Stock_market_predict_random_forest_reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "375d52e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "453af6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500=yf.Ticker(\"^GSPC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48a24413",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500=sp500.history(period=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d028f2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </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>1927-12-30 00:00:00-05:00</th>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-01-03 00:00:00-05:00</th>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>17.760000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-01-04 00:00:00-05:00</th>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>17.719999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-01-05 00:00:00-05:00</th>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>17.549999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-01-06 00:00:00-05:00</th>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25 00:00:00-04:00</th>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4181.419922</td>\n",
       "      <td>4186.770020</td>\n",
       "      <td>3869370000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 00:00:00-04:00</th>\n",
       "      <td>4175.990234</td>\n",
       "      <td>4183.600098</td>\n",
       "      <td>4127.899902</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>4277640000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-27 00:00:00-04:00</th>\n",
       "      <td>4152.930176</td>\n",
       "      <td>4156.700195</td>\n",
       "      <td>4103.779785</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>4019500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-30 00:00:00-04:00</th>\n",
       "      <td>4139.390137</td>\n",
       "      <td>4177.470215</td>\n",
       "      <td>4132.939941</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>3911140000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31 00:00:00-04:00</th>\n",
       "      <td>4171.330078</td>\n",
       "      <td>4195.549805</td>\n",
       "      <td>4153.120117</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>4249470000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24074 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open         High          Low        Close  \\\n",
       "Date                                                                            \n",
       "1927-12-30 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
       "1928-01-03 00:00:00-05:00    17.760000    17.760000    17.760000    17.760000   \n",
       "1928-01-04 00:00:00-05:00    17.719999    17.719999    17.719999    17.719999   \n",
       "1928-01-05 00:00:00-05:00    17.549999    17.549999    17.549999    17.549999   \n",
       "1928-01-06 00:00:00-05:00    17.660000    17.660000    17.660000    17.660000   \n",
       "...                                ...          ...          ...          ...   \n",
       "2023-10-25 00:00:00-04:00  4232.419922  4232.419922  4181.419922  4186.770020   \n",
       "2023-10-26 00:00:00-04:00  4175.990234  4183.600098  4127.899902  4137.229980   \n",
       "2023-10-27 00:00:00-04:00  4152.930176  4156.700195  4103.779785  4117.370117   \n",
       "2023-10-30 00:00:00-04:00  4139.390137  4177.470215  4132.939941  4166.819824   \n",
       "2023-10-31 00:00:00-04:00  4171.330078  4195.549805  4153.120117  4193.799805   \n",
       "\n",
       "                               Volume  Dividends  Stock Splits  \n",
       "Date                                                            \n",
       "1927-12-30 00:00:00-05:00           0        0.0           0.0  \n",
       "1928-01-03 00:00:00-05:00           0        0.0           0.0  \n",
       "1928-01-04 00:00:00-05:00           0        0.0           0.0  \n",
       "1928-01-05 00:00:00-05:00           0        0.0           0.0  \n",
       "1928-01-06 00:00:00-05:00           0        0.0           0.0  \n",
       "...                               ...        ...           ...  \n",
       "2023-10-25 00:00:00-04:00  3869370000        0.0           0.0  \n",
       "2023-10-26 00:00:00-04:00  4277640000        0.0           0.0  \n",
       "2023-10-27 00:00:00-04:00  4019500000        0.0           0.0  \n",
       "2023-10-30 00:00:00-04:00  3911140000        0.0           0.0  \n",
       "2023-10-31 00:00:00-04:00  4249470000        0.0           0.0  \n",
       "\n",
       "[24074 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b4076b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1927-12-30 00:00:00-05:00', '1928-01-03 00:00:00-05:00',\n",
       "               '1928-01-04 00:00:00-05:00', '1928-01-05 00:00:00-05:00',\n",
       "               '1928-01-06 00:00:00-05:00', '1928-01-09 00:00:00-05:00',\n",
       "               '1928-01-10 00:00:00-05:00', '1928-01-11 00:00:00-05:00',\n",
       "               '1928-01-12 00:00:00-05:00', '1928-01-13 00:00:00-05:00',\n",
       "               ...\n",
       "               '2023-10-18 00:00:00-04:00', '2023-10-19 00:00:00-04:00',\n",
       "               '2023-10-20 00:00:00-04:00', '2023-10-23 00:00:00-04:00',\n",
       "               '2023-10-24 00:00:00-04:00', '2023-10-25 00:00:00-04:00',\n",
       "               '2023-10-26 00:00:00-04:00', '2023-10-27 00:00:00-04:00',\n",
       "               '2023-10-30 00:00:00-04:00', '2023-10-31 00:00:00-04:00'],\n",
       "              dtype='datetime64[ns, America/New_York]', name='Date', length=24074, freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a6d82d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500.plot.line(y=\"Close\",use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e05f89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "del sp500[\"Dividends\"]\n",
    "del sp500[\"Stock Splits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e41081d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500[\"Tomorrow\"]=sp500[\"Close\"].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a2cc50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500[\"Target\"]=(sp500[\"Tomorrow\"]>sp500[\"Close\"]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6851122e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500=sp500.loc[\"2000-01-01\":].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb36c5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Tomorrow</th>\n",
       "      <th>Target</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>2000-01-03 00:00:00-05:00</th>\n",
       "      <td>1469.250000</td>\n",
       "      <td>1478.000000</td>\n",
       "      <td>1438.359985</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>931800000</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00-05:00</th>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1397.430054</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1009000000</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00-05:00</th>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1413.270020</td>\n",
       "      <td>1377.680054</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1085500000</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00-05:00</th>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1411.900024</td>\n",
       "      <td>1392.099976</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1092300000</td>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00-05:00</th>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1400.729980</td>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1225200000</td>\n",
       "      <td>1457.599976</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25 00:00:00-04:00</th>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4181.419922</td>\n",
       "      <td>4186.770020</td>\n",
       "      <td>3869370000</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 00:00:00-04:00</th>\n",
       "      <td>4175.990234</td>\n",
       "      <td>4183.600098</td>\n",
       "      <td>4127.899902</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>4277640000</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-27 00:00:00-04:00</th>\n",
       "      <td>4152.930176</td>\n",
       "      <td>4156.700195</td>\n",
       "      <td>4103.779785</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>4019500000</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-30 00:00:00-04:00</th>\n",
       "      <td>4139.390137</td>\n",
       "      <td>4177.470215</td>\n",
       "      <td>4132.939941</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>3911140000</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31 00:00:00-04:00</th>\n",
       "      <td>4171.330078</td>\n",
       "      <td>4195.549805</td>\n",
       "      <td>4153.120117</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>4249470000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5996 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open         High          Low        Close  \\\n",
       "Date                                                                            \n",
       "2000-01-03 00:00:00-05:00  1469.250000  1478.000000  1438.359985  1455.219971   \n",
       "2000-01-04 00:00:00-05:00  1455.219971  1455.219971  1397.430054  1399.420044   \n",
       "2000-01-05 00:00:00-05:00  1399.420044  1413.270020  1377.680054  1402.109985   \n",
       "2000-01-06 00:00:00-05:00  1402.109985  1411.900024  1392.099976  1403.449951   \n",
       "2000-01-07 00:00:00-05:00  1403.449951  1441.469971  1400.729980  1441.469971   \n",
       "...                                ...          ...          ...          ...   \n",
       "2023-10-25 00:00:00-04:00  4232.419922  4232.419922  4181.419922  4186.770020   \n",
       "2023-10-26 00:00:00-04:00  4175.990234  4183.600098  4127.899902  4137.229980   \n",
       "2023-10-27 00:00:00-04:00  4152.930176  4156.700195  4103.779785  4117.370117   \n",
       "2023-10-30 00:00:00-04:00  4139.390137  4177.470215  4132.939941  4166.819824   \n",
       "2023-10-31 00:00:00-04:00  4171.330078  4195.549805  4153.120117  4193.799805   \n",
       "\n",
       "                               Volume     Tomorrow  Target  \n",
       "Date                                                        \n",
       "2000-01-03 00:00:00-05:00   931800000  1399.420044       0  \n",
       "2000-01-04 00:00:00-05:00  1009000000  1402.109985       1  \n",
       "2000-01-05 00:00:00-05:00  1085500000  1403.449951       1  \n",
       "2000-01-06 00:00:00-05:00  1092300000  1441.469971       1  \n",
       "2000-01-07 00:00:00-05:00  1225200000  1457.599976       1  \n",
       "...                               ...          ...     ...  \n",
       "2023-10-25 00:00:00-04:00  3869370000  4137.229980       0  \n",
       "2023-10-26 00:00:00-04:00  4277640000  4117.370117       0  \n",
       "2023-10-27 00:00:00-04:00  4019500000  4166.819824       1  \n",
       "2023-10-30 00:00:00-04:00  3911140000  4193.799805       1  \n",
       "2023-10-31 00:00:00-04:00  4249470000          NaN       0  \n",
       "\n",
       "[5996 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c34b46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4a000441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_split=100, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(min_samples_split=100, random_state=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=RandomForestClassifier(n_estimators=100,min_samples_split=100,random_state=1)\n",
    "\n",
    "train=sp500.iloc[:-100]\n",
    "test=sp500.iloc[-100:]\n",
    "\n",
    "predictors=[\"Close\",\"Volume\",\"High\",\"Low\"]\n",
    "\n",
    "model.fit(train[predictors],train[\"Target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a162cf62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "\n",
    "preds=model.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ab147a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds=pd.Series(preds,index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78d2df28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(test[\"Target\"],preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ec3fe26",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined=pd.concat([test[\"Target\"],preds],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fbbe30e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P/wGPeMQj8OM//uPHXtY7R+j7YdsRjrjosWoMNcDvHaoBcUmffDTGLFqRJ4jKnKDcPbwdSiS7KchIbZ9KnaLJiLB8Wi8yVR1ERvTCkINmVFUgsTFjiYcax4rcsFoy7v56XOSOdi3O2hNccuYmEznmOhnpypKRLnDj1dz83MhItqYB4/Fy59ipeZIRn6YB+7ooXJqGbg8kGVnnISMmsa0qswZwa8O4f6VP7q39k5s7TXKkyIhJRurgdaeUMsBBdD+Q68CmaSLrWjYyMtA0blLPbBvI2L9kLZsa1ppOEtDgpoXrmrl/nSHISHYy8oxnPCOqKH7jG9/ovfY1X/M1+JM/+ZPcTZ3xYSEj1VzISJ+M9DQNxnGFSQ4rYK3iyEg7ICPddj6apkdG+teaTpiMqPGJZ0xG7LfohUGgWzUxoiz263ohy6Np0k8TiuzrPVLDf7TtIKiOFJ1LqtM0TQWlVJ7/AcYFy/0e3SlCRoymQUpPkHm5N7N2cK0oTUbCqNspQkZMMkJoGmFSNp6jMNdzy6CmXTEy4gpYbWRkTEZyEYE6eK201s09Mi5BAUYkL7GuaWRkJ1vLTDLi0TQ8MpK9HybSNEopbIZrsRXKAaTrWug6OK44M9xO7qPR0BtmPZdmZLhJoHZguVzNCHmCqBtzU+foja7pnyaU8AK2QutBHJqmU8poRiqxZmTkgkN0SEz7Egr9XjeJjiVowbECegMadDFYNbUxKxLf4AXHXKnxxrQpUKCFxJq5GgFp5Goa6Lzc49ZO1Izowx367kVr85YmI/Z3tJARoyGSajDIOapGSsHVD+g5T9eMKOs9qnBcVdXBa4X+PzpuxWlGEutaJjLSJGkaX8AqCrKWTQm6Pfs+ExGwCte1UdMzaYqzxZKMTAgLhpRA64KwaBoKy+VW03TkM1UdpWk67WRZgowM8/WSkQ4jTSNcGMYnnsrcMDwaoQDNCMGRI5xZMFZUM2KPm/s0LIFi9b7qUBUlDmGaJp9OkUTuEzadl0+vaWSkbI4h4d6oUSmhaUgy0gWSEdWSc0FI09BzNEbT5CJa0moaidDUGnagaVAnr7vkfMl1MCJ5iK9rmchIlaRpCgWsZC2bElYyIrzPSNe1EI12XLEkIxPCCLQs+GweAWurkZEqT8BqaBrlwJkRUVO30shIgTW+0Yw4AlaqGRHTNOMTTx2C0kl1gTRCcCQVmuaOFVOgu08m+5nlnDKaZhQ6ixdIEgYZCVAVWYJbyfZykRHyNi9hmihgDZU0Gppmks8IIjQN1Yzk0jSV2SlmbaA0zWw+IxNpGkvAKqBpYvuBUiWUQouta6WakWA1je0zIqdpxrVsStDttUI5gHRdawLJ4nHFkoxMiBH+x2w0Db3J2FU689A0XLasGn0Bz0zTDMlIJR2X0jR031rjDpsrSCD8sTTlIx4qWCbMzdFDRqRJg+SYq3ERLUEx9BxjEPpmRqpGJFi05kFpGvtvJhkpXERDyMi4T0qQESJgdWkanUBDFdA0/U+fpqHIiJKVyloDa2RE8a8Pv5fSNJZmpBgZ8Wmo5LqWqxnRCKHbPYehiJLzpTEXTUOOp/R+IF3XYqXXxxFLMjIhrMqKmWiajiYjHcqraTpF4MwqWoWimv4CrqRVL9aE+dLerlM4Unt54zLVND6aob+HfIrBapoiyidtFOTOsZymSVfTFNM0ARSg6ClQEPQJW7L4xWia1tA0pdU0gXEDOhpRUGQkVk0zRcBKaRqHws03PRNW0xQiLrSaplP2DU+swaAVLZRCi61rhchIF6umKUlGZqqm8WkaLXhKm57JaZpJU5wtlmRkQljwv1kY5kFGjGI+l6bRNelKjVd9gqbRDfjECAaNCE1zOCAj0oXBqqYJeBRMqYAJ6SPyaJr+ZxQZcfb1wZlI0yj+xlu08GZtr+9AmgqrmuYU0TSh7160OG/DNI0WSVaqzT4XrHOUrg0dTi9NI9WMKJ+mAWzETVydEqRpIuua7mabWU2zc9v2WdseXxZXK81UTWPRNNJqGuG6FloXjyuWZGRCWDDXbNU0w01GDU8/9QjFSmJjkBE4cGbkBF1pZKSg3XXMgVX3wy2haQIQogVbC6Mxu5CH5fMM1NKlvQY61pqRXGTktNI0kWSkIMkJhcpMcuju9appBp+RqhQZCVZ5TEFGwjSNnmdd5TuwUjdfn6bxqYQ5HVgVSVSm0DSAvU/F5xh5wLMFrJF1LRMZqUw1zRlK0wSraWI0Tf8zta4tNM39KCy4NxPBCAYRsNo287JxtVuf1cOhakiPC/8z1XABN1KnVBqGpnFMz7pRMyKu0jHOijURGQbQjIwEIkT5RNGi1FiRC7ilNxBgdN2U3twlNA0p7c1xNdUx0jT26/T/UpdQSdB9L3GxpO/35qh9RgoT/5Sguayahjh+djwyUquRppHuW0uMqCgyoljkINsh1KNpxn3atmM3W7kD61DaS9DN/ntQmiYTGek6vjcNt64ZzYjQgTXUVoFa0ZMTMNeJdioyQrdnJaACmia1roWKBI4rlmRkQtScZmSmrr3GDj5Ti6IzadscqI6We1UDTSN2SqURomm60WdEjoyM8w3pPAxNk3HmjsfJfj1W7pwaK4qMuJqRdaFmJOYzohf9QmQkWE1zipARC+oWISOnUjOSoGmKBKy8z4jqOjTVcJ6pdqyskjqwKnIudQEBKzU9E2sadOYVpmm6ImREf6ay1hrreGYjIwE7eG5dm9uBFUA7CSGar7TXlgPEqmlk61pIO3VcsSQjE2KE7GHxt1OiMz4jtT1upoC1pU8QdRMt96qHZKTp5qNpaGmvWDNCuOB0Bcx0amWK6VnshuVCx8XVNBJkRNXz+oycIgGrneTkISO+H8hA0xQiI0kBa1FpL28H3xGkwaJpxCXO5BzVAlZVeTq1XD+QcT1R9kVG5q5aehPORAQczYiNdAlpD4ICGAfW1LqWXU2j0aZwMtIRhChfwDpfNY3luiupppH2pllomvt+WE9YMwlYqZdAWdfeQUCmyGfIEwR3Ex+TkRJkRJf22jSNspKRTM1I3QTFVaMdfAa1EizlzE9sQjb1NEI0Tb6ANfL+blxEpyQjp0vAmjtuSGMAALuZ7OBPhwMrRRcoTSO9uVtP/oquDbCphGHO2T4Ykd8VSaSyHVhJS4f+e5C3kN9lAtaxK3FyXVvl2RTojsptiKZxJnycPiNKIcv0LLWuhbRTxxVLMjIhrIqPmatpFJynHwHi0nYK25bCmf5Fy9EbOhlZlSAjxoHV7U1DBayZ1TRVHXQ6LdOMBMYySGr+WCKaplTAKqH8SKv2kqZ2KdMz4NSU9vbjCpIR8pZQb5piAauh2txtTqBpAqZnVHdh0TTSahrL9MzxIJpE07T87xZNQxCBXMSlqq3y+1CvIVFpL62mSa1rBhkRCliH+XrdrylN007bD1MiTNNEqmmE69pC09yPwmoENRtN45T2ZvSm2bgnrgVnhm/izV5fDrdSE2iaOTQjDMTrPb0OX7GkN00QGSlquhe+gK1Oq0B2p1bJuVSR86QMGRnm6upoTpFmJJumiSAj+im2VMAaEiHPZwdPbujkRkZ9Rja7TlTFYEHuakxGXNfn4q697u/W3PNpmipE00zRjHSOZiS2rmUiI0EHVkKvFGln5qJp3DVdsDZI1zWJm/TpjCUZmRAWzJVJpwSDKOZVJk1jQ3rKKi8z5V4MvTEtGUk7sMqREULTJMovy2gaZ6ySahoBtOkuBvk0jaRRHi3tnZOmGX+flaaZIGB1EYyppb1hmsbftjiCNM34e0W69gJ5Jc4+TTPemFTXEofbqTSNIr8W0DQD7aGqBlVVjR5d1rhC9M3QNMpMK7mu5SIjmqZxNSOUXunyk7LZaJqts6YL7gfiahqBGP90xpKMTAir/DRTaBoK1Y6K+bZDFk1jZdFK2apzUwLnn6Drg7P6nyXJSKhRnlI4zPYZoRBvvLQ3C82YsUxYZgfvaEZyq2lEPiPE9GzOappTRdPkakYipmcaUp8qYA1992zTs661z3Eyr5agC9T0DJDtB1O6WsOmaTqYG1ORtoNmeCGaZopwc0B0G4YSE1dWcTRNal0rFLDuuggy0hYgI8Qldkp4a7rgPiNd10JVhscVSzIyIVgB62x28FV2kmMprzvymboxJyjHI64GZGRvCjLC0TTF1TRhvrnEwl0zXT7lM5/+hMZYWlcqYE2fSxU5T0roFJkd/JzISF6SY9E0Ac3I1K69oeQ0GxmhqAhgHTdlaUY6rOqxI7VkP3AC1g6DDbq50Y/b2HUKO0k2JaBpLNMzIeJSOVoJjhIrqqZRJFGMrWvZmpFQaS8V39L9kJnszUrTQCZgFa5rEgPH0xlLMjIhrJvcGU3T1FEecb3fX8B7mNmB1WhGZAZEdJGxraSpfmB4S1EFDA/Ll9E0MWTEHtcYXUkhdMkxn0zT2D/d14GM+Yq2Nw4sMWmzHtydOe5MaW/ZIjqiW+NrilAB2cnIxklGQtU06FBV1ZicivYDoSWDNI09juh8CFAzIQfWw0LnUbNGBkq1o+cYW02TWNd0MrKVrTmVsYN3kZGKvfEfP02TpnCl61poXTyuWJKRCWGJGUnL6SlBe45YsJwAcTl0a9KJC2CMR1zv9zTNvtrmT9jQNI4D6xTNSNVYmhDuqarIG8TTHpwimsaZY663hAgNI9bgJUmDqDfNnKZnlhZFjgj0n3VpGq0ZmYaMcEkuUFBd4CIjAeMw/RSeQ9vp/da41TREwKoIMtKPKzhuomqa8Xep4Na9CXPXS37X3gBNw61ruXbwxoGVWQOG7Vs0TXY1zSkSsEp60ySREfv9xx1LMjIheJpmntLe0dhoOKEkNI3bVIk8QbjUAY31fk/T7Fdb7ykrGUbA6pb2qvzeNEzXXsB9StYXmnyKIaGW65QqGktSTWOg4/7/ua6bIpqGdHA9dV17TxFNI9gPIftwYLxBz2l6FjNZS0aEpqE3Mv36iJRlICMViE7ILvu3tgHh07uEpvHGTc+3crQSnPbLOsdiiTStpunIZ2PrWqZmpAo5sJLtdyXIyGxde8maLpQDSNe1RcB6P4rxQoOVxU8KUrLZm9yM/RlS4bWbJuVl1hOWE3uDgBUAjo6ElIqOQGmv1ZumoGtvqMlWSdfeYClnSTWNoO22J2A9hT4j5Y3yeGRE3FE1M3KTnJDGAKA0zXzVNDH7+WR4NA0vAtXHLEdDFOra61bT0BAlvQJkxBtXlIy4NM0EZITYGoyN8hLrWjYyMghYuWTEoNIFQt65uvZunTVd0rVXWk0TQIyPK5ZkZEJYi9pMXXsV6T8hheV00JtHp2BBpqb7J3OC7tNk5LAwGfFoGhT0piECVtpki1nISmiakGYkp0w41AHYGnc2miZyU9Q0TaEdvKmmcbaR211XGl1mkqMCNy9gdGAtR0biN8jsB8Xt3fb/A6W9+gaVk5xaia3VtRdsNU0/bq5mJF3aK52vHqvSNA1TCk9/jwpumc65yXUtFxkZPu81yiPbVyVo4Snv2hvzGel/pta1xQ7+fhRWieBM1TSKQrGWYCmzmkYpq1Q21jxpvd5DNzwZbI/u8d8Qi07Qm6bbAe0OybCqaShN4980SipgQr1pSiifeNfe/ucpraahNE1JNc0wxyhNc6pMz0Qlrfxn+7/pZKRQM8KgWzFaKBkuMkKSA0vP0bmakRyahiQjTm8apVwEQ5409L+HaBpHiyI4H/xqmv6/IZoGADbBZITSNEQzElvXCpGROE1DHVilNI1ey6aW9hbQNMJ1zayLSzJy3w+7a+/8NI0Fy2X6jPhwpk0d0Kjq2iQOm1JkJEbTAICkI3CApuHayZeYngVpmhLKJ3LDGr0P+v/nd+0VnEuWA2uBgNX4jLivj7+fMpomUzPi3qt05cNk0zMrASHbnqoZCdI0GhmRa4is892jaYZkxE0aZhKw+klOBk0zPMFz14ub7AX3A0EBwjSNcw1TZERwHKMC1tq/8Z92msZd0wVrg3Rdk+jfTmcsyciEsCDIDNv2WFCaxoblcmkaIvSqa3NdhuiNTdUnDttSzQjrwErQEsmTSqCaxoZ47Ru9JDjjJTpWHk3Tvze2zrlUUnHX3sgx186R3cwOrKeOphl/n0zTTDY9m5umiZX2Ku/1IpqmHj8/9qYZaAQlvLkH5hgu7S2nafTcuOvF3b/B84zSNJ3+bGJd08gI1Lg2RcKU9kZpmpJkZCaahmpGSAIaW4Ck6xpX4n6csSQjE6KmT1gZQtNoEJ8Ramwkq6ax+UXFwJmhE/QI/UW8K6ZpfNOzDjW2GLQkEg63s58mWKHhhNLeWbr2inrT2Pv6ILM5mggNUxQZKU9GQv1ZgFOpGckTsNIbulJqtIOfWk0TKB+eTtNQEejOez3nfLA1I2Rt6EAErC4yMlM1zQQBa+UiI5FS7eB8md40yXVtfWL8vGDNMciIawcPmP3bWQ63x1hNQwslZu3ae2ZkI0syMiEsCDJD2xEN4qyZm+S4T0SKwpmJcq+tRkY2ucgI78CqT3A9rigZkfDNpmxWnkAEOwAnEjQuuBuZG66u5VRU01TmKbmatWuvXfVyjNU0zDHXr3dGwFo2P56mCWsakuEJWAOlvZ6AVb4fKstnRK8NA01TkDSIaBq3tLdEM6K9LGI0TWi+tJqGJqSxda3JQ2NNNU0X1ozQ/bNtlez8OBU0jVBDKF3XJJTz6YwlGZkQmiqYtZrGhWILaZp+EEZ1HriJb6v+Is5GRtpQbxp7XGwzkJHKfqriIPSsrr3BpnuwtiMaq9KwcywZsd+rNQISX4l+QmkotiLnyaYIGbF/6qCbFM83Y3v9uBKahv9sp0A6rJYtolyzQ44KFEfEgdVKFFyaRrAfzLlEHFhdmsZ9UBGZ4ImqaexxDkXJ6ZA8OaZnMdotOF+mmqYfILKuVRXRjaQfrEwywiEjhgaz5ye63maiaei+kd4PpOtawySKxxlLMjIhLJgrQ2gaDSJSc9uEp8K9eShG6BXKlndD0tBuhGW4Zr5hmgYgyYgIGdFX0dBkyzzBks2V0DQhO3gzlnioIpomHxlJn0sVgexzHVhjKMCpQkbs3jR5yIhbTdVpAWvhtcY1TuTKx8XhWo+TE7azEAidjBTQNLVD08R8RrJLewMoSYF/iW48Z2ga5npxz7kwMkJ709CpJ9a1nIoaXdrLISM1jzyJrjeTME2laUiFpLCgQbquSR6sTmcsyciEsE3PZqJpSDfW3AZ87s1DEd7SqPID2fKu7i/g7GQk4MBqaBrjwppRTSNospVTMVcF4MgSO3hJp0u3tI6Wcoou/AzTsxIBa+zGa2lGZrWDp+Omz+WQxsCmaaZpRjgtkt5GVkRoGsVQIOZ8kOwHer4bcbumaSp/G8JxZaZn+QJWfUxU5Bp2z7ngeUauA56mCaxrGV4jUdOzwNorS/bm0owU0DTCda1ijs1xxpKMTAjriZspAyuJcDVNnoC1H0vzlrX9hMXEru6Thk7YYKp/c0uU7QFkpM5ARjyaZniZWciKBKwhzUgW5QN2LBp+NY2Ge3vOORmCfkTVhGQk5jaai2CIt5k5blDPodRI05T6jCSe1rNbqkcErLwDa75mhApYOwc1LUNGfPrInXtJz5vKQUa4Sja3VDssYCXICJeMhNa1LGRkELB2kWqaNj8pc9ey0qCJpd0DLXwspOtaqGfXccWSjEwItppmapZpTHj0giMvGfaeMAhvmWor3ZpkJAMZoaVzjAMrMNI/WaW9bjVNxyQjMzS3S5U7cyHpdOkuBvrmAwgXMoHls+mpUeAzwvX64f4/K02TWU0T0nCoDiNNU2oHz5Q0WtuY7DNi8Yrkdy1gldM0lsOo1bUXQf3ANAfWwO/CcfV56dI0oTLq6Lh1QDOSWteykJF0NU2J38psXXtdB1ZBQYN0XRvLrhdk5D4fVmXFqaimmZOmcagDN9qBplE5yUhHkxGHpinSjEhomuEtJdU0LiWRqDDiQmZ6Zr/XTkYEC5mommY8T8QK/yFsyNz92/j7nMhIbs8b7pjr17upyEjCZyS/N02kmoajaTI0RFb1GO3aS9YGHxmZi6bJp39G0zOnIi6ajISQEb83jTWv0LpmkBH5mhOladxkROTjMp2m2bUddi5CmFNNk1jXQlWGxxVLMjIhrCd3AbQuCgK/5/emcd4Ta7XtRNcUJCMUGXFpGi0My0FGXJqGE7BSEyhhcEJYOscS07MoMuLMsaoq7OX0pzGUXwQZIb1pAKHCX88voo/I7a4rDfpVJONyaBjQz7ediIzwNA3I77nIiCtgpcmI7zMyakYkNE3/M0bTlAhNT7nPSNUjpRy66QlYU5oRh6ZJrmsGGZGU9g7IiKp9hCBIgyXWeGrMNoGmcW3yqWg5dp+RrmvNohm5/0RN4V4BtC4KF4rNGNfTjFA4U9lzdsMkI9KmdgBJRioPjjQC1jrnKUVPcqim4Z5gZzQ9S5U7c1EzEL8bXP+cg4xyThFNQ84TII9SiZVZujTNXBBuPk3Dz1EpZRKw0mvN6H6C28gcUAtY987x52U1yut/zzE9UzSx1cdc2TSN39BOkowwtEfsdeG4mqapGxvdDJVRR8clD2LWtFLrWk6zPOLX413Tppomcz/QyU6gadwkTXo/kK5rEjfp0xlLMjIhjADIqv+er7Q3twGf7zPiw5mhm7hq8hpM9eOTsl7nxNdJw64uqaaxNSNcCWZZBUyApilIbES9aci4+zkurIJzSS/6HTIQlyFiBlTW3xQsmHhK5JYMh8WlCq3m9wtRSHODjGwjK7SAdf8B/U+pz0jGfuh70+gENE7hnjKaRjBuHbCDj5uepWia1j4mqXUtCxkh5dLuuR6wVUgjI+TvExrlude0hcDPYHoWqjI8rliSkQlxKrr26pNM6a69OdU0rs8IcQEchV6BzQ7JSJVTTRNwXwXGE3yXoxlxLJQ5rUdKiMuF/s5K2aiA1kcUdQAW0DR0XH0DEnkUZHbtBfIolZg1t9iQKjPmQkZ0mwEAkzUjIR1DPk2jk5EBGbFoGv+mnyNgtc4lszbY4nbPnGwmmqZEK+EJWJnrxbODD9I0gWqa1LpWoBkxOhxr+4FkL7Uf6Psn0DTutWcVNEQoXOm61jDr63HGkoxMCNv0bJ5GeTZNo0SwnA7PIdG0sR4vtFC2rIaniUrSXVdHwH0VINU0WTTNuMgAvNrbcqQUBn0vvc9YELgwJP0cRr8jPxmRCVglNE2/fU3TyNwxh2EjkLkYQs8MOu5m13lPx7H3W0yHAklGSmmaOHWQTU1pZGTPR0a4EtqcxNRURlRuNQ3VDxSUnkqomQk+I25vmlg1TfDcDdA0yXVNIyMi1+cRYfSu6QANlrzW6Pun0DQuMiK8z0jXtZDlwXHFkoxMiNHJEVlC01hULk2T4V/il/aON3dLCMfF8DSRl4xowzMuGRluljk+Iy7ES/ev3mQJTUMWKg7+L7GDj9Xmc+XHOU/DEjSsdmmaHGQkApmL+4Zkhos2uOK82Ptd0zPjM1LYlDIlqsx+Uty6NA2hOlo/MaEmeKmwaRqXwg3RCIL9IqFpXM2IxIHVQUZENE0KGYFCy+zH4LpWoBkxVgrc9nORETUPMuImf7YdvKSaJr6uLY3y7kdhZf2z0TT9ia5QOc6uE6pp6BNE4ASt1gXIiNGM7Hl/0hdEm6MZCdE0zEJWUo4L8HB8if4k9jTBldbluG5KoFiPpslARmI3Xh9Cn4mmkd6A9PsZag7Qpb2adyubG3teBUqJk9Fux+tg/9xhMCFNI6mmoaWrLoVbSiMAENE0BdU0tUFGXAFrmAYLjkuvS26OoXUty/RsvI68yy1U2pvaDxZNU36Ldbdj0zQzJCOCB6vTGUsyMiGsEsGMHjKxoIKq3J433g2JCEJToqZqeJpoSmiaeuX/SZf2ZtE0mt8I0zTGkbKgNw39PEAQjBzTM91cKkbTMNBxHk0j8BmBTdPkIBh06rHeNLnjxkLcNp6Zh6sZMjTN1N40gRtkFmxNPUa0ZsS6ccYcWNPzt8TQVt8qREy5JAJWSsf4VJL3unDcCv17/N4043tch9vguJTi4NCb0LqWg4wQ7xbvmja9fzLpqrloGiepbBVEFK50XQuZQR5XLMnIhLDt4E9FaW/euN6Ng9zcR+qA/6xGRupuI59rhKbR53cZTRPra9H/zKmAob2quA7AJTRNVDPC6FrKaJrwe2sivOvHzUBGImWs7v/n04zkJTkhPUenQOzgp9E0tnYmrGmIhqZoqma8CVr71y/tLWmUVzuaERXRDxyvA2s/32p4QLE6m+thpecCpTg4+/rQuiZFRpSykvqQgNX1s0nTNOTvk5AR+5pWQppGuq4tNM39KCyl+CmhaZToKVmHV02TIWCt1wXIyCmmaTi3U7cJnSQsmoY+oZVQPgLRF0vT5HTulVTTGM1IQTVNxIDKN6Sai6ax/5/aD8HeNN0cNA2nYxj/nqUZMeLVs1lDKsXRNFmakf4nTUa8tWFyb5r5qmmCNM0U0zPYSV1yXZMiI2RM03yQ237u/jXvr2Yt7RVX0wjXNeOOuwhY7/th0zTzVNMYmkYNsKGQplFKmQXenP9Wd8s4j2iSkSxkZEhGOJrGJCNzVNP445b0pgH4cs6S3jSxGxa3r43PiCRpEFB+etEv8RmJlVmO9uPIHjcW7rip8tMghaIoTVOIjHA0TUCjkgyNjKxP8A8OMTt4QaJnOQ5TmsaqprGv+3yfkUD1T8G49UDT1C5Nw+zr5LiE4qg4+ii0rkmREartidE0KnM/OOtYabjrufThVEzTLMjI/SesEsFTQNMohZFjSIy765SB506snbnUDUsd0Gj2TwAAVqogGWGQEQ3FtjlmagE7eBYZyaJpeAFryVgha3kailkMDrLs4OWN8lar1TCuHCWIQeb6/ydyTNoytikdN0SbdErNQNMMHw9uI2Mwk4ycxfL5aqIDa5imAdmes29FAlY5TZNzLow0jdO1l+xT5axT02ia/ldrXVv3a1kaGRnHNI7XzPar3P0wgxU8MB5HvV0lvM9I17URtZo0zdliSUYmREMhyAyhaSxoa/gcxIVeIGftOU/WVT0K4QKIQjNcwKscZIQ6sDqhnzK6KQJWxpSnpGsvECrnRPZYEs0Ia3q2zjE9S3fm1CWU63W/70sdWEM0jT6H5jA9U0qZG5AeN4umCZX2zkrThPdJNLSANUTTMEiDRkZ2ncJOWOLcd+0ddELK7VvVv551zDJomvGYyWmaukn3pkmOS/UWrIA1sK6JNSPjdk25tPVl7ONp5is1PZugFwHG46i3K0XKpeuaBOU9nbEkIxPCcgidq2svKTWLNcNyg0K++smroqpzBxp1Y7XXJyN7KqeaJkbTDD81MpIyIFIKGJ6qXM0It5DloBn9WMPnGTg+R8Aq6XRp8fxDZAlY9ecENM06o0xUh914kK9WOpFxA0oF3VcnhAt6qHFdpxSU7rBaWk3DVXgEkp9kWMgIc9xYmmZ8YpYiRLXVtdcu7dXXedYxk1TTaEQgI8kZfUbC5fnuORYclyrPyT5NrmtGM5Jwk6ZJo15vaThr+glhIj0fTWPvf2nXXum6Zno0LZqR+35Yhj4zde2tLCjWf/oJxeFw4u41NVajT33/M4OmWc9E0+hFtGuE1TRMbX5NaTAzbv8zJ4Gg77fH0omNfBwJz8rRNFkCVsG5pGma9apHRnIQDL9hoP/7CElPR0ZoAigdN0Sb2NU000p7OWdfIBcZ0QJWStMQGsy1jwVMB2dAkIzoS5ggI2M1jX2eUBoh6SLL0R7u78y4qWhMMuIgIww1KBqX2adJmkaMjNg0jYcQOKi0nm/Sbt8IbGdKRtYEGRHQNK1wXVuQkftRWALLmbr2WjRNpHzPDY2M7K/qETUgpbKpbrfrIRnZw5b9OxsxB9ZOJyP5kCkcvplDM3Is3Pv3MyhLAeXDWYm7MTbvGl/LQ0Ykpb0TaJqAaJX+fmJv0KJkIC6hoJsz4xZqRmyapmxubM+jOTQjXBLJoCRNXWE9cJBJvxV6juoEX1O45m6jn6BHhDLlcBu0rGdQkvFckPiMaJrG1YxEzrHYPuAqWrrEuiaupiFz0gkeDefGL5ovef+UShq6Hb1drz1IIImQUtmStex0xpKMTAiLe56JphmdNQdjIylNMyzu++t6PAkZc6AqcIKaZCQHGZlTM6I4ZGTYDJNAlCIjrJtrVjUNvHHc4Pb16MAqSUbS55KGw/dLBKyRcl7D588oYKXjnyUUWYZ0LV2noGgyUvBUx3aDnoWmkVXTAHIXVstxmNI0HbwyT71vgUyX0MAc9VqUcy7o87I2LsocJQZ73Ng+0A8mYJKn0LpWUE1jHK9pOImQeD/MRdNs7ePa0WMOBJNx6bomcZM+nVGUjLz2ta/FRRddhIODA1x66aV4+9vfHn3/m9/8ZjzpSU/CWWedhYc//OH4ju/4Dtx2221FEz6Twnpyr+2FoTgKaRqTjKwac5IZbrWukyjAev8sALnIiHZgjSEjwnbeTKfLmJV0fjIyfJ55QpvbDl5vwu5NMzdNM0D+axnSQMOdOodCSIWmkqD7XDpuqLS3VQotyPEqQEc46qC4ay9H04R8RshcpY68I+Ru0zT0KVlf5wfreiw/zemfEnJdHV7Xx0wiuDWaEY2M6GUxeo6laZq6Gt+TXNfEyMi4P8FqRuxqGrGQ16kKLA29HSNgtdAwBM996brGVSseZ2QnI295y1vw0pe+FK94xStwww034LLLLsMVV1yBG2+8kX3/7//+7+M5z3kOnve85+HP//zP8Yu/+Iv44z/+Yzz/+c+fPPnjDqtE8FTQNBbiEj9hDE2zpjTN8JmqSXZy3NOakarFbitER6KN8oafjRQZYWga5mIxN/pMASvbAbiz/5YzTrSahoGOi2gaqOBxN9U0ewNNU2h6BvCanDkFrPTQSsfldCx6fi3SC3IsuJLGcgfWoZpmTappEhoMQJ6cGk0EKe01HWad7TV1JU96c0p798abaor+qU3yFK6m0b+LBKHarwTMCRFa1zKrabRvTahrr7sfxPt2Jp8RI2Cl9xkg+LAiXdfu83bwr3nNa/C85z0Pz3/+83HxxRfj2muvxYUXXojXve517Pv/8A//EI997GPxkpe8BBdddBG++qu/Gi94wQvwrne9K7iNo6Mj3HHHHda/MzFOCU1DnDVjzbDcoMiIuTBJCVyKptk7OGF+3xwlVOg6BDSNaoQOrBxNE0VGZFPUwTWFmuLmGjU9YxT+Rb1pgODNVsPW++tpPiNAgKaRljEKgkdGMmgaRzOk6LJVIBjnjqF1XhQjI8waEPh9X+q3YiB3EJqGL/uvq0qe9AaraRiahiQjqfPB7dpbcfuaQUaCgtvh8zxNE1jXpMgIob0A5qbsHE/xNUHmNyVcZMRy+qbbcUK6rlkO4mdAZO2tzWaDd7/73bj88sut1y+//HK8853vZD/ztKc9DZ/4xCdw3XXXQSmFz3zmM/ilX/olfOM3fmNwO9dccw3OO+888+/CCy/MmeZpi5GmwYzVNASKZbwEQjEmI6NmpCLcpW5OFaJp9g/ONr9vDoXJiCntldA0h3F0hy6OHk1DNik09HGDgySLuvYKTM84UZ0xupJUvdTpm612utxf98leqc8InS+9CZ8lFetlbu+EcD+E9ByWgBUoSv65kkZOlyQKTjNCTg6rmob8PrqwypKy3vQsTtNYyEgWTcOIQ8nvq6Ymgtv4uI0RsPbzaBhaszMJb3+OKQVs3e55OvRaQJKR5Lq2ElLDTn+nUGnvmJQJKdG5qmm0ZmTYrlW1CQTXBnHX3vsyTXPrrbeibVucPHnSev3kyZP49Kc/zX7maU97Gt785jfj2c9+Nvb29nDBBRfggQ98IP79v//3we1cffXVuP32282/j3/84znTPG1hQf+z0TTDjUENjoDCcfVNY39Vj08JLJwZKO1drbBR/bbEyIigtFdpyBRqfD8XdEEcbsb6pmHTNHHtSyg4SDJV7iwdxw1OzZ6HjFDnSf79WjNSUk3jzt2UYZPXc1xCU6HIk5oUEaBPyq5T6mw0zamqpglVqlDNSGaJc296Nhwn1P39rmaQEdP3ZjpNYypjKkozxscdq2nc0l76nfqfB5bgNuQ1IqVppiAjmqbht63X5ANpubuhaaYiI1oLpJERiNYG6bp2v6imcaF+pVQQ/n/f+96Hl7zkJfjBH/xBvPvd78Zv/uZv4iMf+QiuvPLK4Pj7+/s499xzrX9nYliZpRDBSAVFRrJomq2G7RtzklUMnBlDFDbok4rN4T2yycZomuE6UXphAOKLA2OhzOkzSk3PYqLFrN40OkEKJCNKjbb8dI5ZDqz06SdwPjXaWlxrRqbQNMP/OTplDgfWluznXOGm+7vVmwaYSNPw2yunaZg1IFhNI9sPVmLr9qZxBJZ1PdI0aS+M9Bz1700lP24aGWkGzQhLiZmbu8BvZVj/Gg4ZCa1res1pN3EIkyR3AEfT2MdTLGCdiaY5NMgIMT2rJcmIbF0702ga3zozEg95yEPQNI2Hgtx8880eWqLjmmuuwdOf/nR87/d+LwDgiU98Is4++2xcdtll+OEf/mE8/OEPL5z68YdV5qmTsYnVNFQzUkrTHG6YahqB1mJTrQHci+2RMBmJOLCaE5xSOLtDAIHEkrFQrhiIV99AcqtpYk6QJZoRpfgknN7HOAfWTa5mJEjTDMe7wA/E1SDqU5aeujkW4OntjbCxvqmlkhyuFFT/3s1VTTMLTUMErDoxCTmaMgLW9H4gmgjizkzdOA1NU2VUbQmqaaqOJjnp+aquQ1OZTLz/YZZFf1+vhnGPdl143ChNE1jXDBqLfs3ZOysw4VyaRqgZma2axt6u9XBKt+OEvGvvfZim2dvbw6WXXorrr7/eev3666/H0572NPYz99xzD2oHrmqGsq+kS+AZHhZkL+i0KgnqM2IJlhJJziExPdP3wIqgDRIjHI2M7GagacYnlloGmzK1+Q3zBDvZ9Ix5As5JbKwOwMxFTMefh6YJJSP9dvYGmuZwZmRkTprGuIhmCCxVAKloXZqmBBlJ+IzoRFMUFBlhHhyqYGlvXlVRU4/ISKecrr2WgFV4nkl8RnQH3qoS0WtUH9M4NA23ryvJfBmfkeS6JkVj9f4c1thQbxq9PX1NHO7a+Pkxsx283u740Ktp+AAyIlzXuKT8OCMbR3r5y1+On/mZn8HrX/96vP/978fLXvYy3HjjjYZ2ufrqq/Gc5zzHvP+bvumb8Na3vhWve93r8OEPfxjveMc78JKXvARf8RVfgUc84hHzfZNjCMuufKauvZqfbI3pWV6jvL6axjlZq5qlDtzYVkMyspEmI7HSXgIVSkrtYjQNY0iVi4xw3UPLaJrxvdw1TBddmoOLuXzAgWL5hUI/KR4UlPa6i48RsHJVLzPQNHQ/S/dDyISsvwlUIzpSpBnxx/W9V4SDsaZnRMAa0I+MJngyv5WmHsc1HWYjNE2aSqAajLh+RCqMbUlSozUjsZYO/fmQQBuG69bWjCTWtWY1riOCNUeFNCOOXk9fE1HBrTO/KeFW05hDlkDL5TTNmaUZyaJpAODZz342brvtNrzqVa/CTTfdhEsuuQTXXXcdHvOYxwAAbrrpJstz5Nu//dtx55134id+4ifw3d/93XjgAx+Ir/3ar8W//tf/er5vcUxhwVxCbUdyTErTWE8/KZpm9BkZNSP6kbQRZcu7ag9QwO5I0GEXALrB9CySjNRVNTyp3C56SrFpmv4nZ4A1C01TkNjQ65vjWulrbKO8HAdWIHg+6SdFqb06DZeGMAJWtppmBpqGClilVSQBzYg+fgoVAFVWTZMQsOrtiJJUtpomXaki1s5k0TSVOMmRVNNULOISHrdtd9ArQeXSNJxWSzIuS9MI1rXVQU+hCdBYndj6vWnsddQqcd61Vo8hK04lTQMk7zVi07MzrDdNdjICAFdddRWuuuoq9m9vfOMbvdde/OIX48UvfnHJps7osNwFTxlNIysZNpDeqjE6Bq67ZYze2NZ7QAe02+mlvZawLAcZIXBC7KZRanpmc9d6kxk0DXkvT9Pw782jaTQUy99sVdehHrj5/f39Ydwcmsb+v/4elm37jJoRak8tpX+Ceg6N4qDpE7JTQNMAGaI+lqYhN86JNI2hJavKnLBjb5p++R6Rkf76l4ybYwff1LIqqE47MmOk4psqvK+rSkAHsjSNYF1b7Q/JSGTNMTQNEYgy2x4dbmky0uGc0LhzVdNs7QcOsw8TFZbSde0+T9MsMYZVIjgXTQNK08jHHatpavSWAMqM1Xe3FCIjALpZaJrhTwYZgRAZSdE0/c85u/bmlAnT7XI3LIumYTQjm7aTXfyR496Rp9exmmYCTaPsn0BGGaNke+RcyHUedX/Xu7wT0pdc8JSdu33hAm0hIwkHVraaRrYf+tJeStMQNNaU4FIaLEXTxKkZYKSMpchIx2hGRpqGf6BIJukGGdGfF65rIp2aS9O4yIhei/r3reraoCHR/ctQziXh0jQARC1C5I3y7PcfdyzJyIQYxVkQC01TUYcErFKaZtXTNBbHWtUii+C2HpKR7Qw0DRWargSW8IyFck33r55jNy5kOWGOFQP/5zzA0O1yh5re6C1khDxVJTuqAlFErCVPoCWakRAKwN4k5nBgJciIFBGI9aYBQJrllSAj/c8UTZMMpYDNUE2zR+zgJdU0wsaJFuTurg3ag6PIDl5QTaOIgFVAM7atrxlJoZvjeRaiaTTdMyRc0nUtA43Via13zJ39W9dIzxdgKeeSMHbwZO3oJQH6POOPhXRdqxjU6jhjSUYmhNU07VTYwQtOPB1uozzLPpnW48eQkcEtVZyMaGSEo2kU4bpXg9V8dGHwL+AY31xqB6+TBaWUecouaZQH8FwrfY3O8YDwy1M791I4/MT+6DMirQBx5+3SNE3OE7YgWI2AsFst/Tydo5qQ/HMljb4rrWCg3SGgb46Brr1VQJshpVN4O3i9NtiaEWnS0A8iqaYhSY7gfFDkvNQVlCnn42Ry6tA04nVtrdecNBobRkYYTY7kuM1QTaOUYpGRvrxXi7cZCjdjXeMQwuOMJRmZEKeCptGNpoxi3nFZDAX1GamryhJ8SWmaTiMjG2kyEnNgHf5UVTJkhKNpIk9VpXbwIwow/i2PpiFTjghYq8o2B1w1o7BYVIYbpWnGz+tkpFN9V1VJuEmLi4xUFRXczldN0zuwSukJPhnRczfGZ5O69vLb4/7PBtVWiWgaohnJ3A+0UZ5bTQNFb+6DH0hsXKUArjrF+X0UsMpoJXpeGpomQomJaCWNjGgqSrquiZAR22ckVE1TkTVHpP2agabZtJ1JKk5YNA3ia0PGusaVXR9nLMnIhLCgf6HQNBW6tr/TvWmkNA3p2tvTNPYThET4OXbYlTbK0zQNY3pG6RTN38YQlyhNQ5+qhk1ml/bqz9soAN2OJKqqYo2cdHSR+WVRHxFEjMLhJ/ZGgycpipEyPctxSpVtz6dpUg6hYdOzIRmZgES6KJn7OyCsMNAUTbPXXwOG/wlQIP2GACBjP5DkO0jTUKGp4Bxz55SqphEet874oFSmmibWB6ghtEfY9CySjMTWNaMZiaxlWjMSpGkGZMTyWzk9NA297s5aj+urjZYzFG7GumZ8OhfNyH0/rJvSTDSNKe1VdVaSY9E0tUvTNCJXPp2MqFSDKR1GwBo2Pasq5GlGyE2cfaoqLO11+5HYJbhZQ0VL4mJldWI+H4hCsR1JRg72x4VKimJ4NI1G4xg6RSy4jQQvWCxDRrS9g9GMzNW110VGJN+ZilcB1mekco+fGrVdgFzAWlvVNA5NAz9piCaR7j5LmJ5Jhcf6vKQOubF9PZ2miaxrEmRETNNQTY6EppleTUOTyX1im2+5sHJrQ8a6xhlBHmcsyciEqCkyQqGzCQe3NlBs5ZipxRct6sBaVz6cKenpohpB0kAjVtpLn1gkXTRj1TQTRaf9+20xLH2CyBXDxrpdWhy/E2JDKoAXQw6hyGurZo29RkPzMhTDpWlctKiqCgS3kdAfz3Fg5XQiANH8TEj+uSot9ytmISN7Q8dr7sHBPX5qpFOBzN40tG+VpRkh1TSSJCeQILnztcYVIC5aM0J7B8X0OaL5VnYyIl7XMlyfgwJW11SuEiA5ALuW5YZbkGCGTlRY5qxrXNn1ccaSjEyIsT027BNvQjIyClhdmkauGWkqv5pGTylGb+gOu5UYGdGaEZ+msbrrZpTZWTQNc7GUlOPS9/PISNlY3GGOzS/LhTXiJdA6QkGRwp9+3ll89LFSDILRjzstGeE1IzJ6gn6e/q4maEY4YbSvoxEM5CIjnM8InIE6TafKhKYtTW7VuDZQ7YARmkrt4D2aJqEZESICbTdqWnSMnc3JJtT4t2SSY9CfYT9I1zWRZsRObL1j7uxfsd3+DA6s7nquw9IKMRRuzrrGueMeZyzJyISwoH964CdQNTVZcIqqadYcTVPLXPmGpKFqhclIl+5NU1kC1rJqGu7JOF/AOnxeJyP0ASub8vHnpSNWtZSnGQk/+SsNhw/cfG7liztvg4yQZGRVj9qYqV4jVD+k90HbKewiiAvXQ4jOcVI1DVfm7VXTSGiaQY+gKze4YzaZpiHnk9ubxkVGJPbqAEPT0GSElOeaJEc2XzVoyJRF0/Q/WWREkuSkqmmm+Iy4NI2HjAx6FUrTSEz7ZqimoV3Y6VpnNWXlaJqMdc3yTDoDMpIlGZkQpp08hc6ASSJWvQAYY6M6fFOioReJA5emqWqgquwnrFAYZGQOmqb/adM0ZaZn9Km1xMKdvt+98dLtiMdibmY6DMfPjDkXTdMp+wk0a1z4iI7RjJBEqqrkbqmpoMfMdbEMfoZJQIBx0RyraQpoGopoMttwtx8MAU1TBVCIXDv4hghYW2MHbwtY6wo4kKBvmTRNI3TO1SXnFBlhEz+ClCXnOyQEHk2TWtdEOrUhGdE0jacZ0cjIKGA9kCSRM9I0ev9YaF6MpslY16ymn2eAbmRJRiaEVSJo0TTli7eGIz07eLEDa2ObnlV2k6XYCVoNSUPdbWSTjZT2jpA/8pARjqaxbkbDJkvt4IehpghYuSTJzC9StZQnYLXLNq1tONy8uB/JEO7Co//r+hNkzTcSVIS5R3y7ozc2Ff9dCa8LLjgtkrubZaW9Lk3DCVjdahqNjEi1M8PQpLTXrabRa4bcB8P5bhZNM/5N0xNWd91oNc2wdhF0c6Q0fUpMNF/nO4rXtQydmsJgB+/uF2MHP65lIuSJoZxzgxYk9NumKHzsQUW+rtEE7kwQsS7JyISwdAhWp9V5kJGcBnyuz0hDnyBgV0qEohrg5jqbpvE1IxZdITEgYnhWy+FWb3KqZkQvmESFXxWOxbEMMZomC2mIIGK6aqEUGQnSNPQpnIybKj9NBaVp6npMSOKeFQGaxjM9y7/W9KGZ3JvGICO6mobRjHgoRP83CYLhufkSTQattOvXDCX3wRBW0+Q6u2qapkOqPJ/SNDLNiEfTpNa1DJ2aTp6CNA0t7RVpRjQykvmUQ4Ku53rbgIPCszSNfF2zaZriqc4WSzIyISw7XUGnVUk0RsCaV03j2cFXdnZuaTgCUa37C7jpMkt7Y43ypJqRzk9GdGZv0TS0ZDgjKgdlKaV7rLGimhH/c0WaEe7pp7W5+VxPkJCnhlupNJcLq1vxIGpHHyi7NQLWOappIqW9ogIig4xomkbiM+IgI8J9UFewfUY62NcKlF16GkPJpDQNFcaKaJpYaS/9XhjHlVbTaDt46bomQUYcnZpfTcOV9kpoGr2WTdGM2NqisQIM0bUhZ12zmn4uyMh9O1joDJhI0ziaESlNQwWslU3T0Jt5jN6o94ZkpJXSNNr0jKNpyPYyyuxSpmcdHTcj3KZQMW1H7lg0YnSYvrmLHFgjx11p7UClkZE8OsVdc91qGo+mmejC6lr4S5IcOkelxrnNUU3D2WCHXGmj4QpYuWqakGZEtA9IMlJzNA1NRjp5tZKwmkYjI1UlS3h1yTkt7dXXCkfT1JT2KKBpouuayPW532a6mma8LvJ8RsqTkUOHprGLJdIOrJJ1zWptsQhY79thQf/1PMmIMT1zaZqIf4lSCpsITWPVnkcy5mZYVFdSZCRG09AbW5bpmW8Hzz0ZF2tGHJoml+6hn2G79kaeTMR9Q4A4FNvai760TNSdo/l/Z/9sTDIyj4DVpdZG+kdGUfRj2HNUEd48FRyyFaKuohESsDL9XUw41TTxfTD+3pBqmpHCJdcKOptGkPhgcBsK9aYRzddPRqQ+IykHVo6mia5rGa7P+lwKde0dG+XJ/Fa4ysDcoI7aAKlKsooamGQkY12jS6i0r9WpjCUZmRAVPUHowZ9C05inn0qc5NCbRU/TENV5XVs3n9j10Qw0zUpJkZGwAyvlLvOqafyFjF4nEidZLtwbUOk43Fg0YlVLeQLWCE3jlCROpmncfeLSKRMFrMb0zBtXVk1D/29uGKa8cYKANWJ6ViRgZY6ZpxlhBKyhG4FN04zVND2Fa1PDNboMh1AZTVNbyUh6XOWcl2be4JMR0bihaprUupZTTYPA9exoRqROtPNU09iaEeucjdE0GeuaRdMsyMh9OyweD2CfjHLDLu2FSItiWQe7XXurxn/CCkSz1yMja0kyohRMb5qkA2thNQ1H00QEorEYURZ7fmU0TRgZUQ4KQCMraYicS8oxlxK5QpIINYXzEIy5NCMOTbMnsRYPzHEUsJYjIzHEzcxZhIwMyciea3omKO0lFt8hh1ubpoGPjNQRZKRYwOrTNHZDO4GAlRGh8523BfPV/Xc8ZCSxruVU0wznkqebcLbdO7AKRN1z+IwEaBqrRUiktFeyrlVVZT9QH3MsyciE8C60GTr3ak7UNTaKjasXiKoC1k1fsTByq7W1EMTojWa/v4BFyYgu6wVYmsbqrpvjwGqVBQ6bmpGm4dxGc4Pr+qojZrsvMkzSYaBYfyNeaW9uNU0iGdELVBatFAl3X0topVTCpCh9mRn6+4X63/T/Fwy0HWgaV8BKn9gTPiNA+Lh1Lg2h94HbtRf9umHRCMmuvf6c3N9Z59GoHfxIMZt5MfocWq6cLEs36ISrGUmsaxlrjnLKhN1t8117Y/tXr2VTqmlsOq+mD74RCjd3XYu5SZ/uWJKRCeFllRPKDXWMmhGOpgkgI8NidrBqUFUVGoqM1I0NZ0bO0XUOMtLRZISjaYbNT6mm4eD0wioYt5yT9kvJDa40VDK/LEFo5Fwa4fB+GyKjKxIpmkYvZLnjhsItdz6YQtN0zrU2qTdNeHuTBKxMRco4cP83y28lcIO3OrC6NI1TwVejQ1NVOBgSyG2rwuhONk0jK0vvnPOyn7f/XWiptx53E6RpAg6sqXUty/U5TtPoSp5GagQ4SzXNsKavdTURFbDGaJr+p3Rd42i044olGZkQNKtUFnw2vbTXq6YBwjSNzqKJ2Km24MyI0IvEar+Hm/eQiYxESnvlvWnCpme2Nfjwt1xkxC3tLaR76FichbKppjmVNI2yn0BzkRF32h5N4/iMzFbaa+gfPW6MpnH+r0W2BhmZQNMwrqCz0DRMghTyGbGMxAL7wdKM1I6A1fE2apxqGiByg5f6jEABUOIGfJ2haXwXZbbZpaRKx6VKpOtajh28pmncY+7RNJk+I7PQNI6AlQqXI43yxMlIbX/uOGNJRiaE1U1RgcBnZQdWdR3qaoRiPZv5ACSt+UsK6VGhFz3PYvDder9/wttT2+B7TFg0jZ+MjDQNstp5c3bws5ie1WPiaI1TcAUU0zQldvCcYr61uXmRwp+Zoxmvs39qgW6uMDYUbrmzZNwwTTOMOYGmiSW5oe2z4dE0XGmvS4mMN/PUfvDKxHUSqvSDSgUMKISmaWyH20Di4FFHKvi32iQjacGt3okWTRPTjFgC1rjPiJeMpNY1ETKiaZp+vt73Mtse1zKR2zFTGZgb3gMm3Y+xaprMdS1WGXi6Y0lGJoTfwGgaTdORFZEVsCaqaYx1cKC0t6ripmd7B/0T3j62RiQZnqzuS7NiuR/rKXuV4cBKn/aYC8U15pKGK4YtTWoAR0zmRBtZDLJoj8iT/+jn0L8n32fEQQE0WmT2CexxJ/qMuNSVZFyPptHJiKFpyvVZo5neeAPydDSSYUMCVobqGAce/3aQ0M5Y6CJgEplWm56RbWqaZtXUWA1fMJjsCWkaoEdcaHddpXoKiB1Wn5eMHbz1QNGNf5M7sCozH/16dF3Lcn2OC1gNRSStVmIqA3Njqh28dF3jND3HFUsyMiEsO13K5RXSNLQ1PFe+l6RpOGSkauzOn5HQyUhdKWy2Ca+RiPsq4MCFhV17XaGhUsrrnyINl7uWONIGx9I5J0vTxDQjGfbqsa69xqypjE7xUAfXe8URmh7ORNPoBE0yX89nxDluU+zguZLGkCttNEIOrAKaBkBSbOq5DZsktPJ0ajppAATVVcJqGqBPcqhwMzZft+QcIDe7UGlvqmKr1gJWhqaJrWsZaKxplOdOoXK2fRppmkPXgZUmdRFUMHdd4yoWjyuWZGRCNG4yMrGapiOLwmh6VkVPPoC6r2p+kVTT1I04W94/ODGOee898clG3FcBWzE/GhDdGx6PeZpoPDRjfHtpb5rRydPeRslYrOlZREAmqnTQEaum6dxqmjw6xffUsH96NM1EZMRzdtVOtNFqmvgcU9dELGqXXkWYFoqGSUZOOHMaP6vdO01YNE08ObVoGtXrNwBdTWMjRHWlxiqolMhSWE0DwDi7UvoneNycLrgASdwZmqaqxn2waTv+hujRNMJ1zWhGImuO8wAUpmm4ZERC08yBjAwVPfTBLGaImLmuxWwKTncsyciEoFSBJSwqpWkIMtJaC04ccTEdew2kNyrAKZyZojb2Bjt4ANgcJpKRiPsq4Dxl64Wh24b3DUfTOBeKVV2QmUS4rcxLS4StsSKmZ9GuvSJkJELTDNmE7jaa27U31ZumcZORU9WbJrKgey6xXmlvuVjcQzQx+s+Y16f4jFCaxjM9I8iIUMDal/XaFK6hmChNI+39k0vTVJVIcMuV9qZclCniwgpuvYoW4bqWoxlx+tyYqB2KqK5kbsdz0DSkC7veNjCclxKaRriu1TTJOeZYkpEJ4XU9nEzTMMgIkExyPJqmcmgaYblXVdc4VD3tsj1KISNh91XAMbrSCwMQXhyYp4nKQLz9/3PaY7tRU5gTDAReMBZ3Abt9WGhk0SlRmkYLWG2aRkqn+PqIAE0zVzWNflrLoJVSNM0UfRZ9kg7SNKlkpOvGp+4YTROwgwfSyZ51jlqoKUmGKU3jJZElNI39N03TSObL0jSs2+2YZCXpn2g1zfCWKDJyGC4oMN97oGk8ZMSlaYT6rBl603BrupljZG3IXdeW0t77SViLGoXPCvsxd04yYs6PROfeI7eaxvIZqb0n3lhsqj652B5F4E1gpGlEmpERcQkKyrhqmkA5LlBuemaqMlxxYM5YjChPR4zHzhKaMk/ZOnRpr2cHX+jAGrSDn8lnxGoNANl+CM5RiBbGwkI0mXOL274XW5Ksm9JeDhlxkxGKjMSTMsvAStlrg5k7uWFW0mQvomMJCViBtFldNSTJFk3DICNU17BqajM+O9+gz0hiXdMPQKqDcYp2wxGwemgY2bYWyYrQzVnt4B0H1k7Fq2ky1zXOc+e4YklGJoTNPdOMtezIKrIQaKtvy9wo4cBKldecU6GE2thAIyMRFTpAkBGeprH47mY1XphBZMQXfZnuuE4FDFAiYLWf0KZoRjgeXEeUpsmxV4917TVVC241jVDAGrjxet11c2il2PZUIMmJjBtKDoyD7qRqmvHY6I9nO7DSZGQV6dobqaZJ0WtWSwWHpgGGa8wgB4okDYlkT9i1V48rTSI7XXrM2cHTTTjXXvQ8EziwsutaxgOQGi5o75g7lUqA0EX5FGhGLGuCKvzQm7uuxVDe0x1LMjIxDOdmJQ3zVNMA+uknjrgYB1ZSk86ZA0nOT42M7FI0jdGMpGiaYaMpE6IYTWPQjPHtxdU0jj6iqJpGRNOEq2lkmpEITTOcJ6MDa64dPD9nVxCYO254e864EpomMEefpsmfm4dowj+WSdiaNsnT2SmjY/FKe4tomsqhaerx71Y1Tf/3g9R5FqJpiEiWzr8Wng/KoT0AXpPgnQ8xE7xYNU1sXWvyqeEYTaMTHu0evGm7sK5oDtOzIUHV+8bSqcVomsx1zZieLcnIfT8sN8cItC4JpVvDqwrazMg2uQlpRhwBa8XbwUuy5a1ORjYpZET7jCRoGn2GpQRlUZpmGJNcMMU0jaePyBrGGou7YcUcYvNomrAmwkUHJvuMdPbPubv2+iXDAgGrp+Gwf6aoy1h4/kDM9pJPihunkgZwyvCHm+cEmob6cYRpmpFKcKuVsn1GmHONKxkOjsv4jMS69upTPDquwA6eXQvqenxQCj4A2WtOjKZxkREg3OBwDppm41RIWn26YoaImetazE36dMeSjEwMyzRmAnTcj9F/riWHpRfGxsfVWbRtB6+fIOUCVgDYVn3S0G5SmhGNjPjJCC2RM0+hKRMi8zTBQbwcTROfnhuuuc8cpmesA6tjHEYjC2kwx5zZiFvaK1H404+LaZp5kBG/ZFiCjMRpmqnXmts4MYTEBMP1GAFsMYq2ffdomvH/KRM8qzKCIguapulgbkwVTUZSSWSotJfZl9qB1Ro3QCuN/jdcRZy/edF8CQ1Ff4rWNWO2KHsA8o65RmUqn6oCYtVKo66lNHw7eHK+zml6tviM3H/CumFOeFoD4HVjBRz1dLCaxjlxLZqmthe1ROwGZCSZjHThZIQVmqaQEfcmA/9CobBsLr3i9iOJ2bZLx+KeJlwUgEaWtiPWmdMxa8r3GXFu9K4rrbREVBi+6Vl63KSAdSIK6dJ2IbQoGJvBCl6LVwH7SXg4bj4y4vuMBB1YjSYCYKtpHGREXAUVpGn8fVlXNMlJCW59n5FYbxpvvlHNiEvTCNY1s+YkqOFa017utkfNiF4nVnVlzp2kJmeSZsTWAY5rIaJrQ+66tjiw3o/CgiGn2sFrmoZ0vUyZ3AACmoZWtiSirYdkZCsUsHJN8sjCY5KGlGaEqc13+ebSjr3WWM6Nd8pYHM/qogA09PFpO4Vt6m4X69rr9NTIp2n6n8EEzTUnm0rTBP1L0jRN0GvGsUjPDZc+8LaXoxnRwbgl65snJ7hNl8qS42G+50jhGlNEODRNyoHVoSdGFS95P5fkpOgf1oEV41xh/y6ilZx5ZK1rmTo1Dxlh3G37ihphsjeBpnH7jVmVhebc5wSseeva0pvmfhTWjWliNQ2HjKRMbgDODp5/gpCghrshGemSNI12YGWa5JGv7yMjKZom3JvGuLpOMCpzx5rUm4YzPYvApLSjahLFiJl6OYu+SOFPPz7Mcd241FX/U/TEmhGuRbWxmRc4sLpzNGvmRINBv3Givb2koE8nI3uUpqHIiKZpBnpJJ+1MNU0oabDO0W68PiyKiVR8GCohVa2kx9KaCo6mGf5GhbFSm3mutNej2QCP+mDHJTTNuglVCfLTMWtO6MHKSRpiXXvpzT1drTSDgNVrlDdM2bof+MdXP+MsNM3/hsEtDMWlvaQRlg5bPc2fMKNb38gvchet5ATtBhW6kiIjkY691jbFTylpmmYObxCpKy07lqM3oBGjaayOqilPkMi5NNI0toB1E+uoSkJ/97UuafRosJkFrF6Sk9BKkIVxbSB0BxmZ2CHbTSg7d58UCVgZmkYjI/o64WiaRDVNTUt7q8Z5SmZKe5M+I/qO5SRINPEd/lZBibU+I2LHaEYYobBovsO2a3RY1zVP00xFRuqAZoTsWysZMclTYv8W0jQ9cqqGbdk+I2k7+Lx1bXFgvR+F5ew5E02jyInfCqppDhmfERvOtOcai7YekpFYt0sAsdJem6YZfhFX0/hPVXq4EZaNT40L9wltGk0TQUYiDoi0xXsaGdGrRAQZcZIG0bgYv/vKQQH87rpzCVgDWpTQTZicP6vGPm5zmJ4BPtXWOvtkLppGX4eqXlmvAxLfDnK+kz4q+tSwSnsrzik19eS+sv9v1c6vzPyznV3Jye+antFjK5ovoWlWTea6ltSp2WtOnKYZXx6vi9R+KENGqC2+6zOSkgNk0zSMpue4YklGJoZd/z1RwEpaw/MmNwGahnFgravxSSpH1CRHRrQDq2961jFPP+mnFK6aZtiUe7OcQNOENAJFYzHXb0pUJxabxkzPnCdQvTgCMkplpGlsZMQTmuaYtEXCTdCSJa1kYdRz9GzbJwpYQ6Xe7vaCYQSscZqmhj6vHUoEAs0IvbEwNE1HxIw1W00Tomk0MjLMKYKMsJqRZKM8H900LR1oviPRjNRaB9dh3dR565pUpxbsTROgaaS9fwqraWiS41bTdLSaJtK1N9f0LCnYPg2xJCMTo2Hhs0KaphtLe5uKyYRLuvZW9WgrLciWlanNjzSYAhI0zfj7SNOkkBGGpnEQCJW40cdCl9p6+pMiygfWfGikoGOxxbqEphku33VTmRu9hFJpnRuvy+e7N7W2U9hNWK08AWvCeZTesNw5ut1qS6+1UKn3uL3EACwyMopLPc2IoWlIMpIoybZuLOR7N8yNqYbyXUJTNEJIM1LV1rjSJJIt7Q0gkvRvkmqaCqpPRnLWNSEyUpnSXn7bHk2T9HFx0LvM0OM2dW+XDzjnq6Br74KM/G8YFuc2maYZnTWNM56gZFjfgA5CNI0KUwduKPM0kUhGYjQNuapHmibfgdWnaezXc8ITsM5A+fA0Tf8zVHosFoXGziWH0qqqSuRqaj4+vMWINY2Opv9p3CaJwdMUdMTV0aT8VloLGbGfrDUaVU2laVykzBP1pmiaQeBNS3sBD7FpjC6BEbCKaZqKUApj4kkrKxp0jIlYgqbRrRzcapqqsVCB8bgJxyXVgCE6DBiBg+i4hKZZuzRNal3LrabxTM/8ahqAOgifGppGrw0HhH5t6P0gRtNkrmtuleFxxpKMTAzeprcUGdE0TW3fiJPVNDYy0jjVNDnQnU5GqlbqwMrQNITrFpf2sr1peGplDp1HjiutN5ZTEktjpGn4z4qpjxjl55T22uOmb86jZsRGHdxKIEtwOyEZ8bUo4z7g0CWa5Ok5tk4SOZmmcZ/YO2d7Uppm7SQjTpJkrsPaufFDkJTR5JClaRSbNCSrq8xYAQFrVVtJjrTbMoeM6GtFqR55syhcybj1mIysmjpvXVsnHqy6MbEBmOuZ2bdAhsNtYTXNWEnDmcfF7zO56xrXyPC4YklGJoblMDi5mqb/XEdpGkvAGqBpjGZkcGOsiOlZ3XiNqaJhkpEUTRNGRljdRApxYZ4m3AsldaOPhesEOUXA2jiIDY1YNQ0gEL/pyKim6cdNKPxJjChAiKYZpmAJbssralzomApuOUttmqCEaJpq4rXmdXGeg6YBPPqoNjSNpkQ4ZCSuGWkqkKf4xj6XmYqPpGYkRdPUjWX4VbnjJjUj5ImeXF+dsveraL6Gphk0IznrmvABSJ9LPk3jU1X9fIU0WCky4phYAuNDnW2u6W+/VMC6lPbeD4Ljb6fSNF1VZyEurs+IZXpGniAkJ2g18KyVmKYJO7Ba2xO7IYbLAqdYuLs9GFJJQyxitfkpYzZx0hCjaZhFP6fyZRRrBpAn+hSYkeSktjf6VcTpH7pf1051y2h6Ni0ZsSpSyE8xTcM5sAIeYqNvnpVbRgt6U4uXOPeakfFp224nPyIH2aZnjVtNMz4QKA5xkVbT1D4youdLj61XTcPN11AlyqZpJOtaUjMyHBudjAQErBQdsuabpGnKbq/6uNFkxOrTFbODLxSwngHAyJKMTA2uzK6Ux6bCRH0upXoRAIwDa2137W2dJ95oDE8TzRSahtN2pJARwonr8BxYHfOsnLB4dgQSJmFwrpI6UgIysXdHLAHlaJoMTxA9x5Xrbsokezn0TyjcJMcS3DJJjqUrcP1ADDKiT46J1TQOMrKSPilyvWn6ifU/h2PURGgavW83Ce2M1bW3GilcCtnbSUMpTUM1FGMS4AljE4iLbXpGNqsUi5pGx6U0Te66JnZ9DhxzvW8rZfWaSiNPU2kaez0HnAezGE2Tua4tpmf3o2B700y1g69qvhtwyg5+rQWNsIReOVUo9cCz1t0m/sYITcPylsYNMeDsylXTuKZnOXSTEz5NY2+jbCwmGUnRNNKmdpFzSTH7KqcM16um0Sg9s3/ncGF10aLeUjv89E7nETpunkdGZoyIpj2uuLQ3JGAlNwrVdagr/WXCpb0iB1aWphkfVBrOgTXVOyVI09RQpkmcL4wNzbfiOm+Tc4kmIzVzc2fHHcYaaZpRM5Rc1zJdnz3dhEU3jX9LJ3u+GD8nXPdVwHmYivWmyVzXrG7AxxxLMjIx5uzaqx1YqYBVRNM4sF7jlPbmVKFUQzLSdFNKe5knFikywtrB2+NOSkY8yid7KM9AjYaUppniM1IZzQhD0xT4jLhoUcVC0uXJiKI3VXe+EZqmqSpGaDqPZiRM09TDnIU0jasZIUlkZ/VFiNA0id40DXVgrWun0s4vP01rO3QyolFN1R8kmkzQJnFSAWukIk7Pl3tyl2hGmiEZsWkafxtWCJGRakhsvWNO1qJ17SNaQRfliQ6srm8UIJcDqMx1zeoGfMyxJCMTwxJZ6ouilKYZri5FkpGuQ5KmOYzSNHVWg7l6sLdepZCRiGbElJdxyEhBaa9P08SnxkVFkzvkw5k0YkZBqdK6fJqGW3B8TjqHptHf3XUbdU3PAGBvBkv4mBaFG5ceZy9pMB4OM9E0jobIdaUNRlDAOh63VhsDAuN1kiFgtc73U0nT6NfIeaVIEiB1zuW0TPT66roRNGDRt2Q1Tea6Ju4UHkDDyPdYW8jIgOQcG00Tvs+U0zRFU501lmRkYvA0TWlp7yhgtaDYCCy3aztzAuqLpKrsevwcUVOz1z9NrJLIiHZgDXfttYSmJdU09AkQ5ElxgoB1DjdXfbNmaZoEeiMWmsYoP6YHSPJpmJmji4xwAmExrRQJjtuP0Uo8TeMkpBNpmpBN+UjTJAbQvWk8moYgI2wywjxhJ3xGLAEr7U1DHlQ4AWvaZ2TPfo0IUBVJcjyn1BBN43YDhn0dtEqxCUR0PxCExkJGJOtaZjWN56hsmbcxNE2iYWB5NY0vYK3oMY/0Zcpd19x18ThjSUYmRs3wt1NLexUIFGshLv4JQxdz7VvQIyNj1t9lnKBjMpLSjERoGoMoc8lIvs+I12l3QgKh4UiOOpBG7TxV03CNw9xIWmrrENA09Mlt9KyQC1j3nDJWIw4l++RgFprGv2kcRGglmsy658D4MBtekCURcmDdk9I0W03TOAJWQh91JJGsXOt1jDe1batYjYqVHJJqs5p5UKHajpR/idcoT79GS1KtkmF73JDgltNKhASstmYkkvAOx6mGwp7jwJpc14T9sPS5FKNpViQZOUj6jEzVjHTDduhaOEw5SdMM7xcjI8NQ91UB62tf+1pcdNFFODg4wKWXXoq3v/3t0fcfHR3hFa94BR7zmMdgf38fj3vc4/D617++aMJnWhgBkMApNRXG9IxaPnfxk49eEHtUM8L0cJDwiM1eT9Os1QSahkVGZGV2tmjMvmHk0E1ueKZnHJUkjNjTRNIOXkp7RJojKgOHM8hIhoDVp2n6v/OVDjPQNMKnYXpsXErM69o7G03Tv75yyp2DkUJGVIu2pclImKYB+Bs8a3rmrA0jgkGqXlIomR6rcWkaImAtommGhylyE6+qynL55NAMaW+aVZO5rgkdWA0y4tE0RDOSI2CdStOkNCMxAWvmuhbTv53u8OsyE/GWt7wFL33pS/Ha174WT3/60/FTP/VTuOKKK/C+970Pj370o9nPPOtZz8JnPvMZ/OzP/iw+//M/HzfffDN2ux373vtacPxt6QKpqB08vRFHTj69mK+b8Smyru1qmhwjnPV+v7iu1Tb+RlPaG/MZoQMPrdaTZXYcNGnTNCX9p0ZYPjLHzLG4pwlWvEsi+dSqw5xL4dJecALWnK69tc2VszeKGZARNsmJzJfuQ3ojA2i567Qy+pAdvNknscW53Y7JuKcZMRM21XEAeJrG6rbc4sSeffOy6C2ua69SUKhRAXxDu10LpZRfCu9a1AM2TVPVpu9RU1HTs3hiWjFapn7+FbqBouEq7SRde2t0WNV13romFM1XAs3IqiLHLVWtdAqqaaS+U7nr2plkepadjLzmNa/B8573PDz/+c8HAFx77bX4rd/6Lbzuda/DNddc473/N3/zN/F7v/d7+PCHP4wHP/jBAIDHPvax02Z9BsUImWIGmmaAoqvGOfnC47ruqwCcRnlNVhnran9ARlBe2svCp1JkhKFpgOGpaorp2TDWSNPIdTRuWMfcidSTiVzAmkfTJBX+JPSNfW/l0mD6Rs/MVzBuKFxnV2vcSDLCaUb0WJMdWHXO4Hx3vU+iD4pavAqEq2lUZ5BOAKhWmqYZ57tqaqzqCrtOBbQzVDNiLmLW9dl2Sh0dRXedMkZu48BymoamR9IqncrRSvTIjuodWPUmhJVV4/dT2FtlrmvSappG0zTutikyMb4srlaaUcDKVm3GqmkyNSNnAjKSlbptNhu8+93vxuWXX269fvnll+Od73wn+5lf//Vfx1Oe8hT8m3/zb/DIRz4SX/AFX4Dv+Z7vwb33Bvwm0NM6d9xxh/XvTA2ru24EWheFFrASO/gU4sJZB9vVNJVVKpmK9UDT7M1B02RpRshCOASdb6vUJGol9CQ8xQ6+jKbpv1/S0TRG+TFweBZN46AAUdMzPd8Ze9NY82WSHErreEJT/bdmGk1DEU2llLkRuWgRG5qiqeoxydZh0TQj+hsS3MZubBa9RWkaQjEptppmXAtY7w6WpuksekGPu2YRATlNA8CilViRdOzmTuiiVV3nrWvJCj6dzfTHhr2eh+O5oqW9UppmTgdWSycUvs/krmsVTXKOObKQkVtvvRVt2+LkyZPW6ydPnsSnP/1p9jMf/vCH8fu///s4ODjAr/zKr+DWW2/FVVddhc9+9rNB3cg111yDH/qhH8qZ2rGFEQBZSUNpNY3WAowOlSmOkFNe1241jUEq0nNYH/TJyL4YGeFomnEeJlILA0fTkPna4rcSzYiem62PmDIWS9OkBKxin5EI5WeeQEtpmv6nq4+I8vlTqmlY+iec5FAHX5cSG+3g56Jp7MRDVNpL3Vfd84ccN6VNDFWFOoDk7K8b3L1pcRjTzlSVQ9OMx83QKVZDO0r/dDjHHdgkHSv7NUrTDN+Duwlvhgo+9+k7TNOM34c7F7QglNsHFk3TZK5r+gFom6imidCuGDoQrzD+TW4HPxUZ8UukbZrGn2/uumZ1Az7mKErdXA6S5SWH6LoePnzzm9+Mr/iKr8A3fMM34DWveQ3e+MY3BtGRq6++Grfffrv59/GPf7xkmqcl7JNkHgGrIk8/qZbRo/sqhfSIZoQIvSQn6N6gGdmrWrQxXU+uZqSkmsbxKDDagwlohgvLlzTdm9SbRkqnRM6lKipgzaBpTGkvrJ+8IVU5TcMlkbH9wNE0rguvQRoKE3+KbtHEw1QYRZGRQF8awHpwaAeks0UdhNZjqAAtcba69lLNCEFG9O6tqor4wzD7xzI3I2sLKc01yQhzEwZ4wW3VRWga2PvapgIj1TT1mGztWaZngnVNaCdQS5CRLIRoJgGrs6YDzn2G602Tua65Qu7jjKyl+CEPeQiapvFQkJtvvtlDS3Q8/OEPxyMf+Uicd9555rWLL74YSil84hOfYD+zv7+Pc8891/p3psacXXsNzAnHZySSCbPK67pCZZXA2XONxd6AjADA5ihMpcVKe1neUlhmF/MoyEF43HB1HqwxmzBci3IablM4N+Q+I2GRGsdJ5yAYhqZpbB1NtOxyAk3DoUUxhKglx9k7buZv0641s3sJRQOQCqMoMjJcF+sT/t+I1kfb9neog+hm1PyNJnGMHbxSyiSkNZSDPEWSXlI1Y2mTrGoaXcXCJyN8chqgaeh8EwJWr7yWVAutmsx1TWgHrzUj3DE3SRnrM3KqSnvDNI1KIOW569qZRNNk7a29vT1ceumluP76663Xr7/+ejztaU9jP/P0pz8dn/rUp3DXXXeZ1z74wQ+irms86lGPKpjymRUWjDy1mobYfPMmN/64HL/o0TQZyMj+wfi0tzm8J/zG4amPpWk4dIAiI9xCby7g8TP049NpGgfun4HymWYHn0JGYtU0dhVAP66mPdLnnintzaqmmVDaG9GisAJWSzMyvOZoRqbqsyiiadE0Zp9EPhzyGAHYapoOFXndTUbC+8FCGFmapq+8A8B0lo0kkfRmSekuUgWix6U34VUzPiTx49q0hw5ans2hm67g1opYNc1UZMShO3mQTVcV+UlZ8FpjekflRJSmSWgIc9e1+7Tp2ctf/nL8zM/8DF7/+tfj/e9/P172spfhxhtvxJVXXgmgp1ie85znmPd/67d+K84//3x8x3d8B973vvfhbW97G773e78X/+Sf/BOcOME8WdzHwj5JptE0IMmIpfYX+IxYyuu6suHMjBN0td7DTvWnRRwZkdA0DDKiujGRocFV01g0Dc83S8Nwo8q58c7gWULD3CuTyUhKMxKjaYYnUM4OXoCM6HVHQ/kx75U5NCOcRX4syTFPvFVlC8TBICMTfUbazqFpVraol42QxwhgPThoB9aO0jTOuLEyUQtFGOEl6wbSkZs1WwWVomnofrRoGp+esMZlzocqUEXCJX7WXEkJqzdfYge/t6rz1jWhTk1Tfuz1bOgqRsAaREY0TVOKjISraajrLocK5q5rZxJNk13a++xnPxu33XYbXvWqV+Gmm27CJZdcguuuuw6PecxjAAA33XQTbrzxRvP+BzzgAbj++uvx4he/GE95ylNw/vnn41nPehZ++Id/eL5vcYxhOeNNhI6NA6ujmI/VlXM16U1VoSI9HHJ5xA3WWOEIm0MJTeOX9lpctw79lAL0i4OLqKRomqE0EJiIjBhKYnh9Ek3DLF6JhCm7ay93LhH9gA6xfwnGRXdtaJphWGb/HsxB0zCl3geR/WCZnnk0Tf9zrE4ppGmq8bvTIdYimibQlwawaZrhptRWApqG3Q/DXOswTWOqTSpbt3cQ0ybRpIGlaUbNCEUE+nEb3LNp2eSpInOkQdfIGE2j5/uAfXJbGuZRoS9RzlnXumYfm7Mf2Y9x772+2Hj/IcADLkTX7OGR51R48FkVDg/txOXoAY+G2t2Bc0+szN/W2OGR5zQ4d1957wcAnDgJdDWwqwDu74k4q2nxyHManL3qzPhnr/rXHrDqcFgdAA+4EFg/yBv/rKbzPhuLB6z79+/Xrej9XKzXazRNGQpEIzsZAYCrrroKV111Ffu3N77xjd5rX/iFX+hRO/eXYJOGqTQN7dqbrKbhkBGgMa22a3TbPEThqNrHWTjC7khC0/inEFuCayUjR8C+o/FnnqqqoapIKQweBVOQEX2chjnO4FnC0jQJzvZATNOEn/wrAy+XCVhDBl98b5o5aBr440p8Riq/a695sm7C14Qk6PVFkRFZaa8WsDI0De3aS7pwh45nLIm0zlEiiqQCavPk7iEYMZqGIiPkQYc8EHSmSsdBciLHTQurK1czQo4hh2Zowe1m1/njkt47PU0jW9c2mw0+8olb0X31awBUwEc+4icjX/wSoN1gt/9QvPKZfVLzkY98xHqL+qpXoVItnr56iPnbru3wymc+DHXlvx8A8KWv6PfxHRVwN/P3RDzrCQf4u497GM6v78RHPtKvwU88b4tXPvNhOHsP+Ih6FPD0V/drqrP9yx6u8GUPfhjOPbHl5+bEV59UeNIzH4ZzDnai94figQ98IC644IJgIYskipKRJcawXEIjCIYoCE1jm9xEqmm2PDLSVCPKop+wpCfKBj1qsd0IkJFIozxrjagqoNkH2iMeNo24N2rxKjuuMFxqJdVdNxauMyyNlAPrfq4DK3uzjQhYBQiGfjpdO6W9fHfdOQSszNOwwA6+qsZ7ZdspC0qux6YaRXOiJY1caW+5gHU8bjoZUaiDa0OUrqLnqNW1d5y7Lu1dOQhG9HygZfRsNc3owEpLe1PzDQk36fUSolr3Q8kIoWnWjWxdU0rhpptuQrNe48JH9EkDHvJoWKXMAHBbB7RH2DzgQnR39ALgix5mPyS1N2/RYIdb1o/AQx/0QADAtu3Q3XIXKlS46AKvcBq4ZQuoHfCgxwDrA//viahuvRtHuxaPfNAJPGC/X18/e/cR9u88wrkHazz8YNMnOusTwIMusj574vZ7cfu9W5z/gH085AH73PBW3HznIf767g0edNYeHnZu/lyVUrjnnntw8803A+gLVkpjSUYmhtU/ZaZqGtCuvR1FRvwFMiR20nCmonCmMBnZVnuAArYSzQhL0wS2tzoYkhFGUBaozW+qCi3UQNNMEZ26NE0cwYiF2zOHhrQ3jdz0LMLNcz4jkmoaQ9P0n3eraSjsPc53emkvW86ZcGClx40mCFNpGq7Co7eftxE0NoyANUXT9IO00Wqa9H7waBpKMRnkgEcw2OOWrKYhNI03bvg8C2lGaPWZ8Rmr3GSkwZ3Y+UmOpmkqhXVTi9a13W6He+65B494xCNw1t1D4cT+vk8Nr3phcX2wj+qeDlVd4eDAviG36wqNqrBe75m/rdoO1WozDLvvP+StAKgKODgoSkbq1RYVWhzsH+DgoJ/zeluhWims9vZwsF/1c181/Tbopu/pUG0r7O3te9+Fi/WRQnUErITv50JrP2+++WY87GEPK6ZsyhQ2S5iwS3CnieqozwjfiyBfwNqhthZ3SWyrPsFoY8hIlKYZ52FFTFBGF0gS1FRuFprGwP3263ljDXPifEYSNM0c1TQV42MgHVepUXuzMj4jToImpFOkwZuehbUSrB28IzStI9SlJKyndc7XRCRg5WiaEWmwBaw8uhnVjKRoGqUMneIhI2KahuxHUk3D+WsAccRFJwoeTUOuvdD1EdwP1IGV+IzE1jXdoHBvb0+IVg+fZw95Rd/Rz50K60+B7rODTt79NaT/yvp1f+PmFeGyVk6q2HHWWX1ivt0meppFYklGJoY+X1JJgyiMmdUIxaarafia9IY8QXAK9ljsap2MRDQjEQFrkE6JOSIGIF76pJpj3uaG68CaolNiYW5knIA18OSnYw6aZtSMMMhIYlw65ZGmwfCTSUakgttI5GpRKPxOtR00L6tq/uYujbEihdJCdofZYEQFrOPNb9SAVeR1nk7hEAzLM8Igh5VD01TD98mgU2gjtyBNM4ybgbgYzYhH04zzHRMs+7PB/eDSNBnrWn+d6kSDO57Da7FcRI9F/kq35/miWOOW3epH9Ihs38xRiTII+ZYHlE38/sAoE7QiOpZkZGJYddoTyw3t0l7yJB8TsBq3PkrTwPRw6FBnizV3BhmJ9HSIlPYGEYwYMhKhaQAtNBw2OUdvmgmJjVtuSiOl8NeL+WbXBRYyPeHIuRQzPUskIzSBWjtuox2DFs3iM0Ju9uO44SSnIzcsy6OCIiMN3+tFGhRxMd+78nvhsBFzYCUPDhYyUkTT9D9DpmedGgWsDVwEI5JEUmSNlpCTKq0RcZELY4M0TUX3NX/dBcclpcvrps5f1wTISBW5dXNIQy+sD1O1U+/sI60ZmlcEGcmERswm7os+I0vYMXLPmCxgNTcni6aJj3tokBG7Jl13t1So+FLbSGhkpAslI10LcyFEGuV5N/qYJXzAQpkmESrwVCUJyrPTOU6hfLjrN5XkuH1DghHr2qv8xE2q7aAJlElGPJrGn+8UmoY7/yTCzZ420WO4mpFp+izjQUbGtbsERz5sBKyRahqiGemqcDVN1IGVuvmSGz018OOcUu1xuWSElODWBLEhrxtn1+C4EcQuohkJldQH50vs7vtkJHNdq8I3bjNAFBnxaZp+PnqIWDYyIzLCvjH8Yu4z1vGnIksyMjnmrKYxreFhGxvFq2l8ASulaTo07JNpLNq6RzC6UIOpjvCCbKO8UDISsYQP0jTDnynfPMWoLKKPkIZFzTmRTkbGhTqejKRpmproa6Q0Db2hu03h+O6606tpRk3G+FrUeZTMwypjnbOahintrSuhCZShaeLVNJqm6eBoM0hISpztrr207B/olKZp5EmD5VNDkySma+/KpWkEmpFgNY1SQTQjOF9C09BGeeJ1bbJmxHrH+H+dEEZGLclFqKDa+l4s2xTRjAjDbOEMyEaWZGRiWF0PJ9I02iRJ1bXVOyNWWaEXhYO1i4yMT2XWE5YgukEHokLOhS1JRjiaJoQ6xJCREE1TMzeNIpqm/xkrY5VGTOSYelpbN2NH5ij1ETmXTEWBZXo2+mPsIl7m9B67rl2axj9uUfMsYXA3jYPITY3SE5YfCE1GZqVpxnMhVrZtIuYzYtE0mnatgjfFGJ1iJSMEDaNzN06pzm0xaoJHy+gtO/jx9S6gRTmI0Gt1yGeECL7HfW1/NmiCR3rTrOs6f12r2Lv4EDaKoNibO4+s0Iek0LhZyo2B+qnrGk+88EF40oUPwt6qMa+/6AXPp29OjyfesD1jLh772Mfi2muvlY5YHEtp78QY4X8VhdZFQRxY+ZbRXDLiC1h7zYiGM+twqW0gugEZUSFkpI0jI0ERZxQZCdA0ZD+YG32JzsOhVropY9Fj7kTKorqqKuyvahxuu7goNNIckTc9sxGXVWCFtmkam5LgjptYcBsJ7riNmpEETUPoNeO+Ws1B0/jUAWeyxkZMwEpMxPTDhaiaJiLkbWhpL6VpiGakdhGMWHsAikLS/Uhe7xAoGY6Z4Okn+ojpWQiRlNA0q6YqWNciyIjykwavA30FcJrRqrLXE3/MvLjpppsA9Nfnj//HN+G1r/5X+IsP/IV5WDrsGtzRmfQpuC390mazAZD2GSkjkk5NLMjIxKBldjGhqSQq8mRitU4X9aYZD2VVUXOgOhtR6AYEI4iMaJqGLmYkgk8sGtZmNSP6TmN/iCYRk9AMl6bJRItouGJYGpL+OSLr9si5xDld7gm1KJR+MKW9UdOz8SYRFdxGgi3tjSAjnB089Zlpqip6TUiCMz2riUaluDcN9RkZsgll0TQBZCTSMLCxqmlqq89SWmgaqabxaBrGZyRDwKpRC9dcjB7DoOnZOjBfQtP0yWnmulZVvTHX0Rb3bHb2v22He7Yd7t12ONy2ONy24fds7L8d7vr33xUZ954N04U4EBdccAEuuOACnDx5Eg8451xUVYWHP7x/bb1e47u/60X4ui//YnzRox+KL7n0K/Hzv/qb1uef8Yxn4EUvehF++Ae+D1/zxMfhW775GwAAv/7rv47HP/7xOHHiBJ75zGfiTW96E6qqwuc+9zm9g/Ced/1PfMvfuRwnTpzAhRdeiJe85CW4++67zbgf+9jH8LKXvcwS7p6KWJCRiWE3MJpWbqgIfEp7Z8iqaeykQMO2LerR90N4IqkmgmAAUfdVICZgjYxLXSFJcE225nBN1QtZycVFOXs3qIFWKEQVKpFzSZcZ0mSkqSusmwrbVkXHtTQjjvcKW4JLkpxN21kIjDR4LUokGSH70JSxupqhiWX0/NO6f56wYZARjqYZj5savHi6GE0j2A9VBQu1sASsodLeWHVVDk1TYgfvXFOccV1YMxKupqmrsWpIvK5VNe7dKXzR//VH4ffg05G/5bzH/8z7XvX1OGtPfpulp53ej4eHh3jyl34pnv38F+FhD34g/uz3fwvf9pLvw+dd9Fh85RVfZN7/pje9Cd/67c/Hm37lN/Gwc/bx0Y9+FN/yLd+C7/qu78Lzn/983HDDDfie7/kea3vv//P34p/942/B91z9A/hPb3oDbrnlFrzoRS/Ci170IrzhDW/AW9/6VjzpSU/Cd37nd+Kf/tN/WrAP5LEkIxODa1o1K02TGJejaYAATSO8i4/JSIKmYTxGAMcfgUZRNc2wScU32ZIG/Qw1/iqjfMZx3JA8rekbetSFVVJN4xDv+6sG23YXpX/oDc41ghsdP/256vmWJCMqMm60W21VWZSYNb/ZaBr7Bhlz1zURE7Ay1TQq0psm3r2Y3LgpTUOrU8zNmk8aog6sbm8a8rrpTRMSxnI+IzpJdowQ6T4dqbZAMhLRjNRVZZIj8bp2Cp/kT0Vwa8ojH/lIvPilL8eNn70HD9hf4RlP+mf4zf/6a/jFX/8tfOUV/6d53+d//ufj+/7lD+POwy0ufNBZ+Nf//x/EE57wBPzoj/4oAOAJT3gC3vve9+JHfuRHzGf+w4+/Blf8vX+If3LlC/GY88/G4x//ePz4j/84vuZrvgave93r8OAHPxhN0+Ccc87BBRdccEq/+5KMTAxL2zF1geSg2IQwlqNpgHER6VDbT5SSGJKGOkjThN1XgREx8O3gy31GlOKbbEmDzoVWZpQkNm6fGxqG54/MUYSMRNCwOlBCub+qcddRiqYZ5zdSgXru/v7Vglul9Hx5NCwWBppnkJFt2yMedofm/qdH09B9a27uhdU05LvbNI1AMxJzYCW+HaaaxnU6JRFLTK1zdDteH9TnZmxoJ6dT+GqaznogGDUjPK3EzTckYKXi8RCFa/ZDgKZZVZqmyVzXqhonVhXe98+/DHjAQ+2/3fS/ACh0D7kY77u5L9e++OHnWufi5jMfxF53Lz6390g88PyHmNc/cuvduPtoh0c98Cw88GxyTXQt8Jn39r+fvAQn1nnJ+7ikkOuhbfFv/83/hV94y1twy2duwnazwdHREc4+26YJn/KUpxB7COAv/uIv8OVf/uXWe77iK77C+v+f3nADPvzhv8L/86u/NEp1lULXdfjIRz6Ciy++OGv+U2JJRiYGhZGn0jQcFNtX04Qhac4OHoBlm5zdYG5IRqo2QdOkkBF3e1FkJFTaS28a9ms5QYelkH/Jg1MsGVGChElkUJZJ0wCyJIdSaH6/nv49dDEWC24jwWpRiEnf0a61oGybpiHHn+5bUwIxsZrGEVXGyrb77alEbxpC0wymZ7YDa4hOYQSsFEWwKFyQuff/8e3g56JpQrRSuMrLdWBlaRpPMxJCRkiVIFT+ulbVqKoKZ60rwKVL1jUAhW6/MTqus/YaNATCa9Y19rsam/3aOkfP3luh7RT29+zX0VXDuAD21tkLDFfR8+pXvxqv+4kfx3f/4I/giV/yRHzhyQfgpS+5CpuNbb1+9tljclyBEePCR166rsO3/KNvx3de+UI86sH2+fzoRz86a+5TY0lGJoblS1CFn2ZFYUp7XdOz8FOgoWnWLjIycKtVnY8CrHsEow4mI2H3VSBCVZRU03ACxgLRKX0q78iNbY4+NzTGG2/486KmdgKapnJQJEnlC52fhb6BR0b0fA+3TEdVYXBVOnvkIB5tO5xF8loKv1NBqZXUTCyjt7VIINtLaEZ2R+Mx4QSs9MHBtHdwhKIkRD16KlhohnVNkGoTa9xYSTbVZ9H9qGgyEhKwhpOcEbFzaBpGwBqkaTxkhFQJoitY13TSyh1PZb8n9DZu1OCwVPQhG4sGd9q9/e1vxzd84zfh7/yDZ+OsvRU+77wKH/rIx3Hx4z/Pey/9+Bd+4Rfiuuuus/7+rne9y/r/k578ZPzVBz+Ax37e4/DYhzBIH/oeP7rfz6mMpZpmYoxZP6bTNExnzpRm5JAxPQOoZqRK9kvx5qFpGo2AuGE0I3wuG+xcG/UZ4ZERS2g4gzeIHiu33JmGdcydkFhUy2ia8DHXN57KycokLqz02AQ7GTtTn2oJz50Pq6Y2Alr3xkaTIjpHSyMwsYzeRhfG7+069XqxJf2aUl17u/EpPnQ8o3bw9GZLO3pTDYahaVxkJOYzoheExt6P5HVFtBrScavAeUmvl5BWK2wHP461qlT+uiaxg498XguE3Xe41w75AB05PK/Q9pgT7/M///Pxu//jv+M97/qf+MsPfgAvuOpF+PQtt4UmbLb8ghe8AB/4wAfwL/7Fv8AHP/hB/MIv/ALe+MY39n8f5v+Sl38v/te7/xg/8M9fhve85z340Ic+hF//9V/Hi1/8YjPkYx/7WLztbW/DJz/5Sdx6663Z30kaSzIyMVih6USapiKlvZ2lReGqaXw7eAC2U2EmTVMPba+bLoCMdEIBa5bPCJ+MVOSmMUUzQj9i0zQlycgwJyYbkSRMIov1GE3D+IxY48YErJw+ImJ6Bsj73qS3ab8eSnJo+af1VE3P46nVNIzpWUM1KqFsRCcj9Zr12BmRBtIob6IdfFXZyUhNkLkSBCPctXd8vQ2NG6GVTJLsnJf0egmeY4lqGmAo781d18z17SYN/PENAyP2X0TISEFw0/qBH/gBPOnJT8Y/+8ffgn/8978BF5y8AH/v658Rn2VV4aKLLsIv/dIv4a1vfSue+MQn4nWvex1e8YpXAAD29/u1+Isv+RL87C/+V3z0I3+Fyy67DF/6pV+KH/iBH8DDH/5wM+arXvUqfPSjH8XjHvc4PPShju5mxlhomolhtZOfkaahN2GJz8iBS9OYErgqm6apUslIiqYhAkQrCqpp6M3IfI+JAta+Hb09ftZYkRuW5GlNZLEeOZdCrdolNE3I3ZT+dOcec92URKjU+2Dd4O5N681XMXO0aJpqRprGSXJp2SwbMY8RwEqS7GSEf0KXONFaPiO1bYiokxGXpjkI+XYANk0T6Nob1ozEHFi7YYppF2X38hgdWHkBK9Cf99nrmrBRXoV4GhFCRqI+IgXrVKcU/u6zvhXf+m3PMa89+MEPxs//4i/jI7fejRPrBo9/8Bq45f1Wova7v/u7AIC/vPkua77f/M3fjG/+5m827/uRH/kRPOpRj8LBwYGZ4iVP/jK86Rd+DZ/30Aewc/qqr/oq/Omf/mn2d8mNJRmZGPQpxWo6VRAVA8XGaBqlVFDAWlcKUH0J3FgSKbs46mGhbboUTRP3GfG0HSYZifmM8AtZR8pxp9M0VJeQPZR9zJ2QmZ5F+HyzkTANEaumAWQCVooCuA6sYWSklKYJjBtAcrjeNB1hEWyapuxa46iDuhKYnhnxKs+v27QHKe1Nde2NakYoMtJYcxyTEZdOiaBktLsudfolrwfHFfSm8appJvmMEDEpVP66FoQwHG3HUDLmsy782OPDovsXX4eSEzQRZ/9ON87a19uvvfa1r8WXf/mX4/zzz8c73vEO/OiP/ihe9KIXmb+XzfLUxJKMTIyxzA5RBEMU5OnHpml4SHpDepB4AlYDZ1aiclPrs3t90rAKJSOdMBnJ0YwE2o+PT6oyPUYoqmosUbUN1MpRFq6qNPjdSWQhI8y5ZOBwRygoGZd+78ZBAbLbuwsjKIwNPL3T8k+roZ2FjEylafqfHk3joEVepJARgtiMyEhEwEoSPbf6wRLsEuSQHreuCSAYItOzME0TLO2NJLwjMsILWKM0TSjhJQ8nq6rDLnddCyEjjrZjREb44145rweRkWksjVk/3G/EfkNuW1ozMnzgQx/6EH74h38Yn/3sZ/HoRz8a3/3d342rr75aNNTpjiUZmRiWkGkij00FrLR3Rqiahi40voBVw5l1ECYPhUlGVAgZiTuwBvUYkmqaQNfelgpYC9P5uqr6cdwbW8E4AF9xYRaTyLDRSgcdEXhZP4HWrumZYFwq1rTK0slP3wMiLYwNbo/sI6lOgCZFVG8wVmlhsj6LsyinpcRB+5Jt70fBilcBew0wyUgVPJ460esUsOuU6RfUv0bOd0Kt2F1wQ5qRAO0BeyyepqnQDrc/r0pHYAcfKu2llUsumhEcl9I0qi1Y1zQy4s5XphkJCViTyEiJZwAiyIglrI4hI3b82I/9GH7sx34suD2zlTMgG1kErBPD6lMykccee444pmeBJIdCsHtNCBnJp2lWe72z5DqYjGjTs3hpr7hrL135QzRNp7K/hxu0MseC/DODusK6YTU3C4RIwFqHb7ZpmiaiGSHz0/tRDbRVGBkpF7DSfSSt0qF0HKXErAqoifosm6YZz6tkaW/MYwSwko6OOCqHkBz6EOElZfQ64rr2qrGapvaSnBgyYjKCYDVN2EwtTP/UIZqmZvZ16FzwfEbGNzbVSBuJ17Vgw0lK01TZN2X9ibkFrJ3TSXjcXug/fIhXNZ3kSN9/CmNJRiaGfpCxhKaldvAmq26sG2dIP0Ct4L1+EOSiFbXaJqGTkb1QMpKgaYI3eo2MuN2A6QJd809VnZLd6GNhOUFOqMyxWgA4ETJ1opEnYA0v+mjcZERA05DvbYl6He1E9nwDQbUXoadh182T0nEjTeMIoyeW0XO9abhyZy/ENE03IiOWHTx/cwd85MlC70gCYSXougQ3YHq2aTtfjGvZwYdoGn5crXfynFIxUhkeTUPWyJRmhBt3p8bEyDI9k6xrBqWJ0zQGQEHobS5No18N0TRTkZHA36EQRUYmbv84Y0lGJoYlZpxM0/hQbKyaJmQFD0yjadb7QzKCBE0TKO0NupuGkBF6QwlBvI4fRElQCH4K5SOhaWLjZjXKY7v2appmggNrbZ8P8Y6qAloptD1yaL0bUEAnQI+NAYhcnc+sNA3MuGZ7SQFrDk0Trqapqsp0XPbpqnFe1IdH78Y+qQ5pRsZzg2rL+oHT1TR63CBNwyAjjUFGYjQNj2YY/ZAzrlLKlBlXqstf18I1uMII0TSERp8xRprX3mIQuQl9L+G6Nr7t+LGRJRmZGHZvGrNyFo1FW8NbfUMCSvxQx16A0DQqvzfNer9faPfUln+DKe0N9KYJleCGqmnoDSVC00zRedD50BLD+XvTpOcYtL62NiIRsLqlvelxaXk0vWdYPi5SDwhB2DRNYFxnvhR+t8pYOZoGquhGo5/WrdJeKphNlvYmqmmIgNVyOmWSy1DzOasqjdI0jGbETxoI/eOeD7SMniZJRECvEZeYgNVFBmv0n3eTZOrdEkIkQ+eYIlRUU3X561pQe+XSNDxdETqzDDISon8maka8j1vITXhslXxHYNzjz0WWZGRqUP52Mk1DOFvbZp6/oA4DHXuBkaZpiVOh9Ma7NyAj+9gYF0krEqW9wW6aoUZ5Fk0T4pvDfS2kwY01pUyYu4Al3gdz0TQ+MhJoNkaCMz0DbE2Gz+fPRdPIxrVomtAxo9+94Hob9TK2s6tb7uxFSsBK6SOqGYlUR6X2g03TNNb5F3JKXdWj+NdDyliaprNeD5cM24JbGiNN4zxQEPFliMINGuApZTxPaqiCdS10t1Xs20LZh1tNc6qQkVBvq7BmZNoEEl/7tMaSjEwMy41zJpqmIo3ybGdXRzMyPPEcMMiIgTNJDwfpfXd9MPiMVAq7HYOOJBxYR2MjITISpWmGMTuVTTe5YTtB6tdKkJFhTpyAVTDHLJqGefI3yYiz6B8IkBF6ow/SNM7cDyb4jFjVNJk0Td+ldUxGaNWL9ehYQNWw1AFT7uxFkqYhCIgiNI0EGYlUFbHVNJ0KVr1UVUWMzwI0Te0kSQYxqcOIi9Xg0P5bE3AG5rr2cgZ43Jj9d9RzafPXtWRpr0vDKOdt/OBBZMT8v2yN0qedv3xQ5Ib8cV5g5lhjSUYmhpU0TK6mGZ9+zI3TErC6mpEYMkLgzMyb+P7BiXEbh/f4b0jSNP1Pn6YJICMxmoa7GZVW01C4eIKba1wzguQcRfbq9MnfOZ/006FfTZNGMGjpID0fui48d1Fjv0DQfRQUxno0Tf+z94ZhaBpqegYUJf+UBuV64RT7jJiaT5lmBAhrcqwnf4umgZljS8Sd3rihpNcgIJX9AEV0KTrJCdE03HzHJNleF/jEj5/rZtdZN3hK0/Tt+zLXNfM3GQETQgjkyMg0jCFc2hsa3k2e8iKoRTmGWJKRiTEnTaP9I6Q0jUTA2hXQNPsH40K7iSUjAWQk6G66HpKc9sh+2qffK9i1lyzOxciI3qe2kDM3RDRNZI4ie3WKEDnHXXPzvulZGsEI0TQ7grqJ3TEFQRdrqRbFMj2jZaEUzZpK05AnW0odpAWsw/WQpGlG+gcV0WYwtOdBiKaxTM/8ahqlwnbwQLhaKYemqRjEJSS4rUM0TYASs+ZK0F06LqVpmt53FUDGupayg68cZMQFOgLDGtwyKCAtRUYU3vKmn8GllzwBBwcHuPTSS/H2t7/dQW7SY8s1I7xW5jhiSUYmhrShnSRoA7TxCQ2CapowTbNTYw8HKaJQ1TWOVK8H2Rzd678hUdobRDA0MgLYVI1ZKCrvIua69pZYuPefm0d/EutN0wqSHJG9eh1+8g/1AJEkDdTYjH71XQzBmELTRPZzkKaxNCPja20IGZlC0ziiyvF6DnxwM9A0KQGralF1QpomgJRZgt1uTBSsSqAATRMbN03TjI3yXP8SIHyeJZERgYDVHdemabr8dS1YTePQKZX9KhmA/pkMO5wn4S0XxW/8yi/h3/zQ9+Pl3/t9uOGGG3DZZZfhiiuuwI033jjOL6IZGVki4Vo/dcIzxpKMTAwKI8eefkRjEdMzEU2jO/au/cM4PkHkV9MAwFE1JCMsMqIdWAM0TQg+1ZoRwKZqaNWBEzVzMyrptEvnM9UOnpZW0qBP2ZJGed4Tq7URWurC0zRNqFFeZFwqkNMW+UAPj5vxQ0LTCTQNhxQFhZvEwdeiaSzNCEWOptA0vANruGuv0IG1a6Goq3CMpkl0L64rWGW343mMUdsRG9ctybbmVY2vkeswVDLcj8vb+Ov31kEH1rBXUEhw23Uj+kNLe+XrWt3fobf3AEd39cnk5m7g6O7+WG7vBTZ3o97ei2p7z/j34Z/aDO/Z2q9X27vZ94/jHvb/zyxT+dmf/An8/Wf/Yzz3O/4JLr74Ylx77bW48MIL8R9/6if7N7jZSIC2yV3VPL+UY4jFDn5i8A3tCr0PGMW8RdNk+IxwDqw59MYGewDuwY5DRowDK0/TBKmKetV/F9XZyUigYy9gJxCTaRqS2IQMviThdgDmKjDivWkkAtYIMkLovNxxqaeGnudOKQsZkVY6SCLoOUPGdc2+KPw+ImOwj5mlqSmhaUaqzaqmSZX2GprmBP93Snt0edU0bnJqVQ/RvlVdHk0TREYsmkZZNI2eJT+uPm7j31TXGYO0yqNp9CbCFVtVVWF/1eDebWslvbQZYFN1rANrvDdN1a81b7gi/B4AXxB4/bzh54Hz+gGAL4mOOMT3fyqMojmx2Wzw3j+9Ac+98rusdenyyy/HH/7hH+DbXsIhNxM1I0EtyumPBRmZGPRCm+oKqUG/qrah2JClcYym0YKrnaqL9BFb9MjIbpNP0wRFnFXFG58FOvYCrmagnFoBQpRPOU2jx9JhiTWnlvZGNBHmCbSEpnG+t57njhhjhWma/PM6VMoZm69VTUN9ZiyaJqypkQTbm8bR0bB6gByahiIQomqamICVoWnUSNO4AsvYuHbX3hBNExmXEdx2VHPk0jSBfR0cl8y3/44+MiJe1xi09UyNW2+9FW3b4vyHPtRK3k+ePInPfOYz4wux5EvLlDK3fQbkIgsyMjV4mmYaMkJLezuFsmqaYawWVRElsa33gA4BZEQ7sMZ707D349V+/3TJaUY4moaUWsaesiVhw9uROSbC6qyqlLmILE+NyLiim7tF0zgiwhBNI6qmsb+3/rmJJSNTaJrI02uIVhrpOEKJKad3TlUButdqCU1j6RhgxqXHre0UVo0z75SAlVTTiGma0H4I0jTj33fGKVWuRbGuN8YxFlWFLuDACvDnWdvuoM9GrzcNoxnhaTsfcenF5lV/qLsuf12rKmB1AviO/wd46MXAakBzt/cCt36wR2tPfjE+dPNdONy2eOz5Z+Gcg3Fd+9ynP4YHqs/h6OB87D/oUeP37Tq876Y7AQBf/Ihzxzkc3gn89Yf7h66HPiF8nkSC0pMArG7O4woznPshZES4rp1JmpElGZkYtHfG1OZdY2nvyu5NExKwGgdWTjPSv7e3TR7mmnHn3Vb9RdtyyIimaQJde6OoA4eM0Cc1J9iFbKLp2VTNiIWMkLWaJiOiRnmSrr2Adz4Z220XGREITV09jz7Ptm147qeapokKWJlk1DxV1w3Q7YqSfyOMJVqUprKf2GmiaSLlwBqkacJ6siBCRI8VpWkUQfiiQtNAEsk5w3atTdMM55xUwNrpNQFMNQ05hqFmjNZ86bgEGYFqy9a1uu5ptb0TRLdW9a/V6/5YrjsotP3ve+O6ptYnAHXUJxXkmFdKQa0HgfL6LMIBt/24qxNiekbHQx7yEDRNg1tvvtlKim+++WacfNjJYUIOhhGANOSr2khXHnfcdzCsMzSspmkTaZqxNXyIprHHHR1YwzRNq6ois7CdSUYO/T8mkJHo9ozXCEFGIgJWWk2hIk9VkhidIKd17bUbzAVoGoGANY6MVOxxp9x8qDdNTBgro2kK5huIoBsvMmkaWgGlh5pQSk97P1lde60nUuaDSWRknNNkmoYeK1MmTFDTDqTSZCJNQ0t7STWNW9obGrcjCWHjJCN21177tdS4LRGwQnVl6xpLc+v9aX/Wv9drusoZkvzOyosKlqi9vT180ROfjD98+/8L0kcY119/Pb7qqU+lsw6KPWzkJB2lKPOpiCUZmRh20qCfMsqSEdNzpKmtm3CqN82Bi4yo0TaZ9nDIufHu6iEZ2cY0I6FGeQhvLxMZYXvTTEVGJqIslKOmVReWgFWAjOw6ZSUBXjCIWIybNy6WEcTFTcL0T42M9AyIPfeDCZqR2H4Oum5S2oQcM69MeEIpvdUN2ColJsgId5fZSrv2jkiDcm/6Tsjs4AkyQvbJLuCUCoT3LzVQMyez07W3jQhjDxhaqW3HY+B37eVoGm9Ydr4U/UG3K1zXhr9HktZQhZ5iftPvr8nDjf+JsjXqOf/0hXjrf/m/8XNvegPe//7342UvexluvPFGvOAFLxi3EIMx+BwrGUs1zf0gKH9rlckVREVFahKaJiRgJe/bId8OHgDauk8aOhYZiTuwRjvXcpbwKoyMjJoc+yZVEvRYTdGf2M6liv09NscDp6PqKmScwlRnUW4+XE2TRkb0ftXz3A5JUbQEt6Brr7s9fr48TUNLbTtSFmrGmqDRokkupQ7oNL3y3q4TICPkmHGaEWauIRt/q3qIoIem7L9TRttRRRGXCE1D6SMyX6NFkdI05Ht5jfLofGMCVqZxYqdG9MesO8hc1zhkJHBDD96SmT9Ug2zDylkn8h1f/83/AH/92dtwzY/8MF5y00245JJLcN111+Exj3kM3nfTHWYqBjlxtpebVJxBwMiSjEwN2gRqVpqGE8Z61TQBASvZfgtnURNGOyAj3TaSjIRKe6M0TWY1DXmqCjq7CoNC/u74WeNYNM34ut2hNvz5PXK8jrYdzuJ3Y38+tbCOJ+XmXTicPmFT0RsN98lUHyPtwBqvcphQTRNNciI0DUOnmLEM2pB/AzA6U1rm7dI07tfdEZRQRNOMDxdxmob37bBpGv5BZTQ9i9A0ns/IRJpm7c9XEWQkVE1jUWKsoJmnaUx/mG4897PWtagLq0vDuDd3/Xd//9ZVhRYqgFTkryu69PnZz30+fuj7Xm49pLQCtJ3OI3fri2bkfhAURo55CYjG0tU0ls8IbCiVRNBnhD5Jkx4OOZRE1/TaDsUlI0IHVp6mYTQjEZ8RqsmZIjqln4uJNUXjBKB8+qQWM2Zr6grrIRuIddjlzqfWSkYcO3hC120C9I8rLtbrnd4n3O4I+YFIInYuhJIcSsfRJms+TVPu68OZnlH7eYBBRjbEADBlB08Fod5N3x43uB9o8s3SNMBO9ePGkoZDOq5SGLF8J0liTM9iPiNuNY3ZBU6trd0HyH7NHjchYCXISNa6xuorHD6DBxpEw9psnh5XPo75JBnHXz/I/xUC3yk/Fs3I/Sho6elk0zNjZuU4sCaraSI0DbVNzjjzTDLiNrUDMmiaTM0IS9MMm5xYAdN/rv+5JTfqcjfX/idFWXIQKFG5LPNERzUjtQMRhSy1aXRmDS6gaSZoRjgviDRNU9kogEutTaFpOAGrU1LpaUZ0x97VCf4L0ckpm/awq6OcZCTRo6fyaJpxzTHmZFIHVqspZWXvQ3IdthKahpqeDchIpypUzr6h10rMqIxNcjqajGzM61nrWqo/TSRit/oqWomSv67QtcT9SvS/oTlZr4s3X0XHPJ2xJCMTQ98P5ujaW+tyunpl21IHkpwwTUMEj8QcKIfe0MmI12EXSNI0UaGpRkaoMLYLJyM2TTO8Vmp6ZipHpiEj9HP0hjXeeCXJiID6qP2bbUeFgk4yuEcOcCjJcX0eDE3ThucuFtwyISnl9J1HYeZomd65x39CKT3tuuomzxydB2A8Z0PiVWtOnX1e0xu0M99xP9ivWxQXSRQo7aHt4CvOZ4Q7x+i2YzRNdFyNuPjVNC1zS6mZxC9a6k3N1BRP02StayZB5DQjNk3j3pRNERNL0+jPpLUokqAz8naPBYyo8QXrO9G3y9Y1864zIBtZkpGJwQpNocp4bN310rWDD2hRggJW8r5doemZiiYjqdLe4c9RZCSXpvE9MnLDRQGCc8wYy0JGQjb4TGRZwgc1I3Yy0ltqx8f1aRp7n8S4fCBM/4Qidi4ke7I49uze/p2g0bKrtOzXGubYAhhpmpiRFXkgqeh5HWnsl+5eXFmJQkP2yU5rRpj1hkW06L6KVtMMN2luXEZwq5ORjrml8JSYkKYh5cu2gDVnXctARryvGx6bJrTMX9PbcjdtErXKQ2yt/3G0kPNyJjDiDnossSQjE4Oq/SdbVFs0DTnRk9U0zmEkUH5vm2zPVRJqQDAqmjSY8XVvmrjpWdCBFcigafybUSGYwWpGSjlT67gPEa0icmI/VHZJg0HE7KoFf3+lEBf3yXSkrsI3CQniEgqZ/Xdnie/saprhtY5plDipmmaYn0XTDMMSatAKTdPEkhFyzCwBa2Rt4OzV9dzMfLhqGqXQdvrJPubAGqJpYtU04dJe1mdE0zTMbZBDRqIaIrIfqB28TdOk1zVzTnHIiKPt8B1O4fw/goxExs2JzpluNNg3TUNloiXDiegK7SxoLNU0E8OC6+nNoWvZJ/1YjK3hV7Z/SQgZCXXtpQJW8gSRRUkMCEbVMsmI0Ywk7OCjPiOc6RmHjAxjdtMdWF0UYI6xqMgxxweF4929YGg/zc23quLRhnUDHO7CNE3A9CyGjKyaGqu6wq5T2bqRWIKmn4SV6pOhvZWNSFDTs1bZQlMALHIkDbaDs4MWeevrJuExQudEkJGqru21IEDTuPvWMowj3iB0bdgZBEOm7QjTNAQZqRujGeFLhv35quEBhUNGOOfjeKl3wGeEClgj69p6vUZVVbjlllvw0Ic+FNW2A3YKODoCVsND0NGmfw0KODxEtz2C2u2wOapx2Izb3+x2OITCrmqxOrRR4m63gdrtcHhY47AePnM4jFt1wCGDKkficLOD2m2AusYh81m165Oxew8Psd52ffZyeAR0/fHYtZ15z+HRoejh0/rM4WG2hk4phc1mg1tuuQV1XWNvL1QamI4lGZkYPE2DogWyIo3ybDt4nYLzNM2BK2DV/RtUBUU58YJkpGaTEU3ThEp7hz+zNA2HjERoGmIsVPQ9SLg33uAcJWORp1Mdsac+N0ppmnZY9FvU4FJd7VkRqtJxBYSjHfwgYA1gpQfrBncd7bIt4WNiZmrWd7RrTckzhd/p9eW5uRqKoTwZodeHu098zYhGRiI239aDg37UnUjTUM1I3aAhwskW4Woa1vTMo2kYzUjVYGdKe/2nZc4Ez2hGIrovS/fDng8cTTN6qegqvtS61jQNHvWoR+ETn/gEPvrRjwL3fBbY3AUcbICDvp8MtvcCd98CNPvA7cBtdx3h3m2H7efW+Ov98bZ4++c+i7twF7rV3ag/t7W2c9tdG9y7bbGhn9nc1W9vfRfwubxrZbPrcPOdR1jVFeq73T7BwC1/fS8UgObuAzR33dLvj9trs6Z2ncLNt/fr6vqeEyKExf7MQbGg/6yzzsKjH/1oFq2VxpKMTAxL7BZ5+pFEbdnBYxw3WE0TELASMZlVEplxolXDCc4mI4amCVTTREt7Oc0IgbOdsBw4NbdfeMFob40dNSebiIxYpmexJMwJWede/2arhp3APYFa4waQEdeO24h6E/z7/qrGXUf5FTUxVM6if3YdztFzJBoOilLoHNLMcUIpPUU03fPVKtenIRKwUpqGJNmRLsxcYqoc/xOKHlJx+67TmhEZnWLBPV7X3pEm3kmQESo0NTQNpxnRm6b72ntb0A7e1YxI1rUHPOABePzjH4/tdgu87ZeB//VfgEu/A3jqC/s3/OX/AN7xz4GHPxn4hz+DN/zae/GOv7wV3/U3H49v/sJHmnF+6id+AS/AL+LuC5+Bs//uv7W28Z//2/vwPz5wM678msfh//jCC/sX3/vLwDuuAS56BvCN9vtTccONf41X/vqf4rHnn4Wf/faLvb//s199G7Zdh5//p1+Fh/3BPwc+91Hg7/808MgvBAB89u4NvvNX3wkA+O/f/QzRNu8+2uI7f/UdAIDrXnKZX5kpiKZpsFqtihMZHUsyMjFsB1aHpskdiyAjlulZsGtvSMA6Lgx2Sax8LtX6RP+ZGDISomli7qZrzvRMJyNhvrn3KJhJwLrTlETRMPa8GJpGclFKmtpxmojYog+kERf3XKi8fRJORoB8zUgMGdGC26Nd50Hzeo5co8QxGSkvpefGHcudh3m4mhEjYD0RHphoMAxN45X22vuQs1eneVDtVtOQNWenCIJBUVSEBKyB0t6Qz4jQgVUNn1XMeVlF9rU9rr8fOgWPppGua03T9MaA1Ra46+PA5rPAwbD+VEfDa48GDg5w57bCJ+9scagaHByMqMTtd92BA3wc7dGt1usAcKQafPLOFndsq/Fv3T39uLvbx20J43AY78Hn1t62AOCmu1sc7TpUqz0cHN3Sb6feme00G+CTd7aoK7Cf56KrV/jknf2xW+/v42Dv+FKCIkzlta99LS666CIcHBzg0ksvxdvf/nbR597xjndgtVrhyU9+cslmz8igTaCm0jTaRbFuSDWNQrBOPmx6Nj49e09Y0rkMSUPTbfw/Ch1Y5chIupqm6yhNk5o9H4amKdHQBMaiD5qepiESMp+RcDVNOhkJICMh07PEPuFcNyXRmYftVJJjixaBgaYhCKFP00zQjOhLilwfhqah1x6NXJpGZxR1MyQJOstxaBqG9qBJrk3T1E6FWUQYy2pGHBQyQNOMJbpMMsIk0lrAyJX2Smkabj/0mhGbpsle16Kuz7U1hpuAmhJnaU8hI5rNX6SMb5S7ng9Bjzvbt6pAU0cfEtheTKcxsvfYW97yFrz0pS/FK17xCtxwww247LLLcMUVV+DGG2+Mfu7222/Hc57zHPzNv/k3iyd7JgZtjx2DYiWh22NX1Geki9A02mfEFbASmoZSEjn0Rr2nk5GCapoYLZRpBy9V4ktCz2cXEWvmjsVqRjKQkairKYOIdVooGFjsUvSPiy74+yQwX0FH4Nj2uMZoAF9VxNnBK8VQSVNoGq5keNil1rVHI0fASmka78YfErDa9ISOugZP0xABK4BgknPIVdPouVimZ+PftlH6hzlmkSSZ6wMU7+Kcpmmy1rVYBd+wH2jjRBq7aDLCoJCRB6tUBNfzIajnEuc+XIIcW322jjcXyU9GXvOa1+B5z3senv/85+Piiy/GtddeiwsvvBCve93rop97wQtegG/91m/FU4dWyPeXYM3JgDKaRlfT1JUtkDQntjKZd9spU44Z8hnpUFlizZyTtBmg6BWXjCR8RqJ0BWsHHy7tNZC0msGBVaMAMyQjFj03hOtuGguR6RlD04yakRCCwZeJunPU393QNO147sXnm4uMJBAXznWTIiNkX3oJ0xSaJlLhwXnIAEg3yaNz6sbSXuNIGkA4Q1UkZq5VZZXdWhRTR/ZrKMnhqmkqZk6UpjFVOhEzNUorGZqGo+P6n1zlEjtf51zonNLe7HUtqlMbkJEANbfT13SsdJru38iDVSpGZIT/rN0h3kB746ZdTZUg6HXp0ZKnObKSkc1mg3e/+924/PLLrdcvv/xyvPOd7wx+7g1veAP+6q/+Cv/yX/5L0XaOjo5wxx13WP/O1KC9MywotqRfhhawNitbIMkkORtywYYErB1qy1Mjh95oBmRkpSI0TUAz4jll0jALA3FgFdI0c/emmYOm4TQjsmoaic8Ih4xINSNSmsbZJ0HNSJklfEwjQOdLESJK7dCblnfcDIdTXk3TKdoDxt4nvs+IRkYkNI0jYAWCSI7eB5tdZ44P3XRDBayOIeI2hoywAtYAWkORkaoxwtgYIkARFxU5L2l5drzU2z8XFFPam72usa7P9n4IiZbzaRon2cuIoKP2ENYDKte3qoimGX/3BNunObL22K233oq2bXHy5Enr9ZMnT+LTn/40+5kPfehD+L7v+z68+c1vxmolE8dcc801OO+888y/Cy+8MGeapzUsHg+YxGPrMroqZAdPxqULTEgz0qIu9tRo9npkZO0mI9QcKUXTcGcXh4yIaJpxH8/lMzJFwEob+OmYvbSXqaZJC1jLaJoUWiQS3DKRqjAKNUfTn2msZMRBbyb4jFCaTTlztCrZaIgErOOczPWcomlIBYN2uLVoGkvA2ozVKUolNCOR0l43QbI0LjWhfzjNCCM01clIpLSXludLNSNtB4PSmNLe3HVNoFMLlXOPVUVSmsbZvxkR1AAOwVZuWq0/CgoVqmps+HdfQkZ0uE85wXblbYtv/dZvxQ/90A/hC77gC8TjX3311bj99tvNv49//OMl0zwt4VVVTHGFNKW9jgMrUzKsT9ymrqxW0/Q9HWqrD0sOorDaH5IRV8DakVr7Ipom1ijPf79F0xRcbPZYWh8Rhopzx6Lu6J353unPsxCvG5X/5B97AgXSSU7IbTS1T4qraZLCWEbASqok6Md2bmfhSTTNuC23+itM00gcWM3KbpueWfO1x7UaHA77t7OSEVi0AnUl3tGhvCSHQcncay1UTdNJtBKZ1TRSmmZLkxEeGcla1wSuz1bDU/0WpYj5m2w/uMLYnAhWRw5h6wiZB5UM00UaVGB8nJFVx/OQhzwETdN4KMjNN9/soSUAcOedd+Jd73oXbrjhBrzoRS8C0KuulVJYrVb47d/+bXzt136t97n9/X3s7+/nTO3YwhO7TWjeVQdpGt8w6TDkMULe4z5BZCUjGhmBbfRDXRCDNE0XfvqJakZOG03ThecnHqv/yTXKm81nhDmXksmIEcbm0jQ6uZ0wXyaiZd6I6yVobxprjiZrmO4z0inf2bdhEk0ARMAqo2lqD4XgH1RWg0asUzqJXFuJkE3T1Gi68eax7SiFy2tR2q5vcLhqajFNszWlvRHNCKVp2ggyYigQX7OUHFfxAtbJmpFANY31cKFIdRC3HwxCNBMyEnLUHsJuPRKhaTLXtXo4+e5TNM3e3h4uvfRSXH/99dbr119/PZ72tKd57z/33HPxZ3/2Z3jPe95j/l155ZV4whOegPe85z34yq/8ymmzPwOCPvEpCz6bWtrbvxamafqfnvsqeU+HypRsunNNxXq/v4D34CAjpD9E0oE1qhkRVtOQm8Z0mqb/uS18grDHYmiaTj4/GU0TKe0NPHkdMJUZNIxxmJuMxBJITKFp4sgI67pJjrNF07jHbQY7+J7+s+dIzzkrRAJWOictYI3TNL3fir0faF+ayqNpxvkZd1KzzTHo2mD2byhBcrr2WlU6zn7gj1lMM6KHoRVx3tvYcS0BqyntzVzXYmispmkYaq4jiZDUiTYmxk9FdE2HgKZxrm1pBH11TnNkO5y8/OUvx7d927fhKU95Cp761Kfip3/6p3HjjTfiyiuvBNBTLJ/85Cfxcz/3c6jrGpdccon1+Yc97GE4ODjwXr+vBl28205hZTLWvAVSdR3qSi+2jQ0bcjRNrCZdJyOqtlq+55yj64P+6W9fOcgIaeMdyv6j3XVzfUao6dnU0t5a0zRzICMOPQdKgaTHZa263WCefhTxkOEiRf+4nP1IXQ03zpS2o9D0LKwZ4cs59dzo57zjFri5S4LtTePoaLzFOUfA2o3ISMXpM5w4WNe4d9ua/eC5DZNrpFZk7sDQp0h5+8F1uD17n4zD+YyQh4IdrdLpWlC3ZSq41RS9Go6NYpoUxPY1DbayiqNpcte1dQwZCWtG6LbjTrQzVdMkNCM194BaaC1AI9j+4DRHdjLy7Gc/G7fddhte9apX4aabbsIll1yC6667Do95zGMAADfddFPSc+T+FDQBbpXCyl08hNG2O3Mw6rpBDcLjWTSNLWCV0jTmCUsYe4NmZL/aQnXdyHtT99XAeCN3yfwxqhnhSnsJnJ6hyeDCK2Mtz0VYL4qxQWD68yINBqOJiHHz/bjxJMfVjOifI02TShpyaZr+Z06SQ+dIP+YLWEd9Rm7Q7+neIE0papGAdTxmVtde+jeGVur3w9bQa15iS7v2Dp/R10SHGg1ab9y6rrDX1Ni03ZjsuZoGzmekGu3g9Xeht4p9B3E5WDfmIaVjdV/jtdJFkxGf9rCM3RiaRrSuCdacirmeLcM1IV01i89IsJqGUvf+uVTitA1Qiuo+lowAwFVXXYWrrrqK/dsb3/jG6Gdf+cpX4pWvfGXJZs/IoFmoomLTTOiYtmCumhUa8vRjrcgOTcOKnSyfkbJsee9ghKKPju7FwYnhaTDhvgokMvTcahrmQinvTaOTkTlpmvG1qFbGCa480guWpglz83TcpB18bmmvxKSNiWhlFQKaEaJrqapRT2GOmxFfTqFpxt/HkmGY7fZzdz6USdNURpAep2kAX2zaugk9pWnIg4pSGiVr2f2wv+qTEaMhElXTNMbsy/oMGVOHTkY6pZGRsGZEEc0Ia3rGVtNwDqyZ6xqrU3OqaZhjTjUjFYMaRKuVCmiaw4TPiCU0jVXTZNM0ZwYyUlRNs8QY3hNWoSuk1gIAQNOQNuFqSEacp+SoWx+tpumcp0lh7NNk5F5anx9vkgf4Nzwr2KeUUZznhr5B7EiyNpmm6eIogCS4JCn6vZ3gyiO9YKtp+v0fRkbiCEaQpunGJ01+3DIBa0ozwmlRvPJj77gNb5xC05D5mGvE2Z7fm2aoponSNOMxM72m9IQjFK6bRFrHSSkAY6LgGrbFRZbO/nVvlnQfdgFkJCC4peMaYXWkPJ/tL8Tsg90guNWfYatpctY1gU7N0mMMkaRpOM3IpGqahIDVMj3z7zOlDtXB5Ps0x5KMTAx6TdlcXj5No6Mv7R2GMSXD9skX1YxQmmZXBt2tVmtT1rY9vJtMVLuvxpCR4XvEkJFuB+jv7JZFkxib241XSml3SP2xcZ9M0Yz0P23NiP6bHBkRNcqzaJp+I8FkJJHkuCI3d/+maZrM8zpRAaWTnEOrnBPWZypnjl6jvAl28Ny4HGTfvzHDgZWlafRFzSUj9nGz9hv9fsSBVb+vC/S84cYV0zQRZ1dLcDuMq1qdJHM0zbCJLmUH7wtuuy5QTZOzrglKew01R2kaqleJOtHOVU0T14xY2g6mtNfrai2MYPuD0xxLMjIx6KJmiU0zF8i2Hd9fNyurdwYABhmJ0TQaGamMiVIutVHVNY7QJxybI4KMJNxX6ZyjvWmAcXEQmJ5tckyOAtE4Y00yPWNEjlm9aSQaDOZcUsneNAmaJoA6bE6ZZiSVjPDlnHQu/nFzaZpyASsdd9zeMHcKW7e7MRGPISPkoUGXxYpoGg8ZGT5SV/b7q9o6vzZtR26YMcRF0zSOpqFikJG6wSaCjABhxEUx1zB1YI15Be059A+AoWuvPiCjHXzWukaREX1MHTTWW2/1fE0lTySBPE0+I1bCxJxLpZoRU7m5JCP37bC8/anYNJN/U5SGaFZWN2CuZDgqdiIVF6U0DQBsqj7h2B7dM75oNCNhmibaXXdF/GM0hyuwg7domlLNyCmgaej16/H8kcjzGaE0TcJnJCVgNU+mGObq0jQhOqWsmiZVjh3qR0LnGDxuM3TtpeO6Ohpatm1QESAuYCVzGh2VnRt/9OZuC1iburK/X914c28jyciem+x5XXtJVQbt2mtpRji9hNYQDUlI5LykpdIxY66mrrAeMkE9X760N5emIWuOTig7ez+EHJU7UaM8TjMyv4DVopK4vlWFNM2iGbmfROVApiMUW64ZoaW9ANiW0SaL5vhF0s5754r+MmIzICNbiox0cgEru826GVEVjYwIGuVRx8Wp1TRT9ok7r9LSXpFvB0fTmCfQaZoRPUd3/wa765bSNIl9wpUiuw6+lTNHs9a6lSYZYZcM2+OONA35gElGKhvdc4McM60ZQaJRHhCjaRClaXZtgqZxk0ghTWN1r42Vte7sZERFqmm6jghYhdVVNk3Tr5HZ6xqHxoaqaRzfoGg1DXcNT6qmiazpCCQjVOOSsf7QsNy+jzGWZGSGYM1osmkaWzNi+ZcoRZ5g+hN2dGCN0TR1En6PxXZARnabQpomtE1XUHa6aJphF3pwf9FYEZomx/QshjRwNE2bELAmuvbqm4zrqXHqaRr+7zxNA2tuLpU0S28aMiEX8m+YG5MlXo2dN2ROI00zoIhZNA25sVg0TePNXaRrSNE0jn9QbzUf1uS48x2TkZhXUNpF2Z0vNR4baZrMdY0+OAXQWM5116ZpwgnZth2/16ns2mslyVGaJm9dCzaGPM2xJCMzhC0sKlwgNZqhKlR1bUGxXC8CfeIesNU0mqapJqEA26q/iNlkJNCXBhAIOd1Su2g1jY1mANNLe3dzdO1loM0cAZmMpmGepHWiGXJgTZipuQLC2tm/obmLTNqYSEHHrOuma1nvHjc9xylNKTlkRELTxMSrdE4AajiakRhN496Eh6/E0jTO3GM0zbh/E9U0tOdUVds27NFxO+s9XJJMGw9GKVxmvm2nRlEsKe3NWteqKvkARF1iddgCVm4fUI1LYP9mhB6DXdMxopahrr2lDtU1HfcYY0lGZgjadKu03LDtRggSsE+oKE3DZdGkmsa4VhYc6W3VJw3thpbExZMRihQELwrNuZuFIcyzjjfLznstN9yx5vAZcU2S6N9icSChaZibrdYWcU+gQEDhT8L1Ihhv9HG0qLyaBtb2wuP6mpHaS5gCyEgBTQPQRNdGRlgTKN1+fi+RjJCb0GpIRtAw+gwn3Ju7BblT8aSDjOzaLvH07uxfU07lJEitg4woaSWJS9OEdV80GZEicDYyQmmazHXNdX52kBHumHeJhMxyuDU+LuEHq1SkBKwWGsusDTnWAjRqOu4xxpKMzBB2A6OycsPOgd+t8j2mF0FcwEppmnJkZFf3yEhrISPEgZUJCm0HrwkPGYlpRjREX6YUP9Vjdc6TlHRcWtKqQk8knEjNOLAmOPcAguHSJnrN3CTQIq4EVxKpTsu866Yzx8qeo1faW1BNA1CKyn5aN3ogujhvBB176ZwArNRuGNdxYBWUiVoIgkXTVNa+3LQEOYiILI1ZHdGFWD9pz6mqRkv1EpJqGoFmRELT7DlJjtWszqJpMtc1Fxlxuhdz7R1SZdOrpsZqOBhesldA00Sbn8LRtTDnUmlXc5aWPIZYkpEZwjqYheWGnTb54ZIRxv432sdAK9tV7Rk65UQ7JCPdliAjCZqGLuDBDN1bGCLVNI4p1xRqxavKmCRg5Tlm+rdYUJHapg3c4CNde6cKWEOmZ0EEo7RRXqoBX6AfCf2Mfw4Mb5zQtRcgwljX9IyrLiigaVbokxFZNY2dRFr7jYpOq96Vls69jWo7nOTU69prV6rov6VQgbCANaz76lSawt2X0DQl65r7AORW0wRK9WPoEMAghm6ylxFJZISWHzMPKjkO0DSsh+ljjCUZmSGsLp8RKDYWbmmcVTJMHfcczcg+27W333ZLuNWSm3hb9xew2nAOrHwyQr928KLwFoaIgHXYnfNUwDj6iEmJTf+TQ0ZyBKxARIfB0jRaMxKgaRJJQ8gO3qtUCcy3tFFeOskZbmpK2R4b5Kd3DkSqUyThfne/ay95s+5Lk6RpxuPSuJoRkYDVqaahmhE6NjmXo0mDW60UsoN3aBr7Rpwua1URzQhHaYppmk6R3jSapilY1xIPQNwxt1CZwDnmJpGTTM+E1TShtiPudSONYJfq0xxLMjJDUIfBYprGaTRFzyc7yRmSETFNo58gsqYDYExGbGQk7sBq0zQpZGRIckQ0zfQKmBHuL98n7rzsluP232LB8s1ucOeSWXxCyAij8CfhztHdv8mSy9xqmoQRnEvT0CmH5jhbMhIYl6VptpqmiRie0TkBWHvISPrmrqF6C2VjnrbpPlE5zdzENE3K2dXRJkUQO/e6A9JIGd0PsWoa8TWsH4C2fGkvV6qf2gd0vn7pdF4ysms7c76Fu/YyyAjVjJTSNHr3LsnIfT+ow2ApTaNaGxmhUKzFEebQNKS7ZQkK0A0Jh9oxNE3Nm57ZNE1g4GA1TVj8luoqK4k5x5ram6a31E5QH5zGQEjTAH2LdzfcxnXGDj5xnpQKWGMmV/a4o2BRh1va6x23mWiarZOMcH1KxAJWcg7rZKRuHBRCQKdY5c2df33oa2ubFLCGaBqnmsZKRhrb30PgMxKjJ9xzLPA2dtxOEbSFJCPZ61qymsanaWhvmlNN09AkP0TTWAkT15smA5mlwVFUxxFLMjJDWJxbadde4pqqo6HjujSNoGtvi9rzbMiaU6OfJki3y4RmRDE3Ey+CC0N4IVPmiT4971D4Y01IbBijoFGPIRsj6d1hzqVxI0maxqJ//AXUFRCOJY0Y5p6mU4KCWybSGgF7MeeSWX3MveM2wWcEcGBv8v+aW5wLBKy1cWDVPiMCOsXZDymaRiWoBP9mqXeikyCBfNe6HmzYJfSP9hkJV3lxHa6T55lGyjqF1oi1Nf1csK65/WlcmoZBOu19y5/zflJmysdk8xqCrgF7AWRkPOZ+QQOde+66FuzFdJpjSUZmCOtELq6m0VUSZDHjzNQyu/a6c8wJ1TANphIOrNbNROwzMnwmAvGa/0/yBnH/P30szvRMOm7SYp21fLarANxYNbVZ/Lkkxy2tdPdnaA3Vi65SMO3bJTF6hsTHHasnfF2B+6Rn/ju1msYZ17Wft76mVMBaVd55XBkH1nDy5ML9FuTO0JiWQ7Oka69bemoErE7yMLyeNvxyEmkVo2n8c1WqGbHmMUTRuuaV9gaqaRxBepKm8aqKyqpp9OfXTRXcNyMaC56mmWh6tjiw3g/CEmiV0jSOzwjgcNcOLHcYc+sjPiNmrIKbOJuMJBxYLc3IHNU07g1jCprh3XxmoGk40zPhuGmahjmXTGlveLE7cMs5SRibCWcR1hE2PYsjLqFIJWgHXk8Wfy7uZ+eiadxxK2d7NjIiFLACXjLSGJpGTqdYZnGGpuGvZ4McMOUQBymaxk0ehv+nOtZ6pmcxzQhzlwldxu64lpB2iKJ1zUVGvN40MNsz2+nG3jSh9fzALXkvtIM3JpYBigZwaRr/XMrxOaLBVo8dQyzJyAxBHQbLq2kiNI1VV+4IWBMOrGasgiOtBnOyqs2hafT2IheE+5QisIM3/58hgQiNnRPcBZzTtRcQuJpGqmlCmhGAUfiT8Lr2hm70TliC2wwRayoZ0TfhTdv1kDyDrAXPgYkCVi/JcZMRSzMiFLAC3nlcCezgXRM8y82Xo2msZERO//jVNM55NLzeG5Q5/Bgd17WDN+hmuDw/9Ro3rpUUDVG0rhmjxYDpGUvTxCuVgMj+LdSMhCppAMcZOFZNk03T9D8XmuZ+EKwdfKHpGbX5tsrNajtDH/sYxJIRP7HJiuFpwkpGEg6srkcEP+4xISPejbd4KL43TaYDYlIzwp1LkVbt3rgM/ZOkaQL71xbcym/++qaaguWBMSHREaJp5rCDZ8c12xvmXiJgpfMy/3X0GQLfDssSn7k+6HGKO6WGhKZhmkaXV4uEscM5VkWSZO7YJ7s4m4aBiNI0xZqRgIDVFaTH9kE/X+daK6ymGasjY8gIh8D7NE3uusYKto8hlmRkhrD4xkIem3PWZE++TiBgZWiaUGv4WFRD0mAjIwkHVtNxNTJwgQPr+P/4nGMhpSQkwXV2zS2t89wx/Y30P+m5ZG4m4Y3E6B/XJTZHR5OcLxMpV1pLcLvtrAUxOcdCfZYZx2UozPYiNE1KM0LnZbYjoWnsm5rlQssIvK3Sf4G249ApwYW7D8ncTc83iRZFUE3DrT2h9YirrorRNOJ1LagZ6cdie00pFdXj9PMNJHuZNM1h7OFyCPt+EKZpyjUjSzJynw+rzLOQx+5MOa4Pxdo0jSNgjfqMUDizIBlZ9xdwbSUjcdMzEW+ZUU3jZvmz0jQzmJ7ZLcfzONukd0dtJ6D97/oJNIaMCGgajQJkoEVJwS0T7vbcsAW3rXl/VRENR0hkW6jPMuMEKCpLKKhjK6ymofPS2/F608Ru7jGfkRRNIxGaujSNcx7VtXmgkCQ5vh28AN2MXB/ufuioiHSIonVNaHpm9abpiB5Hv+DON7Af8mma/nOhShrAuR/EuvZmrmvcg9VxxJKMzBBWyVVhuaHRjDCKebsBn42MxLv2TqNpNDLSZNA0+lqO0zQBAyLmaSIoXiwI90Y7B+WjnCcpIKeaRj8NpwSsnGYkkoxEXFgNbRK60RciLqGQ6GjGp/du7FZL3u8lTHPRNIFxWUFfjoDVgVz8rr1hmubQ0BOUptFiAL6KZBRZcpoRJ4F09ScMTaO/t8QO/tAbl3mgCB0/Jrj9cGppmtoaxy3Vt7YtcrgNVwbGIuqoPQS11Wd9RgptHEw34EUzct8PIwAKNDCShK6msWma/ienRTmSVNOo8fCW3HfrARlpOoamSZT2xmmaUNMqCU0zARmZkfLhavNzBWRpzYh/LlUGORAkDQyCoZyEKUcgXKIZkaBFNMnhErqkgLWYprHHHatphmFLetPQeZntyB1YKSLQz6XiaRqyiS5STZN8cmdpmn7bOc6uKkrT2P+PWXBw+6FT9geK1rXEmsOV6ls0DZCnycmtpomZWA5h0NiAA2tOo0573IWmud+EJX6qyxbIUTPC0DSOFkUpdVpomnqvv4BXHXFn1DRNwIFVRtPExWQ0gh4TBTFnZY7lujtElykgk9M01GdkOLalNI2BcsHOVfLUmiVg1Q+KwnE7Z37u7wDVjEylaez/+zQNk4zsFVTTNOlqGs95lJaJczQN2Z95NI1zs2SqafT3HsWbsSqdQcAaEVb717A84e06zETTBHRqLk3jXM8WMpJBV5XSNBLNSEfvM1zX3sx1jetYfByxJCMzhOUwaBbIwtJejqZxSrl23dhMjHdgdThflCUjjU5GFE1GNDIyh2YkbQfvXlhz2MGb/8/gWUIPcy5nK/cZ8TUjscUuNq7bKyYLGUnRSkxIXGkprcTROkHNQcTRVBKh0l4Oss8SsDrncSOxg3du7i3dbwyNSY9TvKFdwCE0StP0v4q69npVJBNpGodWSpmeFWtG3GoalqZBmqYJaXKyq2kiSPcQVnUlQ/nlWguYcTnB9jHEkozMEBZkP2M1DStg7Tq7jwGnGZmpmqYZavMtZETowBrdXoK/pXEqXFPnGIurzc+maVy+2dsIR9OkfQy8SgcSrg9MDvJ0ymmabccmdEF6jVwTJeF999p+vZymCQlYY6W9/cbbTmFHSpyDNI1V2qs5hkiS41bTuK6w42TNtuMlw07CGzkvfZpGrkvifEbKqmkC/bA0TWNOJbu0l67HvPDY1eSUmp5FfKOGaOjDaZSmydSMLA6s95/QT30WglFI01D43eIxyY2JllaKaZqC++5qr09G1hYyEm+UxwkQ/YHjZXY03HFm1YxMQVlYzUjek0mapvFvtpqmiQlYR1dITsBqJ4shdIAdN2XSxoQkOaXjcgldUDc0czWN/r+nB1Jq7E1T4MA6akZiGozxeFr7YSJN4x2zUNdeM/fKoDKxJCc0rmJuwlVVWUlu7LLjHVgjNM1UzYhjeuZez1Ka5nAyTTMUJAgcWDuroIFW0wzvy6VpOFryGGJJRmYIyxmvuJpGIyNUpEa1KOO4+sTdW9X8Is9V0xTceFcH/cLLJiMBZERE06zjCwONU0rTTDj7rWM+RC5N47puesHSNOF9pSOGuLQOUpFT7lxWTQNre6lxufMnTNNMq6YJnVueNXi7GRd9EU1jn1iN1oxEq2nGzxztOptOY5qvyWmaEc1SSpFqj3Q1jXRcgCJ2/HkZO56xcVuXKkHhupZwfebQML+axr9Z+8hIYTWNwIHV6lXGVtPkadbMuDrJWTQj9/2wTM8iIrVoGGEiudACpVwG0guJnbjeNAWIwnpARvbA0TTx3jTRppUZdvAeMjKjz8gUzQgn+ipFRg5DNA1zLlXMU3JoXJ6mseeYgzwl58uErLR3nC/X7CtY3l3odmzGCdB2Xkt1TdEARQLWxhOw+vuvritjuX+0a216i2m+Rm84koZ2/bgdoWlCPiON2ZzI2VWjb5EHCiB+PLn5Smma/EZ57gNQPxZXzt2fi9WIxIjoqlLTs7SA1aJpWDv4Ms3I0pvmfhTWjUmfCNl28IManbnQXJObpNhJzZOMrPb7p8A9tR1fTNI0At5yUmlvYtKRCDVGmzIWNQoayzFlY6QFrMxNxlQtlCEY1FSMm2vU9KwAGZGUelPNCLcPfa3P8MvMXXtNjuMmmlq8Wq+DSbgVbtdeQyvF58vth6oi709pRiJCU2BIRpI0TU1omkiS41XTxOkJeTJiIw0eOoHSZCTu+syX6jslzpLuxcV28BkC1o7cZxjTs9x1jTX5O4ZYkpEZwupTUkrTqMFnhOlN49r/JmvSOz+xKaE39g40MsIkIwkBq8j0TFRNY/9/3t40Uyif/qcreANKqmlCAlYGZRMhI+Fx3V4xWaZnKcEtExLazqqmcebHfdbMsbCMXoe7aLuiXrM454hXAes8blWFyhWLJtvRdzad5qIZcJOR8H5YN5W5bx3tWv9m6XnijwJWxYgkzVyHc2w3CG5jdvDmezC/e+O6VUWdrxkpWtdS1TSMiLM1CJG8JDu1H0KRU9rbtx3xKb9WcK3x4w5DLcjIfT8sLq+Ux9YJBKVphl9d9XRSeT1sm1oZl9Abe/uDgLVqsdsOVI3QgTW6PbMwDM3HcnxGJtE08bHzxuJg3bxxk/bqzLlUMTemnHHd5CAnQSvxGRElI4T+YU3PQpqRwjJ6M06AAvT0QDniVTovOHqHBIW7T4THFuQ+oWuv1eBw2/F0Cr3uaqlmxEFcElomC+kSoG+atuB70xSsa57rs30deToh8nsXS8pSXZGFMTqwRmgaOkfmXHIr5aTBNQk8jliSkRlCH/tQAyNJsALWiiyKjIA1j6bJmg4AYO9gXHyPDoenQ6HPSHR7Z0ijvAkgC6sZUbPTNBw8rKHY8KUbbZTnzDFoKFYyXyY4DUhsXNeUjfus1yhvNpqm/79Xtp2LjJBjYzl4Rm5qgI1o6cS2qqoATTN+LqZp6MclSSTXaJGeS1VlvreKrGV7jhYlJWClSYNUP6SUYu3gi9a1BDXM0jR6P0homslde9M0jd0d3j+XRO7XTHBNAo8jlmRkhrBPkjIBK9dzhO0GLKJp/MSmhN7QyAgAbA4HFMM4sMa79oo1I0qx1QI65u1N44w1A+Xj2kcDch47iTRwlJ+gtDdO09hzzLODL0BG9KEVJSOdJ7Dtf7ffb+ZYWEavw52SK+r1NCNSZIScxy2XjATmu0eSMqsyIkXTqPi4VhLJiGFdlMRHRvxxm7rCejgwR7vWaEaqAOxBj6eECgSATduXOLvISNG65j4AhUzPmOvZuGJnmcrNT9PYflb+uS+iyJmwZAbHGEsyMkNYzniFNM1op+zzoa2jRUmeuJqmIT0cSuiNZrXCRvXb3RzlISMiO3g9XpYd/ARqZcbExjrmQ7hls6lIakaiNE0MGQknDa4JWU6CNk0zEn7PPvGWYE3PQnMs1GeZcRKmZ+ZB0SAjgkoaOi84N9LE2kDpNes4MeWiUpqmH5ecZyKaZhguNa4lNnVKhp2IHU97TBtxSfWmkdM0oa69/VicZkT/HqVpQuZv0XJCP2S9abj7DKVpSjUjPsp7HLEkIzOEZYA1kaahpmdWZ1hy8unSyoNQh0eGpilFATbohapbjYwkHFiz7OCBAR2JCFjdp9cZkZEpiY1ldDdELmcr7trLVNNEBaxrm3en4ZqK+aW94fkelNA0ArTIjLttPbt6do76tC4tow+M61rk+zTNCYiCnMedRYEkkhFK09D9xlwf9BxTiXEPaNLACaCdxER/7y6xlh2QJKeKXMNA/HjS2GvqUXA7OPK2joC1aF3Ta0637b+PgxCxpfqC/aDX4MOpNE1qTYdbKMHQNJnIrA6rG/AxxpKMzBBG1B/g8iShzIk+nkgVveFZNE3KZ0RX09CnkazpmDiqhmRko2maeGmvxXWHotmD+Z67oyi0WVWVBaefKXbwNQNt5pbW6YVnk9W1dyiljGpGwsiI20zLnWu8N00JTSPQjJBxlZMscZ/1NCMzVdOM1FX//5Gm0QJWKTIyHhurEiR5c9f7obWTRsbVk849pmkAHJEl5xDq6EeMZkSKjOza5HlJT6vY5WEJbge6yqdpCta1NX0A8teckB18v70wXRX0GTklNM0wL6c9yDjf/me+HbweakFG7vNhd1MshI6Zrpc2TUOrabTyOoSM6GoaXwybG9sBGdkdOclIChmJba6qbNg08TQh5ZtT4dM0xUOxoi/uqT4WRTSNwFTJU/iT6BxeOYumKelNI0CLbOFmDk2jr4nCahpXvBuqpskWsKZompS2w6VpmGoasktiNEI/LhWwpmka/bVjpb3ufJOmZ8LSXne+fdfemIBVuB40hBpm0FjO+Mv4jIiqabTDbWE1zS6xprtzjNI0WZtmrfCPI5ZkZIYY28kj6SUQilHA6vPCytGiJPlFjqYppDe2Va8N2R65NE1AMyLVTVBBWeIGay9kgkkHYs4yYc+LAvmLwX6khwyAwJO0fmQWCFgjpb2jPsL+e7zsMjFfJiTeK1Y1DbMPQ0nDZJomcIP0enVkC1gDyYiUpiF0VR5NE09yDrct/+RuJSbU9Cy+lu2R8yzlDCz1GaHzPRpKvWPVNOJ1rVmNaO7uyHsA4vqzeEJetpqm/7xSwLZVxTSNyIE14WeV63Okg9O/HUcsycgMYSEjpeWGzCIRUk+naZpBfzIHMjLQNK1L0yTs4JNUhVVRY7gd9q2WR8GMyMgclI9vHy2nadKmZ/4TWcrpsh+Xp1OUUp5mJERVFM2XCUmpNxVCKob3TnftLUtG7G34r/umZ1KaZhzMTkYSdApFBOh+m0rT0ONmtBJ0XvbvY2nvMG4oySG0Uq1pmkA2S/d16vqwTPAYmqZ4XWPXnNoahzM9i63ptuC2nY6MSKppFNhzv1QzstjB34/CEj+VlhtyyAjHEWbYwVO+uvTGu6sDyUiitDdJVVATIq7ckIRF08zgmsqNmxsctNlqSkKajKQa5THn0lhCKSnttcelC22oN43IHbMAGZFU6RzuWpb3TvammaFrL/3dW5xzBazF1TQawQjRNLxwUyUegFifEYHpmUqgvCPikhaw0tMq1WmXmuB1jANr8bpm0NgwTRNHRsLVNIDT+ydzbZH5jOhp8DSNS8FKw/LJOsZYkpEZwnLGKy03jGhGXC1K0q2P0YyU0hu7ur+A283gCZKgacQVJdZTSgZNMycyMgNNY3HM2aW9/ffdtopfCJhzKWUuBdhVDjToNlx9hA6JIVVQcMvESAsJEJdtx5ZHh+3g5yvt5Wgac2yn0DRcNY3AD8RqB888bdP5jnbl/M3ESiK588f1GRHYwbvzhTHjm5GmGWi7OE0THcoOS6cWELAy13OsWqmqKuIP0/H7VxBJV204aw7btRfDdymjaZbS3vtB2DRNHNoMhYpAsV7X3tNI02hkpNveC3S78Q8hmkbqAkg1I5zCn0QITs+NU+HmygnepLuaHj/2Bs898Sbg8H5c6v8wBp2rARecucYg9FMnYA3QE85cx//ryc9J0/jn2FjaO1TTiGma8dgorppGIDRN0zTM9kQ0jaCaxph9yRGXlOmZtFGeO9+2g5eMlNM0jE7No2l8QXqSriJan9KuvRKaxu5Vpufkzzd3XbMsJI4xlmRkhrC6HhpoPRcZ4WgakrGSCyJN0/R/7yb2pgGAdkBG1PZwpGiAME0jNd5h+dv0U9UcaEbo/znBCd5KTc+AAFXD0jQCnxHyZKmYZInOsYimKbKDD7/HLeXs3x8+5iNNE7+5pyKU8HioVy4yYtE0DAKRrKZxe9P4okgLGZmbptFmokz5qDUuQVzqVDVNVjJCS72VndBhwrrGrTmmNw1D07jIiEDrk3qw4kIpJevam6imyUVmzbjMdz+OWJKRGYK3g89NRpwTHyGaRqWzaI6mKURGOoOMHI7uq0CwtJez82Yjwt+6wfH5JZHToTYV4zEfX+uk332IVVObY8yiDSxNIyjtHRa0TvVdVXVYNE2otFdQTVPiwCrqBux2q9VzCiFap4qmMUjn8MKErr1ZNA1xuLUqIziaxhKwCqt0qMAyUk3jaUYkpb3QiN0MNA1JclK9abLWAxaNHapp2Ou5/5n2W5lG02xISV6Upgkg5eN8ywSsnmD7mGJJRmYI60mqtNyQyagt7pqcfLoMLOjWx9E0hShA15CkQUTTCLdnnlKO0gJWq8xzegIxy1jmoZEiD/3PnH0dK8M1RDZNRjQ3L/AZAewyXEvAOszRnWuMpglpUWIheVqjSQ7n4GsnCmSOhWX0ZqzANsZSfVfAOrFRHnM8aXA0TWOZno3H3DpuRvCQMFPbkif3CDJiSnsTiMsBMasbq2nSuq/U9XFA9gOnGSle1yI6NQtpGs7Z1kvK4k60h5sdJKX3btDr6SCGjFAEg0nEc7uG69Br2ULT3A/COONNsIOPdea0Tc8EjfI05QN7ES+JbriA1e5oREaqJiiMEPOW65zS3vF1adksP078/zkxltn5yEPOkwl13fQ34p9LhpsXdO3txx0Xq45BRrxKFYmAte0gdWsU2cHTUk7GwZf+bo0zsWsvPf72Nvqf5jtm0zSBZCRBK3H7oUdG/AoNOve0toN7cmfmNfw+HlupFqVFhfgDRUwD5I1rGYkBStkfKF7X6AOQV9o7vs0kY3o/mJLsRBK5JTR2Bk2jH0SqCqb5IBeWnQBz7os6prPj+mvZccSSjMwQLHw2RzUNyxF2aeU169hYdudVDSnBTbivApA3a7IWhgyaZsIZe9pomow50vJIL5hzyXDzTfgJylP4D0EXG70rsjQjgSQnFq6vCT+u/STcz4vMKaQfmUrTBOg/T8yYK2Atrqbx90NveuY/bdumZwnEJbeaRtMTUsHttjNapnpib5p+XFpV5CMjxetatJqGICM6GZHSVcP+3RQmI9TwLPawla6mmUrTLMnIfT5smqbwac08CvlQrF9NkxA7MU33Smka1QwXcHs00jQBigbIQAeoZiRlBz9Tae+8NI3WFfgC0ZzFIOQJ0g/EwcP6KYrvDeSNu+WfnPSi5xY/xKaeFNwyIaNpxsSJe79F01nIyKmmaYYXJglYGW1G0oGV7gfwNI3lMxIXzbMCS0cnQuc4mp5JBbcdan1eBjLxHJqGJjlcMlK8rkVcn+l5pXejpj3SPi46GSE0dgFNExOvAk7SYO4z5QJ6HZxNwXHEkozMELZTalyBHhzDnOg+BGmffBKaZkBZZijt1Rdw1RIB6yzJSBgydSMEp+fGnA6sFjUH/Xv+uLGmdhxNM3LzwgXdomn8+eXQNEnBLRMS2k7Pte0Utq1OtkI0DfngxGqaxtrG+LpVqg8A28Hwr0QzwtEhGdU0lh18xV/P4tLTXcAO3qVp9E1JWk1DGuXNQtOQJEexjfJKS3vTDqwAoWncEucEomUjIznJSNoKns6x68CeS8XISE3GPcYoSkZe+9rX4qKLLsLBwQEuvfRSvP3tbw++961vfSu+7uu+Dg996ENx7rnn4qlPfSp+67d+q3jCZ2LohwHLtr3QDl4xUGznJDlSnxH62FtMbwzJSL07SrqvAmAFiPy4emG4N03TZCjxY+GiAMdtegYkymWZc8l0Rw10TTbjRmia2P7M6RsiCVFvGkI33rPpv6sF64eeqhPCzVSE9oMlFFRqFLBKu/aS89jWjKSqafq/H247g8pYXXsDNI24SscqPQ3TNCMykkqexvnW6N8TpGkKqmkOhx49rgNr8bpmuT471TRkHP39zX4QlmRvSzUjpkle/DPmPhOgaYp70+gHq/saMvKWt7wFL33pS/GKV7wCN9xwAy677DJcccUVuPHGG9n3v+1tb8PXfd3X4brrrsO73/1uPPOZz8Q3fdM34YYbbpg8+TMlLNOYiV17OSjWOvkyuvaqwJNUTlSD0LRuSTISQUYkGgEATpldopomcGPKjRy30eRYEZOkIpomWk1DtB5I28EDvHU7Zxed60obpZWYMI68kX2yR+4od+tkJEDNsTfhYp+RwDbosd3eC1MhIbaDH79P51Ag/XwFyEiia6+NjMi1HWOZMJMkDa9LutW686204d/MPiOc6VnxuharpqH700FGkCrtXWtkhBzXHJpGr+dCmiboMyK41rhoXCTwmCL+eMXEa17zGjzvec/D85//fADAtddei9/6rd/C6173OlxzzTXe+6+99lrr///qX/0r/Nqv/Rp+4zd+A1/6pV/KbuPo6AhHR0fm/3fccUfuNE9rWJUVhdU0FVfaS0WS5OSTVtOEFq+seQ0XcNMdIWUFD8hMrgDwC4OomiY951DM6cBqUXNDlKjZc2kaXdpbx9rrBsbNcjeNjruV0zQCtKiuK+w1NTZth3s3Pe9uSUMYCqX/w7RqGvpUXTH7pKOoCDC9tNfQKfyibzuPkmOVqKbJoX9E1TQeTSPXjITaPtehhDIxX65Rnipd1yxvowhN0znJiHA/bE8xTcMi5SRB6qRrrxNe+4NjiixkZLPZ4N3vfjcuv/xy6/XLL78c73znO0VjdF2HO++8Ew9+8IOD77nmmmtw3nnnmX8XXnhhzjRPe1jt5CfSNBwUa9M0cjt4q7FW4Z2XRUZiNI2UqrDEZAn3xpkErKemN834WhFNE0MaIjRNarFjaRoGxs1Fi3JdWMcuzvH36fneu2WQEcaQrP/PRAFrggpqOwVshkqa1YH8aZfSNIXVNFZPH0bcblfTyMeV0TTOuKmuvdsOtdI0Df98GzyG7HyJoJlJRqrSdU0/AGkNEEBoGl8z4gt54wjRdkeTEfm8JFbwQAAp57r25gpYzbhZH5s9spKRW2+9FW3b4uTJk9brJ0+exKc//WnRGK9+9atx991341nPelbwPVdffTVuv/128+/jH/94zjRPe+gLYhJNo6FgCU0znLxB0zOmTLj0xlvv9RfwqtsQAWu4tFds/GWV2aWqaejvZwhNo3NDjqYp0oxwNA1X2jvQNJHSXsD2rNDB6Xn8BC0+34NMF1Zpqbe+saU0I5ZGYEaahktMlEK+eJXOC04ykqJpCLVm28EzNA2XjASebC2zOi7xd+Y4IgLx/Xtg+Yzoc386TUNN2rhqmuJ1zWhGCNpV+4mN/vqmxDlhVqfnu9mRh8CCZCS4npup6odTsGtDKU1jpFf3NZoG8CsalFKiKoef//mfxytf+Ur82q/9Gh72sIcF37e/v4/9/f2SqR1LWCVXpdU0EdEp5QhV15qmaqlqmlDL8ZxoBp686TaktDd82rR0EY1Fhs9ITOOQE+66NUkMy9E02gIkY47Rmzvz9CNxYAVscaGZH7NYBTvihsbNdGFtGZ0KP98BGdnoJ+xAosAhI6fIDr5VavQYkYpX6bxgl6Emb+7E0dS2g2dQ0wy6atSM0GqaMDLide0NVtOM8zU0TSBJth8o2LeQcW3tjIuMFK9rWvOj0S7A8yxpMXbQHk3PUshTPx9TTVPasTdF01DNSISmye5Nw+jfjiOykpGHPOQhaJrGQ0FuvvlmDy1x4y1veQue97zn4Rd/8Rfxt/7W38qf6Rkclhq5lMdmEgiuG3BHLoiggHV4jy30ypuOjmavv4BXiiAjApomTzMSL+0NOnBmRlVVqKrxyWcO/clkn5FoNY1/Lhk/BzFN46vtY/szh89PhVLKJECphxW9HzRNY+taqGaIEZMU0zT8NqySeu0xIhWvAs55zGgzBBoMtpomdD2nxmWracKakdbVSkh8RhKmZ6UC1k7Bq6YpXtcMMkJpmnEst1xfvh/6+e52O29MSch9Rsj8mGMu7pjuBNck8Dgia6/t7e3h0ksvxfXXX2+9fv311+NpT3ta8HM///M/j2//9m/Hf/7P/xnf+I3fWDbTMzjYhna5AlZj8+1DkFSL0rWjsU6WgLUwG9HJyFrRapowTSOmKmiZXUbX3ikOrMD8lTmsHXzGHDkEwwTTAVpK01A+350f3YdeubPwRsHO1wmuF05q3Hu4apoQTVeqzxoiZMRlXc+5fWkAW8BaUE0DjK6cTY3k9Sw1Jzu0WtxHqmkyEYGjXZus8sozPaNJWdyBtag3jUXTMBo9TzMiS/a22vQso5IGIA6sidLemq45bDXN/0bICAC8/OUvx7d927fhKU95Cp761Kfip3/6p3HjjTfiyiuvBNDrPT75yU/i537u5wD0ichznvMc/Lt/9+/wVV/1VQZVOXHiBM4777wZv8rxhVX1UshjVwyXy9nBa2SkroBV6KRjyvdKaZrVoBlZq62IphF3rk0sDDSCduAF0QsChXOMjUOP+RCGBplNwOpDsSlu3h93/CxXZpvrSpuDjNDFLZumSeg5+j9MpGms/UCGpcd2MydNkygRJU/G1n5IXc9JgSURsLp6EGe+lh28tLHftkuel3l28COt1CdGFRQqs43idU2vORGaBiBaEeW8Jylg3dnvF4ZUwGppWpg55XYN13Gm2MFnJyPPfvazcdttt+FVr3oVbrrpJlxyySW47rrr8JjHPAYAcNNNN1meIz/1Uz+F3W6HF77whXjhC19oXn/uc5+LN77xjdO/wRkQp46m6X/SXgRdq/nFJgx9G5qGPkHkTUfHar9/ItxTRzIBq9R4h4VM+c8ESzsLwnqwnpKMMKKvaXbwkdJehqZJHVCWpjGVLWHIPOmOmaEZoYtbCrnW871nKO0NJSPWdEubUg4REvLampESAaulsvVfD2gw1s1II1r7oWPWBq4+O4Fg7DoF1e36GcVoGhcZSQluqR18kKbhf4+NuyE9elA35mGoeF3LpWnMfpAlZYamyVxs56dp8tY1rjLwOKJIwHrVVVfhqquuYv/mJhi/+7u/W7KJ+1TMSdNwvWnoyac6AaSXsI/OifV+/zSxh62otFdsic4hI6e4N437+Uk0Db1hDVHStZejU0ww59LIzaccWMnTsDO/oKMpMvh8EU2TgYwM++FelqYJHP+JNI2VlKVoGmlfGjov2DfOFE1TVRX2VzUOt529HxK+QdJGbv1b2j4ZEVXTyDQuh9sW9Wo4LwP0YQlNc7htCTrBJ09FpmdSmsZtUJhK9tpSzYhMwGr3ptHzVn32WlVFyGw/bv/zuGmaiQz8EsB4cdkq57wDWzFPFhZs6NA00ROXgWJLb7zr/V4zsqc2RDMioGlSZ5Z+StncNb4msJKehaYx404fh0NG5vcZoTRNfNE345oyUUrT+PMrtoMX0TT5496zZWia0Hzp+VKwkIY6A1tVbBrSz0FGUj4jEVrJaGe2HE1Dr2cybGJc6nCrOLdjj6ZxbsIC+mdERgI+IwU0zT3U0ZSrSkKuz4i75lQW1OYmI8pNyhLJ3m5bSNMYR20pTaPshGeYV0nXcMB56D3GWJKRGWLMWFFM01QcTcOYnlGaJhgRN9fcWA80zUG1hWoHV9yoz4gQHTD8LUVGJNU0iQknIliZUTgOZ3qWM8dSmmZaNc34PneuOYZUqaCLm3Tce4xWgp+jXU1DzpcCqsZOcv1ttDMIWNnfI3Nl90Pqek6Mu2pqoy9Tkmoac2hlN2GAInb8cS5xYNX7IDbfImRErznOeuO6Kvs0TaKapi0TsGZ37XWTkeGYFtM0zFp2HLEkIzMEJzTNXhyTvWkcmiaKjMxnB793MC7C7dHwlBilaaTJyL4edHwt2JuG/D4xG5mL8mmchQso7U3j0ykm6KI2PNHWBhlJaUYYmoYRuOWanlFviVR0VjKSGFd3Ph3GDfemIR+yng7zk5GQdsZanI2A9dSbngG2XgLIpWnSSQ7r6RPoTSOlJwB6XvLICE1SpGXeG3qOBap/iuzg9ZrjJA1jj5bhbVrIKqwqKi/tzaVp4KCC7TDvMgFrw6C8xxFLMjJD2DTNtGqaioFiqRZFKYFbHwvrlt14D06MVQTtvXcMg6Ub5SW3x/k2SGiayQLWeTQjRsBqde3NH1fkMwKYY2oW/YRm5IARmnLzy3WlNa6bWwlNQzQjiX1y4EDUwbJbC9ahC3J+RU1Ql0KvZyNgzammIbQIRy9EDBEPnKfj3vQscT0nql6Acb1QjBg2ZHqWWsv2mtqgbLUx4wvZwfO/s3PlEIIArZRH0zhrjvPwE6JpqgRdpfftqBkpQ0ZSDqw2TUMfVIZkpFAzYoS7i2bkvh+mtTMVFmXTNIyAtfKTHDWc8NEsmm05njUdE+v1Hjo1zOPwzmGwOXrTHPivBZ4o5kog+s/z4+aPYy9c9Pc8zYhAwAqY88nQNCnNiPED8X0Igo6mSO+THGSE9qVJPg07N6CQxsCiAZgFOSdS22hnF7CmkwZXN9CETM8o0iBAY816wawN7o1eH7extDcuuAUoMjJDaS+nnahDyUh0KDtWjqu38/Cjv67Xm0boD9PqB4rcahrTtTelGSHzYxLxEmQWcJD9Y4wlGZkh7KShrNzQCFgZPrRV8HhLWTXNdH1EVdc4Qp98mGQkRtMIG6N5C4MjJqPBuWOWhi2MnD4OR9Pk7Gp9HA9TyIj2l2EQNHZcrlGeMaQjm3A1IxmVDqkw/d0EO8Q9n7kHf2+syTQN/d0/x4oFrFVgBwv0ZG5SZglYA86jhgqKJjkaWU1rRjqjPZBoXPpxG4PY8edllbEW7XEZRmCfZq1r7gNQ5Z5zNl3Revshrp1ptcC/lKZJCFgtTQtz7o+2ClmbX2ia+1PYvWnKaBrjrBnq2kt60wApAauvP5mij9hUQ/JxlIGMpLbXxJ9SrLeeKppmJgGrUmr4lz+uSRpivWkAs+A0QpqGNl3TwR2bqqqsG5u8mkaOjEj2h/tUmNWbBpgsYLVohKkC1jqAjCS0BwC3H8AjndYxLEBGOMRmeH20QZdU//SfXVUJZCRCDbpBBbd0XtzveaZnzprjJA2uDsxvGBgXsBpkpLSaJiFgbWiyZKGCNjKSu66ZJGdBRu77YZnGCKBYLipzJyM0DaNF0ZxvXMA6n2YEADYYqmd0SVxUMyKkKpoVQG+okQs41vI+N+YqE7ZauKu8MlYaYgGrRkS002WBgDXUUTlH1BudrxM5T2pRmiZQ9WInawWlvYHzqqbX82YaTcNqMwQ3dzMvCxnx14bscdmuvTYFMhaRCMZd1wDGfR90YM28hr31LUjTTEBGPJqGHHfyM13iPAhYW4YCE4RUwMreZ8i8xIaToXHLjIxniyUZmSEMrBtwxpMET9P0P3stikPTiKppxvdMARS2VZ+MVBuNjMR60+jtCTZIF4cItGmXeU5FRuYZi47TKmXRNTnQscgOHjDnU5NwuvTH9U3P3O9tQ+iJ+cYEt07kONJKkRGbpqFcRT4yEirztipVDDKSIWBNlfbm0DRWNU2gpDVjXM5CIETTyEqRG3NO9vMN0TT878FxiZiz1xzxCV7WJZxARlzzL6+0N1FN03GokyCkpb2e0NQ57uZhY9GM/O8bdgnuNAErb3pGkxzBidvpscbDO4WS0MlIrZGRCEUwUgGCgeniEKNpQgLGgghWZkwYp+1UVuUIDa7qxUTlIyNaKNhEjOcAPskJ9a7IcaWN0kpOtFLKDpxmJEDT1M5dbYIlfIj+seZbgoxMqKZh90NCkC4SsK6dhCVYTUPs4CWlyKvaSkaqwHmZI2DV41rvp4lj6bpWVTY97CQNrg5MXy9VQsirEyezH0q79gpNz0KNDHOuNxomyVk0I/f9sOGz0tJevzTOgg0d/jYuYNVQ7DjWFHpjTEYEmpGcihILGRHSNBORETuxmWecnqZR7N9SEa+m8aHYsYQygYwwNvMdI2AFTiFNE6CFYuOOc+Ln5z31FWq0otug+2erBaxMKXooQu6mkmoal6ahpb0hZOQ4aZpVPTawA9DMoBmx5ovh/LH244R1ja45rs+IUyHna0biNI20b5QbWteVpGlcBMM5PqHrOxVcZeBxxJKMzBCWCrvwSc204K78rJ8rGZbQNPSGNcVtdDckI41emGM0TY4LIEVGIu8PuXGWRBDynzCOT9PIx6EIhuIWA3I+qa5DXQ3JXrK0N0zTuAu4CzZI55uKnMTUo2ksNIS+7nywtDElwueCNd8SmiZpB59TTYNkMlKJkhyXpgkkTNQOXiSMdWkafl2KNWeMzRfQyAhZy6asa9aaE6imMTSNflu8mmZV9yJwk4ycIpqGPpxalvCGpiks7XW0MscVSzIyQ9gN7UqREe0fQZ8I+p+UptF0TtAgh1wwVmIz4S7e1kMy0mnnQklvmkxkRFpNMydNM4PpGTDQNORwlwhYO9V3VfWC3MBabaqEMDev48D4gcR9RvqxquH19OKe5TOSYY/vns8hPYe3b0sdj+EmI/zrZQLWhGYkp5omSNMwmhEBTVOxpb32WCNNk070DtY2TRNyYG0C+zo2rvV+SycyYV2L6NTGdbz/fyekq6qqwsG6mUzTuMZ/btDz0kLLXZomc59wNgXHEUsyMkPYdvCF1TQafq8ITcOUDCeraeh2Zyrt3blluNHeNBnbk9I0M5XjAu5TdvlYNk2jrLK4LJqGLEDRihrVWclIiJs34zLajlBHZT3fHAQjp2tviYA1RB3NS9PwSY7eRo0OlbYOzxKwhimQfq6xmztDHURaRQBE3ClCRpwbLDPHUSuhxw3fpPZXjRHfA2EtU4gSS80XYGiaKevaOkLTDEO5NI1ZkxN0laZQy6tpZKW9Zm7OvaZUwGoVYBxjLMnIDGEjGGVPasZnpPGffuiJpxGU4IlLF7raR1lKoq3dZETQm+YUISOTS3szxJrRcSIC1hz0ht6EWYt1cj6pji76Mppm03ZmkQmhViMykp73QU41TYY9fqyaJnr8C5N/dxvc9k6A9E3KErCGKRAABZqRBDKiEwBBbxpNB0toGqkwthEkI6F9HRvXjFlXznwnrGursIDVFYi2buKWoKtKaJq2U9gOJYjJ3jQOGkvPJ5pI5K5ri2bkfhQ2glHGYdcRB9auw0jTIOHWRy4Yyq1OQQE6z6AsLWAVXQ8R/tbaXEbpaSrm6gBMx2mVKurYq8fZ066m0c69XRZNQ0sjN63jQ+Dezyv9U/7EeiippslBRjyaZvzd0mp6yMiUapr472eZZKTi2xeEIlna2wXRBtaJlu3aS4YV0jQUwQjPsRJ3qwV0NU26rD1Xq2UJWL1qmgnrWoSmcc2/jBOt3tmJBoclNA1N6lPVNLWLjJDjTpHZ3DWodr73ccWSjMwQbDVNqc9IzdA0BHEZkZEQTUORkUJzICe62qFlBMjIrNU0M6EZ7uen6k+oWVCbo5VxYqQ+mHOmJgtOO/495HTpjtmPG+9dUUTThAS3JEaULDksb/alf48ds8JSem8bjkalqoATlaZozsrLgjnRKmAjEMHKDEY7w3XtrZmbc0IYSxGMUBdc1A0RbuYhAq2qrLJbGvmlvU7CMYfpGRCvpnG0EyMyMqzJCa2P2b8ZNA2lO1kbfDo/B42ltB/Ve+SuazVZx44zlmRkhuAa2uXbwfsN0Gyaxk1GQjQNEZOVttp2h3SfCgWlvaLtWT4j4VORs+oujdxFUTJWp1QxXwskymXJ+WTTNHHNiFb49+PGu3pSAat0rkHBLQmdrEy1g7ePmfPBwlJ6dxueK21VjchIDkVD59QPPP7OdGF2g6dpHEEpnH0q0nYQTQMQpWmk3WrHcYdkJHI7yaZpqM9Ibc930romoGn0bjRdNSrJ/m3GEucsZKTfyKqusEokI0EBq+qsqeWua82CjNx/Ql9bdgMjlWVRrZGRmoFiaS+CJDLSBTQjE+67KkfAGhBJsiF2YLWfWqdEFPIvHKuvpikrqwMS/V4IVN5l0DR9R1U7yQnN0Qg2M7n8VEVNyPGVH1dWTTMnTRPzL6mratSM5PSloXOK/R5y84yZnoVKe4VVOnWQpqms10MOn6H5akRAxZKRACUWG3d8v4uMTFjXrDXH/jC9ngG56RkwARkRWsED9nd1bSRsmiYXGRmGWZKR+35wDe36F+QLpFkomN40LeEH65RmhDzFcE33SkJ5rbfDT+UjXSEYOPKUYm1uTgFryNxqwlgd8RkpommYpnYmCFRuaUYSNI017s4u/Qv1ppE8UVEomZ0vibFLcD4yEjYkm4+msTSgHlpEaJq9jEoaOieQp2rAoWniTdfGeYRoGrI5CU2zdmmacDWN8Qoy5168Sqeu0shIvumZg37Q0t5JNE3Y9bmh6y3A7IdEUlaVaEa0iWX6erZoGqeaxqZpxJvv31+NiFCKej2VsSQjMwTt4CqBYtkxzMJN3AWpgNXQNFp5LaBpZhKweuK92WgaYTVNwACrJHLcRpNjkeOeJdx1QkrT6JvCTsl2gr7Ba7HpaAdvv88IWAWTr+vKJCQpZCSni3HU9CxGrZkFOX8RjYkqLZomFxlJVdMAcpqmqvjGlyxNIyw9defi0TT65bxxuygyEkG3AuPqcKtpJq1rEZ1aY27KGhkZ3ibav5SmydeMSJARrWXq52ZLAmgSUUrTAMfrNbIkIzOElVELnn640IhHHdKMGGRESNNUzWwlsZWLjERpmhwBaz4yMr1R3nwoCzW7yzJ7cyJK01gitR4ZiS369rgOTRNAKvScpVOPzpdETpm3bwfPI1hBZGQiTeMmuXVNk5EMK3g6JyCcmASbrrnICKxrepwv3T8ygWUtQkZG0zNVy0qGNeLSRRCBKXbwTV2NJ8HUdS2CjHimZ5525nhpGoB/QEVnC1izTc/I+4/TamRJRmYI1g4eKKJpaGmvxWE6yUjQgZU4LOZaMIeichdjAU0j2h4d9xi69k7VnxhNj1JBQzFJRL07zKNQZzQjHWTbcMcNzVH/X/pEtc+4u3LRZqBF66YKHueQUyqA4lL62Db0/8tpGluDwf4eeMp2XTjtahq6NhDNiKD09MClaSK6lvG4pbUoB+uxmiZ2XuZrRijVReY4dV1bhdcco/1zu/bWsv1QVtqbWM+dsCssx3OfakZy1zVLi7LQNPft0E9VFnQGZNE0GuKzkBF64mmaJoWMEEjXchudcKSrtUvTCASsuchIrJrmFJT2Th2HjkE1IyXlwtFmedTYSPuF5CIjHk3DJyPSuUtdWFUGWkQFt3RO7ueDdvAl1TQxmqaucBYO+/9MoGmshoYSAauLEFUVX01j3d3Jw0HIv8SiaSpHyT2hmmZ9GmiaitA0U9c1UTWNLWA1yJO0YWCBz4gUGRnb5JB7TdeOlGjB+uOVDB9TLMnIDGGZnhXTNEM1DdORUpEyrjpV2ktpmpnKWGsvGRF07c2upjm9NM1UvQgdy6JpJlTTHHJIA4GIle5BIU5GbGSkSwlYpcnIWmtREshIJlrkuW7C/3zQDn4yTcMgI9j0/8kt7Q3ZwVMhZkgz4iIjdcXTNHSfCOmfOkQj0JsnJ2AV+pfEkpFsmoYgBT1NM/x/6rpm6dR4nZKppvH2QzwZKaFpDreJ9dwJu/XIOC8joC9Yf9ymn8cVSzIyQwQFrBkuMtyJXNGLYxh3VWn1dQIZmZGm8ZKRGE2T40Ra4MA6tQJGjzVDLmI55E4SsK5jyMgIEXeDZkQJn7zGahrX9Mx+nxGwSpOGmOCWRNa5ANd1058fO8dJNA3/u/7/aHqWS9MEKBD6/2A1DUMdsDQNGbJKPwBFHUI9mkb/ah7D2TH1fGtBMjIFGaloEjeZpkl37XVNz2QOt6U0TaI60gnb7Xuk58Zu6eJNm6DJYQHAOFssycgMYQSsrmYkBxkZzgJOwOoiLhW6tIC1rmdzG633HM1IhKbJKeeU96Yhv0+tgDkFNE07maaJCEJJ6apqc5ERh6YJiItzTM+S8yWRK+r1mqMNEROaQnDDDEXU9GyKgDVE0wBJJIcV8ia69lKjxPC4tPTUmZPbm8acyxIBqwwZmc30bOq6FqumoQUDIKZnjQQhIgLhU1RNA4zf1+6D1k0S0C/IyP0o7BMkDcWyY3A0DS01o4ZHUOnS3hmraRp3Mf7/2jvz6Ciq7I9/q7vT3VkEQfbNsIVFGCIwIIgoA6IgCOMy4IYBHCfgUUD5cUSPOi4DOAqD4CjDAcSFGcQtKIISBGSEEQGVxaDsi5AQAoawZOnl/f7orupX1V3VVV2VVBLu5xxOk+7q16/fq351637vvU9DpjGkXSZQDt5sCXfx/ZbINFyskFp1Uz0o5RT5h3AxIyz+oq/VrlqVWCPl4OP2l8NQmjcQHScQRvNCZmbXXg13f0imCceMmKgzEmVkx3H5x0xxjrFrr8wY4d2FGmXmBVWZRt5fKYtET2ovHzOilU1jUFrh1zenIER+bJWZTcN7uGE8ZkRVBtNAqjOiV6ZxcH2UrQ3h1xOSaSL/p5iRGg6fVQFA5lrX3Ya0N02MbBpFyrATQWlztSi4HT5lMWomrr0uj/4A1kqXaSzKgLFWpmGGL7w82nVGxJMriGDYM8J0ZtMoPRhqmU5SAKvRbJo4AaxGKrCG2uVlGr0xI4nLNJoVWB1AimC+AqtgVKbRmU0jywrTVUxNr0wjcC5/HfIEdxHWOi+NZ9MoZZpIAKupdU2j6rOUTRO1N434G4znIUqgzojRAFa+j3w2jQmZRhAimWxU9KyGo9xgKZGqkJJnJKZMA9kPx+Nk6ncEXOlop0UXcZdSptEVM2JUptEZ/GZWpjHoBdDTViDIDMdH8EQqsMaRaczWGZFkGvlx4tAb9WBUrkwTeV676FniAayyi5pSpjETwMrfUKjKNLHHTrlZWkimib7jlpWD1xPAypdtVxojSplG8ggYDWDVGYSeSJ0RLmbE1LqmI5smGGRgjHFJTDo8I0m8TKO/T5JMk1DMSOTcNyPTyNolY6Rm4+BdZ0BcV2zMNsJWNb8BmizVjPvheLWs6EqQaVwe/TEjRtI5Eyp6ZjaA1cKYEX7erSl6pi3TiBvlaS36snYVZebjFT2zXKYxkuYNLZkG3P8tlGlkRq78NYeD35vGhEyjLNvPlfCOhcvpgEvmseGOFRQXaPEYp57UXq5CaFyZRnxarzxhUKbRZYwoZRoum8aUTKORTSP7PXPP665EW3UyDYuSaUwaI3x8ok2QMWIB/A9Nmf+tFzFLRohx96OUaVKcGicMJ9OoldQ2SlJUAKt6zIihomcJlYO3xjNiVu7h2woyZmyDQAW6ysEHIxvlaS368nYVMo24/ZGqTGNBfzmkfYp0txv7YqvLM5KITKNxXjkFgZNpTASwCgovog6vqTgODiEsUbDoAFZ5HRZjMg2LCmDlZZoYqb2AqifHk+SAuDeN1kZ5WpKYWn9l3eNkGlPrmpZnhFtv+YuyoKvCrVM9QFgD4zINd02IIdMkuq5F4hMTerslkDFiAVHRyAYDWMVYAABwxNiRUpmlk5ykccJF9r2Wu3JNXHvdyfydoaBpOBiqLaF7117+/yZjRgxKEloI3PwYMsIUeBUpuPIPiSw4Yp0R/RVYFTJNvJgRndaIV0tW4jC6k7FXVnVTLWZE8SYTu/bKqpjGGBMpm8ZwnRFeplF6cuL3VxwH6XvH2rXXofhtx2lXZowozx9Faq90nvCSkYqR4+VkGi1jxGicB38uyDbKM7uuaVR9lkJDuBiw0GHx45K8MpnGSGqvKNPoM2DErsiuCcGgqZuh0Pu4dm2CjBEL4G8sgordFPUQ5BaQWDKNzNsCwOvSOOFkMk24f0L0YmsENyfTMA2JBjAq09hY9MzC1N6QZ8SMTCMGhMbJpgmKAazGPCNicTIrdu2V9TduNg1ifl68/vJ9Ur4/WqYRf2vGF1HNomcOLpvGhEwjKOOrdO5zIvYhdGyMbBqlVyeOXCUIAjxhF1WUZ0Qtm0bmcdHwjFT53jThriayrunYmyaUHce45/XIVc5IhVsjMo3B1F5ZhiV3Llkm01DMSM2GPwGCQRiWafit4QXOGJGlmnEWj1evTGNRfITby91NONWDVwHONa/nzNJYGHgqoxy82dgTvi1+8TJTZ6RMyzMSDERiRnS6gdU2yjNdDl6qwKpXpjFm5MR6j2pci1UyTdTnAclCogGsOuqMaAZDOuV9iiXT8AG+DkHXOHhdofnQDmB1RLyb/G9dZS1zO/UVPTMadBqVWcXLNGbWNR279iplGslDFCeryJlAnZEyw+XgudgO7lyK1HfS/dEyZDe+NkHGiAWoyzT6FkhVmUZhrYqLSLKWZ4Rz6UaqjZo0RnjPiEPbMxJJMTPqGVE/XhbAaNKhYTSNVYtIhVzj1UZ5lIGm8g+JeNnEbBrdqb2KjfLUqsSKf+uPGdEbwGpsgZSnc+rsoxmZRp7Rqvg8vuhZ4hvlqcs02sGQoT4ojhMUF2jp4wRd4+AVPSPKZV8p00jncvxYFJfTAbegYuTwH2HUGJHtU8T10ey6ppXay12Q5cOuw5tlOptGp0wTK2YkGDAsiSqJxL8l9HZLIGPEAmTGCF8tNQHPiEymESI/DgBg4SwKr5ZzQqpJENnDwWw6rCvJDR8LfTbTSOvl+6pvbxqd2TSyzAqLZBpLAlhDj+b3ptEICOXOpUjMiMEAVp8YwGpVNo3OAFajdUZUZBq+DdW9aSwOYA0VPUswgJWXaZQBrDriD2QyDX8cn02j9Oro8LiIHtUoOUUh0wRiyTRaXgHxo63cmyZKpuGyacz8hjU254ykzcrlCkmm0TQgnQntTWM0gDVyTeA+hwtgTXRdk209YhNkjFiArLY/Y7oWBp4gd5LzdUb4Cp9A5M7Do3Wuc7v2WpnGWoFQBk1cYyTRCqya5eDV3elGkfRmC2NGGFeBtfLKwQctkGnk/RaJlIM3ZjRUZp2RqOyWuDKNydReRbsewY8kIXwBNiPTKFN79cg04XFwOgT5cTKZhu87dMWpqRoNimyaSAArnzKs1d/Qo3bMSOz/q7dpg0zDrbeymBHdqb2JFD0zUw4+Mudm1h9A/t3tgowRC+DnX14ZT98CyTjPCL8DJx8zwhiTjBGv1rkuy6aR/muaCiEkzwTjyTTS5lI6GrWlAqt44TXVDADuboKZLHqmJXvEyKYxXoFVsWtvDEmCf4zbrpasxGGmHLzuPprKpuH/L29XCl4FTG2Up1qBVXPTNVGmEeTHqVVg1SnTiMZIVDaWmkyjo8w8AHjEU1Qzm0aI+X+t490ubhxk2TTSf42jY6M8PiBdJhHFKf5mKptGZ50RKetFKdOY2KgTkJcpsAsyRixAEARF6XZjC2Q8mQYIF+IJT5dumaYSPCNBpdtZgaGsEkEAnOHFQbdME79ZLYx6AbTgK7CaKnqmVV49hkxjftdehdch/Kf1Mg1ifl68/sZ6jxQzoppNY+3eNN5w9dWg4AJc2gZ4FFZl0whKmSa250gu02i1G3qMKpqnItOEAjfDn6Np5MSPGTG6N02ov6H2omQaqzwjKnvT8Kn6skylOAakKZlGZwVWWaAp5xU0W2eEZJpahKxojGUyDW+MMOMyjYXxET7JMxJPpjGoXYqLQ1XJNJWQ2suYcS8Aj7ZMwwewijEjBmUaX2XJNPo8I7oyq6BTpomKNLVKppG/JgavBlwG40X4PsGsTAN1mUYZ76JjHEQPRpScErVrb7hdQV+7bvEUtTC1F4iMg8PBeUbMrmsOF+dlUco0EU+0bFNJPWPLyTR6d9UGEti1lw805c4ls6m9yh2L7YCMEYuQpVwZTDcULzJ+Jp8O/sQKBJnkGUnWsgeC0Z4RK+IjfELIg6HlGUkodiIpbIzo1ptNyjQWeov4OZeqmybQbqQ4mXadEfF80usZ8SqzaVS8VhEDzWh/9RU90zsmXg3PSORuWPGmBKodS2/lPJrKzxOrr/odXuXbdDTMZ8SpVDvVIdM4o2Sa2MaaQ+fdu1fyjGgFsDrkFzY9Hhfxoy0OQvdK44CYnpGE1jVBAEQD06FijAQVdYN0jIE3KSLTBJj+fom/Ia/ObBpnrOuMBXvTyHYDtgkyRixC0vJklfGMyTRKLZc/r4IsYoxoe0b41N7odhLFF44VCWgYI7L9HHR7RkSZxjq9WYuI3myBMcJJc2ZS63R5RoIBBAMqG52ptis3GtTSrg3HjCiydNSIxA8ZuxMGoi80YhtRbZnYtRdQ/+5eZsIzws+PUlfUISuJ4xAqBR8/tVdvXIPbEc6miRMzItsBVsdaJrardTlJJD3fEzNmxIJ1TWXN4aV2mewhvqCRTeN2RmQavyFjJLFde+UBrAHpZijRdU367iTT1HxkAUAG0w3FOiPKOxZlyrDoXvVozZq4aFgs0/gF0RjR2JeGO5EtlWmslFakMTHdlCz12kw5eHEhqvAHo4sO8bv2MrHOiLHUXqkCq1rRM4N3mpEibfrqjBiNEYj1HtV0ThO79sraVXz3ZJQCAHxOczKNQylr6ok/4GMl+ONibBUBiHKKaDTEz6YJKDywarv26i2mVnkxI7xMw2XTmF3XXLG9sbF/z9A1Bg6HgKSwUWbEGCmTZBqDdUYUe6BFUnt1f7QMZRkJOyBjxCJkO/caLAcf2Y1VXabhA1h1xYwIDktlmkDYM+LXiFeQl1DW2bArfgCr0+PjdQAAIOtJREFU0XLlWlT+rr3G2+ELHlUEFOcML9NIqb2JeUaYSh/Fv3VfJLQCbjkMpXlDEcAa1UeVeTOxay//OcrfiBjAGkjEGOHOZUfUrr064g+SOGMkRil4IHQHLAvq1TEOYmxHVEyDzJMT2bVXHhirfpESp01TplHKSjqQyVWSTGPBuiauOWoyDWPybS10nmOucPE3n9LY08BwnRE+toO7zhjahkOjXSoHXwuQW6zx71J4gkFRplH3jASDTHKvejTLwUeMESurjQbCWS9+TZmGM0aMekY0ZRr5oxmsqkobaiv0aJVMA8S4wMvKwZvMpokr0xjrr95de/UOibzqplKmkT9GvWBaplH0hYVSe30OczKNampvnDTR0Hshk12VyH7femQaseiZbpmGlyi0ZBrpzarHJJKeHyn+xvXRinVNZc2JpOorMsF0yu6SZ0SnXcwY4zbKMxrAKi8hYVQSVW83obdbAhkjFiGzWBOVaVR2kQTCF7zwdLk164xYvzcNEKkvEoC6MSKTafR+ZlXLNAa9ANptcW5dleqmenA5Ine5UUGsvEyT4EZ5gSCDPxBEgL/j5TC8N43BcvCWyDRq57Ik0yTmGVGTf5LDMSN+ZwIBrA4Nz4gRmYYPYI3x+4j8vrnXtdoNXyzjyjR88KYOiUKMGYnagI/DlEzDGwRWrGsq3lhR4oj6PetMSEgSRJlG3+/TF2CSw0m3TCMFsEJFpknQGJHun8kzUuORWaxG0w3DC4jSfcq7YoOMSYuIW2vW+F17Dd7xaiF5RjRlmsj/rQxgtVSmsdCwkRVJUrnQ60EQBPXaHZwrVmBiAKux1F6xXTVXruFde/Vm05iowBpVsl4tTsC0TBPbEPOGi54lFDPCyzSqdUbi702jJdMA4H7f+qQE8ebbH+UZkdcZkaXn62lXR8yILIBV51VHtc6I2XVN5QYo1saXescA4GUafR3jjXn95eAh9ZE/l0SjJmGZhvfs20RCxsgbb7yB1q1bw+v1okePHvjvf/+refzXX3+NHj16wOv1ok2bNliwYEFCna3OmMmmCWpsgCZd8IIR96rHoXHCxMymMX/hZWFjxKfhGeGtat2/CV0yjSit6GxTA0cltMUXPUt0gVRuaifBSX5GZRo3L//4g/IsCQ6j5wkfcMs0NGajEf7yomfy1yIyjYrEkLBMIz7K243INImk9nJtRe3aK8pK8XftlWXTxJRpxEedMo0YM6K8WCp2DJTJa7qyaWK0E9XXBGSapBieESvWNdVsGpXfs45sGgBwGZRpeGPecJ0RWaXvgOpvWy+yMvM2YdgYef/99zF58mQ8/fTT+OGHH3DDDTdgyJAhOHbsWMzjDx8+jKFDh+KGG27ADz/8gKeeegqPPfYYPvroI9Odr05EXPawLJsG4NxyjEmeEztkGskYYeofzgc/6ZdpYgeT8VhZSbYy2goyczINwGe+KGNGomUaveWmnQ4BSeFbqXI/58pV9DHRbJpQu+orr/GiZ5xMo+a9UZVpEsymUalf4gnLNBUJxYwI0gXflEzj4I6L4U6Q/b51ZNOIMSOqMk24b1LRM50SRZIQP+U8saJnfGovl01jWqZRyaZR+z3rlGkkz0hQr2ckNG5ul0O3wS7byZ2XaUwGsMo8+zahXU4zBnPmzMH48ePx0EMPAQDmzp2LL7/8Em+++SZmzpwZdfyCBQvQqlUrzJ07FwDQqVMnbN++Ha+++iruvPNOc72vRoiT+fW+QjS95EcDAId2rMPZI0Vx3+srOoy2iG2MiO6z3J8KcGP4JG9y5n/Abn/UsQCAEztCj4JD7sY1iWiMnCkDPt15MuYxJaW+0EcLBgKppIWhamJGpIXMQslnz4kS6blE2xUlivU/F+JQ0UXp+S5ny9AGQP6+HRBKQ3E7ej0jYru+gB9rdheg6EJ5zD4aLXrGyymf/ngSXhXr+ODpCzE/T0+70eXgVfoonjeFe4HdH+r6HJ5b2B6UOHyof7gIKInsQdOi/AAAIP+SgMMq57sWt8IBJwLqMs3xraoGeJuC3zDccQRXV6QA+64IPRlLpnFwv2/x9SObgLLimO02O78bAHDRx2S/4dQLRRgIICA48PnOk/CHrRFZJsmBdcCZg7HbLd0HQFs+NBPAKouJsWJdU8umCbd3srgMm/YVhT+bG4NAheY51jB4GgCwv6gU+3WcM6fPh36Per0ifB93Hi9G77JStANQcPBHBNwfYrijCF0u1gV2x3YMaHFD2X40c1yA+0JjAE0Nv98KDBkjFRUV2LFjB5588knZ84MHD8aWLVtivud///sfBg8eLHvulltuweLFi+Hz+ZCUFF23ory8HOXl5dLfJSUlUcdUN8QT6tW1+9A1qQw3OoE2h95Dm0Pv6W7DF6OGh9vlQKkvgL9+lofP3A7AAbTduwDYG0fqcnkkN73bwMmuBnOnAQAOFQfw1//8oHms20h+qzt8AeA3sFIeIn6PRPJmFXic1o2J2Aa/sCfabkr4gj4nd5/s+adcxXjYBTTNXyctEfE2K1S2e6HcjxdW5an2MTK++mJRkpwhj4svwDDto11xj9c7Jslu0S0fbXh61Poo7huzb03on0H+BgBuABvlz3cKP35f4MeCOOd7LPZ4kuBGAEkehcwj7sW0fXHoXwx6AujpBnAeQG74yRi/Dzd/Louvb35NtU8dw4+nS4HHuO/UFGcw0AtcCrhkzyc5HZHxXf+SarviWDGn+nkZ2fRO/01FangzHbfLAYhtW7GuhdcyaS4UfczLL0Fefuia43ZyYxv0Ax+NV222Tfhx/f5zyPlF/zmT6tZ/GRb7uGL7r2jpOotHXUCTgo24BxtxjxtAEYAERIfJAOAGtp9JR+gMrHoMGSNFRUUIBAJo3Lix7PnGjRujoKAg5nsKCgpiHu/3+1FUVISmTaOtsJkzZ+L555830jXbmTSoPd7fdhyMAd+U34MrLyTBxVS8FzFggoCya0ajueL5abd2wKqd+QCAtaUPwlOxCu0apGjHJrg8QK+/oFfT+vhTzxa4uXMT419IQaubxuLbjw9gb/If0SfpKs1jB1/TWPN1GT2ygNJioOtdqoe0b5SGrL7p6NK8rv52VRjYqRHuuLY5RvdqZbqt+3q3wqmSMpRWhNy3yW4n7kmw3cmD2uPdb49Gedn3+UZgR8kZeFmoCJff4UZa/4m62516Swfk/HBCCnBrWteLPm3l8/fHa5vjSNFFjPp9S11tCoKAp4Z2wtqfTsU9tk6yC7d3U57VsambnIQpgzLgSXKELoQcjwxoh6/2FqJnej35m7rdC5z+BSg/r+szlBReKMeFMj9aN0iVRWxdKA/g53MO/Jp2G/q4tM/3WKy4OB6dUkrQp3lr+Qt9JgL+stBdtgoBBhwuuoCrUj2olxK+Qek2Ouq4qYM7IC+/BG0bpgL9Hge2vqkp//iCDPuLyrE1eRT6eLjvxOrj0/N3o8jZGH1SQ8/3TK+H+qlu4KbpwPdva9YZKfcHcfC3AOr1z1Y9pm5yEiYNbI80j0u3MfKnni1QWFKGO7q3ALzDgOPfAT3HmV/Xev05JLl0HiF7+qYODXF7t2aSx8LhAEb/vhWQ1gi44YnQ52twrtSHvPNeXGgyAH0cdXR1RRAQ+n46ebBvOs6V+lDhD+KQfzi2lZxCSvBSuC0BLeolo47mTqqxOXLmEi6W++Gta2DtthiBaUWgKTh58iSaN2+OLVu2oE+fPtLzf/vb3/Duu+/i559/jnpPRkYGxo4di+nTp0vPbd68Gf369UN+fj6aNIk+oWJ5Rlq2bIlz586hTh19k0wQBEEQhL2UlJSgbt26ca/fhkyoBg0awOl0RnlBCgsLo7wfIk2aNIl5vMvlwlVXxb7j8Hg88HjU3fYEQRAEQdQeDIlubrcbPXr0QG5uruz53Nxc9O3bN+Z7+vTpE3X82rVr0bNnz5jxIgRBEARBXF4YjgB6/PHHsWjRIixZsgR79+7FlClTcOzYMWRnh/TC6dOnY8yYMdLx2dnZOHr0KB5//HHs3bsXS5YsweLFizF16lTrvgVBEARBEDUWw5Euo0aNwpkzZ/DCCy8gPz8fXbp0werVq3H11VcDAPLz82U1R1q3bo3Vq1djypQp+Oc//4lmzZph3rx5tSqtlyAIgiCIxDEUwGoXegNgCIIgCIKoPui9ftPeNARBEARB2AoZIwRBEARB2AoZIwRBEARB2AoZIwRBEARB2AoZIwRBEARB2AoZIwRBEARB2AoZIwRBEARB2Irx7f1sQCyFUlJSYnNPCIIgCILQi3jdjlfSrEYYI+fPh7YHb9lS3xbnBEEQBEFUH86fP4+6deuqvl4jKrAGg0GcPHkSV1xxBQRBsLs7tZqSkhK0bNkSx48fp2q3NkFzYD80B/ZA424/Vs8BYwznz59Hs2bN4HCoR4bUCM+Iw+FAixYt7O7GZUWdOnVoMbAZmgP7oTmwBxp3+7FyDrQ8IiIUwEoQBEEQhK2QMUIQBEEQhK2QMULI8Hg8eO655+DxeOzuymULzYH90BzYA427/dg1BzUigJUgCIIgiNoLeUYIgiAIgrAVMkYIgiAIgrAVMkYIgiAIgrAVMkYIgiAIgrAVMkYIgiAIgrAVMkYuM7Zv346ysjK7u0EQBEEQEmSMXCYcOnQII0aMQK9evbBixQq7u3PZcvToUbzwwgtYunQptm7dCiC09xJRdRw/fhyfffYZdu/ejUAgACD+jqKEeY4fP44PP/wQ33//PXw+HwAa96rm7NmzKCoqAlD91h0yRmo5jDFMnDgR7du3hyAIqFu3LtLS0uzu1mXJk08+ic6dO+Pbb7/Fyy+/jLvuugt5eXmam0cR1jJ16lR07NgRr732Gvr164dHH30Uhw4dgiAIdGGsRKZPn46MjAzMnj0bffv2xYQJE2jcq5inn34aHTt2xMKFCwGg2q071as3hKXk5OQgNTUVO3bswJYtW5CTk4NOnTphzZo1AOiupCr5+OOPsW7dOqxatQqrV6/Gf/7zH7Rs2RKfffaZ3V27bFiyZAm2bNmCL7/8El988QUWLVqEPXv2YNy4cQBAO4JXElu3bsXKlSvx4YcfYsOGDVi0aBH279+PBx54AACNe2VTXFyM8ePHY926dWjVqhW+/fZbbNu2DUD1ugaQMVLL4E+u06dP47333sPWrVvRu3dvlJaWom3btjh79iwuXbpEi0AlovyRr1y5EsnJyRgwYAAAIDMzE263G0OGDFF9D2EOcTzFx48++ght27ZFv3794HK5cPfddyMzMxObNm3CokWLZMcS1pGTk4NAIIDbbrsNXq8X999/P2bNmoVdu3bhH//4BwAa98okOTkZV199NaZPn47Zs2fjxIkT+OSTT+Dz+aqVZ4qMkVpEaWkpKioqpL/Hjx+PO+64AwAQCASQnJyMBg0a4MCBA0hJSal2mmFtQTkPfr8fHTp0wLFjx7BlyxYcPnwYt99+O3bt2oVp06bh0UcfJePQYvg5EAQBxcXFAIBWrVrJznuv14uMjAxMnz4dfr+f5sAk4oWNH+NGjRohOTkZly5dkp677rrrMHXqVLz44osoLy+ncbcQcQ7EeCiPx4NJkyZh5MiRuPHGGzFgwABs2rQJubm5dnYzCjJGagnTp09Hv379MGzYMMybNw/nz5+Hw+GQFgXxxz5o0CAcOXIEx44dq3aaYW1AOQ/nzp2Dy+XCyJEj0adPH8ycORPt2rWD3+/HBx98gMGDB2P16tX4y1/+AqD6BZXVRJRzUFxcjCuvvBKdOnXC2rVr8dJLL+HMmTOYNm0ali5diueeew5JSUmSd4RIjDlz5mDGjBkA5PEIderUgcvlwldffSU9JwgCHnzwQaSkpJB3xEL4OXA6ndLzdevWldaWxx57DIwx5OTkoKioqPp4RxhRoykvL2d33XUX69y5M1u+fDkbM2YM69y5M7vttttiHr9y5UrWunVr9s0331RxT2s3avMwZMgQ6ZhgMMiWLVvGhg4dykpKSqTnP/30U5aSksJOnz5tR9drDWpzcMsttzDGGDt//jybNGkSa9euHatfvz7r0qUL27p1K2OMsX79+rE5c+bY2f0ay3fffcduuukmJggC6969O9uyZQtjjLGKigrGGGPnzp1jXbt2ZRMnTmSnTp2S3ldWVsaysrLY2LFjmd/vt6XvtQW1OQgEArLjgsEgY4yxuXPnsh49erC33nor6jW7oFvjGs7Bgwexc+dOzJ07F6NGjcLbb7+NhQsXYv369XjllVei3Kb9+vXDiRMnJLc13Ylbg9o8bNy4Ea+88goCgQAEQcCePXvg8XhwxRVXSO89dOgQWrRoIXNjE8ZRm4NNmzZh1qxZSEtLw9y5c7FhwwZs3LgRu3fvRq9evVBeXo5ffvmFtq1PkC+//BINGjTAkiVLpEcASEpKgs/nQ506dZCdnY1169Zh5cqV0vs8Hg/2798Ph8Mhu4snjKM2Bw6HI6bXIzs7G40bN8aaNWuwe/duLFu2TPKo2IatphBhmh07djBBENiZM2cYYxHrdubMmaxevXps3759suOLi4tZ//792RNPPFHlfa3N6J2HKVOmsIEDB7LPP/+c+f1+lpeXx/r3788eeeQR2/peW4g3B7/88ovsePH1d955h/Xu3ZudPHmyajtcwxHH7+jRo9Kd+MyZM1nv3r3ZihUrGGOM+Xw+6fh7772XZWZmsn/961/st99+Yzt27GDdu3dny5cvr/rO1xL0zIHSOyL+nZOTw9q0acOuuuoq5na72auvvlqFPY+GjJEazg8//MCuueYaNn/+fMZY5OSsqKhgrVu3lowOcVHw+/2sffv2LDs7W3KjEuaJNw+TJ09mjDG2detWNmLECOZ2u9ngwYNZWloaGzduHCstLbWt77UFvb8Fv9/PioqK2Mcff8wefvhhlpqayl544QUWDAZtd1XXdA4ePMhGjhzJRo4cyc6ePcsYC8ln4mvPPvssczqdrEePHiw5OZmNHz+e1iGLiTUHSoPkwIEDbMyYMUwQBDZhwgR24cIFO7oqg4yRGs7Zs2fZyJEj2ahRo6Q7O9HwmD17NmvWrJl0Ioq67DvvvBN1l0iYI948NG3aVBr/kydPsk8++YS9/vrrLC8vz7Y+1zaM/BYKCwvZ1KlT2aBBg9iPP/5oW59rE6Iht3jxYta7d2/VGJw9e/awVatWsb1791Zl9y4L9M7B//3f/7EWLVqwXbt2VWX3NKGYkWpMYWEhTp8+LaUoiqlaQChdFADq1auH4cOH4+eff5bKvLtcLgChCOp69erh+PHjACLR1Q888AAyMjKq7HvUdKyYh/r160vz0LRpU4wcORKPPPIIOnXqVJVfpcZi1W/h2LFjAICGDRvi2WefRW5uLrp161aVX6VGoWfcRcTX7rrrLnTu3BmrVq3C/v37AQDff/89gFCM2jXXXIPbbrsNHTt2rIqvUOOxeg4AYNasWTh+/Di6du1a2d3XDRkj1RCfz4fs7Gz0798fw4cPx+23347y8nI4nU5pTweXy4WysjIsX74c48aNQ2ZmJt5//31s2LBBaufXX39Fw4YNcfXVV9v1VWo0Vs9Denq6Td+k5lKZc8AHERNy9I67z+fD22+/Lf0dDAZRp04d3H333QgGg3j++ecxcOBA9OzZE7/99huVEzBAZc0BUP1KwQOgANbqxgcffMDatm3LbrzxRrZ+/Xq2cOFC1qZNGzZx4kTZca+99hqrX78+GzFiBGOMsZ07d7L77ruPud1uNmHCBPbwww+zK664gr355puMMfvTtmoaNA/2Q3NgD0bH/c4775RiE0SOHj3K2rZtywRBYKNHj2YFBQVV+RVqPJfjHJAxUs145JFH2DPPPCOLQn/wwQfZ448/Lv09f/58lp6ezpYtWyYLTAoGg2zGjBnsz3/+Mxs6dCjbvHlzlfa9NkHzYD80B/ZgdNyVxt1XX33F0tLSWGZmJtu+fXuV9bs2cTnOgcBYdSi9RgSDQTgcDhQUFMDn86Fly5YAQlvO33HHHbj33nvRp08f9O3bF36/H+Xl5UhNTZXezxijksoWQPNgPzQH9mB23EXOnDmDtWvX4p577qnqr1DjuZzngIwRG1m4cCEEQUBGRgZuvPFGAPKFdP78+Zg0aRKuv/56OJ1O7Nq1C48++iimT58Or9drZ9drFTQP9kNzYA9WjzsZgsahOQhjhzvmcuff//43a9SoEevTpw/LzMxkDRs2ZC+99BJjjMnKIi9dupRt2rRJcsEtW7aMJScnsyNHjtjS79oGzYP90BzYA427/dAcyCFjpIpZtmwZ69atG1uwYAFjjLETJ06w+fPns9TUVGm/ErV9Gvbu3cucTidbu3ZtlfW3tkLzYD80B/ZA424/NAfRVMP8ntoJC6thPp8PvXv3xpgxYwAAzZo1w7XXXovmzZtj7969AKC6T0NOTg4GDhyIfv36VU2nayE0D/ZDc2APNO72Q3Ogga2m0GXAjh072G+//Sb9XVxcHGXx/vjjj6xJkyZRqVmMhdKzDhw4wB566CHWrFkztnTpUsYYpScahebBfmgO7IHG3X5oDuJDxkgl8eGHH7IWLVqwtm3bslatWrFnnnlGlufNpyHOmTOHXX/99YwxJtunYd++feyJJ55gLVq0YAMGDKAS7glA82A/NAf2QONuPzQH+iFjpBLYtm0b69ixI5s7dy7buXMne+ONN1jDhg3ZhAkTpB1FA4GAlEP+xz/+MeaurZcuXWIbN26kGgkJQvNgPzQH9kDjbj80B8YgY8RCRJfZm2++yVq0aMHOnTsnvfb666+z6667jr344ovSc4FAgAWDQda2bVu2atUqxhhjv/zyCxs9ejQ7duxY1Xa+FkHzYD80B/ZA424/NAeJQQGsFiLmdh8+fBgZGRnSJl0AkJWVhR49emDNmjX46aefAIT2B9i2bRtSUlLQvXt3TJ48Gb/73e9w5swZNGrUyJbvUBugebAfmgN7oHG3H5qDxCBjxAS5ubl47LHH8Nprr+G7776Tnr/++uuxZcsWFBQUAAjtpJiamooRI0ZAEASsXbtWOnb16tXYs2cPOnTogNzcXGzevBlr166Fx+Op8u9TU6F5sB+aA3ugcbcfmgNrIGMkAfLz8zF8+HDcf//9OHv2LBYvXozBgwdLJ+LgwYORnp6Ol19+GUDEUr755pvhcDhw4MABqa2kpCQ0aNAAS5cuxU8//YQePXpU/ReqodA82A/NgT3QuNsPzYHF2K0T1TQuXrzIHnzwQTZq1Ch26NAh6fnf//73LCsrizEWKlbzzjvvMIfDERV0dN9997GbbrpJ+ruwsLBqOl7LoHmwH5oDe6Bxtx+aA+shz4hBUlJS4PF4kJWVhdatW8Pv9wMAhg0bJitW86c//QkjRozAQw89hK+//hqMMRQUFGD//v24//77pfYaNmxoy/eo6dA82A/NgT3QuNsPzYH10EZ5CeDz+ZCUlAQgsinRAw88gOTkZCxcuFB6rqysDEOGDEFeXh4yMzOxZ88etGrVCitWrJB2YyQSh+bBfmgO7IHG3X5oDqyFjBGL6N+/P8aNG4esrCwwxhAMBuF0OnHq1Cns2rUL27ZtQ3p6Ou699167u1qroXmwH5oDe6Bxtx+ag8QhY8QCDh06hL59++Lzzz+XAo8qKirgdrtt7tnlBc2D/dAc2AONu/3QHJiDYkZMINpx33zzDdLS0qQT8Pnnn8ekSZNQWFhoZ/cuG2ge7IfmwB5o3O2H5sAaXPEPIdQQU7W+++473HnnncjNzcXDDz+MS5cu4d13372sCtbYCc2D/dAc2AONu/3QHFhEFWTs1GpKS0tZu3btmCAIzOPxsFmzZtndpcsSmgf7oTmwBxp3+6E5MA/FjFjAzTffjPbt22POnDnwer12d+eyhebBfmgO7IHG3X5oDsxBxogFBAIBOJ1Ou7tx2UPzYD80B/ZA424/NAfmIGOEIAiCIAhboWwagiAIgiBshYwRgiAIgiBshYwRgiAIgiBshYwRgiAIgiBshYwRgiAIgiBshYwRgiAIgiBshYwRgiAIgiBshYwRgiAsISsrC4IgQBAEJCUloXHjxrj55puxZMkSBINB3e0sXboUV155ZeV1lCCIagcZIwRBWMatt96K/Px8HDlyBGvWrMGAAQMwadIkDBs2DH6/3+7uEQRRTSFjhCAIy/B4PGjSpAmaN2+O7t2746mnnsLKlSuxZs0aLF26FAAwZ84cdO3aFampqWjZsiUmTpyICxcuAAA2btyIsWPH4ty5c5KX5a9//SsAoKKiAtOmTUPz5s2RmpqK3r17Y+PGjfZ8UYIgLIWMEYIgKpU//OEP6NatGz7++GMAgMPhwLx587Bnzx68/fbbWL9+PaZNmwYA6Nu3L+bOnYs6deogPz8f+fn5mDp1KgBg7Nix2Lx5M5YvX45du3bh7rvvxq233or9+/fb9t0IgrAG2puGIAhLyMrKQnFxMXJycqJeGz16NHbt2oW8vLyo1z744ANMmDABRUVFAEIxI5MnT0ZxcbF0zMGDB9G+fXv8+uuvaNasmfT8oEGD0KtXL8yYMcPy70MQRNXhsrsDBEHUfhhjEAQBALBhwwbMmDEDeXl5KCkpgd/vR1lZGS5evIjU1NSY7//+++/BGENGRobs+fLyclx11VWV3n+CICoXMkYIgqh09u7di9atW+Po0aMYOnQosrOz8eKLL6J+/fr45ptvMH78ePh8PtX3B4NBOJ1O7NixI2qb9rS0tMruPkEQlQwZIwRBVCrr16/H7t27MWXKFGzfvh1+vx+zZ8+GwxEKWVuxYoXseLfbjUAgIHvu2muvRSAQQGFhIW644YYq6ztBEFUDGSMEQVhGeXk5CgoKEAgEcOrUKXzxxReYOXMmhg0bhjFjxmD37t3w+/2YP38+hg8fjs2bN2PBggWyNtLT03HhwgV89dVX6NatG1JSUpCRkYH77rsPY8aMwezZs3HttdeiqKgI69evR9euXTF06FCbvjFBEFZA2TQEQVjGF198gaZNmyI9PR233norNmzYgHnz5mHlypVwOp3IzMzEnDlz8PLLL6NLly5YtmwZZs6cKWujb9++yM7OxqhRo9CwYUP8/e9/BwC89dZbGDNmDJ544gl06NABt99+O7Zu3YqWLVva8VUJgrAQyqYhCIIgCMJWyDNCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEIStkDFCEARBEISt/D910QIdmFw1xAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76f11db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(train,test,predictors,model):\n",
    "    model.fit(train[predictors],train[\"Target\"])\n",
    "    preds=model.predict(test[predictors])\n",
    "    preds=pd.Series(preds,index=test.index,name=\"Predictions\")\n",
    "    combined=pd.concat([test[\"Target\"],preds],axis=1)\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f22777e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(data,model,predictors,start=2500,step=250):\n",
    "    all_predictions=[]\n",
    "    \n",
    "    for i in range(start,data.shape[0],step):\n",
    "        train=data.iloc[0:i].copy()\n",
    "        test=data.iloc[i:(i+step)].copy()\n",
    "        predictions=predict(train,test,predictors,model)\n",
    "        all_predictions.append(predictions)\n",
    "    return pd.concat(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c6d7cc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1795\n",
       "0    1701\n",
       "Name: Predictions, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions=backtest(sp500,model,predictors)\n",
    "predictions[\"Predictions\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7b8a5a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5220055710306407"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(predictions[\"Target\"],predictions[\"Predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "596dd74e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.541476\n",
       "0    0.458524\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"Target\"].value_counts()/predictions.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8a62c450",
   "metadata": {},
   "outputs": [],
   "source": [
    "horizons=[2,5,60,250,1000]\n",
    "\n",
    "new_predictors=[]\n",
    "\n",
    "for horizon in horizons:\n",
    "    rolling_averages=sp500.rolling(horizon).mean()\n",
    "    \n",
    "    ratio_column=f\"Close_Ratio_{horizon}\"\n",
    "    sp500[ratio_column]=sp500[\"Close\"]/rolling_averages[\"Close\"]\n",
    "    \n",
    "    trend_column=f\"Trend_{horizon}\"\n",
    "    sp500[trend_column]=sp500.shift(1).rolling(horizon).sum()[\"Target\"]\n",
    "    \n",
    "    new_predictors+=[ratio_column,trend_column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "eb8ffa1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Tomorrow</th>\n",
       "      <th>Target</th>\n",
       "      <th>Close_Ratio_2</th>\n",
       "      <th>Trend_2</th>\n",
       "      <th>Close_Ratio_5</th>\n",
       "      <th>Trend_5</th>\n",
       "      <th>Close_Ratio_60</th>\n",
       "      <th>Trend_60</th>\n",
       "      <th>Close_Ratio_250</th>\n",
       "      <th>Trend_250</th>\n",
       "      <th>Close_Ratio_1000</th>\n",
       "      <th>Trend_1000</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00-05:00</th>\n",
       "      <td>1469.250000</td>\n",
       "      <td>1478.000000</td>\n",
       "      <td>1438.359985</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>931800000</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00-05:00</th>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1455.219971</td>\n",
       "      <td>1397.430054</td>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1009000000</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1</td>\n",
       "      <td>0.980453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00-05:00</th>\n",
       "      <td>1399.420044</td>\n",
       "      <td>1413.270020</td>\n",
       "      <td>1377.680054</td>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1085500000</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000960</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00-05:00</th>\n",
       "      <td>1402.109985</td>\n",
       "      <td>1411.900024</td>\n",
       "      <td>1392.099976</td>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1092300000</td>\n",
       "      <td>1441.469971</td>\n",
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       "      <td>1.000478</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-07 00:00:00-05:00</th>\n",
       "      <td>1403.449951</td>\n",
       "      <td>1441.469971</td>\n",
       "      <td>1400.729980</td>\n",
       "      <td>1441.469971</td>\n",
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       "      <td>1457.599976</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.014881</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25 00:00:00-04:00</th>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4181.419922</td>\n",
       "      <td>4186.770020</td>\n",
       "      <td>3869370000</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>0</td>\n",
       "      <td>0.992778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989609</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.953576</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.003576</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.070961</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 00:00:00-04:00</th>\n",
       "      <td>4175.990234</td>\n",
       "      <td>4183.600098</td>\n",
       "      <td>4127.899902</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>4277640000</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.994049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.943640</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.991388</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.058001</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-27 00:00:00-04:00</th>\n",
       "      <td>4152.930176</td>\n",
       "      <td>4156.700195</td>\n",
       "      <td>4103.779785</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>4019500000</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.940485</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.986425</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1.052643</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-30 00:00:00-04:00</th>\n",
       "      <td>4139.390137</td>\n",
       "      <td>4177.470215</td>\n",
       "      <td>4132.939941</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>3911140000</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>1</td>\n",
       "      <td>1.005969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998956</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.952909</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.064990</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31 00:00:00-04:00</th>\n",
       "      <td>4171.330078</td>\n",
       "      <td>4195.549805</td>\n",
       "      <td>4153.120117</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>4249470000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.003227</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.008029</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.960267</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.004127</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1.071585</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5996 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open         High          Low        Close  \\\n",
       "Date                                                                            \n",
       "2000-01-03 00:00:00-05:00  1469.250000  1478.000000  1438.359985  1455.219971   \n",
       "2000-01-04 00:00:00-05:00  1455.219971  1455.219971  1397.430054  1399.420044   \n",
       "2000-01-05 00:00:00-05:00  1399.420044  1413.270020  1377.680054  1402.109985   \n",
       "2000-01-06 00:00:00-05:00  1402.109985  1411.900024  1392.099976  1403.449951   \n",
       "2000-01-07 00:00:00-05:00  1403.449951  1441.469971  1400.729980  1441.469971   \n",
       "...                                ...          ...          ...          ...   \n",
       "2023-10-25 00:00:00-04:00  4232.419922  4232.419922  4181.419922  4186.770020   \n",
       "2023-10-26 00:00:00-04:00  4175.990234  4183.600098  4127.899902  4137.229980   \n",
       "2023-10-27 00:00:00-04:00  4152.930176  4156.700195  4103.779785  4117.370117   \n",
       "2023-10-30 00:00:00-04:00  4139.390137  4177.470215  4132.939941  4166.819824   \n",
       "2023-10-31 00:00:00-04:00  4171.330078  4195.549805  4153.120117  4193.799805   \n",
       "\n",
       "                               Volume     Tomorrow  Target  Close_Ratio_2  \\\n",
       "Date                                                                        \n",
       "2000-01-03 00:00:00-05:00   931800000  1399.420044       0            NaN   \n",
       "2000-01-04 00:00:00-05:00  1009000000  1402.109985       1       0.980453   \n",
       "2000-01-05 00:00:00-05:00  1085500000  1403.449951       1       1.000960   \n",
       "2000-01-06 00:00:00-05:00  1092300000  1441.469971       1       1.000478   \n",
       "2000-01-07 00:00:00-05:00  1225200000  1457.599976       1       1.013364   \n",
       "...                               ...          ...     ...            ...   \n",
       "2023-10-25 00:00:00-04:00  3869370000  4137.229980       0       0.992778   \n",
       "2023-10-26 00:00:00-04:00  4277640000  4117.370117       0       0.994049   \n",
       "2023-10-27 00:00:00-04:00  4019500000  4166.819824       1       0.997594   \n",
       "2023-10-30 00:00:00-04:00  3911140000  4193.799805       1       1.005969   \n",
       "2023-10-31 00:00:00-04:00  4249470000          NaN       0       1.003227   \n",
       "\n",
       "                           Trend_2  Close_Ratio_5  Trend_5  Close_Ratio_60  \\\n",
       "Date                                                                         \n",
       "2000-01-03 00:00:00-05:00      NaN            NaN      NaN             NaN   \n",
       "2000-01-04 00:00:00-05:00      NaN            NaN      NaN             NaN   \n",
       "2000-01-05 00:00:00-05:00      1.0            NaN      NaN             NaN   \n",
       "2000-01-06 00:00:00-05:00      2.0            NaN      NaN             NaN   \n",
       "2000-01-07 00:00:00-05:00      2.0       1.014881      NaN             NaN   \n",
       "...                            ...            ...      ...             ...   \n",
       "2023-10-25 00:00:00-04:00      1.0       0.989609      1.0        0.953576   \n",
       "2023-10-26 00:00:00-04:00      0.0       0.984451      1.0        0.943640   \n",
       "2023-10-27 00:00:00-04:00      0.0       0.984730      1.0        0.940485   \n",
       "2023-10-30 00:00:00-04:00      1.0       0.998956      2.0        0.952909   \n",
       "2023-10-31 00:00:00-04:00      2.0       1.008029      2.0        0.960267   \n",
       "\n",
       "                           Trend_60  Close_Ratio_250  Trend_250  \\\n",
       "Date                                                              \n",
       "2000-01-03 00:00:00-05:00       NaN              NaN        NaN   \n",
       "2000-01-04 00:00:00-05:00       NaN              NaN        NaN   \n",
       "2000-01-05 00:00:00-05:00       NaN              NaN        NaN   \n",
       "2000-01-06 00:00:00-05:00       NaN              NaN        NaN   \n",
       "2000-01-07 00:00:00-05:00       NaN              NaN        NaN   \n",
       "...                             ...              ...        ...   \n",
       "2023-10-25 00:00:00-04:00      26.0         1.003576      123.0   \n",
       "2023-10-26 00:00:00-04:00      26.0         0.991388      123.0   \n",
       "2023-10-27 00:00:00-04:00      26.0         0.986425      122.0   \n",
       "2023-10-30 00:00:00-04:00      27.0         0.997990      123.0   \n",
       "2023-10-31 00:00:00-04:00      27.0         1.004127      124.0   \n",
       "\n",
       "                           Close_Ratio_1000  Trend_1000  \n",
       "Date                                                     \n",
       "2000-01-03 00:00:00-05:00               NaN         NaN  \n",
       "2000-01-04 00:00:00-05:00               NaN         NaN  \n",
       "2000-01-05 00:00:00-05:00               NaN         NaN  \n",
       "2000-01-06 00:00:00-05:00               NaN         NaN  \n",
       "2000-01-07 00:00:00-05:00               NaN         NaN  \n",
       "...                                     ...         ...  \n",
       "2023-10-25 00:00:00-04:00          1.070961       527.0  \n",
       "2023-10-26 00:00:00-04:00          1.058001       527.0  \n",
       "2023-10-27 00:00:00-04:00          1.052643       526.0  \n",
       "2023-10-30 00:00:00-04:00          1.064990       526.0  \n",
       "2023-10-31 00:00:00-04:00          1.071585       526.0  \n",
       "\n",
       "[5996 rows x 17 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "35e93ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2003-12-26 00:00:00-05:00</th>\n",
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       "      <th>2003-12-29 00:00:00-05:00</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-12-31 00:00:00-05:00</th>\n",
       "      <td>1109.640015</td>\n",
       "      <td>1112.560059</td>\n",
       "      <td>1106.209961</td>\n",
       "      <td>1111.920044</td>\n",
       "      <td>1027500000</td>\n",
       "      <td>1108.479980</td>\n",
       "      <td>0</td>\n",
       "      <td>1.001026</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.006997</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.050463</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.151439</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.972073</td>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-02 00:00:00-05:00</th>\n",
       "      <td>1111.920044</td>\n",
       "      <td>1118.849976</td>\n",
       "      <td>1105.079956</td>\n",
       "      <td>1108.479980</td>\n",
       "      <td>1153200000</td>\n",
       "      <td>1122.219971</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.001263</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.046073</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.147024</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0.969348</td>\n",
       "      <td>485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-24 00:00:00-04:00</th>\n",
       "      <td>4235.790039</td>\n",
       "      <td>4259.379883</td>\n",
       "      <td>4219.430176</td>\n",
       "      <td>4247.680176</td>\n",
       "      <td>3821820000</td>\n",
       "      <td>4186.770020</td>\n",
       "      <td>0</td>\n",
       "      <td>1.003620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.997976</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966019</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.018525</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.086850</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-25 00:00:00-04:00</th>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4232.419922</td>\n",
       "      <td>4181.419922</td>\n",
       "      <td>4186.770020</td>\n",
       "      <td>3869370000</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>0</td>\n",
       "      <td>0.992778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.989609</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.953576</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.003576</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.070961</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-26 00:00:00-04:00</th>\n",
       "      <td>4175.990234</td>\n",
       "      <td>4183.600098</td>\n",
       "      <td>4127.899902</td>\n",
       "      <td>4137.229980</td>\n",
       "      <td>4277640000</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>0</td>\n",
       "      <td>0.994049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984451</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.943640</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.991388</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.058001</td>\n",
       "      <td>527.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-27 00:00:00-04:00</th>\n",
       "      <td>4152.930176</td>\n",
       "      <td>4156.700195</td>\n",
       "      <td>4103.779785</td>\n",
       "      <td>4117.370117</td>\n",
       "      <td>4019500000</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>1</td>\n",
       "      <td>0.997594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.984730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.940485</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.986425</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1.052643</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-30 00:00:00-04:00</th>\n",
       "      <td>4139.390137</td>\n",
       "      <td>4177.470215</td>\n",
       "      <td>4132.939941</td>\n",
       "      <td>4166.819824</td>\n",
       "      <td>3911140000</td>\n",
       "      <td>4193.799805</td>\n",
       "      <td>1</td>\n",
       "      <td>1.005969</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998956</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.952909</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.997990</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.064990</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4995 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open         High          Low        Close  \\\n",
       "Date                                                                            \n",
       "2003-12-26 00:00:00-05:00  1094.040039  1098.469971  1094.040039  1095.890015   \n",
       "2003-12-29 00:00:00-05:00  1095.890015  1109.479980  1095.890015  1109.479980   \n",
       "2003-12-30 00:00:00-05:00  1109.479980  1109.750000  1106.410034  1109.640015   \n",
       "2003-12-31 00:00:00-05:00  1109.640015  1112.560059  1106.209961  1111.920044   \n",
       "2004-01-02 00:00:00-05:00  1111.920044  1118.849976  1105.079956  1108.479980   \n",
       "...                                ...          ...          ...          ...   \n",
       "2023-10-24 00:00:00-04:00  4235.790039  4259.379883  4219.430176  4247.680176   \n",
       "2023-10-25 00:00:00-04:00  4232.419922  4232.419922  4181.419922  4186.770020   \n",
       "2023-10-26 00:00:00-04:00  4175.990234  4183.600098  4127.899902  4137.229980   \n",
       "2023-10-27 00:00:00-04:00  4152.930176  4156.700195  4103.779785  4117.370117   \n",
       "2023-10-30 00:00:00-04:00  4139.390137  4177.470215  4132.939941  4166.819824   \n",
       "\n",
       "                               Volume     Tomorrow  Target  Close_Ratio_2  \\\n",
       "Date                                                                        \n",
       "2003-12-26 00:00:00-05:00   356070000  1109.479980       1       1.000845   \n",
       "2003-12-29 00:00:00-05:00  1058800000  1109.640015       1       1.006162   \n",
       "2003-12-30 00:00:00-05:00  1012600000  1111.920044       1       1.000072   \n",
       "2003-12-31 00:00:00-05:00  1027500000  1108.479980       0       1.001026   \n",
       "2004-01-02 00:00:00-05:00  1153200000  1122.219971       1       0.998451   \n",
       "...                               ...          ...     ...            ...   \n",
       "2023-10-24 00:00:00-04:00  3821820000  4186.770020       0       1.003620   \n",
       "2023-10-25 00:00:00-04:00  3869370000  4137.229980       0       0.992778   \n",
       "2023-10-26 00:00:00-04:00  4277640000  4117.370117       0       0.994049   \n",
       "2023-10-27 00:00:00-04:00  4019500000  4166.819824       1       0.997594   \n",
       "2023-10-30 00:00:00-04:00  3911140000  4193.799805       1       1.005969   \n",
       "\n",
       "                           Trend_2  Close_Ratio_5  Trend_5  Close_Ratio_60  \\\n",
       "Date                                                                         \n",
       "2003-12-26 00:00:00-05:00      1.0       1.002176      3.0        1.039354   \n",
       "2003-12-29 00:00:00-05:00      2.0       1.010755      4.0        1.050761   \n",
       "2003-12-30 00:00:00-05:00      2.0       1.007835      4.0        1.049591   \n",
       "2003-12-31 00:00:00-05:00      2.0       1.006997      4.0        1.050463   \n",
       "2004-01-02 00:00:00-05:00      1.0       1.001263      4.0        1.046073   \n",
       "...                            ...            ...      ...             ...   \n",
       "2023-10-24 00:00:00-04:00      1.0       0.997976      1.0        0.966019   \n",
       "2023-10-25 00:00:00-04:00      1.0       0.989609      1.0        0.953576   \n",
       "2023-10-26 00:00:00-04:00      0.0       0.984451      1.0        0.943640   \n",
       "2023-10-27 00:00:00-04:00      0.0       0.984730      1.0        0.940485   \n",
       "2023-10-30 00:00:00-04:00      1.0       0.998956      2.0        0.952909   \n",
       "\n",
       "                           Trend_60  Close_Ratio_250  Trend_250  \\\n",
       "Date                                                              \n",
       "2003-12-26 00:00:00-05:00      34.0         1.137825      135.0   \n",
       "2003-12-29 00:00:00-05:00      34.0         1.150837      135.0   \n",
       "2003-12-30 00:00:00-05:00      34.0         1.150046      135.0   \n",
       "2003-12-31 00:00:00-05:00      34.0         1.151439      136.0   \n",
       "2004-01-02 00:00:00-05:00      33.0         1.147024      135.0   \n",
       "...                             ...              ...        ...   \n",
       "2023-10-24 00:00:00-04:00      26.0         1.018525      123.0   \n",
       "2023-10-25 00:00:00-04:00      26.0         1.003576      123.0   \n",
       "2023-10-26 00:00:00-04:00      26.0         0.991388      123.0   \n",
       "2023-10-27 00:00:00-04:00      26.0         0.986425      122.0   \n",
       "2023-10-30 00:00:00-04:00      27.0         0.997990      123.0   \n",
       "\n",
       "                           Close_Ratio_1000  Trend_1000  \n",
       "Date                                                     \n",
       "2003-12-26 00:00:00-05:00          0.957328       485.0  \n",
       "2003-12-29 00:00:00-05:00          0.969445       486.0  \n",
       "2003-12-30 00:00:00-05:00          0.969833       486.0  \n",
       "2003-12-31 00:00:00-05:00          0.972073       486.0  \n",
       "2004-01-02 00:00:00-05:00          0.969348       485.0  \n",
       "...                                     ...         ...  \n",
       "2023-10-24 00:00:00-04:00          1.086850       528.0  \n",
       "2023-10-25 00:00:00-04:00          1.070961       527.0  \n",
       "2023-10-26 00:00:00-04:00          1.058001       527.0  \n",
       "2023-10-27 00:00:00-04:00          1.052643       526.0  \n",
       "2023-10-30 00:00:00-04:00          1.064990       526.0  \n",
       "\n",
       "[4995 rows x 17 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500=sp500.dropna()\n",
    "sp500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "39f80987",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier(n_estimators=200,min_samples_split=50,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "26087c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(train,test,predictors,model):\n",
    "    model.fit(train[predictors],train[\"Target\"])\n",
    "    preds=model.predict_proba(test[predictors])[:,1]\n",
    "    preds[preds>=.6]=1\n",
    "    preds[preds<.6]=0\n",
    "    preds=pd.Series(preds,index=test.index,name=\"Predictions\")\n",
    "    combined=pd.concat([test[\"Target\"],preds],axis=1)\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "9bcf06f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=backtest(sp500,model,new_predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "daa6926a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    2068\n",
       "1.0     427\n",
       "Name: Predictions, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"Predictions\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b9612a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5339578454332553"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(predictions[\"Target\"],predictions[\"Predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02d124c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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