022-07-11 04:00:00+00:00       17084  101.720884  385.91  386.8700  383.5000   \n",
       "2022-07-12 04:00:00+00:00       21057  102.039440  383.66  386.1600  378.9900   \n",
       "2022-07-13 04:00:00+00:00       26716  101.977478  375.09  381.9200  374.6580   \n",
       "2022-07-14 04:00:00+00:00       24722  101.747120  373.99  379.0498  371.0400   \n",
       "2022-07-15 04:00:00+00:00       27300  102.214854  382.62  385.2500  380.5400   \n",
       "\n",
       "                                                                      \n",
       "                            close     volume trade_count        vwap  \n",
       "timestamp                                                             \n",
       "2015-12-01 05:00:00+00:00  210.68   97858418      337780  209.921410  \n",
       "2015-12-02 05:00:00+00:00  208.54  108069059      367013  209.563055  \n",
       "2015-12-03 05:00:00+00:00  205.58  166224154      546768  206.878936  \n",
       "2015-12-04 05:00:00+00:00  209.66  192878747      556731  208.178631  \n",
       "2015-12-07 05:00:00+00:00  208.27  102027111      374705  208.276128  \n",
       "...                           ...        ...         ...         ...  \n",
       "2022-07-11 04:00:00+00:00  384.23   58100363      473423  384.871994  \n",
       "2022-07-12 04:00:00+00:00  380.83   62027220      512244  382.617620  \n",
       "2022-07-13 04:00:00+00:00  378.83   83575452      671953  378.819688  \n",
       "2022-07-14 04:00:00+00:00  377.91   89349688      637416  375.589384  \n",
       "2022-07-15 04:00:00+00:00  385.13   79318992      488124  383.334989  \n",
       "\n",
       "[1667 rows x 14 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get ten years' worth of historical data for SPY and AGG\n",
    "# Note: Alpaca only goes back to 2015-12-01\n",
    "tenyrs_stocks_bonds1_df = alpaca.get_bars(\n",
    "    symbol = ticker,\n",
    "    timeframe = timeframe,\n",
    "    start = start_date,\n",
    "    end = end_date,\n",
    ").df\n",
    "\n",
    "# Preview tenyrs_stocks_bonds1_df\n",
    "# tenyrs_stocks_bonds1_df\n",
    "\n",
    "# Separate ticker data\n",
    "agg_tenyrs_ticker = tenyrs_stocks_bonds1_df[tenyrs_stocks_bonds1_df[\"symbol\"]==\"AGG\"].drop(\"symbol\", axis = 1)\n",
    "spy_tenyrs_ticker = tenyrs_stocks_bonds1_df[tenyrs_stocks_bonds1_df[\"symbol\"]==\"SPY\"].drop(\"symbol\", axis = 1)\n",
    "\n",
    "# Concatenate the ticker DataFrames\n",
    "tenyrs_stocks_bonds_df = pd.concat([agg_tenyrs_ticker, spy_tenyrs_ticker], axis = 1, keys = [\"AGG\", \"SPY\"])\n",
    "\n",
    "# Display sample data\n",
    "tenyrs_stocks_bonds_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "mc_thirty = MCSimulation(portfolio_data = tenyrs_stocks_bonds_df, \n",
    "                  weights = [.40,.60], \n",
    "                  num_simulation = 500, \n",
    "                  num_trading_days = 252*30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 colspan=\"8\" halign=\"left\">AGG</th>\n",
       "      <th colspan=\"8\" halign=\"left\">SPY</th>\n",
       "    </tr>\n",
       "    <tr>\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>daily_return</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>daily_return</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",
       "      <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>2015-12-01 05:00:00+00:00</th>\n",
       "      <td>108.54</td>\n",
       "      <td>108.8400</td>\n",
       "      <td>108.4300</td>\n",
       "      <td>108.82</td>\n",
       "      <td>4259986</td>\n",
       "      <td>15562</td>\n",
       "      <td>108.754152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.42</td>\n",
       "      <td>210.8200</td>\n",
       "      <td>209.1100</td>\n",
       "      <td>210.68</td>\n",
       "      <td>97858418</td>\n",
       "      <td>337780</td>\n",
       "      <td>209.921410</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02 05:00:00+00:00</th>\n",
       "      <td>108.73</td>\n",
       "      <td>108.7799</td>\n",
       "      <td>108.6100</td>\n",
       "      <td>108.67</td>\n",
       "      <td>2462787</td>\n",
       "      <td>11581</td>\n",
       "      <td>108.684692</td>\n",
       "      <td>-0.001378</td>\n",
       "      <td>210.60</td>\n",
       "      <td>211.0000</td>\n",
       "      <td>208.2300</td>\n",
       "      <td>208.54</td>\n",
       "      <td>108069059</td>\n",
       "      <td>367013</td>\n",
       "      <td>209.563055</td>\n",
       "      <td>-0.010158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03 05:00:00+00:00</th>\n",
       "      <td>108.41</td>\n",
       "      <td>108.4100</td>\n",
       "      <td>107.8100</td>\n",
       "      <td>107.89</td>\n",
       "      <td>4634020</td>\n",
       "      <td>16801</td>\n",
       "      <td>108.040315</td>\n",
       "      <td>-0.007178</td>\n",
       "      <td>208.90</td>\n",
       "      <td>209.1500</td>\n",
       "      <td>204.7511</td>\n",
       "      <td>205.58</td>\n",
       "      <td>166224154</td>\n",
       "      <td>546768</td>\n",
       "      <td>206.878936</td>\n",
       "      <td>-0.014194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04 05:00:00+00:00</th>\n",
       "      <td>108.05</td>\n",
       "      <td>108.3000</td>\n",
       "      <td>108.0000</td>\n",
       "      <td>108.24</td>\n",
       "      <td>2182057</td>\n",
       "      <td>9796</td>\n",
       "      <td>108.192232</td>\n",
       "      <td>0.003244</td>\n",
       "      <td>206.10</td>\n",
       "      <td>209.9700</td>\n",
       "      <td>205.9300</td>\n",
       "      <td>209.66</td>\n",
       "      <td>192878747</td>\n",
       "      <td>556731</td>\n",
       "      <td>208.178631</td>\n",
       "      <td>0.019846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07 05:00:00+00:00</th>\n",
       "      <td>108.30</td>\n",
       "      <td>108.5800</td>\n",
       "      <td>108.2300</td>\n",
       "      <td>108.40</td>\n",
       "      <td>2143773</td>\n",
       "      <td>9104</td>\n",
       "      <td>108.460067</td>\n",
       "      <td>0.001478</td>\n",
       "      <td>209.20</td>\n",
       "      <td>209.7295</td>\n",
       "      <td>207.2000</td>\n",
       "      <td>208.27</td>\n",
       "      <td>102027111</td>\n",
       "      <td>374705</td>\n",
       "      <td>208.276128</td>\n",
       "      <td>-0.006630</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-11 04:00:00+00:00</th>\n",
       "      <td>101.67</td>\n",
       "      <td>101.9200</td>\n",
       "      <td>101.6029</td>\n",
       "      <td>101.70</td>\n",
       "      <td>5758310</td>\n",
       "      <td>17084</td>\n",
       "      <td>101.720884</td>\n",
       "      <td>0.004643</td>\n",
       "      <td>385.91</td>\n",
       "      <td>386.8700</td>\n",
       "      <td>383.5000</td>\n",
       "      <td>384.23</td>\n",
       "      <td>58100363</td>\n",
       "      <td>473423</td>\n",
       "      <td>384.871994</td>\n",
       "      <td>-0.011424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-12 04:00:00+00:00</th>\n",
       "      <td>102.02</td>\n",
       "      <td>102.1900</td>\n",
       "      <td>101.8200</td>\n",
       "      <td>101.84</td>\n",
       "      <td>6664991</td>\n",
       "      <td>21057</td>\n",
       "      <td>102.039440</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>383.66</td>\n",
       "      <td>386.1600</td>\n",
       "      <td>378.9900</td>\n",
       "      <td>380.83</td>\n",
       "      <td>62027220</td>\n",
       "      <td>512244</td>\n",
       "      <td>382.617620</td>\n",
       "      <td>-0.008849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-13 04:00:00+00:00</th>\n",
       "      <td>101.23</td>\n",
       "      <td>102.2400</td>\n",
       "      <td>101.1900</td>\n",
       "      <td>102.21</td>\n",
       "      <td>6514247</td>\n",
       "      <td>26716</td>\n",
       "      <td>101.977478</td>\n",
       "      <td>0.003633</td>\n",
       "      <td>375.09</td>\n",
       "      <td>381.9200</td>\n",
       "      <td>374.6580</td>\n",
       "      <td>378.83</td>\n",
       "      <td>83575452</td>\n",
       "      <td>671953</td>\n",
       "      <td>378.819688</td>\n",
       "      <td>-0.005252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-14 04:00:00+00:00</th>\n",
       "      <td>101.51</td>\n",
       "      <td>101.9800</td>\n",
       "      <td>101.3300</td>\n",
       "      <td>101.85</td>\n",
       "      <td>6224496</td>\n",
       "      <td>24722</td>\n",
       "      <td>101.747120</td>\n",
       "      <td>-0.003522</td>\n",
       "      <td>373.99</td>\n",
       "      <td>379.0498</td>\n",
       "      <td>371.0400</td>\n",
       "      <td>377.91</td>\n",
       "      <td>89349688</td>\n",
       "      <td>637416</td>\n",
       "      <td>375.589384</td>\n",
       "      <td>-0.002429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-15 04:00:00+00:00</th>\n",
       "      <td>101.94</td>\n",
       "      <td>102.3500</td>\n",
       "      <td>101.9000</td>\n",
       "      <td>102.25</td>\n",
       "      <td>8440151</td>\n",
       "      <td>27300</td>\n",
       "      <td>102.214854</td>\n",
       "      <td>0.003927</td>\n",
       "      <td>382.62</td>\n",
       "      <td>385.2500</td>\n",
       "      <td>380.5400</td>\n",
       "      <td>385.13</td>\n",
       "      <td>79318992</td>\n",
       "      <td>488124</td>\n",
       "      <td>383.334989</td>\n",
       "      <td>0.019105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1667 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              AGG                                       \\\n",
       "                             open      high       low   close   volume   \n",
       "timestamp                                                                \n",
       "2015-12-01 05:00:00+00:00  108.54  108.8400  108.4300  108.82  4259986   \n",
       "2015-12-02 05:00:00+00:00  108.73  108.7799  108.6100  108.67  2462787   \n",
       "2015-12-03 05:00:00+00:00  108.41  108.4100  107.8100  107.89  4634020   \n",
       "2015-12-04 05:00:00+00:00  108.05  108.3000  108.0000  108.24  2182057   \n",
       "2015-12-07 05:00:00+00:00  108.30  108.5800  108.2300  108.40  2143773   \n",
       "...                           ...       ...       ...     ...      ...   \n",
       "2022-07-11 04:00:00+00:00  101.67  101.9200  101.6029  101.70  5758310   \n",
       "2022-07-12 04:00:00+00:00  102.02  102.1900  101.8200  101.84  6664991   \n",
       "2022-07-13 04:00:00+00:00  101.23  102.2400  101.1900  102.21  6514247   \n",
       "2022-07-14 04:00:00+00:00  101.51  101.9800  101.3300  101.85  6224496   \n",
       "2022-07-15 04:00:00+00:00  101.94  102.3500  101.9000  102.25  8440151   \n",
       "\n",
       "                                                                   SPY  \\\n",
       "                          trade_count        vwap daily_return    open   \n",
       "timestamp                                                                \n",
       "2015-12-01 05:00:00+00:00       15562  108.754152          NaN  209.42   \n",
       "2015-12-02 05:00:00+00:00       11581  108.684692    -0.001378  210.60   \n",
       "2015-12-03 05:00:00+00:00       16801  108.040315    -0.007178  208.90   \n",
       "2015-12-04 05:00:00+00:00        9796  108.192232     0.003244  206.10   \n",
       "2015-12-07 05:00:00+00:00        9104  108.460067     0.001478  209.20   \n",
       "...                               ...         ...          ...     ...   \n",
       "2022-07-11 04:00:00+00:00       17084  101.720884     0.004643  385.91   \n",
       "2022-07-12 04:00:00+00:00       21057  102.039440     0.001377  383.66   \n",
       "2022-07-13 04:00:00+00:00       26716  101.977478     0.003633  375.09   \n",
       "2022-07-14 04:00:00+00:00       24722  101.747120    -0.003522  373.99   \n",
       "2022-07-15 04:00:00+00:00       27300  102.214854     0.003927  382.62   \n",
       "\n",
       "                                                                              \\\n",
       "                               high       low   close     volume trade_count   \n",
       "timestamp                                                                      \n",
       "2015-12-01 05:00:00+00:00  210.8200  209.1100  210.68   97858418      337780   \n",
       "2015-12-02 05:00:00+00:00  211.0000  208.2300  208.54  108069059      367013   \n",
       "2015-12-03 05:00:00+00:00  209.1500  204.7511  205.58  166224154      546768   \n",
       "2015-12-04 05:00:00+00:00  209.9700  205.9300  209.66  192878747      556731   \n",
       "2015-12-07 05:00:00+00:00  209.7295  207.2000  208.27  102027111      374705   \n",
       "...                             ...       ...     ...        ...         ...   \n",
       "2022-07-11 04:00:00+00:00  386.8700  383.5000  384.23   58100363      473423   \n",
       "2022-07-12 04:00:00+00:00  386.1600  378.9900  380.83   62027220      512244   \n",
       "2022-07-13 04:00:00+00:00  381.9200  374.6580  378.83   83575452      671953   \n",
       "2022-07-14 04:00:00+00:00  379.0498  371.0400  377.91   89349688      637416   \n",
       "2022-07-15 04:00:00+00:00  385.2500  380.5400  385.13   79318992      488124   \n",
       "\n",
       "                                                    \n",
       "                                 vwap daily_return  \n",
       "timestamp                                           \n",
       "2015-12-01 05:00:00+00:00  209.921410          NaN  \n",
       "2015-12-02 05:00:00+00:00  209.563055    -0.010158  \n",
       "2015-12-03 05:00:00+00:00  206.878936    -0.014194  \n",
       "2015-12-04 05:00:00+00:00  208.178631     0.019846  \n",
       "2015-12-07 05:00:00+00:00  208.276128    -0.006630  \n",
       "...                               ...          ...  \n",
       "2022-07-11 04:00:00+00:00  384.871994    -0.011424  \n",
       "2022-07-12 04:00:00+00:00  382.617620    -0.008849  \n",
       "2022-07-13 04:00:00+00:00  378.819688    -0.005252  \n",
       "2022-07-14 04:00:00+00:00  375.589384    -0.002429  \n",
       "2022-07-15 04:00:00+00:00  383.334989     0.019105  \n",
       "\n",
       "[1667 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the simulation input data\n",
    "mc_thirty.portfolio_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <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>0.999693</td>\n",
       "      <td>0.993835</td>\n",
       "      <td>1.005535</td>\n",
       "      <td>0.989969</td>\n",
       "      <td>1.006427</td>\n",
       "      <td>0.987402</td>\n",
       "      <td>0.993627</td>\n",
       "      <td>1.009744</td>\n",
       "      <td>0.996341</td>\n",
       "      <td>1.000719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993497</td>\n",
       "      <td>0.995256</td>\n",
       "      <td>0.992119</td>\n",
       "      <td>1.024029</td>\n",
       "      <td>1.011890</td>\n",
       "      <td>1.004102</td>\n",
       "      <td>1.002493</td>\n",
       "      <td>1.000902</td>\n",
       "      <td>0.995582</td>\n",
       "      <td>1.004435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000149</td>\n",
       "      <td>0.993807</td>\n",
       "      <td>1.019843</td>\n",
       "      <td>0.979387</td>\n",
       "      <td>1.013990</td>\n",
       "      <td>0.999382</td>\n",
       "      <td>1.002037</td>\n",
       "      <td>1.009583</td>\n",
       "      <td>1.005939</td>\n",
       "      <td>1.003073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987749</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.986099</td>\n",
       "      <td>1.025216</td>\n",
       "      <td>1.017563</td>\n",
       "      <td>1.011691</td>\n",
       "      <td>1.005085</td>\n",
       "      <td>1.000235</td>\n",
       "      <td>1.005823</td>\n",
       "      <td>1.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.003092</td>\n",
       "      <td>0.981102</td>\n",
       "      <td>1.023736</td>\n",
       "      <td>0.975336</td>\n",
       "      <td>1.016542</td>\n",
       "      <td>0.993766</td>\n",
       "      <td>1.004271</td>\n",
       "      <td>1.004913</td>\n",
       "      <td>0.994369</td>\n",
       "      <td>0.997301</td>\n",
       "      <td>...</td>\n",
       "      <td>0.983856</td>\n",
       "      <td>0.992676</td>\n",
       "      <td>0.992537</td>\n",
       "      <td>1.020372</td>\n",
       "      <td>1.029013</td>\n",
       "      <td>1.008209</td>\n",
       "      <td>1.004311</td>\n",
       "      <td>0.999433</td>\n",
       "      <td>1.007666</td>\n",
       "      <td>1.001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.011184</td>\n",
       "      <td>0.981997</td>\n",
       "      <td>1.025786</td>\n",
       "      <td>0.989685</td>\n",
       "      <td>1.015579</td>\n",
       "      <td>0.989033</td>\n",
       "      <td>1.008351</td>\n",
       "      <td>1.016452</td>\n",
       "      <td>1.007481</td>\n",
       "      <td>0.981735</td>\n",
       "      <td>...</td>\n",
       "      <td>0.978112</td>\n",
       "      <td>0.995839</td>\n",
       "      <td>0.992778</td>\n",
       "      <td>1.030678</td>\n",
       "      <td>1.039069</td>\n",
       "      <td>1.011237</td>\n",
       "      <td>0.995765</td>\n",
       "      <td>0.996406</td>\n",
       "      <td>0.996191</td>\n",
       "      <td>0.996658</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>7556</th>\n",
       "      <td>6.775425</td>\n",
       "      <td>4.893451</td>\n",
       "      <td>26.678752</td>\n",
       "      <td>10.416443</td>\n",
       "      <td>6.010549</td>\n",
       "      <td>4.646895</td>\n",
       "      <td>2.939955</td>\n",
       "      <td>2.495678</td>\n",
       "      <td>5.461164</td>\n",
       "      <td>4.958401</td>\n",
       "      <td>...</td>\n",
       "      <td>2.009195</td>\n",
       "      <td>9.130062</td>\n",
       "      <td>12.589545</td>\n",
       "      <td>4.813821</td>\n",
       "      <td>2.623424</td>\n",
       "      <td>3.957544</td>\n",
       "      <td>3.896339</td>\n",
       "      <td>2.015662</td>\n",
       "      <td>8.036235</td>\n",
       "      <td>2.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7557</th>\n",
       "      <td>6.822014</td>\n",
       "      <td>4.886519</td>\n",
       "      <td>26.828863</td>\n",
       "      <td>10.241070</td>\n",
       "      <td>6.034902</td>\n",
       "      <td>4.632015</td>\n",
       "      <td>2.933960</td>\n",
       "      <td>2.495413</td>\n",
       "      <td>5.487663</td>\n",
       "      <td>4.944534</td>\n",
       "      <td>...</td>\n",
       "      <td>2.020697</td>\n",
       "      <td>9.119370</td>\n",
       "      <td>12.563367</td>\n",
       "      <td>4.836115</td>\n",
       "      <td>2.606545</td>\n",
       "      <td>3.917829</td>\n",
       "      <td>3.889511</td>\n",
       "      <td>2.004779</td>\n",
       "      <td>8.010791</td>\n",
       "      <td>2.586626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7558</th>\n",
       "      <td>6.979892</td>\n",
       "      <td>4.859052</td>\n",
       "      <td>26.681388</td>\n",
       "      <td>10.243156</td>\n",
       "      <td>5.983398</td>\n",
       "      <td>4.573759</td>\n",
       "      <td>2.938177</td>\n",
       "      <td>2.492086</td>\n",
       "      <td>5.518510</td>\n",
       "      <td>4.948272</td>\n",
       "      <td>...</td>\n",
       "      <td>2.029784</td>\n",
       "      <td>9.181801</td>\n",
       "      <td>12.628082</td>\n",
       "      <td>4.863869</td>\n",
       "      <td>2.628938</td>\n",
       "      <td>3.932668</td>\n",
       "      <td>3.913551</td>\n",
       "      <td>2.005835</td>\n",
       "      <td>8.130988</td>\n",
       "      <td>2.611390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7559</th>\n",
       "      <td>6.974348</td>\n",
       "      <td>4.855276</td>\n",
       "      <td>26.353839</td>\n",
       "      <td>10.102334</td>\n",
       "      <td>6.019979</td>\n",
       "      <td>4.564650</td>\n",
       "      <td>2.950492</td>\n",
       "      <td>2.499609</td>\n",
       "      <td>5.549130</td>\n",
       "      <td>4.969764</td>\n",
       "      <td>...</td>\n",
       "      <td>2.034579</td>\n",
       "      <td>9.367029</td>\n",
       "      <td>12.701784</td>\n",
       "      <td>4.836762</td>\n",
       "      <td>2.602504</td>\n",
       "      <td>3.924864</td>\n",
       "      <td>3.916953</td>\n",
       "      <td>2.020867</td>\n",
       "      <td>8.081721</td>\n",
       "      <td>2.618323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7560</th>\n",
       "      <td>7.011556</td>\n",
       "      <td>4.846556</td>\n",
       "      <td>26.365791</td>\n",
       "      <td>9.992414</td>\n",
       "      <td>6.022370</td>\n",
       "      <td>4.594761</td>\n",
       "      <td>2.972508</td>\n",
       "      <td>2.509586</td>\n",
       "      <td>5.576633</td>\n",
       "      <td>4.964015</td>\n",
       "      <td>...</td>\n",
       "      <td>2.038465</td>\n",
       "      <td>9.391659</td>\n",
       "      <td>12.739945</td>\n",
       "      <td>4.792248</td>\n",
       "      <td>2.619332</td>\n",
       "      <td>3.887338</td>\n",
       "      <td>3.887492</td>\n",
       "      <td>2.007000</td>\n",
       "      <td>8.099885</td>\n",
       "      <td>2.611089</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",
       "0     1.000000  1.000000   1.000000   1.000000  1.000000  1.000000  1.000000   \n",
       "1     0.999693  0.993835   1.005535   0.989969  1.006427  0.987402  0.993627   \n",
       "2     1.000149  0.993807   1.019843   0.979387  1.013990  0.999382  1.002037   \n",
       "3     1.003092  0.981102   1.023736   0.975336  1.016542  0.993766  1.004271   \n",
       "4     1.011184  0.981997   1.025786   0.989685  1.015579  0.989033  1.008351   \n",
       "...        ...       ...        ...        ...       ...       ...       ...   \n",
       "7556  6.775425  4.893451  26.678752  10.416443  6.010549  4.646895  2.939955   \n",
       "7557  6.822014  4.886519  26.828863  10.241070  6.034902  4.632015  2.933960   \n",
       "7558  6.979892  4.859052  26.681388  10.243156  5.983398  4.573759  2.938177   \n",
       "7559  6.974348  4.855276  26.353839  10.102334  6.019979  4.564650  2.950492   \n",
       "7560  7.011556  4.846556  26.365791   9.992414  6.022370  4.594761  2.972508   \n",
       "\n",
       "           7         8         9    ...       490       491        492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000   1.000000   \n",
       "1     1.009744  0.996341  1.000719  ...  0.993497  0.995256   0.992119   \n",
       "2     1.009583  1.005939  1.003073  ...  0.987749  0.991882   0.986099   \n",
       "3     1.004913  0.994369  0.997301  ...  0.983856  0.992676   0.992537   \n",
       "4     1.016452  1.007481  0.981735  ...  0.978112  0.995839   0.992778   \n",
       "...        ...       ...       ...  ...       ...       ...        ...   \n",
       "7556  2.495678  5.461164  4.958401  ...  2.009195  9.130062  12.589545   \n",
       "7557  2.495413  5.487663  4.944534  ...  2.020697  9.119370  12.563367   \n",
       "7558  2.492086  5.518510  4.948272  ...  2.029784  9.181801  12.628082   \n",
       "7559  2.499609  5.549130  4.969764  ...  2.034579  9.367029  12.701784   \n",
       "7560  2.509586  5.576633  4.964015  ...  2.038465  9.391659  12.739945   \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     1.024029  1.011890  1.004102  1.002493  1.000902  0.995582  1.004435  \n",
       "2     1.025216  1.017563  1.011691  1.005085  1.000235  1.005823  1.006253  \n",
       "3     1.020372  1.029013  1.008209  1.004311  0.999433  1.007666  1.001604  \n",
       "4     1.030678  1.039069  1.011237  0.995765  0.996406  0.996191  0.996658  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "7556  4.813821  2.623424  3.957544  3.896339  2.015662  8.036235  2.591667  \n",
       "7557  4.836115  2.606545  3.917829  3.889511  2.004779  8.010791  2.586626  \n",
       "7558  4.863869  2.628938  3.932668  3.913551  2.005835  8.130988  2.611390  \n",
       "7559  4.836762  2.602504  3.924864  3.916953  2.020867  8.081721  2.618323  \n",
       "7560  4.792248  2.619332  3.887338  3.887492  2.007000  8.099885  2.611089  \n",
       "\n",
       "[7561 rows x 500 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast 30 years cumulative returns\n",
    "mc_thirty.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAEICAYAAACzjJuXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAADJIUlEQVR4nOyddXgVR9fAf3M97kKwBA9OgZYCLdRbCnVvqbt7v7q+71t3V0qNOhVaoKUUK17cPUgIcU+uzffHbK7k3hsCpej8nidPdsdX7u7ZM2fOEVJKNBqNRqPRaA5FTPt6ABqNRqPRaDT7Ci0IaTQajUajOWTRgpBGo9FoNJpDFi0IaTQajUajOWTRgpBGo9FoNJpDFi0IaTQajUajOWQ5YAQhIcTbQoiH/6W2Nwohjt/NukcJIVbt6THtLkLxkRCiVAgxZ1+PZ2cIIaQQosNu1r1YCDFxT4/p30YI0VkIsUAIUSmEuHUnZS8XQkwP2K8SQrT790d5cPBvPjd2cRwH5L26O/yT56lm7yKEeEwI8amx3cZ4vpj39bj2NjsVhIQQfwoh6owTVNX4pS+EOE4IsVIIUSOEmCyEaBuQJ4QQzwghio2/Z4UQoom+rjLaqhRCFAghxgkh4gCklNdLKZ/8Jwe7J2j84pZSTpNSdt6XY2rEYOAEoJWU8vBwBYQQLYQQHwgh8o1zvVII8bgQImbvDrX5CCGyjXNvaUiTUn4mpTzxX+hrlBDCadzvJUKI34QQXf5BW081Sr4X+FNKGSelfHVX2pNSxkop1+/GOB4TQriMYyoTQvwlhDiymXWDhLF/GyHEsoDnjafR8+eBXWlrTzw39sTx/4v3aishxGfG87VaCDFHCDF8T/fTRP/h7u892b4UQiwRQpgC0p4SQozaA203KbAZwmtVwF+NMZ6+Rn7gb6rhr12jNm4TQmwwrs0KIUSngLyLhBCbjLyxQojkCOMIbN8rhKgN2L/4n56HBqSUecbzxbOn2mwg4JlaafwtFUL8TwiRsKf72h2aqxG62ThBsYEvfSFEKvAd8DCQDMwDvgyody1wBtAL6AkMB64L14EQYgjwX+BCKWUckAt8tUtHowFoC2yUUlaHyzR+bDOBKOBI41yfACQC7ffWIA8AnpVSxgKtgB3AqF1toIkvq7bAst0f2m7zpXFMqcBk4Ou90Wmg8NocpJTdGp43wDSCnz//3d129xX/1jiN3/J0wAl0Q13Xl4DPhRDn/Av97avznQVcsLc7NYTX2IB78UZgPfB3QLEvA8sEfqQIIa4GrgJOBWJR778iI68b8A4wEsgAaoA3I4wjcAx5wIiAtM8C+tvffw/PGu+bNOAKYAAwY7/4AJdSNvkH/AlcHSHvWuCvgP0YoBboYuz/BVwbkH8VMCtCW3cDY5sYxyjgKWN7KLAF9WW9A8hHCVzDgNVACfBAuLqB9QP2NwLHG9uHowSFMqPd1wGbkTcVkEA1UAWcH6atXOOclaFedqc1GscbwDigEpgNtDfyBOohtgMoBxYD3SOciyzgR+M41wLXBJzfOsBjjO/xMHWfApYApghtZxvHaAl3DwCXAzOMsZahHgwDjfTNxvgvi3T/GOWmB+xLoIOxfSqwAKgw2nosoFyeUbbK+DsysC3gbeD5RsfyA3BnwDn7FigENgC3NudeCxhXVTOv71vAL8Y9ci3gQr2oqoCfgD+M61NnpHUCEoDRxtg2AQ81XJ+dnK+I9cIc02PApwH7XY220gLa+gB1z2817hOzcbyB91TZLlzXm4A1xvkeivrN3oX/N3vFrjx/8N+bVxn3w1Qj/WtgO+p3MxXo1sS1HA4sNK7fX0DPgLzWqA+7QqAY9duPdPw7u2YNv5ES41w2Pj9dgN+M/FXAeQF5w4DlqGfEVuDuCOfmSWBp42sO3GeMSfAPfhfGPfMN8CnqN3l1o3ZC7u+A5+ndqGdYOerj2NGcaxDmGKVxPGswnknG+RwVUGaA0U4ZsAgYaqQPRAkerY39XkaZLsAngBf1vqoC7m3GvTgZeDTSb6pRWRPqGXZchPz/Ap8H7Lc3zmPcTsawEf+7aijqN3Uf6v7/BEgCfjauZ6mx3Sqgfg4wxbi3fkPd4582+n01nOc/jXtshlF+IpAa0Nalxn1WjFKG+Ma2s2eqkRaHeg7cHHAO/jDaKwI+AxKNvHuAbxvVfw14OeA3t94Y5wbg4p1dz6C2mvkgKjQGNqPhJjPyXgHealR+KXC2sV0OHBGQ1w+ojNDPUcZN+TgwCLBHOpHGDeAGHgGswDXGGD83Tm431MOrXbiLQNOCUF/UD8ti3BgrgNsb/TA7hGvLGMta4AHABhxrXJjOAeMoQQlbFuNCjzHyTgLmozQzAvUAbhHhXE1BfT04gN7GsR8XcENMD1fPyJ9FGAEpID+bnQtCbpREb0Y9lPJQAp4dONE45tjGdcONj+AX+1CgB+oh0hMoAM5oYly+toCjUQ8eYewnoe6nLKO9+aj7xQa0Q/1oTtrZjxb1Jfc5SjPRnOtbjrp/Tcb18bUV7nwa+6NRL6c44zhXA1c143xFrBfmmB7D/8CzAU+jftMND72xqC/UGCAdmANcF+meauZ1/Q2lKY7C/5t9wjiPw1BfwUnNeP40FoRGG+OMMtKvNM6BHXgZWBjhWh6GEsKOQN27l6F++3ZjfxFKeIkxrt3gJo5/Z9fMDdyC+p1HEXyvxqDu1SuM/MOMa9HNyM8Hjgq4jw/bld8y6mUngc78g98F6p5xoT4yTQ3nO9JvpdHzdI7RRzLqGXr9zq5BhGOUQEdjnA33gU8QAlqiXpzDjDGeYOw3CPj/Qb1co1CC2c3hnvs7+0NpcT1ATqPfVDnqmb4MuCEgr40x9tuM878B9W5rEJZ/AO5r1EcV0Hcn4/CNGf9v6hnUPRwFpABnA9Goe/NrAhQMqI/8F43yR6OeX00JQutQH2tRxv7TRl5XY7yDjXvneeNeabYgFPA7+tLY7mBcPztKazQVv6DTAvVxmWjsW4z7qC/q91SB/zncgoCPoeb8NWdq7D7UD6Ql8C7wkxCiYQolFnUjBFJuXIBw+eVAbDg7ISnlNOAs1A9lHFAshHixiekFF/AfKaULGINSC78ipayUUi5D3Zg9m3F8jccxX0o5S0rpllJuRL0chjSz+gDUMT8tpXRKKf9ASeQXBpT5Tko5R0rpRglCvQOOJw71tSKklCuklPmNOxBCtEbdfPdJKeuklAuB91Eq1uaQgnrQ/hM2SCk/kmou+UvUl/QTUsp6KeVE1JfNLhtASyn/lFIukVJ6pZSLgS9o/rmfhvoRH2XsnwPMlFJuA/qjHoxPGNdlPfAeTavb7xZClKEEn1jUi6w51/cHKeUM4xjqdjZo4/4+H7jfuHc3Ai+wk+u5m/XOM46pFvXxcI6U0i2EyABOQQn81VLKHSiB4J9OR/xPSlkipaw19l2o+8QlpfwF9SDdHfu6x4xx1gJIKT80zkE96uXUK4LtwTXAO1LK2VJKj5TyY6AedV0PR7247zHarpNShrULaua53yalfM14jtQ2amI4avr6IyP/b5RWpmE6ywV0FULESylLjfxwpBL+t5wfkP9PfxczpZRjjfu58XE0xatSym1SyhKUJrS3kd7UNYiERGkcHhFC2BvlXQL8IqX8xRjjbygTjWFG/mMo7d0cYBvqg213uBSYJqXcEJD2FeqDNc04rkeEEA3PglbG/xNRH3fHoJ4TVxnpO3t3NhcvSktVL6WslVIWSym/lVLWSCkrUYLgEFDG0Khr/rBRfirq2jTFR1LK1ca1/wr/dTwHpQGcLqV0ooRpuYtjB3VNkgGklGullL8ZYytECWxDjLx8lGB0rlHvZKBISjk/4Dx0F0JESSnzDRmg2exUEDJu2EpjcB+jtEINN1kVEN+oSjxKygyXH4+aYgh7wqSUv0opR6BOzOmol8/VEYZWLP1GXQ0/0IKA/FrUzbZLCCE6CSF+FkJsF0JUoFSYqc2sngVsllJ6A9I2oYTIBrYHbNc0jNF4qb6O+qEWCCHeFUI0PrcNfZQYN3mkPpqiGCUx/xMan2eklHvi3B8hlMF9oRCiHLieZp57454ag18ouQglaIL6mssyjITLDGHgAdTcfCSel1ImSikzpZSnSSnX0bzru7k54w0gFfVFtamJNvdUva+klImo416K+poCdX6sQH7A+XkHpRn6JzQ+F8XGB0ADvvt/d9sVQpiFEE8LIdYZv9eNRla4+6YtcFej+6A16rq2BjY1Gl8kmnPum7oP2gJHNBrHxUCmkX826hm7SQgxpQmj9iLC/5Yb0or2wO9iV+/nBsI+52j6GkTEEJzzUNNxgbQFzm3U3mCMcyDVh/IooDvwQqR3TzO4FPi40ZiWG8KeR0r5F2qGpEGYbXgnPSulLJP+j+rmvjubS2HgB5cQIloI8Y5hhF2BEh4SDeE9CyiVwfajm2iaSNcxi4B7Q0pZg3q37CotURo1hBDpQogxQoitxtg/Jfh3/DFK8MX4/4nRdzXqw+R61DNsnNjFxS27s3xeoqZuQGldejVkGEZP7fEbggblG9s7ldQMyX4SSqXZfTfG2JhqlKqwgcxIBVE2HiuBjlLKeNSDIeJKt0ZsA1oHrnBAqUi3NqeylPJVKWVf1NReJ9S8aLg+koWxmm5X+wB+B85sNMZAGn4kzT1fO2NXzv3nKNun1lLKBJR9Q8O5b84D7AvgHKFWLh6B+soGQzVtCDYNf3FSymERWwpPc65v43HubNxFKA1A2yba3JP1kFIWoRYtPCaEaIE6P/Wo+f+G8xMvpezWxDE057ru7ktnZwS2exHqo+l41Jd/tpEe7je7GaVFDrwPoqWUXxh5bSIYnDY+juac+6aOfTMwpdE4YqWUNwBIKedKKU9HCaJjibxo5Hfg7DC/5fOMPlYb+//kd7Gza7ir17ipa7AzHgIeJPi+2wx80qi9GCnl0wBCiJbAo8BHwAuNNErNGrsQYhDqxf/NTooGvhtXoTTjkfpo/O5sh5oSWh2hfFN9BnIXSst6hPH+OrqhC5SmMKmRcXKbXeyvgXz8Wi+EEA3Tcs1GCBGL+t1OM5L+hzqensbYLyH4dzwW6CmE6I7SqvoMxaWUE6SUJ6AE4JUozWazaVIQEkIkCiFOEkI4hBAWY6ne0cAEo8j3KHXU2UIIB0o9tlhKudLIHw3cKYRoKYTIQl2kURH6Ol0IcYEQIkkoDkepxWbtygFFYCEwTAiRLITIBG5vomwcar6xypAqb2iUX4CaKgzHbNQL4l4hhFUIMRQYgfoiaxIhRH9DI2I12mgw0AxCSrkZZRj4P+O69ESpWz9rXDYCL6K+PD42HowY1+dFIURPQyW5FbjE+Nq+kn+2mmwhcJbxpdIBv2o4HHEobVedcf0vCsgrRKk/I/rQkVIuMMq9D0yQUpYZWXOACiHEfUKIKOO4ugsh+u/isezO9W3qfsHQan4F/EcIEWdckztRX0MR2d16AfVXon7H9xpq54moF0W8EMIkhGgv1ErOhmNoJYSwBTSxkOZf13+TOJQQV4x6Qf63ibLvAdcbvzMhhIgRQpxqfFTMQT3cnzbSHcYLEBod/z8996jp1E5CiJHGfWQ1fv+5QgibUMu2EwxtRgVhngMGL6F+yx8IITKNMV+IEhbuadB+/Mu/iybv7zA0dQ2aREr5J2qhx2UByZ8CI4z3lNk4B0OFcisgUO+bD1D3Zz7K+HdXx34ZylA3SFtjvLMC31e3omx/GjQkX6KeFXFCiFao6bOfjeqfGeM+yhBMnkCZTeyqRqgxcShtVJlQqwofbciQUm5CTRs+btxng1HPr93hG9T4Bxq/i8dppsJACGEXygXBWJRB90cBY68yxt6SRooAQ/P1DeqDeY6UMs9oL0MIcZpxHuuNNnbJBcDONEJWlGFag7H0LSjj1VXGwApRatz/GAd0BMHzy++g5iCXoFTx44y0cJSibpQ1qB//p8BzMmB54D/gE5Qh5EbUA//LJsrejXoBV6J+tI3LPoYSIsqEEOcFZhhzpaeh7C2KUAbNlwYIhk0Rb/RXit8S//kIZS9EffluQwmjj0o1N75TpJqzH4j6op0thKgEJqHmp9caxa5B3YTFKO3UX81pOwIvob6MClCqzaau543AE8aYHiHgS9h4sPwHtdyyTAgRyabgC9RXxucBdT2oH3xvlNFiEeqlEM6OJCK7eX0/QNl7lAkhxkYocwtKwFqPWg79OfBhM4a0u/UaeA64VgiRjlL921CrlUpRD5yGKZY/UF+w24UQRUbarlzXf5PRqN/LVtTYI344SSnnoe7t11HHuBY1/R54j3RATcFsQanbIfzx7/a5N152J6KeldtQ0w8NBq+gbI02CjU9cD3+6YDG7RSjpoEcxrEXowSykVLKxs+tf+t30Zz7O3DMEa9BM3kIw6bEaG8zSiP4AOo9tRn17DKhBJMMlE2MRBmnXyGEaLCX+h/wkDH2u8N1JtQH/nk0mhYzuMAYfyXqPnxGKvORBm5GvZS3oYyUffeIVDYs16N+NztQQsCNu3AeIvEyyrC5CPVbGN8o/yLUe7oEJSSN3p1OjPHfgvoIzEedgx0oQSQS9xrP9hKj3/nAwICpusdRNsLlKFnhuzBtfIyyufokIM2EUrJsM9oegnEuDUGzamfH07CSQKPRaA46hBCjgbVSyif29Vg0moMVoaa5ylAmJRt2Uvyf9NMGNfWVKaWs2FPtHjAhNjQajWZXEMrepzNK26HRaPYgQogRxtR4DGr2Ygn+xQr/Rn8mlMZzzJ4UgkALQhqN5uBlO+or9dudlNNoNLvO6ajpqG0oX08X/INVeU1iCFsVKD9Dj+6k+K63r6fGNBqNRqPRHKpojZBGo9FoNJpDlv09SJtmD5Gamiqzs7P39TA0Go3mgGL+/PlFUsq0fT0Ozb+HFoQOEbKzs5k3b96+HoZGo9EcUAghduZ9WXOAo6fGNBqNRqPRHLJoQUij0Wg0Gs0hixaENBqNRqPRHLJoQUij0Wg0Gs0hixaENBqNRqPRHLJoQUij0Wg0Gs0hixaENBqNRqPRHLJoQUij0Wg0ByX1a9dSPXvOvh6GZj9HO1TUaDQazUHJ+uEjAMhduWIfj0SzP6M1QhqNRqPRaA5ZtCCk0Wg0moOOutWrfdu1S5ftw5Fo9ne0IKTRaDSag47qGX/5tmV93T4ciWZ/RwtCGo1Gozno2PHMM75tYbPvw5Fo9ne0IKTRaDSagxuT2Ncj0OzHaEFIo9FoNAcdMYMH+7atWVn7cCSa/R0tCGk0Go3moMPevh2mmBi6LF2CJSlpXw9Hsx+j/QhpNBqN5qDAXVJC7aJFVI4fT/Ws2QiHA2HRrzlN0+g7RKPRaDT/CtWz51D8zjuk3XYrnqoqYgcN+lf7WzMwuH1LVot/tT/NwYEWhDQajUbzr7D1zjvxFBdT/Zdayr63PTyb9GoxTTPQNkL7MUIIhxBijhBikRBimRDicSP9MSHEViHEQuNv2L4eq0aj0YQgZdBu+Q8//Cvd1K9dS93y5SHpwq4FIc3O0Rqh/Zt64FgpZZUQwgpMF0L8auS9JKV8fh+OTaPRaJqmkSBU9u13xJ14IqaoqD3XhdvN+uEjMEVHh+Q5N2zYY/1oDl60Rmg/RiqqjF2r8SebqKLRaDT7D40EoZo5c9hy8y17tAtvnfIa7a2pCe3e6dyjfWkOTrQgtJ8jhDALIRYCO4DfpJSzjaybhRCLhRAfCiH02lCNRrPfIWXod1v1jBl7tI9wApBGsytoQWg/R0rpkVL2BloBhwshugNvAe2B3kA+8EK4ukKIa4UQ84QQ8woLC/fSiDUajUbhLS+PmCc9nrCC0q4i63QcMc0/QwtCBwhSyjLgT+BkKWWBISB5gfeAwyPUeVdK2U9K2S8tLW3vDVaj0Wh2wspu3dn+xBP/uB1vbW1IWuxxxwGQcOaZ/7h9zcGPFoT2Y4QQaUKIRGM7CjgeWCmECHSOcSawdB8MT6PRaCLiqaqKmCfdbgDKvhjT7PaklBS9+x7OvDxArRSrmjED2UgQyv76KzBCi8UeM3SXxqw5NNGrxvZvWgAfCyHMKKH1Kynlz0KIT4QQvVGG0xuB6/bdEDUajSaYupUrKXz5lYj5u2PX4y4spPDFFyl88UVyV65g/fARAFjbtolcSS8t0TQDLQjtx0gpFwN9wqSP3AfD0Wg0mmax4Yymp6Rq5s/f5TZdW7f6tlf29j8WXZvygguaTAhhqIT2gA2S5uBHT41pNBqNZo/h3LgxbLqw2Xzb5sRE33btokXNEoxkfb1/uwkDaUfXrsQMHAiALSd7p+1qNFojpNFoNJo9RvXcuUH78cNOwdG9B/GnnsqWG29UHqC9XgDMCQlsPP8CYOfhN7Y/9njYdHNCAh5jdZqlRQuEECRecAGxxx6HNSP9nx6O5hBAa4Q0Go1G849xbtzIhrPPQdYHOzFMue56Uq68QgklJhNIibdWaXQ8TSyvD9d+OGKOPhpTfDw5Y7+n3U8/AiCE0EKQptlojZBGo9Fo/jHrzzgTWVdH3bJlQemB01N1S5YAsPnqq0Pqe6urMcXE7HK/npISLMnJOLp02eW6Gg1ojZBGo9Fo9gCR7HZMAbZBTVG/du1u9VuzYEHYOGMaTXPRgpBGo9Fo9gqJ558fMU96vOHTpfTFEwubX1MDFj25odl9tCCk0Wg0mn+Fxpoae+dOEcs6N20Km77u5JNZZSyXt+XkkHTRhSFl6hYv/gej1BzqaEFIo9FoNP8IV35+SFrn+fPoOGN6UFrSOedEbCP//vuD9muXLGHHyy8H+QmK7tePjEblNJp/itYnajQajeYfEc6LdDjDZ9FMeyGAjeeeF5JWNWM6wmol58cfQEo2nH7GLo1TowmHFoQ0Go1G848o/+GH3a5riovDW1nZrLJJ5ynhyNGpU3DkerN5t/vXaPTUmEaj0Wj2CdlfjqH9r7/49t0lJQDBQk4Apvh437YvjAYgtCCk+QdoQUij0Wg0e4QW//0vAAmnn7bTsqaEBKJ69cKSmupLWzNwEHlXX0P19Bnh69jtwf395z9qQ8cU0/wD9NSYRqPRaPYICWeegbe2hoSmbHdMJvB6sWZk+JNiY/FWVQFQPX069vbtw1YVdkdwf2edSdW0aSRdEHlZvkazM7RGSKPRaDS7Tf26dQBE9e2LEILkiy/GHBvZQ3T7X8YB0PaT0b60+FNOCSojoqPC1hU2a/C+ELR6+SViBgzYrbFrNKAFIY1Go9H8A0pGfwJAbTMiyAPYsrPJXbkCc0KCLy1++PCgMsVvvR22blNR5zWa3UULQhqNRqPZbfamcOKtqd1rfWkOHbQgpNFoNJrdJpJH6F0hun8/Um+8MWJ+xiMPA2Bt3eof96XRNEYLQhqNRqPZbRxdcwEQ/yDwqTCZSLv1loj5SRdeSM533xI7aNBu96HRREKvGtuPEUI4gKmAHXWtvpFSPiqESAa+BLKBjcB5UsrSfTVOjUZz6GJJV6u/Os+aucfbzvn+OxACIQSOrl33ePsaDWiN0P5OPXCslLIX0Bs4WQgxAPg/YJKUsiMwydjXaDSavY63thYsll0Kn9EcogcMwJGbi6NLlz3arkbTGC0I7cdIRZWxazX+JHA68LGR/jFwxt4fnUaj0YCsq8XkcOy8YDOIOuww33bgqjKN5t9EC0L7OUIIsxBiIbAD+E1KORvIkFLmAxj/0yPUvVYIMU8IMa+wsHCvjVmj0Rw6eGvrEFF7RhBq/e675Hz3LdEDBpB+x+17pE2NZmdoG6H9HCmlB+gthEgEvhdCdN+Fuu8C7wL069dP+6DXaDR7lBVdlKG0tXXrPdKeOTYGc9eutB310R5pT6NpDlojdIAgpSwD/gROBgqEEC0AjP879t3INBrNoY6w71n7II1mb6IFof0YIUSaoQlCCBEFHA+sBH4ELjOKXQb8sE8GqNFoNIBz7bp9PQSNZrfRU2P7Ny2Aj4UQZpTQ+pWU8mchxEzgKyHEVUAecO6+HKRGozn0cGu7Q81BghaE9mOklIuBPmHSi4Hj9v6INBqNRlH61Vf7eghs3ryZkpISevXqta+HojmA0YKQRqPRaHaZotde36f9ezwePvjgAwAtCGn+EdpGSKPRaDQHHC+88MK+HoLmIEELQhqNRqPZbdLvuZsOk37f6/3W1NT4tqs2L4UPT4bNc/f6ODQHPloQ0mg0Gs0uIT0eLOnpxJ82gpSrrsLasuXe7V8Gu0V7/YNPKclbDh8cD6t+9aUXf7aCrY/v+RhomoMLLQhpNBqNZpfYcuNNuHfswOSI2mt9lpWVMXnyZLxeLx6PJyivDge/M1jtfHGBL33NrBmMWf4fygsL9to4NQceWhDSaDQaTbOQUrL1zjupmjIFANMeCq3RHN5/9RWmTJlCQUEBLpcrJD+/UaShmTNnstal/But/GbSXhmj5sBErxrTaDQaTbNwbd1KxS/+qSev07l3OnbXU+X2gMnE6tWriYuLCylSSiISEMD69euZMGECZNqJK4V2Rx2xd8apOSDRGiGNRqPR7BTpclEze05QWtkXY/61/lz1dcoWqL4KnkoHk3pdTZ48mR9//BGA7i1jg+qMZygAo0eP9qXdlTuNmFT9qtNERt8dGo1Go9kpO156mfwHHwxKs3fq9K/0VVlcxKuXnsOLF4yAdX9ELJdT+FvQ/jLRNWw567J/T2DTHPhoQUij0Wg0O6VmbujS9OwxX/wrfS2bEmDTkxg5sn2NM9houp1cD0CHRP+rzYUZ0fGEPTtAzUGFFoQ0Go1Gs1PqliwJ2m/x1JOYoqP3aB/S62XHxvXM+PITf+KCzwDIZEdI+ZZsJ4c8336N1w7A2jKvL20mhyHkXrJl0hyQaGNpjUaj0ewy9s6d93ibC8b/xOSP3/Ptx1vrYK7ad4V5XbVjM+3YjBszT3Era03tWE12UJk/GEys3cZhe3y0moMFLQhpNBqNJgTXjh1Ip5PSTz4h9ZZbg/K6rFiOEGKP91m0RWl3Yi31nJy1mrYxZf7xNPG6suCfIvucM0PyzdK65wapOejQgpBGo9FoQlh79BDfdsnHo4Py/g0h6O9ffmDJpAkAnJK1ijYx5UH5Tmy73fbC/IX0aqEDs2rCo22ENBqNRtNski66aI+36aytCZoSaywEuTBTR7Dzxgv4AYAa986/53uKnntglJqDFS0IaTQajSYI6XZHzMt48IE93p+rvr7J/JXedr7tDmzgBkbTBbVCbMwmpelJqNoWUq8zawGwWPTkhyYy+u7QaDQaTRAygsfoDn9ORpjNe7w/r9dv42MRnpD8b03DARjEXE5gOgC//N0Fu8NFqYhmXunrpNYupjzAv6LdVcV51p+Z1f8ZunfvvsfHrDl40BohjUaj0QQRThBq/9tErJmZYcv//csPbFu9crf78wZooBJtdUF5dQG2QRUoSae+wkzO6gqyFtcCkO5oQ7eEgUH1breOxoxkUGL9v2LTpDl40ILQfowQorUQYrIQYoUQYpkQ4jYj/TEhxFYhxELjb9i+HqtGozl4aBxDrMuSxdhaR3ZsOPnj9/ji4buprawIm//Ty88EO0lsRGVJsW+7VXSwfVAN/gj3I/gdgPW/ZPjSTFJgEibizfFB9aIwptv6XhaxX40GtCC0v+MG7pJS5gIDgJuE8PmQf0lK2dv4+2XfDVGj0RxsSGdwdHdhjbz8vKLI7+iwrCA/bJnVM6cx/s2XIrbx5aP3+bZLnVFBeTWGkXRy6QZsuCk2ZQXln9HqBgAc2DgyI8zKMEdCxH41GtCC0H6NlDJfSvm3sV0JrABa7ttRaTSagxkpJeuOP963L3biPdrj8gtNZdtDBaGqAG1PJNpEl5JgVdNciSmG4HLdVEranUetIQjVldchz/+C6Au/C6pr9iov0sJh5rhLhnEFX3EPb++0T42mAS0IHSAIIbKBPsBsI+lmIcRiIcSHQoikCHWuFULME0LMKyws3FtD1Wg0BzCytjZo32S3N1l+9awZvm2r3RGS/+1/H9lpn+e2XcqV7edhwsvQDoaNkC2Wsq5XMrFK+YQWHjfrbnqBjSPOCh5vXZnx34Mlzk7q0SOJIfgYNJqm0ILQAYAQIhb4FrhdSlkBvAW0B3oD+cAL4epJKd+VUvaTUvZLS0vbW8PVaDQHMN66YGPljAeCl8u7nU5+e+91Kgp3UFdVxfQxfmeL08eMpnDTBl44fzizx37N9nVrKNq8yZfvCTCKll4vo++7lS8fuBkAk4A7cmdgyZ+PlFA+ZR4TZ8yhMDYbgLOuvRXXttAl8jV//gdvbalvf3DeZ7t/8JpDEi0I7ecIIawoIegzKeV3AFLKAimlR0rpBd4DDt+XY9RoNAcP3poa33b2t9+QMGJ4UP6GRfNZ/Pt43rvlKt646oKgvOIteSw2vENP/+JjPnvgjqD8ly8+g8JNGwCY8tlHFG5cT+vyKSFjqC2yse2Bx+j4ww++NOc11weViT1+hG+7eoLfxsgdtEJMrxbT7BwtCO3HCLXm8wNghZTyxYD0FgHFzgSW7u2xaTSa/YvqOXPwlJUh3W6klLvdjivPH83dHBMTki89hp+fCH1ktu/YZPuj770FKSXzf/4egIFpeSFlvC4lwGRtyye6uhprmOX81uy+wQkmwfiN4wE4P8tY5n/z3CbHotGAdqi4vzMIGAksEUIsNNIeAC4UQvQGJLARuG5fDE6j0ewfeJ1O8i69DHtuLvUrVpB6y82k3XTTbrXlylcGzxn3/x+27OzQvjyhDg8DKdseOn3VmPrq6qbzhd93UEx1Ncf+MTl0nNtTgvaTLmjPgoJRACy32+Cx8pA6Gk04tEZoP0ZKOV1KKaSUPQOXykspR0opexjpp0kpw69Z1Wg0hwTu7dsBqF+xAoCi117f5TZKPv2M+nXrfDZC8cOHhy1XHzB1Fo5Z330Zknb2g08G7W9evrjJNio4zLeds35DSH7C6achLHaij/4/X1r556+ysmT3nTpqDl20Rkij0WgOUKTXy6aLLyGqZ49/1o7HQ8FTTwWliTArwEAFSG1M25592LR4QVCaIy6eOsPBos3h4Kw77+KXl5+mzmvlxxf+2+R46qas8W27GvkwynruWeJPPpWtD8/AnOyPQVY+dix/56pX2s9n/txk+xpNIFojpNFoNAco3upqahcsoOTj0SF51bPnNL+d2rqQNJPdFqZkeI3QOQ8+yaXPBWuhkjL8poy129eR8+sZ3NR5VlCZAWcHG1uHIz052bed8fBDxJ96Kl5n09NzbePb7rRdjaYBLQhpNBrNAYpsImp73mWXNdto2lsTarMTyZv0vJ++Ddq//p1PAEhrk01Mot+l2Zn3P+bbbpUa3JbJCNw6sHWoHY+r9+1B+ylrlHYoZsjRJF98McJkouDF+aqd2OB2o+p230hcc+iiBSGNRqM5QGns8wfAEhAYVbpcIflh26lq2ng5kEBfQABWh38KrbrM788nKjaOOz7/gTvH/IQ9Ktg7dYPBtZgQ7KMIoNYxILi/khIAWjzxhL9+tTouc6yVFv/1T7M9/4GHt19zh/U3pNFEQgtCGo1Gc4AiwwhCKVde2WR+OLzVVQCk3nhDk+Xqa6rJ7nUYLTp09qVZrKFTaCmt2gBK8yOEAJtfELordxrR5tDl8A1UbdoMwNz+/YLSzfHxIWXjjm1D4lln+vbTKiC5Coo/+LDJ49BoAtGCkEaj0RygNNZ85K5cQdLFF/n265Yvb1Y7NXOUPZE5MWy0HgC2rFjK61ecz8ZFfwdpgYTJ/xqJilPCSvGWRr6BnMF2RTd0ms1dudOC0iSwjjbkr1un2kgJXh4vHKHG297ytXjr64kfNiwovfQz7V1a03y0IKTRaDT7EOeWLazokkvlpEm7XLdu1aqQNGHY3wDkXX7FTtuQHg87nnseAHvHDhHLbV62xLftdjq58f3PueCJ55TGx+D4q28MX7m2pOlBJLRhOR35hLNZt2E9AJVxceSM/d5XpKGfBkNpU1wtm6+5ku2PPEL6c0833b5G0wRaENJoNJp9gHS5qJw8mZp58wDYctPNVEycuEttONeuDZue+aTfnqZq2rSwZRooGfWxb9vaqlXEcoGaH4/bRVRcPC075waVyTmsPwCdjhgUXPmXe5scAyntWYESwnosUY7yz77gAhxdumDLyQkq6q1RNkqOzmq6rXbJUhYWLmy6fY2mCbQfIY1Go9nL1K9fT9m331LSyJZl6623Eb9yRbPbKf/hRwDihw0jZvBgX7o9QHiomTuP2KOOithG9Sz/knZLRkbEctLr9W27w4S8ALDa7Nz1ZRgfPh2Og4WfQUZ3KAgTEeiMN1n34itBSd26dQMgZ+z3SJffQNtbowylZVUxAM7165m2dRrbjzdxxe9eNJpdRQtCGo1Gs5dZP+zUPdKOrW1bnJs20fLFF4LSo3r29G1XjB9P+p13NK7qozpAY2Sy2eg48y9EGAPoxX9M8G2H2ADtjIWGzc6V4+F/jbROp74A8VnUEqXaTknGFBvnH5PdDna7b99dqOyNdjzt9yr90dKP6Jfgb9IUHbxKTaNpCj01ptFoNHuRpnz7NDU1FbYtj4f4ESNCMwJ8AAUGUW2KBuHBkpSEOTY02GpVcZFvOz4tvfmDrC72b9tiWVWRyo46o/3kdtDtLLwB2iaTx0tNhGX/0isp+SLULgopsQas6m+u2wCNBrQgpNFoNHsNd3Exq/r2i5jv2rIlYl75uHEUvfteUJqnpARLcuhKLyEErV5/rVljij32WADaTxjfrPK9TjyV0+56sFllKd0Iz/nDYLhcTn7emssnG4xYYrcugOhkfvzxR18Zk9eL1xz8asr/32yKP1uBa2tV2G5Oni8pjvcbbUuXi6qpU5s3Rs0hjxaENBqNZi9R9MabyJ0ELS18803cpcoxYc28eZQbQsK2u+6m8MUX8dbU4MzLo+idd/HW1CAcUWHbiTrMH7i0KS1U1R9/AGBJS2tyXLaoaPqcMoLjr7qBjJz2TZb1URasjSpetZLcrUWYvMHjWbhwIQDp6elY3G6SM1tQ8edmpEutEPOUO6ldUsSONxYi3fV4qwuD6l/5m5fVrQRxY973pRW+9roKIhvBnkmjaUDbCGk0Gs1eoPLPPyn9/POweTEDj6T6r5kAFL36Gs61a2n54otsumQkAAmnneYru+qwvkF1PaWlhMMUYFdT/PbbpN7QtLPESHjcbl6++AwAbBGEroh8HDxtV3ruBeQAVQ4rnhsWYW5UvG1mJjH19cQmtqFi/EbMMVai+wYbcFf9fEvYrtJLJfFde5A0ahR5l19O/dq1rD91OAAZf/xOclbLXRu75pBBa4Q0Go1mL7Dl+mBBJPurL8m4Xxn8RvXuQ8ZDD/nypMtF/uOPN6tdjxHhvTEiwGC4YuJvTbbhMFZoNaa+pgZXvd87tTlC/LGweBsHRvVPXfXYUsTqIaf49i0W9U3e7oEHwePBmadiqLkKatj6wHRfOekOjq1WEuvfvnKil7rRX5J3+eWqbG2tL6+6bCd+jDSHNFojpNFoNP8yjY132/82EVvr1ji6d8ecmEj8sGFUTfe/8Ct/nwQB01nSEznaetb//hc2XQiBKSYGb3U17h07wo9LSjCbiRk0KCSvpqKct665mMRMfxT5cOE0QuqVl+H1eIid87wvrarnNWyIG4plzD0h/Ttra4laMgtHyz6+dPf2RdhzT6Nq+tbg8vXBQt8d15j5+CV1bg5bLyl+/sWwYzI1M9SI5tBEa4Q0Go3mX6Zs7NigfVvr1oByUphw+ukIqxVzbIB6o5FNT8moUWHbtXfsiClM6IkGkkZeAoCnuDhsftXkP8HjwV0Sml9TXqbGvj3fl2ax7VwQeuvaS3jnhsvY+scnbK2JY1VFKj8vMjHx/bdDypaMGsXrV5xHdL2TnlPH+Y+ry2khZQGkK1igefqUV5k+IC5s2UCqnwsvIGk0oAWh/RohRGshxGQhxAohxDIhxG1GerIQ4jchxBrjf+QAQRqNZp9TuZOpKQBH9+4R8xpCYDQm9eabm2wz7UZ/yItwWiXnxo0ARPXoGZL3y2uhfZrMja16IjNmU2/GbOrNz1tz2boyfMyzHS+8CFIydOVmUqv8U1mm+PD2PNKtDM1dFwzjupvNRFujOfftnXvjznn55WaPW3PooQWh/Rs3cJeUMhcYANwkhOgK/B8wSUrZEZhk7Gs0mv2UBqeFppgYuixdEraMKap5hsjJl11G5mOP0WnuHOJOPKHJsiJAg+OpCLUlEg5lUB17zNCgdCklhZs2hJTfsXF9s8YYjiRD0NnRwr86zdy5ExkVoavobO3bhG/EpdpY2juJ0jiBw+wg0ZEYsc9pw4/np8O7I5P0t6ImMloQ2o+RUuZLKf82tiuBFUBL4HSgIUDQx8AZ+2SAGo1mp9Qu8Qs+2WO+4K/vxrBq5vSwZbO/HNNkWxn3/x/pd91J0gXnY46LCwp4Gon0e5RdToNWylNVzYouuazokkvBE08CYElODqozZ+zXYdsaMvKqoP3yn8ex8rC+eA2XAN7q6ojjOHLdNgCia+qY1145jty8bQvdtwTbL3kEfDbzUcxZfsEw/RZlPyQNQejDDeo82c1KkDMnJNCY9hMnUFpUiDRbmGfEc9NowqEFoQMEIUQ20AeYDWRIKfNBCUtAWDevQohrhRDzhBDzCgsLwxXRaDT/MhXj/Y4K7R07MuvbMfz8cvho6VG9emHv3BmAuBNOIOWG68n57ltffvJllwVpeZqDvYtqb/ujj+IuLWV1v1CHjsISvG5myeTQ6aYb3/88ZPn8trvvRtbUUP3XX+x44QVW9e1H1y2FPhsnm9vDsEXr6JlX4KsTW15JfkoCJdEOskorsLuVV+nyKBsFycms732MKpjtP05by1isLR3U/f0RADWGWVS8PR4AT3l5yHjnbNyIpbYKk6ueww8/PPIJ0hzy6FVjBwBCiFjgW+B2KWVFc74CAaSU7wLvAvTr1y+yRzWNRvOvYe/Q0bedt3TRTstb0tKoX7WKlGuvIapHj3/cf3SA4LPj6fACWAO1xlL88oLtAMQmJXPJ06+wbfUKouLig8oGroTb8fwLPnuj7OIKnBYz2UXl2DxKyGlVGuwR2lxfR2x9sKPDeR1yODXnZqwmO2s3PMvnnz3A+Tn3YUlXbgBk5RRf2TpjFX8maprN0qIF7nxl1O0yCaxeybTvvsYGmFxO3/J8jSYc+u7YzxFCWFFC0GdSyu+M5AIhRAspZb4QogUQfm2sRqPZ53hr1HRR9pdjmDj+p52Wz3jwAcq+/gZHbq4vLefHH/CUhHecuDMCHSs2RKuPxJtXXxS037pbT2ISk+h4+MCQsjXz5/u2G4SgBjoVRB7r0nZqxVyDkNRAr5YjsJrUWG2mKOq9NciRCWR2U4bcpaM/8JV1WQVn2YeR/9RsUi7rSvtfxrHqsL4UnHYyVX/NpH1hGVlrV1IUYye9a6ghuEYTiJ4a248RSvXzAbBCShm4/vNH4DJj+zLgh709No1G0zwa7GfsnTuzdu6snZa35+SQce89QdNVjk6diBlwhG/f43bh3kOBRdt+Ed7bNUDLLl0j5tUtXbrLfS1ulUZenA2vzR6UbmnRmxxHJ9++3ay0QF898QAln34W5E17veFo+rj8/gCULytm8YxCOi9Zyvpt5VTblbro8LUqbpu1kSZLo2mMFoT2bwYBI4FjhRALjb9hwNPACUKINcAJxr5Go9kP8dbUUGezUl3tnx6y2O1N1FB43C4K8zaGzfvojut544rzg3z8NEX8sGER8xxdugCwednioPQ23XvR87iTI9ZzbdvWrL4DqYiyU5uVQ21WDt8fN9ifYQ4+HzZDM2TySgqeesonTALcf7lawt+uQElES6dvY8Y3a5n56ixObHk59vjgFWeJ7bvs8jg1hxZaENqPkVJOl1IKKWVPKWVv4+8XKWWxlPI4KWVH47/2H6/R7Kc4S0v4I7cN7954uS+tOTG7/vjoHUbfc3PIknVXXR3lOwpwu5x8cNs1vHD+8J22lfVCsE+gdr8o54WFsVG8dPm5lO/YzldPPBBUpvuxJyJMAa+Ikg1ghLjwVFVh2kVNy5a2HaiItnO2fQRXitO57MTbI5Y96uzLaBfXi5OXBB97YVwU0iQYUXCcL80rwWWppKp4IwDmZBUQdltiLEetzKPtfffvltCmOXTQgpBGo9H8i8zesDokraa8DI87eGpr/rixLPnDv1pr8e9qtdkn990aVO7Vy84Jac9ZVxuSFkjjBRb2du0AmNs+C4B18+eE1EluEeDUcNNMeLU3PJWOrK1kdb/+FL/zDgAuU/Br5Jde7Yk7411iT38bLA7s3c4mdvhr5Pa5l/PveZk4lBAoZ5ZgzRnSMMCgNqxLaugy/Tsak1hdR8eyDtxYcrYvzSbAa3YiUetBUqNaU+1wkFVWRVy9OsflP+7cNktz6KIFIY1Go/mXqJ41m411lUFp8WlqSufli8/0pUkp+XP0+0x859Xd6ue1y87daZm2n4wGVJwzgOyvv/LlTR71rm+71wmncNb9j5PRroO/8ka/36Oi5x4Natfq9Rs9T+ncmsHpZwEghImMF7/D1vEkhEVNdSXUBIcDMScpgaxxgNaqT8NHmLd6JX3XBkej7+Awkxy3A4tbtdE6pjN2u/IrtCw5mzkZuaRcdWXY9jQa0IKQRqPR/Gusv/qqkLS0ttm+ben1UrB+LVM+8a+IKtm2hTeuujCoToPGx1kb6oW5AW9ItPdgovv3J3flCl+cs6gePWjVNTisR8cjBnL81TeR07tvcOX4LN9m9cSxEfuodthoGeN3F1Da3svH9j99++W/BHurtrQ6HGvbwTj6q+P9muDo8uFILA5dkdbZ2xKv23/8lnLlt+juo2/m0SOvQlitO21Xc+iiBSGNRqP5F8i79loqbaEeSoTwP3Z/evlpPr3/duaPG+tL++iO66mrCtYiNWhs/v4l8vL38h0FEfMikdyiVfDYiOCjrNYvfNQWhTf0FmYvx2e29u3bOyQipcQlPHjSwwsiwmTG0edSWv33ZK6iio89zrDlPMbU2fy2GZzeJjS+WoI3GosrNfzYAU/1nllhpzk40YKQRqPR7GE85eVUT51GrS1YAMg96hiOufwa3/TYmtl/Nas9q91BdVkpM776NKitAWed79vPW7JzZ42NaWxbdPQlV4QvaAhC7rrQV0am4aQx56Qi2sZG+9KF1URdnYoWb2kqWKtZIEyCVXi5vNJO/fLvg7LXn3A2nPwkv/RqT0FibNgm4omiQ5SaDpNIaho53/599ubI/WsOebQgpNFoNHuY6r+UgLMqU8XwOuXmu7jry58ZdvNdxKemc86DTzSrndPuUiu5qstKefu6kb706976mGE330X/0/xGw7+//wb1NZGnzsLhqvdPRR175fUkpGeGL7hlLgBrxobmJy2/kdwLtmGPd0PXM3zptrbxPkHIcZx/as2WrVabWVKUvVDWo0cCICRYpcS5+teg9nvFnESiPZ0kWwb16X4D7hJRRZnwxzYrtpbiFC4mJP7FDTebufpqv3bqp8XrIp8EzSGPFoQ0Go1mDyOMSPK1hnO/jkcEe2aOTkjcaRtHXXQ5HfoNAGD1rOAgrbHJKQDYoqI56/7HfelVJUW7NM518/wOHrN7HRa54IYpIUkl0Q5SulaC0+8fqXq1msKK7pdB3NGtqDEEs9e+eQ9H1xRij25J+vW9aPX0UbiLlZBksplZkFeKXcKxU/xG0iI6eKrrxJaXUxXgdsCNh7E2JaB58XJpx4c4vcttrDFvp9Yu2Fp8g69s7+S8nZ4LzaGLFoQ0Go1mD1KwYR1bV69kUes0X5q1kSdlW1R042oAnPfo/3zb3YceH+zHJwKBhs2j7rqx2eOc9sXHvu2bPhxDUmZW+IJSsuqbTFaM8edP7J7NrA5ZpPf02zJ5ZRSyXi1hTz6nE9NnTGdpgPfpBS22kjisXdguznzzL9I9wccaffS9RB/3eFDaefIU3/Yk2xLiipQw5RRuX/qmqE14aluBVPNjUa0/wBndPMeTmkMTHWtMo9Fo9hArZkzhl1efUzvJkR0OBvr1ue6tjzFbrSFBTR2xcc3u94Rrb+G3d18DYOOiv8nudRjSiADf0Ffx1s0UbtpAl4FHs33tauaM/RqATj37UvPt99gvuTi84OWux+sOTj+7wwPkmV8JLiaDp80mTZoUtD9jxgz69OlDaqrS9JhiLET1SMPj8XJ4nYV+9cGvI5MjERyJQWmpFiVAzi6ZSG2KBVPRIr4tXkH/Thf4yiyLW0vNCr+zfUvsGo7NvST0uDQaA60R0mg0mp1QVrCdBRN+3mk5nxDUDIZeejVHnHkesckpQULQje9/zvmPP4PJMDBuvJS93WH9Q9rKbO9fsv7tfx8BlHboxQtGMOOrz6itquSTe29h3CvPIqVk25qVvvId8vIp+O9/qZ4R3nC7dv7soH1TvFpplnPEPQC4pZqmq/f6p9bmzAl10Ajw+uuvAyC9Em+1G5PDworp2xhSZyXGK33llrc+Omz9BqqdJcSsV9NiG1PKeSDzBQA8ta2QMnjl2wNbrqZLdFpIGxpNA1ojpNFoNE1QU1HOB7deDUDu4KE4YtTKpdL8rXx4+3UMOn8kA846n+It4VcmjXwmvJPEvqeeETY9Ki6eVl26+fbjUv0v8QFnX8Cg80K1G+nZoVNOJVvVeGZ9+wWzvv3Cl75g/E9BNkfmymo8QPWsmcQeNbhxMxQ8H6z5iR58JwCev5xUHf4VZXOisYoN2DpnwUr4o/Ua1v8S2SanvLwcMa8cAOEwU1WuDLbNxtL56oxOLG1/Fj2RRBnL+WdYVjLI7Y8Z5vIo26Mqh5vfD9+BlFBlaIFsqX7v3GOJpbywF6R1jjgejUZrhDQajaYR6/+ey7bVKwB465qLfemfP3iXb/vD268DYMaXn+By1jPqrhsIRzghZVdIadXWt52R0yFiuVs+/tq3XbY9n5ik5LDlJo96l7Y9+/j2ZYHyP1TywYchZfOuuprapeo8rDuhH9HHPIyw+Zewl81RU1UumYNIzgFgfWGwEGTBzSn8gUd4qLBW8NJLL1Hx2yYAFm/YzOQ/5iO8LjqtUZ6u8xL6kYqVE6hk/pk7+N42mxWWrXxl82usqt0VAPzWf4dK8Po9VjuLTgSgO2ZSMVHplcgYrRHSREYLQhqNRtOI7595nC8evickvTR/K4snTQhJf3Xk2SFpACdcG+r8b1fZsmKJb7t9vyMilgsM5Lp93eomV6ZtWrwAgJikZOKGDvWlrxl6TFC56hkzfNurdpRiTmhNJGqXFVMpQmOetWQ7R7CIsdlj+a3Vb7iFG2tWDHmmIn7fNJH6xOX0XvQ6LQrUFFxttNIMpbsF90/5i2KTWpVmKzyMv1vF8OWGZ3B5lRapPE45SvTWp4f0W4yXGo9kQ/Um1i8ojDhujUYLQhqNRhOADIid9fevocE6S7ZuZs3cmRHr98wr4PTM9nQ/5gRyjzomYrnmcvKNd5Ce3Z5bP/4mJHhqY068XgVotUVFNxmOY+vK5QBc//ZoZKnfa7R7+3akW63AKv8xshfrcHjK66kl2DN0MqXEUMN4u1+Ac5qcVHhcTLT5HUAmla/1bdfHlgDQ3m0mrVBN1TlqWiCkmax2SaTnqOjyUdm5tN6h7IpqNoWulnuEKH6rdGOytsHj9obkazQNaBshjUajCWDGV5/5tiePUhHWW3bpRsm2LdRWlDN/3NigkBiNaVlaReY559IhjL3N7mBzRDHymVd2XhDI6pQLKI1WOA475TT+/jVYwHFu3RK07y4sxNqiBdvuvc+X5mkfQ4J159NLDRHgGyglgfZyC+NiWgFqCbvT7GRLSRlEcDZdHluLrQ6OdHqpM5dTA1idiYBk/czV1FZU8Ev6SawT7YiSdZg94YXSwK/82oJCIIKzSM0hj9YIaTQaTQBzf/w2JC05qyU3vqcEpLS2Ob70w4ad7tuOMVk4ZdE6BGBOTPjXxxkOe3RMSNpRF13u227R0W803Cq3O976euoWLSbmqKN86aWffRZYncz+ZSzOiMdu9k+9TSF87K4Mmehvx1FEhbWaCuJJdvtXxU1qOYkEt7++yeMPllqamEi5Mf1Wkj6bmriNALitFdyUeRbnmc+j3RkXsS5G2V3VlpyM15Xkqz8Rv8sBT8AsXdWcz8OOV6MBLQhpNBqND7fTidfjDkkfdL4Kb5HaJpvCTf4I6kdffLlve+CiNb6QpfZ2/8xAendp7HsopVWbINuhVl17+LbPe/R/1MxRS9Drliwh5Xpl/F38/gc+H0QASe1ryK+O5pgWKkL8c9QyBOUxuxqJ587ebDYpj9bj4mbwbc63pHhj+KPFFCa2mshWS2dcIvicjrXPoc5UR6WlktzlRTitygB74skngRBg9k/rdWcVGbXKOaJHCn5aERBcVjhxV3YH4FJsRCP4gzimEseGev8xHPnok80+h5pDDy0IaTQajcG7N/mDjp7z0FO+7ZhEpXUI1LgcddHlmC1WehyrVimZDduiDlOmYIoJ1czsDSzW4CCvFz31PN4Am6fYpGROvvEOLn/+TeqWLkMampmWL79E2k03AWBOSqLyzw1B7USb/QLWOFx8iDJWjkFw+kvj2WBSq7deb6W0SRPS/SE5Pm7zGT8lB4foqDfX82fWn8xPXsaOtHOZPugZ7EP+j65u5aNoY0vlkdqGk3P4hctTle3T0fUv80O+XwOEtOEsOh6A4diMOgITAquAgTFm0i0CU+2uhR7RHFpoG6H9GCHEh8BwYIeUsruR9hhwDdCwDOIBKeUv+2aEGs3BRW2F8m9z9CVX0rZHb+4cE2wsvXXlMt92dq/DWNEllywhSDObfF+V1ozQFUx7k3MeeopvnnoIUEbTWZ2U/52cPv0AyFi5juL7HmJHWZmvjq11a4TVirV1ayyZHdnxtN8zc407jXZxvX37bmAU9cQCH1JPTtZoJrqS6FnsX1G2KW5Tk2P0CA/V1mqOWXMZoL7IbUntGOiGtebtnFDViQWWDdxsCg5Ou5XIdkotEKwsm020JZ5WMV0YlqCEwjSrCe+4RzBd8G6TY9IcumhBaP9mFPA6MLpR+ktSyuf3/nA0mkODfoazw6ZWacXbHBQDZikxuz0Ry+1t2vbozV1f+r1gZ7bv6Nt3FxdT+NJLIXUsmcqQWNhsONdvxlujVm7Ze5xPifs4uiUFl/cCrxpaoY3xG9nIRi5MWdDkuLz23lTGHU1C0avsiFIapFblymbJETA3MdzZl2QZS1tnGtKuZspc0sz5zkeabF8gWFT6JyZMWE3RtIj2+1+abzmVUH/cGo1CT43tx0gppwIl+3ocGs2hgDfAaDdSsNN+I87ybcvi4pD8duN2HoZjX1FV76a6rCJsnjDCeTjXrcNTvEZJH4Ct/XFB5bZlxxJlNfPS+b3Y+PSpu9S/qX4hr05VmqYFqQswef3LxhwBAmey9DtsrPCcwRRPDzrWf8Lf0h9GRI3ZH/D1IRz8vPlt0uNO5Nyce4KEIIAZ9dqPkCYyWiN0YHKzEOJSYB5wl5SyNFwhIcS1wLUAbdq02YvD02j2T9xOJ6+MPIuUVm044szzyB081Jc3fUxjxWsoR198BTZHFP1GnEn9nHkh+fb27ffkcPco3R+dQJeKbTTWB+X88ENIWVlThCk2dLn5YefnsiLJ8ErtdvJj65e4uFGZDrU21kY5Q+r22OClcpkduqr9tqXdfXmWCIq3ra4ruF3UcDRmFuGh3Fieb02eijVhATUbbgNgVmINuRsqGZ7aJ6SN+9u8wg0DHgvfgUaD1ggdiLwFtAd6oxxzvBCpoJTyXSllPyllv7Q07WJeo3nt8vMAKN6Sxy+vPc8L5w9nzg/fAP4VV6fd+UDE+kIIjjznQqx2ByWffhKUFzNoEKXVTr6Yk8cPC7f+S0ew67g9XsprXTw28wNe+uPFoLwWTz2JvZNf0+Lo2dO3bbIF+xcC8MQLPF6lOZPPd+Di2IKQMt0LhnHS5pN4qSianvYLaWu+nQv/9PB/X3s5bK1/JddJq68E4K+235NlDf8qihdmfiKO/xLNe8TQFhOWhPk4Mn7B7Mj3leu2aQvCFE+9N9iP0Zdx01gYs4rOLu1DSBMZrRE6wJBS+p48Qoj3gP1XF6/R7EdM++LjsEvjp30+isNPPwePsYKqXd/mWZNUT5kKQOajjxDVty+bE1pw9JO/+fJP65W1U0/Qe4MOD/6KSXoZV7DCl/bAwGv5+r3bsJiDBZDsMV+wsqsK+JrnSSAwVOmcU7by6Kf9cJgduN21LKgrB4L9JSV6vJilmVh3LIUxObw54RW2zQwf86yBkuh82tp3/k2ehYnPiOX09HE+/9UXVNpYb/Vik50hoTNFbklLm/+cj2qpgs1+mF/MPV1Sd9qH5tBEa4QOMIQQLQJ2zwSW7quxaDQHEnPGft1kfvmOAoQwYbZYmywXSLXFwYi8DLYkZnHCS1OD8hZsLuOvtXt32fYv639h+PfD8UovUkq+mqsi0I/74d6gcovSOvDO1PUh9bev9Mc1E8APeW9QYa7ilNwbeXTjfwCo89QxY+MO6rytfGXPzT+Rj7dUcvTmMwBwWT1cuP3XiEKQxWPzbZc7/Odorti202M8q+xo33Zrj5khdf7r1dKmXmkzq9z8UOaiwbHTJndoqBSNpgGtEdqPEUJ8AQwFUoUQW4BHgaFCiN6ABDYC1+2r8Wk0BwqBDgIBUitqsHk8bEuKwxGjjHOX/fn7TtvxlJXh2rYN2uZwyhnGws3SWo5/cUpI2bPeVNHSf7/zaDqkx4Xk/xs8NOMhXF4XxbXF9H9iTsRyXmFi1vpibhqYCYu/hMMuA7OFn559iAYf0y3Kq1ngqeL8TveG1H9R3sylziGACirrrYvjRy7HCmR6ExmycTSbVqeE7fejl8z8dcQ9PiGl0u43Ol8iy+lPFs+5akmzmrgEGxaCtWqXFZ7GmNTxXDXr2aD0wFI73JKP+t3v2x+QpafGNJHRgtB+jJTywjDJH+z1gWg0BzBep5N1J59CW+FkU2oClz7yNEVnnwtAz7wdjO/VnhfOH96stlYPOBKAx4bcQBdpYXNsOtW2KJCSvjtWkReXQbEjHq/JvyLqrq8X88NNg/b8gYXB5VXTezO2zQBCNVtJTz/LSyvroAzmrini20eu5ETLZOrXzmXgohE8b+2DNbslro2hgl0gZ7uOoM7kN4iO90ZRYaqlZ6Eg94/3QHqpwR5U59cjh5Do6UVtdGeSRD4VEjy4+XXlm74yn8iWfCgqfEN/n3pOrLFytMfC0Dj/8Tww51VKGgm3CWYlCn2aMo7paRupt/q9Uw+s64lGEwk9NabRaA5qVvXshXvbNrptLaJLn/6U33KbL88E9Nm4HZtL2Q61OyyyfZBrm3/axlFdwUtTX+PJme8D0L9gBU/NfJ/RE//DuB/vC6rXLSuevUV2fDYAby9625f2+tlqmdbfaZ3IPGMEt1ynlr3/TjxHiFv4veBafv5lPX8Qz2EpJ2DvdRElfYZx4b1mRg0L7xhxfuxyak11AFw2wcTQ8lwctbXkThoDMjTS+zFvfYHdfh610crqqERmUmjyYg74Fp/rcuNqZFJli1lFD6eZcg9qqsvgqDgLZmMF2bTsb/i9/UcMiVNtpboT2ZSqHF9Kr40j1p2HKW8RGk0ktCCk0WgOGfrmdAkSaEBNAR2/XL3wG8JReGtrWTfsVKpnzqRi4kRWdMll7bF+nzr/N0+FksgtVfWyqoN9CvXZsdq3bd6LBtNurxLoNm1X033p8VY6363ChhxWqMbUKimaXgGh3/unnkyCrZ9vXwjB9SdPxGOOPO5nWn5IlVB+fL5Nu4GzoswcP+PvkHKbWh/PnH73c+50VdaL5LnEWl5KrGNUfD1v9vdPX/3uDA3keuPWXpgCJr3mVPuN3Ycn2rALsHo30HaTf5XeW5lfUeG4kcI2n1C5/inGpw/m1615EY9Fo9GCkEajOWhxbd8etF+0MtRAuAGLx8NRF6qQD1VTp+Fcv568K65k6623RawDkFBfiVsEP0qfXPIldotK+2SWX6sy4rXp3PfNYtgwldpH0yj6LdTL8+4ydctUtlSpJe/uSrXyK750Au4CtdA07bZbqXHV0O/TfjwoHEF1+6SoeF1/x6zg/aSvmtXf73wPQH6UCq0RXRS83N7e+1LWtT+TqthWdNmqhJxSU/B01mFFKk7bSlMNc1OUFifOqwSfc6psWBvZB+W7guufnGDlfxUPcKr7FF9ayfrbqU9XU5j1x7bA3S2J7R2CV7dpNIFoQUij0Ry01C0NXlRp/sLvNDH93mAj4AxbDunZ7ZAeD1tva1r4CeSTVsXcvG16cD+V5ax6yv9yvm3MAn5bXsCSreV8OW8z977/I7n1o+g3qRPltS5qnf88RMdNk27ybQtLJaeun85rP/nDEKbecAPzCuZR76nHYtkRVNdisuLFy4NtXuPbzD+b1d/X7VdSucIfk8xpUlNTLc4aQvF5jyJyB+FBUo9feBkbXR/URuvSo9mGlzSvlSKiAeiU8h33lEWR4/ZrrVpZBd2SKvir7fcsrQ09V7mJAwAY4/gO6Qo10nbZQx0tajQNaEFIo9EctOR9PAqAvzq0DEr/rXU/ki+7lA/6nuNL6/X3ZJxuL4Uvv9ysts2Gvx3re69DUWgIB3dxMfecpGxifli4jWtG+z1Rf+U5xt/v4xPJfWQ8vy0vYOrqXQ8FMSt/FheNu4iM6AwAHjjiATw1bbh58VhfmbTbVPT2+QXzaeFMI92lvHCsq5jvK/Np6s5dkrUq9As1nppgL9qbMw4D4ONeDm5o/x9+yXfzZayTVxPr8BjCUEmj5Tmf4+Q8qjidWrx1ajn+sRsuwoN/CqxftJne0WZu6fIwi7P+bPKltaF8Ydj0pExb2HSNBrQgpNFoDmJMc9WLvjw6eAXTuz1OwyUF37QewLnD/BHOnzv9Borfe79ZbWe/8XpIWu5Kv9PCNYMGs7WsttljvWb0PC79cA71uxjA9aqf7mfxjhUU1KgpsPM7nU/r/NigMsdEvcF5P53Hh0s/ZEhFX1/6srJZvu1fk2aEbb9raVfO3nA2Z284mxff94/NVdYvqNzcriNxHHY579dNIqu8IxXCy1aLsrn6PsZJkan5x7XUCj+wkWG2alraTJQL/wq10dkfha2zsHgylSIBYSn3pcUXvkxa3khW501sdt+aQw8tCGk0moOSdfNnUx6lBKDr3/uMMZ38xs5VtmhOfmWqb7uBs9b5nSIKh9+OJtXQqAA8cfjluIQZW4tg3zTWMPH8Ro56dJfH3fmh8ZTXuvhrXRH1bg9TVxdSk7+GdZ/eQdnE5/j+4WEw4UGQkjUFFVSvvxvr+jtomIHKe/k13prsj7zzwKVmEIIVxSto4UyjhVOF25lbNJ5aT5WvXJmlksZ0FW3ILctVO8Zy9WNmqaknd4Wabsp0KzuecpOkuPV8TF4zpy2/mY1W/+qxDVYvH8UHxx87OoL3lioh2ZKwhaeSW2CNTsSDl4tz7/Dl/1VxOAOOKebs3rNYUjqN6ZYCPq96l/sHf82CXuuIaf8Spu21WAtWYa9VgnDP6O5h+9JoQPsR0mg0BxnfP/M4AOv/nsvRXi8F8dHkJiTyQ7/TWJPYCodbvZDXF1YDcN2QdrQ851O2XnJJUDtxn32F66lHSbnhBvLzizhz+H+IddXyyYSn6PDnZADsXXOpX74Cc0oK7Y3I85lPPsH2hx9RjaxaweSBK8m85SZu+Ww2a9av5UXrW5ztfLzJY+j1eDgNxvHG/67ETn+eayb77X9KXSlMX/km97V5mdp3/UvnPzsnhbUty0lwxzJmTbADwvWV/iXldtOCsOPovL4vduqplzbO//IrvukwhB8TRuCo8MdZO7vazhsJdVQKyetxbTl/0dkAVAoZtk2An4jlcyNQRmdMrMIvNM21uzgvqpSsShUDbUr8XF+e9Nixxi/COzmblIxovvBsZqHZi6ffet9XvTDVkeK8Nqg/jyXYOFyjCUQLQhqN5oBHSsnCieNo0b4T6/9WL86s0kpi612QkQ5A54w4Frl7M/meoRz+n0m+uhaTIK5nj6D2Vie24rZPVvPrggVsufZaLj37aeosFuosSsNkSVFakZxvvgGvF2HxP0qTzj3XLwgBdR+9j6tHLu+fkgHv3wnAfPt1XBn7FheXv0eKqOAq1z27dLyj3CMDD57E+ip+XPoZt0wIdg3wyON/MOzvGbT5Nvyjvneygxn2DznOMhZoFZR3+I7D6c5azmMcVa4oNpPEB91HqGPadj4AMV6IkmCRUGGSHLNFGYiXm7z8FRUa1w3gV+KIQzACK2vwkN/qfdhyhS9/nsPDPZX+abfnWn7s265a+3/gjQLAs87J1NSjoB6ialIwxVSE7S/eHUP70uiweRoNaEFIo9EcoLjq6nj1MmXsPOCs85n13ZeIgGXsvfPUyqhkq9IGzN5QQpzdQlJ0sOHsJQPaImw2Oi5dypruagplTaISCmZldmXA9uUcs2IKX3U6jkvbWugw6XeEVXk5FiYTmEItDLK//YaNZ/sNsbfeeRdxP73mWwyecsOv/OCsQX4whZLVMSy7NZm68efSt/6dZh37DJnh2757/hcctyXUh4+wxWEz28IKQdtq1gKQF3cpj9ebiWm9FhNlAKQ5TbS09GBh8kI+qFblYm21WGPj6F+4nLlpXUGqc3hsrRWBwC1gnsPNPEeo8NMFEysNjU8LBHHGWWiDmc2dHqZahNY5nyrGE0eFqMPrjsZVNgBXyZE+IQhgVWwn37YlZkPEc1Vhqab+l+Uw7KiIZTSHNtpGSKPRHJAsm6K0OgLJpl/eA0CG8WqcdvNNVNWrl21lvRur2cQnVx3OOyP7svHpU2mRoF6uFouZjjP/wnPEQL7ofAIAjw+4klPOeJ6vDPuiJ244CWvLliF9NCaqWzdav/duUNr2Fw0hZ8CNkNkDWvRk/a9p7FiQQN4VVxJVXMdGx0XMM60MqneN+Wd6izVh+0morworBAGY4rOQLv/5SLm8m297a/UaYlu34HEjFEiD36For5f7ynawxLIOpyXYpqfD8B3YMoLPbyeXmXAETkS9TyyHGa+aCwLCblyd8wQ15jqEyY0tbTyvRz8W1MaZVHKedFGbdw3OwhORnvDx2iwt/dqeqtUPA+Cq7OpL+2HlKyS3DLXf0mga0BohjUZzQDLpw7cAGJKxnr7J2/hkfR921MeGlIs56mgWbleGwK9c0BuAozqmhW3TkpRE948/YOT45bz8Z2QtQ3OIGRQcX6xs8lJaXAAM/T+cW6vY/tyfOCv88bNcVRZIdVHnPZyxePkFF+9Sz82WH0gQ1WTXfR7Sx5hfH4vYf9QRN/Dsw38wmnqOxMK1n59MP/tGfi+4ke9MgmmWM3xlpUcJg99vzafQbMbjVUbUdXUmHA7D27YUbPcmBvXxS7SL2PSfoczvM8kB1BnbxxmvmFeJZQ0e2hkC0XvUkYfJ5996deVo6mRwbLRawJY2HrxWnPUtwh6jtAj6FhWzPF4JP9ITQ+WKp/mMGC4xbwchWVIrKE4dD5wStg2NRmuENBrNgUX+Ymp+f8632zdZ2cWMbLeAfslbOD5zDXd09Ds4NMfG8Jnh3TklJngZfSRO/+qFkDQLodqmphDSQ+tbj6fjbz/50rbU/YBzh2D781OpHh9qFySlmjZKxcSl2JlOPGfXP84zrgsAyZC0GAYZwsVRW/3GzjedeAd3nnqzb/+Skx7mP1bBW9RTDfyOm/Ocj7JSZrCodD7TUgYG9essOp6MsrZkuT2UG1N9XeqdfGcZSnbd55xX/zCnOZ9iHcHasBU2D3PLggWMuoDtywI0QB0xYzamxb7ASc2GW+m/cTgXLbwBgM0yPagdkyMPe+qf2NN/A8IvvRduyd8xcwBwlym3AAIor4FEZzzSHc9tjhq2VtaEra/RgNYIaTSa/R0pYdsCaHkY5M2GD08kGhAMJrNTLjDNV3RIxgbc9YJV3wRoELb+TXztVsDEoA6hXofDdllayg+z7uP0054B4CnvMo695bLmjXf1BFjwKdIWhyV/Beb3+2GLS8NZaaFy7A1Ujg1fbdvsRJwDP4f11UHp/cnmLU8WAP8pNAPRvOIuZ3asX3D4YtOTvNXnKk4543lf2nhCY3cNcz4NOeH7X5t/A9ncwOnOGXT0bmNu+blsoBSAOTK3ecfeiHYBMc3mVLvJd0nGZU3BWXM4SCgqHcKOnDlQCttkCjkuEx4kefYaYnL8UenNMevwVHcK1wWOjF8BcNeoA7urzMEGvJzvsvN2giGWmVvv1vg1hwZaI6TRaPZvZr4O7x0DjyXAhyf6kjMclRxzqVpttMzblokepRFYMzbYvw/vHUPh6tm0S41GNCMA6sbB7WjXaxLxCbWsuaM/G58+lUuevZes1hlN1pNSIr1e6j99iPLFsVTMkxS5nqDEdRcmW+R+Y0e8YTQgqFtT6kuvQyKRpNaWc1tRHtNRUexrkHxtEUR7/OEqSlbFcv6YLznPFn4ZfCSisl9B2IK9Wf/gHcTf5ecCUETSLrXXmDtbfMiCaOVksiFO2KnbhpDlNnFPeRQn1dk4ZcVgVle/TaFM5pxqO+dXO4huG2w0Ht3mQ0zWkqA0V3YsBBpae6Nwt41BGFqnOClItChBLHvQEf/oODQHN1oQ0mg0+y+V22HiQ2Gzylu34cgPNlIhozjV+T+udd3Fcm8bkH6hI/eCbaz0tmac90jWF9XAuLvgu2spHTOGFV1yWd2zkxKwjMjsnrIykjqoaZSYFvVYM9LD9h2OrbfeyparL6bQ+SKVnouo9FwEQK13KObDXw5bx5zcHmH228ZU/XQz0lnN49Rwc8kSqsZex7AJD3Py9FeRXjU9VOJVmp74+uqQ9q746jN+/fEuRlQFxz7LMlaENaZ2421kpo+lj3v3po4Gm5Yw3D2NhDDapy+Ioe/fDmoqUvHKYJ9CF1cFT1H+VpnB5uJbfPutq0NtgmI6PMtA0zok4OqaSOuWsdgzx6pMTwbOXsl0cUTxv7P9wltC52TqTmqJddvOBWDNoYsWhDQazf7LtFBbnQa+9gwF4Cm33xHiMKc/COgZw/9LnbRysvMZf6W578PiL6l46wFMVi+dzlJhKXijP94tSym4pD8J2Soshskc2SFgY+rXrqV2+gRat/klbL7JHocpNlSjZO9+XEha1S93cOz01zmsYDWnnPE8axPUtFjVjzfgqdjKk+WLAZjTQq30OvW0ZzjljOepFxZ+b9WXb/oM4afYwUFtbiORS8vfDEhRx5bhFlyy9FaOr0rhnrIouteHrgI7oi7UgkJImE48x3kPZ6bsypskMlnGcnSthQsqbRxWb+a/njqSos6lqCqR6VU7D69hk/7XkTBXk1CbTsrG4OnIjqYCkrpKPK1jGFrgJkaqsRW3ug9bsoOlbW0kBvR13owqWpQVscUVKqhpNA1oG6H9GCHEh8BwYIeUsruRlgx8CWQDG4HzpJSlkdrQaA5o5gQsQU9uDyXrALjPdQ0bpNIaBAYwDaTeYqNLvd8Z33c2v5PDtscUh5Sv+98xZPXzLxlP7VYVUiYS+Y88RsshXmo9fSOWiTn+SRJOdFHy4Ud462qpW7oM6YkBYOWAS+kya7SvbO+itdw/+HoAnu4/kvd/V8Lcde5iVib7bWWeOPwyvMYS+FtOuoPNjmBhy4ILkfwXrpIhjE640Zce2/FJqtY8wrnVwZqZGlOo8De7kW+ge8qi+CVRnacTsLLQnElbzCDgSmFnmcdL61o1pgbxo9Sj2i2z1JFjs1JaE37Z/U+5b7A1UWnnMqqzGCyr8G44k+9zvgdgYesEtrduRcrWexlnzScuujUukcrlf9poWVIWts2rJ5hod3zoakKNpgGtEdq/GQWc3Cjt/4BJUsqOwCRjX6M5KPj9/Tf4+eVneOH84dTs2OzPeLSM+TkPUuCJ56Utx/JlBOHnxI2zAdgcG7o8vrtoejl8dJqzyfxwuMvL2XDxc5iSz6DE8RHFruDQGXUrfvBtv0IdccceS9tPP6EkSU3fFFpsXEEVd2T25IJTHvOVfeKIy33b3WrWAzAvvTMrk7OD2p+Z5feI3VgIAhDJf+GuDo4Sb036C2GpIarNu9gl/BztpNSkVsSdXGMNaSOQk2qslAgv19X4Bah7TH4nhx0cZk5PtEZ8sUyxCaZZlVDkRfJcYi1/2ZW4VGOt8AlBAAUx24gv7YbNmUharbqe6x2fcN+8BZg8+QBU2jeDLKJlSdMapz/XhsZR02ga0Bqh/Rgp5VQhRHaj5NOBocb2x8CfwH17b1QazZ5j9H23Ur15JVe/8Cql9TYW/fYrILkrdzq8aQTK7Hcly6b+wR+fjuKNnLchNbSd4etnMPLpu4gfdjcAo7oOC8q/xzMGmwj/sixbH0ViO3+U+IJ1uWQM7+CzG/KV+3k99nYJWFKjqF1SRPxxbci77iVs2f7prVaO4QDcsPZefm3VG0fuMfxu5M3DzXd/LueE7GjGZWbSYvAgXkxKptZYll9uj2XkiQ9x3po/mNnCHyT099R+bBmczvLUCMu9msBVMiQkzZH5IwCWmPWstzpZYfOywubh6G0rWJLRNaR8A1YJPZxmlls9DDY1/eroHmVica06LikkwrDbWmP1slp42Wav5OjtH3Gcqx1/tDiC6vgi1nV4P6iNy+f+F7s7Bnt9CiuPGUDKtusA+DD9xeafgIbx1JXtch3NoYMWhA48MqSU+QBSynwhRPOtOTWafcmGaVBTBN3OBMDr9VC6aQ23dZkN7xzBZysGAYI4S31QNdeA2xh/080sSOgdlJ5FHU/+9hLlthhyS/Ng2Pe+vJktujF+wp2cfNKL9Khax1kbprByQwukV2C2eel01nZf2UAhCCD2hheh+Cso+kkZUgPSHENV9ZdUTd/qKxc95Ths2R/69gV+42VXGw/3mMbwnPt8/+Hj5c7xGxhhW0ayyczKrHbUOoOniIqiE3mz11lBaV6TOUgIenj2R7Qrz+eKEx8IPcfNwFnaH1vSXB7Y7ma+tIAR/PTRVW8zoc2R3Gj5kYdcV/Cp54Sgei4BcTGCdI+go0ONu9TtJckSqv/JsZtZXOtldGwd9bKOa6oTcdcv5tSCNfyafiJHb/8IgDlHTyaWyawLqNujuAeDVl8d1N5xSyRLE6Nwm4OvFcDIeU+EP05bKTZnErWOfHJsq8OW0WhAT40d1AghrhVCzBNCzCssLNx5BY3m3+Tj4fD15covEPDShafTLaHAl31H7gxOa7WCNjFlQdXmT5nFdls6fyUf6UuzeVw8OOl1sqqLOebWy4PKv97rLAaal9Pl9O1ssF/EjykP06J/Oe1vageANact7viuENtomb1BTPmPsGYCAFJacMs0tlZ/GVIu3/kBTsPouFrMZxAesus+J7vuc963vcBNlh+Jo5bTqGTy0qcR5ipA8pOzG0s8mXzr7LXzcxYmgvvA/GVk1pTQMXo03iR/3LS6E7KoO1EZVmOqxxyzGkxKcPBG+wWuuoqz8ZLE/aXP802sfzqw/SmF3GhR2qKnrB+R7lEanVOr1XTZSU4rKS5BT7f/+3lalYdyj8QrJYf3tXJER/84T9r8PV22/02cu5C60hdx1/xOTu0mbtykwqGUOcKv5Oq1RCK8E4K+0gesric/JzhkyTWzXuD6ma8Q40oIaaM4bTZSqPFH1bWgvOPhYfvSaEALQgciBUKIFgDG/x2RCkop35VS9pNS9ktLCx9SQKPZK2xf4t9+PBE541UEksOS/dHSf/EcTmF0BgNS/bZBX7iP4aI50WyK9seKmpO2ih9+up92lUqrE9cjjYZVUAB3t/maT6z/A0AI9QdgLZxB55sSyekzG4u3GKr8WqEgZr8FFWpcJa672F7/UUiR56nlKCo5lkosph85SXYMKQPQ0raGElHHw91vJDrnVaLavAeinpWtfgwpe3H+xKB9Z69knN2C/fi0l/5VaZt73Yjz8DTqTshCHDGHuNL3QAjqTmpJZtffiG7zIXGdH+fWRZ/jPCqTuiFK8BM1UGp7JqhdEcZr9pyYS3gkeib3RTu4p95CzxoLm5ySLJsJkMyvdiOBv6vd/JYisc0vxrrGHwHe0eM8+tT9xRnb/Z61PXYHXpudUcM2MfbYjUH9WbwWnl5zI5aaSmrLl9GSOdjNq+hkN3FMnIUWtV7qo/oDMGjDWZilhXUZwZMaTlsJxWmz8JrrqYxfhV0og/cV1U37gNIc2mhB6MDjR6BhTellwA9NlNVo9j1eL7wdvJxb/PYwR6ZuItmuNBaFMp4bXbdzkeshbuNWCsytKD39c+53X4NXmJmTpF6As+w3Ufzee752ojumYPnmbDqM8H8P5MQUYAqjSQEwFS9XG1V+TVRO3afwUCF0C56S2ipeodbrj1gukfxo/oJy05+MDfCbM8A7NKSfIqmcH14fPQqkMiz2OtOxxKwnrsujYC4LqXNSW3/wVFvqJLyZUXhbxuDJjMKRBdHZr1HQZQYPXmrm136xlMUrTUhU3QoSy37EUT2NpHw1XbaxpbKnEVIy9ailqlG7/3FvW1RCW7GdByyfAZLTTX/58qbW+Z1WXul9jTZRI8j2LqCNbb463vjTaOUYQV/3+/TZOpEBMfBkiRI44lyVfJrnX6YvLHakMCEBKUxUt+vG6OPDT1N9u/oFVq4Z59tfVTqdXuZoFm9/nuUlv5AfZaI66TpOW3oLPbYr26f2BcEr2pz2ErwmJ2ank3uYwNUZI7kp80wOWzM5bJ8aDWgbof0aIcQXKMPoVCHEFuBR4GngKyHEVUAecO6+G6FG0wxKw6/WOjJtM1LCq66zeMl7ji99irc3R1T35ovvngQeDqqTKUopt9jxutVLvWZNMSvWZNFqsPI6HN8mjGPAcz+G6kL45e6w45CYwGKj/sz3sS/7TqVJkHXBq60KbXfwrPMJnm3GIferf5sZ9lv4ovpSX5q7qgOusn4kub0U1hwWUudSp1oAaksbjz31T0DVdfVKpgyIL0zHVrOdv478lL/8s4SctXgzTgbzZ9afmF1+G6biVh8SVf4Nc7OOBsBeX0ZaXBWFlWop+RT7nQBs92TxiE0Jlxc6H2RwWT86J68nw7bW19apSUrDVuH2C0kt08ZRNKMtJ3QdhB03qxyXgwMmV7djRsH3DMo4E1OnwVSZKplR3YJ15iyOj/Zrh7ymONLEUXRw9iG3eAk2GbpibWrB1wBsqlpKp+qNPDFpGovL2yOtXoRQ90BeiiDKMo321cXcFDOKGzv+h7EtjuThqW/42qmvWxp6kTQaAy0I7cdIKS+MkBXqhU2j2Q+RXi8/3Xcxp7UCT79rmLdeckSJf3XQh56Tg4SgQC50BQtB1wk1nZTao5IdC4LtQrZMTwYgs1+5SsjoDgXGy+/HW+D+zdDnEvhPsF3Qtc47AMj+P6WJsPEx6aKMnywvsxwP1xoG0GtsV/Ks+6omj3Wd/WLOijqbRWVKszSo/rWgfFfJUAACrfUcrT6kU1UbFpcd70uzJP5NYZtPQtqvSLuTrJqtQWlXzBiH3e0CUuhV3Iut0VspMvK8JivVSf5HyNW/w2pPAoWO4NVz3TO20uB4unep8oP0Tclz3JR5ZugYHJP4PjqO7vVOetbXk3HuZjoljKLFdr+90zGZ6xm7WcX2SpTRLHVDhTWbbh2KKXNH+8oVt3yDcb9VYZEALVleNlOdk5gWxFi6U1z+GwACyZ2507lr3jRIAFNdNgvrrsFsVyvc2hRLesXsYHCcOmdvrnmQLPe1QePOSwsWajWaQPTUmEaj2TmV29UKqsdCDVMb2Ljobwo3bWDUXTeycsYUAJZPm0z7WOW88L2xeUyfsYoXVhzFwhLlDPFVt386KpNiZtpvDmm3Y5WyGbrfPgavG58QZIsL9RZsthlTYpf9BPcbQkN9Bbw1CJzVcM1kuOJX6HEeD2W+xURvf4TXw+XLxtG6sgAnVoRMp4/rPz4hCGC67MKP3kFBfSVQibCUc5PpO9bYR2IWknXpi5s8jY2xxK5lQ4vf6Wja6EsraefXZJw74VWiyv1alG3R/ujvh29ZQoq7zLffoaIDQ/KHcOGU8D5zkqslR9RZOb7GijzGH3y2pVed39W1RxEbEJ7kj/KbmFYRLPzdlp7G8ylJXJ6VwcmtszixTUvyE1bzd+evg8qd0XoKFrGFY1zdMFf14NISK5sTO5HgLCTRHccT+a/Sf+NKQwhSLCmdCkC5A8pRAmv72GLuzA0OF3J0xkZMdT+RWv8Ibbq+RKfWY31CUAMPbVCG1R9lncGHnpOoKAmeQtNoAtEaIY1G0zRPpoEnwNmg1wum4G8oj9vNt//1e24e9+pzdBk0hPa/n4sjUWkgqiv901apiU5ecZ/JUaYl/Ow9EgtuZjlUrKmNjos4rf5JFkv1Ff/y76/S+fR8vO7gqPKJ7WvYsdAvmJntAZqO6GTfZs0OG1u+K6Jt9KPYL1NCRm2FHdfYT6Hl8UiTmVHdTuXIbUvYHJfBZkLti65w3evbHmBaxt2Wr+lnWk3P7NaM3FSIVXqoFyDMdbxhe4WbnLcF1ReOTci6tkFpMR2fRBgrm7a0ngCbrsOa+wN1+D1AH7uggJTi2Yw+1U1Ngl9D89Sql7l6u3IX8Ez189TGKKFPSAsdClycP20bBYmJ/NlDaWAun1TBYdFmWttMiDIXHeYs97V1RIUyZF9QfXrQ+FbUKi3V4prhpFtXc27KfWyz+l8ZOyzBr48ik4lf1/ZiZDsV+DXTfj21nn50K3sQAE/lFcyKgxPLjqR/mQXpUdfIJSXjSv3CW4L3QrYnS57MmhZyHRq4ufMstVECMDViufs73kHs+jxeNb0XsYxGowUhjUYTmWXfBwtBAE8kwWPlQUl5Sxf5tjMclVySsxAeS8ARJpJCorWWwU41bRRHDYeJ1XxnfyyozI/2h8mu+5x0VwlxmbVYorysHd8GUF/2jhQnyZ2riT6iLxVxp1Py/AvKYDomDa7zv0BdHYexacxCANb/7w+q1/6XvtZXiPLW8Ux72FTbiVlCrUgL9NIciZNNs3nb9gqgXvx/bdpCrOEOYGibVpi8JuaaBtLeVMg6r1qpKSwVRGWMo2aTX8B5zn4ffWcWcfWRSRRaLLxROYdjHVPo3OJnAM7/7Sdq7FH09yzjRGs1rReu4fWeZeQnJQL4hCCA+MqW2OvSKEtZiMWl7H8yataR5CnmqC+VfVCcCVrHKxucLg4TUeUzISDqRJHZxFP9X6VdcW/6lLcno+gIHNZ6auqU0feRMd0Ya36aKlNgvLJgjmnbip5R99Kn8CO6R6sVcFHmeQB4A1altfVsotyzlKqtSfwprJR7wONS9kgmi5pS61+zCBzB7S8szaTWY+XIgFWFjXm+7eXcvWkUz7a9grdaX8DF84pIsi9ivujNsIi1NIc6WhDSaDSR+fpy/3Z0CtQYMboaTZHt6OX/4j6xxZqQZiqvX8q8pz5kZmw/vuFBfzrRJBE+8vly+xXY7C5G3PEG5RUxfFCmjInNNi85JxQhgTdnSWAs9GpPwqBbyDr+SrD636Brn1zo27ZEu+lnfga8MNL5f0zz9uRU8yzwtqG5XGD2rz46pm0rMtxuft+sltrHrr2HfE8aC8xb2e6N85VztPqYmo1K2+XI+pzeNfl0H+WhniTGxe5gZNcUjq1Rq+dW/aU8U2ODdT+n03JYKe/tGIVnh4m7lnzIty2OZ8iSOPDPbGGVT+N1PUbshgpKk20kAkuSF9Ktui2nJ4YaIKfbvPSI/gqAU/q8zf9Wv8zIdHUN1qcspGtNF74YWMmGZA+JOx6mc2V33o6LbGz88+ZtDG+t/BctTr+PC9Nh/gZ4PzGBlm43N2WeSc9s/zkeWTWHpJhZdIuBzwpfx1y3lLrapYDAGnMKMaYiBsX5XRZc2fpNuk/8HIkgOukWjuS8sOPIHKKmY59vezknbh9L7Kpt3F6cgjO2A6+1XRFx/BqNkLL5EZY1By79+vWT8+bN29fD0BxI5C+Cd5RGgfu3gD0urI3Q1MJs3nMOI7dqFfd//i3ezy/CvMbv72ZOm/vpf8Lx5LxREFIXoHN6DOMuzWXTWSNofU4a7nZnED1PrfByCgttjp7EZw/dRlaxWiKfe8E2PJjo2udbrv08ONzCuQ//h5ZduzN49OF8+HSwgJV7gRJYvFLQrv6ziIc9mRimmn5nYNxMvqtsx0zZjYWGn6CNjosAeIYb+D5rOi6zk7mbV/G69xJedO5c5xDb+SG+fKYOExCVVk/2caHBX3dGvrMzLWyrKBMmEqWXDxPieC0+hUsmtsORpASu94+4kzO2H8sJhVGkmE+kOKqAHfaZ9Ks8jTjz1yRY1fFnHv0n93y3hVH9/V6qPeZUzJ6isH0DFLV6G2mKwVq3Ape9MwgTjsrfiSv1B7hNckZRalPC3WF1dfztUMLpV3l15HpCXZ9tqk6kbSNHmgBtjpyM02bC7JE88I2KLX1u2hOkm9X027pKNb02tbwnU1rdwmS3k9eOvo8YSw1V+VHEtlBjKM87nrMuf6d5J7gRQoj5Usp+u1VZc0CgNUIajSY8DULQme/C0u/gp1t9WU5p5nn3+VzomcDX1YOYljmY2qRkxIQHgoSgN1YP4KZH7+OYBz4AWhCOLcUVWFIzaD91DgA2gMMHw5z3aBNzKa8+/5hPCPrspNM4JWEWOTVbgoQgi/BgtTl58Iub2Ni3DadOrfUJPhP+yiA12f/B95HnpJAxxOX+H/VFQ+nrXkFO7VzaumH0tGN5q+cZnCjmcrP5e262jGW1xcq1mS05YouVkX+r0A5n5r7G6vzjQ9psjLAWcd2Eet8Kld0RggBa2FYBcFdGKh9s38GV5ZUcv6M9Yx39SbZsor1jJjdt2sAy22gu6NWCD/K+4bo2ygP191s+obbicBKssKP+WZ6asIxX+gc7V2xKCHI6eiBNMaSVubl+QgbvneBle7IJryXYYWuDEAT4hKAry8rJ9QRPqTYQTggCcNrU2fKYlRG3s3sUh+U+j026cAszZ37yAW1qtwIeSlK3cOPC76la6iCmdw2ORH+olrJtoYb1Gk0DetWYRqNpmpZ9fUKQRwomevrSqf4T3vUM5xhe46fMUwGYZ+rK9plf+OvdvoSbPp9A3vzxbJBKCHrb+iJ2nHRJMXMsyoHgJMutsGMFHreb2krDM3F6LouHPMWlP39Dj3WrfE2+f/oFXNH9KdZ51aqiHYl1vHXRxdzW5S9ubDePhR3LKKtcwmC3Xxt00sAC+nZRgtSk6CjeFYM4QvinSqzJ07m3uBR76p/kpSnhyWSBx3qqeFcTZX9e95xJl/qPObHqY/LzbiW5YCiVhtPGwStvolaJb/Ss9xtFnVzdMC0lGW39HxvMt3JLl60IkyQl1++BuYHbOqupv493vBuSF441Xr/xdRv7Qm7NeYYLU2/n8FgVDuSCluqcX9XGH4bjzFYtqEuYC0C5J5dP2/pXW1VmvEKMKXiG4H+bbuPIyl4M2HEilakPUp6ujMb7ra0D4JrfKnjoyxLc1u5kl/Qgti6ZSBxf7ReOXlhxVMRyDdzf4XbfdssqD/MHx/JMtyjcJgs15igeW+xmR2+/x+gh077BUukhf3YGxSvjsUQpuyRnpYX6lDCRejUaAz01doigp8Y0zcLrhbW/wefKDkNGpVCxOYb4pDyEGfrWvUUxkZfQZ1HEX45beSv+DgaVjaWH2EBO/ee+/IapJR4rD5lma3g5Xvf2aGKTknly2jzOuWakL/+DEefy6bCzsLhd3PH+40gkHw/LY8mGPF+ZhxypsNHBU5lbmnW4p7VswQablSUb8uiRo+xYftyyjXSnmW5hQms0xi6hvlHIrIsrTGR57b798uSxPGDzTxuVbYgiMccvFDzX9grsfx/Dwjap9F0XGHBW0skxhbz6PmTaVrGx/nCfb5+PdnxAjTcZgYcbM0P9MLmBPjmRbZ8eKCrhv6l+oeXaH1rS9WZl27V9/WAynel0zjsNgDdN+dhsy/lo4DDqrTbO/HsKues7YPZEhW17Y9ZExrdVfpnOWXQv3/R6lnZOFz9szWdzdQIT8jtS7opiU6s2nFo5h5OyVL9d6j6l2+GFrIrJ5rhpK9naIpfpXZPC9tGh0sPV331I60vHU1dmY+WXaoWhyeLFZPPQfaTfGeTCd7tg6zSAW554OGxbO0NPjR386KkxjeZQREp4vZ9aZTXwVugyDLbOh/eODSq28iM74ObFTiNpd8ERFC8OLwTZcOHESlfTRr5yD+GZHf2B/kFlfrXdpzaiU2CbsvFYVZHKsm0ZdFlcykDHFma2b8k711/KqFue5qtrRmK2efE4TXQ6K5+HHK8xIe97Esw3MSxrJbNFLAPXuiBgZdojRaXkzUuB4c07DaPzC3gqvy8TvbEMTHYz1dKSp1LqmLTxZQCGmBYyxds7Yv3GQhDA2PhqljkuoMSTzqKqsznKGmyPFCgEDek3inYr07nH4cFsCEH1lkrs7jhAsLpuKAAb61XQ0O+KnyI3ehI13kQASh3FnBV7Jm+WLiLTup7ZDjtVJhPfxqklYYdvdJCSWsx9xaUMbdvK12+gEARAH/+BZLZTfntqKjpjLenEko5u5rY7g4e/VN67i9OqIwpB5YnLifHaOSrWRXu7ly6DPqDTOxmIrHqmRdtY4rwJb3QLHKZYRpx0LWveymVC5ZF0bT0c1wnpzLC05up19fQo68JtxykhKGfrZtps38qUvgMAOGP6JHqv/Ju+GWeyg/E4Ep0kdymjZGUCPa9aFTSetT+2ofe1KylcGmb5okZjoDVChwhaI6QJ4vfHYfqLTRbZPj+e0jX+NdYXnPIo5Xa1Gura/I8ZmLCe16MvYB65bBy+huyfwwceBTjTNI2XbG8hJWyp+4h6bzQ/5L0OwLBF63zlZrXPwuFy03VrESlZ1bQapAxkXV7Bua0z2GaxMCq/gK7OYJuPis0O4lur6Zr6cjP2BA/rolrRqnYbXiFxeKUv+GpjHqvpRFxeW17LviIk748pt7KU1izuUMuY4+rIKe5Bv3WXM0fsYFlcsFAYl/t/OFb8H7PsN2EOE+vsMe7gMV7y7c+O78HpfV7n4S9LWN3xS67bfjE1Xkmm1cR31cWYXfEkmgVOKakJjYkKwNtH+v0VXVxeyWcJcUH5Zyy5nWNdi8lZ8jNv97uJ8bmf4DEFOxfsYevGVRlzw3cALN9+CpWJ9ST8GMnRvSItaSYCD5vjC+h1xHgAHF/bmFWitDU5p+RRX3IKXreD9N5fYrK4sK9QF6U+VxLzm4kt8Vn0qHqSIcfH+9r9+LG7cFy3EXcm7FiUzLZZGWRG5ZB5qbJFk5Kw17ZgYzpJpa2x9ZnP9AUn8+hdb4QWagZaI3TwozVCGs2hRNUO+OFmWDNhp0UDhSCAMb8+zimnP8fp23/mcMda5hdk8W6750m+dSK8cxFtxEvkydAo37dbvuFW8/c4Pa1ZsO0ybLMfYUHbDEzRdo5Zoaa1EnJqqNlhY8A6fzT6hLZ+zYnVJBm7NUK0eKBwSZxPELIneCh2taKkaiAfZ27jiarpYLwop3p6cIX1PB40e7nS9SgAW7Ir6Fh5A1AX1OaTc9/js+Mu5MfscdRZ3Thc0Zy85koALilaTnTeYp7pcQHbvAl0df1Ch0UeXo2/kXCsd3cktroDj8Xdwaneibzc4xImpQ8EYGGLPxjobE+8WRBvFrjx0CKhCC+FDHJ3YVxZeEPf4Ykm3g7YbywEASTWpbPcfTpTuyYz6pRjgWNJ2XwtJlnLHeNH8sA1J7C5uoaGuGamugS8jmCD5q6ZvwKwUpwH0q9ZqTf/xbGOo3AhmVtXS9Ixn2KyOMnIE7iAys3RLCzx2zGlVdfg7P1NUNv1uX6BsfoEL4lsIb/yIeBVX3pKzmbKjcgo6b1KqNoWTeYpfoP8xkLQb5uGMHHTMfxYl01V8jK2OZdTbtHmsJrIaEFIozlUmPIcTH5q5+WOfYhnvxOM4ANmZ+TiSklm8PIZALw5/WncuS6GJq3n+BaGJucdZdszxXaHzx4ok2KeThnFsx2u5oyfBpBnPoq1S96iZdmHABwZIPC0H1GALUZ5hV4xpgUgfCu+wuGVYGr08ms/rDBoP8W6he9LRvBE1cig9Jtdt+Bo8yaHb7yPStGZOMsqntiSyMCEYCHI4tjMqxdu45xJTuo6Kg3K5fP+i8SL017Cqu4W4DBK6pTn5qOLihgqBcQTwj2ua/neM5jba2OJqs3i67RjmZSuCl47vpz8Nqtok/s3VX9HE1vUCwtmjnR3BqDM7cWFl3eOVDHRrpv5Mh7h5q/s73k7c0bY8xNbl0y3goG0Le0GxADw1tl+r9HFrZUx9qNXS+5ZWU+X7SthKBStOIWiJWdRkbCCvse8hTlg5RdATMYK1nSooNWcntRjZWDKUFJrJT/bPiDlpOmYjHgZrjYST72Jdb/4haBTFq2DRRbyX3aBDdIfsbLjiWABz4tgpPjGdw6vla8zhMmUXxpUjHanRLb/Kq5NZMyqswH4Gzcx/Z7D5bUQFeuIWEej0VNjhwh6auzQoLCynnenrqO81sWz5/QKzmwiTpiPh4v44KYnGfinih316mm30WHjTyTU1jNojQrjsGxAEudkLwuqJiWs/FI51dvhSODvHj14+bIb+f3VT3Gv/oVIJHepIqN38AqqojWxpHas8u1/vqEXF+Usalw1BCnh9/LbOSHx5bD52XVKSLu7zIFAYBF1XJehpnvKZTT969/ijnITXhlLSewGPLGb2RSzGY/JhRtJp0p/4M6yqBhKouKZkt+KDFnKHMdNQX09bb2E/3N9GtQvwD1lUbx3Qjzbky0MXFHLcYtr6HKeP0BozdIz6F4yGFmfwBMt32F+3HJ2xgmrruC3zsqwe9iK20itaU+0Uz3XnakF9Dz2IVa7+vKM9XbqpYO4mmoqYuO4fnUdx6yswT3iGgDW/vgc7rpEX7smaw1kLqHTke+H9Hmx+BYhPWRvepoq00oA/tuyhmhD8eKqNrPs004AxMR254/+w3n85WuC2vDaJNtfdlFaEs990a9THRUTlP+2vIw4/PdB0p1Wil9whQjBUzccydE5KmDrVRNfJRzPp63mnLvuiHgOm0JPjR38aI2QRnOQMG1NISM/mOPbDxKEvgkTOf36GTDpcVgzkakDP6I8YwDffryAgav9BqcdNv0MQlAe7f+i7jarlAqTg8KkaMZu6caZiUtJcfm1B+l15Zw8dzon1CSFFYIWtE2kz6YyABK6ZgDBglCgELSM8zg6Yzy/53dgUVkL7soNjj9V4TqTeOv3uIH3Cz/B442lj2ssqdaNvjJXOu9mtmhHX9syjirqgUDgEW5+yn2foZVWOrtcJIgaVjsuAwc86r2duRlzSalLob0h/BR4Y/nB1ZYaaeVc+yLGHH4CQnpZWT6CBE8VXmBYqyy2Wi303noc1Y5oZkT3ZmlJG46xrmGyqyOJHoEEtierx+5NXS6BLsHnJrr7WNYzljs3R+ElglFTABfPf5Q4ZzJDRkfx21H/x5fHZOO0CB78uoSsAe8R30bZ/nSyzucDRoKAkh9HkmX6lbrzi3AHmHUVxqwjqa6vb7/bgk+wenfw95Z36HLudUH9fiaV1oW28HOZlb9rzDy73cFjWUqzZo3xkNK1lCdq7sMrzLCxnA+6ncpVy8ZRZ7Vx54g7aLFlO9Pn9MV5RLAPogYahKB8p+CZgii4A06eJuhzpJOMAIfZeXUqEO2M7T14cuhtOL2CtV/2ZnFMX9Z3m40ldi1TdxxL6No6jUahNUKHCFojdHBRWeeix2MqntP3Nw4kt0U8XR4eH1Rm7oPHkxZnh5oSeDYntJHHyqGqkJUzx3HyJP/L6NexyqvzhO45eMzqE//8touoLbbinmEPbWcXkEjanVtIae0zOGyrSLK9S707h0L3a7S0n+YLQhqOFfMz2JTWjZPb/AHAa6uOolPb4SSIl4mvf5otNatIdbRnYdJ2VsV8wzNVC5jq6cGlrvvp4pjN1pzvQ9r01LZiZE0en6eo47R5JTk1yayKVUbado+d1vknsaByoP8Y7Cbqh7bgpVVPc+F2ZT/zVmI8bxpxwBqoj+pDghzGqStW8pu3E4NL0sjrGMPkHlEMLF3HTUn3+cfhjAqZihpXbmVFrRmHSdJx+xD6Tl/O68eWcte3Llzxl1GQ0Z9NaVY+OTaeG8aPYvBJKkr96u9eJb3PGBJz/mruZSF2vIn4Hy3UOlKY2/c+BJKBMx/E7FXTgpWdLFTeHj4USlNc+efrCKeXDtXr2OpoQa05mrqTWgaVSc27HIGHwlYfcNHPH3LKiEmYAmKT3b45Oqh8O5uHWzPUCru7p95LXV0y3TIXszrpe2QE4XG4+wj+d1WoZqs5aI3QwY8WhA4RtCB0cDBpRQFXfdz0dbz0yLaMnrkJgIcto7nKMj6kjMw6AvcZn5L/wINUT5/Oa73O4pecgfQrWMGTMz+gwu5gepeWCCS9k7ZxbOZ6pBdWfpXVZN9/dmnN0JXBQTEbBKouWws59fiVbKn7mVYO//r21d5+RDsfwyLyyLTfiEcmIaUFi0nZ/VQJwbqZyaRIL/T5BIvYjEemIIlmRsFY4m0p9EgKdtD3PzmBceJIAAZZNlCXOoXVSSubHHtTOEuPoH77GUizoP74lti8TvKmnaDOJdDT8NmTbfNwe0Y9HxTZWFJrAcwUt3gBryUJhImjK7ZwXdxtIe2v/OodkrtMwOuKIrNvaPiPmB/NODYIXC0llad6yPg/K04RzymvvEtsrZfHHbeQSXhj8rwpt2OP207GYWNC8rwuO6nPmbBt9XLcm5/Tbd1qXn/+0eAyQnDcm5/TR85jKT0ZRdOrxxa+k0vb47eQ1N4fUd7jNLHwz64s6tiHSe3O8KXHlowmquo3ANpvieGoxcrxoTXGhbvWQlz/HbyTWsvQODddHR7eLbJzaoKTgbEe3im0kzkjk5FTivj8Ii+/Z4YXgmxuuHpTKjc8MTls/s7QgtDBjxaEDhG0IHTg88nMjTz8w7Imy7x0fi9O6JpJ90f9q8LW2S/GWysoWRVLardKdlw2j7LTR4TUXdIinR75hgfm3LacmLOaTvHFbPgrF+f2Un7O7YjwSrrkFxPt8pJRrl50lfHJrEq2MbT7gwiLnWhxIx+tb0tMvROTV3JNnzkU1MWQ4aim1juISvdZZNjv8vX7ClcQ42nPya7evjSL2IpbtjTGv5m3k8fyRulyaureavL4b6aahXj8bYsa/rYv5buc75qst6vEiBRmrV/gc8KokLzc2q/Veb/QRqnHxFaXiZq4EbhjzuFD67m+fGdVGtMWnMzcjl+z3WVixPInMHtKyDzmabpERdaMNTCvsC/jHZdy4dRq2p/6QNgyrd69lt8T+wMSYXZiiSqj3fH/Yfuic4kpGczDR5h59rX/UJCcyv03Kw3VJxM30ur7+6mJTuDUF96m/cYVdFu9kN+PGkH06i20cj1PTwskdaggz2ninCS/0fPyz9vjrLSx6sz1nJ9eH3ZMAO/vOIU58UcSXfh0UPrIX9tgln6BZtSwTbzcOrwm6u7NUbgbaYDa13joM7sV3xyTD8CI6ZmkVNjJSinjwjenRz6ZTaAFoYMfLQgdImhB6MAn+//GBe3fc1JnurdM4Mmfl/P/7Z13mFxV+cc/55bpO9v7pmw2PYQ0UmihhN5BmohiFwQVQVQEFEFEBVEURCmCwA9pSlcggPSQkISEJKRtdje7m+1ldvrMLef3x2yyu9lCQNQluZ/nmWdnzz333HfulPu973nP+z74lYVsbA5z+JRChBDc9XoNN/xjI/NaN/Hd9x4lPzm4pMNI/GNWJjbmK1Xv8sCmOVR0R6gp6svye07lD4bbFYAS19ewZJyI9U00sYMc/X4AmimklHZqdI0/+iczN5LHDuNQTk4fgI++aTeJ5GHXW3w2fciutnqlA021edT/DlO3H8eivBRZlh9DmIAkITVOJjbAjjcJckXFjazPynipVtfWk7AOZoVXsiT9NmeWlTDRMjjLnoc1ZgXRgMayqIq/IcWl7SHiQvC5smK2uVyMRIFmc3Vpcshtq2IqD3S5+crmbzLzqJsB6Kg7jLYVn+XPC36IqaYHnjvd5tJchS1vXsUjS1zcwBUjHnsnPdsX4M2vxRXIeNLG3nI0Zs1bbD/sB4SrX0Sd8VlKhY+xboV/FtncXhahpbgvyWJx2w5mf7Cc/TdlSp+8dNqpWF1w7OtP7eqzvrKH/WozQfdtQT8xM5/JYhtTT6ml4hqFvxy2hL985mvoiff5knEDh2UNzFm0O1c0erElWP0EzZjtY1iyQWH59C4Sk0NcWjy0oNp9yizPsnitfseu/xtW5vCofyYA7bMlv7hy4PdnT3GE0N6PI4Q+pQgh6oAIYAHmh31RHSH06eDdZ/7O1uVv0bw1E7D8uZ//hpKqSdzyf2v43brMj3ztjSdQ3RZlUnEWViRN5F8N+I4uw7YsPIG+3D8bD14AnZEhjwPw18lLeGHcAkKuAE8+dxUArTlBVo0rRCoqSImQfZ4JCaCoTAnMYXb+kTQonYyx8xG73ZW3ihA6GnkyQJn7dBQxOA9Of0/Kcd0H852Wz5EWBmt8m1kQ24+0MPjxmD/whfaTqHM3kVLStGsdPJH/GgAHtRzE2yWZGBi35Salprhgy4+5XZhIM7P++kWyyBI1HDP11wDc0dLG+O5H6Cl9h5aZdzFp+WfwRQpZe9QfGYqDl3fhSWVe/7seN42axnZd456cgavv5vpMvpDfJ2a+wb38iYHJGS9t8A3wbHyOv1Ja911MdWiBeuGyW9k4po7HD5qLkDavvr2VxoMyXp8/cgkXctugfTY9eieTzDcY64ljrHmaaw//LqvMPqEjVYE2PovCIjdnPXTdrvbbLrgSzTK48MGbh7RlKDxpA0NVd8WQ9U+KCbCpJI8xXWHqilR+/AU/BUoIj5IRjF8pSA8ab9t7efy+IMlkt0XcFnyvJMkHCYXpvZ6xllUFfNDqYVFFCN/+UUpbkqi1Kc6s6Cvk+15t/ZCrf37TXkxr/ln84rob9vj19ccRQns/jhD6lNIrhA6QUg5fKrofjhAa3bx09x9Yu3ToZeaX3PxrptyeqZ30bbq5zHMxHHEV2/OO4b1bH2dbZM2uvidccjnTDj2C+L+eZftFAz0J5x97DQ++cD0Af5h/FKgWYxMNCCSV7SG0QB7vzZ078OBS4KvZQmLceKTWt1SnwsqjUc2UXCi0XbhkgB29/+/k9HQRs1xXDno9SSGYP37MgLZ/bLydE6ZllqBPSFZQ49mzWmF7Qr5qEzXh+S1fIS7nsPXor+7aNnb5VdQvHP4CefDyLlbOyqbwgwLGxbbjkWlsYM74MdhCcG1nJzmz+8pNVDOJe5Lf4TfbryU5pYNOq4h8tW3AmLt7MlL+k3HHntn1/92/tSg4+nbcisYBxw5Okgjwy9f/RcWhfWJo8+N/YNHq3+MNZQTJxmlTuWLKl7H61dVe7HmXrpwCSlsbmVqzfoQzNpi1VT3M2pbNouod5MWG9nyNxJUXXkhY3IWpQneW4EC/yTl5gwXRcKRjKm6vyZFvdiJhyJDo1cU5zG0NDbn/I/ZszrnutY9sNzhCaF/AEUKfUhwhNEoYLr//h+4mWfXsE+zY/AEgqH532ZD9LBQiRSfygL+CxZbFF6M1/CCYzx/tm3i1fviimjvv0KuLcrAmeGj3nMQNOX1rtb9V2xdrY2s6sUmzBo0BkNs+H83y0lH0FlKx0IwAEomlx4bsv5MS2riQvqDfTUzgOZawruhlVvkz0yXTYhNpcDcR1T76aqQ9pVizubJ3ymrs8muoW3ADyggr05Yu/zVncjc9CzcOuT1ctwA14ueoxN9YNz1INNDng1jyegd/LD+LC3c8tqst4ldZMa9vSvG59lNZmly66/8pbQs5WpxHeP1fuK3k7AEX+BNr36ZuTAWrTjxwV1tBXYySWILT1yto3i4mnvwD7m33sDaZETxfegHWjc3h/aowaVfmdZ78xhjyI4MzKz9yZCPnvFIxoM33mRpc/mKeVo8j0erl63m/x5aSVZbKF68cOTvzispS8mIJJraFRuyXLDDJOq6LxEQvZkija/LIdcB+2OjFa9q83m/aC2DNpABFIYOy9hSvHZjHJS193lAhJZ8NR7myK7P677HQOM767fsjHmc4HCG09+PkEfr0IoEXhRAS+JOU8s7dOwghvg58HWDs2OEvmg4fk50JCoPlcNmHJ77byUNXX75r6msoLn/kWe55ZRN3vL6Njn4339Ma/sx70uTcLniV4d9P1eq70G8pzefYnDYO8CzgKDvON+MfMDOyiYsmvYNPM7BtWKosZpkUBMJVmFkbSSq9nh8p0HqLa3oSJZh6lJyujGDqLHwHW0mjG0EUy4OlJjBdEbR0mr/JWbzn7qupNTF5Px5h4vZtIe3vixmZs/k8pupRnpjZV/PM3TWfVF4m902q7VgQNiomWmFmxc/s7jxastpo0TRW19ajA9tLPVQXeGiut/hlXkZ0+BTJhQVJxrr7bvTqF16/yz9yMXdxO30J/q7gVg5Yns2s7QbLuYwHJ3j5WeH5g85tcHwmT9MyBhYtPWRZJ8AAEQSQFbM49O1OHjpoDqptU/Xq8eS55/PInJ+zcPvJ7N98OM3Fr8P4Ss6Wa3g1PocKQ+GohtUctvEZtiTO4oRoF89PaKdmYiEXLQd6X0XE0Pn1+/NoyM2INn9CRVoV7FcL+9Vm8/TBzczdkjOkCNo4LkzCYw1oe3P/DqoTKiQ6QD7AdckUU6/IXCKmjOn7TAUnxnjCk8eS9X2xO29MrmDBjh24Y5K0Ai5hgzW0cPJ0aBgPFqGRuQDNmdsD81JsKvESuCuAWSrJbzTwFaVpKrFZUdAx4F7jouJC5o4X/K7Ng/BLKlISzyrBo/dnPlsPHKnwzEIFJDS2ewi9k03klHFD2uLgAI5H6FOLEKJMStkkhCgClgLfklK+Plx/xyP0CdO6Ae7oyy3D55+AnHGQ35d9+NfnZJaIL/7cl3j9/zKZfxEi40XaDX9OLl+94TdsPaRvGfj3D7mIdQV94/X34uxkQtYsjih6GWntT8tTA5MNvltZQnvQz3envoEiIIwfP3HUTLQPNXIcj8W/hSuViyudubAndcFNZ+Tyz6Uv8XZX3zRZQLWIWn137kLvQE17MUUmG3CJvpHP5P+IK4yvc5Pep8lvb+nL3fO3WQ/T7st4vhbVncrs5kyl+52FQ1P1n2eLdQM28JLPy5J4gl8mL+RHnkwMjwXcN3s/xgczy8QPWdZJJEtj7X4ZQdocK2LpG1czM/cJ5iwcfql0x4aTeEw5lyPWJXhksZvGEihudXH+a5l4qsY8lXuPzmay3MiX+RO/Sl/PcStTnHjQNwaN1S1z6XrqB3h2vMi0Ssn8QEYI/b3zZ1S41rMg62EaUjN5uvu6QfsCtBe9DgJcqXwMVw8FbQcN2W8o/rjoOyAgL3ImafUJzny1YsT+XVlpnj60eUDble1d/HNMkCOzTW5p7Uua+eiNQwc5dxa7WV4y8DiHFtYyrrKd2ypzWN6p8OKOzDG2v5LPt07w8cv7LDzFScYsCtFd7adjw9DTfR/G5M80c0plCfW6PsATe/pbNp99feRVdi+dOJdv/XpwWoI9wfEI7f04QmgvQAhxLRCVUg4b7egIoV6i7eDLB6X3btVMw896kwl++QUomwuaC3oa4eXr4f2H4bMPw5TjM32euBDW/nXQsLt+ly/bhNy6lFt+9eiIZhx01uconzGTZeEAP/j7eh6fnsB/448H9InoXs4+8XoO7XyTWeF1uEt8pFsy00gl3vHMyz+WKv+V6EoDmx4rRVq7LSX+TDMuXSKBnzKwvMBUWUNn6xf36JS1lOqUNPcFPE9wL2N+4BEe6byF6d6XOCJ76CXtj3X8imarks2Fy3m9qu98fGPZbwcEWFvC5K6Fl7Omrp7hJkkMTXCmeQ3fXvyHPbJ5J7bpomXV5whvPxB3dmZqJdUzsmC4/pw8pjSmOfut6IB4lDsXXsbMwml0hddSoElyVhzClOjAnDoKBiCwURkqkmWM6z3ez+vG0zMNb6J00PY94dUJf2V77gZsTwWNpZfzhY4bKH6iTwjced5lXPTAzcyra2b1uBKsgElq3lYO22Lyos/H15uieCYnyLMz+7wzJo/smzIi6O8HCZ6br3DPrdaQxz7ijr/ybXkzJ7W/yn4fpJEopL0pvMnBQiTiV6lWNVLVLhYVZUTmd4oKmPGSm0M/yFx3bE2imAPP06szBYevG/q6dMtpChEv/OSvwwgf1QXW4NijR886nZ9c//Oh9/kQHCG09+MIoU8hQgg/oEgpI73PlwLXSSkHZ87rZZ8VQkYi81jzf7D5n7C9X6HKmWfDupEFyy4WXwFmCt7erZbRNR20/mgcD9bNHXq/fqiahkynObQ4QPrA/Tlv3RyKYl38ZenwP9ATTungXv1oLtQzS3+lVGjRnqQp6OfnzW/zN881xNtdbH+5YMB+3V6V16aXohsuotPnD9iW3bU/rnTOoGN1ZimUR7tJyiAgcIkYXfu9x12VJ/Hdp0MAPJe9jX94vzekrSZ9c+0v93yNS2cPXeH+wmW3ArBy3Db0dCtzmhdwbsGl5GoZoXLG1Dv4+6aLdvVvKXSzYVqfF6EjkUeBd2BgdjyZjW+3qunbnr0RIz7wvIyEpcCLczuZs2k15z37JHef9g2quqajhW7lxYV1dPsgJQU9PsiPV3Dm+5ch/gPRBaU13+HGswOcvv5Snpl+O1FXiCVbP8+kznn8q+ohNhUtp2PsAwP2+elvfkAglWLu9tYB7YrHYspprXRt8dO6euBqt/HHtOPNM9j4cCZJphIoAd2L3V0LQDpPkH9sjKJIhE3bSvjbcffwtdqMp2jzhDeZUpNJbVB/wC8Y27GSipa+edyXF/ed9/KmBFOrMzFlO1cL3lAcx1Yhbgum3qTibVSInGYQWygJBSBpCh7Tv024rpgrHriTsa3DF+EF0McejLLfaTSmtlO2ow5j07OoB12CnV1GwF1AdeHPOPzyoT+PH4YjhPZ+HCH0KUQIMQHYOeegAQ9JKUdcG7pPCqHnr4R3PpoHYTh+vbFvymrJCQfz2kvvUjB2PJGOdmKh7iH3Ob5sM94s+F76Qq5fcQ+iqe+7dvWBX2V8uIWvbnh2wD7fOPJ71AdLeH7ZfcjWzMqe0vkhPHlpPLkm4dxDeWDjZURyXkQ1TD4XeJKeRzIxPUKxkXbG02WqKqvnzqV2QhUmfnJDVazDw/zUwBIZq6rcvDPFQ49PUvfWMehyaE/AL8ov5o4xn2H7O0fuarOBqwrzeTbgH3Kf/pzV/RV62MjfJxxGImv6rvaFobU8tfbbu/6/Uf85f4qMB+CGg39Gib9t96H4uvlnKtL1/Nh3LQCf43Gue/DPVJ2fWXVXdIkXzc68DkvReXLxETy1+Bguej5MVe0zFLW/R1duLivnfJNEIEpWeDJhT5p08G3OfvSxQcfrz7Yv5lBdfzq6nbkuljW9SVfuFHx6A13aYDHsn/o0tV3ZeESaYOvRA7aVuV4jPeZ+Orbds6vtn1PuZHteX9JMwzURLb0d75if0CBLCYQeIppzHiiZvEZVjdu5+4YfjmjzSFj7GajrdYQvn8AxNwIQff77rK8o55LLrwZgSXMrB3Z6OG3HyMHSADEVTlkcoMcluF5+n4c5nw1if2Ym1tPVdgtpYfCL8jie3qEWrOomK2bRmvRzuTaB/B4XlT0u/nLyxTQXDFxZeOcNP2RS43Zs4PkFi7ik+ElejM1hc3klT8w+iTPuz5zHcYFuDi+Cv9Rk4sUOLy3n+bJpXPX9Sz/WOXKE0N6PI4T2EfYpIfTmb+Glnwy9LXc8jDsYumqhvrcW0xXbwOhduVT7Bjz1TZj3RTj256DodD72fe57snrEQ/pz8/jcj65k+V/vYu3qLeTNmo8/T6HkT89RmOgZcV8AeYzCDf4vcrGcz1hUyvST2PzYwHIWiZJyesZWUbJicCiYoWlcecK3mBKtJVtJs71qOvM2D1FfrJeX9/eyZrLkZ9W/Z3psG7MjwwdvD8X9ORXclPvhF8aOij8yvX0zdb4ePD2PY7irSBVcREp4GZfYwfIV5+3qu8qexGfSPwXgmkW/Ynywbwl9Gp1ruZHtjN8VGyKkhRRDT6bd/qtr0A2TiY11I5YufeTcc0BCfkcnVTXVVNbWjfh6JPDaob/BVjNCZMHqmwmEa3u3CZLuXCzRxVcu1QGB7LdC7Rv/NMnl24RypuD2reHWWfcOe5yJ6TQV2mweG9+XfkC1TCxF3fX6b379Cub9dXCaAUsI3pp1AGF/gJPe6ouVSuxv032hCWkou3Rggkj3mdfgMsfsPtQgDHc3eiojMJ4q1zl1x+AcUQCnHupnh2/w5+PC9E84RF9PfNM0Dmhs5W/dv8S07+CegzaTKvouBbpKoquHlsLMjYdmxPnTu51Mi+bwarFGouZuGgxBYVcmTmzDjNnM2LBmwDEmZs1hXsExNMa2UOGfjC1takJ/5/A/3fqhr28oHCG09+MIoX2EvVoIvX4TvPKzobcVToUz7wVPNmSXD93nQ9gZ9Fx1wCK2rXxn0Pbc0nKWXHMLh930KnPG5PBeQwiAseEW/vRKX9jWQ5OPIicd5YS6vjGEyw12ksJTLiBhZ6Ya1sSjqOJddkS3c/Bbe1Y485If/ZzLtujMMrNZETNpNob/Xt9/RBbbi/QBRUN3cn1+LueHI1QaJvcU7M+jYy/mhdUDg4RnzfgKdnwpu9NRfhsFOy4BoIcfotToJA+dBGqfFNndA7STKcn7SOFCYPObw68iy9W3PL+DAi7lD0ihUtVQx0lvvcKt53wJL3HmsYIgYf4hTh329S5ZtZyr7v4tAnjuxBOZ+f77jG3IZJp+cd50TDOFYkuOXVeDADZ/N5f62FRmzH6NeEM+7hWHMMF/Gp2mZGU842ma5lEoyduBBxd2hwLxLtTsMtZuuJZrPjt0np0ZHpNKt83mpMrKrO+S9s0n2H4Laf+hXGa9xLk1L7NSjufLs/uKg87/YC2/+v0vaMvNo6g7Mx24o7CY8vaBU2BXfOtK1ldNJunuC3jGtjl07UquuzOzgu9Ptx/HZ0JbQA8j25opvFlHePMIHDuwzMVIHD/1myAg5Z1NuLC3TIqU3PJegsXtQ3sT757g4o+T3LsWCVSyjVoxcdf2M+vT/HBj3wq0pcUaV87y8EXu4mhewBUtp/LtG7hk/I2M/SDJuFbfoGPs5IP9j+SnkfmD2l/xvskXfjI4p9We4AihvR9HCO0j7LVCqGMr3DbEb9Ts86GzGs7+C2SVfOzhH73uRzRsyOQfufyRZ4l0dvCv++7kqK9djDcriBCCtGkz+eqBguLKFfezuKkvb4l/UYqV+RWEN0xgQf0qhCtA4KjrEJpnwH7dIspS11La9HxCvgBPzjkMgBPe+hdXPHgny+dfTcxfyhRfM7pVjFfVKXMN75k5bb6X1qCKofWKESmZH17PL7fewvRYzYC+d+QE+UXVFaT8B6JYEaSay7svRgHwKG9T4Po5P8o7k2eyM0vIk6klGDVHwrAhzlBOO18oepXPR/+Bzx66VMKi5O9pIR9tksqfKi/e1a7WKPSkcpn8+wg/uOQHJF1urnz7EezPrQNFMGVpxqNiK2nCqou4KjAVOOPQwKBj/G5VnGneF2ib9iAAk1/8Mw3mQ7SN28K2N32ZuGYpGOcdT7O5g+lfzrx30lapf/VyEh2TBow35ayv7VrSHXz1l9xX9ASvZ/W934oUXFSqs9XMp8UOUhvaxPXlmTpk77KQ34rvDxhv8vYaWvILKeruoLpiPEeseocf3zMwHm3ZfnPYMraS0199gWA8IxRtIcg6J0kFXbyZM4ceLYuvzRi4Qi03YZJvwBfqDE5o7lsNdnvJQxwaP4v9w5mp1UvneNmapVDYfi/u1GamJMZxWfMX+Hvev7ijfBmaOdD7ZAsPnWPuGtD2JXknF754NuoIn4nFSwLEez+PfkPy2ivRYfvupG7Rj+lssmh4o4SUHUf1WVjxzDFqxk2myJuFGszni+1zBuxXU7yNiyfvx3HVt3LTdx4YaugPxRFCez+OENpH2CuF0I7VcNcRmefTTgbbyniADvoW+PJG3ncPiPeEuOPrmVwy3/jj/aR0P/kBNxvuupOTqkt5MFDNws8dwazbqhFI0i6dk7e+NSDux1+cxL2kh8+UT+Hhrb8a8Xj/CL7B/1XOYHNJX86TWd0mliGJ2eBLWpz/bpwFfo1sdaQJH4kmGjBlGaBy4FFe7t74Y47tfJsOMZcCuXrXaqhf5+ZwX06mJIXZcgLx0GJmWkk2zMzj6m0CkbRZm6fxTJaNy7wLPZi52Cd2nIsZnj3s8YvpZrnnkiG3tstsNCwetI7i9uSpPJD8IZHjLGS/G/2in+i4jPHYoe272tQLLsPXM3WIEXc7ujCx1SQGGocsKRy23x3ySwQJ02oI4pag0mPj6akkmV27q8+mx+4AqWFbIdLhe0kXnMzaxb/ha4VprLTCB28WY23N4YFjuzG1BEiDyfZY3h17NSeIZzmLhwdMzUXI4kJxHwBBGSIscrj6nt+zZOXwnr+428OJvx08jVZot9KuFH/o+Ti/Ns0lW1O8WqQRNCQLugZ7bg47MkBMF3h7niHQ8yiHhOdwYetZXDb+Ztr0vsD0H2xLUhvUCNpn85muo3a1r8lRuXHCaio7XqHZ1YnHdnN++4mEtAgndx824Fg3T3bRVbiBta5pfKX9fc5YP51/Vu2gquxWJr8x8ncEoCP4DgXhRR/aD+DkKReTrVssXDaPX95+/x7tszuOENr7cYTQPsJeJ4SkhJ/mZJ57cuAHdR8rw3N/bNuiectmQq3NlFRN4r7LvwnA8d+6gtpb/8SEde8BUBMsZUK4eaShAOgct5CXDhwPwPnJxXjQB2zXRTVFrstpl7P5g7KYx+YeTmdWzq7t5XGbp94YOYMzQMSKstTbw7uVlbTn1fD8exeO2H+jS+fs8szS7bRnDhO1MfxA/yu1mp9bOk/AtvtKfcTrvo6VGIcr/zXcRS8CYITmkWzOVFEv8bZzkf5PLkgPu2ARyMT/PGwdgYynoTOKy/awzjeDn06L0z1r4PL7st8twFdyEsYYiaVHse7/He3fV6j64A5Mkbno5hiSqqjNu+3/ZEHh8SSyq1kRe5TAfjsIFGXOmV4jsLMkz7efxn3Tz9+Vw8kVewshBKrRgj/8JJ9f+VP8Rt9531ih013exUHLs5BSkgr9Dhh62ifm8fOHL4485TJZbmSLmIZi25zw1r+4/KG7R+y/k9ZLLe6b/AWeFyftUf/+fDNyLi/Yf2Rbds6gbYot+fubMSoSmd/+Yw+O0RkoIaflp5zUVsRFLWfjloOLzP6k4g98sf1UKlN7NsXcrf+FlHU8RdSTK5bxenQRFfo88rXBHkyJJC47MQxJc2wVCwuWk7AnY8jPDjHyyDwVMjglW+OLVdfQ5u7ixfodPN10JN+44/GPPBY4QmhfwBFC+wh7lRAyknBDvzvha0cORk4aFr959AWeWNfFw+OfRRx1LTvMbNa1xJhZkc1+Zdmcd/vbbG9tpSjVzpyetfitOC8VHkGbq4hnnx650vqg442dzT8OqMTYrWL555OLKdTuImkdxlrXc7yjzcSyMkG1NfmlvLjfQgAWdZjctiqBKlpQ6SYtJ6AQwSYbgYEq2ihxD+1t6U+zq4CidBcqmYDdGl3jS6XFdKkjlzQYibRnLhHXRYgek7rtpwy7ygzAsBX+sGUhptRoyU2ydXyYWdN6OKjmfKa1H0TLjLuJlK7Y1d/fMZPy1ZcRK1zDjjkDA1sT0s3DXdfxUsHE3Q+zi8s2bycvVcSb+QbPlwd3tR/39qs8f9DhQ+7z1adfxmMUkmtWYKig93s5Jc3LmLb5wV0enVWVY3jwhDMY11hDS1EFdWMm0lpYNuS4QzGtZgu33XwtyhC/uR9808Ny/QiC6U7ODK/l5AV3EHF5B/R5et2rlDXN454Jj3DfxLNJiCBHNxtcvDXFr6Z5uGlNArcNNjYJkcYvPTw6RudX0z2DjlcaSXB+TRS/YXNSp4/VbKGhfinZrgIOLzkHgIQZZVXni0zImsUbrY8zLjCDRYV9oqwuup5x/hm0puop8QyfudmUkud6MlNybgHHZQ+8IVjW9jT18XqQI4t+BYWzKq+g0yV4rcdA9wmMsI3fhqTMBLJ3+xV8aYnbkAitFW/4btzCQCkv47M/f3jE8YfDEUJ7P44Q2kfYK4SQbcFL1w7M5fP92kHTYBc+sIrnN7R8Iod89LlryDIysR2dniBfOeqH3P/CzwgacX582EmszpuHKj2kpcYvzQib/BuRisL+s54nO7t90HjvrjgN37gmZhSvQDNsbEUw/f0UxZEPj5MYjn/mzOCnRZUIYytRMrY+09DEnwpn8EzeLAyrDVdqcAmQt+saOGj84JVC13R0sSCR5OQxAy/y17d3ckosRjhLoyPPRXF7ipRboaDLIOJXeSP/INrqD6b4oPt4UPsSx/EsZTTzTkgj/mIFR55dN6T90lJojxTy80hk0LbvFiW5I7IfDYXDrAL8CBzdbLAyT6XbPdgjccuf72XOuy8S9XjZNL6KN2fP56nDjtnjsV/41uepKR+LPxGnpLMd3bJ4v2oy3/neT4fd59o3fo//oC1cof5+xLE/X5vmO1uGjq/aE1IK2AK8FkMWcf3mX25kSeBIcl3F/HPHnnmrohP2QzHTICWeZBBPan8i6gtYeYVU2lX0hMYP6G9bHaTDfVNTZb6J7JdzCC83P4glh85ivRN39kU0elaRa4yjO7+S+5YEB/U5oGYLKydMHtR++RPdKK2/Jz1W4cqbHI+Qw9A4Qmgf4dMshKSU7H///rv+v6G9k2eC2bzjziSzm54/nQ0dG0nUfw0rPmGIAWyyjTARPQt7mOXWgXScha0fUB8opjLcTJM/n2++/wSV4YyguvaMbzLN1UncX80L5VFwf4Pja96lQGll6tQ3aWqeTPOOqQhh4/FEmHfAc8O+Hi1tcfDybrSP8dVLCkFKCA4ZN3J25JG4oCfMqQk/l0z7Ma72Luq0O0gGz8DjOZhzt/+ZmZM+wC8yd+ez1/VgA+unB7FHiEtqYAzPcyLn8BBBwrzEsRTQzmxWs4NydjCGOH5cpBjLdipooDEyhpvEVSREnJi/nKzQ8/hiFq3lp6AbKSZs38zm3YrBTm6o5w83/QTvxGNI7ncSS47MYnGbgUTwRlFfcsOzt2fiYv5RpvNKkcZXa9LM7c64e6IaRDXB3VUunqzo89qdX5vmLX+Y2qI9T8J4wbOPc8GL/0TsTL8AuCYfT7p6KdiZC7xSNpe750/lgeNO3uNxAX67Ks4hHcN73ADSGLh2m3IdjiRb6FQKcOn5XLDAQ5uv73xN27qWk14eOYfSR0NDD5yKEf3biL1sobBjzuk00c0RdTouOZ1i+x2WlgR45IjDOOPtKNk+hXtnD79SbE84591XudXJI+QwDI4Q2kf4VAohKaF+GX/5+zncnJfDBJdFo6GQlgMvyGaskkT9wCXegbG3UVA7jzr1QIbiHPd7ZKdjeJJJlixdisvcecGRFM/tIbLDS6LdhbQVXjrrHMbOeJ4OfRZKR4IvNT85aLwb9j+Xq95/mLumHcNCsYywT+cW73dZkF7JIZu380ThEhaktzC2J86BoZc//KUDlxcV0KypXNYVohONK0rzh+2/MJHkA5eLb3eHeMFfykrvYA/CK/WN3DbuQu6qOBNlY5j/axBU9a7uebT2V0z/wmZ078gX3qF4lPN4SnxmyG26TGEI95DbPgpHthhcvy6Je5jKCna8k9iLmVidrRXj+N53fkQ4EMRvSIKm5MAOk0u2pAiaYGCj9xYutYDXizSumOMdNObdy+M0+AQr8jVW5KvcvjLBxOjINa1GQgKr8lRCuuC5Mn2XcLtkS4ov1g4uC/FRiVoSoaTx9zvfJe4vYssguqhHCJOk5eL2LZkpWEtRefD0b9C22/TeBc/dx45gARN6ZtCdU0Bhj0VOzEbtvVQY8X9hpd770Ne6u2xO5ZfgNiSH5p+Cz1RwqV4WH7fnonMneaF2vvzwreRHE7QVFfHcISfTnl/MgWvf4eCarWyvHEd7Vh5PzjkUKTLv8/fef5rvfefHHzLy0DhCaO/HEUL7CKNdCEnTpPuhh4gtX4Gn1EWo+0lWjc+jcMbQWZtNC2LdFvfWn09hPEV2fQjVZdFIBe8EF+LXY+xfsIHVbbPQhEnM9DMppxpXXHLdB3/Et77vZ9qVZVB5XDvKxwidiQuBJiWWEHSqCoWWRYuqMc4c2t3fqqp0qQrT0plEdE2ayrFjyjkrHOGKrhBRRfDZshJatQ8v3dBRfgf66nZEl84MsZ2rtP/jauNLBONtLG7NFDfVhMXUYDv18RyatQLaXAWs8s3iSM/r5O7o5vAph9Az61Hw98VZRUxBbG0erqBB9rgoqitz8a9/uoyu5iCaaRP2Z+ETcR468SJ2FH68/Ex7wjHNBtetSw7ynkWNbtY0/I0dopPWnDIePf3L/GhtjEM6dPwKhFPtvB3+Fz/5/FcB8CTjHP7SM7ytHkBQNYmYKscmmihH0JhuIzIhi3WzD0I1ffzs/QTTwwMFT9uUh+geu5T8badRUDN8zqIPI42JhU1MJNmmtrLQ7FuW/462hclWGXkywMPutygzJZVmFn59GXnm4AB4Q0qqkzaNnmqinT7SkYcAUPSJnD/maR6smUqeO8FhpUnWdwfZ3DPYc6ToEwmXnsYdJ+Tskf0Lm7awtmACZyzbxITmUmxVsLxUoppvsnXqQi556E7uPfUs1ldNA+DY157hiDXrmLd5PYqURPwBTrv5rg85Sq9tcQPbl7H5uLrX+XztcyxS1nILX6Wgs40la15jy6L9qNh+AJY/RKLqcWJPVtAcLGB8qIm8RJgnjzmBvFgPEwOS4367Z8fdHUcI7f04QmgfYTQLofjGjdSecQYrpmhsn+gn2JzHrK8MzHQsJdhSQSIIt02hK2Bz28rPc/tBV7L4nczy3g1TAnTl6EypjlHUmSbhVujMcyGkwJZ+XGaE7KiBJ/Xx7+p3khLw/cICXvEP47KXkgtesknr8NfDMwrLn5DkRqGxUAzqq1lg9sv1gxAY4Rm4DI0vrF6Nx4AHppxEW+pQbE1FWIO/t1/b/mc8/XL1NLuL2RSYTE95DhOy6xgfbGBxxbLhX1NK59I3f85Z2/9OYTJzTg+qbiIr2TemBO49+SweOOGMAfs++b2vkR2Lsr5yIh9MmMzJb76MN5XiuYMOJyca4eD3V+3qm3C7eWXBofTk+5kWz2JscD6bCrPYnKVy6g6D8TGbLVkKnrfvJNBUw7aKo0j7NNqz90fKJGZyBencbJ7R98PQdBYJjW+jU6O2sk5uxZO0iETqEMkoajqFVFSSJeMwg/lkd8/AUtNEg9WATbb0ESzYxrhpryFUA6RASBUpTAzTRbjmEAqr3sZU0khbJb7hdA5oPWHYcwiwRqmj1M5BINisNbFZbQIBmlRREKRFRiSPtwqxbYEHDVNYzEpX0prysDVlc3xQI2ZLXo8O9NBJO4JttSPNdqz0BqQdGtGWoVFxZZ2JovUJWFvA1lKdRw8dGEOk2AlsZbC37JNCdKVwv9uBBKxxAZRQChExELt9RUvopIWMR/QB/efMUrbhxiCFzkp7KmOVVs5MXYtLSE5wbwLAkAq6sKmzcplot3L1DU7RVYehcYTQPsL/TAhZBrxzB81lR9GulpCzdT3WozfTSC3pczJxFVbKheJKE2vwoXst3IWZC68dF8SXTeZ9ZSbedD4TtCQdXXMxpI/J/r+A1cP7zQqQmVb4zpQ30ZS+z7MtFTYmjqAhNZMx7kz9ppiVx/zAo/RYJbQYU5jseR2l369u1DwRXanFrQwOLn7H4+a7xYXEFIFLZApwDkBmhM5py2wO2SDJGjrB8CeCmSf51kGXUhzv5pz3XmNad1++nUZ/Aa7SOJ5pYaLHZ15bGp0WyrhS3LKr3+flPezHOl5JLOHN0MEkcaPmalxz3++pK6tg1dSZTKvvIeUpJ61005klWbb/wIR1+1dv4cY/3YqpK9Qdfh4L5TS03um2JmKU4WcHbSx1f4AhbbqklywlhQebk9LzKJIDC4ECLA0bxHvfEkuNga2hSjcq4M3ewRSZz464lzZT8u9I2kWlzVQH15GK5VMw42ni71zMDLsEq+Itgm0LUKzBq63+XUwpWRO3MCX0WJJkv59fKSXIJKJXeEgpsc16zMTrSGtw4P3uPFV6CiG1kAsaMzW3NM+B2FY7tlGN4prG9OIuUt5mDhCbCBLlBfMwvm9/nnwR3yUepC1osbNYbo0h1C+xkxRgTgpiF3uRvmG8lUkLPJn3/ry3nqepoIhXpwyuvybCadzLBr6eqWorm6y+laBL9C2EpJdVvWU/lH7vtI2gVIlwrGvPy8IkNJVfXn3NHvcfYK8jhPZ6HCG0j/CfFEKWaRPpTJJTnPnhtG2b6578gCmr/sK52l2YUrBROYC3usZjmZ2cOH4VlfEQrTluekw3H4zNZV5zB7kJm1e1eVh6DyXtKZKJ6ayPXfChxzeNWu7Iy8NrxZkXD7E/k0fMbLs7DQUr6Sp7mvWebmwBtgI5OqRCcE6+wcQCixHzF/aS/VeV+EIbY0LmO5V+LUjZk2lcvR4oKSTSDYn5NkpEYBZJIida7Ix1VbrBzgXf2wr+lxXih9gkp9v0uz7QTiGrmM9cVlFEX5kFCbxvzwYFNjCTXLp4UHwZgEKjg3b9o8diDEdx1OLWVyJMdH/8Zfj9WREzCZmSxH/hp0jzhND9HSQ6h1+Gv5OgCikdbI9kLAozUFGVDTyiRehR4mgYHMgGJiorKZZR6jWNHhGgNPwAiMdZnVyEZpXSYtsIID7C4igrvQUj9uzwHXZjenaYfHcn6/WJbNInMM6dpFvkYQqdqK3zeGo2Byc1CmyFp31pdtfsOiZH6tV4hMkqs4JGO2fE441TuihVwkzV2pESuqWXgEhRkmrECseJywB3ZQ8fDL4zZkgCq9xf5x/iaGoYP6BPFbU0ywJyiNEkSjK9JR8pP5iChSotpprVtCqFzEpuYKZnI4+UfZuvXXTVHo/TH0cI7f04Qmgf4T8hhBLRFM/9YR2tNWE0kSRpSXLS9+CZPpt5XYfydHmMO4v9JFfHUbIF1ng/6UCmnIF0q2BJgmGTI9d0824uxDtNZkWSLDQCuJTBCd0MbFpFKxHTYHFXJ1ZJEnV6E4qeJBWqwEr7iTbPJKfyTYrnPkxkxyyyytdiNJaiVzQjdwShNILo9RolmqtQs0K4Ap2Z8WN5oMTQhwg0/m/QLMsoFU2D2g00XuR4nuNUekTurnaPTLCIN3mdI4ddDdefhR0mZ9enOazdYnWuym2T3Lyfq3J8k0GOITm22WBjUOWOSW7CuuDkRoPagMLUsIWpgNuC721KjVjE9K2IQbfVl37QLWC2T6VAE2i7XdDei5vUpzPvxaseg9VukwJLYAroUiTllsKMtMrMtIrod9QuxabL3UFSCfOGWs5kuY31ShUm4JOgC3C5wmi2ymS9h85wGZv0jE1W7zBzUypzUxqtqkmWhGJTwxaSes2i1FLw20N7Pda6TBKKyQRD4RWP5MCUgtdWadRsxrnWEDBycKdGFlntis0rXoNDI++Q11MH7iik+j5zQlPBstn52/xiwZFsyZqM3O3Ma1gUKVHSUqVL+sgWSdJSJUYmWFogOVZby1ili2o5hleMSRSIKB1ycAmS/sxqr+a8zUuZEGnijcMXM7axgYL2Dgrb27FUFUtVcadSaG6LymPb0X0ZoW8mFWr1Ev5sHc8T1iEke4O257Vu5OoVDxDNyeKtQw4m6f34U207r1Zhj4+I1Nn/gw2Uh1oRKkzbNHxh5PoTz+PYXzseIYehcYTQPsK/K4QS6QTX334HT7VOpCh/G+XKDnKb4C11FpoiqRCdbJfFeAuiLCpdyUSljqLidvzE0Rh4KywtHUwXWlsVestiov4daNOf2LXdCpehuMNonRW4qruwxsVQ015SZRFsf+8FwwY+vPj5v03L6vOQW/MJi6kUNa8k4UpQ2rGRt/c7HF2dzMxUlAJ/NmW6SnPZW9jBWsq3nIfEZuv4vyHHvIXiSuwarzs8hQ2+CtQd+Uzv2UZyYwVV7jnY0uS13DQNEyaS66snVLCJrvAhvJI9OM9PVcRiW9Zg4RM0LCoSBsLWWLijm5NbBULzUJKUwy7Vb01sp9jblwyvLVFPlp7Htsha6mMbMewkuuImqOeTpecRSrfRkdxBoWcM3ekWUlaC4Saoxnb0kK3lU59Vju2rxJPdQTq5A9swiefkkZPIobgwn4dT5VQpOahqkrcVgyNzVnFC3Qckm7bQmesmXZbF1ckr2c/azEptMvEh3vccK0xIzeSXEUiKlQglSgRDKoSkl07bTxIdFYtCJUaP4SKhZqa+dDONpaoo0sZUMu45RVqMTUvOSgzOu7MnhBSbOs2m0lBo1FIs89rEFZscK8J+Pe9TGm8i1wiRVFx47MzU7mOlp9PiGbouXo6IExQpWu0AKXRcmJgo2EN8CSaGGjm18S1mxBpoK81jau02dN0giYsSXxeRBg8rC6ZwzwGns6D5AypEiGRxEF8iQdrnoicrB2HbVESamOdfR5VRR+ubQZoppjM/H1tRMDWN+nFjiWZl4U4mkULgTSQobm0l4fXSUlIyKKFof3zhKKFgEBc2UipYtoLAJKq7qWhoYFr1FprLythcNQklmqQw1MVRK96kM7eQ0vYPz+q+O56jj6Ty97d/5P3AEUL7Ao4Q2kf4uELoZzd9j4a8Sny2Taffg+mCHpcPv54kpgoiIsAWbTJ+GcFNii4xeAqmQtYzS64hS4QYbzYyzmpim6uUtHAzjQ0ECQ/oL0wPYU1nFfMJkUsaNz5i+IhTzSSOS7xMibeG9+KLiLcWUpTswDRcuFwJCvMbiKcChNIFNLVPRFEt8ukkP6+J5pYqPN4o8XgQ03IhvGma/UV0uPMxNDdd/mzy0jYxt4+xkSS1PhuBjYJOp89Dp8/P+EiaaVEVTUKrR6FHF0Q1qPcrKBIsJXPX7jUl+WlJUdKm0adgCChLSDbkDBQwlVGLoCFZmzv8KjFfKsH8uk1MbapFNVIoqSTSNAhl5ZAXakdaJqqUCNNETSVA2gjTwHZ5QAj8agoVG1WajA3EUV020rSZ662l1VNKUEQJ2l6w5qIpLShEiVn7k7RPQhLAkiZbe54kJUENgkwnSaJQ09IwwE5/0E0insA2MxdnW9Uwg7lYviBSEeihDpR0CiWV2OXbkIBUNSx/EKmoSJ8HUgYIBYRAKgqalsZfFMWT7CDSlEXCUwSqitAEdlogdRURcGEwsqdjd7RwF5bXj9TdiFQSLR5Gi/aAJUHYoKjYmg4uL0oiimKrKCYY3lwUJQvFFFhWO0bQje0vxBAKDZaXMVonpNNI08SlZALfEQK9qxVXOBOELoWC5fHRowWp1cqozp0MAg5o2Uw4EMTtlozrbGJGczWpHD+mppPwelCkpKm8L8g5t7OL7vw8xmyvp7W4CCVpYvlcI4oQTyIxpFfGT4wYfgAENnIP7zRyOzvJikbZUVaGpWeEpC8aJR4Y/H5MWbuemZs+QP03rjv1hXmgSDr9Cg3FVbw+ezr+xFpcXoupjQlaRBrD0mgugtPfrqG2WHDkxddw+KxzP9bxHCG09+MIoX2EjyuEznjmbt4ODP0bkGP3EBTdBGQMw/aQVHS8xImkSvAn0xhqmlZvEQBJffgf5uxUmJKubrJSceK6m5jHS31e8a4cIB8FISVu0xh0vMJYFz3uAGnNhcs0SGt7loQOwJ9MkNJ1THWgWMmLRQkkE3R5vUR9AcrbGlCxMDU3mmXTkZ2HN51GtS3agpns12U9XfhScVymgW6ZbCssJ+rxoVkm47pbmNxcT3Yihi0UgskYam98CQDSzgiEnX8/YZTe1UxCsUEKLFvHrcdQFItgbivJRBaKauLSk9hSISenBV1LkY7rqC6baKyAZDIL09RJJgOkUsMLE0VJY9sqI1Wu3yNsC8VIIxFId8bDI4w0ro4mhJTYQgFVQ2o6lseLkKBFujCD+Zn/bRslGcfyBuDDSo/Y9i5R83GRUpJWNdz2R8/XtDuaYWD2Cg/VNLE0jUA4jDudRrEznxFTVfEAeiKNL9xFd24uSEi7XFTW1aMoGkoqisdwo+ZXsXJCBTUe+NthRzJxRx2V7c1o0iYrHiWvJ0Tc42drYSn+dBJbUVBSJsFYmL8vOZHxO+oZ095E1JWmPcdPXqiGMa1t9HhDuFL1ZMULCWhZZBVUkmxrZ0p7O/6eTt6cIKkKazTleAiXFLKuUMW044ztCTJrdS21xRbrxqkIWom7QSp7dv5tJYuU7wBUo5Xrqr7NOfMP/ljn2RFCez+OENpH+NgeoRuvpKmohByzHU/MxrAUzKQLzZa9d3W7pU6Tsi8wU1po0sAGElh4ND+b8wuI6T5c8TDBeJLm4gra8/NoySrCVHRUaZGdjhAwY8w1lzM7+g4yMZ2clMWbBfvTpadIJdx0kM345jqkUHh59uGMbapDlRY9wTzye7pIqSrBcDeWqhEK5qEbKTTLAiS6aZDb0wlSUtbaQGFXK95knITHi6HqmC43irTJ6W7HF0ySVZbGk29ipnVae6rYmDeT4mQbvjSgWKTNkWIe+ubwPFqcoLcTxZck3FOcERg5LeiuJEiIxfJIpz2MHbcOtzuGYbjR9RRebwQh+r6nUoJta6iqiWnqxOPZIAXhSAGWpZNM+tE0A683jKJYKMLGMDwoioktVVyuOEbaSyrtw+2Kk0hmEe4pwucPEQkX4PVGAEkq5ScW6ytfoqppQGBZ2sD3fAhcCLJ0k/ycHurycygON5MK5dCV9GPZEoHA3uksUQS4XKhIOn0BelQPKSHxGWncpkbYpZIV6cIjJXFNJxTIpiU7n7qCUuJuL4qU5MfTtPtcH02k7Pzt6y3GWtnZRl4igctI4DJShHx+ErqL3HiUtKKQk4gjBVgIhGWQnUrSllOIK2lQ0rgJTQq6gjkkNBd+TxEthWUc3tjNrI44HlvS6k7SroRB9dLpkuDOBwRjQh1k2x6qvSk6fF7CLjdzukzypBfVTCDsNLnRBKmeOiyRJiswlvJ4EOLddHqTdGYJpMtDyJMiWiIobOggL28SbrsUd1Ql38gh6ummR+nGLlTIzi7EFBJfOpeOzhY6siP0TDDZ7mmmPdRM2AgTMsJ0hpqYv9nCY6v4pE5xY4xgyCDZe49RUyJQpGRsOyDhlVmC9yZ+NIGuKRrjg+NJW2ki6QgpK0XcjA/oU+oqQmgqs3NmUpE9FtPwkGsFWNf0PmakHSNhcEzxIl6rXkP+rPl4Jh6A7i5GFwp+RdAa6uGzkysZ4xn+ZmwkHCG09+MIoX2ETypYOhSJ8lr1y+itLbRuayAZqsNlekGx8foh7YmheWLEPUE6dC+JUJSCUIJCVdKYmkKBtPBFuwhkN5HblSR7WwxigGZhBBSSqpvukhwmhWpJJhS0Ios8fwRdk3SLIHEzSNzQySGE5kthZdvYpkIqC6IuN6GAByvhxpBu3K4QiaiKDHuwOorwZ4PuM5G+CHGrBzOpgpAIAa6gie62UIQFcYHR4cHvMsnuCWC68zFdHrxRg6TqoqWkC8UTRxUmplChR2K1ZNMji3B32wS1NnJSnXhFnOQEC3QF2ePDUr2kfSrxeBAr4KPUVUe7W0cNxsg1EsRdbmzbix8b1dKx3WE0SyKkTcJrY6AhpYKJQBU2CgLL8PV6jSyEMLATLggVEFVsoiKbtOkjHctDxIvw2G5cvk6yFTdFqWIUbzeptIdCKw/dyMJWk5iWSlta0K5ESaoJutQwpgL5VoAgHmzbRghBrauZsBJju6cFqSsUJLNxSRXdrbHN34iaVoiTpFvroVQUkiZNG52Uy2L8SoBskcUR/oMoKx1DU34nHXUteDsLCTWnUe0Ircp2Wgmhqm4WmtOplxFqc7qQapSIJ4ywICUEqq5hqQnidJHtLcR0FyABpaeTcfF8XEkLb84EfKokL+BCjWs0SJWmoIdqzYeWTJNtqujCoF1TEXouuaaO1NIkjCRddgK3qhFUY0RdKUoUmx1pA2mFCVqtFHR3My8xHU1qZKf8qKpKY7ADb34W42UFaZkmq9NNvNCknS5WptZQ6C5gVnwKXtNNu9FBV7oby91P6ObrJLUUbV0tuEKCbtmD5YMsNYBX96EoCmui76NqGqZusyW6FUUoSCmRfPzfc1WolAXKyHHnkOPOQVd0inxFJMwEKSuFX/dT4C3gsIrDGBscy7qOdShCwbRNwukwmtAwbINCXyELSxYiekWpZVtIJB2JDraHt9OeaMeyLabkTaEqpwpdGeidlVJiS5uEmcCn+1D+A97Pj4IjhPZ+HCH0KUUIcRxwK5m5hbullL8Yqf//MqGilHLXj+JHxbANoukoHs2DR/XsGidlZYKmDcugNdyKaqiZH023gq3ahFNhupJdKKk2UmoWQSVFtsuLohfh0jxkqTpxK41UA0TTUbLd2eR6cgfZalo2ihAove74noTB5pZMcdAJhX4KApmVMbZt093dTWNjI6FQCEVRUFUVIQTBYBBN08jOzsYwDIQQJJNJEokEgUAAwzBIJBKk02kSiQSaJpFyOUKMQ3floAgvmpaNEGLXuD6fj9zcXDRNy1wAe7/HkUgETdPwer272oPBgUUqdx5PCIHP50PGTeywgeLT0HI9GIZBMplEURQ0TcPtHlgiw7IsFEXBMAxcI8SiOPx3MSyDcDpMQ6SB7mQ3USNKOB2mOlRNliuL8cHxNEYaqciqwJY2pm0ScAUoD5RT6C2kLFD2PxcdoxFHCO39OELoU4gQQgW2AEcDjcC7wGellIOzAPYymjNLOzg4OIxWHCG09+PI/08nC4BqKWWNlDINPAx8/AJIDg4ODg4O+yiOEPp0Ug70X7vc2Ns2ACHE14UQK4UQK9vbPzxFv4ODg4ODw76GI4Q+nQwVcDNojlNKeaeU8gAp5QGFhYX/BbMcHBwcHBw+XThC6NNJI9A/5XAFMLg2g4ODg4ODg8OIOELo08m7wCQhRKUQwgWcCzz9P7bJwcHBwcHhU8fwef0dRi1SSlMIcQnwApnl83+WUm74H5vl4ODg4ODwqcMRQp9SpJT/AP7xv7bDwcHBwcHh04wzNebg4ODg4OCwz+IkVNxHEEK0A9s/5u4FQMcnaM4nzWi3D0a/jY59/x6j3T4Y/TaOVvvGSSmdZbd7MY4QcvhQhBArR3Nm1dFuH4x+Gx37/j1Gu30w+m0c7fY57L04U2MODg4ODg4O+yyOEHJwcHBwcHDYZ3GEkMOecOf/2oAPYbTbB6PfRse+f4/Rbh+MfhtHu30OeylOjJCDg4ODg4PDPovjEXJwcHBwcHDYZ3GEkIODg4ODg8M+iyOEHEZECHGcEGKzEKJaCPHD/+Jx/yyEaBNCrO/XlieEWCqE2Nr7N7fftit7bdwshDi2X/s8IcS63m2/E0KIT8i+MUKIfwkhNgohNgghvjOabBRCeIQQK4QQa3vt++losq/f2KoQ4j0hxLOj1L663rHXCCFWjjYbhRA5QojHhRCbej+LB44W+4QQU3rP285HWAhx6Wixz8FhF1JK5+E8hnyQqWO2DZgAuIC1wPT/0rEXA3OB9f3afgX8sPf5D4Ff9j6f3mubG6jstVnt3bYCOBAQwD+B4z8h+0qBub3Ps4AtvXaMCht7xwr0PteB5cCi0WJfPzsvAx4Cnh1t73Hv2HVAwW5to8ZG4C/AV3ufu4Cc0WRfPztVoAUYNxrtcx779sPxCDmMxAKgWkpZI6VMAw8Dp/43DiylfB3o2q35VDI//PT+Pa1f+8NSypSUshaoBhYIIUqBoJRymZRSAvf32+ffta9ZSrm693kE2AiUjxYbZYZo779670OOFvsAhBAVwInA3f2aR419IzAqbBRCBMncMNwDIKVMSylDo8W+3VgCbJNSbh+l9jnswzhCyGEkyoGGfv839rb9ryiWUjZDRogARb3tw9lZ3vt89/ZPFCHEeGAOGa/LqLGxd9ppDdAGLJVSjir7gN8C3wfsfm2jyT7IiMcXhRCrhBBfH2U2TgDagXt7pxfvFkL4R5F9/TkX+Gvv89Fon8M+jCOEHEZiqHn40ZhvYTg7/+P2CyECwN+AS6WU4ZG6DmPLf8xGKaUlpZwNVJC5s95vhO7/VfuEECcBbVLKVXu6yzB2/Kff44OllHOB44GLhRCLR+j737ZRIzN9fIeUcg4QIzPVNBz/k3MohHABpwCPfVjXYez4tPwOOXxKcYSQw0g0AmP6/V8BNP2PbAFo7XWT0/u3rbd9ODsbe5/v3v6JIITQyYig/5NS/n002gjQO13yKnDcKLLvYOAUIUQdmSnXI4UQD44i+wCQUjb1/m0DniAzXTxabGwEGns9fQCPkxFGo8W+nRwPrJZStvb+P9rsc9jHcYSQw0i8C0wSQlT23tWdCzz9P7TnaeCC3ucXAE/1az9XCOEWQlQCk4AVvW73iBBiUe8qky/02+ffone8e4CNUspbRpuNQohCIURO73MvcBSwabTYJ6W8UkpZIaUcT+Zz9YqU8vzRYh+AEMIvhMja+Rw4Blg/WmyUUrYADUKIKb1NS4APRot9/fgsfdNiO+0YTfY57Ov8r6O1ncfofgAnkFkRtQ246r943L8CzYBB5o7wK0A+8DKwtfdvXr/+V/XauJl+K0qAA8hcvLYBt9GbTf0TsO8QMu7594E1vY8TRouNwP7Ae732rQd+3Ns+KuzbzdbD6Vs1NmrsIxODs7b3sWHn53+U2TgbWNn7Pj8J5I4y+3xAJ5Ddr23U2Oc8nIeU0imx4eDg4ODg4LDv4kyNOTg4ODg4OOyzOELIwcHBwcHBYZ/FEUIODg4ODg4O+yyOEHJwcHBwcHDYZ3GEkIODg4ODg8M+iyOEHBwcHBwcHPZZHCHk4ODg4ODgsM/y/zoe1VTwExMiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "mc_thirty.plot_simulation();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "mc_thirty.plot_distribution();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retirement Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           500.000000\n",
       "mean              6.587260\n",
       "std               4.525680\n",
       "min               0.956663\n",
       "25%               3.516535\n",
       "50%               5.390842\n",
       "75%               8.301984\n",
       "max              37.183012\n",
       "95% CI Lower      1.659597\n",
       "95% CI Upper     17.827201\n",
       "Name: 7560, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "mc_summary = mc_thirty.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics from the Monte Carlo simulation results\n",
    "mc_summary"
   ]
  },
  {
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $20,000.00 in the portfolio over the next 30 years will end within in the range of $33,191.93 and $356,544.02.\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 = mc_summary[8] * initial_investment\n",
    "ci_upper = mc_summary[9] * initial_investment\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment:,.2f} in the portfolio\"\n",
    "      f\" over the next 30 years will end within in the range of\"\n",
    "      f\" ${ci_lower:,.2f} and ${ci_upper:,.2f}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ten Years Retirement Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a Monte Carlo simulation to forecast ten years cumulative returns\n",
    "mc_ten = MCSimulation(portfolio_data = tenyrs_stocks_bonds_df, \n",
    "                  weights = [.20,.80], \n",
    "                  num_simulation = 500, \n",
    "                  num_trading_days = 252*10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": {
      "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>490</th>\n",
       "      <th>491</th>\n",
       "      <th>492</th>\n",
       "      <th>493</th>\n",
       "      <th>494</th>\n",
       "      <th>495</th>\n",
       "      <th>496</th>\n",
       "      <th>497</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <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>1.011417</td>\n",
       "      <td>1.011617</td>\n",
       "      <td>0.990034</td>\n",
       "      <td>0.983118</td>\n",
       "      <td>1.000677</td>\n",
       "      <td>1.017025</td>\n",
       "      <td>0.999748</td>\n",
       "      <td>0.998718</td>\n",
       "      <td>0.996140</td>\n",
       "      <td>1.000842</td>\n",
       "      <td>...</td>\n",
       "      <td>1.007470</td>\n",
       "      <td>0.999771</td>\n",
       "      <td>0.992351</td>\n",
       "      <td>0.995681</td>\n",
       "      <td>0.997046</td>\n",
       "      <td>0.998639</td>\n",
       "      <td>0.989489</td>\n",
       "      <td>0.997305</td>\n",
       "      <td>1.014766</td>\n",
       "      <td>0.998606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000025</td>\n",
       "      <td>1.018101</td>\n",
       "      <td>0.981801</td>\n",
       "      <td>0.978676</td>\n",
       "      <td>0.996161</td>\n",
       "      <td>1.021775</td>\n",
       "      <td>0.987356</td>\n",
       "      <td>0.990667</td>\n",
       "      <td>0.995298</td>\n",
       "      <td>0.997308</td>\n",
       "      <td>...</td>\n",
       "      <td>1.020095</td>\n",
       "      <td>1.007329</td>\n",
       "      <td>0.997619</td>\n",
       "      <td>0.990389</td>\n",
       "      <td>1.005445</td>\n",
       "      <td>0.999481</td>\n",
       "      <td>0.996761</td>\n",
       "      <td>0.985642</td>\n",
       "      <td>1.036130</td>\n",
       "      <td>1.003613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.996991</td>\n",
       "      <td>1.005862</td>\n",
       "      <td>0.971775</td>\n",
       "      <td>0.968912</td>\n",
       "      <td>0.990516</td>\n",
       "      <td>1.018867</td>\n",
       "      <td>0.973870</td>\n",
       "      <td>0.994863</td>\n",
       "      <td>0.993570</td>\n",
       "      <td>1.007626</td>\n",
       "      <td>...</td>\n",
       "      <td>1.015542</td>\n",
       "      <td>1.009660</td>\n",
       "      <td>1.001467</td>\n",
       "      <td>0.986151</td>\n",
       "      <td>0.994645</td>\n",
       "      <td>1.004994</td>\n",
       "      <td>0.990667</td>\n",
       "      <td>0.988163</td>\n",
       "      <td>1.026907</td>\n",
       "      <td>1.019568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.009612</td>\n",
       "      <td>1.005536</td>\n",
       "      <td>0.966860</td>\n",
       "      <td>0.985693</td>\n",
       "      <td>0.994514</td>\n",
       "      <td>1.009416</td>\n",
       "      <td>0.960921</td>\n",
       "      <td>1.012157</td>\n",
       "      <td>0.995817</td>\n",
       "      <td>0.996654</td>\n",
       "      <td>...</td>\n",
       "      <td>1.013248</td>\n",
       "      <td>1.004016</td>\n",
       "      <td>0.997699</td>\n",
       "      <td>0.990079</td>\n",
       "      <td>1.013171</td>\n",
       "      <td>1.020044</td>\n",
       "      <td>0.986970</td>\n",
       "      <td>0.977241</td>\n",
       "      <td>1.027989</td>\n",
       "      <td>1.023589</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>2516</th>\n",
       "      <td>3.268678</td>\n",
       "      <td>1.887854</td>\n",
       "      <td>1.588447</td>\n",
       "      <td>2.139624</td>\n",
       "      <td>2.790548</td>\n",
       "      <td>2.819932</td>\n",
       "      <td>4.983663</td>\n",
       "      <td>1.456828</td>\n",
       "      <td>1.894986</td>\n",
       "      <td>1.985240</td>\n",
       "      <td>...</td>\n",
       "      <td>2.817324</td>\n",
       "      <td>1.760944</td>\n",
       "      <td>4.087883</td>\n",
       "      <td>1.587227</td>\n",
       "      <td>3.260301</td>\n",
       "      <td>2.283212</td>\n",
       "      <td>1.490039</td>\n",
       "      <td>2.962582</td>\n",
       "      <td>0.626987</td>\n",
       "      <td>1.881145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>3.302250</td>\n",
       "      <td>1.913106</td>\n",
       "      <td>1.564936</td>\n",
       "      <td>2.146777</td>\n",
       "      <td>2.796294</td>\n",
       "      <td>2.779071</td>\n",
       "      <td>5.037787</td>\n",
       "      <td>1.458235</td>\n",
       "      <td>1.881841</td>\n",
       "      <td>2.009469</td>\n",
       "      <td>...</td>\n",
       "      <td>2.853788</td>\n",
       "      <td>1.791247</td>\n",
       "      <td>4.139797</td>\n",
       "      <td>1.582737</td>\n",
       "      <td>3.270657</td>\n",
       "      <td>2.306832</td>\n",
       "      <td>1.484174</td>\n",
       "      <td>2.948001</td>\n",
       "      <td>0.620679</td>\n",
       "      <td>1.872030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2518</th>\n",
       "      <td>3.283888</td>\n",
       "      <td>1.935689</td>\n",
       "      <td>1.561193</td>\n",
       "      <td>2.163704</td>\n",
       "      <td>2.774488</td>\n",
       "      <td>2.741578</td>\n",
       "      <td>4.995778</td>\n",
       "      <td>1.474717</td>\n",
       "      <td>1.874488</td>\n",
       "      <td>1.994079</td>\n",
       "      <td>...</td>\n",
       "      <td>2.868489</td>\n",
       "      <td>1.771951</td>\n",
       "      <td>4.138355</td>\n",
       "      <td>1.607737</td>\n",
       "      <td>3.270055</td>\n",
       "      <td>2.311038</td>\n",
       "      <td>1.505869</td>\n",
       "      <td>2.972833</td>\n",
       "      <td>0.639453</td>\n",
       "      <td>1.885126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>3.295660</td>\n",
       "      <td>1.948047</td>\n",
       "      <td>1.549812</td>\n",
       "      <td>2.209336</td>\n",
       "      <td>2.779799</td>\n",
       "      <td>2.730907</td>\n",
       "      <td>4.980648</td>\n",
       "      <td>1.482800</td>\n",
       "      <td>1.887067</td>\n",
       "      <td>1.978952</td>\n",
       "      <td>...</td>\n",
       "      <td>2.872869</td>\n",
       "      <td>1.769813</td>\n",
       "      <td>4.143946</td>\n",
       "      <td>1.588699</td>\n",
       "      <td>3.276930</td>\n",
       "      <td>2.321435</td>\n",
       "      <td>1.507968</td>\n",
       "      <td>2.970570</td>\n",
       "      <td>0.636867</td>\n",
       "      <td>1.896215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2520</th>\n",
       "      <td>3.259043</td>\n",
       "      <td>1.930355</td>\n",
       "      <td>1.537882</td>\n",
       "      <td>2.213775</td>\n",
       "      <td>2.769912</td>\n",
       "      <td>2.741371</td>\n",
       "      <td>4.975008</td>\n",
       "      <td>1.475233</td>\n",
       "      <td>1.890847</td>\n",
       "      <td>1.961786</td>\n",
       "      <td>...</td>\n",
       "      <td>2.897137</td>\n",
       "      <td>1.772436</td>\n",
       "      <td>4.116362</td>\n",
       "      <td>1.576527</td>\n",
       "      <td>3.272389</td>\n",
       "      <td>2.353766</td>\n",
       "      <td>1.497159</td>\n",
       "      <td>2.972942</td>\n",
       "      <td>0.645364</td>\n",
       "      <td>1.922939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2521 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     1.011417  1.011617  0.990034  0.983118  1.000677  1.017025  0.999748   \n",
       "2     1.000025  1.018101  0.981801  0.978676  0.996161  1.021775  0.987356   \n",
       "3     0.996991  1.005862  0.971775  0.968912  0.990516  1.018867  0.973870   \n",
       "4     1.009612  1.005536  0.966860  0.985693  0.994514  1.009416  0.960921   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "2516  3.268678  1.887854  1.588447  2.139624  2.790548  2.819932  4.983663   \n",
       "2517  3.302250  1.913106  1.564936  2.146777  2.796294  2.779071  5.037787   \n",
       "2518  3.283888  1.935689  1.561193  2.163704  2.774488  2.741578  4.995778   \n",
       "2519  3.295660  1.948047  1.549812  2.209336  2.779799  2.730907  4.980648   \n",
       "2520  3.259043  1.930355  1.537882  2.213775  2.769912  2.741371  4.975008   \n",
       "\n",
       "           7         8         9    ...       490       491       492  \\\n",
       "0     1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n",
       "1     0.998718  0.996140  1.000842  ...  1.007470  0.999771  0.992351   \n",
       "2     0.990667  0.995298  0.997308  ...  1.020095  1.007329  0.997619   \n",
       "3     0.994863  0.993570  1.007626  ...  1.015542  1.009660  1.001467   \n",
       "4     1.012157  0.995817  0.996654  ...  1.013248  1.004016  0.997699   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2516  1.456828  1.894986  1.985240  ...  2.817324  1.760944  4.087883   \n",
       "2517  1.458235  1.881841  2.009469  ...  2.853788  1.791247  4.139797   \n",
       "2518  1.474717  1.874488  1.994079  ...  2.868489  1.771951  4.138355   \n",
       "2519  1.482800  1.887067  1.978952  ...  2.872869  1.769813  4.143946   \n",
       "2520  1.475233  1.890847  1.961786  ...  2.897137  1.772436  4.116362   \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     0.995681  0.997046  0.998639  0.989489  0.997305  1.014766  0.998606  \n",
       "2     0.990389  1.005445  0.999481  0.996761  0.985642  1.036130  1.003613  \n",
       "3     0.986151  0.994645  1.004994  0.990667  0.988163  1.026907  1.019568  \n",
       "4     0.990079  1.013171  1.020044  0.986970  0.977241  1.027989  1.023589  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2516  1.587227  3.260301  2.283212  1.490039  2.962582  0.626987  1.881145  \n",
       "2517  1.582737  3.270657  2.306832  1.484174  2.948001  0.620679  1.872030  \n",
       "2518  1.607737  3.270055  2.311038  1.505869  2.972833  0.639453  1.885126  \n",
       "2519  1.588699  3.276930  2.321435  1.507968  2.970570  0.636867  1.896215  \n",
       "2520  1.576527  3.272389  2.353766  1.497159  2.972942  0.645364  1.922939  \n",
       "\n",
       "[2521 rows x 500 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running a Monte Carlo simulation to forecast ten years cumulative returns\n",
    "mc_ten.calc_cumulative_return()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot simulation outcomes\n",
    "mc_ten.plot_simulation();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot probability distribution and confidence intervals\n",
    "mc_ten.plot_distribution();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           500.000000\n",
       "mean              2.379970\n",
       "std               1.232458\n",
       "min               0.474750\n",
       "25%               1.530983\n",
       "50%               2.126200\n",
       "75%               2.921771\n",
       "max              11.088398\n",
       "95% CI Lower      0.868635\n",
       "95% CI Upper      5.568994\n",
       "Name: 2520, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch summary statistics from the Monte Carlo simulation results\n",
    "mc_ten_summary = mc_ten.summarize_cumulative_return()\n",
    "\n",
    "# Print summary statistics\n",
    "mc_ten_summary"
   ]
  },
  {
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a 95% chance that an initial investment of $20,000.00 in the portfolio over the next 10 years will end within in the range of $17,372.69 and $111,379.87\n"
     ]
    }
   ],
   "source": [
    "# Set initial investment\n",
    "initial_investment_ten = 20000\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 = mc_ten_summary[8] * initial_investment_ten\n",
    "ci_upper_ten = mc_ten_summary[9] * initial_investment_ten\n",
    "\n",
    "# Print results\n",
    "print(f\"There is a 95% chance that an initial investment of ${initial_investment_ten:,.2f} in the portfolio\"\n",
    "      f\" over the next 10 years will end within in the range of\"\n",
    "      f\" ${ci_lower_ten:,.2f} and ${ci_upper_ten:,.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Will weighting the portfolio more heavily toward stocks allow the credit union members to retire after only 10 years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the projected portfolio returns with identical initial investment of $20,000.00, weighting the portfolio more heavily toward stocks will not allow the credit union members to retire after only ten years. With a 40/60 split between bonds and stocks, there is a 95% chance that the portfolio over the next thirty years will end within the range of $33,191.93 and $356,544.02. With a 20/80 split between bonds and stocks, there is a 95% chance that the portfolio over the next ten years will end within in the range of $17,372.69 and $111,379.87.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Based on the projected portfolio returns with identical initial investment of ${initial_investment:,.2f}, weighting the portfolio more heavily toward stocks will not allow the credit union members to retire after only ten years. With a 40/60 split between bonds and stocks, there is a 95% chance that the portfolio over the next thirty years will end within the range of ${ci_lower:,.2f} and ${ci_upper:,.2f}. With a 20/80 split between bonds and stocks, there is a 95% chance that the portfolio over the next ten years will end within in the range of ${ci_lower_ten:,.2f} and ${ci_upper_ten:,.2f}.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "nteract": {
   "version": "0.28.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}