TESLA_RNN_MODEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESLA (TSLA) Trading Signal Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key savefig.frameon in file /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle, line 421 ('savefig.frameon : True')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key verbose.level in file /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle, line 472 ('verbose.level  : silent      # one of silent, helpful, debug, debug-annoying')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key verbose.fileo in file /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle, line 473 ('verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.2/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /Users/Lidiya/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries and dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "%matplotlib inline\n",
    "\n",
    "from joblib import dump, load\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in CSV as Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close\n",
       "Date             \n",
       "2010-06-29   4.78\n",
       "2010-06-30   4.77\n",
       "2010-07-01   4.39\n",
       "2010-07-02   3.84\n",
       "2010-07-06   3.22"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading CSV file from Github Repo (TSLA_DATE_CLOSE.csv)\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/klleung72781/fintech_project_2/main/TSLA_DATE_CLOSE.csv'\n",
    "df = pd.read_csv(url, parse_dates=True, error_bad_lines=False, index_col='Date', infer_datetime_format=True)\n",
    "df = df.sort_index()\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SENT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-08</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SENT\n",
       "Date            \n",
       "2015-01-30  1.00\n",
       "2015-02-08  0.25\n",
       "2015-02-10  0.00\n",
       "2015-02-11  0.50\n",
       "2015-02-12  0.00"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading CSV file from Github Repo (clean_tweeter.csv)\n",
    "\n",
    "url_2 = 'https://raw.githubusercontent.com/klleung72781/fintech_project_2/main/clean_twitter.csv'\n",
    "twitter_df = pd.read_csv(url_2, parse_dates=True, index_col='Date', infer_datetime_format=True)\n",
    "twitter_df.sort_index()\n",
    "twitter_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a function to replace values in the SENT with (0,1,-1)\n",
    "\n",
    "def encode(x):\n",
    "    if x >= -1 and x < -0.5:\n",
    "        return -1\n",
    "    elif x >= -0.5 and x < 0.5:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "twitter_df.SENT = twitter_df.SENT.apply(\n",
    "    lambda x: encode(x)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SENT\n",
       "2015-01-30     1\n",
       "2015-01-31     0\n",
       "2015-02-01     0\n",
       "2015-02-02     0\n",
       "2015-02-03     0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill out missing dates in twitter_df with adding addition dates and 0 values: \n",
    "\n",
    "idx = pd.date_range('01-30-2015', '10-12-2020')\n",
    "\n",
    "\n",
    "twitter_df.index = pd.DatetimeIndex(twitter_df.index)\n",
    "\n",
    "twitter_df = twitter_df.reindex(idx, fill_value=0)\n",
    "twitter_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>4.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>4.77</td>\n",
       "      <td>-0.002092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>4.39</td>\n",
       "      <td>-0.079665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>3.84</td>\n",
       "      <td>-0.125285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>3.22</td>\n",
       "      <td>-0.161458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Daily_return\n",
       "Date                           \n",
       "2010-06-29   4.78           NaN\n",
       "2010-06-30   4.77     -0.002092\n",
       "2010-07-01   4.39     -0.079665\n",
       "2010-07-02   3.84     -0.125285\n",
       "2010-07-06   3.22     -0.161458"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop NAs and calculate daily percent return\n",
    "\n",
    "df['Daily_return'] = df['Close'].dropna().pct_change()\n",
    "df.index = pd.DatetimeIndex(df.index)\n",
    "df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>Actual_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>-0.002092</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>-0.079665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>-0.125285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>-0.161458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-07</th>\n",
       "      <td>-0.018634</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_return  Actual_Value\n",
       "Date                                  \n",
       "2010-06-30     -0.002092             0\n",
       "2010-07-01     -0.079665             0\n",
       "2010-07-02     -0.125285             0\n",
       "2010-07-06     -0.161458             0\n",
       "2010-07-07     -0.018634             0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating DataFrame with returns and Actual Value\n",
    "\n",
    "daily_df = df.copy().drop(columns=[\"Close\"])\n",
    "daily_df[\"Actual_Value\"] = np.where(df['Daily_return'] > 0 ,1, 0)\n",
    "daily_df = daily_df.dropna()\n",
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>4.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>4.77</td>\n",
       "      <td>-0.002092</td>\n",
       "      <td>4.774965</td>\n",
       "      <td>4.774991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>4.39</td>\n",
       "      <td>-0.079665</td>\n",
       "      <td>4.644861</td>\n",
       "      <td>4.646216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>3.84</td>\n",
       "      <td>-0.125285</td>\n",
       "      <td>4.439442</td>\n",
       "      <td>4.443613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>3.22</td>\n",
       "      <td>-0.161458</td>\n",
       "      <td>4.188745</td>\n",
       "      <td>4.197191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "Date                                                                      \n",
       "2010-06-29   4.78           NaN    4.780000    4.780000             0.0   \n",
       "2010-06-30   4.77     -0.002092    4.774965    4.774991             0.0   \n",
       "2010-07-01   4.39     -0.079665    4.644861    4.646216             0.0   \n",
       "2010-07-02   3.84     -0.125285    4.439442    4.443613             0.0   \n",
       "2010-07-06   3.22     -0.161458    4.188745    4.197191             0.0   \n",
       "\n",
       "            crossover_short  crossover_signal  \n",
       "Date                                           \n",
       "2010-06-29              0.0               0.0  \n",
       "2010-06-30             -1.0              -1.0  \n",
       "2010-07-01             -1.0              -1.0  \n",
       "2010-07-02             -1.0              -1.0  \n",
       "2010-07-06             -1.0              -1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set short and long windows\n",
    "\n",
    "short_window = 50\n",
    "long_window = 200\n",
    "\n",
    "# Construct a `Fast` and `Slow` Exponential Moving Average from short and long windows, respectively\n",
    "df['fast_close'] = df['Close'].ewm(halflife=short_window).mean()\n",
    "df['slow_close'] = df['Close'].ewm(halflife=long_window).mean()\n",
    "\n",
    "# Construct a crossover trading signal\n",
    "df['crossover_long'] = np.where(df['fast_close'] > df['slow_close'], 1.0, 0.0)\n",
    "df['crossover_short'] = np.where(df['fast_close'] < df['slow_close'], -1.0, 0.0)\n",
    "df['crossover_signal'] = df['crossover_long'] + df['crossover_short']\n",
    "\n",
    "df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aXZ4dPAYAAAAAANCihgqhBtl9YkLPE2MO2/XmAG+dVLpDSh4QrqlFnLbsMmZIekrSRtM0H2701tuSrg4+v1rSW42OXxXcbew4SRX0DwIAAAAAAIdjPygQyi+rCz234ok22viu5K2RRp8XrqlFnLZUCM2U9F1JpxiGsSr4c7ak+yWdbhjGVkmnBV9L0vuSdkjaJumfkn4U/mkDAAAAAIBI0zgQum/u+I4PtO5VKWmANHBWGGYVmdqyy9hXklqK4U5t5nxT0k2dnBcAAAAAAOjFBqXGdfzifeukQTMlWzu3qu9F+GQAAAAAAEC34Asc2K+q3dvMN6ivkirzpbQRYZpVZCIQAgAAAAAA3YI/EAg9T4lz6eaTh0mSThud2fZBirdYj+mjwjm1iNOOFt0AAAAAAABdZ/GO0tDzPrFO/fzMkfrujIFKjnW2fZC9q63HzDFhnl1kIRACAAAAAADdwvJdZaHnDru1qCkzMbp9gxQsl2JSpD6Dwzm1iMOSMQAAAAAAEBncFdLWT6XsaVJ7tqnvhQiEAAAAAADAUef1Bw5/0uF8+ZBUUyideEfnx4pwLBkDAAAAAABHXWWdV5I0e3iavjerg8u9chdKA2dK2VPCOLPIRIUQAAAAAAA46sqDgdCFU7J18siM9g/g90r710v9jgnzzCITgRAAAAAAAGhi9e5yvbxs9xG9Z3mtFQglxbRjR7HGijZL/nqp38TwTSqCsWQMAAAAAAA0cf5jCyVJF0/NOWL3rKjzSJKSY10dG6Bhu3kqhNqECiEAAAAAANCsep//iN2roUIouaMVQntXS844KXVoGGcVuQiEAAAAAABAs5bsKD0i93F7/dpfWS9JSo7tQCDkqZE2viPlTJNs9jDPLjKxZAwAAAAAADSREOVQVb1P8zcX6oQR6V1+vxn3zVNZrVeGISVEdyAQWv6MVLVHuvDfYZ9bpKJCCAAAAACAXq7e51etxxd6PWVQH0mSw2YckfuXBZeLJUY7Ze/IPfMWSylDpIEzwjyzyEUgBAAAAABAL3f2XxZozK8+Cr32+gOSpIrgVvBHSoyzg8u99q+TMseFdzIRjkAIAAAAAIBebntRTZPXHt/RCYT2Vbrbf1F9tVS6U+o7PvwTimAEQgAAAAAAoAmP35R0ZAIht7eTO5kVbpBkSpljwzKf3oJACAAAAAAANHGgQsh3mDM7r9Oh0/511iNLxtqFQAgAAAAAADQR6iFU6+nye5XXHgiEclJi2j9A3hIpKklKHhDGWUU+tp0HAAAAAABN1PusZVxHYslYeTB0uvvsUfrO5Oz2XVyRL617TZpyjWQcmR3RIgUVQgAAAAAAIMQ0TZVUWyFNjccfqhbqKg1bzh8/NE2p8VHtu3jDW1LAK824qQtmFtkIhAAAAAAAQEh1vU+1Hr+ykq3lW5VdXCVUUWeFT8mxzvZfXLRJik2VUgaHeVaRj0AIAAAAAACE7K+slySNyIyX1PXLxhp6CCXHutp/cfFWKW1kmGfUOxAIAQAAAACAkP2VbknSiMwESVJ5FwdCZbVeOe2G4lz29l9ctFlKHxH+SfUCNJUGAAAAAAAhDYHQ8GAg1FVLxtYVVMjrD6jK7VVCtFNGe5tC1xRLdaVSGoFQRxAIAQAAAACAkH3BQGhoepwkqabeH7axTdMMBT/n/vUrSdKJI9IV25HqoOIt1iNLxjqEJWMAAAAAACCksLJeCdEOZSRGS5Kq68NTIbS7tFYj7/lQn27Y3+T4F1uKlF9W1/4BizZbj2nDwzC73odACAAAAAAAhOyvdCszMVrxUdaioiq3Lyzj7iiukccX0PX/WabH5m/r/IDbP7N2GEvK6fxYvRCBEAAAAAAACPlmZ6kyE6NCgVB1fXgCIa8vEHr+0EdWdc/9c8d3bLDqImnz+9Ixl0k2oo2OoIcQAAAAAACQJK3Nr1BJjUcLt5XIbjMU67KrupMVQs9+nav0hCj5A2aT430To/WdKdn6fHORhmXEt2/QHZ9LAZ80/qJOza03IxACAAAAAACSpNJajyTpoinZkqT4KEenKoS+3FKkX7+9XpL04IUTmrz3/PePldNu0xPfndL+gUu2STKk9FEdnltvR10VAAAAAACQJNV5rB3FrpoxSJIUH+1QVScCoa2F1aHnbm/T3cqGprezKqixkm1Sco7kjO74GL0cFUIAAAAAAECSVBMMf2JcVv1IQpSjU0vGTPPAMrGluWWSrEqh0X0TOzFLWYFQ6rDOjdHLEQgBAAAAAABJBxpIx7isuCA+unNLxhr3DXpn9R5J0ncmZ8tuMzo+SdOUSrZLOdM7PgZYMgYAAAAAACwN/X5inHZJUrTDfshSr/bwHdRI2m4zOhcGSVJ1oeSpokKokwiEAAAAAABAE7EuKxCy24xDdgdrj5qDqotuOjkMIU7JNusxdWjnx+rFCIQAAAAAAOjFvP5Ak9d9Yp2KclhxgcPeuUCo6qD+Q0PT4zo8VkjBcusxbUTnx+rFCIQAAAAAAOjFDl4S9tltJ8kwrGVddputQ4HQvgq36jx+Vbq9TY6P6mwz6YBfWvovKec4KXlA58bq5WgqDQAAAABAL+b2HlQhFOcKPXfYDPkCplbvLldclF3DMhIOO55pmjruvnnKSo7RiMymW8unNBq7QwpWSOW7pFN/1blxQCAEAAAAAEBv1rhCKC2+aWBjtxmqdHt1/mML5bLbtOUPcw47XlFVvSSpoLxOBeV1Td5LjnV2brJFm6zHrCmdGwcsGQMAAAAAoDer9x0IhL4/e0iT9xw2Q+W11rIvz0G9hlqyu6xpCBQfdaAWxWnvZAxRvFmyR7FcLAwIhAAAAAAA6MUaLxkb2bfpkrDGW8S3dblXSXV9k9edXibWWPFWa7t5mz18Yzbi9rlV463pkrG7GwIhAAAAAAB6sYYlYyMy4zVrWFqT9xoHQgGzbc2lS2o8TV73CWsgtEVKGx6+8Q7yUe5HOuF/J2hX5a4uu0d3QSAEAAAAAEAvVu+zKoT+3/nj5DhoSVfjQKjW03Q3spYcXCHUp7N9gxp43VJZrpQ+MjzjHaSivkJPr3taaTFpGpAQ+UvSCIQAAAAAAOjFGiqEop2HLsNyNAqEPL7AIVvUN6e42qOERn2DYl3WuI2PdUjpDskMSGkjOjdOC55a95RyK3P125m/lWEYh7+ghyMQAgAAAACgF2voIdRcIGS3NY0NKuu8hx2vtMaj1Ea7lTWMGx/dyUBo6T8lGVL/SZ0bpwXz8+Zret/pOq7fcV0yfndDIAQAAAAAQC92oELo0IigcYWQJFW0IRAqq/UoOfZAINQwRkJnAqHdS6Vl/5Zm3CSlDu34OC3IrchVbmWuTso5Kexjd1cEQgAAAAAA9GJuX8tLxuwHBUKV7sMHQvW+gKIcB+KGE0dkSFKTkKjdNr0j2ZzSSXd1fIxWfLDzA0kiEAIAAAAAAL1DaMmYo+UeQukJUZKkmvrD9xDy+AJyNQqEzhrXV9fOHKRHLpnY8UnmLpSypkhR8R0fowUr9q/QE2ue0Ek5J6l/fP+wj99dEQgBAAAAANCLNSwZi2pmyZjdbgVCafFWIFTr8R12PK8/IFej3crsNkO/Pm+s+ifHdGyCdeXSnpXSoFkdu/4wXtv6muKccXpg9gNdMn53RSAEAAAAAEAvVu/1yzDUZJlXg4MrhKo7UCHUKaYpvXSl9XzUOeEZsxGv36v5u+fr5JyTFeuMDfv43VknW3wDAAAAAICezB3s+dPcVuumaT2mBXcNa2uFkNNu04LbT1aM69BlaO2Sv0zKXSCd9YCUNblzYzVj4Z6FqvJU6YyBZ4R97O6OQAgAAAAAgF7M7fU321BakmzBkMgZ3H6+PT2EclLCUHGz5n+SI1qaeHnnxzpIubtcv1/8e/WL66cZ/WeEffzujkAIAAAAAIBezO31N9tQWpIyEq2lYjabZDPaViHk8Zty2sOwZKymRFr3ujTybCk6sfPjHeSj3I+0v3a/njv7ObnsndgBrYeihxAAAAAAAL2Y2xtQdDMNpaUDO4T97PSRinM52lgh5G+2H1G7vXSl5K2VZtzc+bGa8dWer5QVn6UJaRO6ZPzujkAIAAAAAIBerN7X8pKxKIddvz5vrNITohQbZW9jDyFTTvuh/YjapWS7lPe1dPL/SdlTOjdWMzx+j5bsXaKZ/Wc22zupNyAQAgAAAACgF3N7A4pqIRBqLM7lUI2nDRVC/jDsMrbpXetxzPmdG6cFCwoWqM5Xp1lZXbOVfU9AIAQAAAAAQC9WVFWvPrHOw55nsxl6Z/UeFVa5WzynzuOXP2Aq1tWJlsWmKW14S+p3jNRnYMfHaUFxXbHuWXiPBicN1nH9jwv7+D0FgRAAAAAAAL2UaZraWVyjIWnxhz13W2G1JOm3b29o8ZyyWo8kKSWuE02aF/xJKlguTbyi42O04v0d76vKU6U/nfgnxThiuuQePQGBEAAAAAAAvZTXb6rO61dK3OErhFzBncP2VtS1eE5pjRUI9YntYCDk90pfP2rtLDb9ho6N0Yoyd5ne2PaGxqaO1fA+w8M+fk9CIAQAAAAAQC/l8QckqU09fxoaRdf7Ai2eU17rldSJCqHcBZK7Qpp0pRTmZs+13lpd+PaF2lmxU9eMuyasY/dEnVjUBwAAAAAAejJvMNxx2g8fCDXsxuVo5dzS0JKxw1ccNWvjO5IzThp6Sseub8XivYtVWFeov57yV52Uc1LYx+9pqBACAAAAAKCX8vrbEwhZj05by5U7ZcElY8kdWTKWu1Ba9YI08izJGf7ePl/v+VoxjhjN7D8z7GP3RARCAAAAAAD0Ug3Lv9qyZMwWqhBqORBq6CGUHNPOCiHTlN7/hZTYX5rzYPuubaNFexZpet/pcto7WL0UYQiEAAAAAADopRoqhFxtqBAalmHtRGZrpbdPWa1HSTHOVpeVNSt/mVS4Xjr+x1JcWvuubYPVRauVV5WnGf1nhH3snopACAAAAACAXqo9TaX/edVUSVKsy97iOWW13vY3lA74pa8ellzx0vgL23dtG5imqV8v/LX6xvXVt4Z+K+zj91QEQgAAAAAA9FJenympbT2EUuJcOiYnWV6/2eI5ZTUe9Ylt55Ks+X+QNr8vnfBzKSqhfde2wfby7dpesV03TLhBCa7wj99TEQgBAAAAANBLefx+SW2rEJKshtK+QMvbzpfWeNSnPQ2la4qlJf+Qxn1HmvXTtl/XDl/v+VqSNKv/rC4Zv6ciEAIAAAAAoJfyhCqEWu4L1JjDbrReIVTrUZ+2LhnLWyI9Nl3yuaXZt7Xtmg74es/XGpw0WP3i+3XZPXoiAiEAAAAAAHopt8+qEIpqa4WQ3Safv+UKobJaT9t7CM37rWSPkq7/VMoc27Zr2mlH+Q4t27+Mreab4TjaEwAAAAAAAEfea8vztTq/XJKUHh/dpmsctpYrhOo8frm9gbYtGctfJu1aKJ15n9R/Ulun3G63f3m7ElwJunLMlV12j56KQAgAAAAAgF5mT3mdbntldeh1RmJUm65z2m2hreoPVl7nkSQlxRymqXThJun5i6SYFGnyd9s24Q7YWbFTm8s2667pdykrPqvL7tNTEQgBAAAAANDL7K2oa/I62tnyVvKNOe02+QLNVwhV1vkktSEQ+vJBye+VbpjfJbuKNZi/e74k6ZQBp3TZPXoyeggBAAAAANDLlNd6Q8/T4ttWHSRZwVFNva/Z9yrd1piJMa3UnpTlSuvflKZeI6UNb/N928sf8OvDnR9qbOpY9Y3r22X36cmoEAIAAAAAoJepqLPCmwe/M0ETcpLafF3fpCgVVdXLHzBltzXdmawyOGZidAsVQtWF0n/Ol+xO6dgbOzbxNnpo2UPaWLpRvz3+t116n56MQAgAAAAAgF6mLFghdPqYzLZvEy+pb2K0fAFTJdX1ykhs2oh6074qSa30I1r8uFS2S7r2Aykpu2MTb4OK+gq9svkVXTDsAs0dPrfL7tPTsWQMAAAAAIBewusPaP7mQu0srlZitEPJsYfp93OQxGB/oEq3T4GAqT9+tFmFVW5J0pb9VcpJiVG/pJhDL/TUSMv+LY0+Vxo4o9O/R2te2vySPAGPrhh9RZfep6ejQggAAAAAgF7iL59u1d/mb5MkTR6QLMMwDnNFU7EuK0ao8/i1pqBCf5u/Tavzy/Xf645VTb1PCVHNBEyeGumZcyR3uTTjls7+Cq16Zcsr+tvKv+nE7BM1MmVkl96rp6NCCAAAAACAXmLTvsrQ8+EZ7d/hK85l7UZW4/HJHgyTCsqsHcuq3D7FRzVTd7LiP9KeldJ3npIGHNuBWbdNna9Oj654VFMyp+iBEx7osvtECiqEAAAAAADoJbz+A1vGD8+Mb/f1McFAqM7jV0Nt0Y7iGp31yJdy2A1lJDTtKySfR1r0mDTgeGn8hR2ddpu8vvV1ldeX6+ZJNyvOGdel94oEh60QMgzj34ZhFBqGsa7RsRTDMD4xDGNr8LFP8LhhGMajhmFsMwxjjWEYk7ty8gAAAAAAoO28/kDo+bCM9gdCDUvGaj1+1Xr9oeOb9lWpvNaruMYVQgG/9OKlUsVuafbPOj7pNvg492M9tPQhTc6YrMkZRBFt0ZYlY89IOuugY3dKmmea5nBJ84KvJWmOpOHBnxskPR6eaQIAAAAAgM7yNakQav+SsdhGS8bqPP4m7xWU1ykpplEgtOYlafs8ac6D0vDTOzbhNgiYAT268lENSx6mx059rN19kXqrwwZCpml+Kan0oMPnS3o2+PxZSd9udPw/pmWxpGTDMPqFaa4AAAAAAKATGnYEG5gaq/5J0Yc5+1CxjZaM1dT7mrxnmlJ2n1jrRUW+9NkfpP6TpGnf79ykD+OzvM+0q3KXrh9/veJd7a966q062lQ60zTNvcHn+yRlBp9nSdrd6Lz84DEAAAAAAHAUmaapwqp6XTdrsL74xckdqqRpWBJW6/HL7Qsc8n52nxirb9C/51i7is15ULJ13X5Wq4tW6+6v7tbAxIE6beBpXXafSNTpvyqmaZqSzMOeeBDDMG4wDGOZYRjLioqKOjsNAAAAAADQij0VbtV6/MpMjOrwGFEOmwxDqvX45A0GQhdNyQ69n90nVlr7ilSRJ130jJQzvbPTbtWTa55UrCNWT5/5tBw29s1qj44GQvsbloIFHwuDxwsk5TQ6Lzt47BCmaT5pmuZU0zSnpqend3AaAAAAAACgLWbe/5kkKTOx/UvFGhhmQKOchXK76+ULWIHQaWMaFg2ZGlz2tfTlg1LGWGlY11bs7K7crQX5C3TxyIuVHkuu0F4djc/elnS1pPuDj281On6zYRj/k3SspIpGS8sAAAAAAMBRUO870AD6kK3h28rnkZ6bqw9sC1S3Ol7LB35fP7Dv09Qlz+o/zj1KNyqU9HqeFJcunftnqQubO1d7qnXXV3fJbrPrwhFdu519pDpsIGQYxouSTpKUZhhGvqRfywqCXjYM4zpJuyRdHDz9fUlnS9omqVbStV0wZwAAAAAA0A7ltd7Q84yOLBmrKJCev1Aq3KAXnd/WLNt6zdrxZ81ySvUVQxRv2FRkJmn0WQ9I066T7M4wzv5QL256UauLVuvhkx5WRmxGl94rUh02EDJN87IW3jq1mXNNSTd1dlIAAAAAACB8ymo9oec5DTuBtceix6TiLdLcf+k/8/tqfnKUpibX6D9Lduv5q76juQ99LknKPe6cMM24Zb6ATy9veVnH9jtWpw/suu3sI13XtfoGAAAAAADdQlmNVSH0wvXHyuVoZxTgqZVWPSeNPk+acJFiXXbVeAMqcfZVkS1dMcGt6I+UZ9Y/o301+3TZyJbqV9AWBEIAAAAAAES48mCFUHKsq30X+n3S2zdL7gpp+g2SpFiXXbUev7x+U067TbGuI7e718KChfrLir/o1AGn6sScE4/YfSMRgRAAAAAAABGuLNhDqE9cO3v7rH1ZWveadPIvpYHHSwoGQvV+ef0BOe2GYpxHrkLomfXPKCMmQw+d8BDbzHcSgRAAAAAAABFuf6VbktSnvRVC3zxpbSF/ws9Dh2JdDtV6ffIFAnLYbbLbrN3Erjl+ULimewhfwKen1z2txXsX67LRl8nZxU2rewPiNAAAAAAAIpjb69eL3+Rp6sA+im5PNU/xNmnPSum03zTZQr6hQsjjM+WyW3Umufd3bTPpv6/6u/659p+amTVTl468tEvv1VtQIQQAAAAAQAT7YkuRCqvqdetpw9t+UcAvvXqNFJUkjb+oyVtJMU5V1Hnl8QfksBvNXx8mATOgPy79o55e97S+NfRbeuK0JxTviu/Se/YWVAgBAAAAABCB/AFTJdX12l1aK0kan5XU9ov3r5f2rZXOe1RKym7yVmZitHwBU/lltXLYui4QCpgB/XXlX/Xshmd13pDzdMf0O7rsXr0RgRAAAAAAABHoH19u14MfbtbY/omKddmVFNOOvju7l1iPQ0465K2MhChJ0sq8cl02fUAYZnqoen+9bvj4Bq0oXKELhl2g3x7/WxlG11Yj9TYEQgAAAAAARBjTNPX84jxJ0vo9lZoxJLV9gcruJVJCPyn50MAnxnWgD9Gdc0Z1eq4Hq/RU6r4l92lF4QrdfezdumjERYRBXYBACAAAAACACLO2oEIF5XWh18cNSW37xfvWSVs+load0qSZdIOGRtLRTlv7qo7aoMpTpSveu0K5lbn6wYQf6LJRl4V1fBxAU2kAAAAAACLMGysL5LLb9ONThkmSTh+T2bYLA37phYulqHjp1F83e4rT0TVRwqbSTfrRpz9SflW+njz9Sd086eYuuQ8sVAgBAAAAABBB3F6/3lm9R6eMytBPThuhi6bmKCcltm0X71kpVRZI33lKShnc7CkNjaQNhW8Z1/ri9fr+x9+Xw+bQb2f+VjP6zwjb2GgegRAAAAAAABHk/bV7VVzt0cXTsmWzGW0PgyRp6yeSDGnoKS2e4rSHr0LI4/foT8v+pBc2vaDM2Ez9d85/1S++X9jGR8sIhAAAAAAAiCC7SmplGNKsYentv3jbp1LWFCk2pcVTwhUIvbDxBf15+Z/l9rt16chLdeMxNyo1ph29jtApBEIAAAAAAESQgvI6ZSREydXeXj9bPpYKlksn393qaU5755aKFVQX6E/L/qRPdn2imVkzdcWoKzQraxY7iR1hBEIAAABAL+TzB+T1m6rz+vXXz7bqezMHt29ZSRuYpqk/vLdR+6vq9dfLJoV1bAAt+2TDfg1Oi2vfRaYpvftTKXOsNOOmVk9tqBBqb34TMAP6LO8z3ffNfar11urasdfqlsm3yGkL705laBt2GQMAAAB6oV+8ukajf/Wh/v3VTj29MFc3/He5vP5AWO/x/JI8/eurnXpn9R75A2ZYx4a0dX+Vxv7qQ+WV1B7tqWh7UbXW5Jcf7WlA0sa9laqo82rL/qr2XbhvrVSZLx33Q8nVepjUkSVj1Z5q/ebr3+inn/9UNsOmZ856Rj+b+jPCoKOIQAgAAADoZQrK6/TGygJJ0t/mb5NkfYl8f+3esN7nzeA9JOnzzYVhHTvSbCusUn5Z+4Kdt1fvUY3Hr5eX7e6iWbXdqX/6Qt/628KjPQ1IWr27XJL0u/PHte/C7fOsx2GnH/bU9i4Z+3z35zrllVP0xrY39IMJP9CHcz/UyJSR7Zsfwo5ACAAAAOhFFu8o0cz7P2ty7MIp2ZKkW/+3qk1j7C6t1bbCKrm9/hbPMU1TuSW1Ou+Y/nLZbVqwtbjDc450/oCp0x7+Uuc8+lW7ruuXFCNJ2lvh7opptVlFrTf0nEqwo2/dngolRDv0nclZ7bswd6GUNkJKyDzsqY42Vgjtq9mnGz+5UT/+7McakjRE/53zX9086WbZbfb2zQ1dgh5CAAAAQC+yMq889PzWU4dr9vA0ZSRE69Xl+ZKk4up6pcVHtTrG9c8u0+bgcpRjspP01s2zDjnn8S+2q7i6XpMHJCu3uEbbi6qbHauizqvc4hodk5PcsV8oAizaXiLJ+ixKazxKiXO16/paj68rpnVYgYCp+z/cpCe/3BE6tq2wWiP7JhyV+fQmtR6fdhbXaGz/pCbHTdPUc4vzNGVgn/Y1aN67Wtr5pTT5qjadbrdZY7d0B6/fq0dWPKI3t70pv+nXDyf+UFePuVqxzvD2KUPnUCEEAAAA9CJltZ7Q86EZ8Zo6KEU5KTEalGp9UXssuISsJQ98uCkUBknS6vwKVdR6VVRVr+W7ynTna2u0v9KtL7cUSZIumZajzMQoFVXVS5Iq3V797KVVuvTJRbrm6W90zG8/1vmPLVSdp+Vqo3BpqGj6fHNhs9VN2wqrdPIfP9eCrUVdPpfGfvfu+tDzNxotszuchiDog3X79Oi8rWGf18G+3l6sL7cUhaqA/r1wZygMOu+Y/qFz0PVuf3WNznn0q9DysAafB/++G5jazuDlvduk2FTppDvbdHqs066Zw1L12BWTQ8e8Aa82lGzQX1f+VZe/f7n+s+E/Oq7fcXrh7Bf0w2N+SBjUDVEhBAAAAPQiS3NLJUmPXjZJ54zvJ0kyDEOf/+Jk/fyV1XphSZ7uOGuUop3NL+l4ZmFu6Pn3Zw/WPxfs1MX/WKTN+6vULylaeyvc+t9Sq6fNlccNUKzLoYzEaH2zs1T1Pr8+WLtXrzcTeqzfU6Gpg1LC/NtadpfW6pYXV2rV7nJdO3OQnl6Yq0um5uiBCyc0Oe9Hz6/QzuIavbt6r2YPT++SuRzMHzC1o6gm9NpmSI/O26qLp+aob1J0i9etyS8Pfc6S9Pjn23XLKcPCvm13cXW9bvzvcl04JVt3vr5WkvTXyybpmOxk/f69jZoxJFVXHDdAZ43tq9W7y7VwW4munTn4kHHqPH457IbqvH4lRtNEuKMKK92acf9noVDu5hdXaMHtp4Teb6g2u2vO6LYPumeVlL9UOvNeKS6tTZcYhvT3747W/tr9embd+/pqz1daX7xe1d5q2Q27RvQZoQdPeFBzBs9p+zxwxBEIAQAAAL3E7tJarc2v0I9OGqpvBSs6Gps1LE2vLs9XQXmdhqbHh47nl9Xq5aW7dc3Mwarz+nX7WSP1o5OGqbDKrae+2hmqGNpb4dbs4WmhfkENy1nOGJOpF5bk6TuPf62cPk2rBE4ema75m4u0Mq+8Q4HQ6t3liouya1hG88uUTNPU7Afnh14/HQy0Xl+ZL1Om/u/sMUqKdaqgvE5b9lvL2hp+n10lNfrVW+v1p4uPOewyuo4qKKuTL2Dqx6cO16PzturdNXu1fFeZ1u+p0D++O7XF6/7w3kZtK7Tme/mxA/TCkjwVVtUrM7HlEKk9TNPUD59boQ/X75MkLdtVFnrvxW/ydMuLKyVJt5wyTMcPs0KE44ak6JMN+w8Zq97n14z756k82GvojR8dr0kD+oRlnr3N/3tvY5M+TSMzm/65n7+pUCeMSFd6Qhv/vHrd0uf3S654adKVhz19R/kOvbLlFb229TXV+epCx0eljNLZg8/W1L5TNSVzijJiM9p2fxxVBEIAAABAL/DV1mJd+dQSuRw2fXfGwGbP6Z9sNSnec1AgNOsBK1DxBr+IZiREhx5PG52pjzfsV7+kaM0Z108/PnWYLvj719pZXKNZwaDgpJEZumBSlt5YWaB1BZU6YUS6vtxSpOtmDdY9547ReX/9Sg99tFlzxvdVdp+2Lyt56KNNemz+dknSez+e1aSfimma2rC3Us9+nSvJqmgwG/U79vpNvbwsXwNT4/Sjk4ZqR7DH0bisRG3cW6lAwNQzX+fqiy1FemFJnn586vA2z6s9Pt5gBS5nj++r/32Tp+XB4GVNfoV8/kCLzXvjow58lTtvQn+9sCRPm/ZVhS0Qyi+rC4VBDWJddo3pl6ivg1Uokpr0CxqSHq+y2nxVur1NqoBW5ZWHwiBJ2l9ZH5Y59jZFVfWat9EK3BbfdapufG65ymq9qvf55bDZlFdaq53FNTp9zOGbQoe882NpywfSab+RopNaPM0X8OmvK/+qf6/7twwZOnfIuRqdOlrpseka2WekBicdWhWG7o9ACAAAAOgFXl1uLS966MIJod2pDtY/2QoT9pQf+D//O4sPLGd6/HMrfEmOOfBl/6enj9DCbcV6+OKJmjE0VZI0/+cnHTL2gxdOUFKMU4kxTl00JVvpCVFy2W2Sz6PfzXTp/17ZoZfec+vCaYOU3m+gYhMPXy302vIDS8/ufG2t3rpppmzBZrfvrd2rm1+wqlhG9U3Qc9cfqx8+t1x1Xr8ev2JKqGoo1mXXhU8sCgUxk3OStbWgWCf+5iV5PF4ly6na6grJ75Ps4f/6tKO4RilxLo3qm6i+SdEqDPZa2lvh1lur9ug7wR3gDuYLmBqWEa8nrpyijMQouRw2zd9UqBNHdH6pW2mNJ/T5/PyMEaqo8+qfC3Yq2mnX6H6JWrarTDFOux67YpJSG1VODU6LkyStK6iQ2+vXmyv36MrjBurBjzY3Gb/e1/X9oiLRfR9slMcX0Ge3nai+SdEyTVPLd5XrzD9/Kafdpq2F1Yp22jRnXL+2DVi4UVr7ijTjZmnWT1s87Zu93+j2L29XibtEF464UNePv15Z8e3cwQzdEoEQAAAAEGGq631y2W1yOazqktW7y/XW6j26ftZgnT+x5S9ymYnRshnSxr1VGvHLDyRT8vgDkqS0+CgVV1thRXLsgUBodL9Erf/dWa1PKBCQsypfv5lcJ1XnSuvftXY12rdGKsvVJDOg96MkbQv+SFJchtR/kpSULcVnSDF9rKa3sSlSfF8psb9iVK9rxjq0fvsu1e/Zrq8+r9QJ44dKNofWr1mvkUaBLpiQpuunuOTY9LxeHpYvVebLeOP3Wha/SxUeKeGrWB1X41VijEeJRo3iVtfpd9HBXbsaim1WBn/i+0r9J1o7MQ2a1WpFRVutza/QsGA11reO6a/SGo9uOnmYfvnmuiZh3MHqfX6lxLk0LMO6dnBqXJMgrzMe/HBT6PkPThyqymAgdNHUbI3pl6j/Lt6lfsnROmVU00qUmcGKsJV55Xp+8S7tqXBrdX65dpXU6nszB6uizqvXVuSr3hsIyzwjXZXbq3veXKe+STG646yRWr6rTKeNztSQ4J+XuZOztTq/QrkltaFrbjppmMZnt+HP5ZpXpDd/KDnjpONvafYUX8CnPy37k17e/LJyEnL0qxm/0sk5J4e9TxWOHgIhAAAAIAIs2VGiN1ft0U9OG65j750nSVr9qzOUFOvUI59uUVp8lG49rfVlT067TekJUXp1eb48vgNf2h02Q+/eMkvH3WeNm9qWfjp+n7R7ibT+dWnLR1LF7qbv9xkk9Z0gjbtQSh2qJxfv16pdxXLIr35Gqe4aZlihUf5Sqa602VvMl6TtwRdRkr4M/ki6Q9IdUZI2B38kGYZdSugnJecoceRMLVqTL2e1X3YFNGlQlhLSMmRGJanQ49Qn22u1bl+NklymTG+9fnJitmKqd0vb50lbPpQMm5Q+SkodKqUMsX6X7KlS0gDJ1rbNnLfur9LaggrdftZISdL1s4fo+tlDJFmhTKXbq6e+2qmBKbH6dON+maZCjbDrfQFr2ZhpStWFOsa2XXW7y/X9e5dp3JD+uvH0YxQVkyBFJ7d5Pg3yy+qUFu/Sny+ZKKfdptT4KC266xRlJESHdou7oHGwGPBLe1cpftci3e5aptSvTV1SZ8jp8Mte4VfAYdNl9SmKqd+va1yblLGwj1QwRuozWIpL09Iiu0YNHayE1P5WU+OoRMkwVFbjUbTTrhhX8w3OI93aggq9uWqPJGnu5CztLq3Vtxt97lcfP0hj+yfqsfnbdPMpw1Tp9un4YJVeq9yV0od3Sv0mSBc9KyX0PeSUvdV79Y81/9BrW1/TuUPO1e3TblefaPo+RRoCIQAAAKCHq6736ZInF0uywpsGp/35Cy2+61Qt31Wmc4/pr4Q27O6UEhel/ZWVTY5t/H9nyWm3KS3epeJqjwamNNPnp2qftG+ttPUTqXiztGel5K6QnLHS4BOsJSlJ2VJcupQy2Kr4aeTcgXW69/7PJElOu6G7Ljhb5bUea1v76lo9PW+VpmWYivGW6cfTE7Ro5Rot3LJfP5gzTckpGfp4S7le+Ga3sqLrVeP2yi+bzhifrfMmDbQqixL7SQn9Q8u+bP6Abln+Qej+ay85Q4p2ypCUIemK4PENeyp19qML9O7yGP3l0hs19dw/W0HXroXW7kxFm63Ay++xLohJkbImWyFRYn/r3vGZUvY0q7opyO316/Q/W+nVGWP6WsHO3tXWT+FG/dFYq6gNhsprvXJLmiXJlOT2Zys6JVvfrixRZlS99MTPpP3r9GDjD3NT8EeyqpqGnSpljpPSRkhJWdacYlJaXAJXXufR+KykJjutNSwz7JsUrW/uOEFp9XnS0qekHZ9LO7+U3OWSpB8Yhrweh6IdXnlNu7xyyG4E5Noao0BitorNJKXKLm39WKqxtkifJklLGk3A7pIZm6aCCpd8MamaOHWWNOw0KW2klNCO/jg9nNt7YGndtU8vVcCUhmfGNzln6qAUPX3t9HYMWiG9+SOptli64hUpOeeQU9YUrdH1H1+vOl+dLht1me4+9u4O/w7o3giEAAAAgB6kqKpeP3xuuX7zrbEal5Wkwkq3pgcrgiRrByhJSolzqaiqXkPvfl+SNDE7uU3jb9xrhUHHZCfpn1dNVY3HL2ewsfFHPzlBZbVeq09PfZWUu1Ba/4a0f531I1kBUPooafR50rDTpaGnSNGJh71v/+QYrbzndF32z8WqD1Ynffepb7S2oCJ4hkMrSyQpQwMnH6OfbYxRv6Ro3THrVEnS6aNN3bD4fenA6hl9f/ZMqYXfu3Gz5htPHNpiWDamf6LOHJupj9bv14VPLFLu/edIQ060fhr4fdbyt31rpLzF0v71Ut4SyVPVdLCkHGnAcVKfQfLYU/QtW4H6GFUatuRjK1Sq2hucXLRGmSly15nqG1yeY8jqiO3duUtRm4t0dcAnb71TihktnXmv/r4moC9y65Xs8GpMmk2B+mr9dHZf66/R1k+kVc83mYpp2FTrSlPs4GkyRp1r/fWKshpEl9d6Nbxh1zbTlPaskPats0K+XQuVUbJdMoNhRWK2NPpcacjJ0qDZGvqHpZIMSaZcDps8PlNZyTFaeOcp8vsCuvaXH+i28SN0y6nDVVBcrgv/9I5SVKlUo0KjEz36wZREpahCNWX7ta98k9LryqRFj0kL/2LdL2Wo9dkPO00aMKNJyBZpaj0HAqGC8joNSY/T8UPbti18i967Tdr8gXTab63gspGAGdCflv1Jr2x5RanRqXr8tMc1KGlQ5+6Hbo1ACAAAAOgh9lbU6ZFPtmrZrjJd9uRi/eq8MfrFq2uanOML7gT2p4uP0bVPLw0dPyYnuU33mDKwj5bvKtOLNxynWFejrwuBgFIDJUotWSEt+tAKgjzV1pKk7GnSuLlS1lTruavtO4U11ifOpTH9EvX6ygKd+NB87WrUG0WSHr74GP3s5dX62curJUk/PW1E6D3DMPTElZN143Mr9J/vTdeofgmh3dBa8qOThmrD3kr95DBL6X55zhh9tN7a3WnV7nJNzEnW6yvy5fObio926Ozx/awv11mTpSnXWBeZpp79cqNy83bp/2bGy5G/xArNcr+S1r2mRDOgR13BG6yNl4aeLI08Wxp4vJSYrX9/sEVPfbVTF07J1tzJWbr8n8ESGo/ksEk5SQ5NHpSmP10yRZK0YecKLTH36sTB6fJnJ+lv87fp+5PPVPyMm6zrqvZLZTulygL9v1cWKtFXqmxfsb61e4Wcm96zgoL+k6TBJ+rU2hKdVO6Xnr+36ZK9qEQrhBn9LSl9pNR/srVkrlFPmY9+cqLOfORLSYaevma6rvjXEtV4rJ5MTrshm6FQ4HfJUyu010zRXqVIpvRlufSPedKZYzN12fQBun6F9ec3984ZVhhVuMH6/Na8LC37t3XD2DQrgMyeImVPl7KmWEugIqDPTV0wEHr1xhnKK63Vecf0D4Wz7eb3SYv+ajWRPvFOadZPmry9v2a/Hl35qN7e/rbmDJ6jn07+qfrFt7E5NXosw2y89+JRMnXqVHPZsmVHexoAAABAtzbozveaPf7T00Zo7uSs0M5Qf7l0os4c21d3v75W+6vcWritRNvvPVt22+G/JFe5varz+JXhdFuVIUWbrVBg19cHKliik6QRc6SJl0s5x0rO8Gx1LkmnPfyFthVWh17fNWeULj92gMpqvMpJidFPX1qlN1ftUazLrnW/OTO0q1hX+8+iXP3qrfWSpJ33na3Bd70fem/upCwNSovT2eP7alhGgl5YkqfH5m9TQbDJ8xNXTtFZ4xr1aQn4tXbrdv3kmc/1m0tmafaEUc32+cktrlFmYrRiXHaZpqmnF+bqd+9ukGTlHZdOy9F9c62eQu+t2avXVuTrl+eMVlmtR995fJEum37gfUnaXlStt1YW6NHPtoWO/eu7U3RaQq607nUp72tr2V+DlCFW8+zs6dayv8SsQ5aZ+QN+lbhLtL9mv0rcJaqor9LPX10mGR7ddGqO/v7FBrmcXp0/OU11vjp9sjFPLqc0MjNRK/MqJBmaPjhVO4tqVeX2qc4bkExDUQ6H6n2mZErfmpglm2GTTTYZhiGbaSqmvkqxNSWKrq9WTOU+RVXtU7Tfp9hAQLGuBMUk5ihx2OlKn3CZkpIG9shGyA1/5pb98jSltaVvV0vKd0vv3Gr1vxpysnT5S5LDGm9v9V69tf0tPbv+WdV4a3TtuGv1k8k/6ZGfF5pnGMZy0zSnNvceFUIAAABAD1Be6znk2PTBKZoysM8hzaIbdhJ7+JKJ8vkDcvsChw+DynZJi/6mhP0blFCyTareLwWXKSkxW8qZLg2abVWEDDqhS7Zgl6RbThmmW/+3Sv/47hQlRDk0Y2iqDMMILel65NJJ+uNFx6iiznvEwiBJ+vakLD38yRaV13pVXe9r8t7rKwskSe+v3asfnDhEd7+xtsn7j3++TWeOzQx9ya7yBHTp8ztUY2bJlZjZYtPnQcFt3CWrAuq00ZmhQMg0pfFZyar11qq4rlj9+5boe6fXa3vtIrl9bp0wZYde37ZYg1euVUpstF5eVqAVuyok2eRIsEkyJNOmebsr5RqXJdekuYqeerl276vSva8v0vnHD9PxY9JUUlei4rpilW79n0rdparwVKiyvlKVnsrQo99suo18dLCw5Kn1UkyaQzGOWC3dF6c4Z5xy0uzKLXZrVX6dDLupjESX3IFy9U0xlRbwyzRNbd5fKa9hymY3ZTNMbSipUMAMKGAGZJqm/KZfdb461Xpr5TN9klNSysE7axVJO1+Qdr4ghwz1ccQoNT5L6XF9NSBxgIYkDVFOQo6y47OVlWAFTt1Nw5KxGGcnmmqvfE565yeSTGnOQ9KxN6jaU62PtryrT/I+0Yr9K1Tnq9OxfY/VPTPu0cDEgWGZO3oGAiEAAHqgep9fNsPoeOk4gB5n+a4ySdKovgn619VTVVrj0YSD+uM8c+00lVQ3DY4cdpviW/tnRU2xtePQhretnbP6jreaEPcZLOVMsxr5Jh65pSPnT8wKBVotcQR3vjqSEqOduvOsUbrz9bWHfMYNqtw+/d8b60KvL5iUpWmDUnT3G2v19fYSzRyWpkDA1PjffBw6x7D5lF+Vr8LaQhXWFarMXaby+nJV1Feo2lOten+93D633H636v31Gj+9RtuLy2TYvPrLNq/u29Ty1vSuTOmvjVYUxjTzsb673/ppYrD0/F7rp0G8M17JUclKjkpWYlSisuOzlRiVqKSoJGXEZKhvXF+lxaQpzhmnb3ZUyetz6OLJw+S0H9qbqaHS7dsT++u33xqnpFhni++/uWqP/u+U4/TD55ervNard26eFdpWvaLWq2N+94Fk82nZPSfI7XNbQZGvVjXeGlXuW6Pi7Z+quGSTynyFKq4sVWHsbi3b943q/PWh+0XZo5Qdn62M2Az1j++v7IRs9Y/rr7SYNKXGpCo1OlWJUYlHPDSq60wgVFMivXGDtO1T1Q+YoS0n3KqN/iot/vxnmp83Xz7Tp8FJgzVn8BzdMOEGZcW3/vccIhOBEAAAPUxucY1O+uPnkqQnrpys00ZnNmmOirbz+AJy2o3Q/7XfX+nWhr2VOnlkxlGeGXqD3aW16psU3eZg95vcUjntht68aaainXZl9zm0T89J7fmzG/BL3zwpLf67VF0oTb5KmvljKXlA28eIEN6AVxX1FSp3l6usvkwV9RUqqy9r+tpdprzyEsUOqtJNnz+t2MFVinHalBzrkMMuldS4Ve7xyUg1lWyXvH6/FnkdWplrKG64Wzd8Ycrxlalop03xIzySYcppl677ovlwKd4ZrzhnnGIcMYqyRynaEa1oe7QGJCWof1y2iisDmjqgv9Jj05URm6HU6NTQ+Q3X3PXaOs3fEqz0MgKSAop2Grrl1KH648cbJQU0pn+87p07Th6/R26/W3+dv0HLd5XpwQumaWh6qhWKRKcq2tH2ZYGDJrX+/rUzB+nphbl6+OKJzVZ5ffzTE2SaUnafGH2+pUiX/XNx6L0P1+8NBUKPf7Fdkl0K2JUW00yz5f7HS5NvtJ7vWSWte1Va/h8F6iu0f9AM5c+4UbsNv3ZW5mp31W4V1hZq/u75KnWXHjKUzbAp3hmvBFdC6DHBlaDkqGT1ie6j5KhkJUUlKSkqSekx6cqMzVRaTJrsto5X99R5/Ypy2NpUCefxe1RUV6SimkIVbf1AhRte0xZvhdaNPEbbvfvkX3i7JKlPVB9dOupSnTrgVE3JnMLSsF6OQAgAgB7moY83h57f+NwKnTgiXc9+rx1bzkKS9PnmQl37zFLFuxz6+GcnqG9itI4N7tS09Q9zqL5Cl5q3cb+ue3aZ5ozrqwnZybpkWo5S4lwtnr9wW7Ge/ipX47OSFN2Z5SMN8pZIC/4kbf1I6jtBuvBpKbvZFhM9mjfgVUldifbV7FNhbaGK6opUUlei/bX7ta9mn/bV7FNZfZmqDt4NrJEYR4z6RPVRcnSyYhzRMv0+2c0EmV5DwzP7qG9SrGyGTWaaoc82FqneZ+qEUZnaU+bWmH7Jctpt+mpriYqqvHL7TblNQ5JNM4akaUJWiuKcccqIzVBGbIbSY9OVEp2ipKgkOW3N73rWHuP6luuzDe7Q679dPkmj+iZo074qBerLJUn7i6M0IX2C6jx+fbWtWCPik7XZv0dzx5zS6fu35NfnjdU954xpMegYkZkQej5zWJreW3OgVKms1itJ2lVSo/8syg0d311aK7vNUP/kmOZv2n+i9TP7NtnWvqp+H/2f+uVerWkDZ0rn/tlqkh1U663Vnuo9KnWXqsRdopK6EpW6S1XtrVa1p1pVnipVeatUUF2g9cXrVVpfKl/Ad8gt7YZdSVFJoT8/DRVWDWFS44Ap3nXgud2wy5SpYvc+xcSWK68yT6XuUhXVFamwtlDFdcXWn+faIisEqitSRX1F05tHSUlxqRqXNkYnpo7RmOBPv7h+hEAIIRACAKAHqaj1at5Gq7bfZbfJ4w/oiy1Fcnv94fmS2EuYpqkVeeUyTamq3qeNeyu1Zf+BJralNR5lJlr/N3xbYZWGpsfzH9AIq8c/3y5J+mDdPn2wbp8q6ry6c86oFs9/+JMt8vgD+uFJwzp34+Kt0rKnpcWPSfYoac6D0rE/6NyYR4jb51ZFfYWqPFVW/xpPpcrcZU2+sJe4rcfK+kpVeapU66s9ZBy7YVWT9I/vrzGpY5QSnaLk6OQmX9r7RFkVH8nRyYqyH1iatq6gQucu/koTsgZoTX6efn7uDE0ddGDbc8/sgDburTx0R7eZVmDR0PT76ztPaTm4CKO5k7NDDaSfu+5YzRpuVdGs31MZOqesxiPTNPWPL7frkU+3qk+sU3Gurv+a2Nb+T9MHpei9NXv1f2eP1rtr9+qFJXl6d/UeHZOTLK8/oBNGpOvLLUWhz1aSZgxJ1QvfP7b5f27H9JGmf9/atn7Tu9Jnv5cemy6Nv1iacZPUd4JinbEa1qftf6+Zpqk6X53K68tVXl+u4rpi7avZp/21+0NLAEvdpdpVuUtr69eqwlOh+kZL1lqVJZ3zRtNDDsOhtNg0pUenaoDh0hSvQ+kVNcrwuJXmSlLGhEuVNu1HSolN499daBWBEAAAPcjba/bI7Q3o3VtmaVxWkt5bs1c3vbBCq3eXa9qglCPaYLWn+u5TS7Rga7Ekqy9Dndev/LI6/WvBztA5d7y2Rn0To7W7rFYLt5Xo0csm6VvH9G/T+D5/QOV13s7tCNMNLcst1YCUWGUkhm83qd7qvTV7tSzYD6jBjqLqFs623luTX64bThii08dktv+GtaXWFvE75kubP5ACPmnSldJZD0hR8e0fL4xqvbXaXbVbeVV52lezT8V1xSqpKwkFPhX1VgPjw32BjnXEKiU6RakxqcqOz1ZSapJVheFMUFpsmjJjM0NLePpE9+lwL5ikGKtq57nFeZJ0SB8jl8N2aBgUlJMSq9d+OEPjs5LlchyZCsTGoVNDGCRZvY4a+AKmqut9qvNa/WrKar1HvD9Ta64+fpDOndBPKXEuje6XqCufWqJKt08LthZr0oBkJcccWkm1aEeJXlq6W5dOb2X5Y8pg6fhbpHHfsbaw/+rP0tqXpcxx0vAzpFHnSP0mttg83ecPaN2eSk3MSZZhGIp1xirWGav+8W37d4XX71WVt8qqOGp49FSp2lutgBmQIUPPLd6lvRVu3TlnjPpE9QlVkSU7E2TbtVB696dS6Q4pNk0aO1cac761KxwhENqIQAgAgG7KNE29+M1uvb92r04ama74KIfueXOdsvvEaGz/REnSyL5WWf0lTy7WbaeP0C2nWjsNeXwBPfP1Th0/NE3jsg7eeaUpt9evSrdX6fFREf1/EvPLavXjF1dqRV556NgNJwzR459vD20lLQV7VmwuanJtcZX1RXRpbqnKajw6Y2xfNed372zQvxdawdLMYal6/vrjwvxbHB0+f0AXPrFIWckxWnhn1y0j6S0WbLX+fC24/WTlltTo8c+3q6Tm0D4yd7y6RlsLq7Qir1yJ0Q5dP3tw229SUyytfdUKgnYH+6/0GSRN+q504u1SYtu+tIZDwAyooKpAW8q2KL86X7sqdym3Mlf5VfnaX7tfATMQOtdhcyg1OlXJUcmKd8VrYOJAqy+LK0mJUYlKdCUeeHQlqk90H6VEpyjG0fXVNpLVWLqx1PiWl/k1Z8rAlMOfFEYtBU8N/+44Y0ymPt6wX+N/87ESog58NYxzda+K04aAatbwNN0/d7zufN3axW1s/0TdfPJwDU2P19zJWfp/725QcXW9CsrrdOfrazWqX6ImthDQhST2l075pTT1OmsJ5bKnpa8flb56WHLEWA3WM8ZYAVLaCKnPIHnk1O1vbdabawq16K5T1S+p/X/+nHanUuwpSok+9M/ENztL9c8FO+Ry91OK6dW3hs4+8GZZrvTC5VLeIimhn3T5y9LQU6RmmncDh0MgBADoNfZW1Kmm3qfPNxfp6uMHdfseMa+tKNDdb6xVWrxLX22zKlrsNkNPXHmgCeSg1ANNZf/0yRb94MShcjls+vXb6/XiN3myGdLiu09VRkLLVR33f7BJz3ydK0nafu/Zh9+augcxTVO/eXu9YlwOOWxGKAz61bljtL2oWlccN0B/mbdVkrUE762bZyojIUpTfv+pJGlASqzySmsbNt7WRU8skiRt+8OcQxp5BwKmnvn6QJXRwm0lKq6uj4hKobxSa9lNQXmd/rVghyrrvDp+WJpMU0qLd2l4o34fONSa/HKtzCvXJdNytCa/Qv9bulunjc5UTkqsclJi9ezXu/T19v0qrHSHKrD+/dVOvbRsd2iMs8b1bfXvY0nWPuR5i6V1r0lrX5Hc5VLqcOnkX0pDTpSyp3Vp5YDH79Ge6j3aUrZFe2v2Kq8yT5tKN2lr+VbV+epC5yW6EjU4abCmZk5VdkK2tf13orX9d6IrsVsH0wnRTb8+NQ5Ruqv/3XCcsg5anjZtUIpW3HO6Cqvc+niDtQy5qt6qGnLaDc0c1kyD5m7i0ukDNCwjXre/tkbnTuivvknRuvU063+GPHmV1QdrTX65vvW3hfpsU6FinHY9t3iXZg5L01njmg/zJVk76U25xvpxV0hbPpbyvpa2fyZtfl9qFFy6JD0i6Y9RNgWeypFSBkgpQ6TUYdZPvwlSUnaHfr+8klpd/I9FodfDM+Ktv7f3rrYawK95yQqqzn1EGn+hFMU/f9Fx3f+fYAAAhMG2wmqd9vAXode1Hr9+HKym6Y7eXbNHP39ltWyG9PWdp+pHzy/X1sJqvX3TrCbb8zrsNiXHOlUebLI54pcf6M2bZurFb/JCYcbOopoWv0iuyS8PhUGS9NmmQhmSTh2d0a2/lB2syu3V6ysKNCAlVkmxTj344SY57TaV1nia9MoY2z9R794yq8nvdu3MQSqt8eiRSyaGjidGO1Tp9umz207UsP/7QJV1Xu0uPdCLZHdZnfaW12lFXpnOmdBfpTX1uvv1dQqY0iOXTNS4rCSd9ciX+s7jX+uJK6doVN+EHvV5Hmxr4YHlTL9/b6MkhfqSSNK8207U0PSju/SouzJNU7e9vFpbC6v167etSrSEaIfunHOgge3Z4/vq0437Nf3eeUpPiNKMIamh5TsNjh/ayhd0d6VVCbTgT1L5LskZZy0bOe3XUubYsP9O/oBfe6r3aFPZJm0p26LNpZu1q3KX8irz5DMPLEVKcCZoVOoofWf4dzQseZhGpYxSdkL3D31aY7MZeuH7x+ryfy6RpB7xexw3JLXZ4ylxrkMamW/43ZmKdti7/fLjqYNS9NltJ7X4/oTsZGX3idGukho9vXCn/rd0t77eXtx6INRYdJI04SLrR5J8Hqlit+r3rNeDL8+TLeCRSz7FG3W6NM2Qy7PP6kdUW3JgjNhU6++/nGOlrClWQBSfKbc9TlFRMZJhNPvn56+fbZXTbmhgapx2FFbq2uSV0rMPSbkLJMMuzfqpNPlqq2IJ6CQCIQBAr7Boe3GT1/M27tf1swcr9gg0zuyIZ4MhzY9PHS6Xw6Z/XT1NgYDZ7H+kL77rVBmGVb2yJr9C335soSRrOdQv31wX2pGlwdLcUsU47bIZht4N7txywwlD9OSXO/T9/yyTJP3ynNG6fvaQLvwNw6fO49cZf/5SeyvcLZ5z4oh01fv8uuGEIYf8B/ivzzv0C/OC209Rpdsrh92mhCiHtuyv0jMLdyhetYqTWzt3bNVPXt+sern0z49XqI9RpVSjSs/OTNNsc75sW0v1bPYmrd1Tqc8e+7fyMpN05rHHWMsN0kdK8Zk9qsfDykbL7CQr0Gjcg+SJz7frDxeMP2J9UXoK0zT123c2aGthtcb2T9Suklp5/QG9dMMMDcs48H/1507O1p8+3qKC8jqlxUfp7dV7JEnjshJ199mjtXRnmb49Kavp4J4aadP70s7PpdUvSQGvVQF0wi+kcXMlV1yn515WX6a8yjzlVuaGHndW7FRBdUGo4sdm2DQgYYCGJg/VyTkna2jyUA1JGtLjg5/WHD80TYvuOkWlzSzz64le/P5x+ueCHbrtjBHd9t+JHZHTJ7ZJkL+zuEb5ZbXK7hPbylUtcLik1KF6Zp30lNeu62YN1oyRGbryqSUaOGmypgzsY21EUFtq9fTJXyoVbpT2rpIWPCyZBwLeaEkB01CdXDKi4uWIilWdT5Jhl8Pp0HVl9botMVqZdq/MhCLZ8mqk+L7SmfdKEy6R4rpv9RZ6nsj5Ox4AgFZU1FmhyGXTczSmf5LueXOdxvzqI513TH8NSo3VbWeMPMwInb//v7/aqc+3FCnKbtPT105TXAtLDeo8fi3NLdOE7CTd2qiKqaX/Y9uwu9jbN8/SR+v36Qf/XS5Job4J5bUHvrSs31MRWvbUYGRmgu4+e7RW7y7X6vxyxUc59c8FO3TtzMHdfvlYTb1Pj87bqr0VbmUlx2hU3wQNy4zXzKFpmj44Rf9dZC0TGBPsudSqQEAq3iIVLFPSvnVKKtsplWzTAkeZnFurFWfU656GQqv3pTXNFV0tD/5IOt4epSn2gOwKyF4ckN7/74HzopKk9BHW/z1OHyUl9LX+gz8pW4rPkBxRwSmZ+ia3VMcOTmn2i/W2wir97t2Nuuec0WFdtlVR59Xbqwp08bQcef2mXliyS2eMydQ/vjtFu0pq1TcpWpv2VWlc/0Sd/9hCvbI8XylxLt119uhWxzVNMyIDguZs3V+ld9fs1TNf5+riqdm6f+6EVqsuXvz+cVqVX67zJvTTve9v1Mcb9uuOs0bp+KFpTauDKvdKX9wvrX/TWhLmjJMmXSGN+bY05KQOBY213lot2rtIG0o2aE3RGu2p3qP9tfubNHF2GA71j++voclDdVy/4zSizwgNSx6mESkjmuzC1Vv0S4rpUN+Y7mjG0FTNGNp8FVFPltUnRq8uz5ckTRvUR+sKKnXf+5v02BWTOzRevc+v+z7YpGNyknXPuWO0Ms9qDP+j51dIkpb98jS9ubJcV82YLFf21AMXemqk/RukygLt2LVTry7cqCjDqxjVK67Wrei6euvfEwoowWWTR14N7p8iIzZRRmyqVe038mzJ1r36OiEyEAgBAHqFGo9fDpuhey8YL9OU7nlznSTpneD/iT9nQj+N6puoQMCU3zTD2l/om52l+sF/lzWp1Fm/p1LTBzffXPTnr66WJH1ncna7vzyfObav5t12oj7fXKRhcW7NsG+UsW2fauP6h/6j9SSbqUTVKdWoUKpRqauz4qQXH9eLtkIZqYXy1ZSprt4n3/3Rsif3l5IHSBmjrcqWmBSrX4Er1voiGpti/RxBpmnq3wtzNTQ9Tjf8Z7k8/oBmD0/TM9dOPyTA+v4JrVQ5Ve61dl0q2iTtWSXtWSnVB5eXOWOl5IFS5ljF5yTpy11ubSgJ6NJZo/XwF3uUGudSTU21Bifb1C+1j06cOFLOhHTr8wl+JkZUoj5Zs1cLtxXrf0vz9M7VQzXetc/a9rt4s1S0WVr3hlRfcejc7C6ZrniV+qKUVO/U5ugEDcrqK8OVoC/z3HJFx2tIvzQtz6vW6DK/1r+RruHThlp/TdKGWSGTs2NfVk3T1NX//kardpfLabfJb5qqdPv0/WB11aA0q/KkIXD8/bfH6YK/f621Bc38Ho14/QHNfmC+Lp2eo5+cNqLN8ymv9ej6Z5fp8mMH6KSRGYcscemO8kpq9f3/LFNuSa0MQ/r5GSMPuwRnQGqsBgR7gv3fOWP0f+eMsfqGVO2XijZKBSukTe9Je1ZINoe1o9CkK6UBM1rcBak53oBXuRW5Wlu8VhtKNmjJ3iXaVblLpkzZDbuG9xmu0amjdXLOyeob11cDEgdoYOJAZcVnyWHjqwN6jqRGu4+lxUfp6uMH6YkvtmvSgh26btbgNv/7td7n12VPLlZMsNH25AHJknRINdXUYO+537+3UQ9dOEEXTc2x3nDFSTnTJE3Tl+U79Xf/CH36sxNUVOXRZf9c3PRmXum+ueMV1druaEAY8U91AECvUFPvU1yUQ4ZhyDCk568/Vlf8a0no/XdX79Wovon63rNLtSy3TOt+e2ZY7ruuoKJJc8ifnjZCf/50ix78cJNe/sEMLc0tVUrcgaa8Ly/drffW7NX1swbr6uMHHf4Gpmn1DNn9jfWFsSxXQ6v3aWjZLunTUr3olLTF+nmmme/Rftllz0uX4tJli0uTUofKcCXprWX5ctV7dV6codjyXdK2T6ytqpsTm3YgMEofFXw+qsvK2j/bVKj/9+6GJsf+873prf/Hva9eKttlNQgtWC5t/VSqssJA2V3WfMdfKGVNlbKnWo14bVYo6JB0UsDUzEBAUQ67vlz5mfLLrOUyy394WqvbM593TH9NzEnW/5bu1sryGPmzJ+nZ3D76zuS5mnVOmmSauv/1rzV/2Rqd2M+nu2bEyXCXSfVV+mj5VtW4y5WgOsXVuVW/fadyYn06pq5S0bUeRZV6dInhk5yS9kl6p9GNDZvV2DR9pFU1kjXF2j65DV+A1u+p1Krd5ZIU2slHko7JTm72/EkD+mjupCwt3lFyyHuBgKmyWo9S46O0rqBC+yrdeuTTra0GQlVur6Kddj3wwSZ9ta1Ym/ZVSVKTbdrvPnuUzhjTVwnRDsW6HKEvakebaZq67ZXVenNlgQKmNCQtTvfNHR9qFN3KhdYyk9KdUuEGqXKPVFlg/VmtLDhwXsZYa0nYMZe12j/EH/CHdvPaXbVbeZV5oed7avbIF/x7Oc4Zp4npE3X24LM1te9UTUif0CurfRCZfnDCEA1Nj9emfZW6+vhBCgRMPfHFdv3+vY3K7hPbYj8h0zT16cZCDc+I16C0OL2+oqDJ7pQ3njhUkhTbyj93fvHqGnn9puZOzpLDZmh1frke+HCzvtlZqlF9EzQ0PV7DMg788/jpa6fp1hdXavrgVF06LSc8HwDQBgRCAIBeobrep/hGS7RmDkvT0v87TU67oZ++tEp/m79NF0zOCm03XlrjCUslwrLcUklWM+M3b5oph81QeZ1HTy/M1ZC73w+d95PThuvphbmhpW1ZfQ5T3VFdJK19WVr5vFQY3DLdGWdtK53YT+p3jJQ2Ul9XpenB+VafoLmTs/T6CuvL5T+/f5I8UalKTc+U3dV0q1qHpEmTKvStv32lLamDdc/VYySfR2vXr9av/rdQcYZbsXLrqqnpmtU3YFW6FG2y+ph4qkLjmI4Y1dvj5IpLli0mSYpKlOLSpYRMKTFLShlqVbMkDWhzhYPXH9B/Fu0KvT5hRLruvWDcgTDINK0v0+W7rKqfok1WD4f9G6weK5IU00caOFMaeLM0aLa1nfBh7m+zGYoKluufPiZTTy/MlSQlxx7+z0h2nxhlJcfoyy3F+scXO1RQXifTNDVreJpkGNpdH6PN5gBt3iPNTp6uaKddy3eV6f6yTZo9PE3fmzlYL60ssPrKBFf//ea8Marx+FVRU6e+cYb++tFaXTY5Q7ef1N/661G4Qdq/3tqVZmMwKcocZ/WWmXFzaElac77YUtTkdUZClOaM63ugP5DfZ1Ws7FsnFSyTCjfqnsJ8ldXVK/B4umxxqVJsqsqNBOVV+LV4R6mSY5yKi7Lrl45aueST+eFC+R0xqjajldx/uBSdKLkSVOZ36bpnl8njrlW0PEo3PMqxeXTWyCSt3rZbjkC9XPKq+qPX9e7HXrnkk1M+nTJugAb272ttHx2fKaUOlRKz21U501k7iqpV5/Xr9RUFSohy6NpZg/WdyVkamNpCLx9PrbR9nrTiv1bwU9uoz5kzTkrKsoK8439sLS/sN7FJNZ4v4FNxXbH21ezTrspd2l6xXTsrdmp35f9v777j47rq/P+/zr1TNepdsiy519iJ45JGCqkkIUsoIZAQOiyhLtnCUhZ2IQl8+UFCdoHQCUtZSgIEkgBxCunFdmLHcZOLXFVttZFG08/vjzuWrchOXCXZej8fj3lo5t47M2f00NHMfc85n7ODHdEdJLP7popG/BEaChuYXTabixsuZnrJdOaUzmFy0aGPkhA50VQWhrjujKEjbc6fUcFjjR2sbe45aCD0y+e284XcKOL9fe9dp5MX8Hm1gvD+t19/Rj1zagu5cl4NX31gPdcsquNtuWnZn/vDau58bBMfOX8qn//Dvsf74bsXDet3iyeV8uS/X0gk4FOflBFlrLWvfdRxtmjRIrt8+fLRboaIiJzEPnDXMrZ3xlh60/nD9q3e2cNV336Si2dX8dA6b/ndK+fVcOncKt502oTB5WuXfvo8plcV8LvlO8gP+rh8Xs1rPu/HfvkCL27v4unPXjS4zVrL1d99mlW5URh75Qd9vOfsBpq743zl6lOGBFiAN8Jl00Ow9o/eaKBknxdmLHo/1J4OtQsGR7Xslc1alq5r4/wZFYT8Lve91Mydf9/MHz92zmtOi/v4r17gvpda+Opb5jGtMp+3f/8ZrIW3Lazj+aZOdnTFuPdj5zB/78gRa73RDB3roX09T698mW3NrRSYGGfU+Cj3J+hq30VxthNnv9okuAEomQzl071RD0X13glw2TQIFXujXRwXjMvXH2zku481cd3iCdx0RoTyVAt0bc3V/nnBK+I50LnvsUPFXjhWu8AbuVQ93/t5FB+41zb3csV/P8GU8giP/MsFh3SfL937Mj/bL8g6e2oZN14wlVNqi3jvXcvojiXZtic27H7fvOZU3rqwDmstc774NwZSGUry/Kz4wiWDU5AyWcsF33iU3oE0z3/+IoK+/b613jvypOkxWPl/sPN5L4xrONurSVG7wAsRc7+PbNZy0W2PUZzn5x2LJxKPD/CeeWGItnpFUhv/6gVBe3/HwUKomEmzLWP59h7OqQ9TZvpI93XQ39WGj6ErZVkMSXwESREmiWOO/HNoFpek8ZPIOoRIETRDi6djHG/0WqTc+zsIFZLy5+PPr/BqNRVNgEilF1LuPcY5/Kmi1lq+8eAGvvPoZgKuQzKT5b5PvI5TJhS98kCItnhLWG97Btb8HlIxKJoIk8/HTlhIrHQS3YVV9BjoTvbQm+ilO9FNT6KH7kQ3vcleOuOdbO7eTFusjex+y2D7HB+TCicxsWAiDYUNTCmawuSiydQV1FEWKtNJpkjOopuXcsmcar76lnlDtltr+Y97X+YXz24fsn1qRYSPvX4ab14w4ZD60ZJbHqI9uu89zjHe9LWuWIrPXTGLD583dXDf802dNLZFedeZDUf5qkQOzhizwlq76ID7FAiJiMjJ7sE1rXz45yt491kNfPlNpxzwmGu+9zTLtnYN2eZzDJtuvYJb7l/LD59o4tMXz2BmdQEf+YVXNXjr164cPLY/kea2pY0EfQ6fvGg6qUyW/KCPJbc+zNlTy7jjHQuGPHZvPMUPHttCYdjHtx/ZxNULJvDh86YMX/1kz2Z44WfQ9LhX4wagYjZMXAJnfcybEnScrGvp5fI7ngDgXy+byf/3tw0s/8LFlOcHicZTLLnlYQZSGX707kVcPKdqyH2t9YKFLR39lOemVH3/hoW89c6nOXdaGT9/5zTYs8mrp7Nn077rXU2QOYKVe9wAVM/zijTXnOqFSrWneSf7x+FE+O8b2pldUzj4TfFr6Uukufibj9HaG+f0+uIh0w8O5PT6YhbUl/CJC6cNjkJ6ZH0bd6/YyfvPmcyiSUPrNj3W2MF7fvI8l59SzeeumM3E0oOsorNxqff3tO2ZfSNSwiVQ3EDWH6GlZ4BdXf3MKPVRnGwbOmoFoGoe1Mz3pqFVneJNtXMcVu7YzdV3/h1jMvz4vQt5ZstufvTEZoyx3P6O+dQUBXlxexczqyPc/cIOHli9C0yWCHG+emkZP39iPelklKBJsHhyEXMbyjA+PwM4uH4/SeOQdAwJIGkzJMmSyKZIZVMk05Y/r2omGkvwjlNLyDdJTLwbJx7FpGKY5ADZxABOOkEi1k+Jm8SXSeICjrXez1z9HF+wiEBhHaH8KgJFdYRKphIsrieYX4MT8EbtGcyQn79bvpPv/n3z4K9o3oRivnXtAvzpPmKdG+nuWEdP+2q6W1bSm+yl23Xo8YfoLqymN6+YbmPpSfTSk+wZnMp1IBF/hKJAEUXBIiYVTaK+oJ7KvEqq8qpoKGygrqBONX5EDsFltz9OXUmYH7938ZDtT2/azXW5qeRff9t8FjaU0N6bYEZV/qtODX6lP61q5tb713HjBVP50p+8EbyfvHAaH7twGgHXUTgrI06BkIiIjFvJdJY5X/wr6awdDDMO5Paljdzx8MZh2zffegW3L23k249uGrZv7ZcvIy/g80b8fOcpVu08cFHdm68+5VW//ctkrVcMOd67b7TLzuXeaIyuJnD83kiO2VfB1Nd7J+Ij9IHyy39ey0+eagK8pcZX/+e+2kp3PdXEf/55LY6Bz10xmw+8bjKZrOUz96zmnhe8lV2+8iZvSff/uHcNZ00p45lcnZmQ3+G5z15MYfgVw+OzWehv90YZ7dnMqs07+cvqZhyy3kk7WebWFnDpnNy0s5IGb4RL0cQTYgUWay0Pr2vng//7PJg0xkmBSVNaYPj3y6fymd+/QCSU5b/fOY+sSZLMJImn4yQyiaGX9L7r8UycRDrJM00tRBNxwDCxJEJNUR75wQA+4yPkCxH2hSkOFlORV4HfGnx9bbjdO/BFWzB9u9nW2omTdQj7XRoqi4iGi4iGCoj6A0RdH1HXT68Dfck+osko0VSUaDJKb7L3VYOM4yXgBAYDkFQ2QzKdxe8aHAMWSyab9UbQHMUopOMh7AQpDBVTHCymKFg0eCkOFg8GPoO39+4PFOF3/a/94CLymj7/h9X86vnt/L+3zOftuXo9W3f3c8E3/g5AwHVovOXyo36ePX0JvvinNZwxuZTrltTjO4aLVYgcDgVCIiIyLj2xsYMbfvw8ANMr8w84XWyvgWSGu1fsYEF9CRNL8rj2B8+wvjVKZUGQjr4E+79d/vMlM/jm0kbuufFsFjaUsGJbF2+982kv/DCGW+9fx0Bq31SZZz574b7liVMD3kifjvXQs8urddPZ5AU/sf2K8vrCUH8mzLjMW0668LWnpx0P2azllgfW8eMnvVBo/1FRAI1tUS69/XEAHvz0eXzq1ytZ19I7uH/jLZezakf3YE2FOTWFrN1v/+tnVvDT9y0hncly4y9foDuWpLM/yZ8/8ToyWcvZX32EvKDL7W8/jbtX7GRda5Sbrz6FhQ0lx/ulv6pUNkVPomfIpTvRTXusnY6BDtpj7XTGO73AJh33gp3Mvp9HE6AYDCFfiKAbJOAGCLkhgr4gfhPgpZ39eBOzLJgsE0tDRIKGgXScgXSMnkQPaXv4zx1yQxQECoZdCgOF5PvzyfPnEXJD9Axk2djWz6Prd3PO1HIum1uLYxwc4+Aad/C6Yxy27xnglgc2MKkswtbdcb5/w2KKQkH8jt8breP4Bi97X2vADRB0vWMcs+/kKp3JMu3zfwEg7Hf53BWz+I97vW/mZ9UUsL6lB8jicyGdzYCxXDmvkgdebobc78rnWn75ocWURhwvbIu2kOjcRCLazED/brLxbsxAFyT7sJkk/bEYyWScAClCLphAGHxhrD8P8iuxBTVEiiZSVNxAUfXpFIW8gCfkO7RRZSJyfMSSaRbd/BDl+UEe/7fXA/C9xzbztb+s54tvnMM7l9SPmUL1IsfCqwVCGlcqIiLHxOaOPp7b0slbTp9AyD/6H6T2jl7ZK5bMvMrREA643HDWpMHbf/nUuXzq1yu9Qr7AF66czdf/uoH5dUVcNLuKby5tpL03zo7OGG+982kA3nx6HflBH9cvqSedtTzf1MmEfKjpXw8vPQrbnoLNj4LNtcU4XuHb0kkw641e/ZySyV5B3IpZMAZGBDiO4fNXzObHTzbx+pkVw/bPqCrgjnecxqd+vZIv3vsy61p6mVlVQPdAkl996Ez8rsOiSaX86ePnUF0Yoiw/SCyZ5qbfrmLp2jYe3dDB7Usbea5pD89u2Vf7Z/XOHl7Y3k00kea/r1vA2dPKOXva8Vm1bH+xVIzmvmaa+5tp7mumLdZGZ7yTzoFOOhOddMe76Yp3EU1FD/oYe0fhlIZKKQmVEHSDwy++ICE3NBjoBNzAYMjzyuP2Xg+53n6fc/Cio/eu3MX2PTHWt0a5f3ULO5pdFk8qZUdLL8999iIs2cERPRmbIZVNsbm9h/f/7HnOmFrEB8+dRFm+i2vcIcFPwD3MAutXvvYhOwpifLnPYXMfnDu9nIsnnXF4z7Efn+sMFosdSGUGw6A/fuwcTptYzM6uGN/9+2YunFnJB/93OVi4f1UvkA94z//Ext3c/Md2PnjuFNY0p/js5RfD5Itp6Rng7d9/hvbeBD9932KqC0Ms29rJZ+7xVmC7632LuWBm5RG3XURGVl7Ax5Xzavjdip28vKuHUyYUsXpXD2WRAO9/3cFX7xM5GWmEkIiIHLUV2zp5653eCJBvXXsaVy+YMKrtSaaznPv1R2jrTfCDGxbyz79bxS1vnsc/nFp7WI/T1hvnnT98lluunsdZU8tIZbI4xtDW3sqH7riHOTUFbGrposj084GFxZxb5/Nq/vTsgL426GuHnp1A7r22ZDLMfiPUn+WtGJRfNaKrIB2N3X0J8oO+A4Z9vfEUZ936MP3JDD7H8PRnL6Sy4NVHQaQzWR5c28Yn/+9F0tl9n0U+f8Vsbnlg3eDt82ZU8L/vX3LMXkcsFWNHdAc7oztpjbWyM7qTtlgbbf1t7OzbSWe8c8jxPuOjJFQyGO6UhEooCZZQHCr2fgaLKQwWDk71qcirGBPLdltr2dTexyW50VsAz3/uomHLn3/m7pf4zfIdAPzv+5dw3ozhod/xbOM//24VK7Z18dt/POuQ6zEdTCyZJp7K8qvntvHXNa1ct6SBdy6ZOCQ82ztlb0F9Md9c2siWjj5+/oEz8LsO33qokW89tG/a6L9eNpNEOstDa9tY39pL1noj3Da2R0llvL/Zkf6dicix8dvlO/i3u18C4KJZlTy+sYM3nTaBb1xz6ii3TOTY05QxERE5LhLpDF/7y3p+u2wHQb9LZ3+SmqIQH339NBZPKuGW+9dxxuRSPn7h9OPajnQmy8od3UwszeO+l1qoKwnzjz9fwY/fs4iLZle99gO8krVeoNP8ohfqxPZA52ZoXe0FPPtP7XqlQL5X0yY/t3pR6RRvtazJ50P+yXviuHV3Px19CYrCfmZUFRzy/XZ2xfjs71cTS2b44bsXURoJcN7XH2V7p7fi1h8+ejYL6l97epi1lmgqSktfC+2xdroT3XTGO2mLtbEruotdfbto6W+hN9k75H5hX5jqSDWVeZXU5ddRV1BHbaSW2nzvUh4uHzI16UTznp88z57+BC/v6uWeG89iYcO+YtTdsSSnfXnp4O01/3UZkVeubHecWWuxlsEV00bTtj39XPU/TzKrppDnm4YGgzec2cDmjj6e3uz1/YUNJdz+9tOoLztI8W4RGdOstfxpVTM/eqKJ1bu8+n//etlMPvb6aaPcMpFjT4GQiIgcF4+ub+d9dy0D4Otvnc+/3fPSAY/72z+dx8xqLyRYsa2LdCbLGVPKjlk7PvXrF7l3ZfOw7S//12XDl24/kL3LpW96GHatgPX3DQ99wiXe0u7FE7GlU/nI/Z2kcfjMFXOZ0VDv7Q8XQ7j0iJauHs+staSz6cECyY3tXTTt7mFBQ4RENs5AaoBYOkY0GaUr0UXnQCddiS72xPfQFe+iM95JV7yLxP5L2eeE3BAT8icwoWACNZEaqiPV1BXUMbFgIrWRWoqDxSf9ii+b2qNcfNvjfPGNc4ZMh9hbMPxb157Gksml1BaHR7GVY0MyncXnGO5esXPw/9k7Fk/kg+dOpjee5prvPcP0ynx+8+GzKMob/SmdInL03vvT5/n7hg5uefMpXH+Gln+Xk49qCImIyFFLprNs74yxozPGBTMrMMbwcu5btfs/+Trm1hYNC4TuvP50/uk3K7nsW4/znetO54KZFbz1zqdxHcPmW6846jZls96UkwOFQcBrh0ED3bD5Yfj717yVvQCChTD1Qq+gc+3pUDTBC3kC+0YCGGBBejOLJ5Uwo6H0gA89HmSyGfrT/fQn++lL9dGf8n72JfsGb/ckeuhKdNEd7yaaijKQGqA/1U8sHSOejnsrZGUS3mpQr/TygZ836AaHTOGaVjyNkmAJ5eFyavJrqMqrojRUSnGomAJ/wUkf+LyWuhLvb/fL960dDITae+P85KkmXMdw2dxqFVDNCfi8MPftiydy5fwawn53yOilZ/79Qsrzg2NiRJOIHBvfue50vv/4Fi4/ZXQWbxAZTQqERETkNd27chef+vXKwdunTixmQnGIB1a3UhoJMLe2CIDffeQsXtrZw1fuW8vc2kIun1fDfatbuP+lFj72qxf4xIXeUOxM9tiMTv17Yzt/eHEXM6sKmFIR4S8vt/Kd605nQkmYwtCrvMX17ISVv4Knvw2JHiifAW/4fzD5PKicfUhLun/k/KnH5DWMJVmbpSPWQUt/y+Dom70jcPaOwumMe6Nz+pJ9xNKx13xM17iDS2gXBArID+RTmVdJnj+PsC+8r2DyAQoqB9wAeb48wv4web488vx5lIXKCPvC4z7kORyvrPvU3D3A2V97BIClnz5PYdBBHGj63CtrMInIiS8S9HHTJTNGuxkio0KBkIiIvKofPL6ZWx9YD8A7l9Tz51XNrNrRzaodUJ4f5Ip51YPHLp5UyuJJpUyvzGdubSEA1y+p5/6XWgD4n0c2Abx6WHMYHl7XDsDvbjyL/ICPl5t7mDeh6OBhQSruTQe7/yaI98DUi+B1/+QVeR4DK3qNhEw2w+6B3eyI7mB7dDvbe7fT3NdMU28TW3u2Es/Eh90n4o9QEiyhNFxKTaSG2WWzvdWn/AVE/BHyA/lE/BHvdiBCvt+7vXc58hO5Bs/J4l8uncE3HmykP5FmQ6u3Qtrc2kKmVOSPcstERERktCgQEhGRIVp74sRTGSaVR7DW8r3HtnDWlDK+d8NCisJ+PnHhNK753jPccFbDQUfJ7L/qztnTynnmsxfynUc38YtntxPyO/QnM1hrD2mUx0AyQ8jv8J1HNzG3tojXTS/H73oBw4ptXVwws4LCkBfmzK8rPvCDWAsdG+D3H/QKQ1fOgQ896i3vfhLK2iy7orvY1L2JTd2b2Nm3k+a+Zm9lrf5W0jY9eKzP+KiKVDGpaBKLqxfTUNBATX4N5eHyIcumy4nt9Fxh7ic37Wb7Hm9k1w/ffcByAiIiIjJOKBASEZEh/uk3L/Lslk7+8NGz6U9k6OxPcsW8aorCXuhSWxzmqX+/8LAes6YozM1Xz+OmS2by2+U7+Npf1hNLZl51RaPeeIrbHmzkrqe3Do5uAJhSHqG+LI9YIsPOrgFOmVB04AdIJ2Dzo16NoO3PeEFQIB/e9lOY9UbwBQ7rNYxVPYkeXup4icauRjZ0bWBL9xaaeppIZpODx5SHy6mJ1DCvfB5vmPwGqvO8wsr1hfXURGrwOfo4cLJbPLmUwpCPf/z5CoyBmVUFVBYo6BMRERnP9AlQREQGPbqhnWe3eMstv+tHzw2O4Jl3sJE3h6k0Ehg8CX3fXcu4+epThixRnspk+dKf1nDj+VN590+ep2l3P8BgGHTRrEq2dcb4+4aOwfvsDapIDXijgDY9BC2rYMtjXn2gQD5UzYXLboXZV0Fx/TF5LaOhN9lLY2cjq3evpqmniZUdK9neu52MzQAwIX8CkwoncVbtWdQX1jOzZCaTiyZTEDj0ZeDl5OR3Hc6YUsbStW1YC7//6Nn4XE3lExERGc8UCImICAAtPQO876fLBm/3J72Q4RMXTuO0icXH7HnqS70Vj55v6uTS2x+n8ebLB1f2Wd8S5VfPbedXz20HvNFAW3b3A5Z3nVbMzReXYPuSbN7axfMvb6CjdSfnNcXhRx1eCJTJjYopneKFP3Ovhsnnn5CjgXoSPWzo3EBjVyMrO1bS2NVIU0/T4P7iYDGnVpzKZZMuY0n1Em+lrVDJKLZYxrqvvmUeL27v4vZrT3vV0XkiIiIyPhhrj81KL0dj0aJFdvny5aPdDBGRceOV9Xt++dw2Pv+HfWt8z6ouYFZ1AW+cX8vFc6qO/gnTCWhbA20vk96+jA1rVpBN9BMkRXlBmNLCCLhBelOGlc0xkvgIOLCoPI07sBt/fA/OflOg9spiyITL8VfPhqp5ULcQGl4HBcegzSPAWktXooutPVsH6/1sj25na89WdvXtGjyuOlLNnNI5zCmbw+yy2cwvn09xqHj0Gi4iIiIiJwRjzApr7QELB+rrIRGRcWZPX4JLbn+c0+uL+e71C9nQGh0Mg+54x2ns7Brgink1TC6PHN0TZVKw7k/w8u9h6xPeql6AL1TE3NpTIFDH09v62Nyb5MKqYmKxGFs7usk3CWZVhMgLuBCpgrp5kF8BkQqIVA657uSV4bhj/60sk82wPbqdLd1bWLNnDZu7N7M9up3W/lb6Un2Dx+X58mgobGBe+TyumXENs0tnM6N0BuXh8lFsvYiIiIicjMb+p2gRETlmXtrZzT98+ykAHlrXzqd/s5L7V3tLwq/4wsWU5R9lkdlsBpoehzV/gJfvgWSfV7Nn1hth+qVQPc+bzpUbnbT84Y3ctrQR1kJ1YYjWmLfk+XPvuYi8wtDRtWWEZW2Wzngnu/p2sb13O9t6tw0u676pexOJTALwVvWqK6hjctFkFlcvZmLBRCYVTmJa8TSqI9WHtPKaiIiIiMjRUiAkIjIOJNNZ/uHbT7K+NQp4U8LWt0YHw6CPv37a0YVBqTi89BtY/mOvlo8bhPnX5IKgy8A5cPHaty+a6AVCQGtvnBsvmMrC+hKqxmgYlMlmaO5vZkv3Flr7W2npb/GmePVuZVvPtiEreznGoSZSw8SCiVw781qml0xnStEUZpbO1DLuIiIiIjLqFAiJiJzgBpIZfvncNt51ZgMhv3vAY259YB3rW6M4Bp793EX4HYcFX1kKMKSo82FL9sOKn8ELP4OO9VBUD2/+vhcEBfNf8+7VRSG2fu1KrLV0x1IU5/nHxAiZrngXW3q2sLFrI639rbTH2tnSs4UNXRtIZ9ODx/mMjwkF3speZ9ecTV1BHdWRahoKG5iQP4GAe+IVsxYRERGR8UGBkIjICe6Ohzfyvcc2s3pXD3e8Y8Hg9vbeOA+ubWNyeYS7nt4KwMZbrsB1DNmst6DAW0+vO7IwaPcmePoOWP8AxHZD+Qy47ncw/ZLB6WCHwxhDSWR0wpO+ZB/L25azbs861nauZWPXxiEFnX2Oj8pwJRMKJnDD7BuoL6xnWvE0avNrKQuV4ToHDuFERERERMYyBUIiIiewVCbLH17cCcBTm/bwg8c3c8dDG6kryWNDW3TIsfd/8nW4jhfWOI5h1ZcuJRI4jDCjrx22PQ1PfQuaXwRfGGZdAYs/CA1nH6uXdNztGdjDsy3P8kLbCyxrWza4lLvBDBZ0ftuMtzGrdBbTiqdRmVeJY45wBJWIiIiIyBilQEhE5AQUT2XY0tHP1j39tPUmeP3MCh7d0MGtD6wHYENblMWTSrAWCkI+PnXxDObWFg15jKKw/9CerLMJXr4bnrgdUv1QUAuXfRXmXg2Ftcf4lR1bnfFONnZtZEd0B41djaxoW0Fjl1ezqCBQwNyyuVw15SrmVczj1IpTCfvCo9xiEREREZGRoUBIROQEM5DMcPFtj7GrewCASMDlu9cv5AM/W0ZjWx+fvmQ6TzTu5vZrTyN8OCOA9pfNQOPfvELRGx6ATBKmXgTnfwZqTgX/2Cn6nMgkaO1vZVd0FzuiO9jUvYkd0R1s7N5Ie6x98Lg8Xx6zSmdx08KbWFC5gPkV8zXyR0RERETGLQVCIiInmG8+uGEwDAr7Xd5yeh3hgMsvPnAGiXSWcMDl+jMajuzBe3bCirtg2Y9hoBPyq2HBDXDOp6DkCB/zKAykB2jpb6E91k7nQCed8U7aYm209rfSFmtjV3QX7QPtQ+6T78+nvrCexdWLmV06mxklM6gvrKc6r1r1fkREREREchQIiYicQLJZy19ebgXg+zcs5PwZFQRcb5SL45gjGxGUjMG6P8OaP0DjX7xts6+CU97mrRbmHtu3CmstPYkemvubaelvoSveRU+ih55kD3sG9tAR66BjoIPdA7vpTnQPu3/QDVKVV0VlXiVn1Z5FXUEdtfm11ERqqC+opyKvQiN/RERERERegwIhEZETyIa2KLu6B/j62+Zz2dzqI3+gWCc0Pe5NCdv6FCR6IK8Mzv93OOWtUDHjmLR398Bu1u5Zy+buzWzq3sT23u009TbRk+gZdmzACVAWLqM8XM7EgoksrFpIVV4VNfk1VOVVURYuozRYSlGwaEwsTS8iIiIiciJTICQicgJZ09wLwOn1JYd/52Q/bHp4X22gbMorED33TTD/Wqg/G5wjH1nT2t/Kuj3reGn3S2zq2sTq3avZE98zuL8yr5KJBRO5tOFSGgobmJA/gZr8GspCZRQFiwi5IQU9IiIiIiIjRIGQiMgJIpO1/Pr57QBMLD3E1bA6t3gjgdbeC01PeCFQoAAWXO+FQHWLwT3E1cZyrLW0x9pp7GpkU/cmVnWsYn3nenb17QLA5/ioL6jnnAnnMKNkBvPK5zG5aDIloSMIsURERERE5LhQICQiMsZF4yl+8uRWnt2yh+XbugAI+g5SKyidhJ3LYOODsHEptK/xthfWwVkf9VYKazj7kEKgvcHP5p7NbO7ePDjta0v3FqKp6OBxE/IncEr5KVw36zrmV8xnVuksQr6xswqZiIiIiIgMp0BIRGSMamyL8vW/buChdW1Dtn/youlDD+zfA1ufgHV/8kYD9XeAcWHyeTDvbTDnTVAy+aDTway1dAx0DIY9m7o3eQFQz2aiyX3BT0mwhKnFU7liyhVMLZ7KtOJpzCiZQVGw6Ji/dhEREREROb4UCImIjAEPrW3jt8t38KHzprB1dz/3vLCTZ7d0kh/0MaemkJsumcH8iUVUFuRG3vTshNV3Q9NjsPkRb1t+FUx6nbc6WP1ZECkbfPxUNsWe/nZ2D+ymPdbOjugOmnqa2NLjBUD7Bz/FwWIv+Jm8L/iZUjSFsnAZIiIiIiJycjDW2tFuA4sWLbLLly8f7Wac9BLpDNks+F2Dz9WSzONNz0CKnliK6qIQftfw8Lp2NrRF8buGhQ2lLGwYmfou/Yk0WWspCB1e3ZqRks1astYO6yPWWqz1lnbf3/Y9MR5rbGdn9wB98TTbO2PkBVxOqS2iaU8/1sJ/XjWXwrCPFdu6uOeFXezuS3DRrErOn1nBqh09rGnu4X8e2QSAS4Yi+ilz+lhSBZ85v5JCG4WBLujZBe1rvUt/h9eA0qkMzHkTXRNPp6uknvZ4J839zTT3eUu6t8faaelvoSPWgWXo//viYDFTiqYwrXjaYPAztXgqpaFSFXcWERERETkJGGNWWGsXHXCfAqGTW2d/kpaeAZ5v6uS2pY1E42kAbrpkBiG/Q01RmIayPFZs66J3IM2NF0wl4BvbYVEinaEvniYccAn73TF54mqtJZHO8teXW9m6p5/NHf3s7Iqxs2uAaxdNpLooxJ6+JOtbe1m2tYu5tYX81z/MZVJ55LCeozeeZk1zD2t29dIbT7Fldz+PrGsnL+BSlh8g4HPIpDO4NsmOji4CNkXQpKkIWRLxGCGS5JkEIZKcWldIOmspiwRJZSx+n0NnX4KMhdriMC09A8ytLeZ108vxuw5Nu/tJZSx5AZcpFfkkMxZjvHo3f3u5lUc3tHFaXRGn1hVRGvGTTGfZ2B7jwXXtFIYDvHVRA/MnFhMJ+ME4JLMwkLJs745TWRimMBSgM5ZiQmkEjONdMKSzlsc37qE/mWb2hCLKIkEiIR8pa/E5DlnIxR528HoW6I2nWL69i6DPJZbKsK2zn96BJIVBQ1t3L23dfcTicQYSCfwmQ1W+Szadwu9kKAk79PTHSKWSFAUNxWHIppNEBwaw6SQuGfwmTdjNEPZZXNIkkwl8JoNLBh9pfCaDjww+kyHgZCCbxsnddsgSdDIYJ0vSphhwDAPGIW5M7rp3e8DnYyBYyEAwj7gvSNQXoD3RPaSez15BN0hNpIbKvEqqI9XU5tdSlVdFebicinAFdQV1muolIiIiInKSUyA0QgYSMYK+II57kGKvI8Ray47OAb5y/1qWrt1Xe+ScaWUsaijlN8t20No7QIgkhcQoNP2ESOIjS21hgOkVISJ+Q2d/H/FkklMn5FNT6Gd2dQHxtMU4PgrCAYzjksgYggE/0WSG/IAPjCGTtXTGEsSSGYpCforCQ2cmrmnu5cXtnYT8LkG/S31piKy1dEQTRBMp2qJxHMcS9Ds09wyQTKUoj7j0xOL0JRJ09cUwZHHIUBp2KMlzSGfSTCwJMJBMkUynae+Lk8xkcYwh4DeEAi5+1/Ee20DQ72KBUycWkR/00dGXxDGGiaVhAj6HdNYLdPICLgbIYujoSxBNZEllLVhLRb4fg2VjW5R4MkUqk8Fms1hriSdTDCRTGMA1aXwmRdDJ4HPSkE3imAyQxXXBMZDIpgGLz2dwjQXjjVKx1uI6FuNANpvFdR0s3r5EJkvWZgfDDwtgwO+CIQM2611MlgyQNYYMDF5P77/NeK/Ru27I5rZlMGSMF6p4F5N7NgbHmtgh+19xjNkXyJD7PdpX3Ce7332ywx7La+Mrt9kxGAIeL65xCLshQr4wYX8eIV+IsC9M2Bcm4otQFamiMq+S0lApJcESysPl1ObXapSPiIiIiIgoEBopZ/x0LiUZw5vKT2PR5IvJ2Hye3dJJT6yPPH+G/GCGdCZOV18fk8tDFOf5sdbSl0gTjadwjCFtLX3xNK4Bv8+hdyCJz3EI+hxc17A7Gifo88KNgM8ha71wIpvNYLMZUpkUnbE4rdF+MGn8ToJJxWDMANZJ0J+O059N0m/TxAzEjEPcMaQwJI0hbSBlDGmdSI4pxoIhd9l73RgcwMHg5K4bwDEGg8EYBweDMQbXOLjGxTUOjnH3XXd8+BwfjuMbPMYxjrcPB59xcIyDtdAdSxFwHdqiCZKpDMV5ASJBHz2xNH2JNEHXIeR3CbgOZflB8oN+rDXEU1mS6Sw+19uXH3CxFnoHUuzcE6MrliSezJLndyjJC1AU8mEAm7XE01mymezga09nsmQylillEfKCLi3dcbr7U5Tm+XEdQzqdJZbIkExl8LsOBoNrwDWQF/AxsSQMlsF2hPw+jONijA/HcTGOL3dxcRwfGB+O68MYH8b1De53HD/G9WPcgPfT8eM4Pszg737oT8c4w7YP24bB5/gI+8KDoU+eLw9f7nFFREREREQO16sFQselqLQx5g3AHYAL/Mha+7Xj8TxjzbnpGWzKbubO7pXYlauG7swA8f1u7z6EB0zkfmaBdO66k7udBVIHuV9e7pKzDAhZyMMhEvARcYrIc0OU+8JEfHmE/GH8bgi/4yeTdYgEggRdP77cSW5fMkNrb5LmnjileT6szZJJZ0mk03T1JSgMucQSafw+h0QqS3VRiLyAN/onlvDCgngqy97sMRxwaSiLUBT254KGJH7Xxe+6lOQF8O09WbYG1/FCDRzXCzJyJ+Wu2Xti7uZOxF3iGUM4EMTv+nBwBk+6HcidUO897fbChYFUhmc2d7KxPcoFMyoZSGb4y8utuMYwt7YQxxh29QwQdB2wlqkVeUyryMc1WTr70zT3Join4cyp5VQW5oExYByM8WIab5qTwfWHcR0/rpMLYvb+zIUvPseXC2G8fXvbPhgk5MKAB1a3sLm9jw+cO5m8wMlVDz6WTJ90r0lERERERGQsO+YjhIwxLtAIXALsxMsj3mmtXXuw+5wsI4Sstdz3Ugu/evwJCrLPU54PC+uLCAfzKAxHiKd9OE6ISChC054EG9tjlEX8lOUHvZNhAwVBPyV5AbJYYokspZEA0USKVMaSymQpzw+SyULWQjqbxTUGg4PP7yMQ8BPwBQj6wgT8IfxuiGC4FL8vhOuM7jQ2ERERERERERlZIz1CaAmwyVq7JffkvwbeBBw0EDpZGGO46tRarjr1WuDaVz32jJFpkoiIiIiIiIjIMMdjOakJwI79bu/MbRMRERERERERkTFg1NYXN8Z82Biz3BizvKOjY7SaISIiIiIiIiIy7hyPQGgXMHG/23W5bUNYa39grV1krV1UUVFxHJohIiIiIiIiIiIHcjwCoWXAdGPMZGNMAHgH8Kfj8DwiIiIiIiIiInIEjnlRaWtt2hjzceBveMvO/8Rau+ZYP4+IiIiIiIiIiByZ47HKGNbaB4AHjsdji4iIiIiIiIjI0Rm1otIiIiIiIiIiIjI6FAiJiIiIiIiIiIwzCoRERERERERERMYZBUIiIiIiIiIiIuOMAiERERERERERkXFGgZCIiIiIiIiIyDijQEhEREREREREZJxRICQiIiIiIiIiMs4oEBIRERERERERGWcUCImIiIiIiIiIjDMKhERERERERERExhkFQiIiIiIiIiIi44wCIRERERERERGRcUaBkIiIiIiIiIjIOGOstaPdBowxHcA2oBzYPcrNERlr1C9EhlKfEBlO/UJkOPULkaHUJ8anBmttxYF2jIlAaC9jzHJr7aLRbofIWKJ+ITKU+oTIcOoXIsOpX4gMpT4hr6QpYyIiIiIiIiIi44wCIRERERERERGRcWasBUI/GO0GiIxB6hciQ6lPiAynfiEynPqFyFDqEzLEmKohJCIiIiIiIiIix99YGyEkIiIiIiIiIiLHmQIhERERERE54RhjzGi3QUTkRDbigZAxxs391D9wkRz1B5GhjDFFuZ/64kIkxxgz1xgTGu12iIwh4dFugMhYo/NtORwj9kHbGHOOMeZnwBeMMaVWxYtEMMYsMcb8EPiMMaZitNsjMpqMMY4xptAYcx/w3wDW2uwoN0tk1Blj5htjngRuBspGuz0io80Yc6Yx5h7gO8aYS/eeAIuMZzrfliMxIoGQMWYK8F3gUaAB+Iox5sqReG6RscgY4xpjvopX6f8p4HTgS8aYqtFtmcjoyYU/UcAPTDDGXAsaJSQCfAG421r7ZmvtLtA3vzJ+GWMuwDuv+D2wAXgXUDKKTRIZdTrfliM1Uh+yFwLrrLV3Af8MrATeaIyZOELPLzLWOMB24O25fvFPwJlo6LPILGA38C3gemNMgbU2q5NfGY9yo+amAH3W2m/ltl1ijCkGNCVAxqt5wDJr7S+Bn+N9idA3uk0SGXWL0fm2HIHjEgjlhnHO2G/TMqDOGDPRWtuFNyKiG3jL8Xh+kbHoFf0iC/yftbbRGBO01jYDO4Hy0WuhyMjav0/sd1K7CUgCTbnLe4wx9Rr2LOPF/v0iN2puN3CuMeZKY8wfgX/Bm1L5r7lj1DfkpHaA84ongGuMMV8EXgBqgO8aY64ZlQaKjAJjzFXGmI8bY87MbVoGTNT5thyuYxoIGWOKjTH3A0uBtxtj8nO74sCTwNtztzcAa4FSFUeUk92B+oW1NmOt7Qaw1iaMMQXAZKB5FJsqMiIO0Cci+53ULgJ6rbVrgDXAl4A7jTF+TR2Tk9mB+gWAtbYX+CnwFeAn1trLgB8BZ+53IiBy0jnYeYW1diXwBmAS8FFr7QV4J79vMMbMHp3WiowMY0yNMebPwL/hTZX8qTHmMmvtFuAZdL4th+lYf7iOAH8DPpG7fl5uewfwLDDPGLPEWpsBdgHnWGvjx7gNImPNK/vFuQc45gxgjbW22RiTb4yZPpINFBlhB3uvAG8qZYEx5jd4H3ZWAI3W2pQKTMtJ7tX6xX14J79766QsB9qAxAi2T2SkHfTzk7X2eaAC2Jrb9AhQAPSPbBNFRtwi4Alr7bnW2q8AdwAfyu17Ap1vy2E66kDIGPNuY8z5xpjCXKHDHwC/xRsVtMQYMyH3B/kM8CJwey7hnwtsN8bkHW0bRMaa1+gXZxhjanPH+XJ3KQZ2GGPehzfk87SRb7XI8XOofQLvhLcCaAUWADcCM/Wtr5yMDqFfTACw1r6EN0Xs48aYcrwiuqcAe0ap6SLHxWF8fgoCTwMfy931IrwV+HTiKyedXL+4IPd3/zBe7ay99gAbc9efQ+fbcpjMkUw9z9V6qAZ+hVcLZTNecv8pa+3u3DHn4A1ZW26t/fl+970NqMOrfv5ua+2Go30RImPBYfaLZdbaX+x3358D1wM/A27PffgXOaEd6XuFMaZ8v/35QMBa2zkKL0HkmDvKz1A3AVOA6cCnrbVrR7j5IsfcUbxXzMWbVlwNpICPW2vXjfwrEDn2XqtfGGP81tqUMeaTwBxr7Uf2u6/Ot+WQHfYIIWOMm6v1UADsstZehPcNbideig+AtfYpvGGcM40xRbkaKeB9w/UBa+0Z+uOUk8UR9ItZxpjC/eps3Y+34tj7FAbJyeAo3isiuQ86rjHGsdb2KQySk8XRfoay1t6GFwRdpjBITgZH2CeKjTHhXK259wDvtdZepDBIThaH2C/2TqO/BLg7d7/K3LZ/Q+fbcogOORDKfTi/FbjVGHM+MBPIAOSmhH0KODu3b68fAvl4xeA2GWNqc8V0o8fsFYiMoqPsFw8Dm40xNdbaX1tr7x7h5oscc8fgvWLLfu8VqhkkJ4Vj9Rkqd3xqRBsvchwcgz6xNVeWYiBXTFfkhHc4/cJamzHGBPBq9TYaY24BlhpjSqy1aZ1vy6E6pEAo9we5Aq+2wya8lS5SwOuNMUtgcGnU/8xd9roS+CiwCpiXW1pb5KRwDPrFSrx+0TJijRY5jvReITKc+oXIUMfw89OuEWu0yHF2mP3iv3J3CwHvxfuSuQC4OLfkvMgh8732IYA3JO2b+83ZXYC3RPYXgTuBhcZbDviPwIXGmEnW2q14hd0uttY+fqwbLjIGqF+IDKU+ITKc+oXIUOoTIsMdbr+oA2qBXwC3WWtXjkaj5cR3qFPGVgC/Nca4udtPAfXW2rsA1xjziVxiWQdkcv+0sdbeq3/achJTvxAZSn1CZDj1C5Gh1CdEhjucfpG11u601j5vrX23wiA5GocUCFlrY9baRG7uInjFqzpy198HzDbG3Af8H/ACDFZGFzlpqV+IDKU+ITKc+oXIUOoTIsMdZr9YAeoXcmwc6pQxwCt0BVigCvhTbnMU+BxwCtC0dz5vrjK6yElP/UJkKPUJkeHUL0SGUp8QGU79Qkba4S47nwX8wG5gfi6l/A+8YWtPqribjFPqFyJDqU+IDKd+ITKU+oTIcOoXMqLM4QaLxpgzgadzl59aa398PBomciJRvxAZSn1CZDj1C5Gh1CdEhlO/kJF0JIFQHXADXjXzxHFplcgJRv1CZCj1CZHh1C9EhlKfEBlO/UJG0mEHQiIiIiIiIiIicmI73BpCIiIiIiIiIiJyglMgJCIiIiIiIiIyzigQEhEREREREREZZxQIiYiIiIiIiIiMMwqERERERERERETGGQVCIiIiMu4ZYzLGmJXGmDXGmFXGmH82xrzq5yRjzCRjzHUj1UYRERGRY0mBkIiIiAgMWGtPs9bOBS4BLge+9Br3mQQoEBIREZETkrHWjnYbREREREaVMabPWpu/3+0pwDKgHGgAfg5Ecrs/bq192hjzLDAbaAJ+Bvw38DXgAiAIfMda+/0RexEiIiIih0GBkIiIiIx7rwyEctu6gZlAFMhaa+PGmOnA/1lrFxljLgD+xVr7xtzxHwYqrbU3G2OCwFPANdbaphF8KSIiIiKHxDfaDRAREREZ4/zAt40xpwEZYMZBjrsUmG+MeVvudhEwHW8EkYiIiMiYokBIRERE5BVyU8YyQDteLaE24FS8+ovxg90N+IS19m8j0kgRERGRo6Ci0iIiIiL7McZUAN8Dvm29ufVFQIu1NgvcALi5Q6NAwX53/RtwozHGn3ucGcaYCCIiIiJjkEYIiYiIiEDYGLMSb3pYGq+I9G25fd8F7jHGvBv4K9Cf2/4SkDHGrALuAu7AW3nsBWOMATqAq0em+SIiIiKHR0WlRURERERERETGGU0ZExEREREREREZZxQIiYiIiIiIiIiMMwqERERERERERETGGQVCIiIiIiIiIiLjjAIhEREREREREZFxRoGQiIiIiIiIiMg4o0BIRERERERERGScUSAkIiIiIiIiIjLO/P8sqJziN6++CAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the EMA of TSLA closing prices\n",
    "\n",
    "df[['Close', 'fast_close', 'slow_close']].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>fast_vol</th>\n",
       "      <th>slow_vol</th>\n",
       "      <th>vol_trend_long</th>\n",
       "      <th>vol_trend_short</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>4.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>4.77</td>\n",
       "      <td>-0.002092</td>\n",
       "      <td>4.774965</td>\n",
       "      <td>4.774991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>4.39</td>\n",
       "      <td>-0.079665</td>\n",
       "      <td>4.644861</td>\n",
       "      <td>4.646216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.054852</td>\n",
       "      <td>0.054852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>3.84</td>\n",
       "      <td>-0.125285</td>\n",
       "      <td>4.439442</td>\n",
       "      <td>4.443613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.062094</td>\n",
       "      <td>0.062246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>3.22</td>\n",
       "      <td>-0.161458</td>\n",
       "      <td>4.188745</td>\n",
       "      <td>4.197191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.068328</td>\n",
       "      <td>0.068645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "Date                                                                      \n",
       "2010-06-29   4.78           NaN    4.780000    4.780000             0.0   \n",
       "2010-06-30   4.77     -0.002092    4.774965    4.774991             0.0   \n",
       "2010-07-01   4.39     -0.079665    4.644861    4.646216             0.0   \n",
       "2010-07-02   3.84     -0.125285    4.439442    4.443613             0.0   \n",
       "2010-07-06   3.22     -0.161458    4.188745    4.197191             0.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "Date                                                                \n",
       "2010-06-29              0.0               0.0       NaN       NaN   \n",
       "2010-06-30             -1.0              -1.0       NaN       NaN   \n",
       "2010-07-01             -1.0              -1.0  0.054852  0.054852   \n",
       "2010-07-02             -1.0              -1.0  0.062094  0.062246   \n",
       "2010-07-06             -1.0              -1.0  0.068328  0.068645   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \n",
       "Date                                                           \n",
       "2010-06-29             0.0              0.0               0.0  \n",
       "2010-06-30             0.0              0.0               0.0  \n",
       "2010-07-01             0.0              0.0               0.0  \n",
       "2010-07-02             1.0              0.0               1.0  \n",
       "2010-07-06             1.0              0.0               1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set short and long volatility windows\n",
    "\n",
    "short_vol_window = 20\n",
    "long_vol_window = 100\n",
    "\n",
    "# Construct a `Fast` and `Slow` Exponential Moving Average from short and long windows, respectively\n",
    "df['fast_vol'] = df['Daily_return'].ewm(halflife=short_vol_window).std()\n",
    "df['slow_vol'] = df['Daily_return'].ewm(halflife=long_vol_window).std()\n",
    "\n",
    "# Construct a crossover trading signal\n",
    "df['vol_trend_long'] = np.where(df['fast_vol'] < df['slow_vol'], 1.0, 0.0)\n",
    "df['vol_trend_short'] = np.where(df['fast_vol'] > df['slow_vol'], -1.0, 0.0) \n",
    "df['vol_trend_signal'] = df['vol_trend_long'] + df['vol_trend_short']\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9BBERERERERGRHAoOlZUKy0RERERERESkuig4VGKOVUBIRERERERERKqXgkMlljD+Si9BRERERERERGRECg6VWJxApZcgIiIiIiIiIjIiBYdKLIEyh0RERERERESkeik4VGLJrH9io/ZDIiIiIiIiIlJlFBwSEREREREREalhCg6VkdUoexERERERERGpMgoOlVhOOMinf24RERERERERqS6KVpSRsZVegYiIiIiIiIhILgWHysjYRKWXICIiIiIiIiKSQ8GhEjNkpQtZp3ILERERERERERHJQ8GhMvI5yhwSERERERERkeqi4FCJ5WQOocwhEREREREREakuCg6VkXGSlV6CiIiIiIiISNHt7RnkyP+5hUc2d1R6KTIBCg6VWPaEMh8KDomIiIiIiMj0s+aFDnoGE/zfqg2VXopMgIJDZWTUkFpERERERESmobaGEOBmEMnUo+BQyWVSh4waUouIiIiIiMg05Pe54YV9PbEKr0QmIlDpBdSCB2a/gUj7U0TUkFpERERERESmoaTjJkYMJtROZSpS5lCJedPKLD58Vn8kIiIiIiIiMv041gsOKSliKlJwqCwMjvErOCQiIiIiIiLTkpc5FFNwaEpScKhMHOPHKDgkIiIiIiIi05CXORRLKjg0FSk4VGIm9dUxfnzqOSQiIiIiIiLTkJM63LV29P2kOik4VA7GqOeQiIiIiIiITFtJRYWmNAWHSsxrSO32HFLmkIiIiIiIiEwvW9r7uPmJHZVehkyCRtmXgzFY48eHModERERERERkerngyn/R3hur9DJkEpQ5VHJu5tCAr446p6fCaxEREREREREpLgWGpj4Fh8rC0B6cQ4vdD7G+Si9GRERERERERCRNwaES86aVtQfmuhf2b63YWkRERERERESKLeTPDS3ENc5+yikoOGSMOccYs94Ys8EY84k814eNMdemrn/AGLMktT1ojLnaGPO4MeYpY8wni7z+KcFi6AjNcb/Zv7myixEREREREREpolAgN7QwEFe/3almzOCQMcYPXAmcCxwOXGqMOXzIbu8EOqy1y4ArgMtT218PhK21K4DjgXd7gaOaYqAzmMoc6txS2bWIiIiIiIiIFNHQ4FC/gkNTTiGZQycAG6y1G621MeAa4IIh+1wAXJ26/AfgZcYYg9uNud4YEwCiQAzoKsrKpwhvlH13cCYJ/LBfwSERERERERGZPsJDgkM9A4kKrUQmqpDg0AIgO6KxNbUt7z7W2gSwH2jDDRT1AjuAzcA3rLXtk1zzFGQw/gB7TJsyh0RERERERGRaGZo5tHP/QIVWIhNV6obUJwBJYD6wFPiwMebAoTsZY95ljFltjFm9Z8+eEi+pvLzMoZDfRwdN0N9R4RWJiIiIiIiIFM+M+lDO91s7+yu0EpmoQoJD24BFWd8vTG3Lu0+qhKwZ2Ae8EbjZWhu31u4G/gWsHPoA1tofW2tXWmtXzpo1a/zPouoZZjaG6ExGsIPdlV6MiIiIiIiISNHMa47kfL9dwaEpp5Dg0EPAwcaYpcaYEHAJcP2Qfa4H3pq6fBGwylprcUvJXgpgjKkHXgw8XYyFTxXeKPtZDWG6bBSnf39F1yMiIiIiIiJSTI3hYPqyMbCtQ8GhqWbM4FCqh9D7gFuAp4DrrLXrjDFfMMa8OrXbz4A2Y8wG4EOAN+7+SqDBGLMON8h0lbX2sWI/iapnDLMaw/QQxRmoqX7cIiIiIiIiMs3ZVDsVgEWtdWzfr+DQVBMoZCdr7Y3AjUO2fTbr8gDu2Pqht+vJt72WeD2HZjaEedZGMDGVlYmIiIiIiMj0YTOxIRbPqGObysqmnFI3pJaUWY1hYgQhqZF+IiIiIiIiMn04WcGh+S0RdnVpWtlUo+BQmcxqCJPEh3HilV6KiIiIiIiISNHYrNSh5miQvliSZHbESKqegkMl5/5BNEeDOMaPsckKr0dERERERESkeBxraakLctXbX8ScJndyWW9MVTNTiYJD5WAMPp8hFArjwwHHqfSKRERERERERIrCAi3RIGcun01D2G1t3DOg4NBUouBQiZms/4dDIXejsodERERERERkmnAs+Ix73Bvwu2GGPz+yrZJLknFScKiMouFUcCipvkMiIiIiIiIyPTjWkooNMb/ZLStTcGhqUXCoxLxR9gCRsPtHgqP0OhEREREREZkerLWYVHTopIPaCPl9HLOopbKLknFRcKgcUn8kQa+sTMEhERERERERmSasBV8qc8gYw8FzGmjvjVV2UTIuCg6VWHbmkPH73QsKDomIiIiIiMg04Vib7jkEMKM+pODQFKPgUFmk/kh8btd2BYdERERERERkunAs6bIygNa6EB19Cg5NJQoOlZMvCIBVQ2oRERERERGZJqy1mKzvZ9SH2NLeh+PYEW8j1UXBoRLL/gMxqcyhREKZQyIiIiIiIjI9WAu+rOjC7KYwjoV3XP0QPYMJHt3SWbG1SWEUHCoLN0Rk/G7mUDKu9DoRERERERGZHob2HLrsxQcwrznCk9u7ePX37uGCK/+lLKIqp+BQiflMdkNqN3MoqbIyERERERERmSaG9hxqjAS54JgFdPTF2LinF4CBRLJSy5MCKDhUQtamAkPGyxxKBYfiCg6JiIiIiIjI9OAM6TkEMKM+SDyZSZbojyk4VM0UHCojr+dQMqmeQyIiIiIiIjJ9+IZEh5qjwZzv++MKDlUzBYfKIpU5FAgBkEwMVnIxIiIiIiIiIkUztOcQQF0okPP9gIJDVU3BoRKyQxtu+d3gkKOG1CIiIiIiIjJNOA55gkP+nO/7Y045lyTjpOBQOaT+SHzBVHBImUMiIiIiIiIyTTjWMiQ2NCxzSGVl1U3BoRIaNqjPHwbASShzSERERERERKYHa8kTHBqSOaTgUFVTcKikcsND/oDKykRERERERGR6sQzvOVQfHlpWpuBQNVNwqITSo+y9oX5+t1u7VVmZiIiIiIiITBOOHd5zKKqG1FOKgkOlZIdkDgW9sjIFh0RERERERGR6yNdzqF5lZVOKgkPlYLxR9m5wyCZVViYiIiIiIiLTg2PBDMscUlnZVKLgUAnZIT2HMtPKFBwSERERERGRacJafEMyh0L+3HCDMoeqm4JDZeRPZQ6h4JCIiIiIiIhME/l6DmVnEvmMeg5VOwWHSsg6QzOHVFYmIiIiIiIi04tjLWaU66NBv8rKqpyCQ+Vg3H9mb5S9ModERERERERkukg6Ft/QujLc0rJTD55JNORXWVmVC4y9i0zU0J5DgUCQhPWBModERERERERkmkg6lkCe4NAzXz4XgJdcvkrBoSqnzKGSGhIc8hviBBQcEhERERERkWkjafNnDnmiQb96DlU5BYfKwv0jCfgUHBIREREREZHpxXEsfjNKcCiknkPVTsGhEhrakDrg8xEjAMl4hVYkIiIiIiIiUlxJa/GPkjkUCarnULVTcKgcUn8jAb8hRgCjzCERERERERGZJhxn+Cj7bNGgn/64U8YVyXgpOFRSQzOHDHEbAEeZQyIiIiIiIjI9JB2Lf5ToQlM0SEevkiSqmYJDZeH+Mwf8PuIEMMnBCq9HREREREREpDjGKitb1Bplc3sfT27vKuOqZDwUHCqhoT2H/KmG1EY9h0RERERERGSacBw7alnZ4hl1ALzyu3eXa0kyTgoOlUPqjyToNwwSwKeyMhEREREREZkmxswcSgWHAGIJ9R6qRgoOldTwaWVxAhgFh0RERERERGSaSBaYOQSwu3ugHEuScVJwqIRsnobUAzZEINlfoRWJiIiIiIiIFJfjjJ45NK85kr7crsbUVUnBoTLy+Qxd1BNOqAmXiIiIiIiITA9jlZUFskaZdfapkqYaKThUQkMbUgN000A4ruCQiIiIiIiITA+Ow6hlZQA3/uepAOzvV3CoGik4VA5ZfyTdpoFIshvs8MCRiIiIiIiIyFTjZg6Nvs/MhhAAnQoOVSUFh0oqT+aQrwG/TUCstwLrERERERERESkeay1Jx+IfI3OoIRIAoGcgUY5lyTgpOFRC6dBQ1h9Jj2lwLwx0lns5IiIiIiIiIkX107ufB+Dpnd2j7hcJ+AG4/Oan+c0DL5R8XTI+Cg6VUrp0LBMc6vU1uhf6O8u+HBEREREREZFiemhTOwCb2/tG3c+X1bD6c9ev469rt9GhyWVVQ8GhErJ5+gplgkMdZV6NiIiIiIiISHF5U8oSeQYyjSSetHzgmrX84t5NJVqVjFdBwSFjzDnGmPXGmA3GmE/kuT5sjLk2df0Dxpglqe1vMsaszfrPMcYcU9ynUP2yKy/7/CorExERERERkenBCw4lxxEc8oQCylepFmP+JIwxfuBK4FzgcOBSY8zhQ3Z7J9BhrV0GXAFcDmCt/Y219hhr7THAZcDz1tq1xVt+dfP+NGxWz6E+lZWJiIiIiIjINBGYRHBoMJ4s9nJkggoJ050AbLDWbrTWxoBrgAuG7HMBcHXq8h+AlxkzrFX5panb1rR+f1PqgsrKREREREREZGrz+9ywQiHBobeedEDO930xBYeqRSHBoQXAlqzvt6a25d3HWpsA9gNtQ/a5GPjdxJY5ReXpOTToqyOJT2VlIiIiIiIiMuXNa44A8J4zDhpz389fcCRvOnFx+vs+ZQ5VjbIU+BljTgT6rLVPjHD9u4wxq40xq/fs2VOOJZVFJjSUSaIKBvz0++phcPQxfyIiIiIiIiLVzusbdOmLFhW0f0M4kL7cr8yhqlFIcGgbkP1TXpjalncfY0wAaAb2ZV1/CaNkDVlrf2ytXWmtXTlr1qxC1j015Mkc8vsMCYKQGKzAgkRERERERESKJ5F0gExj6rGEs5pQ98USJVmTjF8hwaGHgIONMUuNMSHcQM/1Q/a5Hnhr6vJFwCqbmuNujPEBb6AW+w15saGs9ktBvyFuApCMVWZNIiIiIiIiIkUSdyxBv2F42+H8sieU9cedUi1Lxikw1g7W2oQx5n3ALYAf+Lm1dp0x5gvAamvt9cDPgF8ZYzYA7bgBJM9pwBZr7cbiL7/aDc8cCgV8xAkoc0hERERERESmvETSIeArvGNNTnBImUNVY8zgEIC19kbgxiHbPpt1eQB4/Qi3vQN48cSXOJWlU4fSWxrCAQZtUJlDIiIiIiIiMuXFk5aAv7CsIYCQP7usTD2HqkVZGlLXrFTPoezsusZIkAGrsjIRERERERGZ+uJJJyfgM5Zw0J++rIbU1UPBoRLy7d8MgD85kN7WGAkw4KisTERERERERKa+xCQyh/qrYJS9tZZE0sHmGShVSxQcKqHIHZ8HYNHeu9PbGiNB+h0/NqHMIREREREREZna4s74eg4dPr8JgPqQvyrKyr59+7Ms+9RNLP3kjfz2gc2VXk7FKDhUQntjQQCSAz3pbU2RAHECJBMDI91MREREREREZEpIJN1pZYU6bF4Taz7zci47aUlVlJX9+v4X0pe/t+rZCq6kshQcKqH2uBscCjv96W2NkQAxgjhxlZWJiIiIiIjI1JZwHALj6DkEMKM+RF3ITyzpkEhWdpy9z5cJbO3vj1dwJZWl4FAJ2eaFAARWvjW9rTESZJCAyspERERERERkyosnLQFf4ZlDnrqQ25i6r8J9h/xZE6R8ZvzPY7pQcKiEBgm7F07+z/Q2L3PIqiG1iIiIiIiITHGJpENwnJlDANFUcGigwqVl/qzAlm8CQa7pQsGhUkoMkLA+wuFQelNjJEjMBiCp4JCIiIiIiIhMbfFx9hzyRFMj7SvdlDq7l3YNx4YUHCqpxACDBAkHMv/MXuaQSaqsTERERERERKa2eHL8PYcgq6ys0plDKisDFBwqrcQgMUKYrF+wxtS0Mp+CQyIiIiIiIjLFJZyJZQ7VhQIA9MUSxV7SuGSXkvUMJvjWrev5j9+sqeCKKiNQ6QVMZ77kIDETzNnWFAkSI4DPUXBIREREREREprZE0iEQHn9ooSnqHitXekJYdubQYMLhu6s2AHBlpRZUIcocKiGTdDOHsoUDPhImiN8mwNoKrUxERERERERk8ibac6ilWoJDtdxoKIuCQyXkSw4SH5I5ZIzB+lNTzFRaJiIiIiIiIlNYwnEI+MYfWmiukuBQdo/gWqZ/hRIyToy4CQ3bnvSltmmcvYiIiIiIiExhiaQlMIHMoaZoEJ+BfT2VTZoYqZm2rbFKHwWHSsifjJEw4WHbHV8qm0iZQyIiIiIiIjKFxR2H4ASmlfl9hgWtUV5o7yvBqgqXdPIHgQbiTplXUlkKDpWQ3xkk4RueOeQoc0hERERERESmgXhiYj2HAJa01bNpb2+RVzQ+XnDo1UfPz9neW+EpauWm4FAJBWwsU0KWJZM5pOCQiIiIiIiITF0JxxmxNGssS2e6waFKlnAlHMtZh83hiouPyWlO3VXhXkjlpuBQCQWcGEnf8LIy608FjJK19csmIiIiIiIi00s8aQlOcOLXkrZ6ugcT7K1g3yHHsQR8Br/PML8lkt7e0VdbbWAUHCqhoI2R9A/PHLIqKxMREREREZFpIJGcXOYQwKZ9lSstSzgO/lRZ3OIZdentHb21lcyh4FAJBW0Mx5+nIXU6c6i2IpEiIiIiIiIyvcSdiU0rA5jb7Gbq7OmuXOJEMpU5BLnBoT09tZXMoeBQCQVtDJsnOJTepswhERERERERmcISSYegb2KhhYZwAIDewco1f044Nt1raGFrJjj0fIUbZZebgkMlFCKeNziEt00NqUVERERERGSKchyLY5lw5lB9FQSHsjOHFrZG09uf291TqSVVhIJDJRSyMWwgMmy7CaSmlSVUViYiIiIiIiJTU9xxAAhOsOdQfdgPQG8sWbQ1jZebOeSuf/ncxvT25/YoOCTFkEwQME4mSyhLOptIPYdERERERERkivrn07sBN4NoIkJ+HwGfqZrMoUPnNrH2sy/nP1+6jM3tfQwmKhe0KjcFh0okGe93LwTylZWpIbWIiIiIiIhMbZ/+yzoAtu/vn9DtjTHUhwOV7TmUdNI9hwBa6kIcOKsBx8KW9r6KravcFBwqkcEB95fIBPOVlakhtYiIiIiIiExtXsZNIjmxzCFwm1L3DFYuQyc7c8gzo95N6Ojsq51x9goOlUhswI2cmjyZQyaohtQiIiIiIiIytXkZN8kJlpUB1IX8lZ9WNqShttcou6eC6yo3BYdKJD7oBod8oejwK70m1fGBMq5IREREREREpHi8KWVJO/HgUH04QG+ssj2H/CY3ONQYUXBIiiQ+6JaV+fKVlXnbEgoOiYiIiIiIyNTkZQ7FEs6E76Ohgj2HrLUk8pSVeZlDlcxoKjcFh0oknTmUt+eQFxxSWZmIiIiIiIhMTacdPAuAT5132ITvoz7sp7dCPYe8ajhvlL2nIRUc6h5QcEgmKTHoZgX5g8PLykIBP4M2gFXmkIiIiIiIiExRQb8hGvSzsLVuwvdRH6pcWZnXKykwtOdQyA9QsaBVJSg4VCLxmJs55A8PzxwK+n0MEsRRzyERERERERGZouJJOyywMl6VHGXvBYf8Q8rKAn4f0aCfnsHamVYWqPQCpqtkzA38BPI0pA4G3OBQnYJDIiIiIiIiMgV1DcT5xb2bJn0/bnCoMhk6CcftlTS05xC46+pR5pBMVjKVOZQ3OOT3MUhImUMiIiIiIiIyJd27YW9R7qc+5CeWdCbV1HqiRsocAndimaaVyaR5gZ9gOE/PIb9hr23CdGws97JEREREREREJi0c8BflfrzJYH0V6DuU8HoO5QkORYJ+/vboduLJ8getKkHBoRJx4m7mUL7gUNDv4w7nGALbHoKePeVemoiIiIiIiMikDCaKU3LlTQarRJZOJnNoeGjkqR1dAPzgjufKuqZKUXCoRJxUz6FQJH9D6luTKzFYePbWci9NREREREREZFIGi1QGVheu3GSw0TKHPOt3dZdrORWl4FCJOEk36hkMhoddFwz4eNIegOMPw9715V6aiIiIiIiIyKQMxosTHPLKyioxzj6ZHLnnkGdv92C5llNRCg6ViHXcqKffN7wOM+Q3gCFRPxf2byvzykRkOtne2c+ST9zA/Rv3VXopIiIiIlJDil1WVolx9ulpZf6Rg0N7ehQckkmwuBFI4x/+TxxMbYtHZ0PPrrKuS0Sml3+lpkRc+9CWCq9ERERERKaLwUSSj/3hUXZ1jTxh2ysr++S5h07qsepDlQsOjTatzLOnS8EhmQybCg6Z4b9kXnAoEWyAWE9ZlyUi00t/3D1jUxcqzrQIEREREZG7ntnLdau38um/PJH3+uf39tIfcz+Hvv2UpZN6rPpUz6GeCvYc8uc5bv/ze0/G7zN0DybY3x8v99LKTsGhEjHW/cX2mVEyh/z1MKjgkIhMXF8sf3Do365ezRf+9mQlliQiIiIiU1wk6B6z5hsvv2F3N2d+4w6uuP0ZjIHgKCVZhZjV6Pbp3bm/f1L3MxGjZQ4du7iVr190FAAdvbGyrqsSFBwqkVTiEL58PYcC7i9ePFAHg7XR+VxESiORdNN5fVlvaNs6+7n9qV38/F/PV2pZIiIiIjKF1aVLvYZn82xu7wPAsRAO+PJWy4z3seY0hXl+b9+k7mci0tPKRghwtdQFAehU5pBMWCpzyPhGyxyqU1mZiEyKV1b2ozs3prf9ec1WAKJBlZqJiIiIyPh5mUNe6Vi2eGrCF0A4UJzPm0va6tm0r7co9zUemcyh/KGR5mgIgM4+ZQ7JRHk9h0YJDsW84JBTnBGAIlJ7+vK8YW/rdBsHemc6REREREQmIt94+YF45vOnF0SarKUz69m0t3LBocAIDam9z9PqOZRijDnHGLPeGLPBGPOJPNeHjTHXpq5/wBizJOu6o4wx9xlj1hljHjfGRIq4/uplvVKPkYNDg/56d0O8/H8EIjI99GWl+jqOZV/PIL97cDMAPQPln/ggIiIiIlOf1yYl34nIrqxASdEyh2bWs683RtdAeYMw3ij7kaaVLWyN8tf/OIUzls8u57IqYszgkDHGD1wJnAscDlxqjDl8yG7vBDqstcuAK4DLU7cNAL8G3mOtPQI4A5j+ITdI/zXl7TnkBYdMnbtBTalFZIL6ss7c9MYSPLcnE2zuiSWw1ua7mYiIiIjIiDLBoeEnGzv7soNDxckcOmCGe2y8pb28fYfGyhwKB/wcvaiF5uj0z8gv5Cd5ArDBWrvRWhsDrgEuGLLPBcDVqct/AF5m3K5UZwOPWWsfBbDW7rPWln8+XSV4mUP5RtmnGlIP+qLuBvUdEpEJ6hvMvGF39sXTDarBfVPPd7ZHRERERGQ0FjdoMhAf3gJl1frd6cvhIpWVNafKt7r6y5v5nhhlWlmtKeQnuQDYkvX91tS2vPtYaxPAfqANOASwxphbjDFrjDEfm/ySpwjr4Fgzas+hAS84pIllIjJBfbEk3nvZuu376R7MfUPtHVRpmYiIiIiMz0jJ5/9cv5tHNnemvy9WWVlD2JuOVt7Prsmklzmkdsyl/hcIAC8B3pT6+hpjzMuG7mSMeZcxZrUxZvWePXtKvKRycRipmMMLDvV5ZWXKHBKRCeqLJTjpoDaiQT/3Pbdv2CSFocEiEREREZGxjHQsu6drMOf7YpWV1aeCQz1l/uyqzKGMQn6S24BFWd8vTG3Lu0+qz1AzsA83y+gua+1ea20fcCNw3NAHsNb+2Fq70lq7ctasWeN/FtXIWpwR/nm9nkN9vlRD6oGucq1KRKaZvliSpkiQlUtauW/jvpwacFDmkIiIiIiM30h9K+vCuZlCxQoONVYoOJTuOeRXcKiQn+RDwMHGmKXGmBBwCXD9kH2uB96aunwRsMq6v023ACuMMXWpoNHpwJPFWXqVsw6W/L9gwdQvXq9pcDcMdJZpUSIy3fTFktSFAhy7uJVnd/ewu9s9m/PFC44ANLFMRERERMbPyYoNJbO+qQ8FcvYrVllZfYXKysaaVlZLxgwOpXoIvQ830PMUcJ21dp0x5gvGmFendvsZ0GaM2QB8CPhE6rYdwLdwA0xrgTXW2huK/iyqkbUjBof8PoMx0Gu8zKH9ZVyYiEwnfbEEdSE/i2fUYa3bd2hmQ5hjF7cC5T/7IiIiIiLTQSYgtLUjM0EsNCRTqFgNqetCfvcYuVKZQwoOERh7F7DW3ohbEpa97bNZlweA149w21/jjrOvLdbBGSE4ZIwh6PfRTR1goL+zrEsTkemjL5akLuxnfnMEgPU7u5nZEE439VNwSERERETGK7uqbOOeXg5ocxMbnCHlZl7LlMkyxtAQCpS9X6bXcyjflPFao5bcJWNHDA6B+0cUd4C6NujZVb5lici0kXQsgwmHumCAxog7/rOjL05LXZCGSGVSc0VERERk6ssOAT23JzNAycu0Of+oeQAMJIaPup+o+nCgcplD6jmk4FDJWAc7yj9v0G+IJx1oWQydm8u4MBGZLvpi7ptnfdhPYySTCNocDaUzhzStTERERETGKztB6Lk9vcO2z2lys9a7+nOHoUxGfdhP72CyaPdXiKSmlaUpOFQqduRR9uCOs1dwSEQmoy/mvnlGQ/50phBAa12QcMBHwGeUOSQiIiIi45ZdPrZjf3/6shdMmdMUBqB7oHjBoYZwoHLTynwKjehfoFSsxZrRMod8xBLWDQ7t3wJO8dLxRKQ2eMGh+lAgJ3OopS6IMYb6cEDTykRERERk3LzYkM/kTr9Npq5oq3eDQ11F/KzZECl/cCihzKE0BYdKxIzRc6g5GnQjsC2LIRmD3t1lXJ2ITAdeWVk05Ccc8BNOTY9oqQsB7tkXlZWJiIiIyHjZVB1MYyRId1YAyFovc8gtKztkTkPRHrM+VImeQ26ShqaVKThUOtYZcZQ9wKmHzOShTe301S9wN6i0TETGycscqgv5ATdIBG7mEEBjpPxvsCIiIiIyDaQyh5qigZzSsWSq4GVWY5g//vvJfO2io4v2kJUoK1PmUIaCQ6UyRnDo5YfNIZ60PNRe525QcEhExql/SHAonTkUdTOH6ivwBisiIiIiU5/XcagpEszJRPd6Efl9cPwBrekhKMXQUIETm8mk13NIwSEFh0rG4ozyz3vMohb8PsNTHe5BHYNdZVqXiEwXA3E3OBQOuK8jqRMf6cyhhnCAjXt6eWxrZyWWJyIiIiJTlNdzqCkSpGcwgZP6oOkFh4wpfjClEic2lTmUoeBQiZgxMocCfh+zGsJs7fOCQz1lWpmITBf98cy0MsjUgHvBoVmNYXbsH+DV3/tXZRYoIiIiIlOSFwRqigawFnpTvS7To99LEBxqCAeIJy2DifKNs086Fr/PlCTYNdUoOFQiYwWHAGY3hdnSm/oRxBQcEpHx8TKHIsHczKHmqBscOmHpjIqsS0RERESmtuyyMiDdlNr7vFmKTJv61AnP3sHyBYcSqeCQKDhUOtaOGRxqigTpiTkQaoDB7jItTESmi4G42xEwmgoOnXrwTCAzrezkg9rS+5bzDIyIiIiITG02nTnkBoe8ci+vvKwUiTYNqUBUOfsOJR1H/YZSFBwqGYsd45+3LuR3f/HDjdDXXqZ1ich00Z/OHHJfay5/3VH848OnpxsDLmytS+9bzjMwIiIiIjK1Dc0c6up3J5Ylbel69DSE3ROeXpZSOShzKEPBoVKxDs4Y4dT6cMCt3Zx/LLxwb5kWJiLTRbqsLNWQOhL0c9Cshpx9vnjhkQD0lPFNVkRERESmuCGDTrpS4+zT08pK1JAaMv2NyiGp4FCagkMlYqwDY5SV1Yf99A0moeUAGOgsy7pEZPqIJdw0WN8ob2izG8MAdA/Gy7UsEREREZninCGDTvanMocyZWXFD6i0RN3WCPt6Bot+3yNJOFZlZSkKDpWMxRkrOBRKjeoLN7gNqb15gSIiBbCMXe/dmDoDo8whERERESmUHTLoZH+flznkbi9Fts2y2Q34fYYntnUV/b5H4ihzKE3BoVKxhfQcCjCYcEgG6sA6EO8v0+JEZLowYwShGyKp4FAZG/uJiIiIyNTmpS2kg0P9uaPsSxFPiYb8HDKnkUe3dhb/zkfgZg4pLAIKDpWMYexR9vWphluxQL27IdZb6mWJyDRSSLKhV7ut4JCIiIiIFMqbVhb0+6gP+TNlZanto7U1mIyjFzbz+Lb96ccvNfUcylBwqESMdbAFNKQGGDQRd0Osp9TLEpFpxGLHam2WLisr59QHEREREZnavNCMMW72kBcc+tuj24HSNKQGOGphC519cTa395Xk/odSz6EMBYdKxhaQOeQetPUF29wNXdtKvSgRmWbGeitTWZmIiIiIjJeXuWMwNKWCQ72DCR7duh8AX8mCQ80AZes7lHQcZQ6lKDhUKgX0HKoPuWVlnY3L3A07nyj1qkRkOikg2zYa9OMzakgtIiIiIoXzqrqMgda6EP94ehcX//i+9PWlatMzp8mtqmnvLc/EskRSZWUeBYdKxNixew7Vhdwz+p3+NmicD1seKMfSRGSaKGRamTGGhnBAmUMiIiIiUrDssrKPn3soL1k2Myebp1SZQ42prPeuMp3YTDqWgF/BIVBwqGQMdsyeQ43pco8kHHAyvHCvxtmLyLiMNa0MoDESVM8hERERESlYOnMIwzGLWnjjCYvT1/2/U5cS9JcmlBAJ+gn5fWX77JpwLP6haVBP/R3u/2FZHr+aKDhUMmOXlXljAbsGEnDASdCzEzqeL8fiRGQaKHSKg5s5FC/xakRERERkurCp3CEv32FeSzR93VtOWlLSx26IBOgeKP1nV2stdz6zh137B9wNG253W71c+ya4+eMwWFsDoxQcKpFYuI3u8JxR92mKpIJD/XGYc6S7sX1jqZcmItOEtWOXlQG01AXZ1xMr/YJEREREZFpwUucgvXY8c5rC6evaGkIlfezGSKAsmUMv7HMnou3sGoCuHfDr18EPT8ns8Pu3QrJ2su8VHCqREz7wG4792E2j7tOQrqeMQ6TF3Tiwv8QrE5HppJAK6QNn1fPcnto68yEiIiIiE5fJUHc/bXqJDZDpnVsqjWXKHPJ6Dc2gC2777PAdNtwOt/9PzQSIFByqIL/P0BQJ0NEbg4g7sk/BIREpVKEdyg6a1UBHX5x9PeWZ+iAiIiIi04OXpV6XmrRdDo3h8vTLdBz365eCP4fHr3O/OeI17tdTPuh+ve97cN1lNdEbWMGhCpvVGGZ39yBEW9wN/Z2VXI6ITCFuWdnYuUPLZjcAsGG3sodEREREZGyZhtSpryWaTpZPucrKEqnoUJSsE6gX/gBe/X9w2kehbqa7bf2N7vCoaU7BoQqb3RhhT/cgBCLgDylzSETGpZC3aS849Nye3tIuRkRERESmhUxD6vKPeW+MBNnXO8j2zv6SPk4i1VhpH6kqnlM+CMEoHPcWCDfAe+6G1/0MDjnXPVaf5hQcqrB05pAxbmmZgkNSoI17etjdNVDpZUgF2QILy+Y3Rwn5fbzQruCQiIiIiIzNDmlIDfDV167gqre/qDgP0N8Jv70Etj487KqWuiB7e2Kc/NVVOE7pyrniSTdz6NiZFuasgJd/PneHpvmw4iJ44zWwqEjPu4qVtpOUjGl2Y5jd3QNYazGRFgWHpGAv/eadAGz66nkVXolUirUUlDrk8xmaoqOn5/7jqV3MboywYmFz8RYoIiIiIlOSky4ry3zYvOSExcV7gNs+A8/cBIEQvOGXOVfNqM9k6fTEEjnNsIspkXSfZJu/N9PmpYYpc6jCZjeFGYg79AwmUplDnZVekohMIYUm+jaEA/QOjhwceufVq3nV9+4pzqJERERkytnfX/rpUDJ1eNPKilJVtutJuOeK3KbOT17vft32yLBmz15GD8D+vtL9Xno9h4Kx/VA3o2SPM1UoOFRhsxsjAG5pmcrKRKRE6sMBesrQ2E9ERESmnjvW7+boz9/Kfc/tq/RSpEoUtZjr2jfB7Z+DfRsgGYcfn+EmRTTMhf2bYcuDObsfMqcxfbmUQUsvcygY64Roa8keZ6pQcKjCZjeGAdjdpeCQiIyPtbbgJoEN4QDdo2QOiYiISO16Ypt7DHL3s3sqvBKpGl5ZWTEyh7xj3Bs/Cl+cCdsfcb8/6vXQvNgNHnXtSO9+7pFzufKNxwElDg45FrAEBvcrOISCQxU3KxUc2tOTGmevUfYiMg6FvmE3RpQ5JCIiIvm1pnq8tPfGhl33/Ts28MM7nyv3kqTCvMEnvslGhxIxGOx2L2/8Z+51B70U3ngt9O6Bx65JbzbGcNDsegA6S1hWFk86NNCPzyYgqrIyBYcqrD7s9gTvS/cc2j+s5lJEJJ/xvFI0hANubzMRERGRIVqiIweHvnbzer5609PlXpJUmFOszKHdT0IyBoeen9n2qu/Ap3a6waE5h8O8o2H9zTk3a466TahLXVbWYlLTfJU5pGlllVYX8gPQF0u6wSEnDvF+CNVVeGUyVTiOxecrRr6nTDXWjqMhdUTBIRERERndjv0DI17X3hvLmSIl05vNM61s3Aa64NHfuZdf/gU45o0w40CYfVjufoecC3deDr17oX4mkAlYdvYPD1gWS8JxaKbH/UbBIWUOVVo0FRzqjych0uJuVN8hGYfemA74a1nhPYeCI5aVJZ1MDtJgIlmUdYmIiMjU4U1tenzbfv748Na8+zy7q7ucS5IK88rKxpU55CThV6+B+38I+56D762EB37oXjfjQDj0vOGBIYDl5wIW7v9BelMk6CPk97G/P87fH9vOP9fvLngZiaTDD+54jp2jBDsB4klLi0kFhzStTMGhSgv5ffh9hr5YqqwMNM5expR9MK9skNplx1FY1hgJEEs6dPYNP/sSS2TGhXarL5GIiEjNyf5s+eHfP5rz2cDzzO6eci5JKiyTOTQOu5+C51bBzR+Hn7wUena520MNo0eZ5h0NR18Kd38DdjzmPq4xNNcF6eqP877fPsLbr3qIvzyyraBl/OHhrVx+89P89oEXcrZ//A+P8YYf3Zf+Pp50aFXmUJqCQxVmjCHpWK5bvTUTHPrxGZmmXSJ5xJOZN2w1Ga5d4ykra4q4VcS3Pblr2HWxpIJDIiIitSw7OATwpzVu9pDN6oWqzKHa4v3kC81SByDel7k80Amv/AacfwVc8pvRb2cMnP0lwMD6G9Obm6PBnD5YP71nY0HLeH5fb97t167ewoPPt6e/74slM5lDakit4FC12NM9mCkrSwzApnsquh6pbomsN3CNJ69thb5fn3/UfCB/8Cc72Lira/T0WxEREZl+hgaH9vYMAvDr+zOZF88oOFRTvMDguMrKEqnPkV7z6QPPhJXvgAPPGPu29TNh4Up45pb0ppZokBf2ZQJOhZ7E3Jy6TV9s9HYJvYMJmvEaUrcUdN/TmYJDVSD9BzfrEFhyqnt55+MVW49Uv4QyPYTxTStrigYxJv/Eh+zUcX3wExERqT3ZJx6NyZx8fHpn5nPBnu7Bsq9LKmdCZWWJ1O/IKR+ET++GmcvG96AHvwK2r4HunYCbObS9sx9wexAVetyzcY8b8Blpfyf1+94bS9JqurHBegiEx7fWaUjBoSrw76cfRMBnINwIb/s7NC+Gvc9UellSxWIqKxO8N+3C3rL9PkNjOJA3OJR9tjD7Q2Cl7e+P84FrHqEjz1hdERERKR4nFQl4+NNnMbMhzP4+9/NCQyQz3LpLnzlrSiZzaBzhIS9zKBiZWLDl8FeD8cNv3wB97TRFg+nfu9mNEboH4jmljgC/vG8T21IBJHA/13plZV0Dwz/3QmagT99ggjZ/H0bNqAEFh6pCJOgn4dhMaUfTPOjdU9E19ceSfOf2ZzW5qEolktkNqfO/6EltGM/79ZymCFs7+odtz36PXV9FwaHrH93OX9du55u3ra/0UkRERKY177NlwOejJRqkMxUcCgf86X268pxgkukr3XNoPDfyMocCkYk96KzlcOnvYMejsPpn6Z6ZAHOawsSTloF45iR5e2+Mz/51Had8dRWPbO7AWsv2zv50VvxImUNewKk3luACcxf4Ann3qzUKDlWBaNB90R2IpwIxkRbo76jcgnAjsFfc/gxX37upouuQ/LKDQyorq2XjKSyDQ+Y08uzu4cEf72xhYzjA+p3dw87IVMr8ZveDxaa9fWPsKSIiIpPhZRH7fG4pj5dp3FoXTO8zmHAyxysy7aXLygqNDq2/Cf74TvfyZEq0DnkFLDgeVn2Jdzz9/wiQyRwC6M7KBspOZHjN9+/ln+t35zSw9jKHko5l+advSm9P30f3LnxY6Hh+4uudRhQcqgKRoPtjSEdBo60VDw75fe6rwPZONaetRnEnEzHPTqOU2jKeaWUAy+c2srm9j75YbkDRCwUtaI3SM5jIOSNTSZFU4HyfyspERERKKmmzMofqgnSmgkMNYTej4m0nLwF0UrKWZDKHCvy0+df3ZS5PNHPIc+RFABzQ/yQn+9Zxlu9hzun5E4ebTTmlYr2DucHK7Z0D6SbUsxrD6d/XwUSSwawem1397nZf93Z3wwEvmdx6pwkFh6pAZGjmULQF+vdXbkFAa10IgF8oc6gqZU+XUgPh2jaesrJD5jRiLTy7qydnu5c51Bx1zw5mv+lecOW/uOK2yvRA885YdY9QLy4iIiLF4WUO+X2G5miI/X3uiRnvvXh2k5sJMlIPF5l+nKxssoIse1nmcjA6uQc/8T3wluuxoQa+FPg5Pw19k1ft+D+uDH4np7yxbzDOW/238OPgN/lE4Lf09ffRH0/wUt8abku8g2XdD2GtTf9+n+F7hIv8d7K1w81KD/bvcu/o7C9Obr3TREE/amPMOcaY9caYDcaYT+S5PmyMuTZ1/QPGmCWp7UuMMf3GmLWp/35Y5PVPC42pWso9qZGRRFthcD8kKxeZ9zKHAKWPViGvrKy1Lsj6nT1j7C3T1Xirv5bPbQRg/ZCAoh0aHMp60310Syff+cezk1jlxHlBK52lFBERKS3vs6XfZ3Iyh7yMopaoe+JYfYdqh/ezzz4uHJWXLXTRVe6gpcnw+eDA0zGHnMNin9uLt7d5GUt9u0jsyXwuTezbxOeDV3O2/2HeE/g7B71wHWbfs3wt+GNa7H7Oc1Zx38Z9eLNXfhH6Ot8I/oh7n3gOgBP67yFhgtB20OTWO02MGRwyxviBK4FzgcOBS40xhw/Z7Z1Ah7V2GXAFcHnWdc9Za49J/feeIq17WnnRErc7+v0b97kboq3u14HiZQ85jmVXV+ElYtmjrXWGoPp4mUNHzG9mb88g+3o0WrQWWWzhqb7AvFQPn6GjaL0gU0vd8MyhSvJiX9WyHhERkenKCwT4DLREg/TFksQSTjrjojX9GUEnbGpFug9VoWnqyZg7dfvI1xZvEad/DBaeAG0Hs+cVPwCgftM/0leHdq3J2f20F77Hmbedx0zTRbxuNhf676XrvqtxEnEeCb8rvd87t/8PJGKclfwXj884GyLNxVvzFFZI5tAJwAZr7UZrbQy4BrhgyD4XAFenLv8BeJkZ18y72tbWEGbxjDoe35oKBkVa3K8DnUV7jE/95QlO/Mo/8o6xzid7VLrO2lefROrF+oj5TQA8s0vZQ7VqPK+0kaCfUMA37KyfdzYlkzlUHX/zXkZTlfTHFhERmbaSjkPAZzDG0JwKBO3vz4wNb6lT5lCtcZxxZg4lYxAIFXcRs5bDv90G719NZMFRPO0sYs7Wm8BxK1uiux9N7/qCM5ugzfSp7HnLbTweXMHZG74Ie5+m1WSOlw4beAR766cJmQRbZ5xU3DVPYYUEhxYAW7K+35ralncfa20C2A+0pa5baox5xBhzpzHm1Emud9pasaCZx7elgkN1biYRPbuLdv+/e3AzAOu2F5aNFFdwqKrFU5ldi2bUAVkliVJTJhI0yZ5Akr4fRu455Ln+0e3jf7BJUlBIRESkPBKOTQcBvM8D63d2ZzKH6qsru1hKL11WVuiZyMQg+IscHMrSGAnwy+TZzOx8DO50C5Wa2h/nEWcZ74+9j0tjnwbgT8mXcOTAT6mfuZj7l38cH5bAk39O389rm6/lWf9BmAd/BEBX6xElW/NUU+qG1DuAxdbaY4EPAb81xjQN3ckY8y5jzGpjzOo9e/aUeEnV6cgFzWzt6KejNwazU1V7O9YW/XHWbesqaL/ssrIeBYeqTjz1Rj270W0O2KFpTjXJMr5pZeB+4Bv6wc4bfpev55DnDw9vncAKJ8ei6JCIiEg5JJOZ4NDcJrcM/c0/e4C/P7YDyAyrqZbsYim9TEPqcWQOlTA4VBfyc609i+daToZ7roD9W2nd/yRrnIP5m3My25nJ6YPf4qPxd9NDHaGAj9C8I9lm2wg/9ScA3hX7L1pb2/hR4NL0/Saal5RszVNNIcGhbcCirO8Xprbl3ccYEwCagX3W2kFr7T4Aa+3DwHPAIUMfwFr7Y2vtSmvtylmzZo3/WUwDh81zm3Y9u7sHmhdA8yLY8mDR7t8L+N6zYW9B+2dnDu3p0Tj7apNI/XxmNoYxBtoVHKpZ463gbYoEhmUOpaeVeR/8sgLCdSF3mmIlApDZmUODCTXGFxERKZWkzQSHvAEWAKtf6AAgGvIT8vuUOVRDHOv2oCpYiYNDxhgaIwFum/0O97GuOIKAM8BaJ9NM+gU7lyT+9PdNdUFuSp5AqMc9ydlNHUfMb+JPXYez54h38pvEy4iGgyVb81RTSHDoIeBgY8xSY0wIuAS4fsg+1wNvTV2+CFhlrbXGmFmphtYYYw4EDgY2Fmfp04sXjU+PbF50QlGDQ95B1p3P7BnWjDafWGpiQdBv1M+mCsVTP59IwE9zNEhHn4JDtahYZWWeqNeTKOuDnxc4enzb/rIHIbOf39M7ukfeUURERCYl6VgCqUiA118om98YmqIB9RyqIdkBw4IkYhAIl25BuKVlT/uWwXFvTW+7zxleFhb0u+tuDAf5buI19EXnA/D2lx3LOUfOw7FwZfidfCrxTupDgZKueSoZMziU6iH0PuAW4CngOmvtOmPMF4wxr07t9jOgzRizAbd8zBt3fxrwmDFmLW6j6vdYa9uL/BymhfqwG+HsjaXOjs87Grq2FnViWUPY/cXfsb9/zH1jCYeQ38dBsxpYv1MHZdXGy+wK+g2tdSFlDtWoiZRdecGh+zfuS/cR8AJABmiKBHNSxp2sh1i7pWNS6x0vJys6tOrp4vVgExERkVzZPYfy8fsMTZGRTzDJ9OM4tvBJZZDKHCptFk5jOOj2w331d+HjL/DFo25nL+6ksUDW7++6z58DQFM0SBcN/PW0v/HW2MfpaTmUw+Y1snhGHTc+7pZMelnyUmDPIWvtjdbaQ6y1B1lrv5za9llr7fWpywPW2tdba5dZa0+w1m5Mbf+jtfaI1Bj746y1fyvdU5na6lOBm77B1EFZ/Wz3a29hZWCj8aYMHL3I/cMp5EU9nnQIBXwcMqdRwaEqlEg1iQn4fbTWBens0xt1rRrvXMjmaJAt7f1c8uP7+endbiKnF4Px+XDPCmZnDjmWFx/oNsnP7kVWDl5oKBr0c8d6BYdERERKxRkSHDpoVn3O9cZAYzSoUfY1JDlGwHD4DQbBX9rMoaZoIDMsKdpCZyJEyO/j2MUtfOU1K9L7hQJumKMx4h5jtw/Anc7R+P0+jDEsmhFld6qaRsGhjFI3pJYC1aXS2XrSwaGZ7tdN90z6vr0Dv5aomyJaSHAolnAI+g3L5zayrbM/U+4mVcErKwv4DDPqlTlUsyZYVubZ3N4HZGcOmVTmUG5Z2fzmKFD+JpTea9fh85vY0jF2xqOIiIhMTMKxBHyZQ8Pr3p073ttvDE0RlZXVkqS1hU8qA0jGiz/KfojGSO5glb5YggPa6vjze0/h4DkNAESCmd/jptTn3s5UCw4vE6opkvk8XKeysjQFh6pEfSpi2eeVlTUvdL/e+91J37d3/Oj9cRSaORT0+1jY6h4U7i6gT5GUTyIVHAoFfLTWhdRzqEZZxp851JQVHPIyFr3XCGNS6bc5waHMbcrdhNLLemxJrclqtr2IiEhJJB1LVmyItoYwRy7IDJn2GeN+RtAJ45rhOLbwSWVQ8lH2ALMaw2zv7E9/JuyNJalLfZ71ToBGg1kNqVOZQ16VRd7gUFiZQx4Fh6pEwO8jHPDRG0udmZ99GISbMuVlk5A+wKorPDgUS5WVeX843UohrSpez6HszCEdONcmM85h9tmZQ16zPu93x2cMM+qC7EtlonnbvTfWcqeSe7/RzXVBEo7NBM9FRESkqJJDMocgU3UA7jjzuU0Rtnf250w1lulr3A2pk7GSl5UdMb+JroEEW9rdjPK+wUQ6ycI7mfmiJTPS+9eHAhgDHangkD/1K+6Vm3n7iEvBoSpSHw7QO5h18HXIK6B7+6Tv1zvAqgv6CfpNwWVlIb8v/YejFNLqkg4O+X201ocYTDj0x3XgXGsmEhDMDg55QV+v6bQxMLc5yq6uARzHphtWB/w+GsOBspeXek+vuUKZSyIiIrUiX3+Z5tSJZW/70YtaGIg76kdaI5IOVdeQesUCt4fu49vcoU29sWS6LGxmQ5hr3vVirrj4mPT+Pp+hMRxgf39uWZlXggYQVc+hNAWHqkhdyE/fYNYBft1M6N036fu1WQd+zdFgQX1DvLKyRmUOVaVE6qA96DfMSI0bVd+h2jORsrLW+sxZwO2d7lmXdENqY1jQEiGetGzf358OGvl9hsZIoPw9h/DKygrvlyYiIiLjl3CcYf1lWlInZ7yY0bGLWgB4ZHN5p5dKZbhNysdxgzKMsl8+t5Gg36SDQ32xRHrqN8CLD2xLt03wNEWDWZlD7i/zSw6elb5eDakzFByqIg3hQKasDKC+DWLdbv3mJHgHWCZVK/y7Bzfz2b8+Mept4knrlpVF3T8uNaSuLvGEN8relz7Y7+jVz6gWjTM2xOzGzJv2pn1DG1LDi5a6qbj/2rA3sz3Vi6jcrwNecMoriS13cEpERKRW5Msc8t5/TSpotLA1ysyGMI9s6Sz38qQCxt+QOlbynkPhgJ+lM+vZuKcHgN7B5JgNpRsjmcnOXg+ltqyTpcFxRcCmN/1LVJG6kJ/e7Mwhr99QxwuTut/syhOvPOOX941+n960Mi9zSOUc1SXulfv4DDPq3Z9Ru5pS15yJtJma3RhJX960t5fBRDIru9CwfE4jc5rC3PVMJjjkM27mULkzd7yyOe916w0/uq+sjy8iIlIrko4l4B+aOeQeQMdSJyWNMRzQVsfO/QNlX5+U37gbUidL35Aa3M+FXlVLXyzTc2gkTZHhZWWRoLKF8lFwqIrUD80cWnYWGD888sui3L8xMBgvrIGc15C6PuTHZ1RWVm0SSYeAz2CMYUa9mwmyVxPlao5bVja+3KFoyM8XLziC/zrrEBKO5dldPVkNqd37O+WgmTy4qT1TVmYMi2bUsXFvb5GfQWGy+ySJiIhI8SXyZA7NbY4M268pEtBJ4xoxrobUyQRYp+RlZeBOGusaiOOkhpXUhcfOHIqnJj2PKxOqBik4VEXqQ4HcnkPNC+DwV8OaX0Js4gdl6awADJvb+wq6jZs55MMYN3tIwaHqksg6u7OwNUrI7+OZ3WoOWIsm8hZ32UlLOP/oeQA8taMrqyG1e29LZtazp3uQvlSw2hg4Yn4ze7oH2d1dvrOFXubSrMbCPmis+NwtfPCaR0q5JBERkWkp6QwvITpsXtOw/ZqjQfUArBH5fidG3jlVwVDihtTgnuhct72LvtQwnrEyh2Y1Zk/dK+nSpjz981SRurCfnsEhQZijLoGB/bDj0Qnfb6bnUG7DLW8SUT7xpDutDEg1otWbQDXxgnfg1skum93AUzsUHKo1E5lW5lnSVk8k6OPJHV3p1wjv5NCCligA2zr6U9sNR8x3PyCu2941iRWPj/f0GsIBLjhmPge01Y26f/dAgr+snfyERxERkVqTr+fQ0pn1w/ZrKnC4jUx9jh1HWdmzt7hfe/aUbkEpT+1wP4s+sNEd3DRW5tDC1sznx+xg1xHzm5jZUPoyuKlEwaEqUh8KpM/Up7Ue4H7tmvgBTyZzCH77/17MMalJA6MFfOKpsjKAOU0RtqamGkl1SDhOTvO0w+Y1pV8opXZYmFjqEO60huVzm4ZkDrlfZ9TnTsDzGTg8FRx6sgLBIWMUpBYRESmlfD2H8pUUNUczJT0yvTnOOMqwuna4X5e9rHQLSnn/Sw8GMoNVxsocOnRuY/pydrDr7+9/Cas//fISrHDqUnCoirg9h5K5Gxvnul+7d074fr2XbmNg2ewGLnuxG3AaLSU0OzPlkDmNPLtLWSnVJJG0BLJe3A6b18ie7kH29qjvUK2ZTOX04fMaeWpHd9ZUMvfemlI9fjqzxn42RYLMbgzzwr7y9R3yXrt8xjC7MUJnf5zeodmVIiIiMmkJx6ab9WY7fEhpWVMkiLXQM/SEtkw7SWspuEWPV1a26MSSrcfjJTrc8JibPNEwRubQKctmpi9n/46Pt29nLVBwqIrUh/zEEg7xZFbT6EgLBKLQvWPC92vTY6rdPwCvuetowaF40qaDQ7MaQnT26wxBNcn++QAcOMtN+91SYE8pmSYm+Sd5+Lwm9vfH2d6ZKR8DaI66b7KdqdcI781zRn2I9t7yZe84WWVzh85txFp4eqcC1SIiIsXm2NwTj54/vfdk1n42k12RPo7oUzbvdOfkKTUc0WCXO0gpNLwUsdha69wM9zWbOwHS07VHEgn607+3BT+fGqXgUBWpT0U9c5pSGwNN8yZXVpZ1VwAtdamsgFEzh5LMcdxspaaoe4agW2fsq0Y86RDMSv31JpZ1aJx9TbHYSZ31WDqzAYDNqbRc756aIl7mUO7Yz7aGEO29ZcxOyyor85piFlI+OZhIjrmPiIiIZCSSFn+ebr2RoJ+WukxflqbUCSRNLJv+xjWtbKALwo0Unmo0cY2RQLr9iff9WI4/oBWAjl4dK41GwaEqUh926yX39AzkNpptnD/JzKHc7+c0uWMpt4/SR+j4xFo+9vQb4MoX05T6g1O/j+qRcBwCWZlDM1Jv2vt69IJXaybzFuwFijuGBIGGl5W5+7fWhego45nCTKNsw8LWKI2RQEHBoa0d6pEmIiIyHm5D6rH3ayqgAkGmh+QIpYZ5DXZBZPh0u1Lw+QyLWqPp7wtZ4ldft4LXH7+Qkw5qK+HKpj4Fh6pIXcgNwpz1rbv4+2NZwaBJZg5hweAQie8HYH5LlFDAx6a9I/cOaXP2uhf2PMUcZxeQOVCUyosP6Tk0I9VpX5lDtWUSw8qATHDIKxXz3lwjQT+hgC/9++RlJ7XVh3h+b++okw6LKbtRtjGGQ+c2sr6AsrJO/R2IiIiMS9JaAgXM+fayizWxbPpzxp051FzaBWV5xRFz05cPTGXCj2Z2Y4Svv/5oIsHRm1fXOgWHqkh2M601mzsyVzTOdRtSJyf2ImyxvMn/D9581xmw5SH8PsMBM+p4bk8P/3b1au56ZvjIQb+TObg6tOdBAJ7b0zOhx5fiS2RNkwO3X1XI72OfUiVrirWTy971arYzQaDMdU2RYDog7J01ak99f+U/N0z8QcchM2nRffxFrXWjZjx6dDZTRERkfPKNss/H692iioLpL+nYwqeVlTFzCOBj5xzKE59/Bc995ZVEx5hWJoVTcKiK1GX9YuccAC0+GZKD8NRfJ3zfl/pXuRf+8HaI9bFsdgOrnt7N7U/t4i0/f5D+rClpjmNptKlA0Pxjmf3Id6jzxfngtWtz9pPKGZo5ZIyhtT6oOtoaZCZRWFYX8hP0m/TvTXbqcFM0QGd/ZpQ9ZJrbP7SpfcKPOR5eWZm3rHktEXZ1D46ZuaTgkIiIyPgkHKeg4JBXVqaeQ9Of40AByWSugS4Ily84BG5ihRpMF5eCQ1Uku7Fsb3ZT6kPOgbZl8OBPJ3S/1sJhZjMDwVbYvwW2PsSHXnoA2cdX/7fq2fTluOPQYnqI+yJw6kcwPbs41D4PwM/u2TihNUhxxZO5PYfAbUrdruBQTbGTHFdmjKE5GqJ9jMwh7433CxccCbiTw0phIJ7kjw9vZSDuvv5ll5UBzG2OknQsz+4evbRs/c6xsxzXbunkW7eun9R6RUREpotksrDMocZwAGPgwefb2ddTxiEVUnbjakg9uL+smUNSGgoOVZFjFrXw1pMO4NC5jfTGskrIfD5YcirsndiBjLUWn7FsnXmKu+GXr+bgVe9Op4UCPLMrc7AVSzg008tgsBnmHwvAEb5NgJuxIpWXcGzOtDKAGfVBBYdqzGTLygBa64aXj4GbNu6Nqc0eZT+3KVKy/mN3PbOHD//+Ub7pBW1SmUpedtSsVG+tV37n7ry390pzr753E7u7BkZ9rNf94F6+u2oD8aRTjKWLiIhMackRRtkP5fMZ6oJ+bn1yF5f97MEyrEwqZXwNqbvLnjkkxafgUBUJBXx8/oIjWdJWT+/QsfHNC6FvH6z51bjv11r34KcnuiCzceMdtPjcgyefgc3tfemr4klLi+khFmyCpvkQncF/HOz2QBoakJDKSCQdgsockiLwmlJD7uSzpmiQ7tTrUPZnxfktkZzXi8lyHMs/1+/GWkv3gPt4T2xzJ5J5oWjvc8nSVMPBkarKrLWcddgcBhJJrlu9ZdTHDaX+flSCJiIiUnjPIYDeVJuJJwuYIDqWB59v55HsXqtSNQpuSG2tW1amzKEpT8GhKlQfDuSWlQEc/HL3662fdgtAx8ELDllfpuE1ToITzToAFs+oywkqxJMOzaaXeKjZPSpbfi5zt95KQ1ATy6pFLDl8osSMOmUO1Zpi5PG1pJpSQ25pa1Mk83oRCWT6oR0+v4knt3fhFGli2a8feIG3X/UQf39sRzpj0muQ7TWk9s5aLZ/byIEz6znpwPxjSC2wdGYdTZEge7pHT3VvTD0/BYdERETcrPTx9m85ZM7YU6LG8oYf3cdrvn/vpO9Hiq/ghtTxPrBJZQ5NAwoOVaH6sD+3rAxg3tHw0k/DQCfc8slx3Z9NpgJNxgev/j9YejoE6zkz9CQAs5sidPTF0wd7XllZIpj6Az/wTEj084nQ7+nodRtlD8ST9A1do5SNmzk0tKwsTNdAQmUyNcQtK5tcNl9LVnlp9mfCtoZw+nJ91iTFI+Y30z2YYEtHcbKHtne6GYzP7uqmJ5WpFEv9DjvpsrKMg2Y3pINHQznWpvooBccM+ngNNRXwFhERGV/mkGfoicrJGOukjpRf0rH4CvmdeCEV3POHRt9Pqp6CQ1WoLhSgb2jmEMCpH4Glp2X+AAtkrXtf1vjhuLfAW6+HpafxiuSd/PTg+zn7oHqSjk1PHYilMocS4Rb3DpafA8Cbk3/myF1/A+CMr9/B4Z+9ZWJPUCYt4dg8Dandg92RDpxleppsoWdOWVlWoOmSFy1KX27ICQ65QeN12yefSg4QDri/x99dtYFv3OL2GhqMp7IdhzSkBljYGuWFfX15J5Z5PZgKCg6lMoc0ildERMQNBBTScwjgjo+cwdGLWugs4mfOe5/bW7T7kuJwbIGZQ0/8yf1aN6O0C5KSU3CoCjWE/cSSDrHEkAwQY2DGQbBvAyTHkbWTKivLOcI653/xJWOcteW7/Ns9p7LQ7OG///w4V9z2DPGkQws9JMPN7r7hRnj9LwBo698Iz9/NR/q/zTv9N0z8ScqkxPNkDrXWu9H6jl4d7NaOyZd2ZZeV5fYWiqYvN2SVmB0yx51U9tzusSeCFSIUyLwNefEeL7CT6TmUWdiR85vpjydzJix6rHWbVxcSHGqMpDKH+hVMFRERSRSaJQIsmVnPiw5opbMIJ1iWpz5X3POsgkPVpuBssuaF7tcVry/tgqTkFByqQnUh90Asb9nWwpVuXeemuwq/w9Tpd2uyftwzlsLsQ9Pffj3wI258fCff+cezxAf6iJoYjpc5BHDEa3iy/gSOG3gArj6fi/x38ZngbzKn9qWs4kmH4JBU3rZ6twxo1xhTmmT6KMa0shn1WT2HhuQheX156kKZnkORoJ/6kL8oHwgh0xg6W8+gWx5pvbKyrGUdscDNXPr27c8ymMjNsLTYwjOHUmVl+1VWJiIigjOOzCFwM4/7Yslh78Xj5b3HP/yCmlJXm4I/Z8Z6INQAPv/Y+0pVU3CoCnklHD1DJ5YBHHiG+7XjhYLvL91zaOiP+zU/hqWn0778Eo7zPUMI9yAp3uO+ONtoa87um1tPYqHdmbMt2b6p4HVI8SSSlsCQzKHD5rlnXh7b2lmBFUmlFDU4NOS+rnjDMSyf05izD7jZRsUqX/T6C608IPf1pqs/nikry9q+bFam+eWGIdlL1rrZT03RIPv7R8+u9F5nb3pi56j7iYiITHfW2lRD6sIPDZtTmceTHezg9RfctK+XgfjkAk1SXMlCp5V5wSGZ8hQcqkJ1YTfq2hfL8wLZMBd8AWjfCDd9AvYOL60YypK6n6HR3FmHwFuvxx58NmGT4PbQR7gqeDnbdu5wbxdpydl946LXkbDur8zX4hcD0P/8g+N4ZlIs+V6sW+pCHDy7QWdeakgx8vZmNowcHDrr8Dnc8l+nERyS3dNSFyxaxs1APInPwOymcM72zv44Fi9zKLOwgN/HUQvdktendnTn3MaxNl1WtrdnMJ15lI/357N+V/eo+4mIiEx3Xll3Qf1lUlqKlIHrPbZj1ZS62hQ0rSw+AGt+CT062TYdKDhUhbwz2nkbpfoDMOswWPcXeOAH8LtLxr5Dx+tdlP+Pu27piQAs9u3hTP+jmC0PuFdEW3L2a2xq5vTBK/hx4jyuTp7NgA1iN/6zgGckxeb2Vhlu5ZJWHn6ho2hjxqW62VQwZDKOXNCcvlzo5LOWumDRysoG4knCAT/N0dzspP1ZmUNDT1r9+b2nEAn6eGpHblNsixvg8srhPvaHx0ZMd/f+RDr74mxuL87kNRERkanIG/IwNCt9NN5Ai8l+HnCsTZev7+tVH8Bq4hTSh2rL/eVZjJSFgkNV6IC2egA27RvhgGX5ObB/s3u5Z/eY92fTDanz/7ijbQt5wc5Jf790rxvw8Q0pK1uxsIVtzOIriTfRS5Q/JV9Cw1PXwrY1Y65BisumRnYPdfi8JroGEuzt0ZmXWjHZsrJwwM+XX3MkLXVBWrMml42mJRoq2oSSpAN+n0l/yFyQaoS9c/9AOoAzNADm9xmWz2kcHhyyboDr0hMWA/D7h7fyk7s25n3c7GyhR7fuL8pzERERmYq84NB4Rtm31nmDUCb3ecBamNngZg+39+rzazVJFjKtrMutOOE/Hir9gqTkFByqQotaowT9Zlg/jbQT35O5PNgFidFflK2TOnM+QnAI4JPRz/DH5KkAHNHrZg6Z+iHBoawMg0tPWMT/Jt5EPDwD7rx81MeX8klPLFOT3ZpQrPywN514AGs/e3a6Gf5YmuuCdBbpd8yxmSbSAIfMacDvM6zbvj+rrGz47Q6b18RTO7rSQZ5082rcPkp/eu/JAOztyf/6mHQssxrDRII+HtvSWZTnIiIiUklJx7LkEzew8ku3jet2iVSVwXjKyrz37YlkDj23p4fn9/YC7ucAr8R93wjv2VIZSYexM4e6U8Gh5gWlX5CUnIJDVSjg97GkrZ7n9owQHKqfCc2LM9+v+9Pod5h6wbdm5A7yr335mXw4/u+8O/bB9LahmUN+n2FWoxvZXzqznm7q2D73TNh8X1bpmpTDSEEB7yxOsbI6pLqNVF5Yaq2psrJi9eoxZHoX+H2Gg2c38Pi2rkxD6hGCQx19cXan+hNkStDcnY9b3MqsxvCoZWUhv4/5zVF2aMKfiIhMA2s2u30n9/bEeGhTOxf/6L6CpolNJHPIy/gdb88hay0v++adnPmNOwAvOORlDunzazVxrCXPUNlc3Tsh3Ayh+rKsSUpLwaEqddCshpGDQwCv+ym0LgUMbF09+p1ZLzg08gv+Rccv5Lf/diL3+lemtwXqWobtd9rBswCY2+yWfmxrOAoG9sPeZ0ZfgxTXCKMlvbM4yhyqIZOtK5uAlmiIpGPzT1QcJ2vdenbvQ2Y8aVmxoJmHnm9nMOG+duXrq3TYPHek/ZOp0jJv2kn2P8eClijP7sr/Ouo+bqp/koKpIiIyDXh9SwE+/7d1PPB8O49uGbt0eiLBoYZwAL/P0Nk/vvfQx4aUcjsONEQChAM+BYeqTEENqbu3Q+Pc8ixISk7BoSp10Ox6XtjXRzw5QkbO4hPhA2th7pHQOfpYe+tl9YySOQRw8rKZrDww03soGBheYvLFC4/gixccwdmHu/s9Hz3CvWL9DXDv/ymDqEws+Q+YvbIyHezWhqK2HU8mwMlzdvGRX8OW3KmEzV4TyiIEIZ1U9lM0VdKWdCyHz2+iP57kF/94hO8Fv4Pv+eGN75fPbQRI9x3y/i2y/yqOP6CVx7btz5vh5FiLzxhm1Ido71UwVUREpj4n6/1uYUsdAE9sKyA4lLrdmCVEWYwxtETHX2Z+yzp3qlU06B6X2FRfm9lNYbZ19o/rvqS0Rm1IbS2s/R3s3QBN88q7MCkZBYeq1OzGCEnH5p9YlrPj4bDt4dGDMtbrOTT2C/6KBc38InE2a50DCeaZWFAXCnDZSUuIBP2EAz42Mw+iM+AfX4BbPw0bV8FDP4MHfgybx+5ef+u6nfzPX5/QKOkJyPfjbC3S5AiZGtxpZUXynaPgF+cN3/7X/4CfvRw+1wyDbhaOVwJWjOCQxQ3SeK838aRDWyq9/AjfC5zvf4DAb14L8dwPjM3RIDMbQmxpd7eny8qyPsTMbAgTSzj0x4cHvRzrlqC11BWvubaIiEglZR8OhALuYd4P7nxu7BuOMB10LK31oXGPn1+33T2pk7QWa236/fiIec3p6yQ/x7Gc9927+evabWV5vFEbUu9YC395D+x5ChoVHJouFByqUk1R9yz6mC+Si18Mffuga+QXiUIzh8Ada/25xNu4MPYlAmMUmbbWhdxmrwedmdn4+7fDDR+Cmz4Kv704c8Q2gnf96mGuvu8F/vbYjjHXJhkjBdOiQT8hv48OHezWjLzv2Y4Dq74MD/yo8Dvq2ub2D+veOfI+j10DZGWojTOVPB8nVSK5JDWl8eWHz6ExlRYfJiv4dPWrh72etGYFdhxrOcE8RVP/lvT1M+rdIFa+NHWvEXZrXVB/LyIiMi0ks94nvbLrPd2DbGkfYQJyijOkb1+hDp3bOK6Azs79A9z5zB4AYgmHgbjjZvL6YMXCZp7f28t+neAcUV88ybrtXXzgmrVlebykY0cuNdz1ZOZypKUs65HSU3CoSjWE3YOat/z8wdF3bF3ifh2ltCw9yr6A0wEHz2lMXw6NERw6ZG6j+8ZzzlczLwqDWW8QA51ww4dHvY8lbW7K66/vH700TnK5ZWXDGeP2bulUmUxtu//7cNfX4KaPwWA3xMdouJzM6h101bmZCYjWulMOT/0wtBzglo52bi5u5pAFMMxvifLoZ8/mnS9Zmv7lDuKuyx59KWx9EH51Yc5tW+tCOYGf68Jf5LIHL0w/H69B++48ZzXr4x0clXyS1voQA3GH/tjYDTtFRESqWTIrdSh76vHentGze9J9+8b5eEcvbGFbZ3/B2UM7hwyA6OiLpU4SmfRU5HUFlMHVqsRI7UZKxLGjlJXtfNz92rQAlp5WvkVJSSk4VKX6YgU2em05wP3aMUpwJd1HZOzMIa8sCchbVpbt6IXNPLu7h/5QG3xsI1z4g8yVH38B6mfB6p/B5gdGvA+TOkPxwr7eMdcmGXaEhtTgltus1WjumpB3Wll/J9z6qcz3/7sQvjIvXRKWV3+7+3XpadC+ETbc7n6fjLsN7YNReO1PoHMLfHsFbT3rgeL0trLWpuPWzXVBjDEcONPNIvKCQ+ZF/+busPEOWPdnd124I+u9D7zZfRb4zeugcwvHHdBK0G/4+6PDMxMv3f1Nvt33Seb73Mkuyh4SEZGpbqTYwVjtBrx30PFmDh29qAWAx7Z2jrlv72CCf23Ym7uuvnj6c4A3aOKZXd3jWkMtiSczn3V+VEi54CSN2JA61gsP/MCdnv2hJ+HQV5Z8LVIeCg5VqdMPcaeC1YfGCOg0LwJMgZlDY/+4GyOZ4NBYEwuOWthC0rGs274ffH44+tLMldEWeP/DbtbBc/8YZW3ui9yurkFiCTWzHg8zwhv4s7t7WL+rm8e36sxLLRj2e/DsrZnLh5zrfrUOPPmXke9kMPVB7KiL3aDuv77tBoLiqTT0YJ3bBP+UDwDQes2rmUVH0TKHhj6FA9rqWfXh09PBIaKt8LHnYfYR8Pu3wZ/fDdayaEaUrR39OI7FWhiwqdevzffDTR9jZkOYVxwxlz+u2ZqexOLxW3ftJz39VcLEFBwSEZEpz3uv8zLzPWONm3e898hxpg4ducAN6DyxrYvfPPDCqI/zqT8/ztdvcU8uffycQwH3JFP2gAjQxN3RZA8q+tZtpZ0Une4Hle940JuU3bunpGuQ8lNwqEq11IV4zbEL0r09RhQIwezDMmf68/FG2Rfw484OCI0UfPAcvdBN/3zUC0IYA5de6/4HEGmGuSvghXtHvI9LYn/im8Hvs8jsYl/v+Bra1TJbwJyqtVs6yrASqaS8vwe7UzXgH3wc3ngNvOMWCEThjsuhY1P+O0qmAiPBOjjtY7D1IfjRaZn+Q4GI+/Wln4bzr8DYJB+O/LUoU0W8htRDHTirgZBJBYf8QaibAZf+1v3+iT/Cl+dyRGQvgwmH3d2DqVJLy9rFb4NjL4P1N8LnmrkkeBf7++PDehh0+N0A/Jztt/PpwK/pUCmmiIhMcV4WrTfR01Nopu94M4fqQgFmNYa5bvUWPvXnJ/jqzU/n3c9ay1/Wbk9/f+ziFndd/fF0Q2q/z9AUCajn0CgSWZlDZyyfVdLH8uKFeTOHvGFHr/tpSdcg5afgUBVrjAToGSygvOz4t7kTy7atyX/9ODKHxmN2U4S5TREez04lXX6O+5/ngJe4B5oj9Dw5P3Err/Pfw6+CX2Xvfo2vLFTecqKU1x+/EIDP/HUdm/aqXG86y/t7sPdZmLkcWha73y9+Mbz9BujvcKcK5pNIBWYDYTjxXfC2G91Ss7W/drcHo+5Xnx9WvgMOPZ/zffdx3/rtk5406GQ/h752+Pt/wfZHAFg+051ahj8VJG9dAp/aBYdfAIkBzl39byw2u9i0rxfHcQibBI4/BKd/DM50S+tOfvILHGeeGfbBOOQMsMM3l/3LX8+F/n+xv1sTUkREZGpLeJlDqfJsz1hlZV5QabzTygDmt0TTJ4tG6okTG7L9gFRmU0cqc8iLP7TUhcbsj1TL4lk9pUp9UmvU3wnvuK55YUnXIOWn4FAVawgH6BlIjH3wteL17tfn78x/fbrnUPF/3Itn1A1rLpdj2csgMZCecjSUwZLAzxLfLpyN/8xtjCsjSvXwzevrrz86fflfz+3Nv5NMG8NO6OxZDzMPzt224Hg48jVuxs3TNwy/Ey845E8FYxa/GGYf7jagBjejKNvRl9DgdLOi5570NJSJsjYrS/Gp62H1z+GXF4K1vO3EBal1ZWVQBiPwhl/Cv99LMNnPFwK/YMu+XmyqiXbSF4KG2W6A6H2rSYSa+V3oy/S0b8953IAdJGbC+I55I42mn7mr/ovB9s2Tei4iIiKV5JWHLW3LBIcaI4Exy8DTVWUTCA4tbImmL9enpo0Old0rBzIDI9yeQ5mMpSUz67lngz67jiS7rKzU5fBeiWLesrJE6oR+MDr8OpnSFByqYg2RAAnHMjhWL566GVA/G/blb0w2np5D49VcFxz9DefAM+CAU+CWT0HP7mFX+3F4MHIySWs4+o53wN8/UPQ1TldmlMLwd59+IDD2xDmZ2obFjZNx6HgeZi0fvvOpH3G//und8Pgf4HPNsCHVDyzpZQ6lgjDGwIvfm7ntvEzAEYADzyTRspRvBb/P5jt/Nbnn4DgssDuha0emhn2gE578C4FUX6Cc4JBnzhHETvkvzvA/SvOW29MT1qwva9+ZB7PhldcSIMHyP5+XyZxKDBJx+hg0YRqXn8EDLedyZPc9tP/izeCo95mIiExNyTyZQy11wTFLtWw6S2T80aH5LZH05ewJotniWccyR8xvIhL0Ewn6snoOudeduHQGnX1xTRAdgVdWNqsxXPLgkJc5lLcHbTwVHApEhl8nU5qOHKtYYyr63lVI7e3Mg2HfhvzXeQc7ZuxpZePVWhcc/cXJ54dzL4dYDzxz87Cr/TaJE2rkfxJvA8A+8huI9RV9ndPOGMlk7ztzGVCcUeNSvSzWDRKuvxnu+oY7acxJwMxDhu/cegB86Cm3dOyP73S3/e4StyTVG13vZQ4BHHeZ2wT6kt/CjKW59+UPEPh//+AF/wEcv/H7eaJUhVve+yDXDf47fOtQeORXMGeFm+n0+7fBqi+lHi9/77XwS/6T7baNlzz5BXzbHgTA8eXuO3PpUVyXPIPwwG64+5uw+2n4+sEcObCGpPGDz8eJH7yGywPvYl7Xo/CQ6udFRGRqSqbejxsjmQyelmhozJ5DziQqxM9dMY9o0D3GGDE4lMp4+dKFR3LDf54KuNlDnX3xdENqgLZUr9V2DYnIy/t3nN0YpiM16a1UvEBj3p5DcWUOTVcKDlWx5XPdCQD3Prdv7J3bDsofHNq7gbqtd7iXTWE/7ts/dDq/fueJBe3rvbCPas6R0LQAnrll2FV+kvj8QX6dfDmXxT6BwcKmewp67FpmsaOm/jaEAwT9Rm+utcAAv7sYVn0Rfn2Ru21oWZmnaT54Y+HBzTi8+tWwNzXxIhDO3b9uBhx6Xv77qm/j1oYLmR3fBl+aM3LPszHMGtwyZMNyuOgq97KX0eQPko/xB3i//zMkMUTv+B/3Jr7cfWc1hvmi791cseJP4AvAw7+AQbeJfo9pSu+3fs6reDh4PNz0UXj6xgk9FxERkUpKH9BnZXu01AXHHmU/icyh4xa38tQXz+Flh84eMTjk9RzKzmhvjgbp6HMbUnvl5d4gno4R7qfWeeV5sxvDJB1L10Dp2nE4DgRI8OIN33Kzu7M9t8r9qsyhaaegaIEx5hxjzHpjzAZjzCfyXB82xlybuv4BY8ySIdcvNsb0GGM+UqR114SVB7SysDXKHx7eOvbObcvccYL9nbnbf/oyZj30TWDs6WOeZbMbeMnBMwvat7kuyGDCGT390xg4+GzYeEemt0mKnyROKqPpQedQ+gnDuj9NKhOhFozWkBrcn3VrXUhvrtOctbAknhUU3p/qmZMvc8hz8vvc7JwLvg/vuMntCXbfle51Q4NDY3i87WxuDb3MDeLc8qlxrt7VkGgngR/ensosnH2Ym+X00Y1w3FvgyNeN2gRhoGUZN9a9msBed0KK9ec+B2MMsxsj3LY1xO55L4UHfgDAvdEz+EXTu9P7LZ7ZwHuTH3Gbef/j81m92kRERKpfe2+M+ze6J5Szsz2ao8FhI+Y37O6mayCzzfvUPZHgkGdGfWiUzCH3EYKBzP231oVYs7mDWMJJl5V54+xHup9al0hnDrlBmVJ+zk9ay3KzhRWbfwW/ujBzRV+7OxEWhveklClvzOCQMcYPXAmcCxwOXGqMOXzIbu8EOqy1y4ArgMuHXP8t4KbJL7e2+HyG1x23kH89t5ddozV9Bjc4BNA+pO9QPDMBzJakrCwV4R8rQ+XQ89zSsp+fkxMg8uPg4Od/X7uCQUJcb86ER3+n0o4iGO1NWqYHC6wYfNT95sXvhSWnwtlfgnDjyDcKN8K/3wPHvsmdaHbo+dCVCkCPUL41kub6ej7De+Fl/wOb74W9I5S2jiLs9NFHFA44Cd51J5yS6jtW3wav/j+46Oej3v6kA9v4Zvsp6aBQd/0Bw9cZDfLkji4u2Xg2zD+OAV+UD3e8jj3B+el9Fs+oZ1efpf+Uj8Kep+ELM2Dz/e6VhQSKnr8LBrsLe9IiIiJF9varHuSX970AuMcQC1qiNEYCtNaFhmWSn/Wtu7jkR/env/f6y0wiNsSMBvdzZ75SJy+oEczKHDpwVn36c6oXlCr4uKJGxVOZYbOb3M88pfx3SmbXGu552h0YsvFO+Fqq1cBFV4E/fwNymboKyRw6Adhgrd1orY0B1wAXDNnnAuDq1OU/AC8zqTQVY8yFwPPAuqKsuMacuHQG1sLzY40k94JDQ5tSH/rKrG8m8Yo/gtY6t4TDe3FyHMuf1mzl8puf5q9rt2V2XHaWewC5fQ1seTC92U8S6/Nz6QmLed+Zy/jvgTdj5x8Lq69S9tAoLGO/gbfWhfTmWgNanX3umZtXfAXe9nc4+f3ju4PTshI6w00j75dHS32Qjt449qg3QLAefnz6uEuy/DZG3KRKweYfM2IJ2UhOOXgmuxP13HLG9Rw78EP2tR49bJ8FrW5N/EY7n8G338bRfT9gB20541m9sbobZ58FJ7zL3fi7S+Chn7mBos81u82884n1wtWvgm+vUMaRiIhUxMasY4WAz3DnR8/gkc+8nKUz6+nsi7N9yLj57Gmj3kfuiYyy97TVh4glHXoGh5c6xfIEhz7/6iNYNrsh53GVOTQ6r7H3rMbSB4ccawmQ9Znm7//l/uc54jUle2ypnEKCQwuA7KYQW1Pb8u5jrU0A+4E2Y0wD8HHg85Nfam0quPa2dYl7gOhNH0rLvMpHB4bUixZBSyrCv6d7kPue28fvH97Ch657lB/c8RwfuGZt1jIMvOidblPsjXekN/tJksTNaJpRHyJpffQf+jrYvQ7+/B6Ij5ExVaOstaNOKwNlDtUEC61OOzTMmfjpvrkr4CMb3LKuhlnjumlrnftBsC8yFy75tZsdeM2lsPa3Bd9HwImTYOJnnk5YMgOA+9ob6KAp7z/DZ88/gtcdtxCA9r44g7ivW9np84tnuMGhze0D8Mqvw1v/5pbp3vChzB09dg385vXu69I/vgAdmwCwA6kP2P0dcNtnJ/xcREREJqqlLnNyxWcMAb+PgN/Hi1Lvk6tf6ACgdzBzwO81OM5kDk2mrMwNWOT77OmVlWX3HAr4fRy/uDXncZujQYJ+w879+vyfj/dz8srKdpTw3ynpZAWHvKzu9ufcY85Lr5lcmplUrVI3pP4ccIW1tme0nYwx7zLGrDbGrN6zZ0+JlzS1zCi0a38gDCvfAY9fB+3PZ7bbJIlwM7ckV7Jn/kuLvj7vjejjf3yMS39yP395ZHvO9Tm9iCLN7hSinOCQgzXugaH3XHcsfwuc8Un3QCzVH0SGGzNzqN5t9CfTl8XSkmyHxrmTu6OGWW5Z1zjNSAWHd+zvhwPPzFzxl38fnsU4Ar+NZzKHJqA+HKAxHGB3t1uumi9oOrc5wssPnwPAvp7Ma2lOcCiVOfRCe2pa4tLT4LI/u830j3kTnPBu9wPRs7fCNW90J59952jY9jB9vW5wKI7fzTQa2D/h5yMiIjIRLdFMaXh2Q+rD5jVSF/KzelM7AD2xTGaPl03kZQ5N5nB/ZkPmhPFQ8TyZQ+BmIEPmM63fZzhsXhOPbu2cxEqmv4WtUVrrgjy6pbNkj5F0LEGTOo5bdhac+Sk4/RPwn2th+bkle1yprEKCQ9uARVnfL0xty7uPMSYANAP7gBOBrxljNgEfBP7bGPO+oQ9grf2xtXaltXblrFnjO3M93XnBl4Iajh3/drAOvPCvzDYnSbxuHu+Of4hkuLXo6/Nqg3d1uW8EySGzMP/jt0MmGB14hlta1rUdHMftOWQymUMA7f1JOOMTsGAlrPtL0dc8HRRScDcjVVY29Gci00s6c6gCvLTms751F09s74JPboX3rwEMPHYtJBOQHD1AGbBx4kw8OOStwwsOjZQS39YwPFU9Ox27KRKkrT7Eht1Z5zIOOhM+9CRc+H145dfcD0Rzj4LnUhmaxgdXvRKT6vX2g8SrINHvTkQTEREpo4PnNKQvZweHAn4fKxY08/g298RFb1bZ197UCZNMWdnEw0PzW9wS7u15slm8cqigP/f+m6Pu+3/2yeRjF7Xw2Nb9+vyaR/a/yLGLW3lkc2fJHiunrMwXhNM/Bmd+UhlD01whwaGHgIONMUuNMSHgEuD6IftcD7w1dfkiYJV1nWqtXWKtXQJ8G/iKtfZ7xVl6bQgH/DSEA7T3FpAB0rrEbSi784nMNicJPvfHXIo/Ze9F3dMby60zXvX07twbHPUGd+zh798OjrvvsOCQd/B2+Kthx1roeKH4C5/irIUznr8C7v2/Efc5cFYD1sKazR1lXJmUk7UUJ3Nogrw+PYB7li/cCG0HuUHgR38HV58PX5zp9uvJyhjMFphk5hDAzIYwu7vdD6MjfWbJ18dg3faunH2OXtTCI6P9vRgDF/8aXvwf8PIvwL/dDokBgo+4LfcecA5zm4Lf9ll47PeTeEYiIiLj45UaQW5wCNzee16pVnfW+PN9Pe6JlWI0pPaCQ8/vGd4nNd1zKJB76OkdR2RPTjtmcQt9sSTP7NKQh6HSGV7GDaI9u7uH/f2lqRJwy8pSvyvj7AcpU9eYwaFUD6H3AbcATwHXWWvXGWO+YIx5dWq3n+H2GNoAfAgYNu5eJs4tDyogc8gfgKWnw4bbMtucRLpsqxSB3kjQTzSYmYLmpZJ+7XVHpbcNxLNKy2Ye7E5T2nI/bL7PXeJIwaFDz3e/PvGH4i98Gjh+x+/g1k+72Rl5nH3EHOpDfq57aEve62XqC9kB6mxfxTKHFs+o46WHzgbI7Q+w8h3QuTn9Nw7A3z8E3buG3Ye/WJlDXSOXlYHbKBNIB5HqQ36+8frc5tXHLW7huT29dI72ett6AJzzFbf+fsHxsPBFBDfcDECvjdLz+mth9hHwr++oqb6IiJSNzcorCQ8JwsxrjrC7e5CkY3Myh/alPnMXY5R9QzjAkQua+NeGvcOuy9dzCNysXSAnwLFiQTMATw45gSPg/aQMhkPnuUNExhxaNEFu5pAb1MNX/InXUp0K6jlkrb3RWnuItfYga+2XU9s+a629PnV5wFr7emvtMmvtCdbajXnu43PW2m8Ud/m1YUbdOBoLzz/G7TnkjYu3yfQI+1JlAbZmNcDb2zNIXcjPG160iCvfeBwAj20d0n/jyNe5janXu1ONrHF/DYcFh2YcCMteDv/8CmwbUp5W87IOOjf+M+8edaEArzp6Pjc8voO+WP4AklSn7oE4//3nx/NO/MjWmnT7B1Qqcyjg9/Hzt72IRTOibPZ69YCb9XdY6tzB638Bb/gldO+AP/3b8PtwYiQmmTk0qzHMYCplfaTXuaZIkIDPsHO/+9r4/pcdzEXHL8zZ57gD3NLbcaVpX3Al8bnH0mMjPGfn89DWXjjpP2DX4272lIiISDlYd0rZzR88lcZI7vvq3KYISceyt2cwNzhUxMwhgNMPmcXDmztyMoFg5J5D6cyh/syaFs2ow5isHoCSlp055JX2783T46kYkg65ZWVSE0rdkFqKoLV+HCPJZx4CNplpSu1kBYdKUliWmVgG4KTemCBT+7yra0jtcbQFFp2YFRxyM5siQT91IX8mOGQMXPQzCNXD/WpM7bHWUkfWG8Gf3pXbhDzLaYfMoi+WZNNevcFOJb95YDO/fWAzP75z9KbOzV5wqGF2GVY1skWtdbnBIYDX/gQuuNINEh1+AZz2UXj+Lvi/490ys++fDBtu55DBJ2iykzs76DXBhJEnrfh8htb6EDu73OabQ89eAhw21z0Lt3E8Z+FmLafjTTdz5ODP6aaO+5/bByteDzMOchtz33flOJ6JiIjIxFgg4Dccmnovyza32S352rl/IOfEU6bn0OSnlQGcfNBMko5l7ZCTLJngUO79N0WHZw6FA37mNEbSzbIlwzs1bEzms8/entIEhxxrCaqsrOYoODQFjCtzaObB7tfd69yvThKb6jlUothQutGrxzsr4GUC5Q1sHXqeW3ZCpqwM3AbXOc23I83ugeX6GyE+5E2i/XmI1V7Qw1qYYVJ12C/5kPvvcvMn85awzGlyzyp4pTQyNdSH3L+JvWP83TfY1O9BdEaplzSqWY3hnClgAAQjcOybM6nIx77Z/bpvg/t19zr49esAWJrcNOnH94zUkBrc0jJv7OvQvgfgDgAIBXzj/ntxnMzlBze1QyAE777L7T90z7fHbMotIiIyWdbaEU8Ee58Hd3ZlgkOzG8Ns2tebuq2732jvoYWY0+T2PRr62T+WyJ855E099Q154LaGcRz71CCDYWZDKnOoRMGhnFH2vkBJHkOqj4JDU0BrfaiwaWXg9rponA8P/tR9pbdJLF7mUGl4tcGeQOqsQEvqbMCwg0aAE9/jjooGmuKZ2uSZjWG2dgwJAh3xWoj1uAEQL0BkLXz3GLjqHPf7eD8M1kbjOgu0knquC18EJ78PnrkJnvjjsH295oS7S5RyKqXhZeON9Xdf76QyXKItJV7R6FrrCshubJjtBojmHOlONHvdz9JXfaHpfyb1+N4HJBg9JX5GfSjdGymcJ3PIGMOshkz/okI5WYHZXV7vpXADnPx+6N0Nd/yv+g+JiEhJWTv2UIbOvli6rOzUg2dxx/o9/PDO53DSwaHJHS2kTwwP+fyS7jk05MTMohlRPvXKw7jyjccOu599Cg4Nk/1RIhL00xgJpLO/is2xloBRcKjWKDg0BSxqjdIbS7JxT2bE8kA8yVGfu4Wbn9iZu3Mg5DZK3Xwv7HzcnQjm83oOlSY8dPJBM3OXkMpUCvh9NEdHaKbtD8BZnwNgMNCY3nzqspmsfqE998z90tPgqEvg4avgy3Mh1gvtqbZWOx51S1S+PBf+dyHseaaoz61atXqZQ3VtcOpHYP6x8Od3w64nc/bzMip2Dy3tk6rm/amOFXDpbE9NA4y0lHZBY2ipC9I9kCCRdEbf8YIr4T33uBPNjnwdHHo+v2r9Dx4OnTCpx8/OHBqtfHZGVubQ0A+ontlN4XFnDnnjdtvqQ+ztjaXT81l2Fhx5Edz9TVh/07juU0REZDwsI58IbvVOOvXF6RlMEvL7OPmgNgC+etPT6bKvyR4pNEeDGOM+TraReg4ZY/h/px3Iwta6nO1zmiJsG3qyWNJNx73PibMawuwpR+aQyspqhoJDU8C5K+bhM/DSb97JG354H1s7+tjdNUjXQIIv/v3J4Tc49Dz36+b7cJwkcVu6UfYAx6eauHqyJ/3MqB8lLXTB8byWb3Hf7IvTmy48dj6OhRsf25HZz+d3Dyo9X5kP/3dc/vu8//vjXv9UY63NZA7VtbnlO2/6I/jDcO93c/aNBP00RQLKHJpivA9RnX0jlyO198ZoIlVWGR7eX6Cc0mckCxmn6n2iMQYu+Q23NV446cB1TnBojLIyT0M4/1mwOY0Rdo0zc8iLBc1sCBNLOPTGvDNtfnjNj9xpcmt+Oa77FBERGQ83cyj/m2BdyE/I76MjlTlUH/azcknm87v3WX3E92NrC2rl4Pe55U479ucGdkbqOQRA13bozJ2se/i8Jvb2DKotwhDphtSp72c2hEvWkNqdVqaG1LVGwaEpYE5ThBcf6Eb3H9zUzmf/ug5/6sW1N98UqpZF0LQAtjzAC3u6eGSrG0go1bSyaMjPD998PJe/bkVqTZnR9TPGaKa9gYXYrBecZbMbaa0LsiErSwpwM40++DiEGnO3r3yHe2b+Px6E5efBo9fA2uk9IciS1XOoLtVrpr4Njn8bPHYtPPDjnP1nN0XGXSYjleWlXw9r5p4l6VjCJs6gDbh/HxVUaBlcPtbaSb82tdVngkNDg9XZ5rVE05e9JphDzW4KjzvTLpn6tOYFqfZln8XzB+DoS+HZW92Mx1hfzWQ4iohI+VjsiCeCjTG01gfp6I3RM5igIRJg8YxMtk4mODTCHaz6EnztQHjq72Ou48CZ9Ty3J3ewQ2yEzCEAfv92+PaRcP37Ieke18xPvV/r82uu7IbUUNreTEkHgunMIZWV1QoFh6aIha2Zg5pVT+9mMO7+sfYNJvPfYP6xsOMxBgZjOGX4MZ9z5FwuPHbBsO2tdaH8PYdS8nXhmNUYZk++KHjLYvj4Jvdy/Sw4+o3w0s+4E81mLYdXfRsS/fCX97hvMI9eM6HnUu2sdcvKHHy55URn/rdbxnLTR2HHY+nNcyZQJlNpz+zq5pldtdFDKh/vDFtHX5y+fAFgIOE4+Eb9KFg+rXVuoGVoGnkhrJ18j4NQwMdlLz6AH112/LDU9GyvPnp++nJTNP8HndmNYboGEgykXmMHE0mu/OcGbnhsBz+4I//0OGdocGjoB7Xj3wbBKPzkZe5r05Uvgi0PFfr0RERExmRHqyvD6w8Yp2cwQX0ogDGGS09YDGTet0Z8P37qevcz9l/fCwOjTxg9cFZDTisMgHjCfZ/MGxzyWkWs+WW6f2amR5IGOmRLl62nftBtDaGSNqQOk/r3D0RH31mmDQWHpoihfTT+uGYrkInEDzPvGNi3gYPNVhKUdpS9JxzwD9s2o36EnkOePM3zRgwOQSaD6D8ehNf8IJM5A27D20tTAaE1v3R78AzsH+ezmBpm0M1gsAl8WX/C4QZ4bSpr6EenwrdXQPcuZk+gTKbSzr7iLl71f/fwkstX8de12wq6zdM7u7jvuX0lXll5xBOZv+tHhoyDzexj8eGUJfg7Fq+XQXvv+H/PHFuc8NYXLzySVxwxd9R95mdlDjVGRsoccpu4f+3m9fTHktz9zF6+fst6/uO3a7j85qcB6I8ledk37+CBje7vm+PkBofahwbEZyyFd9wM/e3wxB/cbXd9bXxPUEREZAyjvZ/Whfzc9uQubntyV7q0+s0vdoND3vv3iNPKBrvdoTcD+90eoKM4aFY9HX3xnDYT8aSDz7hlZ8OE6t0+hHOPck+gPLcqfdKpfaxhFzXKO3aa2RCmoy8+ds/HCXCsJWIG3ZOQgfDYN5BpofJHFVKQaCg38JJ9hj6e7wXhuLdAtJWAyVxXqrKy0biT1uJZke5c7kmO3IWN2VytZXFuUCjb8nPhrX/LfP/k9eNccfWzWFpNN/3BluFXRlvdnz1A52a451ssao2ys2sgPZ1iqhhMOGzt6Ofjf3xs7J2Bc759N5f+5P4Sr6o8Pve3TC+xvz26Pe8+saSDDweff3hQttwOaKvDZ+CpHePP9ipG5tB43P6h03jbyUuYlwoCDeVlaf78X89z5zO7h12fdCxP7+ziuT29fOmGpwDSU15mNrhBsn35gmRzV7j/eZ69Ff70bk0xk6qxfmc32zrVAFZkqnLLtEd+P102uyF9ORJ0PztkTu6MUlbWvQu6d8DiE2Hp6XDH5fD0DSM+zrxm9310Z1aJdjzp5GYNPfV32HiHezkxAME6eMtfIdwIv76IefWWkN/HI5s7RnvKNa8tNa21FEG0pGOJEsMJRCtzECkVoeDQFPG+ly7jK69ZwR///WQABrL6+uTNLGicAy//PACHGDfLqBJ/1m31IWJJh54RAhP5+o14mUMjBZTGtPQ0+GwHzDwEVn0xk646TVgLM+hhINCSf4fzroAPr4dZh8La33KOuY+kY1lTojfYjt5YuoZ9/c7udBZFsXj9d2rRiUtncM1DW9IlTtm8sjJ/FQSHGiNBls9t4uEXxv875lhb1henZbMb+dyrj8A3wunRYxdlehZ19SeG9XXb3x9nIO4G3b37SDpjlJV5zvyU+/Vdd7j90h67Bp65eaJPRaSoXvHtuzjlq6sqvQwRmSDL6Mfwnz7/cD788kMA6Ox336e84JDXAiJvcOmp1InWgS54zQ9hxoFww0fS/YGGmtPkvhdmZ633DCao9wZBPPIbuPZN8MsLIDHoBocCEffE71mfA5ukYc2PePkRc/jzI9sYTIzQQqMGDWtIXZ/78yumpLVEGXSDQ1IzFByaImY2hHnjiYs5ZlELxuRG473ShmGWng7AMl8q86AMB2A3/OdL+Pv7X5L+fk7q7Lw3PnqofOXRc5oiDMSdyQUzfD64+NcQ74fbPzfx+6lSLaabgVBL/iv9AWicC5f+DmYs5fB/fYAT/M/wwMb2kqzl2C/exrFfvI1zvn0Xr/j2XXz79sk12x0aXEqOM9iUN5Nuinrd8QsB+NOa4aV1XlkZpjpexlce0Mo9G/byh4e3jut2Y7RIKLtoyM9jnzsbgL29g+wc8tr15PaudGbQo1s6gUzPofpQgGjQP/KHtOXnwqf3uD3hzv06NC+Ce78HzvT5nRURkcoY61xLUyTIsYvdEyBez9JoyE9LXZCNe90G0nkzeYOpXn6nfRSa5rs9Lru3wxfb4P4fDtvda3T99I5Mb6LOvjgtdUHo2e32LfKs+zPEB9zJuwDHvhkOexWs+hLvab6fzr44T2ybni0iJiIzyt79Oc1tdv/dnt/bO+JtJspxLFETwyo4VFOq46hCCub3GRa2RnOa9Y54lrrFrSNe7bhnCboHSl9WdMT8Zo5c0Jz+3nuDeGFf/vGXNk/Podcfv4ho0M/V974wucXMWg7HvBHW3wTtz0/uvorIWjvxrKiUGaabwZEyh9I7HQhvvwmirXy07u+seaE0wSHP1g63HOG7qzZM6n5G7KNVoLWpA/bp4IJj3AbK+/KUWbplZbZqgkPHLGoB4CO/f3R8Aboyl5UVojF1dvPbtz3L/970dM51V9z+DD1Zr6W7uwfSwSGfMSxojbJu+ygfZAPuWT78ATjpffDCPfCFVugr7d+nyEistfzqvk2VXobItNDRG+O7/3h23Ce2isEyelkZQGu928tnMKu34fGLW9O9PvPeOp76DB9NZdYe8orMdTd/fNj71+ymCIfMaeDOZ/akt3X0xXin/RN842B3w7lfcz+n/vndbqNrLwBhDFx0FSw8gcPXfokF7GFvCbJipqqhmUNHLmimIRzgng17i/5YblnZAE4gfxm+TE/VcVQh43Lk/OacF8rOkepMjeHTy/7IW2KfAEYfi10qB89pxGfg8RGi/vneyJrrgrxh5UJuXreT/ZOdUnDS+yAZh1s+BbHxRdW3tPfxwWseKUqvnr09gyz5xA1c/+h2ln/mZj7z1ycmfF/WQoQYcX8BL9ahejjlA7wovpoL9w4/u1MKdSH/sOyf7oE4P7rzuYKCBvmCQ4WkFB+90A1K3pX1YWSqCwf8NIYDeQPAiVTPIVslwSHv7BWM77XGKcIo+2IzxrDygNa8v4sPv9CRUyb7/375cLrnkN9neO1xC7h/Y3th0/ZOfLd7hhTgG4fA438oxvJFxuWf63fzmb+uS39fic8KItPBl294kmO/eBvfuu2ZvD3rSq2QKm2vjCz7/W3lkkwfz7wnaxKp1wQvu8fnh/fcA6/6rvv9v77tZsA+fUP6s/YZy2fz0Kb29PtlT28fb+q52t1/1mFwwrvgNT+GxSe52zqzTgj7g/Cq7+BLDvKX8Gfo3zPJk8XTSDo4lPoxBf0+Tjqojbue2TPpE89DOdbtOaTModpSHUcVMi4vP3xOzvejjY/e72+jjwiN4QAvPXR2qZc2TEM4wOHzm1i9Kf9Z8ZHeyF6/chGxhMMt63ZObgEti9zeS+tvhBs/WvDNkvf9kMYrj+Tide/lii9/lDVPPTupZWxPNfn84R3PEUs4/Pr+zRN+EbfY8WWMnPJB1rSeywWxG6C3+GcWsp25fBZ9sSS3Ppn5uf3jqV2s+Nyt/O9NT/OXR8aePBbLOps1LxVwaB8pOy5LU9Q9GzYdMocWtER57XELALep+x3rdw9ryhhPjvP3oMS8sbPgll4VylJ9mUMAb37xATnf//IdJ/Ce0w8CYNO+TKD50S2d6QC9MW7mI8A/ny7gwMAYuPCHsOIN0LoE/vZB6FfzTamsN//0gUovQWRK+sndmSz1rv7yDwEZq+cQZN6r35L1HrdySabXnjG4H86fudWdUOY4btkX5I4zn7sCjn8rHHUJ/Os7cMURcM0b4Zb/BuC4xa3Ek5bn97jvl6G+He7tTvkAvPNW94EWvcjNEgJY/OLchc45nMRbbqCFXg549hfj+WeY1jKD7DM/6Bcf2MbWjv6RK0kmKOngBoe8skKpCdVxVCHj8rLDMsGhxTPqeHRr54gjDK21HDiznsc//woWtlbmj3vlATN4ZHNn3qwRC3mjQ4fNa8JnYGtH/nK0cTnlA7Dy7fDo7+DBnxR0k923f4eW5D5O8j/Jp30/p/fGz0xqCd7I0Cez6q/zZVNt7+zn2TEyDqyFAEmsKbARsTE8seSt+G0S59bPFG060obd3XQMeSM650h3lPh7fr0mve3B5zOBwYHEyJlDfbEEx37hVm76/+ydZXQb19qFnxHbsi0z27Edh5mhgQbaJG3KzMzMcNveMt6vzMycctoU0qRN2jAzc2JmFsz342hGki3ZMiV2Ms9aWZFGo9EIPDPnPfvde60oLD11Wj8ePLEPEJzRnsNtXL1sV3G7RHoeTFyyjMFtdtw9IZydhVWc8uq/PuvY3cqhjlIcignzFIf8eSQFoiMqhwDSoj0XwRePzmBc9zjGdYsFYEtuhc+6ihxfr5OIDTMRYtT7GHE2ijkMTnsLzngf6spFjO+eJW3yHjQ0giHcYvS5vyWvIsCaGhoawRIoiKU9kQNdVHthMerZ/vhxXD8xW12W5jU+0Dlr4OtL4dMz4JWRou159Rcg6YWipz4nvwo9pwsPIhAK2NJ9anqnkqIVWp0rHs86GiwRnudHJMF/ckVIQz2MGSP5XRpJ7wPfeVrXasuFT1EHsos4FHhfNyW6/V0LGkt6bgFOWSZEqkU2asqhI4mOMarQaBa2EM/BeUBaJCVV9oDKiiDOE+3OoPRIqu1Otuf7aeuSG0bZgxhkRVubiLRvDhPug6wJ8PPtsPWPwOsV7YDdi6iwS2xypXJ07f+x3JXN2PJf4K+nobqkbfYH/8Wh0U/+yTHP/d3kc0U7UfApVbqE3nzoPBbdqk/hr6eatZ+BOPetRbww21dRNTjdM/ukKKO82wbtjRSHdhZUUVxl5+EfRXuDyaBT05+CKRI63a9XVedkXTOUKx0Rh0tGrxOH50CKP7vqOXTo08rAI1UHmL0x12/Cmj86apJ7dlw4eneBzmoWn3HPJHFBu3JPCToJ3r9kGIB6nNJJEpIkER/RgmNXYl8Yehls+BE+PBEqAwQNaGhoaGh0eJRJg4NLcJMtOp3kc22m+BABWPNXwrpvxJ0yd8BE4RaQnf5lSTq9UP9c9CNc+Re4HPDKCBJqdwJQVFlLjd1JlNPd8h+R2nAbxsA2Cd+HnIbBVQMvDoTtf8HKT+Gri+HFQVC4rek3e5jhr+tAmZwrKG9b5ZAsy1ioQzZoyqEjCa041ElZfO8kLh6dweReYuBYUh2gtezgpkT7pamKdqATWVy4mbxgZ9+bwhoD53wOFht8fBrUBSg2fHI6vHss3XT7WOzqyU45ifejb6FMZ4M5j8EL/cXJKNDzA+DPlzCQSXdTyIC+mV4zsWEmHnWcT2nWdJj7BHx7NeRvatHrg/AAyiuvbVC08VanXfDOYlwumcKKWkJNYnDd2KxGtV3MsinR9Sa9nj7JEYSZDT6mhoFwuWSy48MAWNqCSPWOhNPlUQ5N6Bnnd51ahwudJCPpOsZh3Kj37IfdKQc9g9VR28psoUbVZDvUJJR/0VYTYWYDtQ4XZoOe2DBRvFQGAcr7iA83k9cS35bpz8K1C4X555zHOm7lTOOwor5xbozV1ObeFRoaRwL9vAJZDoV3VzCeQ/4wG8Q1Wgr5dPnxTLHwhuUw+KLgNmAwQeY4SB4IV8wBl53Ef+5HwkVRpZ3Ve0tJktzKn4ikZu1bka0XD8S+AI46+OhkWPqueEDSwfxnha/oEYS/I7NyLaIkqbYVwpC6FjTl0BFFxxhVaDSb+AgLD57YRz0g1G/vUZCRD/nAK8a9j/4Gi7Lfw5wgPrwNlUMgTl5DLha3Zz/UcOBVVwmFnqStA3I04WYD5bbunBf1KYy+AWpK4aXB8M4xzRq4eV9oG3AAMm/+vZ27Z6z2exHemHGz7HJhkFy4JEPQrx8bZsaFji3dLhMLVn0Gv9wp3oMsw+ZfxYm3YKsoGjkan31Q2ry8W2d0uAjZ/B3xiMLM/K0FrNtfRmFlHVlxVuLDzY0WDLxbxxIppIsuD/PeBUzJDuGPDXkNTK7r45RlkmwWwswG9hS1QTviIcThdKmqlSRbCEdlxwBQ6lUErqx1oMOF1EHayuqjfJ+yLPPruhyq6vxL7OUO2lYGcOZQMcM5KD1SXab4NZiNOvX4m6cWh8Q68eGWls8ax7uNOpe+Awtfbdk2NDSagfex9dGT+1JYWcfOFk5eaGgcydidLqb0SaBfio0DpYeoONSK82kf3U7PnagMOPFFeLAUrl0EF3wb3EYSesPUJzHuns+zxjfYW1DKmW8soI9uJ7XmWDCHN2ufYqxmlti7wPTnQHZBvjtBdPCFsOJjeGsilAbfyt7pqWdIDRBXb6KqrXC6o+y14tCRRcccVWgEjdJiFsiU2uVq3YmiLVD6jv35xjQ2yxEXbm70QFdSVaeaF1//6XIy7p7pM3j2y+SHYNjlsOh12PiTZ3nBVljqNsXrPg2AfVIC311/FFFWk2jbO/ZROPYxsU7uWvj+evEBK8x+GD48yX9hpa6CZearuNPwOfPMNzPbdDvRlPH5kj2s8GOgnNPYRYXsfs1mFAW6xFgBWO3IgJNehZHXwva5sHcJ7F8On54Ji16D14+CV4bDU12gILAJt/heZPLKqnnU9AFvGJ/lA+OT8PWlLLZcx+e9F6LHyb6SauTSfdxb/SxvyA9TXepriF1cWcebf2/D6ZJ9WiNnW++j79fj4P3juT/3FvLLa9nnNvUORLQ9jzTHLpIjLaoBeGfFWzkEcNmYTACfBKzyGlEc0uk6RlsZwOvnD+HOqT0AYSLudMl8vGg3V320jLe9jDq9kTtglL3CmUPT+OuOoxndNVZdphSHJDxSfG/PIYD4CDO5ZTUtV19Mexq6jIGFr4MruPY8DY2W4vAqDo3uKgrRC7ZpbY0aGs2lzuHCZNCTFh1ySCapZGS/Vg3BYDbosOK+9rx2oWgXU4jvCV0nBr+xwRfBqOs5RT+PmiUfcrPha07QL6QobXKz9ysmzCTGDwPPgbM/BeAH5yj+6fkfOP5ZyFkNz/WGha81e9vtQcbdM7nli5Xttn1lUt27LTAixIBRL7W5IbVLlrFQC5oh9RGFVhzq5ES5Byql1YGVQy09UbQVERYjep3kV+7YWLKCUhwKpBgZ+PDvXPnRUgB+Wi1SEJ75dWPjOyNJMPUpiO4Ksx/xFHJeHwO//UfcPvMD7gt/hIquJ9A1LowYq8mz76Ovh1vWQ+owWPkxbPkVKvKF6mje/4mCy3tTG6iK9OX7iJHKudbwA0lSEV11B1huuZpHDe+QX+gxbB4TVcxgaTM/rt4f8C3IshgsNsdzKDbMRITFwPbCShh0Hkz4D5jCYeatsOV3sdKStz1xpToD/PlowIFpYVklf5ju4Mqadzlf9ytT9EsZq18rtgmM3P4iC83X8dWsP7ih8BFGV/3JIMcqxhR86bOd6z5dzuM/b2TtvlJKysv5wvQwtxu+wOr0+DFFVmzlQv2vTarIHiu9i8cPXMHw0APsL+3cxSGHS0av9/xhpEeL4p530au81oEOGUnfcYpDU/smMr1fMiCUgnfNWM39360FIMePxL6qzsGafaX8GUyy1yFAkiS1sKow1m1KXWMXrWXhFgMF7uKQcrGWGWulss7p9z0H+cIw/Aoo3Q2bfmn5G9DQCALFr+38kelkxlpJiDDz77b2TbbU0DgcqXO6MOoluieEs6Ow8qD/HbVGORQXbsYquc9ZoTGt2xGdDo55hP26RJ4wvsPNBuFhVJA+pdmbigkzU1QlJpvoeTxvjvyTG+03cO2nK4SJdba74PTHQ+J6vAPwbRDJvC1FjbL3WiZJEjFWs3ot0lYoaWWacujIQisOdXKiQhtXDrVWYtoW6HQS0VZTAOVQ4OJVXJgZh0sO7KcEzN2UT6FX0aCyNohZdr0Bjr4bCjbBS0Ogpgwc7kF3j+PBYGaJbiAGdw92TJiZGruLrUqCiy0FLvkFrHFC0vq/bHh1lCfic98y2L3A9zVdvi016y2D2Bc5hPMNs4nb8KG6/PXau/nG/CA7F3yH7PLfWiY7xXt0NaM4JEkSiTaLx8PJHAanvytayOY+IZaV7Bb/D79KyHXXfwdPZ4nPpx6Z/95Ntm4/l+l/9iwccA7cswdOfRu57+nESWW8U3Edg3Vb2W7pTZ4pnbOqP/eZ3UkrXsirxucpLc4ndt8cRug2cr3he/HgqOvh8j+pSBrFw8YP6PLZBDa9dbGvWssLkyze2z15d2ItbqJI2MFxumT0Xn+4ijH3TZ+vZP6WAjbmlFFeY8eko8O1lSnGiEWVdXy9bK+6vMjP3//uTtj+d+sx3fn4shG8e7Ewo44NMzdQDnWLF0XSra1Jfep5PERlwjdXwv4VrdtpDY1GUCZgzhyahiRJDOkSxVo/gQkaGhqNY3e6MOl1XD42i7SoUJ77ffNBfX2ZlvuMRoWaCMd9LWwKa/3O6HS8EvdfZjqHs8GVzl/O/nQbeUKzNxMXZkKWUa/1XSGRgGizzymrhfNnCKWToxreGCuuwauLhWn1gdWtfx8dlPpju5gwU9srh5x1GCWnVhw6wuhYowqNZhNi1GMy6CiuaiStrAMQYzX5NedrTDkUHxFcD+2QRz3pY3pdkKfFfmfAqW+JmfkVH0NECgw8D84RklW7y6Ua7PZJFglF57610PN8vREGnO1pTSvZBY5qcobdJdQzKz7xfT23IuenHo/zrP10XGd9RvwNf/CXsz99tr0FKz8DWcaB8BF6uvYRds78n/99d4pCk9zMdqL4cIvqjQJA92Oh90nidnRX6HOKuB3XHUZdBwl9oaZERJjWe/3MfT8AsFeOZaGrF1+N/h5OeV18mf3PQDr9HWbpxqlP+TzhNr7p9T+2yinIv94L2+YAcI7je47TL2bwrJM5bqcoUm0196Hy+FdEC2DqEJznfMEGVxox1Tvose9b6jb7ps3tWPQji776P/J1ceQYU3HpLTzmeJaaus5rUuio11YWYfH4S/3f75uY+vw83vtnJ2a93GGi7BVCTXrMBh2FlXVqSynA8t3FDdqsHM6OcoQKHkmSGNMtllHu9psYq4lyd2Sx6jkUEdhnLWj0RlGENpjhzaNFSouGRjugtJUp7Z0JERZ2FlY1MKrW0NBoHNFWpiPMbGBwemTL1aMtREwIt6w8NK1fIlapWoSdtFExoMTWk+vsNzOt7kk+6fYcFpOx6SfVIy5chNoo168OL09O1W4gvhecN0Okt74/HTb8JOLulWLRIWDJzqKmV2oBgY7KsWGN+3q2BJ1dfL6S1lZ2RNGxRhUazUaSJJJtFnYW+ImJp+P4efRJtrF6b8OZyMY8h+onAXkTqNWstpGodB8kCfqfCemjRSpQ2T6fKpXd6SkOje4ai04SJ6ZX5ngMqxl/F0y8T7SYhYgI90f/qYJ+p4mWs8/PE7MXoLavJSYkc+tj79A3MwmjXscLoddTpI+D766GOY9RoI9nlaE/8119SFz1MhxY1XDfFc+hZv75do2zsjm3nFqHl7pq3J3i/5iucMb72G/bSu3AiyEiGa6eD0kD4efbPX5MgFwjvsePHZMYU/siZ9fdT68+gxq83gzbxcx1DmB0zYsUh3Ujs/sATq99gGJzqkhLy12HVRLfbVjVXkJdFXwbcgrZ9/yLddj5QuEF2CJsnFr3EONqnyNftuFa+Lp4gbIDkL+ZzF/OZ8S6h+nh3MJeczfW9LmdbN1+qhe83azPp6Og/Lb1XilkkiRx3/G9AF81ilFHh4myVxDyZqEU9Fby5ZXXNjC5La/xb1LdmVCOUwDVdU6fZa2OlY1IgjE3i9sfngjfXNW67Wlo+MFzzBHnQKV99bW5WwM+R0NDoyF2p6xeO8aEmf0q5tuTxkJemuKa8V25eGgskim8zVoO7ju+F9ce3ZVV/z2W184f0qJtJEeK4tC2fHHtY/eaVPL2qqTbZLj4JzEZ+8P1nuWzHzkkyZ/P/9E+qjFZhhHSBnTVvsWnmDBTm7eVSe6uCsmkFYeOJLTi0GHA0IxoFu8o8lsw6ShJQF3jrRRW1qmDJx8C7KBqZO3Hq8jup7VoeEY0+eXNnKUZdhnUuQfbJXvUxQ6njNHt+aLXSXx6xUgAnvl1k+dzNofDuDvg8j/g9q3cWHc9P7tGCOPqcXcIVdGfIo5acoj3IBs8A0kAa3wGV4a9LFQ7fz9Dtn0TVQYbzxuvoFYyiwJTfd8f2a0camZRYFz3OKrqnCzZ4RXzHtcdLvpJmFQD415ezcCH3cocSfIMTH+6GbaK5c6qEgCWu7qpm+mdFNHg9eTIdC6238V+Yrl7Wk+m9ElkcM+u3O26FpDhtdF0rd3AB45jeGLg77wdejm/x1zgd997d0lit5zAx47JWHbOhs/OgRf6wyvDfNaz6yzUdJvOYlcPoubcLXygOhnKLL5B7/t3cfnYLLLirD4FFVEc6niH8ZgwMwdKq6m2O5ncK0EtbC3e4WtyW14j1F1Xjss66PvYVpw0MFm93S9VxBhHWAxEhRrbZubwqJvgZuHbxOrP4dneUBbYk0xDo7konkOKWvHsYekAbDhQHvA5GhoaDVGUQyAG61V1Tv/Xve1FK6wkJEkijBphO9BGJNlCuHNqT2whxuCV/fXok2wjLtzML2tyAHB4Xf8X1R8fRGeKa3CFcXfC9jkiofcgYTWJa/P2On7qnFV8YX6EhNd7C3WUm7gwMwWVdewtruKDf3e2PBDDC71DTOhpxaEji443qtBoNiMyoymusrPFj79FY21bB5MYa8NCj3LgCrR7MVZRSPE38+JP7h4XbmZ/STOLQ31PgzM+gORBcNIr/LhqP/d8swa704VB7/nzGJYRrd7OK69t+Pp6Az+4RuNCh2wKE4qiIRfDkrfgt/vA6X7f9YpD2fFhbCuoxDXhAbCJC/JM+1YqI7L5NOpqKN3jMYx2I7ewrWxU1xhMeh1zN9Uz/80cC2FxABworaHa7uTctxaKC5o+p8BduyAsAX67H2pKkXfMBaCcULLjwzh7WBo6Pyd9RQp83oh0YtxKiq5xVv6qykA+5mEAdpq68YzjLN5YmM+jRROxhPs3Qfzw0uG8ecEQXnWexLa+N8Omn8Epfhd75Vgm1z7NV45xrLBNIt5m5dy6/5CfOB7+fUmYhXcinPVm8b1JcH+mCiYdHeMPvB7RVpOauDciM5rLxmQSG2Zi0fYiauxOMu6eScbdM/nRbSR/3oj0Q7m7rWJq30TGZIsCaKhJqN0kSeKCkV34bX0uW/Pa4AIxMg3u2A6J/YTK0UvJp6HRWpRjjnIcn9AznrHdYlmzr7RNBhgaGkcCsiy7DanFtWOscg3rZ4Kz3faBVl4S1Ja3jd9QG6LXSRzfL4k5m/Ior7H7tKMX+FNmjboOLpkFI64RE7XJg+H762Dddwdlf5W9K6qsw+4MspuhGZhqvbowZj+s+nDGhJmoc7i4+fOV/PeHdW1SnNKUQ0cmWnHoMEApXKzYXdzgscYMnw8m0e6T5PcrPTPequN+gN2zhRgxG3TsKW5oWuuoV5w5vl8SwzKi2F1UxcacMjbllAeXEiFJ0OdkuHIuRKZxw2cr+Gzxbgoq6jB5FYf0Oolrj+4KwMgnZnPtJ4F7mFUT3unPQ78zYcHLROycJd6zrmFxqKrOyQFDEly/BIA5YceTEGHmN8cQCEuEv5/xiZWX3W1lzVUOhZoM9E6OYP2BhgbT9fl3WyFblEFtSKSIC83fBP/rgfHnWwE4cWgmv98yjidP6+93G4qJcrxXQSM2zEytw0Vlj1PhzA95Mu5JKvCcdJT0vfpYzQb6ptiwY2Bx+mXCODwsgbrrVzGm9gW2yqnc4biareHDiAs348DAsi6XQXUJfHRqwwSLuirRltYBUWbFDP6KQxG+vx+jXvaNm+0gxISZOOAuDhn1EpIk0Sspgu0FlR5TdODHVeJ4EGY2+N1OZ0CSJD6+fARXj+/qs/yi0RmYDTo+W7wnwDObiTVGtHp2mwLL3gfHwRtwaBzeqAVpr5PxiQOS2V1Uxco9JYdorzQ0OhdKu5PJrfqNViZFD2JrWauv+esq2lQ51FZM759ErcPF35sLsDtlwswGQk1637Yyb7qMgmlPgsEkWs3ie8Mvdwo/os/Pg8VviYnXAIm8KgVbm92SJsvCDxaguI0NogGMDnENX9PteCjaDvP+B7JMQoS41q5weyAu3dV65bLO7Zeq04pDRxRacegwIDlSGMf58+bpKMqhvikRmAw6nvl1E6XuZDXlcBvoRKbTSQxMi/TbmuGsZ2R70egMThyYAsDU5+cx5fm/OfetRa3a5/qD84k949Xbv67LbbC+0gb3v982iQWSBGNvA52RmC1fAQ3byrrGiZPw+v1lYLRwcuxP/GI7i4RwCwcqHDDhHuE79OYE1b9IUk5mLfCayYq1siOAP1V9fJKkek2HKY97Ut2AA9EjGzU+VItDXgWNlCjxW92WXwW9T6JMDmF4RjTT+ycBEBkS2KxQScEqKK+Fsz+BG1dSbErEW3uml0QyniTBen1POP0d8fm9fzzYvVRlP1wPz/aEmo6XyNOYckj5/BSMEh2zrcxqotoufqdGt8Q+PtxCXlkNZTUNjcLDLc03qezoxISZyYixsqetE9lGXg2VefDhSaqXmYZGa/B3zJnSNxEQEwUaGhpNo6hEvNvKoJXBBM2k9cqhCmGZ0MHonij2aV9JFQ6XC4NeXOsFLA55Y7IKNb/BDDvnCcuHn2+HT06H96aJRN6d/8DHp8ODNnEb4O3J8PIQeCgSZt0De5cGVSiSkdXr3uV+Ju1bi8nuLg71uwB6The+qeu/Z0SmUN5vzBETu4u2t744tHanmETVDKmPLDreqEKj2ZgMOiIsBr8Rhq1JLmhLkmwhPHO6UJjkuX2B1LayRnZvRFYM6/eXNRhQ1vccMhl06iyNN82VxHubyyoneIUeiU2dMMUbKaio8/gSxfeEW9Z59kfvu48D0yKJDzfzxl/bkGUZpyyhlyDBZiG/vJbqfhfA5b9DXblINANkl5gVaE6UvUKie7ve/lSyLPP54t3U2J30SY5gZJZQojWIGR96CWQfg2wO5zXHCej0jas94lXlkOczHe5WuS1ye884nDIGvURGjBWAGkfgWRyzQY8txEh+Ra340ZhCG8zI6XUSRr2O7Lgwlu8ucbcNvg8Fm8Rgusp9slT6tH+6VVwYdCAcjRSHlGKiiuzqmMUhr78jRWKfEGEmr7yWs95Y4LOu2aBr8Ld2uBAXbha/17ak60Q44QXYvQDWfNm229Y4InHJDY85ERYjUaFGDpRWB3qahoaGF3XuQBTlnNfFfV2zPf/gtbY3FvISFHUVHa6tDCDcrS7+fMke7E4Zg05HenQoK3YX+6SXBaTHNLh5DRx1M4y/G+L7QHgS7FkEb02A94+DrW4Lh/ePE9fbe5d4nr/wVXh7Eiz/sMmXGiBv5oQs8RtYs6/tJyBNdlH8kUOi4PT3xHv59moSqzYxIC3Ss8vbC1vdFrwrV1yr68zWVm1Ho3NxeF6RH4EEijB0yXIHaCoTKO1FymDJoxwKzMjMaFwyLNvpW31XZjon90ogMtRIRoz/qnZxVfPizJ1eRadQk2/xJdxi9Cl0VNb6Ji3V2p3oJLFvxVVeRYvwBNad9AtfOcZhD433eY7FqOe6Cdks3VXMuv1lOF0yep3E4PRIXLJbFpo8CFKHw+8PwOyH1eIQuub/+caEmXG4ZE/8JzB/awF3f7OGR2eux+mSsYUYibGaGioeDGY4/2tKb9rOU45zmkzBG5YRzfH9khjSJUpdFh9hIT06lFV7xAnT7hLJHgPdJzTFsyUQsWEmn995/Vkjb8+MRTsKxXfUYypMfRL2LIQvLxQKIqWgsvZreHcqODtOalZjyqHs+PrFIWeHLA55/52YDUpxyILDJVPpNuec3CsBaEbCYCek/u+1zRh8EST2Fy2ntQ295jQ0mkOggnSiLYR9xVpxSEMjGOorh6KtJtKjQ5mxfO9B8+4SyqFWXPV3UOWQ8p6251ficLow6iUuHJXBzsIqPliwK/gNHfOQUORfPR9u2whpI6DQK5Wx60Sx7IfrQWcQ1hD3F0Dvk8XjfzwIFXn+tqzyheEBbl9zIneF/tguLYWSO5QGvVG0zZ3xntjXd47l+FTP6xVW1rGnqHXH7xDE9nSmkCbW1Dic6HijCo0WERNmCngQ6gDCIQDiwoVqRml/a8pzCGBQehRGvcTieq1liiHdlD4JrHzgWCJD/XvV5JY1z6Dae6BqMTZU5vR3pxEB3PPNGp/HahxOUqNEkaq+WqAysid3OK5Gp2/YPnNcP9FSNWdjHi5ZRidJDM+MxqiXmL/F7Zt01kfQ83iY938Y9gu/I5nmK4eU1rexT89h3hbhw6N8D9vyKnG4xIxMUqRFNRSuT2PFC2+irSZeOW9wg+9GUS8B2B3iJD+pVzxvXziUK8Y2nloVF25Wn1tjd7KvRBSwlLY0paB1dI847E6Z2RvdJ/GR1wj57c558FgCuBxw5kdwyhuQtw6+uRz2BvaROpgon68/z6Est3JI9R6SXR0uyh7wKQh6K4e8ObZ3wkHdp0OB8ntt84GBJMH4u6B4J7x5dMP2sppSreVMI2jqR9kr9E6KYOWeEs2UWkMjCGrrKYcAbpzUjY055czbEoQHZhsgt3ZCuLasQyqHAE4YIJJBv1q2lwOlNUzpk8CwjCi+WLK7+RtTJldPehWOvkfczhwPF3wLp70tLoxdDrDGiiLMmR/A5X+Kz+fdKVDZ9Pd5jeszsg/81Px9awq3tYSkc0+mxvWAa/4Bl5MJRV/4rNpa5XKIJJ6vtZUdWWjFocOEGKvZbyJCqyWmbYjSsqWkC8gobWWB9zDEpCc1KrSBisUZIO778VP6+dxvbnGoroni0MgsT5qWtxeDLMvYnTLJkUIdVVDuOzBzNZLMFhdupntCGCv2lOCShXIo1GRgUHoUC3e4i2LhiXDSyxAaQ8Qv14nXbIERsZIABzBrrYgFVYoQZTV2VbkUGxa4HUbpSPOXUBYMceEelZvD5cKg0yFJEpN7JzTZXiQUcuKzPfqZudw1QxTorp+YDXgKXcMzookNM/PnBi9vqFPegFHXe+6njYD+Z0G/M2DTL/DZ2cKo+hDjKb41/CzCzAYW/2eS+jvXIXdI5VB6tOdCQrlQjquXtNY1/vCXKceFm6mxu1S1VJvSa7qQlBdugQUvw/rvPX8Abx4NLw1ptpGmxpHHHV+t4rGfNwC+htQgvAKLq+x+W9Y1NDR8UZVDXsWhEwckE2M1MWP53oOyDzK0/KJfljusITXAxJ5xPvclSWJoRjQ7CipbngoWmw1H3w23bYKzPhbLItOF2jx1OHQ7xrNu6hBRPCreCfOeDbhJh6xjaeJZFBqTubzgKQp/eUIosvI3tWwf6yG5vJRDClFdoP+ZZO39ltN0f/OQ4T2iKKOwlcUhi1s5hFYcOqLoeKMKjRYRSDkkIzfZ/nOwsIUYMeoltTAQ7LglLkx4lXijJDrVH0CfOyKdC0d1Ue97JyM1hdMl+6Sg+fvYLhqdwR1TepAVZ6Wi1k6t2yNHSalItgnpZf1WEo9Kyv93kRIZQk5pDU6X5/vKjLGy36v9C3M4nPO5Z5stUIwoBongSdBQfH5Kq+3YnS4M7uJQ/QKXglLoamFtiNTIEPYWV1NR68DhlFXD4mDwbp/M8Sr8dYsPZ8Y1o3j2zAEAGPQ60qNDfAtc5jCY8hg8WAr37IXwBPEln/Y2XPCdMPn94nyRcHYIcTSiHALRnnl0j3hunNSNjGhLx5EGeiFJEpmxovhjdBdwvVvNLjkqwyfF7nBFKYj7CwtoE/qcIuLtZz8kWiZ/u0+ohoq2Q+lu8XsuCWJWNYhZUI3Dk6+W7aXGLs6n9Qv+SpG3gf+choZGA+rqtZUpt7vGh6npne1OayaEHbVCLdNBlUMDUiMbLOsWH4bdKXNvPSV/swlPBEuE5/6IK6m5aBZ3LI9hzsY8z8Rx5jgYcA4seRt2zm9oSeByYpBc1BgikC+fzVx5EBGLn4PvroZXhsPf//MNR2kBklxPOaQw/i504fH8n+l1LjL8zvem+ykqyvezheDpanXvq1FrKzuS0IpDhwkxYWaKquoaGLO5XB1n7ChJStGhtt7yxp8XF+H7nHu+WcPkZ/8G/A+gTxucqt5ujnKorp73SX1PIRAqiOsmZHP7sT2osbvYlFPO5txytYgTKDlObqKgkmizkFtWg0v2XKDHR5gprKhVlSQApA2nasDFABiczT/BRHm1eClFFmVgUFbtUQ7FhZvJKatR1UXe+Is9bg6TeydQ53Qxe0Mu1XanWjwIhtgwE+U1DmrsvkoMvU5iSJdo4iMsXuuaA/d71++p7zIKTnwJts8VpoOHsCXHqRY+A38uep3Ercd0x6ijQ0bZA6S6k9WUwqi32ftZw9J87h+uKIl97VYckiSY8oRomYztLhREb03yPL7xJ/jlrsa3kbsOnukK/7zYPvuo0WH5c6Nv6mb9Y06Se7Ij92ANbDU0OjF2hzjZebeVgZjgPFiJZTJyyz2H6tzG2R3QcwhEW32SzXdSSfFhFK1mbeuP9seGXL5atpdL3l/Ci7O3eB4Ye5vw4Hz/eHg6C8oOeB5ziO/ZpTMRm5DMuxHXiGLOhh/F438+Av/rDvtXtni/1MTi+qEwUV2Qrpir3k3X5dN72YOe77UFXFIngnA05dCRhVYcOkyIDTMhyw0NmGXkgFHxhwLvdiVVTdPE/tVXDn222DMTXr9IAMIX6JRBItY+t7z5xSElFaGiNnArSIq7CJRXVsuxz/3N0f+bC0CU1YTJoGtwIeBqQjmUEGGhsLKOGrsTpVYSF27GJTc0XS4f/zCP2M9nb+LEoN+bQny4mXNHpNMvxcam3HIcTpf6GZbVOITnkF6nDtyv/ngZ1fVaYpTiUEvbyoakR5EQYebd+Ts4UFpDn2Rb009y0z1BXLT8s7VppUOMVwtafZ6etZGe9//iu3DwhTD2VmFOuPiNQ9aS05RyyIc9izqst4ziNVVSLfYvxMvgPT7c4nP/cMXTStuOA4PMsXD2J3D1PzDsctFmBnDFn8JMc9PPsONvz/r7lsFHp0LpPnG/2G3m+fv9UJGv+hloHP5c+v5S9fZpg1Ox1vubjHX7BO4oPHhpSxoanRV/yiFwBxO01wRBPVplJeFwXy8bOu7EzSi3tcPd03oCkOg1IfjN8n0+667bX9oqvzSDV2fCy3O28t2KfWKcENMVrvpLPFBbCs/2hB3zxH2nUhxyt3xFZfJ4+L1sMPTkdccJuNJHi+e8OR72LG7RfukUQ2rJT4CLNYbjax/nhNpH+TTsYvqX/AGfndOi1/F9Ua1ccCShfduHCYqXjN92po5TG/JJ7/F4DjX+nPgIMxW1DqrqGip5/KWRSZLEc2cNZEBaJJtyyoPeN6VF7MLRXciICeWMIakB11UVAfU+b5NeIi7M3FA5RBPKIfcJLqesRi26xLkHlnn1C1wGM+84j8NpaL70V6eTePyUfpw/Mp3yGgcHSmtU5RCAw91WFuLlt1T/vaixxy2cndLpJPok21i1VySWje8e18QzPEzoGU9smJn3/tmpLrt6fFe/68aGmSiqrKe8cvPq3G3U2F0N+9Qn/Ed4Ef12n0iHOwQoZutNGX5T5faj2v1vO+9RyzhlkDCP7JkY0eCxyBBx4fTC2QP56YYxB3W/DibJthAkCTbnBn8cajEGk/j9qi8+GE54HsKTRXvZ+u/FrObHp8O22TDvf2K9Wq99+182fH6eViA6xPy2LocHf1h30F5vYs94/u/MAQ0mL5TriqdnbfKEI2hoaPjFE2Xv+3cUG2amrMahXmO2J7Lcim4Bp3uiSd9xi0PKpJMyeRZt9ajhl3gF16zcU8LxL87nxdlbaSk76xXFb/5iJR8tdE+mRGcJi4KLZ4r7H0yH96eryiGnTuxXss3CD9UDuFB6jCcd57Dr6BdE2qgpDH68GWrKmr1fAdvK3IRlDMaQNpgtPa7kSdcFsOMv0QIXLPZq+O1+2L2QfKJYFDW92fuo0bnRikOHCYqXTP02mg5WG/LxsvEohxonwe1NsnJ3CftLqn3aUfwphxRGd41hxe4SKvy0h/lDSZrIiLEy944JpEUHllEG8hIx6HXEhjc0c/Yoh/xvT0mhkmVP0SU+wv9rtIWgRfF7ySuv9fkMS6vt6HUSx/VLpGeiUOnkV/gWp4JNK2sMpfCVbLPQNS54Y2KjXsfpQ1KZ71YODcuI4ubJ3fyuGxsmlFclVYGVNfW9rJAkuPB76Hs6LHodynP9P7EdCfrzVbxkep3QznvUMib2TGDLY9PoldSwOKQUQE8amELflOCVY50NW6iRYRnR/Lhq/8FJfAqNhuP+B+PvFr9lkxUu/kn4EH15Ifx0C1QXiYv/FR8LaXuNKNJy6tvi/82/wOMpUFkY8GU02pcrP1rG+//uPGivF0ilqNdJ3D+9NwAbc5o/iNHQOJJQJpvM9ZVD7snE9og1r0+rugXU4lDDVN2OQqhb3VjpVvYbvFr4vD9fRfH+9rztLX6tJ3/ZCMCIzGh1WYMxR8YYuGKOMK/eOQ957TeApziUZAuhoKKWGHcRa3utDU58EU54AfLWw5Np4tzstAsfkCDwRNn7Lw59cdUovr32KMZ2i+W9uknUWWJh1j1QniMmgwJN/lTkiQLX15fBvy/CjCuIo5g6/eEfHqLhi1YcOkxQlCwN2hdkOowhNYj9LKwU0c7KUKmp3ctyFw/OfXsRo5/8kyFdIkmJDOHSozK5aHRGwOeNzY7F4ZJZtD24QU6JW4VUP3rdHyaDjqhQYwNVj1GvI86PObgyMAzUVjYsI4pzhqcBUOU+qcWFiQJOIBVSa75WjxdKjWpIDaKIZdBJRIaa+D+3uXN9U++mCl3BoBS+0mNCm90f732ivmJslt9UOfC8xxw/vlNKmohfTypjCEy4V5ysf7rF03ZzkHDKQRaHSveI/8fc2s571HLqey8c1y+RPskNi0WHMycOSGZbfiVb8yoOzgsOvwIm3OO5H9MVznHH2678RPx//P9BSBS8c6yYVQTofZKYCZ36JDiq4YcbIG/DwdlnDb/4871rD4obKaBfelQGFqOOHM13SEOjUer8RNmDtwq89a1ls9bm8OxvgVOv2kQ51IHbysa5leZDM6LUZb/fMo5JPeN9xj/KNXd5raOBF2tzuclrArLW4WdbKYPhkl8guqtQneNpK6vvkXTXjDViwrLf6XDpLLFw/ffwSCz8fLtnxe+vh4Wv+90fRTkUqDikMCorFgwWvkm+TdglvHMMPNkFXhwo0tPqs/gt2DkPNs3EjgG5XHgp6Wjd56fR+dCKQ4cJykC4/mDXJcsdxpAahJrD7pQprbZ7CiZNzHL0SorwOcDWOVxEW008cEJvwsyBD46Du0Rh0Eks3VUc1L4VuS+Qo63BzZrEhZvJKfU92Rv1EjFWoRzyVgooNwMV6iRJYri76FFYWatuHxpeUASruGoMpTgjlEO+B369mi7lLk41UEG1gXLI/d4MLehjjvNKvfJOX6uPUlT0NyiPcLc15QUyLI/pCsc8BJtmwgv9YdUXzd7PluJ0ycRTzIA5l0DhNv8ryTLkrBEx9nE9Dtq+tZZXzxvCzBvHHurdOKgoyqhdhSLxyemSKatp2A7brvSYCv8t8dxPGgBXzwejBTb+RJGtN3bFv2DkNTDhPvHbf3WkuHDVaJK1+0rJuHsm/2wtaOAT11yUY2t7m9gqHkPDMqIDriNJEokRFr9Fdg0NDQ+Kcqh+cUhRoa/f33r13dUfL+PFP7cGjG5vlT5V8S/UNz1BeqgYnhnN+oencFR2rLqsW0I4A9IiySmrodh97PVOHl61t6RFr2VzXycqXpfQyDFZb4DjnlELN8ovIClSXEeX1zjU5/+xIU88mD4S7t4NI64W95e+A9vmiAnJFR/BrLtg6XsNXkoxpJaaCCMJMekZnB7F5+UD4KyPhdpcdor/XxoCB1b7PqFETITWGiM5rvZxHkt4FoAyc1Kjr6Nx+KEVhw4Tws0G0qNDWVBPJSPTcdLKAFLcCUbeA/am9s9i1PPYKX3V+6XV9gaGf4Ge1zUujM1B+g4VuYsyUUEoh0AUKfYW+0b8GvU6+qXayC+v5eYvVqqDQJdaCGtke26lkDKwCDHpCTcbAiYdteZ7jbGa0UlClVRbTybr3cut10kNlEOtTSsDz0m3JabW3m2FjX1XWbFhhJr0LNpR1OAxW4gYCDc6G37UTSKyFODbK2Hzb83e15bgcMoM020iKmc+vDQY3hgnetOXfySKRbnr4akM+OspiO8tWoc0Oizx9Yq8L87eQv8Hfzv4BSJJgnM+h7ieEJ0JYfFwzCMA3Jx/Aq/M8fJmGH8HnD9D3P7yQlj6Lmya5fG50mjAyj0lAJz39iIGP/J7q7al+Gi0d3EozGJgbLdYbj2me6PrJURYmpX8qaFxJBLIkLpLjCgOfbxwF18s2c17/+wIWNzxR43dqRY9FLbn+zeJF8qhw7etDCDU1HBSeHB6FLIMG9ztr95ekxsOtMzzr3tCGKOyYogONXFM7wSgieTR7Ek4zhTq3JKQdMCT+LivpFqd5PbZhsUG056Ci3+G2B7CPPpfr+TQn26Gry/1OfcqbWVSEN9TcmSImATNniRSTQecIxJOK3JEC9nit1SfJHLXQ/YxvD/mT7bIqey09GKy/CpLEk5v8nU0Di+04tBhgiRJTOmTwD9bC3wGHbLcsdLKRmbGoJPgr835zZrhyIr1mC8v310S0Ni5PqlRIRwIUg5f4a7sh1kal2oqxIWZ2VfsG53pdMmcOzydKX0S+H7lfuZuygeaVg4BpLtnl3YWeApOcX78i9rCuUSvk4gPt7CnqIoau5NQk14dkChqHr1OIjbM1KB1rrVpZSBOWADT+zV/RiLeWzlkDSx/Nhl0jO8exx/rc3HVM6VWkrJym5J5n/Qq3LULojLgrycPSoKZ0yVzmt6dLmUKgwOrYNl78MP1olj02iioKRGPJ/Zr9/3RaB2x9Yzl/90m/LL+DSJxr83pMQ2uW+SJKh58IXOn/cnfrgGsdhvEq2RPhnv3Q/po0V752Vkw4/KDv8+dhPrtA63xmFL8KU57bQFfLNndxNotp7LWSbf4cB/fDn8k2SwcKK2hvMZOqZ8QCA0NDU9bmane35PFqOesoWmsP1DGXTPW8NCP6/l8yZ6gt3vNx8sY9MjvuFyyqojecCCQCkkO7opfluGlofBoApTuhYKtncKQOhCKGl5JqPVWDgVUiDeB0yWj10nodBJvXTiUMdmxHCitbvQ5ru7H0b3mA/Js4tosOdJzXogMNRFi1Psv+mccBWd/KtLOlrj9/25cIf5fO0NM0rjPKbomDKm9SYgQac8ulyxSTU95HUZdC5f+JlLTfr4dvrwIZj8CuWsgoTe7isQYpNruZL8cg07XsYuFGm2PVhw6jJjUKwG7U2apl2N/R1MO2UKNDE6PYu6m/GaNsxMifC+81+wrDbCmL/6KK4FQeokDedjUJz7CQnk9T4jSajs6ncTTpyl+PeKkpCqHGvku0qJFweSCUV3UZbHhZvLrKXeCbcdripFZ0fy1OZ+KWifhFgOXj80EfE9mCREWchp4DrVeOTQsI5o5tx/NmcPSmv1c76JUeBOFvGN6J5BXXsvqer+XWncr3WtztzXej67TQUikUBHtWwYLXgnaNLClOFwuJupXijt374YbV0Lvk8EU3nDl+F7tui8arcdk0BFtNanKoW5uiXqDYsyhQJIwxojjzf4SPxe9Jisc8zAY3eb822bDgzaY/bBIP/ngRI+h9RFO/VBEf0maweKdwHPXjDUt3k5jyLJMZZ0Dq7np812CzUJeWS1jn57DgIcPjoJSQ6OzEUg5BHCSO71TYXt+8B50c9yTjJtyy0mJ9ChR/NHAc6iuErb9CTv+9l2xbB8UbhHx9c/1gZeHeEIuOnBbWSAUX6eZq/cD4PS6Tssta5kC0yn7Xm/2TAxn7b6yhgnCXsjI1GFU1VuhJoOqlDcZdMSFm3ln/g7+3pzf8Mmx2TDiGs/96CzRDj7pAeEF9OWFULIHyeXEJUvCVqAJEm0WHC6ZwvqtzukjRPhKrxN80ku3OWL5Y70IYtmRX4nDJatWExpHDlpx6DBCUZ54HwhbJTFtJ0Z3jWHt/lI1TSCY/Qsx6Xn34qHqSa++T04g4sPNFFbUBpVYVhtg1icQA9MiPfvnLigp7ykixIDZoFOl+MGYOEuSxPbHj1PTYZT931Ncpc5I+T4hqN0MyNS+iRRX2fl7Sz4Wo56rx3Xlq6tHccYQT8EmIcLSYNZFeS+t8RwCyIxteTvU/Lsm8OYFQ5pUL03sGY9eJ/Hruhyf5dVerXSfLApiZn7geUJJ8dt/4PvrRIGonVREijLLYY4CnV60AJ35Ady7VxSL+p0Jl8yCi370vZDQ6LDEh5vV9kxFxdZRDH6VYm/AFqa0YXDzWrhpNUSkimXz/g++uVKYWb81EaqD83U7nHHWKxq35vv1bp1VJg3ammq7E1kGayO+fQqJERbqnC41tEFDQ6Mh9gCG1ABDukT53K/frt8YyW5V4j9bC1T1eaD2pgYTwss/hI9OgQ9OEEUihb1Lxf8DzvUs++F68b+h8xWHIkON2EKMLNtVjNMlq8qhEKO+xX5pLpeMd11kdHYMQIOOAW/8XRZO6hUPwKo9JeqE5oXvLva/gSmPwSlvwBkfiPuSBKOuh6hM2PAD/HIXkuzAgS6oiX9lYt1vW3DW0cKL6PolcOxjcMwjHPdXmjqRtb+0hjqHq1UTwRqdE604dBihmPN6H7hEW1nHondyBLIMW/JEH3Cw+zexZwLbHz+uWa81smsMLpmgEsuU4lD9GNJATOubyDfXjuaTy0fw790TuWhUF84dIfqMJUkiM9bK5lxldsgtB23iIFu/2HFsn0QOlNZw59er1GVtVZMY002kPuSX12Ix6NHpJIZlRPvsQ0KEmY055Xy00JPYpRQvDuX5IjUqlGP7JDa5XmSoiVFZMfyy5oBPm0eN3cn47nEk2yw+7y0gBjOc97U4Sa/6FB6OgpntkxLmcLqolQ0U9zir4YMWG5z2FnQZBZnjOuVF3JFIXLiZfPdso1KYDFbR2N4of88FjcUsW2MgqgvcvAZu2wTmCDHzDCIFZebt/tNPjiAc9aRDOWWNtx80RUZMKCcPTGZPUTUXvbuYqrq2TS5TJkwUU+rGSI0K9bnfHNWDhsaRQp1qSN3w4shs8P07a6xgceYbC3jrb08Eu9F9TfrP1gIc7iL0+//u5M+NuQ2e28BKosJrnblPeo7Te5eI9rETXoB79ooWeoVOqBySJIlHTu5LQUUdP67ar57XkiOb55fmPRGrtJUpKCEtjW3Pn4XEoyd7PFO9i/Ebc/y0BkoSDDgb+pzsWWYww5VzYNjlsGkmySVLcaIPqnug0eKQQlQGjL6ewgFXUYv47q8an8UA9wS4oZUTwRqdD604dBhhNuhJiQxh7uY8dVlHaysDSIkUF5oHSsTBqjn7p6iMBnipdhpDUacEIyutc7gw6XVBK60kSWJwehRHZccSZTXx0El9fQ78g7tEsXRnEdV1zhbHv584IJmLRnXhu5X7qXX4Gke39msNMxvUtDeL0f+hQHk/93+3Vl22fr9oI2mtcuhgMaFnPDsLq3wGv9V1TrLjw5g+IJndRVUNPIn8Iklw9D3Q123Ot/Rd+P2/ba8gsldhlhzIIYEThDQ6F/HhFnU2TlEXNmpseRBp1s9Xp4PwRLh2gZC637hSqNfWfg1vjIXa8oPiy9URUQYjn10xEoCPF+5ucUHHJcvoJElN5Plrc77XREPbUFUrfofBKId6J0f43P962d423RcNjcMBu1McAwIFpijtRZmx1kYH64t3FPHYzxsAMZGlJF0u2lGE3eE5vv69uaFvnXrNP/N2oeqsyIPQGDjpFdizGN4cDxX5sH+F8Cw0mIQH3aDzRPs6gCWyeW+8gzChh5jwvPmLlarKMTM2jJ2Flep5tzHW7iul+32/8Je75Us5DitkxFox6CRWuMMH/CHTcPI01GTgvYuH8fmVI30mn739RZskJAom/AciUkgsW0OIVBfUeCJRLQ41fb3xwPfr1Nvp0aGMdZ9/jswz+pGNVhw6zBjfI66ecqj1RYS2RjGOU06Ozd2/dQ9N4aurRgW1bmyYGUlqfJYG4KJ3F/P6X9uCSkELlun9k6isc/L7hly1daMp5ZA/eiWJC3NlMKlG2bdB1U+JhTcH8Fma1DNBvb3G7ZFyv9cJpDOg9OgrvzdZlqm2Owkx6kmLCqHO4QpexWEOg9PfgfvyYPBF8M/zsHFmm+6vTjGbDols0+1qHDriI8zku00hVeVQBykOeae6BG2ibEuFsbeJlsdpT8J5M6BoOzyRCq+OBHvHaJk7mHjPVD94Qm/+3JjHx8GoEv2gDPAm9fIcf2e0cUFGVQ4FURxKiQyhX4pNvf/1sr2Ne7VpaByBKOpzo87/daQyGZcZayWvrLbJ4+2avaU87i4SAVTVOcmvqGVst1iy48P8tq7KMsQ4C2HJW8InceUnUFUIg84XHjNFO+C10bDrH08wgcIZ74sW4ojOGV0ebjFyhds7c5tb3Ti6aww1dhebcxtPLHtt7jamvzQfQD1u11cOhZkNjMyKYfaGPL/bAK/r83rLJ/SMZ2RWDMMyPJN++Y14F23Nq2jY6h0aDVfPb/R91Cc2zIQuiDEQeJKSQbxXZXxQVN+vSOOwRysOHWbEhZkprrKrF24ycosKEu1JjNWEJEFuuaIcat7+Wc2GoIs4Rr2ObvFhTSYDKTMFwbaUBcPIzBjiw838vj7XS2ra/O0oxbS8eoPJtvhWlYN/IBPu4ZnRXDU+C4DrPl3u81hn8Z9IcH9+Smqd3SnjkoWPVarbp2tPUTNmcEDIfI9/Vshx/3hQJH20EYZa4d+iKYcOH+LDzThcMsVVdeoMZmFlnX8vsYOMy2uA8vv6XLbmtUCh0m0yjL9L3M7fCIvfEH8XJcEn8nR2lLYynSRx8VGZRIUa2d3c44ob2T1jHW018e/dEwGCa39tBlXu36HVTyy0P84fma7eziuvZVuAKG0NjSOVTTllJEZYAnohKubvcWFm6pyuJk3rT3h5Pv+4r10Vdffe4ioMOonECIvfAb8MRMol4k63Kb4PZo2HYZd5ZC1dJ/g+LkkQ2fyQkI7E9P7C+Huve5I8JUpMDtYvtMiyTI2X9+S7/+xQbys+m05ZbvBdTuoVz9a8ioDXjMrZNNCw5roJ2fx4/Rh0UsNrem8mP/sXRz35Z8MHQqOZMfAd/mu/KCjlkEGvIzbMTE4TKWsgfJsUjuuXpHrfBfQj1DhsCWokLEnSVEmSNkmStFWSpLv9PG6WJOkL9+OLJEnKcC8fLknSSve/VZIkndLG+69Rj9h6lV6Xq+O1lRn0OmKsZlXm2N77d+rgVJbuKg7KJ6GuDWdDdTqJ7gnh7Cmq8hqANf/NKn3OioGh3IYiT7U41EhR7JLRYiYmOz7MZ3lJdecoDnVLCMeol9QUP0W5YTHqSXN7aewpbsEgTm+AE16Eyjx45xioKmr6OUGgqy0BQAqJanxFjU6D8neWV17rY4Z+/tuLDtUuqXgXh678aBmTn/2rZRuacK9IVsmaAL8/APOfg3engOPImHVUWlMNbr+R+HBLs0xnfbflOS8muc1ow4JQ+DSHSlU5FFw650QvFSkESLfT0DhCqax1MHdTPlP7BvZCfPW8wZwyKEU1Ng7GCyfGKs4dL50zGBCTWwa9jvgIs9/ny7KMBfdxZ+Q1cNU8uGKOZ4XjnoHbN8OdO4SH4mFGkjttd6/7mk45ftYvxPywaj8975+ljguUFirw+O8JQ2rfa3bF0iKQEqmpNGG9TqJfqo3YMHOT54faAJNHObaBfOCc4vcxfyRFhqiTo43RNU5c4294eCpGvY5Yt49to36EGoclTRaHJEnSA68A04DewDmSJPWut9plQLEsy9nAc8BT7uVrgaGyLA8EpgJvSJLUtlc4Gj7Euf+YlTYZ5TDV0RDpPS1rK2supwxKQSfBtyv2BVxHmRwor2lb08/kSIvPRXTrlENKW5RY3hZFNSX+M1BbGYgozFFZMZRUiRPEMb3FIOGUQSmt34GDQJjZwPDMaP7cKKTAymxRiFFPqntW6dU52xqksgVF1ni4eCZUF8HCV5teH6CycXN0RTlEqKYcOlxQTCEPlFZTbXcyrW8iV4zNZPHOooCRxAeLYOy2gkaS4MQXIdzdllC2T7SZ5bRPHHtHQlEOKW0IceHmRmeGG8Nb8StJEheN6tKic0djNKetDMT7eeq0fnx+pfBU2ltSjSzLlHYSBamGRnvy58Y8ah0upjVSHMqOD+e5swaqBu/BJBou3VWEyaCjZ6KnBcyoF8qhPHercn3Msvu4YwyFpP6QMrjhhkOjRRrqYUasu5imeLQl2UKwmvSs2Vfqs97iHWIy75e1IsnW7vU57iuppqiyDqcsN/DWVFKhFR+o+gTrLxofYWbJrpZNKDZVgKpPalQI2/Mrm2xjrLY7sZr0hLhDCpLdlgxtPTGh0fEJRjk0HNgqy/J2WZbrgM+Bk+qtcxLgzt3ja2CSJEmSLMtVsiwro20Lmq9Vu+ORAYqBvCzLHU45BOKgo0ru23kHEyIs9EiMYMMBP8kAbvokCz+FwemRbfraSbYQ8itqqbWLGYCWtPjFWM1CglrmXfBrm4+tt9vPqKn2h4QIz0DHatLTJSa0U50wJvSIZ0teBX9uzFXbekJMOrWdbkteBfd808IBbGI/6HUC/P0MfHgS1AT4nTlq4ePT4Jks2PRLwM0ZasVFjE4rDh029E6KINSk5/f1udTUObGaDaoSY8chbs9RvHKCNflvksh0kWp21TyR8FdTAq+Pgc/OhdJ9h23svfI5Gtx+I/Hh5hb7Srlk33br+AgLZTUOauzO4H2hmkAxyw62OARw1rB0hmdEY9RL7C+p5qOFuxjw8G/Nb8vV0DjMmL+lgMhQI0Mzmj5vKz6Ia+sVLPzhksVEljJJCOIYk2iz4HTJFFTWb5cCC+6ik8k3ZfBIoH4bmMWoY3R2LH9tyvc5diqK/E8W7kKW5QYeat+t2IfL1fCaPcZqwmzQBfbwCdITNDFCFGwKm2jZ8hdq0NwJ4pGZ0ewrqQ5Y0FKotjvVwhBAWnQoL5w9kGfPHBDcC2kcNgRTHEoBvI0D9rqX+V3HXQwqBWIAJEkaIUnSOmANcLVXsUijHVDaFw54zUZ3wNoQ3RPC1H7rg7F/iRFm/tiQF7B31umSmdgznq+vHt2mr5sSGYIsw89rDwAtK+jodZKQoNYzrwt21qAx+qSI4tCBJtQLCRGiRcLudAlZcydJKlMY6B74Xvr+UrWtJ8RdGJpxzWiirabWKThOfEkkSeyYB29P8l/82fAjbP1D3P7lLqjzXxQw1rkvGDVD6sMGq9nAtL5J/LjqAMVVdkKMepLd8vf9QXgBtCdKW1mM1RNf/L9fN7Vuo3qjmLHudgyc/w3EdodNM+G53vDuNHA1nRzT2VCVQ+6DfJzbhLwlxRzhOeS5r5zXe94/ixs+W9H6nQUqlbSyIKLsvdHpJJJsIewrrubLpeLSUFFlamgcqewuqiIr1hpUimtcuBmDTuL/ft/Mr+tyGjwuSXDjxGz1fohRj9mgJ8udvuuUZU9EeanvNa3FVUEP+0Zxx3jkFYcAVd0IopA2oUc8+0qqWemVMlZeI8Yf+0trWLe/TE2aG5weSf9UG9+u2Oc2pPbdtiRJJNksAdtq5SD7Nc5ze7htL2h4HehdqJq/xX8iXTCvoTC2m0hx+3tLPqv3llAcwGC6xu7EbPA9H5w0MIWYMLPf9TUOX9rdkFqW5UWyLPcBhgH3SJJkqb+OJElXSpK0VJKkpfn5+e29S4c1aVGhxIaZWLhdtK7IcsvUKu3N6UNS1dttNRPaGMqF+3lv+ff4cMmi4BHISLClZMaJk/ncTeJ33dLvIiHCono0teXnlWQTM1jFVY33FA/uEkWd08WSHUXUOV0Y658xOzh9vZJ2lLYypZVuSJcopvZNbF16VEgUjL8TTnsLKvPhu2saKoiq3O1k05+Hkl3weLJQG1UWwMLXRAw4YKwtoVo2YTAfmRd2hysTesZRUetQZ+cS3V4IB0oObbKXUhwa5KUcennO1rZLU0seCFf+BRPug35nQP4G+OO/UNy2BsuHGqW9Q+/lOVTndFHaAm82oRzy3FfiiAF+Wn2gdTvqRvHXa0lCZ6LNwq6iKnUm+o8NuW2yTxoanZUDpdVqG05T6HUS718yHIDf1vn+7ciyLFKGJYmLR2cAYDSIg8HlY0U4iNmgU48J932/lm9XeJIMzy59h1OqvhZ3jMHtz+HGyKwY9bZeJzGtbyIGnaQeO+dszOPt+R4D6j1FVdidLgak2vjm2qMYkRnN5txyv21lII5/gVoCg1X19E22oZPEvtTH2/vUXzJacxOLM2KtpEeH8temfE58+R/OeWuh3/Vq6imHNI5cgrkq2Ad429enupf5XcftKWQDfIw1ZFneAFQAfeu/gCzLb8qyPFSW5aFxcXHB771GA3Q6iaOyY5m/tRBZlnF10LayrLgwbnDPjPirnLc1lxyVAcCm3HK/g576kZVtxYDUSExehZSWfheZsVZW7imhrMbepm1lUe50gjum9Gh0vbHdYjEZdPyxIQ+H09WiAcWhxGLUc87wNGLDTKqvVIiXz1KyzUJhZZ3qw9Fi+p4GF3wrWmdmXAZl+z2PKcWigefB+TOg6ySY+yQ82wtm3Q3vHAv2Gkz2UooJa5ffo8ahQ/GZAM9McGyYmQOHWDmkXIeeODCZRfdOUpcHY5YaNKZQGH8HnPoWDLoA/n0JXugvTKtrK2DLH8KFuRPjUNvKlOKQ/5TJYFDSyhSGdInyOeba2yA4wa7EbnsX+n+5Gx60wfa5jT43IcLCqj0l6rF06c7igzLJo6HRUalzuHyuKZpiTLdYBqVHklPme/xXPGv0OokRmaJFrVeiUHifOyKdb64dzV1Te6qTC6v2lHDLF6vU5+tktyrTlgZHcOJpsvvz0eskoqwm0qJDySmrYd3+Ui55f4nPuptzK9hTXIXBfSxMsoVQ63BRWFHrd0I3yRbY4DlYVU9cuJkJPeJ5de427p6x2ucxxYYC4OvlewP6ujXnCnF01xjmuVVIG3P8m2lX1zmb9RvWOHwJZoS3BOgmSVKmJEkm4Gzgh3rr/ABc5L59OvCnLMuy+zkGAEmSugA9gZ1tsucaARmTHUtBRS0bc8qR6XhpZQpK+lVLE12aw8SeCXx7rWgZW767oeeFv8jKtsBk0LH5sWnq/WAr/fW5clwWpdV23p63g7a8BpckiZ1PHs+V47o2ul6oycDIrBje/WcHczbldzrlEEBcuCgAXfjuYsC3ONTTffHVmC9V0CQPgmMfhW1/wqdngt19EVFTAgYLGEyQPVkMlNNGgtOt2spbD7/fT0htPqVymOpdonF4kOI1q+wxfLSwPwhT0vZEUQ7pdRIJERYeOakPQFDpJs1GkuCklyFzvLj/x4PwRAp8choser3tX+8g8tni3YBHHaq2eLfgc6zvOWQ1G9SBIogBYWtRCkwGnSSmohe9AYteEw9+eJIoEv1yF5Q3bHvplSTMcTNjrVw9vivVdqeakNpalu0qptZx+LUdahzeOFyymlQYLIkRDRUoineZToJp/ZLY/vhxvHHBEPXxwelRJERYVH/R+lRKVmokM9yyFowNGjWOGH64YQzvXTxMnWRLiDCTW1rDDq/JaJ0kzL2f+2Mz2/MrVVW50vLtkgmoHMotq/FrBq6cT4MZT5w1TOguPl+yh2W7itSJ63wv+wunS+bCd307HlqSWJweE+qjSPJnsVFjd2ExatedGkEUh9weQdcDvwIbgC9lWV4nSdLDkiSd6F7tHSBGkqStwK2AEnc/BlglSdJK4FvgWlmWGzZQarQpSn/pgm1CPdQW3jTtgRKbqMQltjcZMaLFy18Pr7/IyvagpfWnvik2JvdK4KulHvuvlhaaWkqf5Aj1trGZF0Edgfhws09hzeJVHBroNiJfurONzHJH3wCnvCFSmr6/Fop3woKXweF1IWiNgXM/d69/o1BULH6T9KJ/KSU47wKNzoP3cU4pTCbbQg55JLhLHYyI39uUPiJtJ6c9FU3nfyOilEdcAzqhXuTXe+CjU9T2ys7EtvwK1dRfUQ71To7ApNfx16bmt8q76nkOgW/b3+/rW9/GVeeUMel14jxyYBX8cqd4IKEvZB0tbi96Hf6vB/z6H9H+6uaa8V1Z8+CxzLn9aIZlRAFtowA+UFrNaa/9yz0zDv90O43Di5aozxUvR2/qFxd0OsnvtZ63ssgbHS5caOqP2DAzE3rGq/cTIyws3VXM9Z96PNtcsseYGjyF/ESbZyLHv3LIgsOPGTh4tXwFsY/je3g6ZU57bQHTX5oHwIuzt/ist2qvr3F5SxKLk22+LYb+JkKr7U6f62KNI5egSoSyLP8sy3J3WZa7yrL8mHvZA7Is/+C+XSPL8hmyLGfLsjxcluXt7uUfybLcR5blgbIsD5Zl+bt2eycaKok2C1GhRrbkVYj6cgcdY/ZNsfHRZcO5e1qvg/J6ke4Wqo8W7mowyxmot7itiLC0Ptmrf6qNA6U16uzGwUaJfQdU877ORJLNdxbN5VUpig0z0zXOyuIdjcfMN4u+p8HQS2H9D/CCO+2h/1m+65jD4d4DMPlBmP4cjLkVgEHSlk5n+q3ROJIkYXa3BinFoaRIYWyZU1pzyNpyvNsYAGLChFlquyqa9AYRpTztSXigAG7bBN2OFWq7J7uIomonYqf3bLT7c4ywGBnfI46fVu9X1QDBIssNT9sRIUb19ht/byevlW1/dqfLU+Sv8Rp8XD0fLvwe7t0PZ34kli14Gb66WCTOfXAi0o6/CLcYwV7NQJsoirWFmkn5mOZsymsQwKCh0ZFxuORmq30TIiyU1zqo9GpnV4tDQYz8P79ypGqZUFplp9bhRCc7cWrFoQYEskII97o2V8YFyV7Xin6VQ26/pwd/WMdv9QzFZYKv3NQ3f1Z8RXsmCmXmmOxY9THv473HWiL4a8T61797ihpO/tTYtbYyDYGmHztM6RJjZVdhJXRQQ2qFsd3iDpoBmveBtP6Fp7/IyrZkbPfWe2mlRYvizN5icVA/2N+qxetEtmxX54ujPio7lvuO78U5w9M4a2iaegJWGN01loXbi9qu+CZJouAz6QFxf+B5cOqbDdczhYJOLxKeJt5HrrUHTzvPbpc2R41DS1SoUA9Z3Me8AamRVNU5GfnEbD5aeGgMmp3uwYhy+NPrJLonhPPrupygopbbhPBEOO8rGHIJyE54ezIUbjs4r90GbMmr8Lt8Us948sprmx31LiM3OB8pbWpKYpG/lKPmYHe6MCoDplr3LPKVf3l+CCYr9D4Rrl8mitc754nEuR1/ibazJe/Aa6OJeXMg/7V8wbb8tvMOLK6yM/yx2Xy/sr69pYZGx6QlyqFEm/ib9vZ3U4v1QVyPSpKkJrEOePg3etw3C73sxClpA/z6DM1oqLIyGXR+fSZjwszqd+lvkq6H+9rx5zU5XPnRMt8Hm6EcAlj8n0nYvAr/4PGBO65fkrrsuT+81EQtmEiqb5buz1NQM6TWUNCKQ4cpSUpPrCx3VOHQIaW+lNdfZGVb8n9nDODTy0eo6WAtIT1aGNruLhIX4Qe75uc98/DUaf0O7ou3ARajnsvHZvHEqf156vT+DWZdJvWKp9ruZMG2NlQPAYy6Ds77WhSKmkKn54N+H/KhfHzb7oNGh0BRLyqzc+O9isa/rMlptsKkLVAUS96DkduO7c72/EqmvzSfvzcfxATR6c/BLetAb4KXBsNrY6C04xcItuSK4tBgd3uqQopbbfmlVztwMPibrJjeP5n7ju/FTzeOwWrSs6OgeQWn+tiV1Mmy/VDkTu4JiWy4Ymw2jLkFbl4j2l8v/hkiu8DMW0UCY9bRXML39Nn5Yav2BxomcbakJU9D41DgcLmarfZNcLc05XgN1JVzQLDXd1mxYT739Ti0tjI/nDEkleX3H6Pef+eiocy6aazfNEm9TlIDBfxN0nWJsXK0V0uYd/R8cwNj4sMtqveQuj33b+DUwSn8eP0YBqdH+qhTW+IlmxBhQZJEF0N8uJm5m/N9FGvgbiszaL8dDa04dNiSEGFhW34lOwurOqwh9aHgrzuOBmDpziKf5e3dVmYx6hntJRFtCWlRSnFIDAoOtpfU6OxY3rpwKJsfncZZw9IP6msfDEZmxRBq0vNXWw+GdXrodgwY/BtI1sfpaqga0Dg8UJRDinFplNXjQ7RgeyH/+fbgt1M5XQ3bGCb1SlBvb8717wFUVmP369/WKiQJbKkw7SlxP3cNvDsVln8IFXngtMN7xwvz5A7E1rxyjsqO4Ztrj/JZHmMVf/Ovzm2eCspfyqheJ3H52CxCTQYRpVzWOk+oOofMGFaLtMTf7xcLzRGBnxCZDsc+AhlHwWnvwPi74MaVcN4MNoQO5ayyd4U3kavlysv6E+KVda1Mj9TQOEi0yHPIPeHmPVkpewUEBEPPpHAGePmRFVdUa8ohP0iSRLTX+XZSrwSy4sL47wl91OXeH/ngdOGlFkhJfkxvzznSW0nfnLZABe+JV4fTpZ6TjXod/VJtpESFss/Lm9Bf23FTmAw6YsPMxEdYuHtaT1btKWHG8r0+61TXacohDYFWHDpMmdo3Ub3tOASz0R2VLjFWxnaLZcbyfT5JA65OMCCPCzdjNujY7adX+GBxTO+EThdjHywWo56MGCubAsR8thWBYkkVnC5Z8xs6TImyCuVQmddspdXrYmzuQVBKVNQ6eHH2FurcUeZqcSjAb84VQMJ+94zVnP/OovYx1B54LjxYCud+BeX74Ycb4H/d4OWhsGu+ME/O29j2r9tC9pfWqMV7b7oniFn9+m0DTSHLjc8Me0cpb84tJ+PumQ0mPJpi6a4inrQ/7lkw/CoIiQruyWnDYMK9wjdKb+DHbo/wozxGeBMteMXzJlZ8DHuWBF0wqv9byz0ISaYaGm2BowXn7WRbCEa95GMO7K9Y3xhGvY7vrzuKIV3E364eJ5V27fohEPdP783zZw1U75/uVhT9dMMY/r5zgrr8mTP6c+lRmZw8MMXvdsItnmP618s8RZbmGFIrnDM8nSi3qnhrfoU6ZlN+TimRIRworfYZs7QkkCYz1kqX6FBOGZSC2aBTLSoUahwuzZBaA9CKQ4ctI7Ni1NafQ52G09E4fUgq+0qqfQ7o7a0cagskSaJLTCjr95e67x/iHToMWX+gjAXbCxvEy7YVf27MZcDDv7FsV+CBnKMFM5AanYNrxmcTZjYwMitGXRZqFoaYpw5OIa+8pt1jvGdvyOXZ3zerxQTlYrb+T+6+40VQwLwA6qB97gvLdom8V+h+LFy/BLpOhIHni9S/qEyhcPn4NDiwuv1euxlU1joIMzcMHTDodZwzPL3Zg0Z/nkPeJNo8EdhKwuIXS4JvXZNlmV2FVZTJoaIg9J8cOO7pFp9U0lJSubX2Csq7HCNUSL8/APmb4Pvr4J3J8OcjQW1HjH1kzhyaCojjcWuNtzU02huXS0aWQd9MQ+oQk56RWTG88fd24RGKx3OouZ6DM64ZzezbxmPAiVMb2gXksjGZnDyoYcGnb4qNVK8Cf6jJwAMn9PZRZXkzyn0OT48O5atle1njThRrblsZiInJ328dj0En8e3yfTjdLYpKASglKgS7UybPHXXfkih7gBfPHsQTp/VDkiQSbSIM48sle6ixO3G6ZOocWpS9hkD7FRzGKAe6+v46RzpT+iRi1Ev8sGq/uqyztPJM759MQYVIVOj4e9v5GOf2gNme799gtrX8tk5EUK/d1zBGVMHpkjG0pwGWxiGjX6qNtQ9NISHCIyN/4ayBDO0SxZjsWFwyLN5RxLZ2+v0B7CoUbal7isX/zgBtDJePzeKOKT2Yt6WATTnlzFx9gIIKz7kkJky0TB1oz8h7gOgsuOBbOOEFGHs7nP2JuO+sg59uFtWtsv3w9aVQ3jqT5pbgcslU1TnVIl99EiMsFFbWNavo52oiSELxFKx1OIkNEy0R+RXBn+er6pxEUEmcVApH3QTGlnvhgdJiITF003k4+58L/7wAr7tb7EJjYOFrUHagye3ItRVsMF/CteUvsWzqbkJclbz+1/ZW7ZuGRnujKD2UduHmMNxtlHzRu4sB77ak5u9H17gw9LhwaJ5D7U5cuJmdTx6v+gWd8PJ8wNMW2Fzbh9gwM0f3iOOn1QcaTBCmus2k95VUuV+jZdf/iTYL8W6fq4wYKz+tPsCdM1bz6pytavucllamAVpx6LAmRT2gaMohbyxGPRN7xvsmRHQStcZ5IzxeP52gltXpePCE3oCvQWRbUu42AGzMeFhTDh1ZjM6O5etrRtM7Wfi9XPDOYi7/YGm7vd5O9wy14l3WmEfCucPTMRl0vDN/O9d9upxPF+1WH+saJ1KzPlu8u8Hz2gW9ASbdDwl9IHUoTP4v7FsGz/UVvjlrZ8Arw6G6/ZMUb/liJd+uEMrTKvdFdZjZ/0W1kkjUnEkas7OSWGduwMcHpEbikmHJDs97zS/3bL+qzsGgh39j9gb/2yirsdNNcitn43sHvV+BiHUXCmsxsXPEfyGuF7gc7A4fxN7TZ4q2sneOgeUfNbodXVU+IVIdGbu+Imbu3cwKe5gdy39v9f5paLQnyvm8JeftC0dlABDmjlR3+QkIaA79k60kRlpb9FyN5qO0Z4PwHlI7Y1vw9fVLiWRfSTXVdU4ftakSbKC0gbXEkLo+/VNt6u2yGgfVSnFI8xzSQCsOHdZ0iRHKoaOyY5pY88gjMULI8pUqf2doKwPPbL1G+6CkybVXq0yhe3Z/R0Hg2GdnC1JPNDo/mbGeC/rdRVU+CShtyW5FOeT2LnM14nERZTWRFhXCwu2iBc1bJaSs/8/WwmZHtbcJA86BjLFQ5mWqWVMKLwyEgi0Bn9YWfLtiH7d8sQpATXwJNflXDvVIFEW/BduDT0G8rux5nj9wAbw0BD46BR5Lgi2eIsno7BhMeh1zN+VR5/6deBeH9hZXU1xl57GZG/xuv6zaQXed+3OL6xn0fjXGmxcMAWBfpR6uXUD+aTM4M/9STvh4D5z+LlQVwQ/Xw9vHQEmAFjiHOO7mx4+GuF7Y5DJedz1CzZa5YNcmuTQ6Jg6X+BtsyXnbFmrkxAHJlNf4Thy1VMmeGmHCZm2dElAjeLxb1E577V+vNu3mf3/JkULVs7e42mc8okz03/T5SuZvKXArh1p3jdg/NVK97XTJrNsv1OxaWpkGaMWhwxpJklh+/zG8feGwQ70rHY7s+DDKax30emAWNXan3+jgjooSad9OY8cjmhCTnshQY7t5Dikn4PUHfNvK7F5fpqYcOjIxe12UOV0yueXt0w68s15bmSJiC/SbS7RZVJWRd9G0zus3e0iKQzo9XPg9nPgy9D8bbl4Ll8wClwM+PAlK9x2U3VCKQ2Fmg1DI7FoAqz5XHx+QaiMrzsqXzfAEine5FT+FW2Hbn2Cvgk9OhzcnwMzbCJWrGZ4Zze8bclXT5rzyWrV1rapO/B8oaae8xk6iVIQs6UQ6XBvQJ0XMRO8vqQZJojRhJDnEUFxl58uqQcI7avxdkLtWtAD6K/bYxe9oV/YFcN1C/j76S8ySHcsnJwlTco1DRnWds93Oi52d1iiHQLSJHnBPVqrFhUDbWvYBfHauKIT7w+UAnf9CtUbbkxlr5ePLRqj3lWNwS34JShFod1GVj7WA1atl+Z3524XnUCsvEb0no35avV9tayyuqmvdhjUOC7Ti0GFOtNWkyQT9MKFnPAA1dhcbDpRR53TRWWxexnWPBSC/XLtQaw/SokJZvKPIp2DTFszbkq/ODnqbxH+/ch/d/vMLexUPGK04pAHsbYeCS2WtQ/UNUgo6TlfjHheJEZ5ZaO/BocPpaY3cf6gGjTo9DL4ATn0DItOgyyg47W2oLIBXRoi2s3Ykv7yWyloxGIiv2QkPR8N7U+Hbq+CDE2DvMiRJ4oT+ySzdVUxVkNHsuboEXEhwyzqR2nbOF+KB/cthydvwRCqXhS9kV2EVj/60lqm6xRhx8Jc77a7EfYGvtLHWp9bh4gTdAiTZJT7DNiAh3IxO8hzbvN/rqj0lYEsRCWfHPgp7F4sWwN0LYem78MpIWP0V1LnVbAbxm7MlduXmumtxGMNgzVfivbu0WZFDwVUfL2PkE7NVtbWGB9VzqIXn7YQIC3UOFyVVdv/H46oiyF0vbq/4GDbNhCfTPcmA3mjFoYPOmG6x/N8ZAwDY6z7+tWSuOcldHNpTVNXgGnBsN3Hdr4znWnuFmBrlOa8XeyXoWgN452kcWXSS4bCGRtuSGhXK3NuPBuCVOVsB0S/cGVBan3I0o/F24dqju7Ipt5y35+1o0+0qbR/juseRW1ajtg39vVmkQc1cLQxbNeXQkcvr5w/mxonZAOwpbnkbzWMz1/P2vIZGvqXV4iIwPTqUgoo6quocOF0ykhQ4GlfxzAFfLy6706XG7x7oSL52PabBdQvBHA7fXSfMqtuJX9flUOkugkRXex0vorMgbwN8fy04HWS5/ZkCJYfO3ZTHRwt3qfeNch17TVkeVU+PqXDtQkjsD9Ofh+iujNn6P07Q/ctZ+rm8bnqeV43PM2e9eK8llTVcr/8WW61/E2hXTTlZurY17zbodSRGeFRmFV6FKR/FybDL4NwvAQnenQI/3QL5G+Cby7HuE6auSnEo0WbhO9cYZk7+E8KTYeZt8HAU/HiTKABqHDT+3iwKjw98v85vkfP/ftuknsOONDzKoZYNqZJsop3oQGmNx3PI+xrgxxvhtVFCcSe7i6Mh0fDHg/DaGPj7GeG1VrpXqBe14tBBRym2KJM6LSoOuX8HtY6G1gIfXjqcUVkxQr3bBvVZi1HPa+cN5o9bx6sJZbFhJs4Znt7EMzWOBLTikMYRS0pUCHqdxB8b8gBPJHBHZ7w7UWtEVvQh3pPDk2n9khiRGc33K9u2LUVp3+mdFIFLRo0ljQsXg++57ll/l0vWPIeOUKb2TeLGSd0ItxhYsqOoxdt5a94OHvXjN6MMYjLcknLhTVNHZIgx4LaUYjRASZWdR35aT155DXVOF2EWAzFWE0s6WmE9OgtOegmKd8BbEwO3YLQQ5cL/320FFFUKlY7VVS4W3rQKblwBxz8L+RvhkRh6Va8AAhf8Ln5vCfd/t1a9b5Tt1En1/OXie8HV82DoJXD+17giUnjJ9DJPGt8G4Bj9cqZtfxSA0L3zuN34Fb+Z7mTpj2/w3WPnUFnu+QxCisRvI2/Ata37IOrRPTGcjTnic6hyK6riws0NPdy6T4ETX/TcH34lRKQSt0Isk5XikDvV76Zvt/LZ0C+gzyli/WXvw2/3t+m+azROz8RwAD5auItX52zzeUyWZV76cyvXfbq8zRW3nYHWKocS3UWBxTsKVWNgtVi/9Q/Y8KO4vXYG7Fsq/g6ungc6I+SugT8fhacy4Lk+sGu++KdxUEl12z0ox/iW2FRYjHpirCJ9sv4EoSRJJEeGCK9U2iaQZlq/JLLjw5jUKwEQacjaxKQGaMUhjSMYo17nI610NJIg1ZHom2Jjy2PTmNAj/lDvymFL94TwNjelVmYEleSJ0U/+ydOzNlJaLQaXK/eUIMuyWzmkHZqPVAx6HeO6xzF7Y26jqXbBUFZj97mvbK9bfBgAS3YWUVBRq6ZN+WNMdqzP/Xfm7+CM1xdgd8oY9Tq6xofx9+Z8/t3WwZQc2ZPhop9EvP2fj0IbtsMom9qeX8m1nyzHTB1p8+8WC0Pdn1fvE4UfEtD91/PoKu1T02YCoSgyTHItDskUeMXoLORLf6NcFseSatnEr7YzGVc9Gz4/j4krbxHbwc7QZXdysv1nzC8PgL+ehrpK9FXC06i828ktePeBGZUVw8accrbklquKquy4MP/pj1lHC4+oqU/BtKdFa6Ab2SgGWt4t8Y//eQBOfw9uWA5DL4VVn8KXF0Fl8EbfGi2nW0K4eru83nHF+9pp3pb8g7ZPHQWns57ap2QP/HQrFG6D2oomn98n2UbflAge/HE9Z7y+gEv1v9Bzw0vwoA0+Pk2sdNsmmHgfJA+CHscJVeF1i0TL6eALRUJgfB8IiYIR17TXW9UIQEK4GYNOarX/njImcfk59yfZLOSV1+Jwyq02pPbmgpFdkCSY1EsbU2gItBGIxhGNYu4M8NFlww/hnjQPY2cxSOqkJEeGUFptV81m2wLlZN8rMRyjXpzY520poKxavEa13UlZjWjz0ZRDRzbH9U2ioKKOJTtbrh4CUbzwvsh0uqsa/VNt9E+18ebf28kpq1XVa/7IiLUysWc8E3t6Lhx3FVZhd7gw6XW8deFQQk16vlq6N+A2Dhlpw0QhYfGbbgVRWdPPaQLvz3NLnhj4Xab/xbOCyStGevAFcPanyMZQfjL9h6Kdq9if13DwfIn+F143PsfeQrE9I3U4dI0UhwCLNYKhta+RVfMxA2vfZE32NSx19YCNP2Fw1fKt8yjG1b3APGdffnEOwynpYc5j8Hgy2eteAkCyxrX0Y/DL6UNSMel1fLJoN2XuFsYeieEUVdb5N8fuMgpGXi2mwTPGsOb8Vdxjv4zasIatDdkJYWK9mK5w7GPie930s2i50Xxw2p06h+f7s9cbuHr7j73859aDtk8dBTWtTC9BXRXM+x8sfQdeGgzP9xWKH0W9WFMGuxeBw2MLYDLoeO9icf1ZVefkAeNHdN/4qucFhl8F4Ykw7g64ci70P1Msj0wTLacnviRaaa/9F+7aCdOePAjvWsMbg15HUqRFDXoI1KbdFKcPEa3E/nz8Em0WnC6Z/IraNlEOKYzMimHjI1MZ261tzwcanRdthKlxRJMR47mQH9pFa9PSECiRot7R3a1FuZ5Oiw5l7UNTOHVwCgUVtT6DpuW7ijXPIQ2O7hGHToJ/t7VOFXHyK//w9K+b1PveqTrXHp3NrsIqVu0paVQ5BPDuxcN45vT+PsvKauwY9BK2ECMnDUxh1tocNamlQzHtKeHTs38F/H4/OFtX8HXJMjpcxIaZ3Z+nzCWGWbgS+oqBW/2r9p7HI123GJek56aNF5D8araIqH86Cz49G9b/wF2Gz5mqX0LGewNgwauY5Vrs9dvK/PDiBaN46dyhPHnWMBLiYji97gGKTv2CHSF9+cZ6Ft279+QC+71cY7+F53t/BePuBJ2R8PJt2GU9kjWmVZ9FfWLCzAzLjGLFnhJK3CanSjtSMElXdpONz5yTkLyOf29fOBRbiJE8b489UyhMfw4mPQAbf4LXx0DFkadYOZjUOlwMSLUxOD2SbXm+ahjv5MJ1+8uOONNqn7SymbeKtkcQCp/qYuEV9NoYKNoBr46Ed4+Fn2/32UZcuJkdTxxHuNnLIP7mNXDrBjju6YP0TjRaQ0pkCHuK3IbULdzGWcMCe/6o3lQl1W2oGxKYtQh7DS+04pDGEU2XGI9ySDFl09BIdqdG7Cup8TFWbQ2KakOSxIk4NTKE3LIaKusc9EwMR6+TWLSjCKeroRmhxpGF1WwgIcLCvlaYUivMWO5R9KiDGEliQs84NRGnqeIQiIH/5WMyOXVwCiBUSaFGYXw6MiuaaruTnQWHINK+KfRG4dMz4moxaPvqolYpTZyyzCzTXXws3U8cJSRTSJxUim7oJaLlwx+RaTwechsrXV3F/cKtUFWIvHMefHkBFkkUUvbUhMCv95Dh2oNdZ2lyX6b0SeT4/kmcMiiVlEgLILEzcgQPxz9HibUrF4zsoq770bJ8Ko66C25dz8aul3KF/VYMhsBeUy0lPTqUvUVVlFTbsZr0qjo3mDZdpajg7dcxuXcC549MJ8fLxF9l5HWida9gM8y6q+3ehEYD6hwuTAYdPZMiWLSjiN/X56rfl/K9ZMVZqXW42JRbfih39aDj4zm0e4FYeMt6OOczOG+G+J2W7oYXB0LZPrDYYPmH8NJQkcDnRpIkLh2RCMDCrBsgMh0ikg/229FoIalRoWroQ0uVPSaDjotGdfE5diso45Vlu4tbrEzS0AgGbTSscUSjGLFBy2WgGocfygzNJwt30fe/v7ZJkp1yIa13/85SokJwyaJFxxZipEtMKLsKK3E4NeWQhpiF3FfS/GJLfZ+iLl6ts94z3GaDnvhw8TuPDW+8hUnhvum9OWNIGiCSy6zuWe6uccLD6PMlu3l1bgdtK5n6BEy8XyhNfrxJJPu0ANkl0123j5729XxueoR/LTeKBwIVhtyMnnY+J9c9wlE1LzCg5k2Or32Mh7p/o5osv+44gal1T/GHeTLfG6fxW/S5zdqvxAhR0N6cU87OwioiQ40M6RKlPl5R6+CKD5ZCWDwre9zCXNcg0QbTxqRGhVJYWceB0mpsIUbVbDenrOlCp/LTrW/mmhwZgtMlqyb+KjqdaN0bd4do3fn6UpHWpNHm1DpcmA16+ibbALjiw6Ws3SfaNJXiyJQ+orChhCscKfiklTkdMOAcsIkiOt0mw5TH4LR3QHKrMy79TXii2avh1/8I02l3m9mVo5IA6JeZdNDfh0brGJzuOd62xJBa4aGT+vLIyX0bLM+OD2d4RjSyjN/EQA2NtkIrDmkc0WTGWvnyqlE8eELvQ70rGh2IhAgLOgl+Wy+MW9vCZFPxKlEuGvq4L7IPlNZgMujoEh3KrsIqnFpbmQZiQLyvBRHxSlqQooTMr/AMqH3aH4AodzJKMMohBaVwCkLhpOwrwHv/7OTpWZsaKjw6ApIEY26B9FGw/AN4z93y0UycdeI72RM5gq46d3R38iBI6Nfo847vn8RnV4xkH3GUEsY6OZP3lxZQfsJbXBr5Hi84TsGOgSdMN/C8+WryLJnN2q/U6BB0Etz9zRp2FFRiCzESGSq+32l9EzEbdGzLF+1AdlXp0PaXgIqh6s9rcrCFmtS0u/0lTSuHPMdI3+Upkco2Avw9jL0Nxt4uCkQrPm7hnms0Rq3Dicmg4/Qhqdw4qRsA7/6zg2+W76XOIf7eM2OthFsMgb+nTkJ5jd2/R1YAVOWQBFTmQ30vL0mCfqfD3bvgnM8hviecPwMunSUSxz4+Db64AGQZqySO11ZrRFu9HY2DxCmDUtTb7XUFd8fUHoCnkK6h0R5oxSGNI57hmdFcfFTzLsQ1Dm+Mep0a9w1QWFHX6m06682K902xMTJL+FyZ9Dq6xFhZf6CMpbuKteKQBilRIRwoqWl2Ypni/3H7sT24alwWB0pr1EG30tqoU4pDoaKtyNZIlH19kiNDCLeIolCYuzgUFWrEZPBcThworeGfrQXqoLHDoNOLBLMTX4aS3fDt1VAVpOm30wE5a5HrRIFlT/zRvGS6nJfDbhReQ4am1VfehTWFx3/eyH5iGdQ1hWuO7sq2/Ep2FFSSX18l0wQRFiPT+3taUCLd3+3ah6bw4jmDuPbobPLdHmdK8c7YDsohRUUGYDXpCTGJeOateU2nNqk/9QDFoYDFUp1eJDmlj4I//gsbZ7Zk1zUaoc7hwmzQYTLouGa8aI/8dsU+bv1ylVocMeolMmKsLNtV3Kl9h/o9+BvHPvd30Os73YbUJmclOGshLEDqkzkcekzz3I9Mg2sXiGLSll9FFP3bk8VjxhD/29DosISY9KrHWns1IgxKi2yfDWtoeKEVhzQ0NDT8cNIAzyzQ9yv3tTpWXPXT8DrqHtdPSMfLauycMTRVXa55DmmkRIbgcMnklTetuPDG7i7ImAw6MmOt1DlcbM4THiAub28MYESmMCSOCg2urUzZrjJDqhSZJEki0qvA9MeGXM57exEP/biuWft+UNAbRCvSsY/C5l/h6cyGceibZsG2P8VtWYa9y0Ti2etHoV/3LQB2g5WkKbeQcWzwsdFp0aFcMLILN07MpktMKCcPTOanVfupc7qIsprI9iqsrNtf2uy3dv3EbPV2iFG0sISZDRj1OrLirMgy9Lx/FuU1oiWhPYrQyuAIYPU+8R4m9ozn2xX7+HVdTqPPlWnoOQTeHnCNKFIkSaQ2hSfBlxdCzlpY+w2s/AxK97XkrWh4Uev2HALUgp+C0uJi1Os4b0Q66/aX8feWgkOyn23F7qIqKmodQakglbS2kDr3ccTajEjwyDS4dSMc8wgkDQDJfYEQltD48zQ6JF3jxTG8qq592lsNWlKxxkFA+5VpaGho+EEx3gUoq3Hw77bWXey6/JitKjPi+eW19Em2cf2EbL/P1TjySHG353ibUtudLv7a3HiLo6IcMul1jO4aC8DK3SWAp/1B8b26fmI2n10xkuGZzUtqvHBUBunRoYzv7mmfmNo3Ub29Pb8SgAXbfYsu+eW15JY1r9jVboy+Hk55Xdx+eSj884Lnsc/Ogo9OgS2/i39vT4Rf7wEgZLb4X9abOX1Iqo9apyn0OolHTu7Lrcf24K87JtA/NZLyWgfb8ysx6iSSIj3Kou+uO6rZb6l7QjjH9BaDyup6bTHDMjzf8aYcUSw0tsNAw6DXsfnRae7XFB4cJ7uLiVd9tIwtjZgVywE8h6xmA2FmA0/P2sScjXmBXzy2G1w8EyyR4vv7+hL47moRKb7qc3C0XgF6pKIohxScXsqgl2YLnzGDTsepg1MxGXT8s7VzF4cA+v73V279cpXfx5bvLmbWWtFWqkwcWWrd79ka27wX0hvgqBuFgfVtG4WZdcaYFu+3xqFDUU4qLbwaGp0RrTikoaGh4Yc0LyNfgJ0Fla3anjIB6T3wUWbEC9xta4pfR2GlNog50kn1o5Z45tdNXPTuYlbsDuyVY3coLR46kiItSJKYBf944S7Vj0hR/Oh1EqO6Nj/OPDs+jL/vnKAa0ALcd3xvPrtiJAadxA7330p+mW9r1LDH/mDE47Ob/Xre/LutIKhY9KAYcDac8wU46+D3B4Sh8deXeh7/5HT49AzP/XF3UNPr1u31SQAAOXNJREFUNIrlMEojWl/I9T7GSJJEnyThQ2Yy6MjyUhE1h8m9hGqhqta3OJRos/DbLeMAmLlGDGrbS6FoMuj49+6JvH7+EMC3MHXNJ8upDjCr7imgN3zsgenCF/CS95fw9bK9gT1hQqPhlDfAJVKDSOwP4Ynw7VXwwfTg2wg1fPBWDgFU1HgMcWe5FWFGvYTJoHOb6Xde3yFvD7YfVu33u86pr/7L1R8vBzxFd0u1WxlnS2vdDthS2q8vSaNdOWd4Gkk2C6cMSm16ZQ2NDopWHNLQ0NBoAqNeYm8rL3ZdftrKFHVIRa240E6NEoPFYMxbNQ5vlN/G3uJqquuc3PblKt78eztAo340inLIaNBh1OtItoXw6txt3PfdWt6etwNon6KAyaBjVNcYUqNC2OJuYyuvbTpRpcbu5Otle9WWt8aQZZlz31rE9JfmtXp/VXpMhds2QdYEYWi8doZYfumvkOCVGHPyazDhP5Qd9yqDat+kLLxbq196fPc4eiSINqx/thZgCzXy7JkD+OmGlqsGju2dSJ/kCK6d0LXBY93iw8iI8RSk2tPbTHhTiVZDk0FHdnwYkaFGtuZV+KjfdhRUcuWHSymtsqueQ/6SQ88c5hlw3/7VKj5euCvwi3ebLL7TSQ/Ahd/DNQtg6pOwZ5GIE98+ty3e4hFFnTutTOHCURkN1lGUaGaDjpmrD7Bid+f0HmrO8bG6zqkqh0LKxPFZTSrTOOJIsoWw4J5JZMe3rLgfDO9dPIzr/BzfNTTaCq04pKGhodEEyZEhPu09LUH201YW4R48Ka0ginKooKJ5ZrQahx+hJgNJNgtLdhYx4KHfmLHcE73eWBqQYgJtcg/Uju3j8a5QBuW6diwKpEWHkuulGHr/nx1szSv3Kf5UehWNXpu7jdu/WtWkHw14WqUK2sAg3gdzGFz4nSgInfqWaE1KHwkX/gCX/Q537oCB54Ikqa1P+jaY2TcZdPx0oygEKe1ppw5OpXtCeGNPa5Qoq4mZN44lO77hNiRJYqhbxRMfbvZbhGkv/rh1PH/dMQGAPUVV6vJvl+/lt/W5vDp3a6PKofqU1zRReDSYRYpZaDSYQmHkNXDJLOEH8911UHtktX0UV9ZxzcfLKGqhKlVJK1P4z/G9WPfQFPokR3itI449G91ti6e8+i+Z9/zcir0+NDhl2ed9Xffpcp/frDd7iqtwuGSypP0krH0Luk4Ek9XvuhoabcGEnvHcMaXnod4NjcMYw6HeAQ0NDY2Oym+3jGP9/jK+XLqn1TJ5f21lACsfOIYQk5iR9fYc0dA4rl8S78zf0WD5/kbaqpTWMZNB/M5OHZTKe//s9FmnLQobgajfjvngj+uJCjX6JJftKa6iZ6IYfCmG28H8fRVX2dXbsiy3fXEjfaT4p2CNEf+8cAaIW28pRr2OTY9OVYt57U39gvTBxBZixBZiZLfXQDs2XLTw/LouhxHu9Mb6x0iFxAgLOW7PqgOlLTgedxklTKvfmypaBodfKXyJAIZcLEzKzS0vzHVkPl+yh1/W5pAWHcq9x/Vq1nNdLhm7U/bxHNLrJKxmA+9fMpzdRVX8tSmPsd2E184xvRP4fX2uum5FrUNNNuwMyLLMwLRIPr5sBIMe+Z2Zqw+waHshKZEhPH36ABK9Ugc/W7yb4RnRPGp4F5fegu6kVw7hnmtoaGi0Hk05pKGhoRGA7gnhnDwoRXgotFI5FGhWPDLUpMr1lf97tEI5oHH4cPJA/+0Jjf0W7WpMuTi9e8+AK7RnO5G/qN3iKjuVXj4zq/eW8uAP66iuc6qpLsH4CBV7qR72FB0aTxNPe2jbfYZmg/6gqXhOHJiMUS9x6ZjMg/J69YkPN/ORV0uY4j+0s7BK/S0EKg59c+1o3rxgCIPSI9nb0uOxUiDKXecpDAEsex/+eMjjit0Jqah1sLuwocJFlmU1Uay4Bcoh1eTe0HDIEBduZkiXKG49tgcWd0Lec2cN5MNLh6vrtNavD8DhdPHi7C0HxcvIJYvfYJTVxIBU4QNWUFHHqr2lPPHLBh9T9Pf+2cmqvaV00+2loutxEBG8Qb2GhoZGR0QrDmloaGg0QUpUCHnltdQ6Wh5P6pJlJMm/n4Y3i+6dxNfXjGrx62gcPnRP9PUtyIq1MrZbbKP+V/XbyvwVMdqzOHT6EI8R58vnDmrwuEmv486vV/P+vzv5ZNEudroHs8EphzwDW2/1SU5pDae/9i95ByEJzRVAAdhZGJgWyZbHjlNTdQ42YRahIFGKGN6RzzvcKXeBPtrkyBCO7ZNIWlQoe4r9t/kExeAL4ayPIW0kXPAt3LMPhl0BS96CR+Lg68ugOrDpe0fl1i9WMu6ZOWohSOHnNTm89KdIFCuqrOPpWRubVbCptYsfvbfnUGOEmQ2M6x7HR5eJAtEOr9dyuWSfttJg+XtLPs/+vpm7vl7d7Oc2F6dLVidxbKEmn8dySmsaFMm25lUQQTWy2dbu+6ahoaHR3mjFIQ0NDY0myIwVHgI7WjED6pLloNp5EiIsqpGrxpGN92Dspknd+OzKkSTbGlexeRtSB6I9i0OSJDHanYA2OD2K9Q9PUR975vT+ausJiIGWMkj9ZW0Or83d1qiBrXdbmXdx6JNFu1i6q7hxk+JWsrOgktu+XEWNu0B8kLrADjvuOLYHANsLhOfPphxPtL0S/9zUYTI1KoQDJTU4nK7GV2yMrPFw2a/CI8YcJgyrT3oVso6GtV/DKyM7TbLZnI15THnubzbnis/yud83+zzu7VE2e2Mer87dxsmv/hP09mud4jdvbuSYQmWB+OfFsIxoTHodS3Z6PsfX/tpGn//+SqnX33IwFJSLwvD8rQUtKi41B5csq0X1M4b4pk5tzClnr7sw+fXVozDqJeZt2INZsoO5oUpTQ0NDo7OhXd5oaGhoNMFAd6vM/C3i4rfO4Wr2wESRqmtotIRp/RJJiLCQEhVCQUVtwChvu1MUV7w9bEZmRfus057FIYDXLxjCC2cPJDkyhFCTge+uO4rnzxrIGUPTOHmQp1Xu8yV7KK22k+Uuvj41ayPbGynAlngph7yVI+FuNcr7/+5s43fi4dGZG5ixfC9/bXKbemt/yy2iT4pNTbPamlehxqADbHUXh5r6bNOiQ3G4ZA4E0YoYNHoDDDoPzv8aLvoJqgrghYHwzhQo3dd2r9PGyLLMJe8vYVNuOSEm8Xfw1rwdrN1Xqq7j8lNwLWlGcUZVDukDFG5lGV4ZDs90hVdHQ6kwz7cY9Uzrl8i3K/apaqYfVopoeKW4G+g4Vh+7y3O+XbyzfYt2Lpes/gZPGJDM5ken8eVVo7hoVBdAHLcA+qbYuObobMJxF98sWnFIQ0Oj86MVhzQ0NDSaoEuMlQGpNt7/dyd2p4sxT/3JeW8vatY2XC65yRlxDY36vHTOIE4fkqr6UKVEikS7QANjta3Ma5b//UuGs+jeScRYRYtEexc2IixGTvLySxqYFqkWhY7uEacur3ArAB45uS+9ksTAyltJUp/iSjGgzY4PY8kOzwBRCUIrq3GQX94+SX+2EKHmy3W3rmnFoZZhCzFyzvB0vl2xj405ZeryJJuFrXnBFYd6u38rV3y4lLKa5ilQgiJzLBz3DDiqYc9CeOdYmHkbfHIG/PmYaDmrLhHr2quhZA8cWNX2+xEEtV5G75tyyohzG3yv2O1pi1OS3dKiQ3yeG2xhptbh4kr9j5wxcwC8dhTsWgAlu0VRyOUSCquqQrFy3jp4aSh8cyUsfJ3L0nIpr3GwcLt4PCJEFLB2FlYyY9leet4/i9V7S5rcB6dX2uGSHUX8tHp/oyrD1iAmcjz3TQYdwzOj+e8JfQgzG9ieX4nVpMdi1HPjxGzCJVHokrTikIaGxmGAVhzS0NDQCILzRnRhb3E1N362grzyWhbtKPKJ524Klyy3u2JD4/DjhAHJ/O+MAapXVdd44RUzb0u+3/XrG1KDmMFPiLAwKD0KoHXtOK3EX8vkiMxovr12NADXfrLcpw3Gm+KqOsItBs4cmsrSXcVqG423F1F7GdbGhovCWo5WHGo1V43PwiXLfOFWYIzIjCYz1kqNW6HS1GGyb4qNcd3j2JhTzoJthe2zk0Mvhbt2wrlfgcUGS96GLb/B30/DUxnwVBd40AaPJcLzfeGNcbDkHagNXNxsD7w9m1wyxFhNhJkNbMv3KPDKauzYQoy8fM5gn+f+vOZAUK9R53AxVb9E3MldK9Lenu8Hr4+Fh6PgmSzx2OnvwZVzISQKVn8Bs+6i/+9ncYH+N/YUiM8l0VjDsbol3PDZctbsKwVk7p6xpsl9cLgVkUa9xKtzt3H9pyv4e0tBE89qGd5tZd7odBK9kkSRPspdaDfoddw0RiT/hYRHN3iOhoaGRmdDKw5paGhoBMEZQ1PJirOyYLtnMLKjMHgPIq2tTKMtGJBqY3hGNC/9udUnHl5B9RzSN/ytPX/2QP53xgC6dbA0PINepyYdATz4w7oG62w4UMa6/aVEhZo4dbDwAVHisksqPeoRpeDQ1ih/u3vcfk+a51DLSbKF0Ds5Qi3s3DS5G1lxVvXxpkz79TqJN84fgiSJ30W7YbJC92Phyjlw4fdwxZ9wzCOi+OGPmbfCc31g7pO+RSJnO6ib3NQ3n96YU07XOKuqwhLrOLGa9AxIi2THE8fxz90TSY0K4dYvV7Epp9ynBc0ftQ4nBpwUJRwFV/8DR98LKUMgt15RJ7YbJA+CK2bD8Cvh6HuRLZE8Ynyf9HWvAnBm0eu8aXqOP0x3cMrOh9hivpDrCx/B6WjcRyimeAVLzVfzZsirhCGUOuv3t89375LlgOfqPsnCdDo+BKgRn9vJPUXB3hwW4HehoaGh0YnQLm80NDQ0gkCSJCb2iPfxavje7Z8QDE6trUyjDZAkiUvHZJBfXsvKPSUNHvfXVqYQZjb4pIkdKm4/tjsDUm1EW01+H5+zKY/KWgczVx9gw4Ey7E4X016Yx5KdxUSFGokNMxMfblYN4kuq6+iREM7lYzL5bPFuv3HerUWJXN+Wp5gma3/MrSEzNgyHW3lpNugZkRmjPhaMwDLEpCczxsrGAwdBqWMwC6PqlCFw1I1CUfRgKdy8Bu7YJm7fuQMyxoqCwdwn4Nk+wtR69VfwRBr88ZBow2pjFOXQ5F7xjMyK5sVzBtEtIZyNOWVq21Wdw4VZD2z5A8nlJCUyhJsmdQNgyvN/M/2l+aoK9ssle/h+pa/HUp3DRZqUT21EF0jsC0ffBed/A5f+Jj6DaxfC3bshsZ94QkSyaMs7+i6ku3byd8hExh14Dz4/j7GVvwKQrdvPgKJfMUpOjtMtonz20+K59mrVs8ib6NL1xEplTHDMY5n5amabbkO/c24bf5qCU6S5DMr7Fha+7rsvLqdaxHyg7L/wZDr89TTsWSwe1wypNTQ0DgMMh3oHNDQ0NDoL/VI9UbW2ECNfLNnN1eOzCDU1fSiVtbYyjTZCaQ/bmFPG8EzfVgalrczUgaUt10/sxvUTu1FabfdRP/12yzienrWJPzbk0ue/YhCZGGHhnuN6qutEuqOlM2Ot7CioZP6WAn5dl8vwzGimD0jm7fk72JBTRnpMaJvus6LQUHySlDYXjZaRFuXxvzEbdJwwIJk7vl5Fjd0VdOGtV1IEa/c3rnppVyLTPbdDo+Hin4QX0c93wP7lkL8BvrlcPD7/WTiwUqSixXZvOpINodjZX1KjpmX6o7LWQYZ0gOsyHAxKtsKyO4inH99X9CKnrIYkWwim2iI+rLkBPtkHobFQVcDp055mbt+hzFwrWssOlNWQEhnCXTNWIqPz8QxzVJcSJVWwNyLN88IhkZA+ounPSJL4O/suytbYmb59LgBPRPyHP/Ijudkwg5nOkUzXL2D6gqcgb6koxhRsgptWQ1QXdTOmuhJcssSC5As56sAHdJUO0GXnHfDaezDyahhwLuja5pj3tOEN2I74N+susTCxP+SsZkKvKxiri2GgY7VYPucxzxM1zyENDY3DAK04pKGhoREkk3slqLfvmtqTe79dw6LtRUzoGd/kc7W2Mo22Ij7cTKhJrypnvFGKLcYOXBxSUEyeFbonhHPF2Ez+2JCrLsspq+Gmz1eq96NCxXMyY638tj6X899ZpC7v5vZjmrFsL1P6JLbpviqmvgo7m9FSqtGQ1ChP8U6JSO+TbGPZruKgo8p7JYUzc80BKmodhJk7yOVsSCSc9pa4XZEPS94ShtWh0bDgZZHqFd8HzvxAtGE1whtffI9xwzecc8crRNpsfteprapkrvk2mONZNpKfeMo4hl0FR5FkC2FE6c+kudxqoCrh0yP9cievhETxXJSRvZU6du/8mpSMSL40PUwEVdjLhmKMEOc7nVs947Sl0xIS4uK5vuYaxt40inteeg9bl2Ooqs3nhtIbAZjtGkxSn3EM2fiM50lvTYDxd4lCmk6Pua6YUqzs6ncjX+8KZZecwAOmTxiYuwa+v078O+N96HMKbJoF676BE14Ao68Jd1FFLc99PIPrzj6FxEjfx0BM4pTIYcihMUQnZYpiVeFWyBHFoLQNb/GRIni89FeQXbB3iTDkjjj0qkwNDQ2N1tJBzqYaGhoaHR+r2cCq/x7Lmr2ldE8M495vPZG8TeGU5aDaJTQ0mkKSJDJirD7FocU7iliys0htJfHXVtYZGJYRTWSoMWDUtiLYyYi1UlTpMaJ2usTfJ8Bv63ORZbnNWr9KqurIK69ldNcYXjpnEHd8vZozOkB7XmfGOzlL+a2+cu5g3pm/XU2ua4qeiWK9hdsKmdw7oYm1DwFhcTDhXnHb5RItU4VbYdc/8PJQ6Hs6nPqWf8XLik+4ccu1YIB9v8QRefZzfl9Cl7fWcydjLIy8htJdKzl1wdPs/vkiuOB10ms2USrZsF0/B2pKoKpYFIk2zkR2OOiy+Rcyvj8aZAfD3LvieGEgnPcZJTtWMGreg2JhC4tDadGiELi7Us/vtb24LNTEn7cdzbhn5nB8vyQ+XbybXyNOY8hJGZTt28Tu6NH0Xf9/8Mud6ja6G2xslhNIibVxr2ssXeOsnJzfHR0uXum2nGl7noWvLhb/FIq2w4BzYNhl6qLFs7/hkZxrmPXlGqZe+USDfXXJEEIt62LGEX3hy+J7K9gMcT3AWYdz/U/ov7mUTXFT6JE+Ujypy+gWfS4aGhoaHRGtOKShoaHRDGwhRsZ0i0WWZSxGXdDFIbkRk0sNjeaSGWtlnbulJreshjPfWADAmOxYAAydtBKp00lYDHrAzulDUvl6mVAtPH5KP+79dg3x7qju9GjftjEl0vzUQSl8s2If+RW1xIdbWr0/87bkc8E7wlPklEEpxISZeffiYa3e7pHO0C6edkizQZiRJ9os/Of43kFvY3hWNGnRIdzw2Qr+vXuimiDVIdHpYPqz4nbBVlj4Kix9RyShTXpAKI68WfkJAGtdGfTd+C58VwfHPCwUMWnDYeFrYAqj/+pvANh58TIyMrIBsPU4jndWFXJ24afIrwxnhL2KFeZhDIrp6vsaA87GJMtc8dD/cVfY73QrX8hSV3dec5zAw9avSfnoVCJl4Wm0yZWKOa4nLaGLu8XzvX92YHfKRIYaCTHpWfKfyQDM31rAzoJKOO58jvr+V8prHOx4+Aekrb/D9rmw4UfMVcW8Ll/J0+mRTO2TyLF9Erj1y1W40PGe/Vim3XEd/Hw7rPsWgMW2qQzP+Qv23gp/PwNRGdB9CsN2fQ/A5P1vwh8WGHwhRGeq++p0OLBIdhz6EM/3Fu9+3wYz+v6nQVQ6PRKC/51qaGhodCa04pCGhoZGC5AkifToUHFRGwQul9ZWptF2ZMSGMmtdDnani2d+3aQun7+1AJNe16kNk11u9dMlR2Xw67ocymscTOubSLTVxNAM4bdUvzhUYxeD2NOHpPLNin0s2VHM8f2TWr0vm3M9qU/xEeZWb09DEGLSM7ZbLPO2FGA165t+gh8iLEaePLU/5729iHX7yxjTLbaN97KdiM2G4/8PXHZY+i4U74DzZohCRGUhFG4BSyRbpQxOq3uQDzJ+Y+TKj2Hlxw02pZQ/LVFeSjZJwjzuJs79vgufx83AXrCDBWGTGeRnVyRJIi92JNfUDWNvTSE1iN/4rooEfgu9H53TyRm1D7BE7snGuBg/W2ianonhnDM8nc8W7wYatpN2iQ5VJ1mU9s2iOj0xvU+C3ifB9Od4/IfVzF92gAiLkdcvGIIsy5RV23nwx/Ws3FtCmd5GxOnvwegbOfuV2SzM7c2qe9/B9s8jUH4ANs6E3QuIQRS6Ki0JDP7nBVj+oSja9ToRJAlXnfh7dxga8SxL04rDGhoahy+dU3euoaGh0QHom2Ljr8357AlCPbS7qEozpNZoM3omRuB0yTw2c4OaNKSgxNl3Vl4+dzCTesbTPSGcr64exbVHdyUy1MjUvonEhrmVQ/UMp586rT8AI7JiSI0K4YMFO9tkXxIjLH5va7Sedy8exi83jSXcYmx65QB0TwgHYFPuQUgta0skCU58CU54Hrb9CQ9HwcOx8EwWvDsFNs2kzilTi4kPwi6HwRd5nqszgN4MNyynJLQLnzomEGLyLbCN6xbHKjmbGUM+4qzIT1keMTngrtw8uRvb8yvUwtCr5w1mq5zKR+PmMrrmRZbIPXnjgiFYjC0r4kmSxBOn9lM9uCPrFYe6xoexPb+SUq9W0vqKXIes8zl/SpLExUdl8uP1Y6hzuPh2+T7xmaYMZqW+LwCbi+pg2lNw5odwzb9w0Y/UGG285jiRmwz3wXWLwBoLX14I/+sG31yF/i/RaubQt62hvYaGhkZnQSsOaWhoaLSQO6b0QCdJvDJna5PrrttfysC0yPbfKY0jguP6JXHigGQ+XLCT3PKaQ707bcrwzGjeuXgYRr2OnokR3Dm1ZwMlVITFSM9EURg4pneC6lOj10lcNCqDxTuK2JhT1qb7laAVh9oUo14XtL9QIGLDTERbTWzpbMUhhcEXwXH/g9AYoSRy84NzFO85pwDuQsn05+DMj+CiH+HeA3DvPuToLN4b8Dn3Oi4ntF5xKC06hGiriZV7SqhzOFXTb39M7JlAttvM/T/H9WJa30TCzQbW5NnZTyxXjc9qE4P3S0aL9q3kekbQJw5Ips7p4tsVe9UCUoPikEvGqG84udIv1UbPxHD++8M6fl4jkteUAvJm799EXA/IHMdbI/7gO9cYCsrrkGOy4cq/4KibICIFVn+OcckbAJSGZbX6/WpoaGh0RrTikIaGhkYLSbKFcM7wNL5etpeCitqA69mdLspqHOost4ZGa9HrJI7uEYdLhn+3FdIvxcbnV4481Lt1ULl5skh7WrqzyGf5tH5iILtkZ3GrX8Ph8qiwtOJQx0OSJLrFh/kWAjoTkgTDr4Dbt8LFM+HOHXDvAW6038BXzqMJMxtEoUSnh94nQuY45u8o49x3lzHthXm8MGcHJoO+QTqhJEkMSosUxSGny29hxZsuMVYAQs16JEkiM87Kuv2iuJriJ9WrJdx3fC9+v2UcA+pNkvRNsTEgLZLX/tqGu6O0gRrX4ZQDKm8Vw+v7vxPm3IrB+RavllCFCnf7abXdKQztjRbh5XTVX3DjSqpOfocTax8hL2pIi9+nhoaGRmdGKw5paGhotIJj+yTicMlszgk8OCmuEqlK0daWt09oaNRHGdDJMozqGnPEKdOm9ElkUs947pnWy2d5SmQIERYDGw+0Xjlkd3pa9hQzbI2ORY/EcLbkVqhJfQebLbnl/LutoHUb0ekgY4yIvDeFcs5wkQx246RsymscPsl8D3y/ln+3FbLRfc4x6f1fyg9Mi2RrXgV7iqqbNOs+a2gaep3EgNRIALJirWxw//20tJ2sPjqdRLcAEyT3TutJbplngsWfcsjgL9UNSIsSxaGyGjuyLLO/pBqA9//dyUf12ksr3J5GgE/aIwDRmdR1P5HVclfNH1BDQ+OIRSsOaWhoaLSCjFgxQN9RGNiYurhStAt06DQdjU5HhpfvTlp0aJsN4joLkiTxzsXDOHNYWoPlPZMi1MFta7A7NeVQR6dbQjjltQ72lx6a9spjnvubc99a1ObbjQ0zMyJTmEDPXL2fkqo6vl2xl5QoXyVPRa3D39MZmuFJhAszN54/M7l3Alsfm0bfFBsAmbFh6mMhB+G40i/Vpt426CR+X5+rFnkAKmsdWIz+hyx3TOkBiCJRUWUdNXYXwzPFe7//+3U+61bWOjDphX/Rhwt2NSgoKvZtmj+ghobGkYpWHNLQ0NBoBUkRFkwGXaOpZcqsb7RWHNJoQ7x/T/XTu450eiWGsymnvIFZdzB8t2Ifs9YK/xKHuzi05D+T1XYVjY7FcHcR5Be358yhoqrOf5GmJbhcMgadxIC0SPqn2vhk0W4em7mBW75YxaLtRU1vABiZFc0nl4/g+gnZXHJUZpPre/t6ZcZZ1dsHozgUavIUrz66bASVdU7emred3LIa/t6cz96SKrV9rD4hJj3XTejKrqIq7vx6NQCXHpXJ5F7xAD6qq4paB13jw7hpUjd+WLWf71bu89mW03280GpDGhoaRyralY6GhoZGK9DpJLpEh7KzMHBimVYc0mgPvAdzaW41wd93TGDWzWMP1S51GPqnRlJZ52T57ub7Dt38xUqu/ng54Gkra8qzRePQ0SMxnMHpkXyyaPchay2Dhq1QrcHh8njsTOmTyMacclUhpKQRzrhmdKPbkCSJo7JjuX1Kj2afe7xVifWT0NqbkVnRDEyL5MdVBxjx+GwufHcxuwqrGhhZe3PigBScLpnZG/MASI0K4bwRXQC45P0lfLhgJw6ni4paB2FmPddNyKZnYjgfLdjlsx3l91PfAF9DQ0PjSCGo4pAkSVMlSdokSdJWSZLu9vO4WZKkL9yPL5IkKcO9/BhJkpZJkrTG/f/ENt5/DQ0NjUNORqyVrXkVPPvbJhZuL2zweJHiORSqFYc02pbLxghFgNJqkh4TSs/E1iVAHQ5M65eILcTIO/N3tHgbsiyrhtSGAL4uGh2D6f2T2VFQSX4jwQDtQaHX6019fh5zN+U1WGdrXrlPe2IwuGRPcahXkvDpqapzqo/3dBfE2ouucWGEWwxYTXq6xBwcVeLjp/TjugldkSSJ9OhQn5CH8hoHtpDAnn09En29jFIiQ+juXrZqTwkPfL+O6S/NZ+H2IsLMBvQ6iTHZsazbX+bz3WhtZRoaGkc6TV7tSJKkB14BpgG9gXMkSepdb7XLgGJZlrOB54Cn3MsLgBNkWe4HXAR81FY7rqGhodFR6JtsY0dBJS/+uZWz31zInI2+A4Rit3JI8xzSaGvuO74Xmx6ditlwZPkNNUWoycApg1KYvTGv2QNzhZyyGk051EnoliA8crblBW7vbWvW7y9jyKN/+Cy7/IOlPvdLq+xMfvZv7pqxulnb9lYOKcXeVXtL1MeLq+pUdUuyre29sKxmAyvuP4a1D00hNergFIfOHZHOHVN6AnDVuCw1kl6hKd+kcIvn8chQIymRIXx6xQhumiRSDRUDb6t7O/3TIql1uNRUNgCnrLWVaWhoHNkEMxU2HNgqy/J2WZbrgM+Bk+qtcxLwgfv218AkSZIkWZZXyLK83718HRAiSZIW96GhoXFYcf3EbD68dLh6/8EffU0wiyrrCLcYGsQNa2i0FkmStMJQAIZ0iaLO4WL13tIWPX/OxnwcSnEoQFKSRscgO95dHMr3jS/PL6/1q+b0prCilo8W7Gy2P9XsDbkNljlcsupTBVDp9iH6bsW+Bus2hsslqwWKJJuFCIuBkiq7+riS7LXqv8fy+63jm7XtYDHodYesvapbQjhL75vMkC5R6rIIS+PFoRsmZgNw9rA0db9Hd43lvJHpPutZ3f5G47rFYjHq+GLJbvWxarc6SztXa2hoHKkEc/RLAfZ43d/rXuZ3HVmWHUApEFNvndOA5bIsH1zNr4aGhkY7o9dJjOsepxaIymscPgOEoso6YjTVkIbGQWVc9zgA/tnavJhx5W/1+T82U2V3oJOEt5hGxyUxwoLVpGdrnqc49N2KfQx77A/OfnNhQPXY7sIq/r+9Ow+Tqy4TPf59u9PZupPuQDoLWchCEsISAsQQlrAM++IyzsAdRXHQi8qmKMOMMwIOCurce13GEXXUEXxQGb1wZRBQVFAGg0ASDEhAwhKCbCELIRtJp7t/949zuulKZelOL9Xd9f08Tz+pOudU9VtP15s6563f7/0dfu2vueq/lvLYSx0rIrYdCfqjC45ovb3fp3/OrYtfBN6aCtbRvuhNbZZuj4jW0UN1Q6v40DGT+fQZMwGoHVLVOhKmP7r1wqN4+yH7ALC1cdcjAC+YP4XnPn8GX/yrWQXbRw0bzHvmTuDE/bMG1a/n07zrhg7kXbPHcfPDf+azP3sCgFO+ch9gf0BJ5atHSuMRcSDZVLOP7GT/hyNiUUQsWrVqVU+EJEld7tjp9fzbew5l7aaGgguN1zc3OKVM6mG1Q7KpJct3sZLgjjQ0NjNq2CBe27CVhcvX2m+oD4gIJtdX81ybv/VlP17SevupfErR9u5e+mrr7adX7viY7f3i8Vf48cIXuPK2xwG49/LjOGrqSN4/b9/WY37w0AruW7aKTW2Wme/IyKTG5lRQkJyaj4waWFnBVWcdwAXHTmn3c/V1V545kyOn7M2JM0fv8riI2GkR9wvvnsWFx08F4OU33mzdfuTU7Hvs7y1Yznfvf661iGdxSFK5as8Zz0vAhDb3x+fbdnhMRAwAaoE1+f3xwE+B81JKz+7oF6SUvp1SmpNSmlNfX9+xVyBJvchB42qBrAlmizUbG2xGLZXA5JHVPLfdVKPdaWhqZt6U7KLxkRfWccTkvbojNHWxKSNrdvq3Puvffsd3/vs51m95a2rWmw1NXHfXk0DWU+qZVRtZt7mBF3ax8iTAR3/wCP9w6x9b77c0g7/qrAOYP20kAH94YR0f+N7DvPP6Ba3H3fun4mbVO9OcsqXsW7xtUja96rUN5Tf4fvTwwdz84XlMHlndqeeZNjprUH3qAWNat00f/VYj62vvfLL19qhhXd/HSZL6gvYUhxYC0yJickQMBP4GuH27Y24nazgN8NfAvSmlFBF1wJ3Ap1JKC5Ckfm7CiCGMHj6Ia372BDcsyFZKen1zg99ESiUweWQ1z67axNbGpt0fTLZCWUNTc8EKTV8655DuCk9daEp9NS+te7N1yffBVRW894iJ7J+vWnXdXU9yxHX3tB7fdtTQ5JHVPPvaRmZ/9lec8tX7OvR7W3p+DRxQwY3nz93p6l43PND+lfO2Hzn0ztlZNwdnN+652iFVLL3mVC4+Yb/WbTPHDue2i4/m9kuObt12x6XHMKYbmnxLUl+w2+JQ3kPoEuBu4EngJymlpRHx2Yh4R37YfwB7R8QzwCeBluXuLwH2A66OiCX5z6gufxWS1EsMqKzg1guPAuCanz3Bpq2NrN3ktDKpFE4+YDQbtzbyzd/ucOBykcbmRErZ9J2W1Y8cRdA3HDu9npTg2/dlf+vmZhg+uIobzn8bB+cjOt/c1sRDeYPqtr2W9xtVw6+fzEb2bNnWXLBEfYuv3/s05/z77xk44K1T5/H5qKEWlRXBJ0+ezlmzxhZsP2xiHQueWcNdf3yFlHY/vay5OdF2gbzKimDRlSdx3xUn7Pax2rnqQQOKpp7NnlDHrPF1PPnZ07jj0mNaR/9KUjlq10T6lNJdKaXpKaWpKaXr8m1Xp5Ruz29vSSmdnVLaL6U0N6X0XL792pRSdUppdpuf9o+rlaQ+aPyIoa1NbT9440K2NjZTO6SqxFFJ5efY6fW845B9uP43z/BGm9WedqYhb3o7cEAF91x+HL/8xLHdHaK6yGETR3D6QWO48YFs5bGGpmYGVgZja4cUTEn61RPZKmNrNmaNiU8/aAyHjK8reK6Lf/RIwf3Gpmb+zy+X8fDytTQ0NnPCjHpqh1Rx3V8eXBTHO2eP4+vvPYznv3gml500jRFDq1r75Vz0w0f4dN6raFcam5tbG1K3GFkziAl79cyy8uVoyMBKC0OSyp5dFiWpG9z5sfkAPLR8LbD7ZXgldY9z5kxgW1Pij+1YjaptcWjUsMEFPUnU+x0zbSTrtzSyYm3WN6hllM/HTtyPMw4ew5T6ap7KG0+v3dRAZUVw/XsP4wNHTWp9jvfMncCDz63ljTffKiY+v6awqfmqjVt59DOncNz0XffJvOyk6Txy1cmtjY8Bbn74BTY3NO7iUdmopwrP0CVJPcyPHknqBqOHDyq4P2ywI4ekUjhoXLYM+OMvt6M41PRWcUh9zwFjs791y4IAVflKc/uNGsY3zj2c2RPqWJYXh9Zs2sqIoQOpqAgGV1Xy608exxWnzuCkfJTPt+57lkmfupNX39jCE68UrmR2zpwJtFdEFBQZU4I7H3tll49pSqlo5JAkSd3NTx5J6gYRwW0XH81R+TfG6zY3lDgiqTzVDR3I+BFDeLwjI4dcvr5PmjFmGBGwZLviUOv+0cNYuX4r6zY3sGZjQ+v0X8j6Dl18wn6thZyWPlX3/GklT7y8nqrKYNm1p7P8C2dw3pGTOhRXzaABVFUG75s3kSFVlSxe8foOj1u8Yi3nfOv3bG5o2umy7JIkdRfnOUhSN5k9oY5r33UQ7/vuQ5x0wOhShyOVrf3HDOexF9+gqTlRuYuL7q2Njhzqy4YOHMDkkdU88OxqAKq2+ztOz1cuW7ZyI2s37XgVyXF1QxhYWdE6iuxffv4nKiqCA/ap7dT74unrzgDgmdc28tunVrF641ZG1hSOMP3n259onf44ttb1WyRJPcuzH0nqRlPqa3jgH09k/AgbiUql8o7Z+/DC2s388KEVuzzOkUN939T6Gpat3AhQtDLYjHxU0FMrN2TFoZri4lBFRTCi+q1pwOu3NLJu8zbeP2/fLonvH0+fybo3G7ji/z5atO/AfYa/FUc4ckiS1LM8+5EkSf3a22eNZdb4Wn76h5d2eZw9h/q+dx86rvX2ijWbC/aNrR3MXtUD+eXSV1mzqXBaWVtfOnt26+2p9dlKZ/OnjeyS+A6ZUMe5R+zLgmfX0Ji/31rsnRerDp1YxzlzxnfJ75Mkqb08+5EkSf1aRHDohDqWvbqB5ua00+O2WRzq804/eCw//3i2WuShE+sK9kUEFx0/lfufXs0bb27b4bQyyFY9+8Vl8/nPD8/jh/9zHt889zBGDx/cZTEeOrGOhsZmFm3Xe2jrtmaGDqzkpxcdzSkHjumy3ydJUnt49iNJkvq9/ccOZ1NDE1P+6S4mfepO1mzcWnTMhi3Z8uVOK+vbZo4dzuPXnMqZB48t2te2mfScfffa6XPsP2Y486bszZjawZy+g+fpjONnZP2EFj2/tmB7Q1MzgyxMSpJKxE8gSZLU782dXFgI2H7FqF88/gofvHER4Mih/qBm0ABiB3172v5tj8xXk+xpNYOyxtk3PbiCp1duaN3+57Wbfe9JkkrGTyBJktTvTa2voWbQW4u0bt+PZunL61tve4Hev/3kI0dy9VkH7HLluu72tb85lNc3beOWxS8CsOCZ1fzmqVWsXF88ok2SpJ7g2Y8kSSoLd3/iWO674njqhw1i0Yq1rFizqXVfy+pQ86eNZN+9q0sVonrA3Ml78cFjJpc0hoPH1zJ1VA33LVtFSonH8yXsJUkqFYtDkiSpLIyrG8K+e1dz2oFjuHvpSo7737/lz2s3s7mhkU1bGxk6sJKbPnREwQgjqbu8/ZCx/OnVDby6fgtrNzWUOhxJUpnz7EeSJJWVS0/cj5seXAHA/P/1G/aqHujFuXrc3ElZH6wTv3Qf4+qGAPCt9x1eypAkSWXMkUOSJKmsjBpWuCy5hSGVwv5jhwOwuaGJp1/byNsmjeC0g1zCXpJUGhaHJElS2Vnwqb8o2lY7pKoEkahc1QwawKfPmNl6f/nqTbs4WpKk7uW0MkmSVHbG1Q3hlo8eyQ0PPM/KN7Zw+SkzmDdlr90/UOpCFxw7hYl7D+UjNy3mX/5qVqnDkSSVsUgplTqGAnPmzEmLFi0qdRiSJEmSJEn9RkQsTinN2dE+p5VJkiRJkiSVMYtDkiRJkiRJZczikCRJkiRJUhmzOCRJkiRJklTGLA5JkiRJkiSVMYtDkiRJkiRJZczikCRJkiRJUhmzOCRJkiRJklTGLA5JkiRJkiSVMYtDkiRJkiRJZczikCRJkiRJUhmzOCRJkiRJklTGLA5JkiRJkiSVMYtDkiRJkiRJZczikCRJkiRJUhmzOCRJkiRJklTGLA5JkiRJkiSVMYtDkiRJkiRJZczikCRJkiRJUhmzOCRJkiRJklTGIqVU6hgKRMQqYAUwElhd4nCk3sa8kAqZE1Ix80IqZl5IhcyJ8rRvSql+Rzt6XXGoRUQsSinNKXUcUm9iXkiFzAmpmHkhFTMvpELmhLbntDJJkiRJkqQyZnFIkiRJkiSpjPXm4tC3Sx2A1AuZF1Ihc0IqZl5IxcwLqZA5oQK9tueQJEmSJEmSul9vHjkkSZIkSZKkbmZxSJIkSVKfFxFR6hgkqa8qaXEoIirzf/2PXMJckHYkImrzf/1CQ8pFxIERMbjUcUi9zJBSByD1Jl5vqyNKcqIdEUdHxPeBKyNir2TjI5W5iJgbEd8B/iEi6ksdj1RqEVEREcMj4g7gawAppeYShyWVXETMiojfAdcCe5c6Hqk3iIh5EXErcH1EnNJyQSyVK6+3tSd6vDgUEVOAbwC/AfYFPhcRZ/Z0HFJvEBGVEfEFstUCFgCHAZ+JiNGljUwqrbwQtAGoAsZFxP8ARw9JwJXALSmlv0wpvQR+I6zyFhHHk11b/D/gKeB9wIgShiSVlNfb2lOlOMk+HHgypXQjcDmwBDgrIiaUIBap1CqAF4Bz8py4DJiHw6IlgP2B1cBXgXMjYlhKqdkLYZWjfDTdFGBjSumr+baTI6IOcNqAytnBwMKU0g+Bm8i+VNhY2pCkknobXm9rD3R7cSgf5jm9zaaFwPiImJBSep1stMQ64N3dHYvUG2yXE83AzSmlZRExKKX0MvAiMLJ0EUo9r21etLnAfQZoAJbnPx+IiIkOjVa5aJsX+Wi61cD8iDgzIm4D/o5s2uUV+THmhvq9HVxb3A+cHRFXA48AY4FvRMTZJQlQ6mER8faIuCQi5uWbFgITvN5WR3VbcSgi6iLiTuBXwDkRUZPv2gL8Djgnv/8U8ASwl40V1Z/tKCdSSk0ppXUAKaWtETEMmAy8XMJQpR6zg7yobnOBOwdYn1JaCiwFPgN8MyKqnF6m/mxHeQGQUloP3AB8DvheSulU4LvAvDYXBVK/tLNri5TSEuA0YBJwUUrpeLKL4dMiYmZpopW6X0SMjYifAX9PNpXyhog4NaX0HPB7vN5WB3XnyXU1cDdwaX772Hz7KuBB4OCImJtSagJeAo5OKW3pxnikUts+J+bv4JgjgKUppZcjoiYipvVkgFIJ7OyzArIpl8Mi4sdkJz6LgWUppW02p1Y/t6u8uIPsIrilp8oiYCWwtQfjk0php+dRKaWHgXrg+XzTvcAwYFPPhij1qDnA/Sml+SmlzwH/ClyQ77sfr7fVQV1aHIqI8yLiuIgYnjdJ/DbwE7LRQnMjYlz+5vw98AfgK3nV/0DghYgY2pXxSKW2m5w4IiL2yY8bkD+kDvhzRJxPNiR0ds9HLXWv9uYF2cVvPfAqcChwITDDb4LVH7UjL8YBpJQeI5tGdklEjCRrvnsQsKZEoUvdpgPnUYOAB4CL84eeSLaanxfC6lfynDg+f8/fQ9Znq8Ua4On89kN4va0Ois5OT897Q4wBfkTWP+VZsmr+x1NKq/NjjiYb1rYopXRTm8d+GRhP1kX9vJTSU50KRuoFOpgTC1NKP2jz2JuAc4HvA1/JLwKkPm9PPysiYmSb/TXAwJTS2hK8BKnLdfIc6pPAFGAa8ImU0hM9HL7ULTrxeXEg2fTjMcA24JKU0pM9/wqkrrW7nIiIqpTStoj4GHBASumjbR7r9bbarVMjhyKiMu8NMQx4KaV0Itk3u2vJKvsApJQWkA3znBERtXlfFci++fpQSukI36jqD/YgJ/aPiOFtenLdSbZy2fkWhtRfdOKzojo/6amMiIqU0kYLQ+ovOnsOlVL6MllR6FQLQ+ov9jAv6iJiSN6f7gPA36aUTrQwpP6gnTnRMtX+ZOCW/HGj8m1/j9fbaqc9Kg7lJ+qfBz4fEccBM4AmgHza2MeBo/J9Lb4D1JA1kXsmIvbJm/Fu6NQrkHqBTubEPcCzETE2pfSfKaVbejh8qVt0wWfFc20+K+wxpH6hq86h8uO39WjwUjfpgrx4Pm9f8WbejFfq0zqSEymlpogYSNbbd1lEXAf8KiJGpJQavd5We3W4OJS/OReT9YJ4hmzFjG3ACRExF1qXW/3n/KfFmcBFwKPAwfmS3VKf1wU5sYQsJ17psaClbuZnhVTMvJCKdeF51Es9FrTUjTqYE9fkDxsM/C3Zl87DgJPyZeyldhuw+0OKNANfajO/91CypbevBr4JHB7ZEsO3AX8REZNSSs+TNYQ7KaX0310RuNSLmBNSMfNCKmZeSMXMC6lQR3NiPLAP8APgyymlJaUIWn3fnkwrWwz8JCIq8/sLgIkppRuByoi4NK9kjgea8v+8SSn9l/95q58yJ6Ri5oVUzLyQipkXUqGO5ERzSunFlNLDKaXzLAypMzpcHEopbU4pbc3nOkLW+GpVfvt8YGZE3AHcDDwCrR3WpX7JnJCKmRdSMfNCKmZeSIU6mBOLwZxQ19iTaWVA1iQLSMBo4PZ88wbgn4CDgOUtc3/zDutSv2ZOSMXMC6mYeSEVMy+kQuaEelpnlrJvBqqA1cCsvHp5FdnQtt/ZFE5lyJyQipkXUjHzQipmXkiFzAn1qOhMkTEi5gEP5D83pJT+o6sCk/oic0IqZl5IxcwLqZh5IRUyJ9STOlscGg+8n6wr+tYui0rqo8wJqZh5IRUzL6Ri5oVUyJxQT+pUcUiSJEmSJEl9W2d6DkmSJEmSJKmPszgkSZIkSZJUxiwOSZIkSZIklTGLQ5IkSZIkSWXM4pAkSZIkSVIZszgkSZLURkQ0RcSSiFgaEY9GxOURsctzpoiYFBHv7akYJUmSupLFIUmSpEJvppRmp5QOBE4GTgc+s5vHTAIsDkmSpD4pUkqljkGSJKnXiIiNKaWaNvenAAuBkcC+wE1Adb77kpTSAxHxIDATWA58H/ga8EXgeGAQcH1K6d977EVIkiR1gMUhSZKkNrYvDuXb1gEzgA1Ac0ppS0RMA25OKc2JiOOBv0spnZUf/2FgVErp2ogYBCwAzk4pLe/BlyJJktQuA0odgCRJUh9SBXw9ImYDTcD0nRx3CjArIv46v18LTCMbWSRJktSrWBySJEnahXxaWRPwGlnvoZXAIWS9G7fs7GHApSmlu3skSEmSpE6wIbUkSdJOREQ98C3g6ymbi18LvJJSagbeD1Tmh24AhrV56N3AhRFRlT/P9IioRpIkqRdy5JAkSVKhIRGxhGwKWSNZA+ov5/u+AdwaEecBvwA25dsfA5oi4lHgRuBfyVYweyQiAlgFvKtnwpckSeoYG1JLkiRJkiSVMaeVSZIkSZIklTGLQ5IkSZIkSWXM4pAkSZIkSVIZszgkSZIkSZJUxiwOSZIkSZIklTGLQ5IkSZIkSWXM4pAkSZIkSVIZszgkSZIkSZJUxv4/zajRLZGx+F0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the EMA of TSLA daily return volatility\n",
    "\n",
    "df[['fast_vol', 'slow_vol']].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>fast_vol</th>\n",
       "      <th>slow_vol</th>\n",
       "      <th>vol_trend_long</th>\n",
       "      <th>vol_trend_short</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "      <th>bollinger_mid_band</th>\n",
       "      <th>bollinger_std</th>\n",
       "      <th>bollinger_upper_band</th>\n",
       "      <th>bollinger_lower_band</th>\n",
       "      <th>bollinger_long</th>\n",
       "      <th>bollinger_short</th>\n",
       "      <th>bollinger_signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>4.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>4.780000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>4.77</td>\n",
       "      <td>-0.002092</td>\n",
       "      <td>4.774965</td>\n",
       "      <td>4.774991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>4.39</td>\n",
       "      <td>-0.079665</td>\n",
       "      <td>4.644861</td>\n",
       "      <td>4.646216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.054852</td>\n",
       "      <td>0.054852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>3.84</td>\n",
       "      <td>-0.125285</td>\n",
       "      <td>4.439442</td>\n",
       "      <td>4.443613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.062094</td>\n",
       "      <td>0.062246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>3.22</td>\n",
       "      <td>-0.161458</td>\n",
       "      <td>4.188745</td>\n",
       "      <td>4.197191</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.068328</td>\n",
       "      <td>0.068645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "Date                                                                      \n",
       "2010-06-29   4.78           NaN    4.780000    4.780000             0.0   \n",
       "2010-06-30   4.77     -0.002092    4.774965    4.774991             0.0   \n",
       "2010-07-01   4.39     -0.079665    4.644861    4.646216             0.0   \n",
       "2010-07-02   3.84     -0.125285    4.439442    4.443613             0.0   \n",
       "2010-07-06   3.22     -0.161458    4.188745    4.197191             0.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "Date                                                                \n",
       "2010-06-29              0.0               0.0       NaN       NaN   \n",
       "2010-06-30             -1.0              -1.0       NaN       NaN   \n",
       "2010-07-01             -1.0              -1.0  0.054852  0.054852   \n",
       "2010-07-02             -1.0              -1.0  0.062094  0.062246   \n",
       "2010-07-06             -1.0              -1.0  0.068328  0.068645   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \\\n",
       "Date                                                            \n",
       "2010-06-29             0.0              0.0               0.0   \n",
       "2010-06-30             0.0              0.0               0.0   \n",
       "2010-07-01             0.0              0.0               0.0   \n",
       "2010-07-02             1.0              0.0               1.0   \n",
       "2010-07-06             1.0              0.0               1.0   \n",
       "\n",
       "            bollinger_mid_band  bollinger_std  bollinger_upper_band  \\\n",
       "Date                                                                  \n",
       "2010-06-29                 NaN            NaN                   NaN   \n",
       "2010-06-30                 NaN            NaN                   NaN   \n",
       "2010-07-01                 NaN            NaN                   NaN   \n",
       "2010-07-02                 NaN            NaN                   NaN   \n",
       "2010-07-06                 NaN            NaN                   NaN   \n",
       "\n",
       "            bollinger_lower_band  bollinger_long  bollinger_short  \\\n",
       "Date                                                                \n",
       "2010-06-29                   NaN             0.0              0.0   \n",
       "2010-06-30                   NaN             0.0              0.0   \n",
       "2010-07-01                   NaN             0.0              0.0   \n",
       "2010-07-02                   NaN             0.0              0.0   \n",
       "2010-07-06                   NaN             0.0              0.0   \n",
       "\n",
       "            bollinger_signal  \n",
       "Date                          \n",
       "2010-06-29               0.0  \n",
       "2010-06-30               0.0  \n",
       "2010-07-01               0.0  \n",
       "2010-07-02               0.0  \n",
       "2010-07-06               0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set bollinger band window\n",
    "\n",
    "bollinger_window = 20\n",
    "\n",
    "# Calculate rolling mean and standard deviation\n",
    "df['bollinger_mid_band'] = df['Close'].rolling(window=bollinger_window).mean()\n",
    "df['bollinger_std'] = df['Close'].rolling(window=20).std()\n",
    "\n",
    "# Calculate upper and lowers bands of bollinger band\n",
    "df['bollinger_upper_band']  = df['bollinger_mid_band'] + (df['bollinger_std'] * 2)\n",
    "df['bollinger_lower_band']  = df['bollinger_mid_band'] - (df['bollinger_std'] * 2)\n",
    "\n",
    "# Calculate bollinger band trading signal\n",
    "df['bollinger_long'] = np.where(df['Close'] < df['bollinger_lower_band'], 1.0, 0.0)\n",
    "df['bollinger_short'] = np.where(df['Close'] > df['bollinger_upper_band'], -1.0, 0.0)\n",
    "df['bollinger_signal'] = df['bollinger_long'] + df['bollinger_short']\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Bollinger Bands for TLSA closing prices\n",
    "\n",
    "df[['Close','bollinger_mid_band','bollinger_upper_band','bollinger_lower_band']].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>fast_vol</th>\n",
       "      <th>slow_vol</th>\n",
       "      <th>vol_trend_long</th>\n",
       "      <th>vol_trend_short</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "      <th>bollinger_mid_band</th>\n",
       "      <th>bollinger_std</th>\n",
       "      <th>bollinger_upper_band</th>\n",
       "      <th>bollinger_lower_band</th>\n",
       "      <th>bollinger_long</th>\n",
       "      <th>bollinger_short</th>\n",
       "      <th>bollinger_signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-27</th>\n",
       "      <td>4.11</td>\n",
       "      <td>-0.019093</td>\n",
       "      <td>3.978296</td>\n",
       "      <td>3.975310</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064452</td>\n",
       "      <td>0.067885</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9745</td>\n",
       "      <td>0.459925</td>\n",
       "      <td>4.894351</td>\n",
       "      <td>3.054649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-28</th>\n",
       "      <td>4.14</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>3.987110</td>\n",
       "      <td>3.983427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.062183</td>\n",
       "      <td>0.066017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9425</td>\n",
       "      <td>0.421599</td>\n",
       "      <td>4.785699</td>\n",
       "      <td>3.099301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-29</th>\n",
       "      <td>4.07</td>\n",
       "      <td>-0.016908</td>\n",
       "      <td>3.991452</td>\n",
       "      <td>3.987507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060155</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9075</td>\n",
       "      <td>0.375862</td>\n",
       "      <td>4.659224</td>\n",
       "      <td>3.155776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-30</th>\n",
       "      <td>3.99</td>\n",
       "      <td>-0.019656</td>\n",
       "      <td>3.991378</td>\n",
       "      <td>3.987619</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058298</td>\n",
       "      <td>0.062720</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.8875</td>\n",
       "      <td>0.359105</td>\n",
       "      <td>4.605710</td>\n",
       "      <td>3.169290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-02</th>\n",
       "      <td>4.18</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>4.000554</td>\n",
       "      <td>3.995959</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057807</td>\n",
       "      <td>0.062239</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9045</td>\n",
       "      <td>0.364742</td>\n",
       "      <td>4.633984</td>\n",
       "      <td>3.175016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "Date                                                                      \n",
       "2010-07-27   4.11     -0.019093    3.978296    3.975310             1.0   \n",
       "2010-07-28   4.14      0.007299    3.987110    3.983427             1.0   \n",
       "2010-07-29   4.07     -0.016908    3.991452    3.987507             1.0   \n",
       "2010-07-30   3.99     -0.019656    3.991378    3.987619             1.0   \n",
       "2010-08-02   4.18      0.047619    4.000554    3.995959             1.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "Date                                                                \n",
       "2010-07-27              0.0               1.0  0.064452  0.067885   \n",
       "2010-07-28              0.0               1.0  0.062183  0.066017   \n",
       "2010-07-29              0.0               1.0  0.060155  0.064295   \n",
       "2010-07-30              0.0               1.0  0.058298  0.062720   \n",
       "2010-08-02              0.0               1.0  0.057807  0.062239   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \\\n",
       "Date                                                            \n",
       "2010-07-27             1.0              0.0               1.0   \n",
       "2010-07-28             1.0              0.0               1.0   \n",
       "2010-07-29             1.0              0.0               1.0   \n",
       "2010-07-30             1.0              0.0               1.0   \n",
       "2010-08-02             1.0              0.0               1.0   \n",
       "\n",
       "            bollinger_mid_band  bollinger_std  bollinger_upper_band  \\\n",
       "Date                                                                  \n",
       "2010-07-27              3.9745       0.459925              4.894351   \n",
       "2010-07-28              3.9425       0.421599              4.785699   \n",
       "2010-07-29              3.9075       0.375862              4.659224   \n",
       "2010-07-30              3.8875       0.359105              4.605710   \n",
       "2010-08-02              3.9045       0.364742              4.633984   \n",
       "\n",
       "            bollinger_lower_band  bollinger_long  bollinger_short  \\\n",
       "Date                                                                \n",
       "2010-07-27              3.054649             0.0              0.0   \n",
       "2010-07-28              3.099301             0.0              0.0   \n",
       "2010-07-29              3.155776             0.0              0.0   \n",
       "2010-07-30              3.169290             0.0              0.0   \n",
       "2010-08-02              3.175016             0.0              0.0   \n",
       "\n",
       "            bollinger_signal  \n",
       "Date                          \n",
       "2010-07-27               0.0  \n",
       "2010-07-28               0.0  \n",
       "2010-07-29               0.0  \n",
       "2010-07-30               0.0  \n",
       "2010-08-02               0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming df to trading_signals_df\n",
    "\n",
    "trading_signals_df = df\n",
    "trading_signals_df = trading_signals_df.dropna()\n",
    "trading_signals_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SENT</th>\n",
       "      <th>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
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       "      <th>2010-07-27</th>\n",
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       "      <th>2010-07-28</th>\n",
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       "      <td>4.14</td>\n",
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       "      <td>3.987110</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>2010-07-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.07</td>\n",
       "      <td>-0.016908</td>\n",
       "      <td>3.991452</td>\n",
       "      <td>3.987507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060155</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9075</td>\n",
       "      <td>0.375862</td>\n",
       "      <td>4.659224</td>\n",
       "      <td>3.155776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>-0.019656</td>\n",
       "      <td>3.991378</td>\n",
       "      <td>3.987619</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.062720</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-08-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>4.000554</td>\n",
       "      <td>3.995959</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.062239</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "            SENT  Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "2010-07-27   NaN   4.11     -0.019093    3.978296    3.975310             1.0   \n",
       "2010-07-28   NaN   4.14      0.007299    3.987110    3.983427             1.0   \n",
       "2010-07-29   NaN   4.07     -0.016908    3.991452    3.987507             1.0   \n",
       "2010-07-30   NaN   3.99     -0.019656    3.991378    3.987619             1.0   \n",
       "2010-08-02   NaN   4.18      0.047619    4.000554    3.995959             1.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "2010-07-27              0.0               1.0  0.064452  0.067885   \n",
       "2010-07-28              0.0               1.0  0.062183  0.066017   \n",
       "2010-07-29              0.0               1.0  0.060155  0.064295   \n",
       "2010-07-30              0.0               1.0  0.058298  0.062720   \n",
       "2010-08-02              0.0               1.0  0.057807  0.062239   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \\\n",
       "2010-07-27             1.0              0.0               1.0   \n",
       "2010-07-28             1.0              0.0               1.0   \n",
       "2010-07-29             1.0              0.0               1.0   \n",
       "2010-07-30             1.0              0.0               1.0   \n",
       "2010-08-02             1.0              0.0               1.0   \n",
       "\n",
       "            bollinger_mid_band  bollinger_std  bollinger_upper_band  \\\n",
       "2010-07-27              3.9745       0.459925              4.894351   \n",
       "2010-07-28              3.9425       0.421599              4.785699   \n",
       "2010-07-29              3.9075       0.375862              4.659224   \n",
       "2010-07-30              3.8875       0.359105              4.605710   \n",
       "2010-08-02              3.9045       0.364742              4.633984   \n",
       "\n",
       "            bollinger_lower_band  bollinger_long  bollinger_short  \\\n",
       "2010-07-27              3.054649             0.0              0.0   \n",
       "2010-07-28              3.099301             0.0              0.0   \n",
       "2010-07-29              3.155776             0.0              0.0   \n",
       "2010-07-30              3.169290             0.0              0.0   \n",
       "2010-08-02              3.175016             0.0              0.0   \n",
       "\n",
       "            bollinger_signal  \n",
       "2010-07-27               0.0  \n",
       "2010-07-28               0.0  \n",
       "2010-07-29               0.0  \n",
       "2010-07-30               0.0  \n",
       "2010-08-02               0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concating dataframes to one: \n",
    "\n",
    "trading_signals_df = pd.concat([twitter_df, trading_signals_df], axis=1)\n",
    "trading_signals_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
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       "      <td>44.802278</td>\n",
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       "      <td>37.665206</td>\n",
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       "      <th>2015-02-02</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.036100</td>\n",
       "      <td>44.766314</td>\n",
       "      <td>34.194551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024627</td>\n",
       "      <td>0.029972</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5670</td>\n",
       "      <td>1.342849</td>\n",
       "      <td>43.252698</td>\n",
       "      <td>37.881302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.67</td>\n",
       "      <td>0.035079</td>\n",
       "      <td>44.751221</td>\n",
       "      <td>34.227937</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.030003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.6495</td>\n",
       "      <td>1.480450</td>\n",
       "      <td>43.610399</td>\n",
       "      <td>37.688601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.71</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>44.736886</td>\n",
       "      <td>34.261344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>0.029899</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7220</td>\n",
       "      <td>1.594575</td>\n",
       "      <td>43.911150</td>\n",
       "      <td>37.532850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.20</td>\n",
       "      <td>0.011210</td>\n",
       "      <td>44.729495</td>\n",
       "      <td>34.296358</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024287</td>\n",
       "      <td>0.029807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8225</td>\n",
       "      <td>1.747934</td>\n",
       "      <td>44.318367</td>\n",
       "      <td>37.326633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "            SENT  Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "2015-01-30   1.0  40.72     -0.007797   44.802278   34.166278             1.0   \n",
       "2015-02-02   0.0  42.19      0.036100   44.766314   34.194551             1.0   \n",
       "2015-02-03   0.0  43.67      0.035079   44.751221   34.227937             1.0   \n",
       "2015-02-04   0.0  43.71      0.000916   44.736886   34.261344             1.0   \n",
       "2015-02-05   0.0  44.20      0.011210   44.729495   34.296358             1.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "2015-01-30              0.0               1.0  0.024043  0.029932   \n",
       "2015-02-02              0.0               1.0  0.024627  0.029972   \n",
       "2015-02-03              0.0               1.0  0.025065  0.030003   \n",
       "2015-02-04              0.0               1.0  0.024634  0.029899   \n",
       "2015-02-05              0.0               1.0  0.024287  0.029807   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \\\n",
       "2015-01-30             1.0              0.0               1.0   \n",
       "2015-02-02             1.0              0.0               1.0   \n",
       "2015-02-03             1.0              0.0               1.0   \n",
       "2015-02-04             1.0              0.0               1.0   \n",
       "2015-02-05             1.0              0.0               1.0   \n",
       "\n",
       "            bollinger_mid_band  bollinger_std  bollinger_upper_band  \\\n",
       "2015-01-30             40.6505       1.492647             43.635794   \n",
       "2015-02-02             40.5670       1.342849             43.252698   \n",
       "2015-02-03             40.6495       1.480450             43.610399   \n",
       "2015-02-04             40.7220       1.594575             43.911150   \n",
       "2015-02-05             40.8225       1.747934             44.318367   \n",
       "\n",
       "            bollinger_lower_band  bollinger_long  bollinger_short  \\\n",
       "2015-01-30             37.665206             0.0              0.0   \n",
       "2015-02-02             37.881302             0.0              0.0   \n",
       "2015-02-03             37.688601             0.0             -1.0   \n",
       "2015-02-04             37.532850             0.0              0.0   \n",
       "2015-02-05             37.326633             0.0              0.0   \n",
       "\n",
       "            bollinger_signal  \n",
       "2015-01-30               0.0  \n",
       "2015-02-02               0.0  \n",
       "2015-02-03              -1.0  \n",
       "2015-02-04               0.0  \n",
       "2015-02-05               0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping NaN values:\n",
    "\n",
    "trading_signals_df = trading_signals_df.dropna()\n",
    "trading_signals_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>bollinger_signal</th>\n",
       "      <th>SENT</th>\n",
       "      <th>vol_trend_signal</th>\n",
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       "  <tbody>\n",
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       "      <th>2015-01-30</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2015-02-02</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1432 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            crossover_signal  bollinger_signal  SENT  vol_trend_signal\n",
       "2015-01-30               1.0               0.0   1.0               1.0\n",
       "2015-02-02               1.0               0.0   0.0               1.0\n",
       "2015-02-03               1.0              -1.0   0.0               1.0\n",
       "2015-02-04               1.0               0.0   0.0               1.0\n",
       "2015-02-05               1.0               0.0   0.0               1.0\n",
       "...                      ...               ...   ...               ...\n",
       "2020-09-30               1.0               0.0   0.0              -1.0\n",
       "2020-10-01               1.0               0.0   0.0              -1.0\n",
       "2020-10-02               1.0               0.0   0.0              -1.0\n",
       "2020-10-05               1.0               0.0   0.0              -1.0\n",
       "2020-10-06               1.0               0.0   0.0              -1.0\n",
       "\n",
       "[1432 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set x variable list of features\n",
    "\n",
    "x_var_list = ['crossover_signal', 'bollinger_signal', 'SENT', 'vol_trend_signal']\n",
    "\n",
    "# Filter by x-variable list\n",
    "trading_signals_df[x_var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>bollinger_signal</th>\n",
       "      <th>SENT</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>2015-02-03</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2015-02-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1432 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            crossover_signal  bollinger_signal  SENT  vol_trend_signal\n",
       "2015-01-30               NaN               NaN   NaN               NaN\n",
       "2015-02-02               1.0               0.0   1.0               1.0\n",
       "2015-02-03               1.0               0.0   0.0               1.0\n",
       "2015-02-04               1.0              -1.0   0.0               1.0\n",
       "2015-02-05               1.0               0.0   0.0               1.0\n",
       "...                      ...               ...   ...               ...\n",
       "2020-09-30               1.0               0.0   0.0              -1.0\n",
       "2020-10-01               1.0               0.0   0.0              -1.0\n",
       "2020-10-02               1.0               0.0   0.0              -1.0\n",
       "2020-10-05               1.0               0.0   0.0              -1.0\n",
       "2020-10-06               1.0               0.0   0.0              -1.0\n",
       "\n",
       "[1432 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shift DataFrame values by 1\n",
    "trading_signals_df[x_var_list] = trading_signals_df[x_var_list].shift(1)\n",
    "trading_signals_df[x_var_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SENT</th>\n",
       "      <th>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>fast_vol</th>\n",
       "      <th>slow_vol</th>\n",
       "      <th>vol_trend_long</th>\n",
       "      <th>vol_trend_short</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "      <th>bollinger_mid_band</th>\n",
       "      <th>bollinger_std</th>\n",
       "      <th>bollinger_upper_band</th>\n",
       "      <th>bollinger_lower_band</th>\n",
       "      <th>bollinger_long</th>\n",
       "      <th>bollinger_short</th>\n",
       "      <th>bollinger_signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.19</td>\n",
       "      <td>0.036100</td>\n",
       "      <td>44.766314</td>\n",
       "      <td>34.194551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024627</td>\n",
       "      <td>0.029972</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5670</td>\n",
       "      <td>1.342849</td>\n",
       "      <td>43.252698</td>\n",
       "      <td>37.881302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.67</td>\n",
       "      <td>0.035079</td>\n",
       "      <td>44.751221</td>\n",
       "      <td>34.227937</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025065</td>\n",
       "      <td>0.030003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.6495</td>\n",
       "      <td>1.480450</td>\n",
       "      <td>43.610399</td>\n",
       "      <td>37.688601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.71</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>44.736886</td>\n",
       "      <td>34.261344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>0.029899</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7220</td>\n",
       "      <td>1.594575</td>\n",
       "      <td>43.911150</td>\n",
       "      <td>37.532850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.20</td>\n",
       "      <td>0.011210</td>\n",
       "      <td>44.729495</td>\n",
       "      <td>34.296358</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024287</td>\n",
       "      <td>0.029807</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8225</td>\n",
       "      <td>1.747934</td>\n",
       "      <td>44.318367</td>\n",
       "      <td>37.326633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.47</td>\n",
       "      <td>-0.016516</td>\n",
       "      <td>44.712155</td>\n",
       "      <td>34.328674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024086</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8900</td>\n",
       "      <td>1.825042</td>\n",
       "      <td>44.540084</td>\n",
       "      <td>37.239916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SENT  Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "2015-02-02   1.0  42.19      0.036100   44.766314   34.194551             1.0   \n",
       "2015-02-03   0.0  43.67      0.035079   44.751221   34.227937             1.0   \n",
       "2015-02-04   0.0  43.71      0.000916   44.736886   34.261344             1.0   \n",
       "2015-02-05   0.0  44.20      0.011210   44.729495   34.296358             1.0   \n",
       "2015-02-06   0.0  43.47     -0.016516   44.712155   34.328674             1.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  \\\n",
       "2015-02-02              0.0               1.0  0.024627  0.029972   \n",
       "2015-02-03              0.0               1.0  0.025065  0.030003   \n",
       "2015-02-04              0.0               1.0  0.024634  0.029899   \n",
       "2015-02-05              0.0               1.0  0.024287  0.029807   \n",
       "2015-02-06              0.0               1.0  0.024086  0.029740   \n",
       "\n",
       "            vol_trend_long  vol_trend_short  vol_trend_signal  \\\n",
       "2015-02-02             1.0              0.0               1.0   \n",
       "2015-02-03             1.0              0.0               1.0   \n",
       "2015-02-04             1.0              0.0               1.0   \n",
       "2015-02-05             1.0              0.0               1.0   \n",
       "2015-02-06             1.0              0.0               1.0   \n",
       "\n",
       "            bollinger_mid_band  bollinger_std  bollinger_upper_band  \\\n",
       "2015-02-02             40.5670       1.342849             43.252698   \n",
       "2015-02-03             40.6495       1.480450             43.610399   \n",
       "2015-02-04             40.7220       1.594575             43.911150   \n",
       "2015-02-05             40.8225       1.747934             44.318367   \n",
       "2015-02-06             40.8900       1.825042             44.540084   \n",
       "\n",
       "            bollinger_lower_band  bollinger_long  bollinger_short  \\\n",
       "2015-02-02             37.881302             0.0              0.0   \n",
       "2015-02-03             37.688601             0.0             -1.0   \n",
       "2015-02-04             37.532850             0.0              0.0   \n",
       "2015-02-05             37.326633             0.0              0.0   \n",
       "2015-02-06             37.239916             0.0              0.0   \n",
       "\n",
       "            bollinger_signal  \n",
       "2015-02-02               0.0  \n",
       "2015-02-03               0.0  \n",
       "2015-02-04              -1.0  \n",
       "2015-02-05               0.0  \n",
       "2015-02-06               0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop NAs and replace positive/negative infinity values\n",
    "\n",
    "trading_signals_df.dropna(subset=x_var_list, inplace=True)\n",
    "trading_signals_df.dropna(subset=['Daily_return'], inplace=True)\n",
    "trading_signals_df= trading_signals_df.replace([np.inf, -np.inf], np.nan)\n",
    "trading_signals_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>SENT</th>\n",
       "      <th>Close</th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>fast_close</th>\n",
       "      <th>slow_close</th>\n",
       "      <th>crossover_long</th>\n",
       "      <th>crossover_short</th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>fast_vol</th>\n",
       "      <th>slow_vol</th>\n",
       "      <th>...</th>\n",
       "      <th>vol_trend_short</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "      <th>bollinger_mid_band</th>\n",
       "      <th>bollinger_std</th>\n",
       "      <th>bollinger_upper_band</th>\n",
       "      <th>bollinger_lower_band</th>\n",
       "      <th>bollinger_long</th>\n",
       "      <th>bollinger_short</th>\n",
       "      <th>bollinger_signal</th>\n",
       "      <th>Positive Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>42.19</td>\n",
       "      <td>0.036100</td>\n",
       "      <td>44.766314</td>\n",
       "      <td>34.194551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024627</td>\n",
       "      <td>0.029972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5670</td>\n",
       "      <td>1.342849</td>\n",
       "      <td>43.252698</td>\n",
       "      <td>37.881302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.67</td>\n",
       "      <td>0.035079</td>\n",
       "      <td>44.751221</td>\n",
       "      <td>34.227937</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025065</td>\n",
       "      <td>0.030003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.6495</td>\n",
       "      <td>1.480450</td>\n",
       "      <td>43.610399</td>\n",
       "      <td>37.688601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.71</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>44.736886</td>\n",
       "      <td>34.261344</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024634</td>\n",
       "      <td>0.029899</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.7220</td>\n",
       "      <td>1.594575</td>\n",
       "      <td>43.911150</td>\n",
       "      <td>37.532850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.20</td>\n",
       "      <td>0.011210</td>\n",
       "      <td>44.729495</td>\n",
       "      <td>34.296358</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024287</td>\n",
       "      <td>0.029807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8225</td>\n",
       "      <td>1.747934</td>\n",
       "      <td>44.318367</td>\n",
       "      <td>37.326633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>43.47</td>\n",
       "      <td>-0.016516</td>\n",
       "      <td>44.712155</td>\n",
       "      <td>34.328674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024086</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.8900</td>\n",
       "      <td>1.825042</td>\n",
       "      <td>44.540084</td>\n",
       "      <td>37.239916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SENT  Close  Daily_return  fast_close  slow_close  crossover_long  \\\n",
       "2015-02-02   1.0  42.19      0.036100   44.766314   34.194551             1.0   \n",
       "2015-02-03   0.0  43.67      0.035079   44.751221   34.227937             1.0   \n",
       "2015-02-04   0.0  43.71      0.000916   44.736886   34.261344             1.0   \n",
       "2015-02-05   0.0  44.20      0.011210   44.729495   34.296358             1.0   \n",
       "2015-02-06   0.0  43.47     -0.016516   44.712155   34.328674             1.0   \n",
       "\n",
       "            crossover_short  crossover_signal  fast_vol  slow_vol  ...  \\\n",
       "2015-02-02              0.0               1.0  0.024627  0.029972  ...   \n",
       "2015-02-03              0.0               1.0  0.025065  0.030003  ...   \n",
       "2015-02-04              0.0               1.0  0.024634  0.029899  ...   \n",
       "2015-02-05              0.0               1.0  0.024287  0.029807  ...   \n",
       "2015-02-06              0.0               1.0  0.024086  0.029740  ...   \n",
       "\n",
       "            vol_trend_short  vol_trend_signal  bollinger_mid_band  \\\n",
       "2015-02-02              0.0               1.0             40.5670   \n",
       "2015-02-03              0.0               1.0             40.6495   \n",
       "2015-02-04              0.0               1.0             40.7220   \n",
       "2015-02-05              0.0               1.0             40.8225   \n",
       "2015-02-06              0.0               1.0             40.8900   \n",
       "\n",
       "            bollinger_std  bollinger_upper_band  bollinger_lower_band  \\\n",
       "2015-02-02       1.342849             43.252698             37.881302   \n",
       "2015-02-03       1.480450             43.610399             37.688601   \n",
       "2015-02-04       1.594575             43.911150             37.532850   \n",
       "2015-02-05       1.747934             44.318367             37.326633   \n",
       "2015-02-06       1.825042             44.540084             37.239916   \n",
       "\n",
       "            bollinger_long  bollinger_short  bollinger_signal  Positive Return  \n",
       "2015-02-02             0.0              0.0               0.0              1.0  \n",
       "2015-02-03             0.0             -1.0               0.0              1.0  \n",
       "2015-02-04             0.0              0.0              -1.0              1.0  \n",
       "2015-02-05             0.0              0.0               0.0              1.0  \n",
       "2015-02-06             0.0              0.0               0.0              0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the dependent variable where if daily return is greater than 0, then 1, else, 0.\n",
    "\n",
    "trading_signals_df['Positive Return'] = np.where(trading_signals_df['Daily_return'] > 0, 1.0, 0.0)\n",
    "trading_signals_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Start: 2015-02-02\n",
      "Training End: 2019-10-04\n",
      "Testing Start: 2019-10-07\n",
      "Testing End: 2020-10-06\n"
     ]
    }
   ],
   "source": [
    "# Construct training start and end dates\n",
    "\n",
    "training_start = trading_signals_df.index.min().strftime(format= '%Y-%m-%d')\n",
    "training_end = '2019-10-04'\n",
    "\n",
    "# Construct testing start and end dates\n",
    "testing_start =  '2019-10-07'\n",
    "testing_end = trading_signals_df.index.max().strftime(format= '%Y-%m-%d')\n",
    "\n",
    "# Print training and testing start/end dates\n",
    "print(f\"Training Start: {training_start}\")\n",
    "print(f\"Training End: {training_end}\")\n",
    "print(f\"Testing Start: {testing_start}\")\n",
    "print(f\"Testing End: {testing_end}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crossover_signal</th>\n",
       "      <th>bollinger_signal</th>\n",
       "      <th>SENT</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-02</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-03</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-04</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            crossover_signal  bollinger_signal  SENT  vol_trend_signal\n",
       "2019-09-30              -1.0               0.0   0.0               1.0\n",
       "2019-10-01              -1.0               0.0   1.0               1.0\n",
       "2019-10-02              -1.0               0.0   0.0               1.0\n",
       "2019-10-03              -1.0               0.0   0.0               1.0\n",
       "2019-10-04              -1.0               0.0   1.0               1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the X_train and y_train datasets\n",
    "\n",
    "X_train = trading_signals_df[x_var_list][training_start:training_end]\n",
    "y_train = trading_signals_df['Positive Return'][training_start:training_end]\n",
    "\n",
    "X_train.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>crossover_signal</th>\n",
       "      <th>bollinger_signal</th>\n",
       "      <th>SENT</th>\n",
       "      <th>vol_trend_signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            crossover_signal  bollinger_signal  SENT  vol_trend_signal\n",
       "2020-09-30               1.0               0.0   0.0              -1.0\n",
       "2020-10-01               1.0               0.0   0.0              -1.0\n",
       "2020-10-02               1.0               0.0   0.0              -1.0\n",
       "2020-10-05               1.0               0.0   0.0              -1.0\n",
       "2020-10-06               1.0               0.0   0.0              -1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the X test and y test datasets\n",
    "X_test = trading_signals_df[x_var_list][testing_start:testing_end]\n",
    "y_test = trading_signals_df['Positive Return'][testing_start:testing_end]\n",
    "\n",
    "X_test.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Positive Return</th>\n",
       "      <th>Predicted Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Positive Return  Predicted Value\n",
       "2019-10-07              1.0              0.0\n",
       "2019-10-08              1.0              0.0\n",
       "2019-10-09              1.0              0.0\n",
       "2019-10-10              1.0              0.0\n",
       "2019-10-11              1.0              0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit a SKLearn linear regression using just the training set (X_train, Y_train): (n_est = number of tress, depth is depth in each tree)\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=0)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make a prediction of \"y\" values from the X_test dataset\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Assemble actual y data (Y_test) with predicted y data (from just above) into two columns in a dataframe:\n",
    "Results = y_test.to_frame()\n",
    "Results[\"Predicted Value\"] = predictions\n",
    "Results.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/Lidiya/Desktop/fintech_project_2'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['random_forest_model.joblib']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the pre-trained model\n",
    "\n",
    "from joblib import dump, load\n",
    "dump(model, 'random_forest_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Daily_return</th>\n",
       "      <th>Actual_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>-0.002092</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>-0.079665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>-0.125285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>-0.161458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-07</th>\n",
       "      <td>-0.018634</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_return  Actual_Value\n",
       "Date                                  \n",
       "2010-06-30     -0.002092             0\n",
       "2010-07-01     -0.079665             0\n",
       "2010-07-02     -0.125285             0\n",
       "2010-07-06     -0.161458             0\n",
       "2010-07-07     -0.018634             0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set index as datetime object and drop extraneous columns\n",
    "\n",
    "daily_df.set_index(pd.to_datetime(daily_df.index, infer_datetime_format=True), inplace=True)\n",
    "daily_df.sort_index()\n",
    "#df.drop(columns=['index'], inplace=True)\n",
    "\n",
    "\n",
    "daily_df.dropna(inplace=True)\n",
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily_return</th>\n",
       "      <th>Actual_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>0.027004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>0.009886</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>0.018746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>0.000818</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>0.012870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_return  Actual_Value\n",
       "Date                                  \n",
       "2019-10-07      0.027004             1\n",
       "2019-10-08      0.009886             1\n",
       "2019-10-09      0.018746             1\n",
       "2019-10-10      0.000818             1\n",
       "2019-10-11      0.012870             1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking 253 values from the begining head of the df\n",
    "daily_df = daily_df.iloc[-253:] \n",
    "daily_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1.,\n",
       "       0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 1., 1., 0.,\n",
       "       0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 0., 1.,\n",
       "       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 0., 1.,\n",
       "       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
       "       0., 0., 1., 0., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 0., 0., 1.,\n",
       "       1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictoins on the X_test: \n",
    "model = load('random_forest_model.joblib')\n",
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Daily_return</th>\n",
       "      <th>Actual_Value</th>\n",
       "      <th>Predicted_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>0.027004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>0.009886</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>0.018746</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>0.000818</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>0.012870</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_return  Actual_Value  Predicted_Value\n",
       "Date                                                   \n",
       "2019-10-07      0.027004             1              0.0\n",
       "2019-10-08      0.009886             1              0.0\n",
       "2019-10-09      0.018746             1              0.0\n",
       "2019-10-10      0.000818             1              0.0\n",
       "2019-10-11      0.012870             1              0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add predicted results to DataFrame\n",
    "\n",
    "daily_df[\"Predicted_Value\"] = predictions\n",
    "daily_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted results vs. actual results\n",
    "\n",
    "daily_df[['Actual_Value', 'Predicted_Value']].plot(figsize=(20,10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 last 10 records of predicted vs. actual results\n",
    "\n",
    "daily_df[['Actual_Value', 'Predicted_Value']].tail(10).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Daily_return</th>\n",
       "      <th>Actual_Value</th>\n",
       "      <th>Predicted_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-10-07</th>\n",
       "      <td>0.027004</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-08</th>\n",
       "      <td>0.009886</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-09</th>\n",
       "      <td>0.018746</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-10</th>\n",
       "      <td>0.000818</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-11</th>\n",
       "      <td>0.012870</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Daily_return  Actual_Value  Predicted_Value\n",
       "Date                                                   \n",
       "2019-10-07      0.027004             1             -1.0\n",
       "2019-10-08      0.009886             1             -1.0\n",
       "2019-10-09      0.018746             1             -1.0\n",
       "2019-10-10      0.000818             1             -1.0\n",
       "2019-10-11      0.012870             1             -1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace predicted values 0 to -1 to account for shorting\n",
    "\n",
    "daily_df['Predicted_Value'].replace(0, -1, inplace=True)\n",
    "daily_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate cumulative return of model and plot the result of 3 Signals EWM Windows = 50, 200\n",
    "## removed 'vol_trend_signal' and SENT' and 'bollinger_signal'\n",
    "\n",
    "(1 + (daily_df['Daily_return'] * daily_df['Predicted_Value'])).cumprod().plot(figsize=(15,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set initial capital allocation. (3 Signals EWM Windows = 50, 200)\n",
    "## 'vol_trend_signal' and SENT' and 'bollinger_signal', \n",
    "\n",
    "initial_capital = 400000\n",
    "\n",
    "# Plot cumulative return of model in terms of capital\n",
    "cumulative_return_capital = initial_capital * (1 + (daily_df['Daily_return'] * daily_df['Predicted_Value'])).cumprod()\n",
    "cumulative_return_capital.plot(figsize=(15,7))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Entry and Exit Points of Dual Moving Average Crossover Trading Strategy"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}