{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine = sqlalchemy.create_engine('mysql+mysqldb://sa:we083826@10.63.6.176/multifactor?charset=utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "common_factors = ['EPSAfterNonRecurring',\n",
    "                  'DivP']\n",
    "\n",
    "prod_factor = ['CFinc1', 'BDTO', 'RVOL']\n",
    "common_500 = []\n",
    "\n",
    "factor_weights = 1. / np.array([15.44, 32.72, 49.90, 115.27, 97.76])\n",
    "factor_weights = factor_weights / factor_weights.sum()\n",
    "\n",
    "total_factors = common_factors + common_500 + prod_factor\n",
    "risk_factor_500 = ['CommunicationsAndTransportation',\n",
    "'LeisureServices',\n",
    "'MultiMedia',\n",
    "'PublicUtility',\n",
    "'Agriculture',\n",
    "'ChemicalIndustry',\n",
    "'MedicationAndBio',\n",
    "'CommercialTrade',\n",
    "'DefenseIndustry',\n",
    "'HouseholdAppliances',\n",
    "'ConstructionAndMaterial',\n",
    "'BuildingDecoration',\n",
    "'RealEstate',\n",
    "'DiversifiedMetal',\n",
    "'Machinary',\n",
    "'MotorVehicle',\n",
    "'ElectronicIndustry',\n",
    "'ElectricalEquip',\n",
    "'TextileAndGarment',\n",
    "'Synthetics',\n",
    "'Computer',\n",
    "'LightManufacturing',\n",
    "'Telecoms',\n",
    "'ExtractiveIndustry',\n",
    "'Metal',\n",
    "'FoodAndBeverage',\n",
    "'Size']\n",
    "\n",
    "index_components = '500Weight'\n",
    "return_data = 'D1LogReturn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "commo_factors_df = pd.read_sql('select Date, Code, 申万一级行业, {0} from factor_data'.format(','.join(common_factors)), engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prod_factors_df = pd.read_sql('select Date, Code, {0} from prod_500'.format(','.join(prod_factor)), engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "common_500_df = pd.read_sql('select Date, Code, {0} from common_500'.format(','.join(common_500)), engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "risk_factor_df = pd.read_sql('select Date, Code, {0} from risk_factor_500'.format(','.join(risk_factor_500)), engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index_components_df = pd.read_sql('select Date, Code, {0} from index_components'.format(index_components), engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "return_df = pd.read_sql('select Date, Code, {0} from return_500'.format(return_data), engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge Data\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 6.38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#total_data = pd.merge(commo_factors_df, common_500_df, on=['Date', 'Code'])\n",
    "total_data = pd.merge(commo_factors_df, prod_factors_df, on=['Date', 'Code'])\n",
    "total_data = pd.merge(total_data, risk_factor_df, on=['Date', 'Code'])\n",
    "total_data = pd.merge(total_data, index_components_df, on=['Date', 'Code'])\n",
    "total_data = pd.merge(total_data, return_df, on=['Date', 'Code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_data = total_data[total_data[index_components] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1136728"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process Data\n",
    "--------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from alphamind.data.standardize import standardize\n",
    "from alphamind.data.neutralize import neutralize\n",
    "from alphamind.data.winsorize import winsorize_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "risk_factors_names = risk_factor_500 + ['Market']\n",
    "total_data['Market'] = 1.\n",
    "\n",
    "all_factors = total_data[total_factors]\n",
    "risk_factors = total_data[risk_factors_names]\n",
    "groups = total_data.Date.values.astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 858 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "factor_processed = neutralize(risk_factors.values,\n",
    "                              standardize(winsorize_normal(all_factors.values, groups=groups),\n",
    "                                          groups=groups),\n",
    "                              groups=groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normed_factor = pd.DataFrame(factor_processed, columns=total_factors, index=total_data.Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>DivP</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>BDTO</th>\n",
       "      <th>RVOL</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.808065</td>\n",
       "      <td>1.992147</td>\n",
       "      <td>0.034774</td>\n",
       "      <td>0.390541</td>\n",
       "      <td>1.328794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>3.962310</td>\n",
       "      <td>2.785140</td>\n",
       "      <td>-0.071391</td>\n",
       "      <td>-0.308380</td>\n",
       "      <td>-1.308533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>1.182883</td>\n",
       "      <td>-0.432208</td>\n",
       "      <td>-0.481225</td>\n",
       "      <td>-0.463718</td>\n",
       "      <td>-1.454125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>2.041310</td>\n",
       "      <td>1.227283</td>\n",
       "      <td>-0.239187</td>\n",
       "      <td>-2.175566</td>\n",
       "      <td>-0.479066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>2.133884</td>\n",
       "      <td>0.036218</td>\n",
       "      <td>1.197907</td>\n",
       "      <td>0.007195</td>\n",
       "      <td>-0.433516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EPSAfterNonRecurring      DivP    CFinc1      BDTO      RVOL\n",
       "Date                                                                    \n",
       "2017-05-11              0.808065  1.992147  0.034774  0.390541  1.328794\n",
       "2017-05-11              3.962310  2.785140 -0.071391 -0.308380 -1.308533\n",
       "2017-05-11              1.182883 -0.432208 -0.481225 -0.463718 -1.454125\n",
       "2017-05-11              2.041310  1.227283 -0.239187 -2.175566 -0.479066\n",
       "2017-05-11              2.133884  0.036218  1.197907  0.007195 -0.433516"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_factor.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = normed_factor @ factor_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_data['res'] = res\n",
    "total_data[total_factors] = normed_factor.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Code</th>\n",
       "      <th>申万一级行业</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>DivP</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>BDTO</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>CommunicationsAndTransportation</th>\n",
       "      <th>LeisureServices</th>\n",
       "      <th>...</th>\n",
       "      <th>LightManufacturing</th>\n",
       "      <th>Telecoms</th>\n",
       "      <th>ExtractiveIndustry</th>\n",
       "      <th>Metal</th>\n",
       "      <th>FoodAndBeverage</th>\n",
       "      <th>Size</th>\n",
       "      <th>500Weight</th>\n",
       "      <th>D1LogReturn</th>\n",
       "      <th>Market</th>\n",
       "      <th>res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1740000</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>603766</td>\n",
       "      <td>申万汽车</td>\n",
       "      <td>0.808065</td>\n",
       "      <td>1.992147</td>\n",
       "      <td>0.034774</td>\n",
       "      <td>0.390541</td>\n",
       "      <td>1.328794</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.976978</td>\n",
       "      <td>0.217338</td>\n",
       "      <td>0.015377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.974859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740001</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>603806</td>\n",
       "      <td>申万电气设备</td>\n",
       "      <td>3.962310</td>\n",
       "      <td>2.785140</td>\n",
       "      <td>-0.071391</td>\n",
       "      <td>-0.308380</td>\n",
       "      <td>-1.308533</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.073940</td>\n",
       "      <td>0.070672</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.414886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740002</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>603866</td>\n",
       "      <td>申万食品饮料</td>\n",
       "      <td>1.182883</td>\n",
       "      <td>-0.432208</td>\n",
       "      <td>-0.481225</td>\n",
       "      <td>-0.463718</td>\n",
       "      <td>-1.454125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.476160</td>\n",
       "      <td>0.044564</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.259628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740003</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>603868</td>\n",
       "      <td>申万家用电器</td>\n",
       "      <td>2.041310</td>\n",
       "      <td>1.227283</td>\n",
       "      <td>-0.239187</td>\n",
       "      <td>-2.175566</td>\n",
       "      <td>-0.479066</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.377240</td>\n",
       "      <td>0.069844</td>\n",
       "      <td>-0.002496</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.051215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1740004</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>603883</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>2.133884</td>\n",
       "      <td>0.036218</td>\n",
       "      <td>1.197907</td>\n",
       "      <td>0.007195</td>\n",
       "      <td>-0.433516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.839231</td>\n",
       "      <td>0.122151</td>\n",
       "      <td>0.009676</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.183746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date    Code  申万一级行业  EPSAfterNonRecurring      DivP    CFinc1  \\\n",
       "1740000 2017-05-11  603766    申万汽车              0.808065  1.992147  0.034774   \n",
       "1740001 2017-05-11  603806  申万电气设备              3.962310  2.785140 -0.071391   \n",
       "1740002 2017-05-11  603866  申万食品饮料              1.182883 -0.432208 -0.481225   \n",
       "1740003 2017-05-11  603868  申万家用电器              2.041310  1.227283 -0.239187   \n",
       "1740004 2017-05-11  603883  申万医药生物              2.133884  0.036218  1.197907   \n",
       "\n",
       "             BDTO      RVOL  CommunicationsAndTransportation  LeisureServices  \\\n",
       "1740000  0.390541  1.328794                                0                0   \n",
       "1740001 -0.308380 -1.308533                                0                0   \n",
       "1740002 -0.463718 -1.454125                                0                0   \n",
       "1740003 -2.175566 -0.479066                                0                0   \n",
       "1740004  0.007195 -0.433516                                0                0   \n",
       "\n",
       "           ...     LightManufacturing  Telecoms  ExtractiveIndustry  Metal  \\\n",
       "1740000    ...                      0         0                   0      0   \n",
       "1740001    ...                      0         0                   0      0   \n",
       "1740002    ...                      0         0                   0      0   \n",
       "1740003    ...                      0         0                   0      0   \n",
       "1740004    ...                      0         0                   0      0   \n",
       "\n",
       "         FoodAndBeverage      Size  500Weight  D1LogReturn  Market       res  \n",
       "1740000                0  0.976978   0.217338     0.015377     1.0  0.974859  \n",
       "1740001                0 -1.073940   0.070672     0.001241     1.0  2.414886  \n",
       "1740002                1 -1.476160   0.044564     0.017544     1.0  0.259628  \n",
       "1740003                0 -1.377240   0.069844    -0.002496     1.0  1.051215  \n",
       "1740004                0 -0.839231   0.122151     0.009676     1.0  1.183746  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor Performance (Long Top)\n",
    "---------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from alphamind.portfolio.rankbuilder import rank_build"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "factor_names = total_factors + ['res']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 418 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "use_rank = 100\n",
    "pos_data = rank_build(total_data[factor_names].values, use_rank, groups)\n",
    "pos_df = pd.DataFrame(pos_data, columns=factor_names, index=normed_factor.index) / use_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret_mat = (pos_df.values - total_data[[index_components]].values / 100.) * total_data[['D1LogReturn']].values\n",
    "ret_df = pd.DataFrame(ret_mat, columns=factor_names, index=normed_factor.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>DivP</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>BDTO</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>0.000577</td>\n",
       "      <td>-0.000441</td>\n",
       "      <td>0.001019</td>\n",
       "      <td>-0.001704</td>\n",
       "      <td>-0.003467</td>\n",
       "      <td>-0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>-0.002500</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.001784</td>\n",
       "      <td>-0.000693</td>\n",
       "      <td>0.002551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>0.006047</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>-0.012595</td>\n",
       "      <td>-0.011506</td>\n",
       "      <td>0.004203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>-0.001246</td>\n",
       "      <td>-0.001701</td>\n",
       "      <td>-0.003289</td>\n",
       "      <td>0.001444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.001325</td>\n",
       "      <td>-0.002240</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>-0.003110</td>\n",
       "      <td>-0.001548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EPSAfterNonRecurring      DivP    CFinc1      BDTO      RVOL  \\\n",
       "Date                                                                       \n",
       "2017-05-05              0.000577 -0.000441  0.001019 -0.001704 -0.003467   \n",
       "2017-05-08              0.003790 -0.002500  0.000173  0.001784 -0.000693   \n",
       "2017-05-09              0.006047  0.002719 -0.002804 -0.012595 -0.011506   \n",
       "2017-05-10              0.001344  0.000205 -0.001246 -0.001701 -0.003289   \n",
       "2017-05-11              0.001325 -0.002240  0.001220  0.000001 -0.003110   \n",
       "\n",
       "                 res  \n",
       "Date                  \n",
       "2017-05-05 -0.000006  \n",
       "2017-05-08  0.002551  \n",
       "2017-05-09  0.004203  \n",
       "2017-05-10  0.001444  \n",
       "2017-05-11 -0.001548  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df.groupby(level=0).sum().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c4156b8080>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vglunZlDZ2EpVYys2y2JMaiTXTEjDZts9RLalzcv/zdnCCwsKgN3Db68cl8LD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\nSZIkSVJi/h/chAuEScpnjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c415684898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df['res'].groupby(level=0).sum()[-500:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_df['Code'] = total_data.Code.values\n",
    "pos_df['Ind'] = total_data['申万一级行业'].values\n",
    "pos_df['bm'] = total_data[index_components].values / 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_df.loc['2017-01-01':, :].to_csv('aggregated_pos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "turn_over_table = {}\n",
    "pos_df['Code'] = total_data.Code.values\n",
    "pos_df.reset_index(inplace=True)\n",
    "\n",
    "for name in total_factors + ['res']:\n",
    "    pos_series = pos_df[['Date', 'Code', name]]\n",
    "    pivot_position = pos_series.pivot_table(name, index='Date', columns='Code').fillna(0.)\n",
    "    turn_over_series = pivot_position.diff().abs().sum(axis=1)\n",
    "    turn_over_table[name] = turn_over_series.values\n",
    "    \n",
    "turn_over_table = pd.DataFrame(turn_over_table, index=pos_df.Date.unique())\n",
    "turn_over_table = turn_over_table[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret_tc_long_top_df = ret_df.groupby(level=0).sum() - turn_over_table * 0.0015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41cb3a7f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1dgVHd2ynfc+biB49DpeWvuYOUQghLqCqKntP5rFi3ylW708ns6AMW0st/Tt4EhPRkj5t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FOiwWR1uDNH+LqRHOXD0Ghfpg8MxUWlQBRFyh56CN1fqwh7/308LhrfNeO9QNl2aBsPrX8IZ6Uz\nCy9aSF//vqfcp12wc+vft5JVm8XSS5cS4Rlx4hd1gJL9dfz2bho9BwUy5ba+zZMUuyAy+6Mt5Ffr\nWf3QGAI9Tn6PfYW+gv9b838UagtZMHIBF/e8uNN9NFQb2bexjKwt5Zj1NnxD1CSOCSVuWA+cXCRP\nk7MBURSpLtaRs6OSvNRK9A0WlM4Kovv7ExDuwY7fD+KiVjFUuQ1x6Wf433sPAXe3/z12PHQWHfvr\n9pNVm9V8FGmLmi25ga6BJPglEO/nELrxvvEEugVKiyDHIU+Tx1u73mJT2SZC1CGOgE9RU7ot0JIo\niqwuWs0bqW9wSH+IadHTmJc075hpykRR5KXtL/H9ge+5LuE6Hkl+5PjvpbYctr4PqV+A1QBxFzty\n2YadegyACwGbYCO/Pr9N4KnWe95D1cHcEDSCGVYlLuV7HNZXU73jxU4ejr/zYQEbmgxqv45fvL4E\nVj0B2SscQnjaaxAzqRvuUkLiaCRBexo4bKG5Jv4aHk159Lhf5hV5Ofz+4Zs0lJZR2EOPcmI8T058\nQVq1PgZWu0BJnYHCWj0Hm9yCHftbDZQ3GGn9kfV3d6anv5oofzei/d2J9ncjyl9NpK8aV6duTjFi\nboT3h4CrL9z+T5s8fCfCahfYV65tErgOS26t3sKAcG8+vjYJxTefU/PeQgIfnoffrbd22y1cCCzL\nXcYLW18gyiuK9ye8T4h7SJf1XaGvYNaKWYR7hPP1tK9RHbnC3Ul0dSZ+fGknru4qrngsuY0w/GzT\nQRb8kc3bVw5gxqBj78nvCHmaPO5ccyeN1kbeGf8Ow4KHndKYHTltq8jcUEpVkQ6Vi4I+Q3uQOC4M\n3+CTTAsicUrUHdKTu7OS3J2VNFQbkStkRPT1I3ZIEFH9/FE5O74bq4t1/PnhXkx6K0mqPaj/+ITA\nRx7G75ZbumQcequ+WeRm12aTVZtFgbYAQXQs5vm5+LURuVLgKQc1xhreT3ufX3J/Qa1Uc1v/27g6\n/urTFhXXaDPyecbnfJn5JQq5gtv73871Cdc3B5wSRIGXtr/EDwd+4Ma+N/JQ0kMdf88MdbD9I9j+\nsUNwRY+F2CmOOlFwWAJFwXEgtio7VvmR7Wmn/Mj24gn6OReuK2JBJFcpI1Ol4DdXFekuzvja7Vxr\nd+XKgBQ8I0Y4BKx/LHTFPty8tbByPtTmQZ9LHAv5Pqfm8i4hcSIkQdvNLNm/hJe2v8RlvS7jxZEv\ntrtiajWZ+PfHb9j95wrU3t6Mu+kOtrnn8b/d/8NN5cZTw55iStSU0zz6M0/rCMJHugkfjiB8GE8X\nJdEB7g7h2iooU5S/Gx4u3R8RULBYsNfXI2i12BsaHEd9A2L6UpwqV+N022KUg6ackqVdFEVW7atg\n3o/pjK3I4N6NX+A1fTrBr7x8wU/uThZBFFi4ZyGfZnzK8ODhvDnuzW4J4LS2aC0P/PMANyXexENJ\nD510PzarnWVv7EZTaWD2Y8n49GgRgwerG5n27iZGx/jz6fXJJ/2Z2FW5i3vX3YuzwpkPJ35IH98+\nJ35RJ6gs0JLxTym5uyoRbCKhcd70GxtG9AB/5KcYHE3i+OjqTA4Rm1pJTUkjMhmExvkQkxJEz4EB\nuKiP/V2pbzCz8qMMKgu0JKiyCVq9kB5PP4XvNdccs/2pYrAayNHkNO/Hza7L5mD9webAU74uvs3u\nyoeF7oWSQshgNfB11td8kfkFFruFOXFzuHPAnfi4+JyR8ZToSnh95+usL1lPpGck81PmMyp0FC9u\ne5Gfcn7i5sSbeWDwAyf33ph1kPqlw6W1sbKTL5aBTN50tHrcppx2yo9s3x19tddPq/oO3YOsA305\nvldFF292efrwefV2Nh/ahlqlZk7cHK6Lv44At4DOvz/tYTM7LO0bX3cI69FNbsgqKSuHRPcgCdpu\n5PeDv/P4pscZFz6Ot8e9jVJ+bMtcYdouVn/2AdrqSgZMmsboq2/E2c0xST1Yf5AnNj/Bvtp9TIua\nxhNDnzip6IJnM6IoUq0zH3NP65ERhF1VimMGYor2V+Pjpmr7gymKINiOOOzHfS7arQiNeuwNDQ5h\nqtVi1zZi1+qw6xqx6/TYdQYEnR57owG73oi90Yi90YRosZ3wXmVOTqgiwnGKiMQpMhKnyAicIiJw\nioxE2aMHMkXHVkcPbNyB6f9uJd8rBOtrC7lyVO9O/90lHMGOnt78NCsLVzIrZhZPDnuyW9MhvLD1\nBZbmLOWTSZ8wPGT4SfWx/pv9ZG0uZ9od/eg5qGUCYhdErvx4KzmVOlY/NJagdvZcn4i/C//m8U2P\nE+IewkeTPiLUvfv27Rt1FrL+LSdzYxmNdWbcfZzpOzqEhFGhuHlKUbq7CoPWQv7uKnJ3VnIovwGA\noGhPYlKC6J0UiNqrYxY9m8XOuq/3k7uzkjBZMb02vEnYi8/jPWtmdw6/GaPNSI4mp9mKm12XTZ4m\nD5vo+O71cvZqI3L7+vYlzCPsvBC5oiiSVZvFL7m/sLJgJTqrjgkRE3hg8ANEeUWd6eEB8G/Zv7yy\n4xUKtYVEekZSpC3i1n63ct+g+079PbDbwKztoLg7LAYljsf+uv18kfEFq4pWoZApmN57Ojf1vanL\ntsUAjujIq56ErOXgEw3TXm2xtEtIdCGSoO0mNpRs4P7195MUlMQHEz84pguQQdvAhsWfkbVpPT4h\nYUy+/R7C4hOPamcTbHye8Tkf7f0Ib2dvnhv+HGPDx554EIddTo4r6GyOH4pOiL6OtTn6NSaLBa3B\nRKPBhN5oQm8yYzSZMZrNINhRYkeBgEom4K4CtQrclCKuSnCRCzgrRJQIyDpwTdFmw24WsVtkCBY5\n9taHWY7dKnOcLfJW9TLsFjmI7f8QyhQCCicRhZOAwklA3nRuLnM+XH74Och8e2IZ8V8sFRosxUVY\nioqwFhVhKS5BNLfs55U5OaEKD28WuE6RjrMqIhJVcIvYtVZVUTh7DqJMzlszH+OvcivXDYvk6UsS\num7P7wWAxqTh/vX3s6dqD/cPvp9bEm/p9omv0WZk7u9zabA08PNlP3c6ymvW5nLWf7OfwVMjGX55\nrzZ1X2wu4IXfs3hj9gCuSDo5V+Nvs7/l1R2v0j+gPwsvWnjaFs8EQaQoo4aMDWWUZNUhV8joNTiQ\nfmND6dHL67wQJKcbi9HGwbRqcndWUrJfgyiI+IaoiUkJIiY5CK+Ak8uBLYoiu1YWsX3FQXyEGvpu\ne5Ool5/B6+LO76/uCsx2M7ma3DZ7cnPrc5v3DHqoPNrsx03wSzj56K9nAI1Jwx8H/+CXvF/I1eTi\nrHBmUuQkroy7koGBA8/08I7Carfybfa3fJrxKVfHX81dA+6S/n/Pcoq1xXy17yuW5y3HLtqZHDmZ\nmxNvPrWcw0eSv97hhlyTA3H/gakvg09U1/UvccEjCdpuILUilTvX3Elv7958PuVz1Kq2+8MEwU7m\n+tVsXrIYs0HPkOlXMHTGlShPkDd0f91+ntz8JDmaHC7vfTnzU+Y7XCNr8yH9e0fY+8bKtiLvDGNH\ngR05NuTYRAU25NhRYEMBcgVyhQqFQolCpUKldMLJSYVK5YRMrkSUKRAFOXazDLuZpkNEMInYTQJ2\no4DdZMdutDkOgw3BaMVusCKYrMcdl9zNGYW7a9PhhsJDjdzdDYWHOwpPdxSeHs2H3NMLhZcnCi8v\n5K5ujtQCckXTufVxZJniuKvEoiBgq6zEUlSMpaioldgtxlJc3FbsqlQOsRsZibW0BEtZOVHffYsq\nNo7X/trPxxsPMiTalw+uGYy/uxRZ8EQUaYu4a81dVOgr+O/o/zI1auppu/aBugNc/cfVDAsZxsKL\nFnZ4sldZqOWXN3YRGuPNJfcORN4qynZhjZ6p725keE8/vrgxpdMTSEEUeHf3u3yR+QXjwsfx2pjX\ncFWenOA5VeorDWRuKCN76yEsRht+Ye70GxtK7JAezXs6JY6NKIgcTKsmZ2clRRm12G0Cnv4uxCQH\nEZMShF+oe5ddK393FWu+ykJp0tIvbSHxL83HY+LELuv/VLDareTW5zZbcrNqs8jR5GARLACoVerm\nPJ4Jfgkk+CYQ6RnZfXk8O4ldsLP10FaW5S5jfcl6rIKVRL9EZsTMYFr0tG7Lad2ViKIoCdlzjGpD\nNd9kf8MPB35Ab9UzMnQktyTeQnLQyW9faYPNAts+gA2vOaIoj3oQRt4PqjPzWyNxfiEJ2i4mqzaL\nW1bdQqBbIF9N/eqoPS0l+/ayftGnVBcVEBKXwMRb7yIgIqrD/VvsFj5K/4jPMz4nUOnGCyYlw0v2\nOlxsosdCUN+OCa3jPj9+G7Mgo0xrpaTeQpHGQpHGTKHGzME6M5WNtiYB6xCyIV6uRPm5Eucuo5ez\nQITKRrDcgq9gRtaoRWjaZ2pvve+0od7h8tugRbRY2v9jKJUovLyOOuRN4lPh5d109mxV54XC07PD\nrr1nClEQsFVVYSlsJXSLi7EUFmHXaunx7DN4XNSSTubXtDLm/7QXP7UTn1yfTGKo1xkc/enFbDdT\nb6qn3txyNJgb0Jg0LY/NmuayBnMDOqsOH2cf/nfR/86IlePb7G95Zccrx0zhdSyMOgs/vrQTZDDn\niRRc3VsWvwRB5KpPt5FdruXvh8YQ7NW5yYHVbuWZLc/w+8HfmR07myeGPtHu9ojTidVsJ2dHBRn/\nlFFb1oiTq5L44cEkjg3FO8jtTA/vrGTLz3nsWV2Mq6cTMUmBxKQEERTt2W3CorpYxx/vp2GqN9B3\n/2IGLbgT99Gju+Vap4pVsHKw/mCLJbcui5y6HEx2E+DI49la5Mb7xhPtFX1a/xdKdCUsz1vOr3m/\nUmmoxNvZm0t6XsKMmBnE+kjp2CROD1qLlh8P/MjXWV9TZ6pjQMAAbkm8hbHhY7vGs6GhDP5+Cvb9\n4rDSTn0V4k7fonJ3IIgCdtHuOAv2lsei/ajnJ92m6RAE4ajyI8uGhwy/4L4zJEHbhRQ0FHDjXzfi\nonBh0bRF9FD3aK6rr6xg4zdfkLtjCx7+AYy99mZih43q3ETDZoHcvyF9CXsL1/GknxeFTiqu9Irn\noTEv4+bb68R9dBCrXaBUY2yT9qakooGq8mr0tXV4WIy4Wwx4WA30kFkIVVgJwoKvYMLTasTVrEep\n1yFotQhaLRzn8yNzczuGMPVsEaDN4tSzTRuZm5u0AtyKzLIGbl+cSq3ewmtX9Gf6wHMrZ7Eoihht\nRjTmJiFqamh53EqMthau9eZ6jDZju326Kd3wcfHBy9kLH2fH2dvZGx8XHy7peQlhHicfBfhUEEWR\ne9fdy5byLSy5eAlxvnHtthXsAr+9l86hvAZmPjKYwEjPNvWLthTy7Ip9vDarP3NSOpfQXm/V8+D6\nB9l6aCv3DLyH2/vfftb9T4miyKH8BjL/KSV/dzWCIBKe4Eu/saFE9vNvY6m+kDmU38Avb+wifkQw\n467pc9r+LvoGM38uTKOquJFexX8w6tkrcR829LRc+1SxCTYKGgqa9+Nm1Waxv25/83eKi8KFON+4\nNrlye3r37NJ99iabiTXFa1ieu5ztFduRIWNE6Ahm9p7JuPBxzRGDJSRONyabieV5y/lq31eUNZbR\n27s3NyfezNToqV3zP3BwA/z5CNQcgNipMPUV8I0+9X5xWJtTK1PZVbmLSn3lUWLRJtgQRKFtmWg7\noVi0ia1eJ7SIzLMNlVzFvOR5XN3n6rPuN727kARtJzieC82hxkNc/9f1WOwWFk9bTKSnI0S5xWhg\n+7If2fXHcmQKBUOnzybp0hmonDroFiqKjjxhe5tcio0aUAdCv9mYEmfyv/J1fJP9DWEeYSwYueCE\nSdRFUUTQGxAa6rFq6qkqr6aqrJraimq0VXUYazXY6uuRNepQWwx4NIlWD4sRF/txrKVyOQoPD+Te\nrS2jDmuowtvrCHHq1aZedgJXa4mOU9No5q5vd7OjoI7bx/Tk0al9UJyBCb8gCugsuqMF6BGW1CPL\nrUL7ruKeTp54O3vj7eLtOLc6vJy98HHxaXncJF7P5slgnamOK1ZcgbuTOz9c8lZstxgAACAASURB\nVEO7Lr5bl+Wxe1Ux46/rQ8LItqmEimsNTH13I8lRviy6qXOuxjXGGu5acxc5mhyeHf4sM2JmnNL9\nnA70DWayNpezb1M5+nozHr4uJI4NJX5kcBur9YWG1WLnhwU7EGwiVz0z5LTn97VZ7Kz5LJ38vfX0\nqNnFpEcn4Jly/N+isxW7YKdIW8S+2n1tRK7eqgfASe50lMjt7d27U6m4RFEkqy6LZbnL+PPgn+is\nOkLdQ5nRewbTe09vsxguIXGmsQk2VhWu4vPMz8nV5BKsDuaGvjcwM2bmqW9NsVkc6Zk2vAp2K4x6\nAAZfD56hnQrsVd5Yzq7KXaRWppJakUqxrhhwLGqHe4SjkCtQyBTIZXIUMgUKecvj5rJW5XKZHKVM\n6aiTt23T/Dp5q9cdLj/iOq2vd/i5XCZHKVces03rsmO2aXXd9sZotBlZsG0BG0o3MDFiIs+PfB5P\nJ88T/AXPfSRB2wGsdoHnVuxj6a5SvFxV+KmdCPBwxk/thL+7M66uRlZUPYnRXs9TSe+RFJyIr6uS\nvC3r2bxkMYaGehJGj2fU1Tfg4dvBfLKaItj7I6QvQazNR5S7IPacihBzCWJgEoLVimg0IpjM5BzK\n4Pu9i9DrNETI/fAwyXA3gdoo4mYQcDbYcNLbcNZbcDFaUQjtv5dWhQKTqxt2tRqZlydOPt64+/uh\n9vPDyccXpbdDrDr2lXq1iFV391NKRyNxYkRRpNJQSVljGQqZAlelK65KV1yULs1nlVyF1S7wwm9Z\nfL2tiNEx/iycOxgvt5NfTbUJNrQW7QnFaGvX3gZzQ7urlgqZotlS2lqMNltPm8Ro6zJPJ8+zwg22\nq9lavpU7Vt/BrNhZPDv82aPq83dX8dcnmfQdHcK4a9qmzhEEkWs+205GWQOrHhxDqHfHJxaFDYXc\nueZO6kx1vDH2DcaEjTnlezmd2O0CBWk1ZG4opSynHoVSTu/kQPqNDSMo+vz/4T6SzUtzSV9bwvQH\nBhLWp3OBxroKURTZsXQfqeuq8Gos4j/3J+Gb0v+MjKWrEUSBYm1xs7tydl022bXZ6Kw6wGENifGJ\naRa5ff36EuMTc9SCWr2pnj8K/uCX3F/I0eQ0B3ia0XsGyT2Sz5lAVRIXJqIosqlsE59lfMaeqj34\nOPswJ24Off0c0cTDPMJOXuBqy7H8+TzLNqcQ5byTIYGrkYUMgOBWh080yGSIokiJrqTZAptakUq5\nvhwADycPkgKTSO6RTFJQEn18+5yXc4cTIYoii7MW886udwhSB/H6mNfpF9DvTA+rW5EE7QnQm23c\n/d1u/jlQzYxBobio5FTrLNTqzdQ2Wqgx1EPwR8idqjEW34LdGEWIqZzRtf8SaKmhTh1MWdwUnEOi\n8HN3xt/dGX93JwJlVoKWf4tCUwtmEzKzCZnJgFJXjUKvAbMJ0S5DEJSINhE6+ec3OMtpdJHT6CxD\n5yKj0RX0LiKNrgJ6N5FGFxyHq6zp7HhuVR1/RcxZ4YyzwhkXpQsuCheclc6O8zHKXJRN5U1lzgpn\nXJWuJyw73I9KrrpgXCVa02hppEhbRIG2gCJtEYUNhRRqCynSFh3XvRZAKVfiqnAIXatNSa1OxEnh\nTEKPAHzd1LgqXXFTurWI4Kb3yWK3NIvR1iJVY9ags+javZ5KrnIIUJe2Lr3Nx5HWVBdv3FXu0sSt\nFW/vepsvMr/g7XFvMzGyJahO3SE9P72Sim+ImhkPDUahavs3+2ZbEU8tz+Tlmf2YO6TjaRbSq9O5\nZ+09yJDx/oT3z/kfudryRjI3lHFgWwVWs53ASA/6jQujd3IgStXZvVe+KyjPrWfZW7vpNyaUMXPb\nd10/XRxYs591PxbhZGtk2i19CBnZ90wPqVsQRIFSXSlZdU0itykAldaiBUApU9LbpzcJfgnEeMeQ\nVp3GuuJ1WAUrff36MjNmJlOjp14QlhOJ84/dlbv5PPNzNpZubFPu7+pPmLtD3IZ7hDuErrvjsb+r\n/3HndOlrS9i8NBeAxIh8xvh8jaw6C1GwclClZJe7D6lefuxSCFQJjr3vvs4+JDWJ1+SgZGJ8YqT5\nRSvSq9OZv2E+VcYqHhz8INclXHfezqslQXscqnQmbv5qJ9mHdCy4PPGoSaPJZuLONXeSVpXOMymv\n08MSSdav39GwbyeovTEOmEplQAK1egs1jQ4BXGewgCDw3LYvSKrK4ZDaF5XCjqdSj4+yEaVCwKBw\npkgeRJ48lDqFJ2a5CrNShUWuwqx0cjxXOMrMTWWWpjK9yhWDkwuh/h5E+anb5GyN9lcT4u2KTCZi\ntpsx28yY7CbMdjMmm8nxuCNltqbHreoOl5ntZow2Y5v+j+dGejxkyFpE8WGxrHBuVzB3uKxVX637\n7878o0diFayU6cqahWpBQ5N41RZSY6xpbieXyQlRhxDpFUm0ZzSRnpGEe4Qj4thvarKZMNqMzcfh\n9+zw8wqtlvSyauyYCfVRoFTaWl5jNzantgBHUJQjXXkPC9LDbrzezt7N4tXb2RtXpet5++V4IgRB\nJLeqke0FtewoqMPf3Zn7J8Tgo+6c66tVsHL9n9dTrCvm58t+poe6BxaTjZ9eScWktzLniRTcfdrm\nlC2pMzD1nY0MivDh61uGdPg9+KfkHx7Z8Aj+rv58POnjrs03eIaxGG0c2F5Bxj+laCoMuKhVxI8M\nJnFMKJ7+52cUTavZzvcLdoAocuVTp9/VuD3Ktuew8pMsbHJnLpodTuzko9PRnY+IokhZY1mzq/Lh\no95cj5ezF5f2vJTLe19+3D3zEhLnEg3mBkp0JZTqSh3nxtLm5xX6CsRW1hgXhQuh7qHHFLs93IL5\n+YU9uHk6E9TTk/Q1Jaj6GMhOXM2uqp1omrwhAkQ5yQYDyUYDSSYTPXFC1qNfW0tuQB/oxBaA850G\ncwNP//s060vWMy58HAtGLsDLuZOBQ0URrAaw6I84GlseW9urM7RtZ9E73MlH3AvJNzuCznYBkqBt\nh/zqRm74Yge1jRY+uGYw4/sEtqm3ClYeXP8gG0s38vLQBXinN5D62zKQyUi5bCYpl85C5eJyVL82\nu0DZuwsxfPIhLhcnEBqwBydTLVZnH6qjLqEmeiaNfv1a9g4cMU+VHVFw5Dw2wMOZcB+3syofqV2w\nHyWAjyWYm4VyK8HcobLD/TSJaJt4cumKFDJFu9ZnZ6UzrgrXZqtyZwSzTbBRpC1qY20t1ZW2Gae3\nszdRnlFEekYS5RXVIl49w4+Zw7gzVDSYuOPrVNJLG7h/Qgz3T4hpDhhjFawYbcZmy7tE+9gFkexD\nWrYX1LH9YC07C+vQGByLNT08XahuNOPpouSxaX2YnRTeqaA8JdoSrvjtCuL94vl0wmes+ngfxftq\nueyBQYTFtY2ULooi132+gz3FGv56YAzhvieO+GuwGliRv4KXd7xMvG88CycsxN+1g9sfzjFEUaQs\np57Mf0o5mF6DKIpEJfqROC6MiHhfZOdREKmNP+SQsb6UGfMGERLjc+IXnEbq0g/wx5vb0bqGMmRC\nAMmz+12Qi1+iKFJtrMbb2fus3tMvIdHVWOwWyhvLKW0sbRG8utJm0dva4yy6tj9Tcm5hf/Iadrn9\nQ++CYQwtuYRD/rmIk0pJChlEclAy4R7hyAS7I5/toXTHUbEXDu2Fw95kCidHxo/DAjdkEAQlXtAi\nVxRFvs3+ljd3vUmAawCvjXmtYxkeDHXw+4OQvQJEoeMXVKnBSQ1ObuDk3vS46VCpob4IirdCj37w\nnzch4tQDCZ7XgrZXYi9x+frl9PLuhZuq42keUgvruHVxKkq5jC9uTKF/mHebekEUeGLzE/yR/wcP\nuV6NZX02jZo6+owcy+irb8TTP6Ddvhs3bqTkjjvx6qsmODEPWfwlMGAu9J4ISunHriuwCtY2wtls\nM2O0G1vKbEdYkluJ4yPLOmK1Fjr4T+4kdyLCM4IozyiivJrEq2cUUZ5ReLt4n7iDU8BktfPkskx+\n3l3K5IQg3rpyIO7OZ4c152zFahfILGtge0EdOwrq2FlYh87kWIQI93VlaLQfQ6N9GdbTjzAfVw5U\n6nhqWSapRRqSIn1YcHki8cEddyf8Lf83ntj0BP+nex5xnzdj58aSOPboKMxLdhTz+C8ZLLg8kWuH\nRbapE0WRKkMVBzQHOFB3gP11+zmgOUCxthgRkZGhI3lr7Fud+j48l2nUmNi3qZx9m8sxai14BbiS\nODaUPsODcVGf25ObsgMalr+9h/4XhTF6ztmZnqExM5u/XlxFpU9/YgZ4MeHWQUe5zktISFx4iKJI\nnamuWdwWLZZha4S9E5cR5hlKclAyPnk9SV9WSWicN//5v/7H90ARBNAUwKG0FqF7KN0RSBVA6Qqh\nSRA+xHGEDQG13+m52bOIzJpMHt7wMJX6Su4bfB839L2hfRft/HWw/C7Q1zgsqZ4hTaLUvUmoqtuK\n1cMiVuUGJ4qpI4qQtRz+egJ05TDgapj0PLgHHv91x+G8FrSu0a5i7+d6I0NGpGckMT4xxPrEEucT\nR6xvLCHqkKNWjFdmHOL+H9II83blq5uGEOHXduIniiIvbX+Jtdt+4bKiBOzlGnr0imH8jbcTEht/\n3PFYSkspmDkLlYuZqDGFyOd8Av3ndPl9S5w+RFHEJtiOEsytLckymYwIjwiC1cEousi14mTH+uW/\nhfz3z2x6+qv59PpkovzVZ2w8Zxtmm529pQ1sP1jL9oI6dhVpMFgcga16BqgZGu3L0Gg/hkT7EtJO\nACZBEPl5dykvr9xPg9HKjSOieHBSbIcXDxZ88iE+u+MIGeHCjOtHHFVfVm9kytsb6RfqxVc3D6ZY\nV+QQrXUHmkWsxqxpbh/mHkacbxxxvnEk+CYwInTEaXWtP1uw2wTy91SR+U8Zh/IbUKrkxAwJot/Y\nMAIiPM708DqNxWTj+xd3IJfLuPLpIaiczt69wob0dDY8tYSDoZPpEenGtLsH4+YpLd5KSEg4qCho\n4OdXdzFqTgwDLmqbeu7A9grWLsomIMKDS+8d0LmFSFGE+mIo2wUlO6B0h0PkHt5q5dsLwodCeIrj\nHNCny9xfz2a0Fi3PbXmO1UWrGRM2hgUjF+Dj0srDx2qCtS/AtvfBPw5mfeqwdHcH5kbY+Dpsfd8h\nhMc/ASm3gqLzBpfzWtD2G9BPfOe3d8ipyyFHk8MBzQFKdCXN9e4qd2J9YonxiSHON46cYnc+W2dk\ncHgPPrs++Zh74d7b8DrZy/+kV7katY8vo+feQMLo8SeM8CuYTBRedRXWwjyiJ5TjdM27MPi6Lr9n\nCYkT8W9eDXd/txtBEHnv6sGMjW3fo+B8xmixs6dY43AhLqhlT3E9ZpvD2h4X5MHQnr4MiXYcgR5H\nbx84HvUGC6+tOsCSHcUEejjz9CUJXNwv+Lgulwf3VLPykwwOBeSws98ylk5f2hwwRmfRsb9uP8+s\nXENxYy6x4VqKdAeb96c7yZ3o7dObPr59iPNxCNhYn1g8nM49sdbdVJfoyNxQRs6OCmwWgR49Pek3\nLoxegwLPGevhhu8OkLmpjBnzBhPSu3u9O7oCQ2oqO+e/R1bM1bj5qBl9ZRx+YWo8/FylPMISEhc4\nqz7LpHhfHTe8POKYVtiDadWs+iwT70A3LrtvIGrvU9geZTVCeRqUbIfSnY6zvtpR5+QBYcktVtzQ\nZHA9+79fTwZRFPn+wPe8vvN1fF18eW3Ma460n5VZ8POtULUPUm6DSS84rLHdTU2uIyfxwfUO9/D/\nvA6RRy/qH4/zWtD2DIkXM7LS2nz4DVYDufW5HKg7QI4mp/k4nGMOZIQ3WTVifWKJ9Y0l1ieWQJU/\nX3zxHNpNGShkCoZdNpshl8/GyeXEwUZEUeTQY4/R8OsKwsbU4nHrf2HIbd101xISJ6akzsBti1PJ\nqdTx6NQ+3D6mZ7PYEkURuyBiE0QE0XG220XsTeWtD9sRzx1tBOwC2AThqPYtbURs7fTZ5tr2tn3a\nBcFRd8w2Ytu6I9s0XdsmiFhsAnlVOqx2EZkMEoI9HS7EPX1JifLFt5OBndpjd7GGp5dnsq9cy+gY\nf16Ynkj0MazilQValr+1G99Qd3pfp+LmtTfSP6A/Pi4+7K/bT1ljWXNbV4UXAwMT6OPbh1jfWPr4\n9CHKK+qCTE1wKpgNVvZvdQSRaqg24uqhImFUCH1Hh+Lh27kFjNNJSXYdK95NY8DEcEZdEXOmh9Nh\n9Fu2sG/ef8lIvAOTwh0AhVKOd5Ar3kFqfHq44RPshk+QGu8ebme11VlCQqJr0NWZ+PqprQyYEM7I\nWb3bbVe6v44/PszAzUPF9AcGdV2gP1F0uCqXNInb0h1Qua9pv6jMYbU9bMENGwL+MZ3Kj3u2k1Wb\nxcMbHqa8sZx7/FK4efevyF08Yfr7EDvl9A5GFCH7N/jrcdCWQv8rHYLao2N5uc9rQRsZFCf+9/av\nmP7goHYnKCarnYd+SGPlgSwmDRAY1NtIbr1D5BZri0EQiS1xZ2CeN65mBYZe7txz/9v4BAV3eBya\nJUuoeP4F/PvqCHj4cRh+d1fdooTESWOw2Hhk6V7+yDiEs1KO0CQKj5Om+IyhkMtQyGUo5TIUMhkK\nRdNZfoyjqVzZbhs5SrmMmEB3hvb0JSnSFy/X7nPDtdkFvtlWxJt/52C2Cdw5rhd3jeuFS1NKGW2N\nkZ9eTUXlrGDW/GTcPJ34KvMr3tr1FpGekcT5xhHi1osv15rp7RPHT7dOQaE4NyyJ5wKiIFKSXUfG\nhjIKM2qQAdEDAkgcF0pYnM9ZFcjIYrSx5MXtKFUKrnwyBeU5Jvp069dTdP/DGAJ6Yx80FnNEXxrl\nXtRXGtHWGGk9zfDwdWkjcH2D3fAOUuPqcWGmc5OQOB/59+c80teWcN2C4SdcSKwoaOD399JRquRc\ndv8gfEO6acuUWQdlu1vclEt2gKneUefqA2EpLftwQ5PA2b17xnGaaKzL57nfrmUVjYyUqXnp4q/x\n9TuDi6UWPWx6E7a8BwpnGP84DLn9hEG9zmtBO6DfIPGeie/i7Kbk8gePXtGpN1i4bXEqOws1PHVx\nPLeMim6xUgkCGf+uY/MPX2OsrkUI8UA+LoZ7L3mqU5EKjXvSKLz2atSBRsKfvQvZuEe68hYlJE4J\nURT5MbWEgzV6FDKHYJTLjzg3lbcWg8dvI2sjQNu0OYbIVMrlyOWglMuPKVblMs6LCWyV1sR//8zm\n17RyInzdeH56X4aH+fDL67swaC3MfCQJ3+CWH2ibYEMpVyKKIjd/tZOtB2tZ9cAYIv2kfc/dhbbG\nyL5NZWRtPoRJb8Wnh5sjiNSwYJxcz7wFfP03+8n+t5yZjyTRo2cn0y6cJRhSU9Es+Z7G9esRDAbk\nXl54XHQRbhMnYe01kIZaK5oKPZoKA5oKPfWVBmyWlsB7zmolPoctuj1aLLuS+7KExLmFxWRj0WP/\nEpnox+RbO5baq7askRXvpiHYRS65dwBBUachj7MgQG1eiwW3ZAdU73fUyeSOiMqHLbjhQ8An6tyx\n4u7/A1bci2gxsDRlDq9W/Yu3szevjnmV5B4n1IbdS20+rJwPeWsgMMHhhhw1qt3m57WgTU5OFv/8\neR0r3k1D5axg+gOD8A5y+IKX1Bm48csdlNQZeXPOAC4dEAI4JvhF6bvZtGQxVYX5+EdEMXruDUQP\nSu70pNpWU0PBxZOQ2RqJfm4uiktf6PJ7lJCQOLfYklfDU79mUlil5w7c8WgUmH7fQELjjp125add\npTy8NJ1nLkng5lHRp3m0FyY2q528XVVk/FNGVaEWpbOCuKE96Dc2FL/QM7MaX7yvlt/eS2fQ5AhG\nzGzfNe9cQTCb0f/7L7pVq9CtW4+g0yF3d8f9ovF4TpmCeuRI5C4uiIKITmOivsLQLHIPn426lhzn\nkvuyhMS5RfraEjYvzeWKR5MJiu64MG2oNrDi3TSMOisX39W/3d/ObsWogdJdLSK3NNWRaxVAHdAk\ncJtclUMGguosy4Vu0Ttce3cvgh79YdZnEBDHgboDPLzhYYp1xdw14C5u7XfrGQ1miijCgT9h5WPQ\nUAz9ZsOkF8HzaC/Z817QpqamUlOq49d30pDLZUx/YBDloo2bvtqJ2Wrn0+uTGdrTEbq7PGc/m5cs\noiQrA8+AIEZeeS19Ro5BfhJvpmi1UjxzAsaDVUQ9NBGXm987d1ZsJCQkuhWz1c5nb6ZCoZ41Hjam\nXdqLm0ZGozrClbhSa2LSWxuI6+HBD7cPlyxQZ4CqIi0Z/5SSu7MKu02gz4hgxl4Ve1rdfc0GK0te\n2IGTi4I5T6agVJ1fAk2wWDBs3Yp21d80rl2LvaEBuZsb7uPG4TFlCu5jRiN3PXpCaNJbWyy5rcTu\nMd2Xmyy6kvuyhMSZRxBEvn1mK2ovZ2Y+ktTp1zdqzKz4XxraaiNTb08kqv8Zzqsu2KEqu22wqbqD\njjq5CoL7txW5XqFnbqxlu+GX2xwW0JH3wfin2qQN1Vv1vLjtRf44+AfDgofx8uiXz3zeeosBNr8N\n/77rcD0e9xgMvbONG/IFIWgB6sr1/PrOHsxWO9+6mhA8VXx1UwoxQR7Ulhaz+fvF5O3chpuXN8Nm\nXkn/iVNRKE9+X13VvbOoXZ1F8NyBeD/znSRmJSQkmtn5RwE7fisg9qJQfjBqWZNdRWyQOwsu78eQ\naF/A4S1y66JUNufV8NcDY44ZTEri9GFstJC2upjdq4oJiPBg6u2JXReY5ASsXZzNgW0VzJqfdHpc\n7M4gotWKfscOdKv+Rrd6NXaNBpmrK+5jxuA5ZTLqMWNRuB//f8FmtdNQZTzKoiu5L0tInB3k76ni\nr48zmXpHIr0GnVzuUWOjhd/fS6empJEJN8YTO6RjwYNOG43VDnF72E25bDfYjI46z9CWfbjhQ6FH\nvzaislsQ7A5R+M/L4B4EMz6C6DHHbCqKIsvylvHS9pfwcPLg+RHPMyp0VPs5a08XdQcd1trcVY6A\nXf95vfkeLhhBC/DdmnyKfy7EWS5j2t39CQkU2LL0O7I2rEPl4kzypTNJuvjyDkUuPh7at++m7ON1\neA8LI/iLVSdOMCwhIXHBcGB7BWu+zCJuaA8m3BiPTCZjdVYlz63YR1m9kVmDw3j8P33YlFvNgz+k\n89TF8dw6uueZHrZEEwV7a1jzZRYyOUy+pS8RCX7der3CjBr+eH8vSVMjGXZ5r2691tmGaLNhSN2F\n7u9VaFevxl5dg8zZGfWoUXhOmYz7+PEoPDqemqoz7stega5tRK7kviwh0XX88vou9A1mrnnh1DyP\nLEYbf3ywl/K8esZeFUvi2LAuHGUXY7dCRUbbYFMNTalElS4QMqjFghs+BNxPTugfk/pi+OUOKN4C\nfWfAJW87AlydgFxNLvM2zKOgoYAgtyCmRk1lWvQ0EvwSzqx3y4GVsPJR9A3FbI4dy7rASF6b+N75\nL2hFUeSdNbm8uzaXieG+jChuRF/9LzZTGjIZDJxyCUMun42b56kH2TD/9DyFz32HU7AXkb9tRO5y\n9qZ/kJCQOL2U52r49d00ekR7cdl9A9vkPTVYbCxcl8enmw7iqlIgAjGB7iy9cwQKyVp0VlFfZWDl\nRxnUHdIzbHpPBk+J7JYfd5PeypIXtuOiVjHn8ZRzJk9udyDa7Rj37EG76m90f/+NrbISmUqFesQI\nPKZMweOi8Si8Tz5nZGv3ZU2FgXrJfVlColuoKGjg51d3MWpODAMuCj/l/mwWO6s+zaQwo5Zhl/ck\naWrUqQ/ydKEtbxK4TW7Kh9LBbnHU+US1BJoKHwKBfUFxEsEJ9y6FPx5y7Ef9z+sw4KpOeY2abCbW\nFa9jZeFKNpdtxibYCPcIbxa3MT6nNyJyjbGGf0r+YV3RGraVb8WKgI9dYNMtWee3oN26fQdP/JLB\n0l2lzB4QwOXKPHb9vgyryYTKNZFpd99CTErXBNgQNiyk4JF3sNtdiV6xElXoWbxSJCEhcVrRVOj5\n+bVduHo4MWt+Ei7qY29pyKvS8fTyfewtrefXe0bRO/DcTglwvmI121n/dTa5qVVED/Bn4o0JXR4J\nec2XWeTsrGT2Y8kERHTcEnm+IwoCxvR0dH+vRrdqFdbyclAqUQ8bhseUyXhMnIjSp2sCxXTYfdlN\n2WLRldyXJSTaZdVnmRTvq+OGl0fg5NI135l2u8Dar7LJ3VnJ4CkRDLu817m5wGQ1OURt6Q6HwC3Z\nAY2VjjqVGkIHt1hww1LAzbf9voz18OfDkLHU8ZqZnzhE8inQYG5gXfE6/ir8i+2HtmMX7fT27s3U\nqKlMjZ5KpGfkKfXfHsXaYtYVr2NdyTrSqtIQEQl1D2VCxAQu8klg4I6vUV7zw/kraAcnJYmJd3/I\n5gMV3B1cjVvWegwN9fROGU7SxVex4ftqGjUmLr6rP2F9jvOh6ABi6peUPf48ulJXIj77FPXI9kNL\nS0hIXFgYdRZ+ejUVq9nOrPnJeAUcf1uDKIqYbUJzrlqJsxNRFNm7rpR/f87DK8CVaXf067LciAfT\nqln5UQbJF0cx9FLJ5bw9RFHElLnP4Zb81yqsJSWgUOA2JAXPKVMc4ta/6wOadNZ9efCUSOKGnmV7\n/CQkTjPaWiPfPL2NARPCGTmra6O1C4LIxu9z2LexjL6jQxgzN+7cX0wSRYe7cLOb8naoyATR7qj3\ni2kSuE2uyv5xjm2ORVvgl9sdFuCxj8LoeSdn3T0OtcZa1hStYWXhSnZV7gIgwS+BaVHTmBI1hWD3\noyMRdxRRFMmqy2Jt0VrWl6wnrz4PgHjfeMZHjGdCxARivGPaLFqc13tovSPixOHT72SKKQ27tpbw\nhH6MvvpGgmPiADBoLfz6zh4aqo38585+RPQ9yb1Q6d9T+/I8qtI8CXzofvxuv7ML70JCQuJcxmax\n8+s7e6guaeTyBweds/lDJdqnLEfDqk8zsVkEJtwQT6/Bp7b3ydRo5bsXtuPm6cTsx5JRKC9cV+PO\nIIoi5v370a5ahe6vVVgKC0Emwy0pyeGWPHkSqqCgbh/Hke7LZQc0VJfognn7FwAAIABJREFUuOi6\neOJHnPwkT0LiXOffn3JJX1fKdQuG4+Hb9VvyRFFk2/KD7F5VRExKEBNujEehOM++Py16R4Cp1hGV\njRpHnbOXIy9uyTbwjoSZnzrEbjdToa/g78K/WVmwkszaTAAGBQ5iatRUJkdN7lCUZKtgZVflLocl\ntngdlYZK5DI5SUFJXBR+ERdFXESIe0i7rz+vBW2wn7/48MRhBEb1YvTVN/w/e/cdHsV1t338e3ZX\nfVFFEkj0bjoYV8CSG7jEPe52bMcFJ47f2LHjOM/jxClPYseJa+LEvcRxHNtxbxQXMMWF3sF0JCTU\ne9t23j9WgAABAiStVro/16Vrd2dnZn/AoJ17zplz6Dt63H5dEOqqPbz/+DJK82s465ZR9D/cob9X\nvU3NP25l++cpdDv9NDL/+rfw7OYgIq3OBiwznlvNpiWFTL15JIOObcVBHqRDqS6rZ/ozqyjYUsm4\nM/tw4oUDcBzhidTM51ezaXEhl/7PBLr3UlfjI2GtpWHDhuBoyTNn0LAheIU/Ztw4uk2dQvyUKURk\nHPjkqDX5vH4+/vsKctaVcfp1xzDsRIVa6Xo89T5evnc+fUemMOWmkW36WYunb+XrdzfTd1QKZ908\nsl2nWWt31gan4Nk1J+6OxZA5Aab8HqLa//sjpzKH6VunM33rdL4r+w6HcXBcj+M4u9/ZnNH3DBKi\n9lzUr/XWsiBvAZ9v/5w5uXOo9FQS7Yzm5IyTOa3PaZzS6xSSolt2+0inDrR907rbGe++zdATJ2EO\nMtJwfY2XD55YRnFONVNuGtHyq+vrPsL74nVsmZWOM603/f77X5xu3e8mIkFfvbORJTO2c9LFAxk/\npW3uLZGOw+8NMO/NDaz6cgeZQ5OYetMIYrod3lQMm5YUMv2ZVRx/Xn+OO7d/G1Xa9TRs3kzVzJlU\nzphJw9q1AESPHk381Cl0mzKFyN5HPzjNwfg8fj76+wpy15dxxvXD1f1Yupzln+Uw780NfP8XE0jv\n3/bTj636cgdzXltPRKSTmG4RRMVGEB3nIjougqi4CKIbf6LiXETH7lrW+H6s64gvSMoem8o38cmW\nT5i+dTrbKrfhcrg4OeNkjks/jsUFi/kq/ysa/A0kRCWQ1SuL0/qcxskZJxPjOvzZZjp1oJ1w7LF2\n0eLFLVrXU+fjg78up2BrJWe0ZD6rDbOw/7qSbV9mUF/uov+bbxA1qHXvBxCR8LV67g5mv7qeEZMz\nyLpqqHpudCFrF+Qz59/riekWwVnTRrV47ti6Kg+v/e4b3EnRXPKLYztfV7kOwrNtG5UzZ1I1Yyb1\nq4Ld46KHD9/dLTmqf9tcSPB6/Hz05AryvivjjBuGd7x5M0XaSCBg+devvsKdFMXFdx/bbp+7bXUJ\n21eVUF/rpaHGR32Nl/qa4POGWi8HizaRMS6i41wKwq3AWsva0rVM3xJsuc2vyadnXE9O63Map/c5\nnXFp43A5ju4e33YNtMaYs4DHASfwnLX2wX3eN43vnwPUAtdba5e0ZNvm7DsP7aF46n18/PcV7NhQ\nzmnXDuOYkw/QHWnzHPj3ZexcmUHZ8noyH32E+LPPbvHniEjntn11CR8+uYLexyRx7o9H6wuuCyrc\nVsn0p1dRU9lA1pVDGT7x0N1bpz+zii3Li7jsf44jJVO9fdqDJzd392jJdcuXAxA1ZEiwW/LUqa1+\nodrr8fPR35aTt6GcM384gsHHtf09vSKhtqvnyVnTRjJwXMe49cYGLA11wWBb3xh2G2r2eX4kQTja\nSbS7+RbhqFgX0e6ILh2EAzZAYW0h6bHprXqhv90CrTHGCXwHnAnkAguBK621a5qscw5wO8FAewLw\nuLX2hJZs25zDDbQQ/LL55B8ryFlbRtZVQxl5SubeK2z7Cv51MRUFGeTNrCX5+utJv/cXh/UZItJ5\nFedW8/ZfFhOfEsPFd49v9alcJHzUVXuY+dxqcteVMXxSBqdcPuSAc8luWFTAzOdWc8IFA5hwdr/2\nLVQA8ObnUzVrFpUzZlK3ZAlYS+TAgcFuyVOnEjVkSKucgHkb/Hz4t+Xkb6rgzB8OZ/AEhVrp3N56\naDG1lQ1c/buTwn7kYQXhjqk9A+1JwG+stVMbX/8SwFr7QJN1ngZmW2tfa3y9HsgG+h1q2+YcSaCF\n4AAO059ZxbaVJXtP/Jy7CP55IfUNqWx91xIzejR9XnwB49IJq0g4qa/2YrFEx0W06hXCmvIG/vun\nRdiA5fv3TsCd1PqjOEp4CQQs37y/mSXTt5HWtxtnTRu13+ietZUeXvvtN8R3j+aSe47VCUoH4C0o\npOrTWVTNmEntokUQCBDRtw/xU6bSbepUokcMP6rfHZ56Hx/+bTk7N1cy5cYRGjBOOq2dWyp460+L\n9z6f7oLaMwgPn5xJr6GtMxd3uGhpoG2NxJYJ5DR5nUuwFfZQ62S2cNtW44pwcva0Ucx8bjXz3tiA\nv2gz4/uugdkP4Hclk/tZEs4EP5mPPqIwKxJGbMCy7NMcvn5vEwG/xRXhwJ0cTbfkqMbHaNxJe167\nk6JwtXAuWE+9jw+fXE59rY+L7xqvMCsAOByGky4cSHrfeD59eQ1vPrCQKTeN3H2yYa1lzr/X42nw\ncfp1wxVmO4iI9DSSr76a5KuvxldSQtWnn1E1YwYlL7xAybPPEpGZSbepU4mfOoXo0aMPO9xGRrv4\n3k/G8OFflzPr+dUYBx2mK6ZIa1r+WQ6RMa4uP2WVcZjd998mpLZ8u8MNwgVbKqkorufSew+Z7bqk\nsEltxphbgFsA+vTp0/IN/T4o3QQ7V0LBapwFq5lSs4bPoi/lqy8m43cvZkLfNPKWjcRbuIS+L7/c\nJpO1i0jbqKlo4LOX1pCztowBY1PJGJxIVVk91aX1VJU2ULKyhNpKz37bxXSLCAbd5Gi6JUXjTo7a\n8zo5mphuEVgLs55fTUluNef8eDSpfTTViuxtwLhULu05gU+eWsn7jy3lpIsGMfbM3mxYVMDmZUWc\ndNFAkjPiQl2mNMOVkkLS5ZeRdPll+MrKqP78cypnzKD0lVcofeEFXD17Ej/lTLpNnUrM2LEHnVWh\nqchoF9+7fQwfPLGMmc+uZuothgFjD+NMV6SDqyypY9OSIsae3pvI6LCJEh3K4QbhXVMWVZXWt8lc\nv+GuNY7CHUDTvga9Gpe1ZJ2IFmwLgLX2GeAZCHY5braSmhIoWAUFqxsfV0HhOvA3BN93uKD7UJz9\nTuKM4xJwLLN8u+Yqqgqn0GP+/fT41X3Ejh/Xwj+2iITa1hXFfPbPtfga/GRfPZThkzKabVHxewNU\nlwcDbjDoNgbesgbK8mvYvroEnyew1zZOl4OoOBe1FR5OuWII/UbpQpc0L6lHHN+/dwKfv7yWBW9v\nZOeWCnasLyO9fzxjzzyMC7ASMq6kJBIvuYTESy7BX1FB1RdfUDVjJmX/fo3Sl/+JKzWVblOm0G3q\nFGKPPRbjPHgPj8hoF+fdPpb3n1jGjMZBc/qPUaiVzmHlF7kAjDq1V4gr6ToGjkvj63c3s3lpEWNO\n77pdvA+kNe6hdREc2Ol0gmF0IXCVtXZ1k3XOBX7CnkGhnrDWHt+SbZszYcKxdtFHrzQG15WNj6uh\nKn/PSnFpkD4CeoyE9JHB592HgCsKAOv3U7dyJbNf28CWihSGRW3gtMdu0RQcImHA5/Gz4O1NrJyd\nS/febqbcOIKkHkfeCmatpaHGR9WusFu2J/z2HJTA6FP15SGHZq1l6cztfP3uJhwuB5f/73FHdVxK\n6Pmrq6n+YjZVM2dQ/eVcbEMDzpQUup1xBvFTpxB7wgkHDbcNdT7ef3wZxTlVnD1tFP1G68KYhDdP\nnY+XfzmfviNTmHLTyFCX06X85/ffEBnjatcpkkKtvaftOQd4jODUOy9Ya/9gjLkVwFr7VOO0PX8D\nziI4bc8N1tpFB9r2UJ83ITPCLro5NvjCGQmpQ/eE1vQRwefu/e9Z8ZWVUTNvHtVfzqVm7lz85eVY\nh5ONk+8gxwzgpIsGMn5q36P++xCRtlOyo5qZz6+mNK+GMWf05qQLBh5whFmRUNi5uQK/L0DmkK41\neEdnF6ipoXruXCpnzKB69hxsXR1xkybR669P4IiJOeB2DbXeYKjdUR0MtertIWFs+Wc5zHtzA9//\nxQTS+7dsLm5pHQs/2sK3H27h+gcnEpcQFepy2kW7Btr2NmFIhl3038cbW10HgzOi2fVsIED96jVU\nfzmH6i+/pH7FSrAWZ1IS7lMmEzf5FOImnowjIZFPX1zDhoUFTLp0sJryRTogay2r5uxg/n83Ehnj\n5PTrh9N3REqoyxKRLihQV0f5f9+i4IEHiB0/nl5P/QOn+8BzDNfXBENtSV415/xotH53SVgKBCz/\n+tVXuJOiulQrYUdRklfNf373LVlXDmFkVtfo7t2eoxy3v/gMGH1ps2/5y8upnj+fmi/nUj1vHv6S\nEjCG6NGj6H7bbbizTiF6xIj9Bnc4/fpj8PsCzHtzA84Ix/7z1IpIyNRVefj8n2vZurKEPiNSOP26\nY4iNjwx1WSLSRTliYki+9hpcKcnsuOcXbL/hh/R59hmciYnNrh8dF8H5Px3Le48t5ZN/rOScH42i\nj0KthJkty4qoKqln4vcHhbqULim5ZxyJ6bFsWlrUZQJtS4VnoG3CBgLUr11Lzdy5VM/5krrlyyEQ\nwJmYSNykSbizTiFu4kRcyckH3Y/T6WDKjSP45OmVzPn3elwRDoad1LWHIhfpCHLWlPLpS2uor/Uy\n6bLBjD61l+51F5EOIf6cczDR0ez46R1s+8F19Hnh+QPOlBAdF8EFd4zjvceW8vFTKzn3R6PpPfzg\n5yYiHcmyT3OI7x6tAc5CxBjDwHGpLJm5nbpqDzFuXdjfJTy7HI8bZz9/4AGq53xJ9by5+IuKAYge\nORL3KafgPmUy0aNGHXIUwub4vH4+enIFO9aXceaNIxg8Ib21yxeRFvD7Anz93maWzdpOUo9Yptw0\ngu69NG2OiHQ8NQsWkHPbT4hIT6fPiy8Q0fPAF8Trq728+9hSygtqOfe20fQeplArHd/OzRW89dBi\nJl02mDGn6da8UCnaXsUbf1zIqdcOY/jEjFCX0+Y69T20I2Ni7Jt9++FISMA9cSJxp0zGPWlSq80f\n623w8+HflpO/qYKzbh7JgHG6EiXSnsoLapn5/GqKtlcx4pRMJn5/EBGRh3+BSkSkvdQuWULOLdNw\nxsfT56UXiexz4Cmb6qo9vPfoUioK6zj3ttH0UqiVDm7Gs6vYvqaU6x44WXPPhpC1wfuYE9PjOO/2\nMaEup821NNCG5dCgru7d6fvvfzNk/jwyH3mYxAsvbLUwCxAR5eTc20aT1rcbM55bxdaVxa22bxE5\nMGsta+bn8fofvqWypI6zbx1F9lVDFWZFpMOLHT+ePi+9RKC2lm3XXEvDpk0HXDfGHckFd4wjPjVm\nd68wkY6qsqSOTUuLGDEpQ2E2xIwxDBiXRu66UhpqvaEup8MIz0Cbnk7s+HEYV9v9pwpOij6GlEw3\n059eRc660jb7LBEJjgI649nVfPHKOtL7x3PFfScwYKx6R4hI+IgZOYI+/3wZawNsu+Za6tesOfC6\n3YKhtlv3GD58cjl5GxRqpWNa+UUuAKNO1UBEHcHAcakE/JatK0tCXUqHEZaBtr1ExUZw/v8bS0Ja\nDB//fQV5G8pDXZJIp5S3oZzX//AtW5YVcdJFAzn/p+NwJ3WNOdZEpHOJHjKEfq+8gomOZtt111O7\ndOkB142Nj+TCO8fRLTmaD/62gq0riwn4A+1YrcjBeep8rJmXx6DxqXRLjg51OQKk94snLjGKTUsK\nQ11Kh6FAewjR7uCohO6kaD7823J2bqkIdUkinUbAH+CbDzbz7iNLcDodXHzPsYyf2heHQ6MYi0j4\niuzXj36v/gtnchLbb7yJmq+/PuC6sfGRXHDnOLolRfHRkyt47q65fPDX5SyZsY2dmyvwK+BKCK1d\nkI+n3s+Y0w98T7i0L+MwDBiXyvY1pXjqfaEup0MIy0GhJkyYYBctWtSun1ld1sA7Dy+modbHBXeM\nI7WPRlsVORqVxXXMemE1OzdXMuykHky+fIjuzRGRTsVbWEjOjTfi2badzCcep1t29gHX9dT52Lqq\nmLzvysnbUE7ZzloAXFFOeg6IJ2NwEhmDE0nvF48zQu0R0vYCgeAARO6kKC6++9hQlyNN7PiujHcf\nWcqUmzr3jCydepTjUARaCN4U/87DS/A1BLjwZ+NIyXS3ew0incF3C3cy59X1AGRfPYzBx3XeX8Yi\n0rX5ysrIuelm6tevJ/Mvfyb+rLNatF1tpYe8DeWNP2WU7KgBwBnhoEf/eDIGJ5IxJIke/eNxaeA8\naQOblhQy/ZlVnD1tlGb86GACActLv5hH5pAkpt48MtTltJmWBlo1hxyG+JQYLrhjHO8+vIT3HlvK\nRXeNJ6lHXKjLEgkbnnofX/7nO9Z/vZMeAxI484fDie8eE+qyRETajCspiT4vvUjOtFvZ8bO7CNTV\nk3jRhYfcLjY+kkHHpjHo2DQgOH9t3sby3SF30cdbsR9txeEypPcLBtzMwUmkD4hXbxdpFcs+zSG+\nezT9xrTeTCLSOhwOw4Cxqaz/tgCfx9/lL2qphfYIlO2s4Z2Hl+BwOrjorvEkpOqEXORQCrZUMvOF\n1VQV1zHhnH5MOKcfDqe6zYlI1xCorSX3Jz+hZsFXpP/6VyRfddVR7a+hzkf+xvJgF+WN5RRuq8IG\nLA6HIbVvNzIGJ9JvVAoZg5Na6U8gXcnOzRW89dBiJl02mDGn9Q51OdKMnDWlvP/EMs6+dVSnnRVC\nXY7bWMmOat55ZAmRUS4uunu8Rn4TOYBAwLJ05ja+fX8LsYmRnPnDEWQMSgx1WSIi7S7Q0MCOO39G\n9eefk3b3XaTcdFOr7dtT72Pn5ord9+AWbK0k4Lece9to+o1SC5scnhnPrmL7mlKue+Bktfh3UH5/\ngBd/Po++o1I484YRoS6nTbQ00Kp55AilZLo5//+NpaHOx7uPLqWmvCHUJYl0ONVl9bz/+FK+fncz\nA8alcsV9xyvMikiX5YiKotfjjxF/zjkU/uVhip54gtZqWIiMdtFneAonXjiQi39+LDc+PJnE9Fjm\nvrEBn9ffKp8hXUNlcR2blhQyYlKGwmwH5nQ66D+mO1tXlOD3de3R0BVoj0Ja33jOu30MdZUe3nts\nKbWVnlCXJNJhbF5axH/+71sKtlZx2g+GMeWmEUTFRoS6LBGRkDIREWT8+SESvn8JxX//B4UP/qnV\nQm1TkdEuTrl8CJVFdSydub3V9y+d14rZuWAMo07tFepS5BAGjk/DU+cjd11ZqEsJKQXao9RjQALf\n+8loqkqCLVH11d5QlyQSUl6Pn9mvruOTp1cSnxLD5f9zHMecnIExmltWRATAOJ30/N3vSLr2Wkpf\nfpmdv74f62/9VtTew5MZOD6NxdO3UVlc1+r7l87HU+dj7bw8Bo1P1e10YaD3sGQio51sWloY6lJC\nSv0IWkHG4CTO+dFoPvr7Ct5/YhkX3DH2gC1RNmBpqPNRX+2lvqbJz+7Xu97zUF/to77GS0ONl6i4\nCJJ6xJKYHvxJSo8lsUcs3ZKiMQ4FBekYinKqmPX8asp21jJuSh9OOH8ATpeum4mI7Ms4HKT/zy9x\nxMZS8vTTBOrryXjgjxhX656aTbp0ENtWlzDvzQ2c86PRrbpv6VyqSuuZ/vRKPA1+xp7ZJ9TlSAs4\nIxz0HdWdLcuKCVwV6LKDbSrQtpLew5M565aRfPL0St5/fBkZgxP3Caje3eH0QD2LjIGouAiiG3+6\npUST2sdNVFwE9VVeygpq+e7bAjx1vt3buCIcJKTF7h12G5/rvgdpL9ZaVnyey4J3NhIdF8H5Px1L\n72OSQ12WiEiHZowh7c47cMTGUvToo/iKikj72Z3EjG694OlOiua4c/rx1Tub2LqyWANESbNy15cx\n87lV+LwBzp42irS+8aEuSVpo4PhUNiwsYMeGcnoP65rnXko8rajf6O5MuWkEn760lpK8GmLcEbsD\nakqmm2h3BNFxrmBgdUfs/RgXQVSM65CtrdZaais9lBfUUl5QS9nO4GPh9io2LSncKyzHJUSS2CMu\n2Jrb2KKblB5Lt2S16krrqa308NnLa9m+uoR+o7tz2g+GEeOODHVZIiJho/u0W3AmJVL48CNsvexy\n4k4+iZRptxJ7/HGtcrvGmNN7s3ZBPnNf/45ew5JwRXTtOStlD2sty2bl8NU7G0lMj+XsW0eR1CMu\n1GXJYegzIgVXpIPNS4q6bKDVtD1twAZsSAKj3xugvKh2v7BbXlBLQ+2eVl1nhIPEtBgS0+N2t+aq\nVVeOxLbVJXz20ho89X4mfX8QI07J1L2yIiJHyF9dQ/nr/6HkxZfwFxcTM24cKdNuwZ2VddS/W3PW\nlvL+48s4/rz+HHdu/1aqWMKZp97H5/9cx6YlhQwcl8pp1x2j88AwNf3pleRvquD6Byd2qkYrzUMr\nu1lrqavyUl5QszvklhXUUr6zlsriur1adWMTIhvvz93TspvUIxZ3cjSOTvQfRI6O3xvgq3c2sfzz\nHFIy4zjzxhGkZLhDXZaISKcQaGig/K23KH3uebx5eUQNG0b3abfQbcoUjPPIW1enP7OKrSuLuer+\nE4jvHtOKFUu4KS+o5eOnVlK+s4YTLxzIuCl9dEE6jG1YWMDM51dz0d3jO9X0iAq00iJ+b4CKorrG\nkFuzV8vuXq26LgcJaTG7B6MKht04EnvEEhWjq3ldSWl+DTOfX01JbjWjTu3FyRcPVPc1EZE2YL1e\nKj78iJJnnsGzZQuR/fqRcvPNJJx/Hibi8KdBqy6r59XffEOvoUmc+2MNENVVbVlexKcvrsHhdDDl\n5hFdtptqZ+Kp9/HC3fMYcUoGky8bEupyWo0CrRwVay311d59WnRrKCuopbK4HhvYc9zExkfu1W05\n+DyObilq1e1MrLWsnpvH/Dc34Ipycvp1x2hwERGRdmD9fqpmfUrxM0/TsGYtroyepNx4I4mXXIIj\n+vCmVlkyYxtfvbOJc28brd/hXUwgYFn44RYWfbyV1D7dOGvaSOJT1FLfWXz09xUU51Txgz+e3Gla\n2xVopc34fQEqi+uaDbsNNfu36jadZmjX8wNNayQdU321ly/+tY7Ny4roPTyZ0687hriEqFCXJSLS\npVhrqZk7l+KnnqZuyRKc3buTcv11JF5xBU53y2778PsCvP5/3+L3Bbjy/hPUw6aLqK/xMuuF1Wxf\nXcqwk3uSdeUQ/dt3Muu+yuezl9fy/V9MIL1/5xilWoFWQqKu2rNnMKqdjWG3oJaKorq9WnVj4iP3\nukd3V8tufEp0l51Dq6PKXV/Gpy+uoa7Kw0kXDWTMab071YADIiLhxlpL3aJFFD/1NDXz5+OIjyf5\nmmtIuvYaXElJh9xeA0R1LUU5VUx/eiXVZQ1MvnwIIyZndJoWPNmjvsbLiz+fx5gzenPyxYNCXU6r\nUKCVDsXvD1BZVLfXyMu77tetr/HuXs/hMiSkxu5zr27wJzpOrbrtye8P8O0HW1gyYxuJabFMuXEE\nqX26hbosERFpom7lKkqeeZqqWZ9iYmNJuvxykm+4noi0tINuN+PZVWxZoQGiOrv13+xk9r/WERUX\nwVm3jKTHgIRQlyRt6IMnllFeVMc1vzuxU1y0UKCVsFFf7aWsoJaynTV7Bd3KojoCTVt1u0U06b68\nZxTm+O5q1W1t5YW1zHp+NYXbqhg+sSeTLhtCRJS6JomIdFQNGzZQ/OyzVH70McbhIOGSi0m56SYi\ne/Vqdv2OMECUtZZAwOLUd3ir8/sDzP/vRlZ+kUvG4ESm3jyS2HjNEd/ZrZ67g9mvruey/z2O1N7h\n3wihQCthz+8PUFVcT1nj/bl7tepWN2nVdRoSUmOadF/eM7+uWnUPj7WW9d/s5MvXvsPhNGRfPYxB\nxx78Kr+IiHQcnpwcSp57noq338ZaS/dp0+g+7RZM5P5hJpQDRNWUN/Dhk8spzqnGFeEgMsa1+ycq\nxklktGufZS4i91ketft9J06Xo1O0SLWGmooGZjy7ivyNFYw5vTcnXTxQFw26iLoqDy/eM49jz+7H\nCecPCHU5R02BVjq1+hpvY7jde6qhiqI6Av79W3WDLbtxu7sxq1V3fw11Pub8ez0bFhaQMTiRM24Y\nTrfkwxs9U0REOgZvQQGFf3mYyg8+IGroUHr+8Q/EjBix1zqhGiCqvKCW9x9fRn2Nl9Gn9cLvDeCp\n9+Op8+Gp89HQ+Oip89FQ78fX4D/kPh1Oc+jwG924fL9lweURUc6wD8X5myqY/sxKPHU+Tr12GEOO\n6xHSeurXrWPn7/+PmJEjSbl1Wovu8Zaj8+4jS6it9HDVb04MdSlHTYFWuqSAP0BlcX2TkLundbeu\nqkmrrsPsHoF5r5bd9Fii3V2vVXfn5gpmPr+a6rIGjv9ef8af1VdTLomIdAJVn3/Ozvt/g6+0lJSb\nb6L7j3+Mo0lrbc66Ut5/rP0GiCrcVsmHf1uOtXDe7WNI63vo0VgDAbs74Hrqd4Ve/4GX1fv2Ccd+\nPPU+OMQpr3EYIqOde4XcqCatxLsC8V7L9lkeEe0KyfentZZVc3Yw740NuFOiOXvaKLr3atnI121V\nT/nrr1PwxwdwxMTgr6rCERND8g03kHz99TjdcSGrrbNb8UUuc1//jivvP4HknuH996xAK7KPXa26\nTVt0ywpqqSis3atVN9odsddgVLu6L8enxnS6LjuBgGXxJ1tZ+NFWuiVHceYPR2jACBGRTsZfUUHB\ng3+i4p13iBo8iJ5/+AMxo/fcN9teA0Tlrivl43+sJDougvN/OpbE9Ng2+6x92YDF2xAMtrtDbtPQ\nuzsE+/duJd5nedOxPQ4kInpPuI1q0iIcGeMiap9W4sjovbtO71p+OOcbPo+f2f9ez/qvd9J3ZApn\n3DA8pLdc+auqyP/1r6n6ZDpxkyeT8acH8ZeVUfTY41TNmoUzKYnY3RiHAAAgAElEQVSUabeQdOWV\nOKI0BWBrqylv4KV753PC+f2ZcE54j2KuQCvSQgF/gMqS+r2C7q6wW1fp2b2ew2GIT90zr25KZhyZ\nQ5NxJ4XnL+Oq0npmvbCa/I0VDDk+nawrhxIZ4wp1WSIi0kaqv/yS/F/fj6+wkOQbrif19ttxREe3\nywBRGxcXMuvF1SSmxXLe7WPD8rvTWovPG9jTMtw0/NbvCcfeOj8NTVqJ96wTXN/vDRzys/a6rzja\nuc89w3svXzk7l+Kcao77Xn+OO6dfSKfWq1u1mh0/+xneHTtIveOnpNx4I8axJ5zXrVxJ0aOPUrPg\nK1w9e5J6249JuPBCjEvnH9bnw7uzAO+OHXhzc/HuyMW7Ywee3B148/NIvvoaUm78YYv29dZDi/F6\n/Fxx3/FtXHXbUqAVaQUNtd7dXZb3mnKosJaAL/h/Jzkjjt7Dk+lzTDI9BycSEdnxRwPeuLiQ2a+u\nIxCwZF05lKEnhPYeGxERaR/+qioKH/oz5W++SWT//vT8wx+IHT+OJTO38dXbmzj3x6PpN7p1B4ha\n9eUO5ry2nh79Ezj3ttFdfsBGvy+w373Cu8LuXssOsHzf+4ojY1ycecPwVv93OxzWWsr+9SqFDz2E\nMyWFzEceJnb8+AOuX/P11xQ+8ij1K1YQ2b8/qT/9Kd2mTgn7e5gPxgYC+IqKdwdVb24untxcvLk7\ngq937gSfb88GxuDq0YPIzEy8+fngcDBw5owW/R0t+3Q78/+7kWt+fyIJqe3XE6K1KdCKtKFAwFKa\nV03OmjK2rykhf2MFfl8Ap8tBz0EJwYA7PJmUTHeH+uXsqfcx740NrF2QT3r/eM784fCw/kUnIiJH\npmbBAvLv+xXe/HySf3AtyT/5f7z58MpWHSDKWsuij7fy7Qdb6Dsqhak3jwyLi77hIODfM5BWVFwE\nUSHsYeWvqCD/vvuomvUp7uxsej7wxxYN/mStpfqzzyh87DE8GzcRPWIEqXfeSdzEkzvUuVNLWWvx\nl5XtHVZ37AgG1txcvHl5WI9nr22cqd2JzMgkolev4E9mBpG7nvfosXt08rLXXmPnb3/HgI8/ImrA\noUcvriyu45X7vuKkiwYyfmrfNvnztgcFWpF25PX4yd9Qzva1peSsKaU0rwaAmPhI+hyTTO/hyfQ+\nJjmkc8AVbqtk1gtrKC+s5diz+nLc9/p3unuCRUSk5fzVNRQ98jBl/36NiD594Mf3M2N6fasMEGUD\nlrlvbGDl7FyGntCDU38wTN85nVDdihXsuPNneAsKSLvrLpKvv+6ww6j1+6n44AOKn/gr3rw8Yo8/\nnrSf3UnM2LFtVPWR81dVNR9Wd+zAs2MHtrZ2r/WdCQlNwmomEb0yg4E1M5OIzEwc0S2bTcKbl8fG\n004n7Z57SPnhDS3a5o0/LsQ4DJfee8g82GEp0IqEUHVZAzlrS3f/7Jo3N6WXOxhwRyTTc2BCu0yR\nYAOWZZ/m8PV7m4iNj+SMG4aTOUTD5ouISFDNN9+Sf999eHNy+O6s35DvS+PK+08kIfXIBojy+wJ8\n9tIaNiwqZOwZvTn54kEhva9TWp+1ltKXXqbw4YeJSEsj89FHiBkz5qj2GfB4KH/9DYqfegp/SQnu\n004j9Y6fEj1kSCtV3YIaamvx5uXt3RU4NxfPjly8O/IIVFTstb4jLq75sNr46HS33kjTm8+/AGdi\nIn3/+XKL1l88fStfv7uZH/zx5LCdhlGBVqSDsAFLcW4129eUkLOmlPxNFQT8FleEg4whifRubMFN\n7hnX6l1saioa+PTFNeSuK2PAuFROvWZYl793SURE9heoraXwscfY+foHfH3C/WT0ieb8+7IPez+e\neh/Tn1lFzppSTrp4IOOnhG93R2mev7ycvF/+D9VffIH7jNPJ+MMfcCa03gwJgZoaSl95hZLnnidQ\nU0PC+efR/fbbiezV6+j37fHgy8sLDrTUZPAlT2Nrq7+kZK/1TVRUMy2re7oHOxMT2617dOHDj1Dy\n4osMWTAfZ/yhp7sqL6jl1fu/ZtKlgxlzeu92qLD1KdCKdFCeeh95G8rJWRNsvS3bGeyeEhMfSYw7\nAofT4HQ5mjw6cLrMnsdd7zkdOFzNPzpdBp83wOLp2/B5/Ey+bAjHTOwZlvekiIhI+6ldsoQFD77L\n+uRsTkpYyZhf3djiVqa6ag8f/m0FRdsqOfXaYRxzckYbVyvtrXbJUnbcdRe+4mLSf/5zkq69ps3O\nLfzl5ZQ89xylr/wLGwiQdOmldP/RrbhSUw+4jfX58BUUBAPrrm7BO3J3B1hfQQE0zT4uFxEZGUT2\nytwvrEb26oWze/cOc+5Uu2QJ2666msxHHyH+7LNbtM1/fv8NkTEuLr772Daurm0o0IqEiarSenLW\nlpK/oRxvgx+/3xLwBfD7AwR8Fr8vsHtZwG/3LN/nsTnde7uZcuMIknqE98TaIiLSfrzVtbz2yy/w\nVtYwMec5ev3217gnTzroNlWl9bz/+DKqSuuZetMI+o85cOiQ8GMDAUqef56ixx4nIiODzEceIWbU\nyHb5bG9BIcX/+Dvl/30LExFB8rXX4s46BW9eXpP7WYPP9xsp2OHA1SN9n4GXMoMBtlcvXGlpGGd4\nDFRm/X42nDwRd3Y2GX96sEXbfPvhFhZ+tIXrH5xIXEL4TZWlQCvShVhrCQTs7gAc8FsC/gCxCVE4\ndN+SiIgcptx1pbz32DIGVSygz9JXSbj4YtLv/UWzXR1L82r44K/L8NT7OffHo8gYrHEaOhNfaSl5\n995LzZdz6TZ1Kj3/7/c4u3Vr9zo827ZR9MRfqfzoo72WO1O7E5l5gIGXmowU3Bns+Pk91Mybx+B5\nc1sUxEt2VPOf339L1pVDGJl19F2221tLA61mMRbpBIwxOJ0GpxMiosLjSqOIiHRcvYYlM2hCGluW\nTWTw9UlUvPIPaubNo9sZZxDZrx+R/fsT2b8fJfVxfPSPlTidDi66axzde7V/0JG2U7twITvuuht/\neTk97v81iVdcEbIuuJF9+5L58F/o/qNb8eblBQNsRkaLRwruDNzZWVR+8AF1K1YQO27cIddPzogj\nMT2WTUuLwjLQtpQCrYiIiIjsZ+Ilg9m6soS18ZM57fVsCv/0Jyree49ATXBqupLk4awccRPRto6T\nor4m8J+vKe/fPxh4+/XDmZzcYe4/lMNjAwFKnnmGoif+SmTv3vR++imijzkm1GUBEDVoEFGDBoW6\njJBwT5oETifVc+a0KNAaYxg4LpUlM7dTV+0hxt15WqubUqAVERERkf24k6I4/tz+LHh7Izsnjab/\nK//EWou/pIS1n25gxZxaEiLqON43H8fW9ZR8OR283t3bO7p12x1uI/v1JWpX2O3bF0ecxnboqHzF\nxeTd8wtqFiwg/txz6fHb3+J069+rI3AmJBA7bhzVs+eQdscdLdpm4Pg0Fk/fxpblxQyf2DkHalOg\nFREREZFmjT69F2u/ymfeG9/Re1gSrkgnq1fUMW92HZlDkjj7R1lExXwPCI4w683Px7N1K54tW4OP\nW7dSt3gxlR9+uNfosq60NCL79cPVPQUTEYGJjAw+Nv7Q5LkjMnKv13v97LVdk+eRja8j91nf4QjV\nX2WHYa0lUF2Nr7AQX1HRfo8133xLoLqaHr/7LYmXXqpW9g7GnZ1F4V8exrtzJxE9ehxy/e693XRL\niWbTkiIFWhERERHpWpxOB6dcMYT3Hl3KkhnbCPgti6dvY8DYVM68cTiuiD3jNhiXi8jevYns3Rsm\nT95rP4H6ejzbtu8OucHQu4X6teuwXu+eH49n9yNtMXCp09lsgD5wSG7Be5FNAvW+AfoI9klExBGF\nSGstgaqqZkOqd/fr4DJbX7/f9iY2lojUVKKHDyft7ruIHjq0Nf7GpZW5s7Mp/MvDVM+eQ9IVlx9y\n/V3djld8kUtDrZeo2Ih2qLJ9HVWgNcYkA68D/YCtwGXW2rJm1jsLeBxwAs9Zax9sXH4p8BvgGOB4\na62GLhYRERHpQHoNTWLwhDQWfrQVgOGTMsi6auhhjaLviI4meugQoocOafE21u9vPuzufn2Q97xe\nrNez9zrefbdvbhsveL0EPB4C9XW7Xx/ss9rErtbpiAjYLyRH7hWkrce7O7weLKi60tKIGTkSV1oa\nrsbXTZ+rW3F4iBw4kIjMTKrntCzQQrDb8bJPc9i6soShJxy6VTfcHG0L7b3AZ9baB40x9za+/kXT\nFYwxTuBJ4EwgF1hojHnfWrsGWAVcDDx9lHWIiIiISBs5+ZLBFGyrYshx6Rx/Xv926YZqnM7g1CQd\neBRbay34fAcNyAd9z9Nc8G7Jdo3LGzwYp3NPUG0aVhVUOyVjDO7sbMrfeotAfX2LRnlO7xdPXGIU\nm5YUKtA24wIgu/H5y8Bs9gm0wPHARmvtZgBjzH8at1tjrV3buOwoyxARERGRtuJOiuLa358U6jI6\nHGPM7tZUYmNDXY50Ee7sbMpefZXab77BnZV1yPWNwzBgXCpr5uXhqfcRGd257jo92jvj0621+Y3P\ndwLpzayTCeQ0eZ3buOywGGNuMcYsMsYsKioqOvxKRUREREREwlzs8cdhYmKonjOnxdsMHJeK3xtg\n26qSNqwsNA4ZaI0xnxpjVjXzc0HT9ay1FmiDu/d37/8Za+0Ea+2E1NTUtvoYERERERGRDssRFUXc\nySdTNXt2sNt7C/QclEhMtwg2L+18DYOHbG+21p5xoPeMMQXGmJ7W2nxjTE+gsJnVdgC9m7zu1bhM\nREREREREDpM76xSqP/uMhg0biB5y6MHWHA5D/7GpfPdtAT6PH1ek85DbhIuj7XL8PnBd4/PrgPea\nWWchMNgY098YEwlc0bidiIiIiIiIHKZd985Wzz68bse+Bj/b15S2VVkhcbSB9kHgTGPMBuCMxtcY\nYzKMMR8DWGt9wE+AGcBa4A1r7erG9S4yxuQCJwEfGWNmHGU9IiIiIiIinVpEejpRw485rPtoM4cm\nERXrYtPS5jrVhq+jGuLKWlsCnN7M8jzgnCavPwY+bma9d4B3jqYGERERERGRrqZbdjbFTz2Nr6wM\nV1LSIdd3Oh30H9OdzcuK8fsCOF1H27bZMXSOP4WIiIiIiEgX4s7KgkCAmnnzW7zNwHFpeOp85K4r\na8PK2pcCrYiIiIiISJiJHjUKZ3Iy1bNnt3ib3sckExHt7FTdjhVoRUREREREwoxxOHBnZVE9dy7W\n52vRNs4IB/1Hd2fDwgJy1naOwaEUaEVERERERMKQOyuLQGUldcuWtXibky8ZREJqDB/+bTkbFha0\nYXXtQ4FWREREREQkDMVNmggu12F1O45LiOKiu8bTY0ACM59fzfLPc9quwHagQCsiIiIiIhKGnG43\nsRMmHNb0PQBRsRGc9//GMGBsKvPe2MBX727CWttGVbYtBVoREREREZEw5c7OomHDRjy5Ow5rO1eE\nk6m3jGT45AyWTN/GF6+sI+APtFGVbUeBVkREREREJEy5s7IAqJ4z+7C3dTgM2VcN5bhz+7F2QT6f\nPLUSr8ffyhW2LQVaERERERGRMBXVvz+RfftSPfvwuh3vYozh+PMGkHXVULauKuH9x5ZRX+Nt5Srb\njgKtiIiIiIhIGHNnZ1P7zTcEamuPeB8jT8nkrJtHUri9krf/soSq0vpWrLDtKNCKiIiIiIiEMXd2\nFtbjoebrr49qPwPHp3H+7WOpKavn7T8vpjS/ppUqbDsKtCIiIiIiImEs9thjccTFUf3F7KPeV+bQ\nJC66ezwBv+Xtvyxm5+aKoy+wDSnQioiIiIiIhDETGUncxIlUz5nTKtPvdO/VjUvuOZbo2Ajee3Qp\nW1cUt0KVbUOBVkREREREJMy5s7PxFRbSsHZtq+wvvnsMF//8WJIz4vj4qZWsXZDfKvttbQq0IiIi\nIiIiYc59ymQAqucc2WjHzYmNj+SCO8fRa2gin/9zLUtmbGuVFuDWpEArIiIiIiIS5lzduxM9ejRV\ns2e36n4jo12ce9sYBh+XzlfvbGLemxuwgY4TahVoRUREREREOgF31inUr1iJr6SkVffrdDk484bh\njDmtNys+z2XWi2vw+wKt+hlHSoFWRERERESkE3BnZ4O1VH85t9X3bRyGiZcO4qSLBrJhYQEfPbkc\nT72v1T/ncCnQioiIiIiIdALRw4fjSkujupW7He9ijGH81L6cft0x5K4v591HllJb6WmTz2opBVoR\nEREREZFOwBiDO+sUaubPx3raLmgOO6kn5/xoFGX5Nbz958VUFte12WcdigKtiIiIiIhIJ+HOziZQ\nXU3tkiVt+jn9RnXngjvHUV/r5a2HFlO0vapNP+9AFGhFREREREQ6ibgTT8RERFA9u/Wm7zmQHgMS\nuPjuY3G4DO88vITtq1t3MKqWUKAVERERERHpJBxxccSecEKb3Ue7r+SecXz/ngkkpMXw4ZMrWLsg\nr10+dxcFWhERERERkU7EnZWFZ+tWPFu3tsvnxSVGcdFd4+k1LInP/7mOhR9twdr2matWgVZERERE\nRKQTcWdnAVA9p+27He8SGe3i3NtGM+ykHnz7wRa++Nc6/P62n6tWgVZERERERKQTiezdm8hBA6lq\np27HuzidDk77wTFMOLcfa+fn8/HfV7T5XLUKtCIiIiIiIp2MOyuL2kWL8VdXt+vnGmM44bwBnHrN\nMHLWlvHuI0upqWhos89ToBUREREREelkumVng9dLzfwFIfn84ZMygnPV7qzhrYcWU7azpk0+R4FW\nRERERESkk4kZNw5HfHy73ke7r36junPRXePxefy89dBi8jaWt/pnKNCKiIiIiIh0Msblwj1pEtVz\n5mADbT8404Gk9Y3nknsmENMtkvcfW8bGxYWtun8FWhERERERkU7InZ2Fv6SE+tWrQ1pHQmoMl/z8\nWFL7dGPGc6tY/llOq+1bgVZERERERKQTips8GRwOqr+YHepSiHZHcMEdYxkwNpV5b25g3psbsIGj\nn6tWgVZERERERKQTciUlETNmDNXtPH3PgbginUy9eSSjT+3F8s9ymPHcanxe/1HtU4FWRERERESk\nk3JnZ1O/Zg3egta9d/VIORyGSZcNZuL3B7FpSSHvP76M+hrvke+vFWsTERERERGRDsSdnQ1A9Zeh\nG+14X8YYxp7Rhyk3jaBgayVv/3kxlcV1R7QvBVoREREREZFOKmrIYFw9e4Z0+p4DGTwhnQt+Opba\nSg9vPbSYou1Vh70PBVoREREREZFOyhiDOzuLmgVfEfB4Ql3OfjIGJ3Hx3cficBnefngJ21aXHNb2\nCrQiIiIiIiKdmDsrC1tbS+23C0NdSrOSM+L4/j0TSEyL4aMnV7Bmfl6Lt1WgFRERERER6cTiTjwR\nEx3dYUY7bk5cYhQX3TWeXsOS+OKVdS3eToFWRERERESkE3NERxN3wglUz56NtUc/92tbiYx2ce5t\noxlxSmaLt1GgFRERERER6eTcp2bjzc3Fs3lzqEs5KKfTQfZVQ1u8vgKtiIiIiIhIJ+fOygLo0N2O\nj4QCrYiIiIiISCcX0bMnUUOHUj27403fczQUaEVERERERLoAd3Y2tUuW4K+oCHUpreaoAq0xJtkY\nM8sYs6HxMekA651ljFlvjNlojLm3yfI/G2PWGWNWGGPeMcYkHk09IiIiIiIi0jx3Vhb4/dTMnx/q\nUlrN0bbQ3gt8Zq0dDHzW+Hovxhgn8CRwNjAcuNIYM7zx7VnASGvtaOA74JdHWY+IiIiIiIg0I2bM\naJyJiVR1ovtojzbQXgC83Pj8ZeDCZtY5Hthord1srfUA/2ncDmvtTGutr3G9r4FeR1mPiIiIiIiI\nNMM4nbhPPZXKjz4m77778OTuCHVJR+1oA226tTa/8flOIL2ZdTKBnCavcxuX7euHwCdHWY+IiIiI\niIgcQPov7iHpyiupfP8DNp11Fvm/+nVYB9tDBlpjzKfGmFXN/FzQdD0bnKH3iGbpNcb8L+ADXj3I\nOrcYYxYZYxYVFRUdyceIiIiIiIh0ac7ERHrc978MnDWTpMsuo+Ldd9l09tnk//p+vHl5oS7vsJlg\nDj3CjY1ZD2Rba/ONMT2B2dbaofuscxLwG2vt1MbXvwSw1j7Q+Pp6YBpwurW2tiWfO2HCBLto0aIj\nrltERERERETAm59P8TPPUP7ftwBIvORiuk+bRkTPniGtyxiz2Fo74VDrHW2X4/eB6xqfXwe818w6\nC4HBxpj+xphI4IrG7TDGnAXcA5zf0jArIiIiIiIirSOiZ0963n8/g2ZMJ/GSiyl/6202TZnKzt/9\nDu/OnaEu75COtoU2BXgD6ANsAy6z1pYaYzKA56y15zSudw7wGOAEXrDW/qFx+UYgCihp3OXX1tpb\nD/W5aqEVERERERFpfd4dOyh++hnK334bYwyJl15KyrRbiEhvbrikttPSFtqjCrShokArIiIiIiLS\ndjy5Oyh5+inK33kX43CQeNllpNx8MxHpae3y+Qq0IiIiIiIiclQ8ubkUP/UUFe+8i3E6Sbz8clJu\nvomItLYNtgq0IiIiIiIi0io8OTkU/+MpKt57D+NykXTF5aTcdBOu1NQ2+TwFWhEREREREWlVnm3b\ngsH2/fcxkZEkXXEFKTfdiKt791b9HAVaERERERERaROerVuDwfaDD8AYnElJOBMS9v9JStz93JGQ\ngDMhEWdi42u3G2NMs/tvaaB1tfqfTERERERERDq1yH79yPjTg6TcOo2K99/HX1KKv6ICf3k53vx8\n6tetw19Rga09yOysTuf+ATgxGHxbSoFWREREREREjkhU//6k/fSnB3w/4PEQqKgIht3GwOsvb/K6\nohx/RQWBigp8RUU0bNyIv7y8xZ+vQCsiIiIiIiJtwhEZiSM19fAHjzpAV+T99n8ENYmIiIiIiIiE\nnAKtiIiIiIiIhCUFWhEREREREQlLCrQiIiIiIiISlhRoRUREREREJCwp0IqIiIiIiEhYUqAVERER\nERGRsKRAKyIiIiIiImFJgVZERERERETCkgKtiIiIiIiIhCUFWhEREREREQlLCrQiIiIiIiISlhRo\nRUREREREJCwp0IqIiIiIiEhYUqAVERERERGRsKRAKyIiIiIiImHJWGtDXcNhM8YUAdtacZfdgeJW\n3J90DTpu5EjouJEjoeNGjoSOGzkSOm7kSLTFcdPXWpt6qJXCMtC2NmPMImvthFDXIeFFx40cCR03\nciR03MiR0HEjR0LHjRyJUB436nIsIiIiIiIiYUmBVkRERERERMKSAm3QM6EuQMKSjhs5Ejpu5Ejo\nuJEjoeNGjoSOGzkSITtudA+tiIiIiIiIhCW10IqIiIiIiEhYUqAVaQFjjAl1DSIiIiIisrcuE2iN\nMYONMdGhrkPChwm60xjTy6pvvhwGY8xAY0xMqOuQ8KLvKTlcjd9TfzDGZIe6FgkvxpjRxhh3qOuQ\n8GKMGWaMiQ11Hfvq9IHWGHOBMWYT8DvgWWNMcqhrko7PGPMD4AtgHFCpFlppCWPM1caY1cCfgbeM\nMa5Q1yQdn76n5EgYY8YB3wLHANuNMREhLknCQOP31Argt8DrxpjIUNckHZ8x5ixjzE7gTwSPm+6h\nrqmpTh1oG08KbgSustZeCRQB/2uMGRLayqQjM8ZMBF4C7rbW/sBaW7mrhVbBVg7EGHM+MA240Vp7\nMRAL3Nz4Xqf+XStHTt9TcriafA8NA/5prb3YWrsZ8IewLAkDxphzgFuBH1lrLwIGAuc1vqfzG2mW\nMSYKuAi4xlp7AZAH3GGMGRvayvbodCdZzXSfcAC7uov+B7gEOKfxH0cEAGNMt13PrbXzgYUEr3pj\njLnXGHOeMcatrsfSlDHG2eTlGuBaa+3Xja+fAC4EsNYG2rs26bj2OW52teLre0oOatf5TZPvobMB\nb+N7TwC/NsYcp5Zaaarp+Q0wy1o72Vo73xiTAGxuXMfo/Eaaavo9Za1tAIYAKY2L/kwwX53eUb6n\nOlWgNcbcA8w2xvzZGHMFUAasBK4zxiQBE4BFQA8gI3SVSkdijLkXWGqM+ZMx5sbGxT8GXm7slpMI\n3A782RgzLFR1SsdijPkdwRPItMZFOdbabU1WGQAsaP/KpCNrctykNi5yASvQ95QcRJPzm4eMMdc3\nLn4LONsY8yaQ37jsx8DVIShROqAm5zcPGWN+YK31GmMcxph04GOgnOAFNJ3fyG77fk819jJ7Bxhs\njIm01m4ElgA9gaGhq3SPThFojTEpxpiXCJ4I3ETwnpI7gWTgWaAB+BcwEbgfOJE9V8OlCzPGnAac\nA5wJTAf+zxgz3lq7mGCI/aG19l6CJwg9gb4hK1Y6BGNMlDHml8B1wGhgLOy+gkmT+2Z7AJtCUqR0\nOM0cN+MbW0XyCLbK1qHvKdnHAc5vfmSM6Unwgn0VEGutfQB4APgGGNhRWk0kdPY5v/kE+JMxZrS1\nNmCtLQDOtdZeBdwGDCZ4EVa6sGa+p8bB7l5m2wjmqlMbV58NjKKDZMkOUUQrqAFmWmsvs9YuAz4F\nVgEDrLWbrbU/I3hf29XW2tVALsF/FJEIYKm1dou19gvgceCPANbaJ621ixqfFwGl6LiRYBe/D4Hh\nwNfAqcaY/rvetNb6Gp+OAuYZY4YYY35jjEnZf1fShex73GTTeAJprV1lrb0bfU/J/vY9v/mM4PlN\nBsFj5BOgpzFmYONFtQTAu+sCm3RpzZ3fPLjrTWtteeNjGcF795NCUqV0JM2d3wxsfG8usBM4s3H2\nj2KgEBgUkkr30SkCrbW2HvigySIfMAYoaLLOTmNMb2PMk0AmsL59q5QOKhZI2TVVhrX2QSDNGHPZ\nrhWMMcnGmIcJXq1aGJoypaNovFL5nbW2Bngd6AUcv6tFJDiLhulDMIz8lmDrW7G1tiRUNUvoHeC4\nmdD0uNH3lOzrIOc3xdZaD8FugG8CfzXG/B24nGArrUhz5zc9dp3fGGOcjec3fyHY00jnN13cQb6n\noq21pcC7BAef+5cx5hlgPMGuxyEXdoG26XQGTUcOtdZWNfBlQAkAAAueSURBVFktBSi01m7fZ/Mn\nASfBbhY1bVqodCjGmGOaW26tfYfgKH/fa7L4IYJd1mlseXuN4JXOrMb7BqSLOMhx09D4uBWYB2QR\nHHF014AtUQSvWhYCE621f2uPeqVjOIrjBuDv6HuqSzrIcdPc+c22xvdqG7sb3w8sAyZba2e0ebHS\nYRhjspvck7/boc5vgBEEQ8uu85vv2rpW6TgOctw09z11TOOy76y1vwAeA9YCJzSOsB5yYTNHojHm\nLOB/gK3GmCJr7V3W2sCuUNv43Gmt9RO8olDWuN3U4Nt2JnClThC6nsbRH883xmQ3/gfddTEkovE/\n7mPA7caYRY3vLwAmm+DcbPkEj5vS0FQvoXKQ4+b/t3f3sZZV9RnHv7+ZQXnVmFJsCanEAnYAKYTG\n1hQUazElaWpNgSgWGpPWgErbFFOSCkra/kHS1KRWaR3aojUVS3yhxlCtQUAYCrWIY0tRsA59ARVN\ni4LDBWbm6R9rX+YMnWG8a86cc8/s7ye5ydx9975ZZ99nzl6/vddZ65nvN9cCJwInVNVJtKHpNwAn\nDJ+P1Ih05ualwFKSjwKv9zo1PivIzWT/5jXtx/lski/gE7ZRmegXfxW4ZGJ7Ac95lv7NK6rNhP11\nWv/mOzNvvOZmD7mp3Vyn1lfVCcDjST6W5Pp5tP3ZrOontMPQvbVV9WbgCuCPgcuAU6vqLGhv9MPJ\nfzGwPDX5K4DnVNWfAe+kTbaBnYRxGP5TTvohWpFx5vLwviE3Twy5uQ64Bbisqt4KbADWJnkyyZLF\n7DisIDc7vd8k+R5wP20EyBVtU2IxOw5TzM2WYbvXqRHozQ0792/eBSzNqs2av6FfvKaq3kDru/xp\nkt8Y3k+eXn5nD/2bNUmeSvKYxew4rCA3e7pOPT6fV7Bnq7agnTi522iPvE9L8ne0N++HgXtqWCOp\nqt4BbKTNDgltGMVPA19J8rNJbp39K9A8LOdm+PfyGlp30IbxnUebyW9530tpM0aeRrtZcg3wMuDG\nJBfOst2ar47c3A68fLhI/ATw28AfJfnxJDfMtvWalynm5hhzMx6dubF/M3KTRQfwEPDXwNeGn51b\nVUfRhg8v94vvxP7N6HXkZiMLeJ1alUOOq+pttMV6Pw98JMm/DdtPod0lWAdcSrtT8Hbgu8DxaTO1\nQXtE/tYMM7hpHCZycwtwXZKHhmHDv0CbgvxI4PVV9U+06ce/Bxw7kZuNVXXHcBNFI9GZm/XLuamq\nB4CX+mRtXMyNeuxtbrB/M0oTubmVVpDcRlv/86pqc8t8GXgDbTLUC2n94uPs34xbZ26OX8TrVCWr\na5m7qnodbWz37wIX0KasvzrJpuEx+NYk/1lVhwIPAmckuXs49oAkT82r7Zqf3eTmr5J8sap+P8k7\nh6EW76fl5rQMs84Od8i3Z7X9Z9A+t5e5WZcdS/RoRMyNeuxlbuzfjNQucrOFtgTPY8BvAn+b5O6h\nQLkf+Lkkm4Zj7d+M1F7mZuGuU6uxoL0S+GqSa6rqRcDZwIlJ3rSLfTfQ/iA3Tg7h0fjsJjfrk/x6\nVf0z7Wn+YbSF6LcCFyVZMjfjZm7Uw9yoh7lRj13k5lzgJUNuDkxb2ml536uBv0lys7kZt7HlZtV8\nhrbq6QkSvg68ESBtWvpPAQdX1Wufsf9ltM+S3DPsu3AnX3tvD7l5QVW9nHZH6vYkJyc5H/gRdkxB\nbm5GyNyoh7lRD3OjHs+Sm08Cz6uqX3pGUXI5rV9877CvuRmhseZmbgVtVf18VZ26/P3ECfwosGWi\ngP0mcDPDG3tVnVVVtwHHAWcn+ebsWq15W2FubgROp911unTi17xueZi6xsHcqIe5UQ9zox4rzM1N\nwPHDcadX1U20fvGvJPnWDJutOTM3zcwL2qo6par+HvgEcMzE9gJIWyLl48Bbhsfe3wUOBQ4adr0X\nuDDJBUm+MdvWa146c3MIcFCGNbVqx5p+LnMwEuZGPcyNepgb9ZhCv/gB2kRh59svHg9zs7OZFbTD\nG/UG4GrahAcfZsdT13XLdxSq6mDgH2hTS2+oqiOBU4AnAZI8kORfZ9VuzdcUcrMVIMm2tCnLNQLm\nRj3MjXqYG/WYYr/4vzKsBqL9n7nZtZkVtGlThX8aOD3J9bQ7Cq+q9sHkrQBVdcWw/YXAJbRppD8M\nPAJcOau2avUwN+phbtTD3KiHuVEPc6Me5mbX9uk6tFX1SmApyZ0AST4+bC/aHcn7gIOBpao6gjaO\n+y1J/n34FZdV1cFJtuzLdmp1MTfqYW7Uw9yoh7lRD3OjHuZmz/ZJQVtVhwEfBM4Arq+q+5L878S4\n7lTVV4BXAwcO2x4GzhuOX7M87GZ/PvnamblRD3OjHuZGPcyNepgb9TA3P7h9NeT4SeBzwK/Sxm6f\nA+3EDyd/TZL/Bu6krcP2tMmTr9ExN+phbtTD3KiHuVEPc6Me5uYHNLUntFV1AfAfwKYkj1TVXwDb\ngcOB06rq5iT3LZ/gqloH3A98f/L3jOnky9yoj7lRD3OjHuZGPcyNepibPnv1hLaaH622jtGv0Rbw\nfV9VHZ5kKcmTwD8CDwPnQjvBwx9hK3AYcPRevQItHHOjHuZGPcyNepgb9TA36mFu9l53QVtVa5OE\ndhIfTPJq4CLgf4ANy/sluR+4Cziyqo6pqgPZsQbS7yS5vLv1WjjmRj3MjXqYG/UwN+phbtTD3EzH\nioccV9Va4A+AtVV1A/A8YBu0qaSr6reAh6rqlUluGbZ/oqrW06aZPhR4FXDv8AfUCJgb9TA36mFu\n1MPcqIe5UQ9zM10rekJbbdrou4AXAF+j/SGeoq1/9DJ4esz2FcPX8nHnAO8AbgJOSnLvFNquBWFu\n1MPcqIe5UQ9zox7mRj3MzfTVSor6qjodODrJh4bvrwL+BXgcuDjJqVW1BjgCeA9waZLNw3EkuXXa\nL0Crn7lRD3OjHuZGPcyNepgb9TA307fSz9DeBVw3PCYH2Aj8WJIP0B6ZXzzcUTgK2JpkM7QT78kf\nNXOjHuZGPcyNepgb9TA36mFupmxFBW2SLUmeSLJt2HQm8O3h328C1lfVp4Brgbun10wtMnOjHuZG\nPcyNepgb9TA36mFupq9rHdrhjkKAFwKfHDY/CvwecCKwOcmDU2mh9hvmRj3MjXqYG/UwN+phbtTD\n3ExP77I924EDgO8AJw13ES4Htie5zZOv3TA36mFu1MPcqIe5UQ9zox7mZkpWNCnUTgdW/Qxw+/B1\nTZK/nGbDtH8yN+phbtTD3KiHuVEPc6Me5mY69qagPQo4H3h3kiem2irtt8yNepgb9TA36mFu1MPc\nqIe5mY7uglaSJEmSpHnq/QytJEmSJElzZUErSZIkSVpIFrSSJEmSpIVkQStJkiRJWkgWtJIkSZKk\nhWRBK0nSHFTVtqr6UlXdU1WbquqSqnrW63JVHV1V582qjZIkrXYWtJIkzcfjSU5OcgJwJnAW8K49\nHHM0YEErSdLAdWglSZqDqnosyaET378Y+AJwOPAi4EPAIcOP35bk9qq6A1gPbAY+CLwHuBI4A3gu\n8L4k75/Zi5Akac4saCVJmoNnFrTDtkeAlwCPAtuTLFXVscC1SX6qqs4A3p7kF4f93wwckeQPq+q5\nwEbgnCSbZ/piJEmak3XzboAkSfp/DgDeW1UnA9uA43az32uAk6rq7OH75wPH0p7gSpK037OglSRp\nFRiGHG8DHqZ9lvZbwE/S5rtY2t1hwMVJPjOTRkqStMo4KZQkSXNWVT8M/Dnw3rTPAj0f+EaS7cD5\nwNph10eBwyYO/QxwUVUdMPye46rqECRJGgmf0EqSNB8HVdWXaMOLt9ImgXr38LOrgI9V1QXAp4Hv\nD9u/DGyrqk3AB4A/oc18/MWqKuDbwC/P6gVIkjRvTgolSZIkSVpIDjmWJEmSJC0kC1pJkiRJ0kKy\noJUkSZIkLSQLWkmSJEnSQrKglSRJkiQtJAtaSZIkSdJCsqCVJEmSJC0kC1pJkiRJ0kL6P/6AGRdf\nAAyvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41cde15f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_tc_long_top_df[total_factors + ['res']][-30:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41bcf30b8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UdJPUmDBdN7G/Xl9foLyyWrvjAPByf19zUHtK6/TTi4cpJMj7J5sHBJhT37cLcwAAgC5w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\nJKmttEyIveuhJ1n850X882v25I0HTq+7HEmSJEl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FxKY8fY+iD2XmJ4ekerUE+0ZV2Deq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41d2b5d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_tc_long_top_df['res'][-30:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor Performance (risk neutral)\n",
    "---------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'alphamind.portfolio.linearbuilder' from 'd:\\\\Anaconda3\\\\lib\\\\site-packages\\\\alpha_mind-0.1.0-py3.6.egg\\\\alphamind\\\\portfolio\\\\linearbuilder.py'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphamind.portfolio.linearbuilder as lb\n",
    "import importlib\n",
    "importlib.reload(lb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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      "1: Factor DivP is finished\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2: Factor CFinc1 is finished\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
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      "3: Factor BDTO is finished\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimal\n",
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      "4: Factor RVOL is finished\n",
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     "text": [
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    {
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     "text": [
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      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "optimal\n",
      "5: Factor res is finished\n"
     ]
    }
   ],
   "source": [
    "marke_netural_pos = {}\n",
    "\n",
    "for i, name in enumerate(total_factors + ['res']):\n",
    "\n",
    "    lbound_exposure = -0.01\n",
    "    ubound_exposure = 0.01\n",
    "\n",
    "    def get_benchmark_match_pos(x, name):\n",
    "        er = x[name].values\n",
    "        bm = x[index_components].values / 100.\n",
    "        lbound = 0.\n",
    "        ubound = 0.01 + bm\n",
    "        risk_exposure = x[risk_factors_names].values\n",
    "\n",
    "        status, value , ret = lb.linear_build(er,\n",
    "                                           lbound=lbound,\n",
    "                                           ubound=ubound,\n",
    "                                           risk_exposure=risk_exposure,\n",
    "                                           bm=bm,\n",
    "                                           risk_target=(lbound_exposure, ubound_exposure),\n",
    "                                           solver=None)\n",
    "        print(status)\n",
    "\n",
    "        if status != 'optimal':\n",
    "            return pd.Series(np.ones(len(er)) / len(er))\n",
    "        else:\n",
    "            return pd.Series(ret)\n",
    "    \n",
    "    look_into = risk_factors_names + [index_components, 'Date', name]\n",
    "    res = total_data[look_into].groupby('Date').apply(get_benchmark_match_pos, name=name).values\n",
    "    marke_netural_pos[name] = res\n",
    "    print('{0}: Factor {1} is finished'.format(i, name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_df = pd.DataFrame(marke_netural_pos, index=total_data.Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret_mat = (pos_df.values - total_data[[index_components]].values / 100.) * total_data[['D1LogReturn']].values\n",
    "ret_df = pd.DataFrame(ret_mat, columns=pos_df.columns, index=normed_factor.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41d74a8d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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54YfNIbxLih9bPstm7/p8hs6IPWO7ZXl1RCT4UpJTQ0NNE5eNjiCqhx+LntxOVXEDxLdp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\ndM7SFxxXb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hVcwlyhnEPJLHnjRW58/j90SOh2wfOs+OBt/MIj6X3tjQA47HbqKyvYt2IJ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\neR749aNeFwnsxG7EzbsHszgjjc3vvYmDhycPLlzcKsmQJEkc2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GGw2QAw7N9v/7h3HwAO\nyiNQdZTKUjNra17GI8CRydeEETlwPAquguYq/J58gyOlIchKFbq4OAwHDgDQvC0FFAqcxo5FGxGB\n8fAhslPLqSlpZspN8VSXNJG2Oh8PfyciB3qTvqEQhVIib39V29iGXhZB8sWhSJJEy969lD7yCPqE\nBPwefQSnceM6rWhssBgwDIgGtZqWtD04XzWXj+IqWGVI4f6Grcz1m2sPgg21KNY9jmdYPuV73Kj3\ne5CW/OV4Xj/njK/n8QN7MTY3ETd6POED2reIWfveG2Rs+Anv0HCC+saT/tNqjM1N6Bx7Z81w8ZEs\nAAJEUNrOskPLWHZoGQviFjApdNKFHk6PyKlspqqpleERnmc8bnCYB7eNi2RSXx+SQ9u/uaJTK3n5\niiRCPBxICOrl1kqCIAiCIPymRFAqUPXWWxj27SPw1VfQ928/VVDl441h/36slVV43XEHntctBEAT\nGkprdrb987CwXhlXc30rX/47FVcfPRPDI2jeuRPZam1XSdZ48CAAjiOGYziwH1mWMezbh9LDA3Xr\nEYicQLl5JlTDJbf3x8XrZFZFCc6++IU6kJmppqaoAV2/ftR98w2yzUbTtq3oExJQurmh6xtL046d\n7P3pOB4BjkQO8CZygDe1pc1sX3GMjM1FNFbbCyaF9PMkYWwgAGH97WthZZuNkof+iTowEO3rz5Aj\nNVJauI7S5lLKmsvaPpY1l1HXWgfAK2/+g6jIQdy15zFy63OJcY/hqZ1PoVaomSU7wMo7oaUat2tu\no7o4hdI3PgPA/aqrzviaFmTsR6XREpLQsRrx+AU34ebrT7+xE6kpKWLfmu8pPpxFZPKQc/8mnkFR\nViZO7h64ePv0yvX/iDYWbOQ/u//DxJCJ3Jd834UeTo/ZkVsNwLCzBKVKhcSiqbFd7r+kv3+X+wRB\nEARB+OMSQelfXGt2NtUffIjrjBm4TJ3aYb/ax5emSnv2z2Hw4LbtmjB7UKoK8Eeh753pc/WVBvvH\nCgM5kf3wbG21FyIKOVU0yJCRicrHB6cJE2nevgNLaSkt+/ahT+iL1JoJMdOoOjYMjb4cZ8+OLVZ8\nk+IgE4p3HyA0Nga5pQVjRgbGAxl43WFvw6GNieXYtjxqSpqZtLAv0omqoRMXxtFQtZfKwkYm/q0v\n9VUG+o0KwMm9/X0M6fsxFxSwYWEC766Z2W6fs8YZP0c//B39SfROxM/Rj7X5a/lX6YdYit9FpVDx\n9qS3GWZVc13KQ7yY+gKjCwrxcgmCa75CEZCEV8MXlC1ejPPUqagDA8/4mhYdOkhAnxiUqo5FmtQ6\nHUNm2DOtWgdHlGo1hQcP9EpQWlNSTPbu7SRNvuQv2X+0MwerD7Jo6yL6efbj2dHPtqsy+0e3M6ca\nf1cdoZ4OF3oogiAIgiD8Domg9C+uftVqkGV8HlrU6f6T7VgAdP1OTbM8mR3Vhkf02thOZh8VSoka\niwue2IsxnQxKrU3NNG3ciNOE8egT7Zm/xp83Yi4owH1CIhgBv/5UbWzEK8ip0+DHNXkcfl8sJ21L\nOCHX9gGgeslSkGWcRo20P2tMDLkRapydJaIGn1r/qdYomXFPEnUVBnzDXLp+jvXrQaViuWc2E4In\nMDdmLn4Ofvg5+uGk6Tg1dnTgaK5afRXRbtG8Ovp5AjY9D+nLWKxWMSMwgK90cNu0FyEgCQC32bMw\n5efjduUVZ3w9jc1NVBzPZcScq894HIBKoyEgOpa89D2Mvnohyk6mGp+PlC8+RaXWMHTWmcf8VyHL\nMk+kPIGr1pU3Jr6BXtX9N3oKa1rIq2pmdLTXBQ3wD5c14Omoxdu5fYGt6qZWNhwuZ9aAQPEGhCAI\ngiAInRJ9Sv/izMXFqP38ULl3XkH3ZFCqCQ1F6Xyq5cLJoLQ315OeDEoDot1oMdmDotZjOW3767/9\nFltTEx7XXIMupg+SRkPNp58AoPexARI2775UFzfhFdz5ukhJ78a4xIOYTAoyspWgUNC4di0KV1d0\nJyry5tZ70ewYQH+fcpTK9r8yWgd1lwGpbLViqamhcf16zANiadBYuCLmCkYFjiLKParTgBQgxiOG\nH2f/yLKxrxLw9c2QvgxG3094+CRiTSZSndwgZPipZ9Bo8H1o0VkLThUfzgJZJqhv59VPfylh4hRq\nigv5eck77fq3ni9DYwPZu7aTNOUSHN16t3LzhdJibsFsM9NqbSW9Ip0tRVt4dNujLD+8HIPF0OH4\nHSU7OFJ7hNsTb8dL37GvbGdkWeZf3x9k9PMbWfDRbjYd7bw6dW8yW22U1BkwmKzM/G8KE1/axA8Z\n7dd+f7LjOEazjRtG9X5BNEEQBEEQ/phEpvQvzlxS0q6w0S+pfO1Bqa5f+0CmLSiN6MWgtMaI3lmN\nu58jFfkNqPz8MOUcQ7bZaFy7jup330WX2L8tS6qLi8OQno6k0aDTloBHBPX1SiwmG15BXfcw9Bxx\nEX77D1OVqyQwLAxTbi5OI0cgKZXk7K0gZVUxHs25eFUcBbpesynLMsbMg1S98w6GvXux1te3FWE6\nMnEUKoWKAT4DuvXsfo1VsNxejIm5S6HfLCg/yKAvLuELVx2tshktnbd8+eWYGiorKMzKYPe3X6HS\naPGLjunWGPqOGkdlQT6pK/8Pj4Agki+ZefaTuiFvXxqybKPP0JE9cr3fiyM1R9havJVtxdvYX7Ef\nN50bGoWGkuYSAPQqPStzVvJ2+tvM6zuP0YGjyavPY2r4VD7K/AhvvTeXRFzS5fV35VZz5/J96NQK\nXHRqlAqJA0X1zBsSwrZjlbyw5ghjo71RKHo3G2mx2tiZW8OqAyWsOVhGg8HM49PjMJpteDhouPOz\nvdwzqQ/VTa3sL6rnYEk9k/r6EuUj+ogKgiAIgtA5EZT+xZlLSnAcNqzL/SfblfwyKNUnJOBx3XW4\nTJ583mOwWW2sX3qIpEnB+ISeyjo2Vhtw9tDh7KnDZLSiiIylOTWVvDlzaM06hCYyEr9HH6O6pInK\n4424J/bHkJ6OLj4eRVUm+CdRfKQWoMtMKQDRU3DRPE9RtQFtTB9Mubk4jhyFzWpj45K9+HhBsjUd\n0+GCLi9hbWyk4PobMGZkoHBxwWXKZJReXqg8PFF5e/Ok4X2S9Ek4qLuxpq72OCy5GDSOcP2PEHAi\nkPXtx5CJ/+bTtGc5UHmAwX6DO5yauzeVnV9/TnN9HV7BIVTk59JUYy8y4+4fwIz7H8GisPHY1odo\nMjUxPng8s6JnoZA6nzQx+qoF1JWVsOnTD3H19Sdq0NAuh1127CgWk4mguDP3Ns3ZsxtHN3d8I6LO\n/lr8QfyQ+wOLttqnwMd6xDK/33yO1hyl2dzMvcn34qxxZpDfIDKrMlmSuYS30t/irfS3APjq6Ffs\nq9jHQ0MeQqPsul3KaxuysdlkBoV6UG8wU28w8/eJ0dwzKZqV6SXc80U6D31zgMcv7YeTtnf+azea\n7RnRw2WNOGqUjI/1YXVGKS+vO4pCghV3jOTW/+3h5XVHcdAoiQ905bqR4dwosqSCIAiCIJyBCEr/\nwmSTCUt5+RkzpZrISHwfeQTXyy5tt13SaPBd9GCPjKO+0kB2ajluPvr2QWlNK56BTricKFBkCY3D\nlrIJSaki4D/P4TJ9OpJSyaZ3MsjbX8m8afaMqUP/OKhdRV3kjWz/Jge/CBc8A88QlKrtgW9TgQ5N\ncgKsXYfjqFGU7jtMq1nDAM1SHPsmU71tE6bcY5hWPoN++s0oo09Noa1f8S3GjAx8HnwQtzmXo3Q5\n9Ry7S3eTufYI9/e5v8shyDYbhVkZZG1eR3HaJmYGa/C8dQ24h7U7bmD0dKS050gtS+0QlMo2G+s+\n+C8KhRLf8EiqiwoIjIkjsG8/AmPi8AoJRaFQsjZ/LatzV+Pv6M/mos2szFnJY8MeI9o9usO4JIWC\ni++4j8aqf7L69ee56l/P4xse2f6+ssy+H79j06cfolSrufH1D7qclmu1mMnfv4eYEWOQFH+e1QMr\njq0g2DmYj6d+jLeDd5fHJfsmk+ybTHZtNodrDpNWnsY32d8Q7xnPVTFdZ+EPFNWxPaeah6fFcvOY\nyA77LzvRt/OtTTmsPlDKjAGBXD0khPjAnm2d8ubPxzhc1sh/Lk9gRlIgOrWSynd3sCuvhsQgV3xd\ndHx1y3CKag0Eezig7OWsrSAIgiAIfw4iKO0lpsJC6r78Cq/bb+u16rTny1xeDrKMOrDroFSSJDzm\nX9ur46irsK+xa643tW2TbTKN1UbC+nu1Vc1VjL8E/+GxOI8bh6S2V4+1mm0UHqpBlsEc0R+Vvz9O\nicGwC3Zn2yvlTr4x/qxTGp393KFAgXrSVMLHjELt60PeZxtQ4EGw6SekSTdT++mn5F8xF2uTEfXy\nnQR9uAxdwkBkWab2iy/Q9e+P5/XXYbaZya3NpqKlgoqWCpYeXEqgUyBXxlzZ4b5VBflkbf2ZQ5vX\n0lTfhEZhxWKTyHC5jHG/CEjrysvI2rKBK7eFkXt8PSTdDoDJaOB4RjpKpYqm6iqm3fkP+o4e3+Wz\nbi7ajIvGhdWzV7M6dzUvpb3EFd9fwcL4hdw14K4OWVO1VsfMBx/nfw/fy/evPMv8515D6+AIgNnU\nyvr33iRr60ZC+w+gIHM/u7/9irELbiAndRdKjRr/qBj0zvYg/dC2zZgMhj/F1N2ixiLeTH8TT50n\nqWWpLOy38IwB6emi3aOJdo9mStgUfBx8uDTi0jNW2/0yrRAHjZJ5Q0I63a9QSDw4NZYp/fz4dOdx\nvtlbxGe7CogPdGHekBAuSwzAWdex4vKvUdFo5N0tOcweEDMWDPMAACAASURBVMiVg0+NY1qCP7vy\nahh6ot2LSqkgzMvxvO4lCIIgCMJfiwhKe0FrdjbHF16HtboaXf8EXC666EIPqVPmYvtat7O1Eelt\n9RUtADTXtbZta2k0YbXYcPHUtQWlLSZ1h9ey5Fgd5lYrAI2tGqI3/gy73sUiq8nPUxE9xAdnj46t\nYH7JJTgIdkNTYT7uY0aALJOfpyDIKQ+Nwgi1u/C59y7Knn4O1zg1TblG8q+eT8B/XgCFElNODv7P\nPossy9y54U62l2xvu7ZGoeGV8a+gU9nH0VRbw+GUzWT9/AOVxaVIyIQ71TA2tInIoaNYdUBDdn49\nY2W5rVrp4e1b+OH1F5GR0bvo0R5sZvvar2nKL+FwyhbMRgMKpRKVRkvk4K6nY1ttVrYWbWVU4CjU\nCjUzo2YyNmgsL6a9yAcZH+CgcuCm/jd1OM/RzZ3pf1/El/96iO9feY6ZDzyG2dTK/z39KBV5OYyY\new3DZl/J2vfeZO+P33EsbRcNleWAPdsa1DeeqMHDSV+7Gu+wCEL7d29t7fnYU76HZnMzQ/2HolWe\nff1tV1KKUzhaa5+K22JpocXcQllLGTtLdiIjY5Pt64YvCv31v+capYY7ku4463Hbj1UzLMLzrIFl\nYrAbicFuPDY9jpXpxXy2q4BHVmTyzOpDXJYYwLwhIfQPcj2nKrhbjlZhtsrcMLr9VNxpCf78b+dx\npiWIHqKCIAiCIJwbEZSeI5vJRMN332E8ehT3q+ahPa3gT/mzz4HVHiiZcnKhi79VZZuN1uxjSBr1\nWSun9gZzcTHAGafv/hZOZUpPBaUnK+86e+jQOapRaRRt2053fH85CsmCTVZRW1wPA32gLINCRmM2\n2Ygc0L3MlXNULJBHY0EBMILajD3Um7yJiW/FZBmIJmsl7gP/htNlZajvXIN53w8Uv/Apxff9A0mt\nRhcfj8u0i0ktS2V7yXYWxC1gUugkvPXeeDt4o1VqKcrKZOdXSyk4dBhZBj9dI+P9qokdmIDDoJug\nz1RQ6+jjtYHct15h+1efIUnQUl/HwU0bCIiJZdpdD1BmruSLRfex48MlqLRaYoaNxic8ki3/+5Co\nwcPQ6LrOzB+oOkBtay3jgse1bXPXufP0yKex2Cy8mf4mQ/2H0t+7f4dzA2P6MummO1j77ht8+8JT\nOLi6UVWQz4wHHmtbazp+4U04ubuTf2AfI6+8FhdPb/IP7CV79w42Ln0XgOn3LOr11iBVhipuXXcr\nRquRQKdA3pr4FhFu7dsX2WQbJqup7c2CzpQ0lXD7htvbAk8HlQMOagecNc4s6LeAeTHzeGXPKxxv\nPE6cZ1yX1zkfZfVGcquauXpo51nSzrjq1SwYHsb8YaGkF9axfHcBK9NL+Dy1kDh/F+YNDWFGUgAu\nvyJ7ujW7Ei8nLX392leb9nbWsu6+sd2+jiAIgiAIwi+JoPQcVb3+OtUffAgKBbXLPsNt7hy877gD\nU0EBzdu34/Pgg9T871Nac3I6Pd9mMJB/9TW0HjoEgK5/f9xmzcRl2jSUrj27Dqwr5pISkCTUfn6/\nyf26UlfeMVN6bG8FkkLC80R/UWdPPQ3VHVtplGdm46fKp8HqS22BGoiGsgxybFegdVARGNu9liNO\nwSFIHKOxtAaA/M1pyHIYu3f9SIbah6u8NuDQ9CLq0BgIHoI6MJnQip2U/3AMgy2K4LffQqHV8t6B\n9/DUeXLXgLtOBTuWVsw/PMLKZftQYWaIRzl9+wbhOXwhxM0AB492Y4kcNBS1Ts/Or5cDoHN2wT86\nhun3PoSDiysueHNkrBb3Jg0v3LgUnaN9qmRk8hD0zmeucPrugXdx1jgzKnBUu+2SJPH48MfZXbab\nl9JeYunUpZ0GjgnjJ6NQKFnz9qsgywy6dHa74kcanZ6RV85n5JXz27YFxcUz6qoF1JQUUV1UQNTg\n4R2u29OWZi7FZDPxxPAneHPfm1yx6grGB4/nkohLGBkwkvTKdJ5PfZ6Klgq+m/kdrtrOf+c+O/QZ\nEhKrZ60myDmo04JQz499Hpts67VAe0duFQDDIz1/9bmSJDEgxJ0BIe48Oj2OleklLN9VwGPfZvLv\n1YeY3t+fhy6OxdNJy57jNXywNY+rhoQwOsqr3ZR3m01ma3YVY/v0fnVfQRAEQRD+ekRQeo4MBw+i\ni48n+N13qHrrbWq/+IL6ld+B1YrS2wv3eVfRvH07pi6C0spXX6X10CF8H34Y2Wal/psVlP3rScqf\nfQ7nSRPxeeAB1P69Ox3OXFKCyscHSdN1xc/fwsnpu4ZGM1azDZPRwsEtxfQZ7Ns29dbFU0djTcdM\naVO9lSB9M0pjMXVlrmA1Yy0/Sl5dFBGDvFAqFcg2G4dTNuMbGY1HQFCnY1ColDhpm2moMYLVQn4O\naG07aGhqxKxSs8KYwFXKfVgHXs8POSsZ6jcU/7nv41c1CnT5sOZmclVKdpmO8PfQ6ejW/wtkG9is\nUJpO5oEijJYorpw9nKApN4Jb11kvnaMTN77xAbLNht7ZBYWy41rDy0Zdw1M7n+K7otVcEXMFAK4+\nvmd8nVOKU0gpTuH+QffjrOkYvDqqHbm1/608vetpthZvZUzQmE6v02/sRJRqNUe2b2H4nHlnvOfp\nPAKCunz9z9Wa/DVkVWVhsBiwyBasNiu1xlq2lWxjWvg05vSZw8iAkXyY+SE/5f/Emvw1OKodaTY3\n4+vgS42xhmWHlnH7ifW5p6tvreeb7G+YHDqZEJczZym7ql7cE1KOVePmoO6Qofy1XHRq5g8L5dqh\nIWQU17N8dwH/t6cIlVLBs7MTWLargB8zy/gxsww/Fx3T+/tzaWIA/YNcOVjSQE2zidHR3euhKgiC\nIAiC8GuIoPQcmYtL0MfHo/L0xO+xR/FYMJ+q995DodXisXAhCr0ebWQktWlpyDZbu0qj5rIyaj75\nFLd5V+GxwJ5R8vjb3zBmZVG/4lvqvvwShaMT/k892avPYKmqQuXdvemtvTYGk5Wm2lacPLQ01bTS\n3NDKwS0lWMw2ki8ObTvO2UNHWW59u3NtVhvNZiccwzxQl+ZwuDYeufIoRYZYTGY1kQPsPVaPpe7k\nhzdfAsAvqg/9xkwkZuQY9E7tAzNnV4nGWg3G1P+jxBCMxbCeSj8bjgODsf5g5tO8gVQf3kyZx0+8\nM/BlHr74acbM/Yi0rf/mX9Y8glqMqLQqZm77AFCASguSAptSz57WJPyjAwm84l/QjYyag8uZs+Vz\n+8xl/fH1vJj2IsMDhhPsHNy2T5ZlrLIVleLUr7fVZuXFtBcJcgpiXmzXgeTsPrP5JOsTXt37KqMC\nR3UZbMWOGEPsiM6D1t/KtuJtPLD5ATQKDQ5qB5SSEpVChV6lZ26fudyWeBsA/k7+PDrsURYNWcSO\nkh2sO76OMJcwrul7DYu2LOJ/Wf9Do9QQ5hJGgFMAgU6BKCUli7YswmA1cH3C9RfsGWuaTaw+UMq0\nBP8ey1BKkkT/IDf6B7lhtcmsTC/mn9Ni2ZVbw6S+vlya6M/3+0v5eEc+H2zLY2wfb7ydtWhVCsbF\n+PTIGARBEARBEE4ngtJzIFutmEtLcZkypW2bJjSUgGeeaXecJjIC2WjEXFKKJuhUMaGW1DSQZdzm\nzGnbJkkS+n790Pfrh/HwIVpzc3v9Oax1dSjd3Hr9PgANVQa2f3OM8ERvogf7olBImAwWDmwsAiCw\njztHdpZRU9xMxqYiopJ9cPc7VcHT2VNHa4sFk8GCRm//sTWUlyOjxMnTGdlow1yooiknixzjMNRa\nCQfnJg5u3s/RndtwdHdnwLQZHN66iQ0fvc2mT94nYuAQ4idcRMQAe2sVtyBvjlUqyFv1OZbW/ljM\nRnaHl9OirmHx2Dkc3LKZ7KBGIqtcGbVbz98Vd/K3pOtJ8fIkv6aSfJ2aCS0GvNwj4YafQGcPLI9u\n30J96vOMvXFOj03xlCSJJ0c+yayVs3h026MsmboEhaTg44Mf837G+yglJcumLSPIOYiKlgq+PPIl\nx+qO8dLYl87YC1OtUHPXgLt4YMsDrM5dzaWRl3Z57IVktpr5z+7/EOoSyorLVqBWnn1tpFqhZkzQ\nmHYZ4L8P/Dv3brqX1/a+1uk5Twx/gliP2F81tnqDmeuXpqKUJC5PDsTbWcuE2DNnsbuyJCUPg9nK\nrWMjzn7wOZg/LIwv04p4Y0M2xXUGbhodzoykQGYkBVLfYubjHfm8vO4oAAuGh+LheGFnVQiCIAiC\n8OckgtJzYKmsBLP5rFVrtVFRALQePdo+KN2ThsLREV1s53/sasPDadzwc88NuAvW+no0oaFnP/A8\ntbaYWfXmfmrLWsjZW0naD/kkXxxK1rYSSo/V4+SuJXKgD0d2lrFzZQ7mViuDLg5rd42TFXgba4xt\nPUebCo4D4OTnhYOkhUIozioj1ziMsGQv0lZ/zaGtGwHIjGzkrdoncU92J6SfOyEFWswZu8jevR3X\nK0cy6+KbCR0cRdZeI7urL8Zq/JZiLyPuEWFU1h5FeXEi29w3YdO7sihyMSueeYIr85P4UPkhSHB7\n0u1sL97O9ZGXQ8jYtoBUlmVSv/sad/9AopKH0pP8HP14aMhDPJryKJ9mfcqCuAV8cvATfB18KW0u\n5fqfrkchKShushe0Gu4/vFsVYieHTebDzA/5IOMDpkdM79WiRMdqj/H5kc+pMdZQa6yl0dTINX2v\nYVb0rDOet6loE/kN+bw6/tVuBaRdiXCLYOXMlTSYGihuLKakqYTipmJMNhOJ3okdesGeTYvJwvVL\nUzlQVIerXs2irzMA+PesBApqWpjcz5eBId1b52yx2vh053Gm9PMl2vfMa4XPVUKQK0PCPXh/ax5A\nW1sXAFcHNXdNiGJfQS3bjlVx0+jeCYwFQRAEQRC6FZRKkjQVeA1QAh/IsvzcL/bHAkuAgcAjsiy/\n2NMD/T1pq1rbjaBUcnCg+J57cJ4yBbc5c3AYMhjDnj3oBwxA6mStIIAmLBxrTQ3W+vpeLXr0W2VK\n928opLa8hRn3DqC1xUzq6nw2LLUXeLro+jiiB/tibDYDUF3cTESSd1vgedLJoLSh+rSgtLQUcMUx\nMBBP5wC0O5tIy/ClVXYmKtmPjZ9lg0KBLMnkBLdwS+It1BhqqDJUke9TRXWskRGrFRRvWM8qxS7e\nH/8BSoWVupZiZNnIsTgLH130LjO+ncETO5+g0dTIm6PeJDx4ACOvvJZtn3/CothrKekjcUv/W9qm\ni55kbG7ip7dfoyIvh8m33t1uCndPuSzyMtYXrOf1va/j5+hHhaGCOwbcgb+jP8/seoYotyiujr2a\nZN9kYjxiuhVgKiQFV8dezePbH2dP+R4G+Q1q2/fMzmfwc/TjhoQbemT8H2Z+yJq8NQS7BOOudUdG\nZvGOxXjqPbtc0wqwLn8d7lp3xgb1TNVXF40LLp4u9PXse87XMJqt3PzJHvYV1PLfqwcyLsaH0noD\n9365n4dX2IPTj7bl8ezsBC5PPvva2n2FddS1mJmR1Lstm56/vD/TXt+KWqkg5hfBryRJvH1tMiV1\nBoI9HHp1HIIgCIIg/HWdNSiVJEkJ/Bd7Y5MiIFWSpO9kWc467bAa4G5gZq+M8nemu0Gp0sWFsM+X\nU/f5F9R//z0N33+PJjwcU14eLpdc0uV5mhPtYUx5eeiTknpu4KeRLRZsDQ29XulXlmWy0yoI7ONG\nUIw9QxSR6E1+RhU2m9y27lPnqEahkrBZZAZNC+twHRdPe5uT09vCNFXUAq44BYeisE4lUPslucah\nqJQWgvq6UVtSTFZIPUdjWxkcNqrTfpApLGPnN8tJqyjh5QMvMSxsCsf2plHjqmTh5Lvx0ntxZcyV\nrDu+juvjr28LlIbMmEPJ0UPkr97B/KH/QSEpMJtaqSkuws3Xj9qSYr5/9T801VQx9trriR/XO71q\nJUniieFPMOPbGTyy7REARgSMwM/Rj1WzVp3zdaeGT+WFtBdYenAp/b37o1FqKGkq4fMjn6NSqLgo\n9KKzFv/pjr3lexkfMp6Xx70MQIu5hb+t+RuPbnuUb2d+i4fOo8M5RouRzUWbmRYxrd262QvJYrVx\n9/J9bDtWxYtzE7n4RM/OCG8nXrkikSdXZXH1kBCWpOTzj6/2c7SikQenxKKQ6PKNgo2HK1AqJEZG\n9W5xoTAvR96dn0xdi7nTdas6tZIIb6dOzhQEQRAEQegZ3fmLbghwTJblXABJkj4HZgBtQaksyxVA\nhSRJXUdaf0DNO3diPHQYXVwcuri+KE+02zjV3/Ps1XF1ffrg9/hj+Dz4AI0//UTVO/ZejQ5Dup7K\nqQkPA6A1L7/XglJrYyNAr2dKqwqbqCtvIWnSqUI8kkIiPLF9gSVJknD3dcDFS493SMepinpnNUq1\ngsbT2sI01xhQSmZ0ro4gRRLU14vcfRAabMDQUI1kkalzNlOvaOGyyMs6HV/C+IvY+c3nXNQQz/qS\n7YxyiwPZQPVQV2ZG2d9juXvg3dw98O7241UomHrHffzvob/z5ZMP4+TuQX15ObJsQ+voiNnYiqO7\nO1cu/g8BfX7dmsRfy0vvxe1Jt/Pc7ueIcovCz/H8W/zoVXrmx83nrfS3uGTFJdyeeDulzaUAqCQV\nz6c+z+sTXu+yEFKLuYW7N96NVqlldOBopkdMx0lzKrAxW81UG6spaS5hftyp9jEOageeHfUsc1fN\n5d6N99Lfuz/ZtdkoFUocVA44qh0pbymnxdLSranIv5Uv0gpZm1XO4kvjmPOLLGiEtxNLrxsCwPhY\nHxZ/d5B3N+fyfXoJ1c0m7poQxW3jolD+IiDceKSS5FB3XPXnPj25u0ZHX9iCZ4IgCIIg/LV1JygN\nBApP+7oI6NnFcb9T5f9+ltajR9u+1oSHE/Tf/2IqLkbp7YVCp+v2tRQ6Ha4zZuAybRqtx46h69v1\nNEFNUBCoVJjy8s5r/Gdira0DQOnWe5lSQ6OJnStzUCiktozomcy4dwAqzS+mNFstcPAbpIABOHvo\n2mdKG6w4apvbMk2hl81BuX8PsRPiOZ5rnx48sO8opsf0ZXTQ6E7v6eLtQ1j/ARQeOUKtVw3p2ato\ndTBzz6zHztrmQ+/kzOx//ot9P36PobGB2JFj8QgI4siObajUaibeeHuHCr+95cqYK9lYuLHHprMC\n3JZ4GwN8BvD63td5fPvjAAzxG8LYoLG8kPYCT+54kiSfJKw2K2abGbPNTIJXAkk+SfyU/xO7SncR\n6BTIlqItvLLnFS6NvJQrY64k0CmQ2d/NbstyDvQd2O6+Ue5RPDj4QV7f+zoHKg8Q7R6NJEm0mFto\nMbegUWqYFzuPIX5DeuxZz9e27CoC3fQsHBl+xuPUSgVPz4ynr78LP2aWEuPnzItrj1LVZGLxZf0w\nWWz8fLiCr/cWcai0gUVTe/cNDUEQBEEQhN+D33TumyRJNwM3A4SEnP/Uv94k22yYCgpwmzsH58lT\nMGZmUPna6zSuW4e5uBhNwLmt85LU6jMGpCeP0QQHn3dQajMYKFh4HQ5DhuDzj/va7bPWnwxKeyBT\nWpMLClWH3ps/Pv8j5dVODJ8djc7p7NkevdMvKnu2NsHHl0LJXoibgYvnfad6ldqsNLWocXK0tB3u\n4u/ODa+OR61R8tWn3wIwPukSRkaOO+N9EyZMJv+VvQSX61EWNyLFena74qpnYDCTbmzf47LvqDPf\nrzeoFCo+mPxBj193mP8whk4bysbCjSw7tIwbEm5guP9wChsL+fzI53yd/XX7cUgq3r3oXVYcW0GY\nSxjfzfyOzKpMPj/yOSuyV/DlkS8ZETiirfiSo9qRPu59Otx3Xuw85sXOwybberUHaE+w2WR25dUw\nvpvtUiRJ4tphoVw7LBRZlnlyVRZLUvI5XNbAkbJGalvMeDlpuXlMBAtHhPXu4AVBEARBEH4HuhOU\nFgPBp30ddGLbrybL8nvAewCDBg2Sz+UavxVLZSWy0YiuXz+cRo/CafQoGn74geaUFIxZWThPnXL2\ni5wHTWQErbm59p6T1dUoPT1/VRVUa10dZU8+hWH/fqzNTR2D0roTQWkPrCktfPFiJEsLQc9kg9qe\nPTbt/ZqyShcGuq4iaeyz53bh7a/bA1LPKCjZh5O3jpJjdaxfmoXUXEG1KYiwsPbBrvpEprXkeDYt\nWgv9g5PPepvIQUPRu7gyIktGbZXoN2zyuY33T0qSJCaETGBCyIS2bY8Me4QbE27EbDOjUqhQKVRY\nbBZuWXcLt6y7BYts4R/J/0CSJBK8E0jwTuD+Qffz941/J6U4hREBI9Cr9DhrnM+4LvT3HpACZFc0\nUdNsYlhEx/WvZyNJEo9M64vJYiOzpIGRUV5cPjCI0dFeqJS//2cXBEEQBEHoCd0JSlOBaEmSwrEH\no1cBV/fqqH4HTPn2diOa0zK6DoMHU/vZcgBcJvdu4KKNjqZp4yYa16yh+N77ULi6ok/sjz4xEZdp\n09CGdz1N0FxWRu70S7E1NaEOCcGUk4u1qRml06m+n9a6eqBnMqWbi/1otaq4ftV9SFonKNxF+XGQ\neQJ/xT44vAo8wmH1P8DUYs+qKpQQNREmLe78onWFkPI69JsF/kmw/gnCRmopPqyl5GgdsqEFtWQk\nZHB8p6c3l1XQ6qbCWXP26bNKlZqLbrqDlS/9G5sCLho999xfjL8QX8eOvTffu+g9lh9eTklzSYe2\nLu46d14f/zqv7n2VBf0WEOH652gxsiOnCoBhp7VT+TVUSgXPzEroySEJgiAIgiD8oZw1KJVl2SJJ\n0p3AT9hbwnwky/JBSZJuPbH/HUmS/IA0wAWwSZJ0DxAny3JDL469V5lO9MBUh5zq4+kwZAi1ny1H\n4eKC47BhvXp/XXQ0WK3ULvsMSa/HZfJFGNLTqdq6jeZtKYQt/6zLcw0ZGdiamgh66y0kpYLCW26l\n9VAWDoNP9Vxsm77bA5nSFpuORrOa1M3FJHnuRxMcT5nf5VALfl4tsH4xGBtA6wxBg8BmgaYK2PYK\nhI+ByAntL2g2wpcL7MHrpMVQa/9ehLvnEv7URJBleK0/ePeFUR0DSJvNhrKmFV2/7lctjR4ygql3\n3ktzfR06vePZTxA65evoyz3J93S5303nxuIRi3vsfjabzOajlbRarAwO88DTSdtj1+5KXYuJ/UX1\njIryYmt2JS+uPUqUj5NomSIIgiAIgnCOurWmVJblH4AffrHtndM+L8M+rfdPw1xQgKRWo/Y/Vcn0\nZFDnPHEikkbT1ak9QhsdDUBLWhoOQ4bg/9RTAFS89BLVS5ZiMxiQlEqs9fWovNtXzrSU2quk6gck\ngc0GgCHzYPugtK4OFAoUzudZiMdmo9msBWzsqB3Cfuk++vYNoKy8Hg9/K9rJD8L2N8AjAmb8F9xO\nzAS3tMJ/h8BPj8Kt2+BkD09ZhtX32aftXrUc3MNAdyKbW7LPnl0tz4S6Ahh9f6dDyis6hMoq4RHy\n6zJx8aMnnttrIFwQ67LKeXndUQ6VnnrvK9LbkcFhHoR7OTIh1ge9RolGqcDHpftFyTpT02xi7cEy\nfsgsY/uxKiw2mRfm9OeltUcJcNO1VdcVBEEQBEEQfr3fR5O/3yFT/nHUwcFIylPVYFWengS+/hr6\nxMRev78mNBTUajCb27WF0Scnw/sfUPXWW9R8/AmyyUTIJx/jOOTUH8XmklIknQ6lmxuSJKEK8MeY\nmdnu+tb6epSurkiK81u3ZmmuwSbbA1+9Uz5BfaaQ8XMRNptM3Ehf5H6zkRLmdDxRpYVxD8OKmyFv\n06ls6e73IX0ZjH0IYqedeGg38Ii0B6UAh1YBEsRM63RMBw7vBCA6qnfa6QgXzt6CWnIqmghw03PT\nJ2mEeznyypWJhHg4sDuvlt151aw5WEZdi5lnfzwMgLezlnX3jsHNQUNlYyspx6pobLWQHOJOXIAL\nsiyz4VAFYV6ORPl07Mf50NcH+GpPEVabTIiHAzeOjuDbfcW8tSmHsgYj/5jcnwA3/W/9UgiCIAiC\nIPxpiKC0C6aCgnbrSU/q7bWkJ0kaDdqwMFqzs9EnnQqCHQYOBEmi+v0PUPn4YKmooPXI0fZBaWkp\nan//tsJI+n7xGA4caHd9a11dj0zdNVYVAeDiHkJTbTHlxz5i5n3/pDTHysGNr7Pm7SAuvuO+zk+O\nmwFrFsHeT+xBaX4K/PRPe7A5dlH7YwOSoGCX/fPDqyFkGDh13lsxP/cgAEl9R5738wm/H9VNrdz0\ncRrVzSbi/F3wdNSw5p7RaFX2N46SQz24bVwkABWNRr7fX0pLq4XXNmRz/dJUWi02Dpacyqr6umhJ\nWTSBTUcqufGTNABi/ZyZ3t+fGUmBBHs4UNFg5PPUQqYl+HH7uCj6BbjY29OYLHyy4ziSZO89KgiC\nIAiCIJw7Ud6xE7LZbA9KQy9s2xptH3urjNMzs0oXF7QxMQB43XEHkl6Puaio3Xkng9KT9MkDMRcW\nYi4vb9tmravrkSJHLVX2qcJ+wf24/OEnaaqt5tvn/4mhfhsVeUc5ujMFc6ux85PVOuh/lT3zuft9\n+HweuIfDrHdPTec9KWAANBRBURqUZ0Ds9C7HVFNciFkHbm7dX1Mq/P796/ssGoxmnLQqskobmDso\nuC0g/SUfZx03jArnronR3DUhmgNF9ThqVTwwJYZVd43itauSKG9oZf2hCl5ce4RQTweeuDQOJ62K\nF9ceZdLLm/n5cDnphfa119ePDCc+0LXtjZ5Jfe1FnpKC3fD6DdaxCoIgCIIg/JmJoLQThowMZIMB\n/YCBF3QcbpfPxmPhQlSe7at6Oo0ZgzogANeZM1AHBmAuad+hx1xagirgVFB6ci1pS2pa27aT03fP\nV1OFPdB1dHUhJD6ReU+9iFqrZefXn6PW6rCYWjl+IL3rCwy9GRw84If7wdkf5n8DOpeOxwUMsH/8\n+Wn7x9hTU3dTilN4Oe1lAHsLncpGJK/zXCsrXBDf7ismo6i+w/Z1WeV8t7+EO8dHc9u4SNRKiXlD\ngju5Qkd3T4zi0FNT+fKW4dwxPor4QFcuSfDH10XL3vMuqgAAIABJREFUA/+3n8Nljdw7qQ/XjQzn\n/24bwbZF4+nj68zNn+zh2/RiVAqJ+MD2vyvDIjwJ9XTg8oF/qqX0giAIgiAIF4QISjvRnLIdJAnH\nYUMv6DgcR4zA96FFHbZ7//1uIn5YjUKrRR0YiKn4VFBqM5mwVla1y5TqYmNRODnRkpbats1a2zOZ\n0oaqWvtYPeyBs2dgMFc/8zJ9R43j0vv+idbRkWOpO7q+gEcE3JMB134NN6wFty6y0379AQlyN4JP\nP/t5QIOpgYe3PcySg0sobSrleMNxnBsVuAcGnvezCb+tLUcrueeLdGa/ncLK9FM/0/UGM4+syCDW\nz5nbxkVy29hItj44gVDP7lVJliQJ9S96fqqUChaOCMdilXlgSgyXJQa07Qtyd+DDhYMA+CGjjFh/\nZ3Tq9hlZjUrB5gfGc+2wUARBEARBEITzI4LSTjSnpKCLj++RoK03SEolCp29mqgmMAhz0ak/4C1l\nZQCo/QPaHa9PHtiWKZVtNixVVah8zn8tXFOtPau11ZjRVvDIwcWVaXfdT3hSMpHJQzm6M4WGqoqu\nL6LSQtQk0J0hc6tzAc8o++d9T03dfXf/u9QYawBIKUkhPXcXGouC0LC+5/dgJ1isNl5ed5QXfzrS\nI9cTOmc0W3l8ZSbhXo7E+bvw/JojyLIMwDOrs6huNvHCnEQ0KgUKhYSf6/lV0wW4dWwEBxZP5o7x\nUSgUUrt9Ps46psbbK28nBv0+/x8QBEEQBEH4sxCFjn7B2tiIISMDz5tuvNBD6RZ1YCC2hgasDQ0o\nXVwwl9jXeJ7eygbAYdAgKjdvwVJdDZIEZnOPBKWN9Y0ArG1M4diGPLQKLZWGSmqMNdyTfA8j5l7D\n0V0prHvvTWY++DhK1Xn8yAUMgOpsiL2kbdOGgg1MCBrPocostpdsx69CgxLoE33+U69bTBZu+iSN\nlGPVAAyN8GB0dOfFlYTz887mHPKrW/j0hiGU1BlY9HUGh8saabXY+DKtiFvHRpIQdP7TzU9nz6BK\nXe6fPyyUVQdKGRTm3qP3FQRBEARBENoTmdJfMGZkgNXarqfn75n6xDRVc3ExssVC0+bN9u2nTd8F\ncDy5rjRtD5YKe9ayJ4LS8sYGQE1kQDiHqg9R0FiAk9qJVmsry7KW4erjy5hrriN//16WPXIfVQX5\n536zgfMh+boTU3mhylBFcVMxMZlqLlrrTGrBTkoKsgHwCQ47r+dqMVm4fmkqO3Kq+fesBHshnJUH\nqW5qPa/rCh3lVzXz1qYcpvf3Z3S0NxNifZEk+zrS97bk4KJTceeEqN98XEMjPFl5x0guSxRTwQVB\nEARBEHqTyJT+guGgvZ2ILi7uAo+ke9RB9kIr9d99T/O2bbRmZ+M4ZjTq4PZFYHRxcUh6PS1paSj0\n9qmPqqodsOpHmPYiKDqvYno2tS1GUOh5Ytxj+Hmfqnb7QcYHvLb3NUqaShgwZTpOHp6sf/+/LHv4\nPsbMv56kyZe0VTLttvAx9n8n7K/cj9akoDUtF4XJjHeBTEtdFbLOHQfXc59yaTBZuWFpGrvzanjl\nyiRmJAUS7uXIwiW7mfvODr65fQRuDppzvr5wiizLPLYyE41SwWPT7b9z3s5akoLd+GxXARWNRm4Z\nG4mT9sL8V5UYLKbuCoIgCIIg9DaRKf0FY1YW6sBAVO5/jCl76kD72tGaJUuwGQwEvvYawe++i6Rs\nH2RKGg36pERaUlPbMqXqvG8g7SNY9/g5399osiBJOrw9PNptnxo2FYA1+WsAiB48nAXPv0FwvwR+\n/ugdvn3+SVoaOlZZ/TX2V+4nrsAVm8mMk4cnoysicGtS4+zv9+sD3hMMJis3fJzKrrxqXr7CHpAC\nDI/05OPrh5Bf3cwbPx/r9vVkWWbP8VqeWZ3Fiz8dYcW+IlLzayipM2C1yec0xj+TNZllbM2u4h+T\n++Drcmqd6E2jI9CqFXg7a1k4IuzCDVAQBEEQBEHodSJT+gvGg1l/mCwpgNLNDferr0bl74fHggUo\ntF33THQYPJiqN96kNdseVClb88EjGHa8CUNuBvfuVRKtbKnkQOUBVAoVNrMVSaFF+YvqpkHOQSR5\nJ/H10a/5W9zfUCqUOLq5M+uhxexb8z1b/vcRK55bzDX/fuWcn/1A5QGiatwIjI0lbsxE1r33Br6S\nnojkeABqmk2UNxjp699Ji5kuLN9dwPacal6+IpGZA9pP2xwW4cmc5CA+3XGc+cNCCfPquvqrxWpj\nzcEy3t+Sy/6iejRKBVZZbheIqhQSAW56At30BLnrCXTXE+TuQLiXAwND3M85sP4jWbI9n3AvR+b/\noorttAR/piX4d3GWIAiCIAiC8GcigtLTWBsaMBcU4DZ79oUeSrdJkoTf449161iHQYNAlmn46SeU\nznr7jN3pr8CyOZC1EkbefcbzS5pK+CjzI1Zkr8BkMwEw3xqDWtn5VNZr467l/s3383X21/Rx70OS\nTxKSJDHw4sswGQykfPEphsYG9M7dDxpPqm+tJ6Myg/jmEDwSg4kfP4mjO7dx/MA+PIPsbWVe+OkI\nK9OLSX1kEo7dnP6ZXdGIp6OG2V30n7z3oj78mFHGvPd3svS6IcT4deyHuj6rnH+tOkhhjYFwL0ee\nnhnPjKQA1EoFRbUGiusMFNW2UFxraPt6S3YlFY2tnCg4y5KFgxkfe/5rfn/PCmta2J1Xw/2T+6BS\nikkbgiAIgiAIf1UiKD2N8dBhAHT9/jiZ0l9Dn5iIpFZjKStD66unWR+G2TkeN/8kyPr2jEHpm/ve\n5MOMD0GCGZEzGB04mvUF61FZDqHWdR6UTgqZRJBTEE/tfAqAVbNWEepiz4gFxtpf49LsI0QM/PVF\npb7L+Q6byYxkMOPq7YtCoWTaXfez5X8fEZls7y+bWVxPi8nKyvQSciqbSAp246I43w49J0+XV9V8\nxgyov6uez28ZxnVLUpnzznbenZ/MiEj7WtqmVgtWm8x9X6bj56rjnWuTuSjOF+Vp7UaifJyI8nHq\n9NqtFivFtQamv7GNDYfL//RB6clepCenSAuCIAiCIAh/TSI9cRrjH6zIUXcUHqqhstDetkWh1aJL\ntFeuValb+LE4iq+efgQ5bgYU74E9S8Fm7XANi83CBxkfMNR/KD/O/pHFIxYz0XsAjyf9E1m2oNU6\ndHpvpULJEyOe4Jq+1wCwqXBT2z6/iGgkhYKSo4d+9TPJssyXR75koM7+fXL18QXs/VGn3n4vrj6+\nWKw2jpTbn/vxlZl8uC2Pu5bvY+i/N/DEykwyiztfz3q8uoVQz86f56R+Aa6suGMkvi46Fn6Uynf7\nSzhW0cTAp9Yx/Y2tNBgtvHxFElPj/doFpGejVSmJ8HZiRKQXm45UtvXp/K3ZbDILPtrNEysze+0e\nsizzzd5ihoZ7EOxx5tdbEARBEARB+HMTQelpjFlZqPz8UHl6Xuih9Jh17+xgx8fb2r4+2erGpm6l\nsNJEQ2UF5e5jIGgIfP93eG8s5G2B5iqozgHsa0itspWJoRPxc/SDDU/BC1HUpH4HgJNb1707h/kP\n46EhDxHtHt0uKFXrdPiERVBy9PCvfqa08jTyG/KZ6DICAFcfvw7H5Fc3Y7LY8HPRYbHJ3Do2kv/d\nMJSxfbxZnlrI9De2Me21rXy8PR+z1QbYixyV1hsJ8+w6U3pSoJuer28dQVKwG/d+kc7jKzOx2WSK\nag1MjvMlPvDce2qOjfGmqNZAXlXzOV/jTJpaLdz4cRpHyho73f/VnkK2HK3kqz1FGM0d36ToCfuL\n6smtamb2QJElFQRBEARB+KsT03dPY8zKQtev34UeRo8xNJmoq/yKljoPaB0FWmccBg2iGqhwcMR2\nouhO9oFM/G5YCwe/gXWL4eNLMaOnQQ7B85GNlDSXABDgGABpS2DriwBU7/g/AFz9w886lnFB4/go\n8yOqDdV46u1Bf0CfvmRsXIvVYkGp6v6P4ldHvsJZ40ykFEgppzKlp8sqtQdcz8yKJ72wjjsnRKFV\nKRkV7UV9i5nv9hfz1Z4invjuIPuL6nhpbiIFNS0AZ5y+ezpXBzXvLUhm0sub2Z5TzZWDgrlpTDh+\nrvpuP0tnxvWxB/kz3kzB11WHt5OWKB8n7pwQRV2LmbUHyxgR5UlyqL3icUmdgX98uZ/7p8SQHHr2\nqtHbsitZf6gchQTvLRgE2Kct51c3kxTkxnM/HsbXRUt5QyubjlQwNb7nCw6t2FuEVqXgYlHMSBAE\nQRAE4S9PZEpPsDY1Y8rLQxfX90IPpcdk70xHtlZhMtdh3LYUAIcBA9CG+FGqd0Lv6EBIfH+yd29H\nBoi/HO7cDZOfZhNP80X5MxSl7KKkqYToAieaVv9I3YqHIeoi0LlRXlgKqPCI6HPWsUwOm4yMzKyV\ns/g061Nara0E9e2HpbWVspzsbj9TtaGadQXruCzyMlqqa1BptehdOmYlD5U2oFJIjI725h+TY9Cq\nTq0jdXVQM394GN/dOYr7LurDN3uL+Ty1sC0zGXaW6bunc3PQ8PTMeLydtdw0JpwoH+fz7qkZ7OHA\n83P6M3tgINE+TpisNr5IK2TYsxuY8uoWXlp3lGd/sGeYW0z2rOeO3GqWpOR16/o7c2sAWJtVzuLv\nDjLp5c2Mf3ET1y1JZdZbKTQYLXz4t8F4OGpYnVF2Xs/SGVmWWZ1RxqS+vrjo1D1+fUEQBEEQBOGP\nRQSlJ7QeOQyy/KfKlB7Zth4A2VpL9eZvoSYXhYMDEYtnUyY7E5qQSMyIsdSWlvDTe+vYv6GQ0gIT\nVSHXkV0eAUisW9n6/+zdd3xV9f3H8df3zuy9E0gIYYS9FFCGG8S9R+uqq7Zq7bBV26r92aUdv9pf\nHVWrYsWBe4t7gWwIe5MQQvbeN/fe8/vjMiVAmDcX3s/Hw4fknO8553P4Jnzzud9FSU0po1fEs+zT\nxfxn3UimLkpmdutwSlsTMPZEtjjtfLmmcq+x9E/oz7Qp0+iX0I+H5j3EOW+cw/qYGjCGTUsXd/md\n3lr/Fl6/l0v6XkJDZTmxyamdbp2ysrSBvJQoXI69f4vfdkoe/VKjeWNhCUXVgaQ0uwvDd3c2eVA6\nc+4+lbyU3VfiPVCXjurB784bxGPfH8lrt5zAjDsmcNOEXB68aDDXjM1m4aZaqpva+fn0AlaVNTAo\nM4bPV1V0abjt7A3VDMmKJcrt4PnZRaTGuLnvnAFcMDyTwuoWrj0hh0GZsZw9JJ33l5byzdqqQ/Ze\nAOUN7VQ1tXN8r4R9FxYRERGRo56G7251tC1y5Pf52LJuEWAHOihtSydz+tVw/cd4KjbQ5HWDPY3m\nhmyMzc3Kb2awftGObweH08bEzHf4tPAc6tY1EOa3MSKtnpiJN7G2YBkzVxUDduyuBB5fWoxnKUz8\n2cS9xjQoaRBPnvEks0tn8/f5f+fu+fdyS9bxFC1dzNiLr9jrtSVNgZVaX1n9CiNSRtA7rjczK8o7\nHboLsKq0kbG99z032BjDlMHp/OPTNTjshoRIF7Hh+997Z9uPBY0ORK+kSO4+M9CLX1Bcx9Rvi/jB\n1PkUFNfxm7Py6ZsazdVPz+WrNZWcMXD3Obbb1DR7WFXWyJ2T+nHhiEwinA5iIwLv6/X5mTQwjZP6\nBYYP3zmpH3M21PCjaQt448cn0ju581WD99fKsgaA/do/VkRERESOXuopBXxNTVQ/+yzuPnk4U46O\nbThKVq/A29FKVHhgv82yuJOhbCm8/wtqNweGea5f3MHCGVuwuwZieddxwgU+Jt00gJFnZnPK1fn0\nHBz4u2jdHJifmTJwNCMvvIopt99PfHJgHmlkVBylzR7WVjRR2+zpUmxj0sfw9KSnyYzKZFH4RkrW\nrKC9tYXSplJq22p3KWtZFn+Y/QfOfO1MznztTDY3bebSfpfi83ZQV1ZKXOrucxJrmz2UNbSRn961\nnsszB6dhWTBrfTUXj+x8f9LuZHBmLElRbgqK67hkZBbXj+vF2N6JJEa6uOv1pdu3WunM3I3VAIzJ\nTSQ9Nnx7QgrgsNuYPCht+5Y50WFOnrpmFE67jRumzqeupWv1uy8rSwNJaWd7vIqIiIjIsUdJKVD+\n5z/jLSsn/YEHgh3KIbN+wVzARo+owB6iVXUdMOFOWPQ8NRsC8xF7j+jD9X8bz8W/vpGEjEw+e/ph\nvnjm14SFrSJnaBzheSNxm0asqsCQ0IjkLCzL4rPnVtHcfjLGnkRa/xHUbE1G5xfVdhpLZ6JcUTx8\n8sN4e8aC3+L59x7m0ncvZfJrk3lq6VO0+9oB+KbkG15a/RIX9LmAyTmT6RPfh9OyT2PL6pV0tLfR\nY+CQ3e69Lenpn9a1nrg+KVEM6xHH2NxE7pzUr8vvECw2m+H6cb2YMjiN318wCGMMTruN528YTa+k\nSH7y0mIe/WJdp9fO3lBDuNPOkKyurQ7cIyGCf181kpLaVn40beH2lYoPxqrSRjLjwg+oR1pERERE\njj7HfFLa+MUX1L/6Gok33ED4sGHBDueQWT9vDjZHD9Ii6rA5XNSVlVDZ+zbIPYmytsAKrTlDexMW\n6SSzbw+u+cu/OO8XvyEiNo5Pn36M535xKzXeKOIdm3E1BRLb8NQcVs8po2R1LVn9c3DHXE3iwB3D\nnecX1uxXjHnxeTz+gxfxhBuWfPkx9e31DE0eysMLH+a8N8/jpVUv8Zf5f6FHdA9+M/o3PDTxIV4/\n93XcdjcbFy/AZnfQc1AnSenWrU66OjzUGMNLN43hhRtH47SHxo/ELSf15tHvjdxlAaf89BheumkM\nZw1O56EPV1NW37bbdd+ur2ZUTvx+veeonAT+dOFgZq2v5omvNhx07KvKGrrciy0iIiIiR7/Q+A38\nMPHW1lL629/i7tuXpFt/HOxwDoinzbvbsdrSEurKt2Bz9iYuxktCRhbG1PHVy+uwLniKSpMDJork\nnoGFZizLwths5B03hise+CsX//r3eNpaefkfj+B2FBPZFgaALTaHma+sIy03hvN+OpzzfjocZ25g\nUSC7zfDNuqr97klz2J30GjuGrMpwzk47gyfOeIInz3iSCGcEf5jzB0qbSrn7+Ltx2nftVStcvIDM\n/gNwhe++Uu7K0gaSotwkR7u7HEeY097pgkmhxmm38dPTA6shz1i+68q51U3trC5vZEzu/u/De9HI\nLCb0TeaZmYW0ew9879LGtg7WVzZ3uRdbRERERI5+x2RS2rJwIatHHcema67FV1tHxoN/xuZyBTus\n/dZQ1cpTP/ua0vX1uxzfsiYwPNfmzCI63kFa7zy87YUUL3udJXNrqTdJ2OzxtEbVc/PHN3Paq6fR\n5GkCAr2G2UOGcdbtv6S1oZ41phzn1kSzYLaFp9XLSd/rj81myOoXT2VzBwAXjchk+ZYGzv7nNywo\nCvSYvrZgM1+v3fuqvACnn3UVNsuQ9Vopn099kv62HF45+xWmnTqVe2suIbtu1wSmsaaKyk2F9Bo2\nstP7rSw9tnvi8lKi6JsaxXtLS7Esi8rGdhYX1/HCnE0AB5SUAlw/rhdVTe28U1C627kNlU384pUC\nrn56Lk98tZ7PV1Xg81u8NHcT9721jLtfX8LPpxdw7r9mYlkWE7buxSoiIiIickyuvtv40cf4m5tp\nX7OG5J/cTlh+aO5N2ljThuW3qC1tJr33jjmCFRvXY3O4MLYEYhLDmXjOD3C4XCye8T6fPnk3GD/u\nqIE8uOhPLCxfSJuvjXc2vMMV/QMr4Na11ZHQN5eolGQ2N1aT6G/FZmysmVfLyMnZJGbuWIW1vCEw\nRPSuM/M5fUAa9721jIse+5bxfZL4em0VuUmRfPaLk/b6HolZPbngV/ex/ItPWDzjXRZ+8DZ5o0bj\n9XgoLFjIhrmzueg3D5DVP7BdT2HBQgByOklKvT4/a8ubuPbEnIP5qw15Zw5K5+FP15J/74e0dezo\nvY4Jc3R5Pul3TeiTRL/UaB79fB3nDcvAabexobKJf322jjcXl+By2MiIDeeP7wc+iEiLCaOsoY3o\nMAdhTjsuu434SCfP3zBa28GIiIiIyHbHZFLaPHcOEccdR8Zf/4IjOXR7bDxtgWGULQ27ropavnEd\n4dEZOEwz7sRkiIzi1B/8kJ6DJ/DuPx7B7y0iOimLhRWfcF7eeSyvWs7zK56ntLmU2Vtms7JmJTGu\nGAalxtB3qR3LUYJloohNDmfUlJxdn9XQjtNuiI9wcvqAVE7oncj/fryGp2duJCnKzYaqZtZVNJGX\nsvftRHJHHEfuiONoqqlm8UfvU/DJB7Q1NjD6gstY/uUnzHvr1R1J6aIFRCUkktQje7f7bKhqxuPz\nH9M9pQBXju7J5tpWEiKdZMVHkBUfWFgoPS78gOfNGmO4c1I/bnhuPtNmFxEV5uRXry3BaQ8svHTT\nhN4kR7upafbw2aoK/vfjNfz89L7cekreUTE0WkREREQOj2MuKfXV1dG+chVJt/445Ld/8bQG5pO2\nNO5ISi2/n4qNG4iMH0K0r5x3Cg1VMzdy3Ym96HPcAEaccztLPi0gNj+L5o5mhqcMZ2jyUO755h7+\nu/y/DE0Zyo+H/ZivS76mMLGQvsTj95YSHRnL5JsH4XDZd4mhoqGNlOiw7UlHpNvBb84ewI0TcvF4\n/Yx/6HM+XlG+z6R0m6iERMZdfhWjL7iEisKNZPTtT2tjPatmfonfF0jCi5Yups/oEztNdLatvHus\n74GZGhPG3y4desjve2p+CqN7JXD/OysAGJeXxP9eNmyX+bsJkS4uHpkVEtvriIiIiEjwHXNJacv8\n+WBZRI4eHexQDlrH1kWOWup3JKU1pSV0tLfh9ycQY6/gjUKo9Zdx3YmBfUXHnNeb2tIW6tLXwRYY\nkTKCtMg0cmJyyI3LJdIZWLjopiE30dzexNMzr8bb4SUp0UFS1u69j+WNbaTG7L6gUGpMYHGkQZkx\nfLi8jFtO6r1f7+Z0h5HZLzCsuuegoSz55EPKN6zD5/PS3tJMr+F7mk/aiNNu6J3ctSRY9o8xhieu\nHsWbi0qob+3gpgm52/c1FRERERE5EMfUQkdWRwfVzzyLLTKSsCG7byUSatpaPHS0zqSpdsdCR6Vr\nVwPg8SQT4yin1J9AcU3L9vPucAfn/3Q4S51ziLAl8p8vajHGMDh58PaEFMBmbESHxZCSnQNARGLn\nvcpl9W3bE9DOXDg8i4LiOmauqzrg99y2F+mmZQUULl6AsdnIHtz59j0rSxvIS4kOma1dQlFsuJNr\nTsjh9lP7KCEVERERkYN2TP3mXvXYY7QuWEDa/feH5Gq731WxcRm+tjmUr3sHAG9HB3PeeJm41Aws\nEomxl1NqJVDW0EZbx67beMzcvID62kz+O7tor1t8pPUNzOMMzxqw2znLsihvaCdlL1uvXDm6J5lx\n4fz5g1X4/NaBvCYRMbEk98xh4+IFbFy8gIy++bgjIjste6yvvCsiIiIiEmqOqaS04f0PiBw3jthz\nzg52KIeE3xuYU+lp2QLAwvffoq6slGGTv4cxNiLs1TQ64rEsKKlr3X5ds6eZJl8VYVYW7V4/y0oa\n9viMtN59APhoQzM/e3kxW3a6z9qKJpravQzI2PP8zTCnnV9O7sfSknr+/MHKA37XARNOoWTVcio2\nrt/jVjBVTe1UNLYz4BifTyoiIiIiEkqOmaTUV1eHp7CQiFGjgh3KfvH7/GxZW4e3Y/fezPbWwHYs\nfm8N9RWVzH79ZXqPGk1samAuZpvp4NQB6QBs2mkI76KytQBMyAn0fs4vrNnj8zP65mOz25lTBa8v\nKuGUv33B3z9aTXO7l1lbh+Se0Dtpr+9w3rBMrh6bzZNfb+Tix2bx1uKSvfbOdmbopLOIjA9sI9Jr\n+I46fHNRCS/PC+y/OX1+cZfiERERERGR7uOYWeiodelSAMKHHfoVSQ+n9Ysq+eip5bgjHPQZlUr/\nsemk5ERjjMHTuqPX8rNnn8Tv7eCkq26gaEU7AA02OD0/lfeWlO4yr3Tu5kCP5YReA1m6tol5hTXc\nPLHzhYhiU1K54E+P8vCji7h5Qi5b6tv452frmD5/M+lxYWTFh9MjIWKf73Hv2QPomRDB87OL+MlL\ni0mMdHHnpH5cfnzPLv09OF1uTr7mJlZ+8wXJ2b22H3/407WU1LUyNjeJp7/ZyPg+SXvtuRURERER\nke7lmOkpbS1YAsYQNmhQsEPZL3XlgWSy58BEVn5byqsPzmfmK+sA6Ghr215uw4JZjDrnQuLS0mmp\nb8eGl0rCOKF3ImFOG5uqdySlK6rWYVk2xvbsx3E58cwrrMW/l/mera5oMIYhWXH83xXDee2WsTR7\nvCzaVMcJvRO79B4Ou40bxufy2c9P4r/XH09abBh/+mAVltX1eab9xo7j/Dt/s30rmLoWDxurmvF4\n/Vz42Eyqmjz86KS8Lt9PRERERESC79hJSpcU4M7Lwx4VWluFNFa3ERHr4ozrB3LdQ+PIHpTI2vnl\nWJZFR/uOpDQ8Op7jz78EgOa6diLstdQ5kkiOdtMzIWKX4bubGgsx3gQyYqM4MS+J+tYOFhXX7jGG\nqqZAz2tSVGBxqJHZCTx4UWBF3Il992+vV5vNML5PMhePzKK+tYPKrfc+EAWbA6sO56VEUdfSwe/O\nHcjYLibJIiIiIiLSPRwTw3fbVq2iZc5cYs8/P9ih7LeG6jZiEgNbrrjDHeQMTqRoWTWN1W3bk1Kb\nawADTzkdV1g4AM21LUTaamiKysAYQ8+ESOYW1vDi3E1cMDyTas9momyZGGM4uX8KTrthxvJyRmYn\ndBpDdVNgH9SknVbZnTI4na9/eTJZ8eEH9F59UgIr5K4rbyIles9byuxNQXEdxsD0m8fS7vWRHntg\nsYiIiIiISPAc9T2l3upqin/0I+zx8STf+uNgh7PfGmvaiE7YkbSl5sYCULaxHm9HO8bmxBU5GXdE\n7vYyzbWtRNpqcCVkAXD7qXlkxIZz9+tLGfvgh7SbclLDA3M5Y8KcjO2dxIzlZXscSrujp3TXrV96\nJERsH0q7v/qkBnqs11Y0HdD1EEhK85KjSIh8hjj5AAAgAElEQVR0KSEVEREREQlRR3VSank8bP7J\nT/BV15D1r3/hSE4Odkj7xfJbNNW2EZ24IylNzIjE4bZTtqEBX0cbdqeb7EGJLPl8M401gZ7T5oYO\nIu21xKRkAzAkK473bh/Hf68fSWSPaYCfcRnjtt9z0sBUiqpbuPm/C/h4RTkdPv8ucVQ2teOy24gJ\nO3Qd6ynRbqLDHKytaDyg6y3LomBzHUN7xB2ymERERERE5Mg7apNSy7Io+/0faJ2/gPQ//IHwQQOD\nHdJ+a2nw4Pdau/SU2uw2UrOj2bK2Dr/Pg8PpZsIVfbGAL19Yjdfjw9NuiLDVkNij7/br/Jaft7b8\nlXqWctdxv+bnE87cfu7ikVncNCGXhZtqufG5+Yz546c8M3Pj9vNVjR4So1wH3CvaGWMMfVOjWVt+\nYD2lJXWtVDV5lJSKiIiIiIS4ozYpbZwxg7rp00m88UZizz4r2OEckG09n1EJu865TO0VS/XmJrA6\ncLjcxCSGM+bcXIqWVbP4k8BenW5bPVlZgZ5Sy7J4YPYDzCicwc9H/pzvD7xslwTT7bBzz5R8vr37\nVP5zzSj6pUXzu3dWMGt9YB/Sqqb23YbuHgp9UqJYd4DDdwuKA4scDctSUioiIiIiEsqO2qS0efZs\nbDExJN/xk2CHcsAaqgP7kMYk7jpfMrlnYJEgrA4czaVQPI/BJ2eRkhPD3HcDPZwtNj/pcYH9Qx9f\n8jivrX2NGwffyLWDrt3j85x2G6fmp/Kfa44jNymSn768mPWVTVuTUtchf7++qdFUN3sob2jbd+Hv\nKNhch8tho19a9CGPS0REREREjpyjNin1FBXh6pWDsduDHcoBa6wOJGs7zymFHUmpZXlwGh+UL8Nm\nM5xyVX+29X+2uRw47DZW16zmiYInOCv3LG4bfluXnhvusvPI90bg81tc/Ngslm9pOCw9paNy4gGY\ns7Fmv69dXFzHwIwYXI6j9ltYREREROSYcNT+Ru8pLMKVnR3sMA5K8YoaYpLCcLp3TaxjksJwRziA\nDlw2HzSWAZCYGcXIM7Ox46EjMtBL+sc5fyTGHcPdx9+9X3NC89NjePnmsTS2eYFdt4M5VAakxxDl\ndjBnQ/V+Xef1+Vm6uZ6hGrorIiIiIhLyjsqk1N/Whre0FFdOTrBD2SvLsthYUMnqOWV4O3y7nKso\naqBkTR2DJmbtdp0xhqQe0WB14LJ3QGPp9nOjzkjhmpQbIC6V4oZiFlYs5NqB1xLrjt3v+HonRzFl\ncPr+v1gXOew2RuXE73dP6dqKJlo7fAzTIkciIiIiIiHv0O3x0Y14Nm0C6PY9pR8+sYwNiyoBmPmq\nk0ETsxg0IZPwaCfz3ivEFWZn4LiMTq9N7hnN+jke3LYO6iqK2ZaeNZRtJMzexJyYFgrWTAdgcs7k\nA47x7in9WVXWwBkDUg/4HnszulciX6xetV+LKRUU1wEoKRUREREROQocnUlpYSFAt+4pbWvuYMOi\nSgZPzCR3eDIFnxYz792NLPywiLTcGErW1HHChXm4wjuvotyhScx+pYMIWwsVJYXbk9KK4jU8lxjP\nW54vYfmXDE8ZTnrUgfd2pseG89FPJx7w9fuybV5pQXEdp+Z3LfEt2FxHbLiT7MSIwxaXiIiIiIgc\nGUdnUlpUBIArOye4gexF2YbAlia9R6aQ2TeerP4J1JY1s+Szzaz6tpQ+x6Uy7PQee7w+NTcaLC9h\ndg/x/nravT7WlDXxxtJpvBUdxcmpJ7GhdSNX5l95pF7pgPRNDSzatLaiqctJ6eLieob2iDuk+6aK\niIiIiEhwHFVJaUdJCRV//1+a587BnpyEPSoy2CHtUen6emw2Q0pOzPZj8WmRTLyyHydekofdbttr\n0tXR1g6A0+YjkQbG/uljHL6FtGWvYVB7BH859e+4nc7D/h4HKzbcSWqMmzXljV0q3+Lxsqa8kdPz\nUw5zZCIiIiIiciQcNUmpt7aWDRdehOXxED5kCJFjxwQ7pL0qW19PUo8onK7dt6xxOPe9jU1HW2AP\nU5fNh81YjEptYI1zOhF+i0cv/G9IJKTb9EmJZl1FU5fKLt/SgM9vMVTzSUVEREREjgpHTVLa8MEH\n+OvryXn5JcKHDg12OHvl8/mpKGxg4PjMA76Hpy2wh+mHMW7iHQ6s6MepavfyXN7lxCf1O1ShHhF9\nUqN4aW4xfr+Fzbb3IbnbFjkaou1gRERERESOCiG1JUzb6tVs+sEPaFu9erdz9W++hbtvX8KGDAlC\nZPuneEUN3g4/mf3jD/ge23pKv4gOY0qPDL72VHKXN4rB4+85VGEeMX1To2nt8DFzfRW1zZ69ll1U\nXEdmXDjJh2HfVBEREREROfJCJin1VlVR/MNbaJ71LSU/+zn+1tbt55q++oq2JUuIPf/8brf4jWVZ\nux1b9W0ZYVFOeg5IOOD7dmztKe2wB+5/blMLl075N9j2PfS3u+mTEgXAVf+Zy3mPzKSsvm2PZQuK\n67QVjIiIiIjIUSRkktLaF17EW15Oyi9/iWf9eqoefRTL76fq8X9T/MNbcPfJI+7CC4Id5i7amjp4\n9q6ZfDp1BV6PL3CsuYPCJVX0OS4Vu+PA//o97YGk3OvwM62mjd/3+R4mo3sPW96TvmnRRLsdjO6V\nQHVTO997ajbVTe27latqamdzbauSUhERERGRo0i3m1PaUV5B3fTpxJ57Dq7s7O3Hm776ivChQ0n8\nwXW0r15FzdTnaFu1muavvybmrLNIf+B/sEV0r30r184vp6Xew6pvy6guaWbyzYNYM6cMn9dP/tgD\n3zsUdu0pzbjuc0z0gc9PDbaYMCez7j6FKLeDORtruPaZuVz1n7m8eNMYYsN3LNi0ZHNgPqkWORIR\nEREROXp0u57S6ieeoOqRR1h/5hRKfvlL2tevx1tVRduyZURNnABA8h13gDE0f/stqffcQ8Zf/9Lt\nElKA1XPKSMyM4qwfDaG+ooVX/jifhTM20WtoEsk9ow/q3u0tLUCgpzQmMgW62bDl/RUd5sQYw5jc\nRP591SjWVjRy7TNzqW/p2F7m67VV2G2GQZkxe7mTiIiIiIiEkm7VU+pvb6f+3XeJmjgRV24utS+9\nRMM77+Lu3x+AyPHjAXCmp9PzqScxERGEDxwYzJD3qK6ihfKNDZxwUR45Q5K45O7jeP/xpdSXtzD2\ngt4Hff+WuloAvE4Ll9110PfrTib2TeZfV47g1hcWMvnhr2jr8HF8rwS+WF3JecMyiHB1q29bERER\nERE5CN3mt3vLsqib/gr++noSrrmayBNOIPGmG6mZOpXa56fhzMggLD9/e/mI444LYrT7Vl0S2Hcz\na+sKu3GpEVxy9yha6tuJTT74Xt2m6kr8Di9uW+jsR7o/Jg1M46WbxvLAuyuIdNuZsbwch81wx6l9\ngx2aiIiIiIgcQt0mKS27917qXnkVd//+RIwZA4AjPp6UO+4g8YYbsDwejK3bjTbeo+a6wNYmUXE7\nti5xuuyHJCEFaKquwOf2EsbR1Uu6s5HZ8bz54xMBeG3BZnyWRc/E7jdMW0REREREDly3SUobP/+C\nqFNOIfN//75b8mmPigpSVAeuua4dm90QFnl4ejKbaqtpd3sJt4Udlvt3NxeNzAp2CCIiIiIichh0\ni65HX10dvqoqIkaOxOZ27/uCENBc305ErAtjOzwLEDXV1tLi9hFuU8+hiIiIiIiErm6RlLZv2ACA\nO+/gFwDqLprr2omMPTwJtt/no6WxiaYwHxH20OtFFhERERER2aZ7JKXr1wPg6n0UJaX1nl3mkx5K\nLfV1WJZFfbiPSOfBbS0jIiIiIiISTN0iKfWsW48JC8OZkRHsUA6Z5rp2Ig5TUtpUWwNAbZif6LC4\nw/IMERERERGRI6FbJKXtGzbgyu0VUqvr7k1Huw9Pq5fI2MOzMm5TTTUAzWF+osMSDsszRERERERE\njoSgZ4GW30/72rW4e+cFO5RDprm+HYDIw9xT2hLmIyamx2F5hoiIiIiIyJEQ9KS06pFH8ZaVEXnC\nCcEO5ZBpOcxJaXN1JWDR5vKRGKHhuyIiIiIiErqCmpQ2vP8+VY88QuyFFxJ7/nnMKZ3D1OVTgxnS\nIdFUtzUp7WT13arWKv4898+UNZcd8P3rCpfjdnqwbJAapaRURERERERClyNYD/a3trLl7nsIHzmS\ntPvvY13dOm777DZava30juvNuMxxwQqtSzatqKZ0XT3HnZWDzb5rbt9Q1QZ03lP6r0X/4rW1r/FF\n8ReMzxzPlNwpDE8Zvl/PrizaiCsi8Iz0aM0pFRERERGR0BW0ntKOTZtwJCaS9c+Hsblc/O7b3xHp\njKRHdA8enPvgQfUkHgnz3y9k/vuFfPjEMjyt3l3ObV5ZQ2JmFO7wXXP+zY2beWvdW4zPHI/B8Oa6\nN7n545tZWb2yy8/1ejzU1LXQHm0HINYdc/AvIyIiIiIiEiRdSkqNMZONMauNMeuMMXd1ct4YY/65\n9fwSY8yIfd3T8vnJeuwxHImJbGnaQkFlAVcNuIp7x95LWXMZ5755Lv9Z+h86fB2B8pZFR3vbfr/g\n4Va4pIqXfj+XLWvrAPC0eildV0/2oMRdytW31/OzL36G3WbnvrH38cFFH/D+he8T57f46bvfo6W9\nsUvPqy5aj2UZimJdGMtFWmTaIX8nERERERGRI2WfSakxxg48ApwJDACuMMYM+E6xM4E+W/+7CXhs\nX/d19cohrF9fAD4p+gSA03uezpj0Mbx5/puMTR/LPxb+gwvfvpBZW2ax8psveOzG71NX3j16UFvq\nPfQZlcIFPx+BMfDG3xfy7Rvr2FhQid9vkT1ox7Da+vZ6bvzoRtbVrePvQ24jddplUL+Z5NKlPLi5\nkC2Wh39+eFOXnlu55CsAVsb4iDLZOGxBG4EtIiIiIiJy0LrSU3o8sM6yrA2WZXmAl4DzvlPmPOA5\nK2A2EGeMSd/rg8PDAShpKuGdDe/QP6E/PXx+2PAlmVGZPHzKwzx66qP4LT83f3wzb3zyDB3tbcx8\nZdr+v+Vh0NzgISLWTXpeHJf95ngGnJjBwhmb+OTZlbjCHaTmxgK7JqT/OOkfTJj7X9iyCBY8Cx/8\niuERmVxuT+SF2qUsXvXGPp9buaYAh/FRHFNDkrP3YX5LERERERGRw6srSWkmULzT15u3HtvfMruo\naqni2ulXcOnUsykvXM+lvS+Gj34D0y6G1sBQ2PFZ43n9vNe5esDVtG4uwwJWfv0F5cXFe7v1Yedp\n8+Jt99HsrKPR04grzMHJ3+/P+T8dzqCzUxhwWQx2u4329gZuenUK62pW8nBHDBPeuQs2z4OwWPjm\nf6FqDZz+P9xxzn9J81vc++39tO9lGG/zhkVsWL2RqCgflt1Lj8i+R/CtRUREREREDr0jutCRMeYm\nY8x8Y8x8X1kdg19r5MKvMjnvmwzqHv2I4oJ54PPA6ve3X+O2u7l14C3ENrlY3aMVC7jv71PZUNl0\nJEPfRUu9B4B/r3uUCS9N4M4v72RxxWISe4fzkOdX3L72elbXrObJ965nhbeBv3pjGW+PhthMOPEO\nmPwg+L2QMRzyzyEirif3D7iBjTY//37v+l2e1eHvYOa6dyl//ke8cO+dNLWB7YRAD2nfuO+OohYR\nEREREQktXZmQWAL02OnrrK3H9rcMlmU9ATwBkJqSbEWlxuJqz6HBk0Z73Ze8Utmb8SkOhix6Dfew\nK7dfV7lxIzagNNVDUlsYyZUreG5WIfefN2jHzX1eePtWiO0BY26BiMOzVUp9ez3VNS0A5EU6GR0/\niLc2f8WHhR+SGJZIdVs1sZaN69+7kmZfO2e5Ujnlmk93vYmnBVa+DeN+BsYAcMKYn3Lehvd4umEF\np694lfwBFwPw1sLHeezrqZw+P5kIewSX/frX3FX8LFZZGH0Scg7LO4qIiIiIiBwpXUlK5wF9jDG9\nCCSalwNXfqfM28CtxpiXgNFAvWVZpXu7aYrdg73jUtpsbtxhHaRGtuFveZOvKnL46sM2YmZeRFxW\nb+IyelJRXgnAuIE/Zl7bw5xQaViwajXsnJRuWQQFLwb+XLMBLv5PV95/v7T72jnz9TMZUDuG4ZzN\nWZXzmVi+ntuM4d3YBF6NtXNOQwOT2/08HekkOjqDn5z1zO43ckXAFS/udvjOs57hm9fP5N7Zv+OF\n3NNxhsWyePGnTJqbQmuExVujillb8TwLKr/BUz2J1JiIQ/6OIiIiIiIiR9I+h+9aluUFbgVmACuB\n6ZZlLTfG/NAY88Otxd4HNgDrgCeBH+3rvvXeNGJ7pHP5fePJHZVNjZVHdHoMJ1/3M8b2c5Fpiuko\nmsfamR9TtmwB1c54Lh51Fs29IrAAf+Ey2jp8O25Y+HXg/30nw7pPwO/r9LkHY0X1Cho9jbQ0BIbv\njuqRD3csJeKCJ7k073ymt0fz8wHXMvDWAv42ZSr3X/U18bE9u3z/2Nge/GbQTayyw8sf/QSA2sJ6\nbH7DrQ8+zUWjvsfXJV8T70zDUzOOlGj3IX9HERERERGRI6lL+4lYlvU+gcRz52OP7/RnC/jx/jw4\nMs7Fhb8ahd1uI7lHHGvnxfJx/R3kvAnNPe6kPbeCYa0zmOT/HGM18qb9FHITIxmTO5766NmktJay\nrKSeUTlbh+kWzYTk/jD4EljzIcx5HGqL4PTfgTN8f0Lbo8UViwEYGzsOTAcR6TkQ1zPw35BLdi3c\na/wBPeO0424nf/kzfFK9hEktlYTV2HBEdZCYlM4vkn7Bpf0u5aW5W3jUqiYpSkmpiIiIiIiEtqBt\nchkZ68ZuD3TUJmREApDjddJsLCKL24gkhk1cwiYCyV6iYyPVM/7LuAEnMj3uc3JKylhQVB1ISn1e\n2DQbhlwGuScDBmbcE3hQUxlc/CzYDn5Np0UVi8iOySa/LZcSswGT3Oeg79mZsdG5PNe4mpmFn5FU\n7yY93b/9XM+YnrS0NBLtrifcZT8szxcRERERETlSgpaU7iw+fcfcyNlxfu69YQQ9wtw017XTXO+h\nubaVZTOaeP0dBxctvJXH4xPoV+zl2ffmELZlDpfyCeGeJsg5ESITIX0IlC2F4VfBwqnw8W9h0h8O\nKkbLsiioLGBc5jhaVjUSaa+FxCEH++qdGt3zZJ5esYb/fvNvTvBGkJ8et8v54poWMuMPTe+viIiI\niIhIMHWLpDQ6IQyn2463w88zd00gNjYwLDUxM2p7mYxeYbzxz1U0pF7HqPrngUR+6/8/pqxeRZMV\nzuyk82myjucknx/HpD9CUzkMvBAcbvj2XxCfA8ff2LWAypbBvCch7zTIPweAt9e/TU1bDUOTh9Jc\n7yHWVgtJh2ef0BH5l+Ja/jhWRSsQQY/srF3OF9W0kJcc1fnFIiIiIiIiIaRbJKXGGFKyo7E5bNsT\n0u9K7ZuGw7mGkpgLue7cs3hq8S+ZVRfLs7kjqIxOoLzei+/jP3Daikt45LJTAfD6/Dgm/xnqN8MH\nv4SoVKhaDfP+A31OhzMf2m2+6doFT3H//IeI9fu5YdmL9K34GX+w1fPuhncZmjyUyb0m83LTt2SG\nN0Jk0mH5+wiLSmGs30VseTgRdg9xWbnbz/n8FpuqWzg1P+WwPFtERERERORI6hZJKcDkmwdv27Kz\nU3aHjbTesZSsrWP8Zccz+IqLWDP9XY7/0kttHmzKqCdtbTnzGp7j1QX9qGpq57Ev1vP0taMYedFT\n8Nx58Mo1YPkhdTAsfC6wMNLYHeszVSx7hR8W/B2vO4yy8ER+GFFDwtqplDod/GjIzdw49If4PeDx\nOomKsbHXgLtg5vTn8Xm99BszDndkFO6ICNwRkdjsdv4n/jSerVjOwMTNmNjM7deUNbTh8fnJTog8\nqGeLiIiIiIh0B90mKQ2LdO6zTEafOOa+u5G25g7OPesG2k66nDlvTGfRB2+TsA4gjBG2Un739hJ8\nrs34YmdzzbPNTL12IiOvehNevwl8Hrh8Gjx/Ecz6P4hIgpL5sHkeHzWtoyIxnpdPfpSU5P5c9+G1\ndDRXMbV4PcOYAf2/T83sGUAyUT17HdT7ej0e5rwxHcvvZ95br+5yzuF2ExkZjoVhcFwZxGRsP1dU\n1QxATqL2KBURERERkdDXbZLSrsjIiwMLygsbyB6YSFhkFBO//wOGnTGFVbO+ZmXhIvzfFuBOWUZz\n7EJc0cuxR2/mmqkepl5zOiOveGHHzSb8ItB7+sZN4IyEzBGszj6eBG81+T1OxBjDq+e+hs3YcC55\nFd6+Df51HE01GcB9RI254KDepbqkGMvvZ+L3f0BsWjqelhbaW5ppb2mmtaGBtXNnkRdbT7yrDWJ2\n9JQW1bQAkJ2knlIREREREQl9IZWUxqUFegfrK1pgYOL247EpaYw+/xKyi0cy7dvbGe9axDznBo5r\nGsSs2I1YWY9wzX/b+b9LJnNyvxQsy2JzUm96XP02RCRCSj7Y7Kx+51L6RffDbB2W67Zvnd867AqI\n6wEf3kVT3BWwAKIOsqeysmgjALkjjychI2u386dcdzNMvxpWrYDI5O3HC6ubcTlspMeEHdTzRURE\nREREuoOD37zzCIqIceFw26mvbO30fFqPXEx6DGEbqxi4JoKsrxp5IOYWYiMMtsxHuP7FN7j1hYU8\nuvgxprwxhS+cFqQNApudDn8H6+rW0T+hf+cPzxkHP/yGprQzwMDKr9/lxd/eSWtT4wG9S9WmjThc\nbuLS0vdc6JR74cInwbZjP9KiqhZ6xIdjsx3cfFYREREREZHuIKSSUmMMscnhe0xKAUZPOJukeje9\nyiIxxsbKV97m0aEPkRQZSW6/z3h/9Xz+veQJAP42/294fB4ANtZvpMPfQd+EwDYvrU2NWH7/bvev\n2lxMeJSNgo/fZ8ualbzxp/vZvGIZlmXt17tUFhWS1KMntp0Szt0k5cGgC7d/ubK0gc9XVzAkK27P\n14iIiIiIiISQkEpKgUBSWrHnpDR/zEQAnF7Dqdffgis8nPlPTeV7fS6n3LOC5N7T8XkjuG3wvRQ2\nFHLS9JP4xZe/YNrKaQD0j+9PdUkxT/74B3z81CPb79vW1MRXLzzLyi/+QnPVizRWV9J3zDiqSzbx\n8u/u4pmf3sycN1/pUs+pZVlUFm0kaT8WS2pq9/LjaQuJDXdyz5T8Ll8nIiIiIiLSnYXUnFIIJKWF\nS6vw+61Oh7AmZGSSmNWThqpKBkw8hbjUdF7942/pM6sXjhgHLf5ybNVXsmhFLo+c+ggfF33MNyXf\nUNVaRbgjnB6RWUz/86/oaGtl6WcfkZjZk+IVS9m4aD5+nxe7KxFPSwnG2Dj1+ltwutysmTOTpZ99\nxDcvTmX1t19z5e//hsO559WEG6sraW1sIDm7a0mpZVnc/fpSCqubeeHGMSRHd76Xq4iIiIiISKgJ\nyaTU77Voqm0jJjG80zKnXHczrY0NOF1usocMY/T5lzDnjelcdtbJlPbw0dM/iFWfPU/yuLt44MQJ\n+C0/q2pWAVC2ehUVhes59Qe3MOuVaXzx3JNExicwfPJZ9D/xJN56eCPtdc+QlteLiJhYAAZOPJWB\nE09l7dxZvP23PzLrlWlMuPLaPb5DYcFCAHoOGtKld35+zibeKdjCnZP6MSY3cd8XiIiIiIiIhIiQ\nTEoBGipb95iU9hw0dJevT7jkexQtWUTTrComXXApn3/0BP39ft56/V3yf3Y9NmOjd0RfvB4fi1e+\ngjE28sefTGb+QFobGsgaMBCbzU5dZSteTzFjLv4VAydk7/bcPsefQP8TJ7J4xnuceOn3sTs6/+vd\nuGg+0UnJJGb13Of7ri1v5IF3VnBSv2Rumdh7n+VFRERERERCScglpTFbk9K6ilay9rBQ7nfZ7HZO\nuvpGXrrvl3z29ONkDxnOmg2baVwym3WLz2Hd3AoKl1ThCrcTEbGUlNze/OubYgakxzC2d39enl/C\n24u3UL+6jrNw0WtYbyLjojt9Vt5xY1k180vKN6wlo+/ucz993g6KlhaQP27i9q1n9mbG8jI8Pj8P\nXTxEK+6KiIiIiMhRJ+SS0uj4MNyRDso31DNoQuYey1mWhd9nYXcE1nLK7D+AwadOoqGyghMuu53S\nv07F3fQFHzzyBRGx6ST1iKJsQzX1W1ZjDRjP/322DgCHzeD1W+QmRTLAOPEaSMjY8x6lPQYOBmDT\nsiWdJqWblhbQ0dZKr2GjuvS+i4vryU2OJCVa+5KKiIiIiMjRJ+RW3zU2Q8/8BIpW1GD5d2zD0lDd\nSkVRw/ZjK77ZwlM/+4r1iyq2lznjptu4+NcPUPBJGS5XPpZx0tL4Iqkjyxh5Zk/83s34vV7ergjn\nguGZ3HVmf64f14t3bh3Hpz+fyOCwMLbYfHy2pmqP8UXExJKc3Yvi5QVUbSrkqduu5/U/3cfaed/i\n83qZ9/ZrRMUnkDNs5D7f1bIsFhfXMUxbwIiIiIiIyFEq5HpKAXoOSmTt/Aoqixtprvew6KMiStfV\nA5B/YjqnXJXP5tW1eD1+PnxiGSdckMew03tgjKG+soUNBZWMnNSf1CH/4LE/PsjqN6dSuWgW3pZy\nCItlS1gG06f0x9nso2xDPaa4hdXFLfhrPDRE2Xhx7iZG9IyjtqWDvJSoTuIbysIP3ubFe3+Jw+Wi\nclMhb//1D4THxNLaUM/J19y419V5tymtb6OqqZ2hPZSUioiIiIjI0Skkk9Ie+QkAvPn3RXS0+4hK\ncDP2wt40VLWx/KsScgYlUb25iR4DEnCFOZj1+jrqK1uYcHlfCj7djM1mGHxyFpGxbq66/4/c/dAz\njCuZhdPfTkniZYzsnYar1c8rf56Pp823y7N7DUzkidVb+P5/5lLe0Mbce07FYd+1w3nElHPxdnTQ\nUl/LiZddRXxaBhsXz2fJJx/SXFfL4FMndek9C4rrAJSUioiIiIjIUSskk9LIWDc5Q5Jormtn2Gk9\n6D0yBbvdhs/rp2R1LQtmFFFX3kLvESkcf3YvZr8VxsIZm2ht6mDT8mr6HpdKZGxgr8/BWXFcf90l\n3P1CT25ssmjriOGsIWnMeHIZxm649HJ/zgQAABIvSURBVJ7jcEc4sCywOwyb2z08urKElaUNACwu\nrmNUTsIu8cUkpXDa9bfscqz3yNH0Hjl6v95z8eY6XHYb+emdL6okIiIiIiIS6kIyKQU460e77/Fp\nd9jIHZ7Mwg+LAEjMjMLYDGMvyMPhsjP3nY0ADD1t161YLhiexcbKZqreKSbdbmNsfAwfFm/gpO/1\nI7nnrglhf8IY2iMOp82wqLiOz1ZV7JaUHioFxXXkZ8TgdtgPy/1FRERERESCLeQWOtqXnEGJ2/+c\nlLVjvueoM3PIPzGd/iek73J8m5+d0Y/Tjs8ky+Wiak0dxkDusOROn/HyTWOYduNoRmXH89mqik7L\nHCyf32Lp5nqGZcUelvuLiIiIiIh0B0ddUpqaG4s70oHDadu+pykEVu095ap8Tr16921atolJCqel\nwcPaueWk58URHu3qtFyY047bYWfSwDRWlTVy+RPfMmdD9X7HWtHYRluHr9Nz6yubaPb4NJ9URERE\nRESOakddUmqzGfLHppM9OAmbzezXtanZMQDUV7bSa2jSPstfPTab+84ZwPrKZi57YjZXPjmbNeWN\nXX7eRY/N4ur/zKXD59/t3GItciQiIiIiIscAY1nWvksdBqNGjbLmz58flGfvTV15CyVraul7fBpO\nd9fmcrZ1+Jg2ZxP/+mwtGXHhvHvbOIzZe0Lc1O5l0H0zABjfJ4lR2Qn0TAynZ0IkmXHhPPDeCr5a\nU0nBvWfsd3ItIiIiIiISbMaYBZZljdpXuZBd6OhwiUuNIC41Yr+uCXPauX5cL8KcNn79xjIWbqpj\nZHb8Xq8pqm4GYExuAusqmvh6bdVuZS4cnqmEVEREREREjmpKSg+h84dl8uf3VzF1VuE+k9JN1S0A\n/PbsAQzMiKWtw8fm2lY21TSzubaVXkmRnNB730OIRUREREREQpmS0kMo0u3g3GEZvL6whLYOH2HO\nPQ//LdyalGYnRgKB3ta8lCjyUnZfGVhERERERORoddQtdBRsp/RPobXDx/zC2r2W21TTTFKUiyi3\nPhcQEREREZFjl5LSQ2xs70Rcdhtfrtn7/qWFVS30TNi/uasiIiIiIiJHGyWlh1iEy8HxvRL4ck3l\nXssVVTeTs3XoroiIiIiIyLFKSelhcFK/ZNaUN1FU3Uyrx8d3t91p6/BR2tBGz0T1lIqIiIiIyLFN\nExoPg8mD0vj9eyv591cbeGtRCbHhTiYNSuPMQemMzI5nTXkjloUWNRIRERERkWOektLDICs+ghE9\n43hhziZcDhv56TFMm7OJZ2YW0j8tmgtHZALsc9sYERERERGRo52S0sPknKEZLNxUx3Un5HD3lHya\n2r1Mm13Enz5YxVNfbyQjNoz02PBghykiIiIiIhJUmlN6mFw8MotbT87jx6fkARDldnDtiTlEhzmo\naGxnhHpJRURERERElJQeLtFhTn4xqR8xYc7tx9wOO6fnpwIauisiIiIiIgJKSo+4i0ZmYbcZTsxL\nCnYoIiIiIiIiQac5pUfYiXlJLLr39F16UEVERERERI5V6ikNAiWkIiIiIiIiAUpKRUREREREJGiU\nlIqIiIiIiEjQKCkVERERERGRoFFSKiIiIiIiIkGjpFRERERERESCRkmpiIiIiIiIBI2SUhERERER\nEQkaJaUiIiIiIiISNEpKRUREREREJGiUlIqIiIiIiEjQKCkVERERERGRoFFSKiIiIiIiIkGjpFRE\nRERERESCRkmpiIiIiIiIBI2SUhEREREREQkaY1lWcB5sTCVQFJSHHz2SgKpgByH7TfUWmlRvoUd1\nFlpUX6FJ9RaaVG+hI9TrKtuyrOR9FQpaUioHzxgz37KsUcGOQ/aP6i00qd5Cj+ostKi+QpPqLTSp\n3kLHsVJXGr4rIiIiIiIiQaOkVERERERERIJGSWloeyLYAcgBUb2FJtVb6FGdhRbVV2hSvYUm1Vvo\nOCbqSnNKRUREREREJGjUUyoiIiIiIiJBo6RUREREREREgkZJaTdnjPm5MeaMrX82wY5HusYYE7vT\nn1VvIUD1FHqMMfnBjkH2j9q00KQ2LfSonkKL2jMlpd2WMeYMY8wM4FfA1QCWJgB3e8aYU4wxi4HH\njDH3gOqtuzPGnGeMmQoMDXYs0nXGmH8CHxhjcoIcinSB2rTQpDYt9KhNCz1qzwIcwQ5Adtj6qZYT\nuBeYCPwJcAHHGWOcgFeNQfdljIkC7gEeAOYCU40xEZZl/Sa4kcl3GWOMZVmWMeZkAvXVAYw1xhRZ\nllUb5PCkE9vqbKdDiUANcLox5jnLstqDFJrsgdq00KY2LXSoTQstas86p57SbmLbN6hlWR7gLcuy\nxluW9T5QC1xuWVaHGu/uyxhjA6KAYmCRZVnFwA3AZRqS0b18pzHYCEwC7gRGA0OCFpjs0c51Zoyx\nbz08G3gUuBLoE6zYpHNq00Kb2rTQoTYttKg92zMlpd2AMeZW4HVjzE+NMemWZc3betxpWdaXwAZj\nzJnBjVK+yxjzI2PMRQCWZfkBC0gm0JBjWdYG4A3gf7aW1/yOIPvOz1qaZVmFlmWVWpb1GVAOTDTG\nZAY5TNnJTnV2hzEmw7IsnzHGBUwG3gQ+By43xlxojEkOarACqE0LVWrTQo/atNCi9mzvlJQGmTHm\nAuAa4J8EPtH6tTFm2zwArzEmASgCfEEKUb7DGBNtjHmcwJC0qcYYB4BlWeXASuCOnYrfBRxvjBmo\nXoHg6uRn7TfGmGE7FZkG9CXw6fLO1+kXryD5Tp0NBe4xxozc2vs237KsKmAtcDvwB0B1FWRq00KP\n2rTQpDYttKg92zclpcE3GnjUsqzPgfsJDL34CQQWE7AsqwYIB06G7UNqJIgsy2oEvrQsKw14F3hk\np9O/A4YZY6YYY9xbP21+l8C8Kgmuzn7Wbt920rKsJcA8YNDWxT1+tfW4fvEKns7q7Jat56YYY74m\nsHDOmwSGPzUEI0jZhdq0EKM2LWSpTQstas/2QY1BkOz0SdUG4HsAlmUVEfjHPtIYc95OxacR+GQy\nbGuDIEGyU729vfX/dwBXGGP6AFiW1QQ8BFxO4FOw/wHGA6VHOlYJ2M+ftRcJzJt6GUj6zvVyhOyj\nzuKNMWMJfNo8y7KsYZZlXQ2kAZrrFiRq00KT2rTQozYttKg96zolpUeI+c4yzzt9UvUq0LLTPyJl\nwBdA/k7fyGHAS2i40xG3p3qzLKvZGGOzLKuMwOT0p3Yq8xLwRwJDL5KBM7cOg5IjwBgzyhiTsu3r\nrv6smcBKkw8DS4EhlmXd+Z3r5TDZzzr7lMAvxdMsy/rVTre5wLKsRUckYDngn7Otx9SmBcme6k1t\nWvdljDnNGDNy29dq07q3/awvtWc7UVJ6mBljRhhjPgH+x+xYZWv7JydbhzK9DvzIGGMsy6onsKiA\ne6dv5Lcsy3rSsqyOIx3/sWpv9fbd4WaWZd0F9DLGjDXGpBljRluWtQq4z7KsWyzLKjnC4R+TjDED\njTGzgPuAuJ2O7+tnLWzrz1ob8BPLss6yLEu9AEfAAdZZJBBuWZbfGGPf9vNoWVbbkX+DY89B/Jyp\nTQuivdWb2rTuyRgz3BjzAYHFpfJ2Oq42rRs6wPpSe7YTJaWHydZ/6H9NYOjES5ZlXW1Zlm/rOdu2\nxtkYEwF8BGwBnjDGZADDAe+2e227Tg6/rtTb1n88ooDYnS59EJgJfEWgF0CfRh55PwHesCzrHMuy\n1kCXf9Y64P/bu/+Qvco6juPv737kj7lMKQ0bOaIt1mQOkuiPZNpaIAQVOCTJRf8Ug5aQUqCspO0P\n/1olS3L9UBEmFVJEhMJIwh8ksdRCjVQWkZU/EGNTt9qzb39c16MPtj0+7n52rvva/X7BgZ1zn/Nw\nDp/77Nzfc67rOpCZhzPzuTa7PrGON7PDUP5vtPnn4EbKDLymNXLM3LymjZdanOwCfgDcAuymNuWM\niEVe08bLPOTl9ayyKD1B6pfwbcD9mflDeO0uyiLKMOtExDbKHZVzgWsow3fvBl4Cbmyx35Nujrl9\ni9IM44I6fxmwBdgBrM7yygMNpF4Qzqbks7Mu+0xELANOr/Pb8VwbG2bWHzPr0xxz24bXtLFRb9rc\nDVycmb+gnFOXRumDfRggIm7Ac20smNf8CW98zZ+IWAcczMyH6vwS4C7gcUqb8WeBfwM/A/YAu4Bv\nZOZTM/7G6Zn5ytD7PslGzS0iPgjsz/JycQ3gKJmdCjwMXAt8ljKgw7+AVymjEd6O51pTZtYfM+vT\nqLl5TRveGzObsTyA9cAVwNcz88UofYK/A2zNzKdnrOu5NhDzOkEy02nECVhKaSf+IvBj4KwZn11J\n6ci8rs5/CbgVOH/GOgtaH8MkTvOQ28LWxzBp05tk9jXgr8CmOv8e4CFg/Yx1PNfMzMnMTsppHnLz\nmjYmmVEGlZp+cLSMMnLreUfZ3nPNvE6ayea78+M/wG+Az1Haim+c/iAzdwMb8/XmL3uAs6lt/muf\njoluQ97QqLnZL2p4x8yMMmLkqZTRIckyGMdvqe/T81xrxsz6Y2Z9GjU3r2nDO2pmWdVc/k65gXD5\nzA0915owrxPIovQ4RcSmiFgXEe/IzEOU4dP3AH8BLoqIlXW9yDLi1rQNlL4dBwD8gg7L3Poz18yy\nvE/vK8CmiFgbEZuBj1NeUG1mAzKz/phZn8ytP2/hd8iCLINQLQKeBF6e+XfMbBjmNRz7lL4Fta34\nuymdk48AT1OGc746M1+o66wAPk9pa769LlsAfJTyvqi/UdqZ/3n4I5hM5taf482sLr8CuBBYDVyX\nmY8NvPsTycz6Y2Z9Mrf+jPI7pBY63wYOZObWJgcwYcyrDZ+UzlFELMxSwS8FnsnM9cBmSrvyXdPr\nZeaTwF7gvIh4fx1gIIFnKO/4+pSFzXDMrT8jZLYkIhZn5k+A62tm/uAagJn1x8z6ZG79GfF3yGn1\n469a4AzDvNpZ1HoHxl1ELAS2AQsj4tfA24EpKMNAR8TVwD8iYl3W/oeZ+fOIWEUZIvoM4GOZ+Tjl\nTosGYG79mafMLgWeSJuADMLM+mNmfTK3/phZX8yrPZ+UziLKkM97gbOApyhf1v9S3j/0YXitjfgN\ndZrebiNwPXAvsKYWNhqIufVnHjN7YtAdn2Bm1h8z65O59cfM+mJe48E+pbOIiIuB5Zl5R52/GfgT\n5d1eWzLzQ1H6HZ4D3ETpc7ivbkdm3tdo1yeaufXHzPpjZv0xsz6ZW3/MrC/mNR58Ujq7vcBP6yN9\ngAeA92bmbZTH+1vqnZNlwOHMnB7F7j6/oE2ZW3/MrD9m1h8z65O59cfM+mJeY8CidBaZ+UpmHsrX\n3921AXi+/vsLwKqI+BVwJ/Bwi33U/zO3/phZf8ysP2bWJ3Prj5n1xbzGgwMdzUG9c5LAucAv6+L9\nwHXABcC+LC+i1hgxt/6YWX/MrD9m1idz64+Z9cW82vJJ6dwcARYDLwBr6t2SrcCRzLzfL+jYMrf+\nmFl/zKw/ZtYnc+uPmfXFvBpyoKM5ioiPAA/W6dbM/FHjXdIcmFt/zKw/ZtYfM+uTufXHzPpiXu1Y\nlM5RRCwDrgJ2ZOah1vujuTG3/phZf8ysP2bWJ3Prj5n1xbzasSiVJEmSJDVjn1JJkiRJUjMWpZIk\nSZKkZixKJUmSJEnNWJRKkiRJkpqxKJUkSZIkNWNRKknSCCJiKiIeiYjHIuLRiLgmIma9vkbE8oi4\ncqh9lCRpnFmUSpI0mlczc21mrgY2AJcB33yTbZYDFqWSJOF7SiVJGklEHMjMM2bMvw/4PfBO4Hzg\nDmBJ/fjLmflgRPwOWAXsA24HbgJuBC4BTgG+l5m3DHYQkiQ1ZFEqSdII3liU1mUvAR8A9gNHMvNg\nRKwA7szMiyLiEuDazPxkXf+LwDmZuT0iTgEeADZm5r5BD0aSpAYWtd4BSZJOYouBnRGxFpgCVh5j\nvU8AayLi8jp/JrCC8iRVkqSTmkWpJEnzqDbfnQKeo/QtfRa4kDKOw8FjbQZsycx7BtlJSZLGiAMd\nSZI0TyLiXcD3gZ1Z+secCfwzM48AVwEL66r7gaUzNr0H2BwRi+vfWRkRS5AkaQL4pFSSpNGcFhGP\nUJrqHqYMbLSjfnYzcFdEbALuBl6uy/8ITEXEo8BtwHcpI/L+ISICeB749FAHIElSSw50JEmSJElq\nxua7kiRJkqRmLEolSZIkSc1YlEqSJEmSmrEolSRJkiQ1Y1EqSZIkSWrGolSSJEmS1IxFqSRJkiSp\nGYtSSZIkSVIz/wOhZ/DpnMVRmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41a25ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df[total_factors + ['res']].groupby(level=0).sum()[-500:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>DivP</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>BDTO</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>0.000334</td>\n",
       "      <td>-0.001778</td>\n",
       "      <td>-0.001184</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.004826</td>\n",
       "      <td>-0.000827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.004500</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.004340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>-0.001618</td>\n",
       "      <td>-0.010973</td>\n",
       "      <td>-0.009495</td>\n",
       "      <td>0.004448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>0.001827</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>-0.002453</td>\n",
       "      <td>-0.001798</td>\n",
       "      <td>-0.002364</td>\n",
       "      <td>0.001739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>-0.002562</td>\n",
       "      <td>-0.000172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EPSAfterNonRecurring      DivP    CFinc1      BDTO      RVOL  \\\n",
       "Date                                                                       \n",
       "2017-05-05              0.000334 -0.001778 -0.001184 -0.002115 -0.004826   \n",
       "2017-05-08              0.004500 -0.000004  0.000990  0.004741  0.002026   \n",
       "2017-05-09              0.006346  0.001462 -0.001618 -0.010973 -0.009495   \n",
       "2017-05-10              0.001827  0.000650 -0.002453 -0.001798 -0.002364   \n",
       "2017-05-11              0.000160  0.000374  0.000506  0.001110 -0.002562   \n",
       "\n",
       "                 res  \n",
       "Date                  \n",
       "2017-05-05 -0.000827  \n",
       "2017-05-08  0.004340  \n",
       "2017-05-09  0.004448  \n",
       "2017-05-10  0.001739  \n",
       "2017-05-11 -0.000172  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df[total_factors + ['res']].groupby(level=0).sum().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_df['Code'] = total_data.Code.values\n",
    "pos_df['Ind'] = total_data['申万一级行业'].values\n",
    "pos_df['bm'] = total_data[index_components].values / 100.\n",
    "pos_df.loc['2017-01-01':, :].to_csv('aggregated_pos_risk_neutral.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41c1967b8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N2GKaZjIwAXjeMAwvF9cqIiIiIiJtRGpmMX5eVi4YHMPOJ6dw7sAueFoNRnUPc3dp0so0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\nSZIS8/8BC0HkVspZg7oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41cb14e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df['res'].groupby(level=0).sum().cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41b6352b0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IyHG0Kq+Kb73wOdOHh/OPW6fhdBh+9VY2y3dWMiImkNfXFbGvsZ3F2aU4jOHn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41d7858d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_df['res'].groupby(level=0).sum()[-500:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BDTO</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>DivP</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</th>\n",
       "      <th>Code</th>\n",
       "      <th>bm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BDTO</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051966</td>\n",
       "      <td>0.047731</td>\n",
       "      <td>0.022172</td>\n",
       "      <td>0.147722</td>\n",
       "      <td>0.059691</td>\n",
       "      <td>-0.008545</td>\n",
       "      <td>-0.057697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFinc1</th>\n",
       "      <td>-0.051966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069779</td>\n",
       "      <td>0.191189</td>\n",
       "      <td>-0.006843</td>\n",
       "      <td>0.284444</td>\n",
       "      <td>-0.008969</td>\n",
       "      <td>0.127854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DivP</th>\n",
       "      <td>0.047731</td>\n",
       "      <td>0.069779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242150</td>\n",
       "      <td>0.099527</td>\n",
       "      <td>0.420747</td>\n",
       "      <td>0.007166</td>\n",
       "      <td>0.083998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <td>0.022172</td>\n",
       "      <td>0.191189</td>\n",
       "      <td>0.242150</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011705</td>\n",
       "      <td>0.745078</td>\n",
       "      <td>-0.018250</td>\n",
       "      <td>0.185314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RVOL</th>\n",
       "      <td>0.147722</td>\n",
       "      <td>-0.006843</td>\n",
       "      <td>0.099527</td>\n",
       "      <td>-0.011705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066686</td>\n",
       "      <td>0.071064</td>\n",
       "      <td>0.100383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>0.059691</td>\n",
       "      <td>0.284444</td>\n",
       "      <td>0.420747</td>\n",
       "      <td>0.745078</td>\n",
       "      <td>0.066686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014969</td>\n",
       "      <td>0.147254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code</th>\n",
       "      <td>-0.008545</td>\n",
       "      <td>-0.008969</td>\n",
       "      <td>0.007166</td>\n",
       "      <td>-0.018250</td>\n",
       "      <td>0.071064</td>\n",
       "      <td>0.014969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bm</th>\n",
       "      <td>-0.057697</td>\n",
       "      <td>0.127854</td>\n",
       "      <td>0.083998</td>\n",
       "      <td>0.185314</td>\n",
       "      <td>0.100383</td>\n",
       "      <td>0.147254</td>\n",
       "      <td>-0.014517</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          BDTO    CFinc1      DivP  EPSAfterNonRecurring  \\\n",
       "BDTO                  1.000000 -0.051966  0.047731              0.022172   \n",
       "CFinc1               -0.051966  1.000000  0.069779              0.191189   \n",
       "DivP                  0.047731  0.069779  1.000000              0.242150   \n",
       "EPSAfterNonRecurring  0.022172  0.191189  0.242150              1.000000   \n",
       "RVOL                  0.147722 -0.006843  0.099527             -0.011705   \n",
       "res                   0.059691  0.284444  0.420747              0.745078   \n",
       "Code                 -0.008545 -0.008969  0.007166             -0.018250   \n",
       "bm                   -0.057697  0.127854  0.083998              0.185314   \n",
       "\n",
       "                          RVOL       res      Code        bm  \n",
       "BDTO                  0.147722  0.059691 -0.008545 -0.057697  \n",
       "CFinc1               -0.006843  0.284444 -0.008969  0.127854  \n",
       "DivP                  0.099527  0.420747  0.007166  0.083998  \n",
       "EPSAfterNonRecurring -0.011705  0.745078 -0.018250  0.185314  \n",
       "RVOL                  1.000000  0.066686  0.071064  0.100383  \n",
       "res                   0.066686  1.000000  0.014969  0.147254  \n",
       "Code                  0.071064  0.014969  1.000000 -0.014517  \n",
       "bm                    0.100383  0.147254 -0.014517  1.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df['2017-01-01':].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BDTO</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>DivP</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</th>\n",
       "      <th>Code</th>\n",
       "      <th>bm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BDTO</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018034</td>\n",
       "      <td>0.089668</td>\n",
       "      <td>0.076392</td>\n",
       "      <td>0.191158</td>\n",
       "      <td>0.136115</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-0.016639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFinc1</th>\n",
       "      <td>0.018034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103055</td>\n",
       "      <td>0.196153</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.323619</td>\n",
       "      <td>0.005923</td>\n",
       "      <td>0.189466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DivP</th>\n",
       "      <td>0.089668</td>\n",
       "      <td>0.103055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.221706</td>\n",
       "      <td>0.122082</td>\n",
       "      <td>0.432897</td>\n",
       "      <td>-0.006556</td>\n",
       "      <td>0.115660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <td>0.076392</td>\n",
       "      <td>0.196153</td>\n",
       "      <td>0.221706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029313</td>\n",
       "      <td>0.701345</td>\n",
       "      <td>-0.065806</td>\n",
       "      <td>0.197005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RVOL</th>\n",
       "      <td>0.191158</td>\n",
       "      <td>0.030712</td>\n",
       "      <td>0.122082</td>\n",
       "      <td>0.029313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118286</td>\n",
       "      <td>0.041460</td>\n",
       "      <td>0.118146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>res</th>\n",
       "      <td>0.136115</td>\n",
       "      <td>0.323619</td>\n",
       "      <td>0.432897</td>\n",
       "      <td>0.701345</td>\n",
       "      <td>0.118286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052532</td>\n",
       "      <td>0.156275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code</th>\n",
       "      <td>-0.000042</td>\n",
       "      <td>0.005923</td>\n",
       "      <td>-0.006556</td>\n",
       "      <td>-0.065806</td>\n",
       "      <td>0.041460</td>\n",
       "      <td>-0.052532</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bm</th>\n",
       "      <td>-0.016639</td>\n",
       "      <td>0.189466</td>\n",
       "      <td>0.115660</td>\n",
       "      <td>0.197005</td>\n",
       "      <td>0.118146</td>\n",
       "      <td>0.156275</td>\n",
       "      <td>0.030070</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          BDTO    CFinc1      DivP  EPSAfterNonRecurring  \\\n",
       "BDTO                  1.000000  0.018034  0.089668              0.076392   \n",
       "CFinc1                0.018034  1.000000  0.103055              0.196153   \n",
       "DivP                  0.089668  0.103055  1.000000              0.221706   \n",
       "EPSAfterNonRecurring  0.076392  0.196153  0.221706              1.000000   \n",
       "RVOL                  0.191158  0.030712  0.122082              0.029313   \n",
       "res                   0.136115  0.323619  0.432897              0.701345   \n",
       "Code                 -0.000042  0.005923 -0.006556             -0.065806   \n",
       "bm                   -0.016639  0.189466  0.115660              0.197005   \n",
       "\n",
       "                          RVOL       res      Code        bm  \n",
       "BDTO                  0.191158  0.136115 -0.000042 -0.016639  \n",
       "CFinc1                0.030712  0.323619  0.005923  0.189466  \n",
       "DivP                  0.122082  0.432897 -0.006556  0.115660  \n",
       "EPSAfterNonRecurring  0.029313  0.701345 -0.065806  0.197005  \n",
       "RVOL                  1.000000  0.118286  0.041460  0.118146  \n",
       "res                   0.118286  1.000000 -0.052532  0.156275  \n",
       "Code                  0.041460 -0.052532  1.000000  0.030070  \n",
       "bm                    0.118146  0.156275  0.030070  1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "turn_over_table = {}\n",
    "pos_df['Code'] = total_data.Code.values\n",
    "pos_df.reset_index(inplace=True)\n",
    "\n",
    "for name in total_factors + ['res']:\n",
    "    pos_series = pos_df[['Date', 'Code', name]]\n",
    "    pivot_position = pos_series.pivot_table(name, index='Date', columns='Code').fillna(0.)\n",
    "    turn_over_series = pivot_position.diff().abs().sum(axis=1)\n",
    "    turn_over_table[name] = turn_over_series.values\n",
    "    \n",
    "turn_over_table = pd.DataFrame(turn_over_table, index=pos_df.Date.unique())\n",
    "turn_over_table = turn_over_table[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret_tc_risk_neutral_df = ret_df.groupby(level=0).sum() - turn_over_table * 0.0015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41aeef8d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UqQwfPtx17dVXX+Xzzz/H39+fL7/8EsOom/HdIiIiIiJSN3KziqytUkzr/KKYYKK7Nibn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qiUCmIS8/h65zk+GdMWBwtTKmr12JqpG4hWewsTtj/XAxdrDefzK/C0M8PWvP6Njn8SWZTKyMjc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\nBrUasy6d0F68ZBKk5i1b4DxjBmaNGiOztaGEKj4/OpfdKbsp1dR6ZjzX4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JCW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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41aef7358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_tc_risk_neutral_df[total_factors + ['res']].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c41aad65c0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mwhpGDwjjs/w9FFQ1UVDVxNf+tYqUqCCmpUWxOLOchZuKqWtxAxAd7Et1Yxtu\nr+XZlflMTolk2Y4KSmqb8XU6+N9N04gN9SMm2A+Xw/CflfncMCOFq6cOIjU6qFt/b0REeoJ2CRYR\nERH5ksZWN3e+uIFb56Qzeu9GQF6v5canV/NxZjlfmzaIu+ePPmT/5jYPZ9y/mBB/F299dyZOx+GX\n9S7bUcE/P8pmc1ENdc3tYfSaqckE+blYuKmYyvpWmto8BPk6mTcmgYvGJ1JU3cTK3CriQv0YOzCM\nn72yGY/Xcmp6NDOHRDNrWIxmT0WkT9KxNiIiIiJd8NbGYm597jOGxYXwxm2n4uty8NDH2fxxUSbx\nof40tXl49NpJPLNiFy1tHv5x9UQAXltfyFPLd1G4p5HaZjfPf/MUpqVFdfq51lp272kiu6yeaWlR\n+Ps4gfYlwpsLaxmfFE6Ar/Ogfd0eL8aYI4ZjEZHepmNtRERERLrgva0l+DodZJbW8djSHCYNiuDP\n72Ry/tgErpicxLVPrOLKBSsIC/ChpqmNrz6+ktyKBiobWhmREMq80QkMig48qrAKYIwhKTKQpMjA\nA66H+PsccSyXU8eqiMjJR4FVREREZD9tHi8fbi/jgnEDaGx18/cPdhDq70NKVBB/uGQsgT5OzhwR\nS1JkID84axiPL83lr+9nccbwWL4+M5Vpg6OOeWdfERE5kAKriIiIyH7W7tpDbbObuSNjmZAcwbId\nFdQ1t/HM16fsO/7l8a9N3tf+9jOHcP2MFMICfHqrZBGRk5YCq4iIiMh+dpbXAzAuKZy4UH+e/voU\nvF7LiITQQ/ZRWBUR6RkKrCIiIiL7KalpxmEgJtgPgInJEb1ckYhI/6W380VERET2U1TdTFyovzYx\nEhHpA/QnsYiIiMh+SmqbiA/z7+0yRESETgZWY8w5xphMY0y2MeYnB7l/jTFmozFmkzFmuTFmXGf7\nioiIiPQlxTXNJCiwioj0CUcMrMYYJ/BPYB4wErjKGDPyS81ygVnW2jHAPcCCo+grIiIi0u1eW1/I\nPz7ccVR9rLUUVzeTEBbQQ1WJiMjR6MwM6xQg21qbY61tBV4A5u/fwFq73Fq7Z+/HFcDAzvYVERER\n6W61zW384tXN/OOjbNweb+f7NblpavNohlVEpI/oTGBNBAr2+7x777VD+Trw9jH2FREREemypz7J\no7bZTXObl+y9x9R0RlFNE4BmWEVE+ohu3XTJGDOH9sD642Po+y1jzBpjzJry8vLuLEtEROSEVljd\nxPkPLmVOLm0QAAAgAElEQVThpuLeLqVb1Da34fHaHhu/1e3l6eV5DIsLAWDT7ppO9y2paQbQpksi\nIn1EZwJrIZC03+eBe68dwBgzFngcmG+trTyavgDW2gXW2gxrbUZMTExnahcREekX3tpYxObCWm59\n7jO+89xnZJd9MWPY0OLmW/9ew71vbqWsrrkXq+ycVreXc/66hEsfWU5Di7tbx7bWUlDVyMeZZVQ2\ntPLDs4cR5Otkc+GBgbW+xU1VQ+tBxyjeG1i1JFhEpG9wdaLNamCIMSaV9rB5JXD1/g2MMcnAy8C1\n1tqso+krIiIih/fR9nKGxAZz6pBoXlxdQE1TG898fSoAf3s/i3e3luJyGIpqmnjomkm9XO3hvbe1\nlKKaZopqmpny2/dJjw3mwasmkhwVCLSHzgVLcsgpb+C+S8d2etzK+hbuenUzb28uISzAh+hgP2YP\ni2FUYhib9gus9S1uLnloOcbAojtO6zDO8p0V+DgNsSF+Xf+yIiLSZUcMrNZatzHmO8A7gBN40lq7\nxRhz0977jwC/BKKAh4wxAO69s6UH7dtD30VEROSkU9fcxuq8Kr4xczA/mTecVreX19YX4fFathXX\n8uQneVw1JRm3x8t720rxei0Oh+ntsg/phdX5JIYHcM9Fo/hoezlvbCzi4oc/4akbpjA6MYx739rG\nE8tyAfjumUNIDD/yu6TvbinhZ69sorbJzdyRcby3tZRvnTYYl9PBmMQwnl6ex3kPLMXpMOxpbKWg\nqv091cLqpgPG/7+1u3lzYzHfPT0dl1NH1YuI9AWd+tPYWrvQWjvUWptmrf3t3muP7A2rWGu/Ya2N\nsNaO3/tfxuH6ioiISOd8uL0Mt9cyZ1j76zITkyOob3GTWVLHz17ZRESgLz85ZzjT0qKobmxjW0lt\nL1f8hdc3FPHf1fnU7136u7mwhqU7Krg8I4nTh8dxz0Wjeemm6fi5nFzx6Kc8vjSHJz/J5dT0aABW\n5VYebngAfr9wG996Zi2xIf68ftsMHrsugze+cyrfnzsUgMsyBjJ3ZBzxof5EBvmSGh3M7WcMAWBp\n1hd7ZmSX1XHXq5uZmhrJ7WcO7e7fChEROUadWRIsIiIivaC0tpnfvLGVYXEhTBoUAcCE5HAAfvrK\nJjburuHBqyYQFujDKYOjAPh0ZyWjBoT1Ws2fq6hv4c4X19Pmsdz9xlYuGDuAbSW1RAX5cv2MlH3t\n0mODefmW6XztyVXc+9Y2IgJ9+MfVEzjtjx+xKreKr0wYeNDxcysaaHV7WbA0h0snDeR3XxmDr6v9\n3+HHDPzi+w+PD+Xhrx64TNpay39XF7B0RwVXTkmmuc3Drc+uI8DXyQNXTcDZh2eoRUT6GwVWERGR\nPsjjtdz+wjoaWz3885oJ+5aopkYHERHow4aCamYNjeH8sQkADAgPYFBUIO9tLeW6aSn7wltveWnt\nbto8lr9fOZ7l2ZW8sbGIxlYPf7p0LGEBPge0jQv158WbpvHr17dw1sg4wgN9mZwSycqcqoOO/Vn+\nHi5+aDlBvk4CfZz87NwRR/V9jTHMHBLNO1tKqG5s5Q9vbyeztI6nbphMXKg2WxIR6UsUWEVERPqg\nBz7YwYqcKv582TjSY0P2XTfGMGlQBMuyK7j3otHs3TsCgCsnJ3Pfou2c+ZfFTEgO5+opyUzdO/N6\nPFlreWFVPlNSIpk/PpH54xP5xQUj2V5cu2+m+MtC/X34y+Xj932ekhrJB9vLKK5p6nAm6uvri/B1\nOvBauGlWGpFBvkdd47XTBvHa+iLO/MsSKupbuHl2GrOHxR71OCIi0rMUWEVERPqY5dkVPPDhDi6Z\nOJBLJ3VcEvurC0axp7GVpMjAA67fPDuN9Nhgnl25i6U7KnhtfRFTUiK59fR0ThsSfUC47UlZpfXk\nVTZy06y0fdeC/VxkpER2eoyzRsXz+7e38/Jnhdw6J53cigb+uGg7pbXNFOxp4vThsfztyvH4HeNM\n8tiB4fz2K6P5+SubuXPuUG6dk35M44iISM9SYBUREelDdpbX890X1jE4Ooh7Lhp10DZJkYEdwurn\n5o6MY+7IOJrbPLywKp9Hl+TwtSdXMSU1kr9cPo6BEQfv151W5LRvljRj7+ZJxyI1Oohpg6N4YXU+\nlfWtPLMiDx+nA4/X0uL2cu7YBPx9nF2q87KMJOaPT+z15dMiInJo+hNaRESkj6hqaOXqx1ZgLTx6\nbQaBvsf+78r+Pk6un5HKxz+czT0XjWZLYQ0XP7Sc5jZPN1Z8cCtzK0kMD2BgxJGPpDmcq6YmU1DV\nxFPLc7l00kA+/uFsHr12EueOiefMEd2zfFdhVUSkb9MMq4iISB/x6rpCSmtbePXWGaTHBnfLmH4u\nJ9eeMoiBEQHc8K/VfLS9jHljErpl7IOx1rIyp4pZw2K6vAR53uh4fn7uCGYOjWZ4fCgAscP89a6p\niEg/on9WFBER6SNeXV/I6MRQxieFd/vYpw2JITbEj5fXFdLc5qGsthlrbbc/Z0tRLZUNrZyS2vXN\nnnycDr552uB9YVVERPofzbCKiIgcR9uKa/nNG1sZMzCM6WlRTEmNJNDXRVZpHRt313DXeSN65LlO\nh2H++AE8viyXkb9chNfC7GExPHrtJPxcR34XNLeigTteWEdJbTNjEsMIC/AlLMBn738uwgLbf77v\n7UwiAn04vZuW7IqISP+mwCoiInIc/ePDbNbm72Htrj0sWJKDj9MwISmCnIoGgnydXDh+QI89+7pp\nKRRWN5EeE0yb1/Lwxzv5znPreOiaifg4D1x0tSSrnFfWFZJf1ciuykYq6lsID/RhRlo0O8vr2VpU\nS01TGw2tB74Taww8fcMUooP9eux7iIhI/6HAKiIi0kPaPF5eWF3AaUOiGRQVRHFNE4u2lPD1U1P5\n3plDWbOrimXZFSzPriQpMoD7LhlLbIh/j9WTFBnIQ9dM2vc5IcyfX762hTv+u56/XzEel9NBU6uH\n3y3cxjMrdhEd7MuQ2BDOGB5LclQgF01IJDH8wI2U2jxe6prd1DS1UdPURniADynRQT32HUREpH9R\nYBUREekhv31rG08tz8Pfx8H101PZuLsaay3XnjKIAF8nM4fEMHNITK/Vd920FFravPx24Tb8XA7+\nfOk4Ln/0UzYX1fDNmancedawIx4d4+N0EBnkS2SQ73GqWkRE+hMFVhERkW7S5vHichiMMbywKp+n\nludx1ZRkapvbWLBkJ/4+Tu69aMwhz1DtDd88bTB1zW088GE24waGs6mwhl9fMJLrZ6T2dmkiIiIK\nrCIiIt2hrrmNCx5cxoz0aC6akMgvXtvMaUNjuGf+KFxOB2W1zfg4HUT0wZnIb542mEcW53D/u5kA\nPXrsjYiIyNFQYBUREekG97+bRV5lI3mV+byxoYiBEYE8eOUEXHs3M4oN7bl3U7sqxN+HGelRfJRZ\nzvD4EOL6cK0iItK/6BxWERGRLiqqbuLfn+Zx2aSBJEUGYC08dl0GYYE+vV1ap501Kh6AWUN7751a\nERGRL9MMq4iISBe9s6UEr4WbZ6fx/bOG0ur2MijqxNopd97oeF5bX8jFEwf2dikiIiL7KLCKiEif\ns3tPI2vy9nDe2IQO54P2Re9uKWVIbDCDY4J7u5RjFh7oywvfmtbbZYiIiBxAgVVERPqEVreXD7aV\n8vzqApbuKMdaqGlq42vTU3q7tEPaXlLL8yvzWZVXxU2zBvd2OSIiIicdBVYREekTvv70apbuqCAh\nzJ/bTh/C0h3lPPRxNldMTjriWaC95Q9vb+fjzHIcBs4fO6C3yxERETnpKLCKiEivq6hvYemOCr5+\naio/O3cETofhlNRIrn58Ja+sK+SqKcm9XWIHeRUNfJxZzu1nDOEbM1MJ8T9xNlgSERE5UfT9F4NE\nROSkt3xnJQAXjBuA02EAmJYWxeDoIBZuKu7N0g7pPyt24XIYrpmarLAqIiLSQxRYRUSk132yo4IQ\nfxdjEsP2XTPGcPboeD7dWUl1Y2svVtdRU6uHF9cUcM7o+D59vqqIiMiJToFVRER6lddrWZZdwfS0\nqH2zq5+bNzoet9fy/rayg/a11mKtPR5lHuC19YXUNrv79IZQIiIiJwMFVhER6TXWWn75+mYKq5s4\nd0xCh/tjEsMYEObPos0lHe41tXr46hMrufk/n3VLLR9sK+WxJTlHDMDWWp7+dBfD40PIGBTRLc8W\nERGRg9OmSyIi0ms+y6/mPyvy+ebMVOaPT+xw//Nlwc+uzKe+xU2wn4uyumY+3FbGC6sLWF9Qjb+P\nA7fHi6sL57UuySrn28+sxe21BPm5uHrqoTd5WrtrD9uKa/n9xWMwxhyynYiIiHSdAquIiPSarcW1\nANx4auoh28wbncC/Psnjj4u2s2F3DRsKqgFIDA/g3DHxLNxUQmZpHaMGhB1yjCP57VvbGBQVSEJY\nAL96fTM+TsNlGUkHtLHWsjK3iseX5hLi72L+eB1jIyIi0tMUWEVEpNfsKK0jxM9F/GE2Lpo0KILo\nYF/+/ekuUqOD+MFZQzlzZBzD4kLIr2pk4aYSnliWy4aCau69aAzT0qKOqoYWt4fs8npunpXGN2cO\n5uZn1/LDlzaytbiWn587Yt/M7aLNJdz8bPvy4xtnpBLoq79CRUREepr+thURkV6TVVpHelzwYZfW\nOh2GW2ans7mwhrvnjzrgCJnkyEAig3x5+bNCAG54ahW/OH8kV05O7rCB06HklDfg8VqGxocQFujD\nv2+cwu8WbufJT3LJLKnjH1dPJDLIlxfXFBAf6s/PzxvB7GExXfviIiIi0inadElERHrNjtJ6hsaG\nHLHdjaem8pcrxnc479QYw7iB7UuBb5mdxriB4fz8lc2c/+AyVuRUdqqGrNI6AIbFtdfhcjr45QUj\n+dOlY1mTt4eZ933IL1/bzOKsci6ZlMgF4wbo3FUREZHjRDOsIiLSKyrrW6hsaGVIXHCXxjl9RBzZ\n5fXcOiedQF8nb20q5ndvbePKBSu4akoyv794zGH7Z5XW4XIYUqODDrh+WUYSYweG8/DH2Ty7Mh+v\nhYsnDuxSrSIiInJ0FFhFRKRX7CirB2Bo3JFnWA/n2lMG8dWpyfuWFZ8/dgBnDI/jN29u4flV+Xxt\n+iAWZ5ZzxeQkwgN9O/TPLKknNToIX1fHRUfD4kP425UT+OE5w9ld1UhaTNfCtYiIiBwdLQkWEZFe\nsbWofYfgrgZWoMM7sAG+Tm4/YyjGwC3Pfsbv397Ok8tyO/QrrW1mW3EtQ+MPX0NieABTBx/dZk4i\nIiLSdZphFRGRblVQ1chv3tzK1VOTmTMs9pDtPtxeRlpMEPFhh94huCviw/yZnBLJqtwqAF5au5vL\nMpL4OLOMNbv2sCZvD4XVTQDcMCOlR2oQERGRrlFgFRGRbvPh9lLueGE9tc1uQvxdhwysNU1trMip\n5BszB/doPReNT2RVbhVfPSWZ/6zIZ86fP8bttcSG+JGREsGNp6YyaVDEvo2bREREpG9RYBURkS7z\neC1/ez+LBz/MZmRCKAA7yxsO2X5xVjlur2XuyEPPwHaHKycnkZESQXJkIIuzykmNDubuC0eREhV4\n2KN0REREpG/QO6wiItJlD32UzYMfZnPZpIG8fMt0JqdEsLOsno27q3lyWS7W2n1tNxRU84eF24gL\n9WN8UkSP1uVwGIbGheDv42TxD+bw7xunkBodpLAqIiJygtAMq4iIdNn720qZNCiCP102DoC02GDq\nW9z86KWNbC+po6S2mZ/OG86Lawr4xatbiAnx44nrMnA6jl9wdBzHZ4mIiEj3UGAVEZEuqW1uY1Nh\nDd+Zk77v2ufHv2wvqSMyyJcFS3JYnFlOZmkdM4dE88CVE4gI6njEjIiIiMj+FFhFRKRLVudW4bVw\nStoXx77sf17pny4dS1ZpPX99P4ubZ6fxg7OGHdeZVRERETlxKbCKiJyE9jS0cvXjK7lp1mDmj0/s\n0Wct31mJr8vBxOQv3keNC/Uj2M9Fi9vDKYOjOGNEHN+cmYrLqa0TREREpPP0fw4iIicZay13vbqZ\nbcW1vLmxuMef91n+HsYnhePv49x3zRjDqAGhnDI4iiC/9n8bVVgVERGRo6UZVhGRk0huRQP3vrmV\nD7aXER7ow5q8Krxe22MbDllryS6r56KDzOI+/NVJOLUbr4iIiHSBAquIyEkgs6SOf36UzZsbi/B1\nObjrvBGE+Lv48f9tIqeinvTYkB55blldC3XNbtJjgzvci9SmSiIiItJFCqwiIicwj9fyg/9t4JV1\nhQT5OvnmaYP5xqmDiQnxI7eiAYBVuXt6LLBml9UDHDSwioiIiHSVAquIyAnskcU7eWVdId86bTC3\nzE4jPPCLWc2UqECig/34y3uZrMvfw9empzA6Maxbn6/AKiIiIj2pUztgGGPOMcZkGmOyjTE/Ocj9\n4caYT40xLcaYH3zpXp4xZpMxZr0xZk13FS4i0t+V1DTz1/eyOG9sAj+dN/yAsArtGx/de9FoJqdE\nsnBTMec/uIzLH/2URZtL8Hhtt9SQXVZPiJ+L2BC/bhlPREREZH9HnGE1xjiBfwJzgd3AamPM69ba\nrfs1qwK+C1x0iGHmWGsrulqsiIh8YUtRDW6v5cYZKZhDbG50zuh4zhkdT21zGy+uLuCp5Xnc9J+1\nJEUGcP30VC7PGEiIv88x15BdVk9abPAhny8iIiLSFZ2ZYZ0CZFtrc6y1rcALwPz9G1hry6y1q4G2\nHqhRREQO4vN3VAdHH3k5bqi/D9+YOZiPfzCbR746kfhQf+55cyvTfv8hd7+xhfzKxkP23dPQesgZ\n2ezyei0HFhERkR7TmXdYE4GC/T7vBqYexTMs8L4xxgM8aq1dcBR9RUTkEHaWNxAR6EPEUezG63I6\nOGd0AueMTmDj7mr+9Ukez3y6i6eW5zF3RBw3nprK1NTIfTOmFfUtzPjDhyRGBPDzc0dwxoi4fWOV\n1jZTXtfCiITQbv9uIiIiItDJd1i76FRr7XhgHnCrMea0gzUyxnzLGLPGGLOmvLz8OJQlInJiyymv\nZ3DMsc9ujh0Yzl+vGM8nPzmdW2enszqviisXrOD8B5exbEf7WxxbimppcXupa3bz9afXcPcbW2hx\newDYUFANwLiB3buRk4iIiMjnOhNYC4Gk/T4P3HutU6y1hXt/LQNeoX2J8cHaLbDWZlhrM2JiYjo7\nvIhIv5VT0cDg6KAujxMX6s8Pzh7G8p+cwe8vHkNNUxt3/HcdXq8ls6QWgDdvO5Xrp6fwr0/yuOTh\n5eRVNLBxdw1Oh2HUAAVWERER6RmdCayrgSHGmFRjjC9wJfB6ZwY3xgQZY0I+/xk4C9h8rMWKiEi7\nuuY2yutaujTD+mUBvk6umpLM984cSkV9K1uLa9leUkdsiB9xof78+sJRLLh2EgVVTZz/4DIWbSlh\nSGwwAb7ObqtBREREZH9HfIfVWus2xnwHeAdwAk9aa7cYY27ae/8RY0w8sAYIBbzGmDuAkUA08Mre\nd6FcwHPW2kU981VERPqPnPK9Gy7FdH2G9ctmDo0GYHFWOZkldQyLD9l376xR8YxICOXCfywju6ye\nKzKSDjWMiIiISJd1ZtMlrLULgYVfuvbIfj+X0L5U+MtqgXFdKVBE5GRire2WI2AyS+oASOuBwBob\n4s+oAaF8sK2UHWX1fG3aoAPuJ0UG8usLR3H7C+uZkBze7c8XERER+VynAquIiHTdf1bs4m/v7+Bn\n5w7npbW7uWJyEvPHJx7TWB9nlREX6kdaNy4J3t8ZI+J44IMdAAyNC+lw/8JxA0gMD2CMNlwSERGR\nHqTAKiJyHNQ0tvGndzKpaWrj+y9uwGFg+c5Klu2o4KIJicxIj+70WK1uL0uyKrhgXEK3zNYezM2z\n0iitaebldbuZNCiiw31jDBkpkT3ybBEREZHPHY9jbURE+r2HF++ktrmNp26YzC2z0/joB7O5PGMg\ni7aUcM3jK/nnR9lYa4H242rqmtsOOdbqvCrqW9ycPjzukG26KsDXyX2XjmXTr8/u1o2dRERERI6G\nZlhFRHpYcU0T//okl6+MT2T2sFhmD4sF4I+XjuM380fz4//byJ/eySS7rJ6bZ6dxwYPLmDQogue+\necpBx/s4swxfl4MZ6VE9Xru/j3YAFhERkd6jwCoi0sP+9t4OrIXvzR3a4Z6/j5O/XTGe9Jhg7n8v\ni7c2FtPq8e5bLnzqkI5LhVfl7WH8wHACffVHuIiIiJzctCRYRKQH7Sit439rC7h22iCSIgMP2sYY\nw21nDOGfV0/Ez8fBPReNJjE8gJ++spHssvoD2ja1ethSWMOklI7vlYqIiIicbBRYRUR60H2LMgny\ndXHrnPQjtj1vbAIbfnkW154yiH9eM5GmVg+XPbKcwuqmfW3WF1Tj9lomK7CKiIhIP6DAKiLSQ6oa\nWnl/WynXz0ghMsi3U30cjvZdf8cnhfPit6fR5rHc8p+1vLa+kPUF1SzZUQ7AxGQFVhERETn56QUo\nEZEekllSB3DMx78Mjgnmj5eO5fYX1nH7C+v3XR8SG0x4YOcCsIiIiMiJTIFVRKSH7ChrD6xD4479\nWJhzxyQwZ1gs+VWN7KpsIL+qkfFJ4d1VooiIiEifpsAqItJDskrrCPFzER/q36VxAnydDIsPYVh8\nSDdVJiIiInJi0DusItLvWGvJLqvHWtujz8kqrWdIXDDGmB59joiIiMjJSoFVRPqd97aWcuZfFnP/\nu1k0t3mobmylpKaZ4pommts8Rz1eq9vLf1fnM/cvi/n9wm1AeyjeUVqnWVERERGRLtCSYBHpdz7O\nat9p9x8fZfOPj7IPuOfv4+D9789iYMTBz0zdX2OrmxdWFfDY0hyKa5rxdTl4bX0RXz81ledW5bOn\nsY0hsQqsIiIiIsdKgVVE+p1Pd1Yye1gMZ42Mp7qpFX+XE38fJ42tbu59axsfbS/j2mkphx1j2Y4K\nvvvCOqoaWpmaGsl9l4xlV2UDv3htCz99eRMfbC8DYHyyNkgSEREROVYKrCLSrxRVN5Fb0cA1U5O5\nemryAfestTy1PI+lOyqOGFj//WkeTofhpZum7Tu2Jqu0fXOlD7aXceaIOO65aBQJYQE98TVERERE\n+gW9wyoi/cqy7AoApqdFd7hnjGHmkBg+3VlJm8d72HEyS+uYkhJ5wBmrQ2KDiQxqPx/1komJCqsi\nIiIiXaTAKiL9Rovbw8Mf72RwdBDDD7EZ0swh0dS1uFlfUH3IcRpa3OyqbOywoZIxhqmpkYT4uZgz\nPLZbaxcRERHpj7QkWET6BbfHy+8Xbie3ooGnb5yCw3Hwo2ZmpEfj53Lw8meFTN47e9rm8VLd2Mb6\ngmrufWsrkwZFABw09P7qglFU1Lfg7+PsuS8jIiIi0k8osIrISaegqpGCPY1MGxzFlqJa3t1aylsb\ni/j/9u48Tu66Pvz467P3Zq8cuzk35A4JgQRIOMIhIKAi+sMDzypWq1ZU1Hq0Vttaq21tbbFSEcXW\nGxCpgIAoitwgdwiBJJD72tzZ3ex9fn5/zOyy5NwkOzs7s6/n47EPdr7XvIf3TL77ns+1Zmdi7Op5\ns6sOem5FcT7/b8FEbl+yhYbWDh58aScNbZ29+0OADbubAZgzvny/88dXFDG+omjgX5QkSdIwZMEq\nKet85ubneGZDLWVFeTS0dpITYOGUUXz24uO5dP6Ew57/gbOmcsszm/nNsq28Y2E1k0aOYFRJPlWl\nhTS3d/G5W5ZSUpBL9SjHqEqSJKWSBaukrLJ6RwPPbKjlornjKCvKY/GMMVw4ZyxjSgv7fY0TJ1Xw\nuYtnM3t8Ga+fN/5V+9o7u/mXu1cwZcyIg3YrliRJ0sCwYJWUVW5+ahN5OYFvvP0kKo+gSN3XVRfO\nOuD2grwcrr9iIYV5jlGVJElKNQtWSVnl7mXbuGDO2GMqVg9n4ZTRhz9IkiRJx8xlbSRljR17W9lS\n18KZ08ekOxRJkiQNAAtWSVljSXLt1JMnV6Q5EkmSJA0EC1ZJWWPppjrycgLzJlqwSpIkZQMLVklZ\n47lNdcydUE5RvhMiSZIkZQMLVklD3t7WDjbsbjrkMU+u28PSTXUssDuwJElS1rBglTTkffm2F7j0\nmkfY09R+wP23PruZd37/TxQX5PGuRccNcnSSJElKFQtWSUPanqZ2fvfCVhrbOvn+Q2v227+8Zi9/\ne+syzpw+mof/+gJOqraFVZIkKVtYsEoa0m59djMdXZFTjxvJTx5bz97Wjt599S0dXHnDM4wckc9/\nv+dUigscuypJkpRNLFglDVkxRm5+ahMnTx7JZy6aTWtHNy9srgeguzvyuV8uZUttC9e+91SqygrT\nHK0kSZIGmgWrpCHr2Y11rNrRyLtPm8yJkxJdfV+oSRSs1z+8lntXbOdLb5zLoqmj0xmmJEmSUiQv\n3QFI0sH88qlNjCjI5U0LJlJamMekkcW8sGUvnV3dXPfAGl47ZywfPHtqusOUJElSitjCKmlIamzr\n5M7na3jz/ESxCjBvYjkv1NTzzIZa6ls6uHxhNSGENEcqSZKkVLFgldTrzqU13PPitnSHAcBdS2to\nbu/iXadP7t124qQK1u1q4o6lNeTnBs6dVZnGCCVJkpRqdgmWBCQmMfrHO16krbOb0/56NKNLCtIa\nzy+e2sTscaWcMnlk77YTJ5UTI9zy9GbOnD6GsqL8NEYoSZKkVLOFVRIAy7fuZXdTO41tnfzr3StY\nt6uJ7u6Yllg27G7iuU11vHPR5Fd1+V08vZJ3LKymenQx7z39uLTEJkmSpMFjC6skAB5etQuAN8wb\nzy3PbOaWZzZTnJ/LnAll/P2bTuDk6pEs3VxHfUsH5x8/NqWxLK/ZC8CZ08e8antxQS7ffMeClD63\nJEmShg4LVkkAPPTyTuaML+O7f3YqL9bsZcW2vazYupe7nt/K3932ArPHlXL7czUA/OlvX8uEiuKU\nxfLy9kZCgBlVpSl7DkmSJA19FqySaO/s5pkNtVyxeAo5OYGTqis4qTqx7ukJE8r5wv89z/Ktezlv\ndhw2zmkAACAASURBVBUPvryTlVsbUluw7mhg8qgRFBfkpuw5JEmSNPQ5hlUSa3c10t7V3Vuk9vWW\nUyYxvbKE+dUVXP3ORHfcldsaUhrPqu0NzB5n66okSdJwZwurJFZuTRSgcyeU77cvPzeH2z95NoV5\nORTm5TKhooiXtu1NWSwdXd2s29XEhXPHpew5JEmSlBksWCWxYtteCnJzmFZZcsD95X2Wjzl+fFlK\nW1g37G6ioyvawipJkiS7BEtKtLDOHFtKfu7h/0k4fnwZa3Y20tHVnZJYXkzOEDxrbFlKri9JkqTM\n0a+CNYTwhhDCSyGE1SGELx5g/5wQwp9CCG0hhM8fybmS0m/ltr3MmdC/AnHu+HI6uiJrdjamJJbb\nlmxhfHkRc8ZbsEqSJA13hy1YQwi5wLXAJcAJwHtCCCfsc9ge4FPAfxzFuZLSaNnmerbvbWPu+P3H\nrx7IoqmjyM8N/M/D6wY8ls21zTz48k7eedpk8vrR2itJkqTs1p8xrKcDq2OMawFCCL8ALgOW9xwQ\nY9wB7AghXHqk50o6vJb2Lm5bsoUTJ5Uzv3rkEZ/f1R25d8V2Vm5t4DWzK8kJgQdf3smDL+/k2Y21\nVBTnc+Hcsf26VvWoEXzonGl8/8G1vOf041g4ZdQRx3Mwv3pmCwDvOm3ygF1TkiRJmas/BeskYFOf\nx5uBM/p5/WM5Vxr2Wju6uOGJjVz3wBp2NbaxePoYbvromUd8nbuer+HTv3gOgG/d+zIAIcD8SRV8\n+sJZ/PlZUxk5oqDf1/vUa2fx6yU1fOWOF/j1J84hNycccUwH8sS63Zw4sYJJI1O3xqskSZIyx5CZ\nJTiE8FHgowDHHXdcmqOR0qu1o4tfPLmR7z6whh0NiUL1xEnlPLF2D51d3ft1l23t6OKPK3ZQ39LB\ne8/Y//Pz8KpdjBqRzx8/dz53L9tKaWEe586qZExp4VHFV1KYx5cvnctVNy3hpic38r4zpxzVdfrq\n6o4s3VTH2xdWH/O1JEmSlB36U7BuAfr2z6tObuuPfp8bY7weuB5g0aJFsZ/Xl7JKW2cXv3xqE9fe\nv4Zte1s5fdpovv3uU1g8Ywx3Lq3hgZd2smJrAydVV/Sec+uzm/nX365kZ0MbAAsmVzBvYmL/6h2N\n5OUE/rRmN2dOH8PokoIBKS4B3jR/Ajc+sZH/+P1LXHrSBEaVHLiFtrWji8fX7ua82VWEcPCW2Je3\nN9DU3sUpxx15l2dJkiRlp/7MavIUMCuEMC2EUAC8G7ijn9c/lnOlIWv1jkb+95F1xDgw3620d3Zz\nwxMbuOCbD/D3v36R6lHF3PDhM7j5o2eyeMYYgN6xok9v2NN73gtb6vn8LUuZNLKY771vIYV5Odz4\nxEZerKnnYz97houufpA3//cjbKlr4azkdQZKCIGvXjaPxtZO/v2elw54TGdXNx+/4Vn+/EdPce+K\nHYe83pKNdQCcMnngxsRKkiQpsx22hTXG2BlC+CRwD5AL/DDG+GII4WPJ/d8LIYwHngbKge4QwmeA\nE2KMew90bqpejDRYvvWHl/nNsq2MLy/i0vkTjulaq3c08IEfPsWWuhZOOW4k/3b5fM6ZWblfa+TE\nkcVMrCjiN89vpTvC3pYOfvvCVkaXFPCTD51ORXE+l86fwM1PbeKGJzZSVpjHX5wzjZ8/vgGgt/Ad\nSLPHlfH+xVP46Z828NHXTGdaZUnvvhgjX7ptGfet3EFBXg6/fm4LF58w7qDXenZjLaNLCpgyZsSA\nxylJkqTM1K8xrDHGu4G799n2vT6/byPR3bdf50pDxeodDXTHROHVXw2tHdy7YjsAX//Nci6YU8WI\ngqMfDv6/j6yntrmdH3/wtMN2mz17ZiW3PLOZpzfUAjBqRD7fePt8KorzAfjIudN5YUs9b5o/kQ+c\nNZWK4nzmTijndy9sY0ZV6VHHeCgfP38mNz25kf/+4yquftfJvduv/sPL/PLpzXzqwlnUNrVzyzOb\naGzrJD830NzW9aouxF3dkYde3slpU0cd8vVLkiRpeBkyky5Jgy3G2Nuy+ab5E/jc645/VQvhwfzu\nhW20dXbzxUvm8I3fruTGJzbyjkWTGVGQS/4Rrh3a2dXNPS9u48K54zj/+MMvK/PPbz2Jq147i/Li\nPMqK8vebnXfuhHJ+/1fnvWrb5QuruTyFExlVlRVyxeKp/ODhtbz3jONYNHU0P3t8A/9932refdpk\n/uqiWTyzoZafPb6Bv7/9BZ5ct4ea+hbOmDaaj5w7ndfOGcsT63azo6GNN82fmLI4JUmSlHmO7K9r\nKYus3tHIlroWzp45hvtW7uCiqx/kb299nq31LYc87/bntjBlzAj+8jXTWTx9DN99YA3nfOM+Xv9f\nD/H85rojiuHxtXvY09TOpSeN79fxBXk5HDdmBCNHFAzYUjID4VMXzqJ6VDGf/eVSNu5u5ut3Lef8\n46v4+ltOJITAwimj+Mi507htyRbaOru48rwZbNrTwl/85Gne8F8P819/WMWIglwumnvwLsOSJEka\nfixYNexsrW9h4df+wFfvXA7Av1++gAe/cAHvP3MKv3pmC+d98wG+ftdy6ps79jt3+95WHluzm8sW\nTCSEwFWvncmepnbGVRTR2t7Fh378NE1tnf2O5TfLtjKiILdfratDWWlhHle/82Q21zbztuseo62z\nm7+79ITe5XdCCHz50hP41ZWLufOqc/jrN8zhgS+cz9XvXEAk8uT6Pbx+3niKC3LT/EokSZI0lNgl\nWMPOss317G5q55HVu5g1tpRJI4sB+Mf/N48PnzuN/7p3FT98dB3bG9r47/ec8qpz71xaQ4xw2SmT\nADhrZiU3feRM5k0qZ9X2Rt5+3WP88JF1XHXhrMPG0bc7cFF+5hdqp00dzcfOm8F3H1jDRXPHMXPs\n/mNmF04Z3ft7fm4Obzu1mrecPIkn1+85onHEkiRJGh4sWDXsbNvb2vv7BXNe3bJZPWoE//GOBeTl\nBH6zbCudXd29rYQAty3ZwvzqildNYNR32ZnXzxvH9x9ay5+dOYXRB1mXtMeRdgfOBJ+5aDZ5uTm8\nLVnQ90dOTuDM6QM/g7EkSZIyn12CNexsqWuhIDeHX125mE9cMPOAx5w7q4qG1k6Wbq7v3bZ6RwMv\n1uzlspMPXox94fXH09zeyXfuW33YOO5YuiUrugP3VZCXw2cvns3UfkxeJUmSJB2OBauGnZq6ViaM\nLGLhlNG9y8Hs6+yZYwgBHl61s3fb7UtqyAnw5gUHX3d15tgy3rloMj9/fAOb9jQf9Lgbn9jIL5/e\nzFtOmZQV3YElSZKkVLBgVco0t3fy6Opd6Q5jPzV1LUysKD7kMSNHFDC/eiQPvZwoWGOM3P7cFs6e\nWcnYsqJDnvuZi2YTAnzrDy8fcP8vn97El25bxgXHV/GVN59wdC9CkiRJGgYcw6qUuWvpVv76V89z\n3+fOY3rV/hPwpEtNXQtnzag87HHnzarkO/evpr6lg12NbWyubTloF+K+xlcU8cGzp/H9h9ZQVV4I\nEZ7dWEtuTuDCOeP4l9+u4NxZlVz3voUU5tm6KkmSJB2MBatSZk9zOwDPbqwbMgVrR1c32/e2Mmnk\noVtJAc6dXcU1963mT2t20dbZDcCC6pH9ep4rz5/B85vr+J+H15EbAnMmlLF+VxOPr93D4uljuP79\ni+wKLEmSJB2GBatSprE1sR7pko21XL6wOs3RJGyrb6U7wsSRh+4SDHDy5JGUFubx0KpdjMjPpSAv\nh1nj+ld4VxTnc+NHzqS1o4v83BxycwJrdjZy59IaPvqa6a43KkmSJPWDBatSprGtp2CtS3Mkr6ip\nawFg0qjDF6z5uTksnjGGh17eyaSRxcydUE5+7pEN++7bijqjqpTPXDT7yAKWJEmShjEnXVLKNCRb\nWFdu20tze2eao0moqU8UrP1pYQV43Qnj2FzbwpPr93DSpPJUhiZJkiRpHxasSpnGtg4AuiO9s+2m\nW01dK8BhZwnu8bZTq5k3sZwY4aRJFakMTZIkSdI+LFiVMo1tnZw4qZzZ40r50m0v9HbHrW/p4A/L\nt6clpi11LYwuKej3GNLcnMA/v/UkplWW9GtmYUmSJEkDx4JVKdPY2smYkkKue99C2jq6+PgNz9Le\n2c23713FR376NBt3Nw96TDV1LUzsxwzBfZ08eST3f/58Jo8ekaKoJEmSJB2IBatSpqGtk9KiPGZU\nlfLNdyzguU11fOWOF7n9uS0ALNlUO+gx1dS19Ls7sCRJkqT0smBVyjS2dlJWmJiI+o0nTeAj507j\npic3sqcpsT7rYM8eHGNkS21LvydckiRJkpReLmujlGls66S08JW32F+/YQ7LttSzrb6VqrJClmwa\n3IJ1b0snTe1dVPdjSRtJkiRJ6WcLq1KiqzvS3N5FadErBWt+bg43fPhM7vrUuSycMprlNfW0dnQN\nWkxb6o5sSRtJkiRJ6WXBqpRobEusu9q3hRUSs+6WFuZxynEj6eiKg9otuMaCVZIkScooFqxKiZ6C\ntazowL3Oz5lZSVlRHjc+uXHQYqqp7ylYj2yWYEmSJEnpYcGqlGhs7WlhzT/g/pLCPN5z+nHcvWwr\nW5OFZKptqWuhIDeHypLCQXk+SZIkScfGglUp0djWAfCqMaz7umLxFALwhVueH5SxrDV1rUwYWURO\nTkj5c0mSJEk6dhasSonGtkQBuu8Y1r6qR43gG2+fzyOrd/HJG5fQ0dWd0phcg1WSJEnKLBasSome\nLsEHG8Pa4/KF1Xztsnncu2I7n/3lUrq644DGsbxmL93Ja9bUtTDJJW0kSZKkjGHBqpTo7RJ8iBbW\nHu9fPJUvXjKHO5fW8KVbl/UWmMfqmQ21vPGah7nz+Ro6urrZvrfVGYIlSZKkDHL4akI6Cg09ky4d\npoW1x8fOm0FzWyfX3Lea4oJcvvLmEwjh2Maa3vrsZgD+uGIHpx43iu4Ik5whWJIkScoYtrDqgOpb\nOo7p/J5lbUoK+v+dyF9dPJsPnj2VHz+2niWbjm191vbObn6zbCsAD63ayabaZsA1WCVJkqRMYsGq\n/Ty2Zhenfu0PrN7RcNTXaGztZERBLrlHMCNvCIG/OGcaACu27j3q54bEa6hr7uAtJ0+krrmDe17Y\nBliwSpIkSZnEglX7eWTVLrq6I4+t2X3U16ht7qCi+MBrsB7KxIpiivNzWb2j8aifG2DdriYAPnXh\nLHIC3LpkS+/1JUmSJGUGC9Zh6pkNe/jszc8dcIKjZzfWJv67ofaor19T13JUrZk5OYEZY0uOuWDd\n2dBGfm5g6pgSXj9vPA2tnYwuKaC4IPeYritJkiRp8FiwDlM/f3wjty7Zwq7Gtldt7+zq5vnN9QA8\nu/Hox5FuOcqCFWBmVSlrBqBgrSwtJCcn8IkLZgIwye7AkiRJUkaxYB2GYow8snoXANv2tr5q38vb\nG2lu7+KECeVs3NO8X0HbH93dka31LUddIM4cW0pNfStNyYmbjsbOxjaqygoBOHFSBe85/Tgumjvu\nqK8nSZIkafBZsA5Dq3c0srMhUYhurX91wbpkU6IbcM/kR0fTLXhnYxsdXZFJo46+YAVYs/PoW1l3\nNrRRVVrY+/hf33YSn75o1lFfT5IkSdLgs2AdhnpaVwG279PCumZHE8X5uVw6fwJF+Tk82ufYA7np\nyY2c++/38b0H19DQmlgKZ0tdC3D0a572FKzHMo51Z8MrLaySJEmSMpMF6zD0+NrdVI8qJi8nsG2f\nFtbte1uZUFFEUX4uZ8+o5P6XdhLj/hMz9XhuYx2ba1v4xm9XctY37uOb96zk5W2J5XAmjRxxVPFN\nGVNCQW4OL207umV1uroju5vaqSy1YJUkSZIymQXrMBNj5NmNdZw2dTTjyov2K1i37W1lXHmiZfT8\nOWPZuKeZtcklYg6koa2D6ZUl3PHJszl3ViXffWANX71zOQATj7KFNT83h1njSll+lGux1ja309Ud\nbWGVJEmSMpwF6zBTU9/KzoY2TjluJOPKC/ebdGlbfSvjKxKF5gXHVwFw67ObD3q9htZOyovzmV89\nku/+2ULefdpxtHR0UV6UR1nRka/D2mPuhHJWHmULa8/4XAtWSZIkKbNZsA4zS5JrrJ48eSTjK4pe\nVbB2d0d2NLzSwlo9agQXzhnLtfev4VM3LaG+uWO/6+1t6XhVYXrF4ikATBp1dN2Be8wZX8bOhraj\nmqXYglWSJEnKDhasw8ySjXUU5uUwZ3x5b5fgnjGqu5va6eiKjC9/pdD7/vsX8vnXzebuZVt5w7cf\n4rF9JmFqaO2krCiv9/HcCeVcfMI4Tp866pjiPGFCOQArjqJbcG/B6hhWSZIkKaNZsA4zSzbWctKk\nCgrychhfXkRzexcNyfVOe2YM7ukSDJCXm8MnXzuLX115FsX5ufzZ/z7Bgy/v7N2/t7WT8n26/v7g\nikV89bITjynOucdSsDbawipJkiRlAwvWYSTGyKrtjb3FYE9humlPM0DvBEw9XYL7WjB5JHdedQ6z\nx5bxVzc/x45kcdvQ2kF5nxbWgTKqpIAJFUU8v7n+iM/dVt/KiIJcSgoHPi5JkiRJg8eCdRjZ09RO\nQ1snUytLgMQ41sK8HK78+bOs3tHYO561bwtrXyWFeXznvaewp6md/3t2M22dXbR1dr+qS/BAOmPa\naB5fu+eQy+ocyPKavcwZX5aSmCRJkiQNHgvWYWT97sTyNNMqExMiTRlTwi8+eibN7Z287buP8vvl\n28kJhx77OWtcGXPGl/HIql00tCa6Eh/LbMCHctaMSnY1trFqR2O/z+nqjrxQU8/86pEpiUmSJEnS\n4LFgHUbW7Up0/Z0ypqR32ynHjeK2j5/NuPIiHnp5J1VlheTlHvptce6sSp5eX8uOvYmxouXFqWlh\nXTxjDMB+Ez0dytqdjTS3d3HSpIqUxCRJkiRp8FiwDiMbdjeRE2DyPkvOTB49gv+78iwuOL6K06aO\nPux1zplVRXtXN39csR2AssLUtLBOHj2CyaOLuWNpTe/Mv4ezNDnmdcFkC1ZJkiQp0/WrYA0hvCGE\n8FIIYXUI4YsH2B9CCNck9z8fQji1z771IYRlIYTnQghPD2TwOjLrdjVRPWoEBXn7p72iOJ8fffB0\nvvPeUw9w5qudPnU0Bbk53LN8G0DKxrAC/PlZ03huUx3nf/N+bn5qY+941nte3MbtS7bsd/yyzXWU\nFOQyrbI0ZTFJkiRJGhyHrTRCCLnAtcDFwGbgqRDCHTHG5X0OuwSYlfw5A7gu+d8eF8QY+9+vUymx\nfncTU8aMOPyBh1FckMvUyhGs2NoAQHlxalpYAf7inGmcf3wVX75tGX/zq2X8Yfl2rnrtLD79iyW0\nd3Yzo6qUk6pfaU19fks98yZVkJsTUhaTJEmSpMHRnxbW04HVMca1McZ24BfAZfsccxnw05jwODAy\nhDBhgGPVEfjdC9v405rdvY9jjGzY1cy0ypJDnNV/0ypL6OpOtHamsoUVYEZVKTd++Ez+7tK5PLRq\nF5dd+ygAY0oL+dvbnu89rqOrm+U1e5nv+FVJkiQpK/SnYJ0EbOrzeHNyW3+PicC9IYRnQggfPdiT\nhBA+GkJ4OoTw9M6dO/sRlg7lYz9/hvf84PHex8u21NPQ1smc8eUDcv2+XW5TNUtwXzk5gQ+fO53f\nXHUOZ88cw5feOJePnDuNF7bsZXdjYnzry9sbaOvsZv5kZwiWJEmSskFqm8YSzokxbgkhjAX+EEJY\nGWN8aN+DYozXA9cDLFq06MgW3tRh/fix9ZQU5PLmBQPT8N2zNE4IUFY4GG+jhFnjyrjhw2cC8Miq\nRC/zl7Y1cNbMQpYlJ1yyhVWSJEnKDv1pYd0CTO7zuDq5rV/HxBh7/rsDuI1EF2MNktaOLnY2tHHX\n0q1cvrB6wFpDe1pYSwvyyEnTeNE5E8oAWLEtMZZ26eZ6yovyBmScriRJkqT060/B+hQwK4QwLYRQ\nALwbuGOfY+4ArkjOFnwmUB9j3BpCKAkhlAGEEEqA1wEvDGD8OozNtc3c9ORG2ru6ueKsqQN23anJ\nFtZUj189lMrSQipLC3hp214Alm2pY371SEJwwiVJkiQpGxy22ogxdoYQPgncA+QCP4wxvhhC+Fhy\n//eAu4E3AquBZuCDydPHAbclC4g84MYY4+8G/FXooFZtb+Tnj2/gNbOrmFE1cEu9VJUWUlqYl9IZ\ngvvj+PFlrNzWQGtHFy9ta+DD505PazySJEmSBk6/msdijHeTKEr7bvten98j8IkDnLcWWHCMMeoI\n9axVCvD9h9ayo6GNf3v71AF9jhAC06tKKM7PHdDrHqk548u54YkNLN+6l46u6PhVSZIkKYukrz+n\nUqats7v39+c21TF1zAjOm1014M/zL289acCveaTmjC+jtaOb255NDKvuuyarJEmSpMxmwZqF2jq6\nX/X4A2dNTcnESCcOgdbMc2dVEQL84qmNjCkpYNLI4nSHJEmSJGmA9GfSJWWY1s6u3t9LCnK5fGF1\nGqNJrfEVRZw+dTQdXZGTqiuccEmSJEnKIrawZqHWjkTB+oXXH8/5x1cN2FI2Q9WbF0zkiXV7HL8q\nSZIkZRlbWLNQzxjWKWNGMG9i9hdxb5o/gdOmjuJ188anOxRJkiRJA8gW1izU08JalJfeGXwHy8gR\nBdzysbPSHYYkSZKkAWYLaxZqTU66VJhveiVJkiRlLiuaLNSWnHSpKM1rpEqSJEnSsbBgzUI9LazD\npUuwJEmSpOxkwZqFelpY7RIsSZIkKZNZ0WQhW1glSZIkZQML1izUM0uwLaySJEmSMpkVTRbqWYfV\nFlZJkiRJmcyCNQvZwipJkiQpG1jRZKGeFtbCPNMrSZIkKXNZ0WShto4uCvNyCCGkOxRJkiRJOmoW\nrFmoNVmwSpIkSVIms6rJQm2d3RTlO+GSJEmSpMxmwZqFWju6LFglSZIkZTwL1izU2tFtl2BJkiRJ\nGc+qJgu1ddrCKkmSJCnzWbBmodaObopcg1WSJElShrOqyUJtnV0U5tnCKkmSJCmzWbBmIVtYJUmS\nJGUDq5os1GoLqyRJkqQsYMGahdo6uim0hVWSJElShrOqyULOEixJkiQpG1iwZqE212GVJEmSlAWs\narJQqy2skiRJkrKABWuW6eqOdHRFW1glSZIkZTyrmixT39IBQEVxfpojkSRJkqRjY8GaZWqb2wEY\nNaIgzZFIkiRJ0rGxYM0ydcmCtWKELaySJEmSMpsFa5apa050CbaFVZIkSVKms2DNMrW9BastrJIk\nSZIymwVrlunpEjzSFlZJkiRJGc6CNcvUNreTmxMoL8pLdyiSJEmSdEwsWLNMbXMHFcX5hBDSHYok\nSZIkHRML1ixT39zBSMevSpIkScoCFqxZpra53RmCJUmSJGUFC9YsU9vc4QzBkiRJkrKCBWuWqWtu\nd4ZgSZIkSVnBgjXL1NnCKkmSJClLWLBmkdaOLlo6umxhlSRJkpQVLFizSF1zB4CzBEuSJEnKChas\nWaS2uR3AWYIlSZIkZYV+FawhhDeEEF4KIawOIXzxAPtDCOGa5P7nQwin9vfcgfDTP61nc21zKi6d\nUWrqWgAYX1GU5kgkSZIk6dgdtmANIeQC1wKXACcA7wkhnLDPYZcAs5I/HwWuO4Jzj8m2+lb+4dcv\n8l/3rhrIy2ak9bsTRfvUMSVpjkSSJEmSjl1/WlhPB1bHGNfGGNuBXwCX7XPMZcBPY8LjwMgQwoR+\nnntM1u5sBOC3y7bS0t41kJfOOBt2N1FWlOcswZIkSZKyQn8K1knApj6PNye39eeY/py7n12Nbfzo\n0XX8+NF1/GnN7kMeu3ZXEwBN7V38fvm2w106q63b1cS0yhJCCOkORZIkSZKOWV66A+gRQvgoie7E\nFIyfyVfvXN6779/efhLvOu24A563blcTRfk5jB5RwN3LtnLZyYeth7PWht3NLJg8Mt1hSJIkSdKA\n6E8L6xZgcp/H1clt/TmmP+cCEGO8Psa4KMa46IQJ5Tz3Dxfz5Jcv5LzZVXzx1mU8vvbALa1rdzYy\nrbKUxTMqeXp9LTHGfryk7NPe2c3m2mamjhmR7lAkSZIkaUD0p2B9CpgVQpgWQigA3g3csc8xdwBX\nJGcLPhOojzFu7ee5+8nNCYwcUcDYsiKue9+pTBk9gs/fspTapvb9jl23q4nplSWcNnUUu5vae7sI\nDzdb6lrojk64JEmSJCl7HLZgjTF2Ap8E7gFWAL+MMb4YQvhYCOFjycPuBtYCq4EfAB8/1LlHEuCI\ngjz+850ns2NvG5d8+2GuvX81j6zaRX1LB+2d3WyqbWF6VQmnTRsNwNPr9xzJ5bPG+t2JQn1qpS2s\nkiRJkrJDv8awxhjvJlGU9t32vT6/R+AT/T33SC2cMopbP34Wf3vrMr55z0u926tHFdPVHZlWWcL0\nyhJGlxTw5Lrag453zVYdXd384KG1FOTlMLOqLN3hSJIkSdKAGDKTLh3OiZMquPOqc6hv6WDZ5nqW\nbq7juU11lBXlc/q00YQQWDhlFEs21qY71EEVY+Qrd7zIY2t285/vWECFS9pIkiRJyhIZU7D2qCjO\n55xZlZwzq3K/fXPGl3Hfyh20d3ZTkNef4bmZ7yePrefGJzZy5fkzePvC6nSHI0mSJEkDJququhlV\npXR1RzbsHh4TLz3w0g7+6a7lvO6EcXzhdcenOxxJkiRJGlBZV7ACrN7RmOZIUq+7O/LZXy5l9rgy\nvvWuk8nJCekOSZIkSZIGVFYVrNOrEku6rNmZ/QXrlroW9jS184GzplJSmHE9uyVJkiTpsLKqYC0p\nzGNiRRFrdmZ/l+DVyaJ85tjSNEciSZIkSamRVQUrwIyxpcOiS/Ca5GucWWXBKkmSJCk7ZV/BWlXK\nmp2NNLR2pDuUlFqzs5HRJQWMKilIdyiSJEmSlBJZV7BefMI42jq7ufy6P7G5tjnd4aTM6h2Ntq5K\nkiRJympZV7CePbOSn3zwdGrqW3jLtY/y7MZarr1/Nd97cE26QxtQa3Y2MWNsSbrDkCRJkqSU5YXq\nAgAAFDlJREFUycrpZc+ZVcltHz+LD/34ad5+3WPECCMKcvnQ2dMoyMv8Gn1PUzt7mtp7l/GRJEmS\npGyU+dXbQcwcW8btnzibi+aO49xZlTS3d7F0c126wxoQSzbWAnDChPI0RyJJkiRJqZO1BSvA6JIC\nfnDFIv77PacQAjy2ene6Q+qXmroWbl+yhc6u7gPuv/+lHYwoyGXh1FGDHJkkSZIkDZ6s7BK8r5Ej\nCpg3sZxH1+zi0xfNSnc4h3X9Q2v58WPr+cHDazlt6mjGVxQxvryI8RVFzBxbyv0rd3L2zEoK83LT\nHaokSZIkpcywKFgBzp5RyQ8fXUdDawf//ruX+MBZU5k5dmiOAd24p5nRJQV0dUd+9exmGlo7e/fl\n5wY6uiKfuGBmGiOUJEmSpNQbNgXrwimj+P5Da/npnzbws8c3sH53Ez/7izPSHdYBbdrTzKIpo7j+\nikUANLZ1sq2+lW31rfzy6U08tGonF84dm+YoJUmSJCm1hk3BespxifGeP3p0PQAPr9rFE2t3c8b0\nMQP6PB1d3dzy9GbeduokivKPvMtujJFNtc28ZnZV77bSwjxmji1l5thSzplVSYyREMJAhi1JkiRJ\nQ05WT7rUV1VZIZNHF7OrsY3pVSWMKy/kP37/EjHGAX2eP67YwZduW8Z37199VOfvbGyjtaObyaOK\nD3qMxaokSZKk4WDYFKwAp0xOtLKeO7OST752Fk+tr+WBl3ce0TU27G7isdW79tu+p6mdDbubeHr9\nHgCuf3gtm/Y0H3GMm/a0AHDcmBFHfK4kSZIkZZPhVbAeNxKA06eN4V2LJlM9qpj/PIJW1u7uyMdv\neJY//9FT1DW3927ftKeZN13zMJdd+yiPrN7F9KoSckLgkm8/zPcfXENbZ1e/Y9xcmyhyJ4+yYJUk\nSZI0vA2rgvXS+RN456Jqzju+ioK8HD5z0Wxe2LKX372wrV/n/3rpFl6s2Ut7Vze3L9lCbVM7V//+\nJS695mF2N7VT19zBym0NvH7eeH7zqXM5Y9po/vW3K3n9tx7i/pd29Os5Nu5OFKzVFqySJEmShrlh\nVbCOLSvi3y9fQGlhYq6pt54yiRlVJVz9h5eJMdLZ1c01f1zF85vrDnj+jx5dz5zxZZw0qYLv3L+a\ns//tPq65bzWLZ4zhrqvOYVplCQCLpoxiWmUJ//vnp/GTD51Obk7gL378FKt3NB42xk21zVSVFVJc\n4BqrkiRJkoa3YVWw7is3J3Dl+TNZtaORJ9bt4W9vXcbVf3iZaw8wYVJnVzcrtzZw3uwqPnj2VGqb\nO3j9vPH8/q9ew/ffv4hZ48r44NlTKSnIZdGU0b3nnTe7ipv/cjEFeTl878E1h41p3a4mpoy2dVWS\nJEmShs2yNgdz6UkT+Mc7XuSzNz9HTX0rlaUFPLFuD93dkZycV2bjXberifaubuZMKOOtp1TzxpMm\n7LdszfvPnMLbTq3ubcHtUVlayLtPO46fP76Bz7/ueMZXFB0wls6ubl7Yspd3nTZ54F+oJEmSJGWY\nYd3CClBckMubF0ykpr6V82ZX8cVL5lLX3MFL2xteddyKbYnHc8aXAxxwjdUQwn7Fao93LKqmszvy\nZHIW4QNZtaORlo4uTp488mhfjiRJkiRljWHfwgrwl6+ZTlNbJ3/3prl0dCVmDH587W7mTijvPWbl\n1r3k5QRmVJUe1XPMGltGXk5g5da9/L8FEw94TM/Y2fnVFUf1HJIkSZKUTYZ9CyvA1MoSrnnPKYwt\nK2LSyGKOGz2CO5fW0NrxynI0K7buZebYUgryju5/WUFeDjPHlrJi696DHrN0cz1lRXlMHVNyVM8h\nSZIkSdnEgvUAPvnamTy7sY4P/fgpGts6iTGyYmvDq1pcj8ac8WWs3NZw0P3Pb65jQfXIV42dlSRJ\nkqThyoL1AN65aDLfetcCnli3h/f9zxPc/NQmtu1tZfGMMcd03bkTytla30pdczsvbKnnvpXbiTHR\nBXlrfQsv1uzl9GmjD3MVSZIkSRoeHMN6EG89pZqSgjw+eeMSvnjrMmaPK+Vtp0w6pmvOSbbQ/nHF\nDr72m+XUNXewePoYvnrZPP64YgcxwmUnH3h8qyRJkiQNN7awHsLr5o3nRx88jelVJfzTZSeSl3ts\n/7vmTSynIC+Hz92ylPbObr7w+uNZvnUvl3z7Ya57YDULp4xiiuNXJUmSJAmA0NMldShZtGhRfPrp\np9MdRko8v7mO25fUcPbMMVw4dxx7mtr5j9+/xE1PbuQ/Ll/A2xdWpztESZIkSUqZEMIzMcZF/TrW\ngnVoaGjtoKwoP91hSJIkSVJKHUnBapfgIcJiVZIkSZJezYJVkiRJkjQkWbBKkiRJkoYkC1ZJkiRJ\n0pBkwSpJkiRJGpIsWCVJkiRJQ5IFqyRJkiRpSLJglSRJkiQNSRaskiRJkqQhyYJVkiRJkjQkWbBK\nkiRJkoYkC1ZJkiRJ0pBkwSpJkiRJGpIsWCVJkiRJQ5IFqyRJkiRpSLJglSRJkiQNSSHGmO4Y9hNC\n2AlsSHccGa4S2JXuIHTEzFvmMWeZx5xlHnOWecxZZjJvmSWT8zUlxljVnwOHZMGqYxdCeDrGuCjd\ncejImLfMY84yjznLPOYs85izzGTeMstwyZddgiVJkiRJQ5IFqyRJkiRpSLJgzV7XpzsAHRXzlnnM\nWeYxZ5nHnGUec5aZzFtmGRb5cgyrJEmSJGlIsoVVkiRJkjQkWbBKkiRJkoYkC9YMFUL4XAjhdcnf\nQ7rjUf+EECr6/G7eMoS5yiwhhLnpjkFHxntaZvKelpnMVebwfpZgwZphQgivCyHcA/wNcAVAdCDy\nkBdCeG0I4TnguhDCl8C8ZYIQwmUhhJ8AC9Idi/onhHAN8NsQwtQ0h6J+8J6WmbynZSbvaZnF+9kr\n8tIdgA4v+U1YPvAPwHnAvwIFwGkhhHyg0xvF0BVCKAW+BHwNeBL4SQhhRIzx79IbmQ4khBBijDGE\ncAGJnHUAi0MIG2KMtWkOT/voyVefTWOAPcDFIYSfxhjb0hSaDsJ7WmbznpZZvKdlDu9nB2cL6xDX\n8+aNMbYDv44xnhtjvBuoBd4dY+zwxj50hRBygFJgE7AkxrgJ+DDwLrt5DD373CzWAa8HvgCcAcxP\nW2A6oL75CiHkJjc/DnwXeC8wK12x6cC8p2U272mZxXta5vB+dmgWrENYCOGTwK0hhL8KIUyIMT6V\n3J4fY3wQWBtCuCS9UWpfIYSPhxDeDhBj7AYiUEXiJk+McS1wG/BPyeMdSzIE7PN5Gx9jXB9j3Bpj\nvA/YDpwXQpiU5jCV1CdfnwkhTIwxdoUQCoA3ALcD9wPvDiG8LYRQldZgBXhPy1Te0zKT97TM4f3s\n8CxYh6gQwluBDwDXkPgW7MshhJ4xB50hhNHABqArTSFqHyGEshDC90h0c/tJCCEPIMa4HVgBfKbP\n4V8ETg8hzLM1If0O8Hn7uxDCyX0OuQGYTeJb6b7n+YdZGuyTrwXAl0IIC5Otdk/HGHcBq4BPAf8M\nmKc0856WebynZS7vaZnD+1n/WLAOXWcA340x3g/8I4muHJ+GxMQGMcY9QDFwAfR201EaxRgbgAdj\njOOBu4Br++z+KnByCOGNIYTC5LfUd5EYx6X0O9Dn7VM9O2OMzwNPAScmJxv5m+R2/zBLjwPl68rk\nvjeGEB4mMYnP7SS6VO1NR5B6Fe9pGcZ7WkbznpY5vJ/1gzeEIabPt1trgT8DiDFuIHEjKAkhXNbn\n8BtIfKNZlLxZKE365O2O5H8/A7wnhDALIMbYCPw78G4S3579E3AusHWwY9UrjvDzdhOJsVo3A5X7\nnK9BcJh8jQohLCbxLfVjMcaTY4xXAOMBx9alife0zOQ9LTN5T8sc3s+OjAVrmoV9pqru8+3W/wHN\nff5x2QY8AMzt8yYvAn6BXagG3cHyFmNsCiHkxBi3kRgo/z99jvkF8C8kunNUAZcku1ZpkIQQFoUQ\nxvY87u/nLSRmxfw2sAyYH2P8wj7nKwWOMF9/JPEH8w0xxr/pc5m3xhiXDErAOurPWHKb97Q0OVje\nvKcNbSGEi0IIC3see08buo4wV97P9mHBmiYhhFNDCPcC/xRemQ2s9xuXZPeoW4GPhxBCjLGexAQH\nhX3e5L+OMf4gxtgx2PEPV4fK275d2GKMXwSmhRAWhxDGhxDOiDGuBL4SY7wyxrhlkMMftkII80II\njwFfAUb22X64z1tR8vPWCnw6xnhpjNEWhBQ7ynyVAMUxxu4QQm7P5zHG2Dr4r2D4OYbPmPe0NDpU\n3rynDV0hhFNCCL8lMdnVzD7bvacNMUeZK+9n+7BgHWTJm8CXSXTF+EWM8YoYY1dyX07PjTuEMAL4\nPVADXB9CmAicAnT2XKvnPKVef/KW/IelFKjoc+q/AY8CD5FoPfAbzPT4NHBbjPHNMcaXod+ftw6A\nGGNnjHFHekIflo42X52Q+LfRLqWD7phyBt7T0uSgefOeNvQki5frgR8A3wduJNlFNISQ5z1t6BiA\nXHk/68OCdZAl36AFwCMxxv+B3m9f8khMFU8I4WskvokZB3yOxPTjNwJ1wDfSEfdw18+8/ROJrh0n\nJh9fAlwFXA3Mi4llGzSIkjeM0SRy9J3ktreGEKqBEcnHX8fP25BgvjKPOctM/czb1/CeNqQkv9T5\nHXBujPF2Ep+rC0Ji3HcnQAjhH/HzlnbmamAFvxhLvRDCeUBrjPGJ5OMS4FfAchJ91LcD9cAtwL3A\n9cA/xBhX97nGiBhj82DHPpwda95CCCcADTGxsLoGyQHyVgQsAT4PvIfE5BLbgBYSsyb+BD9vaWO+\nMo85y0zHmjfvaemxb976bA/AhcC7gL+JMe4JiXHI/wX8fYxxTZ9j/bwNAnOVQjFGf1L0A5SR6Je+\nB/ghMKrPvveSGFR9XvLxXwI/Aqb0OSYn3a9hOP4MQN5y0/0ahuPPYfL218B64Irk40nAE8CFfY7x\n82a+/DFnWfczAHnznjaE8kZikqueBqdqErPMTjzA+X7ezFXW/NglOLXagfuA95Hom/6Onh0xxhuB\nd8RXutTcC4wmOb4gOYZk2PdZT5NjzZvjsNLjoHkjMbtlEYmZLImJyUEeJLlmoJ+3tDBfmcecZaZj\nzZv3tPQ4YN5iUjI3m0l8wXB53xP9vA06c5ViFqwDLIRwRQjhvBDCyBhjG4kp4O8FXgYWhRBmJ48L\nMTEzWI+LSYwlaQTwzTu4zFtm6m/eYmLNwE8BV4QQTg4hXAlcRGKBbvM2SMxX5jFnmcm8ZaYj+Fsk\nJyYmxcoDVgFNfa9j3lLPXA0ux7AOgGTf9PEkBkp3A2tITEn96RjjruQxs4APkOjb/vXkthzgHBJr\nYW0k0a995eC/guHJvGWmo81bcvu7gAXAPOBLMcYXBzn8Ycd8ZR5zlpnMW2Y6lr9FkoXQt4DGGOPf\np+UFDCPmKn1sYT1GIYTcmKj6y4AtMcYLgStJ9GO/vue4GOMq4BlgYghhZnKygwhsIbGG2WUWPYPH\nvGWmY8hbSQghP8Z4M/DlZN78gyzFzFfmMWeZybxlpmP8W6Q4ufuzFkCpZ67SKy/dAWSqEEIu8DUg\nN4RwN1AOdEFiKusQwqeBmhDCeTE53jHGeFsIYS6Jaa5LgdfGGJeT+IZGg8C8ZaYBytsFwIpot5KU\nM1+Zx5xlJvOWmcxb5jBXQ4MtrEchJKatfgYYBawm8UbuILG+0unQ2yf9H5M/Pee9A/gycD8wP1n0\naJCYt8w0gHlbMaiBD1PmK/OYs8xk3jKTecsc5mrocAzrUQghnAtMjTH+LPn4u8AyEmuXXRVjXBgS\n4xzHAteQGOO4LnkeMcaH0xT6sGbeMpN5yyzmK/OYs8xk3jKTecsc5mrosIX16DwD/DLZTQDgUeC4\nGOOPSXQZuCr5jUs10Blj7Jlt72HfvGll3jKTecss5ivzmLPMZN4yk3nLHOZqiLBgPQoxxuYYY1t8\nZW2yi4Gdyd8/CMwNIdwF3AQsSUeM2p95y0zmLbOYr8xjzjKTectM5i1zmKuhw0mXjkHyG5cIjAPu\nSG5uAL4EnAisi4lFuDWEmLfMZN4yi/nKPOYsM5m3zGTeMoe5Sj9bWI9NN5AP7ALmJ79l+XugO8b4\niG/eIcu8ZSbzllnMV+YxZ5nJvGUm85Y5zFWaOenSMQohnAk8lvz5UYzxf9MckvrBvGUm85ZZzFfm\nMWeZybxlJvOWOcxVelmwHqMQQjXwfuDqGGNbuuNR/5i3zGTeMov5yjzmLDOZt8xk3jKHuUovC1ZJ\nkiRJ0pDkGFZJkiRJ0pBkwSpJkiRJGpIsWCVJkiRJQ5IFqyRJkiRpSLJglSRJkiQNSRaskiSlQAih\nK4TwXAjhxRDC0hDC50IIh7zvhhCmhhDeO1gxSpI01FmwSpKUGi0xxpNjjPOAi4FLgK8c5pypgAWr\nJElJrsMqSVIKhBAaY4ylfR5PB54CKoEpwM+AkuTuT8YYHwshPA7MBdYBPwGuAb4BnA8UAtfGGL8/\naC9CkqQ0s2CVJCkF9i1Yk9vqgOOBBqA7xtgaQpgF3BRjXBRCOB/4fIzxTcnjPwqMjTF+PYRQCDwK\nvCPGuG5QX4wkSWmSl+4AJEkahvKB74QQTga6gNkHOe51wPwQwuXJxxXALBItsJIkZT0LVkmSBkGy\nS3AXsIPEWNbtwAIS80m0Huw04KoY4z2DEqQkSUOMky5JkpRiIYQq4HvAd2JiLE4FsDXG2A28H8hN\nHtoAlPU59R7gyhBCfvI6s0MIJUiSNEzYwipJUmoUhxCeI9H9t5PEJEtXJ/d9F/hVCOEK4HdAU3L7\n80BXCGEp8GPg2yRmDn42hBCAncBbBusFSJKUbk66JEmSJEkakuwSLEmSJEkakixYJUmSJElDkgWr\nJEmSJGlIsmCVJEmSJA1JFqySJEmSpCHJglWSJEmSNCRZsEqSJEmShiQLVkmSJEnSkPT/AV7tK8CV\nBYzHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c41a086320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_tc_risk_neutral_df['res'][-500:].cumsum().plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparisong\n",
    "--------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EPSAfterNonRecurring    0.003873\n",
       "DivP                    0.003721\n",
       "CFinc1                  0.002895\n",
       "BDTO                    0.004250\n",
       "RVOL                    0.004340\n",
       "res                     0.003877\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_tc_risk_neutral_df[-500:].std()[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EPSAfterNonRecurring    0.004232\n",
       "DivP                    0.004679\n",
       "CFinc1                  0.003828\n",
       "BDTO                    0.004884\n",
       "RVOL                    0.004965\n",
       "res                     0.004683\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_tc_long_top_df[-500:].std()[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EPSAfterNonRecurring    0.000506\n",
       "DivP                    0.000512\n",
       "CFinc1                  0.000279\n",
       "BDTO                    0.000377\n",
       "RVOL                    0.000473\n",
       "res                     0.000575\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_tc_risk_neutral_df[-500:].mean()[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EPSAfterNonRecurring    0.000675\n",
       "DivP                    0.000718\n",
       "CFinc1                  0.000617\n",
       "BDTO                    0.000729\n",
       "RVOL                    0.000897\n",
       "res                     0.000907\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_tc_long_top_df[-500:].mean()[total_factors + ['res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BDTO</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>DivP</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>-0.000155</td>\n",
       "      <td>-0.000598</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>-0.001705</td>\n",
       "      <td>0.003388</td>\n",
       "      <td>0.001898</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>-0.000155</td>\n",
       "      <td>0.003272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>0.002156</td>\n",
       "      <td>-0.000338</td>\n",
       "      <td>-0.001029</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>-0.002159</td>\n",
       "      <td>0.000365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>0.000056</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000858</td>\n",
       "      <td>-0.000681</td>\n",
       "      <td>-0.000889</td>\n",
       "      <td>-0.000552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-06</th>\n",
       "      <td>0.001013</td>\n",
       "      <td>-0.001058</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000748</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-07</th>\n",
       "      <td>0.000275</td>\n",
       "      <td>-0.000519</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>-0.000553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-08</th>\n",
       "      <td>-0.000330</td>\n",
       "      <td>0.002243</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.005170</td>\n",
       "      <td>-0.000312</td>\n",
       "      <td>0.005330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-09</th>\n",
       "      <td>-0.003015</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.001373</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>-0.002622</td>\n",
       "      <td>0.001915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-10</th>\n",
       "      <td>-0.002121</td>\n",
       "      <td>-0.001789</td>\n",
       "      <td>-0.000276</td>\n",
       "      <td>-0.000887</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>-0.000945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-13</th>\n",
       "      <td>-0.001043</td>\n",
       "      <td>-0.000201</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.001253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-14</th>\n",
       "      <td>-0.001677</td>\n",
       "      <td>0.003174</td>\n",
       "      <td>-0.000254</td>\n",
       "      <td>-0.000830</td>\n",
       "      <td>-0.002724</td>\n",
       "      <td>-0.000241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-15</th>\n",
       "      <td>0.000716</td>\n",
       "      <td>-0.000555</td>\n",
       "      <td>-0.001655</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.002565</td>\n",
       "      <td>-0.001172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-16</th>\n",
       "      <td>-0.002242</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.002522</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.002428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-17</th>\n",
       "      <td>-0.001338</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.004215</td>\n",
       "      <td>-0.001100</td>\n",
       "      <td>0.003346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-20</th>\n",
       "      <td>-0.003713</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>0.004442</td>\n",
       "      <td>-0.001345</td>\n",
       "      <td>0.004819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21</th>\n",
       "      <td>-0.001435</td>\n",
       "      <td>-0.000823</td>\n",
       "      <td>0.000742</td>\n",
       "      <td>0.000567</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>0.000297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-22</th>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-0.001769</td>\n",
       "      <td>0.001365</td>\n",
       "      <td>-0.000898</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>-0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-23</th>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.000854</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>-0.000829</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.000346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-24</th>\n",
       "      <td>-0.003309</td>\n",
       "      <td>-0.000886</td>\n",
       "      <td>-0.001104</td>\n",
       "      <td>-0.001112</td>\n",
       "      <td>-0.000498</td>\n",
       "      <td>-0.001365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-27</th>\n",
       "      <td>-0.003999</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.000790</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>-0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-28</th>\n",
       "      <td>-0.001736</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>-0.000313</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.004422</td>\n",
       "      <td>0.002652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-29</th>\n",
       "      <td>0.001258</td>\n",
       "      <td>-0.001314</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>0.004062</td>\n",
       "      <td>-0.002123</td>\n",
       "      <td>0.002073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-30</th>\n",
       "      <td>0.002590</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.003265</td>\n",
       "      <td>0.002260</td>\n",
       "      <td>-0.004564</td>\n",
       "      <td>0.001706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>-0.002721</td>\n",
       "      <td>-0.000947</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.000959</td>\n",
       "      <td>-0.000257</td>\n",
       "      <td>0.002102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>0.001178</td>\n",
       "      <td>-0.001326</td>\n",
       "      <td>-0.000245</td>\n",
       "      <td>0.003320</td>\n",
       "      <td>-0.003578</td>\n",
       "      <td>0.000814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>-0.000020</td>\n",
       "      <td>-0.001328</td>\n",
       "      <td>0.004521</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>-0.001865</td>\n",
       "      <td>-0.001390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>0.003015</td>\n",
       "      <td>-0.001750</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>-0.003173</td>\n",
       "      <td>-0.001565</td>\n",
       "      <td>-0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>0.009948</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>-0.002545</td>\n",
       "      <td>0.005422</td>\n",
       "      <td>-0.000166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>-0.003267</td>\n",
       "      <td>0.004664</td>\n",
       "      <td>0.002775</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>-0.002390</td>\n",
       "      <td>0.001392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>0.001102</td>\n",
       "      <td>-0.003570</td>\n",
       "      <td>-0.002549</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>-0.002049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>-0.000714</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>-0.001160</td>\n",
       "      <td>0.005071</td>\n",
       "      <td>0.005388</td>\n",
       "      <td>0.003467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>-0.002680</td>\n",
       "      <td>0.003912</td>\n",
       "      <td>-0.001180</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>-0.001981</td>\n",
       "      <td>0.006161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>-0.003357</td>\n",
       "      <td>0.003620</td>\n",
       "      <td>-0.000982</td>\n",
       "      <td>0.006027</td>\n",
       "      <td>-0.004240</td>\n",
       "      <td>0.004311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>-0.003999</td>\n",
       "      <td>0.005402</td>\n",
       "      <td>-0.002317</td>\n",
       "      <td>0.005899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>0.003312</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.002388</td>\n",
       "      <td>-0.003323</td>\n",
       "      <td>0.006604</td>\n",
       "      <td>0.001167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>-0.002207</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>-0.001157</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.003325</td>\n",
       "      <td>0.010032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>-0.003535</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>-0.001348</td>\n",
       "      <td>0.003552</td>\n",
       "      <td>-0.001419</td>\n",
       "      <td>0.000995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>-0.003701</td>\n",
       "      <td>-0.002727</td>\n",
       "      <td>0.003747</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>-0.003480</td>\n",
       "      <td>-0.000543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>-0.003914</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>-0.002998</td>\n",
       "      <td>-0.000929</td>\n",
       "      <td>-0.004231</td>\n",
       "      <td>-0.001735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>-0.003667</td>\n",
       "      <td>-0.002152</td>\n",
       "      <td>-0.002587</td>\n",
       "      <td>-0.006548</td>\n",
       "      <td>-0.002341</td>\n",
       "      <td>-0.004432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>-0.001492</td>\n",
       "      <td>-0.003370</td>\n",
       "      <td>-0.004015</td>\n",
       "      <td>-0.001048</td>\n",
       "      <td>-0.004705</td>\n",
       "      <td>-0.003584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>-0.000595</td>\n",
       "      <td>0.002377</td>\n",
       "      <td>-0.001002</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>-0.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-02</th>\n",
       "      <td>-0.001106</td>\n",
       "      <td>-0.003040</td>\n",
       "      <td>0.001395</td>\n",
       "      <td>-0.000559</td>\n",
       "      <td>-0.001258</td>\n",
       "      <td>-0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>-0.000402</td>\n",
       "      <td>-0.002492</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.002166</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.000855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>0.002660</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>-0.000705</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>-0.000439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>-0.002562</td>\n",
       "      <td>-0.001286</td>\n",
       "      <td>-0.002075</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>-0.005019</td>\n",
       "      <td>-0.001046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.004252</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0.004445</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>0.004220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.011270</td>\n",
       "      <td>-0.001675</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.006338</td>\n",
       "      <td>-0.009680</td>\n",
       "      <td>0.004412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.002072</td>\n",
       "      <td>-0.002580</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.001800</td>\n",
       "      <td>-0.002597</td>\n",
       "      <td>0.001638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.000732</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>-0.002799</td>\n",
       "      <td>-0.000266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BDTO    CFinc1      DivP  EPSAfterNonRecurring      RVOL  \\\n",
       "Date                                                                       \n",
       "2017-02-28 -0.000155 -0.000598  0.000572              0.001134 -0.000020   \n",
       "2017-03-01 -0.001705  0.003388  0.001898              0.004106 -0.000155   \n",
       "2017-03-02  0.002156 -0.000338 -0.001029              0.000632 -0.002159   \n",
       "2017-03-03  0.000056 -0.000665 -0.000858             -0.000681 -0.000889   \n",
       "2017-03-06  0.001013 -0.001058  0.000332              0.000748  0.000415   \n",
       "2017-03-07  0.000275 -0.000519  0.000224              0.001302 -0.000148   \n",
       "2017-03-08 -0.000330  0.002243  0.001249              0.005170 -0.000312   \n",
       "2017-03-09 -0.003015  0.000240  0.001373              0.002742 -0.002622   \n",
       "2017-03-10 -0.002121 -0.001789 -0.000276             -0.000887  0.000517   \n",
       "2017-03-13 -0.001043 -0.000201  0.000574              0.001391  0.000439   \n",
       "2017-03-14 -0.001677  0.003174 -0.000254             -0.000830 -0.002724   \n",
       "2017-03-15  0.000716 -0.000555 -0.001655             -0.000734  0.002565   \n",
       "2017-03-16 -0.002242  0.000147  0.002419              0.002522  0.000417   \n",
       "2017-03-17 -0.001338  0.001416  0.000351              0.004215 -0.001100   \n",
       "2017-03-20 -0.003713 -0.002930  0.001413              0.004442 -0.001345   \n",
       "2017-03-21 -0.001435 -0.000823  0.000742              0.000567 -0.000130   \n",
       "2017-03-22 -0.000955 -0.001769  0.001365             -0.000898  0.001399   \n",
       "2017-03-23  0.000722  0.000854  0.002665             -0.000829  0.001369   \n",
       "2017-03-24 -0.003309 -0.000886 -0.001104             -0.001112 -0.000498   \n",
       "2017-03-27 -0.003999  0.000465  0.000790              0.000433  0.001653   \n",
       "2017-03-28 -0.001736  0.004793 -0.000313              0.000535  0.004422   \n",
       "2017-03-29  0.001258 -0.001314  0.003203              0.004062 -0.002123   \n",
       "2017-03-30  0.002590  0.001660  0.003265              0.002260 -0.004564   \n",
       "2017-03-31 -0.002721 -0.000947  0.001081              0.000959 -0.000257   \n",
       "2017-04-05  0.001178 -0.001326 -0.000245              0.003320 -0.003578   \n",
       "2017-04-06 -0.000020 -0.001328  0.004521              0.001827 -0.001865   \n",
       "2017-04-07  0.003015 -0.001750  0.000660             -0.003173 -0.001565   \n",
       "2017-04-10  0.009948 -0.002257  0.002263             -0.002545  0.005422   \n",
       "2017-04-11 -0.003267  0.004664  0.002775              0.000886 -0.002390   \n",
       "2017-04-12  0.001102 -0.003570 -0.002549             -0.001603  0.003363   \n",
       "2017-04-13 -0.000714  0.000580 -0.001160              0.005071  0.005388   \n",
       "2017-04-14 -0.002680  0.003912 -0.001180              0.007112 -0.001981   \n",
       "2017-04-17 -0.003357  0.003620 -0.000982              0.006027 -0.004240   \n",
       "2017-04-18  0.001643  0.001971 -0.003999              0.005402 -0.002317   \n",
       "2017-04-19  0.003312  0.001006  0.002388             -0.003323  0.006604   \n",
       "2017-04-20 -0.002207 -0.003327 -0.001157              0.010385  0.003325   \n",
       "2017-04-21 -0.003535  0.001592 -0.001348              0.003552 -0.001419   \n",
       "2017-04-24 -0.003701 -0.002727  0.003747              0.000076 -0.003480   \n",
       "2017-04-25 -0.003914  0.002130 -0.002998             -0.000929 -0.004231   \n",
       "2017-04-26 -0.003667 -0.002152 -0.002587             -0.006548 -0.002341   \n",
       "2017-04-27 -0.001492 -0.003370 -0.004015             -0.001048 -0.004705   \n",
       "2017-04-28 -0.000595  0.002377 -0.001002             -0.000056  0.002144   \n",
       "2017-05-02 -0.001106 -0.003040  0.001395             -0.000559 -0.001258   \n",
       "2017-05-03 -0.000402 -0.002492  0.001122              0.002166  0.002367   \n",
       "2017-05-04  0.002660  0.000307  0.000882             -0.000705  0.007583   \n",
       "2017-05-05 -0.002562 -0.001286 -0.002075              0.000325 -0.005019   \n",
       "2017-05-08  0.004252  0.000860 -0.000045              0.004445  0.001817   \n",
       "2017-05-09 -0.011270 -0.001675  0.001375              0.006338 -0.009680   \n",
       "2017-05-10 -0.002072 -0.002580  0.000626              0.001800 -0.002597   \n",
       "2017-05-11  0.000732  0.000468  0.000360              0.000143 -0.002799   \n",
       "\n",
       "                 res  \n",
       "Date                  \n",
       "2017-02-28  0.000034  \n",
       "2017-03-01  0.003272  \n",
       "2017-03-02  0.000365  \n",
       "2017-03-03 -0.000552  \n",
       "2017-03-06  0.000058  \n",
       "2017-03-07 -0.000553  \n",
       "2017-03-08  0.005330  \n",
       "2017-03-09  0.001915  \n",
       "2017-03-10 -0.000945  \n",
       "2017-03-13  0.001253  \n",
       "2017-03-14 -0.000241  \n",
       "2017-03-15 -0.001172  \n",
       "2017-03-16  0.002428  \n",
       "2017-03-17  0.003346  \n",
       "2017-03-20  0.004819  \n",
       "2017-03-21  0.000297  \n",
       "2017-03-22 -0.000317  \n",
       "2017-03-23  0.000346  \n",
       "2017-03-24 -0.001365  \n",
       "2017-03-27 -0.000191  \n",
       "2017-03-28  0.002652  \n",
       "2017-03-29  0.002073  \n",
       "2017-03-30  0.001706  \n",
       "2017-03-31  0.002102  \n",
       "2017-04-05  0.000814  \n",
       "2017-04-06 -0.001390  \n",
       "2017-04-07 -0.000614  \n",
       "2017-04-10 -0.000166  \n",
       "2017-04-11  0.001392  \n",
       "2017-04-12 -0.002049  \n",
       "2017-04-13  0.003467  \n",
       "2017-04-14  0.006161  \n",
       "2017-04-17  0.004311  \n",
       "2017-04-18  0.005899  \n",
       "2017-04-19  0.001167  \n",
       "2017-04-20  0.010032  \n",
       "2017-04-21  0.000995  \n",
       "2017-04-24 -0.000543  \n",
       "2017-04-25 -0.001735  \n",
       "2017-04-26 -0.004432  \n",
       "2017-04-27 -0.003584  \n",
       "2017-04-28 -0.000600  \n",
       "2017-05-02 -0.000359  \n",
       "2017-05-03  0.000855  \n",
       "2017-05-04 -0.000439  \n",
       "2017-05-05 -0.001046  \n",
       "2017-05-08  0.004220  \n",
       "2017-05-09  0.004412  \n",
       "2017-05-10  0.001638  \n",
       "2017-05-11 -0.000266  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_tc_risk_neutral_df.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BDTO</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>DivP</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>0.000345</td>\n",
       "      <td>-0.000405</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>-0.001481</td>\n",
       "      <td>0.003454</td>\n",
       "      <td>0.001958</td>\n",
       "      <td>0.004167</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.003307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-02</th>\n",
       "      <td>0.002512</td>\n",
       "      <td>-0.000192</td>\n",
       "      <td>-0.000897</td>\n",
       "      <td>0.000684</td>\n",
       "      <td>-0.001917</td>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-03</th>\n",
       "      <td>0.000322</td>\n",
       "      <td>-0.000550</td>\n",
       "      <td>-0.000774</td>\n",
       "      <td>-0.000659</td>\n",
       "      <td>-0.000619</td>\n",
       "      <td>-0.000480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-06</th>\n",
       "      <td>0.001285</td>\n",
       "      <td>-0.001004</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000759</td>\n",
       "      <td>0.001010</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-07</th>\n",
       "      <td>0.000653</td>\n",
       "      <td>-0.000431</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.001313</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>-0.000527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-08</th>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>-0.000133</td>\n",
       "      <td>0.005436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-09</th>\n",
       "      <td>-0.002579</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.002769</td>\n",
       "      <td>-0.002368</td>\n",
       "      <td>0.002002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-10</th>\n",
       "      <td>-0.001629</td>\n",
       "      <td>-0.001779</td>\n",
       "      <td>-0.000228</td>\n",
       "      <td>-0.000841</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>-0.000854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-13</th>\n",
       "      <td>-0.000604</td>\n",
       "      <td>-0.000081</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>0.000647</td>\n",
       "      <td>0.001352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-14</th>\n",
       "      <td>-0.001164</td>\n",
       "      <td>0.003190</td>\n",
       "      <td>-0.000245</td>\n",
       "      <td>-0.000817</td>\n",
       "      <td>-0.002645</td>\n",
       "      <td>-0.000197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-15</th>\n",
       "      <td>0.001148</td>\n",
       "      <td>-0.000453</td>\n",
       "      <td>-0.001580</td>\n",
       "      <td>-0.000696</td>\n",
       "      <td>0.002663</td>\n",
       "      <td>-0.001089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-16</th>\n",
       "      <td>-0.001776</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.002433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-17</th>\n",
       "      <td>-0.000904</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>0.000411</td>\n",
       "      <td>0.004222</td>\n",
       "      <td>-0.000899</td>\n",
       "      <td>0.003354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-20</th>\n",
       "      <td>-0.003355</td>\n",
       "      <td>-0.002806</td>\n",
       "      <td>0.001515</td>\n",
       "      <td>0.004522</td>\n",
       "      <td>-0.001228</td>\n",
       "      <td>0.004939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-21</th>\n",
       "      <td>-0.000986</td>\n",
       "      <td>-0.000729</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-22</th>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.001615</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>-0.000837</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>-0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-23</th>\n",
       "      <td>0.001144</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>0.002742</td>\n",
       "      <td>-0.000821</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.000360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-24</th>\n",
       "      <td>-0.002831</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.000985</td>\n",
       "      <td>-0.001101</td>\n",
       "      <td>-0.000194</td>\n",
       "      <td>-0.001255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-27</th>\n",
       "      <td>-0.003612</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>-0.000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-28</th>\n",
       "      <td>-0.001237</td>\n",
       "      <td>0.005090</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>0.004609</td>\n",
       "      <td>0.002804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-29</th>\n",
       "      <td>0.001726</td>\n",
       "      <td>-0.001141</td>\n",
       "      <td>0.003331</td>\n",
       "      <td>0.004074</td>\n",
       "      <td>-0.001743</td>\n",
       "      <td>0.002188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-30</th>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.001891</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>0.002533</td>\n",
       "      <td>-0.004189</td>\n",
       "      <td>0.002062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>-0.002202</td>\n",
       "      <td>-0.000607</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.001075</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>0.002303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-05</th>\n",
       "      <td>0.001566</td>\n",
       "      <td>-0.001161</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.003483</td>\n",
       "      <td>-0.003086</td>\n",
       "      <td>0.001049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-06</th>\n",
       "      <td>0.000418</td>\n",
       "      <td>-0.001253</td>\n",
       "      <td>0.004626</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>-0.001578</td>\n",
       "      <td>-0.001276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-07</th>\n",
       "      <td>0.003515</td>\n",
       "      <td>-0.001633</td>\n",
       "      <td>0.000780</td>\n",
       "      <td>-0.003138</td>\n",
       "      <td>-0.001242</td>\n",
       "      <td>-0.000497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-10</th>\n",
       "      <td>0.010344</td>\n",
       "      <td>-0.001869</td>\n",
       "      <td>0.002597</td>\n",
       "      <td>-0.002467</td>\n",
       "      <td>0.005719</td>\n",
       "      <td>-0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-11</th>\n",
       "      <td>-0.002917</td>\n",
       "      <td>0.004775</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>-0.002095</td>\n",
       "      <td>0.001558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-12</th>\n",
       "      <td>0.001632</td>\n",
       "      <td>-0.003372</td>\n",
       "      <td>-0.002442</td>\n",
       "      <td>-0.001558</td>\n",
       "      <td>0.003778</td>\n",
       "      <td>-0.001957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-13</th>\n",
       "      <td>-0.000340</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>0.005126</td>\n",
       "      <td>0.005621</td>\n",
       "      <td>0.003485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>-0.002195</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>-0.001126</td>\n",
       "      <td>0.007136</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>0.006181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>-0.002852</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>-0.000890</td>\n",
       "      <td>0.006042</td>\n",
       "      <td>-0.003997</td>\n",
       "      <td>0.004363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-18</th>\n",
       "      <td>0.001952</td>\n",
       "      <td>0.002299</td>\n",
       "      <td>-0.003828</td>\n",
       "      <td>0.005446</td>\n",
       "      <td>-0.002118</td>\n",
       "      <td>0.005943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-19</th>\n",
       "      <td>0.003627</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.002438</td>\n",
       "      <td>-0.003224</td>\n",
       "      <td>0.006969</td>\n",
       "      <td>0.001252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-20</th>\n",
       "      <td>-0.001818</td>\n",
       "      <td>-0.003174</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>0.010449</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.010200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-21</th>\n",
       "      <td>-0.003144</td>\n",
       "      <td>0.001786</td>\n",
       "      <td>-0.001221</td>\n",
       "      <td>0.003656</td>\n",
       "      <td>-0.001167</td>\n",
       "      <td>0.001166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-24</th>\n",
       "      <td>-0.003351</td>\n",
       "      <td>-0.002242</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>-0.003107</td>\n",
       "      <td>-0.000257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-25</th>\n",
       "      <td>-0.003666</td>\n",
       "      <td>0.002381</td>\n",
       "      <td>-0.002819</td>\n",
       "      <td>-0.000794</td>\n",
       "      <td>-0.003996</td>\n",
       "      <td>-0.001504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-26</th>\n",
       "      <td>-0.003191</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>-0.006412</td>\n",
       "      <td>-0.002127</td>\n",
       "      <td>-0.004093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-27</th>\n",
       "      <td>-0.001140</td>\n",
       "      <td>-0.002812</td>\n",
       "      <td>-0.003887</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>-0.004332</td>\n",
       "      <td>-0.003437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>-0.000244</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.002407</td>\n",
       "      <td>-0.000267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-02</th>\n",
       "      <td>-0.000605</td>\n",
       "      <td>-0.002541</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>-0.000470</td>\n",
       "      <td>-0.000982</td>\n",
       "      <td>-0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>0.000178</td>\n",
       "      <td>-0.002356</td>\n",
       "      <td>0.001190</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>0.002576</td>\n",
       "      <td>0.000877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>-0.000693</td>\n",
       "      <td>0.007809</td>\n",
       "      <td>-0.000187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-05</th>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.001184</td>\n",
       "      <td>-0.001778</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>-0.004826</td>\n",
       "      <td>-0.000827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0.004741</td>\n",
       "      <td>0.000990</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.004340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-09</th>\n",
       "      <td>-0.010973</td>\n",
       "      <td>-0.001618</td>\n",
       "      <td>0.001462</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>-0.009495</td>\n",
       "      <td>0.004448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>-0.001798</td>\n",
       "      <td>-0.002453</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.001827</td>\n",
       "      <td>-0.002364</td>\n",
       "      <td>0.001739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-11</th>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>-0.002562</td>\n",
       "      <td>-0.000172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BDTO    CFinc1      DivP  EPSAfterNonRecurring      RVOL  \\\n",
       "Date                                                                       \n",
       "2017-02-28  0.000345 -0.000405  0.000677              0.001173  0.000098   \n",
       "2017-03-01 -0.001481  0.003454  0.001958              0.004167  0.000128   \n",
       "2017-03-02  0.002512 -0.000192 -0.000897              0.000684 -0.001917   \n",
       "2017-03-03  0.000322 -0.000550 -0.000774             -0.000659 -0.000619   \n",
       "2017-03-06  0.001285 -0.001004  0.000424              0.000759  0.001010   \n",
       "2017-03-07  0.000653 -0.000431  0.000267              0.001313  0.000048   \n",
       "2017-03-08  0.000036  0.002337  0.001291              0.005179 -0.000133   \n",
       "2017-03-09 -0.002579  0.000340  0.001419              0.002769 -0.002368   \n",
       "2017-03-10 -0.001629 -0.001779 -0.000228             -0.000841  0.000768   \n",
       "2017-03-13 -0.000604 -0.000081  0.000616              0.001437  0.000647   \n",
       "2017-03-14 -0.001164  0.003190 -0.000245             -0.000817 -0.002645   \n",
       "2017-03-15  0.001148 -0.000453 -0.001580             -0.000696  0.002663   \n",
       "2017-03-16 -0.001776  0.000316  0.002435              0.002577  0.000464   \n",
       "2017-03-17 -0.000904  0.001535  0.000411              0.004222 -0.000899   \n",
       "2017-03-20 -0.003355 -0.002806  0.001515              0.004522 -0.001228   \n",
       "2017-03-21 -0.000986 -0.000729  0.000820              0.000615  0.000145   \n",
       "2017-03-22 -0.000349 -0.001615  0.001452             -0.000837  0.001707   \n",
       "2017-03-23  0.001144  0.001047  0.002742             -0.000821  0.001677   \n",
       "2017-03-24 -0.002831 -0.000708 -0.000985             -0.001101 -0.000194   \n",
       "2017-03-27 -0.003612  0.000844  0.000919              0.000501  0.001789   \n",
       "2017-03-28 -0.001237  0.005090 -0.000123              0.000587  0.004609   \n",
       "2017-03-29  0.001726 -0.001141  0.003331              0.004074 -0.001743   \n",
       "2017-03-30  0.002931  0.001891  0.003545              0.002533 -0.004189   \n",
       "2017-03-31 -0.002202 -0.000607  0.001275              0.001075 -0.000041   \n",
       "2017-04-05  0.001566 -0.001161  0.000001              0.003483 -0.003086   \n",
       "2017-04-06  0.000418 -0.001253  0.004626              0.001843 -0.001578   \n",
       "2017-04-07  0.003515 -0.001633  0.000780             -0.003138 -0.001242   \n",
       "2017-04-10  0.010344 -0.001869  0.002597             -0.002467  0.005719   \n",
       "2017-04-11 -0.002917  0.004775  0.002902              0.000978 -0.002095   \n",
       "2017-04-12  0.001632 -0.003372 -0.002442             -0.001558  0.003778   \n",
       "2017-04-13 -0.000340  0.000724 -0.001148              0.005126  0.005621   \n",
       "2017-04-14 -0.002195  0.003996 -0.001126              0.007136 -0.001650   \n",
       "2017-04-17 -0.002852  0.003761 -0.000890              0.006042 -0.003997   \n",
       "2017-04-18  0.001952  0.002299 -0.003828              0.005446 -0.002118   \n",
       "2017-04-19  0.003627  0.001225  0.002438             -0.003224  0.006969   \n",
       "2017-04-20 -0.001818 -0.003174 -0.001038              0.010449  0.003556   \n",
       "2017-04-21 -0.003144  0.001786 -0.001221              0.003656 -0.001167   \n",
       "2017-04-24 -0.003351 -0.002242  0.003983              0.000183 -0.003107   \n",
       "2017-04-25 -0.003666  0.002381 -0.002819             -0.000794 -0.003996   \n",
       "2017-04-26 -0.003191 -0.001520 -0.002291             -0.006412 -0.002127   \n",
       "2017-04-27 -0.001140 -0.002812 -0.003887             -0.000818 -0.004332   \n",
       "2017-04-28 -0.000244  0.003314 -0.000859              0.000108  0.002407   \n",
       "2017-05-02 -0.000605 -0.002541  0.001466             -0.000470 -0.000982   \n",
       "2017-05-03  0.000178 -0.002356  0.001190              0.002177  0.002576   \n",
       "2017-05-04  0.003321  0.000407  0.001244             -0.000693  0.007809   \n",
       "2017-05-05 -0.002115 -0.001184 -0.001778              0.000334 -0.004826   \n",
       "2017-05-08  0.004741  0.000990 -0.000004              0.004500  0.002026   \n",
       "2017-05-09 -0.010973 -0.001618  0.001462              0.006346 -0.009495   \n",
       "2017-05-10 -0.001798 -0.002453  0.000650              0.001827 -0.002364   \n",
       "2017-05-11  0.001110  0.000506  0.000374              0.000160 -0.002562   \n",
       "\n",
       "                 res  \n",
       "Date                  \n",
       "2017-02-28  0.000162  \n",
       "2017-03-01  0.003307  \n",
       "2017-03-02  0.000416  \n",
       "2017-03-03 -0.000480  \n",
       "2017-03-06  0.000065  \n",
       "2017-03-07 -0.000527  \n",
       "2017-03-08  0.005436  \n",
       "2017-03-09  0.002002  \n",
       "2017-03-10 -0.000854  \n",
       "2017-03-13  0.001352  \n",
       "2017-03-14 -0.000197  \n",
       "2017-03-15 -0.001089  \n",
       "2017-03-16  0.002433  \n",
       "2017-03-17  0.003354  \n",
       "2017-03-20  0.004939  \n",
       "2017-03-21  0.000332  \n",
       "2017-03-22 -0.000209  \n",
       "2017-03-23  0.000360  \n",
       "2017-03-24 -0.001255  \n",
       "2017-03-27 -0.000026  \n",
       "2017-03-28  0.002804  \n",
       "2017-03-29  0.002188  \n",
       "2017-03-30  0.002062  \n",
       "2017-03-31  0.002303  \n",
       "2017-04-05  0.001049  \n",
       "2017-04-06 -0.001276  \n",
       "2017-04-07 -0.000497  \n",
       "2017-04-10 -0.000009  \n",
       "2017-04-11  0.001558  \n",
       "2017-04-12 -0.001957  \n",
       "2017-04-13  0.003485  \n",
       "2017-04-14  0.006181  \n",
       "2017-04-17  0.004363  \n",
       "2017-04-18  0.005943  \n",
       "2017-04-19  0.001252  \n",
       "2017-04-20  0.010200  \n",
       "2017-04-21  0.001166  \n",
       "2017-04-24 -0.000257  \n",
       "2017-04-25 -0.001504  \n",
       "2017-04-26 -0.004093  \n",
       "2017-04-27 -0.003437  \n",
       "2017-04-28 -0.000267  \n",
       "2017-05-02 -0.000061  \n",
       "2017-05-03  0.000877  \n",
       "2017-05-04 -0.000187  \n",
       "2017-05-05 -0.000827  \n",
       "2017-05-08  0.004340  \n",
       "2017-05-09  0.004448  \n",
       "2017-05-10  0.001739  \n",
       "2017-05-11 -0.000172  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df.groupby(level=0).sum().tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1136228</th>\n",
       "      <td>6</td>\n",
       "      <td>7.277932e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136229</th>\n",
       "      <td>12</td>\n",
       "      <td>-9.730834e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136230</th>\n",
       "      <td>21</td>\n",
       "      <td>-1.768933e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136231</th>\n",
       "      <td>25</td>\n",
       "      <td>-3.091405e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136232</th>\n",
       "      <td>28</td>\n",
       "      <td>1.301938e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136233</th>\n",
       "      <td>31</td>\n",
       "      <td>-2.268243e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136234</th>\n",
       "      <td>49</td>\n",
       "      <td>1.187182e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136235</th>\n",
       "      <td>50</td>\n",
       "      <td>1.125843e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136236</th>\n",
       "      <td>62</td>\n",
       "      <td>-1.502775e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136237</th>\n",
       "      <td>66</td>\n",
       "      <td>-1.986989e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136238</th>\n",
       "      <td>78</td>\n",
       "      <td>-2.007921e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136239</th>\n",
       "      <td>89</td>\n",
       "      <td>-3.291741e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136240</th>\n",
       "      <td>90</td>\n",
       "      <td>3.057174e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136241</th>\n",
       "      <td>99</td>\n",
       "      <td>-2.310975e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136242</th>\n",
       "      <td>158</td>\n",
       "      <td>-3.190520e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136243</th>\n",
       "      <td>400</td>\n",
       "      <td>5.724036e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136244</th>\n",
       "      <td>401</td>\n",
       "      <td>-2.752791e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136245</th>\n",
       "      <td>417</td>\n",
       "      <td>-1.244532e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136246</th>\n",
       "      <td>418</td>\n",
       "      <td>1.269438e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136247</th>\n",
       "      <td>426</td>\n",
       "      <td>-4.260035e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136248</th>\n",
       "      <td>488</td>\n",
       "      <td>1.266650e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136249</th>\n",
       "      <td>501</td>\n",
       "      <td>1.161125e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136250</th>\n",
       "      <td>513</td>\n",
       "      <td>-2.495915e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136251</th>\n",
       "      <td>517</td>\n",
       "      <td>-1.859248e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136252</th>\n",
       "      <td>519</td>\n",
       "      <td>-1.948215e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136253</th>\n",
       "      <td>528</td>\n",
       "      <td>-1.302723e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136254</th>\n",
       "      <td>541</td>\n",
       "      <td>2.590909e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136255</th>\n",
       "      <td>543</td>\n",
       "      <td>-4.534567e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136256</th>\n",
       "      <td>547</td>\n",
       "      <td>-9.153589e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136257</th>\n",
       "      <td>552</td>\n",
       "      <td>-1.690547e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136698</th>\n",
       "      <td>601717</td>\n",
       "      <td>-2.295747e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136699</th>\n",
       "      <td>601777</td>\n",
       "      <td>-3.124021e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136700</th>\n",
       "      <td>601801</td>\n",
       "      <td>-2.371151e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136701</th>\n",
       "      <td>601880</td>\n",
       "      <td>-1.968025e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136702</th>\n",
       "      <td>601886</td>\n",
       "      <td>-1.679214e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136703</th>\n",
       "      <td>601908</td>\n",
       "      <td>-2.240918e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136704</th>\n",
       "      <td>601929</td>\n",
       "      <td>-7.589631e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136705</th>\n",
       "      <td>603001</td>\n",
       "      <td>3.536692e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136706</th>\n",
       "      <td>603005</td>\n",
       "      <td>-2.578518e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136707</th>\n",
       "      <td>603019</td>\n",
       "      <td>-1.773186e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136708</th>\n",
       "      <td>603025</td>\n",
       "      <td>1.040387e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136709</th>\n",
       "      <td>603077</td>\n",
       "      <td>-1.879729e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136710</th>\n",
       "      <td>603169</td>\n",
       "      <td>-2.680008e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136711</th>\n",
       "      <td>603188</td>\n",
       "      <td>1.159270e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136712</th>\n",
       "      <td>603198</td>\n",
       "      <td>5.279958e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136713</th>\n",
       "      <td>603328</td>\n",
       "      <td>1.078667e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136714</th>\n",
       "      <td>603355</td>\n",
       "      <td>-6.642535e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136715</th>\n",
       "      <td>603369</td>\n",
       "      <td>-1.752050e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136716</th>\n",
       "      <td>603377</td>\n",
       "      <td>-1.935353e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136717</th>\n",
       "      <td>603528</td>\n",
       "      <td>1.067776e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136718</th>\n",
       "      <td>603555</td>\n",
       "      <td>-2.277532e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136719</th>\n",
       "      <td>603567</td>\n",
       "      <td>3.407227e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136720</th>\n",
       "      <td>603568</td>\n",
       "      <td>4.186256e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136721</th>\n",
       "      <td>603589</td>\n",
       "      <td>3.730747e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136722</th>\n",
       "      <td>603698</td>\n",
       "      <td>2.293832e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136723</th>\n",
       "      <td>603766</td>\n",
       "      <td>8.770332e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136724</th>\n",
       "      <td>603806</td>\n",
       "      <td>1.070672e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136725</th>\n",
       "      <td>603866</td>\n",
       "      <td>-1.515834e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136726</th>\n",
       "      <td>603868</td>\n",
       "      <td>5.737203e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136727</th>\n",
       "      <td>603883</td>\n",
       "      <td>1.122151e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Code           res\n",
       "1136228       6  7.277932e-03\n",
       "1136229      12 -9.730834e-15\n",
       "1136230      21 -1.768933e-14\n",
       "1136231      25 -3.091405e-14\n",
       "1136232      28  1.301938e-02\n",
       "1136233      31 -2.268243e-14\n",
       "1136234      49  1.187182e-02\n",
       "1136235      50  1.125843e-02\n",
       "1136236      62 -1.502775e-14\n",
       "1136237      66 -1.986989e-14\n",
       "1136238      78 -2.007921e-14\n",
       "1136239      89 -3.291741e-16\n",
       "1136240      90  3.057174e-03\n",
       "1136241      99 -2.310975e-14\n",
       "1136242     158 -3.190520e-14\n",
       "1136243     400  5.724036e-03\n",
       "1136244     401 -2.752791e-14\n",
       "1136245     417 -1.244532e-14\n",
       "1136246     418  1.269438e-02\n",
       "1136247     426 -4.260035e-15\n",
       "1136248     488  1.266650e-02\n",
       "1136249     501  1.161125e-02\n",
       "1136250     513 -2.495915e-14\n",
       "1136251     517 -1.859248e-14\n",
       "1136252     519 -1.948215e-14\n",
       "1136253     528 -1.302723e-14\n",
       "1136254     541  2.590909e-13\n",
       "1136255     543 -4.534567e-15\n",
       "1136256     547 -9.153589e-15\n",
       "1136257     552 -1.690547e-14\n",
       "...         ...           ...\n",
       "1136698  601717 -2.295747e-14\n",
       "1136699  601777 -3.124021e-14\n",
       "1136700  601801 -2.371151e-15\n",
       "1136701  601880 -1.968025e-14\n",
       "1136702  601886 -1.679214e-14\n",
       "1136703  601908 -2.240918e-14\n",
       "1136704  601929 -7.589631e-15\n",
       "1136705  603001  3.536692e-03\n",
       "1136706  603005 -2.578518e-14\n",
       "1136707  603019 -1.773186e-14\n",
       "1136708  603025  1.040387e-02\n",
       "1136709  603077 -1.879729e-14\n",
       "1136710  603169 -2.680008e-14\n",
       "1136711  603188  1.159270e-02\n",
       "1136712  603198  5.279958e-14\n",
       "1136713  603328  1.078667e-02\n",
       "1136714  603355 -6.642535e-15\n",
       "1136715  603369 -1.752050e-14\n",
       "1136716  603377 -1.935353e-14\n",
       "1136717  603528  1.067776e-02\n",
       "1136718  603555 -2.277532e-14\n",
       "1136719  603567  3.407227e-14\n",
       "1136720  603568  4.186256e-14\n",
       "1136721  603589  3.730747e-03\n",
       "1136722  603698  2.293832e-03\n",
       "1136723  603766  8.770332e-03\n",
       "1136724  603806  1.070672e-02\n",
       "1136725  603866 -1.515834e-14\n",
       "1136726  603868  5.737203e-14\n",
       "1136727  603883  1.122151e-02\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df.loc[pos_df.Date == '2017-05-11', ['Code', 'res']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>BDTO</th>\n",
       "      <th>CFinc1</th>\n",
       "      <th>DivP</th>\n",
       "      <th>EPSAfterNonRecurring</th>\n",
       "      <th>RVOL</th>\n",
       "      <th>res</th>\n",
       "      <th>Code</th>\n",
       "      <th>Ind</th>\n",
       "      <th>bm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-5.442673e-15</td>\n",
       "      <td>1.384754e-02</td>\n",
       "      <td>1.384754e-02</td>\n",
       "      <td>5.858232e-12</td>\n",
       "      <td>1.384754e-02</td>\n",
       "      <td>1.384754e-02</td>\n",
       "      <td>6</td>\n",
       "      <td>申万房地产</td>\n",
       "      <td>0.003848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-6.024292e-15</td>\n",
       "      <td>-2.268139e-12</td>\n",
       "      <td>1.162243e-02</td>\n",
       "      <td>-1.926588e-13</td>\n",
       "      <td>1.139228e-02</td>\n",
       "      <td>-1.551458e-14</td>\n",
       "      <td>16</td>\n",
       "      <td>申万家用电器</td>\n",
       "      <td>0.001702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.245228e-14</td>\n",
       "      <td>-8.373102e-13</td>\n",
       "      <td>-7.004716e-15</td>\n",
       "      <td>8.397004e-14</td>\n",
       "      <td>3.452556e-15</td>\n",
       "      <td>-1.506153e-14</td>\n",
       "      <td>28</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>0.001541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>1.090801e-02</td>\n",
       "      <td>-1.150759e-12</td>\n",
       "      <td>2.849116e-03</td>\n",
       "      <td>1.173542e-13</td>\n",
       "      <td>5.825098e-15</td>\n",
       "      <td>-8.062127e-15</td>\n",
       "      <td>32</td>\n",
       "      <td>申万信息设备</td>\n",
       "      <td>0.000908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.224150e-14</td>\n",
       "      <td>1.185763e-12</td>\n",
       "      <td>-1.518487e-14</td>\n",
       "      <td>-2.165287e-13</td>\n",
       "      <td>1.955101e-14</td>\n",
       "      <td>-2.492053e-14</td>\n",
       "      <td>33</td>\n",
       "      <td>申万餐饮旅游</td>\n",
       "      <td>0.000704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.359089e-14</td>\n",
       "      <td>-1.142156e-12</td>\n",
       "      <td>-1.418759e-14</td>\n",
       "      <td>-2.163438e-13</td>\n",
       "      <td>-3.982074e-14</td>\n",
       "      <td>-2.618693e-14</td>\n",
       "      <td>40</td>\n",
       "      <td>申万综合</td>\n",
       "      <td>0.002320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-6.949695e-15</td>\n",
       "      <td>-8.977892e-13</td>\n",
       "      <td>3.329997e-14</td>\n",
       "      <td>-2.500834e-13</td>\n",
       "      <td>8.515782e-14</td>\n",
       "      <td>-2.528259e-14</td>\n",
       "      <td>42</td>\n",
       "      <td>申万房地产</td>\n",
       "      <td>0.001851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>1.423948e-13</td>\n",
       "      <td>-9.418540e-13</td>\n",
       "      <td>1.140850e-02</td>\n",
       "      <td>9.103620e-15</td>\n",
       "      <td>-3.527894e-14</td>\n",
       "      <td>1.020934e-14</td>\n",
       "      <td>43</td>\n",
       "      <td>申万房地产</td>\n",
       "      <td>0.001409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.830578e-15</td>\n",
       "      <td>-9.578553e-13</td>\n",
       "      <td>1.133237e-02</td>\n",
       "      <td>-7.112720e-14</td>\n",
       "      <td>3.572492e-14</td>\n",
       "      <td>4.562759e-14</td>\n",
       "      <td>50</td>\n",
       "      <td>申万电子元器件</td>\n",
       "      <td>0.001332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>3.664544e-04</td>\n",
       "      <td>1.162243e-02</td>\n",
       "      <td>-1.938180e-14</td>\n",
       "      <td>2.942456e-14</td>\n",
       "      <td>7.304879e-14</td>\n",
       "      <td>1.162243e-02</td>\n",
       "      <td>62</td>\n",
       "      <td>申万家用电器</td>\n",
       "      <td>0.001740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>7.909011e-16</td>\n",
       "      <td>-9.922454e-13</td>\n",
       "      <td>-1.728396e-14</td>\n",
       "      <td>-1.798109e-13</td>\n",
       "      <td>-5.450718e-14</td>\n",
       "      <td>-2.428475e-14</td>\n",
       "      <td>70</td>\n",
       "      <td>申万电子元器件</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.316734e-14</td>\n",
       "      <td>-7.921279e-13</td>\n",
       "      <td>-1.268466e-14</td>\n",
       "      <td>-2.255519e-13</td>\n",
       "      <td>1.222315e-02</td>\n",
       "      <td>-2.623912e-14</td>\n",
       "      <td>78</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>0.002223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>1.187557e-02</td>\n",
       "      <td>1.187557e-02</td>\n",
       "      <td>1.585176e-14</td>\n",
       "      <td>1.533049e-12</td>\n",
       "      <td>1.187557e-02</td>\n",
       "      <td>1.187557e-02</td>\n",
       "      <td>90</td>\n",
       "      <td>申万房地产</td>\n",
       "      <td>0.001876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-9.897533e-15</td>\n",
       "      <td>-9.422119e-13</td>\n",
       "      <td>1.137643e-02</td>\n",
       "      <td>7.822182e-14</td>\n",
       "      <td>8.731363e-14</td>\n",
       "      <td>7.375062e-03</td>\n",
       "      <td>96</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.001376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.223368e-14</td>\n",
       "      <td>-1.014353e-12</td>\n",
       "      <td>-4.642382e-15</td>\n",
       "      <td>-1.363045e-13</td>\n",
       "      <td>-5.979274e-14</td>\n",
       "      <td>-2.408006e-14</td>\n",
       "      <td>99</td>\n",
       "      <td>申万交通运输</td>\n",
       "      <td>0.001845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.424662e-14</td>\n",
       "      <td>-1.164820e-12</td>\n",
       "      <td>1.011059e-02</td>\n",
       "      <td>-1.377082e-13</td>\n",
       "      <td>-5.862568e-14</td>\n",
       "      <td>-1.681653e-14</td>\n",
       "      <td>151</td>\n",
       "      <td>申万综合</td>\n",
       "      <td>0.001440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.359589e-14</td>\n",
       "      <td>-1.118671e-12</td>\n",
       "      <td>1.216890e-02</td>\n",
       "      <td>-1.364962e-13</td>\n",
       "      <td>-5.338915e-14</td>\n",
       "      <td>9.101682e-15</td>\n",
       "      <td>155</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.002169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-4.998005e-15</td>\n",
       "      <td>-9.926427e-13</td>\n",
       "      <td>-1.492530e-14</td>\n",
       "      <td>-1.097737e-13</td>\n",
       "      <td>-4.074542e-14</td>\n",
       "      <td>-2.092030e-14</td>\n",
       "      <td>158</td>\n",
       "      <td>申万纺织服装</td>\n",
       "      <td>0.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.152045e-14</td>\n",
       "      <td>-1.106155e-12</td>\n",
       "      <td>-1.246867e-14</td>\n",
       "      <td>-2.022583e-13</td>\n",
       "      <td>-3.008296e-14</td>\n",
       "      <td>-2.413797e-14</td>\n",
       "      <td>407</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.002732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>1.742197e-14</td>\n",
       "      <td>-7.443259e-13</td>\n",
       "      <td>-9.723997e-15</td>\n",
       "      <td>-2.137157e-13</td>\n",
       "      <td>-3.495764e-14</td>\n",
       "      <td>-2.519559e-14</td>\n",
       "      <td>416</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>0.002837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.436807e-14</td>\n",
       "      <td>1.167734e-02</td>\n",
       "      <td>8.412830e-14</td>\n",
       "      <td>1.736713e-14</td>\n",
       "      <td>-2.423859e-14</td>\n",
       "      <td>3.595892e-13</td>\n",
       "      <td>417</td>\n",
       "      <td>申万商业贸易</td>\n",
       "      <td>0.001677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-8.189506e-15</td>\n",
       "      <td>6.144982e-13</td>\n",
       "      <td>-1.618387e-14</td>\n",
       "      <td>1.820976e-14</td>\n",
       "      <td>-4.532650e-14</td>\n",
       "      <td>-2.062242e-14</td>\n",
       "      <td>419</td>\n",
       "      <td>申万商业贸易</td>\n",
       "      <td>0.001471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.135826e-14</td>\n",
       "      <td>1.194865e-02</td>\n",
       "      <td>-1.389857e-14</td>\n",
       "      <td>-1.674484e-14</td>\n",
       "      <td>-1.006814e-14</td>\n",
       "      <td>1.879390e-14</td>\n",
       "      <td>420</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.001949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-6.058033e-15</td>\n",
       "      <td>-1.047790e-12</td>\n",
       "      <td>-1.687521e-14</td>\n",
       "      <td>-2.079266e-13</td>\n",
       "      <td>-4.480860e-14</td>\n",
       "      <td>-2.809885e-14</td>\n",
       "      <td>421</td>\n",
       "      <td>申万交通运输</td>\n",
       "      <td>0.001081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.610918e-14</td>\n",
       "      <td>-7.990683e-13</td>\n",
       "      <td>-1.754944e-14</td>\n",
       "      <td>-2.367097e-13</td>\n",
       "      <td>-6.641460e-14</td>\n",
       "      <td>-2.897801e-14</td>\n",
       "      <td>426</td>\n",
       "      <td>申万公用事业</td>\n",
       "      <td>0.001595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>5.662129e-03</td>\n",
       "      <td>6.314185e-12</td>\n",
       "      <td>-1.064199e-14</td>\n",
       "      <td>-3.773571e-14</td>\n",
       "      <td>1.169830e-02</td>\n",
       "      <td>-8.539897e-15</td>\n",
       "      <td>428</td>\n",
       "      <td>申万餐饮旅游</td>\n",
       "      <td>0.001698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>1.575807e-02</td>\n",
       "      <td>6.393092e-11</td>\n",
       "      <td>-3.019673e-15</td>\n",
       "      <td>-1.632944e-13</td>\n",
       "      <td>4.170264e-04</td>\n",
       "      <td>-1.400162e-14</td>\n",
       "      <td>501</td>\n",
       "      <td>申万商业贸易</td>\n",
       "      <td>0.005758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-1.690329e-14</td>\n",
       "      <td>6.159581e-13</td>\n",
       "      <td>-1.712382e-14</td>\n",
       "      <td>-2.414557e-13</td>\n",
       "      <td>-6.579989e-14</td>\n",
       "      <td>-2.926138e-14</td>\n",
       "      <td>504</td>\n",
       "      <td>申万信息服务</td>\n",
       "      <td>0.000851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>4.813430e-14</td>\n",
       "      <td>-1.036066e-12</td>\n",
       "      <td>3.712570e-13</td>\n",
       "      <td>1.198967e-02</td>\n",
       "      <td>1.198967e-02</td>\n",
       "      <td>1.198967e-02</td>\n",
       "      <td>507</td>\n",
       "      <td>申万综合</td>\n",
       "      <td>0.001990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>-6.754556e-15</td>\n",
       "      <td>1.287073e-02</td>\n",
       "      <td>3.308598e-14</td>\n",
       "      <td>-1.755678e-13</td>\n",
       "      <td>1.791898e-13</td>\n",
       "      <td>1.123035e-14</td>\n",
       "      <td>510</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.002871</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136698</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.048932e-14</td>\n",
       "      <td>3.624986e-12</td>\n",
       "      <td>-1.890341e-13</td>\n",
       "      <td>-8.022924e-14</td>\n",
       "      <td>-4.834719e-13</td>\n",
       "      <td>-2.295747e-14</td>\n",
       "      <td>601717</td>\n",
       "      <td>申万机械设备</td>\n",
       "      <td>0.001861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136699</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.334897e-15</td>\n",
       "      <td>-4.935211e-13</td>\n",
       "      <td>-1.995864e-13</td>\n",
       "      <td>-1.134539e-13</td>\n",
       "      <td>-3.599771e-13</td>\n",
       "      <td>-3.124021e-14</td>\n",
       "      <td>601777</td>\n",
       "      <td>申万汽车</td>\n",
       "      <td>0.001363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136700</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-7.018828e-15</td>\n",
       "      <td>-3.712378e-13</td>\n",
       "      <td>1.139938e-02</td>\n",
       "      <td>-2.346197e-14</td>\n",
       "      <td>-5.369141e-13</td>\n",
       "      <td>-2.371151e-15</td>\n",
       "      <td>601801</td>\n",
       "      <td>申万传媒</td>\n",
       "      <td>0.001399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136701</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-4.503611e-15</td>\n",
       "      <td>-4.593816e-13</td>\n",
       "      <td>-1.719909e-13</td>\n",
       "      <td>-3.557687e-14</td>\n",
       "      <td>1.355234e-14</td>\n",
       "      <td>-1.968025e-14</td>\n",
       "      <td>601880</td>\n",
       "      <td>申万交通运输</td>\n",
       "      <td>0.002456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136702</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.144877e-14</td>\n",
       "      <td>-2.868608e-13</td>\n",
       "      <td>2.059559e-13</td>\n",
       "      <td>-2.926548e-14</td>\n",
       "      <td>-5.242766e-13</td>\n",
       "      <td>-1.679214e-14</td>\n",
       "      <td>601886</td>\n",
       "      <td>申万建筑装饰</td>\n",
       "      <td>0.001276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136703</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>5.807498e-03</td>\n",
       "      <td>-2.234398e-13</td>\n",
       "      <td>1.516918e-14</td>\n",
       "      <td>-7.867539e-14</td>\n",
       "      <td>-5.230315e-13</td>\n",
       "      <td>-2.240918e-14</td>\n",
       "      <td>601908</td>\n",
       "      <td>申万电气设备</td>\n",
       "      <td>0.000822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136704</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>9.274287e-03</td>\n",
       "      <td>-4.788102e-13</td>\n",
       "      <td>-1.034895e-13</td>\n",
       "      <td>-6.535284e-14</td>\n",
       "      <td>1.208880e-02</td>\n",
       "      <td>-7.589631e-15</td>\n",
       "      <td>601929</td>\n",
       "      <td>申万传媒</td>\n",
       "      <td>0.002089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136705</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-6.823632e-15</td>\n",
       "      <td>-5.442645e-13</td>\n",
       "      <td>3.536692e-03</td>\n",
       "      <td>3.536692e-03</td>\n",
       "      <td>1.210157e-13</td>\n",
       "      <td>3.536692e-03</td>\n",
       "      <td>603001</td>\n",
       "      <td>申万纺织服装</td>\n",
       "      <td>0.000815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136706</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>2.518065e-14</td>\n",
       "      <td>-5.521230e-13</td>\n",
       "      <td>-1.795124e-13</td>\n",
       "      <td>-8.648270e-14</td>\n",
       "      <td>-4.481713e-13</td>\n",
       "      <td>-2.578518e-14</td>\n",
       "      <td>603005</td>\n",
       "      <td>申万电子</td>\n",
       "      <td>0.000858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136707</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.302613e-14</td>\n",
       "      <td>-3.581466e-13</td>\n",
       "      <td>5.617407e-14</td>\n",
       "      <td>3.268043e-14</td>\n",
       "      <td>-6.370383e-13</td>\n",
       "      <td>-1.773186e-14</td>\n",
       "      <td>603019</td>\n",
       "      <td>申万计算机</td>\n",
       "      <td>0.002112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136708</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.016327e-14</td>\n",
       "      <td>1.040387e-02</td>\n",
       "      <td>3.501577e-13</td>\n",
       "      <td>3.428278e-03</td>\n",
       "      <td>-4.764346e-13</td>\n",
       "      <td>1.040387e-02</td>\n",
       "      <td>603025</td>\n",
       "      <td>申万电气设备</td>\n",
       "      <td>0.000404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136709</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>1.157241e-02</td>\n",
       "      <td>1.157241e-02</td>\n",
       "      <td>-1.930698e-13</td>\n",
       "      <td>-8.744162e-14</td>\n",
       "      <td>1.157241e-02</td>\n",
       "      <td>-1.879729e-14</td>\n",
       "      <td>603077</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.001572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136710</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.533585e-14</td>\n",
       "      <td>1.022881e-12</td>\n",
       "      <td>-2.091181e-13</td>\n",
       "      <td>-8.265809e-14</td>\n",
       "      <td>-6.511042e-13</td>\n",
       "      <td>-2.680008e-14</td>\n",
       "      <td>603169</td>\n",
       "      <td>申万机械设备</td>\n",
       "      <td>0.001033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136711</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-7.062452e-15</td>\n",
       "      <td>-5.826364e-13</td>\n",
       "      <td>1.159270e-02</td>\n",
       "      <td>1.159270e-02</td>\n",
       "      <td>1.789716e-13</td>\n",
       "      <td>1.159270e-02</td>\n",
       "      <td>603188</td>\n",
       "      <td>申万化工</td>\n",
       "      <td>0.001593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136712</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.290255e-14</td>\n",
       "      <td>-5.750355e-13</td>\n",
       "      <td>1.120171e-02</td>\n",
       "      <td>-4.171493e-14</td>\n",
       "      <td>-5.869934e-13</td>\n",
       "      <td>5.279958e-14</td>\n",
       "      <td>603198</td>\n",
       "      <td>申万食品饮料</td>\n",
       "      <td>0.001202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136713</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.311101e-14</td>\n",
       "      <td>-5.492916e-13</td>\n",
       "      <td>1.078667e-02</td>\n",
       "      <td>1.078667e-02</td>\n",
       "      <td>-5.814909e-13</td>\n",
       "      <td>1.078667e-02</td>\n",
       "      <td>603328</td>\n",
       "      <td>申万电子</td>\n",
       "      <td>0.000787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136714</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.357410e-14</td>\n",
       "      <td>8.192243e-03</td>\n",
       "      <td>-2.259002e-13</td>\n",
       "      <td>-1.236529e-14</td>\n",
       "      <td>-5.634307e-13</td>\n",
       "      <td>-6.642535e-15</td>\n",
       "      <td>603355</td>\n",
       "      <td>申万家用电器</td>\n",
       "      <td>0.001171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136715</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.006894e-14</td>\n",
       "      <td>-4.010745e-13</td>\n",
       "      <td>-9.291079e-14</td>\n",
       "      <td>-8.547219e-14</td>\n",
       "      <td>-4.878902e-13</td>\n",
       "      <td>-1.752050e-14</td>\n",
       "      <td>603369</td>\n",
       "      <td>申万食品饮料</td>\n",
       "      <td>0.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136716</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-3.558801e-15</td>\n",
       "      <td>-1.330419e-13</td>\n",
       "      <td>-2.034255e-13</td>\n",
       "      <td>-7.084991e-14</td>\n",
       "      <td>-6.382112e-13</td>\n",
       "      <td>-1.935353e-14</td>\n",
       "      <td>603377</td>\n",
       "      <td>申万汽车</td>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136717</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.384284e-14</td>\n",
       "      <td>-8.343654e-14</td>\n",
       "      <td>-1.566414e-13</td>\n",
       "      <td>1.067776e-02</td>\n",
       "      <td>-6.538919e-13</td>\n",
       "      <td>1.067776e-02</td>\n",
       "      <td>603528</td>\n",
       "      <td>申万汽车</td>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136718</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-4.063751e-16</td>\n",
       "      <td>-5.170653e-13</td>\n",
       "      <td>-1.575612e-13</td>\n",
       "      <td>-6.501036e-14</td>\n",
       "      <td>-6.023446e-13</td>\n",
       "      <td>-2.277532e-14</td>\n",
       "      <td>603555</td>\n",
       "      <td>申万纺织服装</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136719</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>7.645416e-14</td>\n",
       "      <td>1.427282e-11</td>\n",
       "      <td>-1.248017e-14</td>\n",
       "      <td>-4.156198e-14</td>\n",
       "      <td>-3.106298e-13</td>\n",
       "      <td>3.407227e-14</td>\n",
       "      <td>603567</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>0.000754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136720</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.165697e-14</td>\n",
       "      <td>-4.713726e-13</td>\n",
       "      <td>-1.497327e-13</td>\n",
       "      <td>2.159836e-03</td>\n",
       "      <td>-6.389228e-13</td>\n",
       "      <td>4.186256e-14</td>\n",
       "      <td>603568</td>\n",
       "      <td>申万公用事业</td>\n",
       "      <td>0.000765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136721</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-9.852110e-15</td>\n",
       "      <td>-5.417599e-13</td>\n",
       "      <td>-1.314956e-13</td>\n",
       "      <td>1.270230e-02</td>\n",
       "      <td>3.857811e-13</td>\n",
       "      <td>3.730747e-03</td>\n",
       "      <td>603589</td>\n",
       "      <td>申万食品饮料</td>\n",
       "      <td>0.002702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136722</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-9.638300e-15</td>\n",
       "      <td>1.082295e-02</td>\n",
       "      <td>-1.681487e-13</td>\n",
       "      <td>1.082295e-02</td>\n",
       "      <td>-3.285073e-13</td>\n",
       "      <td>2.293832e-03</td>\n",
       "      <td>603698</td>\n",
       "      <td>申万建筑装饰</td>\n",
       "      <td>0.000823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136723</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>2.515230e-14</td>\n",
       "      <td>-2.921968e-13</td>\n",
       "      <td>1.217338e-02</td>\n",
       "      <td>7.304168e-14</td>\n",
       "      <td>1.217338e-02</td>\n",
       "      <td>8.770332e-03</td>\n",
       "      <td>603766</td>\n",
       "      <td>申万汽车</td>\n",
       "      <td>0.002173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136724</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.228972e-14</td>\n",
       "      <td>-3.548604e-13</td>\n",
       "      <td>1.070672e-02</td>\n",
       "      <td>1.070672e-02</td>\n",
       "      <td>-6.533766e-13</td>\n",
       "      <td>1.070672e-02</td>\n",
       "      <td>603806</td>\n",
       "      <td>申万电气设备</td>\n",
       "      <td>0.000707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136725</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.067845e-14</td>\n",
       "      <td>-5.139348e-13</td>\n",
       "      <td>-2.002676e-13</td>\n",
       "      <td>1.672276e-15</td>\n",
       "      <td>-5.811825e-13</td>\n",
       "      <td>-1.515834e-14</td>\n",
       "      <td>603866</td>\n",
       "      <td>申万食品饮料</td>\n",
       "      <td>0.000446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136726</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-1.399344e-14</td>\n",
       "      <td>-3.758385e-13</td>\n",
       "      <td>1.181260e-13</td>\n",
       "      <td>6.779183e-03</td>\n",
       "      <td>-5.380859e-13</td>\n",
       "      <td>5.737203e-14</td>\n",
       "      <td>603868</td>\n",
       "      <td>申万家用电器</td>\n",
       "      <td>0.000698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136727</th>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>-6.729684e-15</td>\n",
       "      <td>4.043012e-13</td>\n",
       "      <td>-1.618064e-13</td>\n",
       "      <td>1.122151e-02</td>\n",
       "      <td>-5.499560e-13</td>\n",
       "      <td>1.122151e-02</td>\n",
       "      <td>603883</td>\n",
       "      <td>申万医药生物</td>\n",
       "      <td>0.001222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1136728 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date          BDTO        CFinc1          DivP  \\\n",
       "0       2008-01-02 -5.442673e-15  1.384754e-02  1.384754e-02   \n",
       "1       2008-01-02 -6.024292e-15 -2.268139e-12  1.162243e-02   \n",
       "2       2008-01-02 -1.245228e-14 -8.373102e-13 -7.004716e-15   \n",
       "3       2008-01-02  1.090801e-02 -1.150759e-12  2.849116e-03   \n",
       "4       2008-01-02 -1.224150e-14  1.185763e-12 -1.518487e-14   \n",
       "5       2008-01-02 -1.359089e-14 -1.142156e-12 -1.418759e-14   \n",
       "6       2008-01-02 -6.949695e-15 -8.977892e-13  3.329997e-14   \n",
       "7       2008-01-02  1.423948e-13 -9.418540e-13  1.140850e-02   \n",
       "8       2008-01-02 -1.830578e-15 -9.578553e-13  1.133237e-02   \n",
       "9       2008-01-02  3.664544e-04  1.162243e-02 -1.938180e-14   \n",
       "10      2008-01-02  7.909011e-16 -9.922454e-13 -1.728396e-14   \n",
       "11      2008-01-02 -1.316734e-14 -7.921279e-13 -1.268466e-14   \n",
       "12      2008-01-02  1.187557e-02  1.187557e-02  1.585176e-14   \n",
       "13      2008-01-02 -9.897533e-15 -9.422119e-13  1.137643e-02   \n",
       "14      2008-01-02 -1.223368e-14 -1.014353e-12 -4.642382e-15   \n",
       "15      2008-01-02 -1.424662e-14 -1.164820e-12  1.011059e-02   \n",
       "16      2008-01-02 -1.359589e-14 -1.118671e-12  1.216890e-02   \n",
       "17      2008-01-02 -4.998005e-15 -9.926427e-13 -1.492530e-14   \n",
       "18      2008-01-02 -1.152045e-14 -1.106155e-12 -1.246867e-14   \n",
       "19      2008-01-02  1.742197e-14 -7.443259e-13 -9.723997e-15   \n",
       "20      2008-01-02 -1.436807e-14  1.167734e-02  8.412830e-14   \n",
       "21      2008-01-02 -8.189506e-15  6.144982e-13 -1.618387e-14   \n",
       "22      2008-01-02 -1.135826e-14  1.194865e-02 -1.389857e-14   \n",
       "23      2008-01-02 -6.058033e-15 -1.047790e-12 -1.687521e-14   \n",
       "24      2008-01-02 -1.610918e-14 -7.990683e-13 -1.754944e-14   \n",
       "25      2008-01-02  5.662129e-03  6.314185e-12 -1.064199e-14   \n",
       "26      2008-01-02  1.575807e-02  6.393092e-11 -3.019673e-15   \n",
       "27      2008-01-02 -1.690329e-14  6.159581e-13 -1.712382e-14   \n",
       "28      2008-01-02  4.813430e-14 -1.036066e-12  3.712570e-13   \n",
       "29      2008-01-02 -6.754556e-15  1.287073e-02  3.308598e-14   \n",
       "...            ...           ...           ...           ...   \n",
       "1136698 2017-05-11 -1.048932e-14  3.624986e-12 -1.890341e-13   \n",
       "1136699 2017-05-11 -1.334897e-15 -4.935211e-13 -1.995864e-13   \n",
       "1136700 2017-05-11 -7.018828e-15 -3.712378e-13  1.139938e-02   \n",
       "1136701 2017-05-11 -4.503611e-15 -4.593816e-13 -1.719909e-13   \n",
       "1136702 2017-05-11 -1.144877e-14 -2.868608e-13  2.059559e-13   \n",
       "1136703 2017-05-11  5.807498e-03 -2.234398e-13  1.516918e-14   \n",
       "1136704 2017-05-11  9.274287e-03 -4.788102e-13 -1.034895e-13   \n",
       "1136705 2017-05-11 -6.823632e-15 -5.442645e-13  3.536692e-03   \n",
       "1136706 2017-05-11  2.518065e-14 -5.521230e-13 -1.795124e-13   \n",
       "1136707 2017-05-11 -1.302613e-14 -3.581466e-13  5.617407e-14   \n",
       "1136708 2017-05-11 -1.016327e-14  1.040387e-02  3.501577e-13   \n",
       "1136709 2017-05-11  1.157241e-02  1.157241e-02 -1.930698e-13   \n",
       "1136710 2017-05-11 -1.533585e-14  1.022881e-12 -2.091181e-13   \n",
       "1136711 2017-05-11 -7.062452e-15 -5.826364e-13  1.159270e-02   \n",
       "1136712 2017-05-11 -1.290255e-14 -5.750355e-13  1.120171e-02   \n",
       "1136713 2017-05-11 -1.311101e-14 -5.492916e-13  1.078667e-02   \n",
       "1136714 2017-05-11 -1.357410e-14  8.192243e-03 -2.259002e-13   \n",
       "1136715 2017-05-11 -1.006894e-14 -4.010745e-13 -9.291079e-14   \n",
       "1136716 2017-05-11 -3.558801e-15 -1.330419e-13 -2.034255e-13   \n",
       "1136717 2017-05-11 -1.384284e-14 -8.343654e-14 -1.566414e-13   \n",
       "1136718 2017-05-11 -4.063751e-16 -5.170653e-13 -1.575612e-13   \n",
       "1136719 2017-05-11  7.645416e-14  1.427282e-11 -1.248017e-14   \n",
       "1136720 2017-05-11 -1.165697e-14 -4.713726e-13 -1.497327e-13   \n",
       "1136721 2017-05-11 -9.852110e-15 -5.417599e-13 -1.314956e-13   \n",
       "1136722 2017-05-11 -9.638300e-15  1.082295e-02 -1.681487e-13   \n",
       "1136723 2017-05-11  2.515230e-14 -2.921968e-13  1.217338e-02   \n",
       "1136724 2017-05-11 -1.228972e-14 -3.548604e-13  1.070672e-02   \n",
       "1136725 2017-05-11 -1.067845e-14 -5.139348e-13 -2.002676e-13   \n",
       "1136726 2017-05-11 -1.399344e-14 -3.758385e-13  1.181260e-13   \n",
       "1136727 2017-05-11 -6.729684e-15  4.043012e-13 -1.618064e-13   \n",
       "\n",
       "         EPSAfterNonRecurring          RVOL           res    Code      Ind  \\\n",
       "0                5.858232e-12  1.384754e-02  1.384754e-02       6    申万房地产   \n",
       "1               -1.926588e-13  1.139228e-02 -1.551458e-14      16   申万家用电器   \n",
       "2                8.397004e-14  3.452556e-15 -1.506153e-14      28   申万医药生物   \n",
       "3                1.173542e-13  5.825098e-15 -8.062127e-15      32   申万信息设备   \n",
       "4               -2.165287e-13  1.955101e-14 -2.492053e-14      33   申万餐饮旅游   \n",
       "5               -2.163438e-13 -3.982074e-14 -2.618693e-14      40     申万综合   \n",
       "6               -2.500834e-13  8.515782e-14 -2.528259e-14      42    申万房地产   \n",
       "7                9.103620e-15 -3.527894e-14  1.020934e-14      43    申万房地产   \n",
       "8               -7.112720e-14  3.572492e-14  4.562759e-14      50  申万电子元器件   \n",
       "9                2.942456e-14  7.304879e-14  1.162243e-02      62   申万家用电器   \n",
       "10              -1.798109e-13 -5.450718e-14 -2.428475e-14      70  申万电子元器件   \n",
       "11              -2.255519e-13  1.222315e-02 -2.623912e-14      78   申万医药生物   \n",
       "12               1.533049e-12  1.187557e-02  1.187557e-02      90    申万房地产   \n",
       "13               7.822182e-14  8.731363e-14  7.375062e-03      96     申万化工   \n",
       "14              -1.363045e-13 -5.979274e-14 -2.408006e-14      99   申万交通运输   \n",
       "15              -1.377082e-13 -5.862568e-14 -1.681653e-14     151     申万综合   \n",
       "16              -1.364962e-13 -5.338915e-14  9.101682e-15     155     申万化工   \n",
       "17              -1.097737e-13 -4.074542e-14 -2.092030e-14     158   申万纺织服装   \n",
       "18              -2.022583e-13 -3.008296e-14 -2.413797e-14     407     申万化工   \n",
       "19              -2.137157e-13 -3.495764e-14 -2.519559e-14     416   申万医药生物   \n",
       "20               1.736713e-14 -2.423859e-14  3.595892e-13     417   申万商业贸易   \n",
       "21               1.820976e-14 -4.532650e-14 -2.062242e-14     419   申万商业贸易   \n",
       "22              -1.674484e-14 -1.006814e-14  1.879390e-14     420     申万化工   \n",
       "23              -2.079266e-13 -4.480860e-14 -2.809885e-14     421   申万交通运输   \n",
       "24              -2.367097e-13 -6.641460e-14 -2.897801e-14     426   申万公用事业   \n",
       "25              -3.773571e-14  1.169830e-02 -8.539897e-15     428   申万餐饮旅游   \n",
       "26              -1.632944e-13  4.170264e-04 -1.400162e-14     501   申万商业贸易   \n",
       "27              -2.414557e-13 -6.579989e-14 -2.926138e-14     504   申万信息服务   \n",
       "28               1.198967e-02  1.198967e-02  1.198967e-02     507     申万综合   \n",
       "29              -1.755678e-13  1.791898e-13  1.123035e-14     510     申万化工   \n",
       "...                       ...           ...           ...     ...      ...   \n",
       "1136698         -8.022924e-14 -4.834719e-13 -2.295747e-14  601717   申万机械设备   \n",
       "1136699         -1.134539e-13 -3.599771e-13 -3.124021e-14  601777     申万汽车   \n",
       "1136700         -2.346197e-14 -5.369141e-13 -2.371151e-15  601801     申万传媒   \n",
       "1136701         -3.557687e-14  1.355234e-14 -1.968025e-14  601880   申万交通运输   \n",
       "1136702         -2.926548e-14 -5.242766e-13 -1.679214e-14  601886   申万建筑装饰   \n",
       "1136703         -7.867539e-14 -5.230315e-13 -2.240918e-14  601908   申万电气设备   \n",
       "1136704         -6.535284e-14  1.208880e-02 -7.589631e-15  601929     申万传媒   \n",
       "1136705          3.536692e-03  1.210157e-13  3.536692e-03  603001   申万纺织服装   \n",
       "1136706         -8.648270e-14 -4.481713e-13 -2.578518e-14  603005     申万电子   \n",
       "1136707          3.268043e-14 -6.370383e-13 -1.773186e-14  603019    申万计算机   \n",
       "1136708          3.428278e-03 -4.764346e-13  1.040387e-02  603025   申万电气设备   \n",
       "1136709         -8.744162e-14  1.157241e-02 -1.879729e-14  603077     申万化工   \n",
       "1136710         -8.265809e-14 -6.511042e-13 -2.680008e-14  603169   申万机械设备   \n",
       "1136711          1.159270e-02  1.789716e-13  1.159270e-02  603188     申万化工   \n",
       "1136712         -4.171493e-14 -5.869934e-13  5.279958e-14  603198   申万食品饮料   \n",
       "1136713          1.078667e-02 -5.814909e-13  1.078667e-02  603328     申万电子   \n",
       "1136714         -1.236529e-14 -5.634307e-13 -6.642535e-15  603355   申万家用电器   \n",
       "1136715         -8.547219e-14 -4.878902e-13 -1.752050e-14  603369   申万食品饮料   \n",
       "1136716         -7.084991e-14 -6.382112e-13 -1.935353e-14  603377     申万汽车   \n",
       "1136717          1.067776e-02 -6.538919e-13  1.067776e-02  603528     申万汽车   \n",
       "1136718         -6.501036e-14 -6.023446e-13 -2.277532e-14  603555   申万纺织服装   \n",
       "1136719         -4.156198e-14 -3.106298e-13  3.407227e-14  603567   申万医药生物   \n",
       "1136720          2.159836e-03 -6.389228e-13  4.186256e-14  603568   申万公用事业   \n",
       "1136721          1.270230e-02  3.857811e-13  3.730747e-03  603589   申万食品饮料   \n",
       "1136722          1.082295e-02 -3.285073e-13  2.293832e-03  603698   申万建筑装饰   \n",
       "1136723          7.304168e-14  1.217338e-02  8.770332e-03  603766     申万汽车   \n",
       "1136724          1.070672e-02 -6.533766e-13  1.070672e-02  603806   申万电气设备   \n",
       "1136725          1.672276e-15 -5.811825e-13 -1.515834e-14  603866   申万食品饮料   \n",
       "1136726          6.779183e-03 -5.380859e-13  5.737203e-14  603868   申万家用电器   \n",
       "1136727          1.122151e-02 -5.499560e-13  1.122151e-02  603883   申万医药生物   \n",
       "\n",
       "               bm  \n",
       "0        0.003848  \n",
       "1        0.001702  \n",
       "2        0.001541  \n",
       "3        0.000908  \n",
       "4        0.000704  \n",
       "5        0.002320  \n",
       "6        0.001851  \n",
       "7        0.001409  \n",
       "8        0.001332  \n",
       "9        0.001740  \n",
       "10       0.000803  \n",
       "11       0.002223  \n",
       "12       0.001876  \n",
       "13       0.001376  \n",
       "14       0.001845  \n",
       "15       0.001440  \n",
       "16       0.002169  \n",
       "17       0.001373  \n",
       "18       0.002732  \n",
       "19       0.002837  \n",
       "20       0.001677  \n",
       "21       0.001471  \n",
       "22       0.001949  \n",
       "23       0.001081  \n",
       "24       0.001595  \n",
       "25       0.001698  \n",
       "26       0.005758  \n",
       "27       0.000851  \n",
       "28       0.001990  \n",
       "29       0.002871  \n",
       "...           ...  \n",
       "1136698  0.001861  \n",
       "1136699  0.001363  \n",
       "1136700  0.001399  \n",
       "1136701  0.002456  \n",
       "1136702  0.001276  \n",
       "1136703  0.000822  \n",
       "1136704  0.002089  \n",
       "1136705  0.000815  \n",
       "1136706  0.000858  \n",
       "1136707  0.002112  \n",
       "1136708  0.000404  \n",
       "1136709  0.001572  \n",
       "1136710  0.001033  \n",
       "1136711  0.001593  \n",
       "1136712  0.001202  \n",
       "1136713  0.000787  \n",
       "1136714  0.001171  \n",
       "1136715  0.001272  \n",
       "1136716  0.000493  \n",
       "1136717  0.000678  \n",
       "1136718  0.000753  \n",
       "1136719  0.000754  \n",
       "1136720  0.000765  \n",
       "1136721  0.002702  \n",
       "1136722  0.000823  \n",
       "1136723  0.002173  \n",
       "1136724  0.000707  \n",
       "1136725  0.000446  \n",
       "1136726  0.000698  \n",
       "1136727  0.001222  \n",
       "\n",
       "[1136728 rows x 10 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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