从零开始卷量化(21).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64263a95",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T08:26:58.282925Z",
     "start_time": "2022-04-05T08:26:57.283117Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           date  open  close  high   low   volume        amount  swing  \\\n",
      "0    2015-01-05  4.60   4.89  5.02  4.54  8312544  4.274376e+09  10.48   \n",
      "1    2015-01-06  4.79   4.80  4.88  4.68  5634697  2.905527e+09   4.09   \n",
      "2    2015-01-07  4.74   4.99  5.03  4.72  6156267  3.246406e+09   6.46   \n",
      "3    2015-01-08  5.01   4.67  5.01  4.65  5486685  2.829286e+09   7.21   \n",
      "4    2015-01-09  4.63   4.61  4.85  4.60  5149009  2.617789e+09   5.35   \n",
      "...         ...   ...    ...   ...   ...      ...           ...    ...   \n",
      "1662 2022-03-24  3.52   3.54  3.61  3.50  1118599  3.973331e+08   3.11   \n",
      "1663 2022-03-25  3.53   3.51  3.54  3.50   742411  2.614483e+08   1.13   \n",
      "1664 2022-03-28  3.49   3.54  3.56  3.45   914175  3.203564e+08   3.13   \n",
      "1665 2022-03-29  3.55   3.53  3.56  3.52   470735  1.664713e+08   1.13   \n",
      "1666 2022-03-30  3.54   3.55  3.56  3.52   705496  2.499794e+08   1.13   \n",
      "\n",
      "      chg_pct  chg_amount  ...   MAX  MAXINDEX   MIN  MININDEX  MINIDX  \\\n",
      "0        6.77        0.31  ...   NaN         0   NaN         0       0   \n",
      "1       -1.84       -0.09  ...   NaN         0   NaN         0       0   \n",
      "2        3.96        0.19  ...   NaN         0   NaN         0       0   \n",
      "3       -6.41       -0.32  ...   NaN         0   NaN         0       0   \n",
      "4       -1.28       -0.06  ...   NaN         0   NaN         0       0   \n",
      "...       ...         ...  ...   ...       ...   ...       ...     ...   \n",
      "1662     0.00        0.00  ...  4.05      1631  3.42      1655    1655   \n",
      "1663    -0.85       -0.03  ...  4.05      1631  3.42      1655    1655   \n",
      "1664     0.85        0.03  ...  4.05      1631  3.42      1655    1655   \n",
      "1665    -0.28       -0.01  ...  4.05      1631  3.42      1655    1655   \n",
      "1666     0.57        0.02  ...  4.05      1631  3.42      1655    1655   \n",
      "\n",
      "      MAXIDX     MULT   SUB     SUM  symbol  \n",
      "0          0  22.7908  0.48     NaN  600050  \n",
      "1          0  22.8384  0.20     NaN  600050  \n",
      "2          0  23.7416  0.31     NaN  600050  \n",
      "3          0  23.2965  0.36     NaN  600050  \n",
      "4          0  22.3100  0.25     NaN  600050  \n",
      "...      ...      ...   ...     ...     ...  \n",
      "1662    1631  12.6350  0.11  349.21  600050  \n",
      "1663    1631  12.3900  0.04  348.74  600050  \n",
      "1664    1631  12.2820  0.11  348.32  600050  \n",
      "1665    1631  12.5312  0.04  347.88  600050  \n",
      "1666    1631  12.5312  0.04  347.46  600050  \n",
      "\n",
      "[1667 rows x 123 columns]\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "for root,dirs,files in os.walk('./qcsv'):\n",
    "    for file in files[:1]:\n",
    "        symbol=file.split('.')[0]\n",
    "        df=pd.read_csv('./qcsv/'+symbol+'.csv',index_col=0)\n",
    "        df['symbol']=symbol\n",
    "        df['date']=pd.to_datetime(df['date'])\n",
    "        print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acab3b3d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-08T12:09:36.525722Z",
     "start_time": "2022-04-08T12:09:24.244829Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "Quantiles Statistics\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>count %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>factor_quantile</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>1</th>\n",
       "      <td>7</td>\n",
       "      <td>5079918</td>\n",
       "      <td>2.266622e+05</td>\n",
       "      <td>2.758517e+05</td>\n",
       "      <td>112133</td>\n",
       "      <td>79.693685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97146</td>\n",
       "      <td>9432153</td>\n",
       "      <td>9.872588e+05</td>\n",
       "      <td>7.856217e+05</td>\n",
       "      <td>14208</td>\n",
       "      <td>10.097722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>185226</td>\n",
       "      <td>13534986</td>\n",
       "      <td>1.646138e+06</td>\n",
       "      <td>1.407161e+06</td>\n",
       "      <td>5574</td>\n",
       "      <td>3.961480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>268039</td>\n",
       "      <td>16542931</td>\n",
       "      <td>2.333872e+06</td>\n",
       "      <td>1.964154e+06</td>\n",
       "      <td>2811</td>\n",
       "      <td>1.997797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>395115</td>\n",
       "      <td>19938945</td>\n",
       "      <td>2.823808e+06</td>\n",
       "      <td>2.294238e+06</td>\n",
       "      <td>1578</td>\n",
       "      <td>1.121495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>449165</td>\n",
       "      <td>21567829</td>\n",
       "      <td>3.606149e+06</td>\n",
       "      <td>2.925952e+06</td>\n",
       "      <td>985</td>\n",
       "      <td>0.700046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>615010</td>\n",
       "      <td>19998692</td>\n",
       "      <td>3.943014e+06</td>\n",
       "      <td>2.963511e+06</td>\n",
       "      <td>650</td>\n",
       "      <td>0.461959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>668263</td>\n",
       "      <td>21078386</td>\n",
       "      <td>4.685769e+06</td>\n",
       "      <td>3.577250e+06</td>\n",
       "      <td>430</td>\n",
       "      <td>0.305604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>754352</td>\n",
       "      <td>23381800</td>\n",
       "      <td>5.041500e+06</td>\n",
       "      <td>3.491463e+06</td>\n",
       "      <td>331</td>\n",
       "      <td>0.235244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>878317</td>\n",
       "      <td>51354665</td>\n",
       "      <td>8.276947e+06</td>\n",
       "      <td>6.424631e+06</td>\n",
       "      <td>2005</td>\n",
       "      <td>1.424967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    min       max          mean           std   count  \\\n",
       "factor_quantile                                                         \n",
       "1                     7   5079918  2.266622e+05  2.758517e+05  112133   \n",
       "2                 97146   9432153  9.872588e+05  7.856217e+05   14208   \n",
       "3                185226  13534986  1.646138e+06  1.407161e+06    5574   \n",
       "4                268039  16542931  2.333872e+06  1.964154e+06    2811   \n",
       "5                395115  19938945  2.823808e+06  2.294238e+06    1578   \n",
       "6                449165  21567829  3.606149e+06  2.925952e+06     985   \n",
       "7                615010  19998692  3.943014e+06  2.963511e+06     650   \n",
       "8                668263  21078386  4.685769e+06  3.577250e+06     430   \n",
       "9                754352  23381800  5.041500e+06  3.491463e+06     331   \n",
       "10               878317  51354665  8.276947e+06  6.424631e+06    2005   \n",
       "\n",
       "                   count %  \n",
       "factor_quantile             \n",
       "1                79.693685  \n",
       "2                10.097722  \n",
       "3                 3.961480  \n",
       "4                 1.997797  \n",
       "5                 1.121495  \n",
       "6                 0.700046  \n",
       "7                 0.461959  \n",
       "8                 0.305604  \n",
       "9                 0.235244  \n",
       "10                1.424967  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# # print(len(assets))\u001b[39;00m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# # print(assets)\u001b[39;00m\n\u001b[1;32m     31\u001b[0m ret \u001b[38;5;241m=\u001b[39m get_clean_factor_and_forward_returns(assets[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume\u001b[39m\u001b[38;5;124m'\u001b[39m],price,periods\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m10\u001b[39m],quantiles\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,max_loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,groupby\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m---> 32\u001b[0m sheet\u001b[38;5;241m=\u001b[39m\u001b[43malphalens\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtears\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_full_tear_sheet\u001b[49m\u001b[43m(\u001b[49m\u001b[43mret\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/plotting.py:45\u001b[0m, in \u001b[0;36mcustomize.<locals>.call_w_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m plotting_context(), axes_style(), color_palette:\n\u001b[1;32m     44\u001b[0m         sns\u001b[38;5;241m.\u001b[39mdespine(left\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 45\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/tears.py:520\u001b[0m, in \u001b[0;36mcreate_full_tear_sheet\u001b[0;34m(factor_data, long_short, group_neutral, by_group)\u001b[0m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    494\u001b[0m \u001b[38;5;124;03mCreates a full tear sheet for analysis and evaluating single\u001b[39;00m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;124;03mreturn predicting (alpha) factor.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;124;03m    If True, display graphs separately for each group.\u001b[39;00m\n\u001b[1;32m    517\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    519\u001b[0m plotting\u001b[38;5;241m.\u001b[39mplot_quantile_statistics_table(factor_data)\n\u001b[0;32m--> 520\u001b[0m \u001b[43mcreate_returns_tear_sheet\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfactor_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlong_short\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup_neutral\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mby_group\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mset_context\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m    522\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    523\u001b[0m create_information_tear_sheet(\n\u001b[1;32m    524\u001b[0m     factor_data, group_neutral, by_group, set_context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    525\u001b[0m )\n\u001b[1;32m    526\u001b[0m create_turnover_tear_sheet(factor_data, set_context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/plotting.py:47\u001b[0m, in \u001b[0;36mcustomize.<locals>.call_w_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 47\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/tears.py:211\u001b[0m, in \u001b[0;36mcreate_returns_tear_sheet\u001b[0;34m(factor_data, long_short, group_neutral, by_group)\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;124;03mCreates a tear sheet for returns analysis of a factor.\u001b[39;00m\n\u001b[1;32m    184\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;124;03m    If True, display graphs separately for each group.\u001b[39;00m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    207\u001b[0m factor_returns \u001b[38;5;241m=\u001b[39m perf\u001b[38;5;241m.\u001b[39mfactor_returns(\n\u001b[1;32m    208\u001b[0m     factor_data, long_short, group_neutral\n\u001b[1;32m    209\u001b[0m )\n\u001b[0;32m--> 211\u001b[0m mean_quant_ret, std_quantile \u001b[38;5;241m=\u001b[39m \u001b[43mperf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean_return_by_quantile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfactor_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[43m    \u001b[49m\u001b[43mby_group\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdemeaned\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlong_short\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_adjust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_neutral\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m mean_quant_rateret \u001b[38;5;241m=\u001b[39m mean_quant_ret\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m    219\u001b[0m     utils\u001b[38;5;241m.\u001b[39mrate_of_return, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, base_period\u001b[38;5;241m=\u001b[39mmean_quant_ret\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    220\u001b[0m )\n\u001b[1;32m    222\u001b[0m mean_quant_ret_bydate, std_quant_daily \u001b[38;5;241m=\u001b[39m perf\u001b[38;5;241m.\u001b[39mmean_return_by_quantile(\n\u001b[1;32m    223\u001b[0m     factor_data,\n\u001b[1;32m    224\u001b[0m     by_date\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    227\u001b[0m     group_adjust\u001b[38;5;241m=\u001b[39mgroup_neutral,\n\u001b[1;32m    228\u001b[0m )\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/performance.py:491\u001b[0m, in \u001b[0;36mmean_return_by_quantile\u001b[0;34m(factor_data, by_date, by_group, demeaned, group_adjust)\u001b[0m\n\u001b[1;32m    489\u001b[0m     factor_data \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mdemean_forward_returns(factor_data, grouper)\n\u001b[1;32m    490\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m demeaned:\n\u001b[0;32m--> 491\u001b[0m     factor_data \u001b[38;5;241m=\u001b[39m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdemean_forward_returns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfactor_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    493\u001b[0m     factor_data \u001b[38;5;241m=\u001b[39m factor_data\u001b[38;5;241m.\u001b[39mcopy()\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/utils.py:413\u001b[0m, in \u001b[0;36mdemean_forward_returns\u001b[0;34m(factor_data, grouper)\u001b[0m\n\u001b[1;32m    410\u001b[0m     grouper \u001b[38;5;241m=\u001b[39m factor_data\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_level_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    412\u001b[0m cols \u001b[38;5;241m=\u001b[39m get_forward_returns_columns(factor_data\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[0;32m--> 413\u001b[0m factor_data[cols] \u001b[38;5;241m=\u001b[39m \u001b[43mfactor_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrouper\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcols\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m    414\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m factor_data\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/groupby/generic.py:1184\u001b[0m, in \u001b[0;36mDataFrameGroupBy.transform\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1181\u001b[0m \u001b[38;5;129m@Substitution\u001b[39m(klass\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1182\u001b[0m \u001b[38;5;129m@Appender\u001b[39m(_transform_template)\n\u001b[1;32m   1183\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39margs, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1185\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   1186\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1633\u001b[0m, in \u001b[0;36mGroupBy._transform\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1630\u001b[0m func \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mget_cython_func(func) \u001b[38;5;129;01mor\u001b[39;00m func\n\u001b[1;32m   1632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m-> 1633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transform_general\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1635\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m func \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m base\u001b[38;5;241m.\u001b[39mtransform_kernel_allowlist:\n\u001b[1;32m   1636\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not a valid function name for transform(name)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/groupby/generic.py:1171\u001b[0m, in \u001b[0;36mDataFrameGroupBy._transform_general\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1169\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m   1170\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(group, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m, name)\n\u001b[0;32m-> 1171\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mpath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1172\u001b[0m res \u001b[38;5;241m=\u001b[39m _wrap_transform_general_frame(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, group, res)\n\u001b[1;32m   1173\u001b[0m applied\u001b[38;5;241m.\u001b[39mappend(res)\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/groupby/generic.py:1200\u001b[0m, in \u001b[0;36mDataFrameGroupBy._define_paths.<locals>.<lambda>\u001b[0;34m(group)\u001b[0m\n\u001b[1;32m   1196\u001b[0m     slow_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m group: group\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;28mgetattr\u001b[39m(x, func)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\n\u001b[1;32m   1198\u001b[0m     )\n\u001b[1;32m   1199\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m     fast_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m group: \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1201\u001b[0m     slow_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m group: group\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m   1202\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m x: func(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\n\u001b[1;32m   1203\u001b[0m     )\n\u001b[1;32m   1204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fast_path, slow_path\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/alphalens/utils.py:414\u001b[0m, in \u001b[0;36mdemean_forward_returns.<locals>.<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    410\u001b[0m     grouper \u001b[38;5;241m=\u001b[39m factor_data\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_level_values(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    412\u001b[0m cols \u001b[38;5;241m=\u001b[39m get_forward_returns_columns(factor_data\u001b[38;5;241m.\u001b[39mcolumns)\n\u001b[1;32m    413\u001b[0m factor_data[cols] \u001b[38;5;241m=\u001b[39m factor_data\u001b[38;5;241m.\u001b[39mgroupby(grouper)[cols] \\\n\u001b[0;32m--> 414\u001b[0m     \u001b[38;5;241m.\u001b[39mtransform(\u001b[38;5;28;01mlambda\u001b[39;00m x: x \u001b[38;5;241m-\u001b[39m \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m factor_data\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/generic.py:11117\u001b[0m, in \u001b[0;36mNDFrame._add_numeric_operations.<locals>.mean\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  11099\u001b[0m \u001b[38;5;129m@doc\u001b[39m(\n\u001b[1;32m  11100\u001b[0m     _num_doc,\n\u001b[1;32m  11101\u001b[0m     desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn the mean of the values over the requested axis.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11115\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m  11116\u001b[0m ):\n\u001b[0;32m> 11117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/generic.py:10687\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10679\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m  10680\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10681\u001b[0m     axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m lib\u001b[38;5;241m.\u001b[39mNoDefault \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mno_default,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10685\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m  10686\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 10687\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  10688\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m  10689\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/generic.py:10639\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10629\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m  10630\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing the level keyword in DataFrame and Series aggregations is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m  10631\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeprecated and will be removed in a future version. Use groupby \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10634\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m  10635\u001b[0m     )\n\u001b[1;32m  10636\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_by_level(\n\u001b[1;32m  10637\u001b[0m         name, axis\u001b[38;5;241m=\u001b[39maxis, level\u001b[38;5;241m=\u001b[39mlevel, skipna\u001b[38;5;241m=\u001b[39mskipna, numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only\n\u001b[1;32m  10638\u001b[0m     )\n\u001b[0;32m> 10639\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m  10640\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m  10641\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/frame.py:10014\u001b[0m, in \u001b[0;36mDataFrame._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m  10010\u001b[0m ignore_failures \u001b[38;5;241m=\u001b[39m numeric_only \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m  10012\u001b[0m \u001b[38;5;66;03m# After possibly _get_data and transposing, we are now in the\u001b[39;00m\n\u001b[1;32m  10013\u001b[0m \u001b[38;5;66;03m#  simple case where we can use BlockManager.reduce\u001b[39;00m\n\u001b[0;32m> 10014\u001b[0m res, _ \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblk_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_failures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_failures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m  10015\u001b[0m out \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39m_constructor(res)\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m  10016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/internals/managers.py:1392\u001b[0m, in \u001b[0;36mBlockManager.reduce\u001b[0;34m(self, func, ignore_failures)\u001b[0m\n\u001b[1;32m   1389\u001b[0m     nbs \u001b[38;5;241m=\u001b[39m blk\u001b[38;5;241m.\u001b[39mreduce(func, ignore_failures)\n\u001b[1;32m   1390\u001b[0m     res_blocks\u001b[38;5;241m.\u001b[39mextend(nbs)\n\u001b[0;32m-> 1392\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mIndex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# placeholder\u001b[39;00m\n\u001b[1;32m   1393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_failures:\n\u001b[1;32m   1394\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m res_blocks:\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/indexes/base.py:534\u001b[0m, in \u001b[0;36mIndex.__new__\u001b[0;34m(cls, data, dtype, copy, name, tupleize_cols, **kwargs)\u001b[0m\n\u001b[1;32m    528\u001b[0m subarr \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39masarray_tuplesafe(data, dtype\u001b[38;5;241m=\u001b[39m_dtype_obj)\n\u001b[1;32m    529\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;66;03m# with e.g. a list [1, 2, 3] casting to numeric is _not_ deprecated\u001b[39;00m\n\u001b[1;32m    531\u001b[0m     \u001b[38;5;66;03m# error: Incompatible types in assignment (expression has type\u001b[39;00m\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;66;03m# \"Union[ExtensionArray, ndarray[Any, Any]]\", variable has type\u001b[39;00m\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;66;03m# \"ndarray[Any, Any]\")\u001b[39;00m\n\u001b[0;32m--> 534\u001b[0m     subarr \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cast_data_without_dtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[assignment]\u001b[39;49;00m\n\u001b[1;32m    535\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcast_numeric_deprecated\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m    536\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    537\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m subarr\u001b[38;5;241m.\u001b[39mdtype\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index(subarr, dtype\u001b[38;5;241m=\u001b[39mdtype, copy\u001b[38;5;241m=\u001b[39mcopy, name\u001b[38;5;241m=\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/data/anaconda3/envs/finhack/lib/python3.8/site-packages/pandas/core/indexes/base.py:7124\u001b[0m, in \u001b[0;36m_maybe_cast_data_without_dtype\u001b[0;34m(subarr, cast_numeric_deprecated)\u001b[0m\n\u001b[1;32m   7106\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_maybe_cast_data_without_dtype\u001b[39m(\n\u001b[1;32m   7107\u001b[0m     subarr: np\u001b[38;5;241m.\u001b[39mndarray, cast_numeric_deprecated: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   7108\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[1;32m   7109\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   7110\u001b[0m \u001b[38;5;124;03m    If we have an arraylike input but no passed dtype, try to infer\u001b[39;00m\n\u001b[1;32m   7111\u001b[0m \u001b[38;5;124;03m    a supported dtype.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   7121\u001b[0m \u001b[38;5;124;03m    np.ndarray or ExtensionArray\u001b[39;00m\n\u001b[1;32m   7122\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 7124\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaybe_convert_objects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   7125\u001b[0m \u001b[43m        \u001b[49m\u001b[43msubarr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_datetime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   7127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   7128\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_period\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   7129\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   7130\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype_if_all_nat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdatetime64[ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7131\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   7132\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mu\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m   7133\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cast_numeric_deprecated:\n\u001b[1;32m   7134\u001b[0m             \u001b[38;5;66;03m# i.e. we started with a list, not an ndarray[object]\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import alphalens\n",
    "from alphalens.utils import get_clean_factor_and_forward_returns\n",
    "from alphalens.tears import create_full_tear_sheet\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "warnings.simplefilter(action='ignore', category=RuntimeWarning)\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "\n",
    "df_all=pd.DataFrame()\n",
    "path=\"./qcsv/\"\n",
    "for root,dirs,files in os.walk(path):\n",
    "    for file in files[:100]:\n",
    "        symbol=file.split('.')[0]\n",
    "        df=pd.read_csv(path+symbol+'.csv',index_col=0)\n",
    "        df['symbol']=symbol\n",
    "        df['date']=pd.to_datetime(df['date'])\n",
    "        df_all=df_all.append(df,ignore_index=True)\n",
    "\n",
    " \n",
    "        \n",
    "        \n",
    "price = df_all.pivot_table(index='date', columns='symbol', values='close')        \n",
    "df_all.index=df_all['date']\n",
    "price.index = pd.to_datetime(price.index)\n",
    "assets = df_all.set_index( [df_all.index,df_all['symbol']], drop=True,append=False, inplace=False)\n",
    "\n",
    "# # print(len(assets))\n",
    "# # print(assets)\n",
    "ret = get_clean_factor_and_forward_returns(assets['volume'],price,periods=[10],quantiles=None,bins=10,max_loss=1,groupby=None)\n",
    "sheet=alphalens.tears.create_full_tear_sheet(ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ecfb4e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T08:51:59.131085Z",
     "start_time": "2022-04-05T08:51:54.362526Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.01709827604844051\n",
      "0.21514387239001337\n",
      "-0.07947368362620486\n"
     ]
    }
   ],
   "source": [
    "ic=alphalens.performance.factor_information_coefficient(ret)\n",
    "ic10d=ic['10D']\n",
    "ic10d_mean=ic10d.mean()\n",
    "ic10d_std=ic10d.std()\n",
    "ic10d_adjusted=ic10d_mean/ic10d_std\n",
    "print(ic10d_mean)\n",
    "print(ic10d_std)\n",
    "print(ic10d_adjusted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "718fbd8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T09:40:46.684652Z",
     "start_time": "2022-04-05T09:32:00.204403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UPPER\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MIDDLE\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LOWER\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "DEMA\n",
      "Dropped 13.2% entries from factor data: 13.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "EMA90\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "HT-TRENDLINE\n",
      "Dropped 5.2% entries from factor data: 5.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MA90\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MAMA\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FAMA\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MAVP\n",
      "Dropped 9.0% entries from factor data: 9.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MIDPOINT\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MIDPRICE\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SAR\n",
      "Dropped 0.8% entries from factor data: 0.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SAREXT\n",
      "Dropped 0.8% entries from factor data: 0.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SMA\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "T3\n",
      "Dropped 37.1% entries from factor data: 37.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TEMA\n",
      "Dropped 19.4% entries from factor data: 19.4% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TRIMA\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "WMA\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ADX\n",
      "Dropped 13.3% entries from factor data: 13.3% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ADXR\n",
      "Dropped 19.5% entries from factor data: 19.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "APO\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "AROONDOWN\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ARRONUP\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "AROONOSC\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "BOP\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "CCI\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "CMO\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "DX\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACD\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDSIGNAL\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDHIST\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDX\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDSIGNALX\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDHISTX\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDFIX\n",
      "Dropped 8.8% entries from factor data: 8.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDSIGNALFIX\n",
      "Dropped 8.8% entries from factor data: 8.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MACDHISTFIX\n",
      "Dropped 8.8% entries from factor data: 8.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MFI\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MINUS-DI\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MINUS-DM\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MOM\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "PLUS-DI\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "PLUS-DM\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "PPO\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ROC\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ROCR\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ROCR100\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "RSI\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SLOWK\n",
      "Dropped 1.3% entries from factor data: 1.3% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SLOWD\n",
      "Dropped 1.3% entries from factor data: 1.3% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FASTK\n",
      "Dropped 1.1% entries from factor data: 1.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FASTD\n",
      "Dropped 1.1% entries from factor data: 1.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FASTK-RSI\n",
      "Dropped 7.5% entries from factor data: 7.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FASTD-RSI\n",
      "Dropped 7.5% entries from factor data: 7.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TRIX\n",
      "Dropped 19.5% entries from factor data: 19.5% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ULTOSC\n",
      "Dropped 2.7% entries from factor data: 2.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "WILLR\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "AD\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ADOSC\n",
      "Dropped 1.3% entries from factor data: 1.3% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "OBV\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ATR\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "NATR\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TRANGE\n",
      "Dropped 0.8% entries from factor data: 0.8% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "AVGPRICE\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MEDPRICE\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TYPPRICE\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "WCLPRICE\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "HT-DCPERIOD\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "HT-DCPHASE\n",
      "Dropped 5.2% entries from factor data: 5.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "INPHASE\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "QUADRATURE\n",
      "Dropped 3.0% entries from factor data: 3.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SINE\n",
      "Dropped 5.2% entries from factor data: 5.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LEADSINE\n",
      "Dropped 5.2% entries from factor data: 5.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "HT-TRENDMODE\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "BETA\n",
      "Dropped 7.1% entries from factor data: 7.1% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "CORREL\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LINEARREG\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LINEARREG-ANGLE\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LINEARREG-INTERCEPT\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LINEARREG-SLOPE\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "STDDEV\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TSF\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "VAR\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ACOS\n",
      "Dropped 100.0% entries from factor data: 100.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ASIN\n",
      "Dropped 100.0% entries from factor data: 100.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ATAN\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "CEIL\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "COS\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "COSH\n",
      "Dropped 0.9% entries from factor data: 0.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "EXP\n",
      "Dropped 0.9% entries from factor data: 0.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "FLOOR\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LN\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "LOG10\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SIN\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SINH\n",
      "Dropped 0.9% entries from factor data: 0.9% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SQRT\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TAN\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "TANH\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "ADD\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "DIV\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MAX\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MAXINDEX\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MIN\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MININDEX\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MINIDX\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MAXIDX\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "MULT\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SUB\n",
      "Dropped 0.7% entries from factor data: 0.7% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "SUM\n",
      "Dropped 7.0% entries from factor data: 7.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 100.0%, not exceeded: OK!\n",
      "symbol\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor</th>\n",
       "      <th>ic</th>\n",
       "      <th>ir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UPPER</td>\n",
       "      <td>0.018223</td>\n",
       "      <td>0.070991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MIDDLE</td>\n",
       "      <td>0.017141</td>\n",
       "      <td>0.067531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LOWER</td>\n",
       "      <td>0.014510</td>\n",
       "      <td>0.057887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEMA</td>\n",
       "      <td>0.019872</td>\n",
       "      <td>0.076437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EMA90</td>\n",
       "      <td>0.016725</td>\n",
       "      <td>0.065844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>MINIDX</td>\n",
       "      <td>0.015438</td>\n",
       "      <td>0.100577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>MAXIDX</td>\n",
       "      <td>0.016123</td>\n",
       "      <td>0.108360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>MULT</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>0.028966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>SUB</td>\n",
       "      <td>0.003992</td>\n",
       "      <td>0.015515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>SUM</td>\n",
       "      <td>0.017141</td>\n",
       "      <td>0.067531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     factor        ic        ir\n",
       "0     UPPER  0.018223  0.070991\n",
       "1    MIDDLE  0.017141  0.067531\n",
       "2     LOWER  0.014510  0.057887\n",
       "3      DEMA  0.019872  0.076437\n",
       "4     EMA90  0.016725  0.065844\n",
       "..      ...       ...       ...\n",
       "105  MINIDX  0.015438  0.100577\n",
       "106  MAXIDX  0.016123  0.108360\n",
       "107    MULT  0.007353  0.028966\n",
       "108     SUB  0.003992  0.015515\n",
       "109     SUM  0.017141  0.067531\n",
       "\n",
       "[110 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_list=[]\n",
    "for factor in df.columns[12:]:\n",
    "    try:\n",
    "        print(factor)\n",
    "        ret = get_clean_factor_and_forward_returns(assets[factor],price,periods=[10],quantiles=None,bins=10,max_loss=1,groupby=None)\n",
    "        ic=alphalens.performance.factor_information_coefficient(ret)\n",
    "        ic=ic['10D']\n",
    "        ic_mean=ic.mean()\n",
    "        ic_std=ic.std()\n",
    "        ir=ic_mean/ic_std\n",
    "        ic_list.append({'factor':factor,'ic':ic_mean,'ir':ir})\n",
    "        ic_df=pd.DataFrame(ic_list)    \n",
    "    except:\n",
    "        pass\n",
    "ic_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2576732b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T13:00:03.053095Z",
     "start_time": "2022-04-05T13:00:03.040857Z"
    }
   },
   "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>factor</th>\n",
       "      <th>ic</th>\n",
       "      <th>ir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>OBV</td>\n",
       "      <td>0.053776</td>\n",
       "      <td>0.234068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>T3</td>\n",
       "      <td>0.042063</td>\n",
       "      <td>0.153059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>CORREL</td>\n",
       "      <td>0.029773</td>\n",
       "      <td>0.148487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>TRIX</td>\n",
       "      <td>0.036735</td>\n",
       "      <td>0.147415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>BETA</td>\n",
       "      <td>0.025654</td>\n",
       "      <td>0.141117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ROCR100</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ROC</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ADOSC</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>ACOS</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>ASIN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     factor        ic        ir\n",
       "60      OBV  0.053776  0.234068\n",
       "15       T3  0.042063  0.153059\n",
       "76   CORREL  0.029773  0.148487\n",
       "55     TRIX  0.036735  0.147415\n",
       "75     BETA  0.025654  0.141117\n",
       "..      ...       ...       ...\n",
       "47  ROCR100  0.000190  0.000840\n",
       "45      ROC  0.000189  0.000840\n",
       "59    ADOSC  0.000132  0.000720\n",
       "84     ACOS  1.000000       NaN\n",
       "85     ASIN  1.000000       NaN\n",
       "\n",
       "[110 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_df['ic']=abs(ic_df['ic'])\n",
    "ic_df['ir']=abs(ic_df['ir'])\n",
    "ic_df=ic_df.sort_values(by=['ir','ic'],ascending=False) \n",
    "ic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "bb87d2ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T12:55:58.954751Z",
     "start_time": "2022-04-05T12:55:58.942947Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 20:55:58,947) INFO - qlib.Initialization - [config.py:402] - default_conf: client.\n",
      "[29523:MainThread](2022-04-05 20:55:58,951) INFO - qlib.Initialization - [__init__.py:74] - qlib successfully initialized based on client settings.\n",
      "[29523:MainThread](2022-04-05 20:55:58,952) INFO - qlib.Initialization - [__init__.py:76] - data_path={'__DEFAULT_FREQ': PosixPath('/data/wwwroot/Notebook/qbin')}\n"
     ]
    }
   ],
   "source": [
    "import qlib\n",
    "import pandas as pd\n",
    "from qlib.constant import REG_CN\n",
    "from qlib.utils import exists_qlib_data, init_instance_by_config\n",
    "from qlib.workflow import R\n",
    "from qlib.workflow.record_temp import SignalRecord, PortAnaRecord\n",
    "from qlib.utils import flatten_dict\n",
    "provider_uri = \"/home/ubuntu/myquant/qbin\"  # target_dir\n",
    "qlib.init(provider_uri=provider_uri, region=REG_CN)\n",
    "market = \"all\"\n",
    "benchmark = \"000001\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a1d286e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T13:31:06.550422Z",
     "start_time": "2022-04-05T13:30:56.716654Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total alphas:2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 21:30:59,114) INFO - qlib.timer - [log.py:113] - Time cost: 2.390s | Loading data Done\n",
      "[29523:MainThread](2022-04-05 21:30:59,115) INFO - qlib.timer - [log.py:113] - Time cost: 0.000s | fit & process data Done\n",
      "[29523:MainThread](2022-04-05 21:30:59,116) INFO - qlib.timer - [log.py:113] - Time cost: 2.393s | Init data Done\n",
      "[29523:MainThread](2022-04-05 21:30:59,118) INFO - qlib.workflow - [expm.py:315] - <mlflow.tracking.client.MlflowClient object at 0x7f18c0e13a90>\n",
      "[29523:MainThread](2022-04-05 21:30:59,120) INFO - qlib.workflow - [exp.py:257] - Experiment 1 starts running ...\n",
      "[29523:MainThread](2022-04-05 21:30:59,128) INFO - qlib.workflow - [recorder.py:293] - Recorder aa8a4121f181471d97bb4e1501cc58ca starts running under Experiment 1 ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds\n",
      "[20]\ttrain's l2: 0.0354498\tvalid's l2: 0.013724\n",
      "[40]\ttrain's l2: 0.0286271\tvalid's l2: 0.0127152\n",
      "[60]\ttrain's l2: 0.0271469\tvalid's l2: 0.0124235\n",
      "[80]\ttrain's l2: 0.0267866\tvalid's l2: 0.0123116\n",
      "[100]\ttrain's l2: 0.0266845\tvalid's l2: 0.0122628\n",
      "[120]\ttrain's l2: 0.0266439\tvalid's l2: 0.0122273\n",
      "[140]\ttrain's l2: 0.0266233\tvalid's l2: 0.0122059\n",
      "[160]\ttrain's l2: 0.0266114\tvalid's l2: 0.0121941\n",
      "[180]\ttrain's l2: 0.0266016\tvalid's l2: 0.0121884\n",
      "[200]\ttrain's l2: 0.0265955\tvalid's l2: 0.0121837\n",
      "[220]\ttrain's l2: 0.0265907\tvalid's l2: 0.0121795\n",
      "[240]\ttrain's l2: 0.0265875\tvalid's l2: 0.0121765\n",
      "[260]\ttrain's l2: 0.0265854\tvalid's l2: 0.0121739\n",
      "[280]\ttrain's l2: 0.026584\tvalid's l2: 0.012172\n",
      "[300]\ttrain's l2: 0.0265829\tvalid's l2: 0.0121704\n",
      "[320]\ttrain's l2: 0.0265821\tvalid's l2: 0.0121695\n",
      "[340]\ttrain's l2: 0.0265816\tvalid's l2: 0.0121685\n",
      "[360]\ttrain's l2: 0.0265811\tvalid's l2: 0.0121678\n",
      "[380]\ttrain's l2: 0.0265807\tvalid's l2: 0.0121675\n",
      "[400]\ttrain's l2: 0.0265805\tvalid's l2: 0.0121671\n",
      "[420]\ttrain's l2: 0.0265803\tvalid's l2: 0.0121668\n",
      "[440]\ttrain's l2: 0.0265801\tvalid's l2: 0.0121665\n",
      "[460]\ttrain's l2: 0.02658\tvalid's l2: 0.0121664\n",
      "[480]\ttrain's l2: 0.02658\tvalid's l2: 0.0121662\n",
      "[500]\ttrain's l2: 0.0265799\tvalid's l2: 0.0121662\n",
      "[520]\ttrain's l2: 0.0265799\tvalid's l2: 0.0121661\n",
      "[540]\ttrain's l2: 0.0265798\tvalid's l2: 0.012166\n",
      "[560]\ttrain's l2: 0.0265798\tvalid's l2: 0.012166\n",
      "[580]\ttrain's l2: 0.0265798\tvalid's l2: 0.012166\n",
      "[600]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[620]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[640]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[660]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[680]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[700]\ttrain's l2: 0.0265798\tvalid's l2: 0.0121659\n",
      "[720]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[740]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[760]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[780]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[800]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[820]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[840]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[860]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[880]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[900]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[920]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[940]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[960]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[980]\ttrain's l2: 0.0265797\tvalid's l2: 0.0121659\n",
      "[1000]\ttrain's l2: 0.0265794\tvalid's l2: 0.0121662\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[1000]\ttrain's l2: 0.0265794\tvalid's l2: 0.0121662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 21:31:06,547) INFO - qlib.timer - [log.py:113] - Time cost: 1.239s | waiting `async_log` Done\n"
     ]
    }
   ],
   "source": [
    "###################################\n",
    "# train model\n",
    "###################################\n",
    "import sys\n",
    "\n",
    "data_handler_config = {\n",
    "    \"start_time\": \"2015-01-01\",\n",
    "    \"end_time\": \"2022-03-30\",\n",
    "    \"fit_start_time\": \"2015-01-01\",\n",
    "    \"fit_end_time\": \"2020-12-31\",\n",
    "    \"instruments\": market,\n",
    "}\n",
    "\n",
    "task = {\n",
    "    \"model\": {\n",
    "        \"class\": \"LGBModel\",\n",
    "        \"module_path\": \"qlib.contrib.model.gbdt\",\n",
    "        \"kwargs\": {\n",
    "            \"loss\": \"mse\",\n",
    "            \"colsample_bytree\": 0.8879,\n",
    "            \"learning_rate\": 0.0421,\n",
    "            \"subsample\": 0.8789,\n",
    "            \"lambda_l1\": 205.6999,\n",
    "            \"lambda_l2\": 580.9768,\n",
    "            \"max_depth\": 8,\n",
    "            \"num_leaves\": 210,\n",
    "            \"num_threads\": 20,\n",
    "        },\n",
    "    },\n",
    "    \"dataset\": {\n",
    "        \"class\": \"DatasetH\",\n",
    "        \"module_path\": \"qlib.data.dataset\",\n",
    "        \"kwargs\": {\n",
    "            \"handler\": {\n",
    "                \"class\": \"finhackalpha\",\n",
    "                \"module_path\": 'finhackalpha.py',\n",
    "                \"kwargs\": data_handler_config,\n",
    "            },\n",
    "            \"segments\": {\n",
    "                \"train\": (\"2015-01-01\", \"2018-12-31\"),\n",
    "                \"valid\": (\"2019-01-01\", \"2019-12-31\"),\n",
    "                \"test\": (\"2020-01-01\", \"2022-03-30\"),\n",
    "            },\n",
    "        },\n",
    "    },\n",
    "}\n",
    "\n",
    "# model initiaiton\n",
    "model = init_instance_by_config(task[\"model\"])\n",
    "dataset = init_instance_by_config(task[\"dataset\"])\n",
    "\n",
    "# start exp to train model\n",
    "with R.start(experiment_name=\"train_model\"):\n",
    "    R.log_params(**flatten_dict(task))\n",
    "    model.fit(dataset)\n",
    "    R.save_objects(trained_model=model)\n",
    "    rid = R.get_recorder().id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "883175a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-05T13:31:21.871526Z",
     "start_time": "2022-04-05T13:31:07.532140Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 21:31:07,536) INFO - qlib.workflow - [expm.py:315] - <mlflow.tracking.client.MlflowClient object at 0x7f18649dc820>\n",
      "[29523:MainThread](2022-04-05 21:31:07,540) INFO - qlib.workflow - [exp.py:257] - Experiment 2 starts running ...\n",
      "[29523:MainThread](2022-04-05 21:31:07,546) INFO - qlib.workflow - [recorder.py:293] - Recorder 5f7f88c8933b44d68d8f857cb3cfadcc starts running under Experiment 2 ...\n",
      "[29523:MainThread](2022-04-05 21:31:09,011) INFO - qlib.workflow - [record_temp.py:194] - Signal record 'pred.pkl' has been saved as the artifact of the Experiment 2\n",
      "[29523:MainThread](2022-04-05 21:31:09,049) INFO - qlib.backtest caller - [__init__.py:83] - Create new exchange\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'The following are prediction results of the LGBModel model.'\n",
      "                          score\n",
      "datetime   instrument          \n",
      "2020-01-02 000001      0.992933\n",
      "           000002      0.992933\n",
      "           000063      0.989482\n",
      "           000066      0.993903\n",
      "           000069      0.991748\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 21:31:12,159) WARNING - qlib.BaseExecutor - [executor.py:111] - `common_infra` is not set for <qlib.backtest.executor.SimulatorExecutor object at 0x7f185ff5df40>\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfc4f3cc7c9c4026bc4b14c5104b28ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "backtest loop:   0%|          | 0/522 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[29523:MainThread](2022-04-05 21:31:21,831) INFO - qlib.workflow - [record_temp.py:499] - Portfolio analysis record 'port_analysis_1day.pkl' has been saved as the artifact of the Experiment 2\n",
      "[29523:MainThread](2022-04-05 21:31:21,842) INFO - qlib.workflow - [record_temp.py:524] - Indicator analysis record 'indicator_analysis_1day.pkl' has been saved as the artifact of the Experiment 2\n",
      "[29523:MainThread](2022-04-05 21:31:21,868) INFO - qlib.timer - [log.py:113] - Time cost: 0.006s | waiting `async_log` Done\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'The following are analysis results of benchmark return(1day).'\n",
      "                       risk\n",
      "mean               0.000254\n",
      "std                0.023289\n",
      "annualized_return  0.060415\n",
      "information_ratio  0.168149\n",
      "max_drawdown      -0.417922\n",
      "'The following are analysis results of the excess return without cost(1day).'\n",
      "                       risk\n",
      "mean               0.001705\n",
      "std                0.020868\n",
      "annualized_return  0.405679\n",
      "information_ratio  1.260131\n",
      "max_drawdown      -0.441866\n",
      "'The following are analysis results of the excess return with cost(1day).'\n",
      "                       risk\n",
      "mean               0.001553\n",
      "std                0.020862\n",
      "annualized_return  0.369703\n",
      "information_ratio  1.148711\n",
      "max_drawdown      -0.468023\n",
      "'The following are analysis results of indicators(1day).'\n",
      "     value\n",
      "ffr    1.0\n",
      "pa     0.0\n",
      "pos    0.0\n"
     ]
    }
   ],
   "source": [
    "###################################\n",
    "# prediction, backtest & analysis\n",
    "###################################\n",
    "port_analysis_config = {\n",
    "    \"executor\": {\n",
    "        \"class\": \"SimulatorExecutor\",\n",
    "        \"module_path\": \"qlib.backtest.executor\",\n",
    "        \"kwargs\": {\n",
    "            \"time_per_step\": \"day\",\n",
    "            \"generate_portfolio_metrics\": True,\n",
    "        },\n",
    "    },\n",
    "    \"strategy\": {\n",
    "        \"class\": \"TopkDropoutStrategy\",\n",
    "        \"module_path\": \"qlib.contrib.strategy.signal_strategy\",\n",
    "        \"kwargs\": {\n",
    "            \"model\": model,\n",
    "            \"dataset\": dataset,\n",
    "            \"topk\": 50,\n",
    "            \"n_drop\": 10,\n",
    "        },\n",
    "    },\n",
    "    \"backtest\": {\n",
    "        \"start_time\": \"2020-01-01\",\n",
    "        \"end_time\": \"2022-03-01\",\n",
    "        \"account\": 100000000,\n",
    "        \"benchmark\": benchmark,\n",
    "        \"exchange_kwargs\": {\n",
    "            \"freq\": \"day\",\n",
    "            \"limit_threshold\": 0.095,\n",
    "            \"deal_price\": \"close\",\n",
    "            \"open_cost\": 0.0005,\n",
    "            \"close_cost\": 0.0015,\n",
    "            \"min_cost\": 5,\n",
    "        },\n",
    "    },\n",
    "}\n",
    "\n",
    "# backtest and analysis\n",
    "with R.start(experiment_name=\"backtest_analysis\"):\n",
    "    recorder = R.get_recorder(recorder_id=rid, experiment_name=\"train_model\")\n",
    "    model = recorder.load_object(\"trained_model\")\n",
    "\n",
    "    # prediction\n",
    "    recorder = R.get_recorder()\n",
    "    ba_rid = recorder.id\n",
    "    sr = SignalRecord(model, dataset, recorder)\n",
    "    sr.generate()\n",
    "\n",
    "    # backtest & analysis\n",
    "    par = PortAnaRecord(recorder, port_analysis_config, \"day\")\n",
    "    par.generate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "a1964a35",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T12:23:51.930277Z",
     "start_time": "2022-04-06T12:23:51.875901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'class': 'Recorder', 'id': '5f7f88c8933b44d68d8f857cb3cfadcc', 'name': 'mlflow_recorder', 'experiment_id': '2', 'start_time': '2022-04-05 21:31:07', 'end_time': '2022-04-05 21:31:21', 'status': 'FINISHED'}\n"
     ]
    }
   ],
   "source": [
    "from qlib.contrib.report import analysis_model, analysis_position\n",
    "from qlib.data import D\n",
    "recorder = R.get_recorder(recorder_id=ba_rid, experiment_name=\"backtest_analysis\")\n",
    "print(recorder)\n",
    "pred_df = recorder.load_object(\"pred.pkl\")\n",
    "pred_df_dates = pred_df.index.get_level_values(level='datetime')\n",
    "report_normal_df = recorder.load_object(\"portfolio_analysis/report_normal_1day.pkl\")\n",
    "positions = recorder.load_object(\"portfolio_analysis/positions_normal_1day.pkl\")\n",
    "analysis_df = recorder.load_object(\"portfolio_analysis/port_analysis_1day.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "dcad0c33",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T12:23:54.389740Z",
     "start_time": "2022-04-06T12:23:54.374099Z"
    }
   },
   "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>account</th>\n",
       "      <th>return</th>\n",
       "      <th>total_turnover</th>\n",
       "      <th>turnover</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>cost</th>\n",
       "      <th>value</th>\n",
       "      <th>cash</th>\n",
       "      <th>bench</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2020-01-02</th>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>0.026168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>9.995254e+07</td>\n",
       "      <td>2.086017e-16</td>\n",
       "      <td>9.491868e+07</td>\n",
       "      <td>0.949187</td>\n",
       "      <td>4.745934e+04</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>9.491868e+07</td>\n",
       "      <td>5.033865e+06</td>\n",
       "      <td>0.018822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>1.004392e+08</td>\n",
       "      <td>5.271740e-03</td>\n",
       "      <td>1.370435e+08</td>\n",
       "      <td>0.421449</td>\n",
       "      <td>8.769005e+04</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>9.923387e+07</td>\n",
       "      <td>1.205362e+06</td>\n",
       "      <td>-0.006555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>1.011844e+08</td>\n",
       "      <td>7.580930e-03</td>\n",
       "      <td>1.536405e+08</td>\n",
       "      <td>0.165244</td>\n",
       "      <td>1.039204e+05</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>1.007285e+08</td>\n",
       "      <td>4.559182e+05</td>\n",
       "      <td>0.004799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>1.000822e+08</td>\n",
       "      <td>-1.067617e-02</td>\n",
       "      <td>1.755297e+08</td>\n",
       "      <td>0.216329</td>\n",
       "      <td>1.258812e+05</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>9.950508e+07</td>\n",
       "      <td>5.771196e+05</td>\n",
       "      <td>-0.029254</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23</th>\n",
       "      <td>2.471086e+08</td>\n",
       "      <td>5.059375e-03</td>\n",
       "      <td>1.594325e+10</td>\n",
       "      <td>0.119116</td>\n",
       "      <td>1.590158e+07</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>2.463437e+08</td>\n",
       "      <td>7.649429e+05</td>\n",
       "      <td>-0.002465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>2.419503e+08</td>\n",
       "      <td>-2.075865e-02</td>\n",
       "      <td>1.597191e+10</td>\n",
       "      <td>0.115987</td>\n",
       "      <td>1.593025e+07</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>2.412018e+08</td>\n",
       "      <td>7.485270e+05</td>\n",
       "      <td>-0.017295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-25</th>\n",
       "      <td>2.422943e+08</td>\n",
       "      <td>1.500640e-03</td>\n",
       "      <td>1.599116e+10</td>\n",
       "      <td>0.079559</td>\n",
       "      <td>1.594939e+07</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>2.417831e+08</td>\n",
       "      <td>5.111419e+05</td>\n",
       "      <td>-0.000629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>2.413955e+08</td>\n",
       "      <td>-3.665162e-03</td>\n",
       "      <td>1.600200e+10</td>\n",
       "      <td>0.044757</td>\n",
       "      <td>1.596013e+07</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>2.411072e+08</td>\n",
       "      <td>2.882496e+05</td>\n",
       "      <td>-0.009434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>2.435537e+08</td>\n",
       "      <td>9.018947e-03</td>\n",
       "      <td>1.602086e+10</td>\n",
       "      <td>0.078129</td>\n",
       "      <td>1.597910e+07</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>2.430620e+08</td>\n",
       "      <td>4.916666e+05</td>\n",
       "      <td>0.010794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>522 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 account        return  total_turnover  turnover  \\\n",
       "datetime                                                           \n",
       "2020-01-02  1.000000e+08  0.000000e+00    0.000000e+00  0.000000   \n",
       "2020-01-03  9.995254e+07  2.086017e-16    9.491868e+07  0.949187   \n",
       "2020-01-06  1.004392e+08  5.271740e-03    1.370435e+08  0.421449   \n",
       "2020-01-07  1.011844e+08  7.580930e-03    1.536405e+08  0.165244   \n",
       "2020-01-08  1.000822e+08 -1.067617e-02    1.755297e+08  0.216329   \n",
       "...                  ...           ...             ...       ...   \n",
       "2022-02-23  2.471086e+08  5.059375e-03    1.594325e+10  0.119116   \n",
       "2022-02-24  2.419503e+08 -2.075865e-02    1.597191e+10  0.115987   \n",
       "2022-02-25  2.422943e+08  1.500640e-03    1.599116e+10  0.079559   \n",
       "2022-02-28  2.413955e+08 -3.665162e-03    1.600200e+10  0.044757   \n",
       "2022-03-01  2.435537e+08  9.018947e-03    1.602086e+10  0.078129   \n",
       "\n",
       "              total_cost      cost         value          cash     bench  \n",
       "datetime                                                                  \n",
       "2020-01-02  0.000000e+00  0.000000  0.000000e+00  1.000000e+08  0.026168  \n",
       "2020-01-03  4.745934e+04  0.000475  9.491868e+07  5.033865e+06  0.018822  \n",
       "2020-01-06  8.769005e+04  0.000402  9.923387e+07  1.205362e+06 -0.006555  \n",
       "2020-01-07  1.039204e+05  0.000162  1.007285e+08  4.559182e+05  0.004799  \n",
       "2020-01-08  1.258812e+05  0.000217  9.950508e+07  5.771196e+05 -0.029254  \n",
       "...                  ...       ...           ...           ...       ...  \n",
       "2022-02-23  1.590158e+07  0.000119  2.463437e+08  7.649429e+05 -0.002465  \n",
       "2022-02-24  1.593025e+07  0.000116  2.412018e+08  7.485270e+05 -0.017295  \n",
       "2022-02-25  1.594939e+07  0.000079  2.417831e+08  5.111419e+05 -0.000629  \n",
       "2022-02-28  1.596013e+07  0.000044  2.411072e+08  2.882496e+05 -0.009434  \n",
       "2022-03-01  1.597910e+07  0.000079  2.430620e+08  4.916666e+05  0.010794  \n",
       "\n",
       "[522 rows x 9 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_normal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "b6d0e1af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T12:24:02.517848Z",
     "start_time": "2022-04-06T12:24:01.732721Z"
    }
   },
   "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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>amount</th>\n",
       "      <th>swing</th>\n",
       "      <th>chg_pct</th>\n",
       "      <th>chg_amount</th>\n",
       "      <th>turnover</th>\n",
       "      <th>return</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>4121.35</td>\n",
       "      <td>4152.24</td>\n",
       "      <td>4172.66</td>\n",
       "      <td>4121.35</td>\n",
       "      <td>182116772</td>\n",
       "      <td>2.701055e+11</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.36</td>\n",
       "      <td>55.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>4161.22</td>\n",
       "      <td>4144.96</td>\n",
       "      <td>4164.30</td>\n",
       "      <td>4131.86</td>\n",
       "      <td>142826245</td>\n",
       "      <td>2.152163e+11</td>\n",
       "      <td>0.78</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-7.28</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.001756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>4120.52</td>\n",
       "      <td>4129.30</td>\n",
       "      <td>4170.64</td>\n",
       "      <td>4102.38</td>\n",
       "      <td>175309952</td>\n",
       "      <td>2.501821e+11</td>\n",
       "      <td>1.65</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>-15.66</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.003792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>4137.40</td>\n",
       "      <td>4160.23</td>\n",
       "      <td>4161.25</td>\n",
       "      <td>4135.10</td>\n",
       "      <td>139489032</td>\n",
       "      <td>1.963891e+11</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.75</td>\n",
       "      <td>30.93</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.007435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>4139.63</td>\n",
       "      <td>4112.32</td>\n",
       "      <td>4149.81</td>\n",
       "      <td>4101.98</td>\n",
       "      <td>167585851</td>\n",
       "      <td>2.124063e+11</td>\n",
       "      <td>1.15</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.011650</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-23</th>\n",
       "      <td>4582.01</td>\n",
       "      <td>4623.05</td>\n",
       "      <td>4625.30</td>\n",
       "      <td>4579.29</td>\n",
       "      <td>122952916</td>\n",
       "      <td>2.590501e+11</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.07</td>\n",
       "      <td>48.90</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.010577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-24</th>\n",
       "      <td>4592.07</td>\n",
       "      <td>4529.32</td>\n",
       "      <td>4610.88</td>\n",
       "      <td>4488.48</td>\n",
       "      <td>184403059</td>\n",
       "      <td>3.423467e+11</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-2.03</td>\n",
       "      <td>-93.73</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.020694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-25</th>\n",
       "      <td>4564.63</td>\n",
       "      <td>4573.42</td>\n",
       "      <td>4611.86</td>\n",
       "      <td>4561.90</td>\n",
       "      <td>149722583</td>\n",
       "      <td>2.717276e+11</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.97</td>\n",
       "      <td>44.10</td>\n",
       "      <td>0.53</td>\n",
       "      <td>-0.009643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>4563.74</td>\n",
       "      <td>4581.65</td>\n",
       "      <td>4581.65</td>\n",
       "      <td>4530.68</td>\n",
       "      <td>135406979</td>\n",
       "      <td>2.483637e+11</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.18</td>\n",
       "      <td>8.23</td>\n",
       "      <td>0.48</td>\n",
       "      <td>-0.001796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>4597.86</td>\n",
       "      <td>4619.69</td>\n",
       "      <td>4623.43</td>\n",
       "      <td>4583.92</td>\n",
       "      <td>123139220</td>\n",
       "      <td>2.604312e+11</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.83</td>\n",
       "      <td>38.04</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.008234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>522 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               open    close     high      low     volume        amount  \\\n",
       "date                                                                      \n",
       "2020-01-02  4121.35  4152.24  4172.66  4121.35  182116772  2.701055e+11   \n",
       "2020-01-03  4161.22  4144.96  4164.30  4131.86  142826245  2.152163e+11   \n",
       "2020-01-06  4120.52  4129.30  4170.64  4102.38  175309952  2.501821e+11   \n",
       "2020-01-07  4137.40  4160.23  4161.25  4135.10  139489032  1.963891e+11   \n",
       "2020-01-08  4139.63  4112.32  4149.81  4101.98  167585851  2.124063e+11   \n",
       "...             ...      ...      ...      ...        ...           ...   \n",
       "2022-02-23  4582.01  4623.05  4625.30  4579.29  122952916  2.590501e+11   \n",
       "2022-02-24  4592.07  4529.32  4610.88  4488.48  184403059  3.423467e+11   \n",
       "2022-02-25  4564.63  4573.42  4611.86  4561.90  149722583  2.717276e+11   \n",
       "2022-02-28  4563.74  4581.65  4581.65  4530.68  135406979  2.483637e+11   \n",
       "2022-03-01  4597.86  4619.69  4623.43  4583.92  123139220  2.604312e+11   \n",
       "\n",
       "            swing  chg_pct  chg_amount  turnover    return  \n",
       "date                                                        \n",
       "2020-01-02   1.25     1.36       55.66      0.65       NaN  \n",
       "2020-01-03   0.78    -0.18       -7.28      0.51  0.001756  \n",
       "2020-01-06   1.65    -0.38      -15.66      0.63  0.003792  \n",
       "2020-01-07   0.63     0.75       30.93      0.50 -0.007435  \n",
       "2020-01-08   1.15    -1.15      -47.91      0.60  0.011650  \n",
       "...           ...      ...         ...       ...       ...  \n",
       "2022-02-23   1.01     1.07       48.90      0.44 -0.010577  \n",
       "2022-02-24   2.65    -2.03      -93.73      0.66  0.020694  \n",
       "2022-02-25   1.10     0.97       44.10      0.53 -0.009643  \n",
       "2022-02-28   1.11     0.18        8.23      0.48 -0.001796  \n",
       "2022-03-01   0.86     0.83       38.04      0.44 -0.008234  \n",
       "\n",
       "[522 rows x 11 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300 = ak.index_zh_a_hist(symbol=\"000300\", period=\"daily\", start_date=\"20200102\", end_date=\"20220301\")    \n",
    "hs300['return']=hs300['收盘'].shift(1)/hs300['收盘']-1\n",
    "hs300.rename(columns={'日期':'date', '开盘':'open', '收盘':'close', \n",
    "                       '最高':'high', '最低':'low', '成交量':'volume',\n",
    "                       '成交额':'amount', '振幅':'swing', '涨跌幅':'chg_pct',\n",
    "                       '涨跌额':'chg_amount', '换手率':'turnover',\n",
    "                      }, inplace = True)\n",
    "hs300['date'] = pd.to_datetime(hs300['date'], format='%Y-%m-%d')\n",
    "hs300=hs300.set_index('date') \n",
    "\n",
    "hs300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "a790178a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T12:35:41.119941Z",
     "start_time": "2022-04-06T12:35:40.799470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "font_set = FontProperties(fname=r\"/usr/share/fonts/truetype/wqy/wqy-microhei.ttc\", size=12) \n",
    "plt.figure(dpi=100,figsize=(8,3))\n",
    "plt.title(u\"指数增强策略-Alpha2\",fontproperties=font_set)\n",
    "plt.xlabel(u\"时间\",fontproperties=font_set)\n",
    "plt.ylabel(u\"价格\",fontproperties=font_set)\n",
    "\n",
    "cum=report_normal_df['account']/100000000\n",
    "benchcum=hs300['close']/4152.24\n",
    "plt.plot(cum)\n",
    "plt.plot(benchcum)\n",
    "plt.legend([u'finhack','hs300'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "7f3bbd8d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T13:18:37.800978Z",
     "start_time": "2022-04-06T13:18:37.780443Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha(阿尔法):0.5924871831864988\n",
      "\n",
      "beta(贝塔):-0.9467432525147249\n",
      "\n",
      "aggregate_returns(年度收益):datetime\n",
      "2020    1.494838\n",
      "2021    0.130103\n",
      "2022   -0.065362\n",
      "Name: return, dtype: float64\n",
      "\n",
      "annual_return(年化收益率):0.5964180005055404\n",
      "\n",
      "cagr(复合年均增长率):0.5964180005055404\n",
      "\n",
      "annual_volatility(年化波动率):0.22480399592288472\n",
      "\n",
      "info_ratio(信息比率):0.07911116537767503\n",
      "\n",
      "downside_risk(下行风险):0.15108210817513162\n",
      "\n",
      "R方:0.7454065386870743\n",
      "\n",
      "sharpe(夏普比率):2.195291915586847\n",
      "\n",
      "sortino(索提诺比率):3.2665045570390046\n",
      "\n",
      "calmar(卡尔玛比率):3.7937035480375534\n",
      "\n",
      "omega(欧米伽比率):1.4564751513557872\n",
      "\n",
      "max_down(最大回撤):-0.1572126005507367\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import empyrical as ey\n",
    "\n",
    "returns=report_normal_df['return']\n",
    "benchReturns=hs300['return']\n",
    "\n",
    "alpha, beta = ey.alpha_beta(returns = returns, factor_returns = benchReturns, annualization=252)       \n",
    "result = {}\n",
    "result[\"alpha(阿尔法)\"] = alpha\n",
    "result[\"beta(贝塔)\"] = beta\n",
    "result['aggregate_returns(年度收益)']=ey.aggregate_returns(returns,convert_to='yearly')\n",
    "result['annual_return(年化收益率)']=ey.annual_return(returns=returns, period='daily', annualization=252)\n",
    "result['cagr(复合年均增长率)']=ey.cagr(returns)\n",
    "result['annual_volatility(年化波动率)']=ey.annual_volatility(returns)\n",
    "#result['info_ratio']=ey.information_ratio(returns =  returns, factor_returns = benchReturns)\n",
    "result['info_ratio(信息比率)'] = ey.excess_sharpe(returns =  returns, factor_returns = benchReturns)\n",
    "#result['cum_returns(累积收益)']=ey.cum_returns(returns)\n",
    "result['downside_risk(下行风险)']=ey.downside_risk(returns)\n",
    "result['R方'] =ey.stability_of_timeseries(returns)\n",
    "result['sharpe(夏普比率)'] = ey.sharpe_ratio(returns = returns, risk_free=0, period='daily', annualization=None)\n",
    "result['sortino(索提诺比率)'] = ey.sortino_ratio(returns = returns,required_return=0, period='daily', annualization=None, _downside_risk=None)\n",
    "result['calmar(卡尔玛比率)'] = ey.calmar_ratio(returns = returns,period='daily', annualization=None)\n",
    "result['omega(欧米伽比率)'] = ey.omega_ratio(returns = returns, risk_free=0.0, required_return=0.0, annualization=252)\n",
    "result['max_down(最大回撤)']=ey.max_drawdown(returns)\n",
    "for k,v in result.items():\n",
    "    print(k+\":\"+str(v)+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "00d2c61f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-04-06T13:23:22.766510Z",
     "start_time": "2022-04-06T13:23:17.552482Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Performance Metrics</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Strategy    Benchmark\n",
      "-------------------------  ----------  -----------\n",
      "Start Period               2020-01-02  2020-01-02\n",
      "End Period                 2022-03-01  2022-03-01\n",
      "Risk-Free Rate             0.0%        0.0%\n",
      "Time in Market             100.0%      100.0%\n",
      "\n",
      "Cumulative Return          163.51%     -10.28%\n",
      "CAGR﹪                     56.56%      -4.89%\n",
      "\n",
      "Sharpe                     2.2         -0.15\n",
      "Smart Sharpe               2.12        -0.15\n",
      "Sortino                    3.27        -0.23\n",
      "Smart Sortino              3.15        -0.22\n",
      "Sortino/√2                 2.31        -0.16\n",
      "Smart Sortino/√2           2.23        -0.16\n",
      "Omega                      1.46        1.46\n",
      "\n",
      "Max Drawdown               -15.72%     -39.21%\n",
      "Longest DD Days            201         707\n",
      "Volatility (ann.)          22.48%      20.68%\n",
      "R^2                        0.76        0.76\n",
      "Calmar                     3.6         -0.12\n",
      "Skew                       -0.54       0.83\n",
      "Kurtosis                   3.02        4.57\n",
      "\n",
      "Expected Daily %           0.19%       -0.02%\n",
      "Expected Monthly %         3.65%       -0.4%\n",
      "Expected Yearly %          38.12%      -3.55%\n",
      "Kelly Criterion            24.35%      7.03%\n",
      "Risk of Ruin               0.0%        0.0%\n",
      "Daily Value-at-Risk        -2.13%      -2.16%\n",
      "Expected Shortfall (cVaR)  -2.13%      -2.16%\n",
      "\n",
      "Gain/Pain Ratio            0.46        -0.03\n",
      "Gain/Pain (1M)             3.42        -0.12\n",
      "\n",
      "Payoff Ratio               1.28        1.39\n",
      "Profit Factor              1.46        0.97\n",
      "Common Sense Ratio         1.75        0.95\n",
      "CPC Index                  1.07        0.62\n",
      "Tail Ratio                 1.2         0.98\n",
      "Outlier Win Ratio          3.31        3.54\n",
      "Outlier Loss Ratio         3.31        3.77\n",
      "\n",
      "MTD                        0.9%        -0.82%\n",
      "3M                         -3.43%      5.2%\n",
      "6M                         -1.85%      4.49%\n",
      "YTD                        -6.54%      6.94%\n",
      "1Y                         -0.0%       15.52%\n",
      "3Y (ann.)                  56.56%      -4.89%\n",
      "5Y (ann.)                  56.56%      -4.89%\n",
      "10Y (ann.)                 56.56%      -4.89%\n",
      "All-time (ann.)            56.56%      -4.89%\n",
      "\n",
      "Best Day                   4.72%       8.56%\n",
      "Worst Day                  -7.51%      -5.36%\n",
      "Best Month                 24.87%      8.58%\n",
      "Worst Month                -7.09%      -11.31%\n",
      "Best Year                  149.48%     6.94%\n",
      "Worst Year                 -6.54%      -20.46%\n",
      "\n",
      "Avg. Drawdown              -3.23%      -11.24%\n",
      "Avg. Drawdown Days         21          154\n",
      "Recovery Factor            10.4        -0.26\n",
      "Ulcer Index                0.06        0.26\n",
      "Serenity Index             3.71        -0.01\n",
      "\n",
      "Avg. Up Month              6.91%       1.7%\n",
      "Avg. Down Month            -1.45%      -0.63%\n",
      "Win Days %                 57.58%      45.96%\n",
      "Win Month %                66.67%      44.44%\n",
      "Win Quarter %              88.89%      44.44%\n",
      "Win Year %                 66.67%      66.67%\n",
      "Beta                       -0.95       -\n",
      "Alpha                      0.46        -\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>5 Worst Drawdowns</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Start</th>\n",
       "      <th>Valley</th>\n",
       "      <th>End</th>\n",
       "      <th>Days</th>\n",
       "      <th>Max Drawdown</th>\n",
       "      <th>99% Max Drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>66</td>\n",
       "      <td>-15.721260</td>\n",
       "      <td>-14.034816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-19</td>\n",
       "      <td>2021-04-13</td>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>201</td>\n",
       "      <td>-14.641592</td>\n",
       "      <td>-14.394513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>15</td>\n",
       "      <td>-10.100339</td>\n",
       "      <td>-6.172700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-09</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>173</td>\n",
       "      <td>-9.299347</td>\n",
       "      <td>-8.965692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>2020-09-10</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>54</td>\n",
       "      <td>-8.026872</td>\n",
       "      <td>-7.325877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Start      Valley         End  Days  Max Drawdown  99% Max Drawdown\n",
       "1  2020-03-06  2020-03-23  2020-05-11    66    -15.721260        -14.034816\n",
       "2  2021-02-19  2021-04-13  2021-09-08   201    -14.641592        -14.394513\n",
       "3  2020-01-23  2020-02-03  2020-02-07    15    -10.100339         -6.172700\n",
       "4  2021-09-09  2022-02-28  2022-03-01   173     -9.299347         -8.965692\n",
       "5  2020-08-19  2020-09-10  2020-10-12    54     -8.026872         -7.325877"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4>Strategy Visualization</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAFZCAYAAACVNVWUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACJe0lEQVR4nOzdd3hUZdrA4d+ZmWQmvfeEhAChhd6LgNIUsCtYUMGOuqurru6qK+q3u7qu3dW16yoqKoogIoIIKkrvnQQIpPdeJlPe749DhsQECBBIMjz3dXlJzpxz5n3OzJl55q2aUkohhBBCCOFGDK1dACGEEEKIliYJjhBCCCHcjiQ4QgghhHA7kuAIIYQQwu1IgiOEEEIItyMJjhBCCCHcjiQ4QgCapqFpGk888USLnC8tLc11zg8++KBFzinEsbSH99vKlStdZVy5cmVrF0ecAyTBEWeE1WrlxRdfZNiwYQQEBODl5UWXLl245ZZb2L17d2sXr8U88cQTrg/t+sxmM0OGDGHIkCGEhYWdlbKMGTPGVRZN0zCZTERGRnL11Vdz8ODBkz7fBx984DpXWlpayxe4Hap/TTRNw2Aw4Ofnx4ABA3jjjTdau3hCiHpMrV0A4X6Ki4sZO3YsmzdvBsDX15cuXbqQnp7Oe++9R69evejevXsrl/LMioqKYs2aNa3y3J6envTr14+ioiJSUlKYN28eu3fvZseOHa1Snt+rra3F09OztYtx2rp3746fnx/79u1j06ZNzJo1i7CwMK688srWLlqbU1tb29pFEOcgqcERLe6ee+5xJTcPPvggRUVFbNu2jeLiYlavXk2fPn2Apms/mqrGrr/f0qVL6d69O97e3kydOpXy8nJefPFFoqOjCQ0N5Q9/+AN2u911vqaanupqOsaMGXPMGCorK7nsssvo2LEjPj4+mM1munTpwuOPP+76sB4zZgxPPvlko+f64IMPGjUZZGZmYjQa0TSNefPmuY7ZsGGDa79ff/0VgJycHG699VZiYmLw9PQkPj6ev/zlL1it1mZd/7rkat++fdxwww0A7Ny5k8LCQtc+5eXl3H///XTs2BFPT0+ioqK48847KSkpAWDGjBnMnDnTtX/Hjh0bXMfmXte6/f785z8zY8YMAgICmDZtWoPr8/zzzzN9+nT8/PyIiYnh73//e4N4XnzxRXr06IGPjw/+/v707NmzQdl+784770TTNJKTkxtsnzJlCpqmMX78eADWrVvH+PHjCQ0NxWw2ExcXx+TJk9mwYUOzrvPrr7/O2rVr2bZtm2tb/aYXpRSvvfYaffr0wcvLi4CAAC655BJ27drl2qd+jdCCBQsYNWoUXl5edOvWjUWLFjV4vtTUVKZPn05UVBSenp5ER0dzxx13NCpXaWkpM2fOxN/fv9H1rH/dn3vuOa644gq8vb1JTk5m1apVbN68mcGDB+Pj48PIkSPZu3ev69glS5Zw3nnnER4ejqenJ/7+/px33nl89913TZ7/2Wef5bLLLsPb25uHHnqoyWv42muvuWrC3nzzzWZddyGaTQnRgkpKSpTJZFKA6tOnj3I6ncfcd/bs2QpQ9d+GK1ascG1bsWJFo/38/PxU165dXX/36NFDeXl5qcTERNe2t956y3W+um2zZ892bRs9erQC1OjRo4+5X35+vgJURESE6tu3r4qNjXXt8+CDDyqllJo1a5aKiYlxbR8yZIgaMmSIWrRokTp48KBr+/vvv6+UUmrixIkKUFdeeaXree+//34FqC5duiillCooKFDx8fEKUD4+Pqp3797K09NTAWrKlCnHvfZ1ccXHx7u23XDDDQpQAQEBqra2VimllNVqVf3791eA8vT0VL1791Y+Pj4KUP3791e1tbXqqaeeanBN+/btq4YMGaLefvvtU7qunp6eytvbWyUnJ6tp06Y1uD4eHh4qKipKhYaGurYtXbpUKaXUwoULXdu6d++uevTooby9vZXRaDzmdVi9erXrmO3btyullCosLFQeHh4KUHPmzFEOh8P1fOHh4apfv34qIiJCAeqjjz465rnff//9Bu9Pp9Opli1b5tr2yiuvuPa95557GpS97vwBAQFq//79jc7n4eGhunTpory8vFzv9cLCQqWUUikpKSowMFABymAwqG7duqmYmBjXa13/enp5eR3zetbfz2w2q44dO7pe+/DwcBUaGqqSkpJc12r48OGueP79738rDw8PlZiYqPr166d8fX0VoEwmk9qyZUuj83t6eip/f3+VnJysHnjggUb39nvvvac0TVMGg0G98847x7zmQpwqSXBEi1q3bp3rQ+yee+457r6nkuDMmTNHKaXUiBEjXNtWrVqlHA6HKzGYNm2a63ynmuBYrVa1c+fOBuWdPn26AlRsbOxxY1BKNZngfPrpp64voLKyMuV0Ol2J0z/+8Q+llFJPPvmkAlRwcLDKyspSSim1atWqBrEeS11cnp6easiQIapLly6uc3311Veu/f73v/+5vph27NihlFIqLS1NGY3GBte4/pfvwYMHGzzXyV7X0NBQlZ6erpRSym63N7g+w4YNU1arVeXn57u+WB9++GGllFLPPfecAtTYsWNd57TZbOqnn3465nVQSqlu3bopQD366KNKKaXeeustBSh/f39VVVWlCgoKXM9/+PBh13EpKSkqLS3tmOetf01+/9/EiROV3W5XSumvv6ZpClBvvvmmUkqpmpoa1bNnTwWoW2+9tdH57r//fqWUUgsWLHBt++6775RSSs2cOdP1mtWPfePGja7na871rL/fhAkTlNPpVG+//bZrW125HnvsMde2qqoq17HFxcWu5y4qKlJ+fn4KUI899lij83fr1s21v91ub3Bv33nnncpgMCiDwaA++OCD476WQpwqaaISLUrVW7v19x1vW8LFF18MQEJCAgBBQUGMGDECg8FAfHw8ALm5uaf9PEajkTlz5pCUlITZbEbTNObMmQNAVlbWKZ3zsssuIzAwkOrqahYsWMCqVavIyMjAYDBw4403ArB27VoAioqKiI6ORtM0Ro4c6TpHc/r11NbWsnbtWlJSUgDo0aMHI0aMcD1e9xx2u53k5GQ0TSMhIQGHw9Hs5zhZV155JbGxsYB+beubOnUqnp6ehIaGEh4eDhx9DSdOnIinpyfLly8nNDSU4cOH88c//hEPD4/jPt9NN90EwGeffQbA3LlzAZg2bRpeXl6EhIQwbNgwAJKSkkhOTmbq1KmsWLGC6OjoZsXUvXt3hgwZ4tr/+++/56WXXgJg/fr1rnvhjjvuQNM0LBYLO3fuBJq+xnXNiT169HBtq7sOda/ZyJEjGTVqlOvx/v37NzrP8a5nfZMnT3a99nXq7q/ExETXtry8PEB/X82YMYPw8HCMRiPBwcGUl5cDTd8TN910E4GBgUDj1/yNN97A6XTy7LPPul4rIVqadDIWLapr166YTCbsdjurVq1CKXXMRKf+dofDgdFopLS09Ljn9/f3B8BkMjX4u/756idZ9c9f50TPAfDMM8/w9NNPAxAfH09kZCQZGRlkZmbidDpPeHxTLBYL06ZN480332Tu3LmuhGzcuHGuL/86vr6+9OzZs9E56r4wjic+Pp4DBw6wbNkyLr30UlatWsUtt9zCN99802A/Dw+PJr8gIyIimh1Tc6/r8c5ZP6a617XuNUxOTmbnzp188sknbN68ma1bt/Lf//6Xt956izVr1jBw4MAmz3nDDTfw6KOPkpqayqJFi/jpp58AGvTdWb58OZ988gm//voru3btYv78+XzxxRfs2LGDl19++YSxv/7664wZMwan08nIkSNZvXo1zz33HA888ECD/fr06YPFYmmwrakkqu461F2D+tfhZBzvetb3+3up/rb692bdsZMnTyY1NRWTyUSvXr2wWCxs3ryZ2traBu+DOsd7zX19famoqODNN9/khhtucCViQrQkqcERLSogIICpU6cCsHnzZh555JEGnX5//vlnfvzxR4AGH2r79+8HYP78+S1anrrnqDt/ampqs0YT1f3CTkpKIi0tjV9//dXVObo+b29v178rKytPeN4ZM2YAsHTpUlftQt02gEGDBgG4aozWrFnDmjVrWLFiBQ8++GCzR+gYDAYmTpzI3XffDcCiRYtYvXp1g+ew2+289NJLrudYtWoVs2fPZvr06SeM7WSv66nW5qWkpKBpGo8//jjz589nz549+Pv743A4XElLU2JiYlydiW+77TYcDgddu3Z11doopfjtt9+YMWMG7733HmvWrOGWW24BcL0/T0ZdEmCz2QAYOHCgK+Zrr73WdY3XrFnDa6+9xoMPPnhS5x8yZAgAq1atcnVGB9iyZctJl/VUFBYWkpqaCsBTTz3Fli1bmDt37nFf1+M99vrrrxMTE0NKSgoTJ05s1o8OIU6WJDiixb366qv069cP0GtCgoOD6d27NyEhIYwePdo16uT888/HYNDfghdccAFDhgzho48+atGyjB07FoBPP/2U0aNHM3To0GbVwPTu3RuAffv20bFjR+Lj45tsVujWrZvr3z179mTo0KEcOHDgmOcdOnQo3bp1w2azUVhYSGBgIJdffrnr8XvuuYe4uDjKy8vp0aMHvXv3pkuXLgQFBXH11Ve7Rjk11wMPPOAakv3Pf/4T0L9w+/bti1KK4cOH07NnT7p3705AQACTJk1yzXlTP7Zx48YxdOhQ15frqV7Xk/XTTz/RuXNnoqOj6d+/Px07dqSsrAw4+hodS13imJOT0+Bv0Guexo0bR1BQED179qRXr168/fbbzTpvnbvuuouhQ4cSFxfnem9cdtllgD7q7M477wTgL3/5C/Hx8fTp04fg4GAGDhzI0qVLm/UcdR555BECAwOx2+2MGjWKHj160KFDhwbvnTMpODjYVcs4e/ZsevXqRf/+/RvU/pyMuLg4vvvuOwICAtiyZQtTpkyhurq6JYsshCQ4ouUFBwfz22+/8fzzzzN48GBATxT8/f256aabmDBhAqB/gb711lskJCRQWFhIUFAQr7/+eouW5YUXXmDy5Mn4+vpy8OBBHn744QZ9Wo7lkUcecfUhKCsr45prruGuu+5qtN+UKVO47bbbCAkJ4dChQ6xdu5aqqqrjnrt+n4Np06Y1aL4IDQ1lzZo13HrrrYSHh7N7927KysoYNGgQ//znP0+q+Qj0ppC6vh2LFi1i69atmM1mVq5cyf33309CQgIpKSnk5+fTs2dPHnvsMdfw6t69e/O3v/2NiIgIcnJyWLt2LcXFxcCpX9eT1a9fP6644grMZjO7d++mvLycfv368e6777pqaI6lrs8T6DVaddcB9D4hd955J4mJiWRlZbFv3z5iY2O58847ee2115pVtt27d7N27Vry8/Pp1KkTDzzwAK+++qrr8f/85z+88sor9OnTh7y8PA4ePEhUVBSzZs066blyOnfuzPr167nuuusICwsjJSUFu93uupfONE3T+PLLLxk0aBBGoxGHw8HHH39MaGjoKZ+zV69eLFiwALPZzKpVq7jyyitdNWBCtARNnUojrxBCCCFEGyY1OEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO5LgCCGEEMLtSIIjhBBCCLcjCY4QQggh3I4kOEIIIYRwO6bWLoAQ7qKyspIlS5aQlpaGzWYjPDycCRMmEBsb2+T+W7ZsYdOmTdx8880tXpaXXnqJGTNmEBgYyK+//srWrVspLS3F29ubgQMHMmLECNe+JSUlLFiwgIyMDAICApg0aRKJiYktUo68vDyWLl1KVlYW1dXVzJ49+7j7P/nkk3h7e/PAAw9gMOi/vxwOBy+88AJVVVUnPL65tmzZQlpaGpdddhmFhYUsW7aM9PR0nE4nMTExXHjhhYSGhrr2XbduHYWFhZjNZnr16sXYsWNd5RNCtE2S4AjRQmpra4mOjmbChAn4+PiwefNmPvnkE+677z48PT1btWyXX345ERERFBUVMWfOHAICAkhOTgbgyy+/JDY2luuuu46UlBQ+//xz/vCHP+Dj43Paz2swGOjRowcDBw7ks88+a9YxXl5epKSk0LVrVwBSU1Px8vKiqqrqlMrgdDqPm4zU1NSQlJTEpZdeiqenJz/99BNz587lnnvuAcBmszFx4kRiY2OprKxk7ty5/Pbbb4wcOfKUyiOEODskwRGihQQFBTFs2DDX3wMGDGDp0qUUFBQQHR19wuNXrVrFpk2bqKysxN/fnwsuuIDu3bsDR2t7YmNj2bx5MxaLhUmTJtGlS5cTnrd+bU1oaChdu3bl8OHDJCcnU1hYSHZ2NtOnT8fDw4MePXqwdu1adu/ezcCBA0/hKjQUGhpKaGgoRUVFzT6md+/ebNu2zZXgbN26ld69e7NixQrXPps3b+a3336jrKwMb29vRowY4SpvWloaX331FYMHD2bNmjV06tSJyy+//JjPFxMTQ0xMjOvvYcOG8csvv1BVVYW3tzeDBg1yPebv70+vXr1IS0trdjxCiNYhCY4QZ0hOTg4Oh4Pg4OBm7R8UFMTMmTPx9fVl586dzJ8/n9jYWPz8/ADIzMykT58+/PnPf2bjxo0sXLiQ+++/H03TGp3rvvvua/I5lFIcPnyYAQMGAHoTUlBQEGaz2bVPREQEeXl5Jxlty+nWrRvr1q2jpqbGVd4xY8Y0SHB8fHy49tprCQoK4tChQ3z88cfExMQQFRUFQEVFBdXV1dx3330opRo9R9++fenbt2+Tz3/o0CF8fX3x9vY+5uNhYWGnH6gQ4oySRmQhzgCr1cr8+fMZPXo0FoulWcf07NkTPz8/NE0jOTmZ4OBgMjMzXY8HBAQwYMAADAYDffv2paKigsrKypMq18qVK1FKub7ca2trGyQ3AGazmdra2pM6b0symUwkJSWxY8cOdu7cSVJSEiZTw99iSUlJBAcHo2kaCQkJdOrUiUOHDrke1zSN888/H5PJhIeHR7Ofu6ysjMWLFzNhwoQmH9+8eTNZWVkMHz781IITQpw1UoMjRAuz2Wx8+umnxMTEcN555zX7uK1bt7J69WpKSkoAPfmo3+/E19fX9e+6L+2TSUTWrVvHtm3bmDlzpith8PT0xGq1NtjParU22WeotLSU1157zfX3I4880uznPll9+vRh+fLlKKUYN25co8dTUlL46aefKCwsRCnl6tRdx8fHp1FSdCKVlZV89NFHDBw4kF69ejV6fM+ePSxfvpwbbrjhmLU7Qoi2QxIcIVqQ3W7ns88+w9/fn4svvrjZx5WUlPDNN99w4403Ehsbi8Fg4I033mixcm3evJlVq1Yxc+ZM/P39XdvDw8MpLi7GarW6anJyc3Ob/IIPCAg4o0lNfR06dKC8vBxN0+jQoQPFxcWux+x2O59//jmXX345Xbt2xWg0Mnfu3NN6vurqaubMmUPXrl0ZNWpUo8dTU1P55ptvuO6664iIiDit5xJCnB3SRCVEC3E4HHzxxReYTCYuu+yyJvvGHIvNZgNw1Qxs3ry5xfrBbNu2zVXzEBQU1OCxkJAQIiMj+emnn7Db7ezevZvc3FxX5+bTpZTCbrfjcDgAPTmx2+0nPE7TNK699lquueaaRtfR4XDgcDjw9vbGYDCQkpLC/v37T7mMVquVOXPmEBcX12Rt0cGDB/nqq6+YOnVqg87IQoi2TWpwhGgh6enp7Nu3D5PJxDPPPOPafv311xMfH9/kMXVf3mFhYQwbNox3330XTdPo06cPHTp0aJFyrVixgurqat5++23Xtt69ezNlyhQArrrqKr7++mv+9a9/ERAQwNSpU1tkiDjozVovv/yy6+9//OMfBAQEHLMTdH31m5zqM5vNXHjhhcybNw+73U7Xrl1dI65Oxe7du8nKyiI/P58tW7a4tt99990EBATw888/U1NTw8cff+x6LD4+nuuvv/6Un1MIceZpqqkhBkKIM27t2rUcPHiQa665prWLIoQQbkeaqIRoBXa7nb179zZrfhwhhBAnTxIcIc6y3NxcnnvuOcxmM4MHD27t4gghhFuSJiohhBBCuB2pwRFCCCGE25EEp5lOdsbY9sAdYwKJq71xx7gkpvbFXWNzx7hOJiZJcJrp97O9ugN3jAkkrvbGHeOSmNoXd43NHeM6mZgkwRFCCCGE25EERwghhBBuRxIcIYQQQrgdSXCEEEII4XbOibWonE4nBQUFlJSUuBb9O5Vz5ObmtnDJWld7iMlisRAbG4uHh0drF0UIIUQ7ck4kOBkZGWiaRkJCAh4eHie1ynMdu92OyeRel6utx6SUorCwkIyMDDp27NjaxRFCCNGOnBNNVJWVlcTExODp6XlKyY1oHZqmERISQk1NTWsXRQjRxm1ML+GHffmtXQzRhrTdn+8tzGA4J3I5tyMJqWhJVruT7LIaEoK9W7soooXd+cU2AHrf5k+4n7mVS9M2zd2UiadJ44re58Yiv/KtL4Q4J9Tanfzhy21c/cEGfj1Q1NrFES2oxna0b+WX27L4cV8+27PKWrFELa+k2sZbv6VRVmM7peNT8it48af9vPnbIZznyBKUkuCcg7Kyshg4cCB2u71Vnn/Dhg1MmjSpVZ5bnJscTsXj3+1hc6b+pffiT/txOM+ND/lzwc6ccte/31ubzsOLdvPM8pRWLFHL+WxzJpe+u44/L9zJ22sO89nmrGYfW1pjZ+6mTKpqHTy3Yj9OBUVVNu7/eucZLHHbcc40UbVlF198MUVFRRgMBkwmE7179+avf/0rkZGRrV00Ido9pRTPr9jP8pQC7h2VSISfmUe+3c1PqQVckBTW2sUTLWDrkdqav0/qxqKduTiUYltmGU6lMLTzZu7nVuwHIKu0hkg/Mx+uT6dLmA9jOoce97is0hr+tPgAmWW1PL9SP8ddIxN4fVUavx4swuZw4mHU6zh+O1hElL+FjiHu1XQrNThtxAsvvMAvv/zCkiVLCA4O5t///ndrF+mMaK1aI3Huen9dOl9szWL6gFimD4zlgi6hxAVa+GB9Ouocqap3d1syS0kM8WZit3BevbIX45PCsDqc5JSd3FpMGSXVXPfRRl75+QA2h/MMlfbkdA71AeDeUYncOSKBGruTPy/cRWFl7TGPSS2o5Oa5WyitsTM8IQiAYG8PbhwYxxMXdgUgs1QfvLF4Vy73zt/Bo9/uPsORnH2S4LQxZrOZsWPHcvDgQQBqa2t56aWXmDx5MhMmTOCf//yna1RRXVPPnDlzGD9+PBMnTmThwoWuc9XU1PDiiy8yZcoURo8ezS233NJgRNL333/P5MmTGTt2LO+++65r+5tvvsnDDz/M3/72N0aNGsW0adM4dOgQ77//PuPHj2fy5MmsWbPGtf/ChQu56qqrGDVqFJdeeilffvml67G6Mn7wwQdMnDiRJ598slHMc+fO5eqrr27zc/KItmPtoWI+2ZRxwv2+3p7Nf39NY1L3cP4wSp9qwGjQuGFgHLtzK1ifXnKGSyrOtJJqGxvTSxjUIdC1ra4T+cGiqgb7Hq9ZMrushllfbCO9uJqPNmRw22dbqao9tXnTWlJ+hZUre0cxfWAsoxJDXNtTCxqvqp1aUMlT3+/l6R9S9JrLixJ5ekoPLu4ZwStX9MJo0IgL9AL0ZK6oqpYnluwFoLLW/X58npNNVKp8E5RvOKljDEqhTqaq028gml//kyyZnpQsW7aM5ORkAF599VUyMjL45JNPMJlMPPbYY7zzzjvcc889ABQWFlJRUcF3333HmjVrePjhhxkzZgz+/v68/PLL7N+/n/fee4+QkBB27NjRYDTZ1q1b+fLLLzl8+DA33XQTF1xwgWu+mV9++YXnn3+e2bNn89RTT/GHP/yBSy+9lO+++45vvvmGf/7zn65kKjg4mJdeeomYmBg2bdrEH//4R3r27Em3bt1cZSwrK+Obb77B6XSyY8cOVxnefvttVq5cyVtvvUVQUNBJXy9x7tmRXcYDX+/E7nRySc9IfM1Nf4ylFVXx9A8pDE8I4m8Tkho0VUzuEcFbqw/xwbp0BneQ9117tnhXLrUOxWXJUa5tXcJ8MBk0NqaXMKJjsGv7rC+2UlHr4PWrehPodXTy0IJKGw8t3UZlrYO3p/VhX34l/7d0H+vTSxjdKYTWUllrp7TGTpS/BQA/i4nFtw9h0ltr2V9QyZD4o+9dpRSPL95DypHE5/Zh8SQEWfD2NPL4xK6u/eIC9XMdLqnGbDKggE4h3mSW1qCUcquRq1KD00Y8+OCDjBkzhtGjR7N27VpuvPFGlFLMnz+fBx54gICAAHx8fJg5cyZLly51HWcymbj11lsxmUyMHDkSb29vDh06hNPpZOHChTz44IOEh4djNBrp06cPnp6ermNvvfVWLBYLSUlJdOnShX379rke69u3L8OGDcNkMjFu3DiKi4uZMWMGJpOJCRMmkJWVRXm53rFv5MiRxMbGomkaAwYMYOjQoWzevNl1Lk3TuOOOO/D09MRi0W8upRQvvPACa9as4c0335TkRjRLdlkN93+9E5NRw6Fg3eGSY+67fF8+TgWPTUjCZGz4UedpMjCpezgbDpdIZ+N2TCnFV9uy6RXlT+cwH9d2X7OJAXEB/JRa6BoxVFptY3NmGSn5lcz6YhsXvbmGjzakU1hZy8PfH6Sk2sYrVyTTLcKPsUl6/5YDTdSSnE3ZpXoTW1TA0WHvYb5mgr092J6tf/7uyiln1hfbuPbDja7kJtjbg0uTm+7DGejlga/ZSEZxDQcK9RquMZ1DqbE72ZVbcSbDOevOyRocza8/nGTtiuMMz/r73HPPMWTIEBwOBz/99BO33347n3zyCTU1NUyfPt21n1IKp/No23BAQECDclksFqqqqigpKcFqtRIbG3vM5wwODm5wXHV1tevvkJCjv1rMZjOBgYEYjUbX3wBVVVX4+fnx66+/8vbbb3P48GGcTic1NTV07tzZdXxQUJDrmDrl5eXMnz+fp59+Gl9f32ZfJ3Fu+7+l+6h1OHl7Wh9u+2wrzxypobF4GBvtu/ZQCT0i/AjzbXpOlFBfMwoot9ob/JoX7cdX27I5VFzN4xPjGj02PimMvy9LYciLv9A51IdwX/3H3czBcXyyKROr3ckrPx/k25255FfZePWK3iRH+QPg46l/pr7+axpR/hYu7B7O+2sP8/aaQ/z9om70jwuk1u4k1NfztDsxH68j9KFiPQGJD2rY+Xd81zDmbc0mtaCSh7/ZRU65legAC38d14VxSaFYTEY8TQaKihonaJqmN1Oll1RTUmPD32xiVKcQPlh3mBmfbGZIfCD/ubL3acXUVkgNThtjNBq54IILMBgMbN++HbPZzOeff87KlStZuXIlP/30E7/88ssJzxMYGIjZbCYj48T9FE5HbW0tDz30ENOnT2fp0qWsXLmSESNGNOi82VSVp7+/Py+++CJPPvkkW7ZsOaNlFO6h2uZgw+ESpvWLoUuYL7cNi6e42kZOedMdSYura4kOOPaEbwEW/Uus9BTnFXF3lbV2vtqWxR++3M6bv6VRbWvYH6W1O2jvyS3nmeWpGDU9mfm9Cd3CCff1ZEBsAGG+nuzMKcffbGLG4A68dmUv134ZJTU8NTaefrEBDY7vF6P//bfv9rA3r4LXf03D5lA8vGg34/+7mslvr+XWuVuxN7Mz8s6ccjZllDTYtnRvHhe9uYYDhU3XFKUd6UPUIcirwfZr+sWglOLaDzeSX1nLB9f1Y8Etg7midxT+Fg88Tcf/ao8L9GJzRinL9uZzSXIkPSL9WHDrECZ0DWPtoRKs9rbRwfp0SYLTxiilWLlyJeXl5SQmJnL55ZfzwgsvUFSkT0yWl5fH6tWrT3geg8HAJZdcwosvvkh+fj4Oh4Nt27ZRW3vsnvenwmazYbPZCAoKwmg08uuvvzbogHw8AwcO5P/+7/946KGHGvTLEQL0qfe/3aV3PK+w2tmaWYoCko40RXQ6MqS1pLrpBKW02o6/5dg1MwFHam1Kq92vc2VLeOzbPTz9QyqHiqt4Z81hXvrt6PwrDy3cxU2fbD7O0WfepsxSAF6+oleTNXheHka+vmUwb0ztwytX9GLZrGF8f+dQvD2N9IkJ4Nr+MQBc2iuSvlGNa5H/dXF3/nddP8J9Pbl3/g68PPSvy0g/M/eNTuTCbuFszy5zNQsdS3FVLStTC7jtsy3cN38Hvx4o4uoP1nP3vG08+u0eiqps/PfXtAbH/JhSQG65lUPF1UT4mfH6XXyxgV6uYeLdw33pGenXvItW73irw0lypB93jUwAIMLPzMC4QL3M1S37PdFazskmqrbo/vvvx2AwoGkakZGRPPHEE3Tq1Ik//OEPvPPOO8ycOZOSkhLCwsK46qqrGDZs2AnPee+99/Laa69x4403UlVVRVJSEq+++mqLltvHx4cHH3yQv/71r9TW1nLeeecxatSoZh8/dOhQHn/8ce6//35eeeUVV8dkIeqm3p/UPZw/fb2DLUcm6aubqyPIW29yKK5qnOAopSirsblqaZpS1yx1rATpXFZSbWPd4WIu6xXJI+O68MLKA8zbmkVJtY1ALw9WpBYAkFduxWp3smBHDj0ifNmbX8nMwXFNJhwtbUd2OZF+5gYdbX/Po17fK03TMBmP1ib7Hemc3ifav8ljg7w9CfL25NUre3H7Z1uptjm5eUgcdwxPwKBp5JTVsGRPHtuzy+keoScYdqfiiy1ZTOkRgZ/FRFWtgwlv6D/4OgZ7c7Coivu+1n/MeXkY6Rbui9XhZHNGqauDb1pRFQ9/swt/swmDQaP3Mcp3Tb8YfkwpYMCRpORkDO4QyKoDhfxzSvcG1yjY+8g9UWUj0s9y0udtayTBaQO++eabYz5mNpu5++67ufvuuxs9NnDgQBYvXnzMc1ksFh544AEeeOCBBvtER0ezYcOGBnPSvPXWW65/33HHHQ32HzJkSIPzmkwmNmw4Ogpt6tSpTJ06tcnyN1XG328bOXJkg47TQtQfnrsxvdSV3ACuYa51CUrxkQTlw/XpxAd7M7pTCJW1DhyK49fgSBNVk4qqapl45Es5OdIPTdMYmxTK3M2ZbM8uo3+9ppxZ87ZxuLi6wfHbskp5/tJkvD2bn+SsPVTMT6mFzBgch7enkffXpXPjwFhXLRvoyzF4mgyu/iop+RV0Cz/1/ns3DIwlyt/MuK5hlBQXH3O/xBAf/nNlL578fh+jO4W6nj/Cz0yIjye7cspQKorVacXsza/g9VVpbMksJdjbA/uRDuxBXh68e01fvtia5aqt+fB6vR/o19uz+ceyFPblV9I13JddR2ZlDvAykV5Sw7R+Ta8b1S82gLen9XElVydjQFwgH98woNH2QO+G99TxtIcRV5LgCCHanH35R0dzvPTzAYwaOJQ+/LfuF2dQvRoYh1Px6i/63FGr/jjSlbT4N6MGJ62o+pj7nIveXn3I9e9ORyaZiw+qmzulxlXz0SnEm+JqGzcNiuN/69MBuHlIHP9bl87j3+3huUt7Nvs5X/75ACn5lfhbTORWWFm0M5dOId5M6hEBwJLdefztuz3cPCSOWSM6opQiu9TK8HpDwE+WxcPIlJ7Nmy2+W4Qfn97YMCHQNI3EEG/25VXy1NJ9LNp5dB6vH1MKGuz77jV98bOYmNQjvFFz1NCEIHw8jdw9bxuTe0bwycZMAD65YQCHS6pJCjt2Etc3JuCYj52KIK9j14rW9/qqg3y/N595MwY2qAFqayTBEUK0OfkVR/sA7M2r4JYhHTB7GJjcPcK13dNkwMfTSEm1jazSoxNYrj9cTIiP/kF9vCYqnyM1DB+uT2da3+hzfgXqTzZmsGhXLin5lYzuFEKXMB9X7UCglwfeHgY2pJcwd3MmPp5GXruqt+s6xwVa2JpVxqwRHamxO/lscxavrTrIwLjA4zYhgd6sc/hIkvnu2sOu7bOX7MWhFFN6RPDOGj3p2nZkSYbCKhtWh5MY/9ZtRukQ6MX6wyWkFFTi42mkstZB/9gAuoT6ML5rGPd8uR2lIDpAL2ekn4W4QEuDxCrSz8KH1/fnL4t2uZKbsV1CsXgYj5vcnAl1Pxq+2pbNRd3Dm6yhySyt5v11ekK7Oq2YUa04T9CJSIIjhGhz6k9DbzYZuKZ/TJNDuQO9PCiqqm0wCmVDeinDjkxPf7wmKk3TGBofxJpDxWSV1fDhhnS2ZpZxcXIkU/s23SzgrmwOJ/9ZdZC4QC9mjUjgmn4xDZqYNE0jys+Tn/cX4m828Xq95Abg0l5RXNpLn2hvcFwQn2zM5IN16XywLp0Vdw8/5mSMoM+oa3U4GZYQxOq0YkJ9PCk48vo//UMKPp4mDhVXYzRoHDySCGUfSWjrEofWEumvJ8Xndw7ljuHxPL9yP/+c3N31Xn3g/E4cPlL2Ol/dPLjReToEefH+tf1YmVpAlzAfEkN8Gu1zNvia9dd8a1YZ+wurXMtE1PfD3qO1U9/syGmQ4BRUWHlq6T5mDI6jf2zgGS/viUiCI4Rocwqr9C84Dbg0OfKY89TEBXqxv6AKqy0Xo0Gjc6gPWzJLXU0qJ5rf5p7zOrLmUDGrDhS5Vmne82Oq2yc4n2zK4OMNGZzXKYTr+sdSbXNgcyhuGdqBCV3DmzwmMchCYbWD/1zZi67H6fvSJ8af+CAv/C0ebM8u47eDRUzo1vQ5AbZn67Uy95zXkcuSI+kW4cel764DwKngjd/SiA/yYnLPCF5flcZfF+1yzQsT1co1ODEB+vtsTOcQOoX68PpVDeePuaxXVFOHNclsMjDxONfpbNA0jUEdAll/uISs0ppGCY7dqfh6eza9o/3pHxvAh+vTySqtITrAQnFVLXd9uZ2DhVXEB3lLgiOEEPUt2pnD8pQCTJpGuK8n/5zc/bhfpr2i/Hh7zWFSCyq5b3Qi2aU1fLMzl7mbM+kY7E18sNcxj4Wjo0a+3p7donG0JSn5FQR7ezaocZm/LZu8iloW7sjhq63ZeB7pR9E9/NgdVv8wLBof/4Dj1oqBPovwvJmDcDgVE99YzW9pxcdNcJbuySfa30yXUB9Xk8yTF3bl6R9SqLE7OVhYxT8ndyc20MJH6zP4YZ9eg9AxxNu15lRrGZcUSpR/35Mept2W/d9F3bjwzTXklNU02J5TVsM/lqWQUVrDvaMT6Rbhy0fr0/XO51ll7Mgpx2w0EObjydzNmQzqENjqzVeS4Agh2owPN2Rw8Mj08d0jfOlzgk6UdY9f2TuK6/rH8OW2bKpsDg4UVvH3Sd1OOMtsXZ+D0pqjIwp9TmL0T1tXYbVz3UebSArzcY2a2ZldRlpRNfeP6cT4rmH8d9VBFu7MxddsJCbw2DUiZpPhhMlNfUaDxpD4IFanFTWarVcpxf6CKoJ9PFh/uJgbBsU16O8xqUcE53cJZdSrv2LQYGySPnrpx7uHU1RVy4IdOZzXMaRB009r0DTNNfuxuwj29sBsNJB9ZCV2pRRfb8/h5Z8P4FSKhy7ozOhOIWiaxpjOoXy6KdN17PSBsWzMKCG/spZHv93NL38c2VphAJLgCCHaiMzSag4WVjGmUwi/HCw65hIL9Q3uEMh/r+pN39gANE0j4UjTRYcgL8Y1Mbvt75mMBgIsJkpr7Dx5YVdS8iv5YktWuxgCezw5ZTVYPIx8tEGfyXxffiUHC6vYnFnK26sPEeln5qLu4QR6efDQ2C54eRq5uGfkaS878HvDEoJYujeflCNDoOv8tL+QPy/cRXKkHw5Fk00zXh5G5kzvj5/Z1KBcwd6ezBzcoUXLKY7SNI0IfzPZZTVkldbwj2X7WHe4hIFxgTw2oYurWQ7gmv4xLK83YmxYxyCSo/zYkrmTMF/Ppk5/VkmCI4RoE+oW/rtxUBzTB8US0IzaAk3TGNgh0PV3UrgPwd4e3DUiodm/7oO9PamyORjVKYT8ilqsDic1dmej2WPbC6UUF7+zrtH2qf/T564K8/HkxcuTXf2TzCYDD57fudH+LWHokRFUq9OKXAnO93vyeGzxHgB25JQTH+TVZGdW4LjNk+LMSQrzZXVaEas/3AjAX8d15vJeUY2S/r4xAXx8Q3+UgnWHiukV5Y9B07iqTxTf78kH9PmLVqYWMrFb2Fn/0SAJjhCiTcgo0dv844K8TnnxS3+LB0vuGHpSH6R9Y/zpE+OPr9lE0JE+Oduyyk44vLmt2l9Q5fr3BV1C+dPoRFfC8+XMQcQFWs7aF02or5mkMB9WpxUz40itS/0mDYCLmzkXjTh7rh8Qw4qUfAZ2COTR8UnH7cxd12+qfjIa5W+h3GonNb+S1YeKeOXng4xMDD7uaLozQRKcNmDLli288sor7N+/H6PRSEJCAg888AAHDhzg66+/5t133z3lc2dlZXHJJZewZs2aM7oauhCnK6OkGl+z8bhz1zTHyX55PzI+yfXvuokB7/lyO+v+dF67a6ZSSvH08hQ04NHxXbioewSeJgNfzBiI3aEaLdp4NgyIC+SrrdkopVDor3O4rycvX94Ls8lw3H4/onUkR/mz+I6hBHl5nNI9UJcQXfvRRnzNRjqH+pz15AYkwWl1FRUV3HffffzlL39h/Pjx2Gw2tmzZgqdn89ovHQ4HRmP7rEoXor7DxdXEBni1alJRv6kkpaDyrE+0drK+35PH9qwypg+MJdLfQkm1jW1ZZdw4KM41Lw3QqqONwnw9sTqcVNY6yCipprTGzlMXdaVzWOvM9SKaJ9j71PvQnJcYzN0jEyiusvHtrlwu6t46w98lwWllhw/rM3deeOGFABiNRoYOHcrBgwd5+umnsdvtnHfeeRiNRlauXMkTTzyB2WwmOzubTZs28fzzz2Oz2Xj99dfJzMzE19eXSy65xLWe1G233QbA+eefD8Brr71G7969WbBgAR9++CFFRUX07NmTRx99lKgo/QNxzZo1PPvssxQWFnLRRRdx4MABJk2axOTJk5k4cSJvvfUWnTvrbfZFRUVcfPHFLFq0iKCg9lmlL9qGlIJKhsYHtmoZYgO9+O72IVz01lp+PVjUphOcvXkV+my/TsUvB4v46Pp+5B2ZAbpnRNspd3C9RVHXp5cAMOgUFogU7YfFw+hqkvzTmE6tVo5zMsH5dlcuC3fknNQxJzuq4pLkSCb3iDjhfh06dMBoNDJ79mwmTJhAr1698Pf3p2PHjvz1r39tsolqyZIlvPzyy7z00kvYbDa2b9/OU089RWJiIvv37+fuu++ma9eujBkzhrfffptLLrmEFStWuJqoVq5cyfvvv8+///1vOnbsyAcffMCjjz7Ke++9R0lJCQ8//DCzZ89m1KhRfPHFF8yfP59Jkybh4eHBhAkTWLx4MX/84x8B+P777xk8eLAkN+K0FFRYKayspdtx5mE5W0J9zXQN9+W3g0VNjtb5cmsWgzsEEdcKzT11amwO/rZ4D8FeHjw6PokHFuzgow0ZrpWxmzMC7WypG4q/I6eMxbvy6BjsTWgbKp9wX213laxzhK+vL++88w6apvGPf/yD8ePH86c//YnCwsJjHjN69Gj69u2LwWDAbDYzcOBAOnfujMFgoEuXLkycOJGNGzce8/ivvvqKGTNm0LFjR0wmEzfffDN79+4lOzubVatWkZiYyAUXXIDJZOKaa64hNDTUdeyUKVP4/vvvUUpfJXfx4sVMmjSp5S6IOCftytUX1+zWRmoeRnQMYntWGWU1NpbszuPvS/fhcCr25lXwzPJU/vlDyhl77pyyGv66aDfl9ebm+b3523M4WFTF7Au7MiIxmB6R/mzOKCWvQp+7pC2tq1U3meLj3+0ltaCSfrEtu0CkEMdyTtbgTO4R0azalfrsdvsZ66TbsWNHnnjiCQDS0tL429/+xvPPP8+wYcOa3D8iomHZd+zYwauvvsr+/fux2WzYbDbGjh17zOfLzs7m+eef56WXXmqwPS8vj4KCggbn1zSN8PCj7afJyclYLBY2btxIaGgo6enpjBo16iQjFqKhzRmleBg1urWRYcHDE4J5b206S3bn8e8V+wFICvNhyZGhrx7GM9dP6IN16fywL5+ekX5MHxjb5D5pRVUEeXm4Rnr1ivZj3pasI3PG0GDW4tYW9Lu+HKezArgQJ+OcTHDasoSEBKZMmcJXX33F8OHDm9zn901ljz76KFOnTuWVV17BbDbz/PPPU1JS0uS+oCdIN998M+PHj2+UtKWnp5OXl+f6WynV4G/Qa3EWL15MSEgIY8eOxWxuO78WRfu0Pr2EXlH+WNrI3DM9o/zxM5uYv/1oU/bzK/fj1CsuqbY5mn2u1PxKSmtsDGii30mF1c79X+8kq6yGOdP7E+jlweESfUHJL7dm4W8xMTYpFB/Po/fprweL+GpbNt3r1XaNSgzhiy1ZrDpYRIcgL0ytPMNvfUH1hvxP7RvNqERJcMTZIU1UrSwtLY05c+aQm5sLQE5ODt9//z29evUiODiYvLw8bDbbcc9RVVVFQEAAZrOZHTt2sGTJEtdjQUFBGAwGMjOPzj1x5ZVX8v7773PgwAFAH8n1ww8/ADBy5EhSU1NZuXIldrudzz//nIKCggbPd9FFF7Fy5Uq+++47Jk+e3CLXQZy7tmRXsDevgtGdW3fdmvpMBo1Ood6kFuirlD82vgtKQYDFxMSuYWSXWXlt1UFeW3WwyeP3F1bzv3Xp3PvVdq79aCN3frGtyf2eW7GfzZml5JZbefibXby26iDrD5cQF2jBYND4v6X7uPCNNTz1/V6qavWk6r75OwAIrVdLMyAukJ/vGcGCWwbz7jV9W/BKnD5P09GvmX5HZpwW4mxoVg3OihUr+O2338jMzGTw4MHMmDHD9dgdd9yBp6en603bv3//Bo+vWLGCJUuWUF1dTXJyMjfccANeXnrnvM8++4w1a9YQERHBHXfc4eqompqayrJly5g1a1YLhdl2eXt7s2PHDj7++GPKy8vx8/Nj5MiR3HvvvZjNZhITE5k4cSKaprF8+fImz/Hwww/z0ksv8eyzz9K/f3/GjRtHRYXep8FisXDzzTdzyy23YLfbefXVVzn//POpqqrib3/7Gzk5Ofj6+jJ48GDGjRtHYGAgzzzzDM899xyzZ8/moosuokePHnh4HP0VFhkZSbdu3UhPT6dfv35n5ToJ9+RUirc35BDpZ+bK3m1rBe8OgV5sydRXuj6vUwh3Vtnw9jBSWFXL93vz+WBdOl4eBmaNSGiwlMDG9BLu/mY/CoirN8dLUVVtg6G3O7LL+HZXLjcP6YAGLN6dy6aMUkAfeTKyYzDbs8tZuCOHBTty6Bbuy6R6TesV1oa1SCajgeiAtjmnTIiPJ4WVtYRL52JxFjUrwQkICGDy5Mns3LmzydqERx99lMjIxrNR7tq1i0WLFnHfffcRGhrKBx98wNy5c5k5cyYHDx5k586dPP3003z33XcsWbKEa6+9FofDwbx587j99ttPP7p2IDw8nGeeeeaYj7/88ssN/q7rq1PfuHHjGDdu3DHPceedd3LnnXc22FY35LupfkXDhw/nq6++AsDpdDJp0qRG/X4iIiJITk6WX2PitCzZnUdqYQ1PXtgVs6ltVSh3CDo6d0yglwc3D9FHVP24L19fSLJDIL+lFZNVWkNsoBeVtXYeWbSb39KKCfE2MeeGgYT6eLL+cDF3zdtOSn4lQ+KPJjiLd+VhNhq4YWAsvmYTd45I4PHv9vDd7jz6xeg1Hb2j/ekd7c/69BIW785jX36l6/gu7Wgemecv7cl/fjlAUjsqs2j/mvWJ0r9/f/r27YuPz8m9OVevXs3w4cOJi4vDy8uLyy67jA0bNlBbW0tBQQEdOnTAYrGQlJTkagZZtmwZAwYMIDhY2mlby+rVqykvL6e2tpb33nsPpRTJycmux7OyslixYgWXXnppK5ZStHdWu5P//ppG5xALF7bSRGDHkxByNMGpX0NzfpdQfrpnBLcOjQf0hSOnfrCBC177jd/SigGYlBTsakJKCvPFoMGqA0Wuc9gdTpbty2dU55AGM7zOntiV724f0mjW1zGdQtiZU86CHTlc2C2cD6/vxx/O69jyQZ8hPSP9+O/VfdpMHytxbmiRTsYvvPACSikSExO56qqrCAvTV/HNyspq8MVYN5Fcbm4u0dHRLFy4kOrqavbu3UtUVBQFBQVs3bqVBx988JTKUVlZidVqbbTd6XRitx97yGVztMQ52ppjxbRlyxYeffRR7HY7CQkJ/Otf/8JkMmG323nzzTeZO3cuN954IxEREWflmjidToqKik684xHV1dUntX974W5xrT5cRk65lcdHRVFSXNzaxWmkewCEepuICzA3ed2DjHoT0dyN6eRU2LiiZwga8H1KCaPivBocMzYxkK+2ZXFJF19CvD1Ym15OSbWNkbFejc5tAIqKKhtsu6FXIJM7+eJp1PAzG9E0G1XlpVRx9rjb+68+d43NHeNqKqZjVYicdoLzwAMPkJiYSG1tLQsWLOA///kPjz/+OEajkZqaGld/mzpeXl5YrVY6d+7M6NGjee6554iIiOD666/ngw8+YOrUqWzevJmVK1fi5eXFdddd1+xJ5Hx8fJqsZcrNzT3tId5ncph4azlWTLNmzTpm/6e7776bu++++0wXrQGDwXBSNXpFRUVuWQPobnEV7te/xPvGBrbZuL65bShoWpOjkoKBaP8DZJVZMRk0HhrfA6NB4y8TG79Wd4324seDG5i/t5ybBsfxa0YuARYT45M74GFsXtNcaCv3wXa391997hqbO8Z1MjGddqN3UlISJpMJb29vpk2bRlFRkWvEjsVioaampsH+NTU1rmHF48aN429/+xu33347e/fuJTAwkPDwcObNm8fdd9/NgAEDmDdv3ukWUQjRigoqa7n9s60cLGxY33C4uJpgbw98PNtus4XJaDjukOvEI2tXxQRYMB5nv9hALy7uGcEXW7OY8vZavt+bz7iksGYnN0KIk9fid1f9TqfR0dGkp6e7/s7JyUEp1ajDak1NDd999x2XX345eXl5BAUF4eXlRUJCQoPhzafD6XS2yHnE2VU3Y7Jovz7ZmMHmzFI2ZpSwJbOUZXvzqay1c7i4ivhWXO6gJXQ5kuA0Z2K930/a11oLEApxrmhWm4vD4cDpdKKUwul0YrPZMBgM5Obm4nA4iImJcTVRBQQEEB2tD/ccNmwY7733HkOGDCE0NJQFCxYwcODARitlL1y4kPHjx+Pt7U1wcDC5ubmUlZWxd+/eBssEnCofHx8yMzOJiIjAw+PUln8XZ59SisLCQiyWtjn0VZxYeY2dr7ZlA/DL/kLe+DWN0ho7JoOG3am4vFfj0ZftybR+MaSXVDOm84k/p+Lrjcoa0ymE3kfWjRJCnBnNSnAWL17MokWLXH+vXbuWYcOGMXToUD755BOKi4tdc7bcc889rn4dPXr0YNKkSbzyyivU1NSQnJzMNddc0+Dc6enp5OTkMHXqVEAfkn7hhRfyxBNP4O/v71oN+3TExsZSUFDAoUOHTrlTrNPpxGBwr+rk9hCTxWIhNrbp6erF2eFwKmodTrzqjYB5/Ls9+HgaGRgXyLCEYLyP0cz05bYsKo9MUFc3wujS5EgWHFnstr2vSxTi48nTU3o0e/9PbxhArcNJj8jWX1RUCHenKWkDaJZzvbNWeyJxtRylFPd/vZN9+RXMmd6fIG9PSqptjP/vatc+3cJ9mdwjgs6hPgzsEOjanlVaw40fb6JbhC9rD5W4tr92VS/+ty6ddYdL+Pa2IZhslW73ernje9AdY6rjrrG5Y1wnE5N7DQsSQrSob3bmsuqgPiRzzoYM4oO9sTn0/myTuodjtTtZnlLAnrwKzCYDq/440nXsJ5syqLE5efD8ztz48SaqbU6CvT3oGenHM1N6sDuvnHA/c6Mh0UII0RIkwRHiHPfd7lyiAyz0iW7YXJRfYeXFn/bTLyYAh1J8uCGjweN/GtOJQC8PtmeVcfPcLVjtTuwOJ6YjI4N2ZpfTI9KPhGBv3r2mLyn5lQ2WGhjcoXnTPwghxKlo2x0whBBnVHZZDY9/t5db525t9NgXW7KoqnXw2IQkxnTSJ2GJCbDw5/M78acxiQQeWSW6V7Q//5jcDYBdufoaaDtzytmRU07PI31NuoQ1XEdJCCHONKnBEeIc9vHGo7UyBwur6BjizX9/PcgP+wo4XFzNkPhAOgR5cW3/GMweRrqH+9KridE/gzsEEWAx8dqqg1zbP4a/LNqNt4eR8V3DzmY4QgjhIgmOEOeokmobC7bn0C/Gn82ZZaw9pC8c+d7ao3NXXZKsD+M2GQ1M7Xvs1b4DvTy4fkAsr/+axs6ccrqH+/LyFcn4WzyOeYwQQpxJ0kQlxDmoqKqWS95ZS43dyUNjuwDw/Mr93Pf1DhJDvPnruM6MSwpjXFLza2DOO9KMFeLjyfOX9ZTkRgjRqqQGR4hz0NbMMqptTsYlhdI51IfLekXy9XZ9bprHJyTRM8qfK3ofu8amKZ1CvHnywq70ifEn2PvEM/sKIcSZJAmOEOegw8XVADw6PgmAR8Z14d5RiWSV1ZAU5ntK59Q0TToSCyHaDGmiEuIcdLi4ihAfT3zN+m8cTdPwNZtOObkRQoi2RhIcIc5BacXVdAiUNb6EEO5LEhwhzjHFVbXszC6jT0z7XgdKCCGORxIcIc4xqw4W4VCc1AgpIYRobyTBEeIcszO7HF+zkaQwn9YuihBCnDGS4AhxjtmVW063cD80TWvtogghxBkjCY4Q55CiqlpS8ivpcWSNKCGEcFcyD44QbkYphQIM9WponEqxJ7eCn/cXYncqLu4p89UIIdybJDhCuIE5GzJ4/deD3DQojspaBz+mFLDglsEYDXqS8+6aw7y1+hAAozuFkBDs3ZrFFUKIM04SHCHaOaUU87ZmYXMo3llz2LX9p9QCLjgyUurbXbmu7TcMij3rZRRCiLNN+uAI0c6tPVRMZmkNj43vwoiOwa7ty1MKACiorCWztAaAxBBv+kTL/DdCCPcnNThCtHMfbsggyt/MRd0jGNc1jO9257Epo5Q1acU4nIoNh0sAePeavnSPkKUYhBDnBqnBEaIdszmcbMss4/zOoXiaDPh4mriqTzRD44Mos9rJLqthQ3oJfmYTPSP98DDKLS+EODfIp50Q7dju3AqsDmejZRfig70ASCuqYv3hYgbEBrg6HAshxLlAEhwh2rFle/PxMGr0j/1dghOkj5J6fsV+ssqsDOsY1BrFE0KIViMJjhCnIaOkmpT8ilZ5bqvdyeJduYzpHEqgl0eDxwK9PAj29iCrrIabh8RxSXJUq5RRCCFai3QyFuIU2R1O7v1qB3alWHDLYJxKsT27jORI/0bNQUqpFl8a4ceUfMqsdi7vFdnk489M6YGnyUBPmbVYCHEOkhocIU7RV9uyOVxSTVZpDZszSrnri23cOncrn2zKcO2TWlDJxW+v5ZWfD7b483+9PYe4QAsD4gKbfLxfbIAkN0KIc5YkOEIcg1KKLZml3PzpZj7fksWKlALmb8tGKUWF1c7baw4T6WcG4PbPt7Inr4IOgV7M25KFUgqHU/Hxxgxyyq3M25qFU6ljPtfu3HL+8s0ufj1QhDrOfnXl+t+6dDZllHJZr6gGSzIIIYTQSROVEMfwxm+HeG+tPjPw9uxy1/akcF9WphZQUm3j/Wv7MvPTLQA8Mr4LuWVWXvnlIHd8vo1tWaUowKhBjd3JxvQSBnVourPvD/sKWJ6i/zepezhPXtStyf3sTsW/f0zlq23ZTOwaxjX9Ylo0ZiGEcBdSgyNEE4qral3JzUMXdOaZKd154bKeAPy8v5BPN2ZyYbdwkqP8XQtXjugYTIcgfXj25sxSekb5c0lyJO9d248wH0/X+ZqSUVJNXKCFKT0jWLw7j8vfXdeoJqfa5uDBBTv5als2Nw2K46lJ3fA0yS0shBBNkRoccc5QSrG/oIqEEG9MJ5gT5pNNmWjAZzcNpGPI0YUpO4V4uxKVy4507n1kXBfuGtkRH08T8fUWsZzWN5oJ3cIBuKpvNP/9NY3sshqi/C2Nni+9pJr4IG8mdA1j0c5cMkpryK+oJfxIE1hBZS0PLjnIgaIa/jK2M1f2iT6tayGEEO5Ofv6Jc8anmzK59qON3P/1jgbbDxZWUVlrd/1dVmPjiy1ZjE0Ka5DcADxwfifXv3tH+wNgMhoI9fEEICbgaPKSHOXv+vfEbvqil0v25DUql9Xu5GBhFbGBXvSLCXAlX+kl1QDM25rFjXM2kV5q5blLe0pyI4QQzSA1OOKcoJTig3XpAKTmVwLwn18OsjOnjA3ppVzdJ5qHxnbG4VT8b30GlbUOZgyOa3SeQR2C+PiG/lRY7U0ue+BhNPDuNX3RgOh6yU5MgBe9o/1ZtCOX+CBvzu8cgqZplFTbuO2zLdidisHxgVg8jHx18yAueWcdd36xjZGJwaw6UESwtwfPXZjI0MSQM3OBhBDCzUgNjjgnHC6pprjaRoDFRHG1jYIKK/9bn86G9FJAbwIC+GZnDh+uTycxxJuu4U0vTJkU5kv/2MBjPlfvaH96Rfs32j6xWziHS6p5+Jtd7MnTJwf8KbWAtKJqZk9M4rwjyUvEkWYpgENFVfSPDWDRbUNICvU6pdiFEOJc1KwanBUrVvDbb7+RmZnJ4MGDmTFjhuuxzMxMPvroIzIyMggLC+O6666jS5cuDY5dsmQJ1dXVJCcnc8MNN+DlpX9Qf/bZZ6xZs4aIiAjuuOMOgoL0ESapqaksW7aMWbNmtWCo4ly2LasMgNGdQli4M5ePNupz1QRYTJTWHG2e2nsk8Zg9sWuLl2F8Uij//jEVgPyKWrpHwJpDJYT4eDK5R4RrP4OmMXtiEokhPvSQeWyEEOKUNKsGJyAggMmTJzNixIgG2x0OB6+99hp9+/blxRdf5MILL+T111+nslJvAti1axeLFi3innvu4V//+hcOh4O5c+cCcPDgQXbu3MnTTz9N165dWbJkieuc8+bNY9q0aS0ZpzjHbc0sw89sot+RNZs+2ZjJsIQgfrhrOP1i/CmrsQGQUVJDt3DfM5JYBHl78tAFnQHIr7DyxZYsftiXz9guoY1mOZ7SM1KSGyGEOA3NSnD69+9P37598fHxabB979692Gw2JkyYgIeHB0OGDCE8PJzNmzcDsHr1aoYPH05cXBxeXl5cdtllbNiwgdraWgoKCujQoQMWi4WkpCQKCgoAWLZsGQMGDCA4OLiFQxXnsm1ZZfSO9ifY29O1bWpfvbOuv8WDsiO1OJml1cQGNh7l1FIu7x2FQYPPNmfx7I+pjOoUwn2jE8/Y8wkhxLnqtDoZZ2VlERMTg8FwNE+Ki4sjKyvL9XhycrLrsagofcG/3NxcoqOjWbhwIdXV1ezdu5eoqCgKCgrYunUrDz744CmVp7KyEqvVehoRHVt1dTVFRUVn5NytxR1jgsZxlVntHCyqYlS8L0ZbtWt7V38oKirCrDkorrKSV1BIVmkNw+N8z+h1CfYycbCoinAfD/48PILy0pJmHXeuvF7uQGJqX9w1NneMq6mYjlUhcloJjtVqdfWnqePl5eVqoqqpqWnycavVSufOnRk9ejTPPfccERERXH/99XzwwQdMnTqVzZs3s3LlSry8vLjuuutcfXNOxMfHp1EtU0spKipyu1old4wJGse160AhAMM6RxITaAH20zHYm7BQvVNvWEAJFWllpFcbcSjonxB+Rq9LhL8XBVXl9IoOIDIstNnHnSuvlzuQmNoXd43NHeM6mZhOK8Exm81UV1c32FZTU4PFolfxWywWampqGj1uNuujRMaNG8e4ceMA2LRpE4GBgYSHh/Pmm28ye/Zstm7dyrx587jttttOp5jiHLclswyjBj0i/bCYDPz5gs6M7XI0sfC3mKixO/lgXTpGg8bgDoFntDzdI3zZmVNOiI/niXcWQghxSk5rmHh0dDSZmZk4nU7XtvT0dKKjo12Pp6enux7LyclBKUVERESD89TU1PDdd99x+eWXk5eXR1BQEF5eXiQkJJCZmXk6RRTnOKvdyaJduQzsEIiXhxFN05jaN7pBchFg0fP8Dekl3D4sHl/zmZ0eaubgDiSF+biWeBBCCNHymvVJ7nA4cDqdKKVwOp3YbDYMBgNdu3bFw8ODZcuWccEFF7B582Zyc3Pp168fAMOGDeO9995jyJAhhIaGsmDBAgYOHIinZ8NfrgsXLmT8+PF4e3sTHBxMbm4uZWVl7N27l9DQ5lfhC/F7e/MqKKysdY1easoFXUIprrZxUfdwYgLO/Fwz4X5mPr5hwBl/HiGEOJc1K8FZvHgxixYtcv29du1ahg0bxowZM7j77rv58MMP+eabbwgNDWXWrFmufjA9evRg0qRJvPLKK9TU1JCcnMw111zT4Nzp6enk5OQwdepUQB+SfuGFF/LEE0/g7+8vzVPitBRX68O/I+tNnvd7Qd6e3Do0/mwVSQghxFmgqd8vWSyadK531mpP6se1cEcO/7d0HwtuGdxg6YT26Fx4vdyFxNS+uGts7hjXycQkSzUIt1Z6pAYn0MujlUsihBDibJIER7i1kmobnkYNLw95qwshxLlEPvWFWyupthHo5dFoKQQhhBDuTRIc4dZKauwESPOUEEKccyTBEW6tuMpGgEUSHCGEONdIgiPcWkZJNTHtfPSUEEKIkycJjnBbRVW1FFfbSAz1bu2iCCGEOMskwRFu60BBFQCdQs7MAqxCCCHaLklwhNvaX6ivap8YIjU4QghxrpEER7itA4VV+JtNhMqq3UIIcc6RBEe4rQOFlSSGesscOEIIcQ6SBEe4JadS7C+oIlH63wghxDlJEhzhlvYXVFJutdMryq+1iyKEEKIVSIIj3NKG9FIABsYFtm5BhBBCtApJcIRb2pBeQmyAhUh/meRPCCHORZLgCLfjcCo2ZZQwQGpvhBDinCUJjnA7+4tqqLA6GNQhsLWLIoQQopVIgiPcztacCgAGxAa0ckmEEEK0FklwhNtZk15OQrAXob7m1i6KEEKIViIJjnArmzJK2JFbxeW9olq7KEIIIVqRJDjCrby9+jDBXiau6C0JjhBCnMskwRFuo8JqZ1NGCRcmBWHxMLZ2cYQQQrQiSXCE29iZU45TQXK4rB4uhBDnOklwhNvYklmKBnQLkwRHCCHOdZLgCLex6kARvaP98fGU5ikhhDjXSYIj2jW7U7F8Xz63zN3CnrwKzu8S2tpFEkII0QaYWrsAQpyqWruTGZ9sJqWgkpgACw+M6cRVfaIoKy1p7aIJIYRoZZLgiHZnU0YJP+wt4IutWQA8PLYzl/eKwmjQWrlkQggh2gpJcES7kVduZdXBQp7+IRUvDwOBXh4Ee3twRe8oDJokN0IIIY6SBEe0C7V2J7d9toWsMisAb07tQ7dwXxwKSW6EEEI0IgmOaBfeXH2IrDIrQ+ID8bd40C3cF03TMEluI4QQogmS4Ig275cDhXy4Pp3Le0XyyPik1i6OEEKIdkCGiYs2rdrm4Mkle+ka7ssD53du7eIIIYRoJ1qkBuf555/nwIEDGI36BGt+fn784x//AGDfvn18+umn5OfnExsby4033kh0dDQA69at44svvsDDw4ObbrqJrl27AlBZWclLL73EAw88gMViaYkiinZqX34FpTV2/jYxHrNJ8nEhhBDN02JNVFOnTmX06NENtlVUVPD6669z7bXX0r9/f5YvX87rr7/Ok08+CcCnn37KQw89RGFhIXPnzmX27NkAfPXVV0yaNEmSG0FKfiUAXcN8WrkkQggh2pMz+pN48+bNREREMGTIEDw8PJgwYQK1tbXs27ePyspKPDw8iIqKomvXruTn5wOwf/9+ysvL6dev35ksmmgH/vDldv61PBU/s4kIP3NrF0cIIUQ70mI1OAsWLGDBggVERERw6aWX0q1bN7KysoiNjXXtYzAYiImJISsry9UclZmZSXFxMdHR0TgcDubNm8ett956SmWorKzEarW2SDy/V11dTVFR0Rk5d2tpyzFV2xysOVQMwIgOfhQXFzf/2DYc1+mQuNoPial9cdfY3DGupmIKDg5uct8WSXCuuOIKoqKiMBqNbNiwgddee43HHnsMq9WKt3fDlZ29vLyoqanBYDAwc+ZMPvroI0wmEzfccAPLly+nb9++VFdX8/LLL2O327n44otJSmreyBkfHx98fM5MU0ZRUdExL2J71ZZj2pRRAsDlvSL58wWd8TA2v7KxLcd1OiSu9kNial/cNTZ3jOtkYmqRBKdjx46ufw8bNox169axfft2zGYz1dXVDfatrq529a3p3r073bt3dxV606ZN/PnPf+bZZ59l6tSpBAQE8Nxzz/H000+jyWRu5wylFPO2ZmPQ4K6RHU8quRFCCCHgDPXBqUtGoqOjycjIcG1XSpGZmekaRVXfZ599xtVXX43RaCQzM5P4+HhCQ0NxOByUl5efiWKKNuqzzVks25vPnSMSCPTyaO3iCCGEaIdOO8Gpqqpi586d2Gw2HA4Ha9euJSUlheTkZPr160dubi7r1q3DZrOxdOlSPD09GzU5bdmyBT8/Pzp16gRAaGgoe/bsISsrC7vdjq+v7+kWU7QTmaXVvPTzAUZ1CuGmQXGtXRwhhBDt1Gk3UTkcDhYsWEBOTg4Gg4HIyEhmzZpFZGQkALNmzeLTTz/lww8/JCYmhrvuuss1Xw6A1Wpl8eLF3Hvvva5t06ZN46OPPsJms3HddddhMEgTxbliS2YZDqfirhEJssaUEEKIU6YppVRrF6I9ONc7a50tz/2YysKdOay4ewRGw6klOG0xrpYgcbUfElP74q6xuWNcJxOTVI2INmVHTjldw31PObkRQgghQBIc0YYUVFjZmVPO0Pig1i6KEEKIdk4SHNFmLNtXAMD5XUJbuSRCCCHaO0lwRJtgtTuZtzWL5Eg/EkNk3SkhhBCnRxIc0Sa8vfoQh4uruWVoh9YuihBCCDcgCY5odVW1Dr7clsW4pDBGJoa0dnGEEEK4AUlwRKtbtCuXCquD6/rHtHZRhBBCuAlJcESrcirFZ5sySY70o1e0f2sXRwghhJuQBEe0qnWHijlcUs01UnsjhBCiBUmCI1rVytRCvD2MnN9ZhoYLIYRoOZLgiFZTUm3jx5QChiYE4WmSt6IQQoiWc9qLbQpxKqptDq54bz3lVjs3Doxt7eIIIYRwM/KzWbSKAwWVlFvt3Dwkjp5R0rlYCCFEy5IaHHFWlNfYmb1kD9EBFm4e0oEDRVUATO4R2colE0II4Y4kwRFnxdrDxfxyoAgNKKqyYTYZ8DBqRAdYWrtoQggh3JAkOOKM2Z1bzr68CqpsTrZmlmI2GpjQLYxvduYC0DvaH5NBa+VSCiGEcEeS4Igz4nBxNTd+vLnBtn4xAdw1IoGOwd4khfvSRyb2E0IIcYZIgtNOKKXQtKO1HXanYmN6CYM6BGLQ2k4tiFKKrLIa/vvrQQCm9o3m4p4RFFbZiA/yItTXzA2D4lq5lEIIIdydJDhtXHFVLc/+mEp2mZUPruvn2r5kdy5Pfr+PmYPjuGtkx1YsYUOv/HyQORszALiufwx/GtOplUskhBDiXCQJThtVY3PwwIKdrDtc4tpWYbXja9ZfstVpxQB8viWLm4d0wOJhbI1iNrAtq4w5GzM4LzGYO4Yn0CXMp7WLJIQQ4hwl8+C0UXM2ZrDucAm3DOnAjMF6k85X27IBWJlawNK9+UT5m6msdfD8yv2M+c+vZJRUN3ku5axBOarOSDltDiff78njlrlbuGXuFjyNGrcOjadruG+bajoTQghxbpEEpw3allXGm78dol+MP3eOSGBKzwgAXv3lINllNby4cj+dQrz58Pr+xARY+Hp7DpW1DuZsyGh0LqUUZL8LOR+2eDkPFlZx6TvreGzxHoqravnTmEQW3jqEHpF+Lf5cQgghxMmQBKeNKais5Za5WwAY0TEEgJgAL9fjr/x8gKwyK38clUiglwcXH0l+AH7Yl4/N4QRAKSeq9Fco+w2sGWBNRzlrW7SsC3bkUFJj46XLkpk3cxDX9Y8lxMezRZ9DCCGEOBWS4LQxz/2YilGDu0YmMK1fNAAmg8bCWwcD8MO+AvrF+DMsIQiAyT0j0IAQH09Ka+w8v3K/fqLqfVC4SP9PMwFOPdFpIaXVNpbuyaNPdAAjEoOlOUoIIUSbIglOG7IytYDlKQXcMTyBmYMbdhyO8DNjNuov16wRHV1DxiP9LDx7SQ/entqH8V3D+GprNkVVtVC27uiJQybr/7ceBkA5qlG2QjRnWbPKVVxVy68Hisgpr3HVED23Yj/5lbVc0TvqdMMWQgghWpyMojrLVOlqvXYl4sYG89pU1Tp4dnkqXcJ8uKGJ1bUNmkbXCF8CLCb6xQY0eGxM51AAbhoUx7K9+azan8fFvilgSQBzB/AbDKWroOh7VG0BVO4AZSUADeVzM1j0odz1y1NhtWN3Kv61PJUVKfk4lL49yMuDbhG+rDtcwrR+0YzvGtbCV0gIIYQ4fZLgnEWqcicUfgMosBeBh97HJqe8hovf1mtcnrm4ByZj0xVrr13Z67hNQUlhPkT4mfl5904uHmiHoAloXvocOcojAmyFULERvLuCwRetYiNkv0upeSizlseQV17L1X2juWNYNA8u2M7GjEoArhsQQ0ZJDRvTS/DxNHKouJopPSK4dWh8C14dIYQQouVIgnOWKGsW5H0GpiA9ualJcyU4vx0sAmBS93B6H2f5ghPNdaNpGqMSvPhmVyU1PqPw8qo3AWDIJPBJBu8uaEZfAKpsHnjUbOaRHx2kFujDyN9de5jLAj5iY8ZoAJ6Z0p2xSWEopXAoMGIHzYimSeumEEKItksSnLNA2csg539g8IboOyDzVSjfhPLtj6Zp7MguJ9DLgycu7Hraz3Ve+GG+2O7P+rKujDo6wArNI8SVUNV5an0Cyw/onZUf77+NnkElXPfjSK5dMQqAfw7azAU+O1HqNijfiLFqH1gzAQfKtw8Eno9mPLnJ/JSjCiq3g+YJXh3RTIGnE64QQgjRJElwzjDlrNXnoHHWQPSdaCZ/VOAYKFyEvfowc3cZWLo3n2EJQQ36wJzSczmq6O+zHh+P83ly6SFe8PEls6SGST0iSM2vZOX+AkZ2DKZbhB9VtQ6WHygF9CUVpgyJARRvBll4bXUhu3KK6RNcDFYrHPonOOtNIujdHUpXQ/VBVOSNaKaApgvUlNJfoeTHo2U2BULAeeA/VGqFhBBCtBhJcFpYjc3BzpxyMktruLhnOOR9DrVZEHEDmvnIiCPf/pRk/8Af5u5kb4kv5yX48NAFnU//ycvX42mo5ZELYnj0+2xunbsVgC5hPry75jDLUwp487dDXNgtXJ8AELh9WDw3D+mAZtCTqz4d4M24GKx2O2bTaKjcqXeKtpeAVxJ4J6F5hqOq9kDuJ5D3OSr0MvAIRtMaN6EpeznU5oJHIJiC9XOZYyFoPOS8r5+38BtQNggcffrXQAghhEASnBa1IqWAp39IobjaBsD48C1YqnZC8GQ0n+6u/TSjF/9NHUFqqYmnB23mguhcNM0L5Yg/bpOPKlsHRm80n+TGjymHXqti6cSExC7sLDDwycZMAL7ZmcuBoioi/MyMTwpzLYYZ6m3i+gGxGA0Na440TcPi4aH/4Zus//c7mnc3VPCFenKS8QJgQJkCwFEFYVeBRzDYiiD/C1C/m2AwaAKadxKqw1/A6Ad5c6FoKcrSEc3SoenYnTaozUKzSMdmIYQQJyYJzmlQSvHLgSL8LSZ+Si1kzsaMBjP57k7bRr+OgyFgRKNjf83yZWxSAGOHzYDs9yH3IzBYUNGz0DzDm3wuir4HZUfFxTVoFlI1h8FWAI5SCL0YgPtGJTK5RwRv/3aIr7ZlY7U7uW1oB24fnsDYpFAyS2sYGGbE2/PUF+nUAoajLPF6DY0tH2rzoGoX5H1cbydPiJiu71O8DHz7QeB5+kNHYlChl4M1HXI+QJk7gCUe/AejGX1QtgKo2qMnb/YiVOQMNO/T76skhBDCvZ3xBKeqqoo5c+awY8cOvLy8uOiiixgzZgxFRUW89dZb5ObmMnz4cK6++mrXMR999BG9evWib9++Z7p4p2xnTjkfrU9neUqBa9sVvaN48PxOrEzJ45HF+/gmszt9B17UaGh3cVUteRW1dI/wQzMFoCKug9yPwZYH2e+hYu7UdzQGHO2X4ygD55EFMwsXQcT1AKjaXMh6E3CCwaIPAUevhUkK8+Xy3lGs3F8IQJ8YPaFIjvInOcqfoqKi074OmjkGzDGuv5W9HGr2g+YBzlq96coSDz49UX6DwOjbqK+RZvRCRdwIRd+BvRCK90LxMpRntJ64KSt4hILRV0+Cwqeh+fY97bILIYRwX2c8wfn0009xOBw8++yz5Ofn89JLLxEVFcXGjRvp1q0b9913H//4xz8YNGgQCQkJpKamUl5e3maTG5vDycIdOTyzPBWAmYPjiPQ3E+FrYXhHvaPwuAQru7sc4KOURIJWHeYP53Vs8KW+N68CgK7henOU5hkOcX/Sh5JnvQWH/6XvGDYV/PqhlFNfMBP0fjCVO1Dlm/TakMJvAX12YXx6oWkNX9Ih8UGufw+IC2z5C/I7mskPjpF8aKZjL8KpmaMg6mYAVE263lendJXeNydkCvgPgdocyJ8PefNQHmFgCkIzep+JMIQQQrRzZzTBsVqtbNq0iUcffRSLxUJcXBzDhw/n119/paKignHjxmGxWIiPj6egoIC4uDjmzZvH7bfffiaLdUqyivNZn3GYuVvL2JGvb/v0hgF0DtOTFOXU+90oRyVkvcE9PaDGazAfbcjAbDJwx/AE17lWpxXjYdQarbqtmaNRkTfoNRnWTChfhzL6QtVOvQkIIHwq5M7R+7ZU7oTqFH1+m5p0CDy/UbmNBo3XrupFrd2JydA+1ovSLHFgiUP5DwdlP5oYmWNREdMh/XnI/A8AyncAeATp62zV5kLIxUDEsU8uhBDinHBGE5zc3FyUUkRHR7u2xcXFsXv3brp168aePXvo2LEjhw4dYtKkSfzwww/079+f4ODgM1msk7YtbR+3LMgFwMPgBAyMj8mikyMNVX2evsZT0TIweoFJn2tG8wjhwbHdqXWm8M6aw3iaDMwcrHegXZlawJD4IHw8G19+zasTxNyDKlgAZWsg5z1AA++eEHEdmmZARd0CWW/o/V08QiH8miZHMNUZ3CHomI+1ZZrRq/E2jyBU7D16AmjNgrLV+gMeYaDsULQYvGec3YIKIYRoe9QZtG/fPvWnP/2pwbbt27erRx99VFVUVKg333xTPfXUU2rZsmWqoKBAPfPMM6qmpkbNmTNHPfvss2r+/Pkn9XwXXXSRAlz/3X777er2229vsG306NHqiSeeUL6+vq5tUVFR6oknnlD9+/dvsO/999+vrr322gbbrp82Rf38zd8abJtyQSeV8ssjavTQzg22P/HEE2rKlCkNtl197XTV+/EvG2zr37+/euKJJ1RUVJRrm5+fr/ryf39Tl00ZdcZjmjJlinriiScabEtKSlJPPPGESkpKOmFM1157rbr//vtPGJOvr6964okn1OjRo1s0pr887H4xuePrJDFJTBKTxHQmYjoWTakjE6KcAYcPH+aZZ57h9ddfd21bv34933//PY899liDfV9//XUmTpxIeno66enpTJ8+nZdffplx48aRnNx4mPLZVlRU1KhmSTlroWKr3vnVuxuapqGcVsCAZvBw7Wd3Kh79djc/phQwpWcEi3bm8tJlyYxIPPWaKuWs0UcWBYxs8FynG1N7ppy1cOjvWD26Y4m9trWL0+Lc7fWq445xSUzti7vG5o5xnUxMZ3Tq2IiICDRNIzs727UtPT29QZMVwObNmwkICKBTp05kZmYSHx+PpmnEx8eTmZl5Jot4WjSDJ5r/IDSf7q5OxJrB3CjhMBk0/j6pG+clBrNop97U1THk9DrHagYLWtD5p5zcuCPN4Ak+PfCw7UMpe2sXRwghRCs6owmO2Wymf//+LFy4kJqaGjIyMvjtt98YMeLovDA1NTV89913XH755QCEhoayb98+7HY7qamphIaGnskinjUeRgNPT+nB0PggAr08iPQ3t3aR3JNvXwyqBqr2tXZJhBBCtKIzvvjPtdfqTQUPPfQQr7zyClOmTKFr16MTtX3zzTeMHTsWb2+9RmPUqFFUVlbywAMPEBQURL9+/c50Ec8as8nAS5cnM2/GwEZz44gW4tUFp+YFFVtauyRCCCFa0RmfB8fb25s77rjjmI/Xn+APwMvLi3vvvfdMF6vVGA0aAV7SrHSmaJqRWo8eWCq3oBwVaEbfRvsopRrMS6SUQ59jxyMUzSA1a0II4Q5k+WbhdqzmPoADyjc2ekw5a+DwP1F5c10LjlKxVZ9XJ+0pVM5HbaL/jrIVoWrz9I7TQghxlqnaHJSj4vj7OCpR5RuODK6pf2w+KvsD/RxnbhzTCclaVMLtOI0hYOkIZetQAeehafXy+NpccFToSY1nNASO0ufU0TzAfyiU/qLPoBw45oyVTxV+pz9/6MXg3UMffaecrnIqZ62ecDmr9b81M3gE4aXFoar7giX+uPMeCSHE6VCVuyH3Q/3fxgAwx+pL8phjQDnA4AnVB/XPS1Wrz64fegWayR9VsgqKvtVPlLEXMKICR6EFTzjrcUiCI9yT/xB9lfKqvVBvJXfXjNCeUVD0PcojFGy54BmBFjIJVZOmbzcFNrnelarN0dcN0zwh5i40zajPYm0vQvM88QzKqmILlP6s/5E7B4x+KKMP2IpRIZPBKxHSn9Mf9xusr8puLwNbHubqTZC9Xl+U1asLeHcH394nTHaUo0pfpwwNUA0TPiGEqEfV5kPB1+ARDn4D9R+AtRn6jPq/59MLPEKg5Gc4/CzK5Af2Ev0xU4j+OWw9DCU/owLH6CNdzyJJcIR78ukJpkDI+wwV+0cw+evrdNUWAEaIug2y/quv4g7gO+DI//vpK5vnfaYvZBo0FhxVkPuJ/qulNluvAQIo+QWCxkD+5/ovmIBRejJVvBLMURA0wdXXRykFxUuhZKV+bPh1+nmq9ujrboG+2nqR82h5Qi9r0FeouDCHIEuhfkzVXqjcDtUpqLCrG/YpctrQDB6okp+gbL2+gKkpEExBYM1EWTpA8EQ0c2zLX3chmqCcNYBRprVoI5StWB+I4dMdzTPy6HanFTJe0P8IGofmP+joY45qqM3Sa8GrD4BXR/AfrtdA+w3Sa76dVeAxWP8cNAWiGSxHjq0668kNSIIj3JSmmfQlLdJfgvR/g8FHX+ereh94hukrmEfPgvJ1UJ0KfkcSHP+h4NMDin/Qk5HyzYBTby7yiACjH0TdCsXLofh7lPUwVO3Wjy395WjtTPVe8O6K8ojQa0+KvoXSX/VfPL59jjZN+Q+BmkN64lS4SK+VCRyjr8fVKChPNJ+e+srsyqmXoeRHMMehPCOg5iBU7obaLL2Gp3qf3lTn1xdK1+iPWxL1DtW5H6Oi7zruAqhCtASlHPr6cc5aVNStYI5tkJAf99j8+XqTSPAEfULVmgPgGQP2YlB2NEuH4x9vKwaTn/55oPSJcM9kDWZTz1G/+bm1KGctaAbAoK9hWLgIHGVQvBSFUU9WNE+o60vjGal/TtWjGb3Aq5P+X8Dwho95BEPoJcd8/tZaFFkSHOG2NI9QlP8gfU0vp74IKgDh1+iPG70gcLT+X90xmgamAAi7EuXdQ++orOwQMAzN++j0Bip8GuSh16IYfSHuQf2DuHofOGuh8BvIfl9vnzb66x8m/sMg5OIGH+6aZgCvjihLAvgNbPavHE0zoILGQm0mFC48+oA5Vv9wqk4B764Qfr1em+PTC8o3QNB4PcHJfgdyP0RF3+FagV7ZCvT5g3x6SeIjWk5t3tFaz6zXIWAEKnjycZMcVb5JX2fOmqFvqNyufwlX7W24n3cPvXYyeGKje0dZsyDzVfAIQxn99Pc9TpR3d/0+MXhB2VoIGI7mDAJ+N1O9cqInK0Y9cbHlgUdYw+TFaTt6r9Xm6jXC9hKUJREMHmArBs2Iij6ygLSyQ+ES8OqE5tfv6DnK14G9VK89xqh/3vj0POGlVY5KMHj/blSoHTAerT2uOQTZ7+nPbfI/2oQUdrXe/F38vf4jD8AUrP8Iirql1ZOylnBGl2pwJ+f6lNftSf24lKNS/7USMBKKluh9b4IvavYvyONRyqF/MHhGoJkCGz5W/KO+GKgpQK/WtSRC0AWn1Tm46eVCavTaJoMXBIw4WiX8u6HwjcpetgYKFujJWcQNejt54WL0pV6MYPQGo4/eTGaJR9UchtLfIGAomiWh6XNWp0LeZ/oXjk8f8OmJ5nHihV7b8/tQOaqhYiN4JaF5hru2N/laKaX39zL6gtZ4xvO27lRfJ1W2Vu/T4d3zaD8Ov0EQcJ5e+2np6KqxVPYy/Qs4679HTxB9FxR8dSRBAYIv1L+Ii3/Qkw7Qa1cjp6N56BPDKqdN76hvywPNpN/3nhF6EmIrcHXgb8AYcLQjrdFPb8KpOQC+ffUkrTZL3ydgxJEaDE1fDLl6v/6ed1ToSQToiyBrnvoxx2IK0vezl+nvC82knx+l11AZvEHT9Hvwd8mOctZA0fd6gmaO1c9j8AKc+g86zxgIGE51eQ5etq36jy+//noc9mK9r1/IJXotsjVL3+YR1uA93FadzPtQEpxmas8fwsfijjGBxNUcSjn1kVzFy/QPN9A7CwZN1H9F2kv0X6bOGjB30DtnO6tBM+vJj8EMMXcfrf0p3wT5X+jNcabgox/s3j0h6Hz9l3jFFv3Lwbtng+Srrb5eylYIBQshaGyDphCl7Hryao7VO7JXbgdLIlr0ba59fh+TclRDwXx93zpB4yDwgmYl28pWBJXbwGkHSwdwVIJ317Na9X8qr5NyVOu1KGh6LSdKT3bK1zfc0ScZLPF67QYOfZtXJwiZguYZibKX6l/c3t1dr4Vy2sBRqteS5M3V+394RuvNzbU5+nNEzmhQ8wpHEk1HKdRkgDkS7OVUFafgbSrSO9TaChoHYgrWR1raS0BZ9eTD6KsnUJ6RR5KVED1xM/q4alWUUlCyQo9f89ATFqO/fj/VJVso/T12JIlRzlo9eXPW6gmWoxziHtJrnEH/sZH3qV7j4xkB9nK9hlozHU2wfl/2iOlo5qiTeu3aKklwzoC2+iF8OtwxJpC4ToZyVOk1OaYAvZq/Xg2Tclr1TsqlP+kfnMEXQfkmveq9OlWvlfKM0Dsc2nLBIwyibtWHitbm6wmPNb3xk1oS9BEWygZhV1FcUt4mXy9VsFBvJjH6Q9yfIP8r/Ve5R7CeGGIAnEcPMMfo2zzCKTEMJyhUX3PP9SVvLwX/wWAr1Duu12bqI+UCRujXX/PQa9JMwWgm/6PH1hyEosX6cfX5DkALv6plYq3N18uApie1Rt9GiVdxQSaBPjX6PkZfvVbAXgLeSYDSjzN4ud5DSil9xGHVboi+Hc0Sf/T5qvfrnVL9h+jJb/Fy/QGvLnrtiMEbzHHN76tTfUD/0ldK/7IH8O2LFj6tWcc3qPV1WvXXRzMCTjD4gGY8Oo1D5U69o7+t4EjN7LgWqRFuirJmQ+Yr+h9BE/QfGaW/6H+HX4vm21tP9GoO6bUyzir935ZEcJRSXA7BIZHHfoJ2SBKcM8AdvzTdMSaQuFqactr0Dp11vyCVAzJeOvKFq+k1Cj69wLefax848uVsPax/CVbt0+ccqtqj/7uuhidoIsWqd5t7vZStEDJe0ftQAXVD7F2M/npyp5n0zpVFP+i1Akf6MtSaOuMZOlKv6Sleqh8TdRuaV6J+fuXU+2mVrTl6Ts2s1w5oJr3JBYf+Kx+lJ1aRN4A5Xu+sXvy93iQRfh2ad2eUvRwqNulNK5aOzWoadMVa/0u0jnd3CBrf4Fe/9fC7eNpTG5/AFKLXMtRdK6OvnqAYPPXkJXgSWuB5xy9D2Tq9n1rgGFet4KlQTpveYd8jBM3o0+zj2vJnhsr7rOHSM8YAPQkMOO+EiVVbjutUSYJzBpzrb5T2ROI68/QOmE7qd2Y86XPkfAA1GZR5X45/WPc206lRKYfeId1WAFG3Q94nej+JiOv0HfI+02u7/Ic0PrY6Ve8IW7rq6EaPUPAfihYwouG+SkFNmv6FXLZWTwj8Bh0Z/p8K5jjw7qaPhDPHNuizo6zZRzq0FutJjaP86ImNftDhL8e8nspRre/vqNBr5Kp26TUD/kP0L09l0+c1wQlRtwCaXmtQtQd8euu1UCU/6U0o/gP1JM2aqZ/cq9OR6Qgy9GYiry4QOfOM1XC0lLZ0b/2eUnZwVOs1SvZi8Ixq9r3SluM6VZLgnAHn+hulPZG42gdVcxiy39abv4x+EHo5Wv1JGVujTE6b3tRRtVuvHfHtpSc8ylavA/fxh/0qpSjL346/v7/+ZW/0P6kveKXs+iyxXp2O/zxO25HkIl2v4fHtpdeqFX6jz/NkjtGbvVQt5M3TExrPMD1RqRvVpJn1aRH8hzbsZ2QrPjKbdlXDJ426Fc2rk2v6/aPzPNn1Icb1+57YC8EY0C46U7vbvVXHHeM6mZhkmLgQolVolg6oDo9Qmb8BH9t6KPwG5d3lpJooVE263pnUO+nI3EInXwukCpfotTV+fSF/vv6lHjAKzbeXXk7NeKQ/xpFyn+A5NE3DYYpFs5zaF4ummcC7y4n3M3jA75p+lL1cHzWY/faRLUa9z5SzRk9m7MV6XxufZFBOvRNvEwmI5hGEipmlNyc6a8C7O2VlpQR4dXLF2KjMRlO9vzW95kqIViQJjhCi1WhGL2o9e+LjHwY5/4OChajAMfqIIXPckZEqhXoHypKf9aHn5hjgSDNZ7hy970b5evCMQkVMB7Rm90FRxT/onaih4VT0wRNbNtCzRDP5oSJv1kffgJ7EOMr0JT28u53cuTxCIeBokuIwmluyqEKccZLgCCFanebdDRUwUu+74hpCXFdr4jjybweUrEDF3q8Pyc1+R//yDrsaUJA/T5+1GlBenfX5jszRx3xOZSs6OnoncqY+WqnkZ/Af0mb6A50KzbszeHdu7WII0eokwRFCtA2B5+tzmphj9H4jdet2hV2lN5Uom/7/jBf0OWisGfqII58eaAbLkc6YR/qMlP4Cma+hYv/Y5CKoylEBOfpqya7RTd5J7bbmRgjRmCQ4Qog2QTN6Q+R04MgM1PZi8ElG80l2rRWmrBlQuUsfNusRCrH3ueZdqT+qSfkN1GtzSn+FsCsaPI9y1urJjb1QnyX2yNBtIYR7kQRHCNHmaEYf15phDbabY8Eciwoaz/EWTtRMfijfflCxCWXw1kcN1Q2Nrs0FnBBxfbPW+xFCtE+S4Agh2h19FM8Jhl4HjNAnvytdpXdWNvro//cOPFIzJMmNEO5MEhwhhFvSPMNRCbM5nckIhRDtlyQ4Qgi3dTrT/gsh2rf2OxZSCCGEEOIYJMERQgghhNuRBEcIIYQQbkcSHCGEEEK4HUlwhBBCCOF2JMERQgghhNuRBEcIIYQQbkdTSqnWLoQQQgghREuSGhwhhBBCuB1JcIQQQgjhdiTBEUIIIYTbkQRHCCGEEG5HEhwhhBBCuB1JcIQQQgjhdiTBEUIIIYTbkQRHCCGEEG5HEhwhhBBCuB1JcIQQQgjhdiTBOQe422ocDoejtYsghNvdVwB2u721i3DGuOPr5a5a6rU65xMcp9MJuN+bv6KigtTUVAA0TWvl0rSMsrIy5s+fz86dO1u7KC2qpqaGioqK1i5Gi6tLROvuMXdQUVHBtm3bWrsYLa6srIzPP/+cX375pbWL0qKqq6spLCwE3OdzEOTeai5Ti56tnfn++++x2+2MHz8eT09PlFJucRNUV1fzyCOPEB4ezm233UZERAROpxODof3ms1999RU//fQTVquV66+/HsAtXq8vv/yS9evXExISQu/evRk4cCAhISHtPrbFixdTUFDA1VdfjZeXV7uPB8Bms/Hkk09SW1vLn//8Z2JjY9v9fQUN760pU6YAuE1cq1evJjg4mE6dOjF06FA6dOjQ7mOTe6v52u+rfBry8vJ4/fXXWbRoEampqezduxdwjwxfKUV5eTlhYWFERkayatUqgHZ7Q69fv54//elPpKWl8dRTT3HZZZexe/duoP2/Xj/++CN79uzhwQcf5LzzzuPAgQPMmzcPh8PRbmMrKirinXfe4ccff6SgoID169cD7f+1cjqd2Gw2YmNj6datG4sXLwba730FsGHDBu69917S0tJ4+umnmT59Ort27QLad1wAa9euZffu3Tz66KNceumlWK1WPvroI+x2OwaDoV3W2Mu9dfLa97v4FFmtVmJjY3nggQcIDAxk9+7dlJaWAu23qaqu3JqmYTKZsFgsxMXFkZOTw549e4D2WZ1ZVVXF9OnTuf/++wkICKCmpgaDwYDVam3top0ypRRKKbKzs+nduzehoaEMHTqUCRMmUFlZybfffgu0z9fLZrPRoUMH7rrrLpKSktizZw95eXlA+4yn7r4yGAw4HA7Ky8sZOHAgZWVlbNy4EWi/fcI0TePGG2/k/vvvx9vbG03TMJvNFBcXt3bRTlnd65WTk0NMTAyBgYH06NGDiy++GJPJxKefftpgv/ZE7q2Td04kOLW1tWRkZLj+jouLY/jw4SQkJNCnTx8KCgpcv1zaUzZcP666ciulKCgoICYmhqFDh+Lt7e3K9Osy4rZ8c//+tRo9ejQDBgxw3cAxMTGkpqZiNptbq4inpH6buaZpaJpGbm5ug9ciISGBkSNHsmHDBoqKitrFr2ir1Upqaqqrc2pERASDBg0iMTGRHj16YDAYWLt2LdB+agXqYqpfk+ZwOLBarXTo0IGkpCQSEhL4+eefATAajUDbvq/gaFw2mw2AAQMGNLi3goKCyM7OxtvbuzWLedLq3nt19xZAcXFxg8+IwMBArrjiCtauXUtubm67qMWRe+v07y2374OzZMkSvv/+e0JDQwkLC2PQoEH069eP4OBgAPr27cvu3btJSUkhPj6e6OjodtFG+/u4Bg8eTN++fdE0DX9/f9LT0/Hz82PQoEH8/PPPvPnmm4SEhHDVVVe12STuWDE5HA7XG71jx45YLBb27dtHUlJSK5e4eZYtW8bKlSv5wx/+QGRkJDabDQ8PD4YNG8YXX3zBJZdcAug3c8eOHYmNjWX9+vVMnDixlUt+fMuWLWPRokVERETg7e1Nv379GD16NEFBQQAkJiZy4MABUlJSSE1NpXPnzm3+3vp9TP3792fUqFEYjUZMJhNpaWmYzWZGjx7NJ598wgsvvEBwcDAzZsxos/cVHDuu+klB165dMRqN7N69m759+7aLvh3Lli3jq6++4pFHHiEuLs51bw0cOJA33niDSy65BB8fH5RSJCQk0K9fP3788UeuvfbaNh2b3Fstc2+13avRAlJTU9m4cSMPP/wwt912G1FRUXz44YcUFBS4qsUAhg4dSnl5OTt27AD0bLi2trY1i35cTcX1v//9j4KCAgCys7NJTEzEYDBQXl7uuhESExOBtlmdebyYjEajK4O32+1YLJY2GcPvWa1W5s+fz6pVqwgICODrr78GwMPDA4CePXsSEhLC/PnzAf1XSl3i3dZlZWWxadMm/vznP3PvvfcycOBAPv30U/bt29dgv+TkZHx8fFi3bh2g31vV1dWtUeQTaiqmTz75hJSUFEAfadSlSxcsFgtpaWlkZWWRnp5O165dgbZ5X8Gx49q3bx8Gg8H15VFZWUlUVBTl5eVA267NttvtfPvtt6xbt474+HjmzJkD6PeWUork5GSio6P58ssvAT0Wo9FIYGAgmqa16WZFubda7t5yywSn7sswPz8fg8FAZGQk4eHhXHzxxXTr1o33338fOFqt17FjRzp16kROTg4//fQT//nPf9iyZUtrFf+YmhuXv78/W7du5cUXX2Tx4sUMGjSIhIQEsrOzgbZVndncmOrU1YAcOnQIaLtfKqDH1qFDB6655hquvvpqMjMz2bp1q+sxf39/LrzwQpYuXUpGRobrQ7ju/21ZYWEhJSUlREZG4uPjw8iRIxk1ahRff/01ZWVlrv0iIyPp3r071dXVfPvtt7z00kuu6ue25lgxzZ8/n+rqasxmM+vXr+f5559n4cKFjBw5ki5duri+eNrSfVVfc18rf39/bDZbu+jXoWkaHTt25NJLL+Wuu+4iJyfH1VxTV+7rrruO1atXs337dmw2GwaDgZqaGry9vdv0/SX3VsvdW23zjjxNdb88qqurCQkJobKy0vXYjTfeSFpaGjt27EDTNFf7Zq9evdi4cSOff/45ERERDB48uFXKfjzNiWvXrl34+vri6+tLXFwcjz32GNOmTaNz585ER0e3VtGP6WReq7q+Az169ODw4cMopdrslwqAxWKhR48edO/enQ4dOjBo0CBXB+K6uAcMGMCoUaN45513+Pzzz/nggw9ITU11/XJpq5xOJ3Fxca4vQ4Bp06ZRVFTkSuLqfiXHx8ezb98+Fi9eTGxsbJttejteTBs2bMDX15eOHTsSHx/Po48+ysUXX8ygQYPc4rWqu7d69erFnj172nxzh9FopHPnziQnJ+Pv78+UKVP4/PPPXY85HA4SEhKYMmUKCxcu5P333+f9999n06ZNdOvWrZVLf3xyb7XcvdV238Gnoa5WoHv37uzevZvMzExAv8heXl5ccMEFLFmyBACTycTu3bt54YUX6NatG88++yxXX311q5X9eE4U1/nnn8/SpUuJiIjgnnvu4aqrrnJ1tJswYQL9+vVrtbIfy8m8VnVNO3a7nc6dO7f5ToIAXl5egP6hO2TIEEBvi4ajnSOvvfZaLr30UgwGAyaTiccee4y4uLjWKfAJ1F3zmJgYCgsLOXz4sOsXs9Fo5IILLnDFZzQaSUtL44UXXiAuLo5nn32Wq666qtXKfiwniun888/nxx9/xMfHh1tvvZWrrroKi8UCwMCBAxk6dGirlf14Tua1qru3NE1jyJAh7eLe8vT0BPQ4zz//fHx8fFzNvXVxTp48mWuuuYbY2Fh8fHyYPXt2m+27J/dWy99b7TrBKSgooKqqyvV3/ZvS4XAQFRVFz549WbBggWt4sVKKuLg4PDw8XLPHBgUF8ac//YlZs2bh4+Nz1uP4vVONq0OHDmiaRkVFBb6+vg3O2dq/xlritaq7Ma644grOP//8Vo8Jjh2XUqrRLNmhoaGMHDmSlStX4nQ6MZlMriG5/fr148orr2T69On4+fmd5Sgaqz8y5fccDgehoaF07dqV1atXk5OTA+hxdunSBU9PT1d/sICAAO677z7uueeeVr+3TjWmpKQkjEYjBQUFroS1Tlvop9JSrxXAmDFjGDduXJtowjlWXL+/t+qmxrjyyitZunSpq6NxXVydOnVi0qRJTJ06FX9//7MbRBPKyspcNWa/117vrVON6UzfW63/DXEKysrKeP7553nllVd45ZVX+PHHH6murnY1Y9T1YVBKceONN5Kfn88vv/xCcXExmqZRWlpKYGCgKwmIjIwkNja2laNq+bjagpaMqS6hqfu12ZqaE1ddp7+6G9VoNDJgwADi4+N5++23ef755xv0MWoLX5YlJSW88847fPHFF0DDxLhuxI3RaMRms3HFFVdgs9lYvXo1mZmZaJpGVlYW4eHhhIaGAvqPh8jIyFaJpU5Lx9RWnIm42kJi05y4DAYDlZWVDYYa9+nTh759+/LSSy/x7LPP8vLLL7dK+Y+lqKiIV199lTfeeINXXnmFHTt2uJK4umHT7e3eaumYWpqm2kNd5O/873//QynFjBkz+OGHH9izZw/+/v7ceOONrn0+/fRTMjIyuP/++9m4cSOrV6+murqa6OhoNmzYwPTp09tcP5uWjKutDPF0x5ig+XFlZ2dz4403um7g2tpaXn31VVJTUxkzZgzTpk1rrRAaSUtLY+7cudhsNsxmMxdffDHdu3dv1B/j008/ZdeuXTz88MMcPnyY1atXc+DAATp37symTZuYOnUq5513Xpt4vdwxJpC4Pv30U/bs2cOdd95JVFQUoCc/b7zxBtu2beOCCy5g6tSprRVGI1u2bOHLL78kOTmZSZMm8fXXX1NeXs6ECRPo3Lmza7/29Hq1h5jaVYLjdDqxWq3897//5bzzzmPQoEE4nU4OHDjAq6++yp133kmnTp3497//jcViYebMma5htwUFBezZs4f8/HxGjRpFSEhIK0dzlDvG5Y4xwcnHdfPNN7vmrqiqquL111+ntraWWbNmuba3Ffv37ycnJ4e4uDjWrVtHfn4+s2bNAvTq5LKyMj788EOsViszZ850vS41NTXs2rWL/Px811pabYU7xgQSl9Vq5eabb3Z9ZjidTl577TVKSkq455572ty9tXz5cux2u6sTcG1tLf/3f//HNddcQ8+ePSkuLmbOnDnt6vVqFzGpNi4/P18VFRW5/i4tLVWzZ89WO3fubLDfF198of7+978rpZTKzs52bbfb7WenoCfJHeNyx5iUOv24HA6H6//FxcVnvsDNVBdXXfmsVquqrq5WSim1a9cu9cILL6hffvnFtb/dblf5+fkN/nY6nWe30CfgjjEpJXHV+X1cDofDFVdFRcVZLPnx/T6uiooK171fW1urlFLq6aefVuvWrVNKtY/Xqz3G1GZnMq6oqOCjjz4iOzsbHx8f4uPjmTBhAsHBwcTGxrJkyRJ69OjhqtY677zz2LlzJ9u3b6dXr14Nemq3Je4YlzvGBC0XV13VusFgIDAwsBUj0jUV14UXXkhgYKCrQ3SHDh3o1q0b69ato2/fvq4+UKGhoa61tNrS6+WOMYHEdaK46jdbtXZHWzh+XHU8PDwoLS2lsrLSNaLLaDS22derPcfUJjsZZ2Rk8J///AcvLy9mz57N2LFjKS0t5aeffgLgsssuIyUlhZ07d7ra7CwWCwEBAa6e3AaDoU2MsqnPHeNyx5jg3Itr5cqVwNGOzj4+PvTo0QMvLy+WL18ONFzQtS3F5Y4xgcTlbnHVl56ejr+/PwEBAQ22t7W42ntMbedK1lNeXk7fvn256aabMBqNDBw4sEG2GBoaysSJE/n444/Jz88H9CFzVVVVbWIY4LG4Y1zuGBOce3HVfXGoel3yoqKiGDhwIPv372f+/Pk8/vjjbN++vbWKfkzuGBNIXOB+cdXV6h44cICOHTsCsHr1al544QUOHDjQamU/lvYeU5toosrMzCQrK4vIyEji4uLo3LkzkZGRrjVDjEYjdrsdq9XqOuayyy7j8OHDvPvuuyQm/n979/ML1x7Gcfztx6BNSCtC1FTkbkimJH50Y6NpsKEbCbEg8Q9Iumz/BRbV7htLG4mNvWjHooS0TUOEhTIxR4QmDcIwc+5C5tR1XWlu23Ocx+e1E8R5z8aTmXOe71+sr69TUFBAeXl5gCX/ZLHLYhOoK9t18SmGgoICXNdlfX2dvb09enp6qK+vDyrFY7EJ1HUburJtjuNQXFzM2NgYjuPQ39/vnRUYJGtNgQ44mUyGyclJ4vE4sViM5eVluru7efz4Mffv3yeTyZCXl0cmkyGZTHp3a2cfGRwaGmJra4svX77Q0tJCZ2dnkDkei10Wm0Bdl7uyv5uTk8PHjx8ZHx+nu7ubrq6uAGt+XJe1JlDXbeqC8x1aS0tL3L17l/b2dp4/fx5MyAUWmyDgAef79+9sbW3x8uVLKisrWVxc5P3796RSKbq6urzP7Y6Pjzk5OaGqqgo4v7fh7OyMkpISYrEYsVgsyIx/sdhlsQnUdbkL8LYsP3z4kNevX3sr8YNmsQnUdZu6Tk9PKSkpoa+vj9bWVu9YgqBZbIIA7sE5OjryPrPb2NjAcRwqKyvJZDI0NzfT0NDA5uamd6gYQDKZpLCwkNLSUj59+sSLFy+Ym5vz+9KvZbHLYhOo62e7ysrKAv/HYrEJ1HVbu+LxOABPnz4NfBCw2HSZb+/g7OzsMDEx4T1Z0tvbSzQapaioiNXVVe/U0KamJra3t1lbW6Ouro7CwkKWl5dJp9O8efOGRCJBT0/PjTngzmKXxSZQV5i6LDaButQVfJfFpv/iyzs48XicV69eUV1dTV9fH6lUiunpaY6OjqitrWV+ft772Xv37hGNRr0DudLpNNvb2yQSCWpqahgZGbkxL6jFLotNoC4IT5fFJlAXqCtoFpuu9ed2CP4wNTXlvnv3zvt6f3/fHR4ednd3d92FhQV3bGzM/fDhg/f9RCLhDg8Pe1sSP3/+fKO2VGZZ7LLY5LrqygpDl8Um11VXlrqCY7HpOr58RNXW1kZ+/vmfOj09paioiLKyMlKpFLW1tSSTSaanp3nw4AHRaJTNzU0ePXrkbaa8KY8FXmaxy2ITqCtMXRabQF3qCp7Fpuv4MuBkDz5zXZdIJILjOLiuS3l5Ofn5+bS3t7O3t8fbt2+JRCLs7u4yODhIJBLx4/L+N4tdFptAXWHqstgE6lJX8Cw2XcfXx8SzC4FWV1e9FxTgzp07DA0Nsb+/z9evX2lsbPTzsn6ZxS6LTaCuMHVZbAJ1qSt4Fpuu4uuAk12OtrGx4e0NmZ2dZW1tjWfPnlFRUUFpaamfl/RbWOyy2ATqChOLTaCusLHYZbHpKr4OOLm5uaTTaQ4PDzk4OGB0dJRv374xMDBARUWFn5fyW1nsstgE6goTi02grrCx2GWx6Sq+bzJ2HIeVlRUSiQQdHR03ZrX9r7LYZbEJ1BUmFptAXWFjscti02U5rnvh6FYfnJ2dMTMzw5MnT0J749JVLHZZbAJ1hYnFJlBX2Fjssth0me8DjoiIiMif5vtZVCIiIiJ/mgYcERERMUcDjoiIiJijAUdERETM0YAjIiIi5mjAEREREXM04IiIiIg5GnBERETEnL8BBXn149ugeZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x345.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEgCAYAAAC5GAmdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACG0ElEQVR4nOzdd3gUVdvA4d/sbrK76Y30Tkujd0RApEkTQUGxAHZ9rdgr6Pf62lFR7L2BCiqKiCICivTeS4BASO91s9lyvj+GDAQSpASSLOe+Li/J7OzseWZ2Zp85bRQhhECSJEmSJMmF6Bq7AJIkSZIkSQ1NJjiSJEmSJLkcmeBIkiRJkuRyZIIjSZIkSZLLkQmOJEmSJEkuRyY4kiRJkiS5HJnguAhFUVAUhWnTpjXI9tLS0rRtfvrppw2yTUmqT3P4vi1dulQr49KlS0/7/dnZ2Xh5eeHt7U1RUVHDFxDo378/iqLQv3//BttmbGwsiqIwadIkoO5j9emnn2rL0tLSGuyzm4tzsd+Pd/w1vqioCG9vb7y8vMjOzj5nn9ucyQSnHlarlddee41evXrh6+uL2WymdevW3HTTTezcubOxi9dgpk2bpp04xzIajfTo0YMePXrQokWL81KWmotEzX8Gg4HQ0FCuuuoqDhw4cNrbu9AvunU5dp8oioJOp8Pb25suXbrw7rvvNnbxXNqzzz5LRUUFN910E/7+/rWOxe+//37C+ldccQWKohAUFER1dfU5LdvJkrdOnTrRo0cPWrZsWe/7W7RooV0vjEYjcG7Ov5pkS1EUBg8eXOu1Q4cOYTAYzvhmr7ldL/z9/bnpppuoqKjg2WefbeziNEkywalDUVERvXr1YsqUKaxatQqn00nr1q3Jz8/n448/5rfffmvsIp5zYWFhrFq1ilWrVjF8+PDz+tnu7u706NGD+Ph4cnJymDNnDiNHjjyvZTiZc/1jc74kJibSrVs3DAYDGzZs4I477mDu3LmNXawm6WyPeUlJiVbbcf311wNw1VVX4eXlBcDnn39ea/2CggIWLFgAwIQJE3B3dz+rzz8bP/zwA6tWreKpp56qd53hw4dr14uwsLDzUq4//vij1s3mzJkzcTgc5+Wzm4rrrrsOgM8++4ySkpJGLk3TIxOcOtx1111s3LgRgAcffJDCwkK2bNlCUVERK1eupEOHDkDdtR913Qkdu97vv/9OYmIiHh4ejBs3jrKyMl577TXCw8MJCgri7rvvxm63a9ur627kVKpDKyoqGD16NHFxcXh6emI0GmndujVPP/20drHu378/zzzzzAmf9emnn55QDZ2RkYFer0dRFObMmaO9Z926ddp6//zzD6BWxd98881ERETg7u5OTEwMjz76KFar9ZT2f01ytWfPHu3HYPv27RQUFGjrlJWVMWXKFOLi4nB3dycsLIzbb7+d4uJiACZNmsTkyZO19ePi4mrtx1PdrzXrPfTQQ0yaNAlfX1/Gjx9fa/+8+uqrXHfddXh7exMREcF///vfWvG89tprJCUl4enpiY+PD8nJybXKdrzbb78dRVFISUmptXzEiBEoisKgQYMAWLNmDYMGDSIoKAij0UhUVBTDhw9n3bp1p7Sf3377bVavXs2WLVu0ZcfevQshmDlzJh06dMBsNuPr68uoUaPYsWOHts6xd73z5s2jb9++mM1mEhISmD9/fq3PS01N5brrriMsLAx3d3fCw8O57bbbTihXSUkJkydPxsfH54T9eex+f+WVVxgzZgweHh6kpKSwfPlyNm7cSPfu3fH09KRPnz7s3r1be+/ChQu5+OKLCQ4Oxt3dHR8fHy6++GJ+/fXXOrf/0ksvMXr0aDw8PHj44Yfr3IczZ87UasLee++9evf1nDlzsFgshIWF0aVLFwA8PT256qqrADWJKC8v19afPXu2dp7WfFe2bdvGmDFjCAoKwt3dnbi4OB588MFa76vLQw89RHJyMn5+fri5uREeHs7EiRPJysoC1OvTJZdcoq1/ySWX1GqSOr6Jqi7H136c7Pzr1asXiqIwfvz4WtuIiYlBURQeffTRk8YD4ObmhhCCN998EwCLxcKHH36Im5tbnetff/31tG7dGm9vb+2adM8991BaWgr8+/Wiurqa//3vfyQnJ2MymfD19eWiiy5iz549J3zWhx9+SFxcHN7e3owYMeKE5qPZs2fTs2dPPD098fT0ZMCAAdq1s8bff/9Nx44dMZlMdOrU6YTXa3Tt2pWwsDAqKyvlzUldhFRLcXGxMBgMAhAdOnQQTqez3nWnTp0qAHHsblyyZIm2bMmSJSes5+3tLdq2bav9nZSUJMxms4iPj9eWvf/++9r2apZNnTpVW9avXz8BiH79+tW7Xl5engBESEiI6Nixo4iMjNTWefDBB4UQQtxxxx0iIiJCW96jRw/Ro0cPMX/+fHHgwAFt+SeffCKEEGLIkCECEGPHjtU+d8qUKQIQrVu3FkIIkZ+fL2JiYgQgPD09Rfv27YW7u7sAxIgRI06672viiomJ0ZZdf/31AhC+vr6iurpaCCGE1WoVnTt3FoBwd3cX7du3F56engIQnTt3FtXV1eLZZ5+ttU87duwoevToIT744IMz2q/u7u7Cw8NDpKSkiPHjx9faP25ubiIsLEwEBQVpy37//XchhBA//fSTtiwxMVEkJSUJDw8Podfr690PK1eu1N6zdetWIYQQBQUFws3NTQDiyy+/FA6HQ/u84OBg0alTJxESEiIA8cUXX9S77U8++aTW99PpdIpFixZpy2bMmKGte9ddd9Uqe832fX19xb59+07Ynpubm2jdurUwm83ad72goEAIIcTevXuFn5+fAIROpxMJCQkiIiJCO9bH7k+z2Vzv/jx2PaPRKOLi4rRjHxwcLIKCgkSbNm20fdW7d28tnpdfflm4ubmJ+Ph40alTJ+Hl5SUAYTAYxKZNm07Yvru7u/Dx8REpKSnigQceOOHc/vjjj4WiKEKn04kPP/yw3n0uhBDXXXedAMSoUaNqLf/rr79OOM+EEKJ79+7aNUgIIXbs2KGV19PTUyQmJgqdTqfF6HA4hBB1f4eTk5OFr6+vSElJEQkJCUJRFAGIbt26CSGE+OCDD0RiYmKtY92jRw/x7LPPCiGEdj5PnDjxhH1UU+ZjvwcHDhw46fn3xRdfaMev5vuxevVqbd2dO3fWux9ryjJ06FDh5+cnPD09RXFxsfjggw8EIK655po6z21PT08RGBgoOnToUKtcV155pRBC/Ov1YsSIEdprISEhIjExURgMBu0aX7PfzWazMJlMonXr1tr6EyZM0MrxyiuvaMtbtmwpoqKitHNnxYoVQgghsrOztWNtMplEYmKi8PHxqTMuIYQYNWqUAMR1111X7367UMkE5zhr1qzRvkh33XXXSdc9kwTnyy+/FEIIcdFFF2nLli9fLhwOh3byjh8/XtvemSY4VqtVbN++vVZ5ay6ykZGRJ41BiLovYrNmzdJO4tLSUuF0OrXE6bnnnhNCCPHMM88IQAQEBIjMzEwhhBDLly+vFWt9auJyd3cXPXr00C4SAQEB4vvvv9fW++yzz7Qfpm3btgkhhEhLSxN6vb7WPj7+onus092vQUFBIj09XQghhN1ur7V/evXqJaxWq8jLy9N+WB955BEhxNEL2qWXXqpt02aziWXLltW7H4QQIiEhQQDiiSeeEEII8f777wtA+Pj4iMrKSpGfn699/qFDh7T37d27V6SlpdW73WP3yfH/DRkyRNjtdiGEevxrfgjfe+89IYQQVVVVIjk5WQDi5ptvPmF7U6ZMEUIIMW/ePG3Zr7/+KoQQYvLkydoxOzb29evXa593Kvvz2PUGDx4snE6n9uN2bLmefPJJbVllZaX23qKiIu2zCwsLhbe3twDEk08+ecL2ExIStPXtdnutc/v2228XOp1O6HQ68emnn570WAohRNeuXQUg7rnnnhNea9WqlQDEJZdcIoQQYteuXdrnvPbaa0IIIW644QYtuak5vu+884623k8//SSEqPs7vHnzZi0BEkLU2l+pqalCiLqvWzXOJMGpb5kQ6veoRYsWAhBvvPGGEEKIhx9+WIB6k3UyNWUZO3aseOCBBwQgpk+fLtq3by8AsWLFijrP7Y0bN9bazhNPPKF9Hy0Wy0nLu2zZslrHveYcycjIEFlZWbX2u06n05LlK664QkuIhBCioqJCS8Yfe+wxIYQQDodDDB48WABi4MCBQgghnn76aQEIRVHEhg0bTjhmxyc4d999t4CjCat0lGyiOo445tmjx3e8bQg1fUliY2MBtaPYRRddhE6nIyYmBoCcnJyz/hy9Xs+XX35JmzZtMBqNKIrCl19+CUBmZuYZbXP06NH4+flhsViYN28ey5cv5/Dhw+h0Om644QYAVq9eDUBhYSHh4eEoikKfPn20baxatepfP6e6uprVq1ezd+9eAJKSkrjooou012s+w263k5KSgqIoxMbGau3vp/IZp2vs2LFERkYC6r491rhx43B3dycoKIjg4GDg6DEcMmQI7u7uLF68mKCgIHr37s0999xTb1V6jYkTJwLwzTffAGq1NsD48eMxm80EBgbSq1cvANq0aUNKSgrjxo1jyZIlhIeHn1JMiYmJ9OjRQ1v/t99+4/XXXwdg7dq12rlw2223oSgKJpOJ7du3A3Xv45rmxKSkJG1ZzX6oOWZ9+vShb9++2uudO3c+YTsn25/HGj58uHbsa9ScX/Hx8dqy3NxcQP1eTZo0ieDgYPR6PQEBAZSVlQF1nxMTJ07Ez88POPGYv/vuuzidTl566SXtWJ1MTdOpt7d3nZ8DavNgeno6X3zxBaA2w1x77bWAejwAevfurV0nJkyYoG3jZM2Smzdvplu3bnh5eaEoCrfccov22pleC86G0Wjk5ptvBuDjjz8G0JpXTmVf1rjrrrvQ6XRMmzaNLVu20L9/f9q1a1fnuosXLyYlJQWz2YyiKDz33HOAeg3Jy8s76efUfHcBHn30Ue27EB4eTmhoaK1127Vrp3VhqDkPar6727dvp6KiAoDnn38eRVHQ6/VaB/Oac2rr1q0AtG7dmk6dOgGc0Jx3LB8fH+Dod0w6ytDYBWhq2rZti8FgwG63s3z5coQQ9SY6xy53OBzo9fp/7ehV82U0GAy1/j52e8cmWcduv8apdCZ74YUXeP755wG1bTs0NJTDhw+TkZGB0+n81/fXxWQyMX78eN577z1mz56tXWgHDhyo/fjX8PLyIjk5+YRt1PxgnExMTAz79+9n0aJFXH755SxfvpybbrqJn3/+udZ6bm5udf5AhoSEnHJMp7pfT7bNY2OqOa41xzAlJYXt27fz9ddfs3HjRjZv3sw777zD+++/z6pVq+jatWud27z++ut54oknSE1NZf78+SxbtgygVj+BxYsX8/XXX/PPP/+wY8cOfvjhB7777ju2bdvGG2+88a+xv/322/Tv3x+n00mfPn1YuXIlr7zyCg888ECt9Tp06IDJZKq1rK4kqmY/1OyDY/fD6TjZ/jzW8efSscuOPTdr3jt8+HBSU1MxGAy0a9cOk8nExo0bqa6urrNz6smOuZeXF+Xl5bz33ntcf/31WiJWH19fXwAtoTrWxIkTmTp1Kk6nky+++EK7ERkxYsRZj2Bcvnw5EydORAhBYGAgSUlJlJeXa51zG6tT7m233caLL77I5s2b+eijj9i3bx9Go5Grr776lLcRGxvLqFGj+PHHHwG455576lzvq6++4sEHHwTU/n1RUVHk5+ezf/9+oGH3QV3f3bokJCRo34kaZ3pDXdOP6FSurRcaWYNzHF9fX8aNGwfAxo0befzxx2t1+v3rr7/4888/AWpd1Pbt2weonQUbUs1n1Gw/NTWVbdu2/ev7au4G2rRpQ1paGv/88492Z3EsDw8P7d81dxcnU9PR8Pfff9dqF47tfNitWzcArcaoZmTFkiVLePDBBxk7duy/fgaATqdjyJAh/Oc//wFg/vz5rFy5stZn2O12Xn/9de0zli9fztSpU7WRBSeL7XT365lefPbu3YuiKDz99NP88MMP7Nq1Cx8fHxwOh5a01CUiIkLrTHzLLbfgcDho27atVmsjhGDFihVMmjSJjz/+mFWrVnHTTTcBaN/P01GTBNhsNkDtvFgT8zXXXKPt41WrVjFz5kztB+NU9ejRA1B/cI/tMLlp06bTLuuZKCgoIDU1FVCHa2/atInZs2ef9Lie7LW3336biIgI9u7dy5AhQ/71pqNNmzYAdQ4/joqK4tJLLwXUG5ODBw8CtZPZmu/8ihUrtNe//vpr7fX6EuXVq1drx3br1q2sWbNGq2091uleB07FybYZExPDiBEjALj33nsBGDVqFP7+/qf1GTXvjYmJYdSoUXWuU3Mt9Pb25sCBA6xevfqEIeYnK2/Ndxfg5Zdf1m4Qs7OzT6u2PTk5WfuMAQMGsHLlSu2c+vTTT7UBHzWDC1JTU7Xz47vvvqt3uzXfh5rvmHSUTHDq8Oabb2pVgy+88AIBAQG0b9+ewMBA+vXrp406ueSSS9Dp1F04YMAAevTooVUvN5SaC9+sWbPo168fPXv2PKUamPbt2wOwZ88e4uLiiImJqbNZISEhQft3cnIyPXv21O5s6tKzZ08SEhKw2WwUFBTg5+fHFVdcob1+1113ERUVRVlZGUlJSbRv357WrVvj7+/PVVddddrVqA888IA2RPZ///sfoP7gduzYESEEvXv3Jjk5mcTERHx9fRk2bJj2I3JsbAMHDqRnz57aj+uZ7tfTtWzZMlq1akV4eDidO3cmLi5Ou+OqOUb1qUkca0ZhHJtIOhwOBg4ciL+/P8nJybRr144PPvjglLZb484776Rnz55ERUVp343Ro0cD6iiS22+/HVCr5WNiYujQoQMBAQF07dq1znlbTubxxx/Hz88Pu91O3759SUpKIjo6utZ351wKCAjQahmnTp1Ku3bt6Ny580nvsk8mKiqKX3/9FV9fXzZt2sSIESOwWCz1rt+vXz/gaFPT8WqSmZoanpCQEC677DLt9UcffRQvLy8qKipITk4mOTlZS/579+5d71QOx34X2rVrR2JiIi+//PIJ67Vs2VJrNr3hhhvo2bNnrdGSZ+Jk5x+glb8mmTjZKK369O/fn7y8PLZu3XpCM2KNmn1QVlZGfHw88fHxfPvtt6dc3r59+2rJ2MyZM4mIiCAlJYWYmJjTmhPNw8ODqVOnAmqCHB4eTqdOnQgODiYxMVFLWO+88048PT1xOp3a9e3uu++ud7s136ljR8JJKpng1CEgIIAVK1bw6quv0r17d0BNFHx8fJg4caKW/SckJPD+++8TGxtLQUEB/v7+vP322w1alunTpzN8+HC8vLw4cOAAjzzySK0+LfV5/PHHtT4EpaWlXH311dx5550nrDdixAhuueUWAgMDOXjwIKtXr6aysvKk2z62nXz8+PG1mi+CgoJYtWoVN998M8HBwezcuZPS0lK6devG//73v9NqPgK1KaSmb8f8+fPZvHkzRqORpUuXMmXKFGJjY9m7dy95eXkkJyfz5JNPandA7du356mnniIkJITs7GxWr16tzSB7pvv1dHXq1IkxY8ZgNBrZuXMnZWVldOrUiY8++kiroalPTZ8nUGu0avYDqH1Cbr/9duLj48nMzGTPnj1ERkZy++23M3PmzFMq286dO1m9ejV5eXm0bNmSBx54QBt2C/DWW28xY8YMOnToQG5uLgcOHCAsLIw77rjjlGviarRq1Yq1a9cyYcIEWrRowd69e7Hb7XXeSZ8LiqIwd+5cunXrhl6vx+Fw8NVXXxEUFHTG22zXrh3z5s3DaDSyfPlyxo4dq9WAHe+qq67CZDKRlZVVZ3+ZK664olYTw3XXXVcr+UpMTGTlypVcccUVGI1G9uzZQ1RUFA888AC//fabdqN1vEGDBvHiiy8SHh6OxWIhISGBd95554T1AgMDmTFjBlFRURQVFbF69eqznh33ZOdfTdlat24NQGhoKEOGDDmjzwkKCqqzb1ONm266iSlTphAUFERZWRn9+/evc2K8k5V37ty5PPfccyQmJlJYWMihQ4fo2rXrKfd3q/Hwww/z1Vdf0bNnT0pLS9mzZw9+fn5MnDhR65cUGhrKL7/8Qvv27XE4HOh0OubNm1fn9tauXUtWVhZms/m0z8kLgSLOpJFckiRJOi2333477733Hvfcc88p9ZG6EIwaNYqff/6Zhx56iJdeeqmxi9Ps3HvvvcyYMYPbb7+9zsT1QicTHEmSpPMgKyuL1q1boygKhw4dOu3+Jq7kueee4++//+a3337DZDKxd+/eEwYqSCdXVFREdHQ0QghSU1NPGNElyQRHkiRJOs/69+/PX3/9RXx8PNOnT6+3g7AknQ2Z4EiSJEmS5HJkJ2NJkiRJklyOTHAkSZIkSXI5MsGRJEmSJMnlyARHkiRJkiSXIxMcSZIkSZJcjkxwJEmSJElyOTLBkSRJkiTJ5cgER5IkSZIklyMTHEmSJEmSXI5McCRJkiRJcjkywZEkSZIkyeXIBEeSJEmSJJcjExxJkiRJklyOTHAkSZIkSXI5MsGRJEmSJMnlyARHkiRJkiSXIxMcSZIkSZJcjkxwJEmSJElyOTLBkSRJkiTJ5cgER5IkSZIklyMTHEmSJEmSXI5McCRJkiRJcjkywZEkSZIkyeXIBEeSJEmSJJcjExxJkiRJklyOTHAkSZIkSXI5MsGRJEmSJMnlyARHkiRJkiSXIxMcSZIkSZJcjkxwJEmSJElyOTLBkSRJkiTJ5cgER5IkSZIklyMTHEmSJEmSXI5McCRJkiRJcjkywZEkSZIkyeXIBEeSJEmSJJcjExxJkiRJklyOTHAkSZIkSXI5MsGRJEmSJMnlyARHkiRJkiSXIxMcSZIkSZJcjkxwJEmSJElyOTLBkSRJkiTJ5cgER5IkSZIklyMTHEmSJEmSXI5McCRJkiRJcjkywZEkSZIkyeXIBEeSJEmSJJcjExxJkiRJklyOTHAkSZIkSXI5hsYugCS5ioqKChYuXEhaWho2m43g4GAGDx5MZGRknetv2rSJDRs2cOONNzZ4WV5//XUmTZqEn58f//zzD5s3b6akpAQPDw+6du3KRRddpK1bXFzMvHnzOHz4ML6+vgwbNoz4+PgGKUdubi6///47mZmZWCwWpk6detL1n3nmGTw8PHjggQfQ6dT7L4fDwfTp06msrPzX95+qTZs2kZaWxujRoykoKGDRokWkp6fjdDqJiIhg6NChBAUFaeuuWbOGgoICjEYj7dq149JLL9XKJ0lS0yQTHElqINXV1YSHhzN48GA8PT3ZuHEjX3/9Nffddx/u7u6NWrYrrriCkJAQCgsL+fLLL/H19SUlJQWAuXPnEhkZyYQJE9i7dy/ffvstd999N56enmf9uTqdjqSkJLp27co333xzSu8xm83s3buXtm3bApCamorZbKaysvKMyuB0Ok+ajFRVVdGmTRsuv/xy3N3dWbZsGbNnz+auu+4CwGazMWTIECIjI6moqGD27NmsWLGCPn36nFF5JEk6P2SCI0kNxN/fn169eml/d+nShd9//538/HzCw8P/9f3Lly9nw4YNVFRU4OPjw4ABA0hMTASO1vZERkayceNGTCYTw4YNo3Xr1v+63WNra4KCgmjbti2HDh0iJSWFgoICsrKyuO6663BzcyMpKYnVq1ezc+dOunbtegZ7obagoCCCgoIoLCw85fe0b9+eLVu2aAnO5s2bad++PUuWLNHW2bhxIytWrKC0tBQPDw8uuugirbxpaWl8//33dO/enVWrVtGyZUuuuOKKej8vIiKCiIgI7e9evXrx999/U1lZiYeHB926ddNe8/HxoV27dqSlpZ1yPJIkNQ6Z4EjSOZKdnY3D4SAgIOCU1vf392fy5Ml4eXmxfft2fvjhByIjI/H29gYgIyODDh068NBDD7F+/Xp++uknpkyZgqIoJ2zrvvvuq/MzhBAcOnSILl26AGoTkr+/P0ajUVsnJCSE3Nzc04y24SQkJLBmzRqqqqq08vbv379WguPp6ck111yDv78/Bw8e5KuvviIiIoKwsDAAysvLsVgs3HfffQghTviMjh070rFjxzo//+DBg3h5eeHh4VHv6y1atDj7QCVJOqdkI7IknQNWq5UffviBfv36YTKZTuk9ycnJeHt7oygKKSkpBAQEkJGRob3u6+tLly5d0Ol0dOzYkfLycioqKk6rXEuXLkUIof24V1dX10puAIxGI9XV1ae13YZkMBho06YN27ZtY/v27bRp0waDofa9WJs2bQgICEBRFGJjY2nZsiUHDx7UXlcUhUsuuQSDwYCbm9spf3ZpaSkLFixg8ODBdb6+ceNGMjMz6d2795kFJ0nSeSNrcCSpgdlsNmbNmkVERAQXX3zxKb9v8+bNrFy5kuLiYkBNPo7td+Ll5aX9u+ZH+3QSkTVr1rBlyxYmT56sJQzu7u5YrdZa61mt1jr7DJWUlDBz5kzt78cff/yUP/t0dejQgcWLFyOEYODAgSe8vnfvXpYtW0ZBQQFCCK1Tdw1PT88TkqJ/U1FRwRdffEHXrl1p167dCa/v2rWLxYsXc/3119dbuyNJUtMhExxJakB2u51vvvkGHx8fRo4cecrvKy4u5ueff+aGG24gMjISnU7Hu+++22Dl2rhxI8uXL2fy5Mn4+Phoy4ODgykqKsJqtWo1OTk5OXX+wPv6+p7TpOZY0dHRlJWVoSgK0dHRFBUVaa/Z7Xa+/fZbrrjiCtq2bYter2f27Nln9XkWi4Uvv/yStm3b0rdv3xNeT01N5eeff2bChAmEhISc1WdJknR+yCYqSWogDoeD7777DoPBwOjRo+vsG1Mfm80GoNUMbNy4scH6wWzZskWrefD396/1WmBgIKGhoSxbtgy73c7OnTvJycnROjefLSEEdrsdh8MBqMmJ3W7/1/cpisI111zD1VdffcJ+dDgcOBwOPDw80Ol07N27l3379p1xGa1WK19++SVRUVF11hYdOHCA77//nnHjxtXqjCxJUtMma3AkqYGkp6ezZ88eDAYDL7zwgrb82muvJSYmps731Px4t2jRgl69evHRRx+hKAodOnQgOjq6Qcq1ZMkSLBYLH3zwgbasffv2jBgxAoArr7ySH3/8kRdffBFfX1/GjRvXIEPEQW3WeuONN7S/n3vuOXx9fevtBH2sY5ucjmU0Ghk6dChz5szBbrfTtm1bbcTVmdi5cyeZmZnk5eWxadMmbfl//vMffH19+euvv6iqquKrr77SXouJieHaa68948+UJOncU0RdQwwkSTrnVq9ezYEDB7j66qsbuyiSJEkuRzZRSVIjsNvt7N69+5Tmx5EkSZJOn0xwJOk8y8nJ4ZVXXsFoNNK9e/fGLo4kSZJLkk1UkiRJkiS5HFmDI0mSJEmSy5EJzik63RljmwNXjAlkXM2NK8YlY2peXDU2V4zrdGKSCc4pOn62V1fgijGBjKu5ccW4ZEzNi6vG5opxnU5MMsGRJEmSJMnlyARHkiRJkiSXIxMcSZIkSZJcjkxwJEmSJElyORfMs6hKS0vJzc3VHmp4upxOJzk5OQ1cqsbVHGLy9PTUnq4tSZJUnz/35qMAl7QOauyiNFlOIVDgtB4E3JxdEAlOaWkpOTk5REREYDabz+jg2u12DAbX2l1NPSan00lGRgb5+fn1PnhRkk7HxsMlbM8u4+pO4Rj0Mml2FXaHk0d+3gHArBu6EOjhhtlNj8lN38glazg2h5P9BZW0DfY6o/cXVlZz+7db6BXrz/39WzZw6Zqmpvvr1oByc3OJiIjAw8OjsYsinQadTkdISAgHDx6UCY50VpxC8PHqQ7y34iAAaYWVPDm4TSOXSmooqw8Va/++/dvNlFTZGZYYzDOXJTReoRrI1+sPsyevgowSC5sySnlhRCKXtmlxSu/dV2jhp9RDXJ4SyoPztnOgsJIDhZX4md2Y3CP6HJe88V0QtzA2mw2z2dzYxZDOgJubG3a7vbGLITVjhZXV3PP9Vt5bcZChCcGM7xjOvG3ZLEvNb+yiSQ1k0e48vIx67u8XT6SfmZaBHqw+VMzpPonI5nDy5548skqrzlFJT99ry/bzy44cduaUE+Zj5NH5O5m2cNdJ31NRbee1pfu46+d9zFyexuB3V7E1q4y7L44D4LO16dq+SS+y8OC87axKKzznsZxvF0QNDlw4bY6uRh43qS7pRRYsdgdtWpy8un7D4WKe/GUXJVU2Hh/YmtHtQrE7BRszSnhu0V7ahfsQ4OF+nkotnQsFFdUs2ZvPpW2CmNAlkgldIpmzOZMXF6eSUVJFpN/Rm9uyKjsmNx1udTRPHiio5Klfd7E7txyDTmFshzCm9G+JrhGvQXaHE50CTgEfXd2R7NIqHvxpB7/syOXOi+II9jbWWn93bjkr0gpZeaCQjRmlDGvjT3JEAK8s2cfdfeO4vmsU3kYD//tjLxklahJ31adrcQiodjjpGRvQGGGeMxdMgiNJkmtYuDOX/y7ag1GvY8FtPTEa6q6I3p5Vyp3fbSHC18zrY1K0ZMhNr/DMZQnc8NUGXv4zledHJJ3P4ksNbObyA1Q7nNzQLUpb1iPaH4Bfd+ZyS68YAKrtTq78dC0Azw1LpMLmICXUmwAPN37eVcAHa3Mwu+uZNrQtqw8W8c3GTAa2aUHHCN/zH9QRaUUWnAL+77IE2gZ70aaFJw/0b8mrS/exLr2YYUkhVFTb2ZZVRkmVjSd+OVqz89CAVgyMNhEQEMDQxGB8TG4AJIV6A7A9u4yKajsOAb4mAwcLKxslxnNJJjgXoMzMTEaNGsXy5csbpZPxunXrePrpp1mwYMF5/2ypeftk9SHe/ieNaD8zh4otzPhrPw8NaFXnup+uTcfLaODTCZ3wNtX+nrcK8uTylFDmb89BCCFrCpshIQS/7Mjh5+05XN81ktiAo30so/zN9IkP4P2VB1m2r4C2LTzR6xQKK9VRtHfM2QLARXEBKMDyA4X0ivXn6SFtCfJ0p09cgJocfbOZL6/rTNtgL+Zvz2ZHdjn9WgUS5WfG4RSE+5rQ6878uyOEwGp31tsZekd2GYDWsVhRFMZ1Cuer9Yf5fG06nSN9mTJvO3vz1OczBXi44WU00Dc+kDHtwygtLgLQkhuAloEeGPU6tmWVcbjYQpCnO9d1jeT1Zfu54qM1jG4XxsTuUbgCmeA0ASNHjqSwsBCdTofBYKB9+/Y89thjhIaGNnbRJKnJKLHYeOefNAa2CWLa0ARu+3Yz327K5JZeMfiZ3U5Yf0d2GX3iA09IbmpE+Zmpsjsps9pr/QBIqq2ZpfywNYtt2WUkhXgzLtGXgCMtGPsLKjAZ9IT7mhqtfH/tK+CZ3/bgazJwYx0dZu/qE8fag8UUVlSzvNxKYaWNAA835kzqxkerD/HV+sP8c6AQd73CnT3CmNS7lZbo+prdaB3kyd78Cm75ZhPPXpbAM7/tAeC7zZnaZySHevPxNR3/tRmrstrBR6sOEuFnYlhiCOsPl+BvdmPu5kwW783nrbHtSAnzOeF9W7NK8TEaiAk42symUxQeubQV9/+4nZEfrsFk0DGpexQ9Y/zpGOH7rwmXQa+jbYgXczZnYncKbukZzeh2oQgBP2/P5ot16TLBkRrW9OnT6dGjB1arlRdeeIGXX36ZV199tbGL1eBkh2HpVD23aA8Op+DpIW2ZszmTtYeKEcBVHcMxGnTc0C2KR37eQXZp1QkJjsMpKKioJsS7/v41Nf0XcsqsMsE5js3h5P4ft+EQgpRQH/7cm0dqbilfXB+CoiiM/2w9AKvuu5icMiu7c8uJCTBTWGGjS5TveakRW7QnD4APr+6Il/HEn7KWQZ4suK0HXkYDOkWh3Kpee7yMBu7rF09qfjmrDxbzyKWt6RPufkKZ3xzbjqJKG8/+vpuHftqhLR/bIYykEG82Z5Tw0/YctmeV0S5cTU7sTsGmwyV0jvLVPvOBedvZmVOGxeYE4Pk/Uk8o63srDvLm2HYAHCyqZOJXG+kVG8C2rFJSwrxPSKD6xAdyaesgFu/N56qO4fynT9xp7bv2YT5sySzltt4x3NQjGkVRuK5rJHank5nL06isduDh3vyH2J9SgrNkyRJWrFhBRkYG3bt3Z9KkSdprt912G+7uR78cnTt3rvX6kiVLWLhwIRaLhZSUFK6//nptRNM333zDqlWrCAkJ4bbbbsPfX203TU1NZdGiRdxxxx0NFGbzYTQaufTSS5k+fToA1dXVvP322yxatAibzUb//v2ZMmUKJpNJa+qZMGECn332GTqdjv/85z+MGjUKgKqqKt555x0WL15MWVkZrVq1YubMmdpn/fbbb7z//vtUVVUxYcIEbrrpJgDee+899u/fj7u7O8uWLSMsLIyXXnqJP//8k6+//hp3d3eeeuopevbsCcBPP/3E559/Tm5uLv7+/txwww2MHTsWONocNW7cOGbNmkX37t25/PLLa8U8e/Zs5s6dy1tvvUVISMg538dS01dutfPj1mwARqWE8tLiVGrGwySFqH0IwnzUBCW7zEpCiDefrD5EuzAfukb7UVhZjUNACy9jXZsHIPjIa7ll1bQ+tVG3F4TU/Aqu+VxNYP47LIEhCcHM3ZzJC4tT2ZNXge8xNWIDZq6g0uao9f7esf48PqgNId717/vjfbMxgzWHinn00lYUW2x8uymT+/rF4+muflZFtZ0VB4roFu2Hn9kNIQSrDxYzPCm4VtPU8Y5NXI9Pgv7TJw5f02GGJARTUVp8wnsDPd0J9HTnw/EdeeefNL7bnMmn13SiVQtPAC5pFcQvO3P5a38BgZ7ufLcpk8PFFpbuK2BI2xbkllvxcDew4XAJJoOO169I4b0VaezMKcfLqGfakLYYDTrWHCrm6w0Z/LO/kC5Rvrzy5z4qqh2sOFBIpc1R73QGjw5sTYCH2rx0um7uFc3ANkEkH1drFOaj1shllVbRMsiz3vdnlFgorbKTeORcbKpOKcHx9fVl+PDhbN++vc6ZgJ944ok6m1N27NjB/Pnzue+++wgKCuLTTz9l9uzZTJ48mQMHDrB9+3aef/55fv31VxYuXMg111yDw+Fgzpw53HrrrWcfXT1E2QYoW3da79EJgTiduxLvrijenU+zZGpSsmjRIlJSUgB48803OXz4MF9//TUGg4Enn3ySDz/8kLvuuguAgoICysvL+fXXX1m1ahWPPPII/fv3x8fHhzfeeIN9+/bx8ccfExgYyLZt22rNCLx582bmzp3LoUOHmDhxIgMGDCAuTr0T+Pvvv3n11VeZOnUqzz77LHfffTeXX345v/76Kz///DP/+9//+OmnnwAICAjg9ddfJyIigg0bNnDPPfeQnJxMQkKCVsbS0lJ+/vlnnE4n27Zt08rwwQcfsHTpUt5//30twZWkdenF2r+fXLALs5ueSpuDQW1baP0VQo/8gGaVWim22Hj7nzQAFt/Zi9zyagBaeNZfg1PzA/zMb7tZeHvPRh0t0xRszihhQ0YJP27J0pZ1jfIDoEeMem6uSy9md275kWV+VFgdXNI6iDf/PgDA9V0j+W5TJo//spM7esfSqoVnnc2Hx7I7nMz4az/VDkFltZ3U/EqKLTY2ZZTw5th2hHgZefinHaw5VMy1XSK5r188ueXVFFtsJIWe2KxzqhJDvHlueCIAFSdZz92g495+8dzdN67Wd8TbZKBNC08+XZPOV+sPY3McHZL+2+48/M1uFFlsGPU6/vxPb9z0OiJ8TVz16ToSgr3p10qdcTnK38zivfnc9+PR62KEr4nPJnTicEkVyaF1JxF+ZjcevrTu/mf/xtPdcEJyAxB65KZhzaHiehOc7NIqRn+kdtb+7faeTXoU4iklOJ07qz/UaWlpFBcXn/LGV65cSe/evYmKUtvzRo8ezX//+1+uvfZa8vPziY6OxmQy0aZNG/78808AFi1aRJcuXQgIcK3hav/mwQcfRK/XY7FY8Pf356233kIIwQ8//MDs2bPx9VV78k+ePJknn3xSS3AMBgM333wzBoOBPn364OHhwcGDB0lOTuann37ik08+0SbJ69ChQ63PvPnmm7X937p1a/bs2aMlOB07dqRXr14ADBw4kCVLljBp0iT0ej2DBw/mueeeo6ysDG9vb/r06aNts0uXLvTs2ZONGzdqCY6iKFpNXw0hBNOnT2f79u289957eHmd2eyckmtKL7Jo/84ps/LuVe2x2Bx0iz6aBPuZ3TAadGSXVrHhcIm2/PfdeVpic7IanMAj6xRZbBwqsjBvazaZpVUMTwqhb8vAhg6pSamyOcivqCbc16Q1pdz+3RbsTkGkr4l3r2qPl9Gg7aMIXxNe7npeX7YfBbihWxR39YnVau57xvpTWe2gY4Qv/mY3Zvx9gDvmbCEuwIMvr+uMez0j3QB25JRT7RB4uutZl15CgIeaEKUVWnhpcSoXxQWw5shEfqsPFmFzONmTpyZZbVvUX8vQ0OpKgMN8TOzMKadzpC9do/yYvTGTVy9PxmjQEe1n5p7vt+JrdtOGpccGePC/4Yl0ijiaXET4mpkzqSs/b8/h9125+JjcmNwjCl+zG77/khw2tDBvtQZn+tJ9jEgKqbP/2kerD2n/fm/FQR4b2Fr7e2lqPh+vPsTTQ9rS6iQ1QOdLg/TBmT59OkII4uPjufLKK2nRQq3vzczM1GoiAMLCwgDIyckhPDycn376CYvFwu7duwkLCyM/P5/Nmzfz4IMPnlE5KioqsFqtJyx3Op21+36Y26v/nQan03n6z0M6xf4mQghefPFFunfvjsPh4K+//uLWW2/l888/p6qqiuuuu67WujXxOBwOfHyOtP0e+Syj0UhZWRn5+flYrVZCQ0NP6PdS87efn1+t95WXl2O323E6nfj7+2uvGQwGfH19EUJgt9vR69U76LKyMsxmMytWrOCjjz4iPT0dp9NJVVUV8fHxWhn9/f3R6/Xa9hwOB2VlZfzwww/897//xWQynbRvjtPppLDw1Cehslgsp7V+c3EhxFVYaaO4ykFqTjEACnB5YiBxnk5AoaK0uNbddqyfkb9S81i6Nw8PNx0BZgO/bsukZaDaDG5yWCgsrP/5c88PjuWx39N49+9UFu9TP/PPvfn8Niml3vecbkyNrdrhxE2n1Opj8tQfaaw5XE6A2UDPKG98TQbsTsFLQ2LpEOYFOIHqWjF0CTNzoMTGfb0jSA72oKioSHstSA+YobCwkJ5h7vzsbwIEBwor+XlTGpfE+9VbvrkbMjDoFD4Y3YplB0roEeXNjd/vBeDv/YX8vb+QbhFexAeY+GZrPgPfXoFTgF6BAH11g+znMz1eF0d68OdeuKVzC6J8jQyPb41ep37fykut/PfSSBSovR9b6KG6gsLC2vVG/SON9I+s6dxra5S49ELQLsSDrTmVbDiQRbuQo0lKmdXBz7sKmLc1l9GJgegU+H5LFr3DTWzKKudQiZW/0koBeGTeNl4YEkugR8MnaMfHdLLKkLNOcB544AHi4+Oprq5m3rx5vPXWWzz99NPo9XqqqqpOmEHYbDZjtVpp1aoV/fr145VXXiEkJIRrr72WTz/9lHHjxrFx40aWLl2K2WxmwoQJp9x04enpiafniVljTk7OWQ+HPpfPbVIUBb1ej8FgwGAwMGjQIF588UV27tyJ0Wjk22+/rfNRBXq9HkVRapWrZltBQUEYjUays7Np06Z2G27N+jWjto4vg06nq/VaXZ9Ts9zpdPLYY4/xzDPP0L9/fwwGAw888IC2fl3v1ev1+Pj48Oyzz/LYY4/x8ssv07Fjx3r3j06nO60avcLCQpesAXT1uIQQPPT7ZrZmluJtNJAU4s1Tg9sQG+iBoZ6RIYMSKnhr+QECPNyYMaYdi/fm8+2mTLbmVDK6XSjxESd/xEeywQNI05IbUGt2znY/N5VjVVhZzZUfruGmntFM7q6ONFpzqIg1h8tJDPEizMfEkgOFWGxOfIwGLk6MqndfP9ZfEBj47zVbAQHw7eQQnEIw9uO1LNxXytiu8bXWqbY7WZteTGyAmd9Si7miXSitI0NoHan2wfvyOm9WpBXy9vI0AP43qh3VdieeHmZyyqpZmVbIjT3iiAptmM5TZ3q8RgQEMLR9dJN9rtmZxPW/UZ6M/GANuVY9AQEBZJRYmLUhg5+2ZWOxOekTF8CUgQkIAf+kr+PBhQe0945MDmFZagGHSqx8ubXonDwq43RiOutf7JofT4PBwPjx47n33nvJyMjQmp+qqmpPeV1VVYXRqFYbDxw4kIEDBwKwYcMG/Pz8CA4O5r333mPq1Kls3ryZOXPmcMstt5xtMZsNIQTLli2jrKyM+Ph4rrjiCqZPn87DDz9MQEAAubm57Nu3T2s+qo9Op2PUqFG89tprPPvsswQEBLB9+3at2aih2Gw2bDabVkvzzz//sGrVKlq2/PeHuXXt2pX/+7//4+GHH2b69Om1avukC8/u3HK2ZJaSGOLFzpxyYgPMWofO+lzRPpSKajtXdggn2NtIfkU1szZkEBtgZsopPFAw2MuIXqfgcAqeGtyGr9YfpqSq+Y/0+3r9Ycxuer7fkoXV7uTt5WnklFnZn1/BlsxSYvzNzBjTDj+zG9llVbzy5z4mdImoN7mB059VXKcojO0Qzht/7Sc1r0I7lpklVfxnzhYOH5lJ112vaMlXjbbBXrQN9iLS10yroKP9eO646PRGC50PTTW5OVMhXka8jQaW7ctnY0YJi/fkoSgKQxNacG2XSFofM3v4I5e24oEft2sDAO6+OI6eMf48sWAX2WVqa4pTCAorqgk6SXPxudLgVRLHngTh4eGkp6fTvXt3ALKzsxFCnDBSpqqqil9//ZX777+fnJwc/P39MZvNxMbGsnDhwoYuYpM0ZcoUdDodiqIQGhrKtGnTaNmyJXfffTcffvghkydPpri4mBYtWnDllVf+a4IDcO+99zJz5kxuuOEGKisradOmDW+++WaDltvT05MHH3yQxx57jOrqai6++GL69u17yu/v2bMnTz/9NFOmTGHGjBkNnoBJzceWTLV6+6VRSeSXV5/SKBwfkxt3HjNEtkeMP/1aBnJb7xjMp/Akab1OIcLHhF6vMCI5hJwyK++vPEh2WRWh3o03x8vZ2J1bzmvL9gPgYzRow4nnbs6iU4QvV3WM4LbeMdqoolBvE69cnnxOyjIiOYR3/jnAnC2ZPHqp2lfjyQU7teQGYEz78BMeOVBjUFs5vO18UxSFK9qF8vm6w3i667m2SyTjO0XUeT5eHB/I8nv6YHcK8iqs+Hu4MzghmFUHi/h5ew5rDhWx9lAxP2zJ4o87e5//WMQpPI3M4XDgdDqZP38+RUVFXH/99eh0OnJycnA4HERERGhNVNu2bWPq1KkYDAZ27NjBxx9/rI2i+uyzz3B3d2fy5Mm1tv/tt98SGxtL9+7dKSkp4ZlnnmHatGls3LiRrVu3ah1qz9TOnTtJTEw8q22cyyaqxtJcYjrd49dUmgcamqvHNW3hLlamFbHwtp7ndWbhnTlleLobiPY389O2bP7v9z0owKr7Lz7jkVXn61hV2RyUWe1aZ2qLzcHYj9eSV1HN8KRgbu8dS4i3kW82ZhLk5c7AU3wKdV3ONKbH5u9gS2Ypv9zak/yKaoa/vwqngLfGtuNgkYVhicF1zmNzPrn6uXW67A4n/xwopEuU3xkdm+82ZfLSn0fn+7mkVRAvjWqYR6I0eBPVggULmD9/vvb36tWr6dWrFz179uTrr7+mqKgIo9FIfHw8d911l/ajmZSUxLBhw5gxYwZVVVWkpKRw9dVX19p2eno62dnZjBs3DlCHpA8dOpRp06bh4+NzQTVPSdKFbGtmGUmh3uf9sQnHzuXRJUodrShQR+z0asIPHzxYVMmd320ht7ya0e1CeeTS1qTmVZBXUc0jl7biyg7h2rpXd45otHLGBXiweE8+NoeTX3fm4BTw7cSuxAV6aMPPpabFoNdpw9jPxOUpocQHelBaZWftoWKu6hj+7286B04pwRk5ciQjR46s87Vnn332pO8dMGAAAwYMqPf1qKgo7rnnnlrLBg8ezODBg0+laJIkuYDDxRYOFVsa7UJYI8LXzD/39GHEB6v5blMmvWIDqLY7qbI78DG5UW13Mn3pPq7sGH7OhsEKISistGlDtOtyqMjCbd9uweEUXJYYzI9bs4n2N+N/pK9Kt2i/c1K2MxHma0IAH646yMer00kJ9SYusP7J+aTmz92go8uROZQuaX3midLZcq3eUZIkNUt/7S8AoHdc49eYuBt0jG4XyvL9hezLr+C6Lzcw5uO1pBVW8urSfczdksXX6w+fs8//dlMmQ99bRWpe/dPPfbEuHUu1g/fGtefZyxJICfXml+05rDlUjOFIv6KmIvxIWT5enQ7AyBQ5W7l0fsgER5KkRuUUgvnbckgM8SLa3/zvbzgPxnQIQ1Hgf3/s5UBhJSVVdiZ+tZHvj8zyW251qDU7xz2m4HjFFhtL9uaz4kDdc5EcKrJw/ZcbGPfZOoot6vwpX2/IAODR+Tt4558DrDtUjNXu1N7z/ZYsftyaTVKoF/GBai3SoIQW7Cuo5NeducQFeDSpkT1hxyRbUUceNilJ50PT72EqSZJL+2pTLnvzK5g2tG1jF0UT6m2iTQsvbWTXc8MSeHrhbloFeRLpa2LD4WJGfLCa1i08mXll7UlDs8uqeHZRGvuKdlNYeXSSwTX3X1yrf1FOmZVbv91MQYX6WIlB76zUXmsX5gMIPluTzser0/E2Gvj4mo6E+Zh4/g91ErxIv6PJ4NWdIugW5UdhpY2IRnzCd12OHX3zf8MStUdtSNK5JhMcSZIazbLUfL7cnMfwpBCGJZ58Ur7zLTnMm1255ZgMOga1bUGErwlfsxu/7sxl6b4CvIx61qcXU1Ftp7LaQWZJFZ+tTefv/YWYj7wnNsCDhbty2ZNXQVqhpVbfkw9WHqS0ysbsG7pQUmVj4+ESPlh5EIeA6aOT8TO7UW61s/5wCY/P38GLi1O1xxgAtZ4BpFOUWvOTNCV6ncJ1XSL5cv1hWsq+N9J5JBMcSZIaRUaJhakLd9Mm0MxjA1uf99FT/6ZzpC9zN6tNUoqiaA8nnNAlgq5RfljtDu7+fhtv/n1AW6/GLd1Cub6X+iDEQQktGP3hGt5bkcbzIxJRFIVDRRbmb8/myo7h2kMNO0f6MbZDONmlVdrEdl5GA/1aBjIyOZS5W7LQK3BlhzDahfk0aufN03VP3zhu7R0ja2+k80omOJIkNYo/9+RTUe3g8f5RGE/yMMbGMrBNCw4WWrQnLNfwdDfQKdIXi82BQacwd3MWRoOO50ckYjLo2Hi4hKGtjw49D/U2cWefON78+wDfb8kiJcyHL9al46bXMem4GXz9zG51Pn370YGteWhAK/QnmWm4KVMU5ZQmXpSkhiQTHEmSzqldOWU88csuXh2dTGzA0SaKrVllRPiaCPOufzh0Y9IpCrf0iqn3dbObnuRQbzZnltKmhScXx6vPaeoW7X/CAw6v6xrJ+vRiXlh8dPKzG7pFEXSSoeDHa67JjSQ1lqZ323QB2rRpEzfeeCP9+vVjwIAB3HjjjWzfvp2ff/6Zm2666ay2nZmZSdeuXU/6tG5JOleq7U6eXribQ8UWFu7M5Y7vNjPig9U8/stOlqTmkxLq/e8bacJqZgbuEO570vV0isLjg2o/9Pb6rpHnrFySJMkanEZXXl7Offfdx6OPPsqgQYOw2Wxs2rQJd/dTu7NzOBzo9bLqVzp3DhdbqLQ5aHOkE6tTCB6Ytx2dohDjb2ZshzAifOse3v3eijQOFFQC8NHqQwAkBHuxaHce0DTmvTkb4zuF0zXaT5vr5WRCvI08PKAVDqegV5x/nU1RkiQ1HJngNLJDh9SL/tChQwHQ6/X07NmTAwcO8Pzzz2O327n44ovR6/UsXbqUadOmYTQaycrKYsOGDbz66qvYbDbefvttMjIy8PLyYtSoUdx2220A2qMuLrnkEgBmzpxJ+/btmTdvHp9//jmFhYUkJyfzxBNPEBYWBsCqVat46aWXKCgo4LLLLmP//v0MGzaM4cOHM2TIEN5//31atVI7UBYWFjJy5Ejmz5+Pv7+cdt3VpOZVMPHrDVQ7BB+M76CN7Fm+/2gTzNcbMnDXK0zsFsVNPY826fywJYvP1x3m8pRQ5m3L1pb/37AEZm/IYO6WLC5pHYSlrOS8xtSQFEU5rRmNG3umZkm6kFyQCc4vO3L46ZgL7qkQQpzWKI9RKaEMT/r3Ca2io6PR6/VMnTqVwYMH065dO3x8fIiLi+Oxxx7jxx9/5KOPPqr1noULF/LGG2/w+uuvY7PZ2Lp1K88++yzx8fHs27eP//znP7Rt25b+/fvzwQcfMGrUKJYsWaI9I2zp0qV88sknvPzyy8TFxfHpp5/yxBNP8PHHH1NcXMwjjzzC1KlT6du3L9999x0//PADw4YNw83NjcGDB7NgwQLt8Rq//fYb3bt3l8lNMzZvaxZtg71ICKndXHS42MJ/5m5R+344BLd8s7nW6++P64BOB1+uO8zS1AI+WHWIG3tEa+fJ91uyaB3kySOXtiLKz8zP27O5rmskMf5mHhrQirsujsPspsdy3iKVJOlCIvvgNDIvLy8+/PBDFEXhueeeY9CgQdx///0UFBTU+55+/frRsWNHdDodRqORrl270qpVK3Q6Ha1bt2bIkCGsX7++3vd///33TJo0ibi4OAwGAzfeeCO7d+8mKyuL5cuXEx8fz4ABAzAYDFx99dUEBR0djjpixAh+++03ah5Cv2DBAoYNG9ZwO0Q6r5am5vPfRXu5/quN2B3qbLm7cspYsCOHV5fuw2p38tmEztx5USwAyaHeTOgcwe29Y+gU6UuHcF9eHpXMU4Pb4HAKFu7KJb/cyuO/7GRXbjmDE1rgptcxsXsUcyZ3Y3S7MBRFQa9TGv0J0pIkubYL8gozPCnklGpXjmW327UakIYWFxfHtGnTAEhLS+Opp57i1VdfpVevXnWuHxJSu+zbtm3jzTffZN++fdhsNmw2G5deemm9n5eVlcWrr77K66+/Xmt5bm4u+fn5tbavKArBwUcnYEtJScFkMrF+/XqCgoJIT0+nb9++pxmx1BQUW2zarLgA83fkEOZj4v4ft2FzqAnsLT2jiQv0IDYgig4RPsQFeODvcWL/sCEJ6gMfX/5zH0aDjjKrnTHtw2o90VqSJOl8uiATnKYsNjaWESNG8P3339O7d+861zm+qeyJJ55g3LhxzJgxA6PRyKuvvkpxcXGd64KaIN14440MGjTohKQtPT2d3Nxc7W8hRK2/Qa3FWbBgAYGBgVx66aUYjbXnCZGavtS8Cq75Yj06BT6b0ImJX2/kuUVqstMqyJN2Yd7klVcz8cg8LYqi0DnSr97tGQ067r44jlu/3YyPycSbYzrRqsW5edq2JEnSqZBNVI0sLS2NL7/8kpycHACys7P57bffaNeuHQEBAeTm5mKz2U66jcrKSnx9fTEajWzbto2FCxdqr/n7+6PT6cjIyNCWjR07lk8++YT9+/cD6kiuP/74A4A+ffqQmprK0qVLsdvtfPvtt+Tn59f6vMsuu4ylS5fy66+/Mnz48AbZD9L5tfKg2kl42tC2JIV68+ilrfBw05MY4sWbY9vx+KA2vHZFymlNwNcp0peZV7bj82tlciNJUuOTNTiNzMPDg23btvHVV19RVlaGt7c3ffr04d5778VoNBIfH8+QIUNQFIXFixfXuY1HHnmE119/nZdeeonOnTszcOBAysvLATCZTNx4443cdNNN2O123nzzTS655BIqKyt56qmnyM7OxsvLi+7duzNw4ED8/Px44YUXeOWVV5g6dSqXXXYZSUlJuLkdHdIaGhpKQkIC6enpdOrU6bzsJ6lh7cguI9zHyGVHnuw8tkM4YxugOal7tOxsLklS06CImt6iLmznzp0kJiae1TbOZR+cxnIqMTmdToYNG8Z///tfunbtqi1/5plnaNGiBXfeeee5LuZpH7/CwkICApr3/Cp1aai4qmwORn64hh7Rfvx3+NmdFw3BFY+XjKl5cdXYXDGu04lJNlFJJ1i5ciVlZWVUV1fz8ccfI4QgJSVFez0zM5MlS5Zw+eWXN2IppTO1YGcOxRYbV8o5WSRJcmGuVSUhNYgtW7bw5JNPYrPZiIuL49VXX8VkUmdqfeedd/j666+ZNGkSERERjVxS6UwsTS0g2s9Mx4iTP15AkiSpOZMJjnSC2267TZsJ+Xh33HEHd9xxx3kukdRQcsusrE8vZkx7WXsjSZJrkwmOJLmA/QUV/LO/kEvbtKCgopoii42+LQO11w8WVTJrfQaHSywoisI42TwlSZKLu2ASHKfTiU4nuxw1NxdAH/izJoTgyQW72JtXwadr0im1qk+O/+r6ztoDMv/vtz1sziwF4P5+8UT51/1wTEmSJFdxQfzie3p6kpGRQXV1tfzBbEaEEBQUFGj9f6S6fbc5i715FdzYI4oWXkdnGf58bToABRXVbM8uA9RHLYzvJPtOSZLk+i6IGpzIyEjy8/M5ePAgdrv9jLbhijVAzSEmk8lEZGRkYxejybI7BR+uPEi3aD9u6x3LxG7RrEwrZG16MT9ty6bK5mDetmzsTsFX13emVZAnutN4aKwkSVJzdUoJzpIlS1ixYgUZGRl0796dSZMmaa9lZGTwxRdfcPjwYVq0aMGECRNo3bp1rfcuXLgQi8VCSkoK119/PWazWj3+zTffsGrVKkJCQrjtttu0J1KnpqayaNGiBuvMqtPpCA4OrvVMpdN1oc8nIDVNaw8VUWSxcWX7MHSKgoe7nkvbqA+4nLs5ixVpRXy/JYtu0X5ac5UkSdKF4JRu3319fRk+fDgXXXRRreUOh4OZM2fSsWNHXnvtNYYOHcrbb79NRUUFADt27GD+/PncddddvPjiizgcDmbPng3AgQMH2L59O88//zxt27bVHi/gcDiYM2cO48ePb8g4JemccDgFVruzUT7bKQTv/nOQFp7u9I6rnai2D/cB4JGfd5BbZuWGrrIWTJKkC8spJTidO3emY8eOeHrWfr7M7t27sdlsDB48GDc3N3r06EFwcDAbN24E1AnjevfuTVRUFGazmdGjR7Nu3Tqqq6vJz88nOjoak8lEmzZttOcdLVq0iC5dusiaBanJq7I5mDxrI/f9sA2AcqudxXvyTkh48sqtVNkcDf75C3fmsiOnjLsujsPkpq/1mp/ZjeFJIQxLDObbSV3pGSvPJ0mSLixn1QEjMzOTiIiIWv04oqKiyMzM1F4/tv9EWFgYADk5OYSHh3Pw4EEsFgu7d+8mLCyM/Px8Nm/ezIABA86mWJLUYKpsDpam5lNutWNzOCmrUvtwCSF4btFeduaUsy69mBf+2MuID1bz6PydvL38gPb+X3bkMOz91dqTuutTbrWz9lDRKdcGpRVWMnP5ARJDvBiaWHfT67ShbXnmsgRiAzxOMVpJkiTXcVadjK1Wq9afpobZbNaaqKqqqup83Wq10qpVK/r168crr7xCSEgI1157LZ9++injxo1j48aNLF26FLPZzIQJE7S+Of+moqICq9V6NiHVy2KxUFhYeE623VhcMSZouLgW7C7kjZVqsm7QKQghMOgVPhnThr/TSlm4K5ehrf1ZuLeIuVuy6BbhhaIozN2SiaWqih25lWSUVgPw265cxiX5EuFjrPOzPtuQw9db8vBw03Fth2AuTwzATV/7/sNisZCVm8+sLXl8ty0fo17hyf6RFBcVnXWsjckVv4cypubFVWNzxbiOj+lkrT1nleAYjUYsFkutZVVVVdqwXpPJRFVV1QmvG43qRX7gwIEMHDgQgA0bNuDn50dwcDDvvfceU6dOZfPmzcyZM4dbbrnllMrj6el5QjNaQ3HFDrmuGBM0TFx78sqZcSS5uTg+gEBPd8xuemZtyOD73aX8sDWbfi0DeWZEElU/72BpagEvXN6OFQeKWHN4Fz/sKCDa30xcoCdXdw7nxT9SeWNVDh+M74BSxyim1OLDRPiaCPYy8sG6bL7YlMtfd19Ua92FWw7yztoMMkuqGJYYzD194wn0dD9hW82NK34PZUzNi6vG5opxnU5MZ5XghIeH89tvv9Uabpyenk6fPn2019PT0+nevTsA2dnZCCEICQmptZ2qqip+/fVX7r//fnJycvD398dsNhMbG6t1Ppaks7U+vZgPVx1iUNsWjGkfpi0vttjwNhrQ69Rkwmp38vSCXQR4ujP7hi74md20dfcXVDBncxYAU/q3RKco/G94IiVVdnxMbqSEeWvrTh3SVuvsW2618/wfqaxMKzqhQ7DN4WRLZilDE4MZlRzKjbM3UWV3cri4iih/M3/vL+DHrdn8ta+A2AAz717Vni5RfudqN0mSJLmEU0pwHA4HTqcTIQROpxObzYZOp6Nt27a4ubmxaNEiBgwYwMaNG8nJyaFTp04A9OrVi48//pgePXoQFBTEvHnz6Nq1K+7ute86f/rpJwYNGoSHhwcBAQHk5ORQWlrK7t27CQoKaviopQuO3Sl4/JedFFbayC23MqZ9GK8u2cemjBJ25ZZzX794ru0SSbHFxoPztrOvoJLXr0ipldwAvDgyiXf+ScPTXU+4r1pT6abXEXSkJiXC18T4juEoikJy6NFkZ2RyKJ+tSee/v+9hTIcwJnePRq9TyCixcOs3m6myO+kR40+7cB8evbQVLyxOZcwna0kM8WJnTjl6ncLkziHccnHrE5quJEmSpBOdUoKzYMEC5s+fr/29evVqevXqxaRJk/jPf/7D559/zs8//0xQUBB33HGH1kyUlJTEsGHDmDFjBlVVVaSkpHD11VfX2nZ6ejrZ2dmMGzcOUIekDx06lGnTpuHj43PKzVOSdDIbDxdTWGkj3NdETqmVfw4UMntjhvb6ziMz/X69/jCbM0vpExfARXEnVoN6uht48JJW9X6Ooig8OODE1930Om7qGcP//b6H91YcpE9cAAkh3szdnEVueTUvjkxiQGs1mb88JZRXluxDryh4GQ1c2SGMuy+Op6q8RCY3kiRJp0gR8tkFp+RCb8tsTuqK64XFe/llew439Yxm5nK1BsbX7Mb4TuF8sfYwSaHevHp5MnfP3cqmjBIW3t4TT/eGnejb7nAy5pO1ZJVa+b/LEsgqq+K9FQfpGx/IS6OSaq1bUFGNj8lQK6G5kI5Xcydjal5cNTZXjOt0YpK3g5LLczgFS1MLuCgugLgjQ6Yrqh08PrA1EzpH0rqFJ3nlVoQQ7M4tZ3DbFg2e3AAY9Dq+ndgVBXjq1128vTyNS1oF8uTg1iesG+jpLmtrJEmSzoK8gkoub0tmKQUV1QxoHUSI99Fh2j1i1OkHgjzdKaioZk9eBUUWG8lhPuesLCY3PaFHhor3iPHjf8MT8TG5/cu7JEmSpNN1QTxsU7qw/bozB3e9wkXxAbjpdAxu24JJ3aO011t4uZNfUc1TC3ZhNOgY2ObcdmxvF+ZDVmkeXSL96hwyLkmSJJ09meBILu1gUSU/bs1mTIcwrdnpueGJtdaJDfDAKcCgV3hmaNtzXqPy8IBWeBkNjEwJPaefI0mSdCGTCY7k0vblVSCAK9qF1bvOkIRgukb50cKr7lmGG5qv2Y3HBp7Y70aSJElqOLIPjuTScsrVRyUc2/fmeDpFOW/JjSRJknR+yARHcmm5ZVaMeh2+JllZKUmSdCGRCY7k0nLLrQR7u8vOvJIkSRcYmeBILi233EqwbH6SJEm64MgER3JZTiHYl19JlL+5sYsiSZIknWcywZFc1r78CsqsdjpF+DZ2USRJkqTzTCY4ksvaeLgEgE6RMsGRJEm60MgER3JZGzNKCPE2EuZjauyiSJIkSeeZTHAklySEYGNGqWyekiRJukDJBEdySTUP2OwZ69/YRZEkSZIagUxwJJf0685cjAYd/VsFNnZRJEmSpEYgExzJ5dgcThbtzqN/q0DtAZuSJEnShUUmOJLLWZtRTqnVzmWJIY1dFEmSJKmRyARHcilCCH7dU0iAhxs9YmT/G0mSpAuVTHAkl7JgZy5rDpczoXMkBp18/pQkSdKFSiY4ksuwOZxMX7KPlBAPrusa2djFkSRJkhqRTHAkl7H2UDGlVjvjUlqgl7U3kiRJFzSZ4Egu49eduXi66+kU5tnYRZEkSZIamRxDKzVr5VY7Kw4UsiS1gCV787iyYzjuBpm3S5IkXehkgiM1O0II8iuq2ZNbwdSFuyipshPg4caolFBu6RmDw1LW2EWUJEmSGlmDJDivvvoq+/fvR6/XA+Dt7c1zzz0HwJ49e5g1axZ5eXlERkZyww03EB4eDsCaNWv47rvvcHNzY+LEibRt2xaAiooKXn/9dR544AFMJvmgRElVbrWzL7+CFxankppfoS1/a2w7ukb5af1uCi2NVUJJkiSpqWiwGpxx48bRr1+/WsvKy8t5++23ueaaa+jcuTOLFy/m7bff5plnngFg1qxZPPzwwxQUFDB79mymTp0KwPfff8+wYcNkciNpii02Jn69kcySKgBu7RVDiLcRP7Oc70aSJEk60Tltotq4cSMhISH06NEDgMGDB/Pnn3+yZ88eIiIicHNzIywsjKCgIPLy8gDYt28fZWVldOrU6VwWTWpG8iuquXvuVvLKrVyWGIynu56be0ajKHKklCRJklS3Bktw5s2bx7x58wgJCeHyyy8nISGBzMxMIiOPzkei0+mIiIggMzNTa47KyMigqKiI8PBwHA4Hc+bM4eabbz6jMlRUVGC1WhsknuNZLBYKCwvPybYbS3OIyWp3cudPqeRX2nn20hg6h3sBUFRUVO97mkNcZ0LG1XzImJoXV43NFeM6PqaAgIB6122QBGfMmDGEhYWh1+tZt24dM2fO5Mknn8RqteLh4VFrXbPZTFVVFTqdjsmTJ/PFF19gMBi4/vrrWbx4MR07dsRisfDGG29gt9sZOXIkbdq0OaVyeHp64ul5boYIFxYWnnRHNkfNIaaVaYUcLq3mxRGJDGjT4pTe0xziOhMyruZDxtS8uGpsrhjX6cTUIAlOXFyc9u9evXqxZs0atm7ditFoxGKp3ePTYrFofWsSExNJTEzUCr1hwwYeeughXnrpJcaNG4evry+vvPIKzz//vGyOuAB9uzGDr9YfxqBT6BXnWiepJEmSdG6dkwlDapKR8PBwDh8+rC0XQpCRkaGNojrWN998w1VXXYVerycjI4OYmBiCgoJwOByUlclhvxeasio7Ly/ZR2aplV6x/pjd9I1dJEmSJKkZOesEp7Kyku3bt2Oz2XA4HKxevZq9e/eSkpJCp06dyMnJYc2aNdhsNn7//Xfc3d1PaHLatGkT3t7etGzZEoCgoCB27dpFZmYmdrsdLy+vsy2m1Mz8sUftdD4iOYT/Dkts5NJIkiRJzc1ZN1E5HA7mzZtHdnY2Op2O0NBQ7rjjDkJDQwG44447mDVrFp9//jkRERHceeed2nw5AFarlQULFnDvvfdqy8aPH88XX3yBzWZjwoQJ6HRyZtoLycKdubz4ZyqJIV48OaiNfK6UJEmSdNoUIYRo7EI0Bxd6Z63zZW9eOdd+sYFOkb68enkyXsbTz8GbYlwNQcbVfMiYmhdXjc0V4zqdmGTViNSkLNtXAMALIxLPKLmRJEmSJJAJjtTE/L2vkIQQL/w93Bu7KJIkSVIzJhMcqcnYllXKjpwyhiWGNHZRJEmSpGZOJjhSk2BzOJnx1378zG6MTJEJjiRJknR2ZIIjNQkv/ZnKxoxS7ukbh6e77HsjSZIknR2Z4EiNbktmKT9uzebaLpGMTA5t7OJIkiRJLkAmOFKjsjsFLy7eS7CXO7f2imns4kiSJEkuQiY4UqNauDOHPXkV3N+/JR7u8nEMkiRJUsOQCY7UaKrtTr5en0F8oAeXtg5q7OJIkiRJLkQmOFKjKLfaGfb+KvbmV3Brrxj5tHhJkiSpQckERzovhBD8s7+Q3bnlCCHYnFFKSZWdm3pEc2mbFo1dPEmSJMnFyPG40nmxMq2I+37cBsCNPaIoqrShV2Bi96hGLpkkSZLkimSCI50TQgi+25TJyoNFeLjpySypwuymo1OELx+vTgegfbgPZjfZsViSJElqeDLBacbsToFB1/T6rjiFYM6mTF5esg8AT3c9FpuDyxJDuPviOJam5hPtbyYp1LuRSypJkiS5KpngNGFOIcgsqeLXnbmUWe1M6d9Se231wSIemLedxwe2ZlhS03m0wcerD/Hx6kNY7U46R/ryxKA2RPqZcBxJxhRFYWyH8MYupiRJkuTiZILTRGWUWLh77lbSi6u0Zbf2isHLaKDK5uC9FQex2p08/8deukT5caCggh4x/nWORhLFywAFxa/vOSmrEIK9+RX8c6CQ91cepEukLwPbtGBgmxZ4m9SvmE7f9GqaJEmSJNclE5wmqLCymqcW7KKw0sZjA1tRVuXgreUHuGTmCpbf04epC3ezNauUEckh/LI9h6s/X0e51cEzQ9tqtTlCCKjYBooBCheCzoTwvQhFabg+L8UWG2/9fYAVBwrJq6gGoF2YN88MbUuQl7HBPkeSJEmSTpdMcJqY3DIrk77eSF5FNdd2iWRM+3DKrXbeWn4AgFeXpPLn3nzu7BPL5O7RCCH4ZUcuAG//k8aA1kGY3PRQuQNyvz6yVT04q8B6GEwN9ziET9YcYv72bC5pHUSv2AB6x/rLxEaSJElqEuQ8OOeZcFoRtsI6Xyu32rnvh20UVFYzLDGYSUeGUHsZDbw1th0AP2zNpmuUHxO7qa/d0isGg04h2s9MTpmV/8zdqtbeFC89uuHgqwAFKnYinNWI0rWIgoUYq9aq656Bv/cXMGt9Bhe3DOT5EUmMSgmVyY0kSZLUZMganPNI2Esh6yOwlyBiHkfRuWuv2Z2CR+fvYH9hJW9ckULP2IBa7+0e7Yef2Q2nUzBtaFt0R/raRPia+eK6zoR6G/lmYwbvrjjI+r3r6WI4DL59wRSD4pmEKFoCJcvU/47wADjwNyL8FjBG1+q/Y7E5cDgFszZk8Pf+AnbmlAPQv1Ug0f4e/LQtm0g/E3dfHH/udpgkSZIknSGZ4JwnwlYEWR+CvRhwgmUfeCYCaofia7/YQEW1gycHtT4huQFQFIWnBrfBz+xGiHftmpJWQZ4AXNslkq83HOa7DQfo0icc/Pqj6M3qSn79oHgZmKLBqxO4BeE4PBO9s4R9u77igTW9qbTrGBRj4aEOe7hrcTxbCtRh3J0ifIgL8OBQUSWr0or4e38hLQM9eGZoAtH+5nO2zyRJkiTpTMkE5zwQ1XlqzY2wQtjNkPM5lP6D8EhAURR+35VHRbWD8Z3CubxdWL3b6dsy8KSfY3LTMyq+jFk7W5Dn2Y9g/dHkQ/HuBN6daq3/V/k4cq0K7688SFm12lr53W4zZl2klty83Cedfp2GYS3Zgr1yP3p7DjoE7t5twL8lp0vYi6F0HejcwNwK3EMbtOOzJEmSJIFMcM45Yc2C7I9BCAi7FcUYhvAfDAU/YS/bytKsMOZuySIxxIsHL2l1dp9VncOYiLV8taMvDy44zH+HeZNbZqVrtB8HiypZe6iY5FBvEoK9sNqdPPL7YQCi/T35fKwRX3c7TywRfL5T3d6Xg7bTxjMd0rdhBIygjsoyxUH5ZrVPT8g1KObTSHSKlkDZmtrl9kiCFmNQ9J5nFb8kSZIk1ZAJTgPbnl3G5swSrHYnk9orkP0JKO4QfhOK+5GHSvp041D6Su6flUF6RTFR3jbu6ZP8r9sWtgLQGVH0XnWvUPg7kV4Obu4Rzgersxj7yVoAPpvQiU/WHGJpagEAyaHeVNkcAHSJ9OWFkUn4md0AmHElHC62cLDIQtu4vmqCZj0EjnK1xsUtGEVvRlTnQM6XkPMlwhQHBn8w+ILTAr591XWcNij4BaozQO8D7mFQsRU820PAEMidBToPsOyB7M8QYTfX6pckSZIkSWdKJjhnyeZwoigKaQWVvLZsH2sOFWuvjfT+m0BPM4TdjOLmry1XFAP/29aHUruFF/uV0NdvNXrHH4jMeAi5pv4EJutjUHSIiLtQdEf74Qh7idq3p3IH+A/m1rjW+Jg9eG3ZPpwCZi4/wO7cctoGezE8KYTP16ZTVmXnohgfXhndDoO+9mC6SD8zkX5q85ZiDAPjic1minsIIuR6KPwNbAVqnyKhzoVD6SoEenBWqH+bYqE6Sy2fzhP8+qK4BUDEf9TyV2yHnK8g6yM1WTJGgkdbFJ0bwl6mDm8vWw9V+yH8dhT34NM7SJIkSdIF55wnOJWVlXz55Zds27YNs9nMZZddRv/+/SksLOT9998nJyeH3r17c9VVV2nv+eKLL2jXrh0dO3Y818U7Y3vzyvljTz6zN2Tgplcor3bgbTQwul0ov+7IweoQfJvWitsHDkXn5lvrveVWO5uzqpjYPZoBXeIQFfGQ87X6A57xDiLkGnBWqyOgjvRPUZOYI8PL875HBF+NoihqDUvGTMCh1hT59gLg6s4RXN05gi/WpTPjL3UOnYcvjWRw22DGtg/DKQSVZSUnJDenQ3EPhtDr1fIJAc5KqEqDyl1qU5bTBsYIFN9eam1O+UbwTDohgVM8kxEtxqoTEpakAwLQIYyRasyOctCZQDjh8OuIFleieHc+43JLkiRJru+cJzizZs3C4XDw0ksvkZeXx+uvv05YWBjr168nISGB++67j+eee45u3boRGxtLamoqZWVlTTa5OVxs4fVle1i2rwSA3rH+eJsMBHkaub5rJIGe7jzWvYhHFxzkk51hZDgzeXyQJ57uR3f1uvRiHAK6Ram1OopnMsQ/h6g6CNmfH0lYgMAR4HsRwmGBQy+oyzxToGIL5JsQAYMgdzagNjfh0w1FZ6pV3qs6hGsJTv+WQQC4G9SkprIB94uiKKD3BM9k9b/jX9e5gU/3+t/v3QW8uyCEAyp3q81ipetA2MB/CPj0gOpMNQnKm4uwF4HeCzzbgc4MjhKwFTboRIaSJElS83VOExyr1cqGDRt44oknMJlMREVF0bt3b/755x/Ky8sZOHAgJpOJmJgY8vPziYqKYs6cOdx6663nslinLbe4gOmLV3OwVE9qydGRSV9dFUDryESwl0DpCqg+iHB4oRT8yPPd4Yv8W3lnRTq7c8t5YWSSNpz7+y1ZBHq60yHCp9bnKKYYRPjtULpK/a9gAaJ8C1RnqyuY20DweCj0h5LlRzvrBo5Um3H8B51QdpObnq+u74zTKbTEpilTFD14JoFnEsJ/ECBQlCNfU3NLROhkyHwbiv5QlxUsABR1hBqAz0Wg690YRZckSZKaEnEOHTx4UNxxxx21lq1evVr83//9n/juu+/E3LlzRWVlpXjyySdFRkaGWLhwofjtt9/O+PMuu+wygdq+IQBx6623iltvvbXWsn79+olp06YJLy8vbVlYWJiYNm2a6Ny5c611p0yZIq655ppayy6/eaJ446dPai0bfklL4dz3qBgxoGWt5dOmTRP9R42rtWzcNdeK9k/NqbWsc+fOYtq0aSIsLExb5ufrJbLXPybuvbHPOY9pxIgRYtq0abWWtWnTRkybNk20adPmhJhGjBhRa9k111wjpkyZ8q8xeXl5iWnTpol+/fo1aEw/f36PeOqBcS4VkyseJxmTjEnGJGNq6JhORhHiDOfqPwV79+7lnXfeYfr06dqybdu2MXv2bB577DG++uorcnJy6NWrF506deLDDz/kvvvuY86cOWRmZtK6dWtGjx59rop3WgoLCwkIUCfgE8KpjhaqSlOHS+u91In00IFlL7i1QDFFae/NL7fyxIJdbDhcQudIXzYcLmHGmBR61TGh36kS1XlQ8DO0uArF4H3WMbkCYS+GQy9TZeyCOWJMYxenwbna8arhinHJmJoXV43NFeM6nZjOaROV0Wikqqqq1jKLxYLJZMLT07NWU9Tbb7/NlVdeycqVK3E6nTz44IO88cYbbNu2jZSUlHNZzNOmKLr6+5vU0fk1yMvIzCvb896KND5dkw6oQ7XPqgzuLSDsxrPahqtRDH4IzxSMFZsRzuG1RppJkiRJF5Zz2ikjJCQERVHIysrSlqWnpxMeHl5rvY0bN+Lr60vLli3JyMggJiYGRVGIiYkhIyPjXBbxvDHoFP7TJ44ZY1J45NJW+JjcGrtIrsn3IhSqoWxdY5dEkiRJakTnNMExGo107tyZn376iaqqKg4fPsyKFSu46KKLtHWqqqr49ddfueKKKwAICgpiz5492O12UlNTCQoKOpdFPO96xQZwZYfwf19ROiOKKRq7PhxKliOE/ZTeI4RAOMrP+MnqkiRJUtNzzofVXHPNNQA8/PDDzJgxgxEjRtC2bVvt9Z9//plLL70UDw8PAPr27UtFRQUPPPAA/v7+dOrUqc7tSlJ9LKbe6sSHZetPeE04LIj06YiC+WpfKlDn5zn4HKQ9g8j9Rp2z5xwSwolwVp98HWsGwrIfUZ2LcFQcLaskSdI5JoQTYTmAqM4+6Y2isGYhSlbWumYK4UBUpiKyv1SvY/ay81HkOp3zeXA8PDy47bbb6n392An+AMxmM/fee++5LpbkwuyGWDBGQdEShHeXo8PMAazpYMuDkjwQdkTg5WDZr86l45msNm0Z/BH+g9S5feogrIdBMaC4h55ZAbM/A0sqwu9i8EgA9wh1kkdTHChuUL4B8ubUfo/ihpc+FEFrda4fU4zsYyRJ0rlR+BuU/HXkDx3CLQDcgsG9BTit6nWq6hBYD6qrlG9G+PZR+6YWL1EH2wBUbgdA+PRECbr8vIchH9UguR5FAf+B6nPA8n9CBI1WO4aDOlkggHd3KF2tTg5o2as+GqLFWHUkVvESqDqozpjs5q/WnlTuVrdbuQdKV4JiRETeq75esQMqdoD/perflbvVkXRutXv6i+psyPpUnZQQoHiZ+l8Nr87qjM2lK9S/g8aCzgCOCrAVolTsU8uGQJ3pORwCR6KYouvcDcJWoMZWnQPu4aD3gOpcMIaDuZV8irskXaCEEOCsQtGbj1vugKyPoOoAeHUEj7bqNcOWp/6/chdwpDZZMaqT0QqnOi9b7le1P8RvALiHQNVBsOUhhKj3pvFckQmO5JrMrcGnl5qMWNMR7mEQcJn6zCyDPwSNBoMPlKxU1zfFq//3H6w+YqLqIByejvDsAM5yNcGp4ZEAlXsh8x1EwBDImwsIKF+P0PuAoxTQIaIfBb2X+kiNsg2Q/wMIO7iHQtjNIGxq7VHpGnV5+YYjZYmDgMtqTTUAUKYrxN/PU71zqkpT18/+BBF2s/rQ0upMsBwAe4G6jaI/1M9QDOr2j+WZcuRxHzLJkc4tIeyQ/aV6vgWOVGc1P9X3lqwEnRG8OgA69eZA76s+FkbRnzBz+wnvd1bLB/geQwgBthzI/xmq9iPcQ9Vrn3uw+ngge6Ga3AAEDEUx+B73fgc4q9RnC7oFoxjUyWqF70VqLbRwgltQ7Zs7r/bnK7wTnNN5cFzJhT6fQHNSE5cQArI+VE+8YwUMRfHrB6C2HduL1JNSOdolTdhLoPB3qNypnrS+vdTEBgXFFK3WxmR/oV4QFHcIuVa9y6lKU2uFqjPVJ6g7K9Wq3epM9UISfHWd8xYJe4laO+TR5oSLyvFxHX1PMWS+r5a/hmIAIQAHGPwgdDK4tYCSv9U5m4LHQ8V2KPr9yMzYV2t3ccKaBVX71Dj1PqDoajfvnQLhtKk/PMqpd+9zxe+hjOkoYdkPWR8cXRAwDHz7nPRuXpSuU/vG1Zy7Bj/1xqByF6BHe/aeV0c1cfK9+IRERlgOqLW47iFqE7QtXz0/THFHftBtYE0Dr86UWEz4BsXX+r4LpxVQUHTuap+56kwwRte+TjjK1ZsUz2T15qLgJzVRMIaDolfPTb0vBF6mXkeEHUr+Udf3SEBRdOo2StepN1LV2WpZvbsdeeyMUm+CJoRQt2/wO65MFaAzoigGCgsL8TfmQN53auyKXu2fCGrSaCtQZ8E/llsLCL/jhNqdpuJ0vocywTlF8oLVfNSalNFerLYne3WEoiXqhSdwxGn9ANdHOCzqRdgUg2KMqP1awXy1Slfvo160zLHgP+S07l6PV9fxErYiKFqkXuQ9ksA9DBBq4uUWVG+CIooWqzU8ihH8B6h9kyq21V7pyJPf8e4MFTvVC7NfH3ALAZ1JnYupZntOKxQvVauqFSN4tAZzK/DqgKIY1B8Ip7XO5K4pfw+Fw3LSC72oOqTG7NMTxRyvLa/zWNlLoXyT+kNj8FOT2dNMIBvTGSc4+fPVmlRza7AcqQk1twZTrHqOmGKOzB+mU5NwR8nRR7HovSDocvWRLDWJvG8fNWkoWX60udfgp9bKurVQa2jthZDxllrboBjBLQjcAtTPc5Soy0+gA7dAdRt6D/WGw1EKxkiozgNhVbfl1Q68u6gJVvZnR2psj+MWDChqbUm9dGDwBUflkW27q+exs/roNhUDBF2O4t219j6t3KNe16oz1diP3JCozdl5arymeKotBbjb09TPMcWptbvWTPDpgRI4TK2RKd+iLje3VfeR4n5W16lzTSY450BTvgifKVeMCWRcp0IIAZY9UPzX0btkr45q36TqTPWia9mrJj51UQwQfof6f2u62jm7Ku1IR8RQtSnQWQEoR+4U89U7RXNr8O2tXpARKMaIJnu8ROk6yJ8LAUNQ/PqrNRF6T3UW85IVar+m0n/Up927BYP/peqPo6KnqNxQe+bzkr/UBFscM3rOGAHB16K4+Z+8HMKp/uCXbUCtmfNXfwRbjKm3/9W5cCbHSZRtVGsPPNujhFx9JJbFao2isKk/6qIaUNT+Z06L+kbFqM4O79MNRe+lJocV28EzEcXgp27baVMTFXsB5M5R/w/qj7vDAjgg4p4T9q8QQj1mtnz1u2ovpKJoP57ulWoiY8tTt1uTZLgFqoMWFDc1ybIcQHvAseKm1rYoevW4eHU40nRmPPJZTvXcqKk9QacmMdU56rbsxYAe/PpogxaEs1pttsap9u2rzoCQ69UyOEoh9xu1icgQoN5EVGcfqV2KUp+LaC9Uy6UYsOOFwSNCbRrUe5zWsWuqZIJzDjTVi/DZcMWYQMZ1OtREZzfovU+ohQIQ1blQthbQge9FatIjnJD/fe0VdZ4QMAjFp8eR7TqPNovZS9SmOlAvvKJmSKkCYbdQZPFtcsdLOCrUGgB7MaCARyJU7kCdWeOYIfuKu/oDV/pPrfdXmAfjGdQBbLlQ9KeaBHokQcAgsGaoTRUFv6r7wj1YranQe6nNHe6hajOhLV/94bJmqkmAuZWaGFZsU/82RkPIdSgGb/VHseogGLxPWnNXZ6y2Qsj5Qr3LF3Zw2tUaFa/2tfq4WDLmYLLvVveH06J+D3Rm8O4I9jI1YTZGg95bTVb0HmoyY4qF0Em1agWEs0rt1+aRpP5Y53wNOCBozJGardPrGybsxUf6stnUPmpu/uDdvVat2smczrkl7CXqMXGUgSmuVk1mQxP2Ukh/5Zhz5sj3z+APEXcfbV4WdrWmVDhBVGvHzRWvhTLBOQcu9C9KcyLjOvdExc4jd7h6tdnPPbTeZj8hnGAvQnELVJv1qrPUO9jChWDwo8w4AO8WyU2muUY4q9W+W9VZ6iiR/J8BpzrKTVjVBMN/iDrCRO+BYvBVf4gcpWozXvmm47aoA88Utb/TMf1OhC1frUGz5avJiWIAc0v1385KNRl0D1f3rylarQVRdGpyULpG3X+KXq01qj46WzzGaJSIO+qPz1ak1q7ZS9Qko3K3WnZDoFpDJaqP9gUJnajWdpRvUuM2RqkJWMV2tXzG8CMd8I8kfXovQFHfg1DLH37rv05poPZ30TVa00hTOreOJyz7j/STEWrN6pHh2KfSzN6U4zpTMsE5By70L0pzIuNqHkTlLrWjNs4jfQ3GoHif/4k9hcOidqjWGdUf/+xP1KQjZAKKZ4pawyEc2p26sOWDIaD+hM5ZTUXOP3h6eau1GaaYUxjtc0yHVnuJ2pnWq/NJf/BFdZ46pYAtX60xMcWrzSulK9URgwZ/NUlxWo6MqLOrtSrWTLSEpOZ9Pr1QPFqp2xVC7Vyf+02tJjUBKBH3oBjD1I6xigFFZ1Jruxzl6o+vMUKNwVGhJmqmuCbbWfVYrnZu1XDFuJrMwzYlSZLqo3gkIGKepDx/K172tVDwM8I99MgPaCXo3GuParGXqk1px9aC5P+ojkAxtwTf3igebev4pPoJexlkvgOOSoTOdLTTavDVKJ7qQ36Pn89IcTv542MUnTvVxg54eZ/6D8uxNRyKwReONPWd9D3uLdQOuMcQ9lK1SbHw19or673UzuGKTm1+cmuh/tun1wnNQYqigGcSIvJesB5SRxuZW1JaXIyfMUxdR+91dH29p1rzc+w29J7gmXRKsUvSuSITHEmSGo2iN2NzawUB8ZD5AWR/ijD4qFXyHm3VH2x7qTrSpnKHuizkekCnjh4rXa02g1TnQPZnCOORTrde7cG7y0mbRoSjHA79T/3DFKt2kga1dsarw7kM+5xRDD6I6MePjN7Rq303nBYwxapJx+lsyy1AHVVzhFNOmSQ1MzLBkSSp0SluQYiQayDrY8APjDFHJ1f0SDw60qtyN6T9nzrsvub1sMlHhux+qjbDCAcU/AzFSxAR99Q/71D2F+ofXp2gxZVqMmA9rDbtNGOK3gz62MYuhiQ1OpngSJLUJCimWETsM+rMz7ZCdaSWV0cU767qfB0o6rxDlr3qTNJugRB269HmkvBbtW2pk8t9BMWLIWi02q9E2NTOu7YC9Vlfjgp1xmi/vkcKYFSbuiRJcgkywZEkqcmo6V+juAWoj7PQlh9pH/HuAt5dtKer19fRVzHHI3zU542Jih1qB9iauUtAHdYefludQ+MlSXINMsGRJKnZOaWZqP0HHnnysV59VpfeQ/2/ogNzS23COEmSXJNMcCRJckmK3hOCxzV2MSRJaiRn/0AeSZIkSZKkJkYmOJIkSZIkuRyZ4EiSJEmS5HJkgiNJkiRJksuRCY4kSZIkSS5HPmxTkiRJkiSXI2twJEmSJElyOTLBkSRJkiTJ5cgER5IkSZIklyMTHEmSJEmSXI5McCRJkiRJcjkywZEkSZIkyeXIBEeSJEmSJJcjExxJkiRJklyOTHAkSZIkSXI5MsGRJEmSJMnlyATnAuBqT+NwOByNXQRJcrnzCsButzd2Ec4ZVzxerqqhjtUFn+A4nU7A9b785eXlpKamAqAoSiOXpmGUlpbyww8/sH379sYuSoOqqqqivLy8sYvR4GoS0ZpzzBWUl5ezZcuWxi5GgystLeXbb7/l77//buyiNCiLxUJBQQHgOtdBkOfWqTI06Naamd9++w273c6gQYNwd3dHCOESJ4HFYuHxxx8nODiYW265hZCQEJxOJzpd881nv//+e5YtW4bVauXaa68FcInjNXfuXNauXUtgYCDt27ena9euBAYGNvvYFixYQH5+PldddRVms7nZxwNgs9l45plnqK6u5qGHHiIyMrLZn1dQ+9waMWIEgMvEtXLlSgICAmjZsiU9e/YkOjq62ccmz61T13yP8lnIzc3l7bffZv78+aSmprJ7927ANTJ8IQRlZWW0aNGC0NBQli9fDtBsT+i1a9dy//33k5aWxrPPPsvo0aPZuXMn0PyP159//smuXbt48MEHufjii9m/fz9z5szB4XA029gKCwv58MMP+fPPP8nPz2ft2rVA8z9WTqcTm81GZGQkCQkJLFiwAGi+5xXAunXruPfee0lLS+P555/nuuuuY8eOHUDzjgtg9erV7Ny5kyeeeILLL78cq9XKF198gd1uR6fTNcsae3lunb7m/S0+Q1arlcjISB544AH8/PzYuXMnJSUlQPNtqqopt6IoGAwGTCYTUVFRZGdns2vXLqB5VmdWVlZy3XXXMWXKFHx9famqqkKn02G1Whu7aGdMCIEQgqysLNq3b09QUBA9e/Zk8ODBVFRU8MsvvwDN83jZbDaio6O58847adOmDbt27SI3NxdonvHUnFc6nQ6Hw0FZWRldu3altLSU9evXA823T5iiKNxwww1MmTIFDw8PFEXBaDRSVFTU2EU7YzXHKzs7m4iICPz8/EhKSmLkyJEYDAZmzZpVa73mRJ5bp++CSHCqq6s5fPiw9ndUVBS9e/cmNjaWDh06kJ+fr925NKds+Ni4asothCA/P5+IiAh69uyJh4eHlunXZMRN+eQ+/lj169ePLl26aCdwREQEqampGI3GxiriGTm2zVxRFBRFIScnp9axiI2NpU+fPqxbt47CwsJmcRdttVpJTU3VOqeGhITQrVs34uPjSUpKQqfTsXr1aqD51ArUxHRsTZrD4cBqtRIdHU2bNm2IjY3lr7/+AkCv1wNN+7yCo3HZbDYAunTpUuvc8vf3JysrCw8Pj8Ys5mmr+e7VnFsARUVFta4Rfn5+jBkzhtWrV5OTk9MsanHkuXX255bL98FZuHAhv/32G0FBQbRo0YJu3brRqVMnAgICAOjYsSM7d+5k7969xMTEEB4e3izaaI+Pq3v37nTs2BFFUfDx8SE9PR1vb2+6devGX3/9xXvvvUdgYCBXXnllk03i6ovJ4XBoX/S4uDhMJhN79uyhTZs2jVziU7No0SKWLl3K3XffTWhoKDabDTc3N3r16sV3333HqFGjAPVkjouLIzIykrVr1zJkyJBGLvnJLVq0iPnz5xMSEoKHhwedOnWiX79++Pv7AxAfH8/+/fvZu3cvqamptGrVqsmfW8fH1LlzZ/r27Yter8dgMJCWlobRaKRfv358/fXXTJ8+nYCAACZNmtRkzyuoP65jk4K2bdui1+vZuXMnHTt2bBZ9OxYtWsT333/P448/TlRUlHZude3alXfffZdRo0bh6emJEILY2Fg6derEn3/+yTXXXNOkY5PnVsOcW013bzSA1NRU1q9fzyOPPMItt9xCWFgYn3/+Ofn5+Vq1GEDPnj0pKytj27ZtgJoNV1dXN2bRT6quuD777DPy8/MByMrKIj4+Hp1OR1lZmXYixMfHA02zOvNkMen1ei2Dt9vtmEymJhnD8axWKz/88APLly/H19eXH3/8EQA3NzcAkpOTCQwM5IcffgDUu5SaxLupy8zMZMOGDTz00EPce++9dO3alVmzZrFnz55a66WkpODp6cmaNWsA9dyyWCyNUeR/VVdMX3/9NXv37gXUkUatW7fGZDKRlpZGZmYm6enptG3bFmia5xXUH9eePXvQ6XTaj0dFRQVhYWGUlZUBTbs2226388svv7BmzRpiYmL48ssvAfXcEkKQkpJCeHg4c+fOBdRY9Ho9fn5+KIrSpJsV5bnVcOeWSyY4NT+GeXl56HQ6QkNDCQ4OZuTIkSQkJPDJJ58AR6v14uLiaNmyJdnZ2Sxbtoy33nqLTZs2NVbx63Wqcfn4+LB582Zee+01FixYQLdu3YiNjSUrKwtoWtWZpxpTjZoakIMHDwJN90cF1Niio6O5+uqrueqqq8jIyGDz5s3aaz4+PgwdOpTff/+dw4cPaxfhmv83ZQUFBRQXFxMaGoqnpyd9+vShb9++/Pjjj5SWlmrrhYaGkpiYiMVi4ZdffuH111/Xqp+bmvpi+uGHH7BYLBiNRtauXcurr77KTz/9RJ8+fWjdurX2w9OUzqtjneqx8vHxwWazNYt+HYqiEBcXx+WXX86dd95Jdna21lxTU+4JEyawcuVKtm7dis1mQ6fTUVVVhYeHR5M+v+S51XDnVtM8I89SzZ2HxWIhMDCQiooK7bUbbriBtLQ0tm3bhqIoWvtmu3btWL9+Pd9++y0hISF07969Ucp+MqcS144dO/Dy8sLLy4uoqCiefPJJxo8fT6tWrQgPD2+sotfrdI5VTd+BpKQkDh06hBCiyf6oAJhMJpKSkkhMTCQ6Oppu3bppHYhr4u7SpQt9+/blww8/5Ntvv+XTTz8lNTVVu3NpqpxOJ1FRUdqPIcD48eMpLCzUkriau+SYmBj27NnDggULiIyMbLJNbyeLad26dXh5eREXF0dMTAxPPPEEI0eOpFu3bi5xrGrOrXbt2rFr164m39yh1+tp1aoVKSkp+Pj4MGLECL799lvtNYfDQWxsLCNGjOCnn37ik08+4ZNPPmHDhg0kJCQ0culPTp5bDXduNd1v8FmoqRVITExk586dZGRkAOpONpvNDBgwgIULFwJgMBjYuXMn06dPJyEhgZdeeomrrrqq0cp+Mv8W1yWXXMLvv/9OSEgId911F1deeaXW0W7w4MF06tSp0cpen9M5VjVNO3a7nVatWjX5ToIAZrMZUC+6PXr0ANS2aDjaOfKaa67h8ssvR6fTYTAYePLJJ4mKimqcAv+Lmn0eERFBQUEBhw4d0u6Y9Xo9AwYM0OLT6/WkpaUxffp0oqKieOmll7jyyisbrez1+beYLrnkEv788088PT25+eabufLKKzGZTAB07dqVnj17NlrZT+Z0jlXNuaUoCj169GgW55a7uzugxnnJJZfg6empNffWxDl8+HCuvvpqIiMj8fT0ZOrUqU227548txr+3GrWCU5+fj6VlZXa38eelA6Hg7CwMJKTk5k3b542vFgIQVRUFG5ubtrssf7+/tx///3ccccdeHp6nvc4jnemcUVHR6MoCuXl5Xh5edXaZmPfjTXEsao5McaMGcMll1zS6DFB/XEJIU6YJTsoKIg+ffqwdOlSnE4nBoNBG5LbqVMnxo4dy3XXXYe3t/d5juJEx45MOZ7D4SAoKIi2bduycuVKsrOzATXO1q1b4+7urvUH8/X15b777uOuu+5q9HPrTGNq06YNer2e/Px8LWGt0RT6qTTUsQLo378/AwcObBJNOPXFdfy5VTM1xtixY/n999+1jsY1cbVs2ZJhw4Yxbtw4fHx8zm8QdSgtLdVqzI7XXM+tM43pXJ9bjf8LcQZKS0t59dVXmTFjBjNmzODPP//EYrFozRg1fRiEENxwww3k5eXx999/U1RUhKIolJSU4OfnpyUBoaGhREZGNnJUDR9XU9CQMdUkNDV3m43pVOKq6fRXc6Lq9Xq6dOlCTEwMH3zwAa+++mqtPkZN4ceyuLiYDz/8kO+++w6onRjXjLjR6/XYbDbGjBmDzWZj5cqVZGRkoCgKmZmZBAcHExQUBKg3D6GhoY0SS42GjqmpOBdxNYXE5lTi0ul0VFRU1Bpq3KFDBzp27Mjrr7/OSy+9xBtvvNEo5a9PYWEhb775Ju+++y4zZsxg27ZtWhJXM2y6uZ1bDR1TQ1NEc6iLPM5nn32GEIJJkybxxx9/sGvXLnx8fLjhhhu0dWbNmsXhw4eZMmUK69evZ+XKlVgsFsLDw1m3bh3XXXddk+tn05BxNZUhnq4YE5x6XFlZWdxwww3aCVxdXc2bb75Jamoq/fv3Z/z48Y0VwgnS0tKYPXs2NpsNo9HIyJEjSUxMPKE/xqxZs9ixYwePPPIIhw4dYuXKlezfv59WrVqxYcMGxo0bx8UXX9wkjpcrxgQyrlmzZrFr1y5uv/12wsLCADX5effdd9myZQsDBgxg3LhxjRXGCTZt2sTcuXNJSUlh2LBh/Pjjj5SVlTF48GBatWqlrdecjldziKlZJThOpxOr1co777zDxRdfTLdu3XA6nezfv58333yT22+/nZYtW/Lyyy9jMpmYPHmyNuw2Pz+fXbt2kZeXR9++fQkMDGzkaI5yxbhcMSY4/bhuvPFGbe6KyspK3n77baqrq7njjju05U3Fvn37yM7OJioqijVr1pCXl8cdd9wBqNXJpaWlfP7551itViZPnqwdl6qqKnbs2EFeXp72LK2mwhVjAhmX1Wrlxhtv1K4ZTqeTmTNnUlxczF133dXkzq3Fixdjt9u1TsDV1dX83//9H1dffTXJyckUFRXx5ZdfNqvj1SxiEk1cXl6eKCws1P4uKSkRU6dOFdu3b6+13nfffSf++9//CiGEyMrK0pbb7fbzU9DT5IpxuWJMQpx9XA6HQ/t/UVHRuS/wKaqJq6Z8VqtVWCwWIYQQO3bsENOnTxd///23tr7dbhd5eXm1/nY6nee30P/CFWMSQsZV4/i4HA6HFld5efl5LPnJHR9XeXm5du5XV1cLIYR4/vnnxZo1a4QQzeN4NceYmuxMxuXl5XzxxRdkZWXh6elJTEwMgwcPJiAggMjISBYuXEhSUpJWrXXxxRezfft2tm7dSrt27Wr11G5KXDEuV4wJGi6umqp1nU6Hn59fI0akqiuuoUOH4ufnp3WIjo6OJiEhgTVr1tCxY0etD1RQUJD2LK2mdLxcMSaQcf1bXMc2WzV2R1s4eVw13NzcKCkpoaKiQhvRpdfrm+zxas4xNclOxocPH+att97CbDYzdepULr30UkpKSli2bBkAo0ePZu/evWzfvl1rszOZTPj6+mo9uXU6XZMYZXMsV4zLFWOCCy+upUuXAkc7Ont6epKUlITZbGbx4sVA7Qe6NqW4XDEmkHG5WlzHSk9Px8fHB19f31rLm1pczT2mprMnj1FWVkbHjh2ZOHEier2erl271soWg4KCGDJkCF999RV5eXmAOmSusrKySQwDrI8rxuWKMcGFF1fND4c4pkteWFgYXbt2Zd++ffzwww88/fTTbN26tbGKXi9XjAlkXOB6cdXU6u7fv5+4uDgAVq5cyfTp09m/f3+jlb0+zT2mJtFElZGRQWZmJqGhoURFRdGqVStCQ0O1Z4bo9XrsdjtWq1V7z+jRozl06BAfffQR8fHxpKam4u7uTnBwcCNGUpsrxuWKMYGMqyauY0cxuLu7I4QgNTWVgoICxowZQ7t27RorFI0rxgQyrgshrprYsrOz8fb25vXXXyc7O5urr75ae1ZgY3K1mBo1wXE6ncyZM4fly5eTnJzMjh07GDFiBN26dcPf3x+n04ler8fpdJKVlaX11q4ZMjhp0iTS09PZtm0bXbt2ZfDgwY0ZjsYV43LFmEDGdXxcNe9VFIVNmzbxySefMGLECIYPH96I0Rwtl6vFBDKuCykuUOfQ2rBhAx4eHgwcOJD77ruvcQI5hivGBI2c4JSWlpKens5jjz1GWFgY69ev5++//6a6uprhw4dr7XZVVVVYrVYiIiIAtW+D3W7Hx8eH5ORkkpOTGzOME7hiXK4YE8i4jo8L0GZZjoqK4o033tCmxG9srhgTyLgupLhsNhs+Pj6MGzeO3r17a48laGyuGBM0Qh+cyspKrc0uLS2N7OxswsLCcDqddOnShfbt23Po0CHtoWIAWVlZGI1GAgIC2Lx5M48++ij//PPP+S76SbliXK4YE8i4TjWuoKCgRv9hccWYQMZ1oca1fPlyAAYMGNDoiYArxnS881aDk5OTw6xZs7SRJVdddRWRkZGYTCZ2796tPTW0c+fOZGZmsnfvXhISEjAajezYsQOHw8GMGTM4fPgwY8aMaTIPuHPFuFwxJpBxNae4XDEmkHHJuBo/LleMqT7npQZn+fLlvPbaa0RHRzNu3Diqq6uZP38+lZWVtG3bljX/394d65oSRWEc/66YhE5ETCJT6DQaT0BB6Q1IvIAXofECnkGjFzpHpxHRIBMkE6OhwWTf4sbcGxHFPcdmr3y/Tkjs/1QrYdZ8fYWfTSQScBwnfCBXEATYbDZwXRfZbBatVutjLqjELolNALsAc7okNgHsAtj1bhKbnnrdDsG/er2eGo1G4Wvf91Wz2VSe56nJZKI6nY4aj8fh+67rqmazGW5JnE6nH7Wl8kZil8Qmpdh1Y0KXxCal2HXDrveR2PSMlp+oisUiotE/X3W5XBCLxZBKpXA+n5HL5bDdbtHv95HJZOA4DtbrNfL5fLiZ8lNuC7wnsUtiE8Auk7okNgHsYtf7SWx6RsuAc3vwmVIKlmVht9tBKYV0Oo1oNIpyuYz9fo9utwvLsuB5Hur1OizL0nG8/yaxS2ITwC6TuiQ2Aexi1/tJbHpG623it4VA8/k8vKAAEI/H0Wg04Ps+VqsVCoWCzmN9m8QuiU0Au0zqktgEsItd7yex6RGtA85tOdpyuQz3hgyHQywWC1SrVdi2jWQyqfNIP0Jil8QmgF0mkdgEsMs0ErskNj2idcCJRCIIggCn0wnH4xHtdhuHwwG1Wg22bes8yo+S2CWxCWCXSSQ2AewyjcQuiU2PaN9kvNvtMJvN4LouKpXKx6y2/y6JXRKbAHaZRGITwC7TSOyS2HTvl1L/PLpVg+v1isFggFKpZOwflx6R2CWxCWCXSSQ2AewyjcQuiU33tA84RERERK+m/VlURERERK/GAYeIiIjE4YBDRERE4nDAISIiInE44BAREZE4HHCIiIhIHA44REREJA4HHCIiIhLnN2P1zCBQWeamAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x172.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x172.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x172.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAACuCAYAAAAlHEcyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABEiUlEQVR4nO3dd3hb1fnA8e+RZcvbjuPE2YmzJwl7hRkChJGW0tIBpYGWnBYopYvRwE+IQmmBFNoA7YG2zLDKbCFlEwqUDQkJZJAdZziJnTjetqT7++Nc2fKMnTiWpbyf5/Ej6S695+r66tU5556rHMdBCCGEECIReGIdgBBCCCFEV5HERgghhBAJQxIbIYQQQiQMSWyEEEIIkTAksRFCCCFEwpDERgghhBAJQxIbIdqhlLpBKeUopZyoaQvdaQujpjnu3w2xiLM9SqkTo+I7MdbxdAWl1Fy3PDfFOpbuoJSaFfUZDtvHbb3rbufsLgpPiB5FEhuRUKKSjshfUCm1VSn1T6VU4X586w/cv6L9+B4tKKUOUUr9Sym1WSlVq5QqVkp9oJT6fXfG0Z2UUoOBy4A64M/N5g1RSv1dKbVJKVWnlNqulHrFXae9bc5qdtwc02z+fdHzu7xQje/TImneD25zH29RSqn9+D5CxIQ31gEIsZ/UAZ8BecAo4JvAOGDi/ngzx3GO2h/bbY9SaijwBpADlANfAL2BQ4EC4JpujCXFcZy6bnq7nwA+4AXHcbZFxTASeA/Ix37+KwEFHIfdHxs78R5XAP9zt5sHnN8lkfcMLwI7gQnAKcCrsQ1HiK4lNTYiUW1xHOcox3FGAw+70yYopXpHFlBK5Sml7lJKbVBK1SultimlHlNKjejsmzVvimrW/DNLKfWCUqpKKbVWKfXDZutOVUp9ppSqcR+ndrBpawaNSc1gx3EOcRxnKNAXuLKNdQYqpZ5VSlU2j0UplaGUes6dXunWAH2llLpRKZUStVxDrYJS6mql1Gag2J23zp33kFLqt24NUqVS6lGlVE7UNpRS6jKl1GKlVLVSqsyteRrfgd39fffx382mz8MmNUuAoY7jTHQcZ4K7j5Z0YLsR9cC5SqkB7utLgDR3egtKqZlKqbeVUhXuZ7hYKfWT6NqQqM/zD+4xV+Ieb39SSnkjywAnuKucoNpuehqnlHrD3W/LlVJnRb1PhlLqbveYrnHf5wOl1C8iyziOUw+87L78PkIkGsdx5E/+EuYPWAg4wLqoaQ+503YBye60VOyXnQMEsbUd1e7r7cAgd7kb3GlOK++xMGqa4/7d4L4+MWpaHbAWKHNfh4Cx7nIF2MTEcd//S2B38+21UdZLo7Z3DXBwpHzNlouOpaqdWPLdaVuxtV0bo9a7rZXy10btuzXuvHXuvBp3f6+I2saTUduYFzX9S/c9I5/R8HbKPCJqvYOjpucCYXf6AjemCrcc3+zAcTMraruPuo+/BZKA9W45n2jlWLggar1id99GXt/SyvFRB5Rgmywj0y5xl3k/6rPf7b5+H+jfLL4qbG1UVdSyee425kZ9Np8Cq7EJ2WvNyvtzd7mNsf6flT/56+o/qbERiaq/Uup9pdRK7K/SUuAix/5aBfgujc1S33HsL/vDsV/0+dgTf1d5HhiObRIBW1N6ovv8MiAT+yVznOM444Ffd3C7TwPb3O3dgv0i26WUelEpdWwb67zYTiy7gQmO4/RzHOdgx3EGA4+4877TyrZSgLPcfTeq2bxKYLTjOGOAP7nTvqmUGu7WQFzmTtNumYdik5Ec4Np2yjw26vm6qOejsc1OYGuyst3yTAH+GV2r0QH3YhOz2cB5wBDgOVpvyrrZffwYW4bhwOPutF+6zVjRitxlRgKb3WnToKE581N32qeOrXE8ynGcLc228RfH1kRGPpMs4Aj3+Wj38SbH1uCNwDZPNt+n693HQUqp9FbKJUTcksRGJKoU4Egav3C/BN6Nmn+4+1iHTRBwHGcp8Lk7/bAujGW+4ziRmomIAvcxklytchznY/f5Yx3ZqOM4xcBBwB/cbTtAOnAGsFApNaWTsYSAC5RSK91mKAdbIwEwgJZWOI7zkhtLqNm8hU5j/5cn3EeF7ddxOI1JiHHfp8adB9Bef6XcqOflUc+j+wt+iU0ehtP4BX45gFLqejfhjfxd38p77MDW2vQF/upOm9d8IaVUX2zSA/Cs4zg17r6NfH7JwORmq/3LcZwyx3FqsLU70Lj/OyrStNraZxhpnrvRbY56DbgKWwsZbXfU89xOvr8QPZokNiJRrcc2I5yOrZafCvw9RrHsAnAcJxg1rfnVKHt1pY3jOMWO41zj1pr0prGmyQu0VkvRXizXYH/ZjwK2YK/y2uTOa+1cUbw3MTezmMYryiJ/X7WzfFnU86yo55uini92HKfeTR4ifWuGuY8jsAlv5K+t/lSRq62y3e291X4xOmxX1PPIZ9DZK5Mi22jxGTqOcy+2n85cYBm2xmoO8K5SKiNq+ew2YhIi7kliIxKW4zhhx3FeBu52J52llDraff6R+5gCnAuglJqIrQEB27TQHSJfvCOVUpFf99/tyIpKqdPcjslZAI7j7MQ2NUWUtb5mmyI1JSsdxxkGHItNPNrSXjJ2glKqj/v8W1HTv8Du28i6j0U1uRyFbaK6vZ3trox6PqwhEMdZHzXvIKWUVynlo7FGbKW73CzHcVTU36xWC+Y4i4FIMtOitsZdZhuwwX15jlIq1e0wHPn86ml//7Wmyn3MaHepNiiljgC+cBznV47jnEZjcjuAps14Q93HTY7jVCFEApHERhwI5mKbnAB+4z4+Bix1nz+ulPoC+BBby7MDuKObYrsb28nVA7znxtHeF3u0QuB+oFQptUwp9RmNTWnlwAudjCWy7mil1FpsrdfeXsaeCnyllFpBYy3SU47jrHEcZy2NTTy/V0qtd68kKsUmPae2tVHHcVZia5OgsTkx4ipswjQBWINt6hmGTTB+txdlOAPoAzzQzjJz3MfDsH1+1tDY92Wu4zilnXzP5ZHtKaU+V0q91Mn1rwC2ule2fULj1U+V2I7EEZE+OW92cvtC9HiS2IiE5zjOZhr7JZyllJrsNlOcgE0stmA7XVZi+4Mc5ThOtwy05/7qn4H9ZZ+EbV6I7qhb3c7qb2ATsM+xzVAT3eVfAU53E4jO+B3wILZpIhvbCfaeTm4j4mlsQpmDrYV4AnvZdMTl2C/hxdi+LIXYz+Ev7rrtiXyWM6MnOo7zPLaG4n1sQpIE/Ac40nGc9ztbAMdxqhzH2dFK/6HoZR4Bvobtv5WFvYLpc+wVa79pa7123A68hk12J9H5vl4vYmuafO769e72ZjiOswtAKZWMbaKFxn0pRMJQtq+bECJWlFKj3ZqIyOvvYy9RB5ugvNz6mj2PUmodtpnjwbaaebrgPYbS2Ow0xO1ELTpIKXUO8Ay28/FER74ERIKRGhshYu9Jd6C155VS72BrTcA2E7wSw7h6JLc/zd3Y/lFXxDicePQr9/EaSWpEIpIaGyFiTCl1C7aD7UB30mrgSeygeO01RfU43VFjI4QQ7ZHERgghhBAJQ5qihBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCcMb6wCEiCeBQKAv8CfgBCADWAr8wu/3f9DG8rOAH/n9/qn7IZZ1wIl+v39dIBD4NfADYCiwA7jH7/ffFrXsMOB+4EhgA3C53+9/rYvimAjMBQ4Fevv9frWH5R1gOzDA7/cH3WnJwCagz57W70Rcs7D7Z1YgEBgN3AYcAyQBHwFX+P3+Fe6yPwCuAEYBu4FHgd9E4hNCxA+psRGiczKxX4qHAnnAg8CLgUAgM6ZRgQIuBHoBpwOXBwKB70TNfwz4DOgNzAGeCgQCfbroveuBJ4EfdmKdncCMqNcz3Gl7JRAI7OlHWi7wL2AMUAB8CDwfNT8duBLIxyZ/04Bf7W08QojYkRobITrB7/evAf4YNeneQCBwO/YL85M9rR8IBK4BLgH6AhuBOX6//1l33izgR8D72CRhF3Cp3+//TwfiujXq5YpAIPA8cCzwuFtbcQhwqt/vrwaeDgQCVwLnAn/d07Y78N4r3Pcc2YnVHsYmYv92X18IPATcFFkgEAhcBFwFDMLW8PzB7/cbd96JwCPAPODnwKvA99uJ8UNsMhPZ9h3AdYFAoLff7y/x+/1/iVp8UyAQmA+c1InyCCF6CKmxEWIfBAKBKUAKsKqDq6wGjgNygADwSCAQ6B81/0hgBbbm4Fbg74FAoNWmGb/fP8zv969rJSblvscX7qQJwBq/318etdhid3qsPAccHwgEcgOBQC9svM83W2YbcBaQDVwE3BEIBA6Jmt8PW2s2FJjd/A38fv8Dfr9/Vhvvfzyw1e/3l7Qz/4s25gkhejCpsRFiLwUCgWxszUPA7/eXdWQdv9//z6iXTwQCgWuBI2j8Ul/v9/vvc7f/IHAPtulkaydCuwH7o+V+93Um0Dy+MmBgJ7bZ1WqwtTXfxjaj/cud1sDv978Y9fKtQCDwCjYB+tSdFraL+Ws788aBQGAQcDfwizbmXwwchq09E0LEGUlshNgLgUAgDfvF/L7f77+lE+tdiP1CHeZOysTWzkQ0JDB+v78qEAhEluno9i/HNuscF/WFX4Gt9YiWDZQ3m0YgEBgCfBkVw/7sO/QQcAs2sbm6lVhmAH5gNDZRSweWRC2y3e/31zRfrz1uv6JXsJ2rH2tl/tfdmE7x+/07OrNtIUTPIE1RQnRSIBDwYZtSigDdifWGAvcBl2OvHsrFXlXVVVcBXQxcA0zz+/1FUbO+AIYHAoGsqGmTaaWpxe/3b/D7/ZmRv66Iqx1vA/2xNVLvRM9w9/HTwO1AgbuvFtB0XzmdeTO3yesV4F9+v//mVuafjv18zvb7/UuazxdCxAdJbIToBPey5KeAauAHfr8/3InVM7BfxtvdbV0ETOyiuM4HfgdMdzs4N/D7/SuBRYA/EAikBgKBc4CDsIlDV7y3CgQCqdi+Rrjv4dvTen6/3wHOBma6z6OlAD7svgq6tTen7kOM2cDLwLt+v/+aVuafDMwHznU7Ggsh4pQ0RQnROcdgO7RWA7vcpiKAGX6//+021nEA/H7/l4FAYC7wHrZ/yEPAu10U103YS7k/iorpEb/f/2P3+XeAB7CXVG8Avun3+7d30XsPBdZGva4G1tPY3NYmv9/fagddv99fHggErsBeRu7DNvv9ax9iPAc4HJjgXn0WMd7v928Arsd26F4Qtf/e9vv9MxBCxBXlOJ2qzRVCdIL75Xyy3+//eqxjEUKIA4E0RQmxn7jNM18DPo51LEIIcaCQxEaI/SAQCEzCXuG0G7grxuEIIcQBQ5qihBBCCJEwpMZGCCGEEAlDEhshhBBCJIx4u9x7r9rNKisrycjI6OpYulVPLUPRrmo+LSpj+pg+pCUntblcZWUllWEvH2/cxaayGvpkppDqTSLL5+XIYb3werpkjLr9pqfu/46I59hB4u8J4r0MEn9sdEPcrX5xxFtis1dqa2vj8qCItq9lqA2GueKZJfxm+iiG9krv8Ho7Kut4Z00JSzbvZndtkM1lNQzvncGAHB8H9c/hhpdXsLumnrUlVfzshOFN1n13bSk5qV4Ke6ez4Mti7vlwC8keDwNzUimtrqcuGKaspp7Tx/ZlzvTRJPXg5Caej6F4jh0k/p4g3ssg8cdGrOI+IBIbAUu37ObTojIe+biIOdNHN0wv2lXN22tK2VVd3+RvZ3U9ZdX1lFbV0ystmQn9s6isDTGuIIuq+hAfrN/FAx9uZETvDH590gj+76UVDO6VRnKSYkdFHetKq/jPsm0kJ9lkJUkpvjF5AD89rrBJArN0y26ueGYpG3ctZvbRQxnVJxPHcchI8ZLi9VBTH2L1jkr6ZvnonZGCRynCjsO28lryM1LwJklrqhBCiEYdSmy01guBo4CgO6nYGDOijWUvB67F3mRvAXCJMWa3Oy8XuBeYgb0M9mZjzD37EH+P8/GGXdSGwhxbmBfTOIJhh9vfXMX2ijqSPYp1pVX4vB5e+KKY/IwUwNbGvLpiO73SkumX7SM3LZm89GSG904nNy2Z3LRkBuWkMrYgq9XalN019XiUItPnZdWOSua+uYpe6Sn0yUihX3Yqvz97PHXBMJm+JEZmOvTrm99iGxP7Z/PQ+Qfzl3fXcfnTSwi7jY2pXg+Dc9Mora6npLIOgJQkxdA8W9v01fZK+mSkcO7k/qSneElP9nDmhH49vklLCCHE/tWZGpsrjTF/bW8BrfV07N14pwNrsEO4zwN+4C5yl/ueA4ARwKta62XGmDc7GXePdc0LX1JWE+Tub07iiCG9YhJDVV2Ie99bz8vLt3H2hH7srKpndUkVZ08ooLB3Ov9Zto0sn5deaclcNrWQcyf3x6M6nxBkpyY3PL90aiE/OXYYqo3tlJaWtrmdQblp3HzmOK48YThl1UFQsHFnNVvLa0nyKKaNymd3jW0G+2xTGTur6gicPoYPN+ziyUWbqaoLUR8K8791O7npjLEkSy2OEKKL1dfXU1RURE1Np24o3yXC4TDFxcXd/r77al/jTkpKIjc3l/z8fDyejp/Xu7opahZwvzFmEYDWeg7wmdb6J9hOPt8CDjbGlAOLtNb/AC4GekRiEww7JCna/HLek7DjEAw79M/2ccXTSzhyaC9OHJnP9DF9yPR1X6vfn99ew0vLtnHNtFGcPq4vAIcOzuGwwbkMyk3j+4cN3i/vu7f7LaJPpo8+mfbeiSPzm7bL9s5IobB3OscOb6wJG9Unk+8eMhDHgXWlVVz61Odc/vQSZk7sx5FDezXUTAkhxL4qKioiKyuLYcPa/gG3vwSDQbze+Os5si9xO45DfX09xcXFFBUVMWTIkA6v25l3vElrfTOwArjOGPNGK8tMxDY/AWCMWaa1BhiFvbRcGWO+jFp+EZ24Y29lZSW1tbWdCNmqrq5ut8YA4MFPi3l8yXayfUncdfZI+mQkU1MfZmNZLaPy0zr0Ppt211JZF+Lus0ewpbyOt9aWcffba3jk4w3cdMpQCjL3/ou2I2WI+GBtCRdM7sMRBd6GdY4f6INwNaWl1Xsdw77oTPx7o5cHbj1tGA99Vsztb6yioi7EuD5p3DhtKNmp+35C2N/x70/xHDtI/D1BvJehK+Kvqqpi0KBBhEKhLoqq48LhMMFgcM8L9jD7GrfH46GgoICvvvqq1c8vL6/1Lh8dPeNfDXwJ1GHvEvxvrfUUY8xXzZbLBMqaTSsDsrA1NrubzdvlzuuQjIyMvephXVpa2uYOiPjGoakcM6ofcxeu5pElO8nyeVmwrJjKuhCnjO7DkF5pnH/owCbNL839d9MWsn1exg8pYIJSnDIRymuC/PL5L7jkuVUcPbQXYwsyWbx5N2tLqpg6PI+rTh65x+z/rdUlzHuriDu/cRCDcttPsnZU1lG0u46po/uTl9fhXbvfdeQz2Fd5eTC3sD+hsMPy4nICr6zEfLqD3505bp9/YXVH/PtLPMcOEn9PEO9l6Ir4i4uLSU5u+/y/Px2INTbRPB5Ppz6/Dr2jMeaDqJcPaq2/C5wF3NFs0Qpsp+Fo2UA5tsam+bwcd17MDe2VztBe6fzoyCHc9OpKRvXJ5NcnjyTZo3hlxXZe+GIr5TVBrpo2EoCt5TW8taqE2mCY99btpDYY4qvtlXzv0EFNvkSzUr389byD+O/qEhZ8uY03V5UwZUA2Uwvz+NN/11CYl07vjBQyUpIYmpdOvywfSikqaoOsLaliQE4qj35SxNaKOvSTi7n17PFM6N98N1qbyqr5y7vryPZ5GdUns1v2W0+U5FFM6J/Nb2eM5YePLeKxzzbxvUMGxTosIYQQ3WBvU6m2BspbCkwGHgXQWo/F1tR85T46Wutxxphl7vJT3HV6jFPH9uXUsX1bTHtlxTZufGklK7dXsKu6ng07q8n0eakPhfnm5AFkp3k5d/IAZozr22KbHqU4cWQ+J45selWQUorb3lhFitdDMOwQCjuM6ZvJ784cx9/fX8+CZdsalr19RiEvrS5n1mOL+PqkfvzixBGEHYenF29h8sBs/vH+Bt5bt5PhvdP549cn9OgxYbrLmL6Z/Gb6KG58eQXrS6sZ2iuNzWU1lNcGOXFkPieM7I0CKutC3doHSgghxP6zx7O5e4n2kcBb2Mu9vw0cD/y8lcUfAOZrrecDa4GbgCeMMVXutp4Cfqu1vggoxHYc/vY+l6IbnDyqDxt2VuM4kJPqZWSfDCYPyCHsOHt9Fc63pgzgqKG9yE71kpGSxIZd1cx9czUXP/YZZTVBbjlrHMPy0qmqCzEoNcgJ4wZzXlEZ//ef5Vw4/1OUUqwtqQLgiCG53PvtyUwekN3tHdt6sjPGF1ATDPP+up0s3lRGWnIS/bJ9XL9gOdmpXnbXBqkNhjlvygB6pSdz1oQCSirrASjI8u23Dsi1wTDJSWqvrkYTQojmFi1axJ///GdWr15NUlISw4YN45e//CVr1qzhueee4+9///teb3vz5s3MnDmT999/Py6axDoSYTI2QRkLhIDlwNeNMcu11kOwfW/GG2M2GGNe1Vr/FniJxnFsfhq1rcuA+4At2P42N8TLpd5ej+JHRw1tMT2p9RGdO2xwr8Y+M8N7Z3DbzAnc+dZqdtcEOXlUfsMXX6Tj1MGDcpj//UOY++ZqlIKrThrJi8uKufrkkaS2c0uDA9k3DurPNw7q32Talt01vLu2lL6ZPtaWVHHXO2tJTlKY/61vWCYjJYlvHzyQytogxWWVVIWKGgYuTE9J4ucnjOCYvRyv6IePL+L0sX254DBpIhNC7JuKigquvPJKrrnmGqZPn059fT2LFi0iJaVjP8xCoRBJSYnz/aEcZ69uvxQrexVsvHd8g/gvQ0+O33EcNu6qoS4YpqSqjoMGZOM48PcPNrB4Uxm90pJJ9YTp1yuT3LRkeqUl8+XWcp5ctJljC/P4wRGDmTIwp8Pvt72iljPu/YDBuak8fdHh+72GrSfv+46Q+GMv3svQFfEvW7aMcePGdVFEnbOnTrhffvkll156KQsXLmwyfe3atZx//vkEg0F8Ph9JSUksXLiQG264AZ/Px5YtW/j000+ZO3cu9fX13HPPPWzatInMzExmzpyJe1UzZ555JsXFxaSl2R/id999NwcddBDPP/88Dz/8MCUlJUyYMIE5c+bQv7/9Efn+++9z6623UlJSwowZM1izZg1nnHEGZ555Jqeddhr33nsvI0faPqulpaWcffbZvPDCC/Tq1XL8t3b2/YF7rygh2qOUYohbczaSxqvufnpcYcPz5ifGM8YXcMb4Au7/cAOzn1jMpAHZHDwwh69N7NdQC1cXDPPAhxsY2SeTjTurWVZczu7aIJ9s3AXAprIaXlu5g+lj+gBQUx/qUK1bKOxQUllHcXktLy4rpqw6yKT+WUzqn824gky5zYQQB5ghQ4aQlJSE3+/n1FNPZdKkSWRnZ1NYWMi1117balPUSy+9xJ/+9CfuvPNO6uvrWbJkCTfeeCPDhw9n9erVXHbZZYwZM4YTTzyR++67j5kzZ/Lmm282JFgLFy7k/vvv54477mDw4ME88MADzJkzh3/84x/s2rWLq6++muuuu46TTjqJf/7znzz77LOcccYZJCcnc+qpp7JgwQKuuOIKAF5++WWOOOKIVpOavSGJjRB7aXy/LG6bOYF1pVXM/6SIBz/aSGVdkKunjQLgP8uKeeijIlCQnpzESaPyGdwrjY837GLq8DzG9c3khpeW88znWxiYk8oLX2zlh0cN5ZKjG5s8a+pD/HPxZr7cWsG2ilqKy2vZUVFLyK27HJmfwZi+mTzz+RbueGsNJ43M59aZ42OxO4Q44Oyqrqeqbv+Na5OekkRu2p4vMc/MzORvf/sbDz74IDfffDMlJSUcc8wxXHfddW2uc8IJJzBlyhQAfD4fhx12WMO8UaNGcdppp/HJJ59w4okntrr+M888w6xZsygstD8AL774Yu6//362bNnCJ598wvDhwznppJPwer185zvf4ZFHHmlY96yzzuLqq6/mpz/9KUopFixYwIUXXtiBPdIxktgIsY+G5aUzZ/poxvTNZN5/13LK6D58uGEnzy8t5ryDBzD76KEopfB5bU2KPnooIQeSkxRjCrL4rKiMFdvK+c4hg7jvvfUcNCCbI4faXy5zFixn6ZbdHD+iNyPye1GQ5aNvpq/hMT2lsYbng/U7ueKZJeyorJNRl4XYz2qDYc6+7wNqguH99h6pXg+vXXoMHen9UlhYyA033ADAunXruP7665k7dy5HH310q8sXFBQ0eb106VLmzZvH6tWrqa+vp76+nmnTprX5flu2bGHu3LnceeedTaZv27aNHTt2NNm+Uoq+fRuvGJ44cSKpqal88skn5Ofns3HjRo4//vgOlLJjJLERooucOKI3f3h9FT/+5+dM6JfFOZP6cf6hg1o0L3mTPA3/eCeM6M0JI3o3zKsJhrh+wXKOH9Gb9TurWLJ5Nw9fcEiHxiU6fEgufTJ93P3OWuZMH43Xo6isC1JSVU96VseauYQQHePzevj3JUfu9xobn9dDsJPJ07BhwzjrrLN45plnOOaYY1pdpnnfvjlz5nDeeefx5z//GZ/Px9y5c9m1a1ery4JNjC6++GJmzJjRYt7GjRvZtq1xuBLHcZq8Bltrs2DBAnr37s20adPw+XydKmN74iuxaa1K7Lzz4NJLoaoKzjij5fxZs2DmTNixA775zZbzf/IT+Pa3YeNG+P73W87/5S/h7LNhxQpwO1I1cd11cMopsGgRXHlly/m/+x0ccwz873/wm9+0nH/nnTBlCrz2Gtx0U8v5xsCYMSS/9BLce2/L+Q8/DIMHwxNPwF/+0nL+U09Bfj488ID9a27BAkhPh3vugSefbDk/0hnt9tvhhReazktLg//8xz7/7W/h9debzu/dG55+2i564412H0UbNAgi1ZNXXtly/ujRjWWePRtWrmw6f8oUu/8ALrgAioqazj/6aLjlFvv83HOhpKTp/GnT4Prr7fMZM6C62e0mzjoLfvUrALJmzoTmnfeaHXv5wHthB4UdJJBZs+CYWZ069q524Pu7a6gNhll+wWwu+e65jCrdBN/a87HnAR6rDbJ6RyWlOaksvuwabqrIZ+TKxVzx8t+Y0C+r6d3P2zj2aoJhKuuC5Dz4D/5WmsrOJ59h9ntP0Tu9WS1QNx17qXfdBW80u4NLJ449rr0W3nuv6fxuPPYyf/ADKG82Dmknjr29Pu/NmtVl572sYLDp8d9N5z3+/W+YO7fl/E4eey3i35vznt8PHg8oZY8PIHfndnKbf7ZJSeB2iqWoCCorm85PTobhw+3zDRtafvY+HwwbZp+vWwe1tXgcx74v2GM/ct+kNWugvp51mzfzzmefMf2ooygYMoStXi8vv/wyk4YOJa+qim2bNlH/xRcke72QFTUi/cqV4F5AVFVeTk5VFb6dO1m6YwcvvfQSR40fDytW0Ku2Fo9SbHrnHYaOHw99+3LuOefwl3nzGJ2SwohBg6ioquL9JUs45YwzmDp1Krfeeiv/ffxxjj/kEJ5+/XV27NgBW7dCaSnk5THjlFP43gUXkJ6ayo0/+Yk91gAKCiA3F2pqYL17lerWrfaYhabHntuU1lx8JTZC9HDefRwYUSkYmJMKwPDxBTCkF6zYtoe1GmX5vBRk+di4q4bnlmzhsgsP5+D+JVS/Aos37yY31UuK10N6ShJ5jtPikgIHe0PRyroQT7zwJR+n90P3Tmd9aTWZKd6G5jQHwF0/HHYIhx2SlAIF60urKK8NsmzRJs45KY/YDEIvxIEjPTWVpatXM/8//6G8qoqs7GymTp3Kz84+G19SEsMHDeK0Sy9FKcXrjz7a6jaunjWLO+fP59aHHuKQQw/llFNOoWLzZgBSfT4u/vrX+WEgQDAcZt5dd3HSiSdStXYtv5k3j607dpCZns4RkyZxyhlnkJuby+9vuonbbr2VgDHMOPZYxhcW2sTK1a+ggLHDhrGxuJiDx47t0v0hl3vHiXgvg8TffSpqgzz+2Sa+NrEffTJ9lJaW8t9Ntfxz0WYmD8hha3kNH67fxcyJ/VhWXE5Blo9jCvMYX5DF3IWrWbSpjCkDcwiFHQIzxtAvy8elT33Oim2V1AXD1IXCOEBKkuLXJ4/k6cVbWL6tAgBfkockj+L7hw/i4Y+KSE32MHNiP86Z1J8BbsLWEUW77GCYg3ulxdW+b028xw+tl6E+FN7rwUm7W6Jf7t1TReIOh8OcccYZ3HTTTU06KQcCAfr06cOll17a7nbkcm8hDnCZPm+LwSS/Pqk/X5/UOEjhhxt28ovnvmBQTireJMW8t9eyq7qecQWZPHzBIYzond6kXf3Ocyax4MtictOSyfQl4VGKN7/awc2vfsWEflk88L2DqQuGqagLMjg3jWF56Xzn4IEs+LKYpz/fwoMfbuSCwwbx0+MKW7TXB8NOk5quD9bv5KdPL8HjUVxxfCFD0mFyejZZXXCX9v1he0UtaclJeD2KbRV1DMxJTfhbmjz52SZue3M1500ZwK9PHtmhdarrQywrLmd5cQULV5U0jAHVXDDssL2ilvLaIHXBMP2yfORntt7/4o2V29lWUccRQ3P5anslCpg2uk/C7/948N577zF27FgyMjJ46KGHcByHiRMnNszfvHkzb775JvPnz+/y9+6ZZwohxH51xJBePHLBIeSmJZOblkzYcVhTUsWgnNRWOxn7vB7OaTZ68+QB2Rw7PI/Dh/RqtQku0+flvIMH8q0pA3h7TSm/eXEZA3JSOXxILu+v28nakioyUpJ4ZskWphb2ZnjvdLZX1PHmqh2cc1B/+mf7mP9xEdsq6oA19M5IoTAvnYuPHMyAnFRWba9kZJ8MBua0f8f7/eHdtaWs3FbBprIaXvyyGKVsM2BpVT1pyR7G9s3kyKG96Jvlo29KiCOjKgvWlVZRXR9iXEFW22/Qg+2uqeev/1vPyaPyeWrxZgbnppGekkSSR3HY4FwKslomISu3V/DzZ5eyvaKOYXnpbKuoZcmW3fTOSGZreS2by2rYsruGzWU1FJc3DmcA9hY2j194aJPkJhR2uOvttTz6aRHJSR5qF4bJSfVSFwrz4YZdXHfq6O7YFaIdn3/+OXPmzCEYDFJYWMjcuXNJTbW1tn/5y1949NFHmTVrFgMHDuzy95amqDgR72WQ+GOnp8T+6KdF3LFwDQD9s32M6J1BZV2QQwbl8u7aUrweRX5GCqP7ZnL+oYMaLmUvKt5OmZPKutIqPli/k/+4N4f1euwl9DPG9eWyqYWkpySxcVd1w4CF1fVhUpM9DbclqagNsnDVDl5dsZ1po/vg9ShSvR6OGGoTs0+KyvAleeiTlcKOijoG5qayZHM5lXVBemekkJ+Rgtej+LSojD8uXM24giz6Zvo4bkQeuWnJrCut4rjhvVlTUsXy4nKeXryF3bVB+melcPzIfN5dU0qK18Ma9/5up47pw0mj8jlscG6HxiqJpehj6LFPN/HQRxv514+O4JbXvmLxpt0EHYequhApSYo7zpnI8Lx0Fm3azdtrS3hnTSkbdlYztTCP354xlkyfl6q6ECfc9S4ABw3Ipn+2j4E5qfTPtn8DslPJTvWS5FFc+tTnrNxeyeGDczloQDaHDc5l465qbn1jFX84azzj+mVSH3Lom5nC/9bu5Jf/+oJ//fAI+kYlWKWlpewIpVBeE+TQwbkN0ytqg1zxzFIcx6GyPkR1XYhTx/YlScFPjh3WpHZx2bJljB4zFo9q/Sqh5hzH6bJRxeO9KWpfdbYpShKbOBHvZZD4Y6cnxV60q5qQ4zAkN63DJ/3o+B3HYcmWcnLSvAzMSeP5JVt45OMi6kJhKutCVLqX3k4ZmM3SLeVM6JfFmL6Z7Kqu563VJQRDYQ4dnMu6UptcVNSGCDm2KawuFCYYcpqcZDJSkshJS6akso5a95LbJAX6mGFcdOSQduNeV1rFpl013P7GSvrlpHFsYW9Wl1Ry1NBe9MlM4eGPi/isqIyaYJgbTx/DqWP7tru9WCotLSU9K4dPNpZx+5urOHVsH35ybGGTZWrq7VAFC1eX2EuUQ2EmD8xh6vA8jhvem6G9mn7mRbuq6ZWeTEZK+198VXUhPisq463VO1i+rYLlxRU4wIWHDeKnxw9vsmzYcfjuQ58wKj+DaaP7sKW8hm3ldZRVVPHKqp2EHbj4yCEMy0sHYMGyYtbsqORrk/qTkZLE7pog975nr8S585yJTB6QjdejKKsJsnbVCnL6F5Lp89Iv29du/6Ka+hDrd1bj83pwHAiGw2SkeOmX5cOzF81k3Z3YOI5DeW2QtGR7N8S9Hc1cEpuOkcQmTkn8sRPPscOe4y8ur+Xl5dsYkJPK2L6ZbN5dwyvLt3PIoBzeXlNCMOSQlerlsMG5HD+iN5m+xhNtXTDMu2tLCYYdjhuehzfJQ0llHaleD18WlzNlYA5pyUn2F31diLpQmF5pyZ36Jd5e/MFQmEc/3cQ976zlZyeM4JxJ/XrkeEOvLllP4I2NODgcNjiX608b0+YgkCu3V7BpVw2HDMohZz/URJVV17Nky24OG5zb6r5asnk3lzyxCJ83iQE5qfTJTKGurp6ZBw2kuLyWN1ftYNOuGurDYcYXZPHjY4c1uddbaVUdt72xmtdWbm+y3duPy+agCePZWV1Pfcihf7aP7NSW5asPhVlXWoXP6yElyYPXo0jyKHZU1uFxB+rMz0jB5/V0+DiKJAiO47Czuh7HsQMEVteH6JVum5M9XVA75Lg1b7tq6imrDjZMH9s3M2YJmeM4LF++vOsTG611LnAvMAN7V+6bjTH3tLHs5cC1NN7d+xJjzO7ObqcNktjEKYk/duI5djgw4n/ys0389X/r6Z/t49KphUwekN0kAesqa0oqCYdhZJ+MPS/s2lRWzS+fXcKYgmyuOWUUaT0w8WpuR2UdvdKSGzoRt/YZhB2nzWSgtKqOr7ZXkpuWTDAUxudNIrxzE8OGDcObnMyOyjpKKuvwJXnweT1U1YdQSpGZkmSfoxjaK61JMlAXDFNaZWv+KutCeJMUKUkePAoyUrykJXtI8nhwHIfSqnp6ZySTkuShNhimqq6eujBU14WoCzkkuc2oHqUoq6kHYEBOKjluorWzqo5d1fWkJHnIcI+j2mCYLJ+3yWjlYIdrqKwLUV4bpLw2SNhxSE9OIi8jhbpgmOLyWvpn++jVfByrDuiKxKaqqorNmzc33DCzmX26Kuoud9kBwAjgVa31MmPMm9ELaa2nA35gOrAGeACYB/ygM9sRQogDyXkHD+TUsX254aUV/Or5Lwg7DmP7ZnHF8YUcPCiHmvpwiy+k9tQFwzy3dCuby2q4+MjBbK+o4+8fbOC1FdvplZ7MMxcf3m4TUGVdkH8u2syu6iBPLd5MRrKHHx87LC6SGqBDtxRpr4YjLz2FI4c23cY2J5fi4mIGDhxI30wfOaleymuC1IbC9M5IwXHsfvMoxaCc1BY1HCleD/2ybefZulCYytogdSGHulCYXdX1FJc3ji7sUTSMaFwXCtv+YMkeMlK8DEjz4vPaz8FxHFK8HkJhh81lNYTCDvUhh51VdWT6vDjAlt01JHkUHuxI5IV56TgODYlMRa2tmclwx8DK9CXh9bhNTz7bUXvL7lp2VteT7bP9nurDDnnpyY3L7QeO41BdXc2mTZta3P5hT/aY2GitM4BvAQcbY8qBRVrrfwAXA80TklnA/caYRe66c4DPtNY/wWZWHd2OEEIcUHLTkrnznInU1IdYuqWc55Zs4TcvLmNS/2w+2rCLHx87jPOmDNjjpcwfrN/Jra+vYndtkMyUJJ5ctIn6kMMhg3KYd+4kbnx5BX97bwM/O2F4i3XfXVvKe2tLeWn5NlKSPJTV1DMyP5O5pw8hP7vj4xAlovz8fIqKilgRGSE3yu5mr78q7vz2HceJDAKMA5TVBVFKkZ6cRJ0TJpSURCVQ0sb6ZdX1FAfDJLs1QWGfF+VuN6QUte5l9Bs9Co9SBMMOqV4PqV4PKV4PFUpR0ca2g6EwO4JhNtaFbE2XR6GAVHeYA1+SBwdaHJvhcBjPPiQ/ycnJFBQUkJ2d3bkV7c5s+2/27NkHz549u67ZtO/Onj37s1aWXTx79uzzm02rmT179uTObKetv2uvvdbBfuYO4Lz++uvO66+/3mTaVVdd5ZSUlDgFBQUN0yZNmuSUlJQ4F154YZNlly5d6syfP7/JtD/+8Y9OSUlJk2mnnXaaU1JS4px22mlNppeUlDh//OMfm0ybP3++s3Tp0ibTLrzwQqekpMSZPHlyw7SCggKnpKTEueqqqzpUpqKioiZlmjx5clyVacGCBXv8nHpymX7+85/v1bHXE8pUVFS0T8derMv08ccfd/n/U3eW6fe///1eH3tXPbvIGXHmxU2WfeGlV1st044dO5yc3n0bpk2cdJCzqXi7c/q5322y7EOvvO+M+uHvmky7+Q+3Odf96/Mm06ZPP9X534qNzgnTpnf5/1NP/JwOhDLd/tf7nUvufXmvy/TCS6868597qcm0YTMuck6c946TnN27YdrYCZOch9/7yjnr3G/v1zK1lSvssY+N1vo44FljTH7UtBnAPGPMyGbLrgZ+Zox5IWpaMXAutsamQ9tpR/vBtiHe2+gh/ssg8cdOPMcOEn/E9opaLpz/Gd8/fBBTBuSQneol0+fl0U+KqKwL8cZXOyitquPqaSP5xkED2t2W+d86Hv6oiIcvOISK2iDz3l5DaVU9l00t5KRR+S2Wl88gtroy/rDjUFkb6rIBL6vrQ9z2xipSvB6eWbyFgiwfW8trKcjycXC/dH579qQueZ827HUfmwpsR+BoOUB5B5fNdpf1dGI7QgghovTJ9DFzYgEPfriReTVryfJ5G67Sqg87HDwoh5NG5nPK6JaJSXOzjx7K0i3lnP/IJ9SHHA4emM2d50xkUG73D3YoupdHqS4dxTstOYn/O20MACePymf1jiqOLcxjiHs7lFjoSOlWAo7WepwxZpk7bQqwtJVllwKTgUcBtNZjsRnVV+5jR7cjhBCimW9NHkBZdZDRfTNZua2C0X0zmTmhAKVUhweOA7vc9aeOZuHqEo4fkUe/rAO7/4zoGkcM6cURQ3rFOow9JzbGmEqt9VPAb7XWFwGF2A6/325l8QeA+Vrr+cBa4CbgCWNMFUAntiOEEKKZ/Ewf15wyqku21TfLx3lT2m+yEiIedWYcm/toHH/mJmPMPVrrIcCXwHhjzAZ32Z/SdBybHzUbx6bFdrq4TEIIIYQ4QMXbyMNCCCGEEG3af6PrCCGEEEJ0M0lshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkDElshBBCCJEwJLERQgghRMKQxEYIIYQQCUMSGyGEEEIkjIRJbLTWXXe7UiFEt9Jad+zujT2Q1jol1jEIIRrFfWKjte6rtb4FOD3WsewtrXW61jo51nHsrUhSqbWOy+MpXuPXWue492uLS1rr/lrrHwMYY+Lu3i7uuecOYHasY9lbWutMrXVOrOPYW5GkMt7+dyPi9dwDPfv8E3c7M5rW+vfAKuBqoL87La5++bll+Bh4Vmt9odY6M9YxdYbW+jfAX7XWOcaYcBzu/7iM303mFwH3aq1/q7UujHFIneIe9yuAye7ruNjvEVHnnp8Bee60uDqfumX4HHhOa/1rrfVgd3pcfBZa6+uAF7XW+e7/brzt/7g890DPP//EZfON1vrbwF+Bz4AxwA+AU4D74umXn9b6T8DBwPeAM4FfAuO11nOMMaGYBrcH7knwVmAasBT4DmDiZf/Ha/xa64nA34BabOyjgHnYk8za2EXWMVrrw4EngJ3AZGPMWoifGhut9XnAfcAnwBDgXOBi4EZjTDiWsXWG1vpGYCr2vHky9hx0mNb6fGNMMKbB7YHWui/wO+zxX4RNLq+Pl/0fr+ceiJ/zT1xluFHygEuMMScbY7YAmUBIa50R47g6RGvt0Vr3B44CZhtjFhljbgaexx7k345pgB2Tij25zwQWAtO01iMhbn65phGf8XuAO4wxJxhj1mBPMH2In//lAqAOuNwYs1ZrPV5rfXxP+8XXDgf4kXvu2QWEgQqt9cDYhtUxWuskt+npKOAGY8waY8zfgIewCc5l7nI9+XjyAh8C52O/ZE/SWk+BHh93RLyeeyBOzj/KcXp8kojWOh0YaYz5vNn0JGNMyK3Bud0YMzg2Ee5Z8zK41Y5rgZ8ZY553p50NPAf8G3vy3BGjcFvQWicbY+oj+9ydNsAYs1lrfRRwBbDCGBOIbaStc5PeKcBHxpg6d9ogY0xRT46/jbhTgSDwW2wz7NPYE+Vq4A1jTElsom0pKv6PjTG17rR/YBPjXtimqE+A44CfAP8yxlTGJtqWWovfnR4595wK/B0Y25PijtbGMbQWCBhjHnBfH4X9kl0GnGmM2RyTYFuhtU4xxtQ1O/fkG2N2uAnxb4BkY8ysmAbahng990D8nn96VJbVGq31NcAm4B9a6ye01l93p3ujmms+BMq11sfHKMx2tVYGt9rxSeBarfUgd9EjgMeBemBsbKJtSWv9C2C51nqMezL3AkROfsaY94GPgCla66nuOj3m2NJa/xLYDNwJvBDVYbXIfeyR8bcStwYwxtRgf7V+AGQaY76F7af1DeDy2ETbUrP4/621vsyd9WfgIGAj9jj/GnAbtqbymO6PtHWtxK/d6R5sTQ3Am9j/1+nuvB7VT6KVY+hSd9Y9wB+01lPc8pwKPIzt93RiDEJtlRt/pdZ6crNzzw73cS3wIlCotf6au05SzAJuJl7PPRDf558esQPborU+FvgWttr0O8CXwP1a60JjTDDqJJIClNED+wy1UoZl2DIMAW7BVuU9rrVeDRwOzMH+iq2LTcSNtNYZWuvfAZcAW7Ht2kS3wUf9Ey4ASoHvusuEtdbZ3RtxS1rr8dh+EMdhT95PAne3kgT3qPjbiPueSNzGmBpjzHPGmCp3lTeA3cBQ9xdVTLUR/5+11icaYxYBFwFXGmN2u30jbgGGYau1Y669/W+MCUf1h8gDvsCNuyf1k2ijDPO01scZY24D3gLuApYDJwF/AAZj/w9iSmudom3n4O9ivzTvgxbnnsj5/z33L/LFG9Ja53VvxC3F67kH4v/80yMTm6gDdgQQMsasMMascqvqXgcejF7eGLMCSAcOc9ePebnaKcMN2F95jxhjdmI7Df8A+IEx5nRjzDpgG+D0gF9/CttB+3LgF8BBbnNZw6+iSIc99zN4BcjRWl+ntX4V90QTY8OAgcByY8xOtz/BX4Hfaa0LIgv1wPiH0YG4I8eI+zlkAxvdX1SxNoy24883xnxkjKlqdhyV0VgTEmvD6NhxU4ztMxHpI9FjaguAQlqWwWBrajKxHYbPAc43xpxkjFmFbWLoCRcuhIH3geuArwNjtNbnQ+Ml0pEk0v0MngGqtdZ3aq3fAfyxCLqZYcTnuQfi/PwT8wSgNVG/enKBdc2y7x8CR2itZxhjHK21z53+MnCI1lqZHtA7fg9luAhbhjOMMRXAOmPMO1rrZK3140AJsDjWv/7c2F4xxrwOfAo8BvyfOy8UOaijErBPsL/8rgcWub8KYy0Jm5yNipp2JTAU23kP3TiGUE+KvyNxJwH9tNZpWuu/AYdgf/31BG3FPxj7ZRqJ36tth9a/YWteF3ZvmG3qyP6PnHtexHYA9ZiedTWjh5Zl+Bn2M/ieW/tRaoz5SGudqrWej+0c/X73h9qUG9u7xpiX3MQlgG0SoVltfcQmYALwY3e9n3VnvG2I13MPxPn5p0cmNlEH7avYyxEnutM9xpgybBv9NQBRHfpSgLextQwx18EyXA0NScKPgDVABnBRpKNWrLmxYoypBx6BhrZXsAc/boJ5GLY68jOgvzHm1zEIt0HU/l+K/eV6cFTtQD3wJ+zl9RjbKbpHxN+ZuF3XYDuh9waONcZ80I3httDJ+NOBG4Ad2Cads4wxW7s14GY6edxEzj1h7P9GjzifdrAMv3Bfh7TW38RerpsNnGuMKe/2oFthjKmGhvLcBZRoO34KRHU70FqPwv7vrgcGGGOu7u5Yo8XruQfi//wTEdN/RK11odY6N+q1inqebIxZhq2JuUlrnWUaBzFahK127B1V9Xu1Mebu7q6t6YIyRPoUvA2cbow52xjTbW3cbcWvtVaRJr2oMq3BtnVfqu0VCkGt9QB33g5gujHmrG6OPzLyaIsmAHf/r8U2X87C7ZDtludtoEprPcxdfCvdGH8XxD3crR34hxv3OaYbr6Lbx/irtdbD3C/QV4BpxphvmG68mqILjxuAe4wxd5puHv+lC8oQucT+Q+Bs99wT88+g+bnHGOO4P/R+Dfxaa+1zE4LIqLe7gNOMMad187mnQLfRn6Qnn3vcOPY19pief/YkJpd7u210j2NHC94JPAo8aIzZ7R60kctCFfay0NXA7cCTxl4i90tggjHm4m4P3hXvZehE/NnGmN1R6+UBf8HW1uQDYWPMyTGIfwB2f5YYY37abF70ZaE+bPX6m8C7wEPGmKVa64uwiWS3jhkUr3FHxSjxxzB+N7a4LkMn4u9lbD9EtL0KNqi1/ifQD/ujvK8xZhTdTNsB9gy2hqseO9je68Zekp7s1mz0yP0fz7F3RqxqbG4B1htjxmJ7W58K3AGN1bta67uwvfaD2Kqv04CntG3LC2CbeGJ5eWWXlSFGOhr/s81+nVYDfbEd+hbHKKk5HNtZcDy2X9Up7nQP2Op19/Vd2CrVLGyTx0Dgea31Q9iq7dfc5brlGOrquLtbvO73RInffc+4LkMn4/+f1nqcOz3o1uwkY4cE+CBGSc3Xsc1GX2H7in2F7eh7uBtnJDHocfu/q2Pvybr18mj34M3E9rg27uQ/Y6/jX6C1fgz4H/BfoAK4wN3Zj2mt38P2VRmOrelYD91/eeX+KEN32sv4i9x1c7GjI6cDQ40xm7o1+EZebOyLsVd2XAq8FtXM1w87aFoGcIpbvf6qu/9PxV7Bcn0MjqEujTsG4nW/J0r8iVCGzsR/qjFmIzSct/4NDACGxPDcMxS417ide7XWP8PebyvTfT0IuJeeuf+7NPaebL83RWnbjlsXORC1vc/Hm8DPjTGvRC13G3CSMeYwrfVYY8xyd7q3u9uum4v3MnRB/JFRVpOAAtPNo5JG4ge2uCfANCDJGFOhtZ4GXAs8buwliWh7pcFAYy+dR9vLQ0MxSILjMm6Jv2fE78YQ12XogviTsM3djo5qmopB/FtN4/g4qcaOuJ5qjKlxv/jvNMY80ZP2fzzHvq/2W2Kjte6NvY/HeOzlyx8DtxljNmqtH8V+QU7TbucwbXu2P4PtBLzAzdCVieHlk/FehgSN//fuP2Yk5l7YSzynA98yxpREzVNAt1+CG69xS/w9I/5EKEMix99suX7Y2u3jTdQVfbGMP55j7yr7pY+N1vog7NgOZdixBe7EdlKNDOc9Bzhea31aVDZYge0dngZ2wJ8Yn1jiugwJHP9lbmyRwbl2Yq+sKcOOswDuJf/GXk3R3UlNXMYdIfHHNn6I/zIkevzNTAGKTbNhCmIVfzzH3pX2V+fhPsCz2PFYgsaYJ7EDKEUO6LXY3th/1VqPcKdtwQ5mF9NxLKLEexkSNf6Gy9Gjlv0Se4XXsdreAmKl1vrM7g7YFa9xR0j8sRfvZUj4+HXj6PZHY++ZhNb6B1rr17W9sWWsxHPsXaZLOg9rrSdiB6Bbbux9YN51nzu68RKyVGwvawCMMXO01ocAj2it3wemAlXYntrdLt7LcKDFH1XLhDGm2m2Ln4rtIHeNMeZFiVvi7+nxJ0IZDtD4I2UYB2zT9jYIY4GfGntjS4k9hvYpsXEzv7nYmyT+BzhNax0AHjPGbNJ2lN1698Adh60haOiMir1H0sHADOAJY8zt+xLPgViGAzX+qHUd7KXnDwIBY8xNErfE39PjT4QyHMjxu+sXAN/EjgP2R2PMdIm9Z9jXpqh+2Ha6w429dfnFwOnY+zk13CARmy1mYi8tiwzjnWKM2WaMedkYc2UskhpXvJfhgIzf5XV/gSwCcrr5xBivcUdI/LGNH+K/DAds/NoOYlqMvffWMGPMzd0YN8R37PtdpxMbrXWubhwC+3BgrDFmmVsD8BTwL+zASzOjVhsPVBh7Nc5MrfUG7AcRE/FeBom/If7IP/FaY0yVxC3x9+T4E6EMEn9D/D9y459nuuneWvEce3fr8OXe2l4KfDf2lvYh4OfYIZlfAWYbYxa6yw3A3jK+HPAbYyq11jdgB/gpAyZjLyd+uEtLcgCUQeKPTfzxGrfE3zPiT4QySPyxiz+eY4+VDtXYaK1/iB2K+VPsZXlp2B2Yhx3o7XuRZY29Vn4xjTfO8mI7N00GPjLGDIjRiSWuyyDxxyb+eI1b4u8Z8btxxHUZJP7YxR/PscdSR5uiCoEbjTHXGGNWABcCX8N2PHodGKK1/l7U8v8FTsS2nQaxdwAdbIz5vy6LvPPivQwSf2zij9e4IyR+OffsK4k/dvHHc+wx09Grov4KRG6M6MMO5LYWe8+gN7HteH6t9VJjzOfAodhBgkoAjDELujjuvRHvZZD4YyNe446Q+GMv3ssg8cdOPMceM526pYJuHO56MjAfOMTY253nAH/C7tQaYARwiTHm6f0R9L6I9zJI/LERr3FHSPyxF+9lkPhjJ55jj4W9uleU1vpK7P0lvtFs+hDgUGPMs10T3v4T72WQ+GMjXuOOkPhjL97LIPHHTjzH3p06NUCfbhzU7UjgJXfaj4ETgP8zxnwFbOjyKLtQvJdB4o+NeI07QuKPvXgvg8QfO/Eceyx0usbG7Wn9IvAWdrTawdiqr1e7Prz9I97LIPHHRrzGHSHxx168l0Hij514jr277c0tFcZhbzN/EDDXxG7E4H0R72WQ+GMjXuOOkPhjL97LIPHHTjzH3q32JrFZAfwKuMcYU9PF8XSXeC+DxB8b8Rp3hMQfe/FeBok/duI59m61V52HhRBCCCF6on29CaYQQgghRI8hiY0QQgghEoYkNkIIIYRIGJLYCCGEECJhSGIjhBBCiIQhiY0QQgghEoYkNkIIIYRIGJLYCCGEECJh/D+i0t+hxWQj2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x172.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x172.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 599.04x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import quantstats as qs\n",
    "qs.extend_pandas()\n",
    "qs.reports.full(report_normal_df['return'], benchmark=hs300['return'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2be00d48",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}